def parameter_sweep(model, sigma, ns):
generate_equations(model)
logp = [numpy.log10(p.value) for p in model.parameters]
ts = numpy.linspace(0, 20 * 3600, 20 * 60)
solver = Solver(model, ts)
pf.set_fig_params()
plt.figure(figsize=(1.8, 1), dpi=300)
for i in range(ns):
psample = sample_params(logp, 0.05)
res = solver.run(param_values=psample)
signal = res.observables['p53_active']
plt.plot(signal, color=(0.7, 0.7, 0.7), alpha=0.3)
# Highlighted
colors = ['g', 'y', 'c']
for c in colors:
psample = sample_params(logp, 0.05)
res = solver.run(param_values=psample)
signal = res.observables['p53_active']
plt.plot(signal, c)
# Nominal
solver = Solver(model, ts)
res = solver.run()
signal = res.observables['p53_active']
plt.plot(signal, 'r')
plt.xticks([])
plt.xlabel('Time (a.u.)', fontsize=7)
plt.ylabel('Active p53', fontsize=7)
plt.yticks([])
plt.ylim(ymin=0)
pf.format_axis(plt.gca())
plt.savefig(model.name + '_sample.pdf')
def plot_stmt_counts(go_stmt_map, plot_filename, figsize=(3, 3)):
# Put together counts for a figure
pf.set_fig_params()
fig = plt.figure(figsize=(8, 4), dpi=200)
ax = fig.gca()
counts = []
for go_id in go_stmt_map.keys():
counts.append(
(go_stmt_map[go_id]['names'][0], len(go_stmt_map[go_id]['in_go']),
len(go_stmt_map[go_id]['not_in_go'])))
counts.sort(key=lambda x: x[1] + x[2], reverse=True)
indices = np.arange(len(counts))
width = 0.8
in_go = [c[1] for c in counts]
labels = [c[0] for c in counts]
not_in_go = [c[2] for c in counts]
p1 = ax.bar(indices, in_go, width, color='b')
p2 = ax.bar(indices, not_in_go, width, color='r', bottom=in_go)
ax.set_ylabel('No. of stmts')
plt.xlim([-0.2, len(counts)])
plt.xticks(indices + width / 2., labels, rotation='vertical')
plt.legend((p1[0], p2[0]), ('In GO', 'Not in GO'),
frameon=False,
fontsize=7)
plt.subplots_adjust(left=0.08, bottom=0.44, top=0.85, right=0.97)
pf.format_axis(ax)
fig.savefig(plot_filename)
def parameter_sweep(model, sigma, ns):
generate_equations(model)
logp = [numpy.log10(p.value) for p in model.parameters]
ts = numpy.linspace(0, 20*3600, 20*60)
solver = Solver(model, ts)
pf.set_fig_params()
plt.figure(figsize=(1.8, 1), dpi=300)
for i in range(ns):
psample = sample_params(logp, 0.05)
res = solver.run(param_values=psample)
signal = res.observables['p53_active']
plt.plot(signal, color=(0.7, 0.7, 0.7), alpha=0.3)
# Highlighted
colors = ['g', 'y', 'c']
for c in colors:
psample = sample_params(logp, 0.05)
res = solver.run(param_values=psample)
signal = res.observables['p53_active']
plt.plot(signal, c)
# Nominal
solver = Solver(model, ts)
res = solver.run()
signal = res.observables['p53_active']
plt.plot(signal, 'r')
plt.xticks([])
plt.xlabel('Time (a.u.)', fontsize=7)
plt.ylabel('Active p53', fontsize=7)
plt.yticks([])
plt.ylim(ymin=0)
pf.format_axis(plt.gca())
plt.savefig(model.name + '_sample.pdf')
def plot_stmt_counts(go_stmt_map, plot_filename, figsize=(3, 3)):
# Put together counts for a figure
pf.set_fig_params()
fig = plt.figure(figsize=(8, 4), dpi=200)
ax = fig.gca()
counts = []
for go_id in go_stmt_map.keys():
counts.append((go_stmt_map[go_id]['names'][0],
len(go_stmt_map[go_id]['in_go']),
len(go_stmt_map[go_id]['not_in_go'])))
counts.sort(key=lambda x: x[1] + x[2], reverse=True)
indices = np.arange(len(counts))
width = 0.8
in_go = [c[1] for c in counts]
labels = [c[0] for c in counts]
not_in_go = [c[2] for c in counts]
p1 = ax.bar(indices, in_go, width, color='b')
p2 = ax.bar(indices, not_in_go, width, color='r', bottom=in_go)
ax.set_ylabel('No. of stmts')
plt.xlim([-0.2, len(counts)])
plt.xticks(indices + width/2., labels, rotation='vertical')
plt.legend((p1[0], p2[0]), ('In GO', 'Not in GO'), frameon=False,
fontsize=7)
plt.subplots_adjust(left=0.08, bottom=0.44, top=0.85, right=0.97)
pf.format_axis(ax)
fig.savefig(plot_filename)
