graphmap2/scripts/scatterplot8.py at master · lbcb-sci/graphmap2

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#! /usr/bin/python

import os;

import sys;

import math;

import numpy as np;

from scipy.stats.stats import pearsonr

from scipy.stats.stats import spearmanr

from scipy.optimize import curve_fit

USE_MATPLOTLIB = True;

try:

# import matplotlib;

# matplotlib.use('Agg')

import matplotlib.pyplot as plt;

from matplotlib.font_manager import FontProperties;

import seaborn as sns;

except Exception, e:

USE_MATPLOTLIB = False;

print e;

HIGH_DPI_PLOT = False;

# HIGH_DPI_PLOT = True;

def LineFunction(x, b):

return (1*x + b);

def PlotMedianLine(ax, min_x, max_x, l_median, color='r'):

x0 = 0; y0 = 1*x0 + l_median;

x1 = max_x; y1 = 1*x1 + l_median;

ax.plot([x0, x1], [y0, y1], color, lw=1);

def PlotLines(ax, min_x, max_x, l_median, threshold, color='purple'):

threshold_l = threshold * 2.0 / (math.sqrt(2.0));

l_min = l_median - threshold_l;

l_max = l_median + threshold_l;

x0 = 0; y0 = 1*x0 + l_min;

x1 = max_x; y1 = 1*x1 + l_min;

ax.plot([x0, x1], [y0, y1], color, lw=1);

x0 = 0; y0 = 1*x0 + l_max;

x1 = max_x; y1 = 1*x1 + l_max;

ax.plot([x0, x1], [y0, y1], color, lw=1);

def load_csv(csv_path):

fp = open(csv_path, 'r');

lines = fp.readlines();

fp.close()

x = [];

y = [];

c = [];

i = 0;

for line in lines:

split_line = line.split('\t');

if (i == 0):

if (len(split_line) == 6):

query_header = split_line[0];

query_id = int(split_line[1]);

query_length = int(split_line[2]);

l1_used = True if (int(split_line[3]) == 1) else 0;

l_median = float(split_line[4]);

l_maximum_allowed = float(split_line[5]);

elif (len(split_line) == 7):

query_header = split_line[0];

query_id = int(split_line[1]);

query_length = int(split_line[2]);

ref_header = split_line[3];

# ref_id = int(split_line[4]);

# ref_length = int(split_line[5]);

l1_used = True if (int(split_line[4]) == 1) else 0;

l_median = float(split_line[5]);

l_maximum_allowed = float(split_line[6]);

else:

sys.stderr.write('ERROR: Heading line does not contain a valid number of parameters!\n');

exit(1);

i += 1;

continue;

x.append(float(split_line[0].strip()));

y.append(float(split_line[1].strip()));

if (len(split_line) > 2):

c.append(int(split_line[2].strip()));

else:

c.append(0);

i += 1;

return [x, y, c, query_header, query_id, query_length, l1_used, l_median, l_maximum_allowed];

def plot_data(fig, ax, subplot_coords, x, y, c, query_length, ymin, ymax, l_median, threshold_L1_under_max, plot_mode, plot_title, out_png_path=''):

if USE_MATPLOTLIB == True:

# plt.figure();

# plt.clf();

# plt.subplot(subplot_coords);

ax.grid();

# if (subplot_coords == 223 or subplot_coords == 224):

# plt.xlabel('Read coordinates');

# if (subplot_coords == 221 or subplot_coords == 223):

# plt.ylabel('Region coordinates');

# plt.text(0.5, 1.08, 'Walks from the GraphMap output - %s' % plot_title,

ax.text(0.5, 1.02, plot_title,

horizontalalignment='center',

fontsize=12,

transform = ax.transAxes)

# if (subplot_coords == 221 or subplot_coords == 223):

plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

plt.xlim(0, query_length);

plt.ylim(ymin, ymax);

plt.xticks(np.arange(0, query_length, query_length/5))

all_colors = 'bgrcmyk';

colors1 = [all_colors[val%len(all_colors)] for val in c[0::2]];

colors2 = [all_colors[val%len(all_colors)] for val in c[1::2]];

i = 0;

while (i < len(x)):

ax.plot(x[i:(i+2)], y[i:(i+2)], color=all_colors[(c[i]) % len(all_colors)]);

i += 2;

# ax.plot(x, y, 'o');

# ax.scatter(x[0::2], y[0::2], s=10, facecolor='b', lw = 0.2)

# ax.scatter(x[1::2], y[1::2], s=10, facecolor='c', lw = 0.2)

ax.scatter(x[0::2], y[0::2], s=10, edgecolor=colors1, facecolor=colors1, lw = 0.2)

ax.scatter(x[1::2], y[1::2], s=10, edgecolor=colors2, facecolor=colors2, lw = 0.2)

if (plot_mode == 1):

try:

PlotMedianLine(ax, min(x), max(x), l_median, 'r');

PlotLines(ax, min(x), max(x), l_median, threshold_L1_under_max, 'purple');

PlotMedianLine(ax, 0, query_length, l_median, 'r');

PlotLines(ax, 0, query_length, l_median, threshold_L1_under_max, 'purple');

except Exception, e:

sys.stderr.write(str(e) + '\n');

# plt.xlabel('Read coordinates');

# plt.ylabel('Region coordinates');

if (subplot_coords == 223 or subplot_coords == 224):

ax.set_xlabel('Query coordinates');

if (subplot_coords == 221 or subplot_coords == 223):

ax.set_ylabel('Reference coordinates');

