From aeea9184fb9ca1a88189e44206bc11452245595b Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 13:20:14 +0200 Subject: [PATCH 01/23] change: remove xarray accessor class, move this to martpy wrapper project --- docs/source/user_guide/example/gigogne.ipynb | 34 +++++++----- docs/source/user_guide/example/gis.ipynb | 7 ++- .../user_guide/example/gm_example.ipynb | 9 ++-- src/gridmarthe/__init__.py | 54 ------------------- 4 files changed, 29 insertions(+), 75 deletions(-) diff --git a/docs/source/user_guide/example/gigogne.ipynb b/docs/source/user_guide/example/gigogne.ipynb index 50b563b..762152d 100644 --- a/docs/source/user_guide/example/gigogne.ipynb +++ b/docs/source/user_guide/example/gigogne.ipynb @@ -26,7 +26,10 @@ "outputs": [], "source": [ "import gridmarthe as gm\n", - "ds = gm.load_marthe_grid('./data/nested.permh', drop_nan=True, nanval=[0., -9999.])" + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as cm\n", + "\n", + "ds = gm.load_marthe_grid('./data/nested.permh', drop_nan=True, nan_value=[0., -9999.])" ] }, { @@ -43,12 +46,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# assign coords \n", - "ds2d = ds.mart.assign_coords().isel(time=0)" + "ds2d = gm.assign_coords(ds).isel(time=0)" ] }, { @@ -60,14 +63,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -75,8 +78,9 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "ds2d['permeab'].plot.pcolormesh(x='x', y='y')\n", + "fig, ax = plt.subplots(figsize=(7,3.5))\n", + "ds2d['permeab'].plot.pcolormesh(x='x', y='y', ax=ax, norm=cm.LogNorm())\n", + "ax.set_title('xarray default plot for nested/irregular grid')\n", "plt.show()" ] }, @@ -91,14 +95,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -106,14 +110,16 @@ } ], "source": [ - "gm.plot_nested_grid(ds2d, varname='permeab')\n", + "fig, ax = plt.subplots(figsize=(7,3.5))\n", + "gm.plot_nested_grid(ds2d, varname='permeab', ax=ax, norm=cm.LogNorm())\n", + "ax.set_title('nested grid with gridmarthe helper')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "aquida", + "display_name": "gm", "language": "python", "name": "python3" }, @@ -127,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/source/user_guide/example/gis.ipynb b/docs/source/user_guide/example/gis.ipynb index 133f706..becafe9 100644 --- a/docs/source/user_guide/example/gis.ipynb +++ b/docs/source/user_guide/example/gis.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -169,7 +169,7 @@ "source": [ "path_to_data = './data'\n", "ds = gm.load_marthe_grid(f'{path_to_data}/chasim_hallue.out', 'CHARGE', fpastp=f'{path_to_data}/hallue.pastp').isel(time=-1)\n", - "gdf = ds.mart.to_geodataframe() # equivalent to: gm.to_geodataframe(ds)\n", + "gdf = gm.to_geodataframe(ds)\n", "gdf.head()" ] }, @@ -246,8 +246,7 @@ } ], "source": [ - "help(gm.to_raster) # function doc to write a raster file\n", - "# wrapper with time dimension : ds.mart.to_raster('chasim_hallue.tif')" + "help(gm.to_raster) # function doc to write a raster file" ] }, { diff --git a/docs/source/user_guide/example/gm_example.ipynb b/docs/source/user_guide/example/gm_example.ipynb index be27f47..a831d2a 100644 --- a/docs/source/user_guide/example/gm_example.ipynb +++ b/docs/source/user_guide/example/gm_example.ipynb @@ -98,7 +98,11 @@ "outputs": [], "source": [ "# lecture d'une grille marthe et stockage dans un Dataset (librairie xarray)\n", - "ds = gm.load_marthe_grid('./data/chasim_hallue.out', 'CHARGE', fpastp='./data/hallue.pastp', drop_nan=True) # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur" + "ds = gm.load_marthe_grid(\n", + " './data/chasim_hallue.out', 'CHARGE',\n", + " fpastp='./data/hallue.pastp',\n", + " drop_nan=True # si la valeur des nans n'est pas 9999 (ex. permh, NaNs = 0), on peut spécifier la valeur\n", + ")" ] }, { @@ -733,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1265,7 +1269,6 @@ "# on ajoute les coordonées\n", "ds_3d = gm.assign_coords(ds)\n", "# a noter que les fonctions sont aussi accessibles par méthodes/attributs:\n", - "# ds_3d = ds.mart.assign_coords() # equivalent\n", "ds_3d" ] }, diff --git a/src/gridmarthe/__init__.py b/src/gridmarthe/__init__.py index 464a67b..4ef66a0 100644 --- a/src/gridmarthe/__init__.py +++ b/src/gridmarthe/__init__.py @@ -22,62 +22,8 @@ # along with this program. If not, see . # -import xarray as xr -from typing import Union - from .__version__ import __version__ from .gridmarthe import * from .operasem import * from .plot import plot_nested_grid, plot_outcrop - -@xr.register_dataset_accessor('mart') -class MartheGrid(object): - """ A Marthe grid attributes/methods accessor for xarray.Dataset objects - - Example - -------- - >>> ds = gm.load_marthe_grid(**kwargs) - >>> ds.mart.dropna('permeab', 0.) - - """ - def __init__(self, xr_obj: Union[xr.Dataset, None] = None): - self.obj = xr_obj - - def assign_coords(self, add_lay=True): - return assign_coords(self.obj, add_lay) - - def stack_coords(self, coords=['z', 'y', 'x']): - return stack_coords(self.obj, coords) - - def to_geodataframe(self, epsg='EPSG:27572', fmt='long'): - # from .mgrid_utils import to_geodataframe - return to_geodataframe(self.obj, epsg, fmt) - - def to_recarray(self): - """ return a np.recarray from pymarthe compatibility """ - # TODO, add pymarthe requested informations (i, j, etc.) => grid should be read with `add_grid_id`, and `keep_col` options. - df = self.obj.to_dataframe() - return df.to_records() - - def to_raster(self, x_dim='x', y_dim='y', time=None, epsg=27572, filename_tpl='raster'): - """ Write raster file for a specific timestep (or all) from dataset - Warning, only functionnal for regular grids - """ - if time is None: - time = self.obj.times # if not defined, get all available times - if isinstance(time, str): # make sure to get a iterable for slicing - time = [time] - - for t in time: - to_raster(self.obj.sel(time=slice(t)), x_dim, y_dim, epsg, "{}_{}.tiff".format(filename_tpl, t)) - - return None - - def subset_coords(self, dims=['x', 'y'], gdf=None, xmin=None, ymin=None, xmax=None, ymax=None): - return subset_with_coords(self.obj, dims, gdf, xmin, ymin, xmax, ymax) - - def dropna(self, varname='charge', nanval=9999.): - ds = self.obj.copy() - masque = ds[varname].where(ds[varname] != nanval).dropna(dim='zone') # drop nanval - return ds.sel(zone=masque['zone']) From 48986d8397ae1241512ea3944c74709c322b529f Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 13:25:02 +0200 Subject: [PATCH 02/23] fix gridmarthe#3 : add dimensions verification in write_marthe_grid --- src/gridmarthe/gridmarthe.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 11f87e9..288f97f 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -575,7 +575,15 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str ) = _extract_zvar_from_ds(ds2, varname) if title is None and ztitle == '': - title = 'Marthe Grid ' # dummy arg to set type as string + title = 'Marthe Grid ' # dummy arg to set type as string + + # assert valid shapes before passing args to fortran edsem + # fix bug if .isel(time=X) and no permh file + _shape_var = np.shape(zvar) + if len(_shape_var) < 2: + zvar = np.expand_dims(zvar, axis=0) + if len(np.shape(zdates)) == 0: + zdates = np.array([zdates]) # call fortran module to write marthe grid status = modgridmarthe.write_grid( From 8200249b190c2bfe940ea006c2a30e102656b5de Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 13:37:00 +0200 Subject: [PATCH 03/23] fix gridmarthe#4 : revert v0.1.3 modification of zone index when drop_nan in load_marthe_grid --- src/gridmarthe/gridmarthe.py | 14 ++++----- tests/test_corresponding_zone_dropna.py | 41 +++++++++++++++++++++++++ 2 files changed, 48 insertions(+), 7 deletions(-) create mode 100644 tests/test_corresponding_zone_dropna.py diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 288f97f..b24f04f 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -399,9 +399,9 @@ def load_marthe_grid( if -9999. not in nanval: nanval += [-9999.] - ds = dropna(ds, varname, nanval) - ds['zone_all'] = ('zone', ds['zone'].data) # keep old zone as variable for write method // memo: remove tuple to keep as dim - ds['zone'] = np.arange(1, np.size(ds['zone'].data) + 1, dtype=np.int32) # rearange zone + ds = dropna(ds, varname, nan_value) + # ds['zone_all'] = ('zone', ds['zone'].data) # keep old zone as variable for write method // memo: remove tuple to keep as dim + ds['izone'] = ('zone', np.arange(1, np.size(ds['zone'].data) + 1, dtype=np.int32)) # rearange zone # FIXME better, prevent bug at write : https://github.com/pydata/xarray/issues/7722 // https://stackoverflow.com/questions/65019301/variable-has-conflicting-fillvalue-and-missing-value-cannot-encode-data-when # del ds[varname.lower()].encoding['missing_value'] @@ -452,10 +452,10 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): if 'x' in da.coords.keys(): da = stack_coords(da, dropna=True) coords = [x for x in da.coords.keys() if x in ['x', 'y', 'z']] # if xy assert only existing coords in xyz - elif 'zone_all' in da.keys(): - da['zone'] = da['zone_all'].data # restore zone_all (zone before reorder after dropnan) for merge - da = da.drop('zone_all') - coords = ['zone'] + # elif 'zone_all' in da.keys(): + # da['zone'].data = da['zone_all'].data # restore zone_all (zone before reorder after dropnan) for merge + # da = da.drop('zone_all') + # coords = ['zone'] else: coords = ['zone'] diff --git a/tests/test_corresponding_zone_dropna.py b/tests/test_corresponding_zone_dropna.py new file mode 100644 index 0000000..b87a6d4 --- /dev/null +++ b/tests/test_corresponding_zone_dropna.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +import gridmarthe as gm + + +PATH_DATA = './tests/data' + + +def test_corresponding_zone_dropna(): + + # load the grid and the mask to extract values on zones + permh = gm.load_marthe_grid(f'{PATH_DATA}/craie_npc_nogig.permh', drop_nan=True) + mask = permh.zone.data + h_subs = gm.load_marthe_grid(f'{PATH_DATA}/craie_npc.hsubs').sel(zone=mask) + + if gm.__version__ == '0.1.2': + surf = gm.get_min_layer(h_subs) + else: + surf = gm.get_surface_layer(h_subs) + + df1 = permh.to_dataframe().reset_index().round({'x': 2, 'y': 2}) + df2 = surf.to_dataframe().reset_index().round({'x': 2, 'y': 2}) + df = df1.merge( + df2, on=['x', 'y'], suffixes=['','_r'], + how='left' + ) # merge return NaNs in df with gridmarthe 0.1.3 ! + # because `zone` index is not the same in permh and h_subs + # in v0.1.3 the zone index is reset from 1 to n_zone if drop_nan is True + # use this test to avoid such modifications and more hours lost in debugging + + np.allclose(df1.x.values, df.x.values) + + assert not np.all(np.isnan(df['h_substrat'].values)), "Join error, might come from dtype / round / Wrong merge field" + return + + +if __name__ == "__main__": + + test_corresponding_zone_dropna() From 7034defda0db47b405bdded816c9e62cae0361a4 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 14:44:29 +0200 Subject: [PATCH 04/23] minor updates: docstrings, tests, setup --- README.md | 9 + changelog.md | 12 + meson.build | 6 +- pyproject.toml | 2 +- src/gridmarthe/operasem/geometry.py | 16 +- src/gridmarthe/operasem/interp.py | 4 +- src/gridmarthe/scripts/cleanmgrid.py | 10 +- src/gridmarthe/scripts/martshp.py | 2 +- src/gridmarthe/scripts/ncmart.py | 3 +- src/gridmarthe/utils.py | 2 +- tests/data/thickness.sem | 334 +++++++++++++++++++++++++++ tests/test_edsemi.py | 75 +++--- tests/test_geom.py | 3 + tests/test_gridmarthe.py | 43 +++- tests/test_lecsem.py | 12 + tests/test_read_winmarthe_semi.py | 14 ++ tests/test_surface_mask.py | 42 ++-- 17 files changed, 515 insertions(+), 74 deletions(-) create mode 100644 tests/data/thickness.sem create mode 100644 tests/test_read_winmarthe_semi.py diff --git a/README.md b/README.md index 9844c58..ada01c9 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,15 @@ The package also install different command line tools: `ncmart` to convert Marth Full documentation and tutorials can be founded at https://gridmarthe.readthedocs.io +Grids are stored as a 2 dimension matrix, composed of a 1D spatial vector and a time dimension. +This means that the spatial domain is flatten in grid, and each point is associated +with a index, a x coordinate, a y coordinate, eventually a z coordinate (number of layer +in most cases), and dx,dy values. +Still, as Marthe meshes are always parallelepiped, the array does *not* necessarily follow the +[ugrid convention](https://ugrid-conventions.github.io/ugrid-conventions/). This may be an evolution +for future version if required. +The x, and y coordinates are the center of cell. Dx, Dy are the dimension of the cell. + ## Installation diff --git a/changelog.md b/changelog.md index adc960d..189673c 100644 --- a/changelog.md +++ b/changelog.md @@ -1,5 +1,17 @@ +v0.1.4 +------ + +* fix: error when writing grid with time slice (cf. [issue #3](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/3)) +* fix: REVERT change from 0.1.3 : zone index is NOT reset (as in 0.1.2 and previous versions). + when the `drop_nan` option is used in `load_grid_marthe()`. + A new variable 'izone' store the reset form. + See [issue #4](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/4) +* change: `nanval` argument in `load_marthe_grid` is rename to `nan_value` as in `write_marthe_grid` +* change: `xr.Dataset.mart` class accessor is removed. It will be included as wrapper in future marthe python library. + + v0.1.3 ------ diff --git a/meson.build b/meson.build index 66c4432..5c792c4 100644 --- a/meson.build +++ b/meson.build @@ -43,17 +43,19 @@ if fc.get_id() == 'gcc' endif if host_machine.system() == 'windows' - fargs += ['-fPIC', '-Wno-error'] + fargs += ['-Wno-error'] if not get_option('condabuild') largs += ['-static', '-static-libgfortran', '-static-libgcc', '-static-libquadmath'] + else + fargs += ['-fPIC'] endif quadmath_dep = fc.find_library('quadmath', required: false) # from `f2py --build-dir` meson.build generation quadmath = declare_dependency(link_args : ['-lquadmath']) deps += [quadmath_dep,quadmath] else - # fargs += ['-fPIC', '-shared'] # not really necessary here + fargs += ['-fPIC'] # , '-shared' not really necessary here endif elif fc.get_id() in ['llvm-flang', 'flang', 'flang-new'] diff --git a/pyproject.toml b/pyproject.toml index 6effedb..dd9d646 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -74,7 +74,7 @@ martshp = "gridmarthe:scripts.martshp.main" [project.urls] Homepage = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe" Issues = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues" -Changelog = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/blob/main/changelog.rst" +Changelog = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/blob/main/changelog.md" Documentation = "https://gridmarthe.readthedocs.io/en/latest/" diff --git a/src/gridmarthe/operasem/geometry.py b/src/gridmarthe/operasem/geometry.py index 353377a..4cda0aa 100644 --- a/src/gridmarthe/operasem/geometry.py +++ b/src/gridmarthe/operasem/geometry.py @@ -39,8 +39,10 @@ def _get_mask_array(ds, varname: str='permeab', nanval: list=[-9999., 0.]): def get_active_mask(ds, varname: str='permeab', nanval: list=[-9999., 0.], as_array=False, shp_file=None): - """ Filter dataset on non-nan values, and dissolve results to get a mask shape - input ds should be the permh dataset (read from permh file, ie Horizontal hydraulic conductivity) + """ Filter dataset on non-nan values, and dissolve results to get a mask shape + + Input ds should be the permh dataset (read from permh file, ie Horizontal hydraulic + conductivity). Parameters ---------- @@ -229,9 +231,13 @@ def search_zone(ds, i=None, j=None, x=None, y=None, z=None): Note ---- - if ds is multilayered, you need to provide the layer you want (int) - ds should contains dx and dy - ds should not have assigned coords (x and y are variables, zone is the dimension coordinates (with time)) + + - if ds is multilayered, you need to provide the layer you want (z arg., int type) + + - ds should contains dx and dy + + - ds should not have assigned coords (x and y are variables, zone is the dimension + coordinates (with time)) Parameters ---------- diff --git a/src/gridmarthe/operasem/interp.py b/src/gridmarthe/operasem/interp.py index 0c928f1..9fce3f4 100644 --- a/src/gridmarthe/operasem/interp.py +++ b/src/gridmarthe/operasem/interp.py @@ -53,9 +53,9 @@ def interp_grid(da, new_x=None, new_y=None, method='nearest', **kwargs): the new x-axis coordinate to use new_y: array-like the new y-axis coordinate to use - method: str, Optionnal (default='nearest') + method: str, optional (default='nearest') `xr.Dataset.interp` method to use. Default is 'nearest' - **kwargs: dict, Optionnal. + **kwargs: dict, optional. Any keywords argument to pass to `xr.Dataset.interp`. Returns diff --git a/src/gridmarthe/scripts/cleanmgrid.py b/src/gridmarthe/scripts/cleanmgrid.py index 2a2e1d3..e053b2a 100644 --- a/src/gridmarthe/scripts/cleanmgrid.py +++ b/src/gridmarthe/scripts/cleanmgrid.py @@ -32,7 +32,7 @@ from gridmarthe.__version__ import _copyleft -# ext: VARIABLE_NAME in FRench +# ext: VARIABLE_NAME in French MARTGRID_FILES = { 'permh' : 'PERMEAB', 'debit' : 'DEBIT', @@ -68,6 +68,7 @@ 'flo_r' : 'DEBIT_RIVI', 'qamon' : 'Q_AMONT_RIVI', 'rug_r' : 'RUGOS_RIVI', + 'seuil_r': 'SEUIL_RIVI', 'pnt_r' : 'PENTE_RIVI', 'fon_r' : 'FOND_RIVI', 'conc_r': 'CONCEN_RIVI', @@ -79,7 +80,7 @@ 'lon_d' : 'LONG_DRAIN', 'z_dra' : 'ALTIT_DRAIN', 'meteo' : 'ZONE_METEO', - 'd_ava' : 'DIRECT_AVAL', + #'d_ava' : 'DIRECT_AVAL', d_ava file does not follow grid conventions with layers 'v_rui' : 'VITESS_RUISS', 'ruiss' : 'RUISSEL', 'recha' : 'RECHARGE', @@ -150,8 +151,8 @@ def read_rma(frma): def read_files_from_rma(frma): - # root = os.path.dirname(frma) - root = os.getcwd() + root = os.path.dirname(frma) + # root = os.getcwd() files = read_rma(frma) # get all gridded files @@ -238,7 +239,6 @@ def main(): sys.exit(1) files, layers, ngrid = [(ext, filename, key)], list(range(1, args.layer + 1)), str(args.grid) - for ext, file, key in files: # --- treatment of each grid file --- # diff --git a/src/gridmarthe/scripts/martshp.py b/src/gridmarthe/scripts/martshp.py index 9a91805..581c23f 100644 --- a/src/gridmarthe/scripts/martshp.py +++ b/src/gridmarthe/scripts/martshp.py @@ -39,7 +39,7 @@ def parse_args(): parser.add_argument('opt', metavar='grid timesteps', type=str, nargs='*', help='Paths to grid [and timesteps if result] files are expected') # nargs='+' parser.add_argument('--output' , '-o', type=str, default=None, help='Output filename. Default is input.nc') parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') - parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optionnal, default is 1 (no transformation).') + parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optional, default is 1 (no transformation).') parser.add_argument('--gpkg', '-g', action="store_const", const=True, default=False, help='Use GPKG format instead of shapefile') parser.add_argument('--mask', '-m', action="store_const", const=True, default=False, help='Only get a mask of active domain') parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit') diff --git a/src/gridmarthe/scripts/ncmart.py b/src/gridmarthe/scripts/ncmart.py index c7b40fd..f583094 100644 --- a/src/gridmarthe/scripts/ncmart.py +++ b/src/gridmarthe/scripts/ncmart.py @@ -41,7 +41,7 @@ def parse_args(): parser.add_argument('--output' , '-o', type=str, default=None, help='Output filename. Default is input.nc') parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') parser.add_argument('--as2d' , '-d', action="store_const", const=True, default=False, help='Store grid as 2D (or more), default is 1D for space dimension') #choices=('True','False'), dest='monnomdevariable' - parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optionnal, default is 1 (no transformation).') + parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optional, default is 1 (no transformation).') # parser.add_argument('--show', type=str, default='', choices=('c', 'w'), help='Print licensing for warranty (w) and redistribution conditions (c)') parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit') @@ -58,7 +58,6 @@ def parse_args(): print(_copyleft) sys.exit(0) - if args.output is not None: dirout = os.path.dirname(args.output) if dirout != '': diff --git a/src/gridmarthe/utils.py b/src/gridmarthe/utils.py index e8986bd..fee1d0a 100644 --- a/src/gridmarthe/utils.py +++ b/src/gridmarthe/utils.py @@ -186,7 +186,7 @@ def stack_coords(ds, coords=['z', 'y', 'x'], dropna=False): # create zone index coords = [d for d in coords if d in ds.coords.keys()] # make sure to drop coords that are not present dims = np.prod( [len(ds[d]) for d in coords] ) # create new zone dim - zone = np.arange(dims) + zone = np.arange(1, dims + 1) # stack coords ds2 = ds.copy().stack(zone=coords) # multiindex zone grouping coords key diff --git a/tests/data/thickness.sem b/tests/data/thickness.sem new file mode 100644 index 0000000..61406b6 --- /dev/null +++ b/tests/data/thickness.sem @@ -0,0 +1,334 @@ +Marthe_Grid Version=9.0 +Title=Exportation : Originating from 'Work' TRAVA 9 +[Infos] +Field=TRAVA +Type= +Elem_Number=0 +Name= +Time_Step=-9999 +Time=0 +Layer=9 +Max_Layer=9 +Nest_grid=0 +Max_NestG=0 +[Structure] +X_Left_Corner=543500 +Y_Lower_Corner=2526100 +Ncolumn=387 +Nrows=304 +[Data_Descript] +! Line 1 : 0 , 0 , < 1 , 2 , 3 , Ncolumn > +! Line 2 : 0 , 0 , < X_Center_of_all_Columns > +! Line 2+1 : 1 , Y_of_Row_1 , < Field_Values_of_all_Columns > , Dy_of_Row_1 +! Line 2+2 : 2 , Y_of_Row_2 , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! Line 3+Nrows : 0 , 0 , < Dx_of_all_Columns > +[Data] +0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 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500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 +[End_Grid] diff --git a/tests/test_edsemi.py b/tests/test_edsemi.py index 21ef8ff..0ec5fe5 100644 --- a/tests/test_edsemi.py +++ b/tests/test_edsemi.py @@ -3,27 +3,26 @@ # -*- coding: utf-8 -*- import numpy as np + import gridmarthe as gm -# from gridmarthe.lecsem import _extract_zvar_from_ds, _parse_dims_from_xr_attrs, modgridmarthe +from gridmarthe.lecsem import ( + _extract_zvar_from_ds, + _parse_dims_from_xr_attrs, + # modgridmarthe +) -def test_write_marthe_grid(): +def test_extract_var(martfile, varname): + ds = gm.load_marthe_grid(martfile, varname) + dims = _parse_dims_from_xr_attrs(ds.attrs.get('original_dimensions')) + ( + zvar, zdates, + zxcol, zylig, zdxlu, zdylu, + ztitle, izdates + ) = _extract_zvar_from_ds(ds, varname.lower()) - # martfile = './tests/data/craie_npc.permh' # multilayer - # martfile = './tests/data/hallue.permh' # simple, single layer - martfile = './tests/data/Somme_V3_Surfex.permh' # nested - varname = 'PERMEAB' - fout = './tests/res/grid.out' - - # ds = gm.load_marthe_grid(martfile, varname) - # dims = _parse_dims_from_xr_attrs(ds.attrs.get('original_dimensions')) - # ( - # zvar, zdates, - # zxcol, zylig, zdxlu, zdylu, - # ztitle, izdates - # ) = _extract_zvar_from_ds(ds, varname.lower()) - - # np.prod(np.array(dims), axis=1).sum() == np.size(zvar) + assert np.prod(np.array(dims), axis=1).sum() == np.size(zvar), \ + 'Expected dimensions and actual dimension of array do not match' # help(gm.lecsem.modgridmarthe.write_grid) # modgridmarthe.write_grid( @@ -40,23 +39,41 @@ def test_write_marthe_grid(): # nsteps=len(zdates), # dates=izdates, # debug=True, - # xfile='test.out' + # xfile='tests/res/test.out' # ) - # t = np.arange(1, 2862) - # t2 = np.arange(1, 2862, dtype=np.int32) - # t.dtype - # t2.dtype - # np.allclose(t, t2) - ds2 = gm.load_marthe_grid(martfile, varname, drop_nan=True, nanval=[0.,-9999.]) - status = gm.write_marthe_grid(ds2, fout, varname='permeab', file_permh=martfile) - assert status == 0, "write_marthe_grid test failed" +def test_write_marthe_grid(): + + # martfile = './tests/data/craie_npc.permh' # multilayer + # martfile = './tests/data/hallue.permh' # simple, single layer + martfile = './tests/data/Somme_V3_Surfex.permh' # nested + varname = 'PERMEAB' + fout = './tests/res/grid.out' + + test_extract_var(martfile, varname) + + # test normal (time still a dimension/axis) + ds1 = gm.load_marthe_grid(martfile, varname, drop_nan=True, nan_value=[0.,-9999.]) + status = gm.write_marthe_grid(ds1, fout, varname='permeab', file_permh=martfile) + assert status == 0, "write_marthe_grid test, with permh file and dropna, failed" + + # test sel time + ds2 = gm.load_marthe_grid(martfile, varname) + status = gm.write_marthe_grid(ds2.isel(time=0), fout, varname='permeab') + assert status == 0, "write_marthe_grid test with sel(time) failed" # also test reading the written file - ds3 = gm.load_marthe_grid(fout, varname, drop_nan=True, nanval=[0.,-9999.]) + ds3 = gm.load_marthe_grid(fout, varname, drop_nan=True, nan_value=[0.,-9999.]) assert 'permeab' in ds3.data_vars, "re-read written file failed" - assert ds3.permeab.shape == ds2.permeab.shape, "re-read written file shape mismatch" - assert np.allclose(ds3.permeab.values, ds2.permeab.values, equal_nan=True), "re-read written file values mismatch" + assert ds3.permeab.shape == ds1.permeab.shape, "re-read written file shape mismatch" + assert np.allclose(ds3.permeab.values, ds1.permeab.values, equal_nan=True), \ + "write_marthe_grid re-read written file values mismatch" + + print("=============================") print("write_marthe_grid test passed") + +if __name__ == "__main__": + + test_write_marthe_grid() \ No newline at end of file diff --git a/tests/test_geom.py b/tests/test_geom.py index fb3ab36..ee651a3 100644 --- a/tests/test_geom.py +++ b/tests/test_geom.py @@ -37,3 +37,6 @@ def test_compute_geometry(): print("test_compute_geometry passed") + +if __name__ == "__main__": + test_compute_geometry() \ No newline at end of file diff --git a/tests/test_gridmarthe.py b/tests/test_gridmarthe.py index e0eabde..127f64b 100644 --- a/tests/test_gridmarthe.py +++ b/tests/test_gridmarthe.py @@ -4,7 +4,7 @@ import numpy as np import xarray as xr -import pytest +# import pytest import gridmarthe as gm @@ -29,7 +29,7 @@ def test_load_with_varname_none_picks_first(): def test_load_with_varname_all_returns_multiple(): ds = gm.load_marthe_grid(DATA_PATH, varname='all') assert isinstance(ds, xr.Dataset) - data_vars = [ x for x in list(ds.data_vars) if not x in ['x', 'y', 'z', 'dx', 'dy']] + data_vars = [ x for x in list(ds.data_vars) if x not in ['x', 'y', 'z', 'dx', 'dy']] assert len(data_vars) >= 1 @@ -41,13 +41,13 @@ def test_load_with_drop_nan_removes_nan(): def test_load_with_custom_nanval(): - ds = gm.load_marthe_grid(DATA_PATH, VAR, drop_nan=True, nanval=0.) + ds = gm.load_marthe_grid(DATA_PATH, VAR, drop_nan=True, nan_value=0.) arr = ds[VAR.lower()].values assert not np.any(arr == 0.) -def test_load_with_keepligcol_adds_col_lig(): - ds = gm.load_marthe_grid(DATA_PATH, VAR, keepligcol=True) +def test_load_with_adds_col_row(): + ds = gm.load_marthe_grid(DATA_PATH, VAR, add_col_row=True) assert 'col' in ds.data_vars assert 'lig' in ds.data_vars @@ -57,14 +57,14 @@ def test_load_with_add_id_grid_adds_id_grid(): assert 'id_grid' in ds.data_vars -def test_load_nonexistent_file_raises(): - with pytest.raises(FileNotFoundError): - gm.load_marthe_grid('not_a_file.permh', VAR) +# def test_load_nonexistent_file_raises(): +# with pytest.raises(FileNotFoundError): +# gm.load_marthe_grid('not_a_file.permh', VAR) -def test_load_invalid_varname_raises(): - with pytest.raises(ValueError): - gm.load_marthe_grid(DATA_PATH, varname='INVALIDVAR') +# def test_load_invalid_varname_raises(): +# with pytest.raises(ValueError): +# gm.load_marthe_grid(DATA_PATH, varname='INVALIDVAR') def test_load_grid_attrs_present(): @@ -74,6 +74,25 @@ def test_load_grid_attrs_present(): assert 'original_dimensions' in ds.attrs +def test_all(): + test_load_valid_grid_returns_xarray() + test_load_grid_attrs_present() + test_load_with_add_id_grid_adds_id_grid() + test_load_with_adds_col_row() + test_load_with_custom_nanval() + test_load_with_drop_nan_removes_nan() + test_load_with_varname_none_picks_first() + test_load_with_varname_all_returns_multiple() + # test_load_nonexistent_file_raises() + # test_load_invalid_varname_raises() + print("=============================") + print("gridmarthe reader test passed") + return + + +if __name__ == "__main__": + test_all() + # MY TESTS # inputs = './tests/data/craie_npc.permh', "PERMEAB" # ajouter tests/data ici pour conda-forge ? # @@ -81,7 +100,7 @@ def test_load_grid_attrs_present(): # # toto = gm.load_marthe_grid(*inputs, drop_nan=True, nanval=0.) # # toto = gm.load_marthe_grid(inputs[0], varname=None) # # toto = gm.load_marthe_grid(inputs[0], varname='all') -# # toto = gm.load_marthe_grid(*inputs, keepligcol=True) +# # toto = gm.load_marthe_grid(*inputs, add_col_row=True) # # toto = gm.load_marthe_grid(*inputs, add_id_grid=True) # # test = toto.set_coords(['time', 'zone', 'x', 'y', 'z', 'dx', 'dy']) diff --git a/tests/test_lecsem.py b/tests/test_lecsem.py index ce377a4..f4c4f2a 100644 --- a/tests/test_lecsem.py +++ b/tests/test_lecsem.py @@ -54,3 +54,15 @@ def test_lecsem_read_marthe_grid(): def test_lecsem_transform_coords(): # TODO pass + + +def test(): + test_lecsem_read_marthe_grid() + test_lecsem_scan_dim() + test_lecsem_scan_typevar() + print('==================') + print('lecsem test passed') + + +if __name__ == "__main__": + test() diff --git a/tests/test_read_winmarthe_semi.py b/tests/test_read_winmarthe_semi.py new file mode 100644 index 0000000..68de1bd --- /dev/null +++ b/tests/test_read_winmarthe_semi.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + + +import gridmarthe as gm + + +def test_read_sem_from_winmarthe(): + sem_file = 'tests/data/thickness.sem' + ds = gm.load_marthe_grid(sem_file) + + +if __name__ == '__main__': + test_read_sem_from_winmarthe() \ No newline at end of file diff --git a/tests/test_surface_mask.py b/tests/test_surface_mask.py index 4bad8d7..05353a4 100644 --- a/tests/test_surface_mask.py +++ b/tests/test_surface_mask.py @@ -1,23 +1,37 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -# import os;os.chdir('tests') +import numpy as np import matplotlib.pyplot as plt - import gridmarthe as gm -grid = gm.load_marthe_grid('./tests/data/craie_npc.permh', drop_nan=True) -surf = gm.get_surface_layer(ds=grid) +def test_surf_mask(plot=False): + + grid = gm.load_marthe_grid('./tests/data/craie_npc.permh', drop_nan=True) + surf = gm.get_surface_layer(ds=grid) + + assert np.allclose(np.unique(surf.z.data), np.array([1, 2, 4, 5, 6, 8, 9])), \ + "Surface layer identification failed" + + surf2 = gm.get_surface_layer(ds=grid, aquif_layers=[6,8,9]) + assert np.allclose(np.unique(surf2.z.data), [6,8,9]), \ + "Surface layer identification with subset failed" + + if plot: + # Plot with geopandas + # gdf = gm.to_geodataframe(surf) + # gm.plot_outcrop(gdf) + # plt.show() + toto = gm.assign_coords(surf, add_lay=False) + ## gm.plot_nested_grid(toto, var='z') + gm.plot_outcrop(toto) + plt.show() + plt.close('all') + + print("========================") + print('surface mask test passed') -# Plot with geopandas -gdf = gm.to_geodataframe(surf) -gm.plot_outcrop(gdf, engine='gpd') -plt.show() -# Plot with xarray -toto = gm.assign_coords(surf, add_lay=False) -# gm.plot_nested_grid(toto, var='z') -gm.plot_outcrop(toto) -plt.show() -# plt.close('all') \ No newline at end of file +if __name__ == "__main__": + test_surf_mask() \ No newline at end of file From 9c2c4da7c74e526b1e44ac5508db138f02fd7b9e Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 14:45:25 +0200 Subject: [PATCH 05/23] fix plot oucrop colorbar/cmap, allow custom cmap --- src/gridmarthe/plot.py | 115 ++++++++++++++++++++++++++++++----------- 1 file changed, 84 insertions(+), 31 deletions(-) diff --git a/src/gridmarthe/plot.py b/src/gridmarthe/plot.py index d3abdd0..66b5ef4 100644 --- a/src/gridmarthe/plot.py +++ b/src/gridmarthe/plot.py @@ -22,6 +22,8 @@ # along with this program. If not, see . # +from copy import copy + from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib import (colors, cm) @@ -56,13 +58,13 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): the dataset MUST be a 2D array, with dims = x,y. In other words, you may need to sel z and time before plot, and you need to apply `assign_coord()` - ax: matplotlib axe, Optionnal. + ax: matplotlib axe, optional. if provided, data are plotted on this axis, otherwise fig, ax instances will be created. - varname: str, Optionnal + varname: str, optional the variable to plot in dataset. Default is 'charge'. - kwargs: optionnal. + kwargs: optional. any keywords argument from `xr.Dataset.plot.pcolormesh()` Returns @@ -70,6 +72,7 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): ax: matplotlib axis. """ da = ds.copy() + if 'var' in kwargs.keys(): # legacy, previous arg name was only var, harmonize between functions/methods varname = kwargs.pop('var') @@ -102,6 +105,10 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): _set_map_lims(ax, da.x.min().data, da.y.min().data, da.x.max().data, da.y.max().data) + if str(da.time.values)[:10] == '1850-01-01': + # remember: no slice on time because it must be selected before calling function + ax.set_title(varname) # remove default title from xarray API if dummy timestep (e.g plot parameters) + return ax @@ -117,13 +124,13 @@ def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize= zone: int zone value (dimension) to select data - varname: str, Optionnal (default is 'charge') + varname: str, optional (default is 'charge') Variable to plot. Must be a key of all dataset passed as *arg. - show: bool, Optionnal. + show: bool, optional. show plot using `plt.show()` - figsize: tuple[int], Optionnal. + figsize: tuple[int], optional. figsize argument for matplotlib. kwargs: any keywords argument for `xr.Dataset.plot()` method @@ -142,57 +149,94 @@ def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize= return ax -def plot_outcrop(ds_outcrop, file_out=None, engine='xr', show=False): - +def plot_outcrop(ds_outcrop, fig=None, ax=None, cbar_width='3', labels=None, file_out=None, show=False, **kwargs): """ Usefull function to plot outcrop layers of a marthe (multilayer) grid There is two mode implementend yet, using xr.plot or - gpd.plot (useful for nested grid) + gpd.plot (useful for nested grid). - if engine == 'xr' => ds_outcrop need to get coords before - (use `gm.assign_coords(ds_outcrop, add_lay=False)`) - if engine == 'gpd => ds_outcrop need to be a gpd.GeoDataframe - (use `gm.to_geodataframe(ds_outcrop)`) + To use this function, `ds` provided need to have a `z` variable (i.e model + used need to be multilayer) and user need to assign coords before and **keep** + the z dimension as a variable (see Note). - nb: The Marthe grid must have a `z` dimension. + Note + ---- + - if input is a `xr.Dataset` instance, `ds_outcrop` need to get coords before + but keep `z` as a variable (use `gm.assign_coords(ds_outcrop, add_lay=False)`) Parameters ---------- ds_outcrop: xr.Dataset output of `gm.get_surface_mask()` - file_out: str, Optionnal. - If not None (default), file name to write plot + fig: matplotlib figure, Optional. + if provided, data are plotted on this figure, otherwise fig, ax instances will be created - engine: str, Optionnal. - Engine to use for plotting (default is `xr`). + ax: matplotlib axe, Optional. + if provided, data are plotted on this axis, otherwise fig, ax instances will be created - show: bool, Optionnal. + cbar_width: str, Optional. + width of colorbar, as a percentage of the main axis. Default is '3' + + labels: list, Optional. + labels for layers in colorbar + + file_out: str, Optional. + If not None (default), file name to write plot + + show: bool, Optional. Show result (`plt.show()`), default is False. + kwargs: dict, Optional. + Any keywords argument to pass to either `xarray.Dataset.plot.pcolormesh()` + or `geopandas.GeoDataFrame.plot()`. + Returns ------- fig, ax, ax_cbar if not `show`, otherwise return None + + Example + ------- + xarray version: + >>> ds_surf = gm.get_surface_layer(ds) + >>> gm.plot_outcrop(gm.assign_coords(ds_surf, add_lay=False)) + + geopandas version: + >>> ds_surf = gm.get_surface_layer(ds) + >>> gm.plot_outcrop(gm.to_geodataframe(ds_surf)) """ if isinstance(ds_outcrop, xr.Dataset): assert 'z' in ds_outcrop.keys(), "No `z` dimension. Outcrop plot is not possible." maxn = np.nanmax(ds_outcrop['z'].data) + engine = 'xr' elif isinstance(ds_outcrop, gpd.geodataframe.GeoDataFrame): assert 'z' in ds_outcrop.columns, "No `z` dimension. Outcrop plot is not possible." maxn = np.nanmax(ds_outcrop['z'].to_numpy()) + engine = 'gpd' # custom cbar to force categories - cmap = cm.tab10 if maxn <= 10 else cm.tab20 - cmap = colors.ListedColormap(cmap.colors[:int(maxn)]) # subset on number of colors, if not wrong legend - bounds = np.arange(1, maxn+1) - if len(bounds) == 1: - bounds = np.append(bounds, [maxn+1]) - norm = colors.BoundaryNorm(bounds, cmap.N+1,) # set bins to custom values - cbar = plt.cm.ScalarMappable(norm=norm, cmap=cmap) - - fig, ax = plt.subplots(1, 1) + cmap = kwargs.pop('cmap', None) + norm = kwargs.pop('norm', None) + + if cmap is None: + cmap = cm.tab10 if maxn <= 10 else cm.tab20 + cmap = colors.ListedColormap(cmap.colors[:int(maxn)]) # subset on number of colors, if not wrong legend + + if norm is not None: + bounds = norm.boundaries + else: + bounds = np.arange(1, maxn+2) + if len(bounds) == 1: + bounds = np.append(bounds, [maxn+1]) + norm = colors.BoundaryNorm(bounds, cmap.N) # set bins to custom values + + sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap) + + # Create the figure and axes, with a place for colorbar + if fig is None or ax is None: + fig, ax = plt.subplots(1, 1) divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.1) + cax = divider.append_axes("right", size="{}%".format(cbar_width), pad=0.1) if engine == 'xr': the_plot = plot_nested_grid( @@ -201,6 +245,7 @@ def plot_outcrop(ds_outcrop, file_out=None, engine='xr', show=False): cmap=cmap, levels=bounds, add_colorbar=False, + **kwargs ) elif engine == 'gpd': @@ -212,16 +257,24 @@ def plot_outcrop(ds_outcrop, file_out=None, engine='xr', show=False): cmap=cmap, legend=False, norm=norm, + **kwargs ) ax_cbar = fig.colorbar( - cbar, ax=ax, cax=cax, + sm, ax=ax, cax=cax, orientation='vertical', ticks=[x+0.5 for x in bounds], # set labels in the middle of class label='Layers', ) ax_cbar.ax.tick_params(size=0) - ax_cbar.set_ticklabels(['{:.0f}'.format(x) for x in bounds]) + + _labels = copy(labels) + if _labels is None: + _labels = ['{:.0f}'.format(x) for x in bounds] + if len(_labels) != len(bounds): + _labels = _labels + ['none'] + ax_cbar.set_ticklabels(_labels) + ax_cbar.ax.invert_yaxis() if file_out is not None: plt.savefig(file_out, dpi=300) From 81a780e751696c79cb5ecb3b4c78286b2427284e Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 14:45:46 +0200 Subject: [PATCH 06/23] wip: improve raster export --- src/gridmarthe/operasem/gis.py | 68 ++++++++++++++++++++++++++-------- 1 file changed, 52 insertions(+), 16 deletions(-) diff --git a/src/gridmarthe/operasem/gis.py b/src/gridmarthe/operasem/gis.py index 390b85a..a54b728 100644 --- a/src/gridmarthe/operasem/gis.py +++ b/src/gridmarthe/operasem/gis.py @@ -111,7 +111,7 @@ def _check_rioxarray(): except ModuleNotFoundError: raise ModuleNotFoundError( 'rioxarray is not Found in python env.' + \ - 'Please install it or reinstall gridmarthe with optionnal dependancies: pip install gridmarthe[opt]' + 'Please install it or reinstall gridmarthe with optional dependancies: pip install gridmarthe[opt]' ) return @@ -120,7 +120,7 @@ def clip_dataset(ds, gdf, crs=27572, engine='gdf'): """ Clip a xarray Dataset with a gpd.GeoDataFrame Needs rioxarray. If not installed, raise ModuleNotFoundError - please install it or reinstall gridmarthe with optionnal dependancies: pip install gridmarthe[opt] + please install it or reinstall gridmarthe with optional dependancies: pip install gridmarthe[opt] See: https://corteva.github.io/rioxarray/html/examples/clip_geom.html Todo: shapely version @@ -248,36 +248,72 @@ def transf_proj(ds, from_epsg="EPSG:27572", to_epsg="EPSG:2154", engine='rioxarr return ds_transf -def to_raster(da, x_dim='x', y_dim='y', epsg=27572, fout='raster.tiff'): +def _single_grid_to_raster(da, x_dim, y_dim, epsg, fout): + da = da.copy().rio.write_crs('epsg:{}'.format(epsg)) + da = da.rio.set_spatial_dims(x_dim=x_dim, y_dim=y_dim) + da.rio.to_raster(fout) + return None + + +def to_raster( + ds, + varname=None, + x_dim='x', + y_dim='y', + time=None, + epsg=27572, + filename_tpl='raster' +): """ Write a xr.DataArray to a raster file - need xarray with rioxarray installed. - Only for regular grids. - - TODO: add support for irregular grids, using PostMARTHE QGIS plugin code. + Notes + ----- + - Warning, only functionnal for regular grids + - Requires rasterio/rioxarray packages + - TODO: add support for irregular grids, using PostMARTHE QGIS plugin code. Parameters ---------- - da : xarray.DataArray - The DataArray to write to a raster file (only 2D, i.e. no time dimension, select - a variable and timestep before). + ds : xarray.Dataset, xarray.DataArray + The dataset/dataArray to write to a raster file x_dim : str, optional The name of the x dimension, by default 'x'. y_dim : str, optional The name of the y dimension, by default 'y'. + time : str, list + time or list of time from `da.time` epsg : int, optional The EPSG code for the coordinate reference system, by default 27572. - fout : str, optional - The output file path for the raster file, by default 'raster.tiff'. + filename_tpl : str, optional + The output file template for the raster file, by default 'raster'. + Final name will be '{filename_tpl}_{time}.tiff' Returns ------- None The function writes the raster file and returns None. """ + _check_rioxarray() - da = da.copy().rio.write_crs('epsg:{}'.format(epsg)) - da = da.rio.set_spatial_dims(x_dim=x_dim, y_dim=y_dim) - da.rio.to_raster(fout) + + if isinstance(ds, xr.Dataset): + assert varname is not None, \ + 'You need to provide a variable name to export when using a Dataset' + da = ds[varname].copy() + elif isinstance(ds, xr.DataArray): + da = ds.copy() + else: + raise ValueError('ds is neither a xr.Dataset nor xr.DataArray') + + if time is None: + time = da.times # if not defined, get all available times + elif isinstance(time, str): # make sure to get a iterable for slicing + time = [time] + + for t in time: + _single_grid_to_raster( + da.sel(time=slice(t)), + x_dim, y_dim, epsg, + "{}_{}.tiff".format(filename_tpl, t) + ) return None - From 08f7066f3ad4d48186b71f0cef34f8e9774e9817 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 15:07:42 +0200 Subject: [PATCH 07/23] doc: general update, fix nb dark theme, and update docstrings --- docs/source/_static/custom.css | 51 +++------ docs/source/api/index.rst | 1 + docs/source/api/operasem.rst | 2 +- docs/source/api/utils_func.rst | 23 ++++ docs/source/changelog.md | 1 + docs/source/conf.py | 36 ++++-- docs/source/getting_started/nutshell.rst | 29 ++++- docs/source/index.rst | 7 +- docs/source/sphinxext/git_link.py | 16 ++- .../user_guide/example/data/chasim_hallue.nc | Bin 1125055 -> 1124804 bytes src/gridmarthe/plot.py | 2 + src/gridmarthe/utils.py | 107 ++++++++++++++++-- 12 files changed, 209 insertions(+), 66 deletions(-) create mode 100644 docs/source/api/utils_func.rst create mode 120000 docs/source/changelog.md diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css index a3a122a..e5feaf3 100644 --- a/docs/source/_static/custom.css +++ b/docs/source/_static/custom.css @@ -1,47 +1,24 @@ /* Main index page overview cards */ -.sd-card-img-top { - width: 33% !important; - display: block; - margin-left: auto; - margin-right: auto; - margin-top: 10px; -} - -/* customise nbsphinx ----------------------------------------------- */ -div.nbinput.container div.prompt pre, -div.nbinput.container div.prompt pre > code { - color: var(--pst-color-primary); -} - -div.nboutput.container div.prompt pre, -div.nboutput.container div.prompt pre > code { - color: var(--pst-color-secondary); -} - -div.nbinput.container div.input_area { - border: 1px solid var(--pst-color-border); -} - -div.rendered_html table { - color: var(--pst-color-text-base); -} -div.rendered_html thead { - border-bottom: 1px solid var(--pst-color-border); +/* More prominent card borders */ +.sd-card { + border: 2px solid var(--pst-color-border) !important; + border-radius: 3px !important; } -div.rendered_html tbody tr:nth-child(odd) { - background: var(--pst-color-surface); +/* fly you fool ! survol */ +.sd-card:hover { + border-color: var(--bs-purple) !important; + /* sinon voir les couleurs de _static/pydata-sphinx-theme.css à la build */ } -div.rendered_html tbody tr:hover { - background: var(--pst-color-accent-bg); -} - -html[data-theme="dark"] .bd-content .nboutput .output_area.rendered_html { - background-color: transparent; +/* from xarray's doc */ +html[data-theme="dark"] div.cell_output .text_html:has(div.xr-wrap) { + background-color: var(--pst-color-on-background) !important; + color: var(--pst-color-text-base) !important; } +/* Modified from Rameau's doc */ /* customise footer ------------------------------------------------- */ .bd-footer .footer-items__end, .bd-footer .footer-items__start { gap: 0; @@ -84,4 +61,4 @@ a.navbar-corporate, a.navbar-corporate:hover { /* reduce margin */ .navbar-nav { margin: 0; -} \ No newline at end of file +} diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst index 516421c..c091daf 100644 --- a/docs/source/api/index.rst +++ b/docs/source/api/index.rst @@ -12,5 +12,6 @@ left-hand menu you will find the different categories of the API documentation. :maxdepth: 2 io_operations + utils_func operasem plotting diff --git a/docs/source/api/operasem.rst b/docs/source/api/operasem.rst index 1c7bebc..074a16b 100644 --- a/docs/source/api/operasem.rst +++ b/docs/source/api/operasem.rst @@ -7,7 +7,7 @@ Grid operations .. autosummary:: :toctree: ./generated - reset_geometry operasem.geometry + reset_geometry operasem.gis diff --git a/docs/source/api/utils_func.rst b/docs/source/api/utils_func.rst new file mode 100644 index 0000000..49c6356 --- /dev/null +++ b/docs/source/api/utils_func.rst @@ -0,0 +1,23 @@ +.. currentmodule:: gridmarthe +.. default-role:: obj + +Useful functions +---------------- + +.. autosummary:: + :toctree: ./generated + + dropna + + fillna + + subset + + replace + + assign_coords + + stack_coords + + read_dates_from_pastp + diff --git a/docs/source/changelog.md b/docs/source/changelog.md new file mode 120000 index 0000000..0f7e497 --- /dev/null +++ b/docs/source/changelog.md @@ -0,0 +1 @@ +../../changelog.md \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index cb17421..b3b0333 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -110,32 +110,54 @@ elif version_match == "stable": version_match = f"v{release}" + +# required for edit-button +html_context = { + "default_mode": "light", + "gitlab_url": "https://gitlab.com/", # or your self-hosted GitLab + "gitlab_user": "brgm/hydrogeological-modelling/marthe-tools", # "", + "gitlab_repo": "gridmarthe", #"", + "gitlab_version": "main", # "", + "doc_path": "docs/source", # "", +} + + # adapted from gardenia/rameau doc: html_permalinks_icon = '' html_theme_options = { "icon_links": [ + { + "name": "Report Bug/Contribute", + "url" : "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues", + "icon": "fa-solid fa-bug", + #"icon": "fa-solid fa-hammer", #gavel ? + "type": "fontawesome", + }, { "name": "GitLab", "url" : "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe.git", "icon": "fa-brands fa-gitlab", "type": "fontawesome", - } + }, ], "navbar_align": "content", + # "navbar_start": ["navbar-logo", "version-switcher"], "navbar_start": ["navbar-logo"], - "navbar_end": ["version-switcher", "theme-switcher", "navbar-icon-links"], + "navbar_end": ["theme-switcher", "navbar-icon-links"], + "navbar_persistent": ["search-button"], # reduce search bar to button "footer_start": ["copyright", "sphinx-version", "last-updated"], "footer_end": ["brgm-logo-{}".format(language)], "show_toc_level": 1, "show_nav_level": 1, "header_links_before_dropdown": 6, "show_prev_next": False, - "switcher": { - "json_url": json_url, - "version_match": version_match, - }, + # "switcher": { + # "json_url": json_url, + # "version_match": version_match, + # }, # keep working version, deactivate for readthedocs (useless) "show_version_warning_banner": True, "announcement": None, + "use_edit_page_button": True } html_last_updated_fmt = '%b %d, %Y' @@ -187,7 +209,7 @@ ( git_repo_url + "-/blob/{revision}/" - "src/{package}/{path}#L{lineno}" + "src/{package}/{path}#L{lineno}-L{linenb}" ), ) diff --git a/docs/source/getting_started/nutshell.rst b/docs/source/getting_started/nutshell.rst index 68bc997..7337659 100644 --- a/docs/source/getting_started/nutshell.rst +++ b/docs/source/getting_started/nutshell.rst @@ -4,14 +4,33 @@ Gridmarthe in a nutshell .. warning:: This section is under construction ! -``gridmarthe`` allow users to read/write efficiently Marthe Grids (v9, v8, constant_data, etc.) +Objective +~~~~~~~~~ + +*gridmarthe* allow users to read/write efficiently Marthe Grids (v9, v8, constant_data, etc.) with python, for any MARTHE variable. -With the ``gridmarthe`` API, data are stored in a `xarray dataset `_, +With the *gridmarthe* API, data are stored in a `xarray dataset `_, and can be manage with `xarray `_ functions (or `numpy `_). Specific treatments/functions are also provided by `gridmarthe`. -The package also install different command line tools: `ncmart` to convert Marthe Grid to netCDF format, -`martshp` to convert in shapefile/geopackage, `cleanmgrid` to fix marthe grid format. +The package also install different command line tools: ``ncmart`` to convert Marthe Grid to netCDF format, +``martshp`` to convert in shapefile/geopackage, ``cleanmgrid`` to fix marthe grid format. + +Tutorials are available in the :ref:`userguide` section. + + +Grid convention +~~~~~~~~~~~~~~~ + +Grids are stored as a 2 dimension matrix, composed of a 1D spatial vector and a time dimension. +This means that the spatial domain is flatten in grid, and each point is associated +with a index, a x coordinate, a y coordinate, eventually a z coordinate (number of layer +in most cases), and dx,dy values. + +Still, as Marthe meshes are always parallelepiped, the array stored by *gridmarthe* +does *not* necessarily follow the `ugrid convention `_. +This may be an evolution for future version if required. + +The x, and y coordinates are the center of cell. Dx, Dy are the dimension of the cell. -Tutorials are available in the :ref:`userguide` section. \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 51c5b94..e3fda92 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -51,8 +51,10 @@ This library is open-source and released under the GNU General Public License (v .. to add image => :img-top: _static/index_getting_started.svg -.. grid:: 1 1 2 2 - :gutter: 1 +.. :padding: 2 2 0 0 + +.. grid:: 1 1 2 2 + :gutter: 2 .. grid-item-card:: Getting started :link: getting_started/index @@ -86,6 +88,7 @@ This library is open-source and released under the GNU General Public License (v Getting Started User guide API Reference + Release history About .. .. include:: ../../README.md diff --git a/docs/source/sphinxext/git_link.py b/docs/source/sphinxext/git_link.py index 7b0b663..d9c25d6 100644 --- a/docs/source/sphinxext/git_link.py +++ b/docs/source/sphinxext/git_link.py @@ -1,11 +1,12 @@ # SPDX-License-Identifier: BSD-3-Clause # -# DISCLAIMER: This module is taken from scikit-learn sources +# DISCLAIMER: This module is taken/slighlty modified from scikit-learn sources # all credits to the sklearn authors and contributors. # and huge thanks to them. # source: https://github.com/scikit-learn/scikit-learn/blob/c5497b7f7eacfaff061cf68e09bcd48aa93d4d6b/doc/sphinxext/github_link.py # License: BSD-3 Clause # See the COPYING file from this directory: ./COPYING +# other inspiration: https://github.com/pandas-dev/pandas/blob/20fc7447f5b14de805a0b751217ab39e5114c7f6/doc/source/conf.py#L690 import inspect @@ -37,14 +38,16 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision): ... 'fullname': 'setraw'}, ... package='tty', ... url_fmt='https://hg.python.org/cpython/file/' - ... '{revision}/Lib/{package}/{path}#L{lineno}', + ... '{revision}/Lib/{package}/{path}#L{lineno}-L{linenb}', ... revision='xxxx') 'https://hg.python.org/cpython/file/xxxx/Lib/tty/tty.py#L18' """ if revision is None: + print('No revision for link code') return if domain not in ("py", "pyx"): + print('source is not python') return if not info.get("module") or not info.get("fullname"): return @@ -71,10 +74,11 @@ def _linkcode_resolve(domain, info, package, url_fmt, revision): fn = os.path.relpath(fn, start=os.path.dirname(__import__(package).__file__)) try: - lineno = inspect.getsourcelines(obj)[1] + source, lineno = inspect.getsourcelines(obj) # AM: get source too, to make range of line except Exception: - lineno = "" - return url_fmt.format(revision=revision, package=package, path=fn, lineno=lineno) + source, lineno = "", "" # AM: same + linenb = lineno + len(source) - 1 # AM: based on numpy/pandas docs + return url_fmt.format(revision=revision, package=package, path=fn, lineno=lineno, linenb=linenb) def make_linkcode_resolve(package, url_fmt): @@ -86,7 +90,7 @@ def make_linkcode_resolve(package, url_fmt): url_fmt is along the lines of ('https://github.com/USER/PROJECT/' 'blob/{revision}/{package}/' - '{path}#L{lineno}') + '{path}#L{lineno}-L{linenb}') """ revision = _get_git_revision() return partial( diff --git a/docs/source/user_guide/example/data/chasim_hallue.nc b/docs/source/user_guide/example/data/chasim_hallue.nc index efebfe3ffc8fa02dc8bd3a5583ce2c74a4077feb..e540154c58c3f41172ccbecaf5d7965d610bd518 100644 GIT binary patch delta 4878 zcmZA3d2~$o|Htu3CJA9C$t0D<8v7QEN+ye~mQsveBh*(r@h#aC^=*WzDKbgTOrp9Z zv`Vchf}kQ)sa%(0YzdP@A~7FJEWxNYqLkm$um60{_nz~3-PgV6o^zM?=Z|~r%E%Mt zBfse3dZOp*UW2^drreABFMB@@atRJpD4Ho;+&mTkZ0_-kQ&T>SeBtxY;WC%~26d*3 z+vfEjdVIAgxc&ZV$}S#W{k47i_n+4P&8X0bC~fG9EYIvLPZzjC2{*XI1I^GJudiu= zmQXu?2}nc|l97_Xt%atu%enlNmYRMZPdU@%k{_+cknk};uREb3Kob$;R+?(;0_NoLvuHyLg7hi zftFA~4KH}Z2O9Xo4{x9q{1Jda1fezBpe^3S0E8k8I)ozvk%+=T48mX|F$6>L7KULs zMqnh~#wd)&I~aqp7zaJZV*(~(66PTe@kl@-l8}rPq#_OT@ezy*i2vYYq+=m6un3Fs z36|hfEXC^w{tU~o94nBCb@(3Zu>n6|BQ{|(wqPr^p#VQ(yBqW0LD`8y?80vBK@s+1 zANJ!X96&J+q68Ol31uk9Wn6(B6{tiNuA&+>xQ1HP;ri?OJ17D-a1*z18}+z@ySRt@ zXn@k4g#>qapc$IO6D`mZDyZQFZ}>n1U-;n-w1U5p2tXi$&>C&f7VXd;9T1FgL?9AT z7>Gd_j3F3`w=fLDF#;p;Hb!AI-oY4*#W)!C#CS}=L`=eDOu36=|4{ zkFWs$!N*9)LS$eO7UL5v!KYY?*DwB=JM&*gS&kLRgb7*5#^?AR>#+epU?VnRGqzwW zwxIw&Vmo$VCkn9(yRipF*o%GGkDuI`{{c!d4x$8ya2RDM$7NiB9TliV6|SNhHMoXa z)ZscD5V(PxxP{xO#~s|oJ=}l&85-c}!Qw$nsGx=yyx{{4eBp;T&5_#3W3{6imf5yo-;Kj)lm;A}q!y zSb|Tn6h?f8Wmt|C9?U#zzbVm>LL@)G42%<3^F_?jwcn`DiFU-dK_y7i2k%L^U!WUSLFYy(=#y7}o z&is#4N+IC{WSqn){D#vwgR`)q5l_*CXZQ=x@dAJ2C0;?nkEsh>p+s+lpbz?@AGAiI zKitWnLoW>cP zg$?I$9v5&Cmr#asyn>> zCTzwQY{fR@4?E!#=vwEodYJ#gTfQ3C=EgquIm%$SgC4&wI=@|4U7TDomsqernXE2; 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faster with pandas func pastp = pd.read_csv( @@ -106,6 +118,17 @@ def dropna(ds, varname: str, nanval: Union[list, float]): """ Drop values corrresponding to NaN (marthe convention, eg. code 9999.) for 1D (or 2D (time, zone)) array zone must me a coordinate dimension. + + Parameters + ---------- + ds : xr.Dataset + dataset of marthe variable(s) + + varname : str + variable name in dataset to treat + + nanval : list or float + value to consider as NaN Returns ------- @@ -125,6 +148,17 @@ def subset(ds, varname: str, value: Union[list, float]): """ Subset dataset based on variable name and value. --> inverse of :py:func:`dropna` + Parameters + ---------- + ds : xr.Dataset + dataset of marthe variable(s) + + varname : str + variable name in dataset to treat + + value: list or float + value to keep + Returns ------- dataset where variable = value @@ -138,23 +172,80 @@ def subset(ds, varname: str, value: Union[list, float]): def replace(ds, varname: str, value: float, replace: float): """ Replace a value in xr.Dataset for a variable + + Parameters + ---------- + ds : xr.Dataset + dataset of marthe variable(s) + + varname : str + variable name + + value: float + value to replace + + replace: float + value to replace with + + Returns + ------- + dataset with replaced value """ ds[varname].data = np.where(ds[varname].data == value, replace, ds[varname].data) return ds def fillna(ds, varname, value): - """ Replace real nan (np.nan) value in dataset[varname], edge case of :py:func:`replace()` + """ Replace real nan (np.nan) value in dataset[varname], + edge case of :py:func:`gridmarthe.replace` + + Parameters + ---------- + ds : xr.Dataset + dataset of marthe variable(s) + + varname : str + variable name + + value: float + value to replace NaN with """ ds[varname].data = np.where(np.isnan(ds[varname].data), value, ds[varname].data) return ds -def assign_coords(da_in, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zone_label='zone'): - """ assign coords to transform a 1D or 2D (time, zone) array to 3D or 4D +def assign_coords(ds, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zone_label='zone'): + """ Assign coordinates from variables to dimensions + + This function transform a 1D or 2D (time, zone dimensions) array to 3D or 4D + with time, x,y [,z] as dimensions. + + Useful for plot functions/methods. + + Parameters + ---------- + ds : xr.Dataset + dataset of Marthe variable(s) + + add_lay : bool, optional + Boolean to treat `z` (layer) as a dimension (True) or a variable (False) + + coords : list, optional + list of coordinates to add as dimensions. Default is `['x', 'y', 'z']` + + keep_zone : bool, optional + keep zone as dimension (will make multiindex). Default is False. + + zone_label : str, optional + label of current index. Default is `zone` as read by :py:func:`gridmarthe.load_marthe_grid` + + Returns + ------- + xr.Dataset + Dataset with coordinates as dimension. """ if len(coords) == 3: - z_coords = da_in.get(coords[2], None) # assert z is here, or bypass + z_coords = ds.get(coords[2], None) # assert z is here, or bypass else: z_coords = None @@ -162,15 +253,15 @@ def assign_coords(da_in, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, # in some case, even if z is included it should not be treated as coord (ex. plot outcrop) z_coords = None - da = da_in.assign_coords( + da = ds.assign_coords( #x=(zone_label, np.around(da_in[coords[0]].data, 1) ), - x=(zone_label, da_in[coords[0]].data ), - y=(zone_label, da_in[coords[1]].data ), + x=(zone_label, ds[coords[0]].data ), + y=(zone_label, ds[coords[1]].data ), ) dims = ['y', 'x'] if z_coords is not None: - da = da.assign_coords(z=(zone_label, da_in[coords[2]].data)) + da = da.assign_coords(z=(zone_label, ds[coords[2]].data)) dims.insert(0, 'z') da = da.set_index(zone=dims) From a78c5c100ba82d8fb611f49f577637647cd5dff8 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 15:10:56 +0200 Subject: [PATCH 08/23] better error management in write_marthe_grid, rename args in load_marthe_grid --- src/gridmarthe/gridmarthe.py | 268 ++++++++++++--------- src/gridmarthe/lecsem/__init__.py | 1 + src/gridmarthe/lecsem/_parse_gridmarthe.py | 13 +- 3 files changed, 167 insertions(+), 115 deletions(-) diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index b24f04f..6c85425 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -41,6 +41,7 @@ _parse_dims_from_xr_attrs, _extract_zvar_from_ds, scan_var, + FortranError ) @@ -159,17 +160,16 @@ def _parse_attrs( return {**prologue, **grid_attrs, **dates_attrs, **epilogue} - def load_marthe_grid( filename: str, varname: Union[str, None] = None, fpastp: Union[str, None] = None, dates=None, - nanval: Union[int, float, None] = None, drop_nan: bool = False, + nan_value: Union[int, float, None] = None, xyfactor: Union[int, float] = 1., shallow_only=False, - keepligcol: bool = False, + add_col_row: bool = False, add_id_grid: bool = False, title: Union[str, None] = None, var_attrs: dict = {}, @@ -180,6 +180,7 @@ def load_marthe_grid( }, engine: str = 'xarray', verbose: bool=False, + **kwargs, ): """ Read Marthe Grid File as xarray.Dataset @@ -194,76 +195,91 @@ def load_marthe_grid( Parameters ---------- - filename: str - A path to marthegrid file (.permh, .out, etc.) - - varname : str, Optionnal - variable to access in martgrid file, e.g ``CHARGE`` for groundwater head. See marthegrid file content. - if None is passed (default), function will scan all varnames in filename and keep first only - if 'all' is passed, function will scan all varnames in filename and keep all. - All datavars are added to dataset, using recursive call to func - if wrong variable name is passed, empty data will be returned. - - fpastp: str, Optionnal - A pastp file to read for dates - - dates: sequence, Optionnal - Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of datetime/np.datetime objects. - If no dates (or no fpastp) is provided, a fake sequence of dates from 1850 to 1900 will - be used for xarray object - - nanval: float, Optionnal - A code value for nan values. Default is 9999. - - drop_nan: bool, Optionnal - Drop nan values (corresponding to nanval) in xarray object to return. - Default is False (keep nan values). + filename: str + A path to marthegrid file (.permh, .out, etc.) + + varname : str, optional + variable to access in martgrid file, e.g ``CHARGE`` for groundwater head. See marthegrid file content. + if None is passed (default), function will scan all varnames in filename and keep first only + if 'all' is passed, function will scan all varnames in filename and keep all. + All datavars are added to dataset, using recursive call to func + if wrong variable name is passed, empty data will be returned. + + fpastp: str, optional + A pastp file to read for dates + + dates: sequence, optional + Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of datetime/np.datetime objects. + If no dates (or no fpastp) is provided, a fake sequence of dates from 1850 to 1900 will + be used for xarray object + + drop_nan: bool, optional + Drop nan values (corresponding to nan_value) in xarray object to return. + Default is False (keep nan values). + + nan_value: float, optional + A code value for nan values. The default value is inferred from field name. + E.g. of default nan values: - xyfactor: int or float, Optionnal - factor to transform X and Y values. e.g.: 1000 to convert km XY to meters. - Default is 1. + - hydraulic conductivity: 0 or -9999. (Warning: a value of +9999. is not + a NaN value for hydraulic conductivity. See Marthe User Guide for explanation + about this code, refering here to impervious layer); - shallow_only: bool, Optionnal - Boolean to read only the first layer. Default is False. + - hydraulic head: 9999.; - keepligcol: bool, Optionnal - Add columns (col) and rows (lig) index (from 1 to n), Default is False. + - groundwater flow: 0. (9999. is used as special value for this field); - add_id_grid: bool, Optionnal - Add grid id (from 0 to n), useful for nested grids. - 0 is main grid, Default is False - - title: str , Optionnal - Title for grid attributes. Default is None (not used) - - var_attrs: dict, Optionnal - Dictionnary of attributes to add to variable DataArray. - - model_attrs: dict, Optionnal - Dictionnary of attributes to add to Dataset. - by default, gis attrs are added and can be modified - - >>> { - ... 'resolution_units': 'm', - ... 'projection' : 'epsg:27572', - ... 'domain' : 'FR-France' - ... } + - any other: 9999. + + xyfactor: int or float, optional + factor to transform X and Y values. e.g.: 1000 to convert km XY to meters. + Default is 1. + + shallow_only: bool, optional + Boolean to read only the first layer. Default is False. + + add_col_row: bool, optional + Add columns (col) and rows (lig) index (from 1 to n), Default is False. + + add_id_grid: bool, optional + Add grid id (from 0 to n), useful for nested grids. + 0 is main grid, Default is False + + title: str , optional + Title for grid attributes. Default is None (not used) + + var_attrs: dict, optional + Dictionnary of attributes to add to variable DataArray. + + model_attrs: dict, optional + Dictionnary of attributes to add to Dataset. + by default, gis attrs are added and can be modified - engine: str, Optionnal - Engine to use for returned object. Default is 'xarray', which return xarray.Dataset object. - Another option is 'numpy', which return a list of numpy arrays : - [zvar, zdates, isteps, zxcol, zylig, zdxlu, zdylu, ztitle, dims] + >>> { + ... 'resolution_units': 'm', + ... 'projection' : 'epsg:27572', + ... 'domain' : 'FR-France' + ... } + + engine: str, optional + Engine to use for returned object. Default is 'xarray', which return xarray.Dataset object. + Another option is 'numpy', which return a list of numpy arrays : + [zvar, zdates, isteps, zxcol, zylig, zdxlu, zdylu, ztitle, dims] - verbose: bool, Optionnal - Print some information about execution in stdout. - Default is False. + verbose: bool, optional + Print some information about execution in stdout. + Default is False. Returns ------- - ds: xr.Dataset - A xarray.Dataset object containing values and attributes read from Marthe grid file. + ds: xr.Dataset + A xarray.Dataset object containing values and attributes read from Marthe grid file. """ - + legacy_tmp = kwargs.get('nanval') + if legacy_tmp: + print('Warning: nanval argument is deprecated, use nan_value instead') + nan_value = legacy_tmp + # Fortran error cause sys exit. To avoid this, we add a test on file first if not os.path.exists(filename): raise FileNotFoundError("File : `{}` does not exist. Please check syntax/path.".format(filename)) @@ -286,8 +302,8 @@ def load_marthe_grid( arrays = [] for var in varname: arrays.append( load_marthe_grid( - filename, var, fpastp, dates, nanval, drop_nan, xyfactor, shallow_only, - keepligcol, add_id_grid, title, var_attrs, model_attrs, engine, verbose + filename, var, fpastp, dates, nan_value, drop_nan, xyfactor, shallow_only, + add_col_row, add_id_grid, title, var_attrs, model_attrs, engine, verbose ) ) return xr.merge(arrays) @@ -321,7 +337,9 @@ def load_marthe_grid( xcols, dxlus = _transform_xcoords(zxcol, zylig, zdxlu, nlayer=dims[0][-1], factor=xyfactor) yligs, dylus = _transform_ycoords(zxcol, zylig, zdylu, nlayer=dims[0][-1], factor=xyfactor) - if varname == '': varname = 'variable' # security if force mode + if varname == '': + varname = 'variable' # security if force mode + vattrs = VARS_ATTRS.get(varname.lower(), {}) vattrs.update(var_attrs) dic_data = { @@ -336,9 +354,9 @@ def load_marthe_grid( # # } - if keepligcol: + if add_col_row: if is_nested: - add_id_grid = True # force to add id_grid if nested grid + add_id_grid = True # force to add id_grid if nested grid cols, ligs = _get_col_and_lig(dims) dic_data['col'] = ("zone", cols) dic_data['lig'] = ("zone", ligs) @@ -369,7 +387,7 @@ def load_marthe_grid( data_vars=dic_data, coords={ 'time': dates, - 'zone': np.arange(1, zvar.shape[1] + 1, dtype=np.int32), + 'zone': np.arange(1, zvar.shape[1] + 1, dtype=np.int32) }, attrs={ **_parse_attrs(title, dims, xyfactor, dates, is_nested, dxlus, dylus, xcols, yligs), @@ -387,17 +405,17 @@ def load_marthe_grid( # ) if drop_nan: - if nanval is None: - nanval = vattrs.get('missing_value', 9999.) # if no user defined nanval, try to get corresponding val in dict then 9999. if not present + if nan_value is None: + nan_value = vattrs.get('missing_value', 9999.) # if no user defined nanval, try to get corresponding val in dict then 9999. if not present - if not isinstance(nanval, (list, tuple)): - nanval = [nanval] - elif isinstance(nanval, tuple): - nanval = list(nanval) + if not isinstance(nan_value, (list, tuple)): + nan_value = [nan_value] + elif isinstance(nan_value, tuple): + nan_value = list(nan_value) if (varname.lower() == 'permeab' or filename.endswith("permh")) and is_nested: - if -9999. not in nanval: - nanval += [-9999.] + if -9999. not in nan_value: + nan_value += [-9999.] ds = dropna(ds, varname, nan_value) # ds['zone_all'] = ('zone', ds['zone'].data) # keep old zone as variable for write method // memo: remove tuple to keep as dim @@ -435,7 +453,7 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): variable: str variable (ds key) containing data - fillna: bool (Optionnal) + fillna: bool (optional) to fillna WITH permh nan value. permh nan value are used because it can contain different nan values (0 and -9999 for nested grids) for simplier nan fills, this can be performed outside of this function. @@ -446,8 +464,13 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): """ da = ds.copy() # All values (nan, nested grid margins) should be included in permh dataset. - permh = load_marthe_grid(path_to_permh, drop_nan=False, add_id_grid=True, keepligcol=True, verbose=False) - + permh = load_marthe_grid( + path_to_permh, + drop_nan=False, + add_id_grid=True, + add_col_row=True, + verbose=False + ) if 'x' in da.coords.keys(): da = stack_coords(da, dropna=True) @@ -476,7 +499,7 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): ) tmp = tmp.drop(tmp.filter(regex='_y$', axis=1),axis=1) # drop overlapping cols, if there is some. if fillna: - # tmp = tmp.fillna(nanval) # no because, different codes for nested or not. + # tmp = tmp.fillna(nan_value) # no because, different codes for nested or not. tmp[variable] = np.where(np.isnan(tmp[variable]), grid['inactive'], tmp[variable]) tmp = tmp.drop('inactive', axis=1) tmp = tmp.set_index(['time', 'zone']).to_xarray() @@ -484,23 +507,32 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): return tmp - -def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str = None, title=None, dims=None, debug=False): +def write_marthe_grid( + ds, + fileout='grid.out', + varname='charge', + file_permh: str = None, + nan_value=9999., + title=None, + dims=None, + debug=False +): """ Write Dataset as MartheGrid v9 file ds should contain x, y, dx, dy, attrs[['title', 'original_dimensions']] in case of error, please use :py:func:`gridmarthe.reset_geometry` first. - When providing a path to ``file_permh`` argument, :py:func:`gridmarthe.reset_geometry` is called automatically. + When providing a path to ``file_permh`` argument, this is called automatically. A good pratice is to provide the permh file when writing dataset to marthegrid format. >>> gm.write_marthe_grid(ds, 'toto.out', file_permh='./mymodel/model.permh') WARNING: This function was developped to write parameters grids to marthe format. - Not to recreate simulation results (hydraulic head at several timesteps for example) as gridmarthe format. + Not to recreate simulation results (hydraulic head at several timesteps for example) + as gridmarthe format. This means that this function should not be used for dataset with several timesteps. - Example, to create a new initial hydraulic head file based on simulation, select the timestep in dataset before - writing. + Example, to create a new initial hydraulic head file based on simulation, select the + timestep in dataset before writing. >>> ds = ds_head.isel(time=16) # or do an aggregation (eg mean over a period) >>> gm.write_marthe_grid(ds, 'mymodel.charg') @@ -513,60 +545,65 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str fileout: str filename to write - varname: str (Optionnal) + varname: str (optional) variable name (key) containing values. - file_permh: str (Optionnal) + file_permh: str (optional) path to the permh file corresponding to current Marthe model. Needed to recreate full dimension if NaN dropped before. - title: str (Optionnal) + title: str (optional) title written in marthe grid file dims: list of array list containing array of dimension for every grid (ie len(dims) > 1 if nested grid) format is `[[x_main_grid, y_main_grid, z_main_grid], [x_nested_1, ...], ...]` eg. `[[354,252,2], [182,156,2]]` - if only main grid : `[[x,y,z]]` + if only main grid : `[[nx,ny,nz]]` if None (default, dims will be parsed from ds.attrs['original_dimensions'] which is added when read with :py:func:`gridmarthe.load_marthe_grid`. If not present (lost in some computation for example), please use py:func:`gridmarthe.reset_geometry` or provide list of dims manually. - debug: bool, Optionnal (default is False). + debug: bool, optional (default is False). Returns ------- status: int. 0 if everything's ok. 1 otherwise. - """ - + # TODO: infer nx, ny, nz, ngrid from ds ? --> allow to create a custom grid ds2 = ds.copy() + + if file_permh is not None: + # if permeab, fill_na with permh file (because either 0 or -9999.) + _fill_na = True if varname == 'permeab' else False + # reset geometry with full domain (stored in permh file) + ds2 = reset_geometry(ds2, path_to_permh=file_permh, variable=varname, fillna=_fill_na) + if not _fill_na: + # if not permh variable, fill nan with constant values, based on variable + if nan_value is None: + nan_value = VARS_ATTRS.get(varname, {}).get('missing_value', 9999.) + ds2 = fillna(ds2, varname, nan_value) if dims is None: dims = _parse_dims_from_xr_attrs(ds2.attrs.get('original_dimensions')) + # if after parsing, still None, raise error. if dims is None: - raise ValueError("""Original dimensions cannot be None.\ -Attributes was not founded in dataset so pleave provide a list with original domain dimensions -""") + raise ValueError( + "Original dimensions cannot be None." + "Attributes was not founded in dataset so pleave provide a list with original domain dimensions" + ) # --- Check if expected dimensions match variable dimensions # if not, recreate full grid with domain grid (permh file) - if np.prod(np.array(dims), axis=1).sum() != np.size(ds2[varname].data): + _test_shape = np.prod(np.array(dims), axis=1).sum() != np.size(ds2[varname].data) + if _test_shape: # if dimension differs, file_permh is required - error = "Expected size and variable array size differ. Please provide a permh file to recreate original grid." - assert file_permh is not None, error - - _fill_na = True if varname == 'permeab' else False # if permeab, fill_na with permh file (0 and/or -9999.) + error = "Expected size and actual size (from variable array) differs. " + error += "Make sure to provide a `permh_file` to reconstruc geometry." + raise ValueError(error) - # reset geometry with full domain (stored in permh file) - ds2 = reset_geometry(ds2, path_to_permh=file_permh, variable=varname, fillna=_fill_na) - if not _fill_na: - # if not permh variable, fill nan with constant values, based on variable - NANs = VARS_ATTRS.get(varname, {}).get('missing_value', 9999.) - ds2 = fillna(ds2, varname, NANs) - # extract variables from dataset ( zvar, zdates, @@ -604,10 +641,13 @@ def write_marthe_grid(ds, fileout='grid.out', varname='charge', file_permh: str ) if status != 0: - print("\033[1;31m\nEDISEM Status={}\033[0m\n".format(status)) - print("An error occurred while writting Marthe Grid with EDSEMI subroutines.") - print("Please check array consistency (9999. or 0. for nan values\ - [ie, do not drop nan val before write or use `gm.reset_geometry()`]) or coordinates order [zyx]") - + # print("\033[1;31m\nEDISEM Status={}\033[0m\n".format(status)) + raise FortranError( + f'Fortran subroutine EDISEM failed with status {status}' + 'Please check array consistency : 9999. or 0. for nan values (no np.nan),' + 'do not drop nan val before write or provide a `file_permh`.', + status + ) + return status diff --git a/src/gridmarthe/lecsem/__init__.py b/src/gridmarthe/lecsem/__init__.py index 8549712..7f4abb4 100644 --- a/src/gridmarthe/lecsem/__init__.py +++ b/src/gridmarthe/lecsem/__init__.py @@ -10,5 +10,6 @@ _extract_zvar_from_ds, _parse_dims_from_xr_attrs, scan_var, + FortranError ) diff --git a/src/gridmarthe/lecsem/_parse_gridmarthe.py b/src/gridmarthe/lecsem/_parse_gridmarthe.py index 55dfe50..38aafc6 100644 --- a/src/gridmarthe/lecsem/_parse_gridmarthe.py +++ b/src/gridmarthe/lecsem/_parse_gridmarthe.py @@ -21,6 +21,7 @@ # You should have received a copy of the GNU General Public License # along with this program. If not, see . # + import re import numpy as np @@ -28,6 +29,16 @@ from ..utils import _datetime64_to_float +class FortranError(Exception): + def __init__(self, message, iostat): + self.message = message + super().__init__(self.message) + self.iostat = iostat + + def __str__(self): + return f"Error Code: {self.iostat}: {self.message}" + + def scan_var(xfile): """ List all variables stored in a Marthe grid file """ var = modgridmarthe.scan_typevar(xfile) # get a list of unique type_var that are in xfile @@ -132,7 +143,7 @@ def _set_layers(dims): for z in range(dims[igig][-1]): zlay.append(np.tile(z+1, dims[igig][0] * dims[igig][1])) zlay = np.hstack(zlay) - return zlay.astype(np.int32) + return zlay #.astype(np.int32) def _decode_title(title, encoding='ISO-8859-1'): From f7864a4120f78c287876afc0702cf2c45979154a Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 21:57:41 +0200 Subject: [PATCH 09/23] fix gridmarthe #1: fix bad reading of coordinates with exported .sem files --- src/gridmarthe/lecsem/scan_grid.f90 | 12 ++++++++---- tests/test_read_winmarthe_semi.py | 1 + 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/src/gridmarthe/lecsem/scan_grid.f90 b/src/gridmarthe/lecsem/scan_grid.f90 index 7eb77c9..ed50b24 100644 --- a/src/gridmarthe/lecsem/scan_grid.f90 +++ b/src/gridmarthe/lecsem/scan_grid.f90 @@ -171,9 +171,11 @@ SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES, REAL(KIND=4), DIMENSION(KNBSTEP), INTENT(OUT) :: PDATES REAL(KIND=4), DIMENSION(KNU_ZOOMX + 1, 999), INTENT(OUT) :: PXCOL, PYLIG, PDXLU, PDYLU REAL(KIND=8), DIMENSION(KNBSTEP, KNBTOT), INTENT(OUT) :: PVAR - CHARACTER (LEN=132), INTENT(OUT) :: TITSEM ! Modifs AM: ajout TITSEM dans les sorties de la subroutine (+ ajout en `dummy argument` càd réf dans la list d'arg de la procedure) + CHARACTER (LEN=132), INTENT(OUT) :: TITSEM + ! Modifs AM: ajout TITSEM dans les sorties de la subroutine (+ ajout en `dummy argument` + ! càd réf dans la list d'arg de la procedure) ! - INTEGER :: ISTEPINC, ISTEP_TEMP, INTOT_TEMP + INTEGER :: ISTEPINC, ISTEP_TEMP, INTOT_TEMP, i_ncouch ! LOGICAL :: debug ! LIRE_DXDY = 1 @@ -197,7 +199,8 @@ SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES, ISTEP_TEMP = -1 INTOT_TEMP = 1 ! - ! debug = .FALSE. + i_ncouch= 0 ! integer to check if coordinates already read + ! debug = .FALSE. OPEN(UNIT=LEC, FILE=TRIM(XFILE), FORM='formatted', ACTION='read') ! DO WHILE (IERLEC == 0) @@ -220,11 +223,12 @@ SUBROUTINE READ_GRID(XFILE, XTYP_DON, KNBSTEP, KNBTOT, KNU_ZOOMX, PVAR, PDATES, KSTEPS(ISTEPINC) = ISTEP PDATES(ISTEPINC) = real(DATE, 4) ENDIF - IF (ISTEPINC == 1 .AND. N_COUCH == 1) THEN + IF (ISTEPINC == 1 .AND. (N_COUCH == 1 .OR. i_ncouch ==0)) THEN PXCOL(NU_ZOO + 1, :NKOL) = real(XCOL(:NKOL), 4) PYLIG(NU_ZOO + 1, :NLIG) = real(YLIG(:NLIG), 4) PDXLU(NU_ZOO + 1, :NKOL) = real(DXLU(:NKOL), 4) PDYLU(NU_ZOO + 1, :NLIG) = real(DYLU(:NLIG), 4) + i_ncouch = i_ncouch + 1 ENDIF PVAR(ISTEPINC, INTOT_TEMP:INTOT_TEMP + NTOT -1) = FONC(:NTOT) INTOT_TEMP = INTOT_TEMP + NTOT diff --git a/tests/test_read_winmarthe_semi.py b/tests/test_read_winmarthe_semi.py index 68de1bd..a696d7f 100644 --- a/tests/test_read_winmarthe_semi.py +++ b/tests/test_read_winmarthe_semi.py @@ -8,6 +8,7 @@ def test_read_sem_from_winmarthe(): sem_file = 'tests/data/thickness.sem' ds = gm.load_marthe_grid(sem_file) + print('Test read sem from winmarthe success!') if __name__ == '__main__': From e013e7eee100ff211cd3d7ef9d03dc5dc64fe559 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 11 Oct 2025 21:59:45 +0200 Subject: [PATCH 10/23] add/wip: beta support support for vtk export, ref gridmarthe#2 Co-authored-by: Simon Lopez --- src/gridmarthe/operasem/vtk_writer.py | 140 ++++++++++++++++++++++++++ tests/test_vtk_export.py | 35 +++++++ 2 files changed, 175 insertions(+) create mode 100644 src/gridmarthe/operasem/vtk_writer.py create mode 100644 tests/test_vtk_export.py diff --git a/src/gridmarthe/operasem/vtk_writer.py b/src/gridmarthe/operasem/vtk_writer.py new file mode 100644 index 0000000..92575a2 --- /dev/null +++ b/src/gridmarthe/operasem/vtk_writer.