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mfe_plots.py
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mfe_plots.py
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from __future__ import division
from builtins import zip
from builtins import str
from builtins import range
from builtins import object
import sys
from datetime import datetime
import numpy as np
import numpy.linalg
import pandas as pd
from math import log10, sqrt, exp
from bisect import bisect_left
from scipy.stats import pearsonr, spearmanr, kendalltau, linregress, wilcoxon, gaussian_kde
import matplotlib
from matplotlib.collections import PatchCollection
from matplotlib.patches import Rectangle
from matplotlib.lines import Line2D
#matplotlib.use("cairo")
import matplotlib.pyplot as plt
#plt.style.use('ggplot') # Use the ggplot style
from data_helpers import getSpeciesName, getSpeciesFileName, getGenomicGCContent, getSpeciesProperty, getSpeciesShortestUniqueNamesMapping
from sklearn import decomposition
from sklearn.neighbors import KernelDensity
from sklearn.model_selection import GridSearchCV, KFold
import seaborn as sns
import cairo
from pyqtree import Index
from ncbi_entrez import getTaxonomicGroupForSpecies
from rate_limit import RateLimit
from mysql_rnafold import getWindowWidthForComputationTag
def plotMFEProfile(taxId, profileId, data, computationTag=None, yRange=(-12,0), showRandomizedRange=True, showComponents=True, fileLabel="", aspect=15.0, tickValues = range(-100,101,20), linewidth=2):
#fig, (ax1,ax2) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': [2, 1]})
fig, ax1 = plt.subplots()
varsForRange = []
if showComponents:
data[['native']].plot( color="#20b040", label="Native LFE", linewidth=linewidth, ax=ax1, zorder=10)
data[['shuffled']].plot(color="#20b0ff", label="Randomized LFE", linewidth=linewidth, ax=ax1, zorder=10)
ax1.fill_between( data.index, data['native'], data['shuffled'], color="#ffb020", alpha=0.7, zorder=1 )
varsForRange = varsForRange + ['native', 'shuffled']
#data['native'].plot(ax=ax1)
#data['shuffled'].plot(ax=ax1)
if showRandomizedRange:
data[['shuffled75', 'shuffled25']].plot(ax=ax1, style='--', zorder=10)
varsForRange = varsForRange + ['shuffled75', 'shuffled25']
windowWidth = None
if not computationTag is None:
windowWidth = getWindowWidthForComputationTag( computationTag )
dlfe = data['native']-data['shuffled']
ax1.plot( data.index, dlfe, zorder=10, color="#ffb020", label=u"\u0394LFE", linewidth=linewidth )
minY = min(yRange[0], np.min(np.min(data[varsForRange])), min(dlfe) )
maxY = max(yRange[1], np.max(np.max(data[varsForRange])), max(dlfe) )
speciesName = getSpeciesName(taxId)
plt.title(speciesName)
plt.xlabel('Position (nt, window start, from stop codon)')
ax1.set_title("Mean LFE for %s" % speciesName)
#ax1.set_ylabel(u"\u0394LFE")
ax1.set_ylabel(u"LFE (kcal/mol/window)")
ax1.legend(fontsize=8, loc="lower left", bbox_to_anchor=(-0.4,0.0) )
ax1.grid(axis="y")
#data['gc'].plot(ax=ax2)
#ax2.set_title("GC%")
#ax2.set_ylabel('GC% (in window)')
#ax2.grid(True)
if not computationTag is None:
if not fileLabel:
baseName = "mfe_v2_40nt_cds_{}_{}_series{}".format(profileId, getSpeciesFileName(taxId), computationTag)
else:
baseName = "mfe_v2_40nt_cds_{}_{}_series{}_{}".format(profileId, getSpeciesFileName(taxId), computationTag, fileLabel)
else:
if not fileLabel:
baseName = "mfe_v2_40nt_cds_{}_{}".format(profileId, getSpeciesFileName(taxId))
else:
baseName = "mfe_v2_40nt_cds_{}_{}_{}".format(profileId, getSpeciesFileName(taxId), fileLabel)
#profileId = "tbd" # str(args.profile.ProfileId).replace(':', '-')
ax1.set_aspect(aspect)
if not windowWidth is None:
ax1.annotate( "{}nt".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=10, color="black", zorder=50 )
ax1.annotate( "".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=6, arrowprops=dict(arrowstyle='<-', alpha=1.0, linewidth=1.0, color="black"), color="black", zorder=50 )
ax1.set_xticks(list(tickValues))
ax1.set_xticklabels([str(x) for x in tickValues])
plt.xlim( (-100, 100) )
plt.ylim( (minY-0.2, maxY+0.2) )
plt.axvline( x=0.0, color="black", linewidth=linewidth*0.7, alpha=1.0, zorder=1 )
plt.axhline( y=0.0, color="black", linewidth=linewidth*0.7, alpha=1.0, zorder=1 )
plt.savefig("{}.pdf".format( baseName ), bbox_inches="tight", metadata={"CreationDate":datetime.utcnow().isoformat(' ')})
