Zoe and Soren submission

solution1.py

@@ -0,0 +1,75 @@
+import numpy
+import pandas
+from scipy.optimize import minimize
+from scipy.stats import norm
+from scipy.stats import chi2
+from plotnine import *
+
+def linear(p, obs):
+    B0 = p[0]
+    B1 = p[1]
+    sigma = p[2]
+
+    expected = B0 + (B1 * obs.mutation)
+    nll = -1 * norm(expected, sigma).logpdf(obs.ponzr1Counts).sum()
+    return nll
+
+def nullH(p, obs):
+    B0 = p[0]
+    sigma = p[1]
+
+
+    expected = B0
+    nll = -1 * norm(expected, sigma).logpdf(obs.ponzr1Counts).sum()
+    return nll
+
+
+data = pandas.read_csv('ponzr1.csv', header = 0, sep = ',')
+data['mutation'] = data["mutation"].map({'WT' : 0, 'M124K' : 1, 'V456D' : 2, 'I213N' : 3})
+
+subset=data.loc[data.mutation.isin(['0','1']),:]
+
+initialLinGuess = numpy.array([1,1,1])
+linfit = minimize(linear, initialLinGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+initialNullGuess = numpy.array([1,1])
+nullfit = minimize(nullH, initialNullGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+linfit.fun
+nullfit.fun
+
+D=2*(nullfit.fun - linfit.fun)
+print("M124K P value:")
+print(1-chi2.cdf(x=D, df=1))
+
+subset=data.loc[data.mutation.isin(['0','2']),:]
+
+initialLinGuess = numpy.array([1,1,1])
+linfit = minimize(linear, initialLinGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+initialNullGuess = numpy.array([1,1])
+nullfit = minimize(nullH, initialNullGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+linfit.fun
+nullfit.fun
+
+D=2*(nullfit.fun - linfit.fun)
+print("V456D P value:")
+print(1-chi2.cdf(x=D, df=1))
+
+subset=data.loc[data.mutation.isin(['0','3']),:]
+
+initialLinGuess = numpy.array([1,1,1])
+linfit = minimize(linear, initialLinGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+initialNullGuess = numpy.array([1,1])
+nullfit = minimize(nullH, initialNullGuess, method="Nelder-Mead", options={'disp': True}, args=subset)
+
+linfit.fun
+nullfit.fun
+
+D=2*(nullfit.fun - linfit.fun)
+print("I213N P value:")
+print(1-chi2.cdf(x=D, df=1))
+
+

solution2.py

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+import numpy
+import pandas
+from scipy.optimize import minimize
+from scipy.stats import norm
+from scipy.stats import chi2
+from plotnine import *
+
+def monod(p, obs):
+    UMAX = p[0]
+    KS = p[1]
+    sigma = p[2]
+
+    expected = UMAX*(obs.S / (obs.S + KS))
+    nll = -1 * norm(expected, sigma).logpdf(obs.u).sum()
+    return nll
+
+data = pandas.read_csv('MmarinumGrowth.csv', header = 0, sep = ',')
+
+initialGuess = numpy.array([1,1,1])
+fit = minimize(monod, initialGuess, method="Nelder-Mead", options={'disp': True}, args=data)
+
+print(fit.x)

solution3.py

@@ -0,0 +1,53 @@
+import numpy
+import pandas
+from scipy.optimize import minimize
+from scipy.stats import norm
+from scipy.stats import chi2
+from plotnine import *
+
+def const(p, obs):
+    a = p[0]
+    sigma = p[1]
+
+    expected = a
+    nll = -1 * norm(expected, sigma).logpdf(obs.decomp).sum()
+    return nll
+
+def linear(p, obs):
+    a = p[0]
+    b = p[1]
+    sigma = p[2]
+
+    expected = a + b * obs.Ms
+    nll = -1 * norm(expected, sigma).logpdf(obs.decomp).sum()
+    return nll
+
+def hump(p, obs):
+    a = p[0]
+    b = p[1]
+    c = p[2]
+    sigma = p[3]
+
+    expected = a + b*obs.Ms + c*(obs.Ms) * (obs.Ms)
+    nll = -1 * norm(expected, sigma).logpdf(obs.decomp).sum()
+    return nll
+
+data = pandas.read_csv('leafDecomp.csv', header = 0, sep = ',')
+
+initialGuess = numpy.array([1,1,1,1])
+constfit = minimize(const, initialGuess, method="Nelder-Mead", options={'disp': True}, args=data)
+linfit = minimize(linear, initialGuess, method="Nelder-Mead", options={'disp': True}, args=data)
+humpfit = minimize(hump, initialGuess, method="Nelder-Mead", options={'disp': True}, args=data)
+
+
+D=2*(constfit.fun - linfit.fun)
+print("linear over const fit")
+print(1-chi2.cdf(x=D, df=1))
+
+D=2*(humpfit.fun - linfit.fun)
+print("linear over hump fit")
+print(1-chi2.cdf(x=D, df=1))
+
+D=2*(constfit.fun - humpfit.fun)
+print("hump over const fit")
+print(1-chi2.cdf(x=D, df=2))