def report_stmt_types(stmts, plot_prefix=None):
# Number of statements of different types
stmt_types = [type(stmt) for stmt in stmts]
stmt_type_counter = Counter(stmt_types)
fig = plt.figure(figsize=(2, 3), dpi=300)
ax = fig.gca()
sorted_counts = sorted(stmt_type_counter.items(),
key=lambda x: x[1],
reverse=True)
labels = [t.__name__ for t, _ in sorted_counts]
logger.info('Distribution of statement types:')
for stmt_type, count in sorted_counts:
logger.info('%s: %d' % (stmt_type.__name__, count))
if plot_prefix:
x_coords = np.arange(len(sorted_counts))
ax.bar(x_coords, [x[1] for x in sorted_counts])
ax.set_xticks(x_coords + 0.4)
ax.set_xticklabels(labels, rotation=90)
pf.format_axis(ax)
ax.set_ylabel('No. of statements')
plt.subplots_adjust(left=0.29, bottom=0.34)
plt.savefig('%s_stmt_types.pdf' % plot_prefix)
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 report_stmt_counts(results, 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))
zero_pmids = [pmid for pmid, stmts in results.items() if len(stmts) == 0]
logger.info('%s papers with no statements' % len(zero_pmids))
# Distribution of numbers of statements
if plot_prefix:
plot_filename = '%s_stmts_per_paper.png' % plot_prefix
logger.info('Plotting distribution of statements per paper in %s' %
plot_filename)
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.hist(counts, bins=20)
# ax.set_xticks([0, 100, 200, 300, 400])
pf.format_axis(ax)
plt.subplots_adjust(left=0.23, bottom=0.16)
ax.set_xlabel('No. of statements')
ax.set_ylabel('No. of papers')
fig = plt.gcf()
fig.savefig(plot_filename, format='png')
logger.info('plotted...')
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_sites(gene_count_subset, figsize, subplot_params, do_legend=True):
ind = np.array(range(len(gene_count_subset)))
plt.figure(figsize=figsize, dpi=150)
width = 0.8
handle_dict = {}
for ix, (gene, freq) in enumerate(gene_count_subset):
# Plot the stacked bars
bottom = 0
for site_freq, valid, mapped_res, mapped_pos, explanation \
in site_counts[gene]:
# Don't show sites that are valid
mapped = True if mapped_res and mapped_pos else False
if valid:
color = 'gray'
handle_key = 'Valid'
elif mapped and \
explanation.startswith('INFERRED_METHIONINE_CLEAVAGE'):
color = 'b'
handle_key = 'Methionine'
elif mapped and \
explanation.startswith('INFERRED_MOUSE_SITE'):
color = 'c'
handle_key = 'Mouse'
elif mapped and \
explanation.startswith('INFERRED_RAT_SITE'):
color = 'purple'
handle_key = 'Rat'
elif mapped and \
explanation.startswith('INFERRED_ALTERNATIVE_ISOFORM'):
color = 'orange'
handle_key = 'Alternative isoform'
elif mapped:
color = 'g'
handle_key = 'Manually mapped'
elif not mapped and explanation is not None \
and explanation != 'VALID':
color = 'r'
handle_key = 'Curated as incorrect'
elif not valid:
assert False
else:
assert False # Make sure we handled all cases above
handle_dict[handle_key] = \
plt.bar(ix + 0.4, site_freq, bottom=bottom,
color=color, linewidth=0.5, width=width)
bottom += site_freq
plt.xticks(ind + (width / 2.), [x[0] for x in gene_count_subset],
rotation='vertical')
plt.ylabel('Stmts with invalid sites')
plt.xlim((0, max(ind) + 1))
ax = plt.gca()
pf.format_axis(ax)
plt.subplots_adjust(**subplot_params)
if do_legend:
plt.legend(loc='upper right',
handles=list(handle_dict.values()),
labels=list(handle_dict.keys()),
fontsize=pf.fontsize,
frameon=False)
plt.show()
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 report_stmt_counts(results, 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))
zero_pmids = [pmid for pmid, stmts in results.items() if len(stmts) == 0]
logger.info('%s papers with no statements' % len(zero_pmids))
# Distribution of numbers of statements
if plot_prefix:
plot_filename = '%s_stmts_per_paper.png' % plot_prefix
logger.info('Plotting distribution of statements per paper in %s' %
plot_filename)
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.hist(counts, bins=20)
#ax.set_xticks([0, 100, 200, 300, 400])
pf.format_axis(ax)
plt.subplots_adjust(left=0.23, bottom=0.16)
ax.set_xlabel('No. of statements')
ax.set_ylabel('No. of papers')
fig = plt.gcf()
fig.savefig(plot_filename, format='png')
logger.info('plotted...')