plt.savefig("{}.svg".format( baseName ))
plt.close(fig)
def plot2WayMFEComparison(taxId, profileId, data1, data2, computationTag=None, yRange=(-12,0), comment=None):
#fig, (ax1,ax2) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': [2, 1]})
fig, ax1 = plt.subplots()
#data1[['native', 'shuffled']].plot(ax=ax1, zorder=10, style='--')
#data2[['native', 'shuffled']].plot(ax=ax1, zorder=10)
#data['native'].plot(ax=ax1)
#data['shuffled'].plot(ax=ax1)
#data[['shuffled75', 'shuffled25']].plot(ax=ax1, style='--', zorder=10)
ax1.plot( data1.index, data1['native'], zorder=10, label=u"Native", linestyle='dashed', c='#e24a33' )
ax1.plot( data1.index, data1['shuffled'], zorder=10, label=u"Shuffled", linestyle='dashed', c='#348abd' )
ax1.plot( data2.index, data2['native'], zorder=10, label=u"Native", linestyle='solid', c='#e24a33' )
ax1.plot( data2.index, data2['shuffled'], zorder=10, label=u"Shuffled", linestyle='solid', c='#348abd' )
minY = min(yRange[0], np.min(np.min(data1[['native', 'shuffled']])))
maxY = max(yRange[1], np.max(np.max(data1[['native', 'shuffled']])))
windowWidth = None
if not computationTag is None:
windowWidth = getWindowWidthForComputationTag( computationTag )
smfe1 = data1['native']-data1['shuffled']
ax1.plot( data1.index, smfe1, zorder=10, label=u"\u0394LFE", linestyle='dashed', c='#fbc15e' )
smfe2 = data2['native']-data2['shuffled']
ax1.plot( data2.index, smfe2, zorder=10, label=u"\u0394LFE", linestyle='solid', c='#fbc15e' )
speciesName = getSpeciesName(taxId)
plt.title(speciesName)
plt.xlabel('Position (nt, window start, from stop codon)')
ax1.set_title("Mean LFE for %s" % speciesName)
#ax1.set_ylabel(u"\u0394LFE")
ax1.set_ylabel(u"LFE (kcal/mol/window)")
ax1.legend(fontsize=8, loc="lower left", bbox_to_anchor=(-0.4,0.0) )
ax1.grid(True)
#data['gc'].plot(ax=ax2)
#ax2.set_title("GC%")
#ax2.set_ylabel('GC% (in window)')
#ax2.grid(True)
if not computationTag is None:
baseName = "mfe_v2_40nt_cds_{}_{}_series{}_comparison".format(profileId, getSpeciesFileName(taxId), computationTag)
else:
baseName = "mfe_v2_40nt_cds_{}_{}_comparison".format(profileId, getSpeciesFileName(taxId))
#profileId = "tbd" # str(args.profile.ProfileId).replace(':', '-')
ax1.set_aspect(15.0)
plt.axvline( x=0.0, color="black", linewidth=0.5, alpha=1.0, zorder=1 )
if not windowWidth is None:
ax1.annotate( "{}nt".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=10, color="black", zorder=50 )
ax1.annotate( "".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=6, arrowprops=dict(arrowstyle='<-', alpha=1.0, linewidth=1.0, color="black"), color="black", zorder=50 )
if comment:
ax1.annotate( comment, xy=(-100, minY), xytext=(-100, minY), fontSize=10, color="black", zorder=50 )
plt.ylim( (minY-0.2, maxY+0.2) )
plt.savefig("{}.pdf".format( baseName ), bbox_inches="tight", metadata={"CreationDate":datetime.utcnow().isoformat(' ')})
plt.savefig("{}.svg".format( baseName ))
plt.close(fig)
def plotMultipleMFEComparison(taxId, profileId, series, computationTag=None, yRange=(-12,0), plotname="comparison", showComponents=True, showLegend=True, comment=None, xTickValues=range(-100,101,20), yTickValues=range(-7,1,1), lineWidth=2, aspect=15.0, destinations=None):
if not destinations is None:
assert(len(destinations)==len(series))
fig, ax1 = plt.subplots()
#fig, (ax1,ax2) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': [2, 1]})
if showComponents:
lineStyles = ['solid', 'dashed', 'dotted', 'dashdot']
lineColors = ['#e9bb42', '#e9bb42','#e9bb42', '#e9bb42']
else:
lineStyles = ['solid', 'solid', 'solid', 'solid' ]
lineColors = ['#eb7b6d', '#6debbc', '#6dc8eb', '#6da6eb']
minY = 1e10
maxY = -1e10
varsForRange = []
for n, s in enumerate(series):
data, label = s
if showComponents:
ax1.plot( data.index, data['native'], zorder=10, label=u"{}, Native".format(label), linestyle=lineStyles[n], linewidth=lineWidth, c='#e24a33' )
ax1.plot( data.index, data['shuffled'], zorder=10, label=u"{}, Shuffled".format(label), linestyle=lineStyles[n], linewidth=lineWidth, c='#348abd' )
#ax1.plot( data2.index, data2['native'], zorder=10, label=u"Native", linestyle='solid', c='#e24a33' )
#ax1.plot( data2.index, data2['shuffled'], zorder=10, label=u"Shuffled", linestyle='solid', c='#348abd' )
varsForRange = varsForRange + ['native', 'shuffled']
dlfe = data['native']-data['shuffled']
