def latex_mean_low_up(mean, low, up, sig=2):
#latexstr = "%.10f_{-%.10f}^{+%.10f}" % (mean,low,up)
f_low, round_to1 = utils.round_sig(low, return_round_to=True, sig=sig)
f_up, round_to2 = utils.round_sig(up, return_round_to=True, sig=sig)
round_to = max(round_to1, round_to2)
f_mean = round(mean, round_to)
latexstr = "$%s_{-%s}^{+%s}$" % (f_mean, f_low, f_up)
return latexstr
def pheno_pathway_assoc_row_generator(): # doesn't output primary key, let's sqlite3 auto-increment instead
for i, (phecode, genesettype) in enumerate(itertools.product(phecodes, genesettypes)):
phecode_id = phecode_ids[phecode]
filename = 'PheCode_{}_{}.wConditional.txt.gz'.format(phecode, genesettype)
print(i, filename)
with read_maybe_gzip(pheno_dir_path / filename, 'rt') as f:
for i, line in enumerate(f):
try:
parts = line.split()
if len(parts) == 6:
# This is the normal format, with 2 unused columns
name, url, pval_string, _, selected_genes_string, _ = line.split()
elif len(parts) == 4:
# This is the trimmed format without the 2 unused columns
name, url, pval_string, selected_genes_string = line.split()
else:
raise Exception("wrong number of columns")
if pval_string == 'NA' and selected_genes_string == 'NA':
# I don't know why these lines exist but there's a lot of them.
continue
selected_genes = selected_genes_string.split(',')
try:
assert name in pathways, line
assert pathways[name]['url'] == url, line
assert 0 <= float(pval_string) <= 1, line
assert 1 <= len(selected_genes) < 20e3, line
assert all(g in pathways[name]['genes'] for g in selected_genes), line
except Exception: raise Exception(line)
pval = round_sig(float(pval_string), 3)
yield (phecode_id, pathway_ids[name], pval, selected_genes_string)
except Exception as exc:
raise Exception("Failed on line {} of file {} which is {}".format(i, pheno_dir_path/filename, repr(line))) from exc
def pheno_gene_assoc_row_generator():
for phecode, pheno_id in phecode_ids.items():
print(' - reading pheno#{}: {}'.format(pheno_id, phecode))
filepath = '../input_data/genes/OUTF_PheCode_{}.txt.gz'.format(phecode)
with read_maybe_gzip(filepath, 'rt') as f:
delimiter = '\t' if '\t' in next(f) else ' '
with read_maybe_gzip(filepath, 'rt') as f:
for i, row in enumerate(csv.reader(f, delimiter=delimiter)):
try:
if len(row) == 2:
gene, pval_str = row
elif len(row) == 8:
gene, pval_str = row[0], row[4]
else:
raise Exception(
"Wrong number of parts in line {} of file {} which is {}"
.format(i, filepath, repr(row)))
except ValueError as exc:
raise Exception(
"Failed on line {} of file {} which is {}".format(
i, filepath, repr(row))) from exc
if gene in gene_ids and pval_str != "NA":
pval = round_sig(float(pval_str), 3)
assert 0 <= pval <= 1
yield (pheno_id, gene_ids[gene], pval)
def latex_mean(mean, std, sig=2):
#latexstr = "%.10f_{-%.10f}^{+%.10f}" % (mean,low,up)
f_std, round_to = utils.round_sig(std, return_round_to=True, sig=sig)
f_mean = round(mean, round_to)
latexstr = "$%s \pm %s$" % (f_mean, f_std)
return latexstr
text_file = open("output.txt", "w")
print("-------------")
if min_n == -1:
print("gd = ", gd, " not found")
else:
print("t0 = ", t0, "n =", min_n)
# Ahora checkeamos si cumple la condicion
b.calcular(min_n)
mag, pha = b.evaluarAproximacion(fa)
at = -mag
mag, pha = b.evaluarAproximacion(fp)
at_fp = -mag
gd = b.evaluarRetardoDeGrupo(fp, fp * 0.001) * 1000
if at > 40:
print("Gd(fp) = ", round_sig(gd, 4), "ms At(fp) = ",
round_sig(at_fp, 4), " At(fa) = ", round_sig(at, 4),
" Cumple")
print("H(s) = ", etapas.getFacto2orderExpression(b.tf))
print("coef = ", b.coef)
else:
print("Gd(fp) = ", round_sig(gd, 4), " ms At(fp) = ",
round_sig(at_fp, 4), "At(fa) = ", round_sig(at, 4))
print("H(s) = ", etapas.getFacto2orderExpression(b.tf))
print("coef = ", b.coef)
def plot_moments(self,
title='moment diagram',
figsize=(10, 4),
scale=1,
steps=10,
fontsize=12,
round=3):
scale = scale * np.max(self.elements['L']) / 3 / np.max(
np.abs(self.moment))
relativeOffset = scale * np.max(self.elements['L'])
fig, ax = plt.subplots(figsize=figsize)