# sns.despine(offset=10, trim=True);

# plt.setp([a.get_xticklabels() for a in fig.axes[:]], visible=False)

# if (subplot_coords == 221 or subplot_coords == 222):

# plt.setp(ax.get_xticklabels(), visible=False)

# if (out_png_path != ''):

# if (HIGH_DPI_PLOT == False):

# plt.savefig(out_png_path, bbox_inches='tight'); # , dpi=1000);

# else:

# plt.savefig(out_png_path, bbox_inches='tight', dpi=1000);

# print '';

if __name__ == "__main__":

if (len(sys.argv) < 3):

print 'Plots intermediate results from the LCSk-L1 step of the GraphMap algorithm.'

print '';

print 'Usage:';

print '\t%s <path_to_results> local_scores_id_1 [local_scores_id_2 local_scores_id_3 ...]' % sys.argv[0];

print '';

exit(1);

results_path = sys.argv[1];

i = 2;

while (i < len(sys.argv)):

local_scores_id = (sys.argv[i]);

scores_path = '%s/scores-%s' % (results_path, local_scores_id);

lcs_path = '%s/LCS-%s' % (results_path, local_scores_id);

lcsl1_path = '%s/LCSL1-%s' % (results_path, local_scores_id);

l1_path = '%s/double_LCS-%s' % (results_path, local_scores_id);

# boundedl1_path = '%s/boundedl1-%d' % (results_path, local_scores_id);

# data_path = lcs_path;

# print data_path;

# [x, y] = load_csv(data_path + '.csv');

# [l_median, threshold_L1_under_max] = FindHoughLine(x, y, error_rate);

# fig = plt.figure();

sns.set_style("darkgrid");

sns.set_style("white")

# sns.set_style("ticks");

[fig, ((ax1, ax2), (ax3, ax4))] = plt.subplots(2, 2, sharex=True, sharey=True)

# plt.clf();

# fig.subplots_adjust(hspace=-0.5);

fig.subplots_adjust(wspace=0.1);

# fig.subplots_adjust(hspace=.5);

# fig.subplots_adjust(wspace=.5);

# f, ax = plt.subplots(4, sharex=True, sharey=True)

# plt.gca().spines['top'].set_visible(False)

# plt.gca().spines['right'].set_visible(False)

# plt.gca().get_xaxis().tick_bottom()

# plt.gca().get_yaxis().tick_left()

# plt.setp([axarr[0].get_xticklabels(), axarr[1].get_xticklabels(), axarr[1].get_yticklabels(), axarr[3].get_yticklabels()], visible=False)

# plt.setp([axarr[1].get_yticklabels(), axarr[3].get_yticklabels()], visible=False)

data_path = scores_path;

sys.stderr.write('Reading: %s\n' % data_path);

[x, y, c, query_header, query_id, query_length, l1_used, l_median, l_maximum_allowed] = load_csv(data_path + '.csv');

ymin = min(y);

ymax = max(y);

# FindHoughLine(x, y, error_rate);

plot_data(fig, ax1, 221, x, y, c, query_length, ymin, ymax, l_median, l_maximum_allowed, 0, 'Anchors', data_path + '.png');

data_path = lcs_path;

sys.stderr.write('Reading: %s\n' % data_path);

[x, y, c, query_header, query_id, query_length, l1_used, l_median, l_maximum_allowed] = load_csv(data_path + '.csv');

# FindHoughLine(x, y, error_rate);

plot_data(fig, ax2, 222, x, y, c, query_length, ymin, ymax, l_median, l_maximum_allowed, l1_used, 'LCSk', data_path + '.png');

data_path = lcsl1_path;

sys.stderr.write('Reading: %s\n' % data_path);

[x, y, c, query_header, query_id, query_length, l1_used, l_median, l_maximum_allowed] = load_csv(data_path + '.csv');

# FindHoughLine(x, y, error_rate);

plot_data(fig, ax3, 223, x, y, c, query_length, ymin, ymax, l_median, l_maximum_allowed, l1_used, 'LCSk L1 filtered', data_path + '.png');

data_path = l1_path;

sys.stderr.write('Reading: %s\n' % data_path);

[x, y, c, query_header, query_id, query_length, l1_used, l_median, l_maximum_allowed] = load_csv(data_path + '.csv');

# FindHoughLine(x, y, error_rate);

plot_data(fig, ax4, 224, x, y, c, query_length, ymin, ymax, l_median, l_maximum_allowed, l1_used, 'Second LCSk after L1', data_path + '.png');

# data_path = boundedl1_path;

# print data_path;

# [x, y] = load_csv(data_path + '.csv');

# # FindHoughLine(x, y, error_rate);

# plot_data(224, x, y, l_median, threshold_L1_under_max, 1 if (error_rate > 0.0) else 0, 'boundedl1-%d' % local_scores_id, data_path + '.png');

out_png_path = '%s/all-%s-qid_%d.png' % (results_path, local_scores_id, query_id);

if (out_png_path != ''):

if (HIGH_DPI_PLOT == False):

sys.stderr.write('Writing image to file: %s\n\n' % out_png_path);

plt.savefig(out_png_path, bbox_inches='tight'); # , dpi=1000);

else:

sys.stderr.write('Writing image to file: %s\n\n' % out_png_path);

plt.savefig(out_png_path, bbox_inches='tight', dpi=1000);

# scripts/test-scatterplot3.py temp/local_scores/LCS-315.csv temp/local_scores/LCS-314.csv

# scripts/test-scatterplot4.py temp/local_scores/scores-104.csv 0 temp/local_scores/LCS-104.csv 1 temp/local_scores/L1-104.csv 1

#for par in fitpars:

#x1 = (1.0 - par) / 1;

#plt.plot ([0.0, x1], [par, 1.0], 'g');

#plt.xlim([0.0, 1.0]);

#plt.ylim([0.0, 1.0]);

i += 1;

plt.show();

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