if not destinations is None:
count = np.min( destinations[n].nativeMeanProfile.counts())
label = u"{}, \u0394LFE (N={})".format(label, count)
else:
label = u"{}, \u0394LFE".format(label)
ax1.plot( data.index, dlfe, zorder=10, label=label, linestyle=lineStyles[n], linewidth=lineWidth, c=lineColors[n], alpha=0.8 )
#smfe2 = data2['native']-data2['shuffled']
#ax1.plot( data2.index, smfe2, zorder=10, label=u"\u0394LFE", linestyle='solid', c='#fbc15e' )
minY = min(minY, yRange[0], np.min(np.min(data[varsForRange])), min(dlfe) )
maxY = max(maxY, yRange[1], np.max(np.max(data[varsForRange])), max(dlfe) )
windowWidth = None
if not computationTag is None:
windowWidth = getWindowWidthForComputationTag( computationTag )
speciesName = getSpeciesName(taxId)
hfont = {'fontname':'Helvetica'}
plt.title(speciesName, **hfont)
plt.xlabel('Position (nt, window start, from stop codon)', **hfont)
plt.axvline( x=0.0, color="black", linewidth=1.5, alpha=1.0, zorder=1 )
plt.axhline( y=0.0, color="black", linewidth=1.5, alpha=1.0, zorder=1 )
ax1.set_xticks(list(xTickValues))
ax1.set_xticklabels([str(x) for x in xTickValues])
ax1.set_yticks(list(yTickValues))
ax1.set_yticklabels([str(x) for x in yTickValues])
plt.xlim( (-100, 100) )
plt.ylim( (minY-0.2, maxY+0.2) )
if showComponents:
ax1.set_ylabel(u"LFE (kcal/mol/window)", **hfont)
else:
ax1.set_ylabel(u"\u0394LFE (kcal/mol/window)", **hfont)
if showLegend:
ax1.legend(fontsize=8, loc="lower left", bbox_to_anchor=(-0.4,0.0) )
ax1.grid(axis="y")
#data['gc'].plot(ax=ax2)
#ax2.set_title("GC%")
#ax2.set_ylabel('GC% (in window)')
#ax2.grid(True)
if not computationTag is None:
baseName = "mfe_v2_40nt_cds_{}_{}_series{}_{}".format(profileId, getSpeciesFileName(taxId), computationTag, plotname)
else:
baseName = "mfe_v2_40nt_cds_{}_{}_{}".format(profileId, getSpeciesFileName(taxId), plotname)
#profileId = "tbd" # str(args.profile.ProfileId).replace(':', '-')
ax1.set_aspect( aspect )
#plt.axvline( x=0.0, color="black", linewidth=0.5, alpha=1.0, zorder=1 )
if not windowWidth is None:
ax1.annotate( "{}nt".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=10, color="black", zorder=50 )
ax1.annotate( "".format(windowWidth), xy=(180, minY), xytext=(180+windowWidth, minY), fontSize=6, arrowprops=dict(arrowstyle='<-', alpha=1.0, linewidth=1.0, color="black"), color="black", zorder=50 )
if comment:
ax1.annotate( comment, xy=(-100, minY), xytext=(-100, minY), fontSize=10, color="black", zorder=50 )
plt.ylim( (minY-0.2, maxY+0.2) )
plt.savefig("{}.pdf".format( baseName ), bbox_inches="tight", metadata={"CreationDate":datetime.utcnow().isoformat(' ')})
plt.savefig("{}.svg".format( baseName ))
plt.close(fig)
def plot2dProfile( hist2d, hist2d_xedges, hist2d_yedges, taxId ):
fig, ax1 = plt.subplots()
#ax1.imshow( hist2d, cmap='bwr', aspect='auto', norm=cmapNormalizer, interpolation="bilinear" )
ax1.imshow( np.log(hist2d) )
# Add means line
means = np.sum( hist2d * (hist2d_yedges[1:] + hist2d_yedges[:-1])*0.5, axis=1 ) / np.sum(hist2d, axis=1)
plt.plot( (means+30)/60*100, range( hist2d_xedges.shape[0]-1 ), c="black" )
plt.savefig( "mfe_v3_2dhist_{}.png".format( taxId ) )
plt.close(fig)
def plot2dProfileAsRidgeplot( hist2d, hist2d_xedges, hist2d_yedges, taxId ):
#a = pd.DataFrame(np.zeros((199,100)), index=range(-100,99), columns=list(range(-50,50)))
f, ax = plt.subplots()
sns.set(style="white", rc={"axes.facecolor":(0,0,0,0)})
a = pd.DataFrame(hist2d, index=hist2d_xedges[:-1], columns=hist2d_yedges[:-1] )
a = a.iloc[range(0,200,3), :]
df = a.melt(value_vars=hist2d_yedges[:-1])
#df["pos"] = pd.Series( np.tile( np.round(np.array(hist2d_xedges[range(76,125,8)])), 100 ), dtype=np.int )
#np.tile( np.array(np.round(hist2d_xedges[range(76,125,8)]), np.int), 3)
#df["pos"] = pd.Categorical( np.tile( list("ABCDEFG"), 100 ), categories=list("ABCDEFG"), ordered=True)
allPositions = np.array( np.round( hist2d_xedges[range(0,200,3)]), dtype=np.int)
df["pos"] = pd.Series( np.tile( allPositions, 100 ) )
#df["xxyx"] = pd.Series(
pal = sns.cubehelix_palette(len(range(0,200,3)), rot=-.25, light=.7 )
#g = sns.FacetGrid(df, col="pos", hue="pos", aspect=0.05, height=5.0, palette=pal, sharex=False, sharey=True, col_order=list(reversed(hist2d_xedges[range(0,200,3)])) )
g = sns.FacetGrid(df, col="pos", hue="pos", aspect=0.05, height=5.0, palette=pal, sharex=False, sharey=True, col_order=list(reversed(allPositions)) )