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_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_site_count_dist(sites, num_sites=240):
# Plot site frequencies, colored by validity
sites.sort(key=lambda s: s[1], reverse=True)
width = 0.8
plt.figure(figsize=(11, 2), dpi=150)
ind = np.arange(num_sites) + (width / 2.)
for site_ix, site in enumerate(sites[:num_sites]):
if site.valid:
color = 'g'
else:
color = 'r'
plt.bar(site_ix, site.freq, color=color)
ax = plt.gca()
pf.format_axis(ax)
plt.show()
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())
def report_evidence_distribution(stmts, list_length=10, plot_prefix=None):
# Sort by evidence
sorted_stmts = sorted(stmts, key=lambda x: len(x.evidence), reverse=True)
logger.info('Top %d statements by evidence:' % list_length)
for i in range(list_length):
logger.info('%s: %d' % (sorted_stmts[i], len(sorted_stmts[i].evidence)))
# Distribution of pieces of evidence
if plot_prefix:
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.plot([len(stmt.evidence) for stmt in sorted_stmts])
pf.format_axis(ax)
ax.set_xlabel('Statement index')
ax.set_ylabel('No. of sentences')
ax.set_yscale('log')
plt.subplots_adjust(left=0.23, bottom=0.16)
plt.savefig('%s_evidence_dist.pdf' % plot_prefix)
return sorted_stmts
def report_evidence_distribution(stmts, list_length=10, plot_prefix=None):
# Sort by evidence
sorted_stmts = sorted(stmts, key=lambda x: len(x.evidence), reverse=True)
logger.info('Top %d statements by evidence:' % list_length)
for i in range(list_length):
logger.info('%s: %d' % (sorted_stmts[i], len(sorted_stmts[i].evidence)))
# Distribution of pieces of evidence
if plot_prefix:
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.plot([len(stmt.evidence) for stmt in sorted_stmts])
pf.format_axis(ax)
ax.set_xlabel('Statement index')
ax.set_ylabel('No. of sentences')
ax.set_yscale('log')
plt.subplots_adjust(left=0.23, bottom=0.16)
plt.savefig('%s_evidence_dist.pdf' % plot_prefix)
return sorted_stmts
def plot_frequencies(counts, plot_filename, bin_interval):
freq_dist = []
bin_starts = range(0, len(counts), bin_interval)
for bin_start_ix in bin_starts:
bin_end_ix = bin_start_ix + bin_interval
if bin_end_ix < len(counts):
freq_dist.append(np.sum(counts[bin_start_ix:bin_end_ix]))
else:
freq_dist.append(np.sum(counts[bin_start_ix:]))
freq_dist = np.array(freq_dist)
fracs_total = np.cumsum(freq_dist) / float(np.sum(counts))
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.plot(bin_starts, fracs_total)
pf.format_axis(ax)
plt.subplots_adjust(left=0.23, bottom=0.16)
ax.set_xlabel('String index')
ax.set_ylabel('No. of occurrences')
ax.set_ylim([0, 1])
plt.savefig(plot_filename)
def plot_frequencies(counts, plot_filename, bin_interval):
freq_dist = []
bin_starts = xrange(0, len(counts), bin_interval)
for bin_start_ix in bin_starts:
bin_end_ix = bin_start_ix + bin_interval
if bin_end_ix < len(counts):
freq_dist.append(np.sum(counts[bin_start_ix:bin_end_ix]))
else:
freq_dist.append(np.sum(counts[bin_start_ix:]))
freq_dist = np.array(freq_dist)
fracs_total = np.cumsum(freq_dist) / float(np.sum(counts))
fig = plt.figure(figsize=(2, 2), dpi=300)
ax = fig.gca()
ax.plot(bin_starts, fracs_total)
pf.format_axis(ax)
plt.subplots_adjust(left=0.23, bottom=0.16)
ax.set_xlabel('String index')
ax.set_ylabel('No. of occurrences')
ax.set_ylim([0, 1])
plt.savefig(plot_filename)
def plot_pc_pe_mods(all_mods):
dbs = Counter([row.source for row in all_mods])
dbs = sorted([(k, v) for k, v in dbs.items()],
key=lambda x: x[1],
reverse=True)
plt.ion()
width = 0.8
ind = np.arange(len(dbs)) + (width / 2.)