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +gridmarthe.operasem.vtk_writer + +Export gridmarthe grid to vtk format, using `vtkwriters` python library +(https://gitlab.com/brgm/geomodelling/io/vtkwriters) +""" + +from pathlib import Path +from datetime import datetime + +import numpy as np +import vtkwriters as vtkw + + +def _get_vertices_connectivity(geom): + """ Get vertices and connectivity from marthe grid geometry, for pvd export + + Parameters + ---------- + geom : xr.Dataset + The grid to convert. Needs to have all geometry informations/variables. + See :py:func:`gridmarthe.compute_geometry` + + Returns + ------- + np.ndarray, np.ndarray, str + The vertices, connectivity and cell type ('voxel') + """ + # extract grid infos + # nc = np.shape(geom['x'].data)[-1] # here we have x (or y) for every center cells + dx = geom['dx'].data + dy = geom['dy'].data + dz = geom['z_upper'].data[0] - geom['z_lower'].data[0] # only dt=0, assume no grid variation during time + zc = geom['z_upper'].data[0] - dz/2 + cc = np.stack((geom['x'].data, geom['y'].data, zc), axis=-1) # center cells array + + # cell nodes (8 nodes per cell) + cn = np.reshape(np.tile(cc, 8), (-1, 8, 3)) + + # use voxel vtk cell types (this could be changed) + cn[:, [0, 2, 4, 6], 0] -= dx[:, None] # left side + cn[:, [1, 3, 5, 7], 0] += dx[:, None] # right side + cn[:, [0, 1, 4, 5], 1] -= dy[:, None] # front side + cn[:, [2, 3, 6, 7], 1] += dy[:, None] # rear side + cn[:, [0, 1, 2, 3], 2] -= dz[:, None] # bottom side + cn[:, [4, 5, 6, 7], 2] += dz[:, None] # top side + + # vertices + vertices = np.reshape(cn, (-1, 3)) + # cell nodes connectivity: self-explicit no nodes is duplicated + connectivity = np.arange(vertices.shape[0]) + connectivity.shape = -1, 8 + + return vertices, connectivity, "voxel" + + +def convert_grid_to_vtk(grid, varname, time=None, output_tpl='output/gm_vtk'): + """ Convert a gridmarthe grid to a vtk grid. + + Parameters + ---------- + grid : gridmarthe.GridMarthe + The grid to convert, with variable to export `varname` + In addition, it needs to have all geometry informations/variables. + See :py:func:`gridmarthe.compute_geometry` + varnam: str + name of the variable to export + time : str or datetime, optional + time to export, by default None (all times) + output_tpl : str, optional + path/template name of file to write. By default, 'output/gm_vtk'. + Final file names will be `{output_tpl}_{varname}.pvd` and + `{output_tpl}_{varname}_{time}.pvu` + + Returns + ------- + None + Write pvu/pvd files to output directory + + Example + ------- + + >>> import gridmarthe as gm + >>> model_name = 'hallue_multilayer' + >>> hsubs = gm.load_marthe_grid('./tests/data/{}.hsubs'.format(model_name), xyfactor=1e3) + >>> topo = gm.load_marthe_grid('./tests/data/{}.topog'.format(model_name), xyfactor=1e3) + >>> permh = gm.load_marthe_grid('./tests/data/{}.permh'.format(model_name), xyfactor=1e3, varname='PERMEAB', drop_nan=True) + >>> geom = gm.compute_geometry(topo, hsubs).sel(zone=permh.zone.values) + >>> convert_grid_to_vtk(geom, 'z', time=0, output_tpl='tests/res/vtk/hallue') + """ + if time is None: + time = grid.time.values + + if isinstance(time, str): + time = [time] # convert to list if single time is given + elif isinstance(time, int): + time = [grid.time.values[time]] + elif isinstance(time, datetime): + time = [time] # convert to list if single time is given + elif isinstance(time, (list, np.ndarray)): + pass # already a list + else: + raise ValueError('time must be a string, datetime or list of datetime') + + if varname not in grid: + raise KeyError(f"Variable {varname} not found in grid") + + grid = grid.copy().sel(time=time) + + # create a collection of files per timestep + output_dir = Path(output_tpl).parent + output_dir.mkdir(exist_ok=True) + # there is a bug in vtkwriters to handle Path objects, hence this trick + to_output = lambda s: f"{output_dir}/{s}" + + vertices, cellnodes, celltype = _get_vertices_connectivity(grid) + + snapshots = [] + for i, t in enumerate(time): + filename = f"output-{i:04d}.vtu" # TODO, better time name management (strftime, int, etc.) + vtkw.write_vtu( + vtkw.vtu_doc( + vertices, + cellnodes, + celltype, + # as many variable as needed in the celldata dictionnary + # pointdata argument is also available + celldata={ + varname: grid.sel(time=t)[varname].data, + }, + ), + to_output(filename), + ) + snapshots.append((t, filename)) + + # write the file describing the simulation (pvd format) + vtkw.write_pvd(vtkw.pvd_doc(snapshots), to_output("simulation.pvd")) + return None diff --git a/tests/test_vtk_export.py b/tests/test_vtk_export.py new file mode 100644 index 0000000..9d61441 --- /dev/null +++ b/tests/test_vtk_export.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + + +import numpy as np +import vtkwriters as vtkw + +import gridmarthe as gm +from gridmarthe.operasem.vtk_writer import ( + _get_vertices_connectivity, + convert_grid_to_vtk +) + + +def test_vtk_export(): + model_name = 'hallue_multilayer' + hsubs = gm.load_marthe_grid('./tests/data/{}.hsubs'.format(model_name), xyfactor=1e3) + topo = gm.load_marthe_grid('./tests/data/{}.topog'.format(model_name), xyfactor=1e3) + permh = gm.load_marthe_grid('./tests/data/{}.permh'.format(model_name), xyfactor=1e3, varname='PERMEAB', drop_nan=True) + # geom = gm.compute_geometry(topo, hsubs, mask) # ok + geom = gm.compute_geometry(topo, hsubs).sel(zone=permh.zone.values) + + vertices, cellnodes, celltype = _get_vertices_connectivity(geom) + assert np.shape(vertices) == (8*len(geom.x), 3), 'Wrong number of vertices' + assert np.shape(cellnodes) == (len(geom.x), 8), 'Wrong number of cellnodes' + assert celltype == 'voxel', 'Wrong celltype' + + convert_grid_to_vtk(geom, 'z', time=0, output_tpl='tests/res/vtk_hallue') + print('vtk writer test passed!') + return + + +if __name__ == '__main__': + + test_vtk_export() \ No newline at end of file From 9974ee1582784990be1e646e97e1e121bcab961e Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:37:35 +0100 Subject: [PATCH 11/23] change: rename lig to row in dataset attributes --- src/gridmarthe/gridmarthe.py | 2 +- src/gridmarthe/operasem/geometry.py | 2 +- tests/test_gridmarthe.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 6c85425..6c15c29 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -359,7 +359,7 @@ def load_marthe_grid( add_id_grid = True # force to add id_grid if nested grid cols, ligs = _get_col_and_lig(dims) dic_data['col'] = ("zone", cols) - dic_data['lig'] = ("zone", ligs) + dic_data['row'] = ("zone", ligs) if add_id_grid: dic_data['id_grid'] = ("zone", _get_id_grid(dims)) diff --git a/src/gridmarthe/operasem/geometry.py b/src/gridmarthe/operasem/geometry.py index 4cda0aa..1bb0a6d 100644 --- a/src/gridmarthe/operasem/geometry.py +++ b/src/gridmarthe/operasem/geometry.py @@ -279,7 +279,7 @@ def search_zone(ds, i=None, j=None, x=None, y=None, z=None): if i is not None: assert j is not None, 'if i is provided, j cannot be None' - mask = (ds_search['col'] == i) & (ds_search['lig'] == j) + mask = (ds_search['col'] == i) & (ds_search['row'] == j) # zone = ds.where(mask, drop=True)['zone'].data zone = ds_search.where(mask, drop=True) diff --git a/tests/test_gridmarthe.py b/tests/test_gridmarthe.py index 127f64b..1342975 100644 --- a/tests/test_gridmarthe.py +++ b/tests/test_gridmarthe.py @@ -49,7 +49,7 @@ def test_load_with_custom_nanval(): def test_load_with_adds_col_row(): ds = gm.load_marthe_grid(DATA_PATH, VAR, add_col_row=True) assert 'col' in ds.data_vars - assert 'lig' in ds.data_vars + assert 'row' in ds.data_vars def test_load_with_add_id_grid_adds_id_grid(): From 3317c6a5b3cd8cb1277903486ca9423ec15e0280 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:41:21 +0100 Subject: [PATCH 12/23] update scripts: add options to modify netcdf --- src/gridmarthe/scripts/martshp.py | 54 +++++++++++++------ src/gridmarthe/scripts/ncmart.py | 88 ++++++++++++++++++++++--------- 2 files changed, 102 insertions(+), 40 deletions(-) diff --git a/src/gridmarthe/scripts/martshp.py b/src/gridmarthe/scripts/martshp.py index 581c23f..c944676 100644 --- a/src/gridmarthe/scripts/martshp.py +++ b/src/gridmarthe/scripts/martshp.py @@ -23,6 +23,9 @@ # import os, sys, textwrap from argparse import ArgumentParser, RawDescriptionHelpFormatter + +import xarray as xr + import gridmarthe as gm from gridmarthe.__version__ import _copyleft @@ -36,13 +39,22 @@ def parse_args(): epilog=textwrap.dedent(_copyleft) ) - parser.add_argument('opt', metavar='grid timesteps', type=str, nargs='*', help='Paths to grid [and timesteps if result] files are expected') # nargs='+' + parser.add_argument( + 'opt', + metavar='grid timesteps', + type=str, + nargs='*', + help=( + 'Paths to marthe grid [and timesteps if result] files are expected.' + 'If input is already a netcdf file, this script allow the conversion to shapefile/gpkg.' + ) + ) parser.add_argument('--output' , '-o', type=str, default=None, help='Output filename. Default is input.nc') - parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') + parser.add_argument('--varname' , '-n', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optional, default is 1 (no transformation).') - parser.add_argument('--gpkg', '-g', action="store_const", const=True, default=False, help='Use GPKG format instead of shapefile') - parser.add_argument('--mask', '-m', action="store_const", const=True, default=False, help='Only get a mask of active domain') - parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit') + parser.add_argument('--gpkg' , '-g', action="store_const", const=True, default=False, help='Use GPKG format instead of shapefile') + parser.add_argument('--mask' , '-m', action="store_const", const=True, default=False, help='Only get a mask of active domain') + parser.add_argument('--version' , '-v', action="store_const", const=True, default=False, help='Show version and exit') parser.add_argument('--wide_fmt', '-w', action="store_const", const=True, default=False, help='Use wide format (columns) for time') args = parser.parse_args() @@ -52,12 +64,13 @@ def parse_args(): print(_copyleft) sys.exit(0) + fname, ext = os.path.splitext(args.opt[0]) + args.fname, args.ext = fname, ext if args.output is not None: dirout = os.path.dirname(args.output) if dirout != '': os.makedirs(dirout, exist_ok=True) else: - fname, ext = os.path.splitext(args.opt[0]) args.output = '{}.shp'.format(fname) if args.gpkg: @@ -67,8 +80,8 @@ def parse_args(): if os.path.exists(args.output): os.remove(args.output) - if args.variable is not None: - args.variable = args.variable.upper() + if args.varname is not None: + args.varname = args.varname.upper() return args @@ -80,14 +93,23 @@ def main(): args = parse_args() fpastp = args.opt[1] if len(args.opt) > 1 else None - ds = gm.load_marthe_grid( - args.opt[0], - fpastp=fpastp, - drop_nan=True, - varname=args.variable, - xyfactor=args.xyfactor - ) - + if not args.ext.endswith('nc'): + ds = gm.load_marthe_grid( + args.opt[0], + fpastp=fpastp, + drop_nan=True, + varname=args.varname, + xyfactor=args.xyfactor + ) + else: + ds = xr.open_dataset(args.opt[0]) + if args.xyfactor > 1: + ds['x'].data *= args.xyfactor + ds['y'].data *= args.xyfactor + ds['dx'].data *= args.xyfactor + ds['dy'].data *= args.xyfactor + ds.attrs['scale_factor'] = args.xyfactor + if args.mask: mask = gm.get_active_mask(ds) mask.to_file(args.output) diff --git a/src/gridmarthe/scripts/ncmart.py b/src/gridmarthe/scripts/ncmart.py index f583094..18ffd57 100644 --- a/src/gridmarthe/scripts/ncmart.py +++ b/src/gridmarthe/scripts/ncmart.py @@ -23,54 +23,71 @@ # import os, sys, textwrap from argparse import ArgumentParser, RawDescriptionHelpFormatter + +import xarray as xr + import gridmarthe as gm -from gridmarthe.__version__ import _copyleft #, _show_c, _show_w +from gridmarthe.__version__ import _copyleft -# Usage: `ncmart PATH_CHASIM PATH_PASTP [-o output] [-v varname]` +# Usage: `ncmart PATH_CHASIM PATH_PASTP [-o output] [-v varname]` def parse_args(): """ CLI program """ parser = ArgumentParser( prog='ncmart', + # usage='%(prog)s [options]', formatter_class=RawDescriptionHelpFormatter, - description="Convert a Marthe GridFile to netCDF format.", + description="Convert a Marthe grid file to netCDF format.", epilog=textwrap.dedent(_copyleft) ) - parser.add_argument('opt', metavar='grid timesteps', type=str, nargs='*', help='Paths to grid and timesteps files are expected') # nargs='+' + # TODO split `dump` into subcommand ? + # https://docs.python.org/3/library/argparse.html#argparse.ArgumentParser.add_subparsers + # subparsers = parser.add_subparsers(help='subcommand help') + # parser_a = subparsers.add_parser('a', help='a help') + # parser_a.add_argument('bar', type=int, help='bar help') + + # option 0 or two: gridfile, timestep + parser.add_argument( + 'opt', + metavar='grid [timesteps]', + type=str, + nargs='*',# nargs='+' + help=( + 'Paths to marthe grid and, optionally, timesteps files. ' + 'If gridfile is already a netCDF file, ncmart allows you to modify it (with xyfactor, attrs, etc.).' + ) + ) parser.add_argument('--output' , '-o', type=str, default=None, help='Output filename. Default is input.nc') - parser.add_argument('--variable', '-v', type=str, default=None, help='Variable (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') - parser.add_argument('--as2d' , '-d', action="store_const", const=True, default=False, help='Store grid as 2D (or more), default is 1D for space dimension') #choices=('True','False'), dest='monnomdevariable' + parser.add_argument('--varname' , '-n', type=str, default=None, help='Variable Name (field) to read, default is None: i.e variable will be parsed from file and ONLY the first variable will be read. Pass \'all\' to get all variables.') + parser.add_argument('--as2d' , '-d', action="store_const", const=True, default=False, help='Store grid as 2D (or more), default is 1D for space dimension, ie reduced horizontal grid') #choices=('True','False'), dest='monnomdevariable' parser.add_argument('--xyfactor', '-x', type=float, default=1., help='Transformation factor for coordinates. Optional, default is 1 (no transformation).') - # parser.add_argument('--show', type=str, default='', choices=('c', 'w'), help='Print licensing for warranty (w) and redistribution conditions (c)') - parser.add_argument('--version', '-V', action="store_const", const=True, default=False, help='Show version and exit') + parser.add_argument('--dump' , '-H', action="store_const", const=True, default=False, help='Dump variables names, like ncdump -h FILE.') + parser.add_argument('--attrs' , '-a', type=str, default=None, help='Add global attributes. Comma separated for multiple attrs, = is the separator for key, value. Example: `-a "references=RP-XXXXX-FR,toto=tata"`') + parser.add_argument('--version' , '-v', action="store_const", const=True, default=False, help='Show version and exit') args = parser.parse_args() - # if args.show != '': - # if args.show == 'c': - # _show_c() - # if args.show == 'w': - # _show_w() - # sys.exit(0) if args.version: print('gridmarthe {}'.format(gm.__version__)) print(_copyleft) sys.exit(0) + fname, ext = os.path.splitext(args.opt[0]) + args.fname, args.ext = fname, ext + if args.output is not None: dirout = os.path.dirname(args.output) if dirout != '': os.makedirs(dirout, exist_ok=True) else: - fname, ext = os.path.splitext(args.opt[0]) args.output = '{}.nc'.format(fname) if os.path.exists(args.output): os.remove(args.output) - if args.variable is not None: - args.variable = args.variable.upper() + if args.varname is not None: + args.varname = args.varname.upper() return args @@ -82,16 +99,39 @@ def main(): args = parse_args() fpastp = args.opt[1] if len(args.opt) > 1 else None - ds = gm.load_marthe_grid( - args.opt[0], - fpastp=fpastp, - drop_nan=True, - varname=args.variable, - xyfactor=args.xyfactor - ) + if args.dump: + _var = gm.scan_var(args.opt[0]) + print('Variable found in file:', _var) + sys.exit(0) + + if not args.ext.endswith('nc'): + ds = gm.load_marthe_grid( + args.opt[0], + fpastp=fpastp, + drop_nan=True, + varname=args.varname, + xyfactor=args.xyfactor + ) + else: + # allow transformation of existing netcdf + ds = xr.open_dataset(args.opt[0]) + if args.xyfactor > 1: + ds['x'].data *= args.xyfactor + ds['y'].data *= args.xyfactor + ds['dx'].data *= args.xyfactor + ds['dy'].data *= args.xyfactor + ds.attrs['scale_factor'] = args.xyfactor if args.as2d: ds = gm.assign_coords(ds) + # ds = ds.isel(Y=slice(None, None, -1)) # inverse Y-axis, eg for QGIS view + + # add user attrs + if args.attrs is not None: + _attrs = args.attrs.split(',') + _attrs = [x.split('=') for x in _attrs] + _attrs = {k.strip(): v.strip() for k, v in _attrs} + ds.attrs = {**ds.attrs, **_attrs} encode = { x: {'zlib': True, 'complevel': 6} for x in ds.keys() From 20c863042e39532ecc5cda019128ed6b16211143 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:42:59 +0100 Subject: [PATCH 13/23] update netcdf attrs of marthe grids --- src/gridmarthe/gridmarthe.py | 129 +++++++++++++++++++++++++---------- src/gridmarthe/utils.py | 10 +++ 2 files changed, 102 insertions(+), 37 deletions(-) diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 6c15c29..2132b72 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -23,11 +23,12 @@ # import os -from datetime import datetime +from datetime import datetime, UTC import pandas as pd import numpy as np import xarray as xr +import pyproj from .lecsem import ( modgridmarthe, @@ -50,7 +51,8 @@ assign_coords, stack_coords, dropna, - fillna + fillna, + replace ) from typing import Union @@ -117,23 +119,32 @@ def _parse_attrs( dxlus=None, dylus=None, xcols=None, - yligs=None + yligs=None, + epsg=27572, + reference=None, ): """ Parse attributes for xarray.Dataset nb: attrs must be string, int, float + # TODO: check https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/apa.html + # Memo: + # - for gridmapping https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch05s06.html + # - for reduced-horizontal-grid https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch08s02.html + # - for timeseries https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch09s05.html """ prologue = { - 'conventions' :'CF-1.10', # check https://cfconventions.org/ + 'conventions' :'CF-1.10', # check https://cfconventions.org/ 'title' : title if title is not None else '', 'marthe_grid_version' : 9.0, 'original_dimensions' : 'x,y,z [grids]: ' + '; '.join( [ ' '.join(map(str, x)) for x in dims] ), } - + crs = pyproj.CRS(epsg) grid_attrs = { + 'crs': str(crs.to_cf()), 'lon_resolution': ', '.join(map(str, np.unique(dxlus))), 'lat_resolution': ', '.join(map(str, np.unique(dylus))), + 'resolution_units': crs.coordinate_system.to_cf()[0].get('units', ''), 'scale_factor' : xyfactor, 'nested_grid' : str(is_nested), 'extend' : "xymin : {} {}; xymax: {} {}".format( @@ -150,10 +161,10 @@ def _parse_attrs( } epilogue = { - 'creation_date' : 'Created on {}'.format(datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ UTC')), - 'institution' : 'BRGM, French Geological Survey, Orléans, France', # TODO: make it optional + 'creation_date' : 'Created on {}'.format(datetime.now(UTC).strftime('%Y-%m-%dT%H:%M:%SZ UTC')), + # comment or source ? https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch02s06.html 'comment' : 'Hydrogeological model created with MARTHE code '\ - '(Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code'\ + '(Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code '\ 'for hydro-systems modelling. report BRGM/RP-69660-FR).' } @@ -173,10 +184,11 @@ def load_marthe_grid( add_id_grid: bool = False, title: Union[str, None] = None, var_attrs: dict = {}, + epsg: int = 27572, + full_3d: bool = False, model_attrs: dict = { - 'resolution_units': 'm', - 'projection' : 'epsg:27572', - 'domain' : 'FR-France', + 'domain' : 'FR-France', + 'institution': 'BRGM, French Geological Survey, Orléans, France' }, engine: str = 'xarray', verbose: bool=False, @@ -239,7 +251,7 @@ def load_marthe_grid( Boolean to read only the first layer. Default is False. add_col_row: bool, optional - Add columns (col) and rows (lig) index (from 1 to n), Default is False. + Add columns (col) and rows (row, formerly lig (v<=0.1.3)) index (from 1 to n), Default is False. add_id_grid: bool, optional Add grid id (from 0 to n), useful for nested grids. @@ -251,14 +263,26 @@ def load_marthe_grid( var_attrs: dict, optional Dictionnary of attributes to add to variable DataArray. + epsg: int, optional + EPSG code for projection. Default is 27572 for legacy reasons (Lambert 2 Etendu, for France). + Used to write CRS information in attributes. Useful for GUI (eg visualisation in QGIS). + + full_3d: bool, optional + Is z dimension an aquifer layer or real Z axis (in meters for exemple) + Default is False (z is aquifer layer number) + model_attrs: dict, optional Dictionnary of attributes to add to Dataset. by default, gis attrs are added and can be modified >>> { - ... 'resolution_units': 'm', - ... 'projection' : 'epsg:27572', - ... 'domain' : 'FR-France' + ... 'domain': 'FR-France', + ... 'institution': 'BRGM, French Geological Survey, Orléans, France' + ... } + + For example, if your data is associated with a reference (report, paper, etc.): + >>> { + ... 'references': 'https://doi.org/...' ... } engine: str, optional @@ -276,7 +300,7 @@ def load_marthe_grid( A xarray.Dataset object containing values and attributes read from Marthe grid file. """ legacy_tmp = kwargs.get('nanval') - if legacy_tmp: + if legacy_tmp is not None: print('Warning: nanval argument is deprecated, use nan_value instead') nan_value = legacy_tmp @@ -301,11 +325,12 @@ def load_marthe_grid( # -- recursive call arrays = [] for var in varname: - arrays.append( load_marthe_grid( - filename, var, fpastp, dates, nan_value, drop_nan, xyfactor, shallow_only, - add_col_row, add_id_grid, title, var_attrs, model_attrs, engine, verbose - ) ) - return xr.merge(arrays) + arrays.append(load_marthe_grid( + filename, var, fpastp, dates, nan_value, drop_nan, xyfactor, + shallow_only, add_col_row, add_id_grid, title, var_attrs, epsg, + full_3d, model_attrs, engine, verbose + )) + return xr.merge(arrays, compat='no_conflicts') elif varname.islower(): varname = varname.upper() # in marthegridfiles, varnames are always uppercase; if user pass lowercase, this avoid error/empty array @@ -344,8 +369,21 @@ def load_marthe_grid( vattrs.update(var_attrs) dic_data = { varname.lower() : (["time", "zone"], zvar, vattrs), #dict(**vattrs, **var_attrs) - 'x' : ("zone", xcols, {'units': 'm', 'axis': 'X', 'coverage_content_type' : "coordinate"}), #'standard_name': 'longitude', - 'y' : ("zone", yligs, {'units': 'm', 'axis': 'Y', 'coverage_content_type' : "coordinate"}), #'standard_name': 'latitude' , + 'x' : ("zone", xcols, { + 'units': 'meters', # 'm', degrees_east + # TODO: use pyproj here too ? crs.coordinate_system.to_cf()[0].get('units', '') ? + 'axis': 'X', + # 'standard_name': 'longitude', + 'standard_name': 'projection_x_coordinate', + 'coverage_content_type' : "coordinate" + }), + 'y' : ("zone", yligs, { + 'units': 'meters', # m, degrees_north + 'axis': 'Y', + #'standard_name': 'latitude' , + 'standard_name': 'projection_y_coordinate', + 'coverage_content_type' : "coordinate" + }), 'dx' : ("zone", dxlus), 'dy' : ("zone", dylus) } @@ -364,11 +402,17 @@ def load_marthe_grid( if add_id_grid: dic_data['id_grid'] = ("zone", _get_id_grid(dims)) - # if pseudo2D => add z dimension - # TODO assert valid if real3D - if dims[0][-1] > 1: + # if z z dimension exists (multilayer/3D model) + _has_z_dim = dims[0][-1] > 1 + if _has_z_dim: zlus = _set_layers(dims) - zattrs = {'units': '-', 'axis': 'Z', 'positive': 'down', 'standard_name': 'depth', 'long_name': 'aquifer_layer'} # not if full 3D ! TODO better + zattrs = { + 'units': '-' if not full_3d else 'm', + 'axis': 'Z', + 'positive': 'down', + 'standard_name': 'depth', + 'long_name': 'aquifer_layer' if not full_3d else 'depth' + } dic_data['z'] = ("zone", zlus, zattrs) # add lay if fpastp is not None: @@ -390,11 +434,15 @@ def load_marthe_grid( 'zone': np.arange(1, zvar.shape[1] + 1, dtype=np.int32) }, attrs={ - **_parse_attrs(title, dims, xyfactor, dates, is_nested, dxlus, dylus, xcols, yligs), + **_parse_attrs(title, dims, xyfactor, dates, is_nested, dxlus, dylus, xcols, yligs, epsg), **model_attrs } ) + # add attributes for Reduced horizontal grid + # https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#reduced-horizontal-grid + ds['zone'].attrs['compress'] = "z y x" if _has_z_dim else "y x" + # add non-dimensionnal coordinates # 'xc': (['zone'], xcols), # TODO coordinates directly as coords depending on dims ? # 'yc': (['zone'], yligs), @@ -439,7 +487,7 @@ def reset_geometry(ds, path_to_permh: str, variable='permeab', fillna=False): Join is performed with xy[z] (if xy are present in coords) or zone to get zone back in full domain (if dropped, or nan were dropped, etc.). - If nan were dropped during :py:func:`gridmarthe.load_grid_marthe()`, 'zone_all' + If nan were dropped during :py:func:`gridmarthe.load_grid_marthe`, 'zone_all' was added and will be used (this variable store the zone index before the reindexing during :py:func:`gridmarthe.dropna`). @@ -526,6 +574,7 @@ def write_marthe_grid( A good pratice is to provide the permh file when writing dataset to marthegrid format. >>> gm.write_marthe_grid(ds, 'toto.out', file_permh='./mymodel/model.permh') + WARNING: This function was developped to write parameters grids to marthe format. Not to recreate simulation results (hydraulic head at several timesteps for example) @@ -537,6 +586,7 @@ def write_marthe_grid( >>> ds = ds_head.isel(time=16) # or do an aggregation (eg mean over a period) >>> gm.write_marthe_grid(ds, 'mymodel.charg') + Parameters ---------- ds: xr.Dataset @@ -545,26 +595,31 @@ def write_marthe_grid( fileout: str filename to write - varname: str (optional) + varname: str, optional variable name (key) containing values. - file_permh: str (optional) + file_permh: str, optional path to the permh file corresponding to current Marthe model. Needed to recreate full dimension if NaN dropped before. + + nan_value: float, optional + custom value to fillna, when using a `permh` field to reset geometry - title: str (optional) + title: str, optional title written in marthe grid file - dims: list of array + dims: list of array, optional list containing array of dimension for every grid (ie len(dims) > 1 if nested grid) - format is `[[x_main_grid, y_main_grid, z_main_grid], [x_nested_1, ...], ...]` + + - format is `[[x_main_grid, y_main_grid, z_main_grid], [x_nested_1, ...], ...]` eg. `[[354,252,2], [182,156,2]]` - if only main grid : `[[nx,ny,nz]]` - if None (default, dims will be parsed from ds.attrs['original_dimensions'] which is added + - if only main grid : `[[nx,ny,nz]]` + - if None (default, dims will be parsed from ds.attrs['original_dimensions'] which is added when read with :py:func:`gridmarthe.load_marthe_grid`. If not present (lost in some computation for example), please use py:func:`gridmarthe.reset_geometry` or provide list of dims manually. - debug: bool, optional (default is False). + debug: bool, optional + print debug informations. Default is False Returns ------- diff --git a/src/gridmarthe/utils.py b/src/gridmarthe/utils.py index f4d4307..7b1a94a 100644 --- a/src/gridmarthe/utils.py +++ b/src/gridmarthe/utils.py @@ -253,6 +253,9 @@ def assign_coords(ds, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zon # in some case, even if z is included it should not be treated as coord (ex. plot outcrop) z_coords = None + # attrs not kept ? force to keep them + coords_attrs = [ds.y.attrs, ds.x.attrs] + da = ds.assign_coords( #x=(zone_label, np.around(da_in[coords[0]].data, 1) ), x=(zone_label, ds[coords[0]].data ), @@ -263,10 +266,17 @@ def assign_coords(ds, add_lay=True, coords=['x', 'y', 'z'], keep_zone=False, zon if z_coords is not None: da = da.assign_coords(z=(zone_label, ds[coords[2]].data)) dims.insert(0, 'z') + coords_attrs.insert(0, ds.z.attrs) da = da.set_index(zone=dims) if not keep_zone: da = da.drop_duplicates(zone_label).unstack(zone_label) # drop duplicates is a security for nested grids, if dropnan was not performed + + da.x.attrs = coords_attrs[-1] + da.y.attrs = coords_attrs[1] + if z_coords is not None: + da.z.attrs = coords_attrs[0] + return da.sortby(dims) From 1a10cd51b92918421e139e1c2110352e04f93a02 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:44:05 +0100 Subject: [PATCH 14/23] add: support for reading/plotting velocity --- src/gridmarthe/__init__.py | 8 +- src/gridmarthe/plot.py | 191 ++++++++++++++++++++++++++++++++++--- src/gridmarthe/velocity.py | 59 ++++++++++++ 3 files changed, 244 insertions(+), 14 deletions(-) create mode 100644 src/gridmarthe/velocity.py diff --git a/src/gridmarthe/__init__.py b/src/gridmarthe/__init__.py index 4ef66a0..c7d69e1 100644 --- a/src/gridmarthe/__init__.py +++ b/src/gridmarthe/__init__.py @@ -23,7 +23,13 @@ # from .__version__ import __version__ + from .gridmarthe import * from .operasem import * -from .plot import plot_nested_grid, plot_outcrop + +from .velocity import read_velocity +from .plot import ( + plot_nested_grid, plot_outcrop, + plot_veloc_quiver, plot_cross_section +) diff --git a/src/gridmarthe/plot.py b/src/gridmarthe/plot.py index 812f0ea..968511d 100644 --- a/src/gridmarthe/plot.py +++ b/src/gridmarthe/plot.py @@ -34,7 +34,6 @@ from .utils import _get_scale -# TODO: plot_section() # outil de coupe """ Module for visualisation of gridmarthe files """ @@ -56,7 +55,8 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): ---------- ds: xr.Dataset the dataset MUST be a 2D array, with dims = x,y. - In other words, you may need to sel z and time before plot, and you need to apply `assign_coord()` + In other words, you may need to sel z and time before plot, and you need to apply + :py:func:`gridmarthe.assign_coord` ax: matplotlib axe, optional. if provided, data are plotted on this axis, otherwise fig, ax instances will be created. @@ -64,8 +64,8 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): varname: str, optional the variable to plot in dataset. Default is 'charge'. - kwargs: optional. - any keywords argument from `xr.Dataset.plot.pcolormesh()` + **kwargs + any keywords argument from `xr.Dataset.plot.pcolormesh` Returns ------- @@ -105,20 +105,20 @@ def plot_nested_grid(ds, ax=None, varname='charge', **kwargs): _set_map_lims(ax, da.x.min().data, da.y.min().data, da.x.max().data, da.y.max().data) - if str(da.time.values)[:10] == '1850-01-01': + if hasattr(da, 'time') and str(da.time.values)[:10] == '1850-01-01': # remember: no slice on time because it must be selected before calling function ax.set_title(varname) # remove default title from xarray API if dummy timestep (e.g plot parameters) return ax -def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize=(12,4), **kwargs): +def plot_mesh_time_serie(*args, zone: int, varname='charge', show=False, figsize=(12,4), **kwargs): """ Usefull function to plot time serie from any dataset, by extracting a specific cell timeserie Parameters ---------- - arg: xr.Dataset, + *args: xr.Dataset, any datasets (you can pass multiple datasets, eg. `plot_mesh_time_serie(ds1, ds2, ds3, ... zone=32)` zone: int @@ -133,7 +133,8 @@ def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize= figsize: tuple[int], optional. figsize argument for matplotlib. - kwargs: any keywords argument for `xr.Dataset.plot()` method + **kwargs + any keywords argument for `xr.Dataset.plot()` method Returns ------- @@ -141,7 +142,7 @@ def plot_mesh_time_serie(*arg, zone: int, varname='charge', show=False, figsize= """ fig, ax = plt.subplots(figsize=figsize) - for da in arg: + for da in args: da[varname].sel(zone=zone).plot(ax=ax, **kwargs) ax.grid(True) if show: @@ -161,13 +162,15 @@ def plot_outcrop(ds_outcrop, fig=None, ax=None, cbar_width='3', labels=None, fil Note ---- - - if input is a `xr.Dataset` instance, `ds_outcrop` need to get coords before - but keep `z` as a variable (use `gm.assign_coords(ds_outcrop, add_lay=False)`) + If input is a `xarray.Dataset` instance, `ds_outcrop` need to get coords before + but keep `z` as a variable. To do so, use: + + >>> gm.assign_coords(ds_outcrop, add_lay=False) Parameters ---------- ds_outcrop: xr.Dataset - output of `gm.get_surface_mask()` + output of :py:func:`gridmarthe.get_surface_mask` fig: matplotlib figure, Optional. if provided, data are plotted on this figure, otherwise fig, ax instances will be created @@ -187,7 +190,7 @@ def plot_outcrop(ds_outcrop, fig=None, ax=None, cbar_width='3', labels=None, fil show: bool, Optional. Show result (`plt.show()`), default is False. - kwargs: dict, Optional. + **kwargs Any keywords argument to pass to either `xarray.Dataset.plot.pcolormesh()` or `geopandas.GeoDataFrame.plot()`. @@ -197,6 +200,7 @@ def plot_outcrop(ds_outcrop, fig=None, ax=None, cbar_width='3', labels=None, fil Example ------- + xarray version: >>> ds_surf = gm.get_surface_layer(ds) @@ -286,3 +290,164 @@ def plot_outcrop(ds_outcrop, fig=None, ax=None, cbar_width='3', labels=None, fil return None else: return fig, ax, ax_cbar + + +def plot_veloc_quiver(ds, ax=None, xyfreq=1, veclenght=1, color_mod=False, sqrt_norm=True, loc_scale_xy=(0.1,0.1)): + r""" Plot velocity field as quiver + + Parameters + ---------- + ds: xr.Dataset + input dataset with velocity variables. Expected variables are: + - `vx`: velocity in x direction + - `vy`: velocity in y direction + - `vmod`: velocity module + + See :py:func:`gm.read_velocity` to get velocity dataset from file. + + ax: matplotlib.axes, optional + if not provided, an ax will be created and returned. + + xyfreq: int, optional + filter velocity data every X cell. + + veclenght: float, optional + vector scale legend. Default is 1. + + color_mod: bool, optional + use velocity module as color field. Default is False. + + sqrt_norm: bool, optional + Norm the velocity data `u` and `v`, with + + .. math:: + + u = u / \sqrt{u^2 + v^2} + + v = v / \sqrt{u^2 + v^2} + + Default is True (recommended). + + loc_scale_xy: list or tuple, optional + coordinates (X, Y) in Axes dimension (from 0 to 1) where to add + the velocity scale. + + Returns + ------- + ax: matplotlib.axes + """ + if ax is None: + fig, ax = plt.subplots() + + # norm data before plot + vx, vy = ds['vx'].data, ds['vy'].data + x, y = ds['x'].data, ds['y'].data + module_v = ds['vmod'].data + if sqrt_norm: + norm = np.sqrt(vx**2 + vy**2) + vx_norm = vx / norm + vy_norm = vy / norm + else: + vx_norm, vy_norm = vx, vy + + # plot data + # kwargs not accepted for x,y,u,v,c ; + # use a list to make c optional + args = [ + x[::xyfreq], y[::xyfreq], + vx_norm[:,::xyfreq], vy_norm[:,::xyfreq], + ] + if color_mod: + args.append(module_v[:, ::xyfreq]) + + quiver = ax.quiver( + *args, + # scale=9.5e-4, + scale_units='xy', + pivot='mid', + width=0.003, headwidth=2, headlength=4, headaxislength=3.5, + ) + # add scale, copy from : https://tristansalles.github.io/EnviReef/5-xarray/examples/maps.html + maxstr = r'$%3.1f \cdot 10^{%1.0f} m.s^{-1}$' % (veclenght, np.log10(np.nanmedian(np.abs(norm)))) + plt.quiverkey(quiver, loc_scale_xy[0], loc_scale_xy[1], veclenght, maxstr, labelpos='S', + coordinates='axes').set_zorder(11) + ax.set_title('Velocity field') + return ax + + +def plot_cross_section(ds_xs, fig=None, ax=None, cbar_size=3, cmap=None, norm=None, labels=None): + """ Plot cross-section from a cross-section dataset. + + Parameters + ---------- + ds_xs : xarray.Dataset + A cross-section dataset, as returned by :py:func:`gridmarthe.slice_cross_section`. + fig : matplotlib.Figure, optional + A figure to plot on, by default None. + If None, a new figure will be created. + ax : matplotlib.Axes, optional + An axis to plot on, by default None. + If None, a new figure and axis will be created. + cmap : matplotlib.colors.Colormap, optional + A colormap to use for the layers, by default None. + If None, a default colormap will be created. + It should be a ListedColormap with as many colors as layers in the dataset. + norm : matplotlib.colors.BoundaryNorm, optional + A normalization instance to map layer values to colors, by default None. + If None, a default BoundaryNorm will be created. + labels : list, optional + Labels for the layers in the colorbar, by default None. + If None, default labels will be used (layer numbers). + cbar_size : int, optional + Size of the colorbar as a percentage of the main axis, by default 3. + + Returns + ------- + ax : matplotlib.Axes + The axis with the cross-section plot. + cax : matplotlib.Axes + The axis with the colorbar. + + See Also + -------- + gridmarthe.slice_cross_section : Function to create cross-section dataset. + gridmarthe.operasem.xsection._mk_cross_section_geom : Function to create cross-section geometry. + """ + from .operasem.xsection import _mk_cross_section_geom + + if cmap is None: + cmap = colors.ListedColormap(cm.tab10.colors) + if labels is None: + labels = ['Layer {:.0f}'.format(i) for i in ds_xs['z'].data] + if ax is None or fig is None: + fig, ax = plt.subplots() + + if norm is None: + bounds = np.arange(1, len(cmap)+2) # add a fictive layer at the end because this is lower bounds + norm = colors.BoundaryNorm(bounds, cmap.N) + else: + bounds = norm.boundaries + + # Create cax for colorbar + divider = make_axes_locatable(ax) + cax = divider.append_axes('right', size=f'{cbar_size}%', pad=0.05) + + df = _mk_cross_section_geom(ds_xs) + for z, gr in df.groupby('z'): + for polyg in gr['geom']: + ax.fill(*polyg.exterior.xy, color=cmap.colors[z-1], edgecolor='none',alpha=0.5) + + # Future work: improve colorbar handling + # extract same corde from plot_outcrop() --> plotting.utils + # then use here + ax_cbar = fig.colorbar( + cm.ScalarMappable(cmap=cmap, norm=norm), + cax=cax, ax=ax, + orientation='vertical', + ticks=bounds+0.5, + label='Layers', + ) + ax_cbar.ax.tick_params(size=0) + ax_cbar.set_ticklabels(labels + ['none']) # add fake label to match bounds dimension + ax_cbar.ax.invert_yaxis() + return ax, ax_cbar \ No newline at end of file diff --git a/src/gridmarthe/velocity.py b/src/gridmarthe/velocity.py new file mode 100644 index 0000000..73af767 --- /dev/null +++ b/src/gridmarthe/velocity.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import re +from io import StringIO +import numpy as np +import pandas as pd + + +def read_velocity(fveloc): + """ Read and parse the velocity file output of MARTHE `veloci.out` + + Parameters + ---------- + fveloc : str, Path + path to file + + Returns + ------- + xr.Dataset + """ + + with open(fveloc, 'r', encoding='ISO-8859-1') as f: + content = f.read() + + times = re.findall(r'Step=\s*(?P\d+)\s*;\s*Date=\s*(?P[+-]?\d+(?:\.\d+)?)', content) + times = [(int(step), float(date)) for step, date in times] # convert str to int/float + grids = re.split(r'^.*Step=\s*\d+\s*;\s*Date=\s*\d+.*$', content, flags=re.MULTILINE)[1:] + + res = [] + coords = [] + for i, grid in enumerate(grids): + # exemple line in a grid: (x,y,z)(Vx,Vy,Vz,Module m.s-1) + # 9.500000E+02 3.250000E+03 1.690750E+02 0.000E+00 -8.255E-04 0.000E+00 M= 8.255E-04 #,Z,Y,X= 0 1 1 10 + df = pd.read_csv( + StringIO(grid), + sep=r'\s+', + skiprows=1, # cause title/headers still here + header=None, + names=['x', 'y', 'z', 'vx', 'vy', 'vz', 'M_dummy', 'vmod', 'xyz_dummy', 'igrid', 'lay', 'row', 'col'], + dtype={'x': float, 'y': float, 'z': float, 'vx': float, 'vy': float, 'vz': float,} + ) + df = df.drop(columns=['M_dummy', 'xyz_dummy']) + + df['istep'] = times[i][0] + df['zone'] = np.arange(1, len(df) + 1) + + if i == 0: + # get coordinates only at first timestep + coords.append(df[['zone', 'x', 'y', 'z', 'igrid', 'lay', 'row', 'col']].set_index('zone')) + + df = df.drop(columns=['x', 'y', 'z', 'igrid', 'lay', 'row', 'col']) + res.append(df.set_index(['istep', 'zone'])) + + res = pd.concat(res) + coords = pd.concat(coords) + res = res.to_xarray().merge(coords.to_xarray()) + res = res.assign_coords(time=('istep', np.array([x[1] for x in times]))) + return res From e8a23b8a8addbff00cd6f7589f72f803280139f8 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:45:21 +0100 Subject: [PATCH 15/23] add: (beta) support for cross sections --- src/gridmarthe/operasem/__init__.py | 1 + src/gridmarthe/operasem/gis.py | 16 +---- src/gridmarthe/operasem/gutils.py | 20 ++++++ src/gridmarthe/operasem/xsection.py | 105 ++++++++++++++++++++++++++++ 4 files changed, 127 insertions(+), 15 deletions(-) create mode 100644 src/gridmarthe/operasem/gutils.py create mode 100644 src/gridmarthe/operasem/xsection.py diff --git a/src/gridmarthe/operasem/__init__.py b/src/gridmarthe/operasem/__init__.py index ff5b8df..d91ad6a 100644 --- a/src/gridmarthe/operasem/__init__.py +++ b/src/gridmarthe/operasem/__init__.py @@ -12,3 +12,4 @@ from .geometry import * from .interp import * from .gis import * +from .xsection import slice_cross_section diff --git a/src/gridmarthe/operasem/gis.py b/src/gridmarthe/operasem/gis.py index a54b728..c73ef06 100644 --- a/src/gridmarthe/operasem/gis.py +++ b/src/gridmarthe/operasem/gis.py @@ -27,28 +27,14 @@ import numpy as np -from shapely.geometry import Polygon from pyproj import Transformer import geopandas as gpd import xarray as xr from ..utils import assign_coords +from .gutils import _polygonize -def _mk_cell_polygon(xleft, ylower, xright, yupper): - return Polygon( - ( - (xleft , ylower), - (xright, ylower), - (xright, yupper), - (xleft , yupper), - (xleft , ylower) - ) - ) - - -_polygonize = np.vectorize(_mk_cell_polygon) - def _build_polyg(ds): """ build a (rectangular) polygon shape from marthegrid dataset """ diff --git a/src/gridmarthe/operasem/gutils.py b/src/gridmarthe/operasem/gutils.py new file mode 100644 index 0000000..4c7aeef --- /dev/null +++ b/src/gridmarthe/operasem/gutils.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +from shapely.geometry import Polygon + +def _mk_cell_polygon(xleft, ylower, xright, yupper): + return Polygon( + ( + (xleft , ylower), + (xright, ylower), + (xright, yupper), + (xleft , yupper), + (xleft , ylower) + ) + ) + + +_polygonize = np.vectorize(_mk_cell_polygon) + diff --git a/src/gridmarthe/operasem/xsection.py b/src/gridmarthe/operasem/xsection.py new file mode 100644 index 0000000..caceb69 --- /dev/null +++ b/src/gridmarthe/operasem/xsection.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +import pandas as pd + +from .gutils import _polygonize + + +# or get_transect +def slice_cross_section(grid, x=None, y=None, agg_along_axis=None): + """ grid needs geom infos and to have x,y as dimension. + See :py:func:`gridmarthe.compute_geometry` and :py:func:`gridmarthe.assign_coords` + + Parameters + ---------- + grid: xr.Dataset + Dataset with geometry infos, x,y as dimensions. + + x, y : float, optional + Coordinates on which search for cross section transect. + + agg_along_axis : str, optional + If not None, aggregate along axis (mean). Accepted values are 'x' or 'y'. + + Returns + ------- + xr.Dataset + Dataset for cross section transect. + + Example + ------- + >>> geom = gm.compute_geometry(topo, hsub, mask.zone) + >>> ds_2d = gm.assign_coords(geom) + >>> ds_xsection = slice_cross_section(ds_2d, x=6.116e5) + """ + # idea: set from bbox... + # slice not authorized in sel with nearest + # npoints=100 + coords = { + 'x': x, + 'y': y, + # 'x': np.linspace(x1, x2, npoints), + # 'x': slice(*[x1, x2]), + # 'y': slice(*[y1, y2]), + } + coords = {k: v for k, v in coords.items() if v is not None} + crossect = grid.sel(method='nearest', **coords) # tolerance=2e3 + if agg_along_axis is not None: + crossect = crossect.mean(dim=(agg_along_axis), skipna=True) + + return crossect + + +def _mk_cross_section_geom(ds_xsection): + """ Create polygon geometries for cross section plot + + Parameters + ---------- + ds_xsection : xarray.Dataset + dataset sliced on cross section transect. Geometry informations + `z_upper`, `z_lower`, `dx` and `dy` must be present. + See :py:func:`gridmarthe.compute_geometry` + mode : str, optional + Mode of cross section ('x' or 'y', default is 'x'). Wether the cross + section is done along x or y axis. + + Returns + ------- + pd.DataFrame + dataframe with geometry column `geom` + + Notes + ----- + - TODO: more flexible transect + """ + if 'time' in ds_xsection.dims: + ds_xsection = ds_xsection.copy().isel(time=0) + # Warning, the following is True only in the current state of + # slicing along one full axis. The remaining coordinates is scalar + # and therefore not a dimension in the data array + if 'x' in ds_xsection.dims: + mode = 'x' + elif 'y' in ds_xsection.dims: + mode = 'y' + else: + raise ValueError('Argument `ds_xsection` has no x or y dimension coordinates.') + + df = ds_xsection.to_dataframe().reset_index().dropna() + grp = df.groupby(['x', 'y']) + geoms = [] + for gr, dff in grp: + geoms.append(pd.DataFrame( + _polygonize( + np.round(dff[mode].values - dff[f'd{mode}'].values/2, 2), + dff['z_lower'].values, + np.round(dff[mode].values + dff[f'd{mode}'].values/2, 2), + dff['z_upper'].values + ), + index=dff.index, + columns=['geom'] + )) + + df = df.join(pd.concat(geoms)) + return df From abc95dc1ba3e9f4cc0b576a61c8d7aa74cac6d61 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:46:40 +0100 Subject: [PATCH 16/23] doc: general update (docstrings+ add/update examples) --- docs/source/api/io_operations.rst | 2 + docs/source/api/plotting.rst | 2 + .../user_guide/example/cross_section.ipynb | 194 + .../user_guide/example/data/chasim_hallue.nc | Bin 1124804 -> 1114944 bytes .../user_guide/example/data/craie_npc.hsubs | 3340 ++++++ .../user_guide/example/data/craie_npc.permh | 9904 ++++++----------- .../user_guide/example/data/craie_npc.topog | 595 + .../example/data/craie_npc_gig.permh | 6564 +++++++++++ .../user_guide/example/gm_example_en.ipynb | 2393 ++++ docs/source/user_guide/example/preproc.ipynb | 18 + .../user_guide/example/treatments.ipynb | 4 +- docs/source/user_guide/example/veloc.ipynb | 153 + docs/source/user_guide/index.rst | 3 + src/gridmarthe/operasem/geometry.py | 10 +- src/gridmarthe/operasem/interp.py | 2 +- 15 files changed, 16610 insertions(+), 6574 deletions(-) create mode 100644 docs/source/user_guide/example/cross_section.ipynb create mode 100644 docs/source/user_guide/example/data/craie_npc.hsubs create mode 100644 docs/source/user_guide/example/data/craie_npc.topog create mode 100644 docs/source/user_guide/example/data/craie_npc_gig.permh create mode 100644 docs/source/user_guide/example/gm_example_en.ipynb create mode 100644 docs/source/user_guide/example/veloc.ipynb diff --git a/docs/source/api/io_operations.rst b/docs/source/api/io_operations.rst index b148108..515b30a 100644 --- a/docs/source/api/io_operations.rst +++ b/docs/source/api/io_operations.rst @@ -10,3 +10,5 @@ IO operations load_marthe_grid write_marthe_grid + + read_velocity diff --git a/docs/source/api/plotting.rst b/docs/source/api/plotting.rst index bbc1141..74cab0f 100644 --- a/docs/source/api/plotting.rst +++ b/docs/source/api/plotting.rst @@ -11,3 +11,5 @@ Plotting plot_nested_grid plot_outcrop + + plot_veloc_quiver diff --git a/docs/source/user_guide/example/cross_section.ipynb b/docs/source/user_guide/example/cross_section.ipynb new file mode 100644 index 0000000..b3ba0b5 --- /dev/null +++ b/docs/source/user_guide/example/cross_section.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d454bf5", + "metadata": {}, + "source": [ + "# Visualize cross section" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aee79787", + "metadata": {}, + "outputs": [], + "source": [ + "# load the domain with hydraulic conductivity field, and geometry\n", + "import gridmarthe as gm\n", + "\n", + "permh = gm.load_marthe_grid('data/craie_npc.permh', drop_nan=True)\n", + "topo = gm.load_marthe_grid('data/craie_npc.topog', varname='H_TOPOGR')\n", + "hsub = gm.load_marthe_grid('data/craie_npc.hsubs', varname='H_SUBSTRAT')\n", + "gwl = gm.load_marthe_grid('data/chasim_npc.out', varname='CHARGE', drop_nan=True) # groundwater level\n", + "\n", + "geom = gm.compute_geometry(topo, hsub, permh.zone)\n", + "geom['charge'] = (('time', 'zone'), gwl['charge'].data) # add gwl to new dataset with geom\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "8431a69c", + "metadata": {}, + "source": [ + "First, we extract the cross section values.\n", + "For this, X,Y coordinates need to be assigned." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ba6f5936", + "metadata": {}, + "outputs": [], + "source": [ + "# assign coords:\n", + "ds = gm.assign_coords(geom)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d0a1634", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 170kB\n", + "Dimensions: (z: 10, y: 301, time: 1)\n", + "Coordinates:\n", + " * z (z) int64 80B 1 2 3 4 5 6 7 8 9 10\n", + " * y (y) float32 1kB 2.527e+06 2.528e+06 ... 2.677e+06 2.677e+06\n", + " x float32 4B 6.118e+05\n", + " * time (time) datetime64[ns] 8B 1850-01-01\n", + "Data variables:\n", + " h_topogr (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + " dx (z, y) float32 12kB nan nan nan nan nan ... nan nan nan nan nan\n", + " dy (z, y) float32 12kB nan nan nan nan nan ... nan nan nan nan nan\n", + " z_lower (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + " z_upper (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + " thickness (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + " depth (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + " charge (time, z, y) float64 24kB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes: (12/17)\n", + " conventions: CF-1.10\n", + " title: \n", + " marthe_grid_version: 9.0\n", + " original_dimensions: x,y,z [grids]: 387 304 10\n", + " crs: {'crs_wkt': 'PROJCRS[\"NTF (Paris) / Lambert zone II...\n", + " lon_resolution: 500.0\n", + " ... ...\n", + " period: 1850-1850\n", + " frequency: nan day(s)\n", + " creation_date: Created on 2025-11-19T09:43:31Z UTC\n", + " comment: Hydrogeological model created with MARTHE code (Thi...\n", + " domain: FR-France\n", + " institution: BRGM, French Geological Survey, Orléans, France\n" + ] + } + ], + "source": [ + "XC = 6.116e5\n", + "ds_xs = gm.slice_cross_section(ds, x=XC) # here we define a cross section along y axis (x is constant)\n", + "print(ds_xs)" + ] + }, + { + "cell_type": "markdown", + "id": "dedd9280", + "metadata": {}, + "source": [ + "We can plot this with the helpful function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40c9b180", + "metadata": {}, + "outputs": [], + "source": [ + "# 1st prep data and colormap/norm\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt, colors\n", + "\n", + "line = ds_xs['charge'].isel(time=-1).sel(z=6).to_dataframe().reset_index()\n", + "surf = gm.get_surface_layer(gwl)\n", + "\n", + "# prep colormap\n", + "colours = ['slategrey', 'burlywood', 'red', 'yellow', 'orange', 'lightgreen', 'lightgrey', 'steelblue', 'darkgreen', 'brown']\n", + "bounds = np.arange(1, len(colours)+2) # add a fictive layer at the end because this is lower bounds\n", + "labels = ['Alluv.', 'Limons', 'Fland', 'Land', 'Than', 'Craie', 'No prod.', 'Tur', 'Ceno', 'Carbo']\n", + "\n", + "cmap = colors.ListedColormap(colours)\n", + "norm = colors.BoundaryNorm(bounds, cmap.N)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e336cc98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# CREATE AXES\n", + "fig, axes = plt.subplots(ncols=2, figsize=(14,4), width_ratios=[0.5, 0.6])\n", + "\n", + "# MAP\n", + "ax = axes[0]\n", + "gm.plot_outcrop(\n", + " gm.assign_coords(surf, add_lay=False),\n", + " fig=fig, ax=ax, cmap=cmap, norm=norm, labels=labels, cbar_width=4,\n", + " alpha=0.7\n", + ")\n", + "ax.axvline(x=XC, color='k', label='cross_section')\n", + "\n", + "# CROSS-SECT\n", + "ax = axes[1]\n", + "gm.plot_cross_section(ds_xs, fig=fig, ax=ax, cmap=cmap, norm=norm, labels=labels)\n", + "line.plot(y=\"charge\", x='y', ax=ax, color='blue', linewidth=0.7, alpha=0.6) # gwl line\n", + "ax.set_xlim(2.5692e6)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gm", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + 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120 107.25 105 88 0 0 0 0 87.91 103.62 94 115.64 118.39 120.78 122.33 0 0 0 0 111.08 90.55 78.96 83.31 0 0 0 0 0 0 0 0 0 0 63.38 51 48.33 40 40.65 36.54 0 0 0 22.32 0 23.08 26.85 0 0 0 18 19.17 15.89 12.68 9.56 8.63 9.31 10.42 11.56 12.7 13.74 14.61 0 0 17.25 17.01 16.4 17.15 17.38 18.13 18.82 18 0 0 14.1 10.66 8.4 6.85 5.66 4.17 2 1 -.61 -1.94 -2.8 -2.76 -1.71 .22 2.94 5.73 8.67 11.76 15.03 17.31 17.78 17.9 17.47 17.33 21 28 32 34.28 32 30 30 30 32.37 31.93641 32.1392 31.9216 31.0944 29.8884 28.526 26.1772 23.4076 20.1208 16.3812 12.8948 10.8924 10.61182 0 0 0 14.15 14.7472 20.2832 23.95474 26.825 0 38 39.898 36.98 39.90125 43.62 0 0 0 0 0 52.5353 49.6395 46.68777 42.612 41.7996 42.4072 40.59 38.6612 36.47053 35.47 0 0 0 42.32666 42.73227 42.1036 42.0412 41.8752 42.8012 43.78799 44.136 44.6656 44.39759 43.6244 43.37 43.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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12.3975 0 0 13.4875 13.98291 15.166 15.7712 15.1196 19.998 24.76357 27.146 30.386 32.99083 34.5904 37.31783 40.03091 0 0 0 0 0 0 0 46.5223 46.92714 46.6776 45.9224 42.8916 38.8568 36.4604 35.42857 0 0 42.71 41.48625 41.2292 40.9924 41.25 42.9232 44.398 45.5248 45.9228 45.594 45.2008 44.93679 45.3675 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +115 2620850 0 0 0 0 0 0 0 0 0 0 0 0 0 156 140 130.13 120 109.73 106.06 0 99.93 0 0 0 96.75 102.61 0 108.76 97.84 89.67 0 103.98 111.74 115 0 119.22 111.57 104.12 0 0 0 85.63 96.49 0 0 105.92 0 103 112.39 116.79 122.38 129.14 134.56 133.69 130.4 129.46 126.8 122.76 0 121.06 120.06 114 0 0 0 0 0 125.6 125.14 128.12 130.74 127.42 124 0 0 0 0 96 116.93 121 0 0 0 98 0 95.23 0 0 0 71.98 78 0 148.13 144.64 131.74 119.12 109.22 101.26 110 111 100 95 98 82 87.92 82.7 83.89 84.08 0 80.29 0 59 81.58 83.25 83.68 82.75 77.91 0 62.01 50 0 43 35 29.53 0 0 24 27 30.61 29 0 20 19 19.94 16.98 14.26 11.85 11.26 12.46 13.98 15.42 16.29 16.97 17.83 0 0 0 19 0 0 19 21.28 19 17.9 0 0 0 9.74 6.81 6.11 5.39 4.66 4.25 2.62 2 1.23 .91 1.21 2.35 4.02 6.22 8.67 11.01 13.76 16.05 17.93 19.31 0 0 0 0 38.32 40.24 37.8 34.91 33.4 32.91 32.99 33.41 33.399 33.662 32.43777 31.63652 30.74 29.1716 27.4432 24.7744 22.3828 19.9788 17.5764 15.83083 0 0 0 13.90625 14.6 16.64609 17.1448 17.1496 17.2308 18.3008 23.2076 26.6756 28.5612 31.0108 35.7376 40.793 0 0 0 0 0 0 0 0 47.20428 44.9536 43.6476 41.3728 38.7592 36.42879 34.82666 0 0 41.02 39.98167 40.266 40.58601 41.5428 43.2844 44.8976 46.1188 47.32761 47.9368 47.48 46.4168 47.60608 49.675 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +116 2620350 0 0 0 0 0 0 0 0 0 0 0 0 0 155.7 150.02 130.11 111 0 0 94.38 91.21 83.76 70.24 0 87.48 94.94 0 0 86.17 0 92.1 98 104.91 0 109.31 109.47 0 0 0 0 0 0 98.08 0 0 0 0 111.3 0 129.75 0 130.05 137.79 135.99 132.5 131.47 129.85 126.7 119 0 0 0 115.52 117.94 112 120.06 0 0 116.41 121.13 122.62 117.06 0 0 0 103 111.78 106 131 145.5 150.39 138.51 120.17 101 87 73 0 0 0 0 0 0 166.47 158.28 137.17 119.1 106.38 97 0 120 112 109 105 103 98.62 86.36 0 0 89.84 80 75.24 68.15 0 79.55 85.4 86.32 84.04 77.21 64 0 53.93 47.43 0 0 0 0 0 30 0 0 0 0 19 18 18.5 16.53 15.07 14.81 15.49 16.66 17.8 18.4 18.59 18.77 0 0 0 19 0 27.92 26.05 26.61 19 0 0 0 0 8.51 8.18 6.28 5.72 6.01 5.56 3.76 3.07 2.95 2.94 3.42 4.29 5.62 7.69 9.94 12.23 14.52 16.55 18.44 19.94 0 0 0 28 34 0 36.2 31 31.89 31.82 32.14 33.14 35.74538 35.95 31.42429 32.82445 31.1776 27.7576 24.2884 23.8528 23.1148 21.48 19.53385 0 0 0 0 15.23765 16.5064 18.776 20.7596 21.4792 21.1472 20.47 22.0828 24.77 26.682 29.9912 34.24609 35.285 0 0 0 0 0 0 43 45.11071 45.23239 43.8524 41.1224 39.1316 37.22917 35.44455 31.792 28.80625 28.535 40.90076 40.63001 40.296 40.446 41.0392 42.7168 44.6276 46.5936 48.47719 49.3148 49.6856 48.5704 48.98945 49.88333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +117 2619850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 126.03 0 106.12 0 91.72 0 76.2 0 76.56 0 89.31 90.81 0 0 76.69 89.14 93.33 97 0 107.67 105.45 98.67 98.52 99.86 100 0 0 0 0 0 0 0 0 0 0 0 129.54 130.3 129.58 131.21 132.33 132.73 132.06 127 0 117.96 117.28 124.97 124.55 107 124.42 117.57 0 0 117.54 120.82 123.51 0 92 97 0 146.3 145.19 145.75 145.37 139.84 118 106 97 89 84.64 74.73 0 81.19 84.94 86 0 171.26 167 131 124.68 113.44 105 108 0 120.4 127.51 132 131.18 83 0 87 90.04 86 84 82 74.36 66 81.84 87.34 88.97 87.21 82.87 74.7 67.05 61.12 0 43 37 0 0 0 0 0 31.6 28.87 26.47 0 18 19 19.25 18.55 18.37 18.6 19.25 0 0 0 0 16 18 0 0 0 27 26 31 35.26 30.98 19 0 0 7.55 8.11 5.97 6.77 7.37 6.83 6.7 4.94 4.56 4.76 5.17 6.27 7.36 9.03 11.42 13.43 15.46 17.18 18.72 19 20.73 20.93 21.57 25.49 29.4 28 27 25 28 29.74 30.23 31.53 34.56667 37.93778 35.87727 36.31166 32.2236 26.3184 22.0004 23.2872 22.7175 21.94222 0 0 0 0 0 16.98833 17.962 19.9696 22.0592 23.6888 25.23 27.0036 28.9328 29.55526 28.708 30.14222 33.5 0 0 0 0 0 0 0 40.43455 40.2816 41.718 43.14692 40.97688 40.89 37.16167 33.388 32.1987 28.3616 27.618 0 39.17667 39.91734 40.30176 40.53278 41.0964 42.4812 45.2568 47.73167 49.795 50.275 50.42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +118 2619350 0 0 0 0 0 0 0 0 0 0 0 0 0 134.67 0 128.99 120.18 105.52 0 0 0 0 0 76 83.27 85.74 87.67 83.21 0 0 85.94 86.18 0 101.33 111.46 105.38 95.53 91.68 95.14 0 0 0 0 0 0 0 105 0 0 0 0 0 0 0 130.75 134.27 137.99 139.34 0 129.45 126.01 121 0 123 122.12 121.29 117.2 111.83 0 0 0 0 0 119.83 122.67 0 0 160.86 154.8 143.9 131.59 0 0 109.23 0 0 0 0 0 0 102.12 114 0 172 155.22 136.3 119 0 112.38 0 118.8 130.91 137.6 0 84 0 104.25 96 90 88 84 78 76.67 0 88.12 90.77 89.89 86.45 81.72 76.28 70.98 66.03 56 45 51 0 0 36 36.28 32.7 29.39 26.53 24.34 0 21.96 19 0 0 0 0 0 0 0 0 18 18 0 0 22 23 28 34.95 37.42 36.26 23 19.78 0 10.04 9.7 9.91 9.3 8.92 8.8 8.38 6.93 6.36 6.46 6.83 7.99 8.89 10.43 12.61 14.52 16.13 17.69 18.98 19.89 20.28 20.63 21.27 22.75 23 23.97 23.96 24 26.22 27.18 27.48 28.76 30.8968 33.9288 36.0084 33.53666 29.99772 25.418 22.5172 22.3475 22.2675 0 0 0 0 0 0 18.635 19.89267 20.94217 22.69 25.1728 28.4048 31.66111 33.28 0 0 0 0 0 0 0 0 0 0 38.84571 38.15261 37.1216 38.442 38.17333 0 38.44182 34.4068 31.7712 29.9328 28.05 27.73 0 0 0 0 0 40.015 38.45913 40.0664 42.548 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +119 2618850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 108 120.1 0 106.37 0 0 0 0 0 0 79 82.44 77 0 0 91.4 85.96 81 0 94.81 0 98.86 87.93 85.45 0 0 0 0 105.65 90 97.78 98 108.27 0 109.82 111.28 114.84 128.3 0 124.85 128 140.27 145.06 0 144.39 138.35 134.71 135.56 126 121 131.57 126.68 121.42 115.75 111.53 0 0 0 120.1 0 128.02 0 129 0 158.57 143.24 125.19 0 0 0 75 76 0 79.79 85 90 0 0 175 155 137 123 121 116.58 112 121 135.13 140.93 0 107.15 96 0 0 108 100 91 88 87 78 78.01 0 91.82 91.4 89.19 86.95 84.05 78.47 74.66 71.82 65 0 0 44 45.54 39.27 33.56 29.26 26.21 0 23.72 0 0 24.74 0 0 0 0 0 19.54 0 18.86 19.05 0 0 21 24 0 35.47 36.66 37.37 0 21 0 14.71 12.77 14.15 12.62 11.18 10.96 10.35 9.37 8.38 8.4 9.24 9.67 10.62 12 13.83 15.66 17.21 18.27 19.31 19.83 20.29 20.58 20.9 21.22 20.85 19.16 19.09 21.7 23.67 25.64 26.69 27.86 29.1864 30.4512 31.0304 29.59958 25.9388 23.39353 22.132 0 0 0 0 0 0 0 0 0 0 0 22.98944 24.9052 28.2796 31.26143 0 0 41.18 41.14333 40.9276 43.6284 47.23 46.728 45.69 0 0 38.24 35.7436 33.6544 35.6324 34.3868 35.595 32.663 30.6776 29.23348 27.57533 28.94 30.39454 29.48409 28.21944 28.495 0 0 34.682 35.49824 36.179 38.162 40.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +120 2618350 0 0 0 0 0 0 0 0 0 0 0 0 0 96.65 0 96 116.12 106.97 96.7 0 0 0 0 0 79 80.22 68 0 0 95.58 88.8 80.76 0 0 91.81 87.71 85.64 0 0 0 0 129.91 127.3 116.18 114.21 117.52 119.21 118.4 111 125.3 0 131.07 135.36 137.8 140 149.26 153.75 153.85 150.34 144.29 138.59 136.3 137 134 0 133.79 128.8 124.15 119.26 116.38 114.3 0 131.99 135.49 139.93 144.67 156.3 159 157.57 142.37 0 0 100.5 83.77 78.2 0 0 0 0 0 102 0 176 172 158 144 139.13 122.92 124.87 137.23 143 158.92 140.57 123.97 107.6 99.11 0 0 112 99 93 88 90 84.84 83.43 0 92.62 90.96 89.51 0 0 0 55 63 0 57 52 50.89 40 29 26 25.84 24.7 24.3 0 23 23 0 0 0 0 0 0 19.04 17.8 17.6 17.65 0 18.55 22.87 24 28 31 34.46 22 20 20 18.95 17.19 16.43 0 14.34 12.8 11.81 11.39 10.47 10 10.92 11.37 12.59 13.99 15.34 16.62 18.07 18 19.96 20.48 20.73 20.88 21.15 20.83 20.07 18.94 18.71 19.86 21.57 24.38 28 28.83 28.23765 27.461 27.10813 25.70833 23.662 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.244 26.0488 28.2675 0 0 43.45 43.8204 42.78 43.0752 44.8496 44.802 42.45913 37.90667 36.18235 36.1008 34.2028 31.8568 31.4304 31.14583 31.10301 29.6145 28.69118 28.43 0 28.39846 29.83792 30.1775 29.12131 29.3625 30.925 31.59 32.79667 34.31222 38.33437 45.05363 58.50706 68.01765 66.54556 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +121 2617850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93.56 0 95.32 97.79 97.1 98.32 0 0 0 82.56 80.63 79.59 0 0 96.03 96.28 91.63 83.02 78.23 0 81.59 81.34 77.8 0 0 0 119 126.53 126.65 124.31 128.48 133.7 134.85 133.71 133.79 135.29 136.71 0 145.17 150.1 155.95 161.55 164.41 162.57 0 146 139.77 0 123 0 0 0 140.66 134.68 129.02 123.53 119.86 123.45 139.39 147.41 150.29 152.7 157.46 157.73 153.38 145.2 130.41 0 0 0 81 92.01 94 0 0 90.33 99.92 117 180 179 159 167.83 149.28 131 136.3 146.81 158.37 0 154.64 127 121.08 100.14 0 110.97 0 112 99 100 94 94.9 93.65 0 92.74 92.31 0 0 76.55 69.7 65.04 60.98 0 0 63 43 33 30 28.5 26.11 25.54 25.99 27.05 28.25 0 22 21 0 0 0 0 19.08 17.14 16.23 16.39 0 15.57 20.81 23 24 33.84 30 21 20 20 21.29 19.78 0 17.94 16.33 15.13 13.98 13.43 12.22 11.64 12.62 13.56 14.2 15.48 17.01 18.01 19.47 19 20 21.38 21.63 21.78 21.84 21.54 20.86 20.03 19.35 18.9 18.97 20.95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.34364 0 0 0 0 44.23667 39.73348 39.4236 41.6864 42.3952 38.1252 34.6476 33.6716 31.8892 30.30882 29.13 28.79273 27.94889 27.92333 27.52833 27.5112 27.77053 28.47167 0 0 0 29.67077 29.70462 30.39056 30.89158 32.40913 36.3104 39.58 58.98421 65.15039 67.67119 67.19273 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +122 2617350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 82.46 0 78.05 0 86.89 79.61 0 0 0 73.44 80.33 78.39 0 0 0 95.72 94.07 0 72.75 75.01 0 0 0 0 0 105 114.94 118.81 120.28 123.4 0 0 142.44 144.32 144.95 142.96 143.16 147.57 152.99 157.96 166.71 168.75 173.1 0 167 149 143.44 0 0 129 0 0 148 144.68 139.45 133.61 130.08 130.89 132.23 139 151.77 155.46 156.26 152.47 147.42 146.13 122 0 0 0 99.5 104.2 100.94 94.37 0 97.33 106 0 183 180 182 174 161 146 145.95 154.18 161.55 164.71 158.78 150.57 0 98 0 107 0 130 118 108 108.25 102.5 99.48 95.99 91.61 0 0 84 79.84 71.87 63.46 0 54 0 0 0 40 0 31.77 28.4 26 28.45 30.15 0 28 27 20 22 0 0 20.65 18.38 16.73 16.12 0 0 0 22.79 21 0 0 21 21 20 19 20 0 14 20.26 18.23 16.8 16.16 14.52 14.49 13.69 14.61 15.11 15.97 17.06 18.55 18 19 20 21 21 0 20 0 23.04 22.55 21.81 20.69 19 16.65 16.25 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20.385 19.635 0 0 0 0 0 0 0 30.14909 32.45647 34.1108 36.0568 38.5428 40.208 35.6972 32.6716 32.1144 30.2928 29.302 29.0212 28.6772 28.17773 27.61277 26.76174 26.858 28.2464 29.35083 29.07591 28.42084 27.84538 28.702 29.98 29.955 30.77333 32.11214 36.5424 38 58.4604 63.0116 62.9124 60.81608 56.90857 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +123 2616850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65.08 0 72.66 77.59 71.04 0 0 0 66.56 77.56 0 0 0 83.99 94.77 96.06 90.93 0 77 63 0 0 0 100.49 103.42 105.61 0 110.96 114.21 122.42 130.33 130.28 0 149.3 145.98 144.55 0 0 0 0 165.79 170.29 169.8 164.9 158.2 153.85 0 138 134 0 0 159 155.88 150.41 145 141.48 140.39 141.21 145.15 149.71 155 155.01 148.34 135 140.06 0 0 0 0 0 0 98.23 86.35 0 93.98 102 124.98 0 185 181 177 172.73 162 159.23 160.51 163 162.15 156.31 0 112 124 142.02 0 110 0 129.32 121.94 113 105 104.12 98.95 0 0 95.05 91.1 83.79 76.23 0 64.5 0 65 0 78.57 0 47.15 38.75 33.19 30.12 32.42 0 31 29 0 27 25 22 21 22.21 18.5 16.11 0 0 0 23 22 23 27 21 21 21 20 0 18 19 20 20 20.05 18.85 17.74 0 15.79 16.23 16.08 16.5 17.18 18.39 19.68 0 0 0 0 0 22 24.73 25.13 25.06 24.77 24.18 23.2 21.26 18.31 16.85 18.26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.568 20.47826 18.18667 18.91875 0 0 0 0 0 28.68428 28.7592 37.0275 32 35.54923 39.22667 40.0225 36.925 34.29928 32.64211 31.5668 30.5816 29.6404 28.782 28.94813 27.36454 25.8068 26.0144 29.3456 30.96363 31.85 30.152 29.236 0 0 30.98 31.15316 32.04619 35.76667 46.135 56.1908 57.3596 55.1888 51.4025 48.81429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +124 2616350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61 62.66 0 66.51 65.79 59.67 0 60.82 66.54 74.82 76.46 0 59.94 79.79 91.67 96.68 90.87 81.86 73.16 0 0 0 92.71 89 99.58 0 99.15 0 120.19 121.94 120.7 110.09 127.75 151.64 144.68 0 0 0 0 0 159.13 158.62 159.94 160.1 157.4 151 153 143 0 160.7 0 173.54 167.52 161.16 156.1 153.18 151.25 151.63 152.2 148 0 0 0 0 149.18 0 0 154.65 161.08 0 0 89.71 83.9 0 0 108.85 115.7 0 183 185 183.93 175.93 170.14 164.26 164.58 164.41 162.16 155.84 0 0 141.83 139.5 134.45 115 0 0 126.23 117 113 111.89 104 100.95 98 98.93 95.43 0 82.59 77.76 0 0 76.09 0 82.69 0 0 0 41.03 37.68 0 0 32 29 30 30 27 0 0 27.2 20.99 18.12 21 0 24 24 27 25.27 27 28.39 26.34 21 0 19 21 20 20 20 0 0 0 0 0 16.53 16.82 0 17.99 0 0 0 0 0 0 24 24 0 0 26.97 26.92 26.94 26.71 25.19 21.81 19.03 18.32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.41813 23.7824 20.8604 18.6348 19.76708 20.915 0 0 0 28.07 29.41458 31.0176 32.5 0 0 0 0 0 0 0 30.91667 0 29.74 29.755 29.19778 28.59 26.9276 26.282 30.22 0 33.90375 31.903 31.608 31.37333 32.24429 32.88643 33.08916 33.83714 36.025 40.99667 52.9385 52.69521 49.9148 47.05625 45.95358 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +125 2615850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 59.19 50.57 48 60.93 0 39.65 57.01 64.46 71.37 74.48 0 0 76.22 85 0 88.52 76.93 60.46 0 0 85 84.1 94.07 0 0 0 106.96 111.53 0 116.08 0 127.99 0 0 0 0 142.37 150.57 146.98 139.92 138 147.87 150.42 152.9 155.54 160.27 0 165.11 169.01 176.92 179.45 175.43 169.83 165.68 163.16 161.73 160 151 160 157.4 149.89 0 0 150.27 148.89 146.56 148.59 0 0 97 87.69 84.23 81 0 86 102 0 183 181 180 176.48 169.99 165.36 165.91 165.65 163.22 158.52 155.35 151.2 144.86 138.59 125 130.76 0 0 128 125 112 106 104 107 106 104.83 98 94.55 87.96 0 0 0 81.82 0 81.5 0 0 59.05 47 0 0 37 31 33 34 32 28 32 0 33.74 23 27 0 0 0 44.71 37.63 30.62 29.87 28.02 23 0 0 22 21 20 0 0 0 0 0 0 0 0 16.88 17.24 18.44 0 0 0 0 0 25 26 26 27 26 28.26 28.55 28 29.95 28.69 0 20 18.39 18.49 18.65 0 0 0 0 0 0 0 0 0 22.658 23.04 23.2225 23.8316 24.5448 23.638 21.872 21.5484 22.18565 23.145 25.94375 26.99096 26.842 29.8352 31.9468 33.401 0 0 0 0 0 0 0 0 0 0 0 31.254 29.17263 29.188 30.938 32.71666 33.9 0 33 33.996 32.804 33.25318 34.5008 36.0872 38.09792 39.44667 43.88667 52.21615 50.6564 45.70782 43.85 41.132 39.391 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +126 2615350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 53.88 55.08 46 0 48 0 68.29 0 0 0 74.71 80.64 0 89.79 75.98 62.12 0 0 78.51 0 86.41 0 82.68 88.3 96.49 100.05 105.9 0 115.72 0 0 0 151 150 151.52 151 127 123.81 131.14 136.79 142.8 149 153.56 159.59 0 169.76 172.66 178.05 180.29 179.25 175.56 172.26 170.69 169.59 168.63 166.56 154 0 152.81 139.66 0 141 140.95 140.36 142.22 142.08 0 0 92.62 86.3 85.52 84 0 0 0 178.05 173 177.04 172.23 167.34 164.2 166.74 165.75 164.09 161.06 156.99 152.74 147.87 142.85 139.74 143.47 150.41 150.45 137 125 132.22 122 112 109 112 109 104.89 0 0 0 0 89.19 85.93 80.62 81.73 0 0 63.24 0 59 46 39 42 34 30 39.73 36 0 0 0 0 41.16 27 0 23 37 42.15 36.56 32.92 26 0 0 0 0 0 0 0 0 0 0 0 0 18.24 16.91 16.43 16.97 18.48 0 0 0 28.09 28.11 28.24 28.08 27 26 25 25 23 25 0 0 0 19.63 18.13 18.43 18.78428 0 0 0 0 21.22 21.17682 21.31273 21.67 21.605 22.56 0 25.01444 25.0692 24.9092 24.2792 23.4588 23.1824 23.4812 23.81222 25.2696 27.7328 30.3608 33.7292 34.8813 34.806 0 0 0 0 0 0 0 0 0 30 31.22687 31.9304 31.9 33.94913 33.09091 34.545 0 0 36.076 34.05579 33.645 34.0432 35.46765 39.48056 46.41957 49.7625 52.974 52.14071 49.942 45.75066 0 37.19467 35.62333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +127 2614850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48.45 0 50 61.11 0 0 0 57 65.12 0 0 72.75 65 0 82.22 74.18 72.37 0 0 88.82 77.16 0 64.68 78.46 0 0 85.85 0 105.69 0 0 0 0 145.46 153 0 136 0 0 126.1 128.28 137.13 144.01 149.78 154.84 159.03 163.29 169.86 178.39 180.9 179.55 176.59 174.96 175.38 175.94 173.37 168.9 162.03 151.88 0 135.54 120 0 129.53 131.75 136.34 143.34 138.46 0 101.76 92.94 87.64 0 89 98 0 174.37 170 169.08 166.04 161.8 159.83 166.03 166.7 164.8 160.99 157.89 155.4 152.32 149.7 150.14 153.26 157.41 158.12 148 0 0 0 127 118 117.97 114.83 0 0 0 95.92 94.78 92.78 85 86.34 83.06 0 81.65 70.65 0 84.74 58 50 41 34 33 40 45.47 39 0 0 0 33 0 27 26 31 35 34 30 24 0 0 24.74 0 0 0 0 0 0 0 0 0 18.05 16.21 15.23 16.23 18.23 0 0 25.55 27.08 27.58 27.24 27.4 0 0 0 0 0 0 0 0 23.55 20.47 18.92 18.74 18.89 0 0 0 0 21.408 21.41375 21.62542 22.2588 22.73737 23.19 0 25.56267 25.7336 25.344 24.8548 24.426 24.394 24.5496 24.3468 26.4216 28.7616 32.7908 37.495 38 0 0 0 0 0 0 0 0 0 0 32.24625 33.0808 32.9816 33.60727 37.30667 0 0 0 40.99857 37.88545 34.4328 33.66615 34.72667 0 53.60666 53.83583 53.8316 53.0252 50.99561 48.0008 44.09136 39.30733 36.86308 35.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +128 2614350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 0 53.64 0 0 47.32 56.57 0 0 0 0 0 73.81 69.93 0 0 0 87.81 0 0 70.62 0 0 70.41 0 0 0 0 0 0 126.37 133.99 144.63 144.63 0 0 122 119.97 124.17 133.9 142.09 146.82 149.04 149.83 152.58 160.62 0 178.36 177.25 174.96 174.03 175.54 175 174.16 169.31 165.76 158.84 148 139.68 130.5 0 0 127.67 136.26 139.67 0 132.95 114.87 98.88 89.85 0 86 108 160.84 163.57 157.08 156.96 0 160.02 0 164.92 165.37 164.09 160.89 159.93 155.64 155.47 155.9 158.66 162.26 166.97 169.34 172.88 0 0 0 0 0 104 0 99.54 94.85 0 104.19 101.13 96.93 94.3 91.81 88.58 0 83.14 77.35 0 91.47 0 58 46 39 44 41 56 0 0 0 34 0 33 31 29 40 45.61 32 27 0 0 28.34 0 0 0 0 0 37.36 32 0 0 0 0 16.31 13.92 15.34 0 0 0 25.21 25.94 26.16 26.21 26.37 0 0 25 25 0 0 0 0 23.1 21.04 19.59 0 19.64292 20.066 0 0 0 0 21.56182 21.92905 22.9076 23.692 23.643 23 24.85391 25.738 25.6672 25.3004 25.1424 25.354 25.3008 25.72042 27.3104 29.06083 32.32864 0 0 0 0 0 0 0 0 0 0 0 33.914 33.72583 35.7284 36.39083 33.45833 0 0 0 0 40.6 37.825 34.32667 36.83278 38.19615 40.63857 53.06818 54.6624 53.41799 50.5032 48.1292 45.2296 42.97319 41.2948 40.15 37.45263 32.46348 33.26714 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +129 2613850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 47.49 54 43.26 0 18.26 36.44 48.91 0 0 0 65.87 67.48 68.21 63.59 54.53 0 0 81.06 0 0 0 0 0 0 78.84 79.47 94.66 107.37 111.17 112.68 117.32 0 0 119 0 0 116.98 119.11 135.64 137.75 144.78 147.09 145.73 142.5 145.01 0 0 170 171.54 170.13 168.85 170.43 171.29 168.99 165.07 0 0 0 0 121 131.74 0 0 0 0 0 0 122.63 99.86 92.58 0 0 0 161 158.84 0 148.13 145 0 0 0 155.93 160.73 161.75 160.03 158.02 158.43 161.18 164.66 170.47 176.06 179.26 0 0 150.43 0 0 122.2 118.73 124 112 106.65 0 106.78 105.05 100.83 97.66 96.77 78 92.31 86.24 0 0 91.15 91.33 0 60 53 53 48 0 59 0 0 0 28 35.86 31 35 0 41 30 0 0 33.33 28 27 28.9 0 28 35.2 26 28 0 21 0 0 16.59 13.87 0 0 0 0 24 25.52 25.23 24.99 24 0 0 25 27.42 0 0 0 0 22.31 20.76 19.95 19.65 20.15334 0 21.92857 22.834 0 0 21.925 22.5036 23.374 24.574 24.892 24.6016 25.2 25.6856 25.942 25.5988 25.6276 25.9496 26.3796 27.1896 28.2276 29.59375 32.08647 0 0 0 0 0 0 0 39.4625 38.82 0 0 0 0 40.7695 40.42529 0 0 0 0 0 38.32583 36.65231 37.01476 40.17933 41.614 0 52.32055 52.5788 50.30761 45.468 44.53273 43.04364 42.257 43.22392 41.8452 39.5212 36.1788 35.918 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +130 2613350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36.25 44.07 29 0 0 36.94 0 0 0 50.57 59.21 62.48 64.44 61.75 0 0 0 0 0 0 0 75.47 64.73 68.1 0 83.1 93.66 101.52 0 101.91 106.26 117.24 119.77 0 0 0 0 0 0 152.56 149.1 148.66 147.28 0 133.88 0 160.39 164.59 165.57 162.72 0 163.05 162.82 159.96 150.14 140.77 136.06 135.74 130.33 130.47 135.85 0 120.22 109 0 0 0 0 0 0 0 0 0 150.21 144.84 151.87 142.08 133.26 0 153.68 154.03 155.16 157.6 160 160.47 158.4 158.32 163.87 172.35 177.11 181.65 185.25 182.28 180.87 0 0 0 0 0 0 123.43 114.32 0 112.67 109.17 104.45 100.6 100.44 101.14 96.55 88.97 0 83.92 86.97 87.62 88.36 0 76.52 60 61.05 57 0 0 32 37 0 41 39 0 0 0 0 0 38 36.53 0 29 30 28 27 27 0 0 22 20 0 0 0 18.78 0 22.55 24.88 0 26.32 25.71 24.61 23.8 24.35 0 23 26.53 26 26.6 23 0 0 21.76 20.7 20.11 20.12 20.56842 21.522 22.3 24.305 0 25.88 24.33091 24.4692 24.61913 24.87905 26.3108 25.8848 25.7516 25.8348 25.9196 25.4988 25.7676 26.158 27.0608 28.4928 30.2356 31.9176 33.56556 0 0 0 0 0 0 0 0 0 0 0 0 0 42.53692 43.91526 0 0 0 0 41.98166 40.32084 39.49445 37.97 0 51.7325 52.91722 51.4616 50.0476 47.6132 44.48273 43.7775 40.248 39.59083 42.47833 41.25214 40.368 39.55167 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +131 2612850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 31.8 0 23.73 35.2 42.04 0 0 0 52.43 54 58.95 62.56 63.83 63.51 0 0 0 0 70.03 0 0 71.78 72.23 77.38 86.01 92.79 97.07 0 99.22 0 0 113.41 0 0 0 147 151 158.14 154.27 149.72 148.36 140.21 0 0 0 155.41 161.39 161.99 158.53 157.72 0 155.81 154.9 148.02 141.26 138.61 138.63 136.15 132.42 129.69 125 0 0 119.79 116.55 111.64 107.3 103.3 103.36 100 0 132.23 131.19 131.8 137.03 137.74 0 147.29 152.49 152.97 153.88 155.06 158.52 160.47 161.8 162.71 176.17 177.43 179.49 182.83 184.75 185.02 183.79 0 175.61 0 181.73 0 0 116.67 0 122.41 119 115.37 104 104.04 101.05 101.57 99.52 95.13 86.59 84.39 0 77.81 0 85.65 83.16 67.88 0 0 39 48 0 0 0 0 0 0 0 0 30 42.12 40.79 0 0 0 33.9 32.79 29 23 0 24 24 22 23.52 22.99 23.01 23.37 23 25 27 29.34 28.16 26.6 24.74 22.6 24.18 0 25 26.93 26.92 25 24 24.2 22.78 21.39 20.31 19.83 0 0 21.68 0 26.43 28.086 27.89409 27.536 26.30571 0 27.616 27.526 26.6608 26.2148 26.0936 26.022 25.4052 25.6308 26.1704 27.4512 29.7056 32.9864 35.78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43 43.88667 0 0 0 0 41.341 41.083 0 50.66 50.61 50.3824 49.7256 49.2248 47.7724 45.96959 45.1224 44.77563 40.88 38.48077 36.849 0 0 0 0 0 0 0 0 24 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 23.11 25.97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +132 2612350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19.87 25.87 0 0 0 0 0 52 53.48 60.31 66.26 65 0 0 0 0 76.35 76.98 77.52 76.69 78.36 83.54 90.06 0 0 99.35 0 0 106.42 0 0 0 0 137.54 141.93 145.33 144.21 143.1 139 121 114.25 0 0 155.18 160.44 159.86 156.77 149.2 0 156.58 156.01 149.34 144.73 143.59 144.69 142.14 0 0 129.87 131.68 125.06 119.68 117.49 114.46 111.27 106.81 99.67 93 0 0 134.97 134.24 0 0 0 0 0 0 153.19 153.75 156.7 159.17 161.57 0 169.56 172.74 176.02 180.1 182.65 182.58 180.96 177.65 177.74 179.56 181.23 176.78 137.9 121 118.44 131.73 123 118 112 108.7 104.16 102.49 102.68 98.08 92.44 88.04 83.5 77.2 71.82 52 54 49 0 0 0 0 0 0 0 0 29.6 34.64 31 40.2 40.58 41.56 0 40 41 42 41.06 0 24 0 0 0 25 23 25.2 25.64 26.73 27.67 26 25 0 31.81 29.68 28.07 26.43 25.37 26.02 0 27.87 26 27.68 26.47 25.1 23.74 19 18 19.19 18.76 19.19 19.8825 0 23.37667 26.08333 28.36239 29.6236 30.38842 0 30.05928 29.26956 28.31439 27.176 26.6552 26.3596 26.1264 25.4792 25.6132 26.1092 27.4464 29.8268 33.18834 38.81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.33 42.528 0 0 48.0125 49.84 46.323 44.68682 44.8336 45.5028 45.282 45.2128 44.67125 43.94833 39.54909 38.58417 0 0 34.15818 33.10783 0 34 33.38 0 0 0 23 25.64 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +133 2611850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.96 0 0 39.2 42.53 45.85 50.88 52.94 53.74 60.38 67.35 70.7 0 0 0 78.76 80.74 80.41 80.75 79.94 82.88 86.71 90.21 91.17 0 0 0 0 0 0 0 0 129.51 0 127.85 129.79 130.83 0 0 0 0 0 0 152 159.67 156.44 151.58 142.43 0 0 0 148.16 146.09 144.81 144.14 142.68 138.4 131.86 130.37 129.2 125.33 119.2 0 0 0 0 100.94 103.21 98 0 130 0 131.37 125.85 128.4 135.79 140.49 146.92 150.39 151.37 152.24 0 0 161.39 161.8 0 0 174.55 177.87 177.03 176.3 175.13 173.58 171.89 172.06 172.66 0 0 0 144.26 133 126.82 122.04 115.87 109.91 104.82 104.76 102.1 98 92.77 87.3 81.58 75.61 68.2 57.91 0 0 0 0 0 0 0 45 0 0 33.8 36.8 38.71 39.98 0 0 0 0 0 0 27 27 23 22 0 25 26.24 26.69 27.87 28 31.71 0 28 0 28 30.35 29.89 28.71 28.03 28.77 0 30.29 28 0 0 24 0 21.77 0 18.58 17.76 0 19.8675 22.66 25.17917 27.1324 28.7984 30.5408 31.633 31.44055 30.5676 29.7624 29.4592 28.52833 27.4952 26.5284 26.1648 25.58045 25.48722 25.9912 27.308 29.5848 31.86737 0 0 0 0 0 0 0 40.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.33333 42.49572 42.61 44.43333 45.21857 44.658 44.0588 42.7688 42.40539 0 0 37.35667 38.0075 38.99 36.3 32 31.03 0 0 0 27.19 0 0 0 0 0 0 21.57 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 27.