#g.map( sns.lineplot, x="variable", y="value", data=df )
g.map( plt.fill_between, "value", "variable", data=df )
g.map( plt.plot, "value", "variable", color="white", zorder=10, linewidth=1 )
#g.map( sns.lineplot, "value", "variable", color="white", lw=2, data=df )
#def test(x, y, color, label):
# print(x, y)
#g.map(test, "variable", "value")
#g.map( sns.kdeplot, "value", clip_on=False, shade=True )
#g.map( plt.set_yscale, "log" )
g.despine( bottom=True, left=True )
g.fig.subplots_adjust(wspace=-.9)
plt.savefig("ridge_{}.pdf".format(taxId))
plt.savefig("ridge_{}.svg".format(taxId))
# for i in range(8):
# f, ax = plt.subplots()
# plt.plot( a.iloc[i,:] )
# plt.title(i)
# plt.savefig("ridge_{}_debug_{}".format(taxId, i))
f, ax = plt.subplots()
for n, x in enumerate(a.index):
plt.plot( a.iloc[n,:], c="green")
dd = df[df["pos"]==x]
plt.plot( dd["variable"], dd["value"], c="blue")
plt.savefig("ridge_{}_debug.pdf".format(taxId))
class RGBinterpolator(object):
def __init__(self, N, c=[[0.79, 0.01, 0.01], [0.03, 0.26, 0.99], [0.6, 0.94, 0.55]] ):
self.c = c
assert(len(c)==3)
self.delta = [[c[1][0]-c[0][0], c[1][1]-c[0][1], c[1][2]-c[0][2]], [c[2][0]-c[1][0], c[2][1]-c[1][1], c[2][2]-c[1][2]]]
self.N = N
def getColor(self, n):
pos = float(n)/(self.N-1)
if pos < 0.5:
return (self.c[0][0] + self.delta[0][0]*((pos-0.0)/0.5),
self.c[0][1] + self.delta[0][1]*((pos-0.0)/0.5),
self.c[0][2] + self.delta[0][2]*((pos-0.0)/0.5))
else:
return (self.c[1][0] + self.delta[1][0]*((pos-0.5)/0.5),
self.c[1][1] + self.delta[1][1]*((pos-0.5)/0.5),
self.c[1][2] + self.delta[1][2]*((pos-0.5)/0.5))
def plotMultipleDestinations(taxId, plotLabel, profiles, ranges, xpositions):
assert(len(ranges)==len(profiles))
f, ax = plt.subplots()
clr = RGBinterpolator(N=len(profiles))
labels = []
for i, profile in enumerate(profiles):
r = ranges[i]
dlfe = profile.nativeMeanProfile.value() - profile.shuffledMeanProfile.value()
ax.plot( xpositions, dlfe, c=clr.getColor(i), label=profile.subclass )
y = -5.8 + i*0.2
ax.annotate( "", xy=(0,y), xytext=(0.5*(r[0]+r[1]),y), color=clr.getColor(i), arrowprops=dict(arrowstyle='-', alpha=1.0, linewidth=1.5, color=clr.getColor(i)) )
ax.annotate( "{}-{}".format(r[0],r[1]), xy=(min(95, 0.5*(r[0]+r[1])),y), color=clr.getColor(i) )
labels.append("{}-{}".format(r[0],r[1]))
ax.legend(labels)
plt.axvline( x=0.0, color="black", linewidth=0.5, alpha=1.0, zorder=1 )
plt.axhline( y=0.0, color="black", linewidth=0.5, alpha=1.0, zorder=1 )
plt.xlabel( "Distance from stop codon (nt)" )
plt.ylabel( "dLFE" )
speciesName = getSpeciesName(taxId)
plt.title(speciesName)
plt.savefig("{}_{}.pdf".format(plotLabel, taxId))
plt.savefig("{}_{}.svg".format(plotLabel, taxId))
def plotMFEProfileV3(taxid, profileId, df, dLFEData, wilcoxon, transitionPeak, transitionPeakPos, edgeWilcoxon, ProfilesCount):
# TODO - RE-IMPL THIS
pass
# spearman_smfe_CAI_pval spearman_smfe_CAI_rho ... spearman_smfe_gc_pval spearman_smfe_gc_rho
def plotSmoothedLengthEffectData(taxid, plotLabel, df, xvar='Pos'):
f, ax = plt.subplots()
df.plot(x=xvar, y='MinDLFE', color="blue", label="MinDLFE", ax=ax)
df.plot(x=xvar, y='Mean', color="green", label="Mean", ax=ax)
df.plot(x=xvar, y='P25', color="grey", style='--', label="P25", ax=ax)
df.plot(x=xvar, y='P75', color="grey", style='--', label="P75", ax=ax)
df.plot(x=xvar, y='Density', color="black", style='-', label="Density", secondary_y=True, ax=ax)
speciesName = getSpeciesName(taxid)
plt.title(speciesName)
plt.savefig("{}_{}.pdf".format(plotLabel, taxid))
plt.savefig("{}_{}.svg".format(plotLabel, taxid))
def plotMFEvsCUBcorrelation( biasProfiles, cubCorrs ):
yvars = ('spearman_smfe_Nc_rho', 'spearman_smfe_CAI_rho', 'spearman_smfe_Fop_rho', 'spearman_smfe_gc_rho')
print(cubCorrs.columns)
for cdsPos in (0, 25):
#xvals = [x.iat[cdsPos] for x in biasProfiles.values()]
#print(xvar)
for yvar in yvars:
yvals = cubCorrs[yvar].values
yvarWords = yvar.split("_")
xvals = []
for taxid in cubCorrs.index.values:
xvals.append( biasProfiles[taxid].iat[cdsPos] )
#print(xvals)
#print(yvals)
f, ax = plt.subplots()
g = sns.jointplot( xvals, yvals, kind="scatter", dropna=False )
plt.xlabel("dLFE at begin+{}nt".format(cdsPos*10))
plt.ylabel("Spearman correlation vs. {}".format(yvarWords[2]))
plt.ylim((-1,1))