plt.figure(figsize=(2, 2), dpi=150)
for db_ix, (db, db_freq) in enumerate(dbs):
db_mods = [row for row in all_mods if row.source == db]
valid = [row for row in db_mods if row.valid == 1]
invalid = [row for row in db_mods if row.valid == 0]
h_valid = plt.bar(db_ix, len(valid), width=width, color='g')
h_invalid = plt.bar(db_ix,
len(invalid),
width=0.8,
color='r',
bottom=len(valid))
plt.xticks(ind, [db[0] for db in dbs])
ax = plt.gca()
pf.format_axis(ax)
plt.show()
def report_stmt_types(stmts, plot_prefix=None):
# Number of statements of different types
stmt_types = [type(stmt) for stmt in stmts]
stmt_type_counter = Counter(stmt_types)
fig = plt.figure(figsize=(2, 3), dpi=300)
ax = fig.gca()
sorted_counts = sorted(stmt_type_counter.items(), key=lambda x: x[1],
reverse=True)
labels = [t.__name__ for t, n in sorted_counts]
logger.info('Distribution of statement types:')
for stmt_type, count in sorted_counts:
logger.info('%s: %d' % (stmt_type.__name__, count))
if plot_prefix:
x_coords = np.arange(len(sorted_counts))
ax.bar(x_coords, [x[1] for x in sorted_counts])
ax.set_xticks(x_coords + 0.4)
ax.set_xticklabels(labels, rotation=90)
pf.format_axis(ax)
ax.set_ylabel('No. of statements')
plt.subplots_adjust(left=0.29, bottom=0.34)
plt.savefig('%s_stmt_types.pdf' % plot_prefix)
trial_results_uniq.append(len(trial_stmts_uniq))
trial_results_top.append(len(trial_stmts_top))
trial_results_filt.append(len(trial_stmts_filt))
results.append((np.mean(trial_results), np.std(trial_results)))
results_uniq.append((np.mean(trial_results_uniq), np.std(trial_results_uniq)))
results_top.append((np.mean(trial_results_top), np.std(trial_results_top)))
results_filt.append((np.mean(trial_results_filt), np.std(trial_results_filt)))
results = np.array(results)
results_uniq = np.array(results_uniq)
results_top = np.array(results_top)
results_filt = np.array(results_filt)
plt.ion()
plt.figure(figsize=(3, 3), dpi=150)
plt.plot(sample_sizes, results[:,0], label='Raw', marker='.')
plt.plot(sample_sizes, results_uniq[:,0], label='Unique', marker='.')
plt.plot(sample_sizes, results_top[:, 0], label='Top-level', marker='.')
plt.plot(sample_sizes, results_filt[:,0], label='P 0.9', marker='.')