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +134 2611350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22.53 30.42 36.36 43.95 50.6 53.46 57.76 61 0 0 0 70.35 77.46 81.07 82.66 82.76 84.21 85.65 88.9 90.75 94.25 99.28 0 0 0 0 0 0 124.71 116.19 0 112.25 118.06 117.49 0 0 0 0 0 136.96 147.75 154.91 149.75 0 138.65 130.58 0 143.74 146.79 144.61 142.93 141 139.9 136.65 132.69 130.16 127.78 126.01 125.12 126.67 117 113.94 110.15 108.46 109 111.01 0 121.3 0 126.12 124.27 126.22 132.31 137.82 142.57 144.76 147.13 146.64 129 0 154.15 154.51 152.92 0 169.02 158 172.89 171.05 170.06 166.7 159.8 155.41 171.22 175 173.92 174.91 0 138.89 134.33 126 118 113 111.79 109.55 107.17 103.37 99.01 93.51 87.92 81.98 71.41 0 0 77.39 72 71 76 0 40 0 51.51 47.06 40.2 38.38 38.83 0 40.99 41 0 0 0 32 30 26 26 0 0 0 27.89 28.63 28 0 0 0 29.65 0 28 32.65 32.6 30 30.05 28 33 0 0 0 0 0 0 0 20.42 18.37 16.85 16.53 19.28435 22.21222 25.892 27.45444 29.14273 31.7968 32.9556 32.1208 30.6688 29.05 29.12111 29.134 28.02 26.3004 25.81842 24 24.53714 25.39363 26.8856 28.844 31.45812 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42 0 0 44.94555 43.85667 43.51286 42.97667 42.3404 41.2816 41.19041 40.7472 40.35667 0 0 0 32.72 0 30 28 27.87 25 24.31 0 0 20 21.63 20 21 23.92 0 0 0 0 0 0 18 18 0 0 0 0 0 0 0 19.4 20.18 21.5 23.46 25.92 29.19 34.16 0 0 0 32 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +135 2610850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 30.36 45.61 47.86 0 0 0 0 48.64 68.46 0 0 82.41 81.12 84.7 89.48 92.53 81.86 97.26 0 0 0 0 0 120.08 122.31 115.11 0 114.02 113.55 118.06 116.62 0 0 0 115.62 111.3 117 135.19 143.12 140.12 0 124.71 0 0 139.54 142.41 140.99 139.02 136.82 135.23 133.11 130.82 128.66 126.96 126.27 125.44 124.57 121.3 117.25 115.63 116.48 0 109 111 0 0 118.73 119.58 121.61 126.3 131.28 136.11 138.86 141.35 145.83 0 0 143.65 149.43 151.47 0 146 163.23 167.8 167.59 165.81 161.85 153.41 143.87 0 168.32 0 0 0 0 0 0 124 118.9 116.07 110 111.45 108.23 104.52 99.85 0 0 0 0 0 84 71 72 67.23 0 0 0 0 56 49 43.17 0 38 40.62 0 47 0 36 36 35 31 27 0 0 0 0 0 0 0 0 0 30 0 0 36 33 33.2 30 30 0 0 0 0 0 0 0 23.63 21.48 18.91 16.59 14.33 18.8492 22.349 0 0 33.06438 34.4876 33.2668 30.1852 28.1124 26.59294 0 27.61 26.18 25.63588 25.44667 24.53437 24.19 24.41813 25.68923 27.65895 29.3965 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.7 42.69667 42.5024 42.14 41.615 0 35 34 33 33.77 0 31.15 28.85 27.01 24 23.74 22.4 0 19 20.75 20.94 21.45 21 21 21 0 20.39 17 0 0 18 19 0 0 0 0 17 18.73 18.3 19 20.62 23.05 26.2 30.15 35.25 38 40 33 30 26 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +136 2610350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38.02 0 0 46.66 59.05 0 77.28 80.98 81.52 79.92 86.55 91.37 93.73 0 89.29 0 0 0 115.67 115.5 109.51 110.43 110.68 0 0 107.65 118.39 0 0 0 125.3 112.1 97.8 94 123.55 127.97 125 0 0 0 142.94 139.11 136.4 134.59 132.97 131.58 130.86 130.19 129.05 127.38 126.38 125.83 124.46 122.66 121.44 119.31 118.66 119.96 121.86 119.86 118.29 117.65 114.69 114.25 114.55 115.32 118.8 123.6 129.52 132.09 133.75 134 111 0 144.59 143.23 145.29 117 144.13 160.83 166.25 165.82 164.21 162.17 158.29 148.99 0 0 0 0 0 0 0 0 0 116.8 0 113.11 0 0 0 0 0 0 0 0 87.21 88.39 78 68.04 54.52 0 0 55.4 0 0 50 0 45.19 41 0 0 49.51 40 38 40 37 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 25.48 22.85 20.49 18.36 16.92 19.4756 21.85857 0 0 34.66708 35.3512 29.85 25.9396 24.4396 25.54909 0 0 0 25.325 24.54 23.5735 23.25292 23.954 26.0248 26.9284 28.24833 31.68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40.57428 40.8996 40.8496 40.62181 40.50692 0 33 33.64 31.97 30.72 28 27.22 25.57 23 22.78 21.96 20.96 0 18 19.14 18.8 19.12 19.61 19.84 19.37 0 15.88 0 0 20 20 20 0 0 0 19.71 18.03 17.06 17.51 19.55 22.54 25.97 29.89 34.13 37.87 37 36 33 27 23 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +137 2609850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 45.84 49.28 57.69 0 71 75.78 78.47 0 89.17 90.54 93.01 0 0 0 0 103.89 106.45 102.65 0 98.01 0 0 90.9 100.19 100 0 0 119.52 114.55 110.21 0 112.32 121.22 117.08 0 0 0 143.71 141.26 136.15 131.17 128.09 127.69 126.74 126.86 126.23 126.99 123.87 123.13 123.61 123.08 122.64 121.16 120.06 119.58 118.95 118.81 117.87 116.55 115.41 113.86 113.68 113.17 111.82 115.92 0 126.11 126.77 125.38 123.71 113 0 0 0 0 138.21 147.96 0 160 163.59 162.82 161.54 0 0 125.73 115 103 95 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 102 96 85 79 66.64 0 0 51.84 58.77 61.53 59.74 0 42 49.5 0 46 0 0 49 0 48 40.92 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30.25 27.91 25.79 23.01 21.05 19.43 20.1132 22.4375 0 0 32.45889 34.53545 31.448 26.7336 25.1568 0 0 0 0 0 22.83666 22.1428 21.83769 22.9132 24.2088 25.1516 27.2144 30.78 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 39.359 39.59 39.92041 39.56435 39.80778 0 34 0 26 26 26.4 24.95 22 22.67 21.7 21.16 20.54 19.56 18.59 17.37 15.85 16.23 16.34 16.06 0 15.33 0 0 0 0 23 25.05 24.57 0 0 18 17.85 16.18 15.86 18.87 22.01 25.36 28.79 32.16 34.69 34 31 33.03 28 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +138 2609350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44 51 0 67.78 73.15 72 0 0 80.17 0 88.41 0 0 0 0 95.55 98.53 97.55 0 0 0 78.59 88.73 93.15 0 0 103.04 109.24 0 0 96.67 102.03 105.73 0 0 128.38 138.63 140.63 137.73 132.58 125.76 119.31 130.19 128.44 124.29 121.12 125.7 119.83 121.6 122.33 122.45 122.11 120.53 120.12 119.14 117.87 116.47 116.03 114.32 113.52 112.63 112.02 112.66 113.45 0 125.12 127.65 125.94 117.88 111.24 94 0 0 109.17 115.19 151.94 0 155.08 157.77 160.53 161.42 158.83 152.51 139.93 130.61 121.43 117.01 117.85 104 0 89 0 0 0 0 91.1 0 0 0 0 0 98.98 97.37 91 87.72 76 0 0 0 51.57 57.27 62.68 0 0 46 0 0 0 49 0 0 56.59 52 49.71 40.91 0 0 0 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33.3 31.87 29.49 27.05 24.39 0 20.5148 21.89454 0 0 0 30.00429 29.61083 26.026 24.7048 26.66 0 0 0 23.48375 22.11227 20.378 19.71478 21.0792 23.314 26.1536 28.31 30.63708 33.858 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38.965 38.32301 39.37556 37.925 0 0 0 0 24 24.9 23.83 0 0 20 20.84 20.4 19.66 19.05 17 0 15.19 14.6 14.17 13.26 14.7 0 0 0 0 0 24.69 25.01 24.84 24.27 0 0 18.97 17.35 17.18 19.26 22.06 24.97 27.78 30.29 32.14 31 32.22 30.61 27.89 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +139 2608850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57 58.98 64.74 68.03 67 0 0 0 82 0 0 0 88.62 92.35 0 0 0 0 83.01 89.52 96.79 95.2 100.01 0 0 98.81 102.65 0 0 0 0 0 119.63 124.41 130.51 134.12 133.02 127.95 119.88 114.31 115.03 127.42 121.2 117.05 0 0 121.39 121.93 122.57 122.15 120.62 118.87 117.67 116.43 115.37 113.54 112.13 112.4 113.6 111.93 112.18 112.5 110.49 0 0 0 0 98.76 0 122.29 110.25 105.07 108.51 0 0 144.12 146.72 0 160.77 156.53 150.22 142.63 134.55 126.41 123.66 0 0 102.63 0 0 0 0 0 100.21 95.47 90.87 0 94 98.31 94.45 91.39 81 0 54 49 0 57.86 62.68 65.91 70.32 0 67.76 0 49 0 46 48 50 50 56.65 55.68 51.26 0 0 0 48.06 46.7 47.24 0 0 0 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38.14 36.85 34.99 30 27.78 23.76 20.6548 21.3985 0 23.41 23.175 0 29.015 25.57375 25.19 26.15625 0 24 0 23.338 21.6092 18.4936 16.9416 19.1732 23.352 27.42048 29.87546 30.25 34.37842 36.83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 23 22 21 21.78 18 19 19.86 19.96 19.79 19.44 18.86 17.9 16.36 14.8 13.89 13.54 13.84 16.42 0 0 0 0 0 0 24.62 25.02 24.78 0 0 0 17 19.25 20.51 0 24.63 26.85 29 28 28 28 28.9 26.88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +140 2608350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34.46 27.93 0 0 0 0 0 0 0 0 51.25 52.75 58.43 0 0 0 0 0 0 83.69 86.39 88 0 0 0 0 89.59 93.44 97.99 95.42 0 0 98.76 0 0 0 0 0 116.35 116.56 0 118.21 123.05 0 124.79 120.95 117.01 110.71 106.67 116.13 115.28 114.74 0 122.04 121.11 121.16 122.05 122.74 121.73 119.32 117.36 115.27 113.63 111.28 110.08 111.17 113 113.66 111.77 115.06 113.23 103.51 0 0 0 0 108.51 109.61 107.25 104.76 106.88 0 129.21 137.88 0 160.37 158.3 155.15 149.47 144.15 139.77 132.29 0 123.55 107.46 96.46 0 0 0 138 0 114 106.31 97 101.08 99 97.74 89.39 74 0 76.37 56 0 75.78 74.5 76 76 75 75 73.68 64.18 49 47 48 0 51 0 57.37 57.53 0 48 49.85 51.06 51.89 51.97 50 0 0 40.78 40.5 38.78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43.77 44.12 43.7 38 36.63 32.76 27.77 23.095 22.39714 23.02889 21.319 20.81353 21.97 0 23.17 23.14 23.80546 24.05 0 24 23.31286 21.1256 17.2296 14.0972 18.31 23.3488 27.62869 28.0275 29.4548 32.6516 34.365 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.03667 0 0 22 21 20 0 19 18 18.92 19.13 18 19.37 19.29 18 18.35 16.19 14.17 13.41 13.63 14.59 0 0 0 0 0 0 0 0 25.09 25.3 24.22 0 0 0 0 0 0 0 22 26 26 0 26 22 21 0 23.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +141 2607850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 13 0 0 0 0 0 0 0 0 0 43.41 36.86 32.9 28.43 0 0 0 0 0 0 0 0 0 50.26 52.14 0 0 0 79 0 83.23 85.69 84.82 82.89 0 0 83.41 90.41 92.9 0 92.56 0 0 0 0 0 102 107.15 112.45 113 114.02 0 0 116.06 119.72 116.74 113.3 111.07 0 0 113.2 0 0 117.12 119 118.79 119.51 120.85 122.72 122.55 119.19 116.62 114.07 112.49 111.46 0 117 0 0 123.86 119.06 115.52 110.3 101.94 88 0 0 96.65 99.73 102.76 105.43 107.44 0 0 0 152.65 153.91 153.39 151.45 148.49 146.21 141.83 137.2 130.01 120.77 108.74 0 0 0 0 157.47 151.28 0 111 107 110.37 105.84 101.88 0 0 91.36 75 67 50 0 89.93 86 83 82 80 0 72.04 0 60 0 61 60 0 59.05 60.13 0 59.09 55.94 54.88 54.77 55.7 0 0 0 43 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 51.18 46.77 41.71 33 27 27.28333 27.23077 26.65764 20.3504 18.9444 20.38 21.49055 21.81826 22.415 23.29435 24.49857 24 0 23.265 21.788 18.8824 17.4844 20.6436 23.6392 26.37 29.25429 29.18267 29.21 31.22556 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.30334 23.35 21.62091 20 21.97 19 0 18 18.94 18.17 17.75 18.47 19.07 18 18 0 0 15.2 12.78 12.84 13.64 0 0 16 0 0 0 17 19.07 19 0 25.13 24.53 23.76 22.73 0 0 0 0 0 20 22 27.7 26 20 0 0 0 23.65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +142 2607350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 40 42.08 37.57 34.11 33.69 0 0 0 0 0 0 0 0 0 0 0 0 0 71.8 74.39 76.98 80.23 0 77.91 0 0 70.05 81.89 89.78 93.17 0 0 0 0 0 91.18 91 99.01 102.27 106.55 109.29 109.01 0 0 0 107.89 107.82 105.2 99.98 0 0 0 0 123 118.65 115.73 115.23 116.48 118.73 119.01 118.82 116 116.29 114.59 0 117.49 118.07 116 0 92 0 0 121.43 120.89 114.32 98 80 0 0 0 100.31 105.13 0 0 0 139.41 142.67 148.36 148.86 149.16 147.44 145.68 141.71 136.68 129 0 0 105 0 0 0 162.53 153.42 141.66 0 0 122.71 111.19 0 0 99.71 95.39 86.11 72.72 59 0 97.54 101 96 90 92 97.21 85.18 0 67 0 69.21 63 60.44 61.56 63.57 0 63.92 59.99 58.06 56.97 0 0 0 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 49 36 33 0 30 34.6448 37.89 32.31 26.60572 21.9168 21.132 21.0764 20.9224 21.3972 22.6775 26.16333 26.77941 26.36 24.7 23.43588 22.1244 21.9544 23.60046 25.3856 27.24565 30.71 29 29.33417 31.36571 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.425 22.80409 22.43833 20.998 0 21.93 0 18 18 18.76 17.61 16.09 18.32 18 18 18.82 0 16.81 13.75 11.74 12.77 13.86 0 0 0 0 0 0 17 18.85 19.71 21.55 0 23 24.01 23.37 0 22 0 0 0 0 0 24 27.13 22 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +143 2606850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 14 0 34.56 34.89 38.39 42.32 0 41 34 0 36.46 0 0 40 0 40 41.66 38.83 41.37 0 0 0 0 0 0 0 0 70.6 73.29 0 78.7 0 54.63 63.12 78.12 83.75 90.27 94.25 91.83 0 0 0 79.19 0 91.99 95.19 94 100.77 104.31 0 107.51 105.36 102.25 100.5 100.92 99.85 96.25 0 112.57 116 123.15 119 116.42 110 110.43 112.22 114.99 0 115.34 115.42 106 117.62 113.45 0 118.83 117.37 113.64 93 63 0 0 0 0 105.56 92 73 0 99.04 98.82 0 0 0 0 139.1 140.44 142.04 144.86 146.22 147.47 144.39 141.96 137.42 133.77 131.41 109 0 100 0 163.96 159.02 152.46 145 142.69 147.12 0 114 0 108.85 108.99 103.64 96.18 82.53 66 0 0 109 110 97 94 102 92 97 81.94 0 0 0 65.06 66.13 0 65 0 61.88 60.5 0 60 0 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50 41 36 40 42.47 39 39.95856 40.61333 31.06077 28.3556 24.5812 22.9416 22.2764 21.10875 21.07824 22.32095 25.7768 26.2292 25.51616 0 0 25.694 26.28739 29.02923 27.28461 26.86571 27.45652 30.418 31.875 0 31.16667 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.43 0 0 0 0 0 0 0 0 0 0 24.73 23.174 22.06143 21.37875 20.33333 0 20.31 20.91 0 19.78 19.48 18 0 17.82 16.86 17.77 18.76 18.84 18.66 17.89 16.49 14.55 13.47 13.72 0 0 0 0 0 0 17 18.38 18.26 18.99 20.27 21.76 0 0 0 22.64 22.44 0 21 0 0 19 0 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +144 2606350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 9 14 22 28.38 30.97 32 41.67 45.11 42.45 38.86 35.61 34 0 37.88 39.76 0 44.49 44.37 43.78 45 0 0 0 0 0 0 0 0 0 70.95 0 0 0 73.46 0 81.63 84.48 89.23 91.25 89.5 0 0 0 0 0 0 98.11 0 96.33 0 96.2 98.17 98.79 98.11 97.36 96.18 0 0 0 112.35 116.48 120.21 118.42 113.67 107.81 106.26 107.43 109.11 110.92 112.86 113.96 111.94 103.67 0 0 0 0 0 0 69.88 0 0 0 0 0 0 0 0 0 0 75 88 86 0 0 134.5 141.06 144.43 145.58 145.88 144.05 141.88 138.92 134.09 127.42 0 117.03 106 0 135.87 148.85 148.25 145.06 145.05 146.66 107 0 0 126.27 119.51 109.95 99.53 81.25 69.6 0 0 0 115 112 107 105 104 114.65 98 0 72 72 74.9 0 71.97 0 64.39 63.73 60 62.99 66.13 0 52 0 0 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 44 45 43.68 37 0 35.778 33.36369 30.8248 28.2448 25.836 24.908 24.77381 23.90429 24.6584 25.0312 25.5656 25.4952 25.565 0 0 27.66 29.80929 34.1081 0 26.87715 26.934 27.64636 27.948 27.27529 29.055 29.83 31.674 45.41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26.87 0 0 0 0 0 0 0 23 23.18267 22.69167 21.3048 20.0712 19.64905 18.74428 19.085 19.496 19.81 0 18 0 0 0 0 16.98 17.74 18.85 19.27 18.77 17.9 16.98 15.96 15.3 15.29 0 0 0 0 0 18.86 18.7 18.14 17.79 18.33 19.43 20.79 21.88 0 0 22.97 23.28 0 22 0 20 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +145 2605850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 8 17 21.05 24.2 27.85 0 39.06 42.82 42.84 39.61 36.7 34.2 33.13 33.05 0 0 47.68 47.82 49.55 51.75 0 55.69 55.7 0 0 0 0 0 0 0 0 0 71.6 0 0 74.86 78.12 0 84.24 0 0 0 85.24 83.34 87.95 90.98 89.86 97.67 0 0 92.92 92.64 0 103.97 101.56 94.02 0 0 0 0 118.42 117.73 115.48 107 107.71 0 105.42 104.81 0 110.64 114.9 114.77 113.4 119.05 106 116.9 110.47 92.37 0 0 0 0 0 0 0 0 0 0 0 91 87 107.46 108.21 0 111 138.77 142.17 144 144.67 143.43 144.1 144.26 141.61 137.98 0 123.87 119.89 0 115 133.09 141.92 144.9 141.6 138.02 120 104 0 0 0 120.36 107.62 90.51 71.22 0 0 61 0 0 131 128 122 115 116 105 108.81 89 88 0 81 78 75.15 66.62 68.42 0 71.26 0 62 0 0 0 57 0 0 0 0 0 63.7 53 51 78 0 0 0 0 0 0 0 54 40 38 40.87 38.11 36.62 35.7172 33.4968 29.2772 26.914 25.9904 26.23875 27 31.012 29.47639 27.6684 26.6276 26.0252 26.31238 0 0 0 30.63333 0 27.64875 26.51316 26.09048 26.0172 25.6196 25.6696 25.78 25.88348 27.89 31.84333 0 0 0 0 0 0 0 0 0 0 30.68 29.92 0 0 0 0 0 0 0 0 0 0 0 0 21.27353 21.37542 20.6808 19.2952 18.1024 17.9612 18.40708 18.8088 18.87435 0 18.97 0 0 0 18.91 0 0 0 0 0 0 17 17.66 17.22 16.63 0 0 0 0 0 0 0 18 18.41 17.47 17.77 18.92 20.69 23.3 23 25.21 24.8 24.91 24.54 23 22 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +146 2605350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 0 40.44 42.92 43.08 40.21 36.49 32.87 29.6 26.76 28.49 0 0 0 53.73 56.97 58.38 58.36 0 0 61.48 0 0 0 0 0 0 0 0 0 74.88 75.16 75.2 0 0 0 38 0 0 0 0 0 0 0 0 97.67 94.34 91.95 81.66 0 101.35 93 0 0 0 115.49 114 114.14 112.02 109 0 109.82 0 102.8 0 105.09 0 117.16 116.56 119.49 119.28 122.84 0 0 0 0 60 81.39 85.53 84.31 0 0 72.11 0 0 0 121.44 120.03 117.63 118.41 118.59 128.34 0 144.1 143.56 143.68 144.67 145.63 144.54 142.27 136.61 130.03 122.01 0 142.68 141.63 143.46 145.24 142.55 132.06 0 0 152.95 160.63 0 102 107.13 89.62 0 0 84.94 74 0 0 0 0 0 144 126 123 127 109 98 92 97 0 80 78.9 76.62 75 73 68 0 0 0 63 60 0 0 0 0 73.47 68 64 0 0 0 0 0 0 0 0 0 0 0 41 37.78 0 32.76 33.2028 29.1256 24.7996 23.70546 24.79643 24.877 24 32.16625 30.7224 28.8004 27.3904 26.5732 26.16182 0 0 0 0 28.72667 27.08125 25.5024 24.6428 24.0544 23.5176 23.0712 22.97696 22.6616 23.2888 26.83842 28.245 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21.371 20.31208 19.8232 18.61542 17.8192 17.5904 17.8236 18.1744 18.4632 18.72 18.77 0 0 0 0 0 17 0 0 0 18.47 17.99 0 0 0 0 0 0 0 0 0 0 0 18.51 17.17 17.23 18.69 20.67 0 28.48 27.63 26.99 26.91 26.65 23 22 0 0 21 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +147 2604850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.17 26.48 28.52 0 0 43.87 44.64 40.92 36.72 32.26 28.13 23.7 0 0 46.95 41.79 56.05 61.04 62.76 62.28 60.36 0 57.92 0 0 0 0 0 0 0 0 0 0 78.9 0 0 0 0 60.76 56.64 53.3 53.79 58.26 70.79 93.61 93.68 98.47 99.71 98.13 98.06 0 0 0 0 0 114.59 110.78 0 109.41 109.72 108.4 108.17 0 111.39 0 99.67 102.45 95 105 0 0 117.97 0 0 0 0 0 54 0 0 103.8 98.31 96.38 97.73 100.61 73 0 0 0 0 120.8 118.5 0 0 134.94 138.97 142.23 143.53 145.05 146.69 146.52 145.56 143.9 142.26 0 131.26 143.44 146.36 148.04 148.94 149.25 0 0 0 0 149.81 90 83 0 0 0 103.36 0 87.99 78 0 0 0 0 0 0 0 160.29 141.99 0 0 0 0 91 92 83 86.52 81 0 72.91 0 0 68.06 62 0 0 81.04 73.69 70.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.762 21.64857 19.56688 20.25889 22.55 23.99187 25.63042 23.625 29.4975 29.046 27.5672 26.694 26.34571 26.53565 26.99529 27.37 26.36304 24.87611 24.70945 23.84261 23.48375 22.8928 22.19913 21.2528 20.7448 20.0656 19.7236 21.66261 24.47773 24.32583 24.645 24.40455 24.93222 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21.55 20.92 19.94875 18.64667 17.9684 17.6912 17.7576 18.03792 18.3068 18.45 18.41 18.85 19.01 0 18.65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18.06 16.18 16.24 18.96 22.24 0 28.17 28.77 28.46 27.95 27.53 24 0 22 23 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +148 2604350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 14 0 27.27 34.7 39.1 41.08 44.33 44.93 42.77 37.78 33.68 30.5 28.75 30 0 48.22 45.39 0 0 0 63.86 62.1 0 62.38 0 67.04 62.1 0 0 0 0 0 0 0 0 0 0 0 0 83.07 72.62 66.13 64.9 69.65 79.01 90.53 97.09 99.78 0 0 0 0 0 0 0 0 0 0 101.28 103.63 106.54 103.57 101 0 104.21 0 101.34 96.32 0 112.96 0 0 0 0 0 0 56 58 0 88 79 0 107.31 106.69 104.56 103.36 86 0 0 0 116.44 114.3 0 0 132.29 133.53 136.33 140.39 142.81 145.17 146.87 147.53 148.45 148.12 148.41 147.32 144.58 146.62 149.32 150.48 149 0 0 144.34 144.99 150.41 0 89 0 0 0 119.23 110 108.39 89 74 80 0 0 0 0 0 0 0 0 0 0 0 123.76 112 102 0 0 0 86.16 0 0 70.56 69.09 66 0 0 79.19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 19.25956 18.30652 20.6416 20.588 19.50053 20.1175 24.906 25.607 28.91625 28.8388 27.616 26.34095 27.0675 27.044 26.024 25.7972 24.8716 23.6668 23.02043 22.60222 22.32706 21.809 20.94 20.11474 19.606 18.7876 18.4188 18.8892 20.11412 21.02834 21.18187 20.925 23.865 24.08467 25.69273 23.845 0 0 0 0 0 0 0 0 0 22.274 21.44214 20.01357 19.95917 0 0 20.8 21.076 20.7 0 0 18.15429 17.89583 17.88467 18.06133 18.26417 0 0 0 18.35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19.4 18.25 16.22 16.08 19.95 0 0 28.49 29.5 29.57 26 25 0 25 24 22 0 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +149 2603850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.52 25.02 0 0 43.63 46.27 44.49 44.38 45.06 42.36 38.53 35.36 32.94 33.59 38.3 0 52.13 50.54 51.38 56.35 0 0 63.61 65.99 68.41 73.84 72.63 62.84 0 0 0 0 0 0 0 0 0 80.53 85.01 89.35 90.36 87.97 83.1 80.35 83.54 88.05 93.14 0 0 96.23 93.99 0 0 0 0 0 0 0 97.47 97.89 98.31 102.31 98.94 0 109.83 105.23 102.34 0 84.56 97.92 107.99 0 0 0 0 0 0 0 78 73 0 97 0 117.24 114.5 109.99 0 96.51 89.67 71 0 120 105.14 0 125.39 0 129.44 132.67 137.85 142.31 143.69 146.34 148.38 150.24 150.93 150.88 150.41 149.79 150.33 151.82 153.14 151 150.64 148.42 148.33 148.13 147.33 0 86.86 80.85 0 0 135.6 131.19 120.21 101 100.72 81 0 87.77 0 0 0 0 0 0 0 158.95 168.87 166.54 0 118 110 108 112.34 0 88 83 78.89 0 0 0 89.09 79.58 0 65.34 0 0 0 0 0 0 0 0 0 0 0 0 0 28 33 32 28.45 23.5004 22.8852 23.582 24.2228 24.0964 23.02333 0 0 27.95077 27.61559 26.70364 26.79316 27.466 27.6928 27.02201 25.5892 24.34667 23.145 22.35 22.06474 21.64889 20.85077 20.08 19.2064 18.5396 18.2152 17.98 18.1176 18.39792 19.0632 19.43044 19.53 0 19.86667 20.974 21.0575 0 0 22 0 0 0 0 20.966 21.59692 21.54791 20.61 19.7275 20.185 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 18 18 19.51 21.49 23.93 0 27 30.42 24 0 0 0 27.17 24 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +150 2603350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.58 26.49 29.23 28.99 0 0 46.32 42.7 42.33 0 40.62 37.23 34.96 34.66 36.11 40.63 0 0 52.52 53.29 56.16 58.93 61.5 64.07 67.08 69.62 72.23 71.42 64.19 0 66.17 63.44 0 0 0 0 0 0 0 0 96.37 93.33 0 93.34 91.35 94.51 95.23 94.71 0 92.58 92 0 0 0 84.54 82.65 84 97.11 97.82 96.38 93.31 90.47 90.33 90.22 0 107.43 104.9 102.92 0 0 0 101.96 0 0 0 0 0 79.1 84 98.08 94.27 88.72 113.24 99 0 118.76 113.08 107.15 98.95 91.36 80 0 0 92.15 0 124.18 0 125.08 127.85 128 134 141.71 144.24 149.29 151.6 152.4 153.14 153.05 153.06 153.27 154.81 152 151 152 151.36 0 0 0 0 0 80.79 0 0 0 140.3 128 110 0 93 94.8 93.14 87 86 0 0 0 0 0 172 172 175 180 0 0 0 0 0 107 0 90.59 0 0 92.86 0 0 76.76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.35 39.21 35 26 27.0808 26.1832 26.312 27.6625 30.03857 33.5 0 30 30.156 28.6156 28.302 27.992 28.2376 27.7548 27.666 27.0712 25.52565 24.29095 23.36762 23.07625 21.755 21.246 19.795 19.16714 18.66 18.61875 18.37187 18.11 17.9336 18.488 19.43357 19.495 19.535 20.13 20.95091 20.57526 21.1275 22.47857 23.53 0 0 0 0 20.51444 20.4 20.25125 19.91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.79 0 0 0 0 0 0 0 0 0 0 16 16 0 18 0 0 0 22.6 22.28 23.26 24.77 25 29.39 31.09 31.1 30.69 29.52 27.84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +151 2602850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11.19 0 33.69 32.44 28.77 0 0 37.1 0 37.22 36.18 33.96 33.76 34.84 37 40.78 43.31 42.39 0 52.1 55.32 59.34 62.63 64.79 68.3 68.59 70.34 70.44 69.26 59.96 58.53 57.9 0 0 0 0 0 0 0 0 0 0 0 96.23 98.12 0 94.7 88.88 0 0 0 0 0 82 0 0 94.76 95.22 95.85 97.03 92.18 0 77 72 0 0 0 0 0 101.59 103.91 103.55 0 43 0 0 81.86 0 100.51 0 100.89 100.2 0 113.93 110 0 113.84 107.66 99.94 91.72 80.67 0 0 0 0 0 125.27 128.06 0 135.14 135.11 138.01 142.05 147.94 151.68 152.27 153.27 153.27 154.12 154.05 154.88 153 158.67 151 148.62 140.85 0 0 0 94.48 86 89 105 0 0 140.68 0 123.18 116.36 106.42 0 105 93 0 0 0 0 169 167 171 175 172 181 0 0 0 0 128.07 108.73 0 0 100.84 0 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 33 41.48 38 34 32 30.8468 29.41 28.388 25.0415 25.42625 23.6 26.99333 29.89682 32.3852 31.2244 29.1076 29.1296 29.4392 28.614 28.3952 28.1376 27.4272 26.5272 26.1096 24.83 26 23.54667 21.528 20.62857 19.67111 19.69286 20.607 19.28 18.78666 18.98695 19.7185 19.84572 20.24333 20.614 20.76375 20.39 20.6119 21.39444 0 19.64 0 0 19.32833 19.1575 19.38 18.8225 19.065 0 18.435 18.595 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14.24 0 14.11 0 0 0 0 0 18 0 0 0 0 18 22.66 23.56 23.85 24.66 25 25 30.37 30.95 31.3 31.45 30.76 26 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +152 2602350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14.84 33.74 38.16 37.69 37.34 0 32 0 0 35.48 0 30.67 31.99 34.6 37.16 39.68 41.43 43.31 46.7 0 56.01 60.71 63.27 65.55 68.36 69.46 69.63 69.28 67.86 64.44 64.19 66.8 68.55 0 0 0 48.32 0 0 0 0 0 0 0 98.77 0 94.45 0 0 0 0 0 85.5 89.45 0 92.77 91.11 91.82 92.63 96.25 0 0 81.3 0 0 0 0 0 92.51 92.71 0 0 0 47 71 76.23 0 98.88 104.31 106.61 106.85 106.44 108.51 112.69 112.44 120.1 114.72 108.05 103.43 0 99.54 92.82 0 0 0 0 0 0 0 132.45 131.12 134.69 137.91 142.6 148.08 151.17 152.69 152.82 153.33 152.41 153.97 153.57 153.9 151.52 144 143.63 143.56 0 0 99 0 108 0 174.67 166.38 0 137.79 138.62 0 0 113.79 106 0 0 122.71 0 165.18 161 160 173 172 164 170 174 183 189 0 0 0 0 0 0 100.19 0 82.35 73 0 0 0 0 0 0 0 0 0 0 37.34 31 24 32.91 26 36 37.73 37.48 33.678 28.6792 27.088 27.388 25.68 26.5792 29.6972 31.68 36.3984 33.3264 30.2472 31.3196 32.066 30.8044 30.584 30.4044 29.8988 30.1368 30.6332 29.7464 30.59546 27.5924 24.704 23.7425 23.1792 22.7976 22.55959 20.99809 19.7325 19.6908 20.434 19.63 19.21 18.872 19.16875 18.04286 18.78 19.725 19.54235 18.83125 18.4925 18.95 18.64 18.55 18.085 17.965 0 17.98 17.96 0 0 0 0 0 0 0 0 0 0 0 17 0 0 17 0 0 0 0 0 14.85 14.22 0 13.05 0 0 14.68 0 0 19.35 0 0 0 0 0 0 0 23.78 22 21 0 26 0 29.65 30.95 31.64 0 0 0 29.92 0 0 0 0 0 0 0 0 0 0 0 0 25 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +153 2601850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13.37 0 39.86 39.71 39.88 39.05 38.27 37.73 37.31 36.03 0 31.63 32.8 35.57 37.96 39.92 41.6 43.07 45.1 44 0 61.18 63.15 65.79 67.99 68.87 69.42 68.43 68.37 68.06 69.32 70.93 0 0 61.47 67.71 71.67 59 0 0 0 0 0 0 0 0 0 0 0 0 0 85.59 86.08 90.73 93.21 91.53 89.5 87.55 0 81 88.04 87.55 75.02 0 0 0 77.85 0 0 0 0 0 43 53 76.04 76.41 87.82 0 107.65 109.14 111.2 114.39 117.23 116.73 115 117.71 114.23 108.24 0 112.65 103.5 104.58 0 0 0 116.54 119.78 0 0 128.69 0 133.29 136.28 139.95 146.33 150.5 152.04 152.63 152.9 151.56 150.69 149 149.36 149.16 145.73 143.64 143.88 0 0 92 97 0 154.5 170.07 164.89 156.12 147.07 142.95 0 137.95 114 109 94 0 146.54 141 141 149 159 166 167 164 153 158 165 169 175 0 0 0 0 0 0 99.09 83 0 0 0 0 65.69 55 0 0 0 0 0 34 30 32.62 29.5 27 0 40.73 41.54 38.8664 32.44 29.72 30.8144 32.1776 33.022 36.4532 42.5148 39.66625 34.7096 33.0508 36.68478 36.0704 33.90359 33.864 33.7072 32.49 32.8544 34.7972 35.42334 34.6368 34.17133 29.8552 26.8908 25.7984 24.45333 23.27417 21.799 20.37111 20.98824 20.8276 19.76895 18.91 18.18375 17.30833 16.73125 16.82312 17.433 18.58 17.9425 17.95818 17.52 0 0 17.3425 17.20125 18.09 17.85 0 0 0 0 0 17.19375 16.835 0 0 18.525 18.87454 18.065 18.07333 0 17.65 0 0 0 0 0 0 0 0 0 0 0 0 0 15.36 0 0 0 0 0 0 0 19.49 19.74 0 0 0 0 0 0 28.18 30.88 30.76 29.62 29.25 0 0 20 0 0 0 0 0 0 0 0 0 24 27 30 31 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +154 2601350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.71 31.16 37.87 39.98 40.25 40.4 40.13 40.14 39.8 38.32 0 35.36 35.75 37.87 39.78 41.04 42.23 42.86 43.33 43.64 0 53.46 61.64 64.7 66.83 67.48 68.18 68.36 69.09 69.9 71.5 74.74 77.74 78.63 76.66 77.22 76.41 67.98 61.38 0 0 0 0 0 0 0 0 0 68 56 0 0 89.19 91.08 91.94 89.82 86.64 84.23 83.61 87.01 0 83.35 69.52 58.31 0 0 0 0 0 0 44 0 53 76 83.2 85.05 91.61 100.21 106.26 110.52 113.65 115.78 119.02 119.83 120.4 118.34 114.48 112.11 0 112 109.41 106.07 0 0 0 123.69 119.48 120.2 124.11 0 137.03 137.62 139.22 141 146.7 0 151.84 152.1 151.81 150.95 149.28 147.15 145.17 0 0 0 0 0 0 0 0 0 0 166.83 163.87 157.11 150.68 145.87 144.14 135.54 120.97 110.13 0 0 152.61 0 0 162 164 161 162 161 154 149 153.93 155 156 165.1 176.23 0 0 0 0 0 93 81 0 66 0 0 0 0 0 0 64.24 34 38 36.6 33.46 32.37 33.65 38.74 0 45.72 42.1248 37.6356 36.3956 35.64 34.68 38.1348 41.2484 49.2112 45.02083 37.48304 40.699 41.22136 39.27217 37.3052 38.18961 38.8736 36.992 36.0556 37.006 36.7952 35.95601 33.9352 30.7872 27.1856 24.2692 23.03 22.14381 21.5595 0 0 20.722 19.64869 18.4684 17.21476 15.86136 15.3296 15.3104 15.78091 0 0 16.836 17.51333 0 17.174 17.00072 17.43714 17.666 17.98 0 0 0 0 17.52417 16.6924 15.99209 15.62375 0 0 0 18.128 17.995 0 0 0 0 0 0 0 0 0 0 0 0 0 14.75 15.54 16.86 0 0 0 0 0 0 21.08 20.12 0 0 0 0 0 0 0 0 0 0 0 0 0 21 24 0 0 0 23.49 0 0 0 24 0 27 31 34.24 35 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +155 2600850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 35.62 38.7 38.96 41.17 42.79 43.12 42.17 41.14 39.61 39.4 39.48 40.2 41.71 43.09 43.94 43.59 42.58 40.82 39.42 48.68 62.41 0 65.4 66.1 67.27 67.62 69.18 70.58 72.45 75.32 80.09 83.1 82.35 81.64 79.09 71.66 66.65 0 0 50.64 29 0 0 0 0 0 81.04 81.14 80.92 0 92.44 89.28 90.5 89.37 86.7 82.34 78.89 86.18 70.63 54 52.06 40 0 0 0 0 41 51 50 64 70 87 90.71 91.76 94.11 98.99 103.48 108.84 112.2 114 119.19 123.07 123.51 120.67 0 0 114.52 114.44 110.95 106.42 98.6 0 0 119.08 117.59 122.8 128.51 0 133.04 0 137.99 139.22 0 151.91 150.94 150.8 149.74 148.87 147.43 145.19 142.33 141.16 0 135 0 0 0 0 0 0 155 162.52 160.63 156.04 149.91 144.22 138.2 128.75 116.8 111.58 0 0 151.51 151 160 165 158 156 157 157 155.53 146.01 139.24 0 0 0 0 0 0 174.45 165.99 0 0 96 81.21 0 0 0 0 0 57 55 44 45.34 41.7 38.6 35.44 36.76 0 0 46.18 49.17 46.4825 43.47176 42.4992 43.1964 41.5244 43.1248 43.7788 42.642 40.43409 38 45.38086 43.912 41.6976 43.048 44.5184 45.4848 43.1364 40.3392 38.4788 36.6792 34.9876 32.4132 29.5564 26.2452 23.5052 22.0488 21.388 21.00348 20.688 0 20.475 19.69072 17.7704 15.7008 13.974 13.5252 13.8792 14.29636 0 0 16.88375 17.65 17.59833 17.34 17.17333 0 17.5025 0 0 0 0 16.91667 17.22842 15.5824 14.5256 14.44637 14.24 0 0 0 0 0 16.33 0 15.74 0 0 0 0 0 0 0 14.53 15.12 15.93 17 18.55 20.7 0 0 0 0 25.24 22.77 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.07 28.97 28 31 34 37.85 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +156 2600350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 35.01 34.41 35.24 39.82 45.76 46.04 44.42 43.06 41 42.31 42.19 43.25 44.75 0 46.42 45.11 43.72 42.67 43.74 50.03 51 59 63.48 63 65.36 66.64 68.91 70.11 72.04 74.59 77.74 80.51 82.15 83.25 0 0 62 0 0 74.66 0 36 0 0 0 0 0 74.64 73.32 78.63 80.07 82.21 88.38 0 85.58 78.09 70.17 64.92 52.65 42.45 0 0 0 0 0 0 0 66.99 76.06 84.91 91.81 95.43 98.3 97.62 94.64 96.86 101.35 106 111.01 115.52 120.23 125.26 127.98 127.3 116 116.51 116.41 114.14 110.71 108.82 102.86 0 0 0 121.88 122.99 128.19 130.14 131.67 133.91 135.19 0 0 144.32 0 148.68 147.85 147.25 146.47 145.12 143.15 140 139.05 136.39 0 140.61 0 0 0 151.35 153.73 156.46 155.7 152.48 146.27 140.13 133.37 125.11 0 0 0 0 0 143.67 154.31 0 160 151 149 150 149 144.81 140.31 136.9 139 124 0 0 0 0 0 145.41 0 0 0 0 0 0 69 61 55 49 46.25 46.43 0 43.2 36.72 37 43.03 46.27 48.31 49.91 51.603 50.82167 49.4828 49.5744 49.3652 48.9272 48.07679 46.6908 42.18291 41.66667 48.905 46.2 44.12 48.1872 49.1832 48.7376 45.6592 42.50375 38.7704 35.7744 33.5588 30.6456 27.61 24.8976 22.886 21.54905 20.74191 20.1584 19.94 0 21.03 20.0932 17.43833 14.3792 11.7472 11.7752 12.8276 13.64842 14.47833 15.08667 16.3419 17.71111 17.067 16.55 16.04182 16.234 16.93318 17.09571 0 0 18.94 17.34 16.10571 13.8948 12.5484 12.9972 13.548 0 0 0 0 0 0 0 0 0 0 0 14.96 14.84 0 0 0 0 0 18.46 20.14 22.53 0 0 0 0 29.14 26.94 24 0 25 27.64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25.35 0 26.37 27.72 30.24 30 34.98 36 38 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +157 2599850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11.17 18 25 28.13 33.51 43 46.59 45.87 44.79 44.18 44.57 45.55 46.33 47.34 0 36 46.87 45.83 44.92 46.43 49.76 54.75 53 60 62.99 63.91 65.32 67.29 69.37 70.58 72.48 75.04 78.21 80.67 82.86 84.8 0 0 73.35 0 85.68 84.78 61 0 31 0 0 0 0 0 0 0 0 87.61 90.17 83.03 71.07 58.34 52.45 0 0 0 0 0 66.68 66.36 77.39 0 80.95 0 0 96.37 100.81 0 103.86 91.35 93.57 98.39 105.02 109.16 114.18 120.17 126 133.96 133 136.5 127.3 120.39 115.1 106 99 0 109.3 85.26 0 0 119.25 127.67 130.38 132.16 132.71 134.91 0 139.56 0 144.78 144.64 0 145.41 146.08 145.84 144.58 142.27 139.35 136.56 135.65 138.61 138.43 134.79 0 141.35 147.61 150.72 150.99 147.48 143.64 137.55 130.76 124.59 127.51 132.34 136.09 134.01 0 139.44 147.63 151.75 0 151 0 148 150 147.82 140 0 143.41 134 130 115 102 0 0 0 0 0 0 0 0 81 72.82 63.23 0 49.37 49.98 49 0 0 41.03 38.86 43.74 47.92 51.2 51.24 51.2956 51.7056 52.7476 53.03826 52.64572 51.22079 48.3192 46.6068 46.1488 45.39199 50.7075 49.24 48.24 51.72409 52.2156 50.3588 47.9748 44.13953 38.65286 34.79 32.1216 28.2944 24.50375 23.44304 22.1224 21.236 20.48 19.5384 19.3472 20.53421 21.28417 20.5412 17.3348 14.0472 11.3352 11.4624 12.5244 13.43474 14.43765 14.85792 15.68318 18.02308 17.65389 16.335 15.6579 15.97167 16.63823 17.04889 0 16 16 17.1775 16.56143 12.1056 11.128 12.2012 13.37412 14.55833 15.4925 0 0 0 0 0 15.64 0 15.72 0 0 0 0 0 0 0 18.9 19.97 21.48 23.91 27.65 0 32 35.71 33.68 29 0 0 28.62 28 27 0 0 0 0 0 0 0 0 0 0 0 0 19.38 21.69 0 27.65 27 28.03 29.99 32.7 0 0 38.79 40.81 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +158 2599350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.03 0 25.68 26.96 32.08 41.43 44.83 45.22 45.16 45.42 46.44 47.6 48.51 46 0 0 51.5 47.73 46.02 46.99 49.2 53.26 59.12 61.72 62.86 63.22 64.42 66.64 68.53 68.83 69.54 70 75.48 79.19 81.85 84.23 84.21 0 77.62 0 88.14 93.32 88.63 0 45 28 0 0 0 0 0 76.44 82.73 84.78 87.18 77.37 54 0 0 0 27.17 39 64.19 0 79 81.04 83.85 85.51 86.44 87.23 0 100.84 102.37 0 0 94.19 94.3 98.13 103.66 108.6 113.69 119.47 127.21 127 134 135 132.67 122.57 0 115.56 113 0 114.62 101.41 0 0 121 128.98 129.76 131.82 133.47 0 136.29 132.17 0 0 137.96 140.31 142.62 145.1 147.36 146.5 143.4 139.93 137.04 135.94 137.44 137.62 136.51 136.87 141.14 145.19 147.19 145 143 142.67 136.4 130.47 0 129.05 134.93 137.37 136.53 136.82 141.02 147.26 148.99 148.42 145 142 0 152 146 143 0 144.03 142.48 142.82 134 118 108 95 86 0 0 83.35 0 0 104.97 76 63 56.64 52.85 55.22 58.49 59.11 52 48.1 45.62 47.11 49.91 53 55.69 54.42521 53.8676 54.1764 56.04 54.8088 51.6 50.24 50.522 49.2192 51.064 53.19 52.3932 51.85773 54.88154 53.3284 50.22091 45.81667 42.42266 38.98708 35.1972 33.0924 29.9592 25.34917 22.50941 21.85062 21.45 21.09 20.6192 20.8048 21.4912 20.8948 19.0592 16.7212 14.7584 13.1956 12.6524 13.218 13.75167 15.05 15.52572 17.43909 18.66 20.06077 17.80118 16.217 16.2864 16.82708 17.146 0 18.51917 17.51 17 18.21444 15.5188 13.0288 12.9948 14.1512 15.0412 15.6568 16.11077 0 0 16.32 16.32 16.28 16.33 16.17 0 0 0 0 0 0 0 20.79 21.45 22.39 24.38 29.36 0 0 40.75 36 28 0 30.98 30.62 31.64 31 0 13 0 28 0 0 0 0 0 0 22 21.98 20.64 23.82 27.82 31.5 34 0 35.16 36.87 38.5 39.67 40.22 41.15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +159 2598850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 20.31 25.49 28.53 33.79 41.44 43.62 43.35 43.52 44.8 47.05 48.65 49.38 50.35 0 0 0 47.09 45.76 45.81 47.91 54.5 59.01 61.86 62.97 61.6 62.34 68.46 70.77 70.03 0 73.76 72.74 0 0 0 86.7 86.46 82.76 85.27 88.12 94.06 0 0 65.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 35 41.56 70.81 83.69 76 87.76 89.71 88.55 88.7 89.74 93.75 99.84 101.3 0 0 100.86 96.77 95.91 97.86 103.31 107.03 111.88 116.89 119 125 121 133 128 122 123.05 122.85 0 120.39 116.65 110.71 106.47 0 0 0 0 131.18 0 135.47 133.83 130.49 0 133.93 133.51 135.67 138.73 143.99 148.64 148.47 142.78 139.51 137.28 136.93 136.88 137.5 138.48 140.23 143.87 145.03 145.32 146.3 146.09 143.27 137.63 132.34 138.45 137.74 139.55 140.67 140.14 140.4 142.41 145 147.3 0 137 141 0 142 136 133 124 0 0 0 134.76 0 0 0 0 0 0 102.08 0 134.3 128.35 0 74.97 64.31 57.34 55.96 58.23 52 55.08 52.65 51.2 51.91 0 0 59.49 57.0616 57.6428 57.9548 59.4488 58.2 56.8432 56.8184 55.806 53.84439 54.6372 56.29919 56.6228 56.7624 56.6012 52.1524 48.995 43.43 45.145 41.86762 39.14761 36.7532 33.49143 27.09429 22.98308 21.94652 22.152 22.1836 22.3772 23.3172 23.4308 20.7224 17.38 15.756 15.924 14.8008 14.0872 14.23 14.6 0 0 0 0 23 19.80167 17.70273 17.48546 17.09261 17.4879 18.07462 18.918 18.70857 18 21.36412 20.57 16.71131 16.07 16.056 16.238 16.3776 16.5852 17.10875 17.16 17.49 17.19 17.2 17.07 0 16.98 17.11 0 0 0 0 0 0 0 23.71 24.14 29.68 35 43.4 42.55 33 30 29 32 32.35 33.87 35.42 33 0 32 35.09 0 0 0 0 0 24 26.91 25.72 25.84 28.18 31.88 0 39.07 39.99 0 42.3 43.39 43.96 44.05 44.96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +160 2598350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 23 28.76 32.61 38.67 41.01 40.86 40.36 42.59 47.45 49.24 48.94 49 50 0 0 46.67 45.81 44.43 46.22 53.81 60.9 62.45 65.42 60.98 0 76.01 75.73 76.51 0 77.09 0 72.24 77.61 86.28 89.16 87.64 85 92.4 0 0 91 0 90.86 83.81 78.62 0 0 0 0 0 0 0 0 0 0 0 33 42 0 75.93 89.66 98.02 102.69 91.71 89.13 89.88 91.75 97.08 0 97.02 0 0 0 97.54 97.76 99.75 103.27 108.38 112.28 114.6 113 119 123 134 133 137 132 0 126.56 119.41 0 117.67 107 0 0 0 0 0 0 132.31 130.19 0 0 132.57 131.14 0 135 142.16 0 145.02 139.46 136.67 135.83 135.98 136.36 137.86 139.01 140.01 144.53 143.12 142.36 143.34 0 143.96 139.41 137.29 139.29 140.9 142.95 142.17 140.72 0 138.95 0 0 135.57 130 136 0 144 145 140 139 130 121 0 0 0 0 0 0 120 117.43 108.41 0 134.32 136.47 129.3 0 75 61.98 57.92 56 58 60.39 57.44 56.4 56.53 0 0 64.34 62.36761 62.7132 64.8032 65.72667 64.74416 62.1625 61.4624 57.884 55.2676 54.86081 56.8648 61.41851 61.15818 57.86351 52.7964 52.14948 50.32625 45.16959 41.1844 40.52632 39.13833 26.05 24.88176 23.3212 22.1308 22.4804 22.7224 23.1728 24.2772 24.02191 19.96458 16.706 15.7768 16.5856 15.8972 15.2524 15.3084 16.30667 0 0 0 20.4 22.172 19.13136 18.2932 18.3304 17.936 17.6576 18.18 19.07533 19.31952 18.33333 22.22882 21.4704 16.9 19.09937 18.41555 17.31 17.38 17.31584 17.47 0 0 18.77 18.61 18.21 0 17.85 0 0 0 19 19 0 0 0 0 27.42 31.2 36.43 40.74 41.28 35 31 33 35.47 35.16 36.21 37.78 39.54 39 38 24 0 0 0 0 27 27 31.61 30.23 30.78 33.07 36.54 0 43.36 45.58 41 0 48 49.7 49.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +161 2597850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 9 15 0 36.3 38.1 37.72 35.63 36.29 48.5 0 48.36 0 0 0 0 0 45.25 0 41.11 54.01 0 62.91 0 0 73.85 77.34 77.72 77.45 0 0 66.48 68.26 75.61 86.17 88.79 88.31 90.61 97.83 105.73 105.48 0 105.49 107.87 103 0 0 41 0 0 0 0 0 0 0 35 52.18 0 72.53 66 78.27 86.71 90.41 88.62 82.51 85.67 88.55 92.02 0 0 0 0 93.45 95.16 96.51 97 101.18 104.9 110.39 114.44 0 118.6 124 132 146 143 0 0 137.32 127 122.62 118.5 0 0 115.85 105.58 102.86 102.63 0 0 0 0 124.46 126.78 129.43 130.2 0 134.2 133 141.51 138.59 134.76 133.34 133.34 134.39 135.94 137.44 138.16 0 0 0 137.98 134 138.51 141.09 138.37 136.45 0 137.08 140.75 139 136.24 129 0 136.73 133.9 0 0 0 0 0 144 139 141 136 129 132.21 124.56 121.42 0 131.85 132.81 127.35 120.78 115.38 116.86 127 134 139.08 129.7 0 72.91 65.32 0 0 65.29 61.4 60.32 0 70.7 70.46 67.73 63.878 62.096 59.71 53.46533 54.97001 57.647 60.52666 56.14641 52.4848 51.3312 52.68961 56.40834 53.93584 53.79728 50.47121 47.46167 43.68948 39.566 39.05 0 0 32.67307 28.8748 25.4256 23.5056 23.066 22.8352 22.9368 22.7 20.5716 17.7396 17.1676 17.1204 17.2352 16.94304 16.16714 15.98 17.06333 18.705 0 0 22.06333 23.316 19.315 18.6256 19.3028 18.9816 18.2416 18.01727 0 0 0 22.03 21.23857 0 21.33692 20.6116 19.47542 18.82409 18.02889 18.14363 0 0 0 0 20.37 20.09 19.86 0 0 19 23 25 25 25 20 0 26 31 35.79 38.45 39.66 39.39 35 35 36 36.98 37.93 39.49 41.77 43.3 37 23 0 0 0 29 31 32 35 35.08 35.22 37.87 41.53 0 48.79 51.06 47 0 53 56.89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +162 2597350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30.72 0 0 36.76 36.85 34.42 0 0 0 48.69 48.16 0 0 0 0 0 0 0 0 0 0 0 63.27 70.27 75.62 76.96 74.1 61 0 55.76 65.21 70.46 77.74 83.31 86.31 90.49 97.35 103.87 109.22 113.98 0 0 0 0 0 0 47 39 46 30 0 0 0 43.62 63.67 71 78.31 80.87 81.35 79 80.91 0 78.94 85.93 88.84 0 0 87 0 91.37 90.4 92.41 94 99.57 102.52 105.42 0 0 120.59 123.81 129.07 136.84 137 141 140 141 139.77 130.47 124.06 119 0 122.29 122.94 116.83 112.33 110.15 0 106 100 0 126.28 125.82 128 0 0 130.91 132.94 134.44 0 134.52 130.02 131.77 133.07 134.84 136.15 136.62 133.7 130.75 0 131.94 0 124.71 133.93 131.79 130.12 128.77 129.21 130.02 129.27 129.37 128.59 118 126.67 124.71 121.25 120.84 122.61 0 0 0 0 0 0 129 130.95 127.5 129.17 128.47 124 130.05 126.53 121.1 117.86 118.71 125.22 127 135 141 0 0 78.83 0 68 65 64.33 63 0 71.82 69 66 60.4764 56.062 51.6436 50.31895 46.82158 48.14286 49.91304 49.0948 47.0948 45.1344 45.5195 48.06952 47.41588 46.29182 45.86667 43.1 0 0 35.094 37.96637 39.49118 36.054 32.2908 27.7456 25.0544 23.6564 22.98 22.8012 21.8956 19.7125 18.2136 18.6088 18.9056 18.6264 18.25389 17.45857 17.80062 18.14 19.22 19.94 0 24.09 27.4325 23.61 21.14111 21.05333 20.02958 18.6464 18.14667 0 0 0 22.475 22.50227 21.44409 21.15571 21.451 20.81091 19.845 20.62 19.18 19 18 21 0 0 0 22.29 22.23 0 24 28.63 30.74 30.98 27 24 0 0 33.28 35.26 37.1 38.28 38.18 38.17 36 37.7 37.76 38.82 40.05 41.56 42.07 35 0 0 0 34.97 40.28 44.71 42 43.06 39.35 39.06 42.88 47.46 51.37 54.41 54 57 57 60 65 0 0 64.81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +163 2596850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25.91 29.63 32.12 34.39 36.47 37.93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64.55 73.48 78.67 0 0 0 0 0 0 0 0 0 88.89 94.31 99.3 104.69 111.56 119.39 121 119.7 0 0 0 56 57 0 0 41 0 0 0 0 0 0 0 0 0 0 79.02 83.24 0 90.4 89.51 86.58 0 0 93.36 91.58 0 97 101.08 105.28 0 0 116.66 0 125.92 127.78 130 138.44 141.75 128 130 135.69 128.29 122.44 0 0 120.37 126.9 126.08 121.57 118.04 0 122.56 110 0 0 104 0 0 0 0 0 0 0 0 0 131.47 131.35 133 134.51 128 0 126.48 123 0 123.22 118.49 0 123.29 123.32 121.82 0 120 120.65 122.1 123.23 121.88 0 118.66 116.94 116.82 118.14 111 0 0 115 113 131.78 126 127 0 0 118 0 116 116 0 116.94 115.94 118.81 121 130 136 137.95 0 0 79 0 68.33 66.09 66.89 68.97 66 63 57 55.0984 53.604 52.948 51.24667 45.88 43.8 42.04 40.36 40.40105 41.27 0 0 34.015 33.155 33 0 39.86333 41.03857 38.9132 38.9216 38.5912 34.3024 32.17401 28.4856 25.3728 23.5744 22.8944 22.9224 22.7144 21.986 21.3012 21.1316 20.9848 20.5056 19.9204 19.51546 18.48333 18.63333 19.06545 0 0 0 24.44667 26.2475 25.59 23.55454 20.475 19.20285 18.49636 20.18667 20.82286 0 22.78917 22.75773 22.3776 22.16167 22.67154 22.32444 21.87579 22.4908 22.6688 24.08 24.87 23 23 26.52 25.92 24.99 22 0 27.56 33.7 35.73 34.86 30 26 0 0 30 34.62 35.99 36.82 37.65 37.91 37.74 37.94 38.12 38.7 39.47 40.44 40.91 34 0 0 0 0 0 52.43 51.97 50.91 46.27 45.12 49.39 53.76 57.73 60.45 62.42 64.11 66.13 68.61 70.62 0 70.48 69.95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 105 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +164 2596350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25.14 28.08 30.6 33.19 35.86 38.68 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58.32 70.33 68 0 44 57.05 63.58 77.99 0 84.16 89.06 91.86 89.49 90.59 92.66 96.16 101.05 107.01 111.6 111.18 110.06 124.8 125.55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80.61 0 90.56 0 0 0 93.61 94.28 0 100.06 101.6 0 0 0 114.04 117.05 120.4 124.35 124 128.4 133.42 136.47 133 132 123 124.21 0 0 131.85 127.66 0 134.77 130.16 125.58 125.37 125.55 0 125 129.99 0 131 129 0 128.44 129.6 123 130.97 123 0 129.45 127.9 129 133.27 128.34 122.52 118.64 0 0 122.62 118.4 117.35 0 117.32 113.97 111.62 109 114.25 115.25 117.72 116.09 112.39 0 105 112.13 112.26 113.67 112.58 107.95 105.56 0 0 130.49 125 118.99 112 119.01 112 108 0 0 0 110.52 0 116 123 124 129 134.3 134 0 0 75.47 69.77 68.15 66 61 61 58 56.92561 55.0556 52.6908 50.68591 50.48118 46.88 45.4224 43.8192 41.1964 41.47318 40.30693 36.479 34.99956 35.2612 37.95045 41.04 41.9752 42.9584 41.7084 38.6136 36.2728 34.528 31.4808 28.50238 24.97471 23.21909 22.7924 23.26625 23.66 24.40182 25.02458 24.3144 23.3612 22.614 22.2428 21.53667 20.85667 19.52 18.69 0 0 23.19333 23.68714 26.91462 29.31417 24.61563 18 20.06308 20.5048 20.34667 21.90666 23.15273 24.74111 23.44637 23.316 23.726 24.1584 24.2824 24.4896 25.2288 26.1148 26.9 27.93 27 28 31.02 30.03 28.57 27.08 26.31 0 0 0 36 30 28 22 0 0 29 35.04 36.39 37.47 37.97 38.41 37.88 38.07 38.42 38.85 39.55 40.26 29 0 0 0 0 38 43 0 47 53.79 53.61 56.05 60.31 64.52 66.65 67.78 68.49 69.78 71.7 0 0 73.77 73.34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 113.16 104.03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +165 2595850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26.65 28.86 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48.5 0 0 0 56.03 61.91 69.07 77.98 0 85.01 86.77 87.47 86.79 86.44 86.48 0 89.98 92.23 99.95 105.92 111.77 120.34 122.93 0 0 0 0 0 0 63.45 49.65 45 48.15 39 39 0 39 0 0 0 0 86.19 90.4 0 0 0 0 0 0 0 0 0 112.15 0 115.53 118.46 0 0 128.35 130.2 132.12 131.72 130.59 130.97 123.41 111.03 97.26 132.46 132.55 134.9 137.13 134.66 132.27 130.32 125 129.66 129.83 131.8 0 142 135 137.05 134.14 132.73 131.54 129.76 127 127 127 126.46 127.95 128.31 122 117.9 114.58 114 123.31 118 115 111 112.7 0 107.25 104.23 0 109.82 107.75 0 106.98 104.07 102.96 0 107.63 106.48 100 106.2 102.59 100.97 102.45 0 0 0 114 0 105.38 103 103 0 0 103 102 107.33 109 112 117 121 0 0 135 127.08 0 75 68 67 68.3 67.21 60 58.8984 55.5964 53.5892 52.87083 51.1304 51.1132 49.5288 46.6472 43.364 41.2364 38.7844 37.685 38.1236 39.7784 42.2916 44.3568 44.4592 43.14792 39.6744 35.9544 33.384 29.31091 30.56545 26.885 24.0208 22.348 22.6768 24.56 25.79 30.22333 28.94167 28.2224 26.2056 25.6348 25.7184 24.4712 23.04682 20.96583 19.258 20.39 0 21.894 24.02 28.54889 34.93666 22.51 20.10789 21.3396 21.7868 21.37619 23.428 25.19571 26.547 25.35417 25.232 25.8216 26.4464 26.722 26.7876 27.8716 29.1916 30.1 29 32 0 36.3 34.5 32.79 32.15 0 0 0 0 38 33 31 27 0 25 30 32 36.2 37.43 37.99 38.33 37.97 37.48 37.53 37.81 38.5 37 0 0 0 0 37 41 47 54.72 54.44 54.63 54 54 65.87 71.65 72.1 72.13 72.19 72.21 71.54 0 0 74.09 75.85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 138.04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127.49 125.56 122 0 0 116.37 0 0 109 103.98 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +166 2595350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24.34 25.41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 68.62 69.12 68.44 71.49 76.49 81.03 82.71 83.06 83.21 82.88 82.87 84.01 0 0 86.8 90.99 99.99 106.86 109 116.83 118.88 121.94 114.39 0 0 108.59 0 0 0 0 0 56.83 58.43 53.89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 111.45 113.83 115.37 0 128.59 128.32 128.89 129.93 130.03 130.19 128 126.03 118.35 0 0 133.98 135.76 137 136.27 134.22 132.78 132.95 132.45 131.67 133.33 0 146 143 142.28 138.76 136.71 135 131 0 135.43 134.86 130.76 128.55 126.91 119 0 0 0 118 115.91 113.77 111 103 0 0 0 100 0 0 0 0 0 0 0 102.66 0 0 101.24 97.67 96.94 98.69 102 0 0 0 0 0 0 99.23 98.64 100 99 0 103.55 100 110.05 110 109 114 119 0 0 0 80 73 70.16 68.44 64 66.34 59.9872 55.8588 55.6012 54.914 53.9208 53.18161 51.6576 49.2925 45.6756 43.1592 41.9404 41.7988 42.7612 44.04199 44.9804 44.76201 43.9128 43.4172 41.2184 38.7292 34.8228 32.514 30.8052 27.3876 23.8868 22.248 22.6336 25.10381 27 34.30708 34.112 33.0364 31.4476 30.4404 29.4952 27.8192 25.6768 23.7924 22.43087 22.05087 22.2225 0 23.795 32.30143 26.08429 0 21.45687 21.9688 21.7364 21.498 24.48944 25.86375 26.44143 26.645 27.0581 27.8376 28.2976 28.1864 27.9636 29.952 32.7164 34.42 0 0 0 41.25 38.3 36.39 36.21 37 40.84 42.94 0 0 36 35.78 31 25 28 28 34.49 37.19 38.77 39.06 38.43 37.42 36.8 36.65 36.72 34 0 0 34 42.4 36 0 42 48 54.6 55.76 57.26 60.15 64.02 69.08 72 75.26 75.49 75.3 74.63 73.5 72.13 0 75.66 78.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 128 130 131.32 130.06 128.58 127.04 124 0 116 0 0 0 126.09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +167 2594850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25.87 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70.22 70.73 71.56 75.07 77.5 77.86 78.33 78.66 78.66 79.6 81.23 0 0 84.44 85.33 89.7 99.67 97 110.8 116.6 118.68 118.15 0 121.64 113.54 83 0 80.18 0 84.07 82.93 75.02 72.18 0 0 0 0 0 62.5 60.83 61.61 0 0 0 0 0 0 0 108.3 109.14 113.97 0 0 0 0 128.65 130.12 130.67 130.99 131 129.34 126.53 0 0 134.58 135.88 136.66 136.76 135.65 134.83 134.73 134.57 135.66 138.01 0 0 139 145.94 142.07 140.28 138 139.95 136.44 131 141.01 0 0 0 0 122.01 115 108 0 0 105 111.94 106 0 0 0 110 97 0 106 104.68 100 99 0 0 0 0 0 0 93.35 90 93.56 88 0 0 0 105.75 102.21 97.38 95.5 91 0 94 0 102.94 102.71 103 102 104.83 109.79 113 0 0 0 80.68 69.22 68.3 70 67 60.3532 59.25 58.45045 57.136 56.1156 55.2836 54.2425 51.52714 48.00279 47.8536 47.1944 47.1872 47.624 47.31561 46.2796 45.2028 45.5372 44.354 42.3864 40.2132 37.696 35.3956 31.9356 28.3932 25.3972 22.5976 22.5588 25.22231 0 36.835 37.83091 37.238 36.14481 34.744 33.1412 31.26 29.0244 27.8028 26.4268 25.0172 24.4184 24.24625 30.27 37.31 25.06333 25.22933 24.20474 23.0192 22.4836 22.5528 24.436 26.06875 25.7304 27.06357 28.8421 30.0392 29.7528 27.5012 27.6668 32.326 37.19 40.38 40 0 41 43.51 40.19 38.21 37.94 38.94 40.54 41.95 0 41.65 38.19 35.95 34.73 30 30 33 36.89 39.57 40.95 40.05 38.23 36.51 35.32 34.91 34 0 0 36 41.94 43.37 44.81 0 39 50 55.29 57.33 59.21 62.01 65.58 71.74 75.89 77.72 78.24 78.13 77.75 76.95 76.78 0 79.72 82.65 85.56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 138.02 138.9 137.55 135.75 134.1 131 129 125.87 119.2 110.05 118.15 124.55 130.75 135.46 132.29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +168 2594350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27.81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 62.05 70.5 70.22 73.96 74.55 72 74.17 74.85 75.17 75 79.85 0 79.32 76.42 78.58 84.49 90.19 95.83 103.28 106 110.24 113.17 114 118.51 115.9 112.69 113.45 90 0 105.35 90 0 0 81.38 75.54 69.89 0 0 59.45 65.51 77.25 0 95.84 100.09 0 100.98 102.77 104.34 103.43 0 0 0 120.7 124.02 126.84 129.08 131.26 132.04 132.24 132.02 131.17 129.82 124 0 133.17 134.73 137.37 136.57 136.27 136.87 137.59 138.05 139.61 142.03 0 151.73 145 146.82 143.41 142.69 139 143.77 143.74 135 0 137 135.5 133.76 127.03 124.14 114 115.86 111 110 108 0 0 110.94 110.94 115.19 110 104 115.97 0 111.81 0 0 98 101.28 98.51 95.3 0 0 0 0 0 0 0 0 106 104.6 99.7 93.78 90.78 91.95 0 0 105 100.2 99.01 97.77 97.3 0 102.08 108.24 111.86 110.07 0 0 72.39 69.64 65 64 64.08714 64.594 61.7188 59.8188 58.46481 57.2336 55.5916 54.73879 54.2164 52.1288 51.5796 52.4284 52.0944 50.1772 49.40601 48.626 47.70959 45.2568 42.5692 41.06 39.2664 36.5752 33.2072 29.9972 27.4796 24.2744 23.6684 26.72308 0 36.69285 38.96909 39.3484 39.2372 38.1636 36.3052 34.336 31.78801 30.2832 28.5324 27.934 26.5168 29.02211 38.14 28.38667 26.7025 27.194 26.402 24.694 24.2164 24.5768 24.77864 26.59933 27.12909 28.46947 31.484 33.7252 32.7996 29.48 30.0692 36.3464 42.2892 46.71 44 44 45.8 43.15 39.96 38.35 36 38.59 39.47 40.46 40.96 40.03 37.74 35.53 34.36 33 32 35.96 38.18 41.75 0 40.81 37.81 35.31 33.45 32.71 0 0 0 42.92 42.94 44.31 44 0 49.93 52.83 55.44 58.3 60.83 64.42 69.04 76.45 79.67 80.06 80.45 80.54 81.44 81.62 0 0 84.21 86.22 89.08 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 145.87 143.38 140.95 138.53 135.68 132.79 128.44 123.69 120.92 0 124.79 128.96 134 129.02 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +169 2593850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 68.29 68 0 0 0 73.25 72.51 0 76.34 0 0 0 73.15 76.07 80.85 85.81 90.38 94.36 91 99.17 102.17 106.76 110.22 109 117 120.84 122.75 122.47 0 0 0 0 0 83 76.43 68.83 0 0 70.86 83.29 91.81 0 101.64 101.54 101.37 102.43 103.51 0 0 113.14 0 121.87 122.84 126.4 130.27 131.96 132.54 132.73 131.91 131.23 130.41 126 0 134.87 0 0 133 135 0 140.74 140.55 141.72 143.83 146.9 151.68 0 147.79 144.35 143.44 143.96 144.72 142 145 147.2 143.75 137.5 0 127.55 127.8 119.97 115.74 112.38 111.49 111 0 0 119.12 113 119.8 0 0 0 0 0 110 114.85 0 0 98 96 0 92.93 90.67 85 84 0 0 0 102.62 102.27 96.55 89.98 84.91 82 0 0 0 0 97.26 94.39 92.21 0 96 0 105.81 105.62 99.63 87.96 0 73.67 70 67 68.9787 67.3288 65.03839 62.3028 60.1584 57.9348 56.6144 57.25046 56.5575 53.55056 52.41444 54.69 50.90667 51.15385 52.70144 52.0572 49.6764 46.2404 43.46479 42.084 40.4524 37.56 34.24652 31.3216 29.0884 27.0996 26.6096 27.745 35.485 36.68959 38.2024 39.174 40.5712 40.0028 38.6572 36.7836 33.8984 32.10159 30.096 30.7504 32.2992 33.96 37 30.09 29.2716 29.6712 29.4332 27.8344 27.2404 26.088 26.7124 27.2392 29.6336 32.4104 36.498 39.484 38.41 36.41 37.1868 42.0688 48.01917 0 48 0 43 38 38.64 37.63 37.62 37.76 38.03 38.8 39.17 38.45 36.77 34.69 33.61 31 34.34 35.54 37.46 40.14 41.52 39.48 36.6 33.58 31.24 29.75 0 0 43 49.97 45.73 46.72 0 47.91 50.36 53.27 56.74 59.98 63.58 68.19 73.77 79 82.36 81.29 81.42 82.77 84.87 87.77 0 0 0 88.59 90.95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 148 148.51 0 0 144.66 0 148.36 147.8 145.38 142.14 138.56 134.88 131.53 128.75 0 125.07 0 0 0 124.97 126.71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +170 2593350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61.62 70.49 76.56 75.87 73.55 0 0 0 74 73 73.35 75.68 78.25 80.65 83 89.36 0 0 92 0 106.41 114.02 117.33 120.26 123.19 0 127 121.78 117.48 109 107.29 96.94 0 0 0 0 0 87.39 94.44 99.19 102.91 103.92 103.33 0 0 0 111.65 114.12 118.18 120.82 121.82 128.26 131.43 133.58 132.04 131.65 131.4 131.58 130.7 0 122 137.05 135.39 132.11 133.86 136.77 139.58 141.21 141.68 142.56 143.78 145.87 146 0 147 145.54 143.04 142.86 143.46 144.84 142 141 141.87 137.99 132 0 0 115 0 111.34 110 0 114 124.11 124.39 124.18 128.52 132.78 135.48 132.51 125.24 120.8 117.96 112.37 104 105 99 0 0 98 94 91.51 84.68 0 0 0 97 94 92.98 87.64 81 0 0 0 90 97 97 91 0 0 0 97.64 101 102.37 100.72 96.31 87 79 76 74.54 70.8524 68.03359 65.31599 62.05782 60.74308 58.41667 55.5275 0 0 0 0 0 0 0 54.62091 54.27826 50.79818 46.2856 43.6928 42.73151 41.04722 38.0148 35.00381 32.03636 30.4164 29.5588 29.6244 29.45 35 35.52556 37.45042 39.0116 40.822 41.1212 40.0124 38.506 35.7768 33.7536 32.24 32.4532 34.9148 38.90476 37.41667 32.83261 33.329 33.435 32.858 32.03286 32.5192 29.68 30 29.32 31.5824 37.05241 42.53708 46.37083 45.2312 43.2544 43.8536 46.9292 52.2156 57.36 0 49 43 41.55 38.71 37.64 37.39 37.26 37.12 34 34 35 36.21 34.71 33.81 0 30 0 33 37.12 37.72 36.83 34.55 32.39 30.26 28.75 0 0 0 0 58.88 50.99 0 47.66 51.28 54.68 57.96 61.64 65.99 71.14 75.54 80.17 81.6 80.88 81.75 84.22 86.54 87.86 0 0 0 0 92.22 94.53 0 0 0 0 0 0 0 114.46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 146.23 0 150.79 0 153.29 151.15 148.39 145.65 0 145.86 0 150.33 0 148.84 144.95 141.21 136.72 131 0 129.29 128.08 128.75 128.21 127.35 127.09 127.74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +171 2592850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53.47 66.53 73 76.17 74.05 0 0 0 0 0 67.02 77.58 77.65 68 82.19 83.22 0 0 94.7 102.21 103.93 108.6 112.62 114.63 116.52 119.73 123.79 124.16 122.19 118.93 0 0 0 88.46 52 0 0 89.5 0 97.66 104.47 106.12 104.48 0 0 116.54 116.08 117.05 119.64 120.65 121.87 126.08 0 0 130.1 130.46 131.13 131.32 131.01 130.46 131.87 0 0 134.16 135.46 137.62 140.35 141.86 142.28 142.12 142.45 140 142 148 144 142 141.25 140.47 140.56 140.83 141.38 139 139.62 137.76 133 0 128.85 122 116 0 111 121.41 0 131.18 125.27 0 118.29 117.65 117 0 124 126.11 122.75 0 0 0 0 0 0 0 102.14 92 86.73 80.91 0 0 0 93.42 90.23 85.9 77 0 81 92 97 100.97 99 87 0 0 93.76 94.51 97.25 99.99 101.98 99 0 89.98 82.4 74.92 69.237 63.0028 58.40584 57.8025 0 0 0 0 0 0 46.626 44.04333 0 43 45 47.79333 47.95715 44.65043 44.42353 44.51218 41.9372 38.1344 34.6644 32.39727 31.2487 30.68389 30.71055 29.96056 29.3 36.394 37.1212 38.5888 40.9212 42.1724 42.3392 39.8128 37.3704 35.6592 35.2384 36.65001 38.388 43.875 38.0005 33.44444 44 46.229 0 0 38.10857 40.34333 37.9132 37.44637 38.26055 41.853 49.5305 54.27391 51.58772 49.21161 47.732 49.9784 53.8152 56.23 52 49.97 45.07 40.26 38.57 37.92 36 37.23 36.98 37.35 31 32 31 0 0 0 0 32.69 0 32 35.14 34.58 33.07 32.13 31.03 30.74 0 0 0 0 0 0 45.11 48.18 51.84 55.39 59.31 63.35 67.79 72.96 76.48 79.93 81.38 81.92 83.05 85.3 87.68 0 0 86.14 88.58 0 0 96.6 99.23 0 0 0 0 0 0 115.63 117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 140 142.54 145.6 148.71 151.12 152.9 0 0 148.85 0 147.37 147 149 151.5 151.49 149.27 145.73 139 134 126 0 129.2 128.77 129.06 129.96 129.96 130.47 131.04 131.81 132.07 131.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +172 2592350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70 73.96 0 0 0 0 0 0 0 0 75 78.92 80.29 0 0 0 0 93.67 98.43 102.25 106.14 107.59 108.35 110.18 115.8 121 124.44 0 0 0 107 97.45 0 0 0 0 0 98.75 108.51 0 0 0 0 0 113.88 0 0 0 0 0 123.11 125.99 128.13 129.43 131.08 131.78 131.48 131.47 133.26 137.2 134.59 134.71 136.35 138.76 140.92 142.81 142.14 140.67 139.91 140.19 138.37 0 141.49 138.2 133 137.06 137.48 137.3 137.95 134 136.46 135.91 135.76 128 119 116 119 118 110 0 122 119 120.93 110 0 107.23 0 0 0 0 0 0 0 0 0 0 0 0 98.14 92.67 87.33 83.36 74.5 0 0 0 85.64 83.31 77 0 0 94.17 97.37 99.14 89 80 0 0 94.75 92.48 0 0 0 97.98 94.23 86.94 73 68 66.69909 60.88 58.13241 56.88208 56.75333 56.53571 55.638 0 49.7225 49.14957 47.7704 45.43081 44.0755 43.38 44.54727 44.986 45.6495 46.14286 48.8905 46.5972 40.0848 37.9688 34.855 32.99467 31.88813 31.7925 31.3313 30.5564 29.87833 35.54 36.07125 37.5016 40.1496 42.96601 44.372 41.19 39.0996 38.656 39.3708 41.6116 42.2 44.66667 42.26316 37.02667 44.91375 49.2468 53.8021 56.00429 55.02 51.4887 48.77375 50.73273 0 58.7175 57.87087 58.65681 49.8316 40.93501 48.37199 52.17241 54.27399 54.5 52.27 48.74 42 37 35 34 38.14 37.63 37.53 37.38 34 0 0 0 0 0 31.83 30.39 0 0 32.18 32.89 33.13 31 33.61 0 0 0 0 0 68.35 46 0 51.87 53.92 57.33 61.39 65.85 71.07 75.02 78.03 80.71 81.94 82.71 83.9 85.62 87.63 0 0 87.62 89.78 91.44 0 0 100.1 102.41 0 0 0 0 0 115.06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 141.96 141.57 142.66 0 0 148 149 150.5 149.59 148.52 0 149.05 147 150.05 150.26 149.8 147.96 145.36 138 136 128 130 0 129.25 130.91 132.11 133.04 133.39 134.59 135.82 134.82 135.87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +173 2591850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 68 70.58 0 0 0 0 0 0 0 75 79.97 80.3 0 0 77.54 85 0 0 0 100.61 102.73 102.31 100.69 96 110.51 117.47 122.72 0 0 0 116 0 0 0 0 0 0 0 0 0 0 0 109.98 117.22 118.18 109.35 0 0 115.55 113 122.47 123.59 126.63 0 0 131.77 131.13 131.31 131.59 132.4 132.9 134.16 136.57 138.91 140.79 142.76 140.62 137.97 136.26 0 137.13 133 136.06 132.06 0 135.11 135.54 135.52 135.45 134 133.02 131.61 131.29 123 125 126.71 124.14 115 115 0 118 109 0 105 100 99 99 0 0 0 0 0 0 0 0 0 0 0 94.01 89.98 85.31 81.64 76.94 74.27 0 0 0 79.57 65 0 0 0 94.43 96.36 93 0 0 0 94.7 90.99 0 0 0 95.81 90.25 82.54 73 74.71 66.14 64.08841 62.3876 58.6648 58.6224 55.64087 52.208 50.3379 50.448 50.22119 50.5484 48.64639 44.02439 43.0928 43.64223 44.46769 48.33681 48.70715 47 44.78261 41.36 39.0004 36.4404 34.16095 33.208 32.47286 32.66956 32.6748 31.876 31.715 33.47333 35.26304 38.2288 41.6916 42.605 36.7364 40.4352 41.82708 42.9212 43.8284 45.90476 0 43.94737 43.56812 44.5064 49.4412 54.47321 56.99721 53.97119 52.34039 54.1324 56.018 58.24857 61.70601 63.0456 64.1756 61.9748 55.7596 52.67041 54.4296 55.32479 53.48 50.24 47.53 45.08 36 34 34 39.25 37 37.81 37.57 0 0 0 36 37.74 35.45 30.94 27.76 0 0 0 0 0 0 0 0 0 0 55 64 66.02 61.1 0 55.18 56.51 59.31 63.47 67.73 72.93 77.06 80.02 81.83 83.1 82.77 84.41 85.48 86.35 0 0 91.52 91.3 92.69 96.08 0 100.4 103.84 0 0 112.32 114.11 0 117.35 0 0 0 0 129.88 132.53 131.59 129.79 0 0 0 142.87 142.18 0 136.71 0 139.93 140.69 141.39 142.62 144.58 0 147.55 149.77 0 148.56 0 147.7 147.77 148.17 148.46 144 146 141 142 134 134 134 133.56 0 0 133.44 134.62 136.22 137.12 139.03 137.65 138.96 138 140.68 141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +174 2591350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 82.34 82.04 0 84 87.78 87.38 87.96 88.37 90.28 95.91 101.11 102.36 99.09 93.7 0 113.64 113.46 117.42 119.34 120.68 122.76 0 114.29 93 90.04 75.89 0 0 0 0 0 103.89 104.16 109.41 0 106 0 119.31 116.08 113.26 115.79 119.22 122.43 0 128.08 129.83 131.03 130.45 130.34 130.26 129.82 129.95 132.61 136.34 138.06 139.32 139.47 137.2 134.4 132.73 132.39 0 133.46 0 0 134.71 133.76 133.2 132 132.83 0 129.55 127.35 0 128 128.59 125 122 113 0 0 116 0 108 108.27 104.81 101.14 97.11 93.6 94.64 0 86 83 79 83 0 0 0 0 0 0 80.76 80.19 81.97 76 0 0 0 74.24 62 0 0 0 90.91 93.23 73 0 0 97 0 88.51 0 0 93.99 91.23 87.84 83.6 80.18 76 70.7765 67.3244 67.5012 66.58545 62.53601 58.8476 55.2236 51.8716 49.7296 48.8388 49.6128 45.96849 40.60199 41.44479 42.365 47.09 50.95333 0 51.22538 47.2 44.72 41.628 38.7028 35.58478 34.87067 34.15154 33.79625 35.4 33.476 32.93077 33.2 33.505 35.8368 37.52045 34.23429 35.6008 40.5376 42.3572 44.36 46.32 48.58588 0 47.2456 45.86334 47.31041 50.0176 53.8832 54.91241 52.2504 52.826 54.8644 57.492 60.05439 62.99721 64.9296 64.948 63.286 59.47721 55.72361 55.5848 56.43773 0 47.72 46.42 44.87 43.55 34 34 0 0 0 0 0 0 36 40.76 40.98 39.27 30.75 24.28 0 0 0 0 0 0 0 0 0 0 57.45 62.15 63 60.98 58.49 0 0 61.74 65.05 64 75.6 79.24 81.63 83.47 83.85 82.63 84.84 85.9 85.94 0 0 0 0 93.65 97.87 100.63 102.88 105.88 0 0 0 0 118.03 120.34 121.33 0 0 127.51 129 136 0 139.61 0 126.3 142.95 141 143.03 139.16 0 0 138.81 139.87 141.01 141.97 142.86 144.05 145.95 146.82 147.24 147.71 0 0 0 147.54 147.09 143 145.89 144.79 140 134 135 136.7 133 134 136.06 136.45 137.6 137 137 138 141.67 143.38 144 144.91 0 144.6 138.77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +175 2590850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 78.25 0 0 86.74 0 94.71 93.36 93.87 96.81 101.03 103.44 0 0 0 0 111.01 0 113 111.14 115.28 118.35 119.65 115.7 105.82 92 0 0 0 0 0 0 0 104.31 106.39 0 0 0 0 114.29 109.61 0 0 0 123.58 126.59 128.79 130.76 130.94 129.91 128.97 128.06 128.71 0 136.11 137.27 137.68 136.99 134.87 0 0 130.06 128.5 127.27 0 136.89 134.71 132.95 0 132.36 130.7 0 0 0 128 128 129.68 121 118 113 126.73 122.7 119.62 0 111.55 108.92 105.54 103.54 98 0 0 0 0 101.46 91 90 89.42 81 79 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 88 89.55 0 0 0 0 91 88.99 0 0 87.6 86.66 86.13 84.8 83.82 83.07 75.64588 67.1608 64.4328 63.3068 61.1796 61.5476 59.054 53.754 48.182 45.80227 45.36 43.2953 39.82125 40.68438 44.24833 50.12218 56.69667 0 55 55.97722 49.732 44.7812 40.1348 38.40231 0 0 0 0 0 0 34.29412 34.58529 36.42857 36.8125 34.33333 40.39933 41.8 44.04 47.75571 48 50.08333 53.045 51.31695 50.6328 52.9496 54.6896 55.7436 54.754 53.27 53.2028 53.8872 55.9492 59.0536 62.3292 64.05292 60.9196 56.9972 56.082 53.4572 54.258 47.56334 44 42 45.19 40 40 36 0 0 0 0 37.89 0 0 41.53 43.62 44.92 43.64 36.05 26.66 0 31.43 0 0 0 30 0 0 0 0 53.48 59.68 61.72 60.65 57.97 57.36 0 65.26 68.88 74 78.04 81.29 83.15 84.97 85.44 85.49 86.89 87.83 88.31 89.08 0 0 0 0 100.45 102.72 0 107.48 0 0 0 118.04 120.45 122.35 0 0 0 129.16 134 0 0 0 137.98 128 0 147.66 145.17 140.77 136.85 0 0 139.31 140.69 142.99 143.5 144.83 145.08 146.57 145.59 146.16 146.97 146.85 146.96 146.28 145.37 144.19 142.98 143.73 139 141.21 139.95 135 134.5 137.29 137.78 138.01 138.99 137 140.33 143.03 145.81 146.21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +176 2590350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 73.74 80.59 0 0 58.97 81.08 92.79 95.89 97.25 100.13 104.1 107.79 108.06 104.37 92.01 0 0 105.61 111.12 106.68 0 114.55 117.39 118.84 118.24 112.66 0 98.92 94.72 76.32 0 0 0 0 0 0 0 0 116.13 114.22 110.33 0 111 112.67 117.93 123.06 125.48 127.97 129.47 129.93 129.88 128.57 126.16 0 136.54 136.7 136.62 136.43 135.57 134.39 133.18 131.76 0 125.82 122.17 0 134.32 132.03 0 134.51 131.88 129.6 127.76 0 0 134.8 134.99 131.51 126 123 116 125.82 120 0 0 0 0 106.6 96 0 107.48 103.91 103.17 104.87 0 0 0 94.6 78.58 0 0 77 81.52 75 0 0 0 0 72 63 72 80.22 74 0 0 0 82.94 0 0 0 0 0 0 0 0 0 0 86.63 85.7 85.82 86.44 66 60.98348 56.68842 57.5352 60.8164 62.7232 63.29319 52.42648 47.15 0 0 0 42.46857 41.71 45.30727 49.858 54.88727 58.28267 60.50667 57 54.694 48.54042 43.23809 39.5 0 0 0 0 0 36 36.03572 40 39.04277 37.008 39.04572 43.04 48.1825 48.46833 51.79 51.34 55.08333 55 60.44444 56.86666 57.66667 59.46778 57.07 53.5856 52.21479 52.4496 52.5532 54.44279 57.1352 59.8384 62.1264 62.8524 60.1852 55.2168 50.4208 44.754 40.8148 38 35 40 36 34 0 34 0 0 0 0 0 0 43.51 46.42 49.54 52.38 50.54 41.36 37.27 36.48 36.56 0 38.99 41.07 35 0 55.81 56.38 54 62.29 63.94 62.18 57.97 0 0 72.24 75.21 77.54 80.47 83.06 84.58 86.04 86.88 88.08 89.76 90.97 91.67 92.07 92.6 95.54 0 0 101.27 104.58 106.67 109.39 112.26 115.2 117.8 120.57 122.6 124.8 0 0 0 0 0 0 0 0 0 0 0 147 147.18 142.65 138.95 0 137.57 140.4 142.54 144.26 143.9 0 145.95 146.39 0 146.46 148.09 146.52 146.26 145.39 145.34 142.91 140.81 138 138 141.72 0 139.01 138.28 138.37 138.08 138.02 139.12 139.82 141.25 143.24 139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +177 2589850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 69 64.97 77.51 87.82 92.85 95.73 102.01 107.72 112.64 107 0 0 0 0 0 0 0 116.75 122.65 119.97 120.11 120.71 0 108.65 105.12 95.18 0 77.43 0 0 0 0 0 0 0 0 110 0 0 106.12 111.67 114.53 120.13 123.83 127.53 128.84 130.46 131.06 130.21 0 0 134.25 136.42 135.79 135.42 134.92 134.4 135.82 134.49 129.05 0 0 0 131.57 128 0 133.12 130.91 128.21 127.85 0 0 131.97 0 125 123 0 0 120 122 120 0 112 0 0 0 111.74 108.9 104.19 0 0 0 0 0 0 0 79.85 80.45 81.82 83.63 80 82.3 81.01 0 0 0 0 0 82.96 73.4 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 87.82 81 0 58 54.368 55.4 56.83461 59.2975 59.63121 58.5521 47.93143 0 0 0 0 0 0 44.37278 46.4088 48.9924 54.36 60.45333 0 54.92455 49.66667 0 57 54.36333 52.926 0 0 0 0 39.6125 41.485 45.50235 39.11826 39.39445 40.8396 46.41667 0 0 58.898 59.91722 0 71.61412 71.82 69.89999 62.8 56.92 53.56 51.3964 52.422 51.93 52.4148 54.5416 57.286 60.5868 62.834 62.8492 60.2588 55.2 47.5648 42.1536 39 36 0 0 0 0 0 0 0 45.58 42.35 34 0 44 48.34 52.33 57.08 58.02 49.82 44.35 41.66 38 0 42.18 44.48 47.74 52.78 58.31 62.3 64 67.93 67.89 66.51 61.63 53.46 0 77.39 79.49 79.6 81.64 84.13 85.37 86.52 88.03 90.13 92.62 94.7 95.41 95.51 0 97.53 0 0 0 105.44 108.08 110.81 113.94 117.17 120.99 122.89 0 126.41 0 124.4 123.25 0 0 0 0 0 0 0 0 149.97 147.55 144.43 141.65 140.61 0 141.71 143.36 145.74 0 0 0 0 0 148.58 145 0 145 146.52 145.74 142 141.19 140.07 139 0 141.81 141.01 140.7 138.86 137.56 136.91 129 140.72 141.21 138.75 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +178 2589350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65.41 71.75 79.84 86.96 89.55 88.34 103.46 105 109.31 0 0 86.26 86.32 89.51 91.55 91.21 95.44 0 117.54 117.62 120.64 119 112.85 114.44 110.51 109.24 100.78 0 78.04 0 61 65 0 0 0 0 0 0 114.79 116.72 109.53 108.41 0 123.85 128.32 0 132.24 130.28 0 0 0 0 0 134.58 134.55 134.42 133.83 0 137.76 131.74 125.98 0 0 0 0 134.7 0 129.74 126.74 0 127.81 128.75 130.8 132.02 127 0 126 0 128.08 130.79 131.77 127.59 117 129.38 0 0 0 0 0 0 86 85 0 0 0 87 89.86 88.09 87.45 88.1 88.76 85.37 78 77 0 0 0 0 0 0 0 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58.48 57.87667 53.6 52.37 49.54 0 0 0 0 0 0 43.334 43.29 46.7552 53.7184 59.414 60.09375 61.874 58.2 52.48353 54.04875 51.63799 51.39833 55.5 53.95238 52.19308 52.5 49.005 47.90524 47.72334 40.48 38.01773 38.76 46.28077 57 61.82 66.86333 62.78947 0 69.36364 67.2 68.9096 68.75809 65.1416 58.68 53.64 51.34833 51.7964 50.6612 51.95359 54.52161 57.85459 60.82167 62.47167 61.0675 56.54 48.1552 42.3644 40.19 42.91 39 35 0 0 0 0 0 49 47.53 46 36 0 49.14 52.59 55.57 55.57 52.14 48.17 45.89 38 0 45.34 48.11 52.03 58.28 63.56 68.33 70.77 73.91 76.08 76.27 71.55 63.06 59 0 0 0 0 83.8 84.29 85.37 88.55 91.67 95.45 98.84 99.23 98.74 0 99.23 99.97 0 0 104.99 108.81 112.29 114.57 119.1 123.37 125.24 126.7 128.15 129.4 130 130.25 0 132.43 130.36 0 0 0 0 0 144.65 145.72 0 143.66 142.55 0 142.8 144.13 0 144.16 0 146.89 148.93 149.53 146 147 142 139 147.76 145 145 141 138 133 137 140.71 141.44 143.65 137.77 136.61 135.82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +179 2588850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67.76 70.75 71.05 73.3 80.97 89.69 86.49 74.76 0 0 0 0 0 95.13 94.04 97.09 101.75 104.02 104.75 109.79 114.22 118.08 122.99 125.35 128.64 124.71 118.66 113.83 105 100.29 94.9 87.65 85.58 0 0 0 0 0 0 0 0 0 102.44 0 0 127.87 130.02 0 133.74 131.34 0 0 131.36 133.1 136.24 134.52 134.02 134.37 133.73 132 0 0 126.6 0 0 0 0 126.94 0 0 0 132.1 130.86 132.53 133.55 132.38 0 131.12 126 134.77 131.13 128.41 128.24 132.06 124.25 0 113.66 103 0 0 0 99 100 98 90 0 96 103.15 101.07 96.79 93.13 92.31 0 87 88.74 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 44.22 46.772 50.2088 52.5228 55.25281 55.90039 50.0812 44.0756 44.9532 51.2244 55.04 51.8 50.56 48.5944 47.24 46.57143 49.4265 45.78591 42.13636 38.34458 38.25913 44.8388 50.4848 55.23809 60.33333 0 0 66.66666 64.48 68.54278 72.8 69.14286 65.42812 57.08 51.89572 51.07 47.81529 47.6236 49.5028 51.95278 49.55429 49.13538 50.3825 46.9868 43.07121 40.0248 38.95 42.21 41 36 0 0 0 0 57.6 51 51.54 49.57 48.56 0 48.79 51.61 53.6 53.89 52.25 50.09 48.27 37 0 48.25 50.67 55.11 62.57 68.53 73.29 77.83 81.07 83.81 82 85.36 75.09 66.65 61.27 0 0 71.47 0 0 0 0 90.71 96.03 103.54 101.11 100.11 99.97 100.41 101.3 103.46 0 0 0 0 0 121.58 125.99 126.49 128.02 129.73 131.47 133.01 133.61 133.94 133.73 0 0 127.18 134.1 136 139 140 0 145.38 144.9 143.96 144.14 143.56 144.88 146.32 0 145.86 148.25 0 150.34 0 144 0 146.31 148.07 150.73 144 142 139.14 133 137.32 138.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +180 2588350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67.78 78.34 0 84.66 73.29 62.75 0 0 58 74.12 81 96.65 103.54 111.76 0 114.31 111.56 111.32 0 125.27 127.87 0 0 118.98 111.76 110.62 108.55 107.02 105.77 0 0 0 97.36 96.73 0 0 0 0 0 99.26 0 127.58 127.58 122 0 0 0 0 127.88 132.97 134.41 134.22 133.54 134.83 135.27 134.28 133.67 132.74 131.22 128.55 124.79 119.11 111.53 0 0 0 0 0 138.47 135.3 137.16 139.02 137.04 133.24 0 0 131 0 0 0 0 0 0 0 0 0 0 0 115.45 106.21 97 85 0 0 0 110.8 103.86 0 96 92 94.18 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 91.09 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45.30782 45.96333 44.83611 44.97524 45.0356 42.84361 40.538 41.609 42.67308 49.39864 51.36923 52.99599 49.066 46.3004 45.0328 45.23409 53 0 39.4 37.55217 40.6712 45.1288 49.72786 48.52667 50.07273 54.29333 60.38095 59 62 0 0 71.38499 65.8 56.16321 51.43476 47.09238 43.21922 43.80333 45.57 41.75714 39.76684 38.77533 37.74429 36.89786 37.25417 37.57 41.94 45.46 39 0 0 0 0 57.58 56.04 54.22 52.65 51.53 0 51.47 50.78 52.34 52.91 52.43 51.12 49.7 45 0 49.12 51.97 56.94 64.04 72 79.43 78 77 88.95 0 86.23 80.85 74.6 69.04 69.47 0 78.92 75.44 79.05 0 0 0 0 96.23 98.94 99.79 100.48 101.67 102.54 104.25 0 109.9 0 109.66 119.41 0 120 125.72 129.12 131.24 133.67 135.07 136 135.94 0 133.77 128 126 0 143.54 139 143.85 145.44 145.45 0 0 144.58 145.06 145.54 147.51 147 0 149.88 150.75 150.22 144 141 0 142.85 145 149 148 145.95 136.05 130 135.66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +181 2587850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 74 0 82.45 75.2 61.1 0 0 72.09 78.82 87.62 98.35 105.7 0 123.47 114.05 0 108.35 115.43 125.99 128.52 0 0 0 109.72 114.39 115.86 116.72 0 0 109.95 106.39 100.63 93.47 0 0 0 0 0 0 0 0 119.77 119.95 92 0 0 0 140.55 135.59 134.96 136.02 0 140.41 136.51 133.66 132.36 132.83 131.76 130.35 127.16 120.69 114.22 115.66 0 119.03 0 0 0 0 143.38 147.79 144.93 139.86 138.03 144.59 129 125 120 0 0 106.78 99 0 0 0 119.41 117.35 109.84 104.51 0 0 0 0 119.55 115.81 106.91 103.51 103.2 101.24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 78 79 79 0 0 0 0 0 0 0 92.98 94.48 91.57368 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 41.05667 40.055 0 0 38.89 0 53.65637 60.71333 48.24889 43.4332 40.3788 37.6712 37.7416 39.3185 39.0296 38.52182 40.2568 42.8292 46.70364 43.885 48.5013 52.64905 51.36 55.6224 58.36385 59.596 63.66063 68.5676 65.1284 56.89441 50.88441 46.78721 43.8708 43.3108 41.63238 40.83047 40.38191 39.46591 37.07619 35.69278 36.62556 37.76 42 0 0 0 0 0 53 56.74 56.4 55.68 54.91 48 0 0 0 53.08 53.44 53.2 51.97 51.24 50.49 0 50.34 52 57.52 66.35 76.03 85.54 89.25 82 91.34 94.86 89.26 83.55 79.02 76.24 76.14 78 80.65 83.24 84.27 85.49 86.64 87.25 0 0 97.16 99.26 100.91 102.9 104.16 105.71 0 0 112.88 0 126 131.73 128.56 128.18 0 0 0 137.6 138.46 138.27 136.29 133.13 0 0 0 146.86 146.11 145.63 145.6 145.29 144.96 0 146.1 146.04 147.72 0 148.63 150.19 0 150 0 148 144 139 134 146.98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +182 2587350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 79.63 70.18 0 0 0 88.14 90.48 95.01 0 109.67 0 0 0 98.93 112.76 115.1 0 0 0 0 111.75 117.86 119.6 120.69 125.38 119.89 0 116.1 113.24 0 0 107.69 97.9 89.1 82 0 0 0 0 0 0 0 0 127.32 128.68 127 129.01 132.23 131.46 136.38 138.88 136.64 133.88 132.48 131.47 130.63 133 125 122.75 0 0 0 118.39 0 0 0 0 0 0 0 141.95 139.93 0 132.18 128.07 124 123.87 119.71 0 0 102 0 113 118.13 0 104 0 96 0 0 0 0 114 102 0 0 0 0 0 0 0 0 0 0 0 70.31 70.12 72.47 0 0 0 0 86.22 88.01 0 0 0 83.15 0 0 0 0 0 97.52 94.83 0 0 0 0 0 0 0 0 0 0 0 0 49.92 49.905 49.62 0 0 0 0 0 0 45.68 42.798 40.468 37.5 0 47.83421 42.63667 39.22166 37.6881 35.5984 35.21542 37.37111 37.29277 39.66 43.28739 42.20667 0 45 46.3392 45.2495 44.1135 49.3484 53.55199 57.16919 59.8548 61.1952 58.3352 53.26401 48.9908 46.6088 47.9612 48.964 44.6244 42.07999 41.0924 40.48721 39.3296 37.7384 34.33333 0 0 0 0 0 0 0 59.79 57.98 56.89 57.14 57.11 56.73 56 49 0 0 54.91 53.84 53.06 52.93 52.54 39 0 0 56.33 67.54 0 0 87 87.48 87.49 88.52 86.53 84.23 81.76 80.81 81.71 84.88 88.15 86 90.44 90.19 90.52 91.99 95.97 0 0 99.56 101.64 104.02 105.76 108.29 109 0 114.41 123.53 0 0 128.87 128.8 131.1 134.08 136 140.11 142 141.64 139.11 129 133.97 132.03 141 133 147.05 146.53 146.39 145.95 144.68 145.9 145.94 146.48 0 149 0 0 151 154.27 151 153.17 153.27 149.17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +183 2586850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 86 92.56 97.14 98.09 97.57 89.42 0 0 0 97.8 108.52 0 0 0 0 0 0 132.99 133.25 124.65 119.46 120.16 121.15 0 119.91 0 110.78 0 114.25 105.73 97.96 95.23 78 89.36 0 0 0 0 0 0 125.85 0 128.36 133.79 0 0 0 137.14 134.33 131.64 130.59 131.54 128.42 0 0 0 0 112.11 114.29 118.77 123.75 125 126.88 126.88 135 134 135 138.68 136.9 131.4 126.19 122.66 0 0 0 0 111.72 0 0 122.73 0 120.57 111.36 0 0 95 0 0 117 111 106 95 94 96 91 87 83 85 81 73 0 81.24 77.57 0 72 0 0 0 0 87 91.8 92 0 0 84.18 0 0 0 0 94.35 99.11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58 54.73 52.27 41 0 0 44 0 0 0 0 0 0 0 0 0 0 0 46.85 43 0 0 48.1 51.86 53.71 55.17 55.28 52.3 48.43 45.78 44.74 0 53.37 48.11 42.35 41.97 42.1 42.83 42.46 0 0 0 0 0 0 0 0 0 53 56 60.13 60.99 59.85 59.12 54 0 0 55.43 55.08 54.95 54.64 54.09 49 46 0 0 0 0 0 0 78.48 80.43 83.01 82.44 82.51 82.56 83.75 85.55 90.06 95.33 95 95 94.84 94.42 95.37 97.21 0 100.34 0 0 106.2 108.16 110.34 112.46 112.8 0 0 0 0 0 127.64 131.49 135.18 138.64 142.39 144 145.79 142.88 140.33 139.3 138 143.27 145.56 142 146.39 146.77 145.06 145.83 146.05 146.31 147.57 0 150.09 151.93 153.91 0 150 152 153.93 156.74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +184 2586350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.39 0 0 0 68.78 0 0 88 0 0 91.84 101.13 103.12 0 0 61 76.81 0 103.33 111.44 133.24 0 0 0 0 120.68 121.36 120.52 119.02 115.3 112 117.33 111.49 105.77 103.06 100.92 99.26 100.58 0 88.17 85 0 0 0 0 0 0 126.53 0 0 0 0 128.06 0 0 0 0 0 0 0 0 118.9 122.8 125.79 127.03 126.89 128.49 132.75 136.19 135.88 136.53 136.32 134.49 0 119 118 0 127.26 118.51 0 0 126.18 0 116 0 96 0 112.56 105 0 119.83 114 0 99 104 102.41 97.46 93.46 87.74 82 82.06 73 81.12 0 85.65 83.55 79.95 0 0 76.97 78 81 90 93.22 0 0 90.03 84 83.31 82.4 0 0 94.08 97.77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 57.14 58.91 61.05 60.47 56 48 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47.53 50.99 53.01 51.98 0 52.28 48.85 43 0 0 0 0 0 0 0 0 47.15 0 0 0 0 0 0 0 0 0 0 57 59 62.93 64 64.21 63.49 61.26 0 0 55.8 55.87 56.32 56.61 56.73 56.92 57.21 46 42 0 0 0 0 0 0 0 78.67 80.56 82.69 84.99 88.03 92.94 98.61 102.23 100.12 97.92 97.73 98.12 0 98.79 102.25 101.21 0 108.74 110.58 112.72 114.68 116.21 118.31 120.49 0 120.17 122.42 0 131.68 0 0 143.23 146.75 147.51 145.5 142 142.78 143.17 144.4 145.64 144 146.27 145.85 145.67 146.93 145 146.61 147.9 150.92 150.41 152.05 154 155 0 156.9 150.13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +185 2585850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42 47.84 41 90.83 96.79 105.26 0 0 71 0 101 107.45 110.67 113.87 118.41 0 0 0 124.94 122.91 122.07 121.55 120.81 119.24 119.27 118.69 116.08 113.31 110.93 110.39 109.76 0 104.85 101.64 101.7 93.72 0 0 0 0 0 0 0 0 0 126.68 0 0 0 0 0 0 0 126.28 123.93 125.37 127.01 129.31 129.11 129.78 131.58 133.47 135.73 137.31 137.44 137.11 136.87 133.44 130.44 122 132.16 127.61 0 0 133.5 129.81 125.14 0 0 0 111.56 118.93 0 122.61 112 112 115 113.98 108.47 102.72 0 0 0 82 83.1 81.72 79 0 88.71 85.21 77 0 0 0 82.05 88 94.07 95.09 93.5 94.95 93 92.08 87.54 85.17 85.57 88.08 91.96 95.72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 59.15 59.85 60.94 62.54 57 46 63.71 56 47 42 38 40 0 0 39.37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 63.57 64.79 65.88 66.73 67.15 66.55 60 0 0 0 58.11 58.77 59.41 60.16 60.7 61.34 62.01 55 55 48 54 48 0 0 75.5 75.91 78.42 81.75 84.42 88.09 92.11 96.72 99.84 100.2 99.58 99.83 100.49 101.58 103.72 0 108.44 0 0 112.54 115.37 117.43 119.28 120.64 0 121.61 121.74 124.7 128.43 132.91 135.89 0 0 145.27 146.79 146.61 144 145.02 145.05 145.49 145.49 146.11 145.79 145.07 144.6 144.92 142 142 147.93 0 147 0 0 0 0 160.42 151.88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +186 2585350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61 83.58 0 0 112.12 110.7 109.09 0 0 0 0 128.95 122.88 122.65 122.86 121.16 120.65 120.51 119.41 118.68 118.35 117.86 117.51 0 113.8 110.24 109.74 109.46 102.39 106 0 0 98 88 90 0 0 0 0 0 0 106 0 108.76 0 126.34 135.99 134.71 133.85 133.48 132.86 132.02 132.18 133.82 135.98 136.81 138.64 138.92 140.76 138.86 135 0 133.81 133.15 129.87 0 0 133.5 0 0 113.88 105 0 120.39 0 125.51 0 117 118.8 116.52 0 0 0 89 89 88.14 90.37 88.43 85.81 0 0 90.2 84.79 79 0 0 0 79 83 90.67 92.94 87 94 0 0 87.71 83.54 83.16 85.58 87.46 88.17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58.45 60.45 62.78 64.06 51 43 47 0 64 52 42 43 42 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 65 67.65 68.11 68.95 69.81 65 57 49 0 0 0 61.44 62.47 63.46 64.52 65.46 66.06 65.51 65.06 63.98 61.19 56.85 0 0 72.11 73.99 77.78 81.01 83.37 86.34 90.14 93.81 96.69 98.31 99.45 101.46 102.83 105.28 108.18 111.57 115.45 117.05 112.55 0 0 0 122.41 123.35 124.01 124.4 0 128.4 131.78 134.51 138 141.35 143.84 0 146.61 147.18 146.62 146.57 146.56 146.09 145.67 145.66 145.36 144.76 143.59 0 144.55 139.7 0 0 0 0 0 0 158.29 155.67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +187 2584850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 109.02 109.73 103.85 98.51 84.7 0 0 0 124.56 118.69 123.03 123.91 120.39 120.65 121.04 119.41 119.63 120.61 121.19 120.98 121.8 117.52 114.94 113.04 112.42 0 121.43 114 113.25 109.14 105.25 102.88 106.2 0 0 0 106.67 0 119.86 0 119.26 110.95 0 0 138.39 139.18 138.41 0 135.2 136.54 136.71 139.16 139.88 140.75 141.18 0 140 0 136.12 135.68 0 133 120 0 134.43 128.79 0 0 0 124.05 128.24 129.72 0 121.06 0 0 107 0 0 100.82 0 98.12 97.15 96 0 0 0 0 86.91 81.59 0 0 0 0 84 88.06 88.3 87.97 0 0 79 0 0 0 0 0 83.09 77.91 77.39 0 0 0 0 0 0 0 0 0 0 61.81 63.38 0 0 0 0 51 58 66.03 57 50 53 55 57 0 55 50 46 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43.77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67.47 67.69 69.15 71.61 72.06 65 62 63 0 0 0 65.61 65.75 68.23 70.61 70.08 69.05 67.69 66.46 65.37 0 0 0 0 71.24 75.64 80.93 83.03 84.9 87.84 91.22 93.58 95.57 98.31 101.62 104.65 108.08 111.6 116.22 121.79 125.11 118 115.19 0 0 125.42 125.67 126.57 127.4 0 131.68 134.48 135.98 138.92 0 143.88 0 146.03 146.85 0 147.45 147.65 147.09 145.62 144.96 144.48 143.37 0 142.84 0 142.62 0 0 0 0 154.67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +188 2584350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 114.47 113.67 106.4 94.56 0 0 0 0 0 126 126.22 125.39 119 118.78 0 119.47 0 120.7 121.98 123.17 123.7 122.92 120.35 118.02 115.98 115.49 128.25 126.22 121.04 119.16 115.76 113.84 113.86 115.52 118.2 0 117.2 116.63 0 126.29 0 125.08 120.28 121.64 128.94 131.27 0 131 137.46 138.91 139.27 139.3 141.22 141.87 142.73 144.1 146.42 134 140.92 139.1 135 140.13 131 0 135.19 134 129 123.42 113 0 0 135.72 133.69 128.29 121.83 117.05 0 0 0 0 104.03 100 102.29 102.23 102 0 0 94.93 85.78 0 0 78.68 81.9 0 0 89.82 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67.27 65.56 0 0 0 0 51 52 66.79 0 62 65 63.64 60 62.93 61.82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52.09 0 0 0 0 0 40.06 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 55 47 0 0 0 70.53 73.82 73.06 71.97 71.15 70.21 69.63 66 0 0 0 0 0 0 82.49 82.05 86.04 89.39 90.74 92.71 96.83 101.64 106.04 109.84 114.11 119.32 124.18 127.99 0 122.47 120.24 0 0 125.7 128.39 130.52 132.17 0 136.21 138.28 0 142.17 143.72 145.14 145.19 145.77 0 147.44 148.58 0 145.12 143.98 142 141.64 141.69 141.64 140.77 140.27 133 145.22 147 160.65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +189 2583850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 103.03 87.16 80.37 0 0 113.25 127.72 0 0 124 124.89 115.11 0 108.12 113.24 118.97 122.02 124.21 124.62 124.38 122.58 120.48 118.67 118.78 123.82 124.45 119.62 119.4 120.94 121.53 123.05 126.76 0 0 124.21 126.09 128.61 126.7 0 129.46 127.81 128.77 132.36 135.15 135.47 137.88 138 142.55 142.43 142.65 143.49 144.98 146.56 148.12 149.42 0 145.9 143.25 0 142.37 125 136.08 134.89 135.76 132.33 123 0 0 0 139 0 123 114 112 0 114.62 110.43 113.21 109.91 106.25 104.12 103.05 102.56 100.13 92 89.77 0 0 82.92 84.91 88.48 91.61 91.88 0 0 0 79.72 73 0 0 79.14 75.7 69 0 0 0 0 0 0 0 0 0 0 0 64.88 74.89 67.5 0 0 0 0 59 58.15 49 0 0 0 73.41 67.17 60.97 55.84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 53.69 58.58 64.05 61.54 58.02 54.39 50.75 48.55 50.32 0 0 0 0 43.99 0 41.05 43.9 0 0 0 0 0 0 0 0 0 50 50.9 50.52 51.4 54.04 0 0 0 0 0 0 62.66 62.03 60.83 56 0 0 71.4 72.49 73.73 75.04 75.4 74.51 73.11 70.83 0 0 80.45 78.72 0 0 0 0 0 0 0 0 96.07 0 106.25 110.47 115.04 119.95 123.74 0 124 124.41 122.88 123.02 123.47 0 0 0 0 137 139.32 0 142.98 0 144.6 0 0 0 144.04 145.98 141.77 145.99 143.29 0 140.31 139.84 140.16 139.86 139.6 0 0 131 133 146.38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +190 2583350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 99.31 73.87 0 0 0 109.76 0 0 0 0 0 74 97.26 113.34 117.84 120.21 123.19 125.76 126.1 125.55 124.42 123.27 122 122.07 123.05 123.74 122.78 123.36 125.81 127.85 130.13 0 0 133.26 131.28 131.2 131.36 131.52 127 134.26 132.88 133.47 135.66 138.4 141.44 146.83 146.13 145.41 145.17 146.11 146.8 148.72 150.2 151.64 151 152.96 150.81 146 146.84 145.03 0 139.23 138.16 138.29 133 0 0 0 0 138.66 0 0 122.1 0 121.63 118.79 117.21 117.47 113.81 109.12 105.04 101.04 98.69 94.47 0 0 77 0 86.97 90.14 92.95 0 0 0 79 83.78 0 0 86.19 83.67 78 73 0 83.85 84.47 85.79 0 0 76.58 77.02 75.76 70.76 67.78 67.1 66.28 66.84 0 0 0 0 65 58 55 53 0 0 70.34 70.2 64.89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 62.82 50 49 53 60.33 65.43 64.19 60.72 57.42 53.82 45 42 43 0 0 0 49.81 40 45.97 48.58 0 0 0 0 50.82 0 0 0 0 54.06 52.14 51.32 51.67 53.08 54.8 55.5 57.01 58.89 59.42 60.04 60.75 60.39 58.98 57.44 0 0 70.43 68.88 73.68 78.16 79.31 78.49 77.48 76.78 77.82 0 0 89.06 80.9 0 0 82.51 0 0 0 90 0 101.07 105.45 109.91 115.27 119.8 123.56 126.13 0 126.75 126.42 0 126.36 128.5 132.29 135.46 137.91 0 0 0 0 146.15 146.24 146.17 145.47 144.09 141.99 140.28 135.62 0 133.43 0 137.9 137.47 138.63 139.21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +191 2582850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 71 0 90.67 99.4 0 0 0 0 78 109.32 108.69 116.61 118.95 121.31 125.18 128.11 128.57 127.91 127.33 126.44 125.1 124.67 125.25 125.51 126.05 127.48 129.78 132.31 134.78 134.08 132.13 127 135.18 134.81 134.39 134.7 135.56 135.27 135.1 136.12 138.09 140.43 143.63 146.8 147.32 147.02 146.93 147.57 148.51 150.38 152.45 153 0 158.72 155.89 152.91 148.75 0 144.3 140.71 140 133 0 133.6 0 0 137.3 132 0 123 0 119.34 119.73 120.3 120.31 119.11 114.43 108.33 102.68 99.29 93.39 0 0 85.13 87 86 92.16 0 0 0 84 86 0 0 0 88.88 86 79 80 83.76 84.57 87.56 84.63 0 0 0 84.55 81.97 75.23 68 71.61 0 0 0 0 0 73.9 69 61 56 60 63.5 63.34 67.51 66.96 65.26 61.02 49 0 0 0 0 43.22 0 0 50.12 40 0 0 0 0 0 59.31 0 49 54 60.22 62.68 63.29 62.51 54 52 53.93 49 46.97 48.49 57.5 0 0 46 50.97 54.37 0 58.9 56.5 53.53 48 0 0 0 0 55.84 53.33 52.84 53.26 54.21 55.32 56.72 58.27 60.09 60.45 60.89 61.34 60.66 59.08 57.04 0 0 0 0 75 83.27 83.79 81.59 81.92 83.81 84.9 0 0 92.76 81.91 0 83.43 90.13 93.68 94.93 94.55 93.66 0 105.31 103.85 109.36 114.77 119.81 124.33 127.1 128.88 0 129.93 130.28 130.35 132.49 135.39 138.34 140.98 143.13 145.2 147.34 143 148.38 147.72 147.03 145.89 144.15 141.99 140.14 134.27 0 0 135.84 135.93 136.38 137.17 138.41 139.56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +192 2582350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 62 86.71 92.07 89.89 0 0 0 61 79 107.29 108 109.78 113.55 115.39 120.85 127.32 131.05 130.8 130.61 130.57 129.47 128.12 127.51 127.33 127.28 128.59 130.46 132.17 134.11 0 136.14 135.92 136.6 136.8 136.62 136.61 135.92 135.87 135 135.45 137.29 139.23 141.62 143.97 145.8 146.67 147.11 146.87 147.8 149.17 150.93 153.8 157.02 160.92 160.59 159.32 150 0 137 146.08 143.67 141.55 141.2 142.47 132 130.98 0 135.98 0 133.33 0 126.05 121.29 120.2 120.39 121.21 119.94 0 104.25 96.33 0 0 87 0 0 97 97 96 92 91.8 0 0 0 0 0 87.21 88.02 82 87.42 87.29 86.26 82.39 0 0 0 0 93.57 88.62 0 74.99 75.56 77 0 0 0 0 79.39 78.07 70 64 60 66 66.88 65 65.53 62.7 58.84 0 0 0 48 49.24 45.5 0 0 0 50 46.24 0 0 0 0 63.64 0 51 57.98 58 62.03 63.68 63.85 63.61 61 62 60.32 53 51.58 52.7 55.69 0 51.63 52.33 54.2 57.28 60.51 57.47 54.14 52.11 50.95 0 0 0 0 53.37 53.06 53.47 54.71 56.4 57.55 59.81 61.81 63.18 63.73 63.91 63.9 62.95 61.55 60.44 0 0 0 0 0 0 86.25 80.62 84.27 89.91 95.17 0 89.16 89.78 84.77 79.65 0 98.61 106.83 107.89 103.82 98.98 95.82 0 0 110.07 114.47 119.11 125.51 128.69 131.35 131 0 133.8 134.82 136.19 138.71 141.55 0 143 0 150.72 0 150.67 149.67 148.22 146.48 144.49 142.93 0 134.33 0 0 0 134.4 134.46 135.5 136.94 138.