script for plotting a single, single cluster fit

Author: sjkdenny

bin/plotSingleClusterBindingCurve.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+#
+# will find fmax distribution given the single cluster fits on 
+# good binders and good fitters.
+#
+# fmax distribtuion is then enforced for weak binders, allowing
+# good measurements even when no reaching saturation.
+# 
+# Median of cluster fits are bootstrapped to obtain errors on
+# fit parameters.
+#
+# Sarah Denny
+# July 2015
+
+##### IMPORT #####
+import numpy as np
+import pandas as pd
+import sys
+import os
+import argparse
+import seqfun
+import datetime
+import IMlibs
+import seaborn as sns
+import scipy.stats as st
+import matplotlib.pyplot as plt
+sns.set_style("white", {'xtick.major.size': 4,  'ytick.major.size': 4})
+import plotFun
+import findFmaxDist
+import fitFun
+#plt.rc('text', usetex=True)
+### MAIN ###
+
+################ Parse input parameters ################
+
+#set up command line argument parser
+parser = argparse.ArgumentParser(description='bootstrap fits')
+parser.add_argument('-b', '--binding_curves', required=True, metavar=".CPseries.pkl",
+                   help='file containining the binding curve information')
+parser.add_argument('-f', '--single_cluster_fits', required=True, metavar=".CPfitted.pkl",
+                   help='file containining the single cluster fits')
+parser.add_argument('-c', '--concentrations', required=True, metavar="concentrations.txt",
+                    help='text file giving the associated concentrations')
+parser.add_argument('-i', '--cluster', required=True, metavar="clusterID",
+                    help='cluster Id for which to plot')
+
+parser.add_argument('-out', '--out_file', 
+                   help='output filename. default is variant number + ".pdf"')
+
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    
+    variantFilename = args.variant_file
+    annotatedClusterFile  = args.annotated_clusters
+    bindingCurveFilename  = args.binding_curves
+    singleClusterFilename = args.single_cluster_fits
+    cluster = args.cluster
+            
+    # load data
+    concentrations = np.loadtxt(args.concentrations)
+    bindingSeries =  pd.read_pickle(bindingCurveFilename)
+    fittedSingles = pd.read_pickle(singleClusterFilename)
+
+    
+    # plot
+    fig = plt.figure(figsize=(4,3))
+    ax = fig.add_subplot(111)
+    fitParameters = pd.DataFrame(columns=['fmax', 'dG', 'fmin'])
+    fitFun.plotFitCurve(concentrations,
+                        bindingSeries.loc[cluster],
+                        fittedSingles.loc[cluster],
+                        fitParameters, ax=ax)
+
+        
+    plt.savefig( args.out_file)