#pf.set_fig_params()
ax = plt.gca()
ax.set_xscale('log')
ax.set_yscale('log')
pf.format_axis(ax, tick_padding=3, label_padding=3)
ax.set_xlabel('Number of Articles')
ax.set_ylabel('Number of Statements')
plt.legend(loc='lower right', frameon=False)
def plot_annot_stats(csv_file, output_base):
site_df = pd.read_csv(csv_file)
# Drop rows with error_code (invalid gene names in BEL)
site_df = site_df[site_df.ERROR_CODE.isna()]
#sources = site_df.SOURCE.unique()
sources = [
'psp', 'signor', 'hprd', 'pid', 'reactome', 'bel', 'reach', 'sparser',
'rlimsp'
]
fig_valid = plt.figure(figsize=(1.8, 2.2), dpi=150)
fig_mapped = plt.figure(figsize=(1.8, 2.2), dpi=150)
for ix, source in enumerate(sources):
source_anns = site_df[site_df.SOURCE == source]
valid = source_anns[source_anns.VALID == True]
invalid = source_anns[source_anns.VALID != True]
unmapped = invalid[invalid.MAPPED_RES.isna()
| invalid.MAPPED_POS.isna()]
mapped = source_anns[~source_anns.MAPPED_RES.isna()
& ~source_anns.MAPPED_POS.isna()]
print("%s, %d, %d, %d, %d" %
(source, len(valid), len(invalid), len(unmapped), len(mapped)))
# Plot for valid stats
fig_valid.gca().bar(ix,
height=len(valid),
color='blue',
label='In Ref Seq')
fig_valid.gca().bar(ix,
height=len(invalid),
bottom=len(valid),
color='red',
label='Not in Ref Seq')
# Plot for mapped stats
fig_mapped.gca().bar(ix,
height=len(valid),
color='blue',
label='In Ref Seq')
fig_mapped.gca().bar(ix,
height=len(mapped),
bottom=len(valid),
color='green')
fig_mapped.gca().bar(ix,
height=len(unmapped),
bottom=(len(valid) + len(mapped)),
color='red')
plt.xticks(range(len(sources)), sources, rotation="vertical")
plt.ylabel('Unique Site Annotations')
plt.subplots_adjust(left=0.31, bottom=0.25, top=0.90)
#plt.legend(loc='upper right', fontsize=pf.fontsize)
for fig in (fig_valid, fig_mapped):
ax = fig.gca()
pf.format_axis(ax)
fig_valid.savefig('%s_valid_counts.pdf' % output_base)
fig_mapped.savefig('%s_mapped_counts.pdf' % output_base)
results = []
# Now generate the plot of invalid site proportions
fig_valid = plt.figure(figsize=(1.8, 2.2), dpi=150)
fig_mapped = plt.figure(figsize=(1.8, 2.2), dpi=150)
for ix, source in enumerate(sources):
source_anns = site_df[site_df.SOURCE == source]
valid = source_anns[source_anns.VALID == True]
invalid = source_anns[source_anns.VALID != True]
unmapped = invalid[invalid.MAPPED_RES.isna()
| invalid.MAPPED_POS.isna()]
mapped = source_anns[~source_anns.MAPPED_RES.isna()
& ~source_anns.MAPPED_POS.isna()]
pct_mapped = 100 * len(mapped) / len(source_anns)
pct_unmapped = 100 * len(unmapped) / len(source_anns)
pct_valid = 100 * len(valid) / len(source_anns)
pct_invalid = 100 * len(invalid) / len(source_anns)
results.append([
source,
len(source_anns),
len(valid),
round(100 * len(valid) / len(source_anns), 1),
len(invalid),
round(100 * len(invalid) / len(source_anns), 1),
len(mapped),
round(100 * len(mapped) / len(invalid), 1)
])
#print("%s, %d, %d, %d, %d" % (source, len(valid), len(invalid),
# len(unmapped), len(mapped)))
fig_valid.gca().bar(ix, height=pct_valid, color='blue')