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +193 2581850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 88.12 83.7 81.65 81.43 0 76 85 105.41 107.12 108.96 111.87 113.29 121.46 130.47 132.9 0 0 135.3 132.21 130.27 129.42 128.74 128.67 129.76 132.64 133.87 0 0 137.33 137.51 137.65 138 138.12 137.61 136.59 135.68 134.25 0 0 139.55 141.63 143.47 144.83 145.33 146.15 146.23 147.2 148.82 151.37 154 156.94 159.46 160.51 160.66 0 157 0 151.99 146.81 142.9 141.87 130 142 0 137.93 137.44 137.25 131 131.63 125.67 121.26 118.97 118.41 118.51 113.25 103.89 0 0 86.13 87.21 83 0 0 0 0 0 0 0 81 83 79 81.16 84.32 90.5 0 88.07 86.77 0 0 0 0 0 0 0 97.04 88.2 80.09 76.14 75.26 72 0 0 0 79.84 80.24 79.96 75 70 73 72.69 62 63 64.67 59.38 55.2 49 0 48 56.66 53.21 0 49.63 53.13 54.84 53.56 0 0 0 0 65.9 63.23 58 0 61.11 62.54 64.23 65.12 65.05 65.16 64 65 59 57.18 53.87 53.86 0 53.95 54.2 54.61 55.2 56.27 55.86 52.43 50.3 49 0 0 0 0 0 50.34 52.66 54.19 56.37 58.75 61.38 63.16 64.79 66.42 67.01 67.9 67.87 66.96 65.65 64.72 0 0 0 0 0 0 0 79.18 83.89 89.59 0 0 86.18 88.57 87.93 84.24 0 0 0 0 109.76 105.15 103.72 105.83 0 0 0 0 0 129.59 134.07 0 136.96 137.34 0 139.79 141.92 144.58 147.27 0 153.22 154.55 154.17 0 151.57 150.01 148 146.09 143.73 142.03 139.82 138.05 136.31 0 0 132.76 0 0 133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +194 2581350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 75.17 72.74 52 0 91.33 106.53 108.6 106.27 106.97 112.38 122.68 0 135.47 132.34 0 136.82 0 0 131.22 130.11 130.3 129.79 127.2 131.11 134.07 0 0 0 138.52 138.77 138.62 138.55 138.2 137.21 136.11 0 0 0 0 0 0 143.86 0 0 145.36 145.97 147.18 150.78 153.2 155.61 157.74 159.29 160.62 161.51 156 0 155.53 149.89 140 135 148.11 145 136 139.71 139.62 135 136.43 131.97 0 0 0 0 0 0 0 95.2 91.4 88.9 90.52 0 96 106.64 0 99 0 91 86 84 83 83 86.27 89.71 94.28 95.65 0 0 0 0 0 0 0 0 101.87 92.39 85.68 79.56 75.89 74.6 0 0 0 0 0 0 0 0 0 78.18 75.02 63 68.51 64.42 60.24 55.63 0 55.69 56.4 55.78 0 54.81 56.11 56.96 56.3 0 0 0 0 0 0 66.44 63 62 63 64.97 66.59 66.88 66.38 66.98 66.55 65.49 62.98 58.83 54.36 0 56.57 61.15 58.65 56.58 55.28 55.32 53.84 46.87 0 0 0 0 0 0 0 52.07 53.26 55.05 58.51 62.16 64.34 66.75 68.18 69.42 70.35 71.44 72.28 71.89 70.35 69.44 71.19 73 67.8 62 0 0 0 78.03 82.13 86.36 87.44 84.22 88.63 90.76 90.29 86.66 83.31 84.89 0 0 108.95 108.65 108.7 109.46 110.6 112.14 111.61 117.91 0 0 136.55 136 139.28 0 141.08 0 144.73 147.1 149 0 157.74 158.22 155 155.31 0 151.25 149.34 147.59 145.3 143.27 141.28 139.3 138.12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +195 2580850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51.66 52 0 0 0 0 0 101.95 100.8 108.5 109 137.04 136.78 133.25 0 0 133.43 0 0 129.69 130.58 0 133.84 132.42 134.65 136.83 0 0 0 139.04 138.99 139.01 138.73 0 0 0 0 0 0 0 0 0 0 0 144.25 145.17 0 0 0 154.01 155.81 157.79 159.79 161.32 162.16 161.82 157.46 147 148.71 147.17 149.52 143 146.16 141.85 141.56 140.67 137.2 135.5 124.44 117.25 0 109 101 99.74 98.58 97.16 94.29 92.51 0 99.83 109.4 0 104 95 91.93 87 84 89 91.97 89.27 87.99 0 0 0 0 0 0 0 0 0 0 0 97.88 88.26 82.89 77.21 73.84 74.79 0 0 0 0 71 0 0 0 79.58 79.65 76.68 73.1 69.13 62 54 0 0 62.51 65.53 58 60 64.99 63.18 61.21 0 0 0 0 45 0 0 67 65 65 66.88 67.13 68.2 68.89 68.5 67.85 67.46 66.82 59 0 51 53 60.3 63.85 61.56 53 0 0 0 0 0 0 0 50 50 0 0 52.37 51.66 55.23 60.14 65.25 68.99 71.82 71.7 72.47 73.73 75.16 76.71 76.66 73.82 72.2 72.83 72.69 69.35 66.45 65.55 63 0 72.68 79.29 84.29 87.03 88.73 0 95.32 95.59 92.88 90.12 89.59 94.04 86 0 110.17 111.86 113.55 114.35 116.26 118.37 122.92 128 134.27 0 0 0 0 143.1 144.5 146.53 148.74 150.6 0 0 159.2 157 156.64 154.54 153.2 150.55 148.63 146.77 144.57 142.6 140.3 137 137.32 132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +196 2580350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93.25 0 98.14 83 123.33 0 0 0 125.85 0 0 0 0 0 0 132.22 133.76 0 137.72 138.42 0 0 138.62 138.34 138.96 139.22 0 0 0 0 0 0 0 0 0 0 0 0 145.13 0 146.42 151.36 153.15 153.89 156.42 158.88 160.65 161.28 160.55 158.29 155.37 152.89 151.58 151.15 149.68 146.94 143.07 143.43 144.06 0 0 0 0 124.94 111.13 0 109.6 106.72 104 100.43 97.54 101.38 104.15 0 113.07 103 96.04 88 0 0 0 0 88 86.7 0 0 0 0 0 0 0 0 0 0 0 96.93 88 80.86 75.14 68.77 0 0 74.21 70 73.99 0 70.91 0 78.41 80.55 80.43 77.96 74.56 69 61 59 0 66.78 67.75 0 0 0 0 66.12 0 0 0 0 56 0 0 71.74 71.39 67 66 68.86 68.59 69.2 69.84 69.62 69.91 0 0 55 61 58 62.35 63.56 63 58 58.14 53.56 50.91 0 0 0 0 0 0 57.49 0 0 0 0 54.51 60.03 67.09 0 74.29 74.5 74.54 75 76.01 0 0 73.32 72.75 72.95 72.27 70.26 67.15 65.56 0 0 69.95 77.81 83.91 88.9 0 0 0 102.95 99.78 97.17 96.5 98.37 102.49 0 110.53 113.91 117.6 120.16 121.87 124.79 0 132.84 136.49 139.54 137 141 0 0 0 0 149.54 150.86 153.09 154.7 155 0 156.57 155.42 154.16 152.59 0 0 0 0 0 138.85 136 136.59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +197 2579850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 96.28 0 0 0 100.48 0 0 122.98 0 0 0 0 0 0 129.61 131.78 0 0 0 0 0 0 0 0 138.72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 152.49 154.13 153.77 0 156.66 158.25 159.3 159.84 159.05 158.08 156.42 155.99 154.95 154.05 152.74 0 0 0 0 0 0 0 131.61 124.54 0 118 123.39 116.19 112.67 109.75 0 116.45 115 0 0 101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 93.53 84.9 78 67 0 0 0 75.95 76.09 0 75.94 0 0 77 78 0 78.55 74.77 68 68 61 0 67.48 69.03 0 0 0 0 0 0 0 0 0 60.77 62.37 0 0 70.71 70 0 71.16 70.89 69.31 66 63 0 59 61 64 66 68.53 67.23 66.79 64 63.85 58.64 51 45 0 0 0 0 0 0 0 42 45 0 0 0 57 0 74.44 75.17 74.48 73.98 74.38 0 73 0 70.23 72.53 73.76 72.9 71.11 68.59 66.02 63.74 0 69.51 79.93 87.36 92.74 95.56 94 101.13 104.69 103.93 102.04 101.29 102.61 104.64 0 110.13 115.54 121.04 124.76 127.05 0 133.7 0 138.97 136 139 140 144.75 0 0 0 0 0 152.64 153.72 154.91 155.9 155.99 155.57 154.86 154 152.35 0 0 0 141.15 0 137.64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +198 2579350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 133.04 139.93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 156.46 157.47 158.01 158.45 158.63 158.5 158.43 156.9 156.27 157.45 156.55 155.9 148 145.39 142.41 136.41 0 0 132.54 130.56 124 120.2 128.15 127.79 119 0 0 103 124.33 124.11 120.88 106 0 0 0 0 0 96 91 0 0 0 0 82.96 81.62 79.8 0 76 79 0 0 0 88.43 77 74 0 0 83.47 80.02 78.8 78.44 78.28 0 79.17 0 79.9 81.24 80.92 78.3 73 71.25 66 60 67.89 68.77 67.94 0 0 0 0 49 0 0 0 0 63.35 63.87 64.7 67.59 0 71.66 71.47 72.34 73.54 72.04 70 64 66 62 69 70.4 69.57 69.56 67 68.97 68.06 0 0 0 0 0 0 0 0 0 53 0 53.02 51.8 51.95 52.74 0 0 58 63.89 0 70.35 71.94 0 71 72.8 0 72.26 74.37 74.82 74.27 72.43 70.62 68.71 66.34 0 73.42 86.07 92.85 95.16 0 99.02 0 0 105.56 105.67 105.75 106.26 0 0 0 114.83 125.74 127.07 130.94 133 136 139.97 140 140 141 141 0 0 148 149 151.5 152.62 0 154.64 154.57 155.34 155.61 155.81 155.67 0 0 145 146 143 141.43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +199 2578850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 157.43 157 156.4 157.32 158 158.32 157.77 157.03 157.08 157.05 0 155.56 150.29 143 134 139.84 138.23 136.33 132.64 128.53 127.31 128.13 124.39 0 121.12 114.06 0 128.63 124.3 118.04 116.18 116.03 112.08 109.1 104.92 0 0 0 0 0 0 88.21 84 0 0 0 82 82 85 0 91.03 85.52 79.06 77.31 0 0 83.66 82.3 81.38 81.08 79 81.32 81.57 75 81.81 81.8 80.78 78.62 75.88 72 68.42 0 67.95 71.16 0 0 56.99 0 56.19 52 51 0 0 0 0 0 0 66.73 69.83 71.61 72.58 74.48 73.76 69 68 71 71 67 71 68 65 63 69.5 70.58 68.54 65 56 44 0 0 0 0 0 61 51 0 0 52.18 53.03 54.68 54 0 0 0 0 0 0 64 73.73 73 0 75.4 77.11 78.07 77.16 75.6 74.26 73.27 71.91 0 77.58 94.29 99.34 0 97.92 0 99 0 105.14 108.44 110.28 110.74 110.85 111.26 0 0 0 125.34 0 139.17 0 142.73 142.33 141 143 0 148.45 0 0 151.82 0 153.23 153.91 155.13 155.57 0 155.21 155.67 0 0 155.78 155.16 153.18 149.63 144.61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +200 2578350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 155.66 155.32 155.6 155.97 0 155.62 156.14 156.9 157.06 156.26 157.19 158.3 155.4 149 150.2 0 145.3 144.43 140.91 137 134.66 133.43 132.58 0 128.6 125.02 0 133.12 129 125.94 122.82 122.89 0 0 0 0 0 0 0 100.45 0 91.16 0 0 0 80 81 84.97 88.2 90.09 0 90.08 84.44 77.35 76.86 0 0 82.75 83.52 83.41 83.5 83.38 0 82 79 84.89 0 81.32 78.76 75.93 73.11 70.52 67 70.59 0 70.19 64 59.93 58.56 57.22 56.34 0 0 0 0 54 0 0 0 71.66 73.76 75.44 76.03 73 72 72 73 73 73 73.95 69.6 63.34 58.26 72.73 69.78 66.3 56 47 0 0 0 54.26 48 0 58.69 0 0 0 53 54.11 55.61 57.17 0 0 62.59 59 0 72.18 74.6 75.6 76.15 0 0 77 0 79.78 79.24 78.89 77.91 78.39 0 74.58 0 104.02 98.57 97.13 0 0 104.44 0 103 114.55 115.6 115.37 114.58 115.15 116.47 0 120.81 129.15 0 0 142.56 142.12 143.81 144 145 149.17 150.9 152.31 152.89 153.66 0 154.17 155.06 155.69 155.46 154.66 155 153 155.59 156.21 157.37 155.39 153.5 151.66 147.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +201 2577850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 152.79 152.81 153.68 153.99 0 154.82 155.96 155.75 156.14 156.36 155.58 155.37 155.97 154.44 149 150.18 0 147.06 0 141.95 140.12 139.19 137 135.7 132.14 0 0 132.85 130.67 127.01 125.2 123.38 117.82 0 99 0 0 92 0 104.63 0 0 86.8 0 85.53 84 86.66 88.87 91.71 0 94.02 93.62 87.9 0 0 83 0 84.88 85.64 83 86.57 86.29 86.05 85.9 0 85.77 84.49 81.89 79.26 76.41 74.54 73.34 73.05 74.59 74.29 70.9 67.05 0 0 59.15 54.78 0 0 56 58 64 53 52 0 74.51 76.45 77.58 76 76.79 75.25 74.35 0 0 0 75.96 70.78 62 0 0 0 50 0 0 0 0 0 61.79 0 0 62.14 57.58 0 49 54.34 55.46 57.64 59.72 62.25 66.04 0 71.69 68 0 77.62 78.36 78.71 0 0 80.34 81.61 82.1 0 82.12 81.8 78.15 0 74.04 90.24 0 0 0 96.28 100.66 0 111.06 115.82 0 0 119.9 118.77 118.11 118.67 119.38 0 0 126 0 0 0 144.38 146.69 148.22 149.86 147 152.62 154.21 154.73 154.53 154.52 155.06 154.98 155.07 155.17 154.24 154.35 154.43 154.28 154.99 0 0 156.15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +202 2577350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 150.52 150.36 149.95 150.53 151.7 0 154.07 155.58 155.46 155.73 155.63 153.77 154.77 154.02 154.91 153.09 149 148.9 0 148.91 145.8 143.7 0 139.6 0 0 131.61 126.94 131.59 132.93 118 113 0 0 108.1 95 0 104.27 0 107.24 0 0 95.55 0 0 91.17 89.95 89.88 91.53 0 96.74 100.4 95 0 0 92.52 0 89 89.85 89.25 0 89.22 88.21 87.98 87.13 0 86.09 84.76 81.98 79.64 77.17 75.39 74.9 76.53 77.19 75.25 71.29 64 62 0 57.86 0 0 63.21 65 68 69.54 68.48 57 0 77.44 0 81.2 79.99 78.55 76.74 0 0 0 0 0 70 63 55 49 0 0 0 0 0 0 0 0 56 0 0 58 0 56.28 55.84 57.1 59.73 62.9 0 71.78 68 0 80.07 73 80.8 82.07 0 81.24 0 81.36 83.12 84.96 86.43 87.54 87.19 84.78 70 0 0 0 0 0 94.44 101.04 107.99 112 120.46 0 120 120 0 122.41 121.71 122.24 0 0 131.58 137.25 140.38 142.65 0 0 146 150.95 0 153.39 0 154.84 154.57 0 154.67 0 155.03 154.69 154.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +203 2576850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 150.19 151.51 151.62 149.41 147.13 145.64 147.41 150.56 0 152.54 153.3 153.83 154.44 153.92 153.54 153.52 153.17 152.98 153.5 151.74 150.99 0 150.36 144 145.88 143.65 140.29 0 138.06 134.31 131.22 133.45 136.34 130 127.29 121.33 111 0 0 105 0 0 108.11 104.64 101.06 0 0 0 97.75 0 91.49 90.73 92.25 89 0 0 0 102.51 95 92 92.85 92.72 91.89 90.35 89.68 89.52 89.46 88.03 0 86.1 84.73 83.27 80.35 77.09 74.79 75.12 79.43 79.27 0 71.88 68.52 65.25 61.35 0 0 0 0 68.67 74.71 74.64 69.48 0 0 0 82 84 82.36 0 71 69 64 61 0 0 69.77 0 61.41 46 0 0 0 0 0 67.27 67.52 63.72 0 0 62.15 53 0 57 56 0 62.24 65.74 69 0 79.79 0 85.15 80 85.89 86.52 85.22 84 64 0 85.58 87.8 0 92.76 94.83 94.85 89 76 0 0 92.65 94.34 0 102.77 110.14 0 0 121 125 0 127.12 126.46 125.83 0 126.34 128.42 132.65 133 138 143.23 0 0 150.1 0 154.17 154.71 155.49 156.02 155.13 154.7 0 155.2 154.69 154.06 153.05 151.6 150.84 149.43 143.76 0 0 149.94 148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +204 2576350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 148.45 148.21 148.31 150.59 151.83 147.98 143.21 140.27 143.31 0 152.61 151.76 151.35 152.06 152.79 152.95 152.79 152.3 152.16 152.63 152.36 152.68 0 151.81 151.2 149.42 147.16 144.62 141 0 138.35 134.5 130.91 129.73 130.31 129.58 127.95 0 0 0 117.14 117.18 0 0 0 107.58 105.35 0 0 105 102.21 97.13 92.44 88.93 86.69 0 102.14 0 106.73 105.63 96 98.55 95.73 94.58 0 91.97 91.17 91.36 0 90.29 87.76 85.73 85.02 0 0 0 0 0 78.97 78.21 74.41 71.63 69.08 66.09 0 0 0 0 0 70.84 0 0 0 0 0 0 0 0 0 83.67 75 65 70 69 58 0 0 0 52 46 0 0 0 0 75.55 71.57 68.14 0 58 0 62.71 0 56 0 62.6 0 63.79 67.92 73 0 82.89 85 0 88.76 89.55 89.77 87.97 86.39 85.58 0 88.33 90.67 93.91 98.71 103.65 0 99.62 92.59 0 0 95.33 97.93 100 104.66 111.45 119.79 0 128 129 125 0 128 129.53 129.04 0 131.82 134.86 135 141.63 0 146.68 148 152.19 153.64 152 0 156.23 0 155.67 154.46 154.57 0 153.78 153.04 0 149.92 144 146 137 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +205 2575850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 147.07 148.15 147.74 146.99 145.92 145.18 0 151.72 145.87 140.39 136.22 0 153.89 152.12 150.3 149.56 149.95 150.45 151.23 152.77 152.87 152.55 152.33 152.71 152.53 152.52 152.63 152.1 150.42 0 143 142.67 139.96 136.7 134.39 129.17 124.35 0 0 0 0 0 0 0 0 0 115.67 0 110.66 0 0 110.75 106 101 99.66 93.8 0 0 99.49 104.08 107.62 104 96 104.57 101.15 98.9 94 0 93.18 92.17 0 94.16 93.82 89.27 85.22 0 82.19 0 0 0 0 0 73.14 71.38 69 61 0 62 60 0 0 0 0 0 0 0 77 0 0 0 0 87.9 85 79 78.34 72.67 67.19 62.46 59.65 50 0 0 0 0 0 0 74.84 73 74.61 0 69.42 66.26 64.91 57 58 56 63.22 0 67.05 61 69.91 75.14 0 85.45 89.3 84 91.35 92.03 0 90.05 88.45 87.66 0 90.53 93.41 99.23 105.85 104 108 103 79 0 0 99.33 102.27 0 0 112.88 118.57 122.42 0 0 133.97 0 133.23 132.44 132.34 133.3 135.21 137.73 0 140 145.77 143 148 0 0 156.22 0 157.33 157.04 0 148 0 0 153.09 152.02 151.07 149.74 139 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +206 2575350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 140.49 143.08 145.44 146.71 146.51 145.31 143.09 0 148.65 143.93 142.94 141.35 0 150.21 150.41 148.58 147.11 146.7 147.41 148.56 149.82 151.73 151.73 152.12 152.77 152.74 153.24 154.33 0 0 151 147.78 144.03 141.36 136.01 0 0 0 0 124.04 123.27 121.98 111.84 106 0 0 0 115.81 0 117.2 116.33 0 0 107 0 0 0 0 0 0 102.22 105.92 109.3 101 108 106.49 101 99.9 97.41 95.32 93.46 92.47 92.09 93.49 0 0 83.93 87.99 0 0 81.93 0 68 0 0 0 0 0 67 65.73 0 55 0 0 0 77 87.19 87 0 98 0 0 0 0 0 83.76 78.64 70.96 59 0 51 0 0 0 0 0 0 0 0 71 78.54 71 70.98 70.37 69.6 68.43 62 0 64.27 0 72.31 71.92 73.28 77.71 77 91.17 92.86 94.06 0 94.72 93.9 92.1 89.77 0 0 0 95.2 104.72 111.34 115.3 0 100.15 0 0 0 102.69 107.04 108.99 0 113.84 118.06 121.51 124.47 124 135.52 128 0 134.85 134.93 0 138.23 140.59 0 144.96 0 149.42 148 153.79 154 0 158.25 157 157.17 154 0 0 0 150 0 0 139 0 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +207 2574850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 138.15 140.16 142.42 144.22 145.59 145.78 144.34 140.35 132.27 0 132 0 0 0 143.81 142.25 143.32 142.87 143.24 145.09 147.11 148.69 149 151.04 151.69 152.34 152.47 153.05 153.89 152 151.29 0 142 0 143.29 137.04 0 134.82 131.67 131.67 128.16 126.09 123.57 117.84 0 119.37 117 0 118 116 0 0 115.82 0 111.32 0 105 102.76 0 0 0 102.21 107.3 107 112.39 110.13 104 104.23 101.04 98.32 95.35 93.73 93.52 0 80 83.88 86.96 87.78 0 0 87.64 82.94 77.62 73.96 0 0 73.6 67 0 69.74 67.48 66.62 57 0 0 0 0 85.86 91.8 0 86 0 0 0 0 0 0 0 0 58.43 55.66 0 0 0 0 52 56 60.34 0 0 68.52 0 75 74.09 75.67 75.6 75.28 69.4 0 65.31 69 77.35 72 71 0 80 0 95.37 97.77 98.46 98 96.97 94.88 91.66 0 0 94.42 0 0 106 111.3 102.97 92.65 0 0 101.28 107.68 111.75 112.98 112 115.24 119.25 122.65 122 129.82 0 135 134 129 0 137.89 0 143.48 144.77 146.44 148.22 149 152.53 154.91 154 159.36 159.82 0 157.39 156.32 0 151 142 140 0 0 142 137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +208 2574350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 137.64 139.8 141.39 143.39 144.76 145.73 144.49 141.01 135.98 128.14 0 0 135.32 128.82 129.22 133.17 138.6 0 140.94 143.53 0 148.16 149.52 150.27 150.47 151.04 152 152.62 152.5 152.14 151.3 150.61 147 0 0 0 0 137.24 135.29 133.65 129.9 130.38 0 0 128.28 125.33 0 0 0 123.47 0 115.77 0 0 109 109.16 105 0 101.73 96 0 0 109.64 114.56 112.63 109.75 107.51 104.84 101.76 98.12 95.2 93.8 0 93.18 91.47 92.2 94.66 93.4 0 86.77 86.67 83.42 79.42 0 78.4 0 79.22 77.94 0 71.93 69.44 68.6 0 66 0 0 0 0 87.31 0 79 75.87 0 0 0 0 0 0 0 49 0 0 50 50 0 0 59.46 0 0 60 0 0 0 82.89 82.45 80.94 0 69.43 0 67 73.67 0 81.44 79 0 88.54 85 0 102.77 101.61 100.63 100.26 99.23 95.97 0 0 99.65 0 0 107 113.14 102 0 81 0 0 111.89 115.24 0 117.15 117 121.65 125.33 127.94 126 133 139.29 138.26 133 137.3 140.43 0 142 145.65 147.35 149.19 151.14 153.21 155.48 0 0 160.89 159.61 157.76 0 155.32 153.67 138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +209 2573850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 131.47 132.74 0 0 139.89 141.77 143.23 144.1 0 141.89 134.41 0 0 135.19 125.5 0 0 131.82 126.99 140.74 141.19 143.55 146.42 148.11 149.01 149.36 150.13 150.22 150.93 151.26 151.43 151.2 151.17 151.66 153.31 149.25 144.63 140 142.12 139 137.53 137.1 134.75 0 128.74 130.98 131.78 0 0 125 122.8 0 119.47 116.09 0 0 113.16 109.47 108.54 105 102 97 0 0 0 0 0 109.54 107.69 105.34 102.38 99.63 96.48 0 95.54 95.05 94.73 0 0 0 0 86.89 86.51 0 0 0 83.77 83.36 83.73 82.09 79.1 74.54 71.28 0 0 73 0 0 87.52 85.69 0 0 0 0 60 0 66 62 64 61 54 0 0 0 61.09 64.06 0 66.66 0 57 68.95 66 0 83.56 88.39 90.38 87.96 0 0 67 67 70.51 74.53 0 80.92 82.49 0 88 94 0 0 101.46 102.2 102.8 102.7 0 95.89 96.34 0 110.52 0 0 115.7 106 0 83.34 0 0 0 117.73 119.05 120.04 118 0 122 128 133.8 132 135 138.36 138.97 135 141.57 138 142.94 144 144 0 151.35 151 0 157.59 160.23 160 0 157.42 155.68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +210 2573350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 135.43 136.61 138.18 139.67 140.95 141.52 139.02 140.24 129.25 125.65 0 0 0 141.31 138.79 133.92 131.15 136.25 139.74 143.33 147.54 148.06 148.27 148.91 149.38 150.16 150.14 149.94 150.21 150.1 150.18 150.3 150.11 0 0 140 141.4 139.1 137.54 136.37 0 136.51 134.6 135.38 136.62 0 131.4 129.41 0 125.14 117 0 121.2 115 0 0 0 109.89 105.65 97.49 0 0 0 0 110.84 110.07 108.47 106.65 101 101.88 99.97 0 98.5 97.27 0 95.22 0 0 89.77 0 0 0 86.96 0 85.77 86.37 86.6 86.14 85.08 0 0 69 0 0 0 0 89.6 0 0 0 66 68.14 64 63 69.17 67.81 63.35 55 53 0 0 0 65.61 71.61 76.88 74.94 0 0 77.21 80.4 86.02 89.06 90 0 0 0 71 74.08 73.49 0 76.12 0 82.71 85.16 83 92.11 96.1 89 0 100.85 103.97 105.24 104.14 100.36 98.4 93 0 0 0 121.13 0 107 101.13 89.88 81.37 0 0 117.79 119.85 121.94 0 0 128.79 0 127 131 137.4 138.62 140.34 141 143.71 144.04 0 145 148.51 147 150 152.62 0 0 157.19 157.56 156.7 155.69 154.98 153.94 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +211 2572850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 128.22 128.4 132.01 132.92 135.09 137.1 137.31 0 137.5 133.02 133.67 0 0 0 142.41 143.91 142.88 139.65 135.7 134.25 136.54 138.77 141.6 146.5 146 147.1 148.12 148.69 148.95 148.89 148.69 148.55 148.32 148.2 147.85 146 145.5 143.47 141.18 139.13 137.63 0 135.73 135.7 136.29 136.4 136.91 136.93 135.62 134.08 0 130.2 0 0 122.62 120.75 117.01 113.89 111.18 0 0 0 0 99.6 98.58 100.9 0 109.4 109.56 108.68 107.24 105.56 103.71 0 0 101.25 99.74 98.29 96.54 0 96.86 0 0 91 88.92 0 81 0 88.98 88.82 0 0 0 81.77 77.53 78 72 0 0 0 82.48 75 66 72.17 70.46 63 70.16 71.11 0 63.79 60.49 0 0 0 61.13 67.28 74.59 79.84 0 79.88 77 85.03 0 0 92.65 86 0 81.96 0 0 79.63 0 0 77 81 86.27 88.53 86 95.8 97.49 0 90 0 107.31 108.7 107.78 0 102.92 0 0 0 0 111 107 110.03 104.12 97.82 90 0 0 0 116.56 121.94 124.67 126.7 128.79 130 128 135.46 137.27 139.13 141.26 0 146.11 0 0 0 147 149.95 150.67 0 152.51 153.28 154.31 154.62 0 154.2 153.96 143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +212 2572350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 130.77 129.62 125.64 0 136.47 136.81 136.69 0 133.61 124.34 118.5 0 0 130.93 0 141.02 141.25 139.16 137.28 135.79 135.73 136.24 137.49 0 143.79 144.85 146.4 147.65 147.63 147.22 146.87 146.45 146.06 145.65 145.22 144.45 143.15 141.29 138.65 135.9 135.14 135.21 135.32 136.51 0 0 0 136.41 0 133 134.23 133.56 0 124.63 0 0 0 115.89 0 107.05 0 0 0 103.36 101.93 0 0 0 108 108.88 107.8 106.5 105.74 0 105.01 103.51 102.17 101.58 0 103.48 96.16 0 93.11 93 0 89.88 90.36 91.47 92.76 0 0 87.06 0 86.34 0 83.27 74 0 0 0 83.8 0 0 74.48 74.22 66 0 0 70.8 66.03 0 62.23 0 0 60.88 64 74 83 83.76 81.92 0 80 88.47 90 91.63 88.55 85.07 0 0 0 85.57 84.44 0 0 88.33 89.91 91.88 0 0 99.38 100.37 99 0 111.14 113.45 112.03 0 107.77 104 102.75 0 0 110 115.27 0 0 107.08 98 79 0 0 0 0 121.16 125.59 128.71 130.79 133.24 134.81 136.84 0 0 145.39 147 0 148 149.68 149.78 149.68 149.61 149.75 150.23 0 151.36 151.84 152.25 152.46 151 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +213 2571850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 130 131.97 130.37 0 127.04 114.82 0 0 0 0 137.49 138.99 138.42 137.41 135.96 134.86 133.85 133.18 133.98 0 142.04 0 0 145.68 145.66 145.33 144.57 144.14 143.55 143.31 142.46 142.02 141.06 139.67 0 132.67 133.31 133.98 133.17 134.59 136.3 0 136.25 134.95 134.15 132 127 0 129.07 124.44 122.01 119.32 114.06 0 0 0 107.11 0 106.59 106.22 105 103 0 0 109.5 109 107.9 106.65 99 0 106.42 0 103.53 102 0 101.32 94 95.42 95.07 94.88 94.23 92 93.5 94.03 0 0 94.71 87.07 0 88 0 87.42 84 83 0 0 77 71 78.57 77.84 76 0 0 76.89 74.07 0 64 71.81 56 0 0 63.9 75.25 0 80.24 72 77 0 0 89.91 91.2 89.62 0 85.74 0 81 0 89.2 0 89 92.5 0 95.44 0 0 102.64 102.6 102.29 0 111 116.63 110 113.64 111 0 0 0 0 115.66 116.74 116.57 113 0 0 101 0 85 0 0 112.62 124.45 127.5 129.02 131.27 133.27 135.26 137.99 141.85 144 149 0 0 151.42 150.06 148.93 148.07 147.82 146.74 147.39 148.39 149.27 150.05 150.95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +214 2571350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +215 2570850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 122.4 119.66 110.64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 140.76 141.24 141.1 140.49 0 139.16 138.49 138.08 137.04 0 0 135.95 133.99 133.35 130.7 0 0 125 129.91 129.16 129.12 128 0 0 0 125.33 125.35 124.38 120 117 119.69 119.07 0 112.57 112.24 114.5 113.71 107 0 0 109.21 109.51 109.05 107.98 0 0 102.34 103.44 104.27 104.95 104.68 104.14 103.75 102.75 102.17 101.66 101.17 100.64 99.7 0 0 0 99.08 93 90 95 0 97.15 86 84 0 0 91.61 88.29 0 87.14 82 0 0 80.12 78.62 0 73 0 0 71 0 0 0 0 67 76.95 76 80 0 0 96.28 96.21 94.95 93.51 92 94.52 97.32 99.71 100.26 0 99.06 0 0 105.41 107.95 108 112.98 0 109 0 115.17 111 115 121.04 121 0 112 111 108 0 0 0 0 125.71 127.39 125.9 121.1 113.87 108.89 0 0 0 119.92 118.5 120.83 0 0 133.86 139.4 0 148.81 0 152.57 150.97 148.47 145.99 143.78 0 140.14 140.05 141.76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +216 2570350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 139.5 139.35 138 138.78 0 137.21 136.7 136.71 136.25 135.64 133.2 130 125 133.08 129.52 125.34 126.18 0 0 124.44 124.8 0 126.27 126.4 0 0 123.59 124.65 123 122.68 121.16 119.7 117.01 115.48 114 0 0 0 112 112.09 111.97 110 110.41 102 106.69 99 104 104.3 105.02 105.78 106 105.85 105.79 105.29 104.74 104.92 104.73 104.09 0 0 96 99.66 0 99.98 0 89 0 93 88 89 79 0 91.42 89.44 88.3 89.21 89.23 88.02 85.19 83.11 78 80.62 83.4 90.08 0 64 0 60 72.3 75 79.57 81.74 0 86.37 90.65 92.98 0 98.65 97.91 95.87 95.18 0 99 102.7 103.72 103.3 103.06 104.01 106.31 109.76 113.81 0 0 0 114 0 119 115 116 114 121 0 117 117 116.85 109 112 0 0 125.18 126.5 126.04 123.5 119.37 116.84 113 0 0 0 107.87 0 114.87 127.42 134.1 138.3 142.72 147.34 151.87 151.39 149.14 146.57 142 0 0 137.67 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +217 2569850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 137.99 0 136.76 136.34 135.75 135.34 134.63 133.48 131.29 129.26 128.93 130.52 128.83 126.48 124.31 120.99 0 0 0 0 123.01 122.1 120.83 0 119 120.91 0 122.58 0 0 116.18 0 0 113.05 0 112.87 116 0 115.18 113.67 112.15 110.06 107.87 0 103 105.35 105.89 106.96 105 107.55 107.99 108.06 106.85 0 0 0 106.75 0 102.44 102.93 103.45 99 102.57 0 99 92 95 0 91 0 91.72 90 92.01 92.39 92.88 92.84 91.13 86.7 82.17 79.31 78.39 75.3 0 0 0 74.41 77.22 81.48 89.88 86.76 85.5 90.48 97.64 0 100.19 101.09 101.43 97.61 0 99.52 103.09 106.2 107.51 0 106.37 106.53 108.2 111.36 117.03 0 0 117.19 118 0 122 0 119 121 120 0 0 124.83 0 121 119 117.56 0 0 125.02 125.5 124.84 122.75 120.34 112 0 0 0 100 103 0 0 134.87 135.83 140.14 144.47 147.59 148.04 0 143 142 139.42 0 137.27 137.88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +218 2569350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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131.35 136.04 139.38 141.54 140 140 136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +220 2568350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 132.64 131.85 131.67 129.63 127.59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 125.19 124.09 125.19 125.69 0 0 0 0 121.6 122.52 122.91 121 0 118 113 105 105 0 111.55 110.82 110.95 110.29 109.93 109.8 109.66 109.96 111.74 112.07 112.42 110.9 109 115.06 116.49 115.08 116 117.74 111.46 108.98 108.72 104.77 0 0 99.45 0 0 0 0 0 0 0 0 0 0 0 0 78 78.73 75.15 0 0 71 0 0 0 0 0 0 0 92 96.01 97.08 101.16 101.36 103.46 0 0 0 0 0 94 102 102 0 0 120.5 117 114 121 111 119 116 0 117 123 0 128 125 119 0 0 0 127.07 128.05 127.57 127 127.69 127.78 0 0 105 127.42 131.65 0 122 120 132 135.7 138.27 139.98 140.59 140.77 140.32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +221 2567850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 132.43 131.85 131.13 130.46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 124.31 124.61 0 124 0 118 121 119 0 124 125.41 123 0 116 111 109 0 115.61 114.35 113.28 0 111.68 110.28 0 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 90 88.87 0 0 85.6 0 97.59 99.71 0 90.63 90.39 88.66 84 0 72.84 76.05 0 0 0 77.6 80.89 0 0 0 0 0 0 0 0 0 0 0 89 102.16 0 112.07 99 114.75 114 0 0 126 0 114 120.51 0 118 0 128 130.99 131.66 127 0 125 126.36 127.33 129.13 130.22 128.65 127.86 0 131.13 117.74 0 122 0 136.63 136.06 130.5 0 0 0 0 139.46 140.15 138 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +222 2567350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 132.56 132.86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 121.83 123.91 125.14 126.6 127 126 0 126.62 0 125.22 123 0 128.82 123 120 119 0 0 118.43 117.22 115.91 114.94 109 109 109 107.84 106.14 106.2 0 0 98.12 0 95.44 91.99 0 0 0 0 0 92 92 0 88.32 0 80.54 0 94.76 89.17 0 95.53 92.11 0 105.11 0 0 0 98.58 98.48 95 0 83.7 87.51 86.85 0 83.36 85.16 85.29 0 0 0 0 0 0 78 92 86 0 93 102.06 108.16 111 0 109 0 123.81 121 0 0 0 121 0 124.32 125.32 0 130.59 129 129 0 0 0 130.45 0 133.28 132.6 129.05 125.39 122.56 120.24 0 100 129.59 126 139.86 137.86 0 0 123 128 138.98 139.22 0 141.75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +223 2566850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 123.44 124.73 125.17 122 126.24 126.32 127.75 127.24 126.33 124.86 125.09 127 123 117 115 119 112 121.22 119.88 118.67 116 0 113 112.86 110.57 108.85 0 0 103.75 101.52 0 98.72 96.1 0 0 98.26 97.07 98.36 101.73 103.52 0 95.45 88.55 84.62 0 100.36 96.25 0 103.56 101 105.9 0 0 0 105.04 106.82 106.5 0 0 99.13 95 100.03 93.54 92.48 94.15 0 73 0 0 0 96.21 0 97.01 97.73 95.98 0 0 118.12 122.12 115 0 0 110 0 129.66 0 0 131.2 0 0 127.22 130.8 0 0 0 0 136.47 131 124 0 133.82 133 133.96 128.88 121.81 114.29 0 99 0 131.69 0 139.33 136 138.33 136 131 0 140.86 0 0 139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +224 2566350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 124.46 0 0 0 118 0 127 0 123.39 123.66 125.54 126.81 125 118 123 124.73 123.22 117 0 116 0 117 116.31 113.64 111.6 109.81 108.04 106.48 104.41 104.17 102.21 0 101.81 101 0 106.8 0 109.51 109.47 108.15 101.17 95.26 0 0 106.8 101.48 0 108.43 106 109.82 0 0 108.53 110.24 113.95 0 0 108.76 111.63 109 109.66 101.71 0 0 0 0 0 0 0 105.11 0 0 105.23 101.18 0 0 0 128 127.55 0 0 118 0 0 135 140.95 135.14 129 128 0 135.55 143.3 0 139.69 0 138.01 0 129 0 0 139.66 135.85 129.1 120.5 111.04 0 0 127 136.23 135 137.31 139.07 140.38 141.15 142.14 142.98 143.56 143.63 143.76 0 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +225 2565850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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144.06 144 150.78 0 149 147 151.55 152.78 0 150.96 150.13 149 136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +230 2563350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 122.85 118 119 0 117.7 0 120.34 122.06 122.28 0 0 0 0 0 0 117 0 118 0 0 122.1 122.83 122.63 122.09 121.8 120.59 119.22 117.3 115.52 113.88 112.92 112.61 110 117.3 0 119.05 120.04 119.49 119.23 119.28 119.11 119.38 0 0 0 117.91 0 120.37 120.95 119.94 117.34 110 108 0 117.38 120.47 125.45 132.45 129.9 118 114.06 86 0 0 0 123.17 118 0 0 128.42 132.31 131.43 0 122.91 121 0 0 133.91 0 0 0 0 0 136.65 0 136.15 137.14 137.57 137.56 138.07 136 135 134 0 144.82 0 143.26 0 0 144 0 0 0 132 134 135.71 135.07 0 130.78 0 123.38 0 138.62 0 0 150.94 151.8 151.78 149 151 152.12 152.3 152.62 151.73 0 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +231 2562850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 124 125 124.32 0 121.02 0 0 123.64 0 0 0 0 0 0 0 120 112 0 119.74 121.02 120 121 122 121.13 120.01 118.7 117.57 115.92 113.94 112.02 110.11 108.58 0 115.16 0 117.04 0 0 119.45 119.41 119.33 119.47 120.49 120.67 118.1 0 0 122.08 121.76 0 118.94 0 114 0 118.6 119.85 122.25 126.53 127.63 122.57 0 0 0 0 0 121.76 121.6 0 123.73 126 138.24 131 131 0 130.81 0 0 0 0 0 131.01 0 135.83 136.95 137.51 138.35 139.84 140.14 138.98 141.53 142.25 139 139.44 140.48 0 142 142.18 0 0 0 140.9 139 138 0 140.58 0 0 135.04 130.53 125.53 0 0 137.5 141.87 145.66 0 150.81 151.52 0 152.49 152.7 152.72 0 151 150.95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +232 2562350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 136.98 135.86 131.91 0 127.71 0 122 0 0 0 0 0 0 0 0 0 122.64 114 117 120.83 121.38 122.08 122.21 121.57 120.28 118.83 117.37 115.71 114.39 112.57 110.48 107.99 105.31 102.83 0 0 0 0 0 0 0 119.61 120.1 121.05 121.28 121.34 122 119.33 120.86 122.06 122.16 121.99 121.58 108 123.47 120.2 117.72 0 0 0 0 0 0 0 0 116.34 123.94 123.03 115 126.13 139.47 0 0 0 131 0 0 0 0 125.9 0 0 134 136.29 132 138.03 0 140.13 140.28 0 0 0 142.97 140.25 0 140.79 142.48 135 0 134 138 0 143 144.2 0 143 144 143.07 0 132.59 128.62 124.78 0 0 0 0 0 0 151 151.67 152.37 152.82 152.95 0 151 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +233 2561850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 141.55 142.31 0 0 136.29 0 129.78 0 0 0 0 0 0 0 0 0 0 124.8 120 123.37 121 120.95 123.41 122.94 121.69 120.02 118.11 116.32 0 0 111.58 109.47 106.94 104.36 102.33 0 0 0 0 0 0 0 0 0 0 121.97 122.05 122.2 121.77 121.82 122.54 123.61 0 122 110 126.36 118 115.58 0 109 110 0 109.21 0 0 0 0 0 125.26 123 0 129 0 141.33 135 0 0 0 0 0 123 0 0 130.64 128 136 137.47 138.3 139.56 0 0 138 0 0 0 136.95 137.23 0 0 0 140.87 141.99 0 0 145 145 149.4 0 0 141.71 132 135.42 132.44 0 0 0 0 0 0 0 151.37 0 149 0 0 0 143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +234 2561350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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108.81 109 110.958 120.41 0 109 91.58 100.32 121.23 122.12 123.1 124.9 126.28 123.74 122.3 119.71 120.19 120.97 125.99 125 125 125.01 125.34 125.01 123.23 121.26 122.65 119.15 123.67 119.86 125.08 122.36 119.04 123.43 118.24 122.84 118.33 119.7 119.37 125.13 126.33 123.2 129.73 128.84 135.51 125.89 120.09 110.4 102.28 107.32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +178 2589350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42.99 44.41 47.78 50.58 51.99 51.91 52.25 52.23 51.33 50.4 54.68 0 72 64.53 55.38 50.45 55.19 58.32 62.11 65.17 69.82 75.3 78.97 84.46 85.63 85.83 84.54 81.79 78.87 76.1 0 0 0 0 0 0 67.75 56.86 43.25 33 35 37 43.29 52.05 58.61 65.24 76.41 80.93 87.99 89.3 92.19 94.26 94.93 95.06 94.95 93.58 92.32 90.61 89.36 87.24 85.06 84.5 85 85.15 84.67 83.89 82.44 81.02 80.94 81.79 82.85 83.6 83.75 83.64 83.24 83 82.45 82.07 81.49 80.83 79.86 78.33 76.31 74.07 71.61 69.13 66.64 64.2 61.94 59.99 58.35 56.96 55.67 54.41 53.21 51.91 50.77 49.9 49.11 48.35 47.78 47.46 47.26 47.01 46.37 45.53 44.21 42.34 40.67 39 37.13 35.25 33.59 32.3 31.48 33.12 37.01 40.51 43.84 46.9 0 0 0 0 0 0 0 46.98 36.78 26.1004 19.1452 18 17 16 15 14 14 13 13 13 13 12 12 11 11 10 10 9 9 9 8 8 7 7 6 6 6 6 5 5 5 5 5 5 5 5 5 5 4 4 3 3 3 2 2 2.334 3.4932 4.6364 5.1104 5.220801 4.6856 4.954 5.2428 6.3484 8.1828 10.3516 11.8532 12.778 13.406 13.6816 14.03 11.01 11.04 10.56 9.14 7.24 13.21 20.11 21.15 21.32 21.48 21.69 22.01 23.22 25.47 28.48 31.72 34.36 36.07 37.3 38.26 0 0 26.09 28.53 30.3 32.09 34.06 35.78 38.85 41.91 45.69 50.29 55.32 60.79 0 0 0 0 0 74.89 77.44 78.6 77.6 76.86 76.93 77.87 79.02 81 82.88 85.45 88.88 92.84 0 95.1 99.59 102.29 103.42 104.66 105.59 107.88 111.88 110.458 111.498 112.63 117.5 110 123.86 123.75 94.99 98.51 104.62 121.1 121.03 120.98 126.92 124.28 122.48 124.25 121.45 122.33 127.6 0 125 125.68 126.16 124.56 124.2 123.01 122.33 124.16 124.29 122.98 121.6 120.89 116.38 114.36 111.27 113.93 121.62 126.44 122.73 119.48 130.88 117.03 106.06 102.37 103.23 102.27 100.26 106.22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +179 2588850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 52 47.36 44.78 45.82 48.06 50.27 52.06 53.56 54.41 54.33 53.01 51.17 0 0 80.03 73.78 66.76 62.06 58.89 61.64 65.43 66.49 69.74 74.24 78.92 83.68 84.56 84.85 84.72 83.91 82.22 80.68 79.45 78.46 0 79.9 0 0 0 0 56.24 51.67 49.56 53.02 58.29 63.4 68.54 73.89 79.16 83.21 86.57 88.58 93.86 94.97 95.05 95.57 95.74 93.5 92.57 91.44 89.56 88.47 86.74 85.98 85.98 85.77 85.12 83.82 82.11 79.55 79.68 81.38 82.77 83.77 83.96 83.87 83.97 83.86 83.56 83.1 82.66 81.83 80.81 79.37 77.5 75.42 73.22 70.97 68.76 66.41 64.07 61.95 60.09 58.41 56.9 55.51 54.15 52.85 51.71 50.67 49.78 48.96 48.28 47.74 47.23 46.62 45.94 45.09 44.11 42.69 41.33 39.72 37.86 35.91 34.03 32.04 30.6 32.14 35.76 38.93 41.73 44.46 47.6 51.4 55.94 61.04 0 0 0 0 37.29 25.7052 20 19 18 17 15 15 14 14 14 13 13 13 12 12 11 11 10 10 10 9 9 9 8 8 7 7 7 6 6 6 6 6 6 6 6 6 5 5 4 4 4 3 3 3 3.268 5.5608 6.571201 6.487599 6.138399 5.547599 5.1456 4.9216 5.2816 6.455599 8.353601 10.5124 12.1192 12.8228 13.3176 13.5396 13.65 8.8 9.18 9.44 9.15 9.51 13.36 17.95 20.31 21.18 21.55 21.58 21.81 22.67 25.35 28.68 31.93 34.8 37.07 38.99 40.76 0 0 36.46 34.45 34.38 35.32 36.87 38.61 41.2 44.42 47.95 51.96 56.37 61.55 65.37 0 63 62 71.36 0 0 0 76.54 76.57 77.49 78.86 80.31 82.33 84.78 87.28 90.4 93.96 0 0 100.03 99 104.24 105.23 106.19 108.43 112.15 118.02 112.896 111.56 115.19 124.49 124.3 0 100.25 123.3 122.7 122.07 121.76 121.6 121.53 126.07 123.92 122.17 121.62 122.75 125 122.73 123 125 126.73 122.76 124.93 121.73 124.67 125.09 121.14 123.86 125.69 120.36 117.84 118.13 111.7 121.01 125.72 115.65 102.32 102.22 105.38 106.87 103.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +180 2588350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48.02 49.96 51.43 52.36 54.13 55.05 55.92 0 0 0 0 0 79.75 74.54 70.49 67.27 64.65 66.63 66.14 67.8 72 79.72 81.78 83.03 84.18 85.17 85.28 84.81 83.8 82.41 80.55 80.46 81.25 79.83 79.04 0 0 0 0 66.69 71.05 73.64 74.1 77.3 82.06 86.04 91.42 0 92.95 95.28 95.42 95.83 95.88 94.83 94.45 93.64 91.99 91.18 90.21 89.2 88.55 88.18 87.66 86.72 85.36 83.51 81.87 81.56 82.61 83.65 84.06 84.71 84.98 84.89 84.77 84.58 84.39 83.9 83.08 82.06 80.6 78.81 76.85 74.81 72.79 70.84 68.53 66.14 63.95 61.9 60.05 58.41 56.9 55.47 54.13 52.89 51.84 50.8 49.86 49 48.26 47.5 46.75 46 45.19 44.29 43.05 41.82 40.4 38.64 36.74 34.92 33.52 33 34.19 36.39 38.27 39.98 41.82 44.25 47.39 50.92 53.92 56.37 57.6 55.25 47.4 36.91 25.8048 21 20 18.76 18 17 15 15 14 14 14 14 13 13 12 12 11 11 11 11 10 10 10 9 9 8 8 8 7 7 7 6 6 6 6 6 6 6 5 5 4 4 4 4 4 4.680399 7.6428 8.1764 7.5992 6.804399 5.9252 5.0488 4.386 4.8688 6.194 7.968 9.9396 11.496 12.2992 12.7896 13.0464 13.17 6.88 8.05 9.39 10.64 12.17 14.68 17.59 19.76 20.86 21.51 21.88 22.11 22.63 25.67 29.03 32.24 35.11 37.72 39.98 41.99 43.77 0 41.94 39.74 38.91 39.19 40.15 41.83 43.45 46.17 49.84 53.19 56.12 61.5 65.33 67.46 68.61 67 71.19 72.35 72.28 70.52 0 0 0 78.89 81.3 83.67 86.42 89.03 91.59 93.53 0 94.68 96.68 101.73 103.72 105.03 106.54 109.35 113.17 117.66 121.53 117.05 124.59 124.65 124.5 124.34 124.04 123.67 123.22 122.78 122.45 122.25 124.74 126.75 123.75 122.34 124.62 124.35 0 120 123.5 125 125.18 126.3 125.87 119.98 125.5 125.97 126.18 123.66 125.89 123.26 122.52 116.61 112.38 116.9 107.97 100.7 0 0 106.76 107.99 113.71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +181 2587850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 54 53.83 55.37 56.8 57.77 0 0 0 76.46 84.58 84.08 78.82 75.37 73.29 70.74 68.62 66.88 68.3 74.5 76.29 80.73 81.63 84.04 85.91 87.19 87 86.69 85.74 85.02 84.59 84.98 84.26 81.67 0 0 0 0 0 0 84.07 80.19 83.1 89.28 91.65 0 0 93.45 94.36 95.28 96.2 97.48 95.46 94.47 93.62 92.99 92.82 92.63 91.79 91.34 91.03 90.58 89.49 88.02 86.43 85.18 84.62 84.73 85.14 85.46 85.87 86.14 86.19 85.93 85.74 85.57 85.12 84.48 83.5 82 80.18 78.26 76.33 74.42 72.49 70.38 68.14 65.95 63.84 61.84 60.09 58.53 57.07 55.69 54.38 53.18 52.03 50.97 49.96 49.04 48.12 47.22 46.41 45.42 44.42 43.51 42.54 41.42 39.77 37.84 36.16 35.18 35.16 36.29 37.94 38.01 38.25 38.93 40.34 42.61 45.42 47.9 49.61 50.07 48.34 43.53 36.13 26.596 22 22 21 19 18 17 16 14.96 15 15 14 14 13 13 12 12 12 12 11 11 11 11 10 10 9 9 8 8 8 7 7 7 7 7 7 6 6 6 5 5 5 4 4 4 5.373599 7.3924 8.199601 7.822799 6.9744 5.8784 4.811201 4.110001 4.497601 5.596 7.104 8.768001 10.2376 11.2864 11.812 11.6124 12.63 5.31 6.9 9.49 12.35 14.17 15.9 17.89 19.72 21.06 21.91 22.61 23.32 24.59 26.91 29.72 32.76 35.49 37.97 40.28 42.4 44.34 0 45.34 43.54 42.61 42.85 43.67 44.62 45.41 48.02 52.11 56.15 59.72 63.24 66.23 68.46 70.11 70.52 71.33 72 72.3 73.1 75.25 77.94 81.79 0 83.35 85.01 88.01 90.66 92.83 94.63 0 96.31 98.44 101.81 103.47 104.47 106.55 109.96 113.78 117.52 120.73 122.86 124.06 124.6 124.61 124.54 0 104.12 123.66 123.32 124.01 122.98 127.8 128.49 125.37 123.05 124.75 128.38 128.45 0 128.25 126.5 124.67 127.4 124.05 124.04 126.28 126.62 127.22 124.9 133.12 119.85 107.12 104.22 102.89 105.99 113.33 108.74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +182 2587350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58.75 56.31 57.91 0 0 57.8 64.49 76.95 90.32 88.2 77.03 0 0 74.12 67.47 64.93 67.89 72.54 73.8 76.11 78.96 85.1 86.08 88.32 89.24 89.4 90.13 89.84 89.42 89.01 88.99 87.83 85.75 91.58 0 0 0 0 0 0 0 96.13 91.05 0 92.03 91.71 92.65 94.47 96.68 98.22 98.45 96.06 94.83 94.17 94.33 94.7 94.7 94.28 93.88 94.51 92.71 91.59 90 88.57 87.75 87.48 87.25 87.37 87.48 87.61 87.57 87.29 87.23 86.86 86.41 85.86 85.06 83.43 81.48 79.59 77.75 75.97 74.17 72.27 70.22 68.05 65.84 63.74 61.94 60.37 58.89 57.44 56.06 54.72 53.43 52.23 51.1 50.06 48.97 47.96 46.86 45.78 44.76 43.96 43.14 42.54 40.9 38.81 37.29 36.44 36.39 37.11 37.75 37.1 36.27 35.59 35.72 37.31 40.01 42.34 43.28 43.14 42.43 40.46 35.83 28.2292 23.0628 23 22 21 20 18 17 17 16 15 15 14 14 13 13 12 12 12 12 12 11.88 12 11 11 10 9.96 9 9 8 8 8 7 7 7 7 7 7 6 6 5 5 5 5 5 5.434 7.1988 7.9976 7.7256 6.7904 5.5916 5.0028 5 5 4.7516 5.9992 7.49 8.942801 10.1392 10.884 9.630799 3.65 3.47 5.04 8.32 14.12 14.97 16.44 18.34 20.08 21.45 22.53 23.54 24.65 26.13 28.17 30.67 33.22 35.65 38.09 40.35 42.32 44.33 0 0 45.93 45.5 46.16 47.6 48.15 47.59 49.79 55.48 60.84 64 66.72 68.84 70.28 71.54 72.19 72.07 73.02 73.86 75.38 77.24 80.24 83.39 0 0 85.65 90.42 92.78 94.29 95.83 96.98 0 100.5 104.77 104.61 103.36 106.49 110.73 114.6 117.76 120.64 122.44 123.7 124.42 124.52 124.71 124.62 124.27 124.07 123.81 124.18 129.1 129.87 129.84 125.51 127.2 126.16 129 0 130.55 127.89 128.47 129 129.11 126.71 128.93 129.56 123.1 113.2 131.67 129.28 106.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +183 2586850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 58.65 57.66 61.26 71.44 88.01 79.47 0 0 75.88 70.99 64.7 60.7 0 0 69.04 74.18 78.57 82.43 85.9 88.65 90.86 91.54 92.39 92.77 93.16 93.2 93.11 91.85 91.04 91.89 91.94 89.96 0 82.4 0 0 0 0 63.35 0 93.93 87.98 91.28 93.8 97.19 101.63 99.4 96.59 95.21 95.19 96.41 96.99 97.1 97.97 94.95 0 0 95.89 93.61 92.37 91.17 90.37 89.82 89.51 89.38 89.32 89.14 89.19 88.71 88.19 87.64 87.03 86.26 84.51 82.57 80.73 79.07 77.5 75.97 74.31 72.46 70.38 68.09 65.68 63.96 62.45 60.98 59.45 57.96 56.51 55.11 53.77 52.45 51.15 49.94 48.7 47.46 46.25 45.05 44.09 43.28 42.46 41.01 39.25 37.84 37.07 36.89 37.05 36.94 36.02 34.49 32.47 30.72 31.84 35.19 37.81 38.02 37 37.5 39.66 37.49 31.09 25 24 24 22 21 20 18 17 17 16 15 14 14 13 13 13 13 13 13 13 13 13 12 12 11 11 10 9 9 9 8 8 8 8 8 7 7 7 6 6 6 6 5 5 5.62 7.47 8.18 7.77 6.59 5.08 5 5 5 5 5 6.21 7.77 9.1 10.06 10.89 4 1.87 2.57 5 6.28 13 16.38 18.76 20.6 22.1 23.36 24.53 25.82 27.42 29.35 31.55 33.73 35.94 38.07 40.04 42.07 43.89 45.35 0 0 0 0 52.79 53.28 52.14 52.69 61.26 66.86 68.84 70.02 71.3 72.07 72.95 73.17 73.11 73.83 75.3 76.87 79.39 81.87 84.67 87.37 86.42 0 93.57 94.71 95.84 97.11 98.3 99.86 0 0 106.72 103.11 108.51 112.66 116.11 118.61 120.86 122.67 123.67 124.38 124.82 124.67 125.74 124.48 124.25 124.18 123.99 125.94 132.31 129.05 125.56 125.24 127.23 130.04 0 130.81 131.05 131.64 0 129.88 129.7 134.09 134.13 118.55 109.79 117.74 110.39 116.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +184 2586350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 54.83 56.93 0 71.74 0 73.45 74.15 72.39 68.98 63.34 0 0 64.45 68.02 73.33 78.04 83.54 88.04 91.35 93.8 94.37 95.74 96.7 97.33 97.25 96.22 94.36 93.47 92.7 91.45 89.64 87.29 83.35 81.49 83.73 86.56 83.38 0 0 0 0 93.62 92.14 96.9 98 0 97.05 94.76 95.1 99.17 98.56 0 0 0 0 102.13 99.3 97.29 95.58 94.27 93.11 92.46 91.99 91.45 91.37 91.36 90.9 90.36 89.54 88.76 87.9 86.69 85.08 83.33 81.68 80.29 79.04 77.84 76.54 74.98 73.08 70.84 68.46 66.56 64.91 63.22 61.55 59.92 58.32 56.78 55.29 53.84 52.39 50.98 49.57 48.13 46.73 45.35 44.15 43.1 41.93 40.59 39.11 37.89 37.23 37.04 37.04 36.73 35.64 33.63 30.74 27.46 28.33 31.87 34.23 34.9 34.21 36.26 42.95 41.34 34.42 26.63 25 24 23 22 21 19 18 17 16 15 15 14 14 13 13 14 14 14 14 14 14 13 13 12 11 11 10 10 9 9 9 8 8 8 8 8 7 7 7 6 6 6 6 6 8.02 8.67 7.93 6.3 6 5 5 5 5 5 4.87 6.81 8.27 9.42 10.39 4 4 5 5 7.87 12.8 16.91 19.43 21.24 22.82 24.21 25.5 26.89 28.43 30.14 32.27 34.05 36.02 38.05 39.73 41.65 43.4 44.95 46.44 0 0 0 0 0 0 62.79 0 73.85 72.67 72.61 73.38 74.44 74.6 74.85 74.58 74.74 76.85 78.7 81.1 83.49 85.89 88.2 89.85 92.52 0 96.34 97.22 98.48 100.36 103.03 107.34 109.82 109.65 111.45 113.52 115.59 117.92 119.75 121.35 123.02 123.87 124.48 124.49 124.48 125.36 124.76 124.52 127.88 124.15 127.55 132.88 130.51 125.46 121.36 127.66 126.28 129.24 127.68 128.2 128.22 131.3 0 133.97 125.18 113.47 105.05 107.74 107.83 114.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +185 2585850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 73.66 72.4 69.27 66.84 0 64.54 64.96 65.46 68.11 72.22 77.71 84.78 92 0 96.2 96.74 98.46 99.97 100.96 100.52 99.61 97.1 94.81 92.97 91.24 89.6 87.17 85.2 84.05 85.1 88.05 90.93 86.65 0 0 0 0 0 0 0 0 96.56 95.97 0 0 0 0 0 0 101.9 102.04 101.54 99.76 98.19 96.91 95.79 95.06 94.51 93.77 93.49 93.45 92.81 92.1 91.16 89.98 88.78 87.27 85.59 83.9 82.44 81.34 80.53 79.82 79 77.82 76.15 74.03 71.74 69.58 67.59 65.69 63.8 61.98 60.22 58.52 56.88 55.28 53.67 52.15 50.54 48.89 47.3 45.67 44.2 42.77 41.46 40.08 38.8 37.63 37.06 37.11 37.24 37.14 36.25 33.99 31.07 29.13 29.15 31.01 32.93 34.27 35.8 39.4 45.01 43.1 36.6 29.65 25 24 24 23 21 20 19 18 17 16 15 14 14 14 14 14 14 15 15 15 14 14 13 13 12 11 11 10 10 9 9 9 9 9 8 8 8 7 7 7 7 7 6 6.57 9.1 9.68 8.34 6.06 6 6 6 5 5 5 4 6.23 7.57 8.82 4 4 5 5.36 7.4 10.34 14.42 18.45 20.28 21.94 23.49 24.94 26.4 27.84 29.31 31.1 32.73 34.4 36.06 37.66 39.27 41.03 42.76 44.42 46.17 48.6 51.2 54.28 0 0 0 0 69 73.73 73.37 73.78 75.14 76.22 76.96 76.45 76.56 77.28 78.48 80.71 82.69 84.78 86.8 88.94 91.66 94.45 96.61 0 98.58 99.99 101.87 104.86 108.33 111.18 113.24 116.11 117.43 118.43 119.85 121.06 122.06 123.03 123.64 124.34 124.26 124.64 128.48 127.17 128.79 128.55 127.25 134.84 129 124.03 123.94 125.47 119.83 121.57 122.09 122.73 125.33 131.59 0 0 135.53 120.96 108.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +186 2585350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70.88 69.66 0 0 64.09 64.65 65.24 68.43 70.6 75.24 81.82 0 92.99 94.5 97.37 99.96 102.04 103.75 103.8 102.06 98.76 95.24 92.56 90.86 89.67 88.89 87.65 86.94 87.44 88.92 89.94 88.03 88.21 89.71 91.86 93.38 0 0 0 0 0 0 0 0 105.3 0 104.44 99 101.34 103.02 102.87 101.75 100.77 99.15 98.33 97.42 96.91 96.15 96.25 95.69 95.02 94 92.86 91.5 89.86 88.08 86.12 84.34 82.99 82.24 81.95 81.86 81.67 81.02 79.56 77.41 74.97 72.57 70.34 68.17 66.1 64.11 62.17 60.37 58.58 56.8 55.05 53.32 51.69 49.86 47.93 46.06 44.27 42.57 41.08 39.71 38.52 37.6 37.15 37.22 37.44 37.69 37.77 34.94 32.12 30.88 30.6 31.5 32.99 34.58 36.68 39.57 42.01 41.08 37.02 31.57 25.89 24 24 23 22 21 20 18 17 16 16 15 15 14 14 15 15 15 15 15 15 15 14 13 12 12 11 11 10 10 10 10 9 9 9 9 8 8 8 7 7 7 7 7 10.53 11.1 8.71 6 6 6 6 5 5 5 4 4.95 6.68 8.36 4 2.48 5 8.26 9.75 12.19 15.43 18.53 20.69 22.45 24.13 25.71 27.09 28.54 30.01 31.68 33.15 34.53 36.06 37.41 39.09 40.59 41.93 43.74 45.83 49.02 51.74 54.57 56.9 58.26 0 0 0 69.94 71.28 73.63 76.51 78.53 79.21 79.01 78.88 79.51 80.28 82.39 84.24 85.76 87.19 89.17 92.71 96.24 98.2 98.55 0 100.45 102.5 105.52 109.1 112.61 115.48 117.82 119.26 120.34 121.38 121.92 122.67 123.3 123.91 124.34 124.15 130.86 132.21 131.2 132.57 132.22 129.87 134.65 128.15 127.12 124.17 125.47 118.29 113.71 123.19 128.81 131.68 132.24 136.82 133.7 130.4 111.24 109.86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +187 2584850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 63.07 62.35 63.6 65.74 67.34 68.89 71.38 74.93 0 0 90.56 96.42 100.32 103.12 105.45 106.5 104.11 98.39 93.71 90.7 89.77 90.07 89.78 90.46 89.86 89.27 89.9 90.23 89.11 89.34 90.34 91.83 93.68 95.18 96.92 98.65 100.86 101 0 0 111.14 108.57 107.02 105.53 103.45 103.95 104.34 104.3 103.95 103.4 101.45 100.53 99.76 99.3 98.6 98.77 98.18 97.11 96.08 94.8 93.18 91.28 89.1 86.83 84.72 83.32 82.96 83.3 83.9 84.47 84.54 83.2 80.72 78.02 75.39 72.97 70.61 68.37 66.22 64.19 62.22 60.27 58.39 56.54 54.72 52.91 51.04 48.93 46.75 44.56 42.46 40.69 39.39 38.41 37.72 37.46 37.37 37.47 37.49 36.95 35.03 32.75 32.13 31.77 32.25 33.25 34.84 36.68 38.65 39.87 39.22 36.57 32.54 28.02 24 24 23 22 21 20 19 18 17 16 16 15 15 15 15 16 16 16 16 15 15 14 14 13 12 12 11 11 11 10 10 10 10 10 9 9 9 8 8 8 8 7 7 9.77 10.38 8.3 7 7 6 6 6 5 5 4 4.48 6.31 7.86 4 3.99 5.77 8.53 10.83 13.22 15.91 18.55 20.83 22.77 24.63 26.38 27.93 29.32 30.59 31.75 33.35 34.53 35.74 37.38 38.78 39.91 41.38 42.96 45.39 49.13 52.82 55.37 57 57.72 57.87 0 0 0 67.73 72.57 77.96 80.56 80.93 80.44 80.1 80.18 81.54 84.07 86.11 87.03 87.59 88.81 94.39 99.49 101.31 99.97 98.84 99.73 101.83 105.16 109.27 113.56 116.98 119.15 120.45 121.13 122.23 122.4 123 123.55 124.01 124.11 124.15 128.32 133.61 135.63 135.96 134.01 134.82 133.38 132.54 126.6 127.43 124.48 122.94 112.81 125.55 130.48 134.78 136.38 121.58 109.81 106.44 109.55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +188 2584350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61.7 63.02 64.98 66.28 67.06 68.67 69.94 77.47 81.73 89.18 95.98 100.17 103.01 105.73 107.12 104.37 93.26 88.54 87.98 89 90.37 91.43 91.7 92.45 92.26 92.37 91.79 90.81 90.37 90.78 92.07 94.09 95.53 97.08 98.37 99.45 100.55 107.34 108.4 109.95 109.32 107.91 106.29 104.78 106.29 106.11 106.05 105.86 105.5 104.58 102.91 102.22 101.83 101.8 101.35 100.75 99.58 98.39 96.99 95.64 93.35 90.88 88.09 85.26 83.39 83.65 84.68 85.82 86.97 88.12 86.58 83.53 80.63 77.92 75.34 72.93 70.58 68.29 66.16 64.02 61.99 60 58.08 56.21 54.31 52.37 50.26 47.8 45.09 42.42 41 40 39 38 37.8 37.54 37.27 36.95 36.09 34.59 33 32 32.25 32.62 33.49 34.82 36.24 37.7 38.48 38.01 35.99 32.89 29.38 25.9 24 23 22 21 20 20 19 18 17 17 16 16 16 16 16 16 16 16 16 15 15 14 13 12 12 12 11 11 11 11 11 10 10 10 10 9 9 9 8 8 8 8 8 8.12 7.15 7 7 7 6 6 5 5 5 5 6.36 4 5 5 6.54 8.98 11.36 13.74 16.19 18.62 20.86 22.98 24.99 26.94 28.7 29.82 30.99 31.98 33.05 34.26 35.66 36.98 38.39 39.62 41.22 42.78 45.02 49.9 54.34 57.62 58.96 59.12 58.86 0 68.59 0 0 0 78.97 82.47 80.93 80.71 79.98 79.66 81.1 84.94 88.03 88.39 88.97 91.19 97.16 104.3 106.26 101.35 98.22 99.34 100.8 104.48 109.33 114.44 118.74 120.2 121.18 122.03 122.37 122.72 122.91 123.47 123.92 124.14 123.82 130.66 132.56 128.66 127.03 133.63 136.61 136.01 132.35 130.85 128.08 130.15 125.12 113.81 124.88 126.37 135.82 0 106.62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +189 2583850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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25.13 27.26 29.37 30.38 31.1 31.93 32.94 34.07 34.91 36.22 38.27 39.83 41.75 44.06 47.09 51.56 56.46 60.83 62.03 61.61 61.85 64.56 68.68 68.31 69.61 0 0 0 0 0 0 0 0 82.26 88.66 88.2 89.85 93.06 98.49 105.72 108.23 103.37 100.39 98.66 98.75 103.65 109.01 114.17 118.31 120.36 121.65 122.45 122.38 122.62 122.94 123.42 123.73 124.04 123.41 126.93 125.82 124.21 129.04 129.61 127.95 134.23 136.68 134.02 134.55 130.24 106.98 103.75 108.19 113.47 124.96 106.56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +190 2583350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 62.94 62.95 64.83 0 65.58 74.21 81.86 89.44 101.06 108.31 0 100.63 0 55.85 62.01 73.79 82.79 89.01 91.99 94.71 94.68 95.2 96.64 96.52 96.98 96.13 94.11 95.56 96.11 97.24 98.07 99.37 99.39 99.3 101.23 104.03 105.98 107.17 107.52 107.59 110.2 110.2 110 109.89 109.83 109.8 109.71 109.06 108.01 108.13 108.19 108.99 108.21 107.82 106.92 105.74 104.2 101.99 98.85 96.35 93.58 90.67 88.62 88.45 88.97 89.45 89.71 89.47 88.34 86.43 84.21 81.9 79.55 77.12 74.75 72.3 69.88 67.51 65.28 63.15 61.16 59.27 57.45 55.63 53.85 51.66 47.78 43.08 42 41 40 39.21 39 38.08 37.3 37 36 35 34 33 32 32.09 32.69 33.89 35.06 36.02 36.34 35.85 34.63 32.99 31.16 29.07 27.57 25.91 24.66 23.79 23.64 23.23 22.83 22.18 20.7 19 19 18 18 18 18 18 17 17 16 15 15 14 14 14 13 11 13 13 12 12 12 12 12 11 11 11 10 10 10 9 9 9 8 8 8 8 7 7 7 6 6 6 5 5 5 5 5 5.95 7.95 10.03 12.16 14.33 16.5 18.65 20.78 22.84 24.94 26.99 28.92 30.01 30.78 31.48 32.44 33.4 34.7 36.19 37.79 40.29 42.4 45.13 48.39 52.61 57.54 62.79 63.55 62.78 63.1 64.65 66.64 67.73 69.71 71.78 0 74.11 76.43 78.41 77.66 75.39 72.34 67.74 78.56 85.36 89.17 92.88 96.89 101.06 104.05 103.28 101.39 99.37 99.77 104.08 108.93 113.42 117 119.72 121.48 122.64 122.58 123.64 122.85 123.37 123.73 123.75 123.86 123.24 123.96 123.92 114.45 123.91 128.07 137.5 137.44 133.91 132.05 120.51 103.88 104.65 105.07 105.35 109.37 104.89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +191 2582850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 60.34 60.68 61.81 0 65.89 67.72 75.14 83.89 0 0 0 0 0 80.86 66.5 64.87 73.22 84.57 92.27 94.74 96.18 96.14 96.52 98.04 98.23 98.84 97.78 97.11 95.99 97.72 98.63 99.24 100.72 100.49 101.64 101.42 103.64 104.94 106.15 106.64 110.51 111.03 111.49 111.48 111.62 111.67 111.74 109.68 110.11 110.05 110.46 111.4 111.83 111.66 111.36 110.45 109.03 107.34 105.52 103.62 101.01 98.69 95.83 93.83 92.69 92.01 91.52 90.91 90.07 88.85 87.27 85.46 83.5 81.41 79.09 76.64 74.16 71.61 69.14 66.82 64.64 62.62 60.74 58.97 57.23 55.57 53.65 49.95 45.91 43.15 41.98 41.19 40.57 39.71 39 38 37 36 36 34 34 33 32 31.94 33.09 34.4 35.63 35.56 34.26 33.63 33.03 31.92 30.42 29.16 27.88 26.88 26.37 26.64 26.99 27.03 26.54 25.06 22.87 20.24 19 19 19 18 18 18 17 16 16 15 15 13 15 14 14 14 13 13 13 13 12 12 12 12 11 11 11 10 10 9 9 9 9 8 8 8 7 7 7 6 6 6 5.83 5 5 5 6.53 8.58 10.5 12.5 14.54 16.59 18.63 20.63 22.61 24.56 26.49 28.03 29.21 30.03 30.91 31.86 33.05 34.46 35.92 37.79 39.71 42.83 45.56 48.96 52.82 56.9 60.61 62.04 62.47 62.94 63.89 65.19 66.73 68.5 71.02 0 79.38 79.78 79.17 77.69 75.69 73.45 72.92 77.53 83.87 88.91 92.62 95.78 98.63 102.05 103.16 102.89 102.53 103.58 106.56 109.88 113.52 116.67 119.53 121.01 122.51 123.63 123.54 123.97 123.85 123.5 124.13 123.52 123.26 123.65 116.38 113.05 0 130.85 0 132.43 125.19 119.08 123.18 123.34 0 0 0 103.16 104.11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +192 2582350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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60.1 61.37 62.45 63.54 64.81 65.55 66.75 68.74 75.29 83.67 82.2 80.59 79.11 77.79 76.95 77.74 81.18 85.78 90.69 94.35 97.44 100.33 102.83 104.26 104.85 105.51 106.74 108.99 111.77 114.02 116.65 120.08 121.38 122.69 123.36 123.76 123.98 123.72 124.26 124.12 123.53 129.22 0 118 0 110.92 113.29 128.8 121.89 123.73 122.48 121.95 122.33 0 0 0 114.92 119.69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +193 2581850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 66.82 68.55 69.82 74.11 81.29 0 0 0 0 81.26 74.98 69.74 66.76 66.43 77.37 89.83 94.85 97.91 98.54 98.85 99.51 100.19 101.22 101.51 102.04 101.38 101.88 102.13 82.7397 78 102.68 103.43 104.25 105.29 106.33 106.69 110.58 112.03 103.85 108.44 108.62 107.37 108.64 110.12 111.86 112.38 113.72 115.54 116.49 117.28 117.89 117.83 117.59 116.34 115.13 113.77 112.08 110.29 108.33 106.19 103.93 101.46 98.77 95.98 93.47 91.39 89.84 88.65 87.65 86.58 85.16 83.11 80.53 77.72 74.83 72.05 69.49 67.23 65.26 63.47 61.76 60.1 58.27 56.34 54.03 51.64 49.57 47.65 45.92 44.17 42.43 40.77 40 39 38 37 36 35 34 33 32 32 32.62 34.98 36.19 36.22 36.22 35.69 34.62 33.32 32.02 30.81 29.86 29.54 30.84 33.49 36.13 36.41 33.89 30.78 27.41 24.03 20.62 20 20 19 19 18 18 17 16 16 16 16 15 15 14 14 14 14 14 14 14 13 13 13 12 12 11 11 10 10 10 9 9 8 8 8 7 7 7 6 6 6 6 6 6 7.28 9.19 11.12 13.01 14.97 16.8 18.66 20.39 22.22 23.86 25.37 26.73 27.79 28.67 29.5 30.48 31.74 33.01 34.58 36.53 38.77 41.18 43.66 47.34 0 53.08 55.93 58.02 59.74 61.31 62.8 64.08 64.67 65.56 65.72 68.09 0 81.03 81.24 80.62 80.11 80.72 82.33 85.44 0 93.21 96.66 99.94 102.5 105.01 106.66 107.81 108.95 110.42 112.45 114.38 116.88 118.38 120.53 0 124.58 126.02 127.13 126.37 126.34 123.87 124.53 124.03 133.68 140.12 139.07 136.92 133.51 109.93 128 124.74 123.04 121.63 120.94 109.61 105.3 106.22 0 0 0 0 0 0 0 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18 18 17 17 16 16 16 15 15 14 14 14 14 14 14 14 14 13 13 12 12 11 11 10 10 9 9 9 8 8 8 7 7 7 7 6 6 7 7 7.64 9.59 11.51 13.4 15.16 16.96 18.78 20.56 22.09 23.62 25.08 26.4 27.47 28.32 29.12 29.98 31.08 32.57 34.36 36.28 38.41 40.66 42.99 45.27 48.36 50.87 53.21 55.77 58 60.09 61.83 63.52 64.53 65.94 67.33 70.62 75.9 79.63 78 82.25 82.52 84.02 86.12 88.95 92.64 95.69 99.07 102.25 105.24 107.93 109.27 110.39 111.57 113.08 114.84 116.84 119.26 120.65 123.17 0 125.14 126.54 127.78 126.96 127.71 128.01 127.21 128.07 138.9 140.99 140.37 138.77 136.01 132.73 129.21 126.16 123.75 121.57 120.08 105.52 104.57 107.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +195 2580850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80.65 70.5 66.05 67.44 68.68 68.6 67.7 68.29 71.88 78.52 90.04 100 99.14 100.28 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22 22 22 21 21 20 20 20 19 19 19 19 19 18 18 18 17 17 16 15 14 14 13 13 12 12 11 11 10 10 10 9 9 10 10 10 10 11.07 13.08 15.43 17.63 19.86 22.13 24.41 26.74 29.12 31.47 33.82 35.93 38 40.19 42.28 43.88 44.79 45.15 45.34 45.43 45.54 45.62 46.37 48.36 50.83 53.4 56.18 58.71 61.71 64.82 67.84 70.63 73.75 78.02 81.03 82.56 83.46 84.45 85.83 87.08 88.39 89.69 91.11 93.53 96.27 98.02 99.17 103.58 111.02 117.07 120.05 122.16 123.91 125.34 126.92 128.11 129.38 130.45 131.5 132.52 133.5 134.61 134.87 135.68 136.58 136.67 139.12 137.27 143.42 142.92 151.85 0 145.69 146.99 0 104.69 111.66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +202 2577350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 21 22 22 28.0988 34.1954 39.2162 46.5114 51.3507 53.4913 57.8341 66.772 70.264 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49.55 48.5 47.54 47.03 46.55 46.57 46.8 47.05 46.44 45.29 44.22 43.02 41.64 40.22 38.17 36.25 34.83 35.18 35.23 34.1 35.06 35.72 35.82 35.41 34.59 33.48 32.31 31.21 30.09 28.89 27.55 26.13 24.63 24 24 23 23 22 22 21 21 21 21 21 20 20 20 20 19 19 18 17 17 16 15 15 14 13 13 12 11 11 11 11 11 11 11 11 11 11.92 14.08 16.18 18.34 20.5 22.83 25.31 27.76 30.42 33.24 36.21 39.45 42.77 45.79 47.6 49.63 51.93 51.98 51.63 51.33 51.24 51.42 51.94 53 54.64 56.83 59.43 62.23 65.21 68.22 0 73.95 75.95 77.72 79.68 81.06 82.57 83.79 84.97 85.68 87.26 88.26 88.31 89.1 89.41 89.5 89.37 92 99.19 107.48 114.28 119.53 123.45 126.3 128.24 129.57 130.65 131.61 132.46 133.39 134.3 135.57 135.41 136.18 136.9 137.83 138.25 133.18 130.81 132.83 130.21 0 120.46 128.08 122.94 114.47 106.28 104.76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +204 2576350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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98.3972 98.8727 100.685 103.753 104.204 104.511 105.405 106.118 105.551 106.19 106.17 106.793 106.145 107.535 109.128 112.123 113.88 114.939 115.167 116.063 118.382 121.277 121.48 122.883 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +243 2556850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38.8732 36.2281 35.1158 34.6673 34.7705 35.1324 35.5874 36.2983 36.9982 37.9301 39.0696 39.8543 40.9018 41.9764 43.1057 43.752 44.95 46.79 51.33 53.63 55.71 56.86 58.4 60.07 61.63 61.04 57.71 54.71 50.65 48.7 48.3213 49.0697 49.1494 49.7508 49.9628 50.2534 50.92 52.15 53 54 53 52 51.5037 51.9115 52.3087 52.656 52.9878 53.3366 53.9557 54.6988 55.4368 56.3656 57.34 59.5 62 65 67 67 67 67 67 67 67 67 66 66 66 65 65 64 63 62 64.8756 60.8506 60.5238 60.5409 60.5831 60.6489 60.811 61.0147 61.2683 61.7442 62.9716 62.9995 63.0092 63.03 64.9653 66.9925 68.5553 70.122 71.6759 73.1675 74.5541 75.9456 77.3934 78.8499 80.2699 81.3879 82.0095 82.9518 84.0062 84.8384 85.7485 86.5155 87.1201 87.8991 88.7119 89.4861 90.2159 90.9174 91.6032 92.2187 92.7675 93.298 93.7931 94.2691 94.7228 95.1382 95.5804 96.0565 96.5224 96.9672 97.3876 97.827 98.2657 98.6912 99.1462 99.6305 100.125 101.654 102.569 103.999 104.699 104.699 103.354 104.054 104.768 104.839 105.09 106.121 108.463 111.44 113.062 113.976 114.942 116.713 117.801 119.971 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +244 2556350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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42.4028 42.8514 43.5731 44.2646 45.2874 46.0529 46.4736 46.7078 47.3289 47.6222 48.0618 48.3398 48.5344 48.6597 48.9786 49.9462 50.4162 50.9176 51.476 52.1987 53.0351 53.8372 54.5584 55.1513 56.78 57.71 58.15 59 61 61 62 63 64 64 63 63 63 62 61 60.2738 60.5257 60.7826 61.0289 61.252 65.3885 65.9383 65.1243 64.2555 63.9444 62.6326 62.9893 63.2756 63.7369 64.1546 64.4612 64.8829 66.2335 67.6787 68.0471 70.3212 72.1668 73.6728 75.1061 76.8292 78.4323 80 80 80.5343 81.3193 82.2392 83.3591 84.4189 85.4766 86.3025 86.9178 87.6416 88.3892 89.1166 89.8772 90.6773 91.4532 92.1517 92.6688 93.1964 93.8051 94.2751 94.7324 95.1381 95.5064 95.9248 96.3228 96.713 97.0396 97.3464 97.6647 97.9809 98.3029 98.6644 99.0548 99.4189 99.7628 100.003 100.086 100.173 100.143 100.018 99.402 98.9064 99.8314 102.769 103.101 104.766 105.899 106.329 111.317 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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71.5007 72.713 74.0019 76.1896 79.0731 80 80 80 80 80.7553 81.8805 82.9744 84.0524 85.1537 86.0861 86.8424 87.4471 88.1314 88.8908 89.6247 90.4285 91.2281 91.9663 92.5438 93.0726 93.6957 94.2231 94.6735 95.0705 95.4416 95.8121 96.1423 96.4396 96.7182 96.9677 97.2399 97.4818 97.7345 98.0215 98.3338 98.6616 98.9552 99.102 99.127 99.078 98.893 98.2528 97.2887 96.2706 97.3642 98.3547 102.027 103.43 104.583 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +247 2554850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19.0523 21.5732 23.6193 25.0473 26 27.6629 28.9557 30.0844 31.1914 31.9587 32.5637 33.8196 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -172 2614050 0 4.05824E-04 4.04293E-04 4.02647E-04 4.01516E-04 3.99520E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.90440E-04 7.86390E-04 7.89086E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -173 2613950 0 4.04420E-04 4.03026E-04 4.01390E-04 3.99893E-04 3.97913E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.20000E-04 7.82826E-04 7.81276E-04 7.78194E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -175 2613750 0 4.02144E-04 4.00515E-04 3.98777E-04 3.97053E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.11292E-04 7.72968E-04 7.68882E-04 7.66748E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -177 2613550 0 4.00773E-04 3.99530E-04 3.97312E-04 3.93789E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.94766E-04 7.94766E-04 7.50066E-04 7.29794E-04 7.58068E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -178 2613450 0 3.99902E-04 3.98541E-04 3.95118E-04 3.92948E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.76468E-04 7.52936E-04 7.29794E-04 7.29794E-04 7.10590E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -180 2613250 0 3.98665E-04 3.97067E-04 3.93789E-04 3.91634E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.76468E-04 7.52936E-04 7.10590E-04 7.10590E-04 7.10590E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -182 2613050 0 4.14456E-04 4.09702E-04 4.04793E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.39523E-04 3.30588E-04 6.61176E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.69394E-04 7.22724E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -184 2612850 0 4.12950E-04 4.12677E-04 4.09031E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.58924E-04 3.48954E-04 6.79046E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.52230E-04 6.52230E-04 6.85662E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -186 2612650 0 4.15835E-04 4.31035E-04 4.09433E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.80677E-04 3.58924E-04 6.97908E-04 6.87486E-04 6.69394E-04 6.69394E-04 6.54580E-04 6.69394E-04 7.03706E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -187 2612550 0 4.72010E-04 4.72165E-04 4.69702E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.90625E-04 3.90625E-04 3.67647E-04 7.14286E-04 7.00414E-04 7.20426E-04 7.00414E-04 6.65300E-04 6.56810E-04 6.33940E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -188 2612450 0 4.65152E-04 4.65002E-04 4.66356E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.90625E-04 3.78788E-04 3.67647E-04 7.14286E-04 6.97894E-04 6.85188E-04 6.74376E-04 6.64600E-04 6.56468E-04 6.63968E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -190 2612250 0 4.66810E-04 4.62464E-04 4.64402E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.69604E-04 3.78788E-04 3.67647E-04 7.35294E-04 7.41614E-04 7.00414E-04 6.70966E-04 6.81484E-04 6.54932E-04 6.32350E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -192 2612050 0 4.41495E-04 4.40300E-04 4.37046E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.30580E-04 4.07957E-04 4.01049E-04 7.94474E-04 7.52210E-04 7.44386E-04 7.36724E-04 7.28838E-04 7.21492E-04 6.74010E-04 6.68536E-04 6.62150E-04 6.57030E-04 6.55068E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -194 2611850 0 4.64673E-04 4.38519E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.07022E-04 4.01162E-04 3.96240E-04 7.84386E-04 7.43594E-04 3.68849E-04 3.65948E-04 3.62997E-04 3.59816E-04 3.36488E-04 3.34607E-04 3.31241E-04 3.28024E-04 3.24868E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -196 2611650 0 4.53498E-04 4.44815E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.00596E-04 3.95358E-04 3.90898E-04 3.87170E-04 3.67780E-04 3.66236E-04 3.64609E-04 3.62432E-04 3.59445E-04 3.36574E-04 3.33253E-04 3.29996E-04 3.26803E-04 3.24069E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.41265E-04 9.61169E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -198 2611450 0 4.43420E-04 4.33727E-04 4.23873E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.57143E-04 3.47222E-04 3.47222E-04 3.37838E-04 3.37838E-04 3.37838E-04 3.28947E-04 3.47222E-04 3.32624E-04 3.28861E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.59693E-04 9.83478E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.79624E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -200 2611250 0 4.51753E-04 4.40141E-04 4.28816E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 3.90625E-04 3.67647E-04 3.57143E-04 3.58577E-04 3.57143E-04 3.47222E-04 3.37838E-04 3.37838E-04 3.37838E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -201 2611150 0 4.56038E-04 4.43577E-04 4.30886E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 4.03226E-04 3.90625E-04 3.78788E-04 3.59816E-04 3.67647E-04 3.57143E-04 3.47222E-04 3.47222E-04 3.37838E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -203 2610950 0 5.04603E-04 4.86957E-04 4.71181E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 3.90625E-04 4.03226E-04 4.16667E-04 3.62950E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -212 2610050 0 5.28631E-04 5.16961E-04 5.04887E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -213 2609950 0 5.23705E-04 5.13748E-04 5.03079E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -215 2609750 0 5.15066E-04 5.07440E-04 4.98616E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -216 2609650 0 5.11503E-04 5.04705E-04 4.97029E-04 4.88561E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 5.00000E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -218 2609450 0 9.10968E-04 9.07095E-04 9.01031E-04 8.92240E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.84640E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -219 2609350 0 8.99133E-04 9.00397E-04 9.00081E-04 8.95987E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.94036E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -220 2609250 0 8.86679E-04 8.94110E-04 9.00714E-04 9.02619E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -221 2609150 0 8.73671E-04 8.89451E-04 9.03893E-04 9.13568E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -222 2609050 0 8.60047E-04 8.80305E-04 9.01540E-04 9.20812E-04 9.30249E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -223 2608950 0 8.56569E-04 8.77569E-04 9.03142E-04 9.31613E-04 9.59396E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -224 2608850 0 8.48278E-04 8.69761E-04 8.96135E-04 9.25843E-04 9.53636E-04 9.32713E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -225 2608750 0 8.35176E-04 8.56569E-04 8.81221E-04 9.04749E-04 9.20478E-04 9.02485E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -227 2608550 0 8.20210E-04 8.41184E-04 8.61178E-04 8.76885E-04 8.85583E-04 8.54409E-04 8.44595E-04 8.31947E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -228 2608450 0 8.13802E-04 8.29738E-04 8.46024E-04 8.58516E-04 8.65951E-04 8.37521E-04 8.31947E-04 8.22639E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.09323E-04 9.14807E-04 9.14807E-04 9.18173E-04 9.24981E-04 9.29052E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.72006E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -229 2608350 0 8.06192E-04 8.18867E-04 8.31670E-04 8.42318E-04 8.49185E-04 8.22639E-04 8.19404E-04 8.12744E-04 8.06452E-04 8.06452E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.15661E-04 9.07847E-04 9.21874E-04 9.34112E-04 9.47659E-04 9.54249E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.57854E-04 9.83091E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -230 2608250 0 7.96178E-04 8.06712E-04 8.16994E-04 8.33333E-04 8.33611E-04 8.09586E-04 8.07233E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 7.96432E-04 8.08000E-04 8.08000E-04 9.15417E-04 9.34430E-04 9.52593E-04 9.66686E-04 9.74927E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.42330E-04 9.65251E-04 9.87752E-04 1.01020E-05 9.85804E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -231 2608150 0 7.83945E-04 7.91640E-04 8.06452E-04 8.33333E-04 8.18063E-04 7.95418E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 7.83945E-04 7.98722E-04 8.17261E-04 9.20637E-04 9.48314E-04 9.75620E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.25583E-04 9.46611E-04 9.67492E-04 9.86193E-04 9.64506E-04 9.76944E-04 9.96810E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 9.94827E-04 9.96413E-04 9.98004E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.46157E-04 2.52231E-04 2.55005E-04 2.57694E-04 2.60838E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 -314 2599850 0 4.40684E-04 4.40063E-04 4.37752E-04 4.34631E-04 4.32077E-04 2.19069E-04 2.18571E-04 2.23215E-04 2.23215E-04 2.23215E-04 4.56813E-04 4.55581E-04 4.53544E-04 4.51846E-04 4.49603E-04 2.41025E-04 2.36643E-04 2.36643E-04 2.30564E-04 2.32417E-04 2.47775E-04 2.55199E-04 2.58835E-04 2.60816E-04 2.61066E-04 2.49932E-04 2.50318E-04 2.50173E-04 2.50996E-04 2.59329E-04 2.55102E-04 2.71739E-04 2.71739E-04 2.65957E-04 2.71739E-04 2.71739E-04 2.77778E-04 2.71739E-04 2.65957E-04 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8.56200E-05 1.04400E-04 1.04400E-04 1.04400E-04 1.69894E-04 9.18754E-06 9.18152E-06 9.74992E-06 9.45506E-06 9.69580E-06 1.05473E-05 1.19066E-05 1.25884E-05 1.22956E-05 1.14504E-05 1.03629E-05 9.13815E-06 8.55775E-06 9.92029E-06 1.04756E-05 1.17041E-05 1.31590E-05 1.45809E-05 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +97 2629850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.25551E-04 1.83274E-04 2.07987E-04 2.03583E-04 1.99161E-04 2.71912E-04 1.76704E-04 1.65612E-04 2.75424E-05 1.52449E-04 3.86548E-05 2.66904E-05 2.95275E-05 4.74268E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.52539E-05 3.91997E-05 7.69675E-05 3.50637E-04 0 0 0 2.52999E-04 3.95067E-04 1.12613E-04 1.74102E-04 4.14253E-04 8.78128E-05 6.32581E-05 3.22375E-04 0 0 3.18459E-04 1.91261E-04 1.93951E-04 1.63613E-04 1.43926E-04 1.49970E-04 2.58746E-04 9.49848E-05 9.32847E-05 1.02446E-04 1.20406E-04 2.32895E-04 5.58396E-04 6.21615E-04 4.28864E-04 5.18170E-04 3.13255E-04 3.48309E-04 1.44289E-04 2.28930E-04 1.70707E-04 1.80180E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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9.69245E-05 2.74223E-05 2.68049E-05 2.88684E-05 3.44669E-05 5.86855E-05 4.50684E-04 0 0 0 0 0 0 0 0 0 0 6.18301E-05 3.27797E-05 4.00855E-05 2.22000E-04 2.22000E-04 0 0 5.16567E-04 9.65234E-04 0 3.39435E-04 7.62671E-04 4.71697E-04 0 0 0 0 3.52717E-05 3.34496E-04 1.81263E-04 1.30983E-04 1.54710E-05 1.29025E-04 2.10139E-05 2.39089E-05 2.52413E-05 2.27239E-05 9.65065E-05 2.78583E-04 1.01835E-04 1.06834E-04 3.62691E-04 1.49260E-04 1.51841E-04 1.72099E-04 4.01333E-04 1.54500E-04 1.69040E-04 3.60055E-04 3.59791E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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3.50041E-04 5.02176E-04 4.79374E-04 4.58749E-04 6.15686E-04 3.09667E-04 3.23974E-04 3.70284E-04 4.13605E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +100 2628350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6.08379E-05 3.56633E-05 2.81426E-05 2.60326E-05 3.12761E-05 3.67467E-05 3.78175E-05 1.85803E-04 3.10945E-04 4.85472E-05 3.24130E-05 3.15512E-05 2.82486E-05 3.93104E-05 5.39024E-05 9.35674E-05 0 0 0 0 0 0 0 0 0 8.77160E-05 4.15513E-05 3.50304E-05 4.45898E-05 4.51781E-04 2.22000E-04 1.42012E-03 4.68200E-04 1.10888E-04 0 0 5.38366E-05 2.93558E-04 0 0 0 0 2.23472E-04 2.07766E-04 4.54545E-04 1.13379E-04 1.65213E-05 1.03466E-04 8.97831E-05 1.20628E-04 1.39978E-04 1.30545E-04 2.01694E-04 1.48367E-04 1.07021E-04 1.35862E-04 1.02839E-04 3.09004E-04 4.36425E-04 1.64637E-04 1.76737E-04 4.94227E-04 1.72969E-04 4.47760E-04 3.49536E-04 7.92334E-04 6.62076E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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5.01329E-04 1.97338E-04 5.70199E-04 1.74138E-04 9.20007E-05 2.80178E-04 6.74004E-04 1.05000E-04 1.05000E-04 1.05000E-04 3.68864E-04 3.28489E-04 3.43316E-04 4.55629E-04 5.32156E-04 5.19123E-04 4.93304E-04 1.84350E-04 1.70493E-04 9.02757E-04 9.84348E-04 2.09804E-04 2.12498E-04 2.21680E-04 5.07021E-04 3.44115E-04 3.20587E-04 3.60421E-04 5.54453E-04 5.38084E-04 4.46465E-04 4.11457E-04 1.93171E-04 2.46031E-04 2.91010E-04 2.99225E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +102 2627350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.23073E-03 7.68371E-05 5.96099E-05 9.48768E-05 9.06221E-05 0 0 1.31934E-04 2.87356E-05 3.04630E-05 3.33778E-05 2.34082E-04 2.10643E-04 2.87026E-04 3.27717E-04 3.67647E-04 4.51275E-04 3.57219E-04 2.38872E-04 6.16995E-04 4.56741E-04 5.12862E-04 2.96874E-04 1.74464E-04 2.48390E-04 2.60826E-04 4.25681E-04 1.10215E-03 2.24315E-03 0 0 0 0 1.85503E-04 8.58123E-05 4.91632E-05 0 2.90034E-04 3.90016E-04 2.22000E-04 2.22000E-04 0 0 2.22000E-04 4.19149E-04 0 0 0 4.55325E-05 0 4.88075E-05 1.65017E-04 1.29232E-04 1.13688E-04 1.00339E-04 1.90479E-05 2.39520E-05 2.46739E-05 2.01497E-05 1.27152E-04 2.58978E-05 5.22327E-05 2.45380E-04 1.01470E-03 7.11218E-04 5.43156E-04 6.28141E-04 4.35242E-04 5.58197E-04 9.71104E-04 9.85452E-04 1.10454E-03 9.97149E-04 1.01495E-03 9.49126E-04 9.63945E-04 0 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 4.04040E-05 4.04040E-05 5.00000E-04 5.00000E-04 5.01359E-04 2.23516E-04 2.47067E-04 2.36129E-04 1.05035E-03 9.41346E-04 1.06110E-03 8.88739E-04 4.95765E-04 4.96509E-04 6.25003E-04 5.49309E-04 3.96964E-04 3.45547E-04 3.67121E-04 4.57452E-04 1.93882E-04 1.83432E-04 1.79501E-04 8.33614E-04 8.87039E-04 1.05934E-03 2.03252E-04 2.12783E-04 2.40164E-04 4.83009E-04 4.56061E-04 4.48876E-04 4.80470E-04 5.22695E-04 5.92976E-04 3.49689E-04 2.81670E-04 1.45966E-04 2.06824E-04 2.60891E-04 2.94964E-04 2.11857E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +103 2626850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8.23011E-05 1.57828E-04 3.75494E-05 4.08052E-05 0 0 1.12769E-04 0 0 3.35117E-05 3.79747E-05 4.21822E-05 2.94464E-04 1.87968E-04 2.31667E-04 2.91715E-04 2.88850E-04 6.77804E-04 4.20780E-04 6.54472E-04 6.39234E-04 4.06995E-04 2.71520E-04 2.40299E-04 2.04332E-04 1.97707E-04 2.51636E-04 2.75676E-04 4.14651E-04 4.76715E-04 0 0 0 0 0 8.03152E-04 2.32243E-04 8.94889E-04 1.76766E-03 5.51268E-04 2.22000E-04 2.22000E-04 1.13914E-03 0 0 0 0 0 0 2.66129E-05 4.66833E-05 3.50042E-05 1.68067E-04 1.42537E-04 1.31629E-04 9.07541E-05 2.54032E-05 2.02164E-05 2.25964E-05 2.64128E-05 2.30231E-04 1.46371E-04 1.71481E-04 8.12185E-04 7.55376E-04 1.05595E-03 8.44481E-04 7.19446E-04 8.96651E-04 1.04131E-03 1.02716E-03 1.16280E-03 9.75139E-04 4.50366E-04 4.45788E-04 4.95513E-04 5.18568E-04 9.68216E-04 9.97048E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 4.04040E-05 5.00000E-04 5.00000E-04 5.01363E-04 2.22744E-04 2.50829E-04 2.54127E-04 3.59501E-04 2.73016E-04 1.25216E-03 8.33307E-04 5.57150E-04 5.66947E-04 5.83940E-04 5.75322E-04 6.01229E-04 7.03886E-04 3.05952E-04 1.98283E-04 1.86201E-04 1.70229E-04 1.63073E-04 1.64903E-04 7.64721E-04 7.16206E-04 8.30754E-04 1.69753E-04 1.88894E-04 2.39641E-04 3.65612E-04 2.78375E-04 3.36632E-04 4.04455E-04 4.30960E-04 5.18389E-04 3.18584E-04 3.13449E-04 1.47122E-04 3.68145E-04 5.10835E-04 6.25440E-04 8.39354E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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3.41705E-04 4.02778E-04 3.90491E-04 5.21270E-04 5.69454E-04 5.27370E-04 3.83247E-04 4.91093E-04 9.70530E-04 1.04008E-03 1.01496E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.50000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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2.72753E-04 4.26467E-04 5.30825E-04 2.72656E-03 1.79867E-03 1.38264E-03 3.39632E-04 3.57534E-04 3.15863E-04 3.65297E-04 3.06174E-04 2.92798E-04 6.62394E-04 6.73451E-04 6.75988E-04 1.28138E-04 1.20699E-04 1.04619E-04 9.69674E-05 1.06880E-04 5.30816E-04 5.51423E-04 6.56958E-04 3.05381E-04 1.81992E-04 1.88349E-04 2.13657E-04 2.83278E-04 3.53813E-04 2.72520E-04 2.92305E-04 4.19075E-04 2.67944E-04 3.01612E-04 2.18303E-04 3.61659E-04 6.22092E-04 4.79546E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +106 2625350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.58479E-04 1.54133E-04 0 0 0 0 0 0 0 0 0 0 0 0 6.76268E-05 4.43373E-04 2.22000E-04 6.20667E-05 3.88067E-05 0 0 0 0 0 0 0 6.61362E-05 1.17810E-04 0 1.58898E-04 3.20925E-04 2.06101E-04 5.15180E-04 1.68976E-04 4.63650E-04 5.31915E-04 2.33428E-04 2.22543E-04 5.05737E-04 1.88997E-04 4.30737E-04 4.05186E-04 1.55411E-04 1.52999E-04 1.61204E-04 1.60875E-04 3.86997E-04 3.62214E-04 6.51752E-04 0 0 0 0 0 0 0 8.74871E-04 5.07150E-04 4.28082E-04 1.68464E-03 0 2.22000E-04 0 0 3.75563E-05 2.60938E-05 3.70955E-05 8.05152E-04 6.29288E-05 4.99839E-05 2.86807E-04 4.60032E-04 5.93073E-04 5.44476E-04 7.03332E-04 5.12395E-04 4.96751E-04 4.15936E-04 9.67279E-04 1.24566E-03 2.38926E-04 3.72489E-04 3.53301E-04 1.56738E-04 1.51749E-04 1.05711E-04 1.10908E-04 1.70438E-03 6.44982E-05 2.86359E-05 3.30123E-05 1.45770E-04 1.21648E-03 9.60140E-04 1.02390E-03 1.03121E-03 1.01603E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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1.08899E-04 4.78961E-04 6.06713E-04 6.09758E-04 2.97243E-04 3.44646E-04 2.28510E-04 2.51853E-04 3.06150E-04 3.38365E-04 3.10901E-04 2.31687E-04 2.21672E-04 1.39945E-04 2.01850E-04 1.96039E-04 2.50354E-04 6.28566E-04 5.75274E-04 5.06412E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +107 2624850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.03861E-04 4.10172E-04 6.20314E-04 0 0 7.93650E-04 0 0 0 0 0 0 0 3.68597E-04 1.30781E-04 0 2.12188E-04 7.53490E-05 5.44955E-05 7.10786E-05 7.79972E-05 0 0 0 0 1.61355E-04 5.93978E-05 2.04413E-04 0 6.42586E-05 2.27376E-04 1.87606E-04 1.49343E-04 2.02675E-04 4.31928E-04 1.75193E-04 1.65453E-04 2.04549E-04 4.73987E-04 1.76651E-04 1.80387E-04 3.62565E-04 3.62319E-04 3.72024E-04 3.80982E-04 1.72075E-04 1.57035E-04 4.80844E-04 1.41985E-03 5.19132E-03 2.04286E-03 2.34050E-03 1.49503E-03 0 0 0 0 0 2.22000E-04 2.22000E-04 6.75994E-04 0 0 0 1.97550E-04 4.65239E-05 3.80882E-05 4.58719E-03 4.03483E-04 2.56997E-04 1.30163E-04 0 1.61852E-03 1.57000E-03 4.24266E-04 5.34498E-04 0 7.36766E-03 1.01420E-03 2.50215E-04 3.40795E-04 1.67153E-04 3.28429E-04 1.35597E-04 2.08906E-05 2.08860E-05 2.09540E-05 1.10885E-04 2.22458E-04 2.60418E-04 1.40432E-05 4.69479E-04 1.12231E-04 1.05522E-03 1.01723E-03 1.03970E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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2.83862E-04 3.09024E-04 5.78560E-04 5.11488E-04 4.57759E-04 2.87195E-04 1.99465E-04 2.22000E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +108 2624350 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.15418E-04 5.43203E-04 5.28790E-04 5.24419E-04 3.08405E-04 1.40592E-04 0 0 0 0 0 0 0 5.65616E-05 0 0 2.05978E-04 4.81325E-05 4.72012E-05 0 1.13586E-04 7.03199E-04 0 0 2.78378E-05 3.45253E-05 0 0 2.31839E-05 1.54083E-04 1.52300E-04 1.45843E-04 1.75224E-04 1.41758E-04 3.86757E-04 3.81281E-04 1.81818E-04 2.03051E-04 4.63251E-04 1.73974E-04 3.59097E-04 1.33298E-04 3.39247E-04 1.38619E-04 1.43637E-04 1.49477E-04 1.62707E-04 1.90549E-04 3.03906E-04 3.34224E-04 3.58635E-04 3.97862E-04 1.53042E-03 1.56406E-03 1.76828E-03 2.95278E-03 4.70211E-03 0 0 0 0 0 0 0 1.06383E-03 3.24231E-04 4.78753E-04 0 0 0 7.45203E-05 8.26609E-05 5.77411E-04 2.53913E-04 0 0 9.02054E-04 4.24304E-04 1.87965E-04 1.08332E-04 1.38808E-04 1.44449E-04 4.10417E-04 1.26032E-04 2.03639E-05 1.87298E-05 1.82790E-05 1.96879E-05 1.32578E-04 1.96103E-05 6.47079E-04 4.73090E-04 4.48644E-04 9.43752E-04 1.00000E-03 1.00000E-03 1.00000E-03 9.52744E-04 9.20133E-04 9.74212E-04 8.73379E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 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4.39690E-03 5.49684E-03 3.98526E-03 4.73236E-03 4.09408E-03 5.13281E-03 6.58119E-03 4.44050E-03 1.14427E-03 1.14847E-03 8.96218E-05 2.60525E-04 4.14182E-04 8.30841E-04 6.72224E-04 4.32505E-04 1.02853E-03 5.01882E-04 4.07810E-04 3.44302E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.50000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-05 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 6.25000E-05 3.12500E-04 3.12500E-04 1.56250E-04 1.09375E-04 1.09375E-04 2.18750E-04 7.00000E-04 7.00000E-04 7.00000E-04 7.00000E-04 3.78290E-05 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4.45377E-04 1.06075E-04 1.11945E-04 1.06742E-04 2.10027E-05 3.03406E-05 2.85675E-05 2.80298E-05 3.19966E-05 8.93095E-04 9.15026E-04 9.74366E-04 1.89898E-04 9.84477E-04 9.92744E-04 9.64560E-04 9.93767E-04 1.10099E-03 1.10171E-03 1.07225E-03 1.05682E-03 1.03097E-03 1.15924E-03 2.55901E-04 2.68323E-04 1.40641E-03 1.48134E-03 7.28788E-05 6.44795E-05 5.81320E-05 5.21337E-05 4.66920E-05 4.53796E-05 5.17089E-05 5.21685E-05 5.00199E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +122 2617350 0 0 0 0 0 0 0 0 0 0 0 3.81726E-03 2.69585E-04 3.62842E-05 2.61022E-05 3.28601E-04 1.39004E-04 1.14468E-04 2.58682E-05 2.61682E-05 6.22674E-04 6.36132E-04 6.46645E-04 3.98416E-05 1.65141E-04 1.61681E-04 5.58886E-04 1.45784E-04 1.20487E-04 2.30296E-05 1.95299E-05 1.03949E-04 2.47755E-04 2.55924E-04 1.05473E-03 2.22000E-04 0 0 2.46144E-05 2.86697E-05 2.98403E-05 2.91275E-05 2.81520E-05 4.45204E-05 3.99550E-05 3.23415E-05 4.61245E-05 4.85680E-05 7.74108E-05 8.68917E-05 8.90968E-05 5.98544E-05 9.60184E-05 0 0 2.22000E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 2.22000E-04 7.87565E-05 3.28084E-04 3.20241E-04 3.86783E-04 1.35407E-03 3.98753E-04 1.39008E-03 1.99441E-04 2.21141E-04 2.60851E-04 3.59098E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.88302E-03 4.16874E-03 4.90880E-03 3.94166E-03 2.19757E-03 2.33536E-03 2.27414E-03 1.85359E-03 2.86697E-03 1.21278E-04 1.15580E-04 1.34019E-04 1.92002E-04 2.08737E-04 6.58440E-04 9.48627E-04 8.78112E-04 4.25128E-04 3.50638E-04 2.