g.savefig("cub_corrs_{}_vs_dLFE_{}_begin.pdf".format(yvar, cdsPos*10) )
g.savefig("cub_corrs_{}_vs_dLFE_{}_begin.svg".format(yvar, cdsPos*10) )
f, ax = plt.subplots(4,1, figsize=(7,7), sharex=True)
ymax = 64
for i, yvar in enumerate(yvars):
yvals = cubCorrs[yvar].values
yvarWords = yvar.split("_")
yvarName = yvarWords[2]
if yvarName=="gc": yvarName="GC%"
g = sns.distplot(yvals, kde=False, bins=10, ax=ax[i])
meanVal = np.mean(yvals)
# Show mean value
ax[i].axvline( x=meanVal, c="red" )
ax[i].annotate(s="mean={:.2}".format(meanVal), xy=(meanVal-0.015, ymax*0.9), color="red", fontsize=11, horizontalalignment="right" )
# Show x-axis (no correlation)
ax[i].axvline( x=0, c="black" )
# Update axis labels
ax[i].set_xlabel("Spearman correlation vs. {}".format(yvarName))
ax[i].set_ylabel("Num. species")
# Used fixed y-range
ax[i].set_ylim((0, ymax))
# Annotate number of species
ax[0].annotate(s="N={}".format(cubCorrs.shape[0]), xy=(0.82, ymax*0.9), fontsize=11 )
plt.tight_layout()
plt.savefig("cub_corrs_all_hist_only.pdf")
plt.savefig("cub_corrs_all_hist_only.svg")
randomizationTypesLabels = {11:"Codon shuffle", 12:"Vertical shuffle"} # TODO - move this to a good place (resolve dependency problem)
def plotMFEProfileForMultipleRandomizations(taxId, profileId, data):
fig, ax1 = plt.subplots()
arbitraryKey = list(data.keys())[0]
data[arbitraryKey][['native']].plot(ax=ax1)
labels = []
labels.append("Native")
for shuffleType in list(data.keys()):
data[shuffleType][['shuffled']].plot(ax=ax1)
labels.append(randomizationTypesLabels[shuffleType])
ax1.legend(labels)
#L = plt.legend()
#for i, label in enumerate(labels):
# L.get_texts()[i].set_text(label)
#data[arbitraryKey][['shuffled75', 'shuffled25']].plot(ax=ax1, style='--')
speciesName = getSpeciesName(taxId)
plt.title(speciesName)
plt.xlabel('Position (nt, window start, from cds start)')
#tile = getProfileHeatmapTile(99991320, data[11], (-2.9,2.9) )
#print(tile)
#tileData = plt.imread(tile, format='png')
#
#cmapNormalizer = CenterPreservingNormlizer(-2.9, 2.9)
#plt.imshow( np.array( tileData ).reshape(1,-1), cmap='bwr', norm=cmapNormalizer, extent=(1400,1900,-8,-7.5), interpolation='bilinear' )
#plt.imshow( tileData, cmap='bwr', norm=cmapNormalizer, extent=(1400,1900,-8,-7.5), interpolation='bilinear' )
ax1.set_title("Mean LFE for %s" % speciesName)
ax1.set_ylabel('Mean LFE')
#ax1.legend(fontsize=8)
ax1.grid(True)
#profileId = "tbd" # str(args.profile.ProfileId).replace(':', '-')
plt.savefig("mfe_v2_40nt_cds_%s_allshuffles_%s.pdf" % (profileId, getSpeciesFileName(taxId)) )
plt.savefig("mfe_v2_40nt_cds_%s_allshuffles_%s.svg" % (profileId, getSpeciesFileName(taxId)) )
plt.close(fig)
def plotMFEProfileMultiple(taxId, profileId, data, additionalVars, scaleBar=None):
fig, axes = plt.subplots(2+len(additionalVars), sharex=True)
data[['native', 'shuffled']].plot(ax=axes[0])
#data[['shuffled75', 'shuffled25']].plot(ax=axes[0], style='--')
smfe = data['native']-data['shuffled']
axes[1].plot([min(data.index), max(data.index)], [0,0], c='black')
axes[1].plot( data.index, smfe, zorder=10 )
axes[1].set_ylabel(u"\u0394MFE")
speciesName = getSpeciesName(taxId)
plt.xlabel('Position (nt, window start, from cds start)')
axes[0].set_title("Biases for %s" % speciesName)
axes[0].set_ylabel(r'MFE')
axes[0].legend(fontsize=8)
axes[0].grid(True)
plotRange = []
for i, var in enumerate(additionalVars):
currentAxis = i+2
data[var].plot(ax=axes[currentAxis])
#axes[currentAxis].set_title(var)
axes[currentAxis].set_ylabel(var)
axes[currentAxis].grid(True)
plotRange = [min(data[var]), max(data[var])]
yrange = (min(data[var]), max(data[var]))
warn = ''
if( len(data[var]) < 500 ):
warn = ' <!>'
spearman = spearmanr( smfe, data[var])
axes[currentAxis].annotate(s="Signed Spearman r: %1.3f (p<%1.2f%s)" % (spearman.correlation, spearman.pvalue, warn), xy=(max(data.index)*0.8, yrange[0]+(yrange[1]-yrange[0])*0.20), fontsize=6 )
spearman2 = spearmanr( abs(smfe), data[var])
axes[currentAxis].annotate(s="Unsigned Spearman r: %1.3f (p<%1.2f%s)" % (spearman2.correlation, spearman2.pvalue, warn), xy=(max(data.index)*0.8, yrange[0]+(yrange[1]-yrange[0])*0.05), fontsize=6 )
if( not scaleBar is None ):
# Draw a scale-bar
scaleBarPosY = plotRange[0] + 0.75*(plotRange[1]-plotRange[0]) # use the range of the last plot
scaleBarHeight = 0.075*(plotRange[1]-plotRange[0]) # half-height actually...
