Added first version of kmer histo plotting script

estimate_distribution.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt 
+import sys 
+
+filename = sys.argv[1]
+kmap = {}
+
+with open(filename, 'r') as f:
+    for line in f:
+        x, y = line.strip().split('\t')
+        kmap[int(x)] = float(y)
+
+arrx = np.arange(1,max(kmap.keys()) + 1)
+arry = [kmap[x] if x in kmap else 0.0 for x in np.arange(1,max(kmap.keys()) + 1)]
+df = pd.Series(arry, index=arrx)
+rolling5 = df.rolling(window=5).mean()
+rolling20 = df.rolling(window=20).mean()
+rolling50 = df.rolling(window=50).mean()
+ewm5 = df.ewm(span=5,adjust=False).mean()
+ewm20 = df.ewm(span=20,adjust=False).mean()
+ewm50 = df.ewm(span=50,adjust=False).mean()
+
+def get_eq_pos(data):
+    try:
+        return [len(set(dlist))==1 and dlist[0] > 0.0 for dlist in zip(data[0:],data[1:],data[2:],data[3:],data[4:])].index(True)
+    except ValueError:  
+        return 'NA'
+
+def get_smaller_pos(data1, data2, length):
+    smaller = data1 < data2
+    tozip = [smaller[x:] for x in np.arange(0,length)]
+    try:
+        return [len(set(dlist))==1 and dlist[0] > 0.0 for dlist in zip(*tozip)].index(True)
+    except ValueError:  
+        return 1
+
+ewm5min = get_smaller_pos(ewm5.cummin(), ewm5, 6)
+rolling5min = get_smaller_pos(rolling5.cummin(), rolling5, 6)
+dfmin = get_smaller_pos(df.cummin(), df, 6)
+first = get_smaller_pos(df.cummin(), df, 1)
+inflexion = np.diff(df, 2).tolist()
+inf_point = inflexion.index(list(filter(lambda x: x<0, inflexion))[0])
+
+#plt.semilogy(df, color='blue', label='original data')
+#plt.semilogy((df.cummin()), '--', color='blue', label='cum min original data')
+#plt.semilogy(rolling5, color='red', label='rolling window (length 5)')
+#plt.semilogy((rolling5.cummin()), '--', color='red', label='cum min rolling')
+#plt.semilogy(ewm5, color='green', label='exponential window (length5)')
+#plt.semilogy((ewm5.cummin()), '--', color='green', label='cum min exponential')
+#plt.axis([0,200,0.1,max(kmap.values())])
+#plt.xlabel("Depth of kmer")
+#plt.ylabel("#kmers with depth x")
+#plt.title("Smoothing the kmer histogram")
+
+#plt.loglog(rolling5, color='red', label='rolling window (length 5)')
+#plt.loglog((rolling5.cummin()), '--', color='red', label='cum min rolling')
+plt.loglog(df, color='blue', label='original data')
+plt.loglog((df.cummin()), '--', color='blue', label='cum min original data')
+#plt.loglog(ewm5, color='green', label='exponential window (length 5)')
+#plt.loglog((ewm5.cummin()), '--', color='green', label='cum min exponential')
+plt.axis([1,max(kmap.keys()),0.1,max(kmap.values())*10])
+plt.xlabel("Depth of kmer")
+plt.ylabel("#kmers with depth x")
+plt.title("k-mer histogram of cluster")
+
+#plt.plot(df, color='blue')
+#plt.plot((df.cummin()), '--', color='blue')
+#plt.plot(rolling5, color='red')
+#plt.plot((rolling5.cummin()), '--', color='red')
+#plt.plot(ewm5, color='green')
+#plt.plot((ewm5.cummin()), '--', color='green')
+#plt.axis([0,max(ewm5min, rollinblueg5min, dfmin) + 50,0,5])
+
+plt.axvline(dfmin - 1, color='red')
+#plt.axvline(first, color='black')
+#plt.axvline(inf_point - 1, color='green')
+#plt.axvline(turn_point, color='red')
+#plt.axvline(ewm5min, color='green')
+#plt.axvline(rolling5min, color='red')
+#plt.axvline(2, color='black')
+plt.legend(loc='best', prop={'size' : 8})
+plt.savefig(sys.argv[2], dpi=300)
+
+#print get_eq_pos(ewm5.cummin())
+#print get_eq_pos(rolling5.cummin())
+#print get_eq_pos(df.cummin())
+
+#a = np.array([kmap[x] if x in kmap else 0.0 for x in np.arange(1,max(kmap.keys()))])