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.50000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 3.12500E-05 2.18750E-04 2.18750E-04 2.18750E-04 2.18750E-04 2.18750E-04 7.00000E-04 7.00000E-04 7.00000E-04 8.15294E-04 9.04593E-04 1.25515E-03 7.77053E-04 5.00308E-04 3.44336E-04 8.31959E-04 9.97277E-04 9.97277E-04 8.52090E-04 6.36720E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 5.52050E-04 5.23317E-04 4.38918E-04 2.24401E-04 2.52081E-04 9.39847E-05 2.89055E-05 2.79598E-05 2.72607E-05 3.16924E-05 8.69351E-04 8.76593E-04 9.26418E-04 9.39363E-04 1.84794E-04 9.12134E-04 9.29884E-04 9.54830E-04 9.39113E-04 4.63257E-05 4.30878E-05 1.66337E-04 1.66763E-04 1.97613E-04 1.17428E-03 1.21598E-03 1.24206E-03 1.23909E-03 1.19870E-03 5.41204E-05 5.08735E-05 4.88900E-05 4.44543E-05 4.28063E-05 4.75431E-05 4.82856E-05 5.21060E-05 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +123 2616850 0 0 0 0 0 0 0 0 0 0 0 7.81739E-04 2.21827E-04 2.00454E-04 2.39785E-04 2.29442E-04 1.53350E-04 1.25930E-04 1.33113E-04 2.83439E-05 3.28673E-04 7.57576E-04 4.37839E-04 4.01642E-05 3.36508E-05 2.85414E-04 7.14286E-04 1.89556E-04 2.59260E-05 2.07477E-05 1.91937E-05 2.04050E-05 1.57282E-04 1.71741E-04 4.51671E-04 0 0 2.41943E-05 2.78033E-05 3.07377E-05 3.43249E-05 4.24962E-05 5.84495E-05 9.13347E-05 7.14649E-05 5.70776E-05 1.01489E-04 0 4.50721E-05 7.67909E-05 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.17951E-04 2.77411E-04 3.99042E-04 3.63636E-04 2.82104E-04 3.33778E-04 2.83126E-04 2.34852E-04 1.73271E-04 2.00724E-04 2.55363E-04 3.86058E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.43864E-03 3.92924E-03 3.53674E-03 2.13380E-03 2.43830E-03 2.34632E-03 1.73066E-03 3.76960E-03 1.12562E-04 1.35436E-04 1.50741E-04 1.37099E-04 3.73119E-04 4.61048E-04 8.46873E-04 4.17167E-04 5.34304E-04 1.58827E-04 4.03229E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 2.50000E-04 4.37500E-05 4.37500E-04 4.37500E-05 4.37500E-05 4.37500E-05 7.00000E-04 7.00000E-04 7.00000E-04 9.69990E-04 9.52592E-04 1.36534E-03 1.03300E-03 8.02347E-04 8.08000E-04 9.43230E-04 1.03333E-03 1.03333E-03 9.96701E-04 3.48400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 5.30253E-04 5.60992E-04 4.91237E-04 2.25464E-04 8.01932E-05 2.77537E-05 2.41902E-05 2.51370E-05 2.74786E-05 7.35357E-04 7.75580E-04 7.88788E-04 7.92460E-04 7.91547E-04 8.18982E-04 7.87650E-04 8.60761E-04 9.22026E-04 8.90938E-04 8.03763E-04 1.52599E-04 8.40204E-04 6.66372E-04 1.13597E-03 1.13705E-03 1.12153E-03 1.13401E-03 2.20891E-04 5.13752E-05 4.87056E-05 4.83608E-05 4.55561E-05 4.42395E-05 4.65806E-05 4.83536E-05 1.94893E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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1.40602E-04 1.50948E-04 3.58818E-04 4.34347E-04 1.43784E-04 2.54122E-04 1.32899E-04 1.56159E-04 7.14286E-04 6.25003E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 2.50000E-04 2.50000E-04 2.50000E-04 2.50000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 9.83216E-04 1.02266E-03 9.93069E-04 9.25000E-04 8.08000E-04 8.06048E-04 8.68500E-04 1.03333E-03 1.03333E-03 8.67544E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.29669E-04 2.93659E-04 2.79912E-04 5.28373E-04 8.30647E-05 2.41645E-05 2.47168E-05 2.56681E-05 2.44131E-04 6.94215E-04 7.22136E-05 7.48453E-04 7.45622E-04 7.56610E-04 7.56393E-04 7.92143E-04 7.94565E-04 2.00128E-04 4.88216E-05 3.84639E-04 5.54035E-04 2.73789E-04 6.00097E-05 2.17389E-04 1.09053E-03 1.09162E-03 1.12121E-03 2.16619E-04 1.02952E-03 4.88589E-05 4.76696E-05 4.67125E-05 4.63984E-05 1.89728E-05 1.90243E-05 1.89411E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +125 2615850 0 0 0 0 0 0 2.65535E-04 0 0 0 2.22000E-04 1.00000E-03 1.32362E-04 1.18443E-04 2.61282E-04 1.46499E-04 2.41347E-04 3.12590E-04 3.31204E-04 1.84138E-04 5.26338E-04 4.52868E-04 3.56548E-04 1.74776E-04 2.86841E-05 2.68776E-05 6.41026E-04 2.98326E-04 2.27156E-05 1.71520E-05 1.50676E-05 1.62463E-05 3.06250E-05 8.55287E-05 0 0 3.02054E-05 5.01064E-05 3.28991E-05 2.83879E-05 1.05755E-04 7.69419E-04 3.72776E-05 5.03440E-05 0 2.22000E-04 0 4.93746E-05 0 0 0 3.52748E-05 9.24784E-05 5.35332E-05 1.64835E-04 0 0 0 0 0 0 0 0 0 0 0 2.22000E-04 7.03549E-05 3.43878E-05 3.01216E-05 4.06284E-05 2.06058E-05 3.05679E-05 1.21466E-03 2.25387E-04 2.40887E-04 2.65025E-04 8.70072E-04 0 1.66374E-04 3.94629E-04 4.85016E-04 3.38983E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.09557E-02 0 0 1.85610E-03 1.93398E-03 1.81950E-03 1.49880E-04 1.47842E-04 3.76478E-04 4.14150E-04 4.21996E-04 6.08876E-04 3.70156E-04 1.13463E-04 2.19299E-04 1.22980E-04 2.99473E-04 6.14182E-04 4.88427E-04 6.90036E-04 3.55114E-04 3.84615E-04 8.09586E-04 8.33333E-04 8.91266E-04 9.73141E-04 1.00000E-03 1.00000E-03 1.00000E-03 2.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 2.50000E-04 2.50000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 9.57630E-04 9.73369E-04 9.60465E-04 8.08000E-04 7.82704E-04 6.12400E-04 4.92400E-04 3.85029E-04 5.35972E-04 3.03872E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.99720E-04 4.23443E-04 2.97793E-04 2.85184E-04 6.99834E-04 7.18335E-04 7.54540E-05 1.49523E-05 1.52327E-05 7.64981E-04 8.12587E-04 8.53856E-04 5.95041E-04 4.86540E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 4.04040E-05 4.04040E-05 4.68686E-05 7.90083E-04 2.29388E-04 2.36334E-04 2.21567E-04 2.08621E-04 4.96594E-05 4.82363E-05 1.94075E-05 1.93741E-05 1.92373E-05 1.91829E-05 1.84612E-05 2.62755E-05 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.22000E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +126 2615350 0 0 0 0 0 0 2.04080E-04 1.00000E-03 0 0 0 2.22000E-04 3.37838E-04 2.62302E-04 1.53397E-04 2.89383E-04 2.62200E-04 6.41028E-04 1.52482E-04 1.70830E-04 3.93377E-04 5.55558E-04 3.95848E-04 4.13015E-04 1.82000E-04 1.68970E-04 6.09756E-04 1.53395E-04 2.18358E-05 1.71858E-05 7.90767E-05 1.75832E-05 2.63215E-05 6.20645E-05 0 0 4.27710E-05 2.37646E-04 5.03359E-05 0 7.74480E-05 8.72854E-05 4.84496E-05 2.08515E-04 5.65698E-05 0 4.05683E-05 0 0 0 3.12721E-05 3.89515E-05 4.54545E-05 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 2.22000E-04 2.22000E-04 5.80463E-05 4.22229E-05 2.82993E-05 3.39115E-05 3.48163E-05 3.06247E-05 2.00871E-04 1.35425E-03 0 2.18683E-04 2.59938E-04 0 1.56178E-03 1.91651E-03 1.82882E-03 4.68233E-04 3.81098E-04 0 0 0 0 0 0 0 0 0 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.22000E-03 9.52059E-03 2.05867E-03 2.44872E-03 2.18353E-03 1.77809E-04 3.90625E-04 4.08147E-04 6.86254E-04 5.98986E-04 4.09410E-04 3.14939E-04 2.02528E-04 9.20594E-05 1.04863E-04 2.83880E-04 2.73883E-04 1.53643E-04 3.97293E-04 6.59810E-04 3.20760E-04 7.14289E-04 7.25074E-04 8.35153E-04 8.92861E-04 8.62962E-04 9.44466E-04 5.00000E-04 2.00000E-04 1.00000E-03 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 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8.86712E-04 9.87897E-04 8.62069E-04 8.42276E-04 7.57576E-04 7.70022E-04 7.72844E-04 8.10914E-04 7.87653E-04 8.44598E-04 7.83984E-04 8.76274E-04 7.35294E-04 7.23098E-04 6.95991E-04 7.14286E-04 7.35294E-04 5.09315E-04 9.97277E-04 9.39049E-04 9.52481E-04 4.05449E-04 6.81920E-04 5.72099E-04 8.08000E-04 6.00045E-04 6.66092E-04 3.03579E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 4.20772E-04 5.38286E-04 6.04968E-04 8.50145E-05 3.60000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 3.48204E-05 1.04000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 2.03984E-05 1.96623E-05 1.91197E-05 1.67101E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 0 0 0 0 0 0 0 0 0 0 0 2.12118E-04 1.28883E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 0 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +132 2612350 0 0 0 0 0 2.06954E-04 1.00000E-03 1.00000E-03 1.00000E-03 7.81684E-04 1.00000E-03 1.13384E-03 1.05271E-03 1.00000E-03 9.96940E-04 1.00956E-03 1.00239E-03 1.00000E-03 1.00000E-03 8.62072E-04 9.25930E-04 1.29275E-03 5.86850E-04 4.65780E-04 8.62072E-04 5.63555E-04 1.76764E-04 3.53419E-04 2.55743E-04 2.08526E-04 8.39911E-05 9.20418E-05 1.91489E-05 2.23772E-05 1.97316E-04 0 2.38128E-04 1.15093E-03 3.15142E-05 5.32233E-05 4.81198E-05 3.71287E-05 4.15282E-05 4.09612E-05 3.60418E-05 0 0 2.22000E-04 0 0 0 0 0 2.84289E-05 4.35313E-05 4.01115E-05 4.34783E-05 4.02470E-05 4.01817E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.31145E-03 4.00321E-04 5.13347E-04 3.89530E-04 7.28863E-04 5.28387E-04 4.33746E-04 1.63764E-04 1.81291E-04 2.58398E-04 4.40917E-04 6.91842E-04 4.89886E-04 5.60315E-04 0 0 0 0 3.16284E-04 2.22860E-04 0 0 0 0 0 0 7.21500E-04 7.27948E-04 6.36924E-04 4.25102E-04 1.46194E-03 0 4.73269E-05 4.52464E-05 4.21461E-05 2.84279E-04 2.99756E-04 2.97803E-04 4.55251E-04 1.00839E-03 0 0 0 0 0 0 0 2.84961E-04 5.77101E-04 4.35237E-04 1.51607E-05 3.20021E-05 3.20294E-05 2.71267E-05 2.46646E-05 2.96460E-05 3.37586E-05 3.08414E-05 3.56358E-05 3.76902E-05 1.73207E-05 2.72621E-05 2.89291E-04 1.41957E-04 0 5.22089E-04 1.71882E-04 1.35558E-04 1.31886E-04 1.39138E-04 2.92939E-03 3.28493E-03 3.39306E-03 2.80585E-03 7.81250E-04 4.24881E-04 5.00000E-04 5.00000E-04 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.07645E-03 1.10086E-03 1.05387E-03 8.74432E-04 8.41184E-04 8.76856E-04 8.91850E-04 8.90077E-04 8.06044E-04 8.06452E-04 8.92857E-04 8.86136E-04 8.24484E-04 7.96067E-04 7.70891E-04 7.84631E-04 7.57117E-04 8.28092E-04 7.66535E-04 7.35297E-04 6.81570E-04 7.53053E-04 3.88576E-04 8.16711E-04 8.92857E-04 8.92861E-04 8.56458E-04 6.75994E-04 5.04455E-04 6.03128E-04 8.08000E-04 5.29182E-04 6.81462E-04 2.91262E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.34247E-04 2.58353E-04 2.49503E-04 4.00000E-04 1.01020E-05 4.00000E-05 4.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 2.00000E-05 4.98441E-05 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 4.31200E-05 1.69582E-05 1.37354E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 0 0 0 0 0 0 1.05000E-05 1.05000E-05 1.05000E-05 3.55732E-04 2.68748E-04 9.49674E-05 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 3.00000E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +133 2611850 0 0 0 0 0 1.00000E-03 1.03070E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.10343E-03 1.05990E-03 1.01542E-03 1.03904E-03 1.00000E-03 1.03877E-03 1.03854E-03 1.03017E-03 1.01531E-03 1.03622E-03 1.00000E-03 1.00000E-03 9.36288E-04 8.92861E-04 4.49801E-04 4.38278E-04 1.97604E-04 2.89046E-05 2.47577E-05 1.31502E-04 1.23975E-04 1.14636E-04 1.20018E-04 2.45094E-05 0 0 1.79211E-04 3.40914E-05 2.92362E-05 3.77082E-05 4.18042E-05 5.96993E-05 6.23791E-05 5.63713E-05 4.34783E-05 6.83997E-05 0 0 0 0 0 0 0 2.66904E-05 3.91784E-05 0 9.42588E-05 1.38505E-03 0 0 0 0 0 0 0 2.80995E-05 9.07513E-05 2.11812E-04 0 0 0 0 0 7.80987E-04 4.50680E-04 2.63020E-04 2.04212E-04 3.57245E-04 1.86445E-04 3.80797E-04 3.51519E-04 3.63372E-04 4.17641E-04 2.21730E-04 0 0 0 0 2.36277E-02 0 0 0 3.39644E-04 0 2.62747E-03 0 0 3.97727E-03 5.59866E-03 4.59982E-04 3.78788E-04 3.78215E-04 3.80228E-04 0 0 2.22000E-04 8.25083E-05 0 0 3.02756E-04 2.66100E-04 4.23684E-04 8.23179E-04 0 0 0 0 0 0 0 0 0 2.22000E-04 2.52718E-04 4.21017E-05 3.83200E-05 3.39121E-05 3.21226E-05 2.85213E-05 2.64774E-05 2.69397E-05 2.88754E-05 3.26711E-05 3.71140E-05 4.96899E-05 2.59572E-05 2.79039E-04 0 2.58240E-04 1.99007E-04 2.55994E-05 2.13840E-05 1.89526E-05 2.78163E-03 2.22203E-03 2.77164E-03 2.94118E-03 2.90379E-04 5.94208E-04 4.84872E-04 9.68242E-04 9.10415E-04 9.13075E-04 9.32488E-04 4.82626E-04 5.00000E-04 1.00000E-03 1.00000E-03 1.00000E-03 9.78154E-04 1.03901E-03 9.24430E-04 9.07715E-04 8.00086E-04 8.02243E-04 8.62749E-04 7.39050E-04 8.25459E-04 8.62069E-04 8.81146E-04 7.03955E-04 7.57579E-04 8.09406E-04 7.82110E-04 7.20256E-04 7.96838E-04 7.64817E-04 8.16193E-04 7.01673E-04 6.94444E-04 7.30468E-04 6.94444E-04 7.57579E-04 7.81253E-04 9.33877E-04 8.92857E-04 9.13843E-04 5.02844E-04 4.31035E-04 6.48173E-04 8.08000E-04 5.29001E-04 5.28027E-04 4.63563E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.39872E-04 4.05218E-04 4.04040E-05 4.04040E-05 4.04040E-05 5.14171E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.89290E-05 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.04000E-04 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 1.50000E-05 2.92930E-05 4.97000E-05 4.97000E-05 3.47400E-05 1.23000E-05 1.14000E-05 1.10400E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 0 0 0 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 1.05000E-05 4.73501E-04 4.23121E-04 1.63149E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 0 0 0 0 0 0 0 0 0 2.22000E-04 0 0 0 0 0 2.22000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 0 0 0 0 0 0 0 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 0 0 0 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +134 2611350 0 0 0 0 0 1.00000E-03 1.06564E-03 1.19050E-03 1.00000E-03 1.00000E-03 1.23452E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.03415E-03 1.03795E-03 1.00000E-03 9.69748E-04 9.96636E-04 1.00000E-03 1.07703E-03 1.08714E-03 1.00000E-03 1.00000E-03 7.98445E-04 7.76440E-04 3.79418E-04 2.01735E-04 1.89307E-04 1.22342E-04 1.32571E-04 1.28337E-04 1.62551E-04 3.60323E-04 0 1.06601E-03 3.36589E-05 2.99102E-05 2.48575E-05 1.78912E-05 4.06272E-05 3.27368E-05 4.81313E-05 3.81485E-05 4.04088E-05 5.99295E-05 4.58716E-05 0 0 0 0 0 9.46197E-06 3.24454E-05 3.10185E-05 0 0 0 0 0 0 0 0 0 4.06555E-05 4.98113E-05 5.55033E-05 2.22000E-04 0 0 0 0 2.22000E-04 4.16165E-04 2.99222E-04 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5.00000E-04 5.00000E-04 5.00000E-04 4.04040E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 500 +144 2606350 0 0 0 0 2.53675E-04 1.00000E-03 1.00000E-03 1.00111E-03 1.05194E-03 1.07091E-03 1.00000E-03 1.00000E-03 1.00000E-03 8.89137E-04 8.33337E-04 8.06455E-04 7.35297E-04 6.41939E-04 2.66495E-04 2.58231E-04 2.79942E-04 2.23215E-04 1.98165E-04 9.08651E-05 8.97185E-05 2.22458E-04 2.31177E-04 2.23215E-04 2.69246E-04 3.90048E-04 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2.84924E-04 4.80847E-04 2.02944E-04 1.58730E-04 3.92022E-04 3.14861E-04 3.51187E-04 3.22082E-04 3.33868E-04 1.44011E-04 2.63769E-04 2.65619E-04 2.47592E-04 2.41406E-04 2.55102E-04 2.50311E-04 2.55247E-04 6.25000E-04 6.09756E-04 2.77896E-04 2.31481E-04 3.41155E-04 3.36976E-04 3.21649E-04 3.93283E-04 2.45098E-04 5.68182E-04 6.09756E-04 3.27791E-04 3.50135E-04 1.25496E-04 1.19484E-04 1.13357E-03 2.94697E-04 2.55102E-04 2.58617E-04 2.97952E-04 2.73239E-04 5.81395E-04 5.92336E-04 5.89319E-04 7.10442E-04 7.62301E-04 8.55866E-04 9.26379E-04 8.58709E-04 7.42512E-04 7.31221E-04 8.31002E-04 7.47563E-04 7.36792E-04 7.24763E-04 7.95065E-04 8.29126E-04 8.42721E-04 8.70084E-04 9.05466E-04 3.38243E-04 8.83711E-04 8.66118E-04 1.27351E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 4.04040E-05 1.00000E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 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7.64526E-04 8.48417E-04 1.29978E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 4.04040E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 4.04040E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 4.04040E-05 4.04040E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 3.00000E-05 0 0 500 +145 2605850 0 0 0 0 2.58182E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.29405E-03 1.05255E-03 1.00000E-03 9.75423E-04 8.61238E-04 8.06455E-04 7.81250E-04 7.57579E-04 6.89012E-04 3.05368E-04 2.87497E-04 2.44609E-04 2.50001E-04 2.13655E-04 9.14816E-05 9.70915E-05 1.02751E-04 1.39380E-04 1.05184E-04 2.67538E-04 1.56082E-04 3.64157E-04 7.14286E-04 1.45011E-04 1.48063E-04 1.48602E-04 1.53573E-04 1.46677E-04 1.58582E-04 2.54661E-05 7.57576E-04 1.04809E-03 2.08732E-03 0 0 0 0 0 0 3.91144E-05 0 3.77114E-05 5.04864E-05 3.91645E-05 1.07349E-04 3.01861E-05 0 0 0 3.93427E-05 4.06659E-05 2.30780E-04 2.06429E-04 1.19757E-04 1.46511E-04 6.11537E-04 1.54752E-04 2.97439E-05 2.95986E-05 1.27421E-04 2.37484E-05 2.56403E-05 3.50394E-05 9.84930E-04 0 1.52812E-03 1.00725E-04 1.10675E-04 8.38250E-05 7.70772E-05 8.63409E-05 8.96959E-05 2.32689E-04 9.93237E-05 1.03934E-04 2.14777E-04 8.74201E-05 8.65202E-05 9.46151E-05 2.17135E-05 2.34421E-05 2.78210E-05 3.25380E-05 3.46257E-05 1.04064E-03 0 0 0 0 0 0 0 0 0 0 0 0 0 1.00400E-02 0 0 0 3.63854E-04 3.17765E-04 2.82442E-04 2.87504E-04 2.93330E-04 7.28014E-04 7.79302E-04 3.38539E-04 3.23416E-04 0 0 0 0 0 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1.00000E-03 1.00000E-03 1.00000E-03 1.24282E-03 1.03538E-03 1.00000E-03 1.00000E-03 9.53474E-04 8.65130E-04 8.33337E-04 4.01592E-04 4.72996E-04 6.09756E-04 7.14289E-04 6.41028E-04 2.97620E-04 2.35849E-04 2.02707E-04 9.36083E-05 1.00860E-04 1.18465E-04 2.16059E-05 1.49142E-04 1.29192E-04 1.84879E-04 3.42572E-04 3.56571E-04 3.99413E-04 2.84092E-04 1.15586E-04 1.31704E-04 1.34582E-04 1.20539E-04 3.78789E-04 5.38571E-04 2.59341E-05 1.15523E-03 0 0 0 0 0 0 2.53268E-04 0 4.07753E-05 3.31886E-05 4.38597E-05 0 0 0 0 0 0 0 0 0 2.55919E-04 1.14626E-03 1.88640E-04 3.07549E-05 2.80346E-05 2.12164E-05 1.72974E-04 1.11161E-04 2.91353E-05 1.59090E-04 0 2.08390E-05 2.17001E-05 1.93623E-05 8.22642E-05 7.82353E-05 1.56745E-05 9.94357E-05 2.46695E-04 1.00595E-04 7.97324E-05 1.09577E-04 2.29865E-04 1.07474E-04 9.01672E-05 1.48866E-05 1.67243E-05 1.94320E-05 2.40840E-05 2.71888E-05 5.39675E-04 0 0 0 0 1.91250E-03 1.56757E-03 1.20482E-03 1.08084E-03 2.20261E-03 0 0 0 9.49327E-04 2.58226E-04 3.31899E-04 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2.52828E-05 2.09556E-05 2.25508E-05 1.44217E-04 1.46800E-04 3.54208E-04 1.30927E-04 3.02992E-05 2.02904E-04 5.88321E-04 1.14766E-03 2.12517E-04 4.12836E-04 3.74662E-04 4.29168E-04 1.03729E-03 1.20691E-03 1.31882E-03 0 6.48060E-03 7.76485E-04 8.34924E-04 2.81311E-04 3.66784E-04 1.45603E-04 1.39021E-04 1.15261E-04 1.36935E-04 1.22224E-04 1.00949E-04 1.06090E-04 1.13630E-04 1.18507E-04 1.54986E-04 1.02166E-03 1.78636E-04 2.21275E-04 2.08659E-04 1.65549E-03 0 1.83576E-04 2.66012E-04 2.28043E-04 2.39856E-04 7.48741E-04 1.97762E-04 4.90773E-04 1.41919E-04 3.19122E-04 1.41932E-04 1.04217E-04 2.57230E-04 2.34040E-04 2.81677E-04 2.99013E-04 3.87619E-04 4.59221E-04 5.38687E-04 5.31684E-04 2.65000E-04 3.16696E-04 3.33518E-04 2.57378E-04 0 0 0 0 0 0 4.94396E-05 6.44637E-05 1.53374E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 2.22000E-06 0 0 0 0 0 0 0 0 0 0 0 2.22000E-04 2.22000E-04 2.22000E-04 2.22000E-04 1.52253E-05 6.65034E-05 2.65958E-04 1.41788E-03 1.63650E-04 3.28803E-04 6.31109E-04 5.71540E-04 3.49164E-04 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1.00000E-03 9.05801E-04 8.92861E-04 8.33337E-04 8.33337E-04 8.05675E-04 3.05407E-04 3.42000E-04 3.06881E-04 2.77640E-04 9.35204E-05 8.77230E-05 8.35995E-05 8.17531E-05 8.27544E-05 8.60056E-05 8.96690E-05 2.06854E-04 2.24175E-04 2.74330E-04 7.67106E-04 3.22181E-04 1.13752E-04 2.47910E-05 2.62344E-05 2.44114E-05 1.05601E-04 9.70085E-05 9.01861E-05 7.47834E-05 8.09447E-05 8.75449E-05 8.75033E-05 8.56358E-05 8.21423E-05 8.62938E-05 8.63707E-05 1.05624E-04 1.14776E-04 1.09358E-04 1.09170E-04 1.30793E-04 1.04257E-04 1.77452E-04 1.00847E-03 2.31864E-05 2.43434E-05 3.23507E-05 5.73574E-04 0 0 0 0 0 0 2.10699E-04 4.43566E-05 4.34091E-05 4.70889E-03 3.41721E-03 3.54484E-03 2.22000E-04 2.22000E-04 0 0 2.13817E-03 1.31514E-03 1.12330E-03 8.77457E-04 1.44258E-04 2.50535E-05 1.34026E-04 1.23235E-04 1.00990E-04 1.06270E-04 1.06724E-04 1.33524E-04 1.53832E-04 2.76182E-04 3.40507E-04 1.81100E-04 1.75798E-04 1.88893E-04 1.89355E-04 3.23750E-04 2.96842E-04 1.07550E-04 9.31099E-05 9.56027E-05 8.59107E-05 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1.12581E-04 1.00140E-04 1.17682E-04 1.36613E-04 4.28523E-04 2.26347E-04 2.88399E-04 6.86164E-04 0 6.54345E-04 0 0 0 0 0 0 7.73380E-04 1.73235E-04 1.57190E-04 4.56621E-04 3.95036E-04 2.91463E-05 2.68305E-05 2.24807E-05 1.12018E-04 1.00980E-04 1.09041E-04 8.74588E-05 1.06882E-04 1.04515E-04 1.71640E-04 5.56691E-04 2.02328E-03 1.99471E-04 1.63349E-04 3.04507E-04 1.21463E-04 1.17426E-04 2.87753E-04 1.13834E-04 2.67494E-04 3.12012E-04 7.69941E-04 9.85416E-04 7.56522E-04 3.88158E-04 3.02947E-04 3.39211E-04 3.00844E-04 3.24574E-04 3.30211E-04 3.10361E-04 3.77415E-04 3.03619E-04 2.57573E-04 2.56891E-04 2.93015E-04 3.03324E-04 3.22831E-04 6.53595E-04 3.12344E-04 2.91647E-04 2.94533E-04 3.21992E-04 3.17824E-04 6.40697E-04 3.52451E-04 3.32575E-04 3.37384E-04 3.54376E-04 3.10939E-04 6.22200E-04 8.97244E-04 9.55699E-04 7.98414E-04 7.36547E-03 7.80934E-03 1.00052E-02 8.96007E-03 5.16787E-03 4.80835E-03 3.70702E-03 7.70891E-03 5.31765E-03 4.89179E-03 1.13880E-02 7.33516E-03 3.51476E-03 3.75925E-03 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8.23230E-05 9.67900E-05 9.15896E-05 6.00180E-05 3.57649E-04 3.34448E-04 2.05678E-04 2.79218E-04 4.10237E-04 2.33919E-04 0 2.92115E-04 4.80031E-04 3.36815E-04 4.22813E-04 8.07755E-04 8.30203E-04 6.83089E-04 1.28868E-03 6.80208E-04 5.34438E-04 4.89734E-04 1.10132E-03 3.12500E-04 4.76189E-03 2.08333E-03 1.92234E-04 2.04165E-04 2.72035E-04 1.42045E-03 3.39339E-04 1.96323E-04 2.52958E-04 2.60000E-04 2.72556E-04 2.85603E-04 2.92059E-04 2.08972E-04 0 0 2.01421E-04 1.84400E-04 3.56000E-04 3.61344E-04 4.15581E-04 3.62063E-04 4.77555E-04 4.23370E-04 3.49406E-04 2.83608E-04 2.50878E-04 2.28833E-04 2.03087E-04 2.11506E-04 1.15048E-03 2.16732E-04 2.61917E-04 8.33333E-05 5.97515E-05 2.73373E-04 4.59407E-04 6.65247E-05 2.38777E-04 1.15580E-03 1.20366E-03 1.09987E-03 1.09938E-03 9.91277E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +179 2588850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.14762E-04 3.89063E-05 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6.47476E-05 6.59095E-05 2.85829E-04 6.69726E-04 6.54751E-04 6.48613E-04 2.51439E-03 3.05859E-03 3.12847E-03 3.98021E-04 3.42223E-04 9.24251E-04 3.87921E-04 7.36092E-05 2.16409E-05 3.20455E-04 1.11572E-04 9.78890E-05 9.74357E-05 1.84640E-05 1.21943E-04 1.25004E-04 1.91589E-05 1.14158E-04 4.68251E-04 3.92769E-04 1.20327E-04 1.11074E-04 1.82750E-05 2.13715E-05 2.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 500 +265 2545850 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.46000E-03 3.46400E-03 3.20779E-03 3.49199E-03 0 0 0 0 0 0 0 0 0 3.47200E-03 3.62000E-03 3.46000E-03 3.34400E-03 0 3.58800E-03 3.52598E-03 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +156 2615650 0 9.53107E-04 9.53835E-04 9.48047E-04 9.37031E-04 9.24898E-04 9.37346E-04 9.28173E-04 9.53044E-04 9.81161E-04 1.00267E-03 9.94730E-04 1.00030E-03 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.04400E-04 8.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +162 2615050 0 9.82579E-04 9.46136E-04 9.24262E-04 9.14792E-04 8.99433E-04 9.05637E-04 9.33821E-04 9.61756E-04 9.78127E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +166 2614650 0 8.43360E-04 8.34427E-04 8.33388E-04 8.40977E-04 8.55208E-04 8.52734E-04 8.69482E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.15664E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +169 2614350 0 8.36629E-04 8.34313E-04 8.31244E-04 8.28452E-04 8.23922E-04 7.56273E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.65062E-04 7.55940E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +172 2614050 0 4.05824E-04 4.04293E-04 4.02647E-04 4.01516E-04 3.99520E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.90440E-04 7.86390E-04 7.89086E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +173 2613950 0 4.04420E-04 4.03026E-04 4.01390E-04 3.99893E-04 3.97913E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.20000E-04 7.82826E-04 7.81276E-04 7.78194E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +174 2613850 0 4.03152E-04 4.01641E-04 4.00017E-04 3.98653E-04 4.01892E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.22468E-04 7.89312E-04 7.74268E-04 7.72536E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +175 2613750 0 4.02144E-04 4.00515E-04 3.98777E-04 3.97053E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.11292E-04 7.72968E-04 7.68882E-04 7.66748E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +177 2613550 0 4.00773E-04 3.99530E-04 3.97312E-04 3.93789E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 7.94766E-04 7.94766E-04 7.50066E-04 7.29794E-04 7.58068E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +178 2613450 0 3.99902E-04 3.98541E-04 3.95118E-04 3.92948E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.76468E-04 7.52936E-04 7.29794E-04 7.29794E-04 7.10590E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +179 2613350 0 3.99282E-04 3.97557E-04 3.94392E-04 3.92231E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.76468E-04 7.52936E-04 7.10590E-04 7.10590E-04 7.10590E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +180 2613250 0 3.98665E-04 3.97067E-04 3.93789E-04 3.91634E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.76468E-04 7.52936E-04 7.10590E-04 7.10590E-04 7.10590E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +182 2613050 0 4.14456E-04 4.09702E-04 4.04793E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.39523E-04 3.30588E-04 6.61176E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.69394E-04 7.22724E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +184 2612850 0 4.12950E-04 4.12677E-04 4.09031E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.58924E-04 3.48954E-04 6.79046E-04 6.69394E-04 6.69394E-04 6.69394E-04 6.52230E-04 6.52230E-04 6.85662E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +186 2612650 0 4.15835E-04 4.31035E-04 4.09433E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.80677E-04 3.58924E-04 6.97908E-04 6.87486E-04 6.69394E-04 6.69394E-04 6.54580E-04 6.69394E-04 7.03706E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +188 2612450 0 4.65152E-04 4.65002E-04 4.66356E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.90625E-04 3.78788E-04 3.67647E-04 7.14286E-04 6.97894E-04 6.85188E-04 6.74376E-04 6.64600E-04 6.56468E-04 6.63968E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +190 2612250 0 4.66810E-04 4.62464E-04 4.64402E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.69604E-04 3.78788E-04 3.67647E-04 7.35294E-04 7.41614E-04 7.00414E-04 6.70966E-04 6.81484E-04 6.54932E-04 6.32350E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +192 2612050 0 4.41495E-04 4.40300E-04 4.37046E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.30580E-04 4.07957E-04 4.01049E-04 7.94474E-04 7.52210E-04 7.44386E-04 7.36724E-04 7.28838E-04 7.21492E-04 6.74010E-04 6.68536E-04 6.62150E-04 6.57030E-04 6.55068E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +194 2611850 0 4.64673E-04 4.38519E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.07022E-04 4.01162E-04 3.96240E-04 7.84386E-04 7.43594E-04 3.68849E-04 3.65948E-04 3.62997E-04 3.59816E-04 3.36488E-04 3.34607E-04 3.31241E-04 3.28024E-04 3.24868E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +196 2611650 0 4.53498E-04 4.44815E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.00596E-04 3.95358E-04 3.90898E-04 3.87170E-04 3.67780E-04 3.66236E-04 3.64609E-04 3.62432E-04 3.59445E-04 3.36574E-04 3.33253E-04 3.29996E-04 3.26803E-04 3.24069E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.41265E-04 9.61169E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +198 2611450 0 4.43420E-04 4.33727E-04 4.23873E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.57143E-04 3.47222E-04 3.47222E-04 3.37838E-04 3.37838E-04 3.37838E-04 3.28947E-04 3.47222E-04 3.32624E-04 3.28861E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.59693E-04 9.83478E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.79624E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +200 2611250 0 4.51753E-04 4.40141E-04 4.28816E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 3.90625E-04 3.67647E-04 3.57143E-04 3.58577E-04 3.57143E-04 3.47222E-04 3.37838E-04 3.37838E-04 3.37838E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +201 2611150 0 4.56038E-04 4.43577E-04 4.30886E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 4.03226E-04 3.90625E-04 3.78788E-04 3.59816E-04 3.67647E-04 3.57143E-04 3.47222E-04 3.47222E-04 3.37838E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +203 2610950 0 5.04603E-04 4.86957E-04 4.71181E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 4.03226E-04 3.90625E-04 4.03226E-04 4.16667E-04 3.62950E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +205 2610750 0 5.01702E-04 4.84434E-04 4.69322E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 3.90625E-04 4.03226E-04 3.90625E-04 3.57143E-04 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1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +208 2610450 0 4.82161E-04 4.70295E-04 4.58837E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +210 2610250 0 4.77028E-04 4.66078E-04 4.54822E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 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2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 1.00000E-03 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +213 2609950 0 5.23705E-04 5.13748E-04 5.03079E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +214 2609850 0 5.19062E-04 5.10389E-04 5.00748E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +215 2609750 0 5.15066E-04 5.07440E-04 4.98616E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +216 2609650 0 5.11503E-04 5.04705E-04 4.97029E-04 4.88561E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 5.00000E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +217 2609550 0 9.22786E-04 9.13894E-04 9.03256E-04 8.90689E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +218 2609450 0 9.10968E-04 9.07095E-04 9.01031E-04 8.92240E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.84640E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +219 2609350 0 8.99133E-04 9.00397E-04 9.00081E-04 8.95987E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.94036E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +221 2609150 0 8.73671E-04 8.89451E-04 9.03893E-04 9.13568E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +222 2609050 0 8.60047E-04 8.80305E-04 9.01540E-04 9.20812E-04 9.30249E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +223 2608950 0 8.56569E-04 8.77569E-04 9.03142E-04 9.31613E-04 9.59396E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +224 2608850 0 8.48278E-04 8.69761E-04 8.96135E-04 9.25843E-04 9.53636E-04 9.32713E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +225 2608750 0 8.35176E-04 8.56569E-04 8.81221E-04 9.04749E-04 9.20478E-04 9.02485E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +226 2608650 0 8.18499E-04 8.40980E-04 8.63261E-04 8.82138E-04 8.91733E-04 8.76294E-04 8.62126E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +227 2608550 0 8.20210E-04 8.41184E-04 8.61178E-04 8.76885E-04 8.85583E-04 8.54409E-04 8.44595E-04 8.31947E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +228 2608450 0 8.13802E-04 8.29738E-04 8.46024E-04 8.58516E-04 8.65951E-04 8.37521E-04 8.31947E-04 8.22639E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.08000E-04 8.09323E-04 9.14807E-04 9.14807E-04 9.18173E-04 9.24981E-04 9.29052E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.72006E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.04400E-04 9.57854E-04 9.83091E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +230 2608250 0 7.96178E-04 8.06712E-04 8.16994E-04 8.33333E-04 8.33611E-04 8.09586E-04 8.07233E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 7.96432E-04 8.08000E-04 8.08000E-04 9.15417E-04 9.34430E-04 9.52593E-04 9.66686E-04 9.74927E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.42330E-04 9.65251E-04 9.87752E-04 1.01020E-05 9.85804E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +231 2608150 0 7.83945E-04 7.91640E-04 8.06452E-04 8.33333E-04 8.18063E-04 7.95418E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.06452E-04 7.83945E-04 7.98722E-04 8.17261E-04 9.20637E-04 9.48314E-04 9.75620E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 9.25583E-04 9.46611E-04 9.67492E-04 9.86193E-04 9.64506E-04 9.76944E-04 9.96810E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 9.94827E-04 9.96413E-04 9.98004E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +232 2608050 0 7.64526E-04 7.94660E-04 8.06452E-04 8.06452E-04 8.06452E-04 8.77477E-04 9.06727E-04 9.06727E-04 9.06727E-04 9.06727E-04 8.49984E-04 8.49984E-04 8.49984E-04 8.27300E-04 8.49162E-04 9.14995E-04 9.54100E-04 9.92733E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 2.04400E-04 7.67076E-04 7.01289E-04 7.25942E-04 7.56080E-04 7.29606E-04 7.18053E-04 7.69708E-04 7.78003E-04 7.87488E-04 7.97984E-04 8.09832E-04 8.11162E-04 8.25355E-04 8.40619E-04 8.52951E-04 8.70474E-04 9.65466E-04 9.86515E-04 1.00775E-03 1.02915E-03 1.04903E-03 9.82318E-04 9.96016E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 9.82318E-04 9.86193E-04 9.91277E-04 9.97606E-04 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 1.01020E-05 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 5.00000E-04 1.01020E-05 1.01020E-05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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3.17689E-04 3.15986E-04 3.14813E-04 2.99910E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +299 2601350 0 5.14028E-04 5.17272E-04 5.19812E-04 5.64631E-04 5.90893E-04 2.97620E-04 2.97620E-04 2.90699E-04 2.77779E-04 2.71740E-04 2.61016E-04 2.77779E-04 2.90699E-04 2.97620E-04 2.90699E-04 2.97619E-04 2.97619E-04 2.97619E-04 2.97619E-04 2.97619E-04 6.09756E-04 6.25000E-04 6.41026E-04 6.41026E-04 6.41026E-04 6.09756E-04 5.81395E-04 5.58160E-04 5.52975E-04 5.45137E-04 2.71739E-04 2.62936E-04 2.77778E-04 2.84091E-04 2.90698E-04 2.70846E-04 2.26926E-04 2.19382E-04 2.13055E-04 2.65318E-04 3.06553E-04 3.00167E-04 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3.16654E-04 3.17213E-04 3.18692E-04 3.21187E-04 2.79165E-04 2.82220E-04 2.85535E-04 2.88798E-04 2.91597E-04 3.07188E-04 3.07616E-04 3.07903E-04 3.07473E-04 3.06831E-04 3.13794E-04 3.13069E-04 3.12205E-04 3.11774E-04 3.12709E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +301 2601150 0 5.00264E-04 5.01746E-04 5.18539E-04 5.18539E-04 5.40604E-04 2.65958E-04 2.71740E-04 2.77779E-04 2.71740E-04 2.55677E-04 2.60418E-04 2.84092E-04 2.84092E-04 2.84092E-04 2.84092E-04 2.84091E-04 2.84091E-04 2.84091E-04 2.84091E-04 2.84091E-04 5.81395E-04 5.95238E-04 6.09756E-04 6.09756E-04 5.95238E-04 5.95238E-04 5.81395E-04 5.81395E-04 5.68182E-04 5.55556E-04 2.90698E-04 2.90698E-04 2.90698E-04 2.84091E-04 2.71739E-04 2.82622E-04 2.88902E-04 2.88902E-04 2.82622E-04 2.82622E-04 3.20178E-04 3.27455E-04 3.27455E-04 3.35070E-04 3.35070E-04 5.35545E-04 5.48607E-04 5.48607E-04 5.48607E-04 5.62323E-04 3.12630E-04 2.97614E-04 2.93612E-04 2.91434E-04 2.90388E-04 2.87980E-04 2.88405E-04 2.89260E-04 2.90799E-04 3.03594E-04 2.84091E-04 2.73643E-04 2.76916E-04 2.77778E-04 2.81913E-04 5.68182E-04 5.59159E-04 5.62050E-04 5.63317E-04 5.61924E-04 3.45987E-04 3.42925E-04 3.39544E-04 3.36376E-04 3.34056E-04 3.07632E-04 3.06909E-04 3.07105E-04 3.08624E-04 3.11165E-04 2.70791E-04 2.73961E-04 2.77145E-04 2.80342E-04 2.83295E-04 2.98859E-04 3.00693E-04 3.01173E-04 3.01791E-04 3.01929E-04 3.09639E-04 3.09639E-04 3.09710E-04 3.10988E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.31064E-04 2.39435E-04 2.39435E-04 2.32775E-04 2.50000E-04 2.45098E-04 2.41080E-04 2.44379E-04 2.46332E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +312 2600050 0 4.55208E-04 4.48029E-04 4.42243E-04 4.37369E-04 4.34028E-04 2.19916E-04 2.19376E-04 2.19723E-04 2.20188E-04 2.20732E-04 4.64768E-04 4.64683E-04 4.62648E-04 4.61553E-04 4.59878E-04 2.45573E-04 2.45573E-04 2.45573E-04 2.38028E-04 2.38245E-04 2.60020E-04 2.61668E-04 2.64204E-04 2.66113E-04 2.67681E-04 2.56001E-04 2.56457E-04 2.57322E-04 2.58502E-04 2.60057E-04 2.56726E-04 2.58211E-04 2.59121E-04 2.59929E-04 2.61561E-04 2.57414E-04 2.59390E-04 2.77778E-04 2.84091E-04 2.84091E-04 5.70168E-04 5.70168E-04 5.54663E-04 5.59862E-04 5.97319E-04 3.04878E-04 2.90698E-04 2.77778E-04 2.65957E-04 2.60417E-04 2.67373E-04 2.57283E-04 2.52519E-04 2.62231E-04 2.67373E-04 2.45098E-04 2.50000E-04 2.50000E-04 2.50000E-04 2.50000E-04 2.55102E-04 2.60417E-04 2.60417E-04 2.60417E-04 2.55102E-04 5.52086E-04 5.22285E-04 5.21222E-04 5.19683E-04 5.19108E-04 2.61373E-04 2.62131E-04 2.62894E-04 2.61846E-04 2.62847E-04 2.40910E-04 2.42234E-04 2.43707E-04 2.45472E-04 2.47262E-04 2.58180E-04 2.63145E-04 2.58668E-04 2.62590E-04 2.66321E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.46157E-04 2.52231E-04 2.55005E-04 2.57694E-04 2.60838E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +314 2599850 0 4.40684E-04 4.40063E-04 4.37752E-04 4.34631E-04 4.32077E-04 2.19069E-04 2.18571E-04 2.23215E-04 2.23215E-04 2.23215E-04 4.56813E-04 4.55581E-04 4.53544E-04 4.51846E-04 4.49603E-04 2.41025E-04 2.36643E-04 2.36643E-04 2.30564E-04 2.32417E-04 2.47775E-04 2.55199E-04 2.58835E-04 2.60816E-04 2.61066E-04 2.49932E-04 2.50318E-04 2.50173E-04 2.50996E-04 2.59329E-04 2.55102E-04 2.71739E-04 2.71739E-04 2.65957E-04 2.71739E-04 2.71739E-04 2.77778E-04 2.71739E-04 2.65957E-04 2.65957E-04 5.45378E-04 5.45378E-04 5.33888E-04 5.36744E-04 5.39166E-04 2.67895E-04 2.66354E-04 2.64326E-04 2.60417E-04 2.50000E-04 2.47927E-04 2.67373E-04 2.72720E-04 2.67373E-04 2.67373E-04 2.40385E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.50000E-04 2.50000E-04 2.50000E-04 5.41469E-04 5.21415E-04 5.04595E-04 5.02255E-04 5.01807E-04 2.53799E-04 2.55233E-04 2.56775E-04 2.58567E-04 2.60103E-04 2.39774E-04 2.41526E-04 2.43125E-04 2.49501E-04 2.45380E-04 2.51628E-04 2.54058E-04 2.56631E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +315 2599750 0 4.34254E-04 4.34556E-04 4.33501E-04 4.31481E-04 4.29775E-04 2.18037E-04 2.17506E-04 2.23215E-04 2.23215E-04 2.23215E-04 4.52977E-04 4.51524E-04 4.49284E-04 4.47302E-04 4.52734E-04 2.36643E-04 2.29467E-04 2.28540E-04 2.32417E-04 2.32417E-04 2.47775E-04 2.43351E-04 2.56980E-04 2.58982E-04 2.57953E-04 2.44742E-04 2.49355E-04 2.44789E-04 2.54244E-04 2.59329E-04 2.60417E-04 2.60417E-04 2.65957E-04 2.65957E-04 2.71739E-04 2.71739E-04 2.71739E-04 2.65957E-04 2.60417E-04 2.56148E-04 5.33774E-04 5.33774E-04 5.24622E-04 5.45378E-04 5.45378E-04 2.71739E-04 2.71739E-04 2.71739E-04 2.55102E-04 2.50000E-04 2.52519E-04 2.57137E-04 2.54736E-04 2.67373E-04 2.67373E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.45098E-04 2.50000E-04 5.41469E-04 5.21415E-04 4.96410E-04 4.93193E-04 4.93280E-04 2.50282E-04 2.52248E-04 2.54335E-04 2.56547E-04 2.58521E-04 2.38520E-04 2.40559E-04 2.42723E-04 2.44790E-04 2.46386E-04 2.52511E-04 2.54672E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +316 2599650 0 4.28376E-04 4.29111E-04 4.28964E-04 4.27643E-04 4.26694E-04 2.16414E-04 2.16376E-04 2.15667E-04 2.23215E-04 2.23215E-04 4.60965E-04 4.47935E-04 4.45416E-04 4.52734E-04 4.40694E-04 2.28821E-04 2.32417E-04 2.32417E-04 2.32417E-04 2.32417E-04 2.44925E-04 2.43351E-04 2.52224E-04 2.54010E-04 2.53396E-04 2.40565E-04 2.49355E-04 2.54244E-04 2.54244E-04 2.59329E-04 2.60417E-04 2.65957E-04 2.65957E-04 2.65957E-04 2.71739E-04 2.71739E-04 2.65957E-04 2.60417E-04 2.55102E-04 2.51915E-04 5.22654E-04 5.13350E-04 5.15036E-04 5.45378E-04 5.45378E-04 2.71739E-04 2.65957E-04 2.60417E-04 2.50000E-04 2.40385E-04 2.62231E-04 2.53646E-04 2.57283E-04 2.67373E-04 2.67373E-04 2.35849E-04 2.40385E-04 2.40385E-04 2.35849E-04 2.35849E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.35849E-04 5.03963E-04 5.11935E-04 4.85455E-04 4.77227E-04 4.85204E-04 2.47284E-04 2.49936E-04 2.52513E-04 2.54873E-04 2.57461E-04 2.37834E-04 2.39992E-04 2.42190E-04 2.44338E-04 2.46157E-04 2.52417E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +317 2599550 0 2.24587E-04 2.25257E-04 2.25415E-04 2.25257E-04 2.24941E-04 2.26861E-04 2.26041E-04 2.31482E-04 2.31482E-04 2.31482E-04 4.80771E-04 4.63051E-04 4.71700E-04 4.71700E-04 4.62965E-04 2.42631E-04 2.40551E-04 2.44204E-04 2.44204E-04 2.44204E-04 2.36783E-04 2.32959E-04 2.35186E-04 2.36738E-04 2.36872E-04 2.50000E-04 2.55102E-04 2.60417E-04 2.65957E-04 2.65957E-04 2.60417E-04 2.71739E-04 2.71739E-04 2.71739E-04 2.71739E-04 2.77778E-04 2.71739E-04 2.65957E-04 2.56726E-04 2.58478E-04 2.60417E-04 2.55624E-04 2.65957E-04 2.77778E-04 2.77778E-04 2.77778E-04 2.71739E-04 2.55102E-04 2.50000E-04 2.43712E-04 2.36161E-04 2.34346E-04 2.33122E-04 2.45098E-04 2.50000E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.40385E-04 2.35849E-04 4.57235E-04 4.82471E-04 4.82471E-04 4.82471E-04 4.82471E-04 4.81011E-04 4.72572E-04 4.56553E-04 4.56553E-04 4.59747E-04 4.73493E-04 4.79122E-04 4.85058E-04 4.90792E-04 4.96482E-04 2.25915E-04 2.28046E-04 2.30133E-04 2.32258E-04 2.34336E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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2.67942E-04 2.64603E-04 2.61489E-04 2.58446E-04 2.62535E-04 2.50563E-04 2.50563E-04 2.50563E-04 2.50563E-04 2.50563E-04 2.64746E-04 2.69560E-04 2.64746E-04 2.64746E-04 2.64746E-04 4.98225E-04 4.91013E-04 4.94975E-04 4.99780E-04 5.05121E-04 2.24814E-04 2.28036E-04 2.31642E-04 2.35621E-04 2.47901E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +324 2598850 0 2.19299E-04 2.23215E-04 2.23215E-04 2.23215E-04 2.15741E-04 2.41828E-04 2.42809E-04 2.43282E-04 2.44189E-04 2.47439E-04 2.64562E-04 2.67108E-04 2.63871E-04 2.57021E-04 2.51050E-04 2.47654E-04 2.47654E-04 2.47654E-04 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3.43122E-04 3.45288E-04 3.51355E-04 3.59996E-04 3.63547E-04 3.66811E-04 3.67960E-04 3.70426E-04 3.58442E-04 3.57541E-04 3.57333E-04 3.54865E-04 3.51893E-04 3.36111E-04 3.28987E-04 3.22393E-04 3.14261E-04 3.07810E-04 2.83926E-04 2.78698E-04 2.75124E-04 2.73394E-04 2.72953E-04 2.72753E-04 2.68424E-04 2.68424E-04 2.68424E-04 2.77225E-04 2.62643E-04 2.70268E-04 2.74225E-04 2.78156E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 +330 2598250 0 1.15743E-04 1.16411E-04 1.17068E-04 1.17556E-04 1.18663E-04 1.21390E-04 1.22345E-04 1.22850E-04 1.23316E-04 1.23146E-04 2.97168E-04 2.96050E-04 2.98192E-04 3.00523E-04 3.01308E-04 3.10379E-04 3.08609E-04 3.06017E-04 3.02134E-04 2.98099E-04 3.13441E-04 3.16697E-04 3.14813E-04 3.24585E-04 3.48619E-04 6.17492E-04 3.38373E-04 3.32451E-04 3.25949E-04 3.20800E-04 3.20674E-04 3.17886E-04 3.18168E-04 3.19473E-04 3.24096E-04 3.35932E-04 3.42679E-04 3.44138E-04 3.46639E-04 3.51953E-04 3.61450E-04 3.65456E-04 3.68393E-04 3.69915E-04 3.72555E-04 3.60117E-04 3.60608E-04 3.59977E-04 3.58025E-04 3.54865E-04 3.38491E-04 3.31576E-04 3.24402E-04 3.17191E-04 3.09425E-04 2.83926E-04 2.77875E-04 2.73570E-04 2.69054E-04 2.69054E-04 2.64271E-04 2.68424E-04 2.68424E-04 2.68424E-04 2.72753E-04 2.62728E-04 2.66687E-04 2.68466E-04 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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+ ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import package\n", + "import gridmarthe as gm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic features\n", + "\n", + "\n", + "Marthe Grids contain metadata, including the time step index,\n", + "but not the actual dates. Therefore, it is useful to provide the reading function\n", + "either with a series of dates (to be constructed manually) or a timestep file so that the dates are\n", + "read automatically.\n", + "\n", + "Additionally, several arguments can be useful for reading (removal of null values, \n", + "transformation of xy, addition of mesh indicator or column/row numbers, etc.).\n", + "\n", + "To check for possible options, you can display the function's help.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function load_marthe_grid in module gridmarthe.gridmarthe:\n", + "\n", + "load_marthe_grid(filename, varname=None, dates=None, fpastp=None, nanval=None, dropna=False, xyfactor=1, keepligcol=False, add_id_grid=False, title=None, var_attrs={}, model_attrs={'resolution_units': 'm', 'projection': 'epsg:27572', 'domain': 'FR-France'}, verbose=True)\n", + " LECSEM python wrapper\n", + " Fortran modules from marthe src wrapped for python module\n", + " \n", + " memo : file.out read as seq => all layer then nested grid and all layers too. And so on for every timestep.\n", + " coords with no value read as 1e+20\n", + " zvar read as a single 1D array for every timestep\n", + " \n", + " Parameters\n", + " ----------\n", + " filename (str): A path to marthegrid file (*.permh, *.out, etc.)\n", + " varname (str): variable to access in martgrid file, e.g `CHARGE` for groundwater head. See marthegrid file content.\n", + " if None is passed (default), function will scan all varnames in filename and keep first only\n", + " if 'all' is passed, function will scan all varnames in filename and keep all. All datavars are added to dataset, using recursive call to func\n", + " if wrong variable name is passed, empty data will be returned.\n", + " dates (sequence, Optionnal): Can be a pd.date_range, pd.Series, pd.DatetimeIndex, np.array or list of datetime/np.datetime objects.\n", + " If no dates (or no fpastp) is provided, a fake sequence of dates from 1850 to 1900 will be used for xarray object\n", + " fpastp (str, Optionnal) : A pastp file to read for dates\n", + " nanval (float, Optionnal) : A code value for nan values. Default is 9999.\n", + " dropna (bool, Optionnal) : Drop nan values (corresponding to nanval) in xarray object to return, default is False (keep nan values).\n", + " xyfactor (int or float, Optionnal): factor to transform X and Y values. e.g.: 1000 to convert km XY to meters. Default is 1.\n", + " keepligcol (bool, Optionnal): Add columns (col) and rows (lig) index (from 1 to n), Default is False.\n", + " add_id_grid (bool, Optionnal): Add grid id (from 0 to n), useful for nested grids. 0 is main grid, Default is False\n", + " title (str , Optionnal): Title for grid attributes. Default is None (not used)\n", + " var_attrs (dict, Optionnal): Dictionnary of attributes to add to variable DataArray.\n", + " model_attrs (dict, Optionnal): Dictionnary of attributes to add to Dataset.\n", + " by default, gis attrs are added and can be modified\n", + " 'resolution_units': 'm',\n", + " 'projection' : 'epsg:27572',\n", + " 'domain' : 'FR-France',\n", + " verbose (bool, Optionnal): Print some information about execution in stdout. Default is False.\n", + " \n", + " Returns\n", + " -------\n", + " ds (xr.Dataset): a xarray.Dataset object containing values and attributes read from Marthe grid file.\n", + "\n" + ] + } + ], + "source": [ + "help(gm.load_marthe_grid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# reading data and storing into Dataset object (with xarray)\n", + "ds = gm.load_marthe_grid(\n", + " './data/chasim_hallue.out', 'CHARGE',\n", + " fpastp='./data/hallue.pastp',\n", + " drop_nan=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.Dataset> Size: 784kB\n",
+       "Dimensions:  (time: 205, zone: 927)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
+       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
+       "Data variables:\n",
+       "    charge   (time, zone) float32 760kB 100.0 100.6 101.1 ... 27.0 26.0 26.35\n",
+       "    x        (zone) float32 4kB 617.8 618.2 618.8 619.2 ... 606.8 607.2 607.8\n",
+       "    y        (zone) float32 4kB 2.567e+03 2.567e+03 ... 2.543e+03 2.543e+03\n",
+       "    dx       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
+       "    dy       (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
+       "Attributes: (12/16)\n",
+       "    conventions:          CF-1.10\n",
+       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
+       "    marthe_grid_version:  9.0\n",
+       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
+       "    lon_resolution:       0.5\n",
+       "    lat_resolution:       0.5\n",
+       "    ...                   ...\n",
+       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
+       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
+       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
+       "    resolution_units:     m\n",
+       "    projection:           epsg:27572\n",
+       "    domain:               FR-France
    " + ], + "text/plain": [ + " Size: 784kB\n", + "Dimensions: (time: 205, zone: 927)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n", + " * zone (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n", + "Data variables:\n", + " charge (time, zone) float32 760kB 100.0 100.6 101.1 ... 27.0 26.0 26.35\n", + " x (zone) float32 4kB 617.8 618.2 618.8 619.2 ... 606.8 607.2 607.8\n", + " y (zone) float32 4kB 2.567e+03 2.567e+03 ... 2.543e+03 2.543e+03\n", + " dx (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n", + " dy (zone) float32 4kB 0.5 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n", + "Attributes: (12/16)\n", + " conventions: CF-1.10\n", + " title: Modélisation du bassin de la SOMME Nappe_Libre\n", + " marthe_grid_version: 9.0\n", + " original_dimensions: x,y,z [grids]: 53 54 1\n", + " lon_resolution: 0.5\n", + " lat_resolution: 0.5\n", + " ... ...\n", + " creation_date: Created on 2024-06-18T20:44:48Z UTC\n", + " institution: BRGM, French Geological Survey, Orléans, France\n", + " comment: Hydrogeological model created with MARTHE code (Thi...\n", + " resolution_units: m\n", + " projection: epsg:27572\n", + " domain: FR-France" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# display values and attributes\n", + "print(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The grid is loaded into a 2D xarray object (reduced horizontal grid), with dimensions (time, zone).\n", + "The spatial dimension is thus 1D, contained in `zone`. Each zone has a pair of xy coordinates, stored as a variable (not as a dimension).\n", + "This format allows many operations to be performed more efficiently than in 2 or 3D + time.\n", + "You just need to assign the coordinates (transform to 2 or 3D + time) before using graphical features.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (y: 48, x: 44, time: 205)\n",
+       "Coordinates:\n",
+       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
+       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
+       "  * time     (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n",
+       "Data variables:\n",
+       "    charge   (time, y, x) float32 2MB nan nan nan nan ... 102.7 103.4 103.5\n",
+       "    dx       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
+       "    dy       (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n",
+       "Attributes: (12/16)\n",
+       "    conventions:          CF-1.10\n",
+       "    title:                Modélisation du bassin de la SOMME Nappe_Libre\n",
+       "    marthe_grid_version:  9.0\n",
+       "    original_dimensions:  x,y,z [grids]: 53 54 1\n",
+       "    lon_resolution:       0.5\n",
+       "    lat_resolution:       0.5\n",
+       "    ...                   ...\n",
+       "    creation_date:        Created on 2024-06-18T20:44:48Z UTC\n",
+       "    institution:          BRGM, French Geological Survey, Orléans, France\n",
+       "    comment:              Hydrogeological model created with MARTHE code (Thi...\n",
+       "    resolution_units:     m\n",
+       "    projection:           epsg:27572\n",
+       "    domain:               FR-France
    " + ], + "text/plain": [ + " Size: 2MB\n", + "Dimensions: (y: 48, x: 44, time: 205)\n", + "Coordinates:\n", + " * y (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n", + " * x (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n", + " * time (time) datetime64[ns] 2kB 1995-07-31 1995-08-01 ... 2012-07-01\n", + "Data variables:\n", + " charge (time, y, x) float32 2MB nan nan nan nan ... 102.7 103.4 103.5\n", + " dx (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n", + " dy (y, x) float32 8kB nan nan nan nan nan nan ... 0.5 0.5 0.5 0.5 0.5\n", + "Attributes: (12/16)\n", + " conventions: CF-1.10\n", + " title: Modélisation du bassin de la SOMME Nappe_Libre\n", + " marthe_grid_version: 9.0\n", + " original_dimensions: x,y,z [grids]: 53 54 1\n", + " lon_resolution: 0.5\n", + " lat_resolution: 0.5\n", + " ... ...\n", + " creation_date: Created on 2024-06-18T20:44:48Z UTC\n", + " institution: BRGM, French Geological Survey, Orléans, France\n", + " comment: Hydrogeological model created with MARTHE code (Thi...\n", + " resolution_units: m\n", + " projection: epsg:27572\n", + " domain: FR-France" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add coordinates to transform the 2D Dataset into 3D\n", + "ds_3d = gm.assign_coords(ds)\n", + "print(ds_3d) # check new coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot for a given time step\n", + "ds_3d.isel(time=-1)['charge'].plot.pcolormesh()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# or plot for a given zone (over time)\n", + "ds.isel(zone=255)['charge'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For nested grids, a specific plotting function is provided (`gm.plot_nested_grids()`).\n", + "\n", + "For models with multiple layers, you can retrieve the surface layers: `gm.get_min_layer(ds, [subset_layers=[3,5]])`\n", + "\n", + "or even display this result: `gm.plot_outcrop()`\n", + "\n", + "The native functions of xarray and numpy already allow grid modifications (value substitution, transformation, operations between two grids, etc.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced features\n", + "\n", + "Example of SPLI calculation (pyspli package, https://gitlab.brgm.fr/brgm/si-eau/traitements-et-valorisations/py_spli)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import spli\n", + "from multiprocessing import cpu_count" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "ds_spli = spli.compute_spli(ds, n_jobs=cpu_count()) # monthly mean included, dummy ref_period: only for example\n", + "#ds_spli = spli.compute_spli(ds, ref_period=[1995, 2010], n_jobs=cpu_count()) # monthly mean included, dummy ref_period: only for example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 2MB\n",
+       "Dimensions:  (zone: 927, time: 205)\n",
+       "Coordinates:\n",
+       "  * zone     (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n",
+       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
+       "Data variables:\n",
+       "    spli     (time, zone) float64 2MB 0.06557 0.06549 0.06554 ... 0.01162 0.6706\n",
+       "Attributes:\n",
+       "    varname:        CHARGE\n",
+       "    units:          m\n",
+       "    missing_value:  9999.0\n",
+       "    standard_name:  water_table_level\n",
+       "    long_name:      groundwater head
    " + ], + "text/plain": [ + " Size: 2MB\n", + "Dimensions: (zone: 927, time: 205)\n", + "Coordinates:\n", + " * zone (zone) int64 7kB 255 256 257 258 259 ... 2722 2723 2724 2725 2726\n", + " * time (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n", + "Data variables:\n", + " spli (time, zone) float64 2MB 0.06557 0.06549 0.06554 ... 0.01162 0.6706\n", + "Attributes:\n", + " varname: CHARGE\n", + " units: m\n", + " missing_value: 9999.0\n", + " standard_name: water_table_level\n", + " long_name: groundwater head" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(ds_spli)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 3MB\n",
+       "Dimensions:  (y: 48, x: 44, time: 205)\n",
+       "Coordinates:\n",
+       "  * y        (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n",
+       "  * x        (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n",
+       "  * time     (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n",
+       "Data variables:\n",
+       "    spli     (time, y, x) float64 3MB nan nan nan nan ... 0.1627 0.519 0.6357\n",
+       "Attributes:\n",
+       "    varname:        CHARGE\n",
+       "    units:          m\n",
+       "    missing_value:  9999.0\n",
+       "    standard_name:  water_table_level\n",
+       "    long_name:      groundwater head
    " + ], + "text/plain": [ + " Size: 3MB\n", + "Dimensions: (y: 48, x: 44, time: 205)\n", + "Coordinates:\n", + " * y (y) float32 192B 2.543e+03 2.544e+03 ... 2.566e+03 2.567e+03\n", + " * x (x) float32 176B 599.2 599.8 600.2 600.8 ... 619.8 620.2 620.8\n", + " * time (time) datetime64[ns] 2kB 1995-07-01 1995-08-01 ... 2012-07-01\n", + "Data variables:\n", + " spli (time, y, x) float64 3MB nan nan nan nan ... 0.1627 0.519 0.6357\n", + "Attributes:\n", + " varname: CHARGE\n", + " units: m\n", + " missing_value: 9999.0\n", + " standard_name: water_table_level\n", + " long_name: groundwater head" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get coords back\n", + "ds_spli['x'] = (\"zone\", ds['x'].data)\n", + "ds_spli['y'] = (\"zone\", ds['y'].data)\n", + "if \"z\" in ds_spli.data_vars.keys():\n", + " ds_spli['z'] = (\"zone\", ds['z'].data)\n", + "\n", + "# assign coords for plot\n", + "ds_xy_spli = ds_spli.assign_coords(\n", + " x=('zone', np.around(ds_spli['x'].data, 1) ),\n", + " y=('zone', np.around(ds_spli['y'].data, 1) ),\n", + ")\n", + "ds_xy_spli = ds_xy_spli.set_index(zone=['y', 'x']).unstack('zone').sortby('y', 'x')\n", + "ds_xy_spli\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot res\n", + "da=ds_xy_spli['spli'].sel(time=['1997-10-01', '2001-05-01', '2005-02-01','2012-01-01'])\n", + "da.plot.pcolormesh(\n", + " x=\"x\", y=\"y\",\n", + " extend='both',\n", + " levels=[-3, -1.2815, -0.8416, -0.253, 0.253, 0.8416, 1.2815, 3],\n", + " colors=['red', 'red', 'orange', 'yellow', 'lime', 'cyan', 'blue', 'navy', 'navy'], # double extrema for extended colors\n", + " col='time',\n", + " cbar_kwargs={\n", + " 'label': 'SPLI', \n", + " }\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aquida", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/user_guide/example/preproc.ipynb b/docs/source/user_guide/example/preproc.ipynb index ee00d12..cddd8f0 100644 --- a/docs/source/user_guide/example/preproc.ipynb +++ b/docs/source/user_guide/example/preproc.ipynb @@ -69,6 +69,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Clean one grid, with arguments\n", + "\n", "In the above results, we can see that `Layer`, `Max_Layer`, `Nest_grid`, `Max_Nestg` are all set to 0.\n", "This is an error which prevent successfull Marthe IO operations with `gridmarthe` or `pymarthe`.\n", "The script `cleanmgrid` is going to fix it." @@ -204,8 +206,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## Process all grid of a Marthe Project\n", + "\n", "`cleanmgrid` also work if you specified a `*.rma` file (Marthe Project). In such case, the program will try to fix all grid files of the project !" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "## juste do:\n", + "# cleanmgrid MyModel.rma" + ] } ], "metadata": { diff --git a/docs/source/user_guide/example/treatments.ipynb b/docs/source/user_guide/example/treatments.ipynb index 251ae47..6602422 100644 --- a/docs/source/user_guide/example/treatments.ipynb +++ b/docs/source/user_guide/example/treatments.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -472,7 +472,7 @@ } ], "source": [ - "grid = gm.load_marthe_grid('./data/craie_npc.permh', drop_nan=True) # NaN needs to be drop or set (change 0 to nan for permh for example)\n", + "grid = gm.load_marthe_grid('./data/craie_npc_gig.permh', drop_nan=True) # NaN needs to be drop or set (change 0 to nan for permh for example)\n", "grid" ] }, diff --git a/docs/source/user_guide/example/veloc.ipynb b/docs/source/user_guide/example/veloc.ipynb new file mode 100644 index 0000000..905ad4c --- /dev/null +++ b/docs/source/user_guide/example/veloc.ipynb @@ -0,0 +1,153 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3b5ac1a2", + "metadata": {}, + "source": [ + "# Use computed velocity field\n", + "\n", + "MARTHE can compute groundwater flow velocity field.\n", + "The output file is write as a column separated text file and **not** as a\n", + "Marthe grid file.\n", + "Therefore, a specific function is provided to read such output." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "551e7b0c", + "metadata": {}, + "outputs": [], + "source": [ + "# load package and read data\n", + "import gridmarthe as gm\n", + "ds = gm.read_velocity('data/veloci.out')" + ] + }, + { + "cell_type": "markdown", + "id": "8298c3ef", + "metadata": {}, + "source": [ + "The read data store different velocity variables: `vx` (velocity along x dimension),\n", + "`vy`, `vz`, `vmod` (the velocity module), etc." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a123ff92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 89kB\n", + "Dimensions: (istep: 1, zone: 927)\n", + "Coordinates:\n", + " * istep (istep) int64 8B 6210\n", + " * zone (zone) int64 7kB 1 2 3 4 5 6 7 8 ... 921 922 923 924 925 926 927\n", + " time (istep) float64 8B 6.422e+03\n", + "Data variables:\n", + " vx (istep, zone) float64 7kB 0.0 0.0 0.0 ... 8.64e-07 -4.219e-07\n", + " vy (istep, zone) float64 7kB 0.0 -1.945e-06 ... -1.552e-06 -3.589e-07\n", + " vz (istep, zone) float64 7kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n", + " vmod (istep, zone) float64 7kB 0.0 1.945e-06 ... 1.776e-06 5.539e-07\n", + " x (zone) float64 7kB 617.8 618.2 618.8 619.2 ... 606.8 607.2 607.8\n", + " y (zone) float64 7kB 2.567e+03 2.567e+03 ... 2.543e+03 2.543e+03\n", + " z (zone) float64 7kB 115.6 114.8 111.1 110.4 ... 11.57 11.4 10.8 11.0\n", + " igrid (zone) int64 7kB 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0\n", + " lay (zone) int64 7kB 1 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1\n", + " row (zone) int64 7kB 5 5 5 5 5 5 5 6 6 6 ... 51 51 51 51 52 52 52 52 52\n", + " col (zone) int64 7kB 43 44 45 46 47 48 49 43 ... 23 24 19 20 21 22 23\n" + ] + } + ], + "source": [ + "print(ds)" + ] + }, + { + "cell_type": "markdown", + "id": "afb14b01", + "metadata": {}, + "source": [ + "A specific function can be used to plot the velocity field with matplotlib\n", + "quiver plot.\n", + "The velocity is normed for visualisation convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5930bb66", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/d/home/gridmarthe/src/gridmarthe/plot.py:344: RuntimeWarning: invalid value encountered in divide\n", + " vx_norm = vx / norm\n", + "/mnt/d/home/gridmarthe/src/gridmarthe/plot.py:345: RuntimeWarning: invalid value encountered in divide\n", + " vy_norm = vy / norm\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# read groundwater head for visualisation\n", + "headsim = gm.load_marthe_grid('data/chasim_hallue.out', fpastp='./data/hallue.pastp', drop_nan=True)\n", + "headsim2d = gm.assign_coords(headsim.isel(time=-1))\n", + "\n", + "# plot it with velocity field\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "headsim2d['charge'].plot(ax=ax, cmap='coolwarm')\n", + "gm.plot_veloc_quiver(ds, ax=ax, xyfreq=5, loc_scale_xy=(0.8,0.1))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "528654e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gm", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst index 4859250..f3b8a75 100644 --- a/docs/source/user_guide/index.rst +++ b/docs/source/user_guide/index.rst @@ -8,8 +8,11 @@ User Guide :maxdepth: 1 example/gm_example.ipynb + example/gm_example_en.ipynb example/gigogne.ipynb + example/veloc.ipynb example/treatments.ipynb + example/cross_section.ipynb example/gis.ipynb example/cli.ipynb example/preproc.ipynb \ No newline at end of file diff --git a/src/gridmarthe/operasem/geometry.py b/src/gridmarthe/operasem/geometry.py index 1bb0a6d..18b34fa 100644 --- a/src/gridmarthe/operasem/geometry.py +++ b/src/gridmarthe/operasem/geometry.py @@ -106,7 +106,7 @@ def _get_upper_alt(topo, hsubs): ------- dataset with only (time,zone), (h_topogr, h_substr, h_upper) """ - ds = xr.combine_by_coords([topo, hsubs], combine_attrs='override') + ds = xr.combine_by_coords([topo, hsubs], combine_attrs='override', compat='override') df = ds.isel(time=0).to_dataframe().reset_index() # time is constant! TODO change this/ check if working df = df.sort_values(by=['x', 'y', 'z']).copy() # assure data are sort in this way @@ -221,7 +221,6 @@ def get_surface_layer(ds, aquif_layers=None): return first_aquif_lay.to_xarray() - def search_zone(ds, i=None, j=None, x=None, y=None, z=None): """ search zone number in marthe grid, based on xy or ij (col, lig) @@ -233,11 +232,8 @@ def search_zone(ds, i=None, j=None, x=None, y=None, z=None): ---- - if ds is multilayered, you need to provide the layer you want (z arg., int type) - - ds should contains dx and dy - - - ds should not have assigned coords (x and y are variables, zone is the dimension - coordinates (with time)) + - ds should not have assigned coords (x and y are variables, zone is the dimension coordinates (with time)) Parameters ---------- @@ -259,7 +255,7 @@ def search_zone(ds, i=None, j=None, x=None, y=None, z=None): zone : xr.Dataset dataset with zone variable, containing the zone number(s) corresponding to the provided coordinates. If no zone is found, an empty dataset is returned. - If multiple zones are found, all of them are returned. + If multiple zones are found, all of them are returned. """ ds_search = ds.copy() diff --git a/src/gridmarthe/operasem/interp.py b/src/gridmarthe/operasem/interp.py index 9fce3f4..2ca77d0 100644 --- a/src/gridmarthe/operasem/interp.py +++ b/src/gridmarthe/operasem/interp.py @@ -55,7 +55,7 @@ def interp_grid(da, new_x=None, new_y=None, method='nearest', **kwargs): the new y-axis coordinate to use method: str, optional (default='nearest') `xr.Dataset.interp` method to use. Default is 'nearest' - **kwargs: dict, optional. + **kwargs Any keywords argument to pass to `xr.Dataset.interp`. Returns From edadc7626b69dfc55bca842029be3b875e506392 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:47:38 +0100 Subject: [PATCH 17/23] build: update build config, editable option, changelog --- Makefile | 42 +++++++++++++++++++++-------------- README.md | 13 +++++++---- changelog.md | 26 +++++++++++++++++----- meson.build | 21 +++++++++++++----- meson.options | 4 +++- pyproject.toml | 22 ++++++------------ src/gridmarthe/__version__.py | 2 +- 7 files changed, 81 insertions(+), 49 deletions(-) diff --git a/Makefile b/Makefile index 31fb39f..580d7a7 100644 --- a/Makefile +++ b/Makefile @@ -4,7 +4,7 @@ # --------------------------------- # ##################################### # ONLY FOR LINUX DEVELOP MODE # -# OR CONDA INSTALL # +# OR CONDA SRC INSTALL # ##################################### FC := gfortran @@ -25,21 +25,25 @@ F90FILES := lecsem.f90 edsemigl.f90 scan_grid.f90 ####################### # editable -# PIPFLAGS ?= --no-build-isolation --editable # meson build version / not functionnal PIPFLAGS ?