axes[-1].plot([ 5, 5+scaleBar], [scaleBarPosY, scaleBarPosY], c='black')
axes[-1].plot([ 5, 5], [scaleBarPosY-scaleBarHeight, scaleBarPosY+scaleBarHeight], c='black')
axes[-1].plot([ 5+scaleBar, 5+scaleBar], [scaleBarPosY-scaleBarHeight, scaleBarPosY+scaleBarHeight], c='black')
#profileId = "tbd" # str(args.profile.ProfileId).replace(':', '-')
plt.savefig("mfe_v2_40nt_cds_vars_%s_%s.pdf" % (profileId, getSpeciesFileName(taxId)) )
plt.savefig("mfe_v2_40nt_cds_vars_%s_%s.svg" % (profileId, getSpeciesFileName(taxId)) )
plt.close(fig)
def plotXY(taxId, profileId, data, xvar, yvar, title, computationTag=None):
fig, ax1 = plt.subplots()
data[[yvar]].plot(ax=ax1)
#plt.annotate(s="n= %d" % len(yvals), xy=(0.28, 17.0), fontsize=6 )
#plt.xlim([.25,.75])
plt.xlabel(xvar.replace('_',' '))
plt.ylabel(yvar.replace('_',' '))
plt.title( title )
plt.grid(True)
#plt.legend(loc=(0,1), scatterpoints=1, ncol=3, fontsize='small')
if not computationTag is None:
baseName = "mfe_v2_40nt_%s_vs_%s_%s_%s_series%d" % (yvar, xvar, profileId, getSpeciesFileName(taxId), computationTag)
else:
baseName = "mfe_v2_40nt_%s_vs_%s_%s_%s" % (yvar, xvar, profileId, getSpeciesFileName(taxId))
plt.savefig("{}.pdf".format(baseName))
plt.savefig("{}.svg".format(baseName))
plt.close(fig)
def scatterPlot(taxId, profileId, data, xvar, yvar, title="%s", colorvar=None, label="genelevel"):
data = data.copy()
data.dropna(subset=(xvar, yvar), inplace=True)
fig, ax1 = plt.subplots()
if colorvar is None:
data.plot(x=xvar, y=yvar, ax=ax1, kind='scatter')
else:
data.plot(x=xvar, y=yvar, c=colorvar, s=3.0, ax=ax1, kind='scatter')
################################
xvals = data[xvar]
yvals = data[yvar]
#print(data.head())
# Linear correlation and factors
pearson = pearsonr(xvals, yvals)
spearman = spearmanr(xvals, yvals)
kendall = kendalltau(xvals, yvals)
l = linregress(xvals, yvals)
abline_x = np.arange(min(xvals), max(xvals)*1.1, (max(xvals)-min(xvals)) )
abline_y = abline_x * l.slope + l.intercept
plt.plot(abline_x, abline_y, '--')
topr = max(yvals)*1.05
left = min(xvals)
scaler = topr / 20
# plot the linear approximation
plt.annotate(s="Pearson $\\rho$: %1.3f (p<%g)" % (pearson[0], pearson[1]), xy=(left, topr-scaler*1), fontsize=6 )
plt.annotate(s="Pearson $r^2$: %1.3f" % (pearson[0]**2,), xy=(left, topr-scaler*2), fontsize=6 )
plt.annotate(s="Spearman r: %1.3f (p<%g)" % (spearman.correlation, spearman.pvalue), xy=(left, topr-scaler*3), fontsize=6 )
plt.annotate(s="Kendall's tau: %1.3f (p<%g)" % (kendall.correlation, kendall.pvalue), xy=(left, topr-scaler*4), fontsize=6 )
plt.annotate(s="n= %d" % len(yvals), xy=(left, topr-scaler*5), fontsize=6 )
################################3
#plt.annotate(s="n= %d" % len(data), xy=(0.3, 4), fontsize=6 )
#plt.xlim([-200,1000])
#plt.xlim([.25,.75])
plt.xlabel(xvar.replace('_',' '))
plt.ylabel(yvar.replace('_',' '))
plt.title( title % getSpeciesName(taxId) )
plt.grid(True)
#plt.legend(loc=(0,1), scatterpoints=1, ncol=3, fontsize='small')
plt.savefig("mfe_v2_40nt_%s_%s_vs_%s_%s_%s.pdf" % (label, yvar, xvar, profileId, getSpeciesFileName(taxId)))
plt.savefig("mfe_v2_40nt_%s_%s_vs_%s_%s_%s.svg" % (label, yvar, xvar, profileId, getSpeciesFileName(taxId)))
plt.close(fig)
class CenterPreservingNormlizer(matplotlib.colors.Normalize):
def __init__(self, negativeRange, positiveRange):
self._negativeRange = negativeRange
assert(positiveRange>0.0)
self._positiveRange = positiveRange
assert(negativeRange<0.0)
# Declare to the rest of the API what is our output range
self.vmin=0
self.vmax=1
#def __call__(self, values, clip=None):
def __call__(self, values):
outHalfScale = 0.5*(float(self.vmax)-float(self.vmin))
outCenter = self.vmin + outHalfScale
out = values.copy()
factor = self._positiveRange / outHalfScale
#print("+factor: %g" % (factor))
values[values > 0.0] /= factor
factor = self._negativeRange / outHalfScale*-1
#print("-factor: %g" % (factor))
values[values <= 0.0] /= factor
values += outCenter
values = 1-values
#assert(np.all(values >= self.vmin))
#assert(np.all(values <= self.vmax))
# Use the logistic function (https://en.wikipedia.org/wiki/Logistic_function) to increase 'contrast'
# To test using gnuplot:
# plot [0:1] 1/(1+exp(-15*(x-0.5)))
#
steepness = 15.0
return 1 / (1+np.exp(-steepness*(values-0.5)))
"""
Plot the profile for a single species (contained in 'data'), save into a file, and return its name.
taxId - taxId of the species to plot
data - profile data for multiple species (including the one specified by taxId...)