ax.grid(which='major', linestyle=':', linewidth='0.5', color='black')
element_summary = []
for i in range(len(self.elements)):
#elements
x_el = [
utils.df_value(self.nodes, self.elements['n1'][i], 'name',
'x'),
utils.df_value(self.nodes, self.elements['n2'][i], 'name', 'x')
]
y_el = [
utils.df_value(self.nodes, self.elements['n1'][i], 'name',
'y'),
utils.df_value(self.nodes, self.elements['n2'][i], 'name', 'y')
]
ax.plot(x_el, y_el, '-', linewidth=3, marker='o', color='black')
#moments
x1, x2, y1, y2 = elmCoord(self.elements, self.nodes, i)
s = (y2 - y1) / self.elements['L'][i]
c = (x2 - x1) / self.elements['L'][i]
if self.elements['name'][i] in self.forces_distributed[
'element'].values:
x_mom = [x_el[0]]
y_mom = [y_el[0]]
moments = []
L = self.elements['L'][i]
steps = steps
w1 = utils.df_value(self.forces_distributed,
self.elements['name'][i], 'element', 'n1')
w2 = utils.df_value(self.forces_distributed,
self.elements['name'][i], 'element', 'n2')
for j in range(0, steps + 1):
x = L / steps * j
w2_temp = w1 + (w2 - w1) * x / L
# area=np.trapz([w1,w2_temp], x=[0,L])
# leverarm = w2_temp-utils.centroidX([[0,w1],[x,w2_temp]])
area = (w1 + w2_temp) / 2 * x
if area != 0:
leverarm = (w1 * x**2 / 2 +
(w2_temp - w1) * x**2 / 6) / area
else:
leverarm = 0
moment = -(-self.moment[i * 2] + self.shear[i * 2] * x +
area * leverarm) #upsidedown moments
moments.append(moment)
x_mom.append(
utils.df_value(self.nodes, self.elements['n1'][i],
'name', 'x') + x + moment * s * scale)
y_mom.append(
utils.df_value(self.nodes, self.elements['n1'][i],
'name', 'y') + moment * c * scale)
# x_mom.append(utils.df_value(self.nodes,self.elements['n2'][i],'name','x')+s*self.moment[i*2+1]*scale)
# y_mom.append(utils.df_value(self.nodes,self.elements['n2'][i],'name','y')+c*self.moment[i*2+1]*scale)
x_mom.append(x_el[1])
y_mom.append(y_el[1])
ax.text(x_mom[1] + relativeOffset * c,
y_mom[1] + utils.matchSign(relativeOffset, y_mom[1]),
utils.round_sig(moments[0], round),
fontsize=fontsize)
ax.text(x_mom[-2] - 4 * relativeOffset * c,
y_mom[-2] + utils.matchSign(relativeOffset, y_mom[-2]),
utils.round_sig(moments[-1], round),
fontsize=fontsize)
if utils.maxAbs(moments[0], moments[-1]) > 0:
indice = min(
(val, idx) for (idx, val) in enumerate(moments))[1]
else:
indice = max(
(val, idx) for (idx, val) in enumerate(moments))[1]
ax.text(x_mom[indice + 1],
y_mom[indice] +
utils.matchSign(relativeOffset, y_mom[indice + 1]),
utils.round_sig(moments[indice], round),
fontsize=fontsize)
element_summary.append([
self.elements['name'][i],
utils.round_sig(moments[0], round),
utils.round_sig(moments[-1], round),
utils.round_sig(moments[indice], round), self.shear[i * 2],
self.shear[i * 2 + 1]
])
else:
x_mom = [
x_el[0],
utils.df_value(self.nodes, self.elements['n1'][i], 'name',
'x') + s * self.moment[i * 2] * scale,
utils.df_value(self.nodes, self.elements['n2'][i], 'name',
'x') - s * self.moment[i * 2 + 1] * scale,
x_el[1]
]
y_mom = [
y_el[0],
utils.df_value(self.nodes, self.elements['n1'][i], 'name',
'y') + c * self.moment[i * 2] * scale,
utils.df_value(self.nodes, self.elements['n2'][i], 'name',
'y') - c * self.moment[i * 2 + 1] * scale,
y_el[1]
]
ax.text(x_mom[1] + relativeOffset * c,
y_mom[1] + utils.matchSign(relativeOffset, y_mom[1]),
utils.round_sig(self.moment[i * 2], round),
fontsize=fontsize)
ax.text(x_mom[-2] - 4 * relativeOffset * c,
y_mom[-2] + utils.matchSign(relativeOffset, y_mom[-2]),
utils.round_sig(self.moment[i * 2 + 1], round),
fontsize=fontsize)
element_summary.append([
self.elements['name'][i],
utils.round_sig(self.moment[i * 2], round),
utils.round_sig(self.moment[i * 2 + 1], round),
(utils.round_sig(self.moment[i * 2], round) +
utils.round_sig(self.moment[i * 2 + 1], round)) / 2,
self.shear[i * 2], self.shear[i * 2 + 1]
])
ax.plot(x_mom, y_mom, '-', linewidth=2, marker='', color='blue')
self.element_summary = pd.DataFrame(
element_summary, columns=['name', 'm1', 'm2', 'mMid', 'S1', 'S2'])
plt.axis('equal')
ax.set_title(title)
plt.show()