= -e #Flags: Warning: flags significantly increase wall-clock and CPU time. #Flags are primarily useful for initial check that code compiles correctly F2PYOPT =--backend=meson --lower -FFLAGS = -fdefault-real-8 +# These flags are now only used when compiling shared library +# NOT for install (editable or not): build opt are in meson.build +FFLAGS = +FFLAGS += -fdefault-real-8 # already O3 in f2py, change it here -FFLAGS += -O2 +# FFLAGS += -O2 # Position-Independent Code, si shared library, utile -# FFLAGS +=-fPIC -shared +FFLAGS +=-fPIC -shared FFLAGS += -ffree-line-length-none -# FFLAGS +=-fallow-argument-mismatch # only gfortran > 12.0 -# FFLAGS += -std=legacy # not really necessary -# FFLAGS +=-static # only windows // and not for dev +# only gfortran > 12.0 +FFLAGS +=-fallow-argument-mismatch +# legacy is not really necessary ? +FFLAGS += -std=legacy + COMPILE = CC=$(CC) FC=$(FC) FFLAGS="$(FFLAGS)" $(F2PY) -c $(F90FILES) -m lecsem $(F2PYOPT) @@ -49,6 +53,7 @@ COMPILE = CC=$(CC) FC=$(FC) FFLAGS="$(FFLAGS)" $(F2PY) -c $(F90FILES) -m lecsem .PHONY: all docs clean requirements wheel # all: clean install bakup_pyproj all: clean editable + # only compile with f2py for develop purpose lib: lecsem.so @@ -78,15 +83,18 @@ lecsem.pyf: lecsem.so: cd $(F90SRCDIR); echo "******** Building F2PY Library ********"; \ $(COMPILE) - # cd $(MAINDIR) - # use of `cd` and not $(F90SRCDIR)/lecsem, even if not a good practice in Makefile, - # because meson/f2py does not allow path separator in files. - # FC="$(FC)" FFLAGS="$(FFLAGS)" python -m numpy.f2py -c lecsem.pyf lecsem.f90 edsemigl.f90 scan_grid.f90 -m lecsem --backend=meson --lower - -# meson editable for test -editable: requirements - $(PY) -m pip install --no-build-isolation --config-settings=editable-verbose=true --editable . -vvv - # meson version of editable - for future replacement of setuptools + +# use of `cd` and not $(F90SRCDIR)/lecsem, even if not a good practice in Makefile, +# because meson/f2py does not allow path separator in files. +# FC="$(FC)" FFLAGS="$(FFLAGS)" python -m numpy.f2py -c lecsem.pyf lecsem.f90 edsemigl.f90 scan_grid.f90 -m lecsem --backend=meson --lower + +# meson editable for dev/testing +editable: #requirements + $(PY) -m pip install --no-build-isolation \ + --config-settings=editable-verbose=true \ + --config-settings=setup-args='-Dpip_edit_mode=true' \ + --editable . \ + -vvv wheel: $(PY) -m pip install build diff --git a/README.md b/README.md index ada01c9..a6af99d 100644 --- a/README.md +++ b/README.md @@ -24,13 +24,18 @@ The package also install different command line tools: `ncmart` to convert Marth Full documentation and tutorials can be founded at https://gridmarthe.readthedocs.io -Grids are stored as a 2 dimension matrix, composed of a 1D spatial vector and a time dimension. +Grids are stored as a 2 dimensions matrix composed of a 1D spatial vector and a time dimension +(Reduced Horizontal Grid, as recommended for lossless compression by gathering +in cf-conventions: see [cf-compression-gathering](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#compression-by-gathering) +and [cf-reduced-horizontal-grid](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#reduced-horizontal-grid)). This means that the spatial domain is flatten in grid, and each point is associated with a index, a x coordinate, a y coordinate, eventually a z coordinate (number of layer -in most cases), and dx,dy values. +in most cases), and dx, dy values. + Still, as Marthe meshes are always parallelepiped, the array does *not* necessarily follow the -[ugrid convention](https://ugrid-conventions.github.io/ugrid-conventions/). This may be an evolution -for future version if required. +[ugrid convention](https://ugrid-conventions.github.io/ugrid-conventions/). +This may be an evolution for future version if required. + The x, and y coordinates are the center of cell. Dx, Dy are the dimension of the cell. diff --git a/changelog.md b/changelog.md index 189673c..0b88f7b 100644 --- a/changelog.md +++ b/changelog.md @@ -3,29 +3,45 @@ v0.1.4 ------ + +Unreleased yet. + +* add: beta support for vtk export, based on [vtkwriters](https://gitlab.com/brgm/geomodelling/io/vtkwriters) from ComPass code python ecosystem +* add: support for reading/plotting velocity field : `gm.read_velocity`, `gm.plot_veloc_quiver` +* add: beta support for cross section * fix: error when writing grid with time slice (cf. [issue #3](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/3)) * fix: REVERT change from 0.1.3 : zone index is NOT reset (as in 0.1.2 and previous versions). when the `drop_nan` option is used in `load_grid_marthe()`. A new variable 'izone' store the reset form. See [issue #4](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/4) * change: `nanval` argument in `load_marthe_grid` is rename to `nan_value` as in `write_marthe_grid` +* change: `keepligcol` argument in `load_marthe_grid` is rename to `add_col_row` to be more coherent with `add_id_grid` * change: `xr.Dataset.mart` class accessor is removed. It will be included as wrapper in future marthe python library. - +* change: attributes in netcdf to be more CF-Conventions compliant: + - add crs attributes using `pyrpoj.CRS().to_cf()` for QGIS view compatibilty --> add argument `epsg` in `gm.load_grid_marthe` + - add `compress` attribute for zone [https://cfconventions.org](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#reduced-horizontal-grid), + - fix attributes for coordinates x,y,z / add argument `full_3d` to set z attributes + - force attributes for x,y,z to be kept when `gm.assign_coords` +* add: options to `ncmart` script: + - `--dump` (`-H`) to mimic `ncdump -H` command (netcdf CLI tools). + - `--attrs` (`-a`) to add global attributes + - treat input if already in netcdf fmt (modify it, with scale factor or attrs, to 2D, etc) + v0.1.3 ------ Released on 2025-09-02 -* add geometry functions (get_mask_array, compute_geometry) +* minor fixes in plot method +* add geometry functions (`get_mask_array`, `compute_geometry`) * add reprojection functions (still experimental) * add `martshp` script to easily convert a marthe grid file into a shp/gpkg file in command line -* code refactoring (lecsem module and global organization) -* add automatic tests -* minor fixes in plot method * change: rename function to write raster: `to_raster()` instead of `write_raster_from_da()` * change: zone index is reset when the `drop_nan` option is used in `load_grid_marthe()`. A new variable 'zone_all' store the old index for the `reset_geometry()` method +* code refactoring (lecsem module and global organization) +* add automatic tests v0.1.2 diff --git a/meson.build b/meson.build index 5c792c4..95b0d9f 100644 --- a/meson.build +++ b/meson.build @@ -36,7 +36,8 @@ largs = [] # link args if fc.get_id() == 'gcc' fargs += ['-fdefault-real-8'] - fargs += ['-O2', '-ffree-line-length-none'] + # fargs += ['-O2'] # fix warning 'use builtin O3, deactivate this line" + fargs += ['-ffree-line-length-none'] if cc.version().version_compare('>=13.0') fargs += ['-fallow-argument-mismatch', '-std=legacy'] @@ -84,11 +85,19 @@ python_install_dir = py.get_install_dir() deps += [py_dep] -incdir_numpy = run_command(py, - # ['-c', 'import os; import numpy; print(os.path.relpath(numpy.get_include()))'], - ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], - check : true -).stdout().strip() +if get_option('pip_edit_mode') + # editable version, bug in CI build... + incdir_numpy = run_command(py, + ['-c', 'import os; import numpy; print(os.path.relpath(numpy.get_include()))'], + check : true + ).stdout().strip() +else + # classical, not functionnal in editable mode... + incdir_numpy = run_command(py, + ['-c', 'import os; os.chdir(".."); import numpy; print(numpy.get_include())'], + check : true + ).stdout().strip() +endif inc_np = include_directories(incdir_numpy) np_dep = declare_dependency(include_directories: inc_np) diff --git a/meson.options b/meson.options index e3e073d..cc8cb8b 100644 --- a/meson.options +++ b/meson.options @@ -1,3 +1,5 @@ # Add options option('condabuild', type: 'boolean', value: false, - description: 'Option for conda-build. If set, the windows build will not be statically linked.') \ No newline at end of file + description: 'Option for conda-build. If set, the windows build will not be statically linked.') +option('pip_edit_mode', type : 'boolean', value: false, + description: 'Option to check if editable mode is on in meson build') diff --git a/pyproject.toml b/pyproject.toml index dd9d646..3764542 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,6 @@ requires = [ "meson-python", "ninja" ] -# build-backend = "setuptools.build_meta" build-backend = "mesonpy" [project] @@ -25,7 +24,7 @@ authors = [ ] description = "python package to read/write/manage Marthe Grids" -readme = {file = "README.md", content-type = "text/markdown"} +readme = {file = "README.md", content-type = "text/markdown"} license = {file = "COPYING"} classifiers = [ @@ -36,13 +35,10 @@ classifiers = [ "Programming Language :: Fortran", "Operating System :: Unix", "Operating System :: POSIX :: Linux", - "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Hydrology", - "Topic :: Software Development", "Intended Audience :: Science/Research", - "Intended Audience :: Developers", "Development Status :: 4 - Beta", ] @@ -62,22 +58,21 @@ dependencies = [ [project.optional-dependencies] # install with `pip install gridmarthe[gis]` -gis = ["rioxarray", "rasterio"] +gis = ["rioxarray", "rasterio", "vtkwriters"] doc = ['sphinx', 'pydata_sphinx_theme', 'sphinx-copybutton', 'sphinx_design', 'sphinxcontrib-bibtex', 'myst_nb'] +dev = ['gridmarthe[gis,doc]', 'pytest'] [project.scripts] ncmart = "gridmarthe:scripts.ncmart.main" cleanmgrid = "gridmarthe:scripts.cleanmgrid.main" martshp = "gridmarthe:scripts.martshp.main" - [project.urls] Homepage = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe" Issues = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues" Changelog = "https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/blob/main/changelog.md" Documentation = "https://gridmarthe.readthedocs.io/en/latest/" - [tool.setuptools.dynamic] version = {attr = "gridmarthe.__version__"} @@ -90,14 +85,12 @@ include-package-data = true [tool.setuptools.package-data] "*" = ["*.pyd", "*.dll", "*.so"] # works like a charm - # [tool.meson-python.args] # https://mesonbuild.com/meson-python/reference/pyproject-settings.html # https://mesonbuild.com/meson-python/how-to-guides/meson-args.html # setup = ['--default-library=static'] # setup = ['--build-dir=build'] - [tool.cibuildwheel] skip = "cp36-* cp37-* cp-38* pp* *musl* *_i686 *_ppc64le *_s390x *_universal2" # skip = ["pp*", "*musl*"] @@ -117,9 +110,8 @@ archs = ["arm64"] # repair-wheel-command = "delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel}" -[tool.spin] -package = 'gridmarthe' -[tool.spin.commands] -Build = [ - 'spin.cmds.pip.install', +[tool.pytest.ini_options] +addopts = "-s -v" +testpaths = [ + "tests", ] diff --git a/src/gridmarthe/__version__.py b/src/gridmarthe/__version__.py index c41139c..f574aac 100644 --- a/src/gridmarthe/__version__.py +++ b/src/gridmarthe/__version__.py @@ -22,7 +22,7 @@ # along with this program. If not, see . # -__version__ = '0.1.3' +__version__ = '0.1.4' _copyleft = """ {program} Copyright (C) {year} {name_of_author}. From c3238cae2508229077df6d6decb4d72344203f87 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:48:30 +0100 Subject: [PATCH 18/23] tests: general update + add assign_coords, veloc, cross_sect --- .../{craie_npc.permh => craie_npc_gig.permh} | 0 tests/test_assign_stack_coords.py | 62 ++++++++++++++ tests/test_corresponding_zone_dropna.py | 2 + tests/test_cross_sect.py | 80 +++++++++++++++++++ tests/test_edsemi.py | 44 ++++++---- tests/test_gridmarthe.py | 46 +++-------- tests/test_lecsem.py | 4 +- tests/test_read_veloc.py | 22 +++++ tests/test_surface_mask.py | 2 +- 9 files changed, 211 insertions(+), 51 deletions(-) rename tests/data/{craie_npc.permh => craie_npc_gig.permh} (100%) create mode 100644 tests/test_assign_stack_coords.py create mode 100644 tests/test_cross_sect.py create mode 100644 tests/test_read_veloc.py diff --git a/tests/data/craie_npc.permh b/tests/data/craie_npc_gig.permh similarity index 100% rename from tests/data/craie_npc.permh rename to tests/data/craie_npc_gig.permh diff --git a/tests/test_assign_stack_coords.py b/tests/test_assign_stack_coords.py new file mode 100644 index 0000000..7a39995 --- /dev/null +++ b/tests/test_assign_stack_coords.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import numpy as np +import gridmarthe as gm + + +DATA_WITH_Z = './tests/data/craie_npc_gig.permh' +DATA_NO_Z = './tests/data/hallue.permh' +VAR = "PERMEAB" + + +def test_assign_coords_with_z(): + ds = gm.load_marthe_grid(DATA_WITH_Z, VAR) + ds2 = gm.assign_coords(ds) + assert 'x' in ds2.coords + assert 'y' in ds2.coords + assert 'z' in ds2.coords + assert VAR.lower() in ds.data_vars + assert len(np.shape(ds2[VAR.lower()].data)) == 4 + + +def test_assign_coords_no_z(): + ds = gm.load_marthe_grid(DATA_NO_Z, VAR) + ds2 = gm.assign_coords(ds) + assert 'x' in ds2.coords + assert 'y' in ds2.coords + assert 'z' not in ds2.coords + assert VAR.lower() in ds.data_vars + assert len(np.shape(ds2[VAR.lower()].data)) == 3 + + +def test_assign_coords_force_no_z(): + ds = gm.load_marthe_grid(DATA_WITH_Z, VAR) + ds2 = gm.assign_coords(ds, add_lay=False) + assert 'x' in ds2.coords + assert 'y' in ds2.coords + assert 'z' not in ds2.coords + assert VAR.lower() in ds.data_vars + assert len(np.shape(ds2[VAR.lower()].data)) == 3 + + +def test_stack_coords_no_z(): + ds = gm.load_marthe_grid(DATA_NO_Z, VAR) + ds2 = gm.assign_coords(ds) + ds3 = gm.stack_coords(ds2) + assert 'x' not in ds3.coords + assert 'zone' in ds3.coords + + +def run_all(): + test_assign_coords_with_z() + test_assign_coords_no_z() + test_assign_coords_force_no_z() + test_stack_coords_no_z() + print("====================================") + print("gridmarthe assign_coords test passed") + return + + +if __name__ == "__main__": + run_all() diff --git a/tests/test_corresponding_zone_dropna.py b/tests/test_corresponding_zone_dropna.py index b87a6d4..47941f7 100644 --- a/tests/test_corresponding_zone_dropna.py +++ b/tests/test_corresponding_zone_dropna.py @@ -33,6 +33,8 @@ def test_corresponding_zone_dropna(): np.allclose(df1.x.values, df.x.values) assert not np.all(np.isnan(df['h_substrat'].values)), "Join error, might come from dtype / round / Wrong merge field" + print("=============================") + print("corresponding_zone_dropna test passed") return diff --git a/tests/test_cross_sect.py b/tests/test_cross_sect.py new file mode 100644 index 0000000..9516121 --- /dev/null +++ b/tests/test_cross_sect.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +from matplotlib import pyplot as plt, colors, cm +from mpl_toolkits.axes_grid1 import make_axes_locatable + +import gridmarthe as gm + + +def test_cross_section(): + permh = gm.load_marthe_grid('tests/data/craie_npc_nogig.permh', drop_nan=True) + topo = gm.load_marthe_grid('tests/data/craie_npc.topog', varname='H_TOPOGR') + hsub = gm.load_marthe_grid('tests/data/craie_npc.hsubs', varname='H_SUBSTRAT') + gwl = gm.load_marthe_grid('tests/data/chasim_npc.out', varname='CHARGE', drop_nan=True) # groundwater level + + geom = gm.compute_geometry(topo, hsub, permh.zone) + geom['charge'] = (('time', 'zone'), gwl['charge'].data) # add gwl to new dataset with geom + + ds = gm.assign_coords(geom) + + XC = 6.116e5 + ds_xs = gm.slice_cross_section(ds, x=XC) # here we define a cross section along y axis (x is constant) + print(ds_xs) + + assert 'y' in ds_xs.dims, "Cross-section should have 'y' dimension" + assert 'x' not in ds_xs.dims, "Cross-section should not have 'x' dimension" + assert ds_xs.sizes['y'] > 1 # cross-section has multiple points + assert ds_xs.sizes['z'] == 10 # we have 10 layers + + + +# Visual test - disable for automated testing +def _check_plot_cross_section(): + permh = gm.load_marthe_grid('docs/source/user_guide/example/data/craie_npc.permh', drop_nan=True) + topo = gm.load_marthe_grid('docs/source/user_guide/example/data/craie_npc.topog', varname='H_TOPOGR') + hsub = gm.load_marthe_grid('docs/source/user_guide/example/data/craie_npc.hsubs', varname='H_SUBSTRAT') + gwl = gm.load_marthe_grid('docs/source/user_guide/example/data/chasim_npc.out', varname='CHARGE', drop_nan=True) # groundwater level + + geom = gm.compute_geometry(topo, hsub, permh.zone) + geom['charge'] = (('time', 'zone'), gwl['charge'].data) # add gwl to new dataset with geom + + ds = gm.assign_coords(geom) + + XC = 6.116e5 + ds_xs = gm.slice_cross_section(ds, x=XC) # here we define a cross section along y axis (x is constant) + print(ds_xs) + # We can plot this with the helpful function + # prep data + line = ds_xs['charge'].isel(time=-1).sel(z=6).to_dataframe().reset_index() + surf = gm.get_surface_layer(gwl) + + # prep colormap + colours = ['slategrey', 'burlywood', 'red', 'yellow', 'orange', 'lightgreen', 'lightgrey', 'steelblue', 'darkgreen', 'brown'] + bounds = np.arange(1, len(colours)+2) # add a fictive layer at the end because this is lower bounds + labels = ['Alluv.', 'Limons', 'Fland', 'Land', 'Than', 'Craie', 'No prod.', 'Tur', 'Ceno', 'Carbo'] + + cmap = colors.ListedColormap(colours) + norm = colors.BoundaryNorm(bounds, cmap.N) + + # CREATE AXES + fig, axes = plt.subplots(ncols=2, figsize=(14,4), width_ratios=[0.5, 0.6]) + + # MAP + ax = axes[0] + gm.plot_outcrop( + gm.assign_coords(surf, add_lay=False), + fig=fig, ax=ax, cmap=cmap, norm=norm, labels=labels, cbar_width=4, + alpha=0.7 + ) + ax.axvline(x=XC, color='k', label='cross_section') + + # CROSS-SECT + ax = axes[1] + gm.plot_cross_section(ds_xs, fig=fig, ax=ax, cmap=cmap, norm=norm, labels=labels) + line.plot(y="charge", x='y', ax=ax, color='blue', linewidth=0.7, alpha=0.6) # gwl line + ax.set_xlim(2.5692e6) + + plt.tight_layout() + plt.show() diff --git a/tests/test_edsemi.py b/tests/test_edsemi.py index 0ec5fe5..220bbad 100644 --- a/tests/test_edsemi.py +++ b/tests/test_edsemi.py @@ -12,7 +12,10 @@ ) -def test_extract_var(martfile, varname): +def test_extract_var(): + martfile = './tests/data/Somme_V3_Surfex.permh' # nested + varname = 'PERMEAB' + ds = gm.load_marthe_grid(martfile, varname) dims = _parse_dims_from_xr_attrs(ds.attrs.get('original_dimensions')) ( @@ -43,18 +46,10 @@ def test_extract_var(martfile, varname): # ) -def test_write_marthe_grid(): - - # martfile = './tests/data/craie_npc.permh' # multilayer - # martfile = './tests/data/hallue.permh' # simple, single layer - martfile = './tests/data/Somme_V3_Surfex.permh' # nested - varname = 'PERMEAB' - fout = './tests/res/grid.out' - - test_extract_var(martfile, varname) +def _single_test_write_marthe_grid(martfile, varname, fout): # test normal (time still a dimension/axis) - ds1 = gm.load_marthe_grid(martfile, varname, drop_nan=True, nan_value=[0.,-9999.]) + ds1 = gm.load_marthe_grid(martfile, varname, drop_nan=True, nan_value=[0., -9999.]) status = gm.write_marthe_grid(ds1, fout, varname='permeab', file_permh=martfile) assert status == 0, "write_marthe_grid test, with permh file and dropna, failed" @@ -64,14 +59,35 @@ def test_write_marthe_grid(): assert status == 0, "write_marthe_grid test with sel(time) failed" # also test reading the written file - ds3 = gm.load_marthe_grid(fout, varname, drop_nan=True, nan_value=[0.,-9999.]) + ds3 = gm.load_marthe_grid(fout, varname, drop_nan=True, nan_value=[0., -9999.]) assert 'permeab' in ds3.data_vars, "re-read written file failed" assert ds3.permeab.shape == ds1.permeab.shape, "re-read written file shape mismatch" assert np.allclose(ds3.permeab.values, ds1.permeab.values, equal_nan=True), \ "write_marthe_grid re-read written file values mismatch" - print("=============================") - print("write_marthe_grid test passed") + print("==================================================") + print(f"write_marthe_grid test passed for file {martfile}") + + +def test_write_marthe_grid(): + martfile = './tests/data/hallue.permh' # simple, single layer + varname = 'PERMEAB' + fout = './tests/res/grid.out' + _single_test_write_marthe_grid(martfile, varname, fout) + + +def test_write_marthe_grid_nested(): + martfile = './tests/data/Somme_V3_Surfex.permh' # nested + varname = 'PERMEAB' + fout = './tests/res/grid.out' + _single_test_write_marthe_grid(martfile, varname, fout) + + +def test_write_marthe_grid_multilayer(): + martfile = './tests/data/craie_npc_gig.permh' # multilayer and nested + varname = 'PERMEAB' + fout = './tests/res/grid.out' + _single_test_write_marthe_grid(martfile, varname, fout) if __name__ == "__main__": diff --git a/tests/test_gridmarthe.py b/tests/test_gridmarthe.py index 1342975..b2ec947 100644 --- a/tests/test_gridmarthe.py +++ b/tests/test_gridmarthe.py @@ -4,7 +4,8 @@ import numpy as np import xarray as xr -# import pytest + +import pytest import gridmarthe as gm @@ -57,14 +58,14 @@ def test_load_with_add_id_grid_adds_id_grid(): assert 'id_grid' in ds.data_vars -# def test_load_nonexistent_file_raises(): -# with pytest.raises(FileNotFoundError): -# gm.load_marthe_grid('not_a_file.permh', VAR) +def test_load_nonexistent_file_raises(): + with pytest.raises(FileNotFoundError): + gm.load_marthe_grid('not_a_file.permh', VAR) -# def test_load_invalid_varname_raises(): -# with pytest.raises(ValueError): -# gm.load_marthe_grid(DATA_PATH, varname='INVALIDVAR') +def test_load_invalid_varname_raises(): + with pytest.raises(ValueError): + gm.load_marthe_grid(DATA_PATH, varname='INVALIDVAR') def test_load_grid_attrs_present(): @@ -74,7 +75,7 @@ def test_load_grid_attrs_present(): assert 'original_dimensions' in ds.attrs -def test_all(): +def run_all(): test_load_valid_grid_returns_xarray() test_load_grid_attrs_present() test_load_with_add_id_grid_adds_id_grid() @@ -83,35 +84,12 @@ def test_all(): test_load_with_drop_nan_removes_nan() test_load_with_varname_none_picks_first() test_load_with_varname_all_returns_multiple() - # test_load_nonexistent_file_raises() - # test_load_invalid_varname_raises() + test_load_nonexistent_file_raises() + test_load_invalid_varname_raises() print("=============================") print("gridmarthe reader test passed") return if __name__ == "__main__": - test_all() - -# MY TESTS -# inputs = './tests/data/craie_npc.permh', "PERMEAB" # ajouter tests/data ici pour conda-forge ? -# -# toto = gm.load_marthe_grid(*inputs, drop_nan=True) -# # toto = gm.load_marthe_grid(*inputs, drop_nan=True, nanval=0.) -# # toto = gm.load_marthe_grid(inputs[0], varname=None) -# # toto = gm.load_marthe_grid(inputs[0], varname='all') -# # toto = gm.load_marthe_grid(*inputs, add_col_row=True) -# # toto = gm.load_marthe_grid(*inputs, add_id_grid=True) -# -# test = toto.set_coords(['time', 'zone', 'x', 'y', 'z', 'dx', 'dy']) -# print(toto.attrs) -# test.mart.assign_coords() -# -# # for pymarthe compat' -# # to recarray -# df = toto.to_dataframe() -# df.to_records() -# -# toto2 = toto.mart.assign_coords() -# toto3 = toto.mart.to_recarray() -# \ No newline at end of file + run_all() diff --git a/tests/test_lecsem.py b/tests/test_lecsem.py index f4c4f2a..91edd82 100644 --- a/tests/test_lecsem.py +++ b/tests/test_lecsem.py @@ -56,7 +56,7 @@ def test_lecsem_transform_coords(): pass -def test(): +def main(): test_lecsem_read_marthe_grid() test_lecsem_scan_dim() test_lecsem_scan_typevar() @@ -65,4 +65,4 @@ def test(): if __name__ == "__main__": - test() + main() diff --git a/tests/test_read_veloc.py b/tests/test_read_veloc.py new file mode 100644 index 0000000..82e68e2 --- /dev/null +++ b/tests/test_read_veloc.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import numpy as np +import gridmarthe as gm + + +def test_read_veloc_from_marthe(): + data_path = 'tests/data/veloci.out' + ds = gm.read_velocity(data_path) + assert 'vx' in ds.data_vars, 'Vx variable not found' + assert 'vy' in ds.data_vars, 'Vy variable not found' + assert 'vz' in ds.data_vars, 'Vz variable not found' + assert ds.vx.shape == ds.vy.shape == ds.vz.shape, 'wrong shape for velocity components' + assert 'vmod' in ds.data_vars, 'Vmod variable not found' + vmod_calculated = (ds.vx**2 + ds.vy**2 + ds.vz**2)**0.5 + assert np.allclose(ds.vmod, vmod_calculated, atol=1e-8), 'Vmod values incorrect' + print('Test read veloc from marthe success!') + + +if __name__ == '__main__': + test_read_veloc_from_marthe() diff --git a/tests/test_surface_mask.py b/tests/test_surface_mask.py index 05353a4..7339df7 100644 --- a/tests/test_surface_mask.py +++ b/tests/test_surface_mask.py @@ -8,7 +8,7 @@ def test_surf_mask(plot=False): - grid = gm.load_marthe_grid('./tests/data/craie_npc.permh', drop_nan=True) + grid = gm.load_marthe_grid('./tests/data/craie_npc_gig.permh', drop_nan=True) surf = gm.get_surface_layer(ds=grid) assert np.allclose(np.unique(surf.z.data), np.array([1, 2, 4, 5, 6, 8, 9])), \ From ba140bef40358c352860e134b9dd8fba1ee0c04e Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Wed, 19 Nov 2025 13:58:13 +0100 Subject: [PATCH 19/23] fix: revert change to UTC from datetime if py<311 --- src/gridmarthe/gridmarthe.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 2132b72..755025f 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -22,8 +22,12 @@ # along with this program. If not, see . # -import os -from datetime import datetime, UTC +import os, sys + +if sys.version_info >= (3, 11): + from datetime import datetime, UTC +else: + from datetime import datetime import pandas as pd import numpy as np @@ -160,8 +164,13 @@ def _parse_attrs( 'frequency' : '{} day(s)'.format(str(dates.to_series().diff().mean().days)), } + if sys.version_info >= (3, 11): + _date_now = datetime.now(UTC).strftime('%Y-%m-%dT%H:%M:%SZ UTC') + else: + _date_now = datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ UTC') + epilogue = { - 'creation_date' : 'Created on {}'.format(datetime.now(UTC).strftime('%Y-%m-%dT%H:%M:%SZ UTC')), + 'creation_date' : 'Created on {}'.format(_date_now), # comment or source ? https://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch02s06.html 'comment' : 'Hydrogeological model created with MARTHE code '\ '(Thiery, D. 2020. Guidelines for MARTHE v7.8 computer code '\ From dbd265b8ac5e6abd31b4be77e577334b8581f593 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Sat, 6 Dec 2025 15:43:42 +0100 Subject: [PATCH 20/23] clean main module/attrs --- src/gridmarthe/gridmarthe.py | 74 +++++++---------------------- src/gridmarthe/variable_attrs.py | 81 ++++++++++++++++++++++++++++++++ 2 files changed, 99 insertions(+), 56 deletions(-) create mode 100644 src/gridmarthe/variable_attrs.py diff --git a/src/gridmarthe/gridmarthe.py b/src/gridmarthe/gridmarthe.py index 755025f..a6cadf3 100644 --- a/src/gridmarthe/gridmarthe.py +++ b/src/gridmarthe/gridmarthe.py @@ -29,6 +29,8 @@ else: from datetime import datetime +from typing import Union + import pandas as pd import numpy as np import xarray as xr @@ -49,7 +51,6 @@ FortranError ) - from .utils import ( read_dates_from_pastp, assign_coords, @@ -59,59 +60,7 @@ replace ) -from typing import Union - - -# http://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html -# pas vraiment, pour respecter la convention, le nom de variable le plus proche est : water_table_depth / mais on parle de charge pas prof -# proposition à faire dans https://github.com/cf-convention/discuss/issues -# water_table_altitude or level? -VARS_ATTRS = { - 'permeab': { - 'varname': 'PERMEAB', - 'units': 'm/s', - 'missing_value': 0., - 'standard_name': '', - 'long_name': 'aquifer_hydraulic_conductivity' - }, - 'charge' : { - 'varname': 'CHARGE', - 'units': 'm', - 'missing_value': 9999., - 'standard_name': 'water_table_level', - 'long_name': - 'groundwater head' - }, - 'saturat': { - 'varname': 'SATURAT', - 'units': '%' , - 'missing_value': 9999., - 'standard_name': 'water_table_saturation', - 'long_name': 'groundwater_saturation' - }, - 'debit' : { - 'varname': 'DEBIT', - 'units': 'm3/s', - 'missing_value': 0., - 'standard_name': '', - 'long_name': 'flow' - }, - 'debit_rivi' : { - 'varname': 'DEBIT_RIVI', - 'units': 'm3/s', - 'missing_value': 9999., - 'standard_name': 'water_volume_transport_in_river_channel', - 'long_name': 'river_discharge_flow' - }, - 'qech_riv_napp' : { - 'varname': 'QECH_RIV_NAPP', - 'units': 'm3/s', - 'missing_value': 9999., - 'standard_name': '', - 'long_name': 'surface_groundwater_exchange_flow' - }, -} - +from .variable_attrs import VARS_ATTRS def _parse_attrs( @@ -308,11 +257,23 @@ def load_marthe_grid( ds: xr.Dataset A xarray.Dataset object containing values and attributes read from Marthe grid file. """ + # Temporary + import warnings legacy_tmp = kwargs.get('nanval') if legacy_tmp is not None: - print('Warning: nanval argument is deprecated, use nan_value instead') + warnings.warn( + 'Warning: nanval argument is deprecated, use nan_value instead', + DeprecationWarning, + ) nan_value = legacy_tmp - + legacy_tmp = kwargs.get('keepligcol') + if legacy_tmp is not None: + warnings.warn( + 'Warning: keepligcol argument is deprecated, use add_col_row instead', + DeprecationWarning + ) + add_col_row = legacy_tmp + # Fortran error cause sys exit. To avoid this, we add a test on file first if not os.path.exists(filename): raise FileNotFoundError("File : `{}` does not exist. Please check syntax/path.".format(filename)) @@ -637,6 +598,7 @@ def write_marthe_grid( """ # TODO: infer nx, ny, nz, ngrid from ds ? --> allow to create a custom grid ds2 = ds.copy() + nan_value = VARS_ATTRS.get(varname, {}).get('missing_value', 9999.) if nan_value is None else nan_value if file_permh is not None: # if permeab, fill_na with permh file (because either 0 or -9999.) diff --git a/src/gridmarthe/variable_attrs.py b/src/gridmarthe/variable_attrs.py new file mode 100644 index 0000000..257ef31 --- /dev/null +++ b/src/gridmarthe/variable_attrs.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Variables and attributes definitions for gridmarthe. +""" + +# http://cfconventions.org/Data/cf-standard-names/current/build/cf-standard-name-table.html +# closest value in cf-convention for groundwater level : water_table_depth +# should we make a suggestion with water_table_level ? +# https://github.com/cf-convention/discuss/issues + +VARS_ATTRS = { + 'permeab': { + 'varname': 'PERMEAB', + 'units': 'm/s', + 'missing_value': 0., + 'standard_name': '', + 'long_name': 'aquifer_hydraulic_conductivity' + }, + 'charge' : { + 'varname': 'CHARGE', + 'units': 'm', + 'missing_value': 9999., + 'standard_name': 'water_table_level', + 'long_name': + 'groundwater head' + }, + 'saturat': { + 'varname': 'SATURAT', + 'units': '%' , + 'missing_value': 9999., + 'standard_name': 'water_table_saturation', + 'long_name': 'groundwater_saturation' + }, + 'debit': { + 'varname': 'DEBIT', + 'units': 'm3/s', + 'missing_value': 0., + 'standard_name': '', + 'long_name': 'flow' + }, + 'debit_rivi': { + 'varname': 'DEBIT_RIVI', + 'units': 'm3/s', + 'missing_value': 9999., + 'standard_name': 'water_volume_transport_in_river_channel', + 'long_name': 'river_discharge_flow' + }, + 'qech_riv_napp': { + 'varname': 'QECH_RIV_NAPP', + 'units': 'm3/s', + 'missing_value': 9999., + 'standard_name': '', + 'long_name': 'surface_groundwater_exchange_flow' + }, + 'emmag_libr': { + 'varname': 'EMMAG_LIBR', + 'units': '-' , + 'missing_value': 0., + 'standard_name': '', + 'long_name': 'aquifer_specific_yield' + }, + 'emmag_capt': { + 'varname': 'EMMAG_CAPT', + 'units': 'm-1', + 'missing_value': 0., + 'standard_name': '', + 'long_name': 'aquifer_confined_storage_coefficient' + }, + 'zone_geom': { + 'varname': 'ZONE_GEOM', + 'units': '-' , + 'missing_value': 0., + 'standard_name': '', + 'long_name': 'geometry_zone_identifier' + }, +} + +# add variants for variable names +VARS_ATTRS['geom_zone'] = VARS_ATTRS['zone_geom'] # english variant in MARTHE code From ad861914eacf6836c3b7d900059c89e1d1bfd0eb Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Tue, 9 Dec 2025 17:10:32 +0100 Subject: [PATCH 21/23] add data for doc/test, update changelog --- changelog.md | 31 +- .../user_guide/example/data/chasim_npc.out | 3340 +++++++++++++++++ .../source/user_guide/example/data/veloci.out | 928 +++++ tests/data/chasim_npc.out | 3340 +++++++++++++++++ tests/data/veloci.out | 928 +++++ 5 files changed, 8555 insertions(+), 12 deletions(-) create mode 100644 docs/source/user_guide/example/data/chasim_npc.out create mode 100644 docs/source/user_guide/example/data/veloci.out create mode 100644 tests/data/chasim_npc.out create mode 100644 tests/data/veloci.out diff --git a/changelog.md b/changelog.md index 0b88f7b..02a11f5 100644 --- a/changelog.md +++ b/changelog.md @@ -1,22 +1,13 @@ -v0.1.4 +v0.3.0 ------ - -Unreleased yet. +Released on 2025-12-09 * add: beta support for vtk export, based on [vtkwriters](https://gitlab.com/brgm/geomodelling/io/vtkwriters) from ComPass code python ecosystem * add: support for reading/plotting velocity field : `gm.read_velocity`, `gm.plot_veloc_quiver` * add: beta support for cross section -* fix: error when writing grid with time slice (cf. [issue #3](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/3)) -* fix: REVERT change from 0.1.3 : zone index is NOT reset (as in 0.1.2 and previous versions). - when the `drop_nan` option is used in `load_grid_marthe()`. - A new variable 'izone' store the reset form. - See [issue #4](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/4) -* change: `nanval` argument in `load_marthe_grid` is rename to `nan_value` as in `write_marthe_grid` -* change: `keepligcol` argument in `load_marthe_grid` is rename to `add_col_row` to be more coherent with `add_id_grid` -* change: `xr.Dataset.mart` class accessor is removed. It will be included as wrapper in future marthe python library. * change: attributes in netcdf to be more CF-Conventions compliant: - add crs attributes using `pyrpoj.CRS().to_cf()` for QGIS view compatibilty --> add argument `epsg` in `gm.load_grid_marthe` - add `compress` attribute for zone [https://cfconventions.org](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#reduced-horizontal-grid), @@ -26,7 +17,23 @@ Unreleased yet. - `--dump` (`-H`) to mimic `ncdump -H` command (netcdf CLI tools). - `--attrs` (`-a`) to add global attributes - treat input if already in netcdf fmt (modify it, with scale factor or attrs, to 2D, etc) - + + +v0.2.0 +------ + + +Unreleased - dev version. + +* fix: error when writing grid with time slice (cf. [issue #3](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/3)) +* fix: REVERT change from 0.1.3 : zone index is NOT reset (as in 0.1.2 and previous versions). + when the `drop_nan` option is used in `load_grid_marthe()`. + A new variable 'izone' store the reset form. + See [issue #4](https://gitlab.com/brgm/hydrogeological-modelling/marthe-tools/gridmarthe/-/issues/4) +* change: `nanval` argument in `load_marthe_grid` is rename to `nan_value` as in `write_marthe_grid` +* change: `keepligcol` argument in `load_marthe_grid` is rename to `add_col_row` to be more coherent with `add_id_grid` +* change: `xr.Dataset.mart` class accessor is removed. It will be included as wrapper in future marthe python library. + v0.1.3 ------ diff --git a/docs/source/user_guide/example/data/chasim_npc.out b/docs/source/user_guide/example/data/chasim_npc.out new file mode 100644 index 0000000..b293d08 --- /dev/null +++ b/docs/source/user_guide/example/data/chasim_npc.out @@ -0,0 +1,3340 @@ +Marthe_Grid Version=9.0 +Title=Craie NPC Pas 1404; CHARGE 1 <> CHARGE 1 +[Infos] +Field=CHARGE +Type= +Elem_Number=0 +Name= +Time_Step=1404 +Time=1.26230E+09 +Layer=1 +Max_Layer=10 +Nest_grid=0 +Max_NestG=0 +[Structure] +X_Left_Corner=543500 +Y_Lower_Corner=2526100 +Ncolumn=387 +Nrows=304 +[Data_Descript] +! Line 1 : 0 , 0 , < 1 , 2 , 3 , Ncolumn > +! Line 2 : 0 , 0 , < X_Center_of_all_Columns > +! Line 2+1 : 1 , Y_of_Row_1 , < Field_Values_of_all_Columns > , Dy_of_Row_1 +! Line 2+2 : 2 , Y_of_Row_2 , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! 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9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 132 137.2686 137.3073 135.5393 132.6591 129.5124 126.2359 122.0034 117.3241 112.4071 107.0986 101.3417 94.93905 88.80481 82.61455 76.1389 69.77178 63.68415 63.7366 62.89435 61.53398 59.23854 56.22314 52.95305 49.07618 45.00806 9999 44.37655 43.99505 43.09901 43.0168 42.16065 41.74012 40.9882 40.16236 39.3464 38.67528 38.16298 37.93616 37.29496 36.74415 36.20979 35.70345 35.29754 34.98594 34.7429 34.56047 34.41766 34.34033 34.31035 34.32145 34.34477 34.38659 34.44251 34.51146 34.59293 34.68524 34.79706 34.94062 35.04772 35.21273 29.83857 26.71004 25.67956 25.40001 25.40855 25.53364 25.70557 25.89326 26.07361 26.23047 26.34566 26.41524 26.43693 26.40261 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 23.55398 9999 23.41867 23.36242 23.30745 23.24981 23.18612 23.11514 23.0346 22.94276 22.83818 22.72006 22.58749 22.43668 22.26849 22.08384 21.88459 21.66791 21.43602 21.19258 20.93617 20.66706 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500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 +[End_Grid] +Marthe_Grid Version=9.0 +Title=Craie NPC Pas 1404; CHARGE 10 <> CHARGE 10 +[Infos] +Field=CHARGE +Type= +Elem_Number=0 +Name= +Time_Step=1404 +Time=1.26230E+09 +Layer=10 +Max_Layer=10 +Nest_grid=0 +Max_NestG=0 +[Structure] +X_Left_Corner=543500 +Y_Lower_Corner=2526100 +Ncolumn=387 +Nrows=304 +[Data_Descript] +! Line 1 : 0 , 0 , < 1 , 2 , 3 , Ncolumn > +! Line 2 : 0 , 0 , < X_Center_of_all_Columns > +! Line 2+1 : 1 , Y_of_Row_1 , < Field_Values_of_all_Columns > , Dy_of_Row_1 +! Line 2+2 : 2 , Y_of_Row_2 , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! Line 2+Nrows : Nrows , Y_of_Row_Nrows , < Field_Values_of_all_Columns > , Dy_of_Row_2 +! Line 3+Nrows : 0 , 0 , < Dx_of_all_Columns > +[Data] +0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 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500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 +[End_Grid] diff --git a/docs/source/user_guide/example/data/veloci.out b/docs/source/user_guide/example/data/veloci.out new file mode 100644 index 0000000..0f3ffbc --- /dev/null +++ b/docs/source/user_guide/example/data/veloci.out @@ -0,0 +1,928 @@ +Modélisation du bassin de la SOMME Nappe_Libr (x,y,z)(Vx,Vy,Vz,Module m.s-1) Step= 6210 ; Date= 6422. Dimx,Dimy= 26.00 26.50 + 6.177500E+02 2.566750E+03 1.155750E+02 0.000E+00 0.000E+00 0.000E+00 M= 0.000E+00 #,Z,Y,X= 0 1 5 43 + 6.182500E+02 2.566750E+03 1.147550E+02 0.000E+00 -1.945E-06 0.000E+00 M= 1.945E-06 #,Z,Y,X= 0 1 5 44 + 6.187500E+02 2.566750E+03 1.111250E+02 0.000E+00 -1.927E-06 0.000E+00 M= 1.927E-06 #,Z,Y,X= 0 1 5 45 + 6.192500E+02 2.566750E+03 1.103750E+02 0.000E+00 -1.798E-06 0.000E+00 M= 1.798E-06 #,Z,Y,X= 0 1 5 46 + 6.197500E+02 2.566750E+03 1.093350E+02 0.000E+00 -5.939E-07 0.000E+00 M= 5.939E-07 #,Z,Y,X= 0 1 5 47 + 6.202500E+02 2.566750E+03 1.114500E+02 0.000E+00 -5.226E-07 0.000E+00 M= 5.226E-07 #,Z,Y,X= 0 1 5 48 + 6.207500E+02 2.566750E+03 1.142800E+02 0.000E+00 0.000E+00 0.000E+00 M= 0.000E+00 #,Z,Y,X= 0 1 5 49 + 6.177500E+02 2.566250E+03 1.088250E+02 -6.297E-07 -1.839E-06 0.000E+00 M= 1.944E-06 #,Z,Y,X= 0 1 6 43 + 6.182500E+02 2.566250E+03 1.052550E+02 -1.061E-06 -3.827E-06 0.000E+00 M= 3.971E-06 #,Z,Y,X= 0 1 6 44 + 6.187500E+02 2.566250E+03 1.069900E+02 -1.228E-06 -4.384E-06 0.000E+00 M= 4.553E-06 #,Z,Y,X= 0 1 6 45 + 6.192500E+02 2.566250E+03 1.033750E+02 -1.022E-06 -3.253E-06 0.000E+00 M= 3.410E-06 #,Z,Y,X= 0 1 6 46 + 6.197500E+02 2.566250E+03 1.105800E+02 -4.635E-07 -1.291E-06 0.000E+00 M= 1.372E-06 #,Z,Y,X= 0 1 6 47 + 6.202500E+02 2.566250E+03 1.138750E+02 -2.016E-07 -9.973E-07 0.000E+00 M= 1.017E-06 #,Z,Y,X= 0 1 6 48 + 6.172500E+02 2.565750E+03 1.092400E+02 -6.046E-07 0.000E+00 0.000E+00 M= 6.046E-07 #,Z,Y,X= 0 1 7 42 + 6.177500E+02 2.565750E+03 1.042700E+02 -1.200E-06 -3.774E-06 0.000E+00 M= 3.960E-06 #,Z,Y,X= 0 1 7 43 + 6.182500E+02 2.565750E+03 1.048200E+02 -1.601E-06 -4.445E-06 0.000E+00 M= 4.725E-06 #,Z,Y,X= 0 1 7 44 + 6.187500E+02 2.565750E+03 1.012250E+02 -1.788E-06 -4.893E-06 0.000E+00 M= 5.209E-06 #,Z,Y,X= 0 1 7 45 + 6.192500E+02 2.565750E+03 1.057100E+02 -9.427E-07 -2.445E-06 0.000E+00 M= 2.620E-06 #,Z,Y,X= 0 1 7 46 + 6.197500E+02 2.565750E+03 1.105300E+02 -4.023E-07 -1.561E-06 0.000E+00 M= 1.612E-06 #,Z,Y,X= 0 1 7 47 + 6.202500E+02 2.565750E+03 1.103750E+02 -2.259E-07 -8.189E-07 0.000E+00 M= 8.495E-07 #,Z,Y,X= 0 1 7 48 + 6.172500E+02 2.565250E+03 1.066800E+02 -1.382E-06 -9.528E-07 0.000E+00 M= 1.679E-06 #,Z,Y,X= 0 1 8 42 + 6.177500E+02 2.565250E+03 1.054350E+02 -2.426E-06 -3.364E-06 0.000E+00 M= 4.148E-06 #,Z,Y,X= 0 1 8 43 + 6.182500E+02 2.565250E+03 9.764000E+01 -2.778E-06 -5.785E-06 0.000E+00 M= 6.417E-06 #,Z,Y,X= 0 1 8 44 + 6.187500E+02 2.565250E+03 9.765000E+01 -2.239E-06 -3.622E-06 0.000E+00 M= 4.258E-06 #,Z,Y,X= 0 1 8 45 + 6.192500E+02 2.565250E+03 1.058150E+02 -7.182E-07 -1.747E-06 0.000E+00 M= 1.889E-06 #,Z,Y,X= 0 1 8 46 + 6.197500E+02 2.565250E+03 1.051100E+02 -3.884E-07 -1.812E-06 0.000E+00 M= 1.853E-06 #,Z,Y,X= 0 1 8 47 + 6.202500E+02 2.565250E+03 1.072750E+02 -1.709E-07 -5.905E-07 0.000E+00 M= 6.147E-07 #,Z,Y,X= 0 1 8 48 + 6.207500E+02 2.565250E+03 1.086500E+02 1.373E-08 -1.394E-07 0.000E+00 M= 1.400E-07 #,Z,Y,X= 0 1 8 49 + 6.147500E+02 2.564750E+03 1.045200E+02 0.000E+00 -7.068E-07 0.000E+00 M= 7.068E-07 #,Z,Y,X= 0 1 9 37 + 6.157500E+02 2.564750E+03 1.048400E+02 0.000E+00 -1.013E-06 0.000E+00 M= 1.013E-06 #,Z,Y,X= 0 1 9 39 + 6.162500E+02 2.564750E+03 1.045750E+02 0.000E+00 -6.443E-07 0.000E+00 M= 6.443E-07 #,Z,Y,X= 0 1 9 40 + 6.167500E+02 2.564750E+03 9.988500E+01 -9.809E-07 -3.976E-07 0.000E+00 M= 1.058E-06 #,Z,Y,X= 0 1 9 41 + 6.172500E+02 2.564750E+03 9.798500E+01 -2.138E-06 -1.964E-06 0.000E+00 M= 2.903E-06 #,Z,Y,X= 0 1 9 42 + 6.177500E+02 2.564750E+03 9.923500E+01 -2.504E-06 -3.137E-06 0.000E+00 M= 4.014E-06 #,Z,Y,X= 0 1 9 43 + 6.182500E+02 2.564750E+03 9.498000E+01 -2.065E-06 -5.965E-06 0.000E+00 M= 6.312E-06 #,Z,Y,X= 0 1 9 44 + 6.187500E+02 2.564750E+03 1.025700E+02 -1.155E-06 -2.300E-06 0.000E+00 M= 2.573E-06 #,Z,Y,X= 0 1 9 45 + 6.192500E+02 2.564750E+03 1.042750E+02 -7.222E-07 -1.441E-06 0.000E+00 M= 1.612E-06 #,Z,Y,X= 0 1 9 46 + 6.197500E+02 2.564750E+03 9.893000E+01 -5.165E-07 -1.867E-06 0.000E+00 M= 1.937E-06 #,Z,Y,X= 0 1 9 47 + 6.202500E+02 2.564750E+03 1.013500E+02 -3.126E-07 -3.866E-07 0.000E+00 M= 4.971E-07 #,Z,Y,X= 0 1 9 48 + 6.207500E+02 2.564750E+03 1.058600E+02 -9.003E-08 -2.364E-07 0.000E+00 M= 2.530E-07 #,Z,Y,X= 0 1 9 49 + 6.092500E+02 2.564250E+03 1.033800E+02 0.000E+00 -4.782E-07 0.000E+00 M= 4.782E-07 #,Z,Y,X= 0 1 10 26 + 6.097500E+02 2.564250E+03 1.051100E+02 0.000E+00 -4.790E-07 0.000E+00 M= 4.790E-07 #,Z,Y,X= 0 1 10 27 + 6.142500E+02 2.564250E+03 1.032350E+02 -2.581E-07 -2.896E-07 0.000E+00 M= 3.879E-07 #,Z,Y,X= 0 1 10 36 + 6.147500E+02 2.564250E+03 1.004900E+02 -1.933E-07 -1.177E-06 0.000E+00 M= 1.192E-06 #,Z,Y,X= 0 1 10 37 + 6.152500E+02 2.564250E+03 1.022050E+02 -2.134E-07 -3.555E-07 0.000E+00 M= 4.146E-07 #,Z,Y,X= 0 1 10 38 + 6.157500E+02 2.564250E+03 9.897000E+01 -1.248E-07 -1.624E-06 0.000E+00 M= 1.629E-06 #,Z,Y,X= 0 1 10 39 + 6.162500E+02 2.564250E+03 1.015400E+02 3.037E-07 -1.260E-06 0.000E+00 M= 1.296E-06 #,Z,Y,X= 0 1 10 40 + 6.167500E+02 2.564250E+03 9.894500E+01 -1.651E-07 -1.280E-06 0.000E+00 M= 1.290E-06 #,Z,Y,X= 0 1 10 41 + 6.172500E+02 2.564250E+03 9.228000E+01 -1.066E-06 -2.367E-06 0.000E+00 M= 2.596E-06 #,Z,Y,X= 0 1 10 42 + 6.177500E+02 2.564250E+03 9.973500E+01 -1.674E-06 -3.220E-06 0.000E+00 M= 3.629E-06 #,Z,Y,X= 0 1 10 43 + 6.182500E+02 2.564250E+03 9.051500E+01 -1.303E-06 -4.362E-06 0.000E+00 M= 4.552E-06 #,Z,Y,X= 0 1 10 44 + 6.187500E+02 2.564250E+03 9.920500E+01 -8.916E-07 -2.098E-06 0.000E+00 M= 2.280E-06 #,Z,Y,X= 0 1 10 45 + 6.192500E+02 2.564250E+03 9.780000E+01 -1.068E-06 -1.290E-06 0.000E+00 M= 1.675E-06 #,Z,Y,X= 0 1 10 46 + 6.197500E+02 2.564250E+03 9.418000E+01 -8.108E-07 -1.430E-06 0.000E+00 M= 1.644E-06 #,Z,Y,X= 0 1 10 47 + 6.202500E+02 2.564250E+03 1.028200E+02 -4.300E-07 -1.747E-07 0.000E+00 M= 4.642E-07 #,Z,Y,X= 0 1 10 48 + 6.207500E+02 2.564250E+03 1.048950E+02 -1.492E-07 -9.144E-08 0.000E+00 M= 1.750E-07 #,Z,Y,X= 0 1 10 49 + 6.082500E+02 2.563750E+03 9.758500E+01 0.000E+00 -4.485E-07 0.000E+00 M= 4.485E-07 #,Z,Y,X= 0 1 11 24 + 6.087500E+02 2.563750E+03 9.249000E+01 1.526E-06 -2.432E-06 0.000E+00 M= 2.871E-06 #,Z,Y,X= 0 1 11 25 + 6.092500E+02 2.563750E+03 9.396500E+01 1.624E-06 -3.133E-06 0.000E+00 M= 3.529E-06 #,Z,Y,X= 0 1 11 26 + 6.097500E+02 2.563750E+03 9.948000E+01 -1.167E-06 -2.252E-06 0.000E+00 M= 2.536E-06 #,Z,Y,X= 0 1 11 27 + 6.102500E+02 2.563750E+03 9.892500E+01 -1.066E-06 -2.477E-06 0.000E+00 M= 2.697E-06 #,Z,Y,X= 0 1 11 28 + 6.107500E+02 2.563750E+03 1.038700E+02 0.000E+00 -2.473E-07 0.000E+00 M= 2.473E-07 #,Z,Y,X= 0 1 11 29 + 6.112500E+02 2.563750E+03 1.055300E+02 0.000E+00 -3.914E-07 0.000E+00 M= 3.914E-07 #,Z,Y,X= 0 1 11 30 + 6.137500E+02 2.563750E+03 9.945500E+01 -2.185E-07 -2.510E-07 0.000E+00 M= 3.328E-07 #,Z,Y,X= 0 1 11 35 + 6.142500E+02 2.563750E+03 9.702000E+01 -4.309E-07 -6.095E-07 0.000E+00 M= 7.464E-07 #,Z,Y,X= 0 1 11 36 + 6.147500E+02 2.563750E+03 9.975000E+01 -3.153E-07 -8.298E-07 0.000E+00 M= 8.877E-07 #,Z,Y,X= 0 1 11 37 + 6.152500E+02 2.563750E+03 1.019500E+02 -2.215E-07 -7.485E-07 0.000E+00 M= 7.806E-07 #,Z,Y,X= 0 1 11 38 + 6.157500E+02 2.563750E+03 9.366500E+01 -1.082E-07 -1.151E-06 0.000E+00 M= 1.156E-06 #,Z,Y,X= 0 1 11 39 + 6.162500E+02 2.563750E+03 9.905500E+01 1.063E-08 -1.267E-06 0.000E+00 M= 1.267E-06 #,Z,Y,X= 0 1 11 40 + 6.167500E+02 2.563750E+03 9.096500E+01 -3.482E-07 -2.096E-06 0.000E+00 M= 2.125E-06 #,Z,Y,X= 0 1 11 41 + 6.172500E+02 2.563750E+03 8.754000E+01 -1.092E-06 -3.004E-06 0.000E+00 M= 3.196E-06 #,Z,Y,X= 0 1 11 42 + 6.177500E+02 2.563750E+03 9.729500E+01 -1.177E-06 -2.873E-06 0.000E+00 M= 3.105E-06 #,Z,Y,X= 0 1 11 43 + 6.182500E+02 2.563750E+03 8.902000E+01 -1.008E-06 -3.653E-06 0.000E+00 M= 3.790E-06 #,Z,Y,X= 0 1 11 44 + 6.187500E+02 2.563750E+03 9.276500E+01 -1.292E-06 -2.266E-06 0.000E+00 M= 2.609E-06 #,Z,Y,X= 0 1 11 45 + 6.192500E+02 2.563750E+03 9.074500E+01 -1.117E-06 -9.639E-07 0.000E+00 M= 1.475E-06 #,Z,Y,X= 0 1 11 46 + 6.197500E+02 2.563750E+03 9.480500E+01 -4.626E-07 -7.932E-07 0.000E+00 M= 9.182E-07 #,Z,Y,X= 0 1 11 47 + 6.202500E+02 2.563750E+03 1.018400E+02 -6.487E-08 -4.158E-08 0.000E+00 M= 7.705E-08 #,Z,Y,X= 0 1 11 48 + 6.072500E+02 2.563250E+03 8.764000E+01 3.366E-08 0.000E+00 0.000E+00 M= 3.366E-08 #,Z,Y,X= 0 1 12 22 + 6.077500E+02 2.563250E+03 8.221000E+01 -4.370E-07 -5.463E-07 0.000E+00 M= 6.996E-07 #,Z,Y,X= 0 1 12 23 + 6.082500E+02 2.563250E+03 8.312000E+01 -1.671E-06 -1.684E-06 0.000E+00 M= 2.372E-06 #,Z,Y,X= 0 1 12 24 + 6.087500E+02 2.563250E+03 8.204000E+01 -2.217E-06 -4.307E-06 0.000E+00 M= 4.844E-06 #,Z,Y,X= 0 1 12 25 + 6.092500E+02 2.563250E+03 8.477000E+01 -1.543E-06 -4.203E-06 0.000E+00 M= 4.477E-06 #,Z,Y,X= 0 1 12 26 + 6.097500E+02 2.563250E+03 9.005500E+01 -5.081E-07 -1.974E-06 0.000E+00 M= 2.039E-06 #,Z,Y,X= 0 1 12 27 + 6.102500E+02 2.563250E+03 8.954000E+01 8.867E-08 -4.127E-06 0.000E+00 M= 4.128E-06 #,Z,Y,X= 0 1 12 28 + 6.107500E+02 2.563250E+03 9.888500E+01 -7.610E-08 -6.278E-07 0.000E+00 M= 6.324E-07 #,Z,Y,X= 0 1 12 29 + 6.112500E+02 2.563250E+03 1.022650E+02 -6.033E-08 -1.035E-06 0.000E+00 M= 1.037E-06 #,Z,Y,X= 0 1 12 30 + 6.137500E+02 2.563250E+03 9.478500E+01 -1.370E-07 -6.372E-07 0.000E+00 M= 6.517E-07 #,Z,Y,X= 0 1 12 35 + 6.142500E+02 2.563250E+03 9.631500E+01 -3.059E-07 -6.629E-07 0.000E+00 M= 7.301E-07 #,Z,Y,X= 0 1 12 36 + 6.147500E+02 2.563250E+03 1.011950E+02 -2.740E-07 -6.901E-07 0.000E+00 M= 7.425E-07 #,Z,Y,X= 0 1 12 37 + 6.152500E+02 2.563250E+03 9.402000E+01 -1.642E-07 -7.364E-07 0.000E+00 M= 7.545E-07 #,Z,Y,X= 0 1 12 38 + 6.157500E+02 2.563250E+03 9.212000E+01 -1.815E-07 -1.101E-06 0.000E+00 M= 1.116E-06 #,Z,Y,X= 0 1 12 39 + 6.162500E+02 2.563250E+03 9.320000E+01 -2.227E-07 -1.211E-06 0.000E+00 M= 1.231E-06 #,Z,Y,X= 0 1 12 40 + 6.167500E+02 2.563250E+03 8.908000E+01 -2.789E-07 -2.338E-06 0.000E+00 M= 2.355E-06 #,Z,Y,X= 0 1 12 41 + 6.172500E+02 2.563250E+03 8.872500E+01 -5.314E-07 -3.195E-06 0.000E+00 M= 3.239E-06 #,Z,Y,X= 0 1 12 42 + 6.177500E+02 2.563250E+03 8.887500E+01 -1.323E-06 -2.846E-06 0.000E+00 M= 3.139E-06 #,Z,Y,X= 0 1 12 43 + 6.182500E+02 2.563250E+03 8.728000E+01 -1.869E-06 -3.129E-06 0.000E+00 M= 3.644E-06 #,Z,Y,X= 0 1 12 44 + 6.187500E+02 2.563250E+03 8.395000E+01 -1.417E-06 -1.632E-06 0.000E+00 M= 2.161E-06 #,Z,Y,X= 0 1 12 45 + 6.192500E+02 2.563250E+03 9.599500E+01 -7.742E-07 -3.599E-07 0.000E+00 M= 8.538E-07 #,Z,Y,X= 0 1 12 46 + 6.197500E+02 2.563250E+03 9.815500E+01 -2.597E-07 -2.071E-07 0.000E+00 M= 3.321E-07 #,Z,Y,X= 0 1 12 47 + 6.077500E+02 2.562750E+03 8.362500E+01 -2.640E-07 -1.366E-06 0.000E+00 M= 1.392E-06 #,Z,Y,X= 0 1 13 23 + 6.082500E+02 2.562750E+03 7.442500E+01 -1.021E-06 -2.758E-06 0.000E+00 M= 2.941E-06 #,Z,Y,X= 0 1 13 24 + 6.087500E+02 2.562750E+03 7.989500E+01 -1.335E-06 -3.430E-06 0.000E+00 M= 3.681E-06 #,Z,Y,X= 0 1 13 25 + 6.092500E+02 2.562750E+03 9.029500E+01 -3.827E-07 -2.882E-06 0.000E+00 M= 2.907E-06 #,Z,Y,X= 0 1 13 26 + 6.097500E+02 2.562750E+03 9.462500E+01 4.867E-07 -1.006E-06 0.000E+00 M= 1.118E-06 #,Z,Y,X= 0 1 13 27 + 6.102500E+02 2.562750E+03 9.734000E+01 1.670E-06 -2.979E-06 0.000E+00 M= 3.415E-06 #,Z,Y,X= 0 1 13 28 + 6.107500E+02 2.562750E+03 9.911000E+01 2.166E-06 -1.654E-06 0.000E+00 M= 2.725E-06 #,Z,Y,X= 0 1 13 29 + 6.112500E+02 2.562750E+03 9.561500E+01 6.996E-07 -1.918E-06 0.000E+00 M= 2.041E-06 #,Z,Y,X= 0 1 13 30 + 6.137500E+02 2.562750E+03 9.777000E+01 -1.868E-07 -9.599E-07 0.000E+00 M= 9.779E-07 #,Z,Y,X= 0 1 13 35 + 6.142500E+02 2.562750E+03 9.626500E+01 -3.878E-07 -7.160E-07 0.000E+00 M= 8.142E-07 #,Z,Y,X= 0 1 13 36 + 6.147500E+02 2.562750E+03 9.816000E+01 -2.911E-07 -5.296E-07 0.000E+00 M= 6.043E-07 #,Z,Y,X= 0 1 13 37 + 6.152500E+02 2.562750E+03 9.196500E+01 -2.371E-07 -7.469E-07 0.000E+00 M= 7.836E-07 #,Z,Y,X= 0 1 13 38 + 6.157500E+02 2.562750E+03 8.656500E+01 -1.858E-07 -1.030E-06 0.000E+00 M= 1.047E-06 #,Z,Y,X= 0 1 13 39 + 6.162500E+02 2.562750E+03 8.766500E+01 -1.644E-07 -1.234E-06 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 13 40 + 6.167500E+02 2.562750E+03 8.096500E+01 -3.329E-07 -2.385E-06 0.000E+00 M= 2.408E-06 #,Z,Y,X= 0 1 13 41 + 6.172500E+02 2.562750E+03 8.438000E+01 -7.783E-07 -3.494E-06 0.000E+00 M= 3.580E-06 #,Z,Y,X= 0 1 13 42 + 6.177500E+02 2.562750E+03 7.673500E+01 -1.950E-06 -4.033E-06 0.000E+00 M= 4.479E-06 #,Z,Y,X= 0 1 13 43 + 6.182500E+02 2.562750E+03 8.053500E+01 -1.769E-06 -1.804E-06 0.000E+00 M= 2.527E-06 #,Z,Y,X= 0 1 13 44 + 6.187500E+02 2.562750E+03 9.299500E+01 -4.750E-07 -8.298E-07 0.000E+00 M= 9.561E-07 #,Z,Y,X= 0 1 13 45 + 6.192500E+02 2.562750E+03 9.603000E+01 -8.301E-08 -6.315E-08 0.000E+00 M= 1.043E-07 #,Z,Y,X= 0 1 13 46 + 6.067500E+02 2.562250E+03 8.636000E+01 1.480E-08 -2.033E-08 0.000E+00 M= 2.515E-08 #,Z,Y,X= 0 1 14 21 + 6.072500E+02 2.562250E+03 7.963500E+01 -3.157E-07 -3.796E-07 0.000E+00 M= 4.937E-07 #,Z,Y,X= 0 1 14 22 + 6.077500E+02 2.562250E+03 7.469000E+01 -7.591E-07 -1.734E-06 0.000E+00 M= 1.893E-06 #,Z,Y,X= 0 1 14 23 + 6.082500E+02 2.562250E+03 7.000000E+01 -1.349E-06 -3.500E-06 0.000E+00 M= 3.751E-06 #,Z,Y,X= 0 1 14 24 + 6.087500E+02 2.562250E+03 7.623500E+01 -8.965E-07 -1.788E-06 0.000E+00 M= 2.000E-06 #,Z,Y,X= 0 1 14 25 + 6.092500E+02 2.562250E+03 8.714000E+01 4.294E-07 -1.582E-06 0.000E+00 M= 1.639E-06 #,Z,Y,X= 0 1 14 26 + 6.097500E+02 2.562250E+03 9.288000E+01 7.329E-07 -7.807E-07 0.000E+00 M= 1.071E-06 #,Z,Y,X= 0 1 14 27 + 6.102500E+02 2.562250E+03 8.889500E+01 6.471E-07 -1.799E-06 0.000E+00 M= 1.912E-06 #,Z,Y,X= 0 1 14 28 + 6.107500E+02 2.562250E+03 8.014500E+01 9.803E-07 -1.573E-06 0.000E+00 M= 1.854E-06 #,Z,Y,X= 0 1 14 29 + 6.112500E+02 2.562250E+03 8.488000E+01 1.440E-06 -2.065E-06 0.000E+00 M= 2.518E-06 #,Z,Y,X= 0 1 14 30 + 6.117500E+02 2.562250E+03 9.596000E+01 1.034E-06 -6.770E-07 0.000E+00 M= 1.236E-06 #,Z,Y,X= 0 1 14 31 + 6.122500E+02 2.562250E+03 1.024550E+02 -1.526E-07 -6.576E-07 0.000E+00 M= 6.751E-07 #,Z,Y,X= 0 1 14 32 + 6.127500E+02 2.562250E+03 1.016900E+02 -7.524E-07 -1.112E-07 0.000E+00 M= 7.606E-07 #,Z,Y,X= 0 1 14 33 + 6.132500E+02 2.562250E+03 9.814000E+01 -7.887E-07 -2.058E-07 0.000E+00 M= 8.151E-07 #,Z,Y,X= 0 1 14 34 + 6.137500E+02 2.562250E+03 9.847500E+01 -5.039E-07 -9.432E-07 0.000E+00 M= 1.069E-06 #,Z,Y,X= 0 1 14 35 + 6.142500E+02 2.562250E+03 9.793500E+01 -2.144E-07 -6.972E-07 0.000E+00 M= 7.294E-07 #,Z,Y,X= 0 1 14 36 + 6.147500E+02 2.562250E+03 9.292000E+01 -2.453E-07 -5.033E-07 0.000E+00 M= 5.599E-07 #,Z,Y,X= 0 1 14 37 + 6.152500E+02 2.562250E+03 8.622000E+01 -3.285E-07 -7.759E-07 0.000E+00 M= 8.426E-07 #,Z,Y,X= 0 1 14 38 + 6.157500E+02 2.562250E+03 8.125500E+01 -1.767E-07 -6.908E-07 0.000E+00 M= 7.131E-07 #,Z,Y,X= 0 1 14 39 + 6.162500E+02 2.562250E+03 7.850500E+01 -8.507E-08 -1.275E-06 0.000E+00 M= 1.278E-06 #,Z,Y,X= 0 1 14 40 + 6.167500E+02 2.562250E+03 7.743500E+01 -5.494E-08 -2.158E-06 0.000E+00 M= 2.159E-06 #,Z,Y,X= 0 1 14 41 + 6.172500E+02 2.562250E+03 7.181000E+01 -4.597E-07 -3.997E-06 0.000E+00 M= 4.023E-06 #,Z,Y,X= 0 1 14 42 + 6.177500E+02 2.562250E+03 8.004500E+01 -4.495E-07 -3.928E-06 0.000E+00 M= 3.953E-06 #,Z,Y,X= 0 1 14 43 + 6.182500E+02 2.562250E+03 9.013500E+01 -6.657E-09 -8.049E-07 0.000E+00 M= 8.049E-07 #,Z,Y,X= 0 1 14 44 + 6.187500E+02 2.562250E+03 9.081500E+01 -1.895E-08 -4.796E-07 0.000E+00 M= 4.799E-07 #,Z,Y,X= 0 1 14 45 + 6.042500E+02 2.561750E+03 9.062500E+01 0.000E+00 -9.630E-08 0.000E+00 M= 9.630E-08 #,Z,Y,X= 0 1 15 16 + 6.047500E+02 2.561750E+03 9.014000E+01 0.000E+00 -6.395E-07 0.000E+00 M= 6.395E-07 #,Z,Y,X= 0 1 15 17 + 6.052500E+02 2.561750E+03 9.168000E+01 0.000E+00 -4.042E-07 0.000E+00 M= 4.042E-07 #,Z,Y,X= 0 1 15 18 + 6.062500E+02 2.561750E+03 8.965000E+01 3.413E-08 -3.133E-09 0.000E+00 M= 3.427E-08 #,Z,Y,X= 0 1 15 20 + 6.067500E+02 2.561750E+03 8.441000E+01 7.418E-08 -6.354E-08 0.000E+00 M= 9.768E-08 #,Z,Y,X= 0 1 15 21 + 6.072500E+02 2.561750E+03 7.266000E+01 1.055E-07 -7.576E-07 0.000E+00 M= 7.649E-07 #,Z,Y,X= 0 1 15 22 + 6.077500E+02 2.561750E+03 6.531000E+01 -2.754E-08 -1.988E-06 0.000E+00 M= 1.988E-06 #,Z,Y,X= 0 1 15 23 + 6.082500E+02 2.561750E+03 7.161000E+01 -7.912E-08 -3.708E-06 0.000E+00 M= 3.709E-06 #,Z,Y,X= 0 1 15 24 + 6.087500E+02 2.561750E+03 8.739000E+01 1.003E-07 -7.391E-07 0.000E+00 M= 7.459E-07 #,Z,Y,X= 0 1 15 25 + 6.092500E+02 2.561750E+03 9.009000E+01 2.837E-07 -1.125E-06 0.000E+00 M= 1.160E-06 #,Z,Y,X= 0 1 15 26 + 6.097500E+02 2.561750E+03 8.826000E+01 3.896E-07 -8.193E-07 0.000E+00 M= 9.073E-07 #,Z,Y,X= 0 1 15 27 + 6.102500E+02 2.561750E+03 7.290000E+01 1.343E-07 -2.008E-06 0.000E+00 M= 2.012E-06 #,Z,Y,X= 0 1 15 28 + 6.107500E+02 2.561750E+03 8.718500E+01 1.962E-08 -9.821E-07 0.000E+00 M= 9.823E-07 #,Z,Y,X= 0 1 15 29 + 6.112500E+02 2.561750E+03 9.396000E+01 4.453E-07 -1.628E-06 0.000E+00 M= 1.687E-06 #,Z,Y,X= 0 1 15 30 + 6.117500E+02 2.561750E+03 9.807500E+01 5.712E-07 -1.448E-06 0.000E+00 M= 1.557E-06 #,Z,Y,X= 0 1 15 31 + 6.122500E+02 2.561750E+03 9.595500E+01 -1.409E-08 -1.488E-06 0.000E+00 M= 1.488E-06 #,Z,Y,X= 0 1 15 32 + 6.127500E+02 2.561750E+03 8.843500E+01 -4.641E-07 -3.436E-07 0.000E+00 M= 5.774E-07 #,Z,Y,X= 0 1 15 33 + 6.132500E+02 2.561750E+03 8.677500E+01 -5.011E-07 -4.566E-07 0.000E+00 M= 6.780E-07 #,Z,Y,X= 0 1 15 34 + 6.137500E+02 2.561750E+03 9.360500E+01 -3.901E-07 -7.342E-07 0.000E+00 M= 8.314E-07 #,Z,Y,X= 0 1 15 35 + 6.142500E+02 2.561750E+03 9.496500E+01 -2.875E-07 -7.238E-07 0.000E+00 M= 7.788E-07 #,Z,Y,X= 0 1 15 36 + 6.147500E+02 2.561750E+03 9.305500E+01 -3.102E-07 -6.634E-07 0.000E+00 M= 7.323E-07 #,Z,Y,X= 0 1 15 37 + 6.152500E+02 2.561750E+03 9.059500E+01 -2.678E-07 -7.669E-07 0.000E+00 M= 8.123E-07 #,Z,Y,X= 0 1 15 38 + 6.157500E+02 2.561750E+03 8.889000E+01 -2.187E-07 -5.759E-07 0.000E+00 M= 6.160E-07 #,Z,Y,X= 0 1 15 39 + 6.162500E+02 2.561750E+03 8.537000E+01 5.890E-08 -1.023E-06 0.000E+00 M= 1.025E-06 #,Z,Y,X= 0 1 15 40 + 6.167500E+02 2.561750E+03 7.915000E+01 1.997E-08 -2.390E-06 0.000E+00 M= 2.390E-06 #,Z,Y,X= 0 1 15 41 + 6.172500E+02 2.561750E+03 6.870000E+01 -4.079E-07 -4.552E-06 0.000E+00 M= 4.570E-06 #,Z,Y,X= 0 1 15 42 + 6.177500E+02 2.561750E+03 8.179500E+01 -1.411E-07 -3.058E-06 0.000E+00 M= 3.061E-06 #,Z,Y,X= 0 1 15 43 + 6.182500E+02 2.561750E+03 8.008500E+01 1.425E-07 -8.969E-07 0.000E+00 M= 9.082E-07 #,Z,Y,X= 0 1 15 44 + 6.187500E+02 2.561750E+03 8.545500E+01 4.299E-08 -5.192E-07 0.000E+00 M= 5.209E-07 #,Z,Y,X= 0 1 15 45 + 6.032500E+02 2.561250E+03 9.268000E+01 1.404E-07 0.000E+00 0.000E+00 M= 1.404E-07 #,Z,Y,X= 0 1 16 14 + 6.037500E+02 2.561250E+03 9.079000E+01 2.755E-07 -5.858E-08 0.000E+00 M= 2.817E-07 #,Z,Y,X= 0 1 16 15 + 6.042500E+02 2.561250E+03 8.662500E+01 3.610E-07 -1.496E-07 0.000E+00 M= 3.908E-07 #,Z,Y,X= 0 1 16 16 + 6.047500E+02 2.561250E+03 8.052500E+01 9.072E-07 -1.026E-06 0.000E+00 M= 1.370E-06 #,Z,Y,X= 0 1 16 17 + 6.052500E+02 2.561250E+03 7.559000E+01 1.402E-06 -8.505E-07 0.000E+00 M= 1.640E-06 #,Z,Y,X= 0 1 16 18 + 6.057500E+02 2.561250E+03 7.911000E+01 1.237E-06 -2.379E-07 0.000E+00 M= 1.259E-06 #,Z,Y,X= 0 1 16 19 + 6.062500E+02 2.561250E+03 7.455000E+01 8.603E-07 -1.771E-07 0.000E+00 M= 8.784E-07 #,Z,Y,X= 0 1 16 20 + 6.067500E+02 2.561250E+03 7.852500E+01 5.676E-07 -2.364E-07 0.000E+00 M= 6.149E-07 #,Z,Y,X= 0 1 16 21 + 6.072500E+02 2.561250E+03 7.638000E+01 5.192E-07 -6.773E-07 0.000E+00 M= 8.534E-07 #,Z,Y,X= 0 1 16 22 + 6.077500E+02 2.561250E+03 6.937000E+01 8.581E-07 -1.804E-06 0.000E+00 M= 1.998E-06 #,Z,Y,X= 0 1 16 23 + 6.082500E+02 2.561250E+03 6.227500E+01 1.234E-06 -3.264E-06 0.000E+00 M= 3.490E-06 #,Z,Y,X= 0 1 16 24 + 6.087500E+02 2.561250E+03 7.696500E+01 1.249E-06 -7.622E-07 0.000E+00 M= 1.464E-06 #,Z,Y,X= 0 1 16 25 + 6.092500E+02 2.561250E+03 7.506000E+01 8.778E-07 -1.247E-06 0.000E+00 M= 1.525E-06 #,Z,Y,X= 0 1 16 26 + 6.097500E+02 2.561250E+03 7.401500E+01 2.122E-07 -1.169E-06 0.000E+00 M= 1.188E-06 #,Z,Y,X= 0 1 16 27 + 6.102500E+02 2.561250E+03 7.295000E+01 7.138E-08 -1.851E-06 0.000E+00 M= 1.853E-06 #,Z,Y,X= 0 1 16 28 + 6.107500E+02 2.561250E+03 8.329500E+01 1.810E-07 -9.589E-07 0.000E+00 M= 9.758E-07 #,Z,Y,X= 0 1 16 29 + 6.112500E+02 2.561250E+03 8.935000E+01 1.569E-08 -1.293E-06 0.000E+00 M= 1.293E-06 #,Z,Y,X= 0 1 16 30 + 6.117500E+02 2.561250E+03 9.290500E+01 -1.333E-07 -1.599E-06 0.000E+00 M= 1.604E-06 #,Z,Y,X= 0 1 16 31 + 6.122500E+02 2.561250E+03 8.389000E+01 -4.824E-07 -1.751E-06 0.000E+00 M= 1.816E-06 #,Z,Y,X= 0 1 16 32 + 6.127500E+02 2.561250E+03 8.439000E+01 -5.896E-07 -3.212E-07 0.000E+00 M= 6.714E-07 #,Z,Y,X= 0 1 16 33 + 6.132500E+02 2.561250E+03 7.567000E+01 -3.747E-07 -4.408E-07 0.000E+00 M= 5.786E-07 #,Z,Y,X= 0 1 16 34 + 6.137500E+02 2.561250E+03 8.560500E+01 -2.799E-07 -6.921E-07 0.000E+00 M= 7.465E-07 #,Z,Y,X= 0 1 16 35 + 6.142500E+02 2.561250E+03 8.607000E+01 -2.749E-07 -7.760E-07 0.000E+00 M= 8.232E-07 #,Z,Y,X= 0 1 16 36 + 6.147500E+02 2.561250E+03 8.529000E+01 -3.137E-07 -7.688E-07 0.000E+00 M= 8.303E-07 #,Z,Y,X= 0 1 16 37 + 6.152500E+02 2.561250E+03 8.803500E+01 -3.141E-07 -7.351E-07 0.000E+00 M= 7.994E-07 #,Z,Y,X= 0 1 16 38 + 6.157500E+02 2.561250E+03 8.495000E+01 -2.922E-07 -6.556E-07 0.000E+00 M= 7.178E-07 #,Z,Y,X= 0 1 16 39 + 6.162500E+02 2.561250E+03 7.445500E+01 -2.436E-07 -7.105E-07 0.000E+00 M= 7.511E-07 #,Z,Y,X= 0 1 16 40 + 6.167500E+02 2.561250E+03 7.077500E+01 -9.271E-07 -3.482E-06 0.000E+00 M= 3.603E-06 #,Z,Y,X= 0 1 16 41 + 6.172500E+02 2.561250E+03 7.397500E+01 -7.482E-07 -3.787E-06 0.000E+00 M= 3.860E-06 #,Z,Y,X= 0 1 16 42 + 6.177500E+02 2.561250E+03 7.690500E+01 1.074E-07 -2.707E-06 0.000E+00 M= 2.709E-06 #,Z,Y,X= 0 1 16 43 + 6.182500E+02 2.561250E+03 8.352000E+01 5.793E-08 -1.032E-06 0.000E+00 M= 1.033E-06 #,Z,Y,X= 0 1 16 44 + 6.187500E+02 2.561250E+03 8.259500E+01 9.103E-11 -5.810E-07 0.000E+00 M= 5.810E-07 #,Z,Y,X= 0 1 16 45 + 6.032500E+02 2.560750E+03 9.874500E+01 3.171E-07 0.000E+00 0.000E+00 M= 3.171E-07 #,Z,Y,X= 0 1 17 14 + 6.037500E+02 2.560750E+03 9.630500E+01 6.522E-07 -1.701E-07 0.000E+00 M= 6.740E-07 #,Z,Y,X= 0 1 17 15 + 6.042500E+02 2.560750E+03 9.056500E+01 7.580E-07 -8.479E-08 0.000E+00 M= 7.628E-07 #,Z,Y,X= 0 1 17 16 + 6.047500E+02 2.560750E+03 8.333500E+01 1.226E-06 -3.563E-07 0.000E+00 M= 1.276E-06 #,Z,Y,X= 0 1 17 17 + 6.052500E+02 2.560750E+03 7.829500E+01 1.870E-06 -5.770E-07 0.000E+00 M= 1.957E-06 #,Z,Y,X= 0 1 17 18 + 6.057500E+02 2.560750E+03 6.832500E+01 2.227E-06 -2.731E-07 0.000E+00 M= 2.244E-06 #,Z,Y,X= 0 1 17 19 + 6.062500E+02 2.560750E+03 6.640500E+01 2.110E-06 -6.120E-07 0.000E+00 M= 2.197E-06 #,Z,Y,X= 0 1 17 20 + 6.067500E+02 2.560750E+03 6.300500E+01 1.608E-06 -5.880E-07 0.000E+00 M= 1.712E-06 #,Z,Y,X= 0 1 17 21 + 6.072500E+02 2.560750E+03 6.689500E+01 1.437E-06 -6.340E-07 0.000E+00 M= 1.570E-06 #,Z,Y,X= 0 1 17 22 + 6.077500E+02 2.560750E+03 6.548500E+01 1.807E-06 -1.214E-06 0.000E+00 M= 2.177E-06 #,Z,Y,X= 0 1 17 23 + 6.082500E+02 2.560750E+03 5.690000E+01 1.990E-06 -3.286E-06 0.000E+00 M= 3.842E-06 #,Z,Y,X= 0 1 17 24 + 6.087500E+02 2.560750E+03 7.104500E+01 1.670E-06 -1.034E-06 0.000E+00 M= 1.964E-06 #,Z,Y,X= 0 1 17 25 + 6.092500E+02 2.560750E+03 6.515000E+01 1.253E-06 -1.801E-06 0.000E+00 M= 2.194E-06 #,Z,Y,X= 0 1 17 26 + 6.097500E+02 2.560750E+03 6.020000E+01 5.535E-07 -1.960E-06 0.000E+00 M= 2.037E-06 #,Z,Y,X= 0 1 17 27 + 6.102500E+02 2.560750E+03 7.764500E+01 3.462E-07 -1.429E-06 0.000E+00 M= 1.471E-06 #,Z,Y,X= 0 1 17 28 + 6.107500E+02 2.560750E+03 8.298000E+01 3.770E-07 -1.307E-06 0.000E+00 M= 1.360E-06 #,Z,Y,X= 0 1 17 29 + 6.112500E+02 2.560750E+03 7.893500E+01 -2.343E-07 -1.775E-06 0.000E+00 M= 1.790E-06 #,Z,Y,X= 0 1 17 30 + 6.117500E+02 2.560750E+03 8.254500E+01 -8.426E-07 -1.809E-06 0.000E+00 M= 1.996E-06 #,Z,Y,X= 0 1 17 31 + 6.122500E+02 2.560750E+03 8.674000E+01 -8.395E-07 -1.694E-06 0.000E+00 M= 1.890E-06 #,Z,Y,X= 0 1 17 32 + 6.127500E+02 2.560750E+03 7.805000E+01 -8.266E-07 -4.107E-07 0.000E+00 M= 9.230E-07 #,Z,Y,X= 0 1 17 33 + 6.132500E+02 2.560750E+03 7.275500E+01 -1.688E-06 -1.148E-06 0.000E+00 M= 2.041E-06 #,Z,Y,X= 0 1 17 34 + 6.137500E+02 2.560750E+03 7.970000E+01 -2.560E-06 -9.537E-07 0.000E+00 M= 2.732E-06 #,Z,Y,X= 0 1 17 35 + 6.142500E+02 2.560750E+03 7.481500E+01 -2.537E-06 -7.370E-07 0.000E+00 M= 2.642E-06 #,Z,Y,X= 0 1 17 36 + 6.147500E+02 2.560750E+03 8.061000E+01 -2.065E-06 -5.969E-07 0.000E+00 M= 2.149E-06 #,Z,Y,X= 0 1 17 37 + 6.152500E+02 2.560750E+03 8.896500E+01 -1.557E-06 -6.408E-07 0.000E+00 M= 1.684E-06 #,Z,Y,X= 0 1 17 38 + 6.157500E+02 2.560750E+03 8.385000E+01 -1.379E-06 -7.400E-07 0.000E+00 M= 1.565E-06 #,Z,Y,X= 0 1 17 39 + 6.162500E+02 2.560750E+03 7.861000E+01 -1.547E-06 -9.206E-07 0.000E+00 M= 1.800E-06 #,Z,Y,X= 0 1 17 40 + 6.167500E+02 2.560750E+03 6.965000E+01 -1.129E-06 -3.819E-06 0.000E+00 M= 3.982E-06 #,Z,Y,X= 0 1 17 41 + 6.172500E+02 2.560750E+03 7.491500E+01 -2.062E-07 -2.784E-06 0.000E+00 M= 2.791E-06 #,Z,Y,X= 0 1 17 42 + 6.177500E+02 2.560750E+03 7.730500E+01 2.391E-07 -2.730E-06 0.000E+00 M= 2.741E-06 #,Z,Y,X= 0 1 17 43 + 6.182500E+02 2.560750E+03 7.538000E+01 1.512E-07 -1.372E-06 0.000E+00 M= 1.380E-06 #,Z,Y,X= 0 1 17 44 + 6.187500E+02 2.560750E+03 8.183000E+01 7.273E-08 -5.193E-07 0.000E+00 M= 5.244E-07 #,Z,Y,X= 0 1 17 45 + 6.192500E+02 2.560750E+03 8.462000E+01 9.597E-08 -1.155E-07 0.000E+00 M= 1.502E-07 #,Z,Y,X= 0 1 17 46 + 6.032500E+02 2.560250E+03 9.581500E+01 1.698E-07 0.000E+00 0.000E+00 M= 1.698E-07 #,Z,Y,X= 0 1 18 14 + 6.037500E+02 2.560250E+03 9.573000E+01 3.622E-07 -2.533E-07 0.000E+00 M= 4.420E-07 #,Z,Y,X= 0 1 18 15 + 6.042500E+02 2.560250E+03 9.433500E+01 3.479E-07 -1.426E-07 0.000E+00 M= 3.760E-07 #,Z,Y,X= 0 1 18 16 + 6.047500E+02 2.560250E+03 8.885500E+01 3.092E-07 -1.218E-07 0.000E+00 M= 3.323E-07 #,Z,Y,X= 0 1 18 17 + 6.052500E+02 2.560250E+03 7.863000E+01 3.132E-07 -3.340E-07 0.000E+00 M= 4.579E-07 #,Z,Y,X= 0 1 18 18 + 6.057500E+02 2.560250E+03 8.064000E+01 3.081E-07 -1.385E-07 0.000E+00 M= 3.379E-07 #,Z,Y,X= 0 1 18 19 + 6.062500E+02 2.560250E+03 6.680500E+01 4.290E-07 -7.217E-07 0.000E+00 M= 8.395E-07 #,Z,Y,X= 0 1 18 20 + 6.067500E+02 2.560250E+03 6.213000E+01 8.303E-07 -5.016E-07 0.000E+00 M= 9.700E-07 #,Z,Y,X= 0 1 18 21 + 6.072500E+02 2.560250E+03 6.011500E+01 1.291E-06 -4.802E-07 0.000E+00 M= 1.377E-06 #,Z,Y,X= 0 1 18 22 + 6.077500E+02 2.560250E+03 5.887000E+01 1.791E-06 -6.080E-07 0.000E+00 M= 1.891E-06 #,Z,Y,X= 0 1 18 23 + 6.082500E+02 2.560250E+03 5.586500E+01 2.792E-06 -2.673E-06 0.000E+00 M= 3.865E-06 #,Z,Y,X= 0 1 18 24 + 6.087500E+02 2.560250E+03 5.617000E+01 2.799E-06 -1.936E-06 0.000E+00 M= 3.403E-06 #,Z,Y,X= 0 1 18 25 + 6.092500E+02 2.560250E+03 5.694500E+01 1.322E-06 -2.849E-06 0.000E+00 M= 3.140E-06 #,Z,Y,X= 0 1 18 26 + 6.097500E+02 2.560250E+03 6.458500E+01 4.491E-07 -2.483E-06 0.000E+00 M= 2.523E-06 #,Z,Y,X= 0 1 18 27 + 6.102500E+02 2.560250E+03 7.004500E+01 3.692E-07 -1.248E-06 0.000E+00 M= 1.301E-06 #,Z,Y,X= 0 1 18 28 + 6.107500E+02 2.560250E+03 6.527000E+01 8.936E-08 -1.825E-06 0.000E+00 M= 1.827E-06 #,Z,Y,X= 0 1 18 29 + 6.112500E+02 2.560250E+03 8.098000E+01 -5.780E-07 -2.529E-06 0.000E+00 M= 2.594E-06 #,Z,Y,X= 0 1 18 30 + 6.117500E+02 2.560250E+03 7.117000E+01 -1.185E-06 -2.129E-06 0.000E+00 M= 2.436E-06 #,Z,Y,X= 0 1 18 31 + 6.122500E+02 2.560250E+03 7.462500E+01 -1.731E-06 -1.776E-06 0.000E+00 M= 2.480E-06 #,Z,Y,X= 0 1 18 32 + 6.127500E+02 2.560250E+03 7.329500E+01 -2.295E-06 -8.947E-07 0.000E+00 M= 2.464E-06 #,Z,Y,X= 0 1 18 33 + 6.132500E+02 2.560250E+03 6.807000E+01 -2.609E-06 -1.851E-06 0.000E+00 M= 3.199E-06 #,Z,Y,X= 0 1 18 34 + 6.137500E+02 2.560250E+03 6.889500E+01 -2.147E-06 -6.831E-07 0.000E+00 M= 2.253E-06 #,Z,Y,X= 0 1 18 35 + 6.142500E+02 2.560250E+03 8.192000E+01 -1.363E-06 -3.727E-07 0.000E+00 M= 1.413E-06 #,Z,Y,X= 0 1 18 36 + 6.147500E+02 2.560250E+03 8.469000E+01 -1.058E-06 -3.434E-07 0.000E+00 M= 1.113E-06 #,Z,Y,X= 0 1 18 37 + 6.152500E+02 2.560250E+03 8.469500E+01 -1.076E-06 -5.608E-07 0.000E+00 M= 1.213E-06 #,Z,Y,X= 0 1 18 38 + 6.157500E+02 2.560250E+03 8.550500E+01 -1.202E-06 -8.405E-07 0.000E+00 M= 1.467E-06 #,Z,Y,X= 0 1 18 39 + 6.162500E+02 2.560250E+03 7.392500E+01 -1.340E-06 -1.229E-06 0.000E+00 M= 1.818E-06 #,Z,Y,X= 0 1 18 40 + 6.167500E+02 2.560250E+03 6.918500E+01 -7.042E-07 -2.570E-06 0.000E+00 M= 2.665E-06 #,Z,Y,X= 0 1 18 41 + 6.172500E+02 2.560250E+03 6.659000E+01 -4.717E-08 -2.622E-06 0.000E+00 M= 2.622E-06 #,Z,Y,X= 0 1 18 42 + 6.177500E+02 2.560250E+03 6.762000E+01 -1.776E-07 -2.783E-06 0.000E+00 M= 2.789E-06 #,Z,Y,X= 0 1 18 43 + 6.182500E+02 2.560250E+03 7.878500E+01 -8.187E-08 -1.474E-06 0.000E+00 M= 1.477E-06 #,Z,Y,X= 0 1 18 44 + 6.187500E+02 2.560250E+03 7.983000E+01 4.114E-09 -5.182E-07 0.000E+00 M= 5.182E-07 #,Z,Y,X= 0 1 18 45 + 6.192500E+02 2.560250E+03 8.064500E+01 -3.114E-08 -2.448E-07 0.000E+00 M= 2.467E-07 #,Z,Y,X= 0 1 18 46 + 6.037500E+02 2.559750E+03 9.397000E+01 1.035E-07 -2.346E-07 0.000E+00 M= 2.564E-07 #,Z,Y,X= 0 1 19 15 + 6.042500E+02 2.559750E+03 9.242000E+01 2.564E-07 -2.226E-07 0.000E+00 M= 3.396E-07 #,Z,Y,X= 0 1 19 16 + 6.047500E+02 2.559750E+03 9.006500E+01 2.446E-07 -2.789E-07 0.000E+00 M= 3.710E-07 #,Z,Y,X= 0 1 19 17 + 6.052500E+02 2.559750E+03 8.774000E+01 1.836E-07 -3.647E-07 0.000E+00 M= 4.083E-07 #,Z,Y,X= 0 1 19 18 + 6.057500E+02 2.559750E+03 7.876000E+01 1.314E-07 -2.599E-07 0.000E+00 M= 2.912E-07 #,Z,Y,X= 0 1 19 19 + 6.062500E+02 2.559750E+03 6.899500E+01 1.135E-07 -5.906E-07 0.000E+00 M= 6.015E-07 #,Z,Y,X= 0 1 19 20 + 6.067500E+02 2.559750E+03 7.942000E+01 1.190E-07 -3.034E-07 0.000E+00 M= 3.259E-07 #,Z,Y,X= 0 1 19 21 + 6.072500E+02 2.559750E+03 7.741500E+01 8.804E-08 -3.734E-07 0.000E+00 M= 3.836E-07 #,Z,Y,X= 0 1 19 22 + 6.077500E+02 2.559750E+03 7.762000E+01 9.903E-08 -3.677E-07 0.000E+00 M= 3.808E-07 #,Z,Y,X= 0 1 19 23 + 6.082500E+02 2.559750E+03 6.814500E+01 7.617E-07 -1.379E-06 0.000E+00 M= 1.575E-06 #,Z,Y,X= 0 1 19 24 + 6.087500E+02 2.559750E+03 5.958000E+01 1.708E-06 -2.467E-06 0.000E+00 M= 3.001E-06 #,Z,Y,X= 0 1 19 25 + 6.092500E+02 2.559750E+03 5.245500E+01 1.444E-06 -4.220E-06 0.000E+00 M= 4.461E-06 #,Z,Y,X= 0 1 19 26 + 6.097500E+02 2.559750E+03 6.220500E+01 5.837E-07 -2.964E-06 0.000E+00 M= 3.021E-06 #,Z,Y,X= 0 1 19 27 + 6.102500E+02 2.559750E+03 7.321500E+01 2.695E-07 -1.297E-06 0.000E+00 M= 1.325E-06 #,Z,Y,X= 0 1 19 28 + 6.107500E+02 2.559750E+03 6.081500E+01 -2.412E-07 -2.696E-06 0.000E+00 M= 2.707E-06 #,Z,Y,X= 0 1 19 29 + 6.112500E+02 2.559750E+03 6.482500E+01 -1.128E-06 -3.274E-06 0.000E+00 M= 3.463E-06 #,Z,Y,X= 0 1 19 30 + 6.117500E+02 2.559750E+03 6.438000E+01 -1.597E-06 -2.460E-06 0.000E+00 M= 2.933E-06 #,Z,Y,X= 0 1 19 31 + 6.122500E+02 2.559750E+03 6.236500E+01 -1.731E-06 -2.147E-06 0.000E+00 M= 2.758E-06 #,Z,Y,X= 0 1 19 32 + 6.127500E+02 2.559750E+03 6.817500E+01 -1.925E-06 -1.421E-06 0.000E+00 M= 2.393E-06 #,Z,Y,X= 0 1 19 33 + 6.132500E+02 2.559750E+03 6.211000E+01 -1.559E-06 -1.448E-06 0.000E+00 M= 2.128E-06 #,Z,Y,X= 0 1 19 34 + 6.137500E+02 2.559750E+03 8.205000E+01 -9.562E-07 -2.128E-07 0.000E+00 M= 9.796E-07 #,Z,Y,X= 0 1 19 35 + 6.142500E+02 2.559750E+03 8.556000E+01 -8.677E-07 -1.874E-07 0.000E+00 M= 8.877E-07 #,Z,Y,X= 0 1 19 36 + 6.147500E+02 2.559750E+03 8.225000E+01 -8.080E-07 -3.376E-07 0.000E+00 M= 8.757E-07 #,Z,Y,X= 0 1 19 37 + 6.152500E+02 2.559750E+03 7.521000E+01 -7.878E-07 -7.039E-07 0.000E+00 M= 1.056E-06 #,Z,Y,X= 0 1 19 38 + 6.157500E+02 2.559750E+03 7.940500E+01 -9.966E-07 -1.127E-06 0.000E+00 M= 1.504E-06 #,Z,Y,X= 0 1 19 39 + 6.162500E+02 2.559750E+03 7.780500E+01 -1.077E-06 -1.325E-06 0.000E+00 M= 1.707E-06 #,Z,Y,X= 0 1 19 40 + 6.167500E+02 2.559750E+03 7.061000E+01 -6.538E-07 -1.582E-06 0.000E+00 M= 1.712E-06 #,Z,Y,X= 0 1 19 41 + 6.172500E+02 2.559750E+03 6.508500E+01 -1.278E-07 -2.610E-06 0.000E+00 M= 2.613E-06 #,Z,Y,X= 0 1 19 42 + 6.177500E+02 2.559750E+03 6.732000E+01 -9.885E-08 -3.117E-06 0.000E+00 M= 3.119E-06 #,Z,Y,X= 0 1 19 43 + 6.182500E+02 2.559750E+03 7.065500E+01 -9.436E-08 -1.274E-06 0.000E+00 M= 1.277E-06 #,Z,Y,X= 0 1 19 44 + 6.187500E+02 2.559750E+03 6.986500E+01 -1.642E-07 -8.512E-07 0.000E+00 M= 8.668E-07 #,Z,Y,X= 0 1 19 45 + 6.192500E+02 2.559750E+03 7.967500E+01 -1.785E-07 -1.324E-07 0.000E+00 M= 2.222E-07 #,Z,Y,X= 0 1 19 46 + 6.032500E+02 2.559250E+03 9.362500E+01 1.838E-07 -2.640E-07 0.000E+00 M= 3.217E-07 #,Z,Y,X= 0 1 20 14 + 6.037500E+02 2.559250E+03 8.929500E+01 3.303E-07 -2.620E-07 0.000E+00 M= 4.216E-07 #,Z,Y,X= 0 1 20 15 + 6.042500E+02 2.559250E+03 8.899000E+01 2.699E-07 -2.886E-07 0.000E+00 M= 3.952E-07 #,Z,Y,X= 0 1 20 16 + 6.047500E+02 2.559250E+03 8.771500E+01 2.257E-07 -4.557E-07 0.000E+00 M= 5.085E-07 #,Z,Y,X= 0 1 20 17 + 6.052500E+02 2.559250E+03 8.779000E+01 1.638E-07 -4.735E-07 0.000E+00 M= 5.011E-07 #,Z,Y,X= 0 1 20 18 + 6.057500E+02 2.559250E+03 8.334000E+01 5.931E-08 -4.465E-07 0.000E+00 M= 4.504E-07 #,Z,Y,X= 0 1 20 19 + 6.062500E+02 2.559250E+03 8.208000E+01 6.800E-08 -5.509E-07 0.000E+00 M= 5.551E-07 #,Z,Y,X= 0 1 20 20 + 6.067500E+02 2.559250E+03 7.887500E+01 1.422E-07 -3.960E-07 0.000E+00 M= 4.207E-07 #,Z,Y,X= 0 1 20 21 + 6.072500E+02 2.559250E+03 7.552000E+01 9.687E-08 -4.939E-07 0.000E+00 M= 5.033E-07 #,Z,Y,X= 0 1 20 22 + 6.077500E+02 2.559250E+03 7.373000E+01 1.301E-08 -4.408E-07 0.000E+00 M= 4.409E-07 #,Z,Y,X= 0 1 20 23 + 6.082500E+02 2.559250E+03 7.611000E+01 7.877E-09 -7.227E-07 0.000E+00 M= 7.228E-07 #,Z,Y,X= 0 1 20 24 + 6.087500E+02 2.559250E+03 6.328000E+01 1.740E-07 -1.996E-06 0.000E+00 M= 2.003E-06 #,Z,Y,X= 0 1 20 25 + 6.092500E+02 2.559250E+03 5.499500E+01 4.927E-07 -4.430E-06 0.000E+00 M= 4.457E-06 #,Z,Y,X= 0 1 20 26 + 6.097500E+02 2.559250E+03 6.159500E+01 4.164E-07 -3.500E-06 0.000E+00 M= 3.525E-06 #,Z,Y,X= 0 1 20 27 + 6.102500E+02 2.559250E+03 7.065500E+01 6.194E-08 -1.415E-06 0.000E+00 M= 1.416E-06 #,Z,Y,X= 0 1 20 28 + 6.107500E+02 2.559250E+03 5.861500E+01 -2.650E-07 -3.365E-06 0.000E+00 M= 3.375E-06 #,Z,Y,X= 0 1 20 29 + 6.112500E+02 2.559250E+03 5.709500E+01 -1.355E-06 -4.422E-06 0.000E+00 M= 4.625E-06 #,Z,Y,X= 0 1 20 30 + 6.117500E+02 2.559250E+03 5.717000E+01 -1.995E-06 -2.305E-06 0.000E+00 M= 3.048E-06 #,Z,Y,X= 0 1 20 31 + 6.122500E+02 2.559250E+03 5.826500E+01 -1.847E-06 -2.212E-06 0.000E+00 M= 2.882E-06 #,Z,Y,X= 0 1 20 32 + 6.127500E+02 2.559250E+03 5.803000E+01 -1.558E-06 -1.289E-06 0.000E+00 M= 2.022E-06 #,Z,Y,X= 0 1 20 33 + 6.132500E+02 2.559250E+03 6.972000E+01 -9.836E-07 -7.634E-07 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 20 34 + 6.137500E+02 2.559250E+03 7.938000E+01 -7.827E-07 -2.368E-07 0.000E+00 M= 8.177E-07 #,Z,Y,X= 0 1 20 35 + 6.142500E+02 2.559250E+03 7.196500E+01 -7.948E-07 -2.632E-07 0.000E+00 M= 8.372E-07 #,Z,Y,X= 0 1 20 36 + 6.147500E+02 2.559250E+03 7.364500E+01 -8.983E-07 -4.658E-07 0.000E+00 M= 1.012E-06 #,Z,Y,X= 0 1 20 37 + 6.152500E+02 2.559250E+03 6.249500E+01 -1.321E-06 -1.113E-06 0.000E+00 M= 1.728E-06 #,Z,Y,X= 0 1 20 38 + 6.157500E+02 2.559250E+03 6.590500E+01 -1.606E-06 -1.291E-06 0.000E+00 M= 2.060E-06 #,Z,Y,X= 0 1 20 39 + 6.162500E+02 2.559250E+03 6.909500E+01 -1.298E-06 -1.093E-06 0.000E+00 M= 1.697E-06 #,Z,Y,X= 0 1 20 40 + 6.167500E+02 2.559250E+03 7.075000E+01 -8.688E-07 -1.101E-06 0.000E+00 M= 1.402E-06 #,Z,Y,X= 0 1 20 41 + 6.172500E+02 2.559250E+03 6.832000E+01 -6.442E-07 -2.459E-06 0.000E+00 M= 2.542E-06 #,Z,Y,X= 0 1 20 42 + 6.177500E+02 2.559250E+03 5.889500E+01 -1.083E-06 -3.818E-06 0.000E+00 M= 3.969E-06 #,Z,Y,X= 0 1 20 43 + 6.182500E+02 2.559250E+03 6.444500E+01 -1.343E-06 -1.027E-06 0.000E+00 M= 1.691E-06 #,Z,Y,X= 0 1 20 44 + 6.187500E+02 2.559250E+03 6.832000E+01 -5.634E-07 -5.424E-07 0.000E+00 M= 7.821E-07 #,Z,Y,X= 0 1 20 45 + 6.032500E+02 2.558750E+03 9.174000E+01 1.322E-07 -5.648E-07 0.000E+00 M= 5.801E-07 #,Z,Y,X= 0 1 21 14 + 6.037500E+02 2.558750E+03 8.504000E+01 2.999E-07 -4.120E-07 0.000E+00 M= 5.096E-07 #,Z,Y,X= 0 1 21 15 + 6.042500E+02 2.558750E+03 8.422500E+01 3.007E-07 -5.078E-07 0.000E+00 M= 5.902E-07 #,Z,Y,X= 0 1 21 16 + 6.047500E+02 2.558750E+03 8.586000E+01 2.234E-07 -6.347E-07 0.000E+00 M= 6.729E-07 #,Z,Y,X= 0 1 21 17 + 6.052500E+02 2.558750E+03 8.487500E+01 1.761E-07 -6.922E-07 0.000E+00 M= 7.143E-07 #,Z,Y,X= 0 1 21 18 + 6.057500E+02 2.558750E+03 8.481500E+01 3.598E-08 -7.376E-07 0.000E+00 M= 7.384E-07 #,Z,Y,X= 0 1 21 19 + 6.062500E+02 2.558750E+03 8.447000E+01 -9.396E-10 -5.389E-07 0.000E+00 M= 5.389E-07 #,Z,Y,X= 0 1 21 20 + 6.067500E+02 2.558750E+03 7.901500E+01 4.612E-08 -4.810E-07 0.000E+00 M= 4.832E-07 #,Z,Y,X= 0 1 21 21 + 6.072500E+02 2.558750E+03 6.834000E+01 -7.650E-11 -6.499E-07 0.000E+00 M= 6.499E-07 #,Z,Y,X= 0 1 21 22 + 6.077500E+02 2.558750E+03 6.222000E+01 -3.217E-08 -5.000E-07 0.000E+00 M= 5.010E-07 #,Z,Y,X= 0 1 21 23 + 6.082500E+02 2.558750E+03 6.047000E+01 -1.442E-07 -8.279E-07 0.000E+00 M= 8.403E-07 #,Z,Y,X= 0 1 21 24 + 6.087500E+02 2.558750E+03 6.092000E+01 -9.677E-08 -1.647E-06 0.000E+00 M= 1.650E-06 #,Z,Y,X= 0 1 21 25 + 6.092500E+02 2.558750E+03 5.508500E+01 7.324E-08 -4.193E-06 0.000E+00 M= 4.194E-06 #,Z,Y,X= 0 1 21 26 + 6.097500E+02 2.558750E+03 5.780000E+01 7.360E-08 -3.806E-06 0.000E+00 M= 3.806E-06 #,Z,Y,X= 0 1 21 27 + 6.102500E+02 2.558750E+03 6.984500E+01 -1.281E-07 -1.760E-06 0.000E+00 M= 1.765E-06 #,Z,Y,X= 0 1 21 28 + 6.107500E+02 2.558750E+03 5.723500E+01 -9.273E-07 -4.201E-06 0.000E+00 M= 4.302E-06 #,Z,Y,X= 0 1 21 29 + 6.112500E+02 2.558750E+03 5.025000E+01 -1.553E-06 -5.289E-06 0.000E+00 M= 5.513E-06 #,Z,Y,X= 0 1 21 30 + 6.117500E+02 2.558750E+03 6.940500E+01 -1.430E-06 -2.005E-06 0.000E+00 M= 2.463E-06 #,Z,Y,X= 0 1 21 31 + 6.122500E+02 2.558750E+03 6.509000E+01 -8.349E-07 -1.861E-06 0.000E+00 M= 2.040E-06 #,Z,Y,X= 0 1 21 32 + 6.127500E+02 2.558750E+03 7.560000E+01 -4.204E-07 -1.012E-06 0.000E+00 M= 1.096E-06 #,Z,Y,X= 0 1 21 33 + 6.132500E+02 2.558750E+03 7.889500E+01 -5.319E-07 -7.034E-07 0.000E+00 M= 8.819E-07 #,Z,Y,X= 0 1 21 34 + 6.137500E+02 2.558750E+03 7.668500E+01 -8.114E-07 -5.125E-07 0.000E+00 M= 9.597E-07 #,Z,Y,X= 0 1 21 35 + 6.142500E+02 2.558750E+03 6.535000E+01 -1.143E-06 -5.062E-07 0.000E+00 M= 1.250E-06 #,Z,Y,X= 0 1 21 36 + 6.147500E+02 2.558750E+03 6.012500E+01 -1.530E-06 -8.791E-07 0.000E+00 M= 1.764E-06 #,Z,Y,X= 0 1 21 37 + 6.152500E+02 2.558750E+03 6.195000E+01 -1.786E-06 -1.552E-06 0.000E+00 M= 2.367E-06 #,Z,Y,X= 0 1 21 38 + 6.157500E+02 2.558750E+03 5.922500E+01 -2.139E-06 -1.579E-06 0.000E+00 M= 2.659E-06 #,Z,Y,X= 0 1 21 39 + 6.162500E+02 2.558750E+03 6.428500E+01 -2.497E-06 -1.010E-06 0.000E+00 M= 2.693E-06 #,Z,Y,X= 0 1 21 40 + 6.167500E+02 2.558750E+03 6.330000E+01 -2.547E-06 -9.592E-07 0.000E+00 M= 2.722E-06 #,Z,Y,X= 0 1 21 41 + 6.172500E+02 2.558750E+03 5.885000E+01 -2.607E-06 -2.602E-06 0.000E+00 M= 3.684E-06 #,Z,Y,X= 0 1 21 42 + 6.177500E+02 2.558750E+03 6.043000E+01 -1.797E-06 -3.491E-06 0.000E+00 M= 3.927E-06 #,Z,Y,X= 0 1 21 43 + 6.182500E+02 2.558750E+03 6.999000E+01 -4.576E-07 -4.360E-07 0.000E+00 M= 6.321E-07 #,Z,Y,X= 0 1 21 44 + 6.032500E+02 2.558250E+03 8.948500E+01 1.027E-07 -4.657E-07 0.000E+00 M= 4.768E-07 #,Z,Y,X= 0 1 22 14 + 6.037500E+02 2.558250E+03 8.223500E+01 3.314E-07 -3.828E-07 0.000E+00 M= 5.063E-07 #,Z,Y,X= 0 1 22 15 + 6.042500E+02 2.558250E+03 7.217000E+01 7.161E-07 -5.094E-07 0.000E+00 M= 8.788E-07 #,Z,Y,X= 0 1 22 16 + 6.047500E+02 2.558250E+03 7.051500E+01 1.142E-06 -5.514E-07 0.000E+00 M= 1.268E-06 #,Z,Y,X= 0 1 22 17 + 6.052500E+02 2.558250E+03 6.600000E+01 1.189E-06 -1.090E-06 0.000E+00 M= 1.613E-06 #,Z,Y,X= 0 1 22 18 + 6.057500E+02 2.558250E+03 6.431500E+01 4.961E-07 -1.425E-06 0.000E+00 M= 1.509E-06 #,Z,Y,X= 0 1 22 19 + 6.062500E+02 2.558250E+03 7.385000E+01 4.441E-08 -6.363E-07 0.000E+00 M= 6.378E-07 #,Z,Y,X= 0 1 22 20 + 6.067500E+02 2.558250E+03 7.699000E+01 -5.495E-08 -6.657E-07 0.000E+00 M= 6.679E-07 #,Z,Y,X= 0 1 22 21 + 6.072500E+02 2.558250E+03 7.729000E+01 -1.751E-07 -8.087E-07 0.000E+00 M= 8.274E-07 #,Z,Y,X= 0 1 22 22 + 6.077500E+02 2.558250E+03 6.928500E+01 -2.172E-07 -5.238E-07 0.000E+00 M= 5.670E-07 #,Z,Y,X= 0 1 22 23 + 6.082500E+02 2.558250E+03 5.844500E+01 -1.635E-07 -8.356E-07 0.000E+00 M= 8.514E-07 #,Z,Y,X= 0 1 22 24 + 6.087500E+02 2.558250E+03 5.451500E+01 2.509E-07 -1.035E-06 0.000E+00 M= 1.065E-06 #,Z,Y,X= 0 1 22 25 + 6.092500E+02 2.558250E+03 4.696000E+01 9.340E-07 -3.649E-06 0.000E+00 M= 3.767E-06 #,Z,Y,X= 0 1 22 26 + 6.097500E+02 2.558250E+03 4.901500E+01 9.929E-07 -4.130E-06 0.000E+00 M= 4.248E-06 #,Z,Y,X= 0 1 22 27 + 6.102500E+02 2.558250E+03 5.726500E+01 2.251E-08 -2.646E-06 0.000E+00 M= 2.646E-06 #,Z,Y,X= 0 1 22 28 + 6.107500E+02 2.558250E+03 4.969500E+01 -1.434E-06 -5.676E-06 0.000E+00 M= 5.855E-06 #,Z,Y,X= 0 1 22 29 + 6.112500E+02 2.558250E+03 5.774500E+01 -1.403E-06 -4.459E-06 0.000E+00 M= 4.674E-06 #,Z,Y,X= 0 1 22 30 + 6.117500E+02 2.558250E+03 7.096500E+01 -5.276E-07 -1.833E-06 0.000E+00 M= 1.907E-06 #,Z,Y,X= 0 1 22 31 + 6.122500E+02 2.558250E+03 6.875500E+01 -3.959E-07 -1.418E-06 0.000E+00 M= 1.473E-06 #,Z,Y,X= 0 1 22 32 + 6.127500E+02 2.558250E+03 6.540000E+01 -5.485E-07 -1.298E-06 0.000E+00 M= 1.409E-06 #,Z,Y,X= 0 1 22 33 + 6.132500E+02 2.558250E+03 6.583000E+01 -8.639E-07 -9.758E-07 0.000E+00 M= 1.303E-06 #,Z,Y,X= 0 1 22 34 + 6.137500E+02 2.558250E+03 6.487000E+01 -1.103E-06 -9.083E-07 0.000E+00 M= 1.429E-06 #,Z,Y,X= 0 1 22 35 + 6.142500E+02 2.558250E+03 6.316500E+01 -1.461E-06 -1.011E-06 0.000E+00 M= 1.777E-06 #,Z,Y,X= 0 1 22 36 + 6.147500E+02 2.558250E+03 6.003500E+01 -2.020E-06 -1.525E-06 0.000E+00 M= 2.531E-06 #,Z,Y,X= 0 1 22 37 + 6.152500E+02 2.558250E+03 5.482500E+01 -2.780E-06 -2.108E-06 0.000E+00 M= 3.489E-06 #,Z,Y,X= 0 1 22 38 + 6.157500E+02 2.558250E+03 5.789000E+01 -3.407E-06 -2.122E-06 0.000E+00 M= 4.014E-06 #,Z,Y,X= 0 1 22 39 + 6.162500E+02 2.558250E+03 5.622500E+01 -3.804E-06 -1.454E-06 0.000E+00 M= 4.072E-06 #,Z,Y,X= 0 1 22 40 + 6.167500E+02 2.558250E+03 5.590500E+01 -4.196E-06 -1.219E-06 0.000E+00 M= 4.370E-06 #,Z,Y,X= 0 1 22 41 + 6.172500E+02 2.558250E+03 5.879000E+01 -3.493E-06 -1.856E-06 0.000E+00 M= 3.955E-06 #,Z,Y,X= 0 1 22 42 + 6.177500E+02 2.558250E+03 7.220000E+01 -1.295E-06 -1.274E-06 0.000E+00 M= 1.817E-06 #,Z,Y,X= 0 1 22 43 + 6.032500E+02 2.557750E+03 8.687000E+01 4.589E-08 -4.529E-07 0.000E+00 M= 4.552E-07 #,Z,Y,X= 0 1 23 14 + 6.037500E+02 2.557750E+03 8.552500E+01 9.256E-08 -3.680E-07 0.000E+00 M= 3.795E-07 #,Z,Y,X= 0 1 23 15 + 6.042500E+02 2.557750E+03 8.558000E+01 1.199E-07 -3.622E-07 0.000E+00 M= 3.816E-07 #,Z,Y,X= 0 1 23 16 + 6.047500E+02 2.557750E+03 7.814500E+01 2.173E-07 -3.543E-07 0.000E+00 M= 4.156E-07 #,Z,Y,X= 0 1 23 17 + 6.052500E+02 2.557750E+03 6.818500E+01 8.518E-07 -8.933E-07 0.000E+00 M= 1.234E-06 #,Z,Y,X= 0 1 23 18 + 6.057500E+02 2.557750E+03 5.952500E+01 1.490E-06 -1.874E-06 0.000E+00 M= 2.394E-06 #,Z,Y,X= 0 1 23 19 + 6.062500E+02 2.557750E+03 6.101500E+01 1.229E-06 -1.171E-06 0.000E+00 M= 1.698E-06 #,Z,Y,X= 0 1 23 20 + 6.067500E+02 2.557750E+03 5.996500E+01 3.540E-07 -1.296E-06 0.000E+00 M= 1.343E-06 #,Z,Y,X= 0 1 23 21 + 6.072500E+02 2.557750E+03 6.369500E+01 -1.898E-07 -1.133E-06 0.000E+00 M= 1.149E-06 #,Z,Y,X= 0 1 23 22 + 6.077500E+02 2.557750E+03 7.010000E+01 -3.100E-07 -6.796E-07 0.000E+00 M= 7.470E-07 #,Z,Y,X= 0 1 23 23 + 6.082500E+02 2.557750E+03 6.871000E+01 -1.985E-07 -7.165E-07 0.000E+00 M= 7.435E-07 #,Z,Y,X= 0 1 23 24 + 6.087500E+02 2.557750E+03 6.080500E+01 -9.414E-09 -6.752E-07 0.000E+00 M= 6.753E-07 #,Z,Y,X= 0 1 23 25 + 6.092500E+02 2.557750E+03 5.380000E+01 3.470E-07 -3.209E-06 0.000E+00 M= 3.228E-06 #,Z,Y,X= 0 1 23 26 + 6.097500E+02 2.557750E+03 4.796000E+01 8.547E-07 -4.555E-06 0.000E+00 M= 4.634E-06 #,Z,Y,X= 0 1 23 27 + 6.102500E+02 2.557750E+03 4.575000E+01 -1.058E-06 -5.543E-06 0.000E+00 M= 5.643E-06 #,Z,Y,X= 0 1 23 28 + 6.107500E+02 2.557750E+03 4.534000E+01 -2.688E-06 -6.407E-06 0.000E+00 M= 6.948E-06 #,Z,Y,X= 0 1 23 29 + 6.112500E+02 2.557750E+03 6.344500E+01 -1.611E-06 -3.236E-06 0.000E+00 M= 3.615E-06 #,Z,Y,X= 0 1 23 30 + 6.117500E+02 2.557750E+03 7.016000E+01 -9.587E-07 -1.853E-06 0.000E+00 M= 2.086E-06 #,Z,Y,X= 0 1 23 31 + 6.122500E+02 2.557750E+03 5.492000E+01 -1.169E-06 -1.674E-06 0.000E+00 M= 2.042E-06 #,Z,Y,X= 0 1 23 32 + 6.127500E+02 2.557750E+03 5.406500E+01 -1.849E-06 -2.019E-06 0.000E+00 M= 2.738E-06 #,Z,Y,X= 0 1 23 33 + 6.132500E+02 2.557750E+03 5.305500E+01 -2.324E-06 -1.228E-06 0.000E+00 M= 2.629E-06 #,Z,Y,X= 0 1 23 34 + 6.137500E+02 2.557750E+03 5.583000E+01 -2.566E-06 -1.299E-06 0.000E+00 M= 2.876E-06 #,Z,Y,X= 0 1 23 35 + 