from __future__ import print_function
import setup
from os.path import join
from dev_stages import dev_stages
from scalers import LogScaler
from project_dirs import results_dir
filename = join(results_dir(), 'dev-stages.txt')
with open(filename, 'w') as f:
scaler = LogScaler()
header = '{:<30} {:<8} {:<10} {:<10}'.format('Full Name', 'Label', 'Age',
'Log Scale')
print(header, file=f)
print(len(header) * '-', file=f)
for stage in dev_stages:
name = stage.name
short_name = stage.short_name
age = stage.central_age
log_age = stage.scaled(scaler).central_age
print('{:<30} {:<8} {:<10.3g} {:<10.3g}'.format(
name, short_name, age, log_age),
file=f)
fontsize=xtick_fontsize,
fontstretch='condensed',
rotation=90)
# set y ticks (first and last only)
ax.set_ylabel('expression level', fontsize=fontsize)
ticks = ax.get_yticks()
ticks = np.array([ticks[0], ticks[-1]])
ax.set_yticks(ticks)
ax.set_yticklabels(['{:g}'.format(t) for t in ticks], fontsize=fontsize)
return fig
cfg.verbosity = 1
age_scaler = LogScaler()
data = GeneData.load('both').scale_ages(age_scaler)
shapes = [
Sigmoid('sigmoid_wide'),
Poly(1, 'poly1'),
Poly(3, 'poly3'),
Spline()
]
GRs = [
('ADRB1', 'A1C', (5, 8)),
('GLRA2', 'STC', (5, 12)),
('TUBA1A', 'V1C', (10, 14)),
]
import setup
from load_data import load_data
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
from plots import *
from all_fits import *
import config as cfg
from fitter import Fitter
from shapes.sigmoid import Sigmoid
from shapes.spline import Spline
from scalers import LogScaler
cfg.verbosity = 1
data = load_data(pathway='serotonin', scaler=LogScaler())
series = data.get_one_series('HTR1A', 'MD')
x = series.ages
y = series.single_expression
fitter = Fitter(Spline())
theta, sigma, LOO_predictions, _ = fitter.fit(x, y)
spline = theta[0]
preds = spline(x)
print preds