def export():
with pp(r"E:\Instagram\new.pdf") as new:
g = f.Date_reported[10169:10315]
h = f.New_cases[10169:10315]
mt.plot(h, g, color="red", marker="o")
mt.grid(True)
mt.title("COVID-19 Daily Updates in INDIA")
mt.ylabel("Date")
mt.xlabel("No. of cases")
new.savefig()
mt.close()
def write(self, subdirectory, filename):
pl.legend(fontsize="x-small", ncol=self.legend_columns,
mode="expand", title=self.legend_title)
if not self.pdf:
pdf_path = os.path.join(self.get_base_path(), "all.pdf")
self.pdf = pp(pdf_path)
print("wrote: {}".format(pdf_path))
self.pdf.savefig(transparent=True)
figure_path = self.get_full_path(subdirectory, filename)
pl.savefig(figure_path)
print("wrote: {}".format(figure_path))
pl.close('all')
def plot_throughput(args):
df = pd.read_csv(args.csvfile)
plot_groups = list(df.groupby(by=['testcase', 'symbol_size', 'type']))
if args.outfile:
pdf = pp(args.outfile)
# Group the plots
for (test, symbol_size, type), df in plot_groups:
def density_to_string(density):
if not np.isnan(density):
return "density {}".format(density)
else:
return ""
# Combine the testcase and benchmark columns into one (used for labels)
if not 'density' in df:
df['test'] = df['testcase'].map(str) + '.' + df['benchmark']
df = df.drop(['testcase', 'benchmark'], axis=1)
else:
df['test'] = df['testcase'].map(str) + '.' + df['benchmark'] +\
' ' + df['density'].map(density_to_string)
df = df.drop(['testcase', 'benchmark', 'density'], axis=1)
group = df.groupby(by=['test', 'symbols'])
def compute_throughput(group):
s = group['throughput']
s = pd.Series([s.mean(), s.std()], ['mean', 'std'])
return s
df = group.apply(compute_throughput)
df = df.unstack(level=0)
df['mean'].plot(title="Throughput {} {} p={}B".format(
test, type, symbol_size),
kind='bar')
if args.outfile:
pdf.savefig(transparent=True)
if args.outfile:
pdf.close()
else:
plt.show()
def plot_metric(df,metric,varying_parameter,fixed_parameters,cases,density):
df_group = df.groupby(by=fixed_parameters)
all_figures_filename = "all_" + density + "_" + metric + "_vs_" + \
varying_parameter + ".pdf"
pdf = pp(all_figures_filename)
for keys, group in df_group:
p = group.pivot_table(metric,cols=cases,rows=varying_parameter).plot(
kind=pltkind[(metric,varying_parameter)])
plt.title(get_plot_title(fixed_parameters,keys),fontsize=font_size)
set_axis_properties(p,metric,varying_parameter,group)
set_plot_legend(p,metric,varying_parameter)
filename = get_filename(metric,varying_parameter,
fixed_parameters,keys,density)
plt.savefig(filename,bbox_inches='tight')
pdf.savefig(bbox_inches='tight')
plt.close()
pdf.close()
def main():
usage = 'usage: %prog [options] '
parser = OptionParser(usage)
parser.add_option('-r',
dest='runName',
default='spt6',
type=str,
help='Name of the run')
parser.add_option(
'-s',
dest='save_path',
type=str,
default='/Users/umut/Projects/intragenicTranscription/results/')
parser.add_option(
'-a',
dest='annotation',
type=str,
default=
'/Users/umut/Projects/intragenicTranscription/data/annotation/ScerTSSannot.csv'
)
parser.add_option("-o", action="store_true", dest="overwrite")
(options, args) = parser.parse_args()
# options.save_path = options.save_path+options.runName+'/peaks_motifs/'
options.save_path = \
options.save_path + options.runName + '/peaks_motifs_p_thresh_0.05/'
gdb = genome.db.GenomeDB(
path='/Users/umut/Projects/genome/data/share/genome_db',
assembly='sacCer3')
# open data 'tracks' for DNase and MNase
gerp = gdb.open_track('phyloP')
pl.ioff()
df = pd.read_csv(options.save_path + options.runName +
'_intragenic_peaks.csv',
index_col=0)
consScoreSE = np.zeros((df[df['orientation'] == 'sense'].shape[0], 1000))
print('getting conservation score for sense')
for i in tq(range(len(df[df['orientation'] == 'sense']))):
chname = df[df['orientation'] == 'sense']['chr'].iloc[i]
st = df[df['orientation'] == 'sense']['peak_position'].iloc[i]
strand = df[df['orientation'] == 'sense']['strand'].iloc[i]
if strand == '+':
consScoreSE[i, :] = gerp.get_nparray(chname, st - 500 + 1,
st + 500)
else:
consScoreSE[i, :] = np.flipud(
gerp.get_nparray(chname, st - 500 + 1, st + 500))
consScoreAS = np.zeros(
(df[df['orientation'] == 'antisense'].shape[0], 1000))
print('getting conservation score for antisense')
for i in tq(range(len(df[df['orientation'] == 'antisense']))):
chname = df[df['orientation'] == 'antisense']['chr'].iloc[i]
st = df[df['orientation'] == 'antisense']['peak_position'].iloc[i]
strand = df[df['orientation'] == 'antisense']['strand'].iloc[i]
if strand == '-':
consScoreAS[i, :] = gerp.get_nparray(chname, st - 500 + 1,
st + 500)
else:
consScoreAS[i, :] = np.flipud(
gerp.get_nparray(chname, st - 500 + 1, st + 500))
print('plotting...')
pfile = pp(options.save_path + 'Figures/intragenic_conservation_sense.pdf')
xran = np.arange(-500, 500)
tmth = 0.1
for wn in [3, 50, 75]:
fig = pl.figure()
pl.plot(xran,
tm(np.apply_along_axis(sm.smooth, 1, consScoreSE,
wn)[:, (wn - 1):-(wn - 1)],
tmth,
axis=0),
'r',
label='Sense')
pl.xlabel('Position from intragenic TSS (bp)')
pl.ylabel('Average GERP score (a.u.)')
pl.title('Smoothing window: ' + str(wn) + 'bp')
pl.legend(loc='center left', bbox_to_anchor=(1, 0.5))
pfile.savefig()
pl.close(fig)
pfile.close()
pfile = pp(options.save_path +
'Figures/intragenic_conservation_antisense.pdf')
xran = np.arange(-500, 500)
tmth = 0.1
for wn in [3, 50, 75]:
fig = pl.figure()
pl.plot(xran,
tm(np.apply_along_axis(sm.smooth, 1, consScoreAS,
wn)[:, (wn - 1):-(wn - 1)],
tmth,
axis=0),
'r',
label='Antisense')
pl.xlabel('Position from intragenic TSS (bp)')
pl.ylabel('Average GERP score (a.u.)')
pl.title('Smoothing window: ' + str(wn) + 'bp')
pl.legend(loc='center left', bbox_to_anchor=(1, 0.5))
pfile.savefig()
pl.close(fig)
pfile.close()
np.savetxt(options.save_path + 'ConservationScoreGERP_sense.csv',
consScoreSE)
np.savetxt(options.save_path + 'ConservationScoreGERP_antisense.csv',
consScoreAS)
gerp.close()