yrange - y-range scale (this allows all tiles to have the same scale)
"""
def getProfileHeatmapTile(taxId, data, yrange, ticks=False, profileStep=10, phylosignalProfiles=None, profilesGroup=0, profileTilesFirstLineFix=False, wilcoxonDLFEZero=None, wilcoxonDLFE50_100=None):
if not taxId in data:
print("getProfileHeatmapTile(): taxId {} not found".format(taxId))
return None
assert(len(yrange)==2)
assert(yrange[0] < yrange[1])
isSecondaryPlotActive = (((not wilcoxonDLFEZero is None) and (taxId in wilcoxonDLFEZero )) or
((not wilcoxonDLFE50_100 is None) and (taxId in wilcoxonDLFE50_100)))
#fig, ax = plt.subplots()
if isSecondaryPlotActive:
fig, (ax1,ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3, 1]})
else:
fig, ax1 = plt.subplots()
ax2 = None
# read profile data
series = data[taxId]
if profileTilesFirstLineFix: # fix for some end-profiles with invalid first element (i.e. based on very partial data)
series = series[1:]
cmapNormalizer = CenterPreservingNormlizer(yrange[0], yrange[1])
phylosignalNormalizer = None
if not phylosignalProfiles is None:
phylosignalNormalizer = CenterPreservingNormlizer(np.min(phylosignalProfiles.values), np.max(phylosignalProfiles.values) )
#pvalNormalizer = None
#if not (wilcoxonDLFEZero is None and wilcoxonDLFE50_100 is None):
# pvalNormalizer = matplotlib.colors.Normalize(vmin=-10,vmax=-1.5)
pvalNormalizer = None
if isSecondaryPlotActive:
pvalNormalizer = CenterPreservingNormlizer( -50, 50 )
# read phylosignal profile (if used)
phylosignalProfile = None
if( not phylosignalProfiles is None and taxId in phylosignalProfiles.index ): # note: this does not support end-referenced profiles
phylosignalProfile = phylosignalProfiles.loc[taxId,:]
if( len(series) < len(phylosignalProfile) ):
phylosignalProfile = phylosignalProfile[:len(series)]
assert(phylosignalProfile.index[0] == "Profile.1")
assert(len(phylosignalProfile)==len(series))
#print( "Got phylosignal for {}".format(taxId) )
wilcoxonDLFEZeroProfile = None
#if( not wilcoxonDLFEZero is None and taxId in wilcoxonDLFEZero ):
# wilcoxonDLFEZeroProfile = wilcoxonDLFEZero[taxId]
if isSecondaryPlotActive:
wilcoxonDLFEZeroProfile = wilcoxonDLFE50_100[taxId]
imdata = np.array(series).reshape(1,-1)
#ax.axis(xmin=series.index[0], xmax=series.index[-1])
ax1.imshow( imdata, cmap='bwr', aspect='auto', norm=cmapNormalizer, interpolation="bilinear" )
# if( not phylosignalProfile is None ):
# ax2.imshow( phylosignalProfile.values.reshape(1,-1), cmap='bwr', aspect='auto', norm=phylosignalNormalizer, interpolation="bilinear" )
if isSecondaryPlotActive:
assert( not wilcoxonDLFEZeroProfile is None )
# test values (legend)
#valuesForPlotting = ( np.log10( np.maximum( 1e-320, np.concatenate( (np.linspace( 0, 1, 100), np.linspace( 1, 0, 100)) ))) * np.repeat( (-1,1), 100 ) ).reshape(1,-1)
valuesForPlotting = ( np.log10( np.maximum( 1e-320, wilcoxonDLFEZeroProfile['pval'].values ) ) * wilcoxonDLFEZeroProfile['sign'].values * -1 ).reshape(1,-1)
ax2.imshow( valuesForPlotting, cmap='bwr', aspect='auto', norm=pvalNormalizer, interpolation="bilinear" )
#else: # if phylosignal is not shown, the profile will be also be plotted on the 2nd axis
# ax2.imshow( imdata, cmap='bwr', aspect='auto', norm=cmapNormalizer, interpolation="bilinear" )
#pos = list(ax1.get_position().bounds)
#taxname = getSpeciesFileName(taxId)
#taxDescriptor = "%s.%s" % (taxname[0], taxname[1:9])
#fig.text(pos[0]-0.01, pos[1]+pos[3]/2., taxDescriptor, va='center', ha='right', fontsize=8)
ax1.set_yticks(())
if not ax2 is None:
ax2.set_yticks(())
if ticks:
tickValues = list(range(10, len(series)-10, 10))
ax1.set_xticks(tickValues)
ax1.set_xticklabels(["" for x in tickValues])
else:
ax1.set_xticks(())
if profilesGroup == 0:
tileFilename = "heatmap_profile_taxid_{}.png".format(taxId)
else:
tileFilename = "heatmap_profile_taxid_{}_g{}.png".format(taxId, profilesGroup)
plt.savefig(tileFilename, orientation='portrait', bbox_inches='tight')
#plt.savefig("heatmap_profile_taxid_%d.svg" % taxId, orientation='portrait')
plt.close(fig)
return tileFilename
"""
Plot the profile for a single species (contained in 'data'), save into a file, and return its name.
taxId - taxId of the species to plot
data - profile data for multiple species (including the one specified by taxId...)
yrange - y-range scale (this allows all tiles to have the same scale)
"""
def getLegendHeatmapTile(yrange):
assert(len(yrange) == 2)
assert(yrange[0] <= yrange[1])
fig, ax = plt.subplots()
b = 1.2
series = np.linspace( b**yrange[0], 1 - b**(-yrange[1]), 100) # Create a range whose logit image will cover the range yrange...
print(series)
#series = np.linspace( yrange[0], yrange[1], 100) # linear scale
series = np.log( series / (1-series)) / np.log(b) # Logit function (inverse of logistic function)
#print(series)
cmapNormalizer = CenterPreservingNormlizer(yrange[0], yrange[1])
imdata = series
imdata = np.vstack((imdata,imdata)) # pretty crude trick borrowed from matplotlib examples...