fig_valid.gca().bar(ix,
height=pct_invalid,
bottom=pct_valid,
color='red')
fig_mapped.gca().bar(ix, height=pct_valid, color='blue')
fig_mapped.gca().bar(ix,
height=pct_mapped,
bottom=pct_valid,
color='green')
fig_mapped.gca().bar(ix,
height=pct_unmapped,
bottom=(pct_valid + pct_mapped),
color='red')
plt.xticks(range(len(sources)), sources, rotation="vertical")
plt.ylabel('Pct. Unique Site Annotations')
plt.subplots_adjust(left=0.31, bottom=0.25, top=0.90)
for fig in (fig_valid, fig_mapped):
ax = fig.gca()
pf.format_axis(ax)
fig_valid.savefig('%s_valid_pcts.pdf' % output_base)
fig_mapped.savefig('%s_mapped_pcts.pdf' % output_base)
result_df = pd.DataFrame.from_records(results,
columns=[
'SOURCE', 'TOTAL', 'VALID',
'VALID_PCT', 'INVALID',
'INVALID_PCT', 'MAPPED',
'MAPPED_PCT_INVALID'
])
result_df.to_csv('%s_result_df.csv' % output_base)
return result_df
pa = PysbAssembler()
pa.add_statements(stmts)
pa.make_model()
t = np.linspace(0, 25000)
sol = Solver(pa.model, t)
sol.run()
pf.set_fig_params()
plt.ion()
plt.figure(figsize=(1, 1), dpi=300)
species_names = [str(s) for s in pa.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())
# Condition 1
off = []
on = ['Growth_factor_proteins']
avgs = get_sim_avgs(bn_str, off=off, on=on)
jun_ext_noinh = avgs['JUN']
# Condition 2
off = ['MAP2K1', 'MAP2K2']
on = ['Growth_factor_proteins']
avgs = get_sim_avgs(bn_str, off=off, on=on)
jun_ext_inh = avgs['JUN']
# Condition 3
off = ['MAP2K1', 'MAP2K2', 'MAPK8', 'MAPK9']
on = ['Growth_factor_proteins']
avgs = get_sim_avgs(bn_str, off=off, on=on)
jun_ext_inh2 = avgs['JUN']
pf.set_fig_params()
plt.figure(figsize=(4,4), dpi=300)
plt.ion()
plt.plot(jun_basic_noinh, 'r', linewidth=2, label='Basic model, no inhibitor')
plt.plot(jun_basic_inh, 'r--', linewidth=2, label='Basic model, MEK inhibitor')
plt.plot(jun_ext_noinh, 'b', linewidth=2, label='Extended model, no inhibitor')
plt.plot(jun_ext_inh, 'b--', linewidth=2, label='Extended model, MEK inhibitor')
plt.plot(jun_ext_inh2, 'g--', linewidth=2, label='Extended model, MEK+JNK inhibitor')
plt.ylim(-0.01, 1.01)
plt.ylabel('Average JUN activity')
plt.xlabel('Time steps')
plt.legend()
pf.format_axis(plt.gca())
plt.show()
total_paths = np.sum(path_counts)
print(path_counts)
print(total_paths)
# Plot num paths vs length
plt.show
plt.figure(figsize=(5, 2), dpi=150)
ypos = list(range(1, MAX_DEPTH + 1))
plt.bar(ypos, path_counts, align='center')
#plt.xticks(ypos, str_names[:num_genes], rotation='vertical')
ax = plt.gca()
plt.ylabel('Number of paths')
plt.xlabel('Path length')
ax.set_yscale('log')
#plt.subplots_adjust(bottom=0.3)
pf.format_axis(ax)
"""
cfpg_list = []
total_cf_paths = 0
for i, pg in enumerate(result['pg_list']):
print("Generating CFPG %d" % i)
cfpg = CFPG.from_pg(pg)
total_cf_paths += cfpg.count_paths()
print("total paths (with cycles)", total_paths)
print("total cycle-free paths", total_cf_paths)
"""
# Length distribution
pg_path_lengths = Counter([len(p) - 1 for p in result['pg_paths']])
#nx_path_lengths = Counter([len(p)-1 for p in result['nx_paths']])
lengths = range(1, MAX_DEPTH + 1)