6.142500E+02 2.557750E+03 5.619500E+01 -2.928E-06 -1.643E-06 0.000E+00 M= 3.358E-06 #,Z,Y,X= 0 1 23 36 + 6.147500E+02 2.557750E+03 5.655500E+01 -3.188E-06 -2.083E-06 0.000E+00 M= 3.808E-06 #,Z,Y,X= 0 1 23 37 + 6.152500E+02 2.557750E+03 5.175500E+01 -3.338E-06 -2.808E-06 0.000E+00 M= 4.362E-06 #,Z,Y,X= 0 1 23 38 + 6.157500E+02 2.557750E+03 5.309500E+01 -3.182E-06 -2.127E-06 0.000E+00 M= 3.828E-06 #,Z,Y,X= 0 1 23 39 + 6.162500E+02 2.557750E+03 6.078500E+01 -2.342E-06 -1.321E-06 0.000E+00 M= 2.689E-06 #,Z,Y,X= 0 1 23 40 + 6.167500E+02 2.557750E+03 7.274000E+01 -1.447E-06 -1.208E-06 0.000E+00 M= 1.885E-06 #,Z,Y,X= 0 1 23 41 + 6.172500E+02 2.557750E+03 7.644500E+01 -5.427E-07 -5.213E-07 0.000E+00 M= 7.525E-07 #,Z,Y,X= 0 1 23 42 + 6.027500E+02 2.557250E+03 8.241500E+01 -2.151E-08 -7.681E-08 0.000E+00 M= 7.977E-08 #,Z,Y,X= 0 1 24 13 + 6.032500E+02 2.557250E+03 8.247500E+01 4.174E-09 -5.032E-07 0.000E+00 M= 5.032E-07 #,Z,Y,X= 0 1 24 14 + 6.037500E+02 2.557250E+03 8.093000E+01 5.840E-08 -4.847E-07 0.000E+00 M= 4.882E-07 #,Z,Y,X= 0 1 24 15 + 6.042500E+02 2.557250E+03 7.823000E+01 7.693E-08 -4.713E-07 0.000E+00 M= 4.776E-07 #,Z,Y,X= 0 1 24 16 + 6.047500E+02 2.557250E+03 7.971000E+01 4.153E-08 -4.496E-07 0.000E+00 M= 4.515E-07 #,Z,Y,X= 0 1 24 17 + 6.052500E+02 2.557250E+03 7.893500E+01 1.259E-08 -4.286E-07 0.000E+00 M= 4.288E-07 #,Z,Y,X= 0 1 24 18 + 6.057500E+02 2.557250E+03 5.799500E+01 7.371E-07 -1.079E-06 0.000E+00 M= 1.306E-06 #,Z,Y,X= 0 1 24 19 + 6.062500E+02 2.557250E+03 5.650000E+01 1.165E-06 -1.871E-06 0.000E+00 M= 2.204E-06 #,Z,Y,X= 0 1 24 20 + 6.067500E+02 2.557250E+03 5.199500E+01 7.866E-07 -1.880E-06 0.000E+00 M= 2.038E-06 #,Z,Y,X= 0 1 24 21 + 6.072500E+02 2.557250E+03 5.606500E+01 5.006E-07 -1.583E-06 0.000E+00 M= 1.660E-06 #,Z,Y,X= 0 1 24 22 + 6.077500E+02 2.557250E+03 5.950000E+01 2.364E-08 -1.138E-06 0.000E+00 M= 1.138E-06 #,Z,Y,X= 0 1 24 23 + 6.082500E+02 2.557250E+03 6.303500E+01 -1.395E-07 -6.545E-07 0.000E+00 M= 6.692E-07 #,Z,Y,X= 0 1 24 24 + 6.087500E+02 2.557250E+03 6.264500E+01 -8.793E-08 -7.929E-07 0.000E+00 M= 7.978E-07 #,Z,Y,X= 0 1 24 25 + 6.092500E+02 2.557250E+03 5.697500E+01 -5.922E-07 -3.840E-06 0.000E+00 M= 3.886E-06 #,Z,Y,X= 0 1 24 26 + 6.097500E+02 2.557250E+03 5.210500E+01 -1.078E-06 -4.698E-06 0.000E+00 M= 4.820E-06 #,Z,Y,X= 0 1 24 27 + 6.102500E+02 2.557250E+03 4.181500E+01 -1.126E-06 -8.310E-06 0.000E+00 M= 8.386E-06 #,Z,Y,X= 0 1 24 28 + 6.107500E+02 2.557250E+03 6.157000E+01 -1.217E-06 -6.352E-06 0.000E+00 M= 6.467E-06 #,Z,Y,X= 0 1 24 29 + 6.112500E+02 2.557250E+03 6.318500E+01 -1.385E-06 -2.966E-06 0.000E+00 M= 3.274E-06 #,Z,Y,X= 0 1 24 30 + 6.117500E+02 2.557250E+03 5.846000E+01 -1.688E-06 -2.110E-06 0.000E+00 M= 2.702E-06 #,Z,Y,X= 0 1 24 31 + 6.122500E+02 2.557250E+03 5.478000E+01 -1.845E-06 -2.037E-06 0.000E+00 M= 2.748E-06 #,Z,Y,X= 0 1 24 32 + 6.127500E+02 2.557250E+03 4.861500E+01 -2.683E-06 -3.342E-06 0.000E+00 M= 4.286E-06 #,Z,Y,X= 0 1 24 33 + 6.132500E+02 2.557250E+03 5.223000E+01 -3.526E-06 -1.522E-06 0.000E+00 M= 3.840E-06 #,Z,Y,X= 0 1 24 34 + 6.137500E+02 2.557250E+03 5.058000E+01 -4.211E-06 -2.131E-06 0.000E+00 M= 4.720E-06 #,Z,Y,X= 0 1 24 35 + 6.142500E+02 2.557250E+03 4.890500E+01 -5.350E-06 -1.647E-06 0.000E+00 M= 5.597E-06 #,Z,Y,X= 0 1 24 36 + 6.147500E+02 2.557250E+03 5.099500E+01 -5.365E-06 -1.897E-06 0.000E+00 M= 5.691E-06 #,Z,Y,X= 0 1 24 37 + 6.152500E+02 2.557250E+03 5.454500E+01 -3.698E-06 -1.996E-06 0.000E+00 M= 4.202E-06 #,Z,Y,X= 0 1 24 38 + 6.157500E+02 2.557250E+03 6.020500E+01 -1.908E-06 -1.283E-06 0.000E+00 M= 2.300E-06 #,Z,Y,X= 0 1 24 39 + 6.162500E+02 2.557250E+03 6.971000E+01 -1.116E-06 -7.528E-07 0.000E+00 M= 1.346E-06 #,Z,Y,X= 0 1 24 40 + 6.167500E+02 2.557250E+03 7.581500E+01 -4.789E-07 -4.567E-07 0.000E+00 M= 6.618E-07 #,Z,Y,X= 0 1 24 41 + 6.017500E+02 2.556750E+03 8.504000E+01 1.412E-08 -3.902E-08 0.000E+00 M= 4.150E-08 #,Z,Y,X= 0 1 25 11 + 6.022500E+02 2.556750E+03 8.319000E+01 2.972E-08 -4.526E-08 0.000E+00 M= 5.415E-08 #,Z,Y,X= 0 1 25 12 + 6.027500E+02 2.556750E+03 7.646000E+01 9.661E-08 -1.179E-07 0.000E+00 M= 1.524E-07 #,Z,Y,X= 0 1 25 13 + 6.032500E+02 2.556750E+03 7.566500E+01 2.665E-07 -4.671E-07 0.000E+00 M= 5.378E-07 #,Z,Y,X= 0 1 25 14 + 6.037500E+02 2.556750E+03 7.143500E+01 4.619E-07 -5.109E-07 0.000E+00 M= 6.887E-07 #,Z,Y,X= 0 1 25 15 + 6.042500E+02 2.556750E+03 6.847000E+01 5.747E-07 -6.127E-07 0.000E+00 M= 8.400E-07 #,Z,Y,X= 0 1 25 16 + 6.047500E+02 2.556750E+03 6.404500E+01 3.939E-07 -8.124E-07 0.000E+00 M= 9.029E-07 #,Z,Y,X= 0 1 25 17 + 6.052500E+02 2.556750E+03 7.384000E+01 2.531E-08 -7.335E-07 0.000E+00 M= 7.340E-07 #,Z,Y,X= 0 1 25 18 + 6.057500E+02 2.556750E+03 7.602000E+01 9.660E-09 -3.195E-07 0.000E+00 M= 3.196E-07 #,Z,Y,X= 0 1 25 19 + 6.062500E+02 2.556750E+03 6.402000E+01 2.179E-07 -2.219E-06 0.000E+00 M= 2.229E-06 #,Z,Y,X= 0 1 25 20 + 6.067500E+02 2.556750E+03 5.613500E+01 3.238E-07 -2.063E-06 0.000E+00 M= 2.088E-06 #,Z,Y,X= 0 1 25 21 + 6.072500E+02 2.556750E+03 4.745500E+01 2.715E-07 -2.038E-06 0.000E+00 M= 2.056E-06 #,Z,Y,X= 0 1 25 22 + 6.077500E+02 2.556750E+03 5.402500E+01 2.983E-07 -1.443E-06 0.000E+00 M= 1.473E-06 #,Z,Y,X= 0 1 25 23 + 6.082500E+02 2.556750E+03 6.126500E+01 1.860E-07 -9.510E-07 0.000E+00 M= 9.690E-07 #,Z,Y,X= 0 1 25 24 + 6.087500E+02 2.556750E+03 4.925000E+01 -1.049E-06 -1.994E-06 0.000E+00 M= 2.253E-06 #,Z,Y,X= 0 1 25 25 + 6.092500E+02 2.556750E+03 4.318500E+01 -2.556E-06 -5.403E-06 0.000E+00 M= 5.978E-06 #,Z,Y,X= 0 1 25 26 + 6.097500E+02 2.556750E+03 4.889000E+01 -2.861E-06 -4.922E-06 0.000E+00 M= 5.693E-06 #,Z,Y,X= 0 1 25 27 + 6.102500E+02 2.556750E+03 4.516500E+01 -2.888E-06 -9.644E-06 0.000E+00 M= 1.007E-05 #,Z,Y,X= 0 1 25 28 + 6.107500E+02 2.556750E+03 4.150500E+01 -7.075E-06 -5.767E-06 0.000E+00 M= 9.127E-06 #,Z,Y,X= 0 1 25 29 + 6.112500E+02 2.556750E+03 4.724500E+01 -1.077E-05 -3.529E-06 0.000E+00 M= 1.134E-05 #,Z,Y,X= 0 1 25 30 + 6.117500E+02 2.556750E+03 4.390500E+01 -1.058E-05 -2.143E-06 0.000E+00 M= 1.080E-05 #,Z,Y,X= 0 1 25 31 + 6.122500E+02 2.556750E+03 4.601500E+01 -6.206E-06 -1.406E-06 0.000E+00 M= 6.364E-06 #,Z,Y,X= 0 1 25 32 + 6.127500E+02 2.556750E+03 4.619000E+01 -5.009E-06 -2.036E-06 0.000E+00 M= 5.407E-06 #,Z,Y,X= 0 1 25 33 + 6.132500E+02 2.556750E+03 4.928000E+01 -7.233E-06 -9.142E-07 0.000E+00 M= 7.291E-06 #,Z,Y,X= 0 1 25 34 + 6.137500E+02 2.556750E+03 5.073000E+01 -5.585E-06 -1.718E-06 0.000E+00 M= 5.843E-06 #,Z,Y,X= 0 1 25 35 + 6.142500E+02 2.556750E+03 5.973000E+01 -3.513E-06 -6.916E-07 0.000E+00 M= 3.581E-06 #,Z,Y,X= 0 1 25 36 + 6.147500E+02 2.556750E+03 5.682000E+01 -2.055E-06 -9.692E-07 0.000E+00 M= 2.272E-06 #,Z,Y,X= 0 1 25 37 + 6.152500E+02 2.556750E+03 6.823000E+01 -1.107E-06 -6.913E-07 0.000E+00 M= 1.305E-06 #,Z,Y,X= 0 1 25 38 + 6.157500E+02 2.556750E+03 7.128000E+01 -7.616E-07 -6.362E-07 0.000E+00 M= 9.924E-07 #,Z,Y,X= 0 1 25 39 + 6.162500E+02 2.556750E+03 7.519500E+01 -3.506E-07 -3.350E-07 0.000E+00 M= 4.849E-07 #,Z,Y,X= 0 1 25 40 + 6.012500E+02 2.556250E+03 8.180500E+01 2.354E-08 -2.249E-08 0.000E+00 M= 3.256E-08 #,Z,Y,X= 0 1 26 10 + 6.017500E+02 2.556250E+03 7.806000E+01 4.119E-08 -1.051E-07 0.000E+00 M= 1.129E-07 #,Z,Y,X= 0 1 26 11 + 6.022500E+02 2.556250E+03 7.751000E+01 3.595E-08 -1.119E-07 0.000E+00 M= 1.175E-07 #,Z,Y,X= 0 1 26 12 + 6.027500E+02 2.556250E+03 7.816500E+01 6.799E-08 -7.516E-08 0.000E+00 M= 1.013E-07 #,Z,Y,X= 0 1 26 13 + 6.032500E+02 2.556250E+03 7.181000E+01 2.658E-07 -2.209E-07 0.000E+00 M= 3.456E-07 #,Z,Y,X= 0 1 26 14 + 6.037500E+02 2.556250E+03 6.551000E+01 6.168E-07 -2.841E-07 0.000E+00 M= 6.791E-07 #,Z,Y,X= 0 1 26 15 + 6.042500E+02 2.556250E+03 6.121000E+01 8.706E-07 -6.101E-07 0.000E+00 M= 1.063E-06 #,Z,Y,X= 0 1 26 16 + 6.047500E+02 2.556250E+03 5.913000E+01 9.105E-07 -1.045E-06 0.000E+00 M= 1.386E-06 #,Z,Y,X= 0 1 26 17 + 6.052500E+02 2.556250E+03 5.790500E+01 4.928E-07 -1.326E-06 0.000E+00 M= 1.415E-06 #,Z,Y,X= 0 1 26 18 + 6.057500E+02 2.556250E+03 7.055000E+01 1.199E-07 -2.416E-07 0.000E+00 M= 2.697E-07 #,Z,Y,X= 0 1 26 19 + 6.062500E+02 2.556250E+03 5.304000E+01 2.806E-07 -2.077E-06 0.000E+00 M= 2.096E-06 #,Z,Y,X= 0 1 26 20 + 6.067500E+02 2.556250E+03 4.623000E+01 -8.603E-08 -2.576E-06 0.000E+00 M= 2.578E-06 #,Z,Y,X= 0 1 26 21 + 6.072500E+02 2.556250E+03 5.366000E+01 -1.726E-07 -1.804E-06 0.000E+00 M= 1.812E-06 #,Z,Y,X= 0 1 26 22 + 6.077500E+02 2.556250E+03 5.373500E+01 2.179E-07 -1.381E-06 0.000E+00 M= 1.398E-06 #,Z,Y,X= 0 1 26 23 + 6.082500E+02 2.556250E+03 4.727500E+01 -8.484E-08 -1.620E-06 0.000E+00 M= 1.622E-06 #,Z,Y,X= 0 1 26 24 + 6.087500E+02 2.556250E+03 4.059000E+01 -9.833E-07 -3.685E-06 0.000E+00 M= 3.814E-06 #,Z,Y,X= 0 1 26 25 + 6.092500E+02 2.556250E+03 4.284500E+01 -1.616E-06 -6.208E-06 0.000E+00 M= 6.415E-06 #,Z,Y,X= 0 1 26 26 + 6.097500E+02 2.556250E+03 3.950000E+01 -3.523E-06 -4.966E-06 0.000E+00 M= 6.088E-06 #,Z,Y,X= 0 1 26 27 + 6.102500E+02 2.556250E+03 4.000000E+01 -9.090E-06 -6.971E-06 0.000E+00 M= 1.146E-05 #,Z,Y,X= 0 1 26 28 + 6.107500E+02 2.556250E+03 4.259000E+01 -9.826E-06 -3.852E-06 0.000E+00 M= 1.055E-05 #,Z,Y,X= 0 1 26 29 + 6.112500E+02 2.556250E+03 4.350000E+01 -8.934E-06 -2.876E-06 0.000E+00 M= 9.385E-06 #,Z,Y,X= 0 1 26 30 + 6.117500E+02 2.556250E+03 4.600000E+01 -8.581E-06 -1.390E-06 0.000E+00 M= 8.693E-06 #,Z,Y,X= 0 1 26 31 + 6.122500E+02 2.556250E+03 6.038500E+01 -4.928E-06 -8.741E-07 0.000E+00 M= 5.005E-06 #,Z,Y,X= 0 1 26 32 + 6.127500E+02 2.556250E+03 7.292000E+01 -2.369E-06 3.550E-07 0.000E+00 M= 2.396E-06 #,Z,Y,X= 0 1 26 33 + 6.132500E+02 2.556250E+03 7.002000E+01 -2.602E-06 -9.386E-08 0.000E+00 M= 2.603E-06 #,Z,Y,X= 0 1 26 34 + 6.137500E+02 2.556250E+03 6.492500E+01 -2.007E-06 -4.669E-07 0.000E+00 M= 2.061E-06 #,Z,Y,X= 0 1 26 35 + 6.142500E+02 2.556250E+03 6.795500E+01 -1.545E-06 -7.692E-08 0.000E+00 M= 1.547E-06 #,Z,Y,X= 0 1 26 36 + 6.147500E+02 2.556250E+03 6.356000E+01 -1.157E-06 -2.294E-07 0.000E+00 M= 1.179E-06 #,Z,Y,X= 0 1 26 37 + 6.152500E+02 2.556250E+03 6.548500E+01 -7.903E-07 -2.737E-07 0.000E+00 M= 8.364E-07 #,Z,Y,X= 0 1 26 38 + 6.157500E+02 2.556250E+03 7.132000E+01 -3.093E-07 -2.924E-07 0.000E+00 M= 4.257E-07 #,Z,Y,X= 0 1 26 39 + 6.002500E+02 2.555750E+03 8.489500E+01 1.902E-08 -3.074E-08 0.000E+00 M= 3.614E-08 #,Z,Y,X= 0 1 27 8 + 6.007500E+02 2.555750E+03 8.165500E+01 4.634E-08 -3.184E-08 0.000E+00 M= 5.623E-08 #,Z,Y,X= 0 1 27 9 + 6.012500E+02 2.555750E+03 7.972000E+01 1.049E-07 -3.367E-08 0.000E+00 M= 1.102E-07 #,Z,Y,X= 0 1 27 10 + 6.017500E+02 2.555750E+03 7.118500E+01 2.329E-07 -8.065E-08 0.000E+00 M= 2.465E-07 #,Z,Y,X= 0 1 27 11 + 6.022500E+02 2.555750E+03 6.914500E+01 1.790E-07 -3.006E-07 0.000E+00 M= 3.498E-07 #,Z,Y,X= 0 1 27 12 + 6.027500E+02 2.555750E+03 7.452000E+01 4.096E-08 -1.113E-07 0.000E+00 M= 1.186E-07 #,Z,Y,X= 0 1 27 13 + 6.032500E+02 2.555750E+03 7.786500E+01 5.552E-08 -9.814E-08 0.000E+00 M= 1.128E-07 #,Z,Y,X= 0 1 27 14 + 6.037500E+02 2.555750E+03 7.562500E+01 8.473E-08 -1.790E-07 0.000E+00 M= 1.980E-07 #,Z,Y,X= 0 1 27 15 + 6.042500E+02 2.555750E+03 6.688500E+01 3.361E-07 -3.824E-07 0.000E+00 M= 5.091E-07 #,Z,Y,X= 0 1 27 16 + 6.047500E+02 2.555750E+03 5.942500E+01 1.064E-06 -6.599E-07 0.000E+00 M= 1.252E-06 #,Z,Y,X= 0 1 27 17 + 6.052500E+02 2.555750E+03 5.524500E+01 1.674E-06 -1.589E-06 0.000E+00 M= 2.308E-06 #,Z,Y,X= 0 1 27 18 + 6.057500E+02 2.555750E+03 5.823000E+01 1.085E-06 -9.287E-07 0.000E+00 M= 1.428E-06 #,Z,Y,X= 0 1 27 19 + 6.062500E+02 2.555750E+03 4.965500E+01 6.161E-07 -1.756E-06 0.000E+00 M= 1.861E-06 #,Z,Y,X= 0 1 27 20 + 6.067500E+02 2.555750E+03 4.285000E+01 3.057E-07 -3.521E-06 0.000E+00 M= 3.535E-06 #,Z,Y,X= 0 1 27 21 + 6.072500E+02 2.555750E+03 5.836000E+01 3.595E-08 -1.322E-06 0.000E+00 M= 1.322E-06 #,Z,Y,X= 0 1 27 22 + 6.077500E+02 2.555750E+03 5.062000E+01 6.486E-08 -1.550E-06 0.000E+00 M= 1.551E-06 #,Z,Y,X= 0 1 27 23 + 6.082500E+02 2.555750E+03 4.672500E+01 -3.228E-07 -2.260E-06 0.000E+00 M= 2.283E-06 #,Z,Y,X= 0 1 27 24 + 6.087500E+02 2.555750E+03 3.957000E+01 -6.833E-07 -4.405E-06 0.000E+00 M= 4.458E-06 #,Z,Y,X= 0 1 27 25 + 6.092500E+02 2.555750E+03 3.542000E+01 -3.356E-06 -4.269E-06 0.000E+00 M= 5.430E-06 #,Z,Y,X= 0 1 27 26 + 6.097500E+02 2.555750E+03 3.850000E+01 -5.564E-06 -2.941E-06 0.000E+00 M= 6.293E-06 #,Z,Y,X= 0 1 27 27 + 6.102500E+02 2.555750E+03 5.005500E+01 -3.569E-06 -1.575E-06 0.000E+00 M= 3.901E-06 #,Z,Y,X= 0 1 27 28 + 6.107500E+02 2.555750E+03 5.966500E+01 -2.285E-06 -2.338E-06 0.000E+00 M= 3.269E-06 #,Z,Y,X= 0 1 27 29 + 6.112500E+02 2.555750E+03 6.673000E+01 -1.553E-06 -1.244E-06 0.000E+00 M= 1.990E-06 #,Z,Y,X= 0 1 27 30 + 6.117500E+02 2.555750E+03 6.508000E+01 -1.953E-06 -8.937E-07 0.000E+00 M= 2.148E-06 #,Z,Y,X= 0 1 27 31 + 6.122500E+02 2.555750E+03 6.286000E+01 -2.544E-06 -1.279E-06 0.000E+00 M= 2.848E-06 #,Z,Y,X= 0 1 27 32 + 6.127500E+02 2.555750E+03 7.880500E+01 -3.187E-06 4.702E-08 0.000E+00 M= 3.188E-06 #,Z,Y,X= 0 1 27 33 + 6.132500E+02 2.555750E+03 7.934500E+01 -2.112E-06 1.291E-08 0.000E+00 M= 2.112E-06 #,Z,Y,X= 0 1 27 34 + 6.137500E+02 2.555750E+03 7.773000E+01 -8.507E-07 -2.161E-07 0.000E+00 M= 8.778E-07 #,Z,Y,X= 0 1 27 35 + 6.142500E+02 2.555750E+03 7.648000E+01 -4.312E-07 -4.610E-08 0.000E+00 M= 4.337E-07 #,Z,Y,X= 0 1 27 36 + 6.147500E+02 2.555750E+03 7.117500E+01 -2.306E-07 -4.094E-08 0.000E+00 M= 2.342E-07 #,Z,Y,X= 0 1 27 37 + 6.152500E+02 2.555750E+03 7.020500E+01 -8.074E-08 -6.476E-08 0.000E+00 M= 1.035E-07 #,Z,Y,X= 0 1 27 38 + 5.992500E+02 2.555250E+03 8.781000E+01 6.540E-08 4.251E-10 0.000E+00 M= 6.540E-08 #,Z,Y,X= 0 1 28 6 + 5.997500E+02 2.555250E+03 8.410500E+01 1.784E-07 -9.321E-09 0.000E+00 M= 1.786E-07 #,Z,Y,X= 0 1 28 7 + 6.002500E+02 2.555250E+03 7.903000E+01 2.570E-07 -4.870E-08 0.000E+00 M= 2.615E-07 #,Z,Y,X= 0 1 28 8 + 6.007500E+02 2.555250E+03 7.580000E+01 2.779E-07 -6.349E-08 0.000E+00 M= 2.851E-07 #,Z,Y,X= 0 1 28 9 + 6.012500E+02 2.555250E+03 7.124000E+01 2.199E-07 -1.267E-07 0.000E+00 M= 2.538E-07 #,Z,Y,X= 0 1 28 10 + 6.017500E+02 2.555250E+03 7.437500E+01 1.506E-07 -4.761E-08 0.000E+00 M= 1.579E-07 #,Z,Y,X= 0 1 28 11 + 6.022500E+02 2.555250E+03 6.503500E+01 2.889E-07 -3.324E-07 0.000E+00 M= 4.404E-07 #,Z,Y,X= 0 1 28 12 + 6.027500E+02 2.555250E+03 6.664500E+01 2.561E-07 -3.276E-07 0.000E+00 M= 4.158E-07 #,Z,Y,X= 0 1 28 13 + 6.032500E+02 2.555250E+03 7.186000E+01 1.334E-07 -1.121E-07 0.000E+00 M= 1.743E-07 #,Z,Y,X= 0 1 28 14 + 6.037500E+02 2.555250E+03 6.966000E+01 1.011E-07 -3.096E-07 0.000E+00 M= 3.257E-07 #,Z,Y,X= 0 1 28 15 + 6.042500E+02 2.555250E+03 7.230500E+01 5.102E-08 -1.801E-07 0.000E+00 M= 1.872E-07 #,Z,Y,X= 0 1 28 16 + 6.047500E+02 2.555250E+03 6.963000E+01 1.194E-07 -2.163E-07 0.000E+00 M= 2.471E-07 #,Z,Y,X= 0 1 28 17 + 6.052500E+02 2.555250E+03 6.127500E+01 4.133E-07 -1.150E-06 0.000E+00 M= 1.222E-06 #,Z,Y,X= 0 1 28 18 + 6.057500E+02 2.555250E+03 4.562500E+01 5.404E-07 -1.539E-06 0.000E+00 M= 1.631E-06 #,Z,Y,X= 0 1 28 19 + 6.062500E+02 2.555250E+03 4.355500E+01 4.270E-07 -1.536E-06 0.000E+00 M= 1.594E-06 #,Z,Y,X= 0 1 28 20 + 6.067500E+02 2.555250E+03 3.856000E+01 5.989E-07 -3.576E-06 0.000E+00 M= 3.626E-06 #,Z,Y,X= 0 1 28 21 + 6.072500E+02 2.555250E+03 4.732000E+01 4.447E-07 -1.557E-06 0.000E+00 M= 1.619E-06 #,Z,Y,X= 0 1 28 22 + 6.077500E+02 2.555250E+03 4.578000E+01 8.585E-08 -1.675E-06 0.000E+00 M= 1.677E-06 #,Z,Y,X= 0 1 28 23 + 6.082500E+02 2.555250E+03 4.034000E+01 -9.026E-07 -3.519E-06 0.000E+00 M= 3.633E-06 #,Z,Y,X= 0 1 28 24 + 6.087500E+02 2.555250E+03 3.420000E+01 -3.194E-06 -5.500E-06 0.000E+00 M= 6.360E-06 #,Z,Y,X= 0 1 28 25 + 6.092500E+02 2.555250E+03 3.773500E+01 -3.370E-06 -2.422E-06 0.000E+00 M= 4.150E-06 #,Z,Y,X= 0 1 28 26 + 6.097500E+02 2.555250E+03 5.560500E+01 -2.194E-06 -1.078E-06 0.000E+00 M= 2.445E-06 #,Z,Y,X= 0 1 28 27 + 6.102500E+02 2.555250E+03 5.462500E+01 -1.971E-06 -7.925E-07 0.000E+00 M= 2.124E-06 #,Z,Y,X= 0 1 28 28 + 6.107500E+02 2.555250E+03 5.057000E+01 -1.560E-06 -1.408E-06 0.000E+00 M= 2.102E-06 #,Z,Y,X= 0 1 28 29 + 6.112500E+02 2.555250E+03 6.572500E+01 -1.117E-06 -1.052E-06 0.000E+00 M= 1.535E-06 #,Z,Y,X= 0 1 28 30 + 6.117500E+02 2.555250E+03 7.646000E+01 -1.482E-06 -1.517E-06 0.000E+00 M= 2.121E-06 #,Z,Y,X= 0 1 28 31 + 6.122500E+02 2.555250E+03 8.386000E+01 -1.858E-06 -1.957E-06 0.000E+00 M= 2.698E-06 #,Z,Y,X= 0 1 28 32 + 6.127500E+02 2.555250E+03 9.062000E+01 -1.987E-06 2.581E-07 0.000E+00 M= 2.003E-06 #,Z,Y,X= 0 1 28 33 + 6.132500E+02 2.555250E+03 9.058500E+01 -8.588E-07 3.408E-07 0.000E+00 M= 9.239E-07 #,Z,Y,X= 0 1 28 34 + 6.137500E+02 2.555250E+03 8.209000E+01 -1.319E-07 -3.934E-08 0.000E+00 M= 1.376E-07 #,Z,Y,X= 0 1 28 35 + 5.992500E+02 2.554750E+03 8.813500E+01 7.444E-08 4.906E-10 0.000E+00 M= 7.444E-08 #,Z,Y,X= 0 1 29 6 + 5.997500E+02 2.554750E+03 8.369500E+01 1.839E-07 -8.778E-09 0.000E+00 M= 1.841E-07 #,Z,Y,X= 0 1 29 7 + 6.002500E+02 2.554750E+03 7.717500E+01 2.800E-07 5.452E-09 0.000E+00 M= 2.800E-07 #,Z,Y,X= 0 1 29 8 + 6.007500E+02 2.554750E+03 7.062500E+01 3.515E-07 -5.427E-09 0.000E+00 M= 3.515E-07 #,Z,Y,X= 0 1 29 9 + 6.012500E+02 2.554750E+03 6.664000E+01 5.687E-07 -1.137E-07 0.000E+00 M= 5.800E-07 #,Z,Y,X= 0 1 29 10 + 6.017500E+02 2.554750E+03 6.224000E+01 6.432E-07 -1.407E-07 0.000E+00 M= 6.584E-07 #,Z,Y,X= 0 1 29 11 + 6.022500E+02 2.554750E+03 6.111000E+01 6.832E-07 -1.741E-07 0.000E+00 M= 7.050E-07 #,Z,Y,X= 0 1 29 12 + 6.027500E+02 2.554750E+03 5.627500E+01 8.405E-07 -4.484E-07 0.000E+00 M= 9.526E-07 #,Z,Y,X= 0 1 29 13 + 6.032500E+02 2.554750E+03 6.335500E+01 7.759E-07 -3.160E-07 0.000E+00 M= 8.378E-07 #,Z,Y,X= 0 1 29 14 + 6.037500E+02 2.554750E+03 6.132500E+01 6.054E-07 -4.515E-07 0.000E+00 M= 7.552E-07 #,Z,Y,X= 0 1 29 15 + 6.042500E+02 2.554750E+03 6.690500E+01 5.705E-07 -2.609E-07 0.000E+00 M= 6.274E-07 #,Z,Y,X= 0 1 29 16 + 6.047500E+02 2.554750E+03 6.152000E+01 6.339E-07 -1.275E-07 0.000E+00 M= 6.466E-07 #,Z,Y,X= 0 1 29 17 + 6.052500E+02 2.554750E+03 5.217500E+01 7.860E-07 -8.403E-07 0.000E+00 M= 1.151E-06 #,Z,Y,X= 0 1 29 18 + 6.057500E+02 2.554750E+03 4.207000E+01 1.018E-06 -1.349E-06 0.000E+00 M= 1.690E-06 #,Z,Y,X= 0 1 29 19 + 6.062500E+02 2.554750E+03 3.710500E+01 1.171E-06 -1.581E-06 0.000E+00 M= 1.967E-06 #,Z,Y,X= 0 1 29 20 + 6.067500E+02 2.554750E+03 3.635500E+01 1.433E-06 -3.025E-06 0.000E+00 M= 3.347E-06 #,Z,Y,X= 0 1 29 21 + 6.072500E+02 2.554750E+03 3.874500E+01 1.300E-06 -2.433E-06 0.000E+00 M= 2.758E-06 #,Z,Y,X= 0 1 29 22 + 6.077500E+02 2.554750E+03 4.227000E+01 9.339E-07 -1.619E-06 0.000E+00 M= 1.869E-06 #,Z,Y,X= 0 1 29 23 + 6.082500E+02 2.554750E+03 3.286000E+01 -1.411E-06 -4.484E-06 0.000E+00 M= 4.701E-06 #,Z,Y,X= 0 1 29 24 + 6.087500E+02 2.554750E+03 3.428000E+01 -2.845E-06 -4.983E-06 0.000E+00 M= 5.738E-06 #,Z,Y,X= 0 1 29 25 + 6.092500E+02 2.554750E+03 4.138500E+01 -1.389E-06 -2.370E-06 0.000E+00 M= 2.747E-06 #,Z,Y,X= 0 1 29 26 + 6.097500E+02 2.554750E+03 6.337500E+01 -8.354E-07 -9.174E-07 0.000E+00 M= 1.241E-06 #,Z,Y,X= 0 1 29 27 + 6.102500E+02 2.554750E+03 6.544500E+01 -6.620E-07 -7.076E-07 0.000E+00 M= 9.690E-07 #,Z,Y,X= 0 1 29 28 + 6.107500E+02 2.554750E+03 6.260500E+01 -5.510E-07 -9.443E-07 0.000E+00 M= 1.093E-06 #,Z,Y,X= 0 1 29 29 + 6.112500E+02 2.554750E+03 6.324000E+01 -5.340E-07 -9.726E-07 0.000E+00 M= 1.110E-06 #,Z,Y,X= 0 1 29 30 + 6.117500E+02 2.554750E+03 7.501000E+01 -1.032E-06 -1.554E-06 0.000E+00 M= 1.866E-06 #,Z,Y,X= 0 1 29 31 + 6.122500E+02 2.554750E+03 8.539000E+01 -1.247E-06 -8.524E-07 0.000E+00 M= 1.511E-06 #,Z,Y,X= 0 1 29 32 + 6.002500E+02 2.554250E+03 8.001500E+01 2.042E-08 1.620E-08 0.000E+00 M= 2.606E-08 #,Z,Y,X= 0 1 30 8 + 6.007500E+02 2.554250E+03 7.567000E+01 5.762E-08 4.105E-08 0.000E+00 M= 7.075E-08 #,Z,Y,X= 0 1 30 9 + 6.012500E+02 2.554250E+03 7.296500E+01 1.422E-07 1.071E-08 0.000E+00 M= 1.426E-07 #,Z,Y,X= 0 1 30 10 + 6.017500E+02 2.554250E+03 6.599500E+01 3.588E-07 -7.908E-08 0.000E+00 M= 3.674E-07 #,Z,Y,X= 0 1 30 11 + 6.022500E+02 2.554250E+03 6.461500E+01 5.906E-07 -7.085E-08 0.000E+00 M= 5.948E-07 #,Z,Y,X= 0 1 30 12 + 6.027500E+02 2.554250E+03 6.243000E+01 8.573E-07 -2.226E-07 0.000E+00 M= 8.857E-07 #,Z,Y,X= 0 1 30 13 + 6.032500E+02 2.554250E+03 5.288000E+01 1.291E-06 -3.111E-07 0.000E+00 M= 1.328E-06 #,Z,Y,X= 0 1 30 14 + 6.037500E+02 2.554250E+03 4.988000E+01 1.349E-06 -6.973E-07 0.000E+00 M= 1.518E-06 #,Z,Y,X= 0 1 30 15 + 6.042500E+02 2.554250E+03 5.163500E+01 6.773E-07 -8.481E-07 0.000E+00 M= 1.085E-06 #,Z,Y,X= 0 1 30 16 + 6.047500E+02 2.554250E+03 6.637000E+01 1.554E-07 -9.391E-08 0.000E+00 M= 1.816E-07 #,Z,Y,X= 0 1 30 17 + 6.052500E+02 2.554250E+03 5.598000E+01 3.013E-07 -5.824E-07 0.000E+00 M= 6.557E-07 #,Z,Y,X= 0 1 30 18 + 6.057500E+02 2.554250E+03 5.107000E+01 5.709E-07 -9.391E-07 0.000E+00 M= 1.099E-06 #,Z,Y,X= 0 1 30 19 + 6.062500E+02 2.554250E+03 4.485000E+01 7.275E-07 -1.532E-06 0.000E+00 M= 1.696E-06 #,Z,Y,X= 0 1 30 20 + 6.067500E+02 2.554250E+03 3.507000E+01 1.066E-06 -2.383E-06 0.000E+00 M= 2.610E-06 #,Z,Y,X= 0 1 30 21 + 6.072500E+02 2.554250E+03 3.563500E+01 1.275E-06 -3.050E-06 0.000E+00 M= 3.306E-06 #,Z,Y,X= 0 1 30 22 + 6.077500E+02 2.554250E+03 3.618000E+01 1.277E-06 -1.457E-06 0.000E+00 M= 1.937E-06 #,Z,Y,X= 0 1 30 23 + 6.082500E+02 2.554250E+03 3.200000E+01 -3.913E-07 -3.067E-06 0.000E+00 M= 3.091E-06 #,Z,Y,X= 0 1 30 24 + 6.087500E+02 2.554250E+03 3.972000E+01 -1.560E-06 -2.780E-06 0.000E+00 M= 3.188E-06 #,Z,Y,X= 0 1 30 25 + 6.092500E+02 2.554250E+03 4.742500E+01 -8.977E-07 -1.855E-06 0.000E+00 M= 2.061E-06 #,Z,Y,X= 0 1 30 26 + 6.097500E+02 2.554250E+03 6.512500E+01 -7.151E-07 -7.510E-07 0.000E+00 M= 1.037E-06 #,Z,Y,X= 0 1 30 27 + 6.102500E+02 2.554250E+03 7.025000E+01 -5.296E-07 -6.700E-07 0.000E+00 M= 8.540E-07 #,Z,Y,X= 0 1 30 28 + 6.107500E+02 2.554250E+03 6.477000E+01 -3.707E-07 -8.199E-07 0.000E+00 M= 8.999E-07 #,Z,Y,X= 0 1 30 29 + 6.112500E+02 2.554250E+03 7.009000E+01 -2.182E-07 -8.923E-07 0.000E+00 M= 9.186E-07 #,Z,Y,X= 0 1 30 30 + 6.117500E+02 2.554250E+03 7.665500E+01 -1.009E-07 -1.087E-06 0.000E+00 M= 1.092E-06 #,Z,Y,X= 0 1 30 31 + 6.007500E+02 2.553750E+03 7.442000E+01 4.444E-08 1.862E-08 0.000E+00 M= 4.818E-08 #,Z,Y,X= 0 1 31 9 + 6.012500E+02 2.553750E+03 7.174000E+01 1.044E-07 1.342E-08 0.000E+00 M= 1.053E-07 #,Z,Y,X= 0 1 31 10 + 6.017500E+02 2.553750E+03 7.210500E+01 1.093E-07 1.767E-09 0.000E+00 M= 1.094E-07 #,Z,Y,X= 0 1 31 11 + 6.022500E+02 2.553750E+03 7.188000E+01 1.002E-07 -5.671E-08 0.000E+00 M= 1.151E-07 #,Z,Y,X= 0 1 31 12 + 6.027500E+02 2.553750E+03 6.913000E+01 1.040E-07 -7.969E-08 0.000E+00 M= 1.310E-07 #,Z,Y,X= 0 1 31 13 + 6.032500E+02 2.553750E+03 6.698000E+01 1.494E-07 -1.073E-07 0.000E+00 M= 1.839E-07 #,Z,Y,X= 0 1 31 14 + 6.037500E+02 2.553750E+03 5.968500E+01 4.202E-07 -6.916E-07 0.000E+00 M= 8.092E-07 #,Z,Y,X= 0 1 31 15 + 6.042500E+02 2.553750E+03 4.820000E+01 9.232E-07 -1.105E-06 0.000E+00 M= 1.440E-06 #,Z,Y,X= 0 1 31 16 + 6.047500E+02 2.553750E+03 5.068500E+01 8.640E-07 -4.403E-07 0.000E+00 M= 9.697E-07 #,Z,Y,X= 0 1 31 17 + 6.052500E+02 2.553750E+03 5.479500E+01 4.966E-07 -5.128E-07 0.000E+00 M= 7.139E-07 #,Z,Y,X= 0 1 31 18 + 6.057500E+02 2.553750E+03 5.362500E+01 4.619E-07 -7.641E-07 0.000E+00 M= 8.929E-07 #,Z,Y,X= 0 1 31 19 + 6.062500E+02 2.553750E+03 4.611500E+01 5.194E-07 -1.513E-06 0.000E+00 M= 1.600E-06 #,Z,Y,X= 0 1 31 20 + 6.067500E+02 2.553750E+03 3.758500E+01 6.495E-07 -2.016E-06 0.000E+00 M= 2.118E-06 #,Z,Y,X= 0 1 31 21 + 6.072500E+02 2.553750E+03 3.520500E+01 9.996E-07 -3.009E-06 0.000E+00 M= 3.171E-06 #,Z,Y,X= 0 1 31 22 + 6.077500E+02 2.553750E+03 3.300000E+01 8.151E-07 -2.913E-06 0.000E+00 M= 3.025E-06 #,Z,Y,X= 0 1 31 23 + 6.082500E+02 2.553750E+03 3.027000E+01 2.280E-08 -2.699E-06 0.000E+00 M= 2.699E-06 #,Z,Y,X= 0 1 31 24 + 6.087500E+02 2.553750E+03 3.833000E+01 -6.390E-07 -2.672E-06 0.000E+00 M= 2.748E-06 #,Z,Y,X= 0 1 31 25 + 6.092500E+02 2.553750E+03 4.866500E+01 -8.069E-07 -1.533E-06 0.000E+00 M= 1.732E-06 #,Z,Y,X= 0 1 31 26 + 6.097500E+02 2.553750E+03 6.500500E+01 -4.739E-07 -6.321E-07 0.000E+00 M= 7.900E-07 #,Z,Y,X= 0 1 31 27 + 6.102500E+02 2.553750E+03 7.072000E+01 -3.809E-07 -6.664E-07 0.000E+00 M= 7.676E-07 #,Z,Y,X= 0 1 31 28 + 6.107500E+02 2.553750E+03 6.947500E+01 -2.807E-07 -7.508E-07 0.000E+00 M= 8.015E-07 #,Z,Y,X= 0 1 31 29 + 6.112500E+02 2.553750E+03 6.847500E+01 -1.480E-07 -7.942E-07 0.000E+00 M= 8.078E-07 #,Z,Y,X= 0 1 31 30 + 6.117500E+02 2.553750E+03 6.996000E+01 -4.493E-08 -8.203E-07 0.000E+00 M= 8.216E-07 #,Z,Y,X= 0 1 31 31 + 6.012500E+02 2.553250E+03 7.733500E+01 7.223E-08 -2.641E-09 0.000E+00 M= 7.228E-08 #,Z,Y,X= 0 1 32 10 + 6.017500E+02 2.553250E+03 7.323000E+01 1.503E-07 -6.943E-08 0.000E+00 M= 1.656E-07 #,Z,Y,X= 0 1 32 11 + 6.022500E+02 2.553250E+03 6.975000E+01 1.339E-07 -1.381E-07 0.000E+00 M= 1.924E-07 #,Z,Y,X= 0 1 32 12 + 6.027500E+02 2.553250E+03 6.772000E+01 1.080E-07 -1.387E-07 0.000E+00 M= 1.758E-07 #,Z,Y,X= 0 1 32 13 + 6.032500E+02 2.553250E+03 6.397500E+01 1.094E-07 -1.192E-07 0.000E+00 M= 1.617E-07 #,Z,Y,X= 0 1 32 14 + 6.037500E+02 2.553250E+03 5.830500E+01 2.813E-07 -3.081E-07 0.000E+00 M= 4.172E-07 #,Z,Y,X= 0 1 32 15 + 6.042500E+02 2.553250E+03 4.960000E+01 7.776E-07 -4.826E-07 0.000E+00 M= 9.152E-07 #,Z,Y,X= 0 1 32 16 + 6.047500E+02 2.553250E+03 3.897500E+01 1.127E-06 -6.488E-07 0.000E+00 M= 1.301E-06 #,Z,Y,X= 0 1 32 17 + 6.052500E+02 2.553250E+03 3.975500E+01 1.220E-06 -5.302E-07 0.000E+00 M= 1.330E-06 #,Z,Y,X= 0 1 32 18 + 6.057500E+02 2.553250E+03 3.451000E+01 1.253E-06 -7.194E-07 0.000E+00 M= 1.445E-06 #,Z,Y,X= 0 1 32 19 + 6.062500E+02 2.553250E+03 3.432000E+01 1.092E-06 -1.706E-06 0.000E+00 M= 2.026E-06 #,Z,Y,X= 0 1 32 20 + 6.067500E+02 2.553250E+03 3.938500E+01 6.339E-07 -2.232E-06 0.000E+00 M= 2.320E-06 #,Z,Y,X= 0 1 32 21 + 6.072500E+02 2.553250E+03 3.893000E+01 4.943E-07 -2.645E-06 0.000E+00 M= 2.691E-06 #,Z,Y,X= 0 1 32 22 + 6.077500E+02 2.553250E+03 2.907500E+01 -2.054E-07 -5.218E-06 0.000E+00 M= 5.222E-06 #,Z,Y,X= 0 1 32 23 + 6.082500E+02 2.553250E+03 2.965000E+01 -9.263E-07 -3.941E-06 0.000E+00 M= 4.048E-06 #,Z,Y,X= 0 1 32 24 + 6.087500E+02 2.553250E+03 3.535500E+01 -7.663E-07 -3.055E-06 0.000E+00 M= 3.150E-06 #,Z,Y,X= 0 1 32 25 + 6.092500E+02 2.553250E+03 4.377500E+01 -6.200E-07 -1.148E-06 0.000E+00 M= 1.305E-06 #,Z,Y,X= 0 1 32 26 + 6.097500E+02 2.553250E+03 6.727500E+01 -6.060E-07 -6.954E-07 0.000E+00 M= 9.224E-07 #,Z,Y,X= 0 1 32 27 + 6.102500E+02 2.553250E+03 6.505500E+01 -6.431E-07 -6.674E-07 0.000E+00 M= 9.268E-07 #,Z,Y,X= 0 1 32 28 + 6.107500E+02 2.553250E+03 6.420500E+01 -4.912E-07 -6.749E-07 0.000E+00 M= 8.347E-07 #,Z,Y,X= 0 1 32 29 + 6.112500E+02 2.553250E+03 6.281500E+01 -2.770E-07 -6.540E-07 0.000E+00 M= 7.102E-07 #,Z,Y,X= 0 1 32 30 + 6.117500E+02 2.553250E+03 6.290500E+01 -8.388E-08 -5.826E-07 0.000E+00 M= 5.886E-07 #,Z,Y,X= 0 1 32 31 + 6.017500E+02 2.552750E+03 7.289500E+01 1.295E-07 -5.153E-08 0.000E+00 M= 1.393E-07 #,Z,Y,X= 0 1 33 11 + 6.022500E+02 2.552750E+03 6.414000E+01 2.673E-07 -2.220E-07 0.000E+00 M= 3.474E-07 #,Z,Y,X= 0 1 33 12 + 6.027500E+02 2.552750E+03 6.435000E+01 2.293E-07 -2.312E-07 0.000E+00 M= 3.256E-07 #,Z,Y,X= 0 1 33 13 + 6.032500E+02 2.552750E+03 5.994500E+01 1.352E-07 -2.301E-07 0.000E+00 M= 2.669E-07 #,Z,Y,X= 0 1 33 14 + 6.037500E+02 2.552750E+03 6.315000E+01 5.092E-08 -2.022E-07 0.000E+00 M= 2.085E-07 #,Z,Y,X= 0 1 33 15 + 6.042500E+02 2.552750E+03 5.841000E+01 4.068E-08 -1.856E-07 0.000E+00 M= 1.900E-07 #,Z,Y,X= 0 1 33 16 + 6.047500E+02 2.552750E+03 4.746500E+01 2.300E-07 -4.270E-07 0.000E+00 M= 4.850E-07 #,Z,Y,X= 0 1 33 17 + 6.052500E+02 2.552750E+03 4.644000E+01 5.315E-07 -3.625E-07 0.000E+00 M= 6.433E-07 #,Z,Y,X= 0 1 33 18 + 6.057500E+02 2.552750E+03 4.249000E+01 9.099E-07 -5.000E-07 0.000E+00 M= 1.038E-06 #,Z,Y,X= 0 1 33 19 + 6.062500E+02 2.552750E+03 3.741000E+01 1.173E-06 -1.993E-06 0.000E+00 M= 2.313E-06 #,Z,Y,X= 0 1 33 20 + 6.067500E+02 2.552750E+03 3.196500E+01 9.295E-07 -2.837E-06 0.000E+00 M= 2.986E-06 #,Z,Y,X= 0 1 33 21 + 6.072500E+02 2.552750E+03 3.451000E+01 3.390E-07 -2.929E-06 0.000E+00 M= 2.948E-06 #,Z,Y,X= 0 1 33 22 + 6.077500E+02 2.552750E+03 2.864000E+01 -1.200E-06 -6.480E-06 0.000E+00 M= 6.590E-06 #,Z,Y,X= 0 1 33 23 + 6.082500E+02 2.552750E+03 2.928000E+01 -1.609E-06 -3.079E-06 0.000E+00 M= 3.474E-06 #,Z,Y,X= 0 1 33 24 + 6.087500E+02 2.552750E+03 3.648000E+01 -7.703E-07 -2.923E-06 0.000E+00 M= 3.023E-06 #,Z,Y,X= 0 1 33 25 + 6.092500E+02 2.552750E+03 4.443000E+01 -6.592E-07 -1.028E-06 0.000E+00 M= 1.221E-06 #,Z,Y,X= 0 1 33 26 + 6.097500E+02 2.552750E+03 5.091500E+01 -7.447E-07 -8.738E-07 0.000E+00 M= 1.148E-06 #,Z,Y,X= 0 1 33 27 + 6.102500E+02 2.552750E+03 5.766500E+01 -7.312E-07 -5.818E-07 0.000E+00 M= 9.344E-07 #,Z,Y,X= 0 1 33 28 + 6.107500E+02 2.552750E+03 5.670500E+01 -5.307E-07 -5.523E-07 0.000E+00 M= 7.660E-07 #,Z,Y,X= 0 1 33 29 + 6.112500E+02 2.552750E+03 5.901500E+01 -3.343E-07 -5.158E-07 0.000E+00 M= 6.147E-07 #,Z,Y,X= 0 1 33 30 + 6.117500E+02 2.552750E+03 6.173500E+01 -1.158E-07 -3.854E-07 0.000E+00 M= 4.024E-07 #,Z,Y,X= 0 1 33 31 + 6.022500E+02 2.552250E+03 6.931500E+01 1.515E-07 -1.201E-07 0.000E+00 M= 1.934E-07 #,Z,Y,X= 0 1 34 12 + 6.027500E+02 2.552250E+03 5.979000E+01 2.605E-07 -3.284E-07 0.000E+00 M= 4.192E-07 #,Z,Y,X= 0 1 34 13 + 6.032500E+02 2.552250E+03 5.178000E+01 2.293E-07 -3.300E-07 0.000E+00 M= 4.018E-07 #,Z,Y,X= 0 1 34 14 + 6.037500E+02 2.552250E+03 5.648500E+01 1.586E-07 -3.262E-07 0.000E+00 M= 3.627E-07 #,Z,Y,X= 0 1 34 15 + 6.042500E+02 2.552250E+03 6.045000E+01 2.532E-08 -2.960E-07 0.000E+00 M= 2.971E-07 #,Z,Y,X= 0 1 34 16 + 6.047500E+02 2.552250E+03 5.891000E+01 -3.983E-09 -2.475E-07 0.000E+00 M= 2.475E-07 #,Z,Y,X= 0 1 34 17 + 6.052500E+02 2.552250E+03 5.968000E+01 6.713E-08 -2.258E-07 0.000E+00 M= 2.355E-07 #,Z,Y,X= 0 1 34 18 + 6.057500E+02 2.552250E+03 5.505500E+01 1.283E-07 -2.717E-07 0.000E+00 M= 3.005E-07 #,Z,Y,X= 0 1 34 19 + 6.062500E+02 2.552250E+03 4.108000E+01 3.108E-07 -1.959E-06 0.000E+00 M= 1.984E-06 #,Z,Y,X= 0 1 34 20 + 6.067500E+02 2.552250E+03 3.300000E+01 6.887E-07 -2.907E-06 0.000E+00 M= 2.988E-06 #,Z,Y,X= 0 1 34 21 + 6.072500E+02 2.552250E+03 3.052500E+01 5.205E-07 -3.748E-06 0.000E+00 M= 3.784E-06 #,Z,Y,X= 0 1 34 22 + 6.077500E+02 2.552250E+03 2.716500E+01 -7.472E-07 -8.328E-06 0.000E+00 M= 8.361E-06 #,Z,Y,X= 0 1 34 23 + 6.082500E+02 2.552250E+03 3.528500E+01 -1.403E-06 -2.163E-06 0.000E+00 M= 2.578E-06 #,Z,Y,X= 0 1 34 24 + 6.087500E+02 2.552250E+03 3.491500E+01 -9.050E-07 -2.474E-06 0.000E+00 M= 2.635E-06 #,Z,Y,X= 0 1 34 25 + 6.092500E+02 2.552250E+03 4.269500E+01 -6.478E-07 -1.063E-06 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 34 26 + 6.097500E+02 2.552250E+03 4.483000E+01 -7.792E-07 -9.605E-07 0.000E+00 M= 1.237E-06 #,Z,Y,X= 0 1 34 27 + 6.102500E+02 2.552250E+03 5.496000E+01 -7.361E-07 -4.182E-07 0.000E+00 M= 8.466E-07 #,Z,Y,X= 0 1 34 28 + 6.107500E+02 2.552250E+03 6.349500E+01 -5.524E-07 -4.162E-07 0.000E+00 M= 6.916E-07 #,Z,Y,X= 0 1 34 29 + 6.112500E+02 2.552250E+03 6.075500E+01 -4.413E-07 -4.639E-07 0.000E+00 M= 6.402E-07 #,Z,Y,X= 0 1 34 30 + 6.117500E+02 2.552250E+03 6.118500E+01 -1.956E-07 -1.345E-07 0.000E+00 M= 2.374E-07 #,Z,Y,X= 0 1 34 31 + 6.027500E+02 2.551750E+03 6.426000E+01 1.025E-07 -3.101E-07 0.000E+00 M= 3.266E-07 #,Z,Y,X= 0 1 35 13 + 6.032500E+02 2.551750E+03 5.230500E+01 2.470E-07 -3.357E-07 0.000E+00 M= 4.168E-07 #,Z,Y,X= 0 1 35 14 + 6.037500E+02 2.551750E+03 4.522500E+01 3.236E-07 -4.144E-07 0.000E+00 M= 5.258E-07 #,Z,Y,X= 0 1 35 15 + 6.042500E+02 2.551750E+03 4.533500E+01 1.789E-07 -6.202E-07 0.000E+00 M= 6.455E-07 #,Z,Y,X= 0 1 35 16 + 6.047500E+02 2.551750E+03 5.753000E+01 7.049E-09 -2.608E-07 0.000E+00 M= 2.608E-07 #,Z,Y,X= 0 1 35 17 + 6.052500E+02 2.551750E+03 5.992500E+01 3.344E-08 -2.433E-07 0.000E+00 M= 2.455E-07 #,Z,Y,X= 0 1 35 18 + 6.057500E+02 2.551750E+03 5.343500E+01 1.252E-07 -2.261E-07 0.000E+00 M= 2.585E-07 #,Z,Y,X= 0 1 35 19 + 6.062500E+02 2.551750E+03 4.198500E+01 3.306E-07 -1.788E-06 0.000E+00 M= 1.818E-06 #,Z,Y,X= 0 1 35 20 + 6.067500E+02 2.551750E+03 3.982000E+01 6.172E-07 -2.647E-06 0.000E+00 M= 2.718E-06 #,Z,Y,X= 0 1 35 21 + 6.072500E+02 2.551750E+03 2.632500E+01 1.473E-06 -3.637E-06 0.000E+00 M= 3.924E-06 #,Z,Y,X= 0 1 35 22 + 6.077500E+02 2.551750E+03 2.690000E+01 -6.019E-08 -6.115E-06 0.000E+00 M= 6.115E-06 #,Z,Y,X= 0 1 35 23 + 6.082500E+02 2.551750E+03 4.151500E+01 -1.745E-06 -1.544E-06 0.000E+00 M= 2.330E-06 #,Z,Y,X= 0 1 35 24 + 6.087500E+02 2.551750E+03 5.194000E+01 -8.356E-07 -1.810E-06 0.000E+00 M= 1.993E-06 #,Z,Y,X= 0 1 35 25 + 6.092500E+02 2.551750E+03 5.944000E+01 -5.694E-07 -1.236E-06 0.000E+00 M= 1.361E-06 #,Z,Y,X= 0 1 35 26 + 6.097500E+02 2.551750E+03 6.320000E+01 -1.013E-06 -1.308E-06 0.000E+00 M= 1.654E-06 #,Z,Y,X= 0 1 35 27 + 6.102500E+02 2.551750E+03 6.619000E+01 -1.138E-06 -1.645E-07 0.000E+00 M= 1.150E-06 #,Z,Y,X= 0 1 35 28 + 6.107500E+02 2.551750E+03 6.683000E+01 -7.361E-07 -1.720E-07 0.000E+00 M= 7.559E-07 #,Z,Y,X= 0 1 35 29 + 6.112500E+02 2.551750E+03 6.222000E+01 -2.558E-07 -2.330E-07 0.000E+00 M= 3.460E-07 #,Z,Y,X= 0 1 35 30 + 6.027500E+02 2.551250E+03 6.366000E+01 1.436E-07 -1.147E-07 0.000E+00 M= 1.838E-07 #,Z,Y,X= 0 1 36 13 + 6.032500E+02 2.551250E+03 5.942000E+01 3.475E-07 -2.816E-07 0.000E+00 M= 4.472E-07 #,Z,Y,X= 0 1 36 14 + 6.037500E+02 2.551250E+03 4.911000E+01 5.303E-07 -3.080E-07 0.000E+00 M= 6.133E-07 #,Z,Y,X= 0 1 36 15 + 6.042500E+02 2.551250E+03 3.983000E+01 7.786E-07 -7.443E-07 0.000E+00 M= 1.077E-06 #,Z,Y,X= 0 1 36 16 + 6.047500E+02 2.551250E+03 3.964500E+01 6.198E-07 -5.962E-07 0.000E+00 M= 8.600E-07 #,Z,Y,X= 0 1 36 17 + 6.052500E+02 2.551250E+03 4.461000E+01 1.631E-07 -4.638E-07 0.000E+00 M= 4.917E-07 #,Z,Y,X= 0 1 36 18 + 6.057500E+02 2.551250E+03 5.164500E+01 -1.686E-09 -1.860E-07 0.000E+00 M= 1.860E-07 #,Z,Y,X= 0 1 36 19 + 6.062500E+02 2.551250E+03 3.417500E+01 2.140E-07 -1.532E-06 0.000E+00 M= 1.547E-06 #,Z,Y,X= 0 1 36 20 + 6.067500E+02 2.551250E+03 2.708000E+01 3.461E-07 -2.644E-06 0.000E+00 M= 2.666E-06 #,Z,Y,X= 0 1 36 21 + 6.072500E+02 2.551250E+03 2.600000E+01 3.705E-07 -2.074E-06 0.000E+00 M= 2.107E-06 #,Z,Y,X= 0 1 36 22 + 6.077500E+02 2.551250E+03 3.101000E+01 -1.756E-07 -2.280E-06 0.000E+00 M= 2.287E-06 #,Z,Y,X= 0 1 36 23 + 6.082500E+02 2.551250E+03 5.856000E+01 -8.045E-07 -1.095E-06 0.000E+00 M= 1.359E-06 #,Z,Y,X= 0 1 36 24 + 6.087500E+02 2.551250E+03 6.059500E+01 -7.094E-07 -1.332E-06 0.000E+00 M= 1.509E-06 #,Z,Y,X= 0 1 36 25 + 6.092500E+02 2.551250E+03 6.417500E+01 -5.003E-07 -1.233E-06 0.000E+00 M= 1.330E-06 #,Z,Y,X= 0 1 36 26 + 6.097500E+02 2.551250E+03 6.739000E+01 -2.088E-07 -1.340E-06 0.000E+00 M= 1.357E-06 #,Z,Y,X= 0 1 36 27 + 6.032500E+02 2.550750E+03 5.966000E+01 6.755E-08 -2.159E-07 0.000E+00 M= 2.262E-07 #,Z,Y,X= 0 1 37 14 + 6.037500E+02 2.550750E+03 5.490000E+01 1.796E-07 -1.878E-07 0.000E+00 M= 2.598E-07 #,Z,Y,X= 0 1 37 15 + 6.042500E+02 2.550750E+03 4.762000E+01 3.330E-07 -5.882E-07 0.000E+00 M= 6.759E-07 #,Z,Y,X= 0 1 37 16 + 6.047500E+02 2.550750E+03 3.391000E+01 5.957E-07 -7.898E-07 0.000E+00 M= 9.893E-07 #,Z,Y,X= 0 1 37 17 + 6.052500E+02 2.550750E+03 3.487000E+01 6.452E-07 -8.196E-07 0.000E+00 M= 1.043E-06 #,Z,Y,X= 0 1 37 18 + 6.057500E+02 2.550750E+03 3.834500E+01 9.759E-08 -6.624E-07 0.000E+00 M= 6.696E-07 #,Z,Y,X= 0 1 37 19 + 6.062500E+02 2.550750E+03 3.334500E+01 -3.042E-07 -1.356E-06 0.000E+00 M= 1.390E-06 #,Z,Y,X= 0 1 37 20 + 6.067500E+02 2.550750E+03 3.027500E+01 -9.200E-07 -3.409E-06 0.000E+00 M= 3.531E-06 #,Z,Y,X= 0 1 37 21 + 6.072500E+02 2.550750E+03 2.800000E+01 1.669E-07 -2.425E-06 0.000E+00 M= 2.431E-06 #,Z,Y,X= 0 1 37 22 + 6.077500E+02 2.550750E+03 3.171000E+01 9.347E-07 -3.923E-06 0.000E+00 M= 4.033E-06 #,Z,Y,X= 0 1 37 23 + 6.082500E+02 2.550750E+03 3.820500E+01 -3.642E-07 -1.540E-06 0.000E+00 M= 1.583E-06 #,Z,Y,X= 0 1 37 24 + 6.087500E+02 2.550750E+03 5.257000E+01 -6.868E-07 -1.235E-06 0.000E+00 M= 1.413E-06 #,Z,Y,X= 0 1 37 25 + 6.092500E+02 2.550750E+03 5.695000E+01 -5.509E-07 -1.071E-06 0.000E+00 M= 1.204E-06 #,Z,Y,X= 0 1 37 26 + 6.097500E+02 2.550750E+03 6.289000E+01 -2.213E-07 -9.118E-07 0.000E+00 M= 9.382E-07 #,Z,Y,X= 0 1 37 27 + 6.032500E+02 2.550250E+03 5.278000E+01 6.229E-08 -1.440E-07 0.000E+00 M= 1.569E-07 #,Z,Y,X= 0 1 38 14 + 6.037500E+02 2.550250E+03 4.605000E+01 1.658E-07 -1.491E-07 0.000E+00 M= 2.230E-07 #,Z,Y,X= 0 1 38 15 + 6.042500E+02 2.550250E+03 4.323500E+01 2.134E-07 -5.335E-07 0.000E+00 M= 5.746E-07 #,Z,Y,X= 0 1 38 16 + 6.047500E+02 2.550250E+03 4.001500E+01 2.880E-07 -6.134E-07 0.000E+00 M= 6.777E-07 #,Z,Y,X= 0 1 38 17 + 6.052500E+02 2.550250E+03 3.643000E+01 4.347E-07 -9.177E-07 0.000E+00 M= 1.015E-06 #,Z,Y,X= 0 1 38 18 + 6.057500E+02 2.550250E+03 2.933500E+01 3.947E-07 -1.254E-06 0.000E+00 M= 1.315E-06 #,Z,Y,X= 0 1 38 19 + 6.062500E+02 2.550250E+03 2.983000E+01 2.253E-08 -1.629E-06 0.000E+00 M= 1.629E-06 #,Z,Y,X= 0 1 38 20 + 6.067500E+02 2.550250E+03 3.080500E+01 -5.118E-07 -4.370E-06 0.000E+00 M= 4.400E-06 #,Z,Y,X= 0 1 38 21 + 6.072500E+02 2.550250E+03 2.480000E+01 2.687E-07 -5.980E-06 0.000E+00 M= 5.986E-06 #,Z,Y,X= 0 1 38 22 + 6.077500E+02 2.550250E+03 2.474000E+01 2.762E-07 -5.947E-06 0.000E+00 M= 5.954E-06 #,Z,Y,X= 0 1 38 23 + 6.082500E+02 2.550250E+03 2.769000E+01 -8.587E-07 -2.013E-06 0.000E+00 M= 2.188E-06 #,Z,Y,X= 0 1 38 24 + 6.087500E+02 2.550250E+03 3.546000E+01 -9.039E-07 -1.224E-06 0.000E+00 M= 1.521E-06 #,Z,Y,X= 0 1 38 25 + 6.092500E+02 2.550250E+03 5.472000E+01 -7.989E-07 -9.367E-07 0.000E+00 M= 1.231E-06 #,Z,Y,X= 0 1 38 26 + 6.097500E+02 2.550250E+03 5.888000E+01 -3.586E-07 -3.397E-07 0.000E+00 M= 4.939E-07 #,Z,Y,X= 0 1 38 27 + 6.027500E+02 2.549750E+03 5.802500E+01 2.800E-08 -1.853E-08 0.000E+00 M= 3.358E-08 #,Z,Y,X= 0 1 39 13 + 6.032500E+02 2.549750E+03 5.350500E+01 1.002E-07 -9.374E-08 0.000E+00 M= 1.372E-07 #,Z,Y,X= 0 1 39 14 + 6.037500E+02 2.549750E+03 4.779500E+01 1.897E-07 -1.115E-07 0.000E+00 M= 2.201E-07 #,Z,Y,X= 0 1 39 15 + 6.042500E+02 2.549750E+03 4.147000E+01 3.846E-07 -4.431E-07 0.000E+00 M= 5.867E-07 #,Z,Y,X= 0 1 39 16 + 6.047500E+02 2.549750E+03 3.523000E+01 5.304E-07 -5.962E-07 0.000E+00 M= 7.980E-07 #,Z,Y,X= 0 1 39 17 + 6.052500E+02 2.549750E+03 3.513000E+01 5.349E-07 -9.013E-07 0.000E+00 M= 1.048E-06 #,Z,Y,X= 0 1 39 18 + 6.057500E+02 2.549750E+03 2.901500E+01 6.625E-07 -1.308E-06 0.000E+00 M= 1.466E-06 #,Z,Y,X= 0 1 39 19 + 6.062500E+02 2.549750E+03 2.744000E+01 8.314E-07 -1.914E-06 0.000E+00 M= 2.087E-06 #,Z,Y,X= 0 1 39 20 + 6.067500E+02 2.549750E+03 2.532500E+01 1.318E-06 -4.298E-06 0.000E+00 M= 4.495E-06 #,Z,Y,X= 0 1 39 21 + 6.072500E+02 2.549750E+03 2.285000E+01 -1.262E-07 -1.036E-05 0.000E+00 M= 1.036E-05 #,Z,Y,X= 0 1 39 22 + 6.077500E+02 2.549750E+03 2.879500E+01 -1.519E-06 -6.630E-06 0.000E+00 M= 6.802E-06 #,Z,Y,X= 0 1 39 23 + 6.082500E+02 2.549750E+03 4.679000E+01 -7.543E-07 -1.898E-06 0.000E+00 M= 2.043E-06 #,Z,Y,X= 0 1 39 24 + 6.087500E+02 2.549750E+03 5.561000E+01 -7.340E-07 -1.498E-06 0.000E+00 M= 1.668E-06 #,Z,Y,X= 0 1 39 25 + 6.092500E+02 2.549750E+03 5.738500E+01 -4.720E-07 -4.527E-07 0.000E+00 M= 6.540E-07 #,Z,Y,X= 0 1 39 26 + 6.027500E+02 2.549250E+03 5.649500E+01 4.782E-08 -1.798E-08 0.000E+00 M= 5.109E-08 #,Z,Y,X= 0 1 40 13 + 6.032500E+02 2.549250E+03 4.803000E+01 1.269E-07 -7.173E-08 0.000E+00 M= 1.458E-07 #,Z,Y,X= 0 1 40 14 + 6.037500E+02 2.549250E+03 4.838000E+01 1.592E-07 -1.145E-07 0.000E+00 M= 1.961E-07 #,Z,Y,X= 0 1 40 15 + 6.042500E+02 2.549250E+03 4.558500E+01 2.591E-07 -2.505E-07 0.000E+00 M= 3.604E-07 #,Z,Y,X= 0 1 40 16 + 6.047500E+02 2.549250E+03 3.942500E+01 5.947E-07 -4.156E-07 0.000E+00 M= 7.256E-07 #,Z,Y,X= 0 1 40 17 + 6.052500E+02 2.549250E+03 3.836000E+01 8.422E-07 -9.587E-07 0.000E+00 M= 1.276E-06 #,Z,Y,X= 0 1 40 18 + 6.057500E+02 2.549250E+03 3.781000E+01 7.987E-07 -1.308E-06 0.000E+00 M= 1.532E-06 #,Z,Y,X= 0 1 40 19 + 6.062500E+02 2.549250E+03 3.163500E+01 1.061E-06 -1.632E-06 0.000E+00 M= 1.947E-06 #,Z,Y,X= 0 1 40 20 + 6.067500E+02 2.549250E+03 2.784000E+01 2.636E-06 -2.679E-06 0.000E+00 M= 3.758E-06 #,Z,Y,X= 0 1 40 21 + 6.072500E+02 2.549250E+03 2.250000E+01 -1.774E-07 -7.530E-06 0.000E+00 M= 7.533E-06 #,Z,Y,X= 0 1 40 22 + 6.077500E+02 2.549250E+03 2.474500E+01 -2.534E-06 -4.559E-06 0.000E+00 M= 5.216E-06 #,Z,Y,X= 0 1 40 23 + 6.082500E+02 2.549250E+03 3.624500E+01 -6.285E-07 -1.470E-06 0.000E+00 M= 1.599E-06 #,Z,Y,X= 0 1 40 24 + 6.087500E+02 2.549250E+03 4.756500E+01 -1.955E-07 -1.611E-06 0.000E+00 M= 1.623E-06 #,Z,Y,X= 0 1 40 25 + 6.032500E+02 2.548750E+03 5.728000E+01 5.646E-08 -3.399E-08 0.000E+00 M= 6.590E-08 #,Z,Y,X= 0 1 41 14 + 6.037500E+02 2.548750E+03 5.515500E+01 7.633E-08 -2.002E-07 0.000E+00 M= 2.142E-07 #,Z,Y,X= 0 1 41 15 + 6.042500E+02 2.548750E+03 5.431000E+01 3.526E-08 -2.034E-07 0.000E+00 M= 2.064E-07 #,Z,Y,X= 0 1 41 16 + 6.047500E+02 2.548750E+03 5.317000E+01 4.718E-08 -2.162E-07 0.000E+00 M= 2.213E-07 #,Z,Y,X= 0 1 41 17 + 6.052500E+02 2.548750E+03 4.377000E+01 1.976E-07 -9.006E-07 0.000E+00 M= 9.220E-07 #,Z,Y,X= 0 1 41 18 + 6.057500E+02 2.548750E+03 3.674500E+01 5.198E-07 -1.260E-06 0.000E+00 M= 1.363E-06 #,Z,Y,X= 0 1 41 19 + 6.062500E+02 2.548750E+03 3.705500E+01 7.197E-07 -1.378E-06 0.000E+00 M= 1.555E-06 #,Z,Y,X= 0 1 41 20 + 6.067500E+02 2.548750E+03 2.720000E+01 9.304E-07 -1.279E-06 0.000E+00 M= 1.581E-06 #,Z,Y,X= 0 1 41 21 + 6.072500E+02 2.548750E+03 2.100000E+01 -4.722E-07 -1.064E-06 0.000E+00 M= 1.164E-06 #,Z,Y,X= 0 1 41 22 + 6.077500E+02 2.548750E+03 2.638000E+01 -1.123E-06 -2.042E-06 0.000E+00 M= 2.331E-06 #,Z,Y,X= 0 1 41 23 + 6.082500E+02 2.548750E+03 2.940000E+01 -1.619E-07 -1.205E-06 0.000E+00 M= 1.216E-06 #,Z,Y,X= 0 1 41 24 + 6.087500E+02 2.548750E+03 4.419000E+01 -3.951E-08 -1.432E-06 0.000E+00 M= 1.433E-06 #,Z,Y,X= 0 1 41 25 + 6.037500E+02 2.548250E+03 4.998000E+01 5.570E-08 -2.823E-07 0.000E+00 M= 2.878E-07 #,Z,Y,X= 0 1 42 15 + 6.042500E+02 2.548250E+03 4.319000E+01 1.133E-07 -2.507E-07 0.000E+00 M= 2.751E-07 #,Z,Y,X= 0 1 42 16 + 6.047500E+02 2.548250E+03 3.629000E+01 7.060E-08 -2.877E-07 0.000E+00 M= 2.962E-07 #,Z,Y,X= 0 1 42 17 + 6.052500E+02 2.548250E+03 3.184500E+01 1.987E-07 -6.316E-07 0.000E+00 M= 6.621E-07 #,Z,Y,X= 0 1 42 18 + 6.057500E+02 2.548250E+03 3.300500E+01 6.007E-07 -8.978E-07 0.000E+00 M= 1.080E-06 #,Z,Y,X= 0 1 42 19 + 6.062500E+02 2.548250E+03 2.873500E+01 9.623E-07 -1.124E-06 0.000E+00 M= 1.480E-06 #,Z,Y,X= 0 1 42 20 + 6.067500E+02 2.548250E+03 2.439500E+01 6.590E-07 -6.579E-07 0.000E+00 M= 9.312E-07 #,Z,Y,X= 0 1 42 21 + 6.072500E+02 2.548250E+03 2.150000E+01 1.378E-07 -7.289E-07 0.000E+00 M= 7.419E-07 #,Z,Y,X= 0 1 42 22 + 6.077500E+02 2.548250E+03 3.333000E+01 2.001E-07 -1.004E-06 0.000E+00 M= 1.024E-06 #,Z,Y,X= 0 1 42 23 + 6.082500E+02 2.548250E+03 4.726000E+01 3.455E-07 -1.158E-06 0.000E+00 M= 1.208E-06 #,Z,Y,X= 0 1 42 24 + 6.087500E+02 2.548250E+03 4.943500E+01 1.600E-07 -1.566E-06 0.000E+00 M= 1.574E-06 #,Z,Y,X= 0 1 42 25 + 6.037500E+02 2.547750E+03 4.691500E+01 4.151E-08 -3.235E-07 0.000E+00 M= 3.261E-07 #,Z,Y,X= 0 1 43 15 + 6.042500E+02 2.547750E+03 4.113000E+01 6.613E-08 -3.030E-07 0.000E+00 M= 3.102E-07 #,Z,Y,X= 0 1 43 16 + 6.047500E+02 2.547750E+03 4.217000E+01 6.967E-08 -3.520E-07 0.000E+00 M= 3.589E-07 #,Z,Y,X= 0 1 43 17 + 6.052500E+02 2.547750E+03 4.010500E+01 1.253E-07 -4.624E-07 0.000E+00 M= 4.791E-07 #,Z,Y,X= 0 1 43 18 + 6.057500E+02 2.547750E+03 3.331500E+01 2.943E-07 -5.817E-07 0.000E+00 M= 6.519E-07 #,Z,Y,X= 0 1 43 19 + 6.062500E+02 2.547750E+03 2.765500E+01 4.949E-07 -8.151E-07 0.000E+00 M= 9.535E-07 #,Z,Y,X= 0 1 43 20 + 6.067500E+02 2.547750E+03 2.100000E+01 7.127E-07 -1.980E-07 0.000E+00 M= 7.397E-07 #,Z,Y,X= 0 1 43 21 + 6.072500E+02 2.547750E+03 2.385000E+01 9.289E-07 -6.609E-07 0.000E+00 M= 1.140E-06 #,Z,Y,X= 0 1 43 22 + 6.077500E+02 2.547750E+03 3.700500E+01 1.017E-06 -9.140E-07 0.000E+00 M= 1.367E-06 #,Z,Y,X= 0 1 43 23 + 6.082500E+02 2.547750E+03 4.188500E+01 9.285E-07 -1.272E-06 0.000E+00 M= 1.575E-06 #,Z,Y,X= 0 1 43 24 + 6.087500E+02 2.547750E+03 4.795500E+01 1.054E-06 -1.599E-06 0.000E+00 M= 1.916E-06 #,Z,Y,X= 0 1 43 25 + 6.092500E+02 2.547750E+03 4.120500E+01 6.294E-07 -6.450E-07 0.000E+00 M= 9.012E-07 #,Z,Y,X= 0 1 43 26 + 6.037500E+02 2.547250E+03 4.450500E+01 3.508E-08 -3.931E-07 0.000E+00 M= 3.946E-07 #,Z,Y,X= 0 1 44 15 + 6.042500E+02 2.547250E+03 4.563500E+01 6.420E-08 -3.672E-07 0.000E+00 M= 3.728E-07 #,Z,Y,X= 0 1 44 16 + 6.047500E+02 2.547250E+03 4.101000E+01 5.789E-08 -3.684E-07 0.000E+00 M= 3.730E-07 #,Z,Y,X= 0 1 44 17 + 6.052500E+02 2.547250E+03 3.338500E+01 2.365E-08 -4.981E-07 0.000E+00 M= 4.987E-07 #,Z,Y,X= 0 1 44 18 + 6.057500E+02 2.547250E+03 2.991000E+01 7.953E-09 -4.594E-07 0.000E+00 M= 4.595E-07 #,Z,Y,X= 0 1 44 19 + 6.062500E+02 2.547250E+03 2.274000E+01 -3.206E-08 -8.193E-07 0.000E+00 M= 8.199E-07 #,Z,Y,X= 0 1 44 20 + 6.067500E+02 2.547250E+03 1.977000E+01 4.416E-07 -2.484E-07 0.000E+00 M= 5.067E-07 #,Z,Y,X= 0 1 44 21 + 6.072500E+02 2.547250E+03 2.585500E+01 1.126E-06 -3.591E-07 0.000E+00 M= 1.182E-06 #,Z,Y,X= 0 1 44 22 + 6.077500E+02 2.547250E+03 2.503500E+01 1.457E-06 -8.303E-07 0.000E+00 M= 1.677E-06 #,Z,Y,X= 0 1 44 23 + 6.082500E+02 2.547250E+03 2.931500E+01 1.470E-06 -1.517E-06 0.000E+00 M= 2.112E-06 #,Z,Y,X= 0 1 44 24 + 6.087500E+02 2.547250E+03 3.388500E+01 1.177E-06 -1.574E-06 0.000E+00 M= 1.965E-06 #,Z,Y,X= 0 1 44 25 + 6.092500E+02 2.547250E+03 3.012500E+01 8.691E-07 -1.484E-06 0.000E+00 M= 1.720E-06 #,Z,Y,X= 0 1 44 26 + 6.097500E+02 2.547250E+03 2.249000E+01 4.085E-07 -3.330E-07 0.000E+00 M= 5.270E-07 #,Z,Y,X= 0 1 44 27 + 6.102500E+02 2.547250E+03 2.306000E+01 6.904E-08 -1.313E-07 0.000E+00 M= 1.483E-07 #,Z,Y,X= 0 1 44 28 + 6.037500E+02 2.546750E+03 3.959500E+01 5.335E-08 -4.611E-07 0.000E+00 M= 4.642E-07 #,Z,Y,X= 0 1 45 15 + 6.042500E+02 2.546750E+03 3.923000E+01 1.234E-07 -4.540E-07 0.000E+00 M= 4.705E-07 #,Z,Y,X= 0 1 45 16 + 6.047500E+02 2.546750E+03 3.564500E+01 6.594E-08 -4.881E-07 0.000E+00 M= 4.926E-07 #,Z,Y,X= 0 1 45 17 + 6.052500E+02 2.546750E+03 3.092500E+01 -5.874E-08 -6.337E-07 0.000E+00 M= 6.364E-07 #,Z,Y,X= 0 1 45 18 + 6.057500E+02 2.546750E+03 2.813000E+01 -2.538E-07 -6.155E-07 0.000E+00 M= 6.657E-07 #,Z,Y,X= 0 1 45 19 + 6.062500E+02 2.546750E+03 2.489000E+01 -5.146E-07 -1.021E-06 0.000E+00 M= 1.143E-06 #,Z,Y,X= 0 1 45 20 + 6.067500E+02 2.546750E+03 1.932500E+01 7.228E-07 -9.770E-07 0.000E+00 M= 1.215E-06 #,Z,Y,X= 0 1 45 21 + 6.072500E+02 2.546750E+03 2.244500E+01 2.474E-06 2.281E-07 0.000E+00 M= 2.485E-06 #,Z,Y,X= 0 1 45 22 + 6.077500E+02 2.546750E+03 2.714500E+01 2.784E-06 -8.830E-07 0.000E+00 M= 2.920E-06 #,Z,Y,X= 0 1 45 23 + 6.082500E+02 2.546750E+03 2.787500E+01 2.209E-06 -2.216E-06 0.000E+00 M= 3.129E-06 #,Z,Y,X= 0 1 45 24 + 6.087500E+02 2.546750E+03 2.652500E+01 1.089E-06 -2.459E-06 0.000E+00 M= 2.689E-06 #,Z,Y,X= 0 1 45 25 + 6.092500E+02 2.546750E+03 1.977000E+01 -3.681E-07 -2.544E-06 0.000E+00 M= 2.571E-06 #,Z,Y,X= 0 1 45 26 + 6.097500E+02 2.546750E+03 1.916500E+01 -7.620E-07 -3.274E-07 0.000E+00 M= 8.293E-07 #,Z,Y,X= 0 1 45 27 + 6.102500E+02 2.546750E+03 2.527500E+01 -1.881E-07 -1.290E-07 0.000E+00 M= 2.281E-07 #,Z,Y,X= 0 1 45 28 + 6.037500E+02 2.546250E+03 3.691000E+01 6.758E-08 -4.724E-07 0.000E+00 M= 4.772E-07 #,Z,Y,X= 0 1 46 15 + 6.042500E+02 2.546250E+03 3.585000E+01 1.773E-07 -5.313E-07 0.000E+00 M= 5.601E-07 #,Z,Y,X= 0 1 46 16 + 6.047500E+02 2.546250E+03 3.400000E+01 1.688E-07 -6.520E-07 0.000E+00 M= 6.735E-07 #,Z,Y,X= 0 1 46 17 + 6.052500E+02 2.546250E+03 3.180000E+01 3.749E-08 -8.160E-07 0.000E+00 M= 8.168E-07 #,Z,Y,X= 0 1 46 18 + 6.057500E+02 2.546250E+03 2.783000E+01 -1.820E-07 -9.293E-07 0.000E+00 M= 9.470E-07 #,Z,Y,X= 0 1 46 19 + 6.062500E+02 2.546250E+03 2.602000E+01 -5.792E-07 -1.436E-06 0.000E+00 M= 1.548E-06 #,Z,Y,X= 0 1 46 20 + 6.067500E+02 2.546250E+03 1.935000E+01 -3.471E-07 -2.798E-06 0.000E+00 M= 2.819E-06 #,Z,Y,X= 0 1 46 21 + 6.072500E+02 2.546250E+03 1.995000E+01 2.815E-06 -2.914E-06 0.000E+00 M= 4.052E-06 #,Z,Y,X= 0 1 46 22 + 6.077500E+02 2.546250E+03 2.696000E+01 4.927E-06 -1.834E-06 0.000E+00 M= 5.257E-06 #,Z,Y,X= 0 1 46 23 + 6.082500E+02 2.546250E+03 2.571500E+01 2.802E-06 -4.488E-06 0.000E+00 M= 5.291E-06 #,Z,Y,X= 0 1 46 24 + 6.087500E+02 2.546250E+03 1.650000E+01 -1.152E-06 -5.816E-06 0.000E+00 M= 5.929E-06 #,Z,Y,X= 0 1 46 25 + 6.092500E+02 2.546250E+03 1.600000E+01 -1.735E-06 -1.821E-06 0.000E+00 M= 2.515E-06 #,Z,Y,X= 0 1 46 26 + 6.032500E+02 2.545750E+03 3.543830E+01 1.421E-08 -5.348E-08 0.000E+00 M= 5.534E-08 #,Z,Y,X= 0 1 47 14 + 6.037500E+02 2.545750E+03 3.518500E+01 1.217E-07 -4.384E-07 0.000E+00 M= 4.550E-07 #,Z,Y,X= 0 1 47 15 + 6.042500E+02 2.545750E+03 3.420000E+01 2.917E-07 -5.365E-07 0.000E+00 M= 6.107E-07 #,Z,Y,X= 0 1 47 16 + 6.047500E+02 2.545750E+03 3.404500E+01 3.460E-07 -7.530E-07 0.000E+00 M= 8.287E-07 #,Z,Y,X= 0 1 47 17 + 6.052500E+02 2.545750E+03 3.243000E+01 2.245E-07 -1.035E-06 0.000E+00 M= 1.059E-06 #,Z,Y,X= 0 1 47 18 + 6.057500E+02 2.545750E+03 2.902000E+01 -1.487E-08 -1.242E-06 0.000E+00 M= 1.242E-06 #,Z,Y,X= 0 1 47 19 + 6.062500E+02 2.545750E+03 2.468000E+01 -4.006E-07 -2.000E-06 0.000E+00 M= 2.040E-06 #,Z,Y,X= 0 1 47 20 + 6.067500E+02 2.545750E+03 2.306500E+01 -1.795E-07 -3.637E-06 0.000E+00 M= 3.641E-06 #,Z,Y,X= 0 1 47 21 + 6.072500E+02 2.545750E+03 1.812000E+01 3.882E-06 -9.453E-06 0.000E+00 M= 1.022E-05 #,Z,Y,X= 0 1 47 22 + 6.077500E+02 2.545750E+03 2.509000E+01 8.535E-06 -2.061E-06 0.000E+00 M= 8.781E-06 #,Z,Y,X= 0 1 47 23 + 6.082500E+02 2.545750E+03 1.450000E+01 9.599E-07 -7.792E-06 0.000E+00 M= 7.851E-06 #,Z,Y,X= 0 1 47 24 + 6.087500E+02 2.545750E+03 1.500000E+01 -4.150E-06 -4.085E-06 0.000E+00 M= 5.823E-06 #,Z,Y,X= 0 1 47 25 + 6.032500E+02 2.545250E+03 3.503500E+01 7.254E-08 -5.065E-08 0.000E+00 M= 8.847E-08 #,Z,Y,X= 0 1 48 14 + 6.037500E+02 2.545250E+03 3.381000E+01 2.384E-07 -3.160E-07 0.000E+00 M= 3.959E-07 #,Z,Y,X= 0 1 48 15 + 6.042500E+02 2.545250E+03 3.289000E+01 4.789E-07 -3.656E-07 0.000E+00 M= 6.025E-07 #,Z,Y,X= 0 1 48 16 + 6.047500E+02 2.545250E+03 3.198000E+01 6.562E-07 -7.554E-07 0.000E+00 M= 1.001E-06 #,Z,Y,X= 0 1 48 17 + 6.052500E+02 2.545250E+03 2.947500E+01 5.292E-07 -1.283E-06 0.000E+00 M= 1.388E-06 #,Z,Y,X= 0 1 48 18 + 6.057500E+02 2.545250E+03 2.772000E+01 2.646E-07 -1.487E-06 0.000E+00 M= 1.510E-06 #,Z,Y,X= 0 1 48 19 + 6.062500E+02 2.545250E+03 2.899500E+01 2.855E-07 -2.136E-06 0.000E+00 M= 2.155E-06 #,Z,Y,X= 0 1 48 20 + 6.067500E+02 2.545250E+03 2.558000E+01 7.643E-07 -2.919E-06 0.000E+00 M= 3.017E-06 #,Z,Y,X= 0 1 48 21 + 6.072500E+02 2.545250E+03 1.762000E+01 5.241E-06 -1.010E-05 0.000E+00 M= 1.138E-05 #,Z,Y,X= 0 1 48 22 + 6.077500E+02 2.545250E+03 1.756000E+01 1.580E-05 -5.680E-06 0.000E+00 M= 1.679E-05 #,Z,Y,X= 0 1 48 23 + 6.082500E+02 2.545250E+03 1.550000E+01 1.357E-05 -1.858E-06 0.000E+00 M= 1.369E-05 #,Z,Y,X= 0 1 48 24 + 6.037500E+02 2.544750E+03 3.293000E+01 1.386E-07 -1.179E-07 0.000E+00 M= 1.820E-07 #,Z,Y,X= 0 1 49 15 + 6.042500E+02 2.544750E+03 3.403000E+01 4.170E-07 -1.149E-07 0.000E+00 M= 4.325E-07 #,Z,Y,X= 0 1 49 16 + 6.047500E+02 2.544750E+03 3.318000E+01 9.533E-07 -3.650E-07 0.000E+00 M= 1.021E-06 #,Z,Y,X= 0 1 49 17 + 6.052500E+02 2.544750E+03 3.270000E+01 9.441E-07 -1.882E-06 0.000E+00 M= 2.105E-06 #,Z,Y,X= 0 1 49 18 + 6.057500E+02 2.544750E+03 3.158500E+01 5.479E-07 -1.579E-06 0.000E+00 M= 1.671E-06 #,Z,Y,X= 0 1 49 19 + 6.062500E+02 2.544750E+03 3.296500E+01 7.846E-07 -1.857E-06 0.000E+00 M= 2.016E-06 #,Z,Y,X= 0 1 49 20 + 6.067500E+02 2.544750E+03 2.767000E+01 9.780E-07 -2.666E-06 0.000E+00 M= 2.840E-06 #,Z,Y,X= 0 1 49 21 + 6.072500E+02 2.544750E+03 2.120500E+01 2.877E-06 -5.784E-06 0.000E+00 M= 6.460E-06 #,Z,Y,X= 0 1 49 22 + 6.077500E+02 2.544750E+03 1.767000E+01 6.974E-06 -8.047E-06 0.000E+00 M= 1.065E-05 #,Z,Y,X= 0 1 49 23 + 6.082500E+02 2.544750E+03 1.458500E+01 4.790E-06 -5.311E-08 0.000E+00 M= 4.790E-06 #,Z,Y,X= 0 1 49 24 + 6.052500E+02 2.544250E+03 3.172500E+01 2.419E-07 -2.075E-06 0.000E+00 M= 2.089E-06 #,Z,Y,X= 0 1 50 18 + 6.057500E+02 2.544250E+03 3.213000E+01 4.118E-07 -1.643E-06 0.000E+00 M= 1.694E-06 #,Z,Y,X= 0 1 50 19 + 6.062500E+02 2.544250E+03 3.103500E+01 3.870E-07 -1.642E-06 0.000E+00 M= 1.687E-06 #,Z,Y,X= 0 1 50 20 + 6.067500E+02 2.544250E+03 2.634500E+01 6.608E-07 -2.527E-06 0.000E+00 M= 2.612E-06 #,Z,Y,X= 0 1 50 21 + 6.072500E+02 2.544250E+03 2.400000E+01 1.236E-06 -3.634E-06 0.000E+00 M= 3.838E-06 #,Z,Y,X= 0 1 50 22 + 6.077500E+02 2.544250E+03 1.700500E+01 2.700E-06 -4.894E-06 0.000E+00 M= 5.589E-06 #,Z,Y,X= 0 1 50 23 + 6.082500E+02 2.544250E+03 1.300000E+01 2.117E-06 -7.393E-06 0.000E+00 M= 7.690E-06 #,Z,Y,X= 0 1 50 24 + 6.052500E+02 2.543750E+03 2.565000E+01 9.864E-07 -9.259E-07 0.000E+00 M= 1.353E-06 #,Z,Y,X= 0 1 51 18 + 6.057500E+02 2.543750E+03 2.799000E+01 1.297E-06 -2.571E-06 0.000E+00 M= 2.879E-06 #,Z,Y,X= 0 1 51 19 + 6.062500E+02 2.543750E+03 2.437000E+01 4.736E-07 -1.779E-06 0.000E+00 M= 1.841E-06 #,Z,Y,X= 0 1 51 20 + 6.067500E+02 2.543750E+03 1.964000E+01 8.359E-07 -1.976E-06 0.000E+00 M= 2.145E-06 #,Z,Y,X= 0 1 51 21 + 6.072500E+02 2.543750E+03 1.887500E+01 1.474E-06 -3.250E-06 0.000E+00 M= 3.568E-06 #,Z,Y,X= 0 1 51 22 + 6.077500E+02 2.543750E+03 1.458500E+01 3.331E-06 -2.282E-06 0.000E+00 M= 4.038E-06 #,Z,Y,X= 0 1 51 23 + 6.082500E+02 2.543750E+03 1.100000E+01 2.698E-06 -4.735E-06 0.000E+00 M= 5.450E-06 #,Z,Y,X= 0 1 51 24 + 6.057500E+02 2.543250E+03 9.981500E+00 7.227E-11 -1.490E-06 0.000E+00 M= 1.490E-06 #,Z,Y,X= 0 1 52 19 + 6.062500E+02 2.543250E+03 1.157500E+01 4.003E-07 -9.375E-07 0.000E+00 M= 1.019E-06 #,Z,Y,X= 0 1 52 20 + 6.067500E+02 2.543250E+03 1.140000E+01 1.629E-06 -7.723E-07 0.000E+00 M= 1.802E-06 #,Z,Y,X= 0 1 52 21 + 6.072500E+02 2.543250E+03 1.080000E+01 8.640E-07 -1.552E-06 0.000E+00 M= 1.776E-06 #,Z,Y,X= 0 1 52 22 + 6.077500E+02 2.543250E+03 1.100000E+01 -4.219E-07 -3.589E-07 0.000E+00 M= 5.539E-07 #,Z,Y,X= 0 1 52 23 diff --git a/tests/data/chasim_npc.out b/tests/data/chasim_npc.out new file mode 100644 index 0000000..b293d08 --- /dev/null +++ b/tests/data/chasim_npc.out @@ -0,0 +1,3340 @@ +Marthe_Grid Version=9.0 +Title=Craie NPC Pas 1404; CHARGE 1 <> CHARGE 1 +[Infos] +Field=CHARGE +Type= +Elem_Number=0 +Name= +Time_Step=1404 +Time=1.26230E+09 +Layer=1 +Max_Layer=10 +Nest_grid=0 +Max_NestG=0 +[Structure] +X_Left_Corner=543500 +Y_Lower_Corner=2526100 +Ncolumn=387 +Nrows=304 +[Data_Descript] +! 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Line 3+Nrows : 0 , 0 , < Dx_of_all_Columns > +[Data] +0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 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9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 132 137.2686 137.3073 135.5393 132.6591 129.5124 126.2359 122.0034 117.3241 112.4071 107.0986 101.3417 94.93905 88.80481 82.61455 76.1389 69.77178 63.68415 63.7366 62.89435 61.53398 59.23854 56.22314 52.95305 49.07618 45.00806 9999 44.37655 43.99505 43.09901 43.0168 42.16065 41.74012 40.9882 40.16236 39.3464 38.67528 38.16298 37.93616 37.29496 36.74415 36.20979 35.70345 35.29754 34.98594 34.7429 34.56047 34.41766 34.34033 34.31035 34.32145 34.34477 34.38659 34.44251 34.51146 34.59293 34.68524 34.79706 34.94062 35.04772 35.21273 29.83857 26.71004 25.67956 25.40001 25.40855 25.53364 25.70557 25.89326 26.07361 26.23047 26.34566 26.41524 26.43693 26.40261 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 23.55398 9999 23.41867 23.36242 23.30745 23.24981 23.18612 23.11514 23.0346 22.94276 22.83818 22.72006 22.58749 22.43668 22.26849 22.08384 21.88459 21.66791 21.43602 21.19258 20.93617 20.66706 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500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 +[End_Grid] +Marthe_Grid Version=9.0 +Title=Craie NPC Pas 1404; CHARGE 10 <> CHARGE 10 +[Infos] +Field=CHARGE +Type= +Elem_Number=0 +Name= +Time_Step=1404 +Time=1.26230E+09 +Layer=10 +Max_Layer=10 +Nest_grid=0 +Max_NestG=0 +[Structure] +X_Left_Corner=543500 +Y_Lower_Corner=2526100 +Ncolumn=387 +Nrows=304 +[Data_Descript] +! 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Line 3+Nrows : 0 , 0 , < Dx_of_all_Columns > +[Data] +0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 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9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +300 2528350 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +301 2527850 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +302 2527350 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +303 2526850 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +304 2526350 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 9999 500 +0 0 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 +[End_Grid] diff --git a/tests/data/veloci.out b/tests/data/veloci.out new file mode 100644 index 0000000..0f3ffbc --- /dev/null +++ b/tests/data/veloci.out @@ -0,0 +1,928 @@ +Modélisation du bassin de la SOMME Nappe_Libr (x,y,z)(Vx,Vy,Vz,Module m.s-1) Step= 6210 ; Date= 6422. Dimx,Dimy= 26.00 26.50 + 6.177500E+02 2.566750E+03 1.155750E+02 0.000E+00 0.000E+00 0.000E+00 M= 0.000E+00 #,Z,Y,X= 0 1 5 43 + 6.182500E+02 2.566750E+03 1.147550E+02 0.000E+00 -1.945E-06 0.000E+00 M= 1.945E-06 #,Z,Y,X= 0 1 5 44 + 6.187500E+02 2.566750E+03 1.111250E+02 0.000E+00 -1.927E-06 0.000E+00 M= 1.927E-06 #,Z,Y,X= 0 1 5 45 + 6.192500E+02 2.566750E+03 1.103750E+02 0.000E+00 -1.798E-06 0.000E+00 M= 1.798E-06 #,Z,Y,X= 0 1 5 46 + 6.197500E+02 2.566750E+03 1.093350E+02 0.000E+00 -5.939E-07 0.000E+00 M= 5.939E-07 #,Z,Y,X= 0 1 5 47 + 6.202500E+02 2.566750E+03 1.114500E+02 0.000E+00 -5.226E-07 0.000E+00 M= 5.226E-07 #,Z,Y,X= 0 1 5 48 + 6.207500E+02 2.566750E+03 1.142800E+02 0.000E+00 0.000E+00 0.000E+00 M= 0.000E+00 #,Z,Y,X= 0 1 5 49 + 6.177500E+02 2.566250E+03 1.088250E+02 -6.297E-07 -1.839E-06 0.000E+00 M= 1.944E-06 #,Z,Y,X= 0 1 6 43 + 6.182500E+02 2.566250E+03 1.052550E+02 -1.061E-06 -3.827E-06 0.000E+00 M= 3.971E-06 #,Z,Y,X= 0 1 6 44 + 6.187500E+02 2.566250E+03 1.069900E+02 -1.228E-06 -4.384E-06 0.000E+00 M= 4.553E-06 #,Z,Y,X= 0 1 6 45 + 6.192500E+02 2.566250E+03 1.033750E+02 -1.022E-06 -3.253E-06 0.000E+00 M= 3.410E-06 #,Z,Y,X= 0 1 6 46 + 6.197500E+02 2.566250E+03 1.105800E+02 -4.635E-07 -1.291E-06 0.000E+00 M= 1.372E-06 #,Z,Y,X= 0 1 6 47 + 6.202500E+02 2.566250E+03 1.138750E+02 -2.016E-07 -9.973E-07 0.000E+00 M= 1.017E-06 #,Z,Y,X= 0 1 6 48 + 6.172500E+02 2.565750E+03 1.092400E+02 -6.046E-07 0.000E+00 0.000E+00 M= 6.046E-07 #,Z,Y,X= 0 1 7 42 + 6.177500E+02 2.565750E+03 1.042700E+02 -1.200E-06 -3.774E-06 0.000E+00 M= 3.960E-06 #,Z,Y,X= 0 1 7 43 + 6.182500E+02 2.565750E+03 1.048200E+02 -1.601E-06 -4.445E-06 0.000E+00 M= 4.725E-06 #,Z,Y,X= 0 1 7 44 + 6.187500E+02 2.565750E+03 1.012250E+02 -1.788E-06 -4.893E-06 0.000E+00 M= 5.209E-06 #,Z,Y,X= 0 1 7 45 + 6.192500E+02 2.565750E+03 1.057100E+02 -9.427E-07 -2.445E-06 0.000E+00 M= 2.620E-06 #,Z,Y,X= 0 1 7 46 + 6.197500E+02 2.565750E+03 1.105300E+02 -4.023E-07 -1.561E-06 0.000E+00 M= 1.612E-06 #,Z,Y,X= 0 1 7 47 + 6.202500E+02 2.565750E+03 1.103750E+02 -2.259E-07 -8.189E-07 0.000E+00 M= 8.495E-07 #,Z,Y,X= 0 1 7 48 + 6.172500E+02 2.565250E+03 1.066800E+02 -1.382E-06 -9.528E-07 0.000E+00 M= 1.679E-06 #,Z,Y,X= 0 1 8 42 + 6.177500E+02 2.565250E+03 1.054350E+02 -2.426E-06 -3.364E-06 0.000E+00 M= 4.148E-06 #,Z,Y,X= 0 1 8 43 + 6.182500E+02 2.565250E+03 9.764000E+01 -2.778E-06 -5.785E-06 0.000E+00 M= 6.417E-06 #,Z,Y,X= 0 1 8 44 + 6.187500E+02 2.565250E+03 9.765000E+01 -2.239E-06 -3.622E-06 0.000E+00 M= 4.258E-06 #,Z,Y,X= 0 1 8 45 + 6.192500E+02 2.565250E+03 1.058150E+02 -7.182E-07 -1.747E-06 0.000E+00 M= 1.889E-06 #,Z,Y,X= 0 1 8 46 + 6.197500E+02 2.565250E+03 1.051100E+02 -3.884E-07 -1.812E-06 0.000E+00 M= 1.853E-06 #,Z,Y,X= 0 1 8 47 + 6.202500E+02 2.565250E+03 1.072750E+02 -1.709E-07 -5.905E-07 0.000E+00 M= 6.147E-07 #,Z,Y,X= 0 1 8 48 + 6.207500E+02 2.565250E+03 1.086500E+02 1.373E-08 -1.394E-07 0.000E+00 M= 1.400E-07 #,Z,Y,X= 0 1 8 49 + 6.147500E+02 2.564750E+03 1.045200E+02 0.000E+00 -7.068E-07 0.000E+00 M= 7.068E-07 #,Z,Y,X= 0 1 9 37 + 6.157500E+02 2.564750E+03 1.048400E+02 0.000E+00 -1.013E-06 0.000E+00 M= 1.013E-06 #,Z,Y,X= 0 1 9 39 + 6.162500E+02 2.564750E+03 1.045750E+02 0.000E+00 -6.443E-07 0.000E+00 M= 6.443E-07 #,Z,Y,X= 0 1 9 40 + 6.167500E+02 2.564750E+03 9.988500E+01 -9.809E-07 -3.976E-07 0.000E+00 M= 1.058E-06 #,Z,Y,X= 0 1 9 41 + 6.172500E+02 2.564750E+03 9.798500E+01 -2.138E-06 -1.964E-06 0.000E+00 M= 2.903E-06 #,Z,Y,X= 0 1 9 42 + 6.177500E+02 2.564750E+03 9.923500E+01 -2.504E-06 -3.137E-06 0.000E+00 M= 4.014E-06 #,Z,Y,X= 0 1 9 43 + 6.182500E+02 2.564750E+03 9.498000E+01 -2.065E-06 -5.965E-06 0.000E+00 M= 6.312E-06 #,Z,Y,X= 0 1 9 44 + 6.187500E+02 2.564750E+03 1.025700E+02 -1.155E-06 -2.300E-06 0.000E+00 M= 2.573E-06 #,Z,Y,X= 0 1 9 45 + 6.192500E+02 2.564750E+03 1.042750E+02 -7.222E-07 -1.441E-06 0.000E+00 M= 1.612E-06 #,Z,Y,X= 0 1 9 46 + 6.197500E+02 2.564750E+03 9.893000E+01 -5.165E-07 -1.867E-06 0.000E+00 M= 1.937E-06 #,Z,Y,X= 0 1 9 47 + 6.202500E+02 2.564750E+03 1.013500E+02 -3.126E-07 -3.866E-07 0.000E+00 M= 4.971E-07 #,Z,Y,X= 0 1 9 48 + 6.207500E+02 2.564750E+03 1.058600E+02 -9.003E-08 -2.364E-07 0.000E+00 M= 2.530E-07 #,Z,Y,X= 0 1 9 49 + 6.092500E+02 2.564250E+03 1.033800E+02 0.000E+00 -4.782E-07 0.000E+00 M= 4.782E-07 #,Z,Y,X= 0 1 10 26 + 6.097500E+02 2.564250E+03 1.051100E+02 0.000E+00 -4.790E-07 0.000E+00 M= 4.790E-07 #,Z,Y,X= 0 1 10 27 + 6.142500E+02 2.564250E+03 1.032350E+02 -2.581E-07 -2.896E-07 0.000E+00 M= 3.879E-07 #,Z,Y,X= 0 1 10 36 + 6.147500E+02 2.564250E+03 1.004900E+02 -1.933E-07 -1.177E-06 0.000E+00 M= 1.192E-06 #,Z,Y,X= 0 1 10 37 + 6.152500E+02 2.564250E+03 1.022050E+02 -2.134E-07 -3.555E-07 0.000E+00 M= 4.146E-07 #,Z,Y,X= 0 1 10 38 + 6.157500E+02 2.564250E+03 9.897000E+01 -1.248E-07 -1.624E-06 0.000E+00 M= 1.629E-06 #,Z,Y,X= 0 1 10 39 + 6.162500E+02 2.564250E+03 1.015400E+02 3.037E-07 -1.260E-06 0.000E+00 M= 1.296E-06 #,Z,Y,X= 0 1 10 40 + 6.167500E+02 2.564250E+03 9.894500E+01 -1.651E-07 -1.280E-06 0.000E+00 M= 1.290E-06 #,Z,Y,X= 0 1 10 41 + 6.172500E+02 2.564250E+03 9.228000E+01 -1.066E-06 -2.367E-06 0.000E+00 M= 2.596E-06 #,Z,Y,X= 0 1 10 42 + 6.177500E+02 2.564250E+03 9.973500E+01 -1.674E-06 -3.220E-06 0.000E+00 M= 3.629E-06 #,Z,Y,X= 0 1 10 43 + 6.182500E+02 2.564250E+03 9.051500E+01 -1.303E-06 -4.362E-06 0.000E+00 M= 4.552E-06 #,Z,Y,X= 0 1 10 44 + 6.187500E+02 2.564250E+03 9.920500E+01 -8.916E-07 -2.098E-06 0.000E+00 M= 2.280E-06 #,Z,Y,X= 0 1 10 45 + 6.192500E+02 2.564250E+03 9.780000E+01 -1.068E-06 -1.290E-06 0.000E+00 M= 1.675E-06 #,Z,Y,X= 0 1 10 46 + 6.197500E+02 2.564250E+03 9.418000E+01 -8.108E-07 -1.430E-06 0.000E+00 M= 1.644E-06 #,Z,Y,X= 0 1 10 47 + 6.202500E+02 2.564250E+03 1.028200E+02 -4.300E-07 -1.747E-07 0.000E+00 M= 4.642E-07 #,Z,Y,X= 0 1 10 48 + 6.207500E+02 2.564250E+03 1.048950E+02 -1.492E-07 -9.144E-08 0.000E+00 M= 1.750E-07 #,Z,Y,X= 0 1 10 49 + 6.082500E+02 2.563750E+03 9.758500E+01 0.000E+00 -4.485E-07 0.000E+00 M= 4.485E-07 #,Z,Y,X= 0 1 11 24 + 6.087500E+02 2.563750E+03 9.249000E+01 1.526E-06 -2.432E-06 0.000E+00 M= 2.871E-06 #,Z,Y,X= 0 1 11 25 + 6.092500E+02 2.563750E+03 9.396500E+01 1.624E-06 -3.133E-06 0.000E+00 M= 3.529E-06 #,Z,Y,X= 0 1 11 26 + 6.097500E+02 2.563750E+03 9.948000E+01 -1.167E-06 -2.252E-06 0.000E+00 M= 2.536E-06 #,Z,Y,X= 0 1 11 27 + 6.102500E+02 2.563750E+03 9.892500E+01 -1.066E-06 -2.477E-06 0.000E+00 M= 2.697E-06 #,Z,Y,X= 0 1 11 28 + 6.107500E+02 2.563750E+03 1.038700E+02 0.000E+00 -2.473E-07 0.000E+00 M= 2.473E-07 #,Z,Y,X= 0 1 11 29 + 6.112500E+02 2.563750E+03 1.055300E+02 0.000E+00 -3.914E-07 0.000E+00 M= 3.914E-07 #,Z,Y,X= 0 1 11 30 + 6.137500E+02 2.563750E+03 9.945500E+01 -2.185E-07 -2.510E-07 0.000E+00 M= 3.328E-07 #,Z,Y,X= 0 1 11 35 + 6.142500E+02 2.563750E+03 9.702000E+01 -4.309E-07 -6.095E-07 0.000E+00 M= 7.464E-07 #,Z,Y,X= 0 1 11 36 + 6.147500E+02 2.563750E+03 9.975000E+01 -3.153E-07 -8.298E-07 0.000E+00 M= 8.877E-07 #,Z,Y,X= 0 1 11 37 + 6.152500E+02 2.563750E+03 1.019500E+02 -2.215E-07 -7.485E-07 0.000E+00 M= 7.806E-07 #,Z,Y,X= 0 1 11 38 + 6.157500E+02 2.563750E+03 9.366500E+01 -1.082E-07 -1.151E-06 0.000E+00 M= 1.156E-06 #,Z,Y,X= 0 1 11 39 + 6.162500E+02 2.563750E+03 9.905500E+01 1.063E-08 -1.267E-06 0.000E+00 M= 1.267E-06 #,Z,Y,X= 0 1 11 40 + 6.167500E+02 2.563750E+03 9.096500E+01 -3.482E-07 -2.096E-06 0.000E+00 M= 2.125E-06 #,Z,Y,X= 0 1 11 41 + 6.172500E+02 2.563750E+03 8.754000E+01 -1.092E-06 -3.004E-06 0.000E+00 M= 3.196E-06 #,Z,Y,X= 0 1 11 42 + 6.177500E+02 2.563750E+03 9.729500E+01 -1.177E-06 -2.873E-06 0.000E+00 M= 3.105E-06 #,Z,Y,X= 0 1 11 43 + 6.182500E+02 2.563750E+03 8.902000E+01 -1.008E-06 -3.653E-06 0.000E+00 M= 3.790E-06 #,Z,Y,X= 0 1 11 44 + 6.187500E+02 2.563750E+03 9.276500E+01 -1.292E-06 -2.266E-06 0.000E+00 M= 2.609E-06 #,Z,Y,X= 0 1 11 45 + 6.192500E+02 2.563750E+03 9.074500E+01 -1.117E-06 -9.639E-07 0.000E+00 M= 1.475E-06 #,Z,Y,X= 0 1 11 46 + 6.197500E+02 2.563750E+03 9.480500E+01 -4.626E-07 -7.932E-07 0.000E+00 M= 9.182E-07 #,Z,Y,X= 0 1 11 47 + 6.202500E+02 2.563750E+03 1.018400E+02 -6.487E-08 -4.158E-08 0.000E+00 M= 7.705E-08 #,Z,Y,X= 0 1 11 48 + 6.072500E+02 2.563250E+03 8.764000E+01 3.366E-08 0.000E+00 0.000E+00 M= 3.366E-08 #,Z,Y,X= 0 1 12 22 + 6.077500E+02 2.563250E+03 8.221000E+01 -4.370E-07 -5.463E-07 0.000E+00 M= 6.996E-07 #,Z,Y,X= 0 1 12 23 + 6.082500E+02 2.563250E+03 8.312000E+01 -1.671E-06 -1.684E-06 0.000E+00 M= 2.372E-06 #,Z,Y,X= 0 1 12 24 + 6.087500E+02 2.563250E+03 8.204000E+01 -2.217E-06 -4.307E-06 0.000E+00 M= 4.844E-06 #,Z,Y,X= 0 1 12 25 + 6.092500E+02 2.563250E+03 8.477000E+01 -1.543E-06 -4.203E-06 0.000E+00 M= 4.477E-06 #,Z,Y,X= 0 1 12 26 + 6.097500E+02 2.563250E+03 9.005500E+01 -5.081E-07 -1.974E-06 0.000E+00 M= 2.039E-06 #,Z,Y,X= 0 1 12 27 + 6.102500E+02 2.563250E+03 8.954000E+01 8.867E-08 -4.127E-06 0.000E+00 M= 4.128E-06 #,Z,Y,X= 0 1 12 28 + 6.107500E+02 2.563250E+03 9.888500E+01 -7.610E-08 -6.278E-07 0.000E+00 M= 6.324E-07 #,Z,Y,X= 0 1 12 29 + 6.112500E+02 2.563250E+03 1.022650E+02 -6.033E-08 -1.035E-06 0.000E+00 M= 1.037E-06 #,Z,Y,X= 0 1 12 30 + 6.137500E+02 2.563250E+03 9.478500E+01 -1.370E-07 -6.372E-07 0.000E+00 M= 6.517E-07 #,Z,Y,X= 0 1 12 35 + 6.142500E+02 2.563250E+03 9.631500E+01 -3.059E-07 -6.629E-07 0.000E+00 M= 7.301E-07 #,Z,Y,X= 0 1 12 36 + 6.147500E+02 2.563250E+03 1.011950E+02 -2.740E-07 -6.901E-07 0.000E+00 M= 7.425E-07 #,Z,Y,X= 0 1 12 37 + 6.152500E+02 2.563250E+03 9.402000E+01 -1.642E-07 -7.364E-07 0.000E+00 M= 7.545E-07 #,Z,Y,X= 0 1 12 38 + 6.157500E+02 2.563250E+03 9.212000E+01 -1.815E-07 -1.101E-06 0.000E+00 M= 1.116E-06 #,Z,Y,X= 0 1 12 39 + 6.162500E+02 2.563250E+03 9.320000E+01 -2.227E-07 -1.211E-06 0.000E+00 M= 1.231E-06 #,Z,Y,X= 0 1 12 40 + 6.167500E+02 2.563250E+03 8.908000E+01 -2.789E-07 -2.338E-06 0.000E+00 M= 2.355E-06 #,Z,Y,X= 0 1 12 41 + 6.172500E+02 2.563250E+03 8.872500E+01 -5.314E-07 -3.195E-06 0.000E+00 M= 3.239E-06 #,Z,Y,X= 0 1 12 42 + 6.177500E+02 2.563250E+03 8.887500E+01 -1.323E-06 -2.846E-06 0.000E+00 M= 3.139E-06 #,Z,Y,X= 0 1 12 43 + 6.182500E+02 2.563250E+03 8.728000E+01 -1.869E-06 -3.129E-06 0.000E+00 M= 3.644E-06 #,Z,Y,X= 0 1 12 44 + 6.187500E+02 2.563250E+03 8.395000E+01 -1.417E-06 -1.632E-06 0.000E+00 M= 2.161E-06 #,Z,Y,X= 0 1 12 45 + 6.192500E+02 2.563250E+03 9.599500E+01 -7.742E-07 -3.599E-07 0.000E+00 M= 8.538E-07 #,Z,Y,X= 0 1 12 46 + 6.197500E+02 2.563250E+03 9.815500E+01 -2.597E-07 -2.071E-07 0.000E+00 M= 3.321E-07 #,Z,Y,X= 0 1 12 47 + 6.077500E+02 2.562750E+03 8.362500E+01 -2.640E-07 -1.366E-06 0.000E+00 M= 1.392E-06 #,Z,Y,X= 0 1 13 23 + 6.082500E+02 2.562750E+03 7.442500E+01 -1.021E-06 -2.758E-06 0.000E+00 M= 2.941E-06 #,Z,Y,X= 0 1 13 24 + 6.087500E+02 2.562750E+03 7.989500E+01 -1.335E-06 -3.430E-06 0.000E+00 M= 3.681E-06 #,Z,Y,X= 0 1 13 25 + 6.092500E+02 2.562750E+03 9.029500E+01 -3.827E-07 -2.882E-06 0.000E+00 M= 2.907E-06 #,Z,Y,X= 0 1 13 26 + 6.097500E+02 2.562750E+03 9.462500E+01 4.867E-07 -1.006E-06 0.000E+00 M= 1.118E-06 #,Z,Y,X= 0 1 13 27 + 6.102500E+02 2.562750E+03 9.734000E+01 1.670E-06 -2.979E-06 0.000E+00 M= 3.415E-06 #,Z,Y,X= 0 1 13 28 + 6.107500E+02 2.562750E+03 9.911000E+01 2.166E-06 -1.654E-06 0.000E+00 M= 2.725E-06 #,Z,Y,X= 0 1 13 29 + 6.112500E+02 2.562750E+03 9.561500E+01 6.996E-07 -1.918E-06 0.000E+00 M= 2.041E-06 #,Z,Y,X= 0 1 13 30 + 6.137500E+02 2.562750E+03 9.777000E+01 -1.868E-07 -9.599E-07 0.000E+00 M= 9.779E-07 #,Z,Y,X= 0 1 13 35 + 6.142500E+02 2.562750E+03 9.626500E+01 -3.878E-07 -7.160E-07 0.000E+00 M= 8.142E-07 #,Z,Y,X= 0 1 13 36 + 6.147500E+02 2.562750E+03 9.816000E+01 -2.911E-07 -5.296E-07 0.000E+00 M= 6.043E-07 #,Z,Y,X= 0 1 13 37 + 6.152500E+02 2.562750E+03 9.196500E+01 -2.371E-07 -7.469E-07 0.000E+00 M= 7.836E-07 #,Z,Y,X= 0 1 13 38 + 6.157500E+02 2.562750E+03 8.656500E+01 -1.858E-07 -1.030E-06 0.000E+00 M= 1.047E-06 #,Z,Y,X= 0 1 13 39 + 6.162500E+02 2.562750E+03 8.766500E+01 -1.644E-07 -1.234E-06 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 13 40 + 6.167500E+02 2.562750E+03 8.096500E+01 -3.329E-07 -2.385E-06 0.000E+00 M= 2.408E-06 #,Z,Y,X= 0 1 13 41 + 6.172500E+02 2.562750E+03 8.438000E+01 -7.783E-07 -3.494E-06 0.000E+00 M= 3.580E-06 #,Z,Y,X= 0 1 13 42 + 6.177500E+02 2.562750E+03 7.673500E+01 -1.950E-06 -4.033E-06 0.000E+00 M= 4.479E-06 #,Z,Y,X= 0 1 13 43 + 6.182500E+02 2.562750E+03 8.053500E+01 -1.769E-06 -1.804E-06 0.000E+00 M= 2.527E-06 #,Z,Y,X= 0 1 13 44 + 6.187500E+02 2.562750E+03 9.299500E+01 -4.750E-07 -8.298E-07 0.000E+00 M= 9.561E-07 #,Z,Y,X= 0 1 13 45 + 6.192500E+02 2.562750E+03 9.603000E+01 -8.301E-08 -6.315E-08 0.000E+00 M= 1.043E-07 #,Z,Y,X= 0 1 13 46 + 6.067500E+02 2.562250E+03 8.636000E+01 1.480E-08 -2.033E-08 0.000E+00 M= 2.515E-08 #,Z,Y,X= 0 1 14 21 + 6.072500E+02 2.562250E+03 7.963500E+01 -3.157E-07 -3.796E-07 0.000E+00 M= 4.937E-07 #,Z,Y,X= 0 1 14 22 + 6.077500E+02 2.562250E+03 7.469000E+01 -7.591E-07 -1.734E-06 0.000E+00 M= 1.893E-06 #,Z,Y,X= 0 1 14 23 + 6.082500E+02 2.562250E+03 7.000000E+01 -1.349E-06 -3.500E-06 0.000E+00 M= 3.751E-06 #,Z,Y,X= 0 1 14 24 + 6.087500E+02 2.562250E+03 7.623500E+01 -8.965E-07 -1.788E-06 0.000E+00 M= 2.000E-06 #,Z,Y,X= 0 1 14 25 + 6.092500E+02 2.562250E+03 8.714000E+01 4.294E-07 -1.582E-06 0.000E+00 M= 1.639E-06 #,Z,Y,X= 0 1 14 26 + 6.097500E+02 2.562250E+03 9.288000E+01 7.329E-07 -7.807E-07 0.000E+00 M= 1.071E-06 #,Z,Y,X= 0 1 14 27 + 6.102500E+02 2.562250E+03 8.889500E+01 6.471E-07 -1.799E-06 0.000E+00 M= 1.912E-06 #,Z,Y,X= 0 1 14 28 + 6.107500E+02 2.562250E+03 8.014500E+01 9.803E-07 -1.573E-06 0.000E+00 M= 1.854E-06 #,Z,Y,X= 0 1 14 29 + 6.112500E+02 2.562250E+03 8.488000E+01 1.440E-06 -2.065E-06 0.000E+00 M= 2.518E-06 #,Z,Y,X= 0 1 14 30 + 6.117500E+02 2.562250E+03 9.596000E+01 1.034E-06 -6.770E-07 0.000E+00 M= 1.236E-06 #,Z,Y,X= 0 1 14 31 + 6.122500E+02 2.562250E+03 1.024550E+02 -1.526E-07 -6.576E-07 0.000E+00 M= 6.751E-07 #,Z,Y,X= 0 1 14 32 + 6.127500E+02 2.562250E+03 1.016900E+02 -7.524E-07 -1.112E-07 0.000E+00 M= 7.606E-07 #,Z,Y,X= 0 1 14 33 + 6.132500E+02 2.562250E+03 9.814000E+01 -7.887E-07 -2.058E-07 0.000E+00 M= 8.151E-07 #,Z,Y,X= 0 1 14 34 + 6.137500E+02 2.562250E+03 9.847500E+01 -5.039E-07 -9.432E-07 0.000E+00 M= 1.069E-06 #,Z,Y,X= 0 1 14 35 + 6.142500E+02 2.562250E+03 9.793500E+01 -2.144E-07 -6.972E-07 0.000E+00 M= 7.294E-07 #,Z,Y,X= 0 1 14 36 + 6.147500E+02 2.562250E+03 9.292000E+01 -2.453E-07 -5.033E-07 0.000E+00 M= 5.599E-07 #,Z,Y,X= 0 1 14 37 + 6.152500E+02 2.562250E+03 8.622000E+01 -3.285E-07 -7.759E-07 0.000E+00 M= 8.426E-07 #,Z,Y,X= 0 1 14 38 + 6.157500E+02 2.562250E+03 8.125500E+01 -1.767E-07 -6.908E-07 0.000E+00 M= 7.131E-07 #,Z,Y,X= 0 1 14 39 + 6.162500E+02 2.562250E+03 7.850500E+01 -8.507E-08 -1.275E-06 0.000E+00 M= 1.278E-06 #,Z,Y,X= 0 1 14 40 + 6.167500E+02 2.562250E+03 7.743500E+01 -5.494E-08 -2.158E-06 0.000E+00 M= 2.159E-06 #,Z,Y,X= 0 1 14 41 + 6.172500E+02 2.562250E+03 7.181000E+01 -4.597E-07 -3.997E-06 0.000E+00 M= 4.023E-06 #,Z,Y,X= 0 1 14 42 + 6.177500E+02 2.562250E+03 8.004500E+01 -4.495E-07 -3.928E-06 0.000E+00 M= 3.953E-06 #,Z,Y,X= 0 1 14 43 + 6.182500E+02 2.562250E+03 9.013500E+01 -6.657E-09 -8.049E-07 0.000E+00 M= 8.049E-07 #,Z,Y,X= 0 1 14 44 + 6.187500E+02 2.562250E+03 9.081500E+01 -1.895E-08 -4.796E-07 0.000E+00 M= 4.799E-07 #,Z,Y,X= 0 1 14 45 + 6.042500E+02 2.561750E+03 9.062500E+01 0.000E+00 -9.630E-08 0.000E+00 M= 9.630E-08 #,Z,Y,X= 0 1 15 16 + 6.047500E+02 2.561750E+03 9.014000E+01 0.000E+00 -6.395E-07 0.000E+00 M= 6.395E-07 #,Z,Y,X= 0 1 15 17 + 6.052500E+02 2.561750E+03 9.168000E+01 0.000E+00 -4.042E-07 0.000E+00 M= 4.042E-07 #,Z,Y,X= 0 1 15 18 + 6.062500E+02 2.561750E+03 8.965000E+01 3.413E-08 -3.133E-09 0.000E+00 M= 3.427E-08 #,Z,Y,X= 0 1 15 20 + 6.067500E+02 2.561750E+03 8.441000E+01 7.418E-08 -6.354E-08 0.000E+00 M= 9.768E-08 #,Z,Y,X= 0 1 15 21 + 6.072500E+02 2.561750E+03 7.266000E+01 1.055E-07 -7.576E-07 0.000E+00 M= 7.649E-07 #,Z,Y,X= 0 1 15 22 + 6.077500E+02 2.561750E+03 6.531000E+01 -2.754E-08 -1.988E-06 0.000E+00 M= 1.988E-06 #,Z,Y,X= 0 1 15 23 + 6.082500E+02 2.561750E+03 7.161000E+01 -7.912E-08 -3.708E-06 0.000E+00 M= 3.709E-06 #,Z,Y,X= 0 1 15 24 + 6.087500E+02 2.561750E+03 8.739000E+01 1.003E-07 -7.391E-07 0.000E+00 M= 7.459E-07 #,Z,Y,X= 0 1 15 25 + 6.092500E+02 2.561750E+03 9.009000E+01 2.837E-07 -1.125E-06 0.000E+00 M= 1.160E-06 #,Z,Y,X= 0 1 15 26 + 6.097500E+02 2.561750E+03 8.826000E+01 3.896E-07 -8.193E-07 0.000E+00 M= 9.073E-07 #,Z,Y,X= 0 1 15 27 + 6.102500E+02 2.561750E+03 7.290000E+01 1.343E-07 -2.008E-06 0.000E+00 M= 2.012E-06 #,Z,Y,X= 0 1 15 28 + 6.107500E+02 2.561750E+03 8.718500E+01 1.962E-08 -9.821E-07 0.000E+00 M= 9.823E-07 #,Z,Y,X= 0 1 15 29 + 6.112500E+02 2.561750E+03 9.396000E+01 4.453E-07 -1.628E-06 0.000E+00 M= 1.687E-06 #,Z,Y,X= 0 1 15 30 + 6.117500E+02 2.561750E+03 9.807500E+01 5.712E-07 -1.448E-06 0.000E+00 M= 1.557E-06 #,Z,Y,X= 0 1 15 31 + 6.122500E+02 2.561750E+03 9.595500E+01 -1.409E-08 -1.488E-06 0.000E+00 M= 1.488E-06 #,Z,Y,X= 0 1 15 32 + 6.127500E+02 2.561750E+03 8.843500E+01 -4.641E-07 -3.436E-07 0.000E+00 M= 5.774E-07 #,Z,Y,X= 0 1 15 33 + 6.132500E+02 2.561750E+03 8.677500E+01 -5.011E-07 -4.566E-07 0.000E+00 M= 6.780E-07 #,Z,Y,X= 0 1 15 34 + 6.137500E+02 2.561750E+03 9.360500E+01 -3.901E-07 -7.342E-07 0.000E+00 M= 8.314E-07 #,Z,Y,X= 0 1 15 35 + 6.142500E+02 2.561750E+03 9.496500E+01 -2.875E-07 -7.238E-07 0.000E+00 M= 7.788E-07 #,Z,Y,X= 0 1 15 36 + 6.147500E+02 2.561750E+03 9.305500E+01 -3.102E-07 -6.634E-07 0.000E+00 M= 7.323E-07 #,Z,Y,X= 0 1 15 37 + 6.152500E+02 2.561750E+03 9.059500E+01 -2.678E-07 -7.669E-07 0.000E+00 M= 8.123E-07 #,Z,Y,X= 0 1 15 38 + 6.157500E+02 2.561750E+03 8.889000E+01 -2.187E-07 -5.759E-07 0.000E+00 M= 6.160E-07 #,Z,Y,X= 0 1 15 39 + 6.162500E+02 2.561750E+03 8.537000E+01 5.890E-08 -1.023E-06 0.000E+00 M= 1.025E-06 #,Z,Y,X= 0 1 15 40 + 6.167500E+02 2.561750E+03 7.915000E+01 1.997E-08 -2.390E-06 0.000E+00 M= 2.390E-06 #,Z,Y,X= 0 1 15 41 + 6.172500E+02 2.561750E+03 6.870000E+01 -4.079E-07 -4.552E-06 0.000E+00 M= 4.570E-06 #,Z,Y,X= 0 1 15 42 + 6.177500E+02 2.561750E+03 8.179500E+01 -1.411E-07 -3.058E-06 0.000E+00 M= 3.061E-06 #,Z,Y,X= 0 1 15 43 + 6.182500E+02 2.561750E+03 8.008500E+01 1.425E-07 -8.969E-07 0.000E+00 M= 9.082E-07 #,Z,Y,X= 0 1 15 44 + 6.187500E+02 2.561750E+03 8.545500E+01 4.299E-08 -5.192E-07 0.000E+00 M= 5.209E-07 #,Z,Y,X= 0 1 15 45 + 6.032500E+02 2.561250E+03 9.268000E+01 1.404E-07 0.000E+00 0.000E+00 M= 1.404E-07 #,Z,Y,X= 0 1 16 14 + 6.037500E+02 2.561250E+03 9.079000E+01 2.755E-07 -5.858E-08 0.000E+00 M= 2.817E-07 #,Z,Y,X= 0 1 16 15 + 6.042500E+02 2.561250E+03 8.662500E+01 3.610E-07 -1.496E-07 0.000E+00 M= 3.908E-07 #,Z,Y,X= 0 1 16 16 + 6.047500E+02 2.561250E+03 8.052500E+01 9.072E-07 -1.026E-06 0.000E+00 M= 1.370E-06 #,Z,Y,X= 0 1 16 17 + 6.052500E+02 2.561250E+03 7.559000E+01 1.402E-06 -8.505E-07 0.000E+00 M= 1.640E-06 #,Z,Y,X= 0 1 16 18 + 6.057500E+02 2.561250E+03 7.911000E+01 1.237E-06 -2.379E-07 0.000E+00 M= 1.259E-06 #,Z,Y,X= 0 1 16 19 + 6.062500E+02 2.561250E+03 7.455000E+01 8.603E-07 -1.771E-07 0.000E+00 M= 8.784E-07 #,Z,Y,X= 0 1 16 20 + 6.067500E+02 2.561250E+03 7.852500E+01 5.676E-07 -2.364E-07 0.000E+00 M= 6.149E-07 #,Z,Y,X= 0 1 16 21 + 6.072500E+02 2.561250E+03 7.638000E+01 5.192E-07 -6.773E-07 0.000E+00 M= 8.534E-07 #,Z,Y,X= 0 1 16 22 + 6.077500E+02 2.561250E+03 6.937000E+01 8.581E-07 -1.804E-06 0.000E+00 M= 1.998E-06 #,Z,Y,X= 0 1 16 23 + 6.082500E+02 2.561250E+03 6.227500E+01 1.234E-06 -3.264E-06 0.000E+00 M= 3.490E-06 #,Z,Y,X= 0 1 16 24 + 6.087500E+02 2.561250E+03 7.696500E+01 1.249E-06 -7.622E-07 0.000E+00 M= 1.464E-06 #,Z,Y,X= 0 1 16 25 + 6.092500E+02 2.561250E+03 7.506000E+01 8.778E-07 -1.247E-06 0.000E+00 M= 1.525E-06 #,Z,Y,X= 0 1 16 26 + 6.097500E+02 2.561250E+03 7.401500E+01 2.122E-07 -1.169E-06 0.000E+00 M= 1.188E-06 #,Z,Y,X= 0 1 16 27 + 6.102500E+02 2.561250E+03 7.295000E+01 7.138E-08 -1.851E-06 0.000E+00 M= 1.853E-06 #,Z,Y,X= 0 1 16 28 + 6.107500E+02 2.561250E+03 8.329500E+01 1.810E-07 -9.589E-07 0.000E+00 M= 9.758E-07 #,Z,Y,X= 0 1 16 29 + 6.112500E+02 2.561250E+03 8.935000E+01 1.569E-08 -1.293E-06 0.000E+00 M= 1.293E-06 #,Z,Y,X= 0 1 16 30 + 6.117500E+02 2.561250E+03 9.290500E+01 -1.333E-07 -1.599E-06 0.000E+00 M= 1.604E-06 #,Z,Y,X= 0 1 16 31 + 6.122500E+02 2.561250E+03 8.389000E+01 -4.824E-07 -1.751E-06 0.000E+00 M= 1.816E-06 #,Z,Y,X= 0 1 16 32 + 6.127500E+02 2.561250E+03 8.439000E+01 -5.896E-07 -3.212E-07 0.000E+00 M= 6.714E-07 #,Z,Y,X= 0 1 16 33 + 6.132500E+02 2.561250E+03 7.567000E+01 -3.747E-07 -4.408E-07 0.000E+00 M= 5.786E-07 #,Z,Y,X= 0 1 16 34 + 6.137500E+02 2.561250E+03 8.560500E+01 -2.799E-07 -6.921E-07 0.000E+00 M= 7.465E-07 #,Z,Y,X= 0 1 16 35 + 6.142500E+02 2.561250E+03 8.607000E+01 -2.749E-07 -7.760E-07 0.000E+00 M= 8.232E-07 #,Z,Y,X= 0 1 16 36 + 6.147500E+02 2.561250E+03 8.529000E+01 -3.137E-07 -7.688E-07 0.000E+00 M= 8.303E-07 #,Z,Y,X= 0 1 16 37 + 6.152500E+02 2.561250E+03 8.803500E+01 -3.141E-07 -7.351E-07 0.000E+00 M= 7.994E-07 #,Z,Y,X= 0 1 16 38 + 6.157500E+02 2.561250E+03 8.495000E+01 -2.922E-07 -6.556E-07 0.000E+00 M= 7.178E-07 #,Z,Y,X= 0 1 16 39 + 6.162500E+02 2.561250E+03 7.445500E+01 -2.436E-07 -7.105E-07 0.000E+00 M= 7.511E-07 #,Z,Y,X= 0 1 16 40 + 6.167500E+02 2.561250E+03 7.077500E+01 -9.271E-07 -3.482E-06 0.000E+00 M= 3.603E-06 #,Z,Y,X= 0 1 16 41 + 6.172500E+02 2.561250E+03 7.397500E+01 -7.482E-07 -3.787E-06 0.000E+00 M= 3.860E-06 #,Z,Y,X= 0 1 16 42 + 6.177500E+02 2.561250E+03 7.690500E+01 1.074E-07 -2.707E-06 0.000E+00 M= 2.709E-06 #,Z,Y,X= 0 1 16 43 + 6.182500E+02 2.561250E+03 8.352000E+01 5.793E-08 -1.032E-06 0.000E+00 M= 1.033E-06 #,Z,Y,X= 0 1 16 44 + 6.187500E+02 2.561250E+03 8.259500E+01 9.103E-11 -5.810E-07 0.000E+00 M= 5.810E-07 #,Z,Y,X= 0 1 16 45 + 6.032500E+02 2.560750E+03 9.874500E+01 3.171E-07 0.000E+00 0.000E+00 M= 3.171E-07 #,Z,Y,X= 0 1 17 14 + 6.037500E+02 2.560750E+03 9.630500E+01 6.522E-07 -1.701E-07 0.000E+00 M= 6.740E-07 #,Z,Y,X= 0 1 17 15 + 6.042500E+02 2.560750E+03 9.056500E+01 7.580E-07 -8.479E-08 0.000E+00 M= 7.628E-07 #,Z,Y,X= 0 1 17 16 + 6.047500E+02 2.560750E+03 8.333500E+01 1.226E-06 -3.563E-07 0.000E+00 M= 1.276E-06 #,Z,Y,X= 0 1 17 17 + 6.052500E+02 2.560750E+03 7.829500E+01 1.870E-06 -5.770E-07 0.000E+00 M= 1.957E-06 #,Z,Y,X= 0 1 17 18 + 6.057500E+02 2.560750E+03 6.832500E+01 2.227E-06 -2.731E-07 0.000E+00 M= 2.244E-06 #,Z,Y,X= 0 1 17 19 + 6.062500E+02 2.560750E+03 6.640500E+01 2.110E-06 -6.120E-07 0.000E+00 M= 2.197E-06 #,Z,Y,X= 0 1 17 20 + 6.067500E+02 2.560750E+03 6.300500E+01 1.608E-06 -5.880E-07 0.000E+00 M= 1.712E-06 #,Z,Y,X= 0 1 17 21 + 6.072500E+02 2.560750E+03 6.689500E+01 1.437E-06 -6.340E-07 0.000E+00 M= 1.570E-06 #,Z,Y,X= 0 1 17 22 + 6.077500E+02 2.560750E+03 6.548500E+01 1.807E-06 -1.214E-06 0.000E+00 M= 2.177E-06 #,Z,Y,X= 0 1 17 23 + 6.082500E+02 2.560750E+03 5.690000E+01 1.990E-06 -3.286E-06 0.000E+00 M= 3.842E-06 #,Z,Y,X= 0 1 17 24 + 6.087500E+02 2.560750E+03 7.104500E+01 1.670E-06 -1.034E-06 0.000E+00 M= 1.964E-06 #,Z,Y,X= 0 1 17 25 + 6.092500E+02 2.560750E+03 6.515000E+01 1.253E-06 -1.801E-06 0.000E+00 M= 2.194E-06 #,Z,Y,X= 0 1 17 26 + 6.097500E+02 2.560750E+03 6.020000E+01 5.535E-07 -1.960E-06 0.000E+00 M= 2.037E-06 #,Z,Y,X= 0 