ax.imshow( imdata, cmap='bwr', aspect=2.0, norm=cmapNormalizer, interpolation="bilinear" )
def roundTowardZero(x):
if x < -0.5:
return round(x)+1
elif x > 0.5:
return round(x)-1
else:
return 0
#ax.set_title(taxId)
ax.set_yticks(())
#ax.axis(xmin=yrange[0], xmax=yrange[1])
tick_values = list(sorted(list(range(int(roundTowardZero(yrange[0])), int(roundTowardZero(yrange[1]))+1, 2 )) + [-1.0, 1.0])) # Put ticks at integer intervals, plus two special ticks at -.5 and .5 (since this regions spans much of the graph)
#print(tick_values)
tick_positions = [bisect_left(series, x) for x in tick_values] # use bisect to find the approximate position of each tick
#print(tick_positions)
ax.set_xticks( tick_positions ) # set tick positions
ax.set_xticklabels( ["%.2g" % x for x in tick_values], size="xx-large" ) # set tick labels
tileFilename = "heatmap_profile_legend"
plt.savefig("{}.svg".format(tileFilename), orientation='landscape', bbox_inches='tight' )
plt.savefig("{}.png".format(tileFilename), orientation='landscape', bbox_inches='tight', dpi=1000)
plt.close(fig)
return tileFilename
def getNodeDiversityPlot(clusterRadius, groupRadius, identifier):
fig, ax = plt.subplots()
scale=50.0
shapeScale=400.0
ax.scatter(0, 0, s=clusterRadius*scale*shapeScale, facecolors="none", edgecolors="black" )
ax.scatter(0, 0, s=groupRadius*scale*shapeScale, facecolors="none", edgecolors="blue" )
ax.set_aspect("equal")
ax.set_xticks( [] )
ax.set_yticks( [] )
ax.set_xlim((-scale*0.2, scale*0.2))
ax.set_ylim((-scale*0.2, scale*0.2))
tileName = "node_diversity_{}.png".format(identifier)
plt.savefig( tileName, bbox_inches='tight', dpi=100)
plt.close(fig)
return tileName
def toStrWithUnicodeMinusSign(x):
if x >= 0.0:
return "{}".format(x)
else:
return "\u2212{}".format(-x)
speciesColors = ['#0ac22c', '#86dd52']
def brighten(hexcolor):
assert(len(hexcolor)==7)
assert(hexcolor[0]=="#")
sqrt_brighten = lambda x: sqrt(x)
from_hex = lambda b: int(b,16)/255
to_hex = lambda f: hex(round(f*255))[2:]
r = sqrt_brighten(from_hex(hexcolor[1:3]))
g = sqrt_brighten(from_hex(hexcolor[3:5]))
b = sqrt_brighten(from_hex(hexcolor[5:7]))
return "#{}{}{}".format( to_hex(r), to_hex(g), to_hex(b) )
def plotProfilesDists( profiles, category=None, xticks=None, xticklabels=None, highlightSpecies=(), boxplotTicks=None, yrange=(-4,1), boxplotWidth=0.05, xrange=(-100,100), colors=('#2040f0',), defaultLabel="All", showBoxplots=True, fileBaseName=None, aspect=0.2 ):
sns.set(style="ticks")
assert(len(yrange)==2)
assert(yrange[1] > yrange[0])
fig, ax = plt.subplots()
xvals = profiles.iloc[0,:].index
if not category is None:
assert(xvals[-1]==category)
xvals = xvals[:-1]
#transformToFigureXscale = lambda x: (x-100)/20+10
transformToFigureXscale = lambda x: (x-190)/100+3.8
xvals2 = [transformToFigureXscale(x) for x in xvals]
legendLines = []
legendCaptions = []
pointsForKde = pd.DataFrame()
def plotCategory( profiles, plotIQR=True, plotOutliers=True, color='#2030f0', boxplotXoffset=0.0, boxplotWidth=boxplotWidth, label="category" ):
nonlocal pointsForKde
#ax.set_aspect(6.0)
#p = sns.boxplot( data=profiles.melt(id_vars=()), x='variable', y='value', color='#2398ad', fliersize=2.5, ax=ax )
# bp = ax.boxplot( profiles.loc[:,boxplotTicks],
# positions=list(boxplotTicks),
# widths=0.2,
# flierprops=dict(markersize=2),
# patch_artist=True )
medians = profiles.median( axis=0)
q25 = profiles.quantile(0.25, axis=0)
q75 = profiles.quantile(0.75, axis=0)
iqr = q75-q25
#print(profiles.shape)
#print(q25.shape)
#print(q75.shape)
#print(iqr.shape)
ocolor = brighten(color)
if profiles.shape[-1] == q25.shape[-1]+1:
q1iqr = np.maximum( (q25 - 1.5*iqr), np.amin(profiles, axis=0)[:-1] )
q3iqr = np.minimum( (q75 + 1.5*iqr), np.amax(profiles, axis=0)[:-1] )
else:
assert(profiles.shape[-1] == q25.shape[-1])
q1iqr = np.maximum( (q25 - 1.5*iqr), np.amin(profiles, axis=0) )
q3iqr = np.minimum( (q75 + 1.5*iqr), np.amax(profiles, axis=0) )
ax.plot( xvals2, medians, color='#000000', zorder=1 )
if profiles.shape[-1] == q25.shape[-1]+1:
#x.fill_between( xvals2[:-1], q1iqr, q3iqr, color='white', alpha=1.0, zorder=1 )
#ax.fill_between( xvals2[:-1], q3iqr, q1iqr, color='white', alpha=1.0, zorder=1 )
ax.fill_between( xvals2[:-1], q25, q75, color=color, alpha=1.0, zorder=1 )
ax.fill_between( xvals2[:-1], q25, q75, color=color, alpha=0.7, zorder=2 )
if plotIQR:
ax.plot( xvals2[:-1], q1iqr, color=ocolor, zorder=2 )
ax.plot( xvals2[:-1], q3iqr, color=ocolor, zorder=2 )
else:
assert(profiles.shape[-1] == q25.shape[-1])
#ax.fill_between( xvals2, q1iqr, q3iqr, color='white', alpha=1.0, zorder=1 )
#ax.fill_between( xvals2, q3iqr, q1iqr, color='white', alpha=1.0, zorder=1 )
ax.fill_between( xvals2, q25, q75, color=color, alpha=1.0, zorder=1 )
ax.fill_between( xvals2, q25, q75, color=color, alpha=0.7, zorder=2 )
if plotIQR:
ax.plot( xvals2, q1iqr, color=ocolor, zorder=2 )
ax.plot( xvals2, q3iqr, color=ocolor, zorder=2 )
#
# Plot outliers (for this category only)
allPoints = profiles.melt()
allPoints['value'] = allPoints['value'].astype('float')
allPoints = pd.merge( allPoints, pd.DataFrame({'q1iqr':q1iqr}), how='inner', left_on='variable', right_index=True )
allPoints = pd.merge( allPoints, pd.DataFrame({'q3iqr':q3iqr}), how='inner', left_on='variable', right_index=True )
allPoints['figx'] = allPoints['variable'].transform( transformToFigureXscale )
if plotOutliers:
# plot negative outliers
od1 = allPoints[ allPoints['value'] <= allPoints['q1iqr'] ]