def plot_results(g, stmt_uuids, stmt_nodes, stmt_node_nums, stmt_uuid_nums):
norm_node_counts = np.array(stmt_node_nums) / len(g)
norm_uuid_counts = np.array(stmt_uuid_nums) / len(g.edges())
pf.set_fig_params()
plt.ion()
lengths = range(1, len(norm_uuid_counts)+1)
plt.figure(figsize=(2, 2), dpi=150)
plt.plot(lengths, norm_uuid_counts, color='orange', alpha=0.8,
label='Statements')
plt.plot(lengths, norm_node_counts, color='blue', alpha=0.8, label='Nodes')
plt.legend(loc='upper left', fontsize=pf.fontsize, frameon=False)
ax = plt.gca()
pf.format_axis(ax)
def plot_result(model, sol):
pf.set_fig_params()
plt.ion()
plt.figure(figsize=(1, 1), dpi=300)
species_names = [str(s) for s in model.species]
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='u', Y187='u')")],
'b', label='MAPK1.uu')
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='p', Y187='u')")] +
sol.y[:, species_names.index("MAPK1(T185='u', Y187='p')")],
'g', label='MAPK1.p')
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='p', Y187='p')")],
'r', label='MAPK1.pp')
plt.xlabel('Time')
plt.ylabel('Amount')
plt.legend(loc='upper right', fontsize=4)
plt.xticks([])
plt.yticks([])
pf.format_axis(plt.gca())
def plot_result(model, sol):
pf.set_fig_params()
plt.ion()
plt.figure(figsize=(1, 1), dpi=300)
species_names = [str(s) for s in model.species]
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='u', Y187='u')")],
'b', label='MAPK1.uu')
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='p', Y187='u')")] +
sol.y[:, species_names.index("MAPK1(T185='u', Y187='p')")],
'g', label='MAPK1.p')
plt.plot(t, sol.y[:, species_names.index("MAPK1(T185='p', Y187='p')")],
'r', label='MAPK1.pp')
plt.xlabel('Time')
plt.ylabel('Amount')
plt.legend(loc='upper right', fontsize=4)
plt.xticks([])
plt.yticks([])
pf.format_axis(plt.gca())
import numpy as np
from indra.util import plot_formatting as pf
from collections import Counter
from indra.statements import *
from indra.preassembler import Preassembler
from indra.preassembler.hierarchy_manager import hierarchies
from copy import deepcopy
from indra.databases import uniprot_client
import sys
from indra.preassembler import grounding_mapper as gm
import logging
logger = logging.getLogger('reading_results')
pf.set_fig_params()
def load_file(stmts_file):
logger.info("Loading results...")
with open(stmts_file, 'rb') as f:
results = pickle.load(f)
return results
def report_stmt_counts(paper_stmts, plot_prefix=None):
counts_per_paper = [(pmid, len(stmts)) for pmid, stmts in results.items()]
counts = np.array([tup[1] for tup in counts_per_paper])
logger.info("%.2f +/- %.2f statements per paper" %
(np.mean(counts), np.std(counts)))
logger.info("Median %d statements per paper" % np.median(counts))
from __future__ import absolute_import, print_function, unicode_literals
from builtins import dict, str
import sys
import glob
import pickle
from os.path import join as pjoin
from indra.tools import assemble_corpus as ac
from collections import OrderedDict, Counter, namedtuple, defaultdict
from indra.preassembler.sitemapper import SiteMapper, default_site_map
from indra.util import write_unicode_csv, read_unicode_csv
from indra.util import plot_formatting as pf
import numpy as np
from matplotlib import pyplot as plt
pf.set_fig_params()
SiteInfo = namedtuple('SiteInfo', [
'gene', 'res', 'pos', 'valid', 'mapped', 'mapped_res', 'mapped_pos',
'explanation', 'freq', 'source'
])
def map_statements(stmts, source, outfile=None):
"""Tabulate valid, invalid, and mapped sites from a set of Statements."""
# Look for errors in database statements
sm = SiteMapper(default_site_map)
valid_stmts, mapped_stmts = sm.map_sites(stmts)
# Collect stats from SiteMapper itself
sites = []
for site_key, mapping in sm._cache.items():