1 17 27 + 6.102500E+02 2.560750E+03 7.764500E+01 3.462E-07 -1.429E-06 0.000E+00 M= 1.471E-06 #,Z,Y,X= 0 1 17 28 + 6.107500E+02 2.560750E+03 8.298000E+01 3.770E-07 -1.307E-06 0.000E+00 M= 1.360E-06 #,Z,Y,X= 0 1 17 29 + 6.112500E+02 2.560750E+03 7.893500E+01 -2.343E-07 -1.775E-06 0.000E+00 M= 1.790E-06 #,Z,Y,X= 0 1 17 30 + 6.117500E+02 2.560750E+03 8.254500E+01 -8.426E-07 -1.809E-06 0.000E+00 M= 1.996E-06 #,Z,Y,X= 0 1 17 31 + 6.122500E+02 2.560750E+03 8.674000E+01 -8.395E-07 -1.694E-06 0.000E+00 M= 1.890E-06 #,Z,Y,X= 0 1 17 32 + 6.127500E+02 2.560750E+03 7.805000E+01 -8.266E-07 -4.107E-07 0.000E+00 M= 9.230E-07 #,Z,Y,X= 0 1 17 33 + 6.132500E+02 2.560750E+03 7.275500E+01 -1.688E-06 -1.148E-06 0.000E+00 M= 2.041E-06 #,Z,Y,X= 0 1 17 34 + 6.137500E+02 2.560750E+03 7.970000E+01 -2.560E-06 -9.537E-07 0.000E+00 M= 2.732E-06 #,Z,Y,X= 0 1 17 35 + 6.142500E+02 2.560750E+03 7.481500E+01 -2.537E-06 -7.370E-07 0.000E+00 M= 2.642E-06 #,Z,Y,X= 0 1 17 36 + 6.147500E+02 2.560750E+03 8.061000E+01 -2.065E-06 -5.969E-07 0.000E+00 M= 2.149E-06 #,Z,Y,X= 0 1 17 37 + 6.152500E+02 2.560750E+03 8.896500E+01 -1.557E-06 -6.408E-07 0.000E+00 M= 1.684E-06 #,Z,Y,X= 0 1 17 38 + 6.157500E+02 2.560750E+03 8.385000E+01 -1.379E-06 -7.400E-07 0.000E+00 M= 1.565E-06 #,Z,Y,X= 0 1 17 39 + 6.162500E+02 2.560750E+03 7.861000E+01 -1.547E-06 -9.206E-07 0.000E+00 M= 1.800E-06 #,Z,Y,X= 0 1 17 40 + 6.167500E+02 2.560750E+03 6.965000E+01 -1.129E-06 -3.819E-06 0.000E+00 M= 3.982E-06 #,Z,Y,X= 0 1 17 41 + 6.172500E+02 2.560750E+03 7.491500E+01 -2.062E-07 -2.784E-06 0.000E+00 M= 2.791E-06 #,Z,Y,X= 0 1 17 42 + 6.177500E+02 2.560750E+03 7.730500E+01 2.391E-07 -2.730E-06 0.000E+00 M= 2.741E-06 #,Z,Y,X= 0 1 17 43 + 6.182500E+02 2.560750E+03 7.538000E+01 1.512E-07 -1.372E-06 0.000E+00 M= 1.380E-06 #,Z,Y,X= 0 1 17 44 + 6.187500E+02 2.560750E+03 8.183000E+01 7.273E-08 -5.193E-07 0.000E+00 M= 5.244E-07 #,Z,Y,X= 0 1 17 45 + 6.192500E+02 2.560750E+03 8.462000E+01 9.597E-08 -1.155E-07 0.000E+00 M= 1.502E-07 #,Z,Y,X= 0 1 17 46 + 6.032500E+02 2.560250E+03 9.581500E+01 1.698E-07 0.000E+00 0.000E+00 M= 1.698E-07 #,Z,Y,X= 0 1 18 14 + 6.037500E+02 2.560250E+03 9.573000E+01 3.622E-07 -2.533E-07 0.000E+00 M= 4.420E-07 #,Z,Y,X= 0 1 18 15 + 6.042500E+02 2.560250E+03 9.433500E+01 3.479E-07 -1.426E-07 0.000E+00 M= 3.760E-07 #,Z,Y,X= 0 1 18 16 + 6.047500E+02 2.560250E+03 8.885500E+01 3.092E-07 -1.218E-07 0.000E+00 M= 3.323E-07 #,Z,Y,X= 0 1 18 17 + 6.052500E+02 2.560250E+03 7.863000E+01 3.132E-07 -3.340E-07 0.000E+00 M= 4.579E-07 #,Z,Y,X= 0 1 18 18 + 6.057500E+02 2.560250E+03 8.064000E+01 3.081E-07 -1.385E-07 0.000E+00 M= 3.379E-07 #,Z,Y,X= 0 1 18 19 + 6.062500E+02 2.560250E+03 6.680500E+01 4.290E-07 -7.217E-07 0.000E+00 M= 8.395E-07 #,Z,Y,X= 0 1 18 20 + 6.067500E+02 2.560250E+03 6.213000E+01 8.303E-07 -5.016E-07 0.000E+00 M= 9.700E-07 #,Z,Y,X= 0 1 18 21 + 6.072500E+02 2.560250E+03 6.011500E+01 1.291E-06 -4.802E-07 0.000E+00 M= 1.377E-06 #,Z,Y,X= 0 1 18 22 + 6.077500E+02 2.560250E+03 5.887000E+01 1.791E-06 -6.080E-07 0.000E+00 M= 1.891E-06 #,Z,Y,X= 0 1 18 23 + 6.082500E+02 2.560250E+03 5.586500E+01 2.792E-06 -2.673E-06 0.000E+00 M= 3.865E-06 #,Z,Y,X= 0 1 18 24 + 6.087500E+02 2.560250E+03 5.617000E+01 2.799E-06 -1.936E-06 0.000E+00 M= 3.403E-06 #,Z,Y,X= 0 1 18 25 + 6.092500E+02 2.560250E+03 5.694500E+01 1.322E-06 -2.849E-06 0.000E+00 M= 3.140E-06 #,Z,Y,X= 0 1 18 26 + 6.097500E+02 2.560250E+03 6.458500E+01 4.491E-07 -2.483E-06 0.000E+00 M= 2.523E-06 #,Z,Y,X= 0 1 18 27 + 6.102500E+02 2.560250E+03 7.004500E+01 3.692E-07 -1.248E-06 0.000E+00 M= 1.301E-06 #,Z,Y,X= 0 1 18 28 + 6.107500E+02 2.560250E+03 6.527000E+01 8.936E-08 -1.825E-06 0.000E+00 M= 1.827E-06 #,Z,Y,X= 0 1 18 29 + 6.112500E+02 2.560250E+03 8.098000E+01 -5.780E-07 -2.529E-06 0.000E+00 M= 2.594E-06 #,Z,Y,X= 0 1 18 30 + 6.117500E+02 2.560250E+03 7.117000E+01 -1.185E-06 -2.129E-06 0.000E+00 M= 2.436E-06 #,Z,Y,X= 0 1 18 31 + 6.122500E+02 2.560250E+03 7.462500E+01 -1.731E-06 -1.776E-06 0.000E+00 M= 2.480E-06 #,Z,Y,X= 0 1 18 32 + 6.127500E+02 2.560250E+03 7.329500E+01 -2.295E-06 -8.947E-07 0.000E+00 M= 2.464E-06 #,Z,Y,X= 0 1 18 33 + 6.132500E+02 2.560250E+03 6.807000E+01 -2.609E-06 -1.851E-06 0.000E+00 M= 3.199E-06 #,Z,Y,X= 0 1 18 34 + 6.137500E+02 2.560250E+03 6.889500E+01 -2.147E-06 -6.831E-07 0.000E+00 M= 2.253E-06 #,Z,Y,X= 0 1 18 35 + 6.142500E+02 2.560250E+03 8.192000E+01 -1.363E-06 -3.727E-07 0.000E+00 M= 1.413E-06 #,Z,Y,X= 0 1 18 36 + 6.147500E+02 2.560250E+03 8.469000E+01 -1.058E-06 -3.434E-07 0.000E+00 M= 1.113E-06 #,Z,Y,X= 0 1 18 37 + 6.152500E+02 2.560250E+03 8.469500E+01 -1.076E-06 -5.608E-07 0.000E+00 M= 1.213E-06 #,Z,Y,X= 0 1 18 38 + 6.157500E+02 2.560250E+03 8.550500E+01 -1.202E-06 -8.405E-07 0.000E+00 M= 1.467E-06 #,Z,Y,X= 0 1 18 39 + 6.162500E+02 2.560250E+03 7.392500E+01 -1.340E-06 -1.229E-06 0.000E+00 M= 1.818E-06 #,Z,Y,X= 0 1 18 40 + 6.167500E+02 2.560250E+03 6.918500E+01 -7.042E-07 -2.570E-06 0.000E+00 M= 2.665E-06 #,Z,Y,X= 0 1 18 41 + 6.172500E+02 2.560250E+03 6.659000E+01 -4.717E-08 -2.622E-06 0.000E+00 M= 2.622E-06 #,Z,Y,X= 0 1 18 42 + 6.177500E+02 2.560250E+03 6.762000E+01 -1.776E-07 -2.783E-06 0.000E+00 M= 2.789E-06 #,Z,Y,X= 0 1 18 43 + 6.182500E+02 2.560250E+03 7.878500E+01 -8.187E-08 -1.474E-06 0.000E+00 M= 1.477E-06 #,Z,Y,X= 0 1 18 44 + 6.187500E+02 2.560250E+03 7.983000E+01 4.114E-09 -5.182E-07 0.000E+00 M= 5.182E-07 #,Z,Y,X= 0 1 18 45 + 6.192500E+02 2.560250E+03 8.064500E+01 -3.114E-08 -2.448E-07 0.000E+00 M= 2.467E-07 #,Z,Y,X= 0 1 18 46 + 6.037500E+02 2.559750E+03 9.397000E+01 1.035E-07 -2.346E-07 0.000E+00 M= 2.564E-07 #,Z,Y,X= 0 1 19 15 + 6.042500E+02 2.559750E+03 9.242000E+01 2.564E-07 -2.226E-07 0.000E+00 M= 3.396E-07 #,Z,Y,X= 0 1 19 16 + 6.047500E+02 2.559750E+03 9.006500E+01 2.446E-07 -2.789E-07 0.000E+00 M= 3.710E-07 #,Z,Y,X= 0 1 19 17 + 6.052500E+02 2.559750E+03 8.774000E+01 1.836E-07 -3.647E-07 0.000E+00 M= 4.083E-07 #,Z,Y,X= 0 1 19 18 + 6.057500E+02 2.559750E+03 7.876000E+01 1.314E-07 -2.599E-07 0.000E+00 M= 2.912E-07 #,Z,Y,X= 0 1 19 19 + 6.062500E+02 2.559750E+03 6.899500E+01 1.135E-07 -5.906E-07 0.000E+00 M= 6.015E-07 #,Z,Y,X= 0 1 19 20 + 6.067500E+02 2.559750E+03 7.942000E+01 1.190E-07 -3.034E-07 0.000E+00 M= 3.259E-07 #,Z,Y,X= 0 1 19 21 + 6.072500E+02 2.559750E+03 7.741500E+01 8.804E-08 -3.734E-07 0.000E+00 M= 3.836E-07 #,Z,Y,X= 0 1 19 22 + 6.077500E+02 2.559750E+03 7.762000E+01 9.903E-08 -3.677E-07 0.000E+00 M= 3.808E-07 #,Z,Y,X= 0 1 19 23 + 6.082500E+02 2.559750E+03 6.814500E+01 7.617E-07 -1.379E-06 0.000E+00 M= 1.575E-06 #,Z,Y,X= 0 1 19 24 + 6.087500E+02 2.559750E+03 5.958000E+01 1.708E-06 -2.467E-06 0.000E+00 M= 3.001E-06 #,Z,Y,X= 0 1 19 25 + 6.092500E+02 2.559750E+03 5.245500E+01 1.444E-06 -4.220E-06 0.000E+00 M= 4.461E-06 #,Z,Y,X= 0 1 19 26 + 6.097500E+02 2.559750E+03 6.220500E+01 5.837E-07 -2.964E-06 0.000E+00 M= 3.021E-06 #,Z,Y,X= 0 1 19 27 + 6.102500E+02 2.559750E+03 7.321500E+01 2.695E-07 -1.297E-06 0.000E+00 M= 1.325E-06 #,Z,Y,X= 0 1 19 28 + 6.107500E+02 2.559750E+03 6.081500E+01 -2.412E-07 -2.696E-06 0.000E+00 M= 2.707E-06 #,Z,Y,X= 0 1 19 29 + 6.112500E+02 2.559750E+03 6.482500E+01 -1.128E-06 -3.274E-06 0.000E+00 M= 3.463E-06 #,Z,Y,X= 0 1 19 30 + 6.117500E+02 2.559750E+03 6.438000E+01 -1.597E-06 -2.460E-06 0.000E+00 M= 2.933E-06 #,Z,Y,X= 0 1 19 31 + 6.122500E+02 2.559750E+03 6.236500E+01 -1.731E-06 -2.147E-06 0.000E+00 M= 2.758E-06 #,Z,Y,X= 0 1 19 32 + 6.127500E+02 2.559750E+03 6.817500E+01 -1.925E-06 -1.421E-06 0.000E+00 M= 2.393E-06 #,Z,Y,X= 0 1 19 33 + 6.132500E+02 2.559750E+03 6.211000E+01 -1.559E-06 -1.448E-06 0.000E+00 M= 2.128E-06 #,Z,Y,X= 0 1 19 34 + 6.137500E+02 2.559750E+03 8.205000E+01 -9.562E-07 -2.128E-07 0.000E+00 M= 9.796E-07 #,Z,Y,X= 0 1 19 35 + 6.142500E+02 2.559750E+03 8.556000E+01 -8.677E-07 -1.874E-07 0.000E+00 M= 8.877E-07 #,Z,Y,X= 0 1 19 36 + 6.147500E+02 2.559750E+03 8.225000E+01 -8.080E-07 -3.376E-07 0.000E+00 M= 8.757E-07 #,Z,Y,X= 0 1 19 37 + 6.152500E+02 2.559750E+03 7.521000E+01 -7.878E-07 -7.039E-07 0.000E+00 M= 1.056E-06 #,Z,Y,X= 0 1 19 38 + 6.157500E+02 2.559750E+03 7.940500E+01 -9.966E-07 -1.127E-06 0.000E+00 M= 1.504E-06 #,Z,Y,X= 0 1 19 39 + 6.162500E+02 2.559750E+03 7.780500E+01 -1.077E-06 -1.325E-06 0.000E+00 M= 1.707E-06 #,Z,Y,X= 0 1 19 40 + 6.167500E+02 2.559750E+03 7.061000E+01 -6.538E-07 -1.582E-06 0.000E+00 M= 1.712E-06 #,Z,Y,X= 0 1 19 41 + 6.172500E+02 2.559750E+03 6.508500E+01 -1.278E-07 -2.610E-06 0.000E+00 M= 2.613E-06 #,Z,Y,X= 0 1 19 42 + 6.177500E+02 2.559750E+03 6.732000E+01 -9.885E-08 -3.117E-06 0.000E+00 M= 3.119E-06 #,Z,Y,X= 0 1 19 43 + 6.182500E+02 2.559750E+03 7.065500E+01 -9.436E-08 -1.274E-06 0.000E+00 M= 1.277E-06 #,Z,Y,X= 0 1 19 44 + 6.187500E+02 2.559750E+03 6.986500E+01 -1.642E-07 -8.512E-07 0.000E+00 M= 8.668E-07 #,Z,Y,X= 0 1 19 45 + 6.192500E+02 2.559750E+03 7.967500E+01 -1.785E-07 -1.324E-07 0.000E+00 M= 2.222E-07 #,Z,Y,X= 0 1 19 46 + 6.032500E+02 2.559250E+03 9.362500E+01 1.838E-07 -2.640E-07 0.000E+00 M= 3.217E-07 #,Z,Y,X= 0 1 20 14 + 6.037500E+02 2.559250E+03 8.929500E+01 3.303E-07 -2.620E-07 0.000E+00 M= 4.216E-07 #,Z,Y,X= 0 1 20 15 + 6.042500E+02 2.559250E+03 8.899000E+01 2.699E-07 -2.886E-07 0.000E+00 M= 3.952E-07 #,Z,Y,X= 0 1 20 16 + 6.047500E+02 2.559250E+03 8.771500E+01 2.257E-07 -4.557E-07 0.000E+00 M= 5.085E-07 #,Z,Y,X= 0 1 20 17 + 6.052500E+02 2.559250E+03 8.779000E+01 1.638E-07 -4.735E-07 0.000E+00 M= 5.011E-07 #,Z,Y,X= 0 1 20 18 + 6.057500E+02 2.559250E+03 8.334000E+01 5.931E-08 -4.465E-07 0.000E+00 M= 4.504E-07 #,Z,Y,X= 0 1 20 19 + 6.062500E+02 2.559250E+03 8.208000E+01 6.800E-08 -5.509E-07 0.000E+00 M= 5.551E-07 #,Z,Y,X= 0 1 20 20 + 6.067500E+02 2.559250E+03 7.887500E+01 1.422E-07 -3.960E-07 0.000E+00 M= 4.207E-07 #,Z,Y,X= 0 1 20 21 + 6.072500E+02 2.559250E+03 7.552000E+01 9.687E-08 -4.939E-07 0.000E+00 M= 5.033E-07 #,Z,Y,X= 0 1 20 22 + 6.077500E+02 2.559250E+03 7.373000E+01 1.301E-08 -4.408E-07 0.000E+00 M= 4.409E-07 #,Z,Y,X= 0 1 20 23 + 6.082500E+02 2.559250E+03 7.611000E+01 7.877E-09 -7.227E-07 0.000E+00 M= 7.228E-07 #,Z,Y,X= 0 1 20 24 + 6.087500E+02 2.559250E+03 6.328000E+01 1.740E-07 -1.996E-06 0.000E+00 M= 2.003E-06 #,Z,Y,X= 0 1 20 25 + 6.092500E+02 2.559250E+03 5.499500E+01 4.927E-07 -4.430E-06 0.000E+00 M= 4.457E-06 #,Z,Y,X= 0 1 20 26 + 6.097500E+02 2.559250E+03 6.159500E+01 4.164E-07 -3.500E-06 0.000E+00 M= 3.525E-06 #,Z,Y,X= 0 1 20 27 + 6.102500E+02 2.559250E+03 7.065500E+01 6.194E-08 -1.415E-06 0.000E+00 M= 1.416E-06 #,Z,Y,X= 0 1 20 28 + 6.107500E+02 2.559250E+03 5.861500E+01 -2.650E-07 -3.365E-06 0.000E+00 M= 3.375E-06 #,Z,Y,X= 0 1 20 29 + 6.112500E+02 2.559250E+03 5.709500E+01 -1.355E-06 -4.422E-06 0.000E+00 M= 4.625E-06 #,Z,Y,X= 0 1 20 30 + 6.117500E+02 2.559250E+03 5.717000E+01 -1.995E-06 -2.305E-06 0.000E+00 M= 3.048E-06 #,Z,Y,X= 0 1 20 31 + 6.122500E+02 2.559250E+03 5.826500E+01 -1.847E-06 -2.212E-06 0.000E+00 M= 2.882E-06 #,Z,Y,X= 0 1 20 32 + 6.127500E+02 2.559250E+03 5.803000E+01 -1.558E-06 -1.289E-06 0.000E+00 M= 2.022E-06 #,Z,Y,X= 0 1 20 33 + 6.132500E+02 2.559250E+03 6.972000E+01 -9.836E-07 -7.634E-07 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 20 34 + 6.137500E+02 2.559250E+03 7.938000E+01 -7.827E-07 -2.368E-07 0.000E+00 M= 8.177E-07 #,Z,Y,X= 0 1 20 35 + 6.142500E+02 2.559250E+03 7.196500E+01 -7.948E-07 -2.632E-07 0.000E+00 M= 8.372E-07 #,Z,Y,X= 0 1 20 36 + 6.147500E+02 2.559250E+03 7.364500E+01 -8.983E-07 -4.658E-07 0.000E+00 M= 1.012E-06 #,Z,Y,X= 0 1 20 37 + 6.152500E+02 2.559250E+03 6.249500E+01 -1.321E-06 -1.113E-06 0.000E+00 M= 1.728E-06 #,Z,Y,X= 0 1 20 38 + 6.157500E+02 2.559250E+03 6.590500E+01 -1.606E-06 -1.291E-06 0.000E+00 M= 2.060E-06 #,Z,Y,X= 0 1 20 39 + 6.162500E+02 2.559250E+03 6.909500E+01 -1.298E-06 -1.093E-06 0.000E+00 M= 1.697E-06 #,Z,Y,X= 0 1 20 40 + 6.167500E+02 2.559250E+03 7.075000E+01 -8.688E-07 -1.101E-06 0.000E+00 M= 1.402E-06 #,Z,Y,X= 0 1 20 41 + 6.172500E+02 2.559250E+03 6.832000E+01 -6.442E-07 -2.459E-06 0.000E+00 M= 2.542E-06 #,Z,Y,X= 0 1 20 42 + 6.177500E+02 2.559250E+03 5.889500E+01 -1.083E-06 -3.818E-06 0.000E+00 M= 3.969E-06 #,Z,Y,X= 0 1 20 43 + 6.182500E+02 2.559250E+03 6.444500E+01 -1.343E-06 -1.027E-06 0.000E+00 M= 1.691E-06 #,Z,Y,X= 0 1 20 44 + 6.187500E+02 2.559250E+03 6.832000E+01 -5.634E-07 -5.424E-07 0.000E+00 M= 7.821E-07 #,Z,Y,X= 0 1 20 45 + 6.032500E+02 2.558750E+03 9.174000E+01 1.322E-07 -5.648E-07 0.000E+00 M= 5.801E-07 #,Z,Y,X= 0 1 21 14 + 6.037500E+02 2.558750E+03 8.504000E+01 2.999E-07 -4.120E-07 0.000E+00 M= 5.096E-07 #,Z,Y,X= 0 1 21 15 + 6.042500E+02 2.558750E+03 8.422500E+01 3.007E-07 -5.078E-07 0.000E+00 M= 5.902E-07 #,Z,Y,X= 0 1 21 16 + 6.047500E+02 2.558750E+03 8.586000E+01 2.234E-07 -6.347E-07 0.000E+00 M= 6.729E-07 #,Z,Y,X= 0 1 21 17 + 6.052500E+02 2.558750E+03 8.487500E+01 1.761E-07 -6.922E-07 0.000E+00 M= 7.143E-07 #,Z,Y,X= 0 1 21 18 + 6.057500E+02 2.558750E+03 8.481500E+01 3.598E-08 -7.376E-07 0.000E+00 M= 7.384E-07 #,Z,Y,X= 0 1 21 19 + 6.062500E+02 2.558750E+03 8.447000E+01 -9.396E-10 -5.389E-07 0.000E+00 M= 5.389E-07 #,Z,Y,X= 0 1 21 20 + 6.067500E+02 2.558750E+03 7.901500E+01 4.612E-08 -4.810E-07 0.000E+00 M= 4.832E-07 #,Z,Y,X= 0 1 21 21 + 6.072500E+02 2.558750E+03 6.834000E+01 -7.650E-11 -6.499E-07 0.000E+00 M= 6.499E-07 #,Z,Y,X= 0 1 21 22 + 6.077500E+02 2.558750E+03 6.222000E+01 -3.217E-08 -5.000E-07 0.000E+00 M= 5.010E-07 #,Z,Y,X= 0 1 21 23 + 6.082500E+02 2.558750E+03 6.047000E+01 -1.442E-07 -8.279E-07 0.000E+00 M= 8.403E-07 #,Z,Y,X= 0 1 21 24 + 6.087500E+02 2.558750E+03 6.092000E+01 -9.677E-08 -1.647E-06 0.000E+00 M= 1.650E-06 #,Z,Y,X= 0 1 21 25 + 6.092500E+02 2.558750E+03 5.508500E+01 7.324E-08 -4.193E-06 0.000E+00 M= 4.194E-06 #,Z,Y,X= 0 1 21 26 + 6.097500E+02 2.558750E+03 5.780000E+01 7.360E-08 -3.806E-06 0.000E+00 M= 3.806E-06 #,Z,Y,X= 0 1 21 27 + 6.102500E+02 2.558750E+03 6.984500E+01 -1.281E-07 -1.760E-06 0.000E+00 M= 1.765E-06 #,Z,Y,X= 0 1 21 28 + 6.107500E+02 2.558750E+03 5.723500E+01 -9.273E-07 -4.201E-06 0.000E+00 M= 4.302E-06 #,Z,Y,X= 0 1 21 29 + 6.112500E+02 2.558750E+03 5.025000E+01 -1.553E-06 -5.289E-06 0.000E+00 M= 5.513E-06 #,Z,Y,X= 0 1 21 30 + 6.117500E+02 2.558750E+03 6.940500E+01 -1.430E-06 -2.005E-06 0.000E+00 M= 2.463E-06 #,Z,Y,X= 0 1 21 31 + 6.122500E+02 2.558750E+03 6.509000E+01 -8.349E-07 -1.861E-06 0.000E+00 M= 2.040E-06 #,Z,Y,X= 0 1 21 32 + 6.127500E+02 2.558750E+03 7.560000E+01 -4.204E-07 -1.012E-06 0.000E+00 M= 1.096E-06 #,Z,Y,X= 0 1 21 33 + 6.132500E+02 2.558750E+03 7.889500E+01 -5.319E-07 -7.034E-07 0.000E+00 M= 8.819E-07 #,Z,Y,X= 0 1 21 34 + 6.137500E+02 2.558750E+03 7.668500E+01 -8.114E-07 -5.125E-07 0.000E+00 M= 9.597E-07 #,Z,Y,X= 0 1 21 35 + 6.142500E+02 2.558750E+03 6.535000E+01 -1.143E-06 -5.062E-07 0.000E+00 M= 1.250E-06 #,Z,Y,X= 0 1 21 36 + 6.147500E+02 2.558750E+03 6.012500E+01 -1.530E-06 -8.791E-07 0.000E+00 M= 1.764E-06 #,Z,Y,X= 0 1 21 37 + 6.152500E+02 2.558750E+03 6.195000E+01 -1.786E-06 -1.552E-06 0.000E+00 M= 2.367E-06 #,Z,Y,X= 0 1 21 38 + 6.157500E+02 2.558750E+03 5.922500E+01 -2.139E-06 -1.579E-06 0.000E+00 M= 2.659E-06 #,Z,Y,X= 0 1 21 39 + 6.162500E+02 2.558750E+03 6.428500E+01 -2.497E-06 -1.010E-06 0.000E+00 M= 2.693E-06 #,Z,Y,X= 0 1 21 40 + 6.167500E+02 2.558750E+03 6.330000E+01 -2.547E-06 -9.592E-07 0.000E+00 M= 2.722E-06 #,Z,Y,X= 0 1 21 41 + 6.172500E+02 2.558750E+03 5.885000E+01 -2.607E-06 -2.602E-06 0.000E+00 M= 3.684E-06 #,Z,Y,X= 0 1 21 42 + 6.177500E+02 2.558750E+03 6.043000E+01 -1.797E-06 -3.491E-06 0.000E+00 M= 3.927E-06 #,Z,Y,X= 0 1 21 43 + 6.182500E+02 2.558750E+03 6.999000E+01 -4.576E-07 -4.360E-07 0.000E+00 M= 6.321E-07 #,Z,Y,X= 0 1 21 44 + 6.032500E+02 2.558250E+03 8.948500E+01 1.027E-07 -4.657E-07 0.000E+00 M= 4.768E-07 #,Z,Y,X= 0 1 22 14 + 6.037500E+02 2.558250E+03 8.223500E+01 3.314E-07 -3.828E-07 0.000E+00 M= 5.063E-07 #,Z,Y,X= 0 1 22 15 + 6.042500E+02 2.558250E+03 7.217000E+01 7.161E-07 -5.094E-07 0.000E+00 M= 8.788E-07 #,Z,Y,X= 0 1 22 16 + 6.047500E+02 2.558250E+03 7.051500E+01 1.142E-06 -5.514E-07 0.000E+00 M= 1.268E-06 #,Z,Y,X= 0 1 22 17 + 6.052500E+02 2.558250E+03 6.600000E+01 1.189E-06 -1.090E-06 0.000E+00 M= 1.613E-06 #,Z,Y,X= 0 1 22 18 + 6.057500E+02 2.558250E+03 6.431500E+01 4.961E-07 -1.425E-06 0.000E+00 M= 1.509E-06 #,Z,Y,X= 0 1 22 19 + 6.062500E+02 2.558250E+03 7.385000E+01 4.441E-08 -6.363E-07 0.000E+00 M= 6.378E-07 #,Z,Y,X= 0 1 22 20 + 6.067500E+02 2.558250E+03 7.699000E+01 -5.495E-08 -6.657E-07 0.000E+00 M= 6.679E-07 #,Z,Y,X= 0 1 22 21 + 6.072500E+02 2.558250E+03 7.729000E+01 -1.751E-07 -8.087E-07 0.000E+00 M= 8.274E-07 #,Z,Y,X= 0 1 22 22 + 6.077500E+02 2.558250E+03 6.928500E+01 -2.172E-07 -5.238E-07 0.000E+00 M= 5.670E-07 #,Z,Y,X= 0 1 22 23 + 6.082500E+02 2.558250E+03 5.844500E+01 -1.635E-07 -8.356E-07 0.000E+00 M= 8.514E-07 #,Z,Y,X= 0 1 22 24 + 6.087500E+02 2.558250E+03 5.451500E+01 2.509E-07 -1.035E-06 0.000E+00 M= 1.065E-06 #,Z,Y,X= 0 1 22 25 + 6.092500E+02 2.558250E+03 4.696000E+01 9.340E-07 -3.649E-06 0.000E+00 M= 3.767E-06 #,Z,Y,X= 0 1 22 26 + 6.097500E+02 2.558250E+03 4.901500E+01 9.929E-07 -4.130E-06 0.000E+00 M= 4.248E-06 #,Z,Y,X= 0 1 22 27 + 6.102500E+02 2.558250E+03 5.726500E+01 2.251E-08 -2.646E-06 0.000E+00 M= 2.646E-06 #,Z,Y,X= 0 1 22 28 + 6.107500E+02 2.558250E+03 4.969500E+01 -1.434E-06 -5.676E-06 0.000E+00 M= 5.855E-06 #,Z,Y,X= 0 1 22 29 + 6.112500E+02 2.558250E+03 5.774500E+01 -1.403E-06 -4.459E-06 0.000E+00 M= 4.674E-06 #,Z,Y,X= 0 1 22 30 + 6.117500E+02 2.558250E+03 7.096500E+01 -5.276E-07 -1.833E-06 0.000E+00 M= 1.907E-06 #,Z,Y,X= 0 1 22 31 + 6.122500E+02 2.558250E+03 6.875500E+01 -3.959E-07 -1.418E-06 0.000E+00 M= 1.473E-06 #,Z,Y,X= 0 1 22 32 + 6.127500E+02 2.558250E+03 6.540000E+01 -5.485E-07 -1.298E-06 0.000E+00 M= 1.409E-06 #,Z,Y,X= 0 1 22 33 + 6.132500E+02 2.558250E+03 6.583000E+01 -8.639E-07 -9.758E-07 0.000E+00 M= 1.303E-06 #,Z,Y,X= 0 1 22 34 + 6.137500E+02 2.558250E+03 6.487000E+01 -1.103E-06 -9.083E-07 0.000E+00 M= 1.429E-06 #,Z,Y,X= 0 1 22 35 + 6.142500E+02 2.558250E+03 6.316500E+01 -1.461E-06 -1.011E-06 0.000E+00 M= 1.777E-06 #,Z,Y,X= 0 1 22 36 + 6.147500E+02 2.558250E+03 6.003500E+01 -2.020E-06 -1.525E-06 0.000E+00 M= 2.531E-06 #,Z,Y,X= 0 1 22 37 + 6.152500E+02 2.558250E+03 5.482500E+01 -2.780E-06 -2.108E-06 0.000E+00 M= 3.489E-06 #,Z,Y,X= 0 1 22 38 + 6.157500E+02 2.558250E+03 5.789000E+01 -3.407E-06 -2.122E-06 0.000E+00 M= 4.014E-06 #,Z,Y,X= 0 1 22 39 + 6.162500E+02 2.558250E+03 5.622500E+01 -3.804E-06 -1.454E-06 0.000E+00 M= 4.072E-06 #,Z,Y,X= 0 1 22 40 + 6.167500E+02 2.558250E+03 5.590500E+01 -4.196E-06 -1.219E-06 0.000E+00 M= 4.370E-06 #,Z,Y,X= 0 1 22 41 + 6.172500E+02 2.558250E+03 5.879000E+01 -3.493E-06 -1.856E-06 0.000E+00 M= 3.955E-06 #,Z,Y,X= 0 1 22 42 + 6.177500E+02 2.558250E+03 7.220000E+01 -1.295E-06 -1.274E-06 0.000E+00 M= 1.817E-06 #,Z,Y,X= 0 1 22 43 + 6.032500E+02 2.557750E+03 8.687000E+01 4.589E-08 -4.529E-07 0.000E+00 M= 4.552E-07 #,Z,Y,X= 0 1 23 14 + 6.037500E+02 2.557750E+03 8.552500E+01 9.256E-08 -3.680E-07 0.000E+00 M= 3.795E-07 #,Z,Y,X= 0 1 23 15 + 6.042500E+02 2.557750E+03 8.558000E+01 1.199E-07 -3.622E-07 0.000E+00 M= 3.816E-07 #,Z,Y,X= 0 1 23 16 + 6.047500E+02 2.557750E+03 7.814500E+01 2.173E-07 -3.543E-07 0.000E+00 M= 4.156E-07 #,Z,Y,X= 0 1 23 17 + 6.052500E+02 2.557750E+03 6.818500E+01 8.518E-07 -8.933E-07 0.000E+00 M= 1.234E-06 #,Z,Y,X= 0 1 23 18 + 6.057500E+02 2.557750E+03 5.952500E+01 1.490E-06 -1.874E-06 0.000E+00 M= 2.394E-06 #,Z,Y,X= 0 1 23 19 + 6.062500E+02 2.557750E+03 6.101500E+01 1.229E-06 -1.171E-06 0.000E+00 M= 1.698E-06 #,Z,Y,X= 0 1 23 20 + 6.067500E+02 2.557750E+03 5.996500E+01 3.540E-07 -1.296E-06 0.000E+00 M= 1.343E-06 #,Z,Y,X= 0 1 23 21 + 6.072500E+02 2.557750E+03 6.369500E+01 -1.898E-07 -1.133E-06 0.000E+00 M= 1.149E-06 #,Z,Y,X= 0 1 23 22 + 6.077500E+02 2.557750E+03 7.010000E+01 -3.100E-07 -6.796E-07 0.000E+00 M= 7.470E-07 #,Z,Y,X= 0 1 23 23 + 6.082500E+02 2.557750E+03 6.871000E+01 -1.985E-07 -7.165E-07 0.000E+00 M= 7.435E-07 #,Z,Y,X= 0 1 23 24 + 6.087500E+02 2.557750E+03 6.080500E+01 -9.414E-09 -6.752E-07 0.000E+00 M= 6.753E-07 #,Z,Y,X= 0 1 23 25 + 6.092500E+02 2.557750E+03 5.380000E+01 3.470E-07 -3.209E-06 0.000E+00 M= 3.228E-06 #,Z,Y,X= 0 1 23 26 + 6.097500E+02 2.557750E+03 4.796000E+01 8.547E-07 -4.555E-06 0.000E+00 M= 4.634E-06 #,Z,Y,X= 0 1 23 27 + 6.102500E+02 2.557750E+03 4.575000E+01 -1.058E-06 -5.543E-06 0.000E+00 M= 5.643E-06 #,Z,Y,X= 0 1 23 28 + 6.107500E+02 2.557750E+03 4.534000E+01 -2.688E-06 -6.407E-06 0.000E+00 M= 6.948E-06 #,Z,Y,X= 0 1 23 29 + 6.112500E+02 2.557750E+03 6.344500E+01 -1.611E-06 -3.236E-06 0.000E+00 M= 3.615E-06 #,Z,Y,X= 0 1 23 30 + 6.117500E+02 2.557750E+03 7.016000E+01 -9.587E-07 -1.853E-06 0.000E+00 M= 2.086E-06 #,Z,Y,X= 0 1 23 31 + 6.122500E+02 2.557750E+03 5.492000E+01 -1.169E-06 -1.674E-06 0.000E+00 M= 2.042E-06 #,Z,Y,X= 0 1 23 32 + 6.127500E+02 2.557750E+03 5.406500E+01 -1.849E-06 -2.019E-06 0.000E+00 M= 2.738E-06 #,Z,Y,X= 0 1 23 33 + 6.132500E+02 2.557750E+03 5.305500E+01 -2.324E-06 -1.228E-06 0.000E+00 M= 2.629E-06 #,Z,Y,X= 0 1 23 34 + 6.137500E+02 2.557750E+03 5.583000E+01 -2.566E-06 -1.299E-06 0.000E+00 M= 2.876E-06 #,Z,Y,X= 0 1 23 35 + 6.142500E+02 2.557750E+03 5.619500E+01 -2.928E-06 -1.643E-06 0.000E+00 M= 3.358E-06 #,Z,Y,X= 0 1 23 36 + 6.147500E+02 2.557750E+03 5.655500E+01 -3.188E-06 -2.083E-06 0.000E+00 M= 3.808E-06 #,Z,Y,X= 0 1 23 37 + 6.152500E+02 2.557750E+03 5.175500E+01 -3.338E-06 -2.808E-06 0.000E+00 M= 4.362E-06 #,Z,Y,X= 0 1 23 38 + 6.157500E+02 2.557750E+03 5.309500E+01 -3.182E-06 -2.127E-06 0.000E+00 M= 3.828E-06 #,Z,Y,X= 0 1 23 39 + 6.162500E+02 2.557750E+03 6.078500E+01 -2.342E-06 -1.321E-06 0.000E+00 M= 2.689E-06 #,Z,Y,X= 0 1 23 40 + 6.167500E+02 2.557750E+03 7.274000E+01 -1.447E-06 -1.208E-06 0.000E+00 M= 1.885E-06 #,Z,Y,X= 0 1 23 41 + 6.172500E+02 2.557750E+03 7.644500E+01 -5.427E-07 -5.213E-07 0.000E+00 M= 7.525E-07 #,Z,Y,X= 0 1 23 42 + 6.027500E+02 2.557250E+03 8.241500E+01 -2.151E-08 -7.681E-08 0.000E+00 M= 7.977E-08 #,Z,Y,X= 0 1 24 13 + 6.032500E+02 2.557250E+03 8.247500E+01 4.174E-09 -5.032E-07 0.000E+00 M= 5.032E-07 #,Z,Y,X= 0 1 24 14 + 6.037500E+02 2.557250E+03 8.093000E+01 5.840E-08 -4.847E-07 0.000E+00 M= 4.882E-07 #,Z,Y,X= 0 1 24 15 + 6.042500E+02 2.557250E+03 7.823000E+01 7.693E-08 -4.713E-07 0.000E+00 M= 4.776E-07 #,Z,Y,X= 0 1 24 16 + 6.047500E+02 2.557250E+03 7.971000E+01 4.153E-08 -4.496E-07 0.000E+00 M= 4.515E-07 #,Z,Y,X= 0 1 24 17 + 6.052500E+02 2.557250E+03 7.893500E+01 1.259E-08 -4.286E-07 0.000E+00 M= 4.288E-07 #,Z,Y,X= 0 1 24 18 + 6.057500E+02 2.557250E+03 5.799500E+01 7.371E-07 -1.079E-06 0.000E+00 M= 1.306E-06 #,Z,Y,X= 0 1 24 19 + 6.062500E+02 2.557250E+03 5.650000E+01 1.165E-06 -1.871E-06 0.000E+00 M= 2.204E-06 #,Z,Y,X= 0 1 24 20 + 6.067500E+02 2.557250E+03 5.199500E+01 7.866E-07 -1.880E-06 0.000E+00 M= 2.038E-06 #,Z,Y,X= 0 1 24 21 + 6.072500E+02 2.557250E+03 5.606500E+01 5.006E-07 -1.583E-06 0.000E+00 M= 1.660E-06 #,Z,Y,X= 0 1 24 22 + 6.077500E+02 2.557250E+03 5.950000E+01 2.364E-08 -1.138E-06 0.000E+00 M= 1.138E-06 #,Z,Y,X= 0 1 24 23 + 6.082500E+02 2.557250E+03 6.303500E+01 -1.395E-07 -6.545E-07 0.000E+00 M= 6.692E-07 #,Z,Y,X= 0 1 24 24 + 6.087500E+02 2.557250E+03 6.264500E+01 -8.793E-08 -7.929E-07 0.000E+00 M= 7.978E-07 #,Z,Y,X= 0 1 24 25 + 6.092500E+02 2.557250E+03 5.697500E+01 -5.922E-07 -3.840E-06 0.000E+00 M= 3.886E-06 #,Z,Y,X= 0 1 24 26 + 6.097500E+02 2.557250E+03 5.210500E+01 -1.078E-06 -4.698E-06 0.000E+00 M= 4.820E-06 #,Z,Y,X= 0 1 24 27 + 6.102500E+02 2.557250E+03 4.181500E+01 -1.126E-06 -8.310E-06 0.000E+00 M= 8.386E-06 #,Z,Y,X= 0 1 24 28 + 6.107500E+02 2.557250E+03 6.157000E+01 -1.217E-06 -6.352E-06 0.000E+00 M= 6.467E-06 #,Z,Y,X= 0 1 24 29 + 6.112500E+02 2.557250E+03 6.318500E+01 -1.385E-06 -2.966E-06 0.000E+00 M= 3.274E-06 #,Z,Y,X= 0 1 24 30 + 6.117500E+02 2.557250E+03 5.846000E+01 -1.688E-06 -2.110E-06 0.000E+00 M= 2.702E-06 #,Z,Y,X= 0 1 24 31 + 6.122500E+02 2.557250E+03 5.478000E+01 -1.845E-06 -2.037E-06 0.000E+00 M= 2.748E-06 #,Z,Y,X= 0 1 24 32 + 6.127500E+02 2.557250E+03 4.861500E+01 -2.683E-06 -3.342E-06 0.000E+00 M= 4.286E-06 #,Z,Y,X= 0 1 24 33 + 6.132500E+02 2.557250E+03 5.223000E+01 -3.526E-06 -1.522E-06 0.000E+00 M= 3.840E-06 #,Z,Y,X= 0 1 24 34 + 6.137500E+02 2.557250E+03 5.058000E+01 -4.211E-06 -2.131E-06 0.000E+00 M= 4.720E-06 #,Z,Y,X= 0 1 24 35 + 6.142500E+02 2.557250E+03 4.890500E+01 -5.350E-06 -1.647E-06 0.000E+00 M= 5.597E-06 #,Z,Y,X= 0 1 24 36 + 6.147500E+02 2.557250E+03 5.099500E+01 -5.365E-06 -1.897E-06 0.000E+00 M= 5.691E-06 #,Z,Y,X= 0 1 24 37 + 6.152500E+02 2.557250E+03 5.454500E+01 -3.698E-06 -1.996E-06 0.000E+00 M= 4.202E-06 #,Z,Y,X= 0 1 24 38 + 6.157500E+02 2.557250E+03 6.020500E+01 -1.908E-06 -1.283E-06 0.000E+00 M= 2.300E-06 #,Z,Y,X= 0 1 24 39 + 6.162500E+02 2.557250E+03 6.971000E+01 -1.116E-06 -7.528E-07 0.000E+00 M= 1.346E-06 #,Z,Y,X= 0 1 24 40 + 6.167500E+02 2.557250E+03 7.581500E+01 -4.789E-07 -4.567E-07 0.000E+00 M= 6.618E-07 #,Z,Y,X= 0 1 24 41 + 6.017500E+02 2.556750E+03 8.504000E+01 1.412E-08 -3.902E-08 0.000E+00 M= 4.150E-08 #,Z,Y,X= 0 1 25 11 + 6.022500E+02 2.556750E+03 8.319000E+01 2.972E-08 -4.526E-08 0.000E+00 M= 5.415E-08 #,Z,Y,X= 0 1 25 12 + 6.027500E+02 2.556750E+03 7.646000E+01 9.661E-08 -1.179E-07 0.000E+00 M= 1.524E-07 #,Z,Y,X= 0 1 25 13 + 6.032500E+02 2.556750E+03 7.566500E+01 2.665E-07 -4.671E-07 0.000E+00 M= 5.378E-07 #,Z,Y,X= 0 1 25 14 + 6.037500E+02 2.556750E+03 7.143500E+01 4.619E-07 -5.109E-07 0.000E+00 M= 6.887E-07 #,Z,Y,X= 0 1 25 15 + 6.042500E+02 2.556750E+03 6.847000E+01 5.747E-07 -6.127E-07 0.000E+00 M= 8.400E-07 #,Z,Y,X= 0 1 25 16 + 6.047500E+02 2.556750E+03 6.404500E+01 3.939E-07 -8.124E-07 0.000E+00 M= 9.029E-07 #,Z,Y,X= 0 1 25 17 + 6.052500E+02 2.556750E+03 7.384000E+01 2.531E-08 -7.335E-07 0.000E+00 M= 7.340E-07 #,Z,Y,X= 0 1 25 18 + 6.057500E+02 2.556750E+03 7.602000E+01 9.660E-09 -3.195E-07 0.000E+00 M= 3.196E-07 #,Z,Y,X= 0 1 25 19 + 6.062500E+02 2.556750E+03 6.402000E+01 2.179E-07 -2.219E-06 0.000E+00 M= 2.229E-06 #,Z,Y,X= 0 1 25 20 + 6.067500E+02 2.556750E+03 5.613500E+01 3.238E-07 -2.063E-06 0.000E+00 M= 2.088E-06 #,Z,Y,X= 0 1 25 21 + 6.072500E+02 2.556750E+03 4.745500E+01 2.715E-07 -2.038E-06 0.000E+00 M= 2.056E-06 #,Z,Y,X= 0 1 25 22 + 6.077500E+02 2.556750E+03 5.402500E+01 2.983E-07 -1.443E-06 0.000E+00 M= 1.473E-06 #,Z,Y,X= 0 1 25 23 + 6.082500E+02 2.556750E+03 6.126500E+01 1.860E-07 -9.510E-07 0.000E+00 M= 9.690E-07 #,Z,Y,X= 0 1 25 24 + 6.087500E+02 2.556750E+03 4.925000E+01 -1.049E-06 -1.994E-06 0.000E+00 M= 2.253E-06 #,Z,Y,X= 0 1 25 25 + 6.092500E+02 2.556750E+03 4.318500E+01 -2.556E-06 -5.403E-06 0.000E+00 M= 5.978E-06 #,Z,Y,X= 0 1 25 26 + 6.097500E+02 2.556750E+03 4.889000E+01 -2.861E-06 -4.922E-06 0.000E+00 M= 5.693E-06 #,Z,Y,X= 0 1 25 27 + 6.102500E+02 2.556750E+03 4.516500E+01 -2.888E-06 -9.644E-06 0.000E+00 M= 1.007E-05 #,Z,Y,X= 0 1 25 28 + 6.107500E+02 2.556750E+03 4.150500E+01 -7.075E-06 -5.767E-06 0.000E+00 M= 9.127E-06 #,Z,Y,X= 0 1 25 29 + 6.112500E+02 2.556750E+03 4.724500E+01 -1.077E-05 -3.529E-06 0.000E+00 M= 1.134E-05 #,Z,Y,X= 0 1 25 30 + 6.117500E+02 2.556750E+03 4.390500E+01 -1.058E-05 -2.143E-06 0.000E+00 M= 1.080E-05 #,Z,Y,X= 0 1 25 31 + 6.122500E+02 2.556750E+03 4.601500E+01 -6.206E-06 -1.406E-06 0.000E+00 M= 6.364E-06 #,Z,Y,X= 0 1 25 32 + 6.127500E+02 2.556750E+03 4.619000E+01 -5.009E-06 -2.036E-06 0.000E+00 M= 5.407E-06 #,Z,Y,X= 0 1 25 33 + 6.132500E+02 2.556750E+03 4.928000E+01 -7.233E-06 -9.142E-07 0.000E+00 M= 7.291E-06 #,Z,Y,X= 0 1 25 34 + 6.137500E+02 2.556750E+03 5.073000E+01 -5.585E-06 -1.718E-06 0.000E+00 M= 5.843E-06 #,Z,Y,X= 0 1 25 35 + 6.142500E+02 2.556750E+03 5.973000E+01 -3.513E-06 -6.916E-07 0.000E+00 M= 3.581E-06 #,Z,Y,X= 0 1 25 36 + 6.147500E+02 2.556750E+03 5.682000E+01 -2.055E-06 -9.692E-07 0.000E+00 M= 2.272E-06 #,Z,Y,X= 0 1 25 37 + 6.152500E+02 2.556750E+03 6.823000E+01 -1.107E-06 -6.913E-07 0.000E+00 M= 1.305E-06 #,Z,Y,X= 0 1 25 38 + 6.157500E+02 2.556750E+03 7.128000E+01 -7.616E-07 -6.362E-07 0.000E+00 M= 9.924E-07 #,Z,Y,X= 0 1 25 39 + 6.162500E+02 2.556750E+03 7.519500E+01 -3.506E-07 -3.350E-07 0.000E+00 M= 4.849E-07 #,Z,Y,X= 0 1 25 40 + 6.012500E+02 2.556250E+03 8.180500E+01 2.354E-08 -2.249E-08 0.000E+00 M= 3.256E-08 #,Z,Y,X= 0 1 26 10 + 6.017500E+02 2.556250E+03 7.806000E+01 4.119E-08 -1.051E-07 0.000E+00 M= 1.129E-07 #,Z,Y,X= 0 1 26 11 + 6.022500E+02 2.556250E+03 7.751000E+01 3.595E-08 -1.119E-07 0.000E+00 M= 1.175E-07 #,Z,Y,X= 0 1 26 12 + 6.027500E+02 2.556250E+03 7.816500E+01 6.799E-08 -7.516E-08 0.000E+00 M= 1.013E-07 #,Z,Y,X= 0 1 26 13 + 6.032500E+02 2.556250E+03 7.181000E+01 2.658E-07 -2.209E-07 0.000E+00 M= 3.456E-07 #,Z,Y,X= 0 1 26 14 + 6.037500E+02 2.556250E+03 6.551000E+01 6.168E-07 -2.841E-07 0.000E+00 M= 6.791E-07 #,Z,Y,X= 0 1 26 15 + 6.042500E+02 2.556250E+03 6.121000E+01 8.706E-07 -6.101E-07 0.000E+00 M= 1.063E-06 #,Z,Y,X= 0 1 26 16 + 6.047500E+02 2.556250E+03 5.913000E+01 9.105E-07 -1.045E-06 0.000E+00 M= 1.386E-06 #,Z,Y,X= 0 1 26 17 + 6.052500E+02 2.556250E+03 5.790500E+01 4.928E-07 -1.326E-06 0.000E+00 M= 1.415E-06 #,Z,Y,X= 0 1 26 18 + 6.057500E+02 2.556250E+03 7.055000E+01 1.199E-07 -2.416E-07 0.000E+00 M= 2.697E-07 #,Z,Y,X= 0 1 26 19 + 6.062500E+02 2.556250E+03 5.304000E+01 2.806E-07 -2.077E-06 0.000E+00 M= 2.096E-06 #,Z,Y,X= 0 1 26 20 + 6.067500E+02 2.556250E+03 4.623000E+01 -8.603E-08 -2.576E-06 0.000E+00 M= 2.578E-06 #,Z,Y,X= 0 1 26 21 + 6.072500E+02 2.556250E+03 5.366000E+01 -1.726E-07 -1.804E-06 0.000E+00 M= 1.812E-06 #,Z,Y,X= 0 1 26 22 + 6.077500E+02 2.556250E+03 5.373500E+01 2.179E-07 -1.381E-06 0.000E+00 M= 1.398E-06 #,Z,Y,X= 0 1 26 23 + 6.082500E+02 2.556250E+03 4.727500E+01 -8.484E-08 -1.620E-06 0.000E+00 M= 1.622E-06 #,Z,Y,X= 0 1 26 24 + 6.087500E+02 2.556250E+03 4.059000E+01 -9.833E-07 -3.685E-06 0.000E+00 M= 3.814E-06 #,Z,Y,X= 0 1 26 25 + 6.092500E+02 2.556250E+03 4.284500E+01 -1.616E-06 -6.208E-06 0.000E+00 M= 6.415E-06 #,Z,Y,X= 0 1 26 26 + 6.097500E+02 2.556250E+03 3.950000E+01 -3.523E-06 -4.966E-06 0.000E+00 M= 6.088E-06 #,Z,Y,X= 0 1 26 27 + 6.102500E+02 2.556250E+03 4.000000E+01 -9.090E-06 -6.971E-06 0.000E+00 M= 1.146E-05 #,Z,Y,X= 0 1 26 28 + 6.107500E+02 2.556250E+03 4.259000E+01 -9.826E-06 -3.852E-06 0.000E+00 M= 1.055E-05 #,Z,Y,X= 0 1 26 29 + 6.112500E+02 2.556250E+03 4.350000E+01 -8.934E-06 -2.876E-06 0.000E+00 M= 9.385E-06 #,Z,Y,X= 0 1 26 30 + 6.117500E+02 2.556250E+03 4.600000E+01 -8.581E-06 -1.390E-06 0.000E+00 M= 8.693E-06 #,Z,Y,X= 0 1 26 31 + 6.122500E+02 2.556250E+03 6.038500E+01 -4.928E-06 -8.741E-07 0.000E+00 M= 5.005E-06 #,Z,Y,X= 0 1 26 32 + 6.127500E+02 2.556250E+03 7.292000E+01 -2.369E-06 3.550E-07 0.000E+00 M= 2.396E-06 #,Z,Y,X= 0 1 26 33 + 6.132500E+02 2.556250E+03 7.002000E+01 -2.602E-06 -9.386E-08 0.000E+00 M= 2.603E-06 #,Z,Y,X= 0 1 26 34 + 6.137500E+02 2.556250E+03 6.492500E+01 -2.007E-06 -4.669E-07 0.000E+00 M= 2.061E-06 #,Z,Y,X= 0 1 26 35 + 6.142500E+02 2.556250E+03 6.795500E+01 -1.545E-06 -7.692E-08 0.000E+00 M= 1.547E-06 #,Z,Y,X= 0 1 26 36 + 6.147500E+02 2.556250E+03 6.356000E+01 -1.157E-06 -2.294E-07 0.000E+00 M= 1.179E-06 #,Z,Y,X= 0 1 26 37 + 6.152500E+02 2.556250E+03 6.548500E+01 -7.903E-07 -2.737E-07 0.000E+00 M= 8.364E-07 #,Z,Y,X= 0 1 26 38 + 6.157500E+02 2.556250E+03 7.132000E+01 -3.093E-07 -2.924E-07 0.000E+00 M= 4.257E-07 #,Z,Y,X= 0 1 26 39 + 6.002500E+02 2.555750E+03 8.489500E+01 1.902E-08 -3.074E-08 0.000E+00 M= 3.614E-08 #,Z,Y,X= 0 1 27 8 + 6.007500E+02 2.555750E+03 8.165500E+01 4.634E-08 -3.184E-08 0.000E+00 M= 5.623E-08 #,Z,Y,X= 0 1 27 9 + 6.012500E+02 2.555750E+03 7.972000E+01 1.049E-07 -3.367E-08 0.000E+00 M= 1.102E-07 #,Z,Y,X= 0 1 27 10 + 6.017500E+02 2.555750E+03 7.118500E+01 2.329E-07 -8.065E-08 0.000E+00 M= 2.465E-07 #,Z,Y,X= 0 1 27 11 + 6.022500E+02 2.555750E+03 6.914500E+01 1.790E-07 -3.006E-07 0.000E+00 M= 3.498E-07 #,Z,Y,X= 0 1 27 12 + 6.027500E+02 2.555750E+03 7.452000E+01 4.096E-08 -1.113E-07 0.000E+00 M= 1.186E-07 #,Z,Y,X= 0 1 27 13 + 6.032500E+02 2.555750E+03 7.786500E+01 5.552E-08 -9.814E-08 0.000E+00 M= 1.128E-07 #,Z,Y,X= 0 1 27 14 + 6.037500E+02 2.555750E+03 7.562500E+01 8.473E-08 -1.790E-07 0.000E+00 M= 1.980E-07 #,Z,Y,X= 0 1 27 15 + 6.042500E+02 2.555750E+03 6.688500E+01 3.361E-07 -3.824E-07 0.000E+00 M= 5.091E-07 #,Z,Y,X= 0 1 27 16 + 6.047500E+02 2.555750E+03 5.942500E+01 1.064E-06 -6.599E-07 0.000E+00 M= 1.252E-06 #,Z,Y,X= 0 1 27 17 + 6.052500E+02 2.555750E+03 5.524500E+01 1.674E-06 -1.589E-06 0.000E+00 M= 2.308E-06 #,Z,Y,X= 0 1 27 18 + 6.057500E+02 2.555750E+03 5.823000E+01 1.085E-06 -9.287E-07 0.000E+00 M= 1.428E-06 #,Z,Y,X= 0 1 27 19 + 6.062500E+02 2.555750E+03 4.965500E+01 6.161E-07 -1.756E-06 0.000E+00 M= 1.861E-06 #,Z,Y,X= 0 1 27 20 + 6.067500E+02 2.555750E+03 4.285000E+01 3.057E-07 -3.521E-06 0.000E+00 M= 3.535E-06 #,Z,Y,X= 0 1 27 21 + 6.072500E+02 2.555750E+03 5.836000E+01 3.595E-08 -1.322E-06 0.000E+00 M= 1.322E-06 #,Z,Y,X= 0 1 27 22 + 6.077500E+02 2.555750E+03 5.062000E+01 6.486E-08 -1.550E-06 0.000E+00 M= 1.551E-06 #,Z,Y,X= 0 1 27 23 + 6.082500E+02 2.555750E+03 4.672500E+01 -3.228E-07 -2.260E-06 0.000E+00 M= 2.283E-06 #,Z,Y,X= 0 1 27 24 + 6.087500E+02 2.555750E+03 3.957000E+01 -6.833E-07 -4.405E-06 0.000E+00 M= 4.458E-06 #,Z,Y,X= 0 1 27 25 + 6.092500E+02 2.555750E+03 3.542000E+01 -3.356E-06 -4.269E-06 0.000E+00 M= 5.430E-06 #,Z,Y,X= 0 1 27 26 + 6.097500E+02 2.555750E+03 3.850000E+01 -5.564E-06 -2.941E-06 0.000E+00 M= 6.293E-06 #,Z,Y,X= 0 1 27 27 + 6.102500E+02 2.555750E+03 5.005500E+01 -3.569E-06 -1.575E-06 0.000E+00 M= 3.901E-06 #,Z,Y,X= 0 1 27 28 + 6.107500E+02 2.555750E+03 5.966500E+01 -2.285E-06 -2.338E-06 0.000E+00 M= 3.269E-06 #,Z,Y,X= 0 1 27 29 + 6.112500E+02 2.555750E+03 6.673000E+01 -1.553E-06 -1.244E-06 0.000E+00 M= 1.990E-06 #,Z,Y,X= 0 1 27 30 + 6.117500E+02 2.555750E+03 6.508000E+01 -1.953E-06 -8.937E-07 0.000E+00 M= 2.148E-06 #,Z,Y,X= 0 1 27 31 + 6.122500E+02 2.555750E+03 6.286000E+01 -2.544E-06 -1.279E-06 0.000E+00 M= 2.848E-06 #,Z,Y,X= 0 1 27 32 + 6.127500E+02 2.555750E+03 7.880500E+01 -3.187E-06 4.702E-08 0.000E+00 M= 3.188E-06 #,Z,Y,X= 0 1 27 33 + 6.132500E+02 2.555750E+03 7.934500E+01 -2.112E-06 1.291E-08 0.000E+00 M= 2.112E-06 #,Z,Y,X= 0 1 27 34 + 6.137500E+02 2.555750E+03 7.773000E+01 -8.507E-07 -2.161E-07 0.000E+00 M= 8.778E-07 #,Z,Y,X= 0 1 27 35 + 6.142500E+02 2.555750E+03 7.648000E+01 -4.312E-07 -4.610E-08 0.000E+00 M= 4.337E-07 #,Z,Y,X= 0 1 27 36 + 6.147500E+02 2.555750E+03 7.117500E+01 -2.306E-07 -4.094E-08 0.000E+00 M= 2.342E-07 #,Z,Y,X= 0 1 27 37 + 6.152500E+02 2.555750E+03 7.020500E+01 -8.074E-08 -6.476E-08 0.000E+00 M= 1.035E-07 #,Z,Y,X= 0 1 27 38 + 5.992500E+02 2.555250E+03 8.781000E+01 6.540E-08 4.251E-10 0.000E+00 M= 6.540E-08 #,Z,Y,X= 0 1 28 6 + 5.997500E+02 2.555250E+03 8.410500E+01 1.784E-07 -9.321E-09 0.000E+00 M= 1.786E-07 #,Z,Y,X= 0 1 28 7 + 6.002500E+02 2.555250E+03 7.903000E+01 2.570E-07 -4.870E-08 0.000E+00 M= 2.615E-07 #,Z,Y,X= 0 1 28 8 + 6.007500E+02 2.555250E+03 7.580000E+01 2.779E-07 -6.349E-08 0.000E+00 M= 2.851E-07 #,Z,Y,X= 0 1 28 9 + 6.012500E+02 2.555250E+03 7.124000E+01 2.199E-07 -1.267E-07 0.000E+00 M= 2.538E-07 #,Z,Y,X= 0 1 28 10 + 6.017500E+02 2.555250E+03 7.437500E+01 1.506E-07 -4.761E-08 0.000E+00 M= 1.579E-07 #,Z,Y,X= 0 1 28 11 + 6.022500E+02 2.555250E+03 6.503500E+01 2.889E-07 -3.324E-07 0.000E+00 M= 4.404E-07 #,Z,Y,X= 0 1 28 12 + 6.027500E+02 2.555250E+03 6.664500E+01 2.561E-07 -3.276E-07 0.000E+00 M= 4.158E-07 #,Z,Y,X= 0 1 28 13 + 6.032500E+02 2.555250E+03 7.186000E+01 1.334E-07 -1.121E-07 0.000E+00 M= 1.743E-07 #,Z,Y,X= 0 1 28 14 + 6.037500E+02 2.555250E+03 6.966000E+01 1.011E-07 -3.096E-07 0.000E+00 M= 3.257E-07 #,Z,Y,X= 0 1 28 15 + 6.042500E+02 2.555250E+03 7.230500E+01 5.102E-08 -1.801E-07 0.000E+00 M= 1.872E-07 #,Z,Y,X= 0 1 28 16 + 6.047500E+02 2.555250E+03 6.963000E+01 1.194E-07 -2.163E-07 0.000E+00 M= 2.471E-07 #,Z,Y,X= 0 1 28 17 + 6.052500E+02 2.555250E+03 6.127500E+01 4.133E-07 -1.150E-06 0.000E+00 M= 1.222E-06 #,Z,Y,X= 0 1 28 18 + 6.057500E+02 2.555250E+03 4.562500E+01 5.404E-07 -1.539E-06 0.000E+00 M= 1.631E-06 #,Z,Y,X= 0 1 28 19 + 6.062500E+02 2.555250E+03 4.355500E+01 4.270E-07 -1.536E-06 0.000E+00 M= 1.594E-06 #,Z,Y,X= 0 1 28 20 + 6.067500E+02 2.555250E+03 3.856000E+01 5.989E-07 -3.576E-06 0.000E+00 M= 3.626E-06 #,Z,Y,X= 0 1 28 21 + 6.072500E+02 2.555250E+03 4.732000E+01 4.447E-07 -1.557E-06 0.000E+00 M= 1.619E-06 #,Z,Y,X= 0 1 28 22 + 6.077500E+02 2.555250E+03 4.578000E+01 8.585E-08 -1.675E-06 0.000E+00 M= 1.677E-06 #,Z,Y,X= 0 1 28 23 + 6.082500E+02 2.555250E+03 4.034000E+01 -9.026E-07 -3.519E-06 0.000E+00 M= 3.633E-06 #,Z,Y,X= 0 1 28 24 + 6.087500E+02 2.555250E+03 3.420000E+01 -3.194E-06 -5.500E-06 0.000E+00 M= 6.360E-06 #,Z,Y,X= 0 1 28 25 + 6.092500E+02 2.555250E+03 3.773500E+01 -3.370E-06 -2.422E-06 0.000E+00 M= 4.150E-06 #,Z,Y,X= 0 1 28 26 + 6.097500E+02 2.555250E+03 5.560500E+01 -2.194E-06 -1.078E-06 0.000E+00 M= 2.445E-06 #,Z,Y,X= 0 1 28 27 + 6.102500E+02 2.555250E+03 5.462500E+01 -1.971E-06 -7.925E-07 0.000E+00 M= 2.124E-06 #,Z,Y,X= 0 1 28 28 + 6.107500E+02 2.555250E+03 5.057000E+01 -1.560E-06 -1.408E-06 0.000E+00 M= 2.102E-06 #,Z,Y,X= 0 1 28 29 + 6.112500E+02 2.555250E+03 6.572500E+01 -1.117E-06 -1.052E-06 0.000E+00 M= 1.535E-06 #,Z,Y,X= 0 1 28 30 + 6.117500E+02 2.555250E+03 7.646000E+01 -1.482E-06 -1.517E-06 0.000E+00 M= 2.121E-06 #,Z,Y,X= 0 1 28 31 + 6.122500E+02 2.555250E+03 8.386000E+01 -1.858E-06 -1.957E-06 0.000E+00 M= 2.698E-06 #,Z,Y,X= 0 1 28 32 + 6.127500E+02 2.555250E+03 9.062000E+01 -1.987E-06 2.581E-07 0.000E+00 M= 2.003E-06 #,Z,Y,X= 0 1 28 33 + 6.132500E+02 2.555250E+03 9.058500E+01 -8.588E-07 3.408E-07 0.000E+00 M= 9.239E-07 #,Z,Y,X= 0 1 28 34 + 6.137500E+02 2.555250E+03 8.209000E+01 -1.319E-07 -3.934E-08 0.000E+00 M= 1.376E-07 #,Z,Y,X= 0 1 28 35 + 5.992500E+02 2.554750E+03 8.813500E+01 7.444E-08 4.906E-10 0.000E+00 M= 7.444E-08 #,Z,Y,X= 0 1 29 6 + 5.997500E+02 2.554750E+03 8.369500E+01 1.839E-07 -8.778E-09 0.000E+00 M= 1.841E-07 #,Z,Y,X= 0 1 29 7 + 6.002500E+02 2.554750E+03 7.717500E+01 2.800E-07 5.452E-09 0.000E+00 M= 2.800E-07 #,Z,Y,X= 0 1 29 8 + 6.007500E+02 2.554750E+03 7.062500E+01 3.515E-07 -5.427E-09 0.000E+00 M= 3.515E-07 #,Z,Y,X= 0 1 29 9 + 6.012500E+02 2.554750E+03 6.664000E+01 5.687E-07 -1.137E-07 0.000E+00 M= 5.800E-07 #,Z,Y,X= 0 1 29 10 + 6.017500E+02 2.554750E+03 6.224000E+01 6.432E-07 -1.407E-07 0.000E+00 M= 6.584E-07 #,Z,Y,X= 0 1 29 11 + 6.022500E+02 2.554750E+03 6.111000E+01 6.832E-07 -1.741E-07 0.000E+00 M= 7.050E-07 #,Z,Y,X= 0 1 29 12 + 6.027500E+02 2.554750E+03 5.627500E+01 8.405E-07 -4.484E-07 0.000E+00 M= 9.526E-07 #,Z,Y,X= 0 1 29 13 + 6.032500E+02 2.554750E+03 6.335500E+01 7.759E-07 -3.160E-07 0.000E+00 M= 8.378E-07 #,Z,Y,X= 0 1 29 14 + 6.037500E+02 2.554750E+03 6.132500E+01 6.054E-07 -4.515E-07 0.000E+00 M= 7.552E-07 #,Z,Y,X= 0 1 29 15 + 6.042500E+02 2.554750E+03 6.690500E+01 5.705E-07 -2.609E-07 0.000E+00 M= 6.274E-07 #,Z,Y,X= 0 1 29 16 + 6.047500E+02 2.554750E+03 6.152000E+01 6.339E-07 -1.275E-07 0.000E+00 M= 6.466E-07 #,Z,Y,X= 0 1 29 17 + 6.052500E+02 2.554750E+03 5.217500E+01 7.860E-07 -8.403E-07 0.000E+00 M= 1.151E-06 #,Z,Y,X= 0 1 29 18 + 6.057500E+02 2.554750E+03 4.207000E+01 1.018E-06 -1.349E-06 0.000E+00 M= 1.690E-06 #,Z,Y,X= 0 1 29 19 + 6.062500E+02 2.554750E+03 3.710500E+01 1.171E-06 -1.581E-06 0.000E+00 M= 1.967E-06 #,Z,Y,X= 0 1 29 20 + 6.067500E+02 2.554750E+03 3.635500E+01 1.433E-06 -3.025E-06 0.000E+00 M= 3.347E-06 #,Z,Y,X= 0 1 29 21 + 6.072500E+02 2.554750E+03 3.874500E+01 1.300E-06 -2.433E-06 0.000E+00 M= 2.758E-06 #,Z,Y,X= 0 1 29 22 + 6.077500E+02 2.554750E+03 4.227000E+01 9.339E-07 -1.619E-06 0.000E+00 M= 1.869E-06 #,Z,Y,X= 0 1 29 23 + 6.082500E+02 2.554750E+03 3.286000E+01 -1.411E-06 -4.484E-06 0.000E+00 M= 4.701E-06 #,Z,Y,X= 0 1 29 24 + 6.087500E+02 2.554750E+03 3.428000E+01 -2.845E-06 -4.983E-06 0.000E+00 M= 5.738E-06 #,Z,Y,X= 0 1 29 25 + 6.092500E+02 2.554750E+03 4.138500E+01 -1.389E-06 -2.370E-06 0.000E+00 M= 2.747E-06 #,Z,Y,X= 0 1 29 26 + 6.097500E+02 2.554750E+03 6.337500E+01 -8.354E-07 -9.174E-07 0.000E+00 M= 1.241E-06 #,Z,Y,X= 0 1 29 27 + 6.102500E+02 2.554750E+03 6.544500E+01 -6.620E-07 -7.076E-07 0.000E+00 M= 9.690E-07 #,Z,Y,X= 0 1 29 28 + 6.107500E+02 2.554750E+03 6.260500E+01 -5.510E-07 -9.443E-07 0.000E+00 M= 1.093E-06 #,Z,Y,X= 0 1 29 29 + 6.112500E+02 2.554750E+03 6.324000E+01 -5.340E-07 -9.726E-07 0.000E+00 M= 1.110E-06 #,Z,Y,X= 0 1 29 30 + 6.117500E+02 2.554750E+03 7.501000E+01 -1.032E-06 -1.554E-06 0.000E+00 M= 1.866E-06 #,Z,Y,X= 0 1 29 31 + 6.122500E+02 2.554750E+03 8.539000E+01 -1.247E-06 -8.524E-07 0.000E+00 M= 1.511E-06 #,Z,Y,X= 0 1 29 32 + 6.002500E+02 2.554250E+03 8.001500E+01 2.042E-08 1.620E-08 0.000E+00 M= 2.606E-08 #,Z,Y,X= 0 1 30 8 + 6.007500E+02 2.554250E+03 7.567000E+01 5.762E-08 4.105E-08 0.000E+00 M= 7.075E-08 #,Z,Y,X= 0 1 30 9 + 6.012500E+02 2.554250E+03 7.296500E+01 1.422E-07 1.071E-08 0.000E+00 M= 1.426E-07 #,Z,Y,X= 0 1 30 10 + 6.017500E+02 2.554250E+03 6.599500E+01 3.588E-07 -7.908E-08 0.000E+00 M= 3.674E-07 #,Z,Y,X= 0 1 30 11 + 6.022500E+02 2.554250E+03 6.461500E+01 5.906E-07 -7.085E-08 0.000E+00 M= 5.948E-07 #,Z,Y,X= 0 1 30 12 + 6.027500E+02 2.554250E+03 6.243000E+01 8.573E-07 -2.226E-07 0.000E+00 M= 8.857E-07 #,Z,Y,X= 0 1 30 13 + 6.032500E+02 2.554250E+03 5.288000E+01 1.291E-06 -3.111E-07 0.000E+00 M= 1.328E-06 #,Z,Y,X= 0 1 30 14 + 6.037500E+02 2.554250E+03 4.988000E+01 1.349E-06 -6.973E-07 0.000E+00 M= 1.518E-06 #,Z,Y,X= 0 1 30 15 + 6.042500E+02 2.554250E+03 5.163500E+01 6.773E-07 -8.481E-07 0.000E+00 M= 1.085E-06 #,Z,Y,X= 0 1 30 16 + 6.047500E+02 2.554250E+03 6.637000E+01 1.554E-07 -9.391E-08 0.000E+00 M= 1.816E-07 #,Z,Y,X= 0 1 30 17 + 6.052500E+02 2.554250E+03 5.598000E+01 3.013E-07 -5.824E-07 0.000E+00 M= 6.557E-07 #,Z,Y,X= 0 1 30 18 + 6.057500E+02 2.554250E+03 5.107000E+01 5.709E-07 -9.391E-07 0.000E+00 M= 1.099E-06 #,Z,Y,X= 0 1 30 19 + 6.062500E+02 2.554250E+03 4.485000E+01 7.275E-07 -1.532E-06 0.000E+00 M= 1.696E-06 #,Z,Y,X= 0 1 30 20 + 6.067500E+02 2.554250E+03 3.507000E+01 1.066E-06 -2.383E-06 0.000E+00 M= 2.610E-06 #,Z,Y,X= 0 1 30 21 + 6.072500E+02 2.554250E+03 3.563500E+01 1.275E-06 -3.050E-06 0.000E+00 M= 3.306E-06 #,Z,Y,X= 0 1 30 22 + 6.077500E+02 2.554250E+03 3.618000E+01 1.277E-06 -1.457E-06 0.000E+00 M= 1.937E-06 #,Z,Y,X= 0 1 30 23 + 6.082500E+02 2.554250E+03 3.200000E+01 -3.913E-07 -3.067E-06 0.000E+00 M= 3.091E-06 #,Z,Y,X= 0 1 30 24 + 6.087500E+02 2.554250E+03 3.972000E+01 -1.560E-06 -2.780E-06 0.000E+00 M= 3.188E-06 #,Z,Y,X= 0 1 30 25 + 6.092500E+02 2.554250E+03 4.742500E+01 -8.977E-07 -1.855E-06 0.000E+00 M= 2.061E-06 #,Z,Y,X= 0 1 30 26 + 6.097500E+02 2.554250E+03 6.512500E+01 -7.151E-07 -7.510E-07 0.000E+00 M= 1.037E-06 #,Z,Y,X= 0 1 30 27 + 6.102500E+02 2.554250E+03 7.025000E+01 -5.296E-07 -6.700E-07 0.000E+00 M= 8.540E-07 #,Z,Y,X= 0 1 30 28 + 6.107500E+02 2.554250E+03 6.477000E+01 -3.707E-07 -8.199E-07 0.000E+00 M= 8.999E-07 #,Z,Y,X= 0 1 30 29 + 6.112500E+02 2.554250E+03 7.009000E+01 -2.182E-07 -8.923E-07 0.000E+00 M= 9.186E-07 #,Z,Y,X= 0 1 30 30 + 6.117500E+02 2.554250E+03 7.665500E+01 -1.009E-07 -1.087E-06 0.000E+00 M= 1.092E-06 #,Z,Y,X= 0 1 30 31 + 6.007500E+02 2.553750E+03 7.442000E+01 4.444E-08 1.862E-08 0.000E+00 M= 4.818E-08 #,Z,Y,X= 0 1 31 9 + 6.012500E+02 2.553750E+03 7.174000E+01 1.044E-07 1.342E-08 0.000E+00 M= 1.053E-07 #,Z,Y,X= 0 1 31 10 + 6.017500E+02 2.553750E+03 7.210500E+01 1.093E-07 1.767E-09 0.000E+00 M= 1.094E-07 #,Z,Y,X= 0 1 31 11 + 6.022500E+02 2.553750E+03 7.188000E+01 1.002E-07 -5.671E-08 0.000E+00 M= 1.151E-07 #,Z,Y,X= 0 1 31 12 + 6.027500E+02 2.553750E+03 6.913000E+01 1.040E-07 -7.969E-08 0.000E+00 M= 1.310E-07 #,Z,Y,X= 0 1 31 13 + 6.032500E+02 2.553750E+03 6.698000E+01 1.494E-07 -1.073E-07 0.000E+00 M= 1.839E-07 #,Z,Y,X= 0 1 31 14 + 6.037500E+02 2.553750E+03 5.968500E+01 4.202E-07 -6.916E-07 0.000E+00 M= 8.092E-07 #,Z,Y,X= 0 1 31 15 + 6.042500E+02 2.553750E+03 4.820000E+01 9.232E-07 -1.105E-06 0.000E+00 M= 1.440E-06 #,Z,Y,X= 0 1 31 16 + 6.047500E+02 2.553750E+03 5.068500E+01 8.640E-07 -4.403E-07 0.000E+00 M= 9.697E-07 #,Z,Y,X= 0 1 31 17 + 6.052500E+02 2.553750E+03 5.479500E+01 4.966E-07 -5.128E-07 0.000E+00 M= 7.139E-07 #,Z,Y,X= 0 1 31 18 + 6.057500E+02 2.553750E+03 5.362500E+01 4.619E-07 -7.641E-07 0.000E+00 M= 8.929E-07 #,Z,Y,X= 0 1 31 19 + 6.062500E+02 2.553750E+03 4.611500E+01 5.194E-07 -1.513E-06 0.000E+00 M= 1.600E-06 #,Z,Y,X= 0 1 31 20 + 6.067500E+02 2.553750E+03 3.758500E+01 6.495E-07 -2.016E-06 0.000E+00 M= 2.118E-06 #,Z,Y,X= 0 1 31 21 + 6.072500E+02 2.553750E+03 3.520500E+01 9.996E-07 -3.009E-06 0.000E+00 M= 3.171E-06 #,Z,Y,X= 0 1 31 22 + 6.077500E+02 2.553750E+03 3.300000E+01 8.151E-07 -2.913E-06 0.000E+00 M= 3.025E-06 #,Z,Y,X= 0 1 31 23 + 6.082500E+02 2.553750E+03 3.027000E+01 2.280E-08 -2.699E-06 0.000E+00 M= 2.699E-06 #,Z,Y,X= 0 1 31 24 + 6.087500E+02 2.553750E+03 3.833000E+01 -6.390E-07 -2.672E-06 0.000E+00 M= 2.748E-06 #,Z,Y,X= 0 1 31 25 + 6.092500E+02 2.553750E+03 4.866500E+01 -8.069E-07 -1.533E-06 0.000E+00 M= 1.732E-06 #,Z,Y,X= 0 1 31 26 + 6.097500E+02 2.553750E+03 6.500500E+01 -4.739E-07 -6.321E-07 0.000E+00 M= 7.900E-07 #,Z,Y,X= 0 1 31 27 + 6.102500E+02 2.553750E+03 7.072000E+01 -3.809E-07 -6.664E-07 0.000E+00 M= 7.676E-07 #,Z,Y,X= 0 1 31 28 + 6.107500E+02 2.553750E+03 6.947500E+01 -2.807E-07 -7.508E-07 0.000E+00 M= 8.015E-07 #,Z,Y,X= 0 1 31 29 + 6.112500E+02 2.553750E+03 6.847500E+01 -1.480E-07 -7.942E-07 0.000E+00 M= 8.078E-07 #,Z,Y,X= 0 1 31 30 + 6.117500E+02 2.553750E+03 6.996000E+01 -4.493E-08 -8.203E-07 0.000E+00 M= 8.216E-07 #,Z,Y,X= 0 1 31 31 + 6.012500E+02 2.553250E+03 7.733500E+01 7.223E-08 -2.641E-09 0.000E+00 M= 7.228E-08 #,Z,Y,X= 0 1 32 10 + 6.017500E+02 2.553250E+03 7.323000E+01 1.503E-07 -6.943E-08 0.000E+00 M= 1.656E-07 #,Z,Y,X= 0 1 32 11 + 6.022500E+02 2.553250E+03 6.975000E+01 1.339E-07 -1.381E-07 0.000E+00 M= 1.924E-07 #,Z,Y,X= 0 1 32 12 + 6.027500E+02 2.553250E+03 6.772000E+01 1.080E-07 -1.387E-07 0.000E+00 M= 1.758E-07 #,Z,Y,X= 0 1 32 13 + 6.032500E+02 2.553250E+03 6.397500E+01 1.094E-07 -1.192E-07 0.000E+00 M= 1.617E-07 #,Z,Y,X= 0 1 32 14 + 6.037500E+02 2.553250E+03 5.830500E+01 2.813E-07 -3.081E-07 0.000E+00 M= 4.172E-07 #,Z,Y,X= 0 1 32 15 + 6.042500E+02 2.553250E+03 4.960000E+01 7.776E-07 -4.826E-07 0.000E+00 M= 9.152E-07 #,Z,Y,X= 0 1 32 16 + 6.047500E+02 2.553250E+03 3.897500E+01 1.127E-06 -6.488E-07 0.000E+00 M= 1.301E-06 #,Z,Y,X= 0 1 32 17 + 6.052500E+02 2.553250E+03 3.975500E+01 1.220E-06 -5.302E-07 0.000E+00 M= 1.330E-06 #,Z,Y,X= 0 1 32 18 + 6.057500E+02 2.553250E+03 3.451000E+01 1.253E-06 -7.194E-07 0.000E+00 M= 1.445E-06 #,Z,Y,X= 0 1 32 19 + 6.062500E+02 2.553250E+03 3.432000E+01 1.092E-06 -1.706E-06 0.000E+00 M= 2.026E-06 #,Z,Y,X= 0 1 32 20 + 6.067500E+02 2.553250E+03 3.938500E+01 6.339E-07 -2.232E-06 0.000E+00 M= 2.320E-06 #,Z,Y,X= 0 1 32 21 + 6.072500E+02 2.553250E+03 3.893000E+01 4.943E-07 -2.645E-06 0.000E+00 M= 2.691E-06 #,Z,Y,X= 0 1 32 22 + 6.077500E+02 2.553250E+03 2.907500E+01 -2.054E-07 -5.218E-06 0.000E+00 M= 5.222E-06 #,Z,Y,X= 0 1 32 23 + 6.082500E+02 2.553250E+03 2.965000E+01 -9.263E-07 -3.941E-06 0.000E+00 M= 4.048E-06 #,Z,Y,X= 0 1 32 24 + 6.087500E+02 2.553250E+03 3.535500E+01 -7.663E-07 -3.055E-06 0.000E+00 M= 3.150E-06 #,Z,Y,X= 0 1 32 25 + 6.092500E+02 2.553250E+03 4.377500E+01 -6.200E-07 -1.148E-06 0.000E+00 M= 1.305E-06 #,Z,Y,X= 0 1 32 26 + 6.097500E+02 2.553250E+03 6.727500E+01 -6.060E-07 -6.954E-07 0.000E+00 M= 9.224E-07 #,Z,Y,X= 0 1 32 27 + 6.102500E+02 2.553250E+03 6.505500E+01 -6.431E-07 -6.674E-07 0.000E+00 M= 9.268E-07 #,Z,Y,X= 0 1 32 28 + 6.107500E+02 2.553250E+03 6.420500E+01 -4.912E-07 -6.749E-07 0.000E+00 M= 8.347E-07 #,Z,Y,X= 0 1 32 29 + 6.112500E+02 2.553250E+03 6.281500E+01 -2.770E-07 -6.540E-07 0.000E+00 M= 7.102E-07 #,Z,Y,X= 0 1 32 30 + 6.117500E+02 2.553250E+03 6.290500E+01 -8.388E-08 -5.826E-07 0.000E+00 M= 5.886E-07 #,Z,Y,X= 0 1 32 31 + 6.017500E+02 2.552750E+03 7.289500E+01 1.295E-07 -5.153E-08 0.000E+00 M= 1.393E-07 #,Z,Y,X= 0 1 33 11 + 6.022500E+02 2.552750E+03 6.414000E+01 2.673E-07 -2.220E-07 0.000E+00 M= 3.474E-07 #,Z,Y,X= 0 1 33 12 + 6.027500E+02 2.552750E+03 6.435000E+01 2.293E-07 -2.312E-07 0.000E+00 M= 3.256E-07 #,Z,Y,X= 0 1 33 13 + 6.032500E+02 2.552750E+03 5.994500E+01 1.352E-07 -2.301E-07 0.000E+00 M= 2.669E-07 #,Z,Y,X= 0 1 33 14 + 6.037500E+02 2.552750E+03 6.315000E+01 5.092E-08 -2.022E-07 0.000E+00 M= 2.085E-07 #,Z,Y,X= 0 1 33 15 + 6.042500E+02 2.552750E+03 5.841000E+01 4.068E-08 -1.856E-07 0.000E+00 M= 1.900E-07 #,Z,Y,X= 0 1 33 16 + 6.047500E+02 2.552750E+03 4.746500E+01 2.300E-07 -4.270E-07 0.000E+00 M= 4.850E-07 #,Z,Y,X= 0 1 33 17 + 6.052500E+02 2.552750E+03 4.644000E+01 5.315E-07 -3.625E-07 0.000E+00 M= 6.433E-07 #,Z,Y,X= 0 1 33 18 + 6.057500E+02 2.552750E+03 4.249000E+01 9.099E-07 -5.000E-07 0.000E+00 M= 1.038E-06 #,Z,Y,X= 0 1 33 19 + 6.062500E+02 2.552750E+03 3.741000E+01 1.173E-06 -1.993E-06 0.000E+00 M= 2.313E-06 #,Z,Y,X= 0 1 33 20 + 6.067500E+02 2.552750E+03 3.196500E+01 9.295E-07 -2.837E-06 0.000E+00 M= 2.986E-06 #,Z,Y,X= 0 1 33 21 + 6.072500E+02 2.552750E+03 3.451000E+01 3.390E-07 -2.929E-06 0.000E+00 M= 2.948E-06 #,Z,Y,X= 0 1 33 22 + 6.077500E+02 2.552750E+03 2.864000E+01 -1.200E-06 -6.480E-06 0.000E+00 M= 6.590E-06 #,Z,Y,X= 0 1 33 23 + 6.082500E+02 2.552750E+03 2.928000E+01 -1.609E-06 -3.079E-06 0.000E+00 M= 3.474E-06 #,Z,Y,X= 0 1 33 24 + 6.087500E+02 2.552750E+03 3.648000E+01 -7.703E-07 -2.923E-06 0.000E+00 M= 3.023E-06 #,Z,Y,X= 0 1 33 25 + 6.092500E+02 2.552750E+03 4.443000E+01 -6.592E-07 -1.028E-06 0.000E+00 M= 1.221E-06 #,Z,Y,X= 0 1 33 26 + 6.097500E+02 2.552750E+03 5.091500E+01 -7.447E-07 -8.738E-07 0.000E+00 M= 1.148E-06 #,Z,Y,X= 0 1 33 27 + 6.102500E+02 2.552750E+03 5.766500E+01 -7.312E-07 -5.818E-07 0.000E+00 M= 9.344E-07 #,Z,Y,X= 0 1 33 28 + 6.107500E+02 2.552750E+03 5.670500E+01 -5.307E-07 -5.523E-07 0.000E+00 M= 7.660E-07 #,Z,Y,X= 0 1 33 29 + 6.112500E+02 2.552750E+03 5.901500E+01 -3.343E-07 -5.158E-07 0.000E+00 M= 6.147E-07 #,Z,Y,X= 0 1 33 30 + 6.117500E+02 2.552750E+03 6.173500E+01 -1.158E-07 -3.854E-07 0.000E+00 M= 4.024E-07 #,Z,Y,X= 0 1 33 31 + 6.022500E+02 2.552250E+03 6.931500E+01 1.515E-07 -1.201E-07 0.000E+00 M= 1.934E-07 #,Z,Y,X= 0 1 34 12 + 6.027500E+02 2.552250E+03 5.979000E+01 2.605E-07 -3.284E-07 0.000E+00 M= 4.192E-07 #,Z,Y,X= 0 1 34 13 + 6.032500E+02 2.552250E+03 5.178000E+01 2.293E-07 -3.300E-07 0.000E+00 M= 4.018E-07 #,Z,Y,X= 0 1 34 14 + 6.037500E+02 2.552250E+03 5.648500E+01 1.586E-07 -3.262E-07 0.000E+00 M= 3.627E-07 #,Z,Y,X= 0 1 34 15 + 6.042500E+02 2.552250E+03 6.045000E+01 2.532E-08 -2.960E-07 0.000E+00 M= 2.971E-07 #,Z,Y,X= 0 1 34 16 + 6.047500E+02 2.552250E+03 5.891000E+01 -3.983E-09 -2.475E-07 0.000E+00 M= 2.475E-07 #,Z,Y,X= 0 1 34 17 + 6.052500E+02 2.552250E+03 5.968000E+01 6.713E-08 -2.258E-07 0.000E+00 M= 2.355E-07 #,Z,Y,X= 0 1 34 18 + 6.057500E+02 2.552250E+03 5.505500E+01 1.283E-07 -2.717E-07 0.000E+00 M= 3.005E-07 #,Z,Y,X= 0 1 34 19 + 6.062500E+02 2.552250E+03 4.108000E+01 3.108E-07 -1.959E-06 0.000E+00 M= 1.984E-06 #,Z,Y,X= 0 1 34 20 + 6.067500E+02 2.552250E+03 3.300000E+01 6.887E-07 -2.907E-06 0.000E+00 M= 2.988E-06 #,Z,Y,X= 0 1 34 21 + 6.072500E+02 2.552250E+03 3.052500E+01 5.205E-07 -3.748E-06 0.000E+00 M= 3.784E-06 #,Z,Y,X= 0 1 34 22 + 6.077500E+02 2.552250E+03 2.716500E+01 -7.472E-07 -8.328E-06 0.000E+00 M= 8.361E-06 #,Z,Y,X= 0 1 34 23 + 6.082500E+02 2.552250E+03 3.528500E+01 -1.403E-06 -2.163E-06 0.000E+00 M= 2.578E-06 #,Z,Y,X= 0 1 34 24 + 6.087500E+02 2.552250E+03 3.491500E+01 -9.050E-07 -2.474E-06 0.000E+00 M= 2.635E-06 #,Z,Y,X= 0 1 34 25 + 6.092500E+02 2.552250E+03 4.269500E+01 -6.478E-07 -1.063E-06 0.000E+00 M= 1.245E-06 #,Z,Y,X= 0 1 34 26 + 6.097500E+02 2.552250E+03 4.483000E+01 -7.792E-07 -9.605E-07 0.000E+00 M= 1.237E-06 #,Z,Y,X= 0 1 34 27 + 6.102500E+02 2.552250E+03 5.496000E+01 -7.361E-07 -4.182E-07 0.000E+00 M= 8.466E-07 #,Z,Y,X= 0 1 34 28 + 6.107500E+02 2.552250E+03 6.349500E+01 -5.524E-07 -4.162E-07 0.000E+00 M= 6.916E-07 #,Z,Y,X= 0 1 34 29 + 6.112500E+02 2.552250E+03 6.075500E+01 -4.413E-07 -4.639E-07 0.000E+00 M= 6.402E-07 #,Z,Y,X= 0 1 34 30 + 6.117500E+02 2.552250E+03 6.118500E+01 -1.956E-07 -1.345E-07 0.000E+00 M= 2.374E-07 #,Z,Y,X= 0 1 34 31 + 6.027500E+02 2.551750E+03 6.426000E+01 1.025E-07 -3.101E-07 0.000E+00 M= 3.266E-07 #,Z,Y,X= 0 1 35 13 + 6.032500E+02 2.551750E+03 5.230500E+01 2.470E-07 -3.357E-07 0.000E+00 M= 4.168E-07 #,Z,Y,X= 0 1 35 14 + 6.037500E+02 2.551750E+03 4.522500E+01 3.236E-07 -4.144E-07 0.000E+00 M= 5.258E-07 #,Z,Y,X= 0 1 35 15 + 6.042500E+02 2.551750E+03 4.533500E+01 1.789E-07 -6.202E-07 0.000E+00 M= 6.455E-07 #,Z,Y,X= 0 1 35 16 + 6.047500E+02 2.551750E+03 5.753000E+01 7.049E-09 -2.608E-07 0.000E+00 M= 2.608E-07 #,Z,Y,X= 0 1 35 17 + 6.052500E+02 2.551750E+03 5.992500E+01 3.344E-08 -2.433E-07 0.000E+00 M= 2.455E-07 #,Z,Y,X= 0 1 35 18 + 6.057500E+02 2.551750E+03 5.343500E+01 1.252E-07 -2.261E-07 0.000E+00 M= 2.585E-07 #,Z,Y,X= 0 1 35 19 + 6.062500E+02 2.551750E+03 4.198500E+01 3.306E-07 -1.788E-06 0.000E+00 M= 1.818E-06 #,Z,Y,X= 0 1 35 20 + 6.067500E+02 2.551750E+03 3.982000E+01 6.172E-07 -2.647E-06 0.000E+00 M= 2.718E-06 #,Z,Y,X= 0 1 35 21 + 6.072500E+02 2.551750E+03 2.632500E+01 1.473E-06 -3.637E-06 0.000E+00 M= 3.924E-06 #,Z,Y,X= 0 1 35 22 + 6.077500E+02 2.551750E+03 2.690000E+01 -6.019E-08 -6.115E-06 0.000E+00 M= 6.115E-06 #,Z,Y,X= 0 1 35 23 + 6.082500E+02 2.551750E+03 4.151500E+01 -1.745E-06 -1.544E-06 0.000E+00 M= 2.330E-06 #,Z,Y,X= 0 1 35 24 + 6.087500E+02 2.551750E+03 5.194000E+01 -8.356E-07 -1.810E-06 0.000E+00 M= 1.993E-06 #,Z,Y,X= 0 1 35 25 + 6.092500E+02 2.551750E+03 5.944000E+01 -5.694E-07 -1.236E-06 0.000E+00 M= 1.361E-06 #,Z,Y,X= 0 1 35 26 + 6.097500E+02 2.551750E+03 6.320000E+01 -1.013E-06 -1.308E-06 0.000E+00 M= 1.654E-06 #,Z,Y,X= 0 1 35 27 + 6.102500E+02 2.551750E+03 6.619000E+01 -1.138E-06 -1.645E-07 0.000E+00 M= 1.150E-06 #,Z,Y,X= 0 1 35 28 + 6.107500E+02 2.551750E+03 6.683000E+01 -7.361E-07 -1.720E-07 0.000E+00 M= 7.559E-07 #,Z,Y,X= 0 1 35 29 + 6.112500E+02 2.551750E+03 6.222000E+01 -2.558E-07 -2.330E-07 0.000E+00 M= 3.460E-07 #,Z,Y,X= 0 1 35 30 + 6.027500E+02 2.551250E+03 6.366000E+01 1.436E-07 -1.147E-07 0.000E+00 M= 1.838E-07 #,Z,Y,X= 0 1 36 13 + 6.032500E+02 2.551250E+03 5.942000E+01 3.475E-07 -2.816E-07 0.000E+00 M= 4.472E-07 #,Z,Y,X= 0 1 36 14 + 6.037500E+02 2.551250E+03 4.911000E+01 5.303E-07 -3.080E-07 0.000E+00 M= 6.133E-07 #,Z,Y,X= 0 1 36 15 + 6.042500E+02 2.551250E+03 3.983000E+01 7.786E-07 -7.443E-07 0.000E+00 M= 1.077E-06 #,Z,Y,X= 0 1 36 16 + 6.047500E+02 2.551250E+03 3.964500E+01 6.198E-07 -5.962E-07 0.000E+00 M= 8.600E-07 #,Z,Y,X= 0 1 36 17 + 6.052500E+02 2.551250E+03 4.461000E+01 1.631E-07 -4.638E-07 0.000E+00 M= 4.917E-07 #,Z,Y,X= 0 1 36 18 + 6.057500E+02 2.551250E+03 5.164500E+01 -1.686E-09 -1.860E-07 0.000E+00 M= 1.860E-07 #,Z,Y,X= 0 1 36 19 + 6.062500E+02 2.551250E+03 3.417500E+01 2.140E-07 -1.532E-06 0.000E+00 M= 1.547E-06 #,Z,Y,X= 0 1 36 20 + 6.067500E+02 2.551250E+03 2.708000E+01 3.461E-07 -2.644E-06 0.000E+00 M= 2.666E-06 #,Z,Y,X= 0 1 36 21 + 6.072500E+02 2.551250E+03 2.600000E+01 3.705E-07 -2.074E-06 0.000E+00 M= 2.107E-06 #,Z,Y,X= 0 1 36 22 + 6.077500E+02 2.551250E+03 3.101000E+01 -1.756E-07 -2.280E-06 0.000E+00 M= 2.287E-06 #,Z,Y,X= 0 1 36 23 + 6.082500E+02 2.551250E+03 5.856000E+01 -8.045E-07 -1.095E-06 0.000E+00 M= 1.359E-06 #,Z,Y,X= 0 1 36 24 + 6.087500E+02 2.551250E+03 6.059500E+01 -7.094E-07 -1.332E-06 0.000E+00 M= 1.509E-06 #,Z,Y,X= 0 1 36 25 + 6.092500E+02 2.551250E+03 6.417500E+01 -5.003E-07 -1.233E-06 0.000E+00 M= 1.330E-06 #,Z,Y,X= 0 1 36 26 + 6.097500E+02 2.551250E+03 6.739000E+01 -2.088E-07 -1.340E-06 0.000E+00 M= 1.357E-06 #,Z,Y,X= 0 1 36 27 + 6.032500E+02 2.550750E+03 5.966000E+01 6.755E-08 -2.159E-07 0.000E+00 M= 2.262E-07 #,Z,Y,X= 0 1 37 14 + 6.037500E+02 2.550750E+03 5.490000E+01 1.796E-07 -1.878E-07 0.000E+00 M= 2.598E-07 #,Z,Y,X= 0 1 37 15 + 6.042500E+02 2.550750E+03 4.762000E+01 3.330E-07 -5.882E-07 0.000E+00 M= 6.759E-07 #,Z,Y,X= 0 1 37 16 + 6.047500E+02 2.550750E+03 3.391000E+01 5.957E-07 -7.898E-07 0.000E+00 M= 9.893E-07 #,Z,Y,X= 0 1 37 17 + 6.052500E+02 2.550750E+03 3.487000E+01 6.452E-07 -8.196E-07 0.000E+00 M= 1.043E-06 #,Z,Y,X= 0 1 37 18 + 6.057500E+02 2.550750E+03 3.834500E+01 9.759E-08 -6.624E-07 0.000E+00 M= 6.696E-07 #,Z,Y,X= 0 1 37 19 + 6.062500E+02 2.550750E+03 3.334500E+01 -3.042E-07 -1.356E-06 0.000E+00 M= 1.390E-06 #,Z,Y,X= 0 1 37 20 + 6.067500E+02 2.550750E+03 3.027500E+01 -9.200E-07 -3.409E-06 0.000E+00 M= 3.531E-06 #,Z,Y,X= 0 1 37 21 + 6.072500E+02 2.550750E+03 2.800000E+01 1.669E-07 -2.425E-06 0.000E+00 M= 2.431E-06 #,Z,Y,X= 0 1 37 22 + 6.077500E+02 2.550750E+03 3.171000E+01 9.347E-07 -3.923E-06 0.000E+00 M= 4.033E-06 #,Z,Y,X= 0 1 37 23 + 6.082500E+02 2.550750E+03 3.820500E+01 -3.642E-07 -1.540E-06 0.000E+00 M= 1.583E-06 #,Z,Y,X= 0 1 37 24 + 6.087500E+02 2.550750E+03 5.257000E+01 -6.868E-07 -1.235E-06 0.000E+00 M= 1.413E-06 #,Z,Y,X= 0 1 37 25 + 6.092500E+02 2.550750E+03 5.695000E+01 -5.509E-07 -1.071E-06 0.000E+00 M= 1.204E-06 #,Z,Y,X= 0 1 37 26 + 6.097500E+02 2.550750E+03 6.289000E+01 -2.213E-07 -9.118E-07 0.000E+00 M= 9.382E-07 #,Z,Y,X= 0 1 37 27 + 6.032500E+02 2.550250E+03 5.278000E+01 6.229E-08 -1.440E-07 0.000E+00 M= 1.569E-07 #,Z,Y,X= 0 1 38 14 + 6.037500E+02 2.550250E+03 4.605000E+01 1.658E-07 -1.491E-07 0.000E+00 M= 2.230E-07 #,Z,Y,X= 0 1 38 15 + 6.042500E+02 2.550250E+03 4.323500E+01 2.134E-07 -5.335E-07 0.000E+00 M= 5.746E-07 #,Z,Y,X= 0 1 38 16 + 6.047500E+02 2.550250E+03 4.001500E+01 2.880E-07 -6.134E-07 0.000E+00 M= 6.777E-07 #,Z,Y,X= 0 1 38 17 + 6.052500E+02 2.550250E+03 3.643000E+01 4.347E-07 -9.177E-07 0.000E+00 M= 1.015E-06 #,Z,Y,X= 0 1 38 18 + 6.057500E+02 2.550250E+03 2.933500E+01 3.947E-07 -1.254E-06 0.000E+00 M= 1.315E-06 #,Z,Y,X= 0 1 38 19 + 6.062500E+02 2.550250E+03 2.983000E+01 2.253E-08 -1.629E-06 0.000E+00 M= 1.629E-06 #,Z,Y,X= 0 1 38 20 + 6.067500E+02 2.550250E+03 3.080500E+01 -5.118E-07 -4.370E-06 0.000E+00 M= 4.400E-06 #,Z,Y,X= 0 1 38 21 + 6.072500E+02 2.550250E+03 2.480000E+01 2.687E-07 -5.980E-06 0.000E+00 M= 5.986E-06 #,Z,Y,X= 0 1 38 22 + 6.077500E+02 2.550250E+03 2.474000E+01 2.762E-07 -5.947E-06 0.000E+00 M= 5.954E-06 #,Z,Y,X= 0 1 38 23 + 6.082500E+02 2.550250E+03 2.769000E+01 -8.587E-07 -2.013E-06 0.000E+00 M= 2.188E-06 #,Z,Y,X= 0 1 38 24 + 6.087500E+02 2.550250E+03 3.546000E+01 -9.039E-07 -1.224E-06 0.000E+00 M= 1.521E-06 #,Z,Y,X= 0 1 38 25 + 6.092500E+02 2.550250E+03 5.472000E+01 -7.989E-07 -9.367E-07 0.000E+00 M= 1.231E-06 #,Z,Y,X= 0 1 38 26 + 6.097500E+02 2.550250E+03 5.888000E+01 -3.586E-07 -3.397E-07 0.000E+00 M= 4.939E-07 #,Z,Y,X= 0 1 38 27 + 6.027500E+02 2.549750E+03 5.802500E+01 2.800E-08 -1.853E-08 0.000E+00 M= 3.358E-08 #,Z,Y,X= 0 1 39 13 + 6.032500E+02 2.549750E+03 5.350500E+01 1.002E-07 -9.374E-08 0.000E+00 M= 1.372E-07 #,Z,Y,X= 0 1 39 14 + 6.037500E+02 2.549750E+03 4.779500E+01 1.897E-07 -1.115E-07 0.000E+00 M= 2.201E-07 #,Z,Y,X= 0 1 39 15 + 6.042500E+02 2.549750E+03 4.147000E+01 3.846E-07 -4.431E-07 0.000E+00 M= 5.867E-07 #,Z,Y,X= 0 1 39 16 + 6.047500E+02 2.549750E+03 3.523000E+01 5.304E-07 -5.962E-07 0.000E+00 M= 7.980E-07 #,Z,Y,X= 0 1 39 17 + 6.052500E+02 2.549750E+03 3.513000E+01 5.349E-07 -9.013E-07 0.000E+00 M= 1.048E-06 #,Z,Y,X= 0 1 39 18 + 6.057500E+02 2.549750E+03 2.901500E+01 6.625E-07 -1.308E-06 0.000E+00 M= 1.466E-06 #,Z,Y,X= 0 1 39 19 + 6.062500E+02 2.549750E+03 2.744000E+01 8.314E-07 -1.914E-06 0.000E+00 M= 2.087E-06 #,Z,Y,X= 0 1 39 20 + 6.067500E+02 2.549750E+03 2.532500E+01 1.318E-06 -4.298E-06 0.000E+00 M= 4.495E-06 #,Z,Y,X= 0 1 39 21 + 6.072500E+02 2.549750E+03 2.285000E+01 -1.262E-07 -1.036E-05 0.000E+00 M= 1.036E-05 #,Z,Y,X= 0 1 39 22 + 6.077500E+02 2.549750E+03 2.879500E+01 -1.519E-06 -6.630E-06 0.000E+00 M= 6.802E-06 #,Z,Y,X= 0 1 39 23 + 6.082500E+02 2.549750E+03 4.679000E+01 -7.543E-07 -1.898E-06 0.000E+00 M= 2.043E-06 #,Z,Y,X= 0 1 39 24 + 6.087500E+02 2.549750E+03 5.561000E+01 -7.340E-07 -1.498E-06 0.000E+00 M= 1.668E-06 #,Z,Y,X= 0 1 39 25 + 6.092500E+02 2.549750E+03 5.738500E+01 -4.720E-07 -4.527E-07 0.000E+00 M= 6.540E-07 #,Z,Y,X= 0 1 39 26 + 6.027500E+02 2.549250E+03 5.649500E+01 4.782E-08 -1.798E-08 0.000E+00 M= 5.109E-08 #,Z,Y,X= 0 1 40 13 + 6.032500E+02 2.549250E+03 4.803000E+01 1.269E-07 -7.173E-08 0.000E+00 M= 1.458E-07 #,Z,Y,X= 0 1 40 14 + 6.037500E+02 2.549250E+03 4.838000E+01 1.592E-07 -1.145E-07 0.000E+00 M= 1.961E-07 #,Z,Y,X= 0 1 40 15 + 6.042500E+02 2.549250E+03 4.558500E+01 2.591E-07 -2.505E-07 0.000E+00 M= 3.604E-07 #,Z,Y,X= 0 1 40 16 + 6.047500E+02 2.549250E+03 3.942500E+01 5.947E-07 -4.156E-07 0.000E+00 M= 7.256E-07 #,Z,Y,X= 0 1 40 17 + 6.052500E+02 2.549250E+03 3.836000E+01 8.422E-07 -9.587E-07 0.000E+00 M= 1.276E-06 #,Z,Y,X= 0 1 40 18 + 6.057500E+02 2.549250E+03 3.781000E+01 7.987E-07 -1.308E-06 0.000E+00 M= 1.532E-06 #,Z,Y,X= 0 1 40 19 + 6.062500E+02 2.549250E+03 3.163500E+01 1.061E-06 -1.632E-06 0.000E+00 M= 1.947E-06 #,Z,Y,X= 0 1 40 20 + 6.067500E+02 2.549250E+03 2.784000E+01 2.636E-06 -2.679E-06 0.000E+00 M= 3.758E-06 #,Z,Y,X= 0 1 40 21 + 6.072500E+02 2.549250E+03 2.250000E+01 -1.774E-07 -7.530E-06 0.000E+00 M= 7.533E-06 #,Z,Y,X= 0 1 40 22 + 6.077500E+02 2.549250E+03 2.474500E+01 -2.534E-06 -4.559E-06 0.000E+00 M= 5.216E-06 #,Z,Y,X= 0 1 40 23 + 6.082500E+02 2.549250E+03 3.624500E+01 -6.285E-07 -1.470E-06 0.000E+00 M= 1.599E-06 #,Z,Y,X= 0 1 40 24 + 6.087500E+02 2.549250E+03 4.756500E+01 -1.955E-07 -1.611E-06 0.000E+00 M= 1.623E-06 #,Z,Y,X= 0 1 40 25 + 6.032500E+02 2.548750E+03 5.728000E+01 5.646E-08 -3.399E-08 0.000E+00 M= 6.590E-08 #,Z,Y,X= 0 1 41 14 + 6.037500E+02 2.548750E+03 5.515500E+01 7.633E-08 -2.002E-07 0.000E+00 M= 2.142E-07 #,Z,Y,X= 0 1 41 15 + 6.042500E+02 2.548750E+03 5.431000E+01 3.526E-08 -2.034E-07 0.000E+00 M= 2.064E-07 #,Z,Y,X= 0 1 41 16 + 6.047500E+02 2.548750E+03 5.317000E+01 4.718E-08 -2.162E-07 0.000E+00 M= 2.213E-07 #,Z,Y,X= 0 1 41 17 + 6.052500E+02 2.548750E+03 4.377000E+01 1.976E-07 -9.006E-07 0.000E+00 M= 9.220E-07 #,Z,Y,X= 0 1 41 18 + 6.057500E+02 2.548750E+03 3.674500E+01 5.198E-07 -1.260E-06 0.000E+00 M= 1.363E-06 #,Z,Y,X= 0 1 41 19 + 6.062500E+02 2.548750E+03 3.705500E+01 7.197E-07 -1.378E-06 0.000E+00 M= 1.555E-06 #,Z,Y,X= 0 1 41 20 + 6.067500E+02 2.548750E+03 2.720000E+01 9.304E-07 -1.279E-06 0.000E+00 M= 1.581E-06 #,Z,Y,X= 0 1 41 21 + 6.072500E+02 2.548750E+03 2.100000E+01 -4.722E-07 -1.064E-06 0.000E+00 M= 1.164E-06 #,Z,Y,X= 0 1 41 22 + 6.077500E+02 2.548750E+03 2.638000E+01 -1.123E-06 -2.042E-06 0.000E+00 M= 2.331E-06 #,Z,Y,X= 0 1 41 23 + 6.082500E+02 2.548750E+03 2.940000E+01 -1.619E-07 -1.205E-06 0.000E+00 M= 1.216E-06 #,Z,Y,X= 0 1 41 24 + 6.087500E+02 2.548750E+03 4.419000E+01 -3.951E-08 -1.432E-06 0.000E+00 M= 1.433E-06 #,Z,Y,X= 0 1 41 25 + 6.037500E+02 2.548250E+03 4.998000E+01 5.570E-08 -2.823E-07 0.000E+00 M= 2.878E-07 #,Z,Y,X= 0 1 42 15 + 6.042500E+02 2.548250E+03 4.319000E+01 1.133E-07 -2.507E-07 0.000E+00 M= 2.751E-07 #,Z,Y,X= 0 1 42 16 + 6.047500E+02 2.548250E+03 3.629000E+01 7.060E-08 -2.877E-07 0.000E+00 M= 2.962E-07 #,Z,Y,X= 0 1 42 17 + 6.052500E+02 2.548250E+03 3.184500E+01 1.987E-07 -6.316E-07 0.000E+00 M= 6.621E-07 #,Z,Y,X= 0 1 42 18 + 6.057500E+02 2.548250E+03 3.300500E+01 6.007E-07 -8.978E-07 0.000E+00 M= 1.080E-06 #,Z,Y,X= 0 1 42 19 + 6.062500E+02 2.548250E+03 2.873500E+01 9.623E-07 -1.124E-06 0.000E+00 M= 1.480E-06 #,Z,Y,X= 0 1 42 20 + 6.067500E+02 2.548250E+03 2.439500E+01 6.590E-07 -6.579E-07 0.000E+00 M= 9.312E-07 #,Z,Y,X= 0 1 42 21 + 6.072500E+02 2.548250E+03 2.150000E+01 1.378E-07 -7.289E-07 0.000E+00 M= 7.419E-07 #,Z,Y,X= 0 1 42 22 + 6.077500E+02 2.548250E+03 3.333000E+01 2.001E-07 -1.004E-06 0.000E+00 M= 1.024E-06 #,Z,Y,X= 0 1 42 23 + 6.082500E+02 2.548250E+03 4.726000E+01 3.455E-07 -1.158E-06 0.000E+00 M= 1.208E-06 #,Z,Y,X= 0 1 42 24 + 6.087500E+02 2.548250E+03 4.943500E+01 1.600E-07 -1.566E-06 0.000E+00 M= 1.574E-06 #,Z,Y,X= 0 1 42 25 + 6.037500E+02 2.547750E+03 4.691500E+01 4.151E-08 -3.235E-07 0.000E+00 M= 3.261E-07 #,Z,Y,X= 0 1 43 15 + 6.042500E+02 2.547750E+03 4.113000E+01 6.613E-08 -3.030E-07 0.000E+00 M= 3.102E-07 #,Z,Y,X= 0 1 43 16 + 6.047500E+02 2.547750E+03 4.217000E+01 6.967E-08 -3.520E-07 0.000E+00 M= 3.589E-07 #,Z,Y,X= 0 1 43 17 + 6.052500E+02 2.547750E+03 4.010500E+01 1.253E-07 -4.624E-07 0.000E+00 M= 4.791E-07 #,Z,Y,X= 0 1 43 18 + 6.057500E+02 2.547750E+03 3.331500E+01 2.943E-07 -5.817E-07 0.000E+00 M= 6.519E-07 #,Z,Y,X= 0 1 43 19 + 6.062500E+02 2.547750E+03 2.765500E+01 4.949E-07 -8.151E-07 0.000E+00 M= 9.535E-07 #,Z,Y,X= 0 1 43 20 + 6.067500E+02 2.547750E+03 2.100000E+01 7.127E-07 -1.980E-07 0.000E+00 M= 7.397E-07 #,Z,Y,X= 0 1 43 21 + 6.072500E+02 2.547750E+03 2.385000E+01 9.289E-07 -6.609E-07 0.000E+00 M= 1.140E-06 #,Z,Y,X= 0 1 43 22 + 6.077500E+02 2.547750E+03 3.700500E+01 1.017E-06 -9.140E-07 0.000E+00 M= 1.367E-06 #,Z,Y,X= 0 1 43 23 + 6.082500E+02 2.547750E+03 4.188500E+01 9.285E-07 -1.272E-06 0.000E+00 M= 1.575E-06 #,Z,Y,X= 0 1 43 24 + 6.087500E+02 2.547750E+03 4.795500E+01 1.054E-06 -1.599E-06 0.000E+00 M= 1.916E-06 #,Z,Y,X= 0 1 43 25 + 6.092500E+02 2.547750E+03 4.120500E+01 6.294E-07 -6.450E-07 0.000E+00 M= 9.012E-07 #,Z,Y,X= 0 1 43 26 + 6.037500E+02 2.547250E+03 4.450500E+01 3.508E-08 -3.931E-07 0.000E+00 M= 3.946E-07 #,Z,Y,X= 0 1 44 15 + 6.042500E+02 2.547250E+03 4.563500E+01 6.420E-08 -3.672E-07 0.000E+00 M= 3.728E-07 #,Z,Y,X= 0 1 44 16 + 6.047500E+02 2.547250E+03 4.101000E+01 5.789E-08 -3.684E-07 0.000E+00 M= 3.730E-07 #,Z,Y,X= 0 1 44 17 + 6.052500E+02 2.547250E+03 3.338500E+01 2.365E-08 -4.981E-07 0.000E+00 M= 4.987E-07 #,Z,Y,X= 0 1 44 18 + 6.057500E+02 2.547250E+03 2.991000E+01 7.953E-09 -4.594E-07 0.000E+00 M= 4.595E-07 #,Z,Y,X= 0 1 44 19 + 6.062500E+02 2.547250E+03 2.274000E+01 -3.206E-08 -8.193E-07 0.000E+00 M= 8.199E-07 #,Z,Y,X= 0 1 44 20 + 6.067500E+02 2.547250E+03 1.977000E+01 4.416E-07 -2.484E-07 0.000E+00 M= 5.067E-07 #,Z,Y,X= 0 1 44 21 + 6.072500E+02 2.547250E+03 2.585500E+01 1.126E-06 -3.591E-07 0.000E+00 M= 1.182E-06 #,Z,Y,X= 0 1 44 22 + 6.077500E+02 2.547250E+03 2.503500E+01 1.457E-06 -8.303E-07 0.000E+00 M= 1.677E-06 #,Z,Y,X= 0 1 44 23 + 6.082500E+02 2.547250E+03 2.931500E+01 1.470E-06 -1.517E-06 0.000E+00 M= 2.112E-06 #,Z,Y,X= 0 1 44 24 + 6.087500E+02 2.547250E+03 3.388500E+01 1.177E-06 -1.574E-06 0.000E+00 M= 1.965E-06 #,Z,Y,X= 0 1 44 25 + 6.092500E+02 2.547250E+03 3.012500E+01 8.691E-07 -1.484E-06 0.000E+00 M= 1.720E-06 #,Z,Y,X= 0 1 44 26 + 6.097500E+02 2.547250E+03 2.249000E+01 4.085E-07 -3.330E-07 0.000E+00 M= 5.270E-07 #,Z,Y,X= 0 1 44 27 + 6.102500E+02 2.547250E+03 2.306000E+01 6.904E-08 -1.313E-07 0.000E+00 M= 1.483E-07 #,Z,Y,X= 0 1 44 28 + 6.037500E+02 2.546750E+03 3.959500E+01 5.335E-08 -4.611E-07 0.000E+00 M= 4.642E-07 #,Z,Y,X= 0 1 45 15 + 6.042500E+02 2.546750E+03 3.923000E+01 1.234E-07 -4.540E-07 0.000E+00 M= 4.705E-07 #,Z,Y,X= 0 1 45 16 + 6.047500E+02 2.546750E+03 3.564500E+01 6.594E-08 -4.881E-07 0.000E+00 M= 4.926E-07 #,Z,Y,X= 0 1 45 17 + 6.052500E+02 2.546750E+03 3.092500E+01 -5.874E-08 -6.337E-07 0.000E+00 M= 6.364E-07 #,Z,Y,X= 0 1 45 18 + 6.057500E+02 2.546750E+03 2.813000E+01 -2.538E-07 -6.155E-07 0.000E+00 M= 6.657E-07 #,Z,Y,X= 0 1 45 19 + 6.062500E+02 2.546750E+03 2.489000E+01 -5.146E-07 -1.021E-06 0.000E+00 M= 1.143E-06 #,Z,Y,X= 0 1 45 20 + 6.067500E+02 2.546750E+03 1.932500E+01 7.228E-07 -9.770E-07 0.000E+00 M= 1.215E-06 #,Z,Y,X= 0 1 45 21 + 6.072500E+02 2.546750E+03 2.244500E+01 2.474E-06 2.281E-07 0.000E+00 M= 2.485E-06 #,Z,Y,X= 0 1 45 22 + 6.077500E+02 2.546750E+03 2.714500E+01 2.784E-06 -8.830E-07 0.000E+00 M= 2.920E-06 #,Z,Y,X= 0 1 45 23 + 6.082500E+02 2.546750E+03 2.787500E+01 2.209E-06 -2.216E-06 0.000E+00 M= 3.129E-06 #,Z,Y,X= 0 1 45 24 + 6.087500E+02 2.546750E+03 2.652500E+01 1.089E-06 -2.459E-06 0.000E+00 M= 2.689E-06 #,Z,Y,X= 0 1 45 25 + 6.092500E+02 2.546750E+03 1.977000E+01 -3.681E-07 -2.544E-06 0.000E+00 M= 2.571E-06 #,Z,Y,X= 0 1 45 26 + 6.097500E+02 2.546750E+03 1.916500E+01 -7.620E-07 -3.274E-07 0.000E+00 M= 8.293E-07 #,Z,Y,X= 0 1 45 27 + 6.102500E+02 2.546750E+03 2.527500E+01 -1.881E-07 -1.290E-07 0.000E+00 M= 2.281E-07 #,Z,Y,X= 0 1 45 28 + 6.037500E+02 2.546250E+03 3.691000E+01 6.758E-08 -4.724E-07 0.000E+00 M= 4.772E-07 #,Z,Y,X= 0 1 46 15 + 6.042500E+02 2.546250E+03 3.585000E+01 1.773E-07 -5.313E-07 0.000E+00 M= 5.601E-07 #,Z,Y,X= 0 1 46 16 + 6.047500E+02 2.546250E+03 3.400000E+01 1.688E-07 -6.520E-07 0.000E+00 M= 6.735E-07 #,Z,Y,X= 0 1 46 17 + 6.052500E+02 2.546250E+03 3.180000E+01 3.749E-08 -8.160E-07 0.000E+00 M= 8.168E-07 #,Z,Y,X= 0 1 46 18 + 6.057500E+02 2.546250E+03 2.783000E+01 -1.820E-07 -9.293E-07 0.000E+00 M= 9.470E-07 #,Z,Y,X= 0 1 46 19 + 6.062500E+02 2.546250E+03 2.602000E+01 -5.792E-07 -1.436E-06 0.000E+00 M= 1.548E-06 #,Z,Y,X= 0 1 46 20 + 6.067500E+02 2.546250E+03 1.935000E+01 -3.471E-07 -2.798E-06 0.000E+00 M= 2.819E-06 #,Z,Y,X= 0 1 46 21 + 6.072500E+02 2.546250E+03 1.995000E+01 2.815E-06 -2.914E-06 0.000E+00 M= 4.052E-06 #,Z,Y,X= 0 1 46 22 + 6.077500E+02 2.546250E+03 2.696000E+01 4.927E-06 -1.834E-06 0.000E+00 M= 5.257E-06 #,Z,Y,X= 0 1 46 23 + 6.082500E+02 2.546250E+03 2.571500E+01 2.802E-06 -4.488E-06 0.000E+00 M= 5.291E-06 #,Z,Y,X= 0 1 46 24 + 6.087500E+02 2.546250E+03 1.650000E+01 -1.152E-06 -5.816E-06 0.000E+00 M= 5.929E-06 #,Z,Y,X= 0 1 46 25 + 6.092500E+02 2.546250E+03 1.600000E+01 -1.735E-06 -1.821E-06 0.000E+00 M= 2.515E-06 #,Z,Y,X= 0 1 46 26 + 6.032500E+02 2.545750E+03 3.543830E+01 1.421E-08 -5.348E-08 0.000E+00 M= 5.534E-08 #,Z,Y,X= 0 1 47 14 + 6.037500E+02 2.545750E+03 3.518500E+01 1.217E-07 -4.384E-07 0.000E+00 M= 4.550E-07 #,Z,Y,X= 0 1 47 15 + 6.042500E+02 2.545750E+03 3.420000E+01 2.917E-07 -5.365E-07 0.000E+00 M= 6.107E-07 #,Z,Y,X= 0 1 47 16 + 6.047500E+02 2.545750E+03 3.404500E+01 3.460E-07 -7.530E-07 0.000E+00 M= 8.287E-07 #,Z,Y,X= 0 1 47 17 + 6.052500E+02 2.545750E+03 3.243000E+01 2.245E-07 -1.035E-06 0.000E+00 M= 1.059E-06 #,Z,Y,X= 0 1 47 18 + 6.057500E+02 2.545750E+03 2.902000E+01 -1.487E-08 -1.242E-06 0.000E+00 M= 1.242E-06 #,Z,Y,X= 0 1 47 19 + 6.062500E+02 2.545750E+03 2.468000E+01 -4.006E-07 -2.000E-06 0.000E+00 M= 2.040E-06 #,Z,Y,X= 0 1 47 20 + 6.067500E+02 2.545750E+03 2.306500E+01 -1.795E-07 -3.637E-06 0.000E+00 M= 3.641E-06 #,Z,Y,X= 0 1 47 21 + 6.072500E+02 2.545750E+03 1.812000E+01 3.882E-06 -9.453E-06 0.000E+00 M= 1.022E-05 #,Z,Y,X= 0 1 47 22 + 6.077500E+02 2.545750E+03 2.509000E+01 8.535E-06 -2.061E-06 0.000E+00 M= 8.781E-06 #,Z,Y,X= 0 1 47 23 + 6.082500E+02 2.545750E+03 1.450000E+01 9.599E-07 -7.792E-06 0.000E+00 M= 7.851E-06 #,Z,Y,X= 0 1 47 24 + 6.087500E+02 2.545750E+03 1.500000E+01 -4.150E-06 -4.085E-06 0.000E+00 M= 5.823E-06 #,Z,Y,X= 0 1 47 25 + 6.032500E+02 2.545250E+03 3.503500E+01 7.254E-08 -5.065E-08 0.000E+00 M= 8.847E-08 #,Z,Y,X= 0 1 48 14 + 6.037500E+02 2.545250E+03 3.381000E+01 2.384E-07 -3.160E-07 0.000E+00 M= 3.959E-07 #,Z,Y,X= 0 1 48 15 + 6.042500E+02 2.545250E+03 3.289000E+01 4.789E-07 -3.656E-07 0.000E+00 M= 6.025E-07 #,Z,Y,X= 0 1 48 16 + 6.047500E+02 2.545250E+03 3.198000E+01 6.562E-07 -7.554E-07 0.000E+00 M= 1.001E-06 #,Z,Y,X= 0 1 48 17 + 6.052500E+02 2.545250E+03 2.947500E+01 5.292E-07 -1.283E-06 0.000E+00 M= 1.388E-06 #,Z,Y,X= 0 1 48 18 + 6.057500E+02 2.545250E+03 2.772000E+01 2.646E-07 -1.487E-06 0.000E+00 M= 1.510E-06 #,Z,Y,X= 0 1 48 19 + 6.062500E+02 2.545250E+03 2.899500E+01 2.855E-07 -2.136E-06 0.000E+00 M= 2.155E-06 #,Z,Y,X= 0 1 48 20 + 6.067500E+02 2.545250E+03 2.558000E+01 7.643E-07 -2.919E-06 0.000E+00 M= 3.017E-06 #,Z,Y,X= 0 1 48 21 + 6.072500E+02 2.545250E+03 1.762000E+01 5.241E-06 -1.010E-05 0.000E+00 M= 1.138E-05 #,Z,Y,X= 0 1 48 22 + 6.077500E+02 2.545250E+03 1.756000E+01 1.580E-05 -5.680E-06 0.000E+00 M= 1.679E-05 #,Z,Y,X= 0 1 48 23 + 6.082500E+02 2.545250E+03 1.550000E+01 1.357E-05 -1.858E-06 0.000E+00 M= 1.369E-05 #,Z,Y,X= 0 1 48 24 + 6.037500E+02 2.544750E+03 3.293000E+01 1.386E-07 -1.179E-07 0.000E+00 M= 1.820E-07 #,Z,Y,X= 0 1 49 15 + 6.042500E+02 2.544750E+03 3.403000E+01 4.170E-07 -1.149E-07 0.000E+00 M= 4.325E-07 #,Z,Y,X= 0 1 49 16 + 6.047500E+02 2.544750E+03 3.318000E+01 9.533E-07 -3.650E-07 0.000E+00 M= 1.021E-06 #,Z,Y,X= 0 1 49 17 + 6.052500E+02 2.544750E+03 3.270000E+01 9.441E-07 -1.882E-06 0.000E+00 M= 2.105E-06 #,Z,Y,X= 0 1 49 18 + 6.057500E+02 2.544750E+03 3.158500E+01 5.479E-07 -1.579E-06 0.000E+00 M= 1.671E-06 #,Z,Y,X= 0 1 49 19 + 6.062500E+02 2.544750E+03 3.296500E+01 7.846E-07 -1.857E-06 0.000E+00 M= 2.016E-06 #,Z,Y,X= 0 1 49 20 + 6.067500E+02 2.544750E+03 2.767000E+01 9.780E-07 -2.666E-06 0.000E+00 M= 2.840E-06 #,Z,Y,X= 0 1 49 21 + 6.072500E+02 2.544750E+03 2.120500E+01 2.877E-06 -5.784E-06 0.000E+00 M= 6.460E-06 #,Z,Y,X= 0 1 49 22 + 6.077500E+02 2.544750E+03 1.767000E+01 6.974E-06 -8.047E-06 0.000E+00 M= 1.065E-05 #,Z,Y,X= 0 1 49 23 + 6.082500E+02 2.544750E+03 1.458500E+01 4.790E-06 -5.311E-08 0.000E+00 M= 4.790E-06 #,Z,Y,X= 0 1 49 24 + 6.052500E+02 2.544250E+03 3.172500E+01 2.419E-07 -2.075E-06 0.000E+00 M= 2.089E-06 #,Z,Y,X= 0 1 50 18 + 6.057500E+02 2.544250E+03 3.213000E+01 4.118E-07 -1.643E-06 0.000E+00 M= 1.694E-06 #,Z,Y,X= 0 1 50 19 + 6.062500E+02 2.544250E+03 3.103500E+01 3.870E-07 -1.642E-06 0.000E+00 M= 1.687E-06 #,Z,Y,X= 0 1 50 20 + 6.067500E+02 2.544250E+03 2.634500E+01 6.608E-07 -2.527E-06 0.000E+00 M= 2.612E-06 #,Z,Y,X= 0 1 50 21 + 6.072500E+02 2.544250E+03 2.400000E+01 1.236E-06 -3.634E-06 0.000E+00 M= 3.838E-06 #,Z,Y,X= 0 1 50 22 + 6.077500E+02 2.544250E+03 1.700500E+01 2.700E-06 -4.894E-06 0.000E+00 M= 5.589E-06 #,Z,Y,X= 0 1 50 23 + 6.082500E+02 2.544250E+03 1.300000E+01 2.117E-06 -7.393E-06 0.000E+00 M= 7.690E-06 #,Z,Y,X= 0 1 50 24 + 6.052500E+02 2.543750E+03 2.565000E+01 9.864E-07 -9.259E-07 0.000E+00 M= 1.353E-06 #,Z,Y,X= 0 1 51 18 + 6.057500E+02 2.543750E+03 2.799000E+01 1.297E-06 -2.571E-06 0.000E+00 M= 2.879E-06 #,Z,Y,X= 0 1 51 19 + 6.062500E+02 2.543750E+03 2.437000E+01 4.736E-07 -1.779E-06 0.000E+00 M= 1.841E-06 #,Z,Y,X= 0 1 51 20 + 6.067500E+02 2.543750E+03 1.964000E+01 8.359E-07 -1.976E-06 0.000E+00 M= 2.145E-06 #,Z,Y,X= 0 1 51 21 + 6.072500E+02 2.543750E+03 1.887500E+01 1.474E-06 -3.250E-06 0.000E+00 M= 3.568E-06 #,Z,Y,X= 0 1 51 22 + 6.077500E+02 2.543750E+03 1.458500E+01 3.331E-06 -2.282E-06 0.000E+00 M= 4.038E-06 #,Z,Y,X= 0 1 51 23 + 6.082500E+02 2.543750E+03 1.100000E+01 2.698E-06 -4.735E-06 0.000E+00 M= 5.450E-06 #,Z,Y,X= 0 1 51 24 + 6.057500E+02 2.543250E+03 9.981500E+00 7.227E-11 -1.490E-06 0.000E+00 M= 1.490E-06 #,Z,Y,X= 0 1 52 19 + 6.062500E+02 2.543250E+03 1.157500E+01 4.003E-07 -9.375E-07 0.000E+00 M= 1.019E-06 #,Z,Y,X= 0 1 52 20 + 6.067500E+02 2.543250E+03 1.140000E+01 1.629E-06 -7.723E-07 0.000E+00 M= 1.802E-06 #,Z,Y,X= 0 1 52 21 + 6.072500E+02 2.543250E+03 1.080000E+01 8.640E-07 -1.552E-06 0.000E+00 M= 1.776E-06 #,Z,Y,X= 0 1 52 22 + 6.077500E+02 2.543250E+03 1.100000E+01 -4.219E-07 -3.589E-07 0.000E+00 M= 5.539E-07 #,Z,Y,X= 0 1 52 23 From a0fdbd10656eec6bab1541e97fe128a2d6046fa4 Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Tue, 9 Dec 2025 17:27:25 +0100 Subject: [PATCH 22/23] update version to 0.3 --- src/gridmarthe/__version__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gridmarthe/__version__.py b/src/gridmarthe/__version__.py index f574aac..4b76cc6 100644 --- a/src/gridmarthe/__version__.py +++ b/src/gridmarthe/__version__.py @@ -22,7 +22,7 @@ # along with this program. If not, see . # -__version__ = '0.1.4' +__version__ = '0.3.0' _copyleft = """ {program} Copyright (C) {year} {name_of_author}. From 088eefee79c5d4a253b56ec6bed6fa598fd1334c Mon Sep 17 00:00:00 2001 From: Adrien Manlay Date: Tue, 9 Dec 2025 17:34:53 +0100 Subject: [PATCH 23/23] ci: drop macos build (gh ci), add future version of ci with gitlab --- .github/workflows/build.yml | 4 +- .gitlab_brgm-ci.yml | 140 ++++++++++++++++++++++++++++++++++++ 2 files changed, 142 insertions(+), 2 deletions(-) create mode 100644 .gitlab_brgm-ci.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 69054f9..0692858 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -13,7 +13,7 @@ jobs: strategy: matrix: # mac os 14 at least - runner with Apple Silicon - os: [ubuntu-latest, windows-latest, macos-14] + os: [ubuntu-latest, windows-latest] # macos-14, drop mac-os build env: FC: gfortran GCC_V: 12 # only for MACOS @@ -61,7 +61,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: "3.10" + python-version: "3.12" - name: Install cibuildwheel run: pip install cibuildwheel diff --git a/.gitlab_brgm-ci.yml b/.gitlab_brgm-ci.yml new file mode 100644 index 0000000..99c1850 --- /dev/null +++ b/.gitlab_brgm-ci.yml @@ -0,0 +1,140 @@ +# Gitlab CI config - Courtesy of T. Janvier (BRGM) +default: # linux image on linux runnner + image: nexus-docker.brgm.fr/brgm/miniconda3:latest + tags: + - brgm + +.windows: # windows image on windows runnner + image: nexus-docker.brgm.fr/brgm/miniconda3:windows + tags: + - windows-2022-docker + + +stages: + - build + - test + - deploy + + +.version_tag: + only: # see https://peps.python.org/pep-0440/ + variables: + - $CI_COMMIT_TAG =~ /^(?P\D+)?(?P(?P\d+(?:\.\d+)*)(?P
    (?:a|b|rc)\d+?)?((?P\.post\d+))?(?P\.dev\d+)?(?P\+\S+)?)$/
+  except:
+    - branches
+
+
+# ----- Build ----- #
+
+.build:
+  stage: build
+  extends: .version_tag
+  when: manual
+  script:
+    # build multiple versions using cibuildwheel
+    # - python -m cibuildwheel --output-dir ./dist 
+    # build standalone environment for testing (current package + all dependencies)
+    # - python -m pip wheel -w ./dist/wheelhouse .[test]
+    - python -m pip wheel -w ./dist/ .
+  artifacts:
+    paths:
+    - dist/
+    expose_as: wheel
+    expire_in: 12 hours
+    
+
+build-linux:
+  when: manual
+  extends: .build
+  before_script:
+    - echo "gridmarthe ci/cd pipeline"
+    - python --version
+    - mamba install gfortran
+    - gfortran --version
+    - python -m pip install --upgrade pip setuptools wheel
+    - python -m pip install cibuildwheel build
+    # uncomment next line if you need internal dependencies
+    # - python -m pip config set global.index-url ${REGISTRY_NEXUS}/pypi-all/simple
+
+build-windows:
+  when: manual
+  extends: 
+    - .windows
+    - .build
+  before_script:
+    - echo "gridmarthe ci/cd pipeline"
+    - python --version
+    - gfortran --version
+    - pip install --upgrade pip setuptools wheel
+    - pip install cibuildwheel build
+
+
+# ----- Test config ----- #
+
+.test:
+  stage: test
+  extends: .version_tag
+  when: manual
+  script:
+    # - python -c 'import gridmarthe'
+    - pytest tests/
+  # allow_failure: true
+
+
+test-linux:
+  needs: ["build-linux"]
+  extends: .test
+  before_script:
+    - echo "gridmarthe ci/cd pipeline"
+    - python --version
+    - mamba install gfortran
+    - gfortran --version
+    - python -m pip install --no-index --no-deps dist/*none*.whl # no-index: avoid search pypi index ; no-deps, wheels already build
+    - python -m pip install --no-index --no-deps dist/*linux*.whl
+
+
+test-windows:
+  needs: ["build-windows"]
+  extends: 
+    - .windows
+    - .test
+  before_script:
+    - echo "gridmarthe ci/cd pipeline"
+    - python --version
+    - gfortran --version
+    - pip install --no-index --no-deps dist/*none*.whl
+    - pip install --no-index --no-deps dist/*win*.whl
+
+
+
+# ----- Publish ----- #
+
+.deploy:
+  extends: .version_tag
+  stage: deploy
+  except:
+    - branches
+
+
+deploy-nexus_snapshot:
+  needs: ["build-linux", "build-windows"]
+  extends: .deploy
+  before_script:
+    - echo ${NEXUS_URL}
+    - echo ${REGISTRY_NEXUS_USER}
+    - echo ${REGISTRY_NEXUS}
+    - pip install twine
+  script:
+    - twine upload -u ${REGISTRY_NEXUS_USER} -p ${REGISTRY_NEXUS_PASSWORD} --repository-url ${REGISTRY_NEXUS}/pypi-snapshots/ dist/*.whl
+  when: manual
+
+
+deploy-pip:
+  needs: ["build-linux", "build-windows"]
+  extends: .deploy
+  before_script:
+    - pip install twine
+  script:
+    - twine upload --skip-existing --verbose dist/*.whl
+  when: manual
+

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