def Plot_dose_response_poly(data, save_fig, save_data, ec50_poly,
save_poly_coeffs, namedata):
'''This function normalizes a set of data points for plotting on a dose response curve.
They are normalized so that the highest drug concentration's data = 0, lowest = 1.
A polynomial is fit to the data, and we measure where 0.5 (50% effect) crosses on that line.'''
normalized = (data - data[0]) / (data[-1] - data[0])
normalized = np.absolute(normalized)
np.savetxt(join2(save_dir, save_data), normalized, delimiter=',')
# fit a 3 degree polynomial. nm is the ranks and other crap
poly_coeffs, residual, nm1, nm2, nm3 = np.polyfit(concs_nums,
normalized,
ec50_poly,
full=True)
r_squared = 1 - residual / (len(normalized) * normalized.var())
# make a function out of it
f = np.poly1d(poly_coeffs)
np.savetxt(join2(save_dir, save_poly_coeffs), f)
EC50 = x_for_known_y_poly(f)
ec50s_file.write(('%s Endpoint\t EC50 = ' % namedata + '%.3f' % EC50 +
'\tR-squared = ' + '%.3f' % r_squared + '\n'))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
plt.scatter(concs_nums, normalized, s=75, color='#756E71')
plt.ylabel('effect (0 to 1)')
plt.xlabel('concentration')
plt.plot(rangeconc, f(rangeconc), 'k', EC50, f(EC50), 'r<', ms=8)
plt.grid(True)
ax.tick_params(axis='x')
ax.xaxis.tick_bottom()
ax.tick_params(axis='y')
ax.yaxis.tick_left()
plt.savefig(join2(save_dir, save_fig))
plt.close()
def Plot_dose_response_sig(data, save_fig, save_data, namedata):
'''This function normalizes a set of data points for plotting on a dose response curve.
They are normalized so that the highest drug concentration's data = 0, lowest = 1.
A sigmoidal is fit to the data, and we measure where 0.5 (50% effect) crosses on that line.'''
normalized = (data - data[0]) / (data[-1] - data[0])
normalized = np.absolute(normalized)
np.savetxt( join2(save_dir, save_data), normalized, delimiter=',')
p_guess = ( np.median( concs_nums ),np.median( normalized ),1.0,1.0 )
p, cov, infodict, mesg, ier = scipy.optimize.leastsq(residuals, p_guess, args=(concs_nums, normalized), full_output=1)
x0,y0,c,k = p
pxp = sigmoid(p, rangeconc)
ss_err =(infodict['fvec']**2).sum()
ss_tot =((normalized - normalized.mean())**2).sum()
r_squared = 1- (ss_err / ss_tot)
EC50 = x_for_known_y_sig( sigmoid, p )
ec50s_file.write(('%s Endpoint\t EC50 = ' %namedata + '%.3f' %EC50 + '\tR-squared = ' + '%.3f' %r_squared + '\n'))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
plt.scatter(concs_nums, normalized, s=75, color='#756E71')
plt.ylabel('effect (0 to 1)')
plt.xlabel('concentration')
plt.plot(rangeconc, pxp, 'k', EC50, sigmoid(p, EC50), 'r<', ms=9)
ax.tick_params(axis='x')
ax.xaxis.tick_bottom()
ax.tick_params(axis='y')
ax.yaxis.tick_left()
plt.grid(True)
plt.savefig( join2(save_dir, save_fig))
plt.close()
def Plot_dose_response_poly(data, save_fig, save_data, ec50_poly, save_poly_coeffs, namedata):
'''This function normalizes a set of data points for plotting on a dose response curve.
They are normalized so that the highest drug concentration's data = 0, lowest = 1.
A polynomial is fit to the data, and we measure where 0.5 (50% effect) crosses on that line.'''
normalized = (data - data[0]) / (data[-1] - data[0])
normalized = np.absolute(normalized)
np.savetxt( join2(save_dir, save_data), normalized, delimiter=',')
# fit a 3 degree polynomial. nm is the ranks and other crap
poly_coeffs, residual, nm1, nm2, nm3 = np.polyfit(concs_nums, normalized, ec50_poly, full=True)
r_squared = 1 - residual / (len(normalized) * normalized.var())
# make a function out of it
f = np.poly1d(poly_coeffs)
np.savetxt( join2(save_dir, save_poly_coeffs), f)
EC50 = x_for_known_y_poly(f)
ec50s_file.write(('%s Endpoint\t EC50 = ' %namedata + '%.3f' %EC50 + '\tR-squared = ' + '%.3f' %r_squared + '\n'))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
plt.scatter(concs_nums, normalized, s=75, color='#756E71')
plt.ylabel('effect (0 to 1)')
plt.xlabel('concentration')
plt.plot(rangeconc, f(rangeconc),'k', EC50, f(EC50),'r<', ms=8)
plt.grid(True)
ax.tick_params(axis='x')
ax.xaxis.tick_bottom()
ax.tick_params(axis='y')
ax.yaxis.tick_left()
plt.savefig( join2(save_dir, save_fig))
plt.close()
def Plot_dose_response_sig(data, save_fig, save_data, namedata):
'''This function normalizes a set of data points for plotting on a dose response curve.
They are normalized so that the highest drug concentration's data = 0, lowest = 1.
A sigmoidal is fit to the data, and we measure where 0.5 (50% effect) crosses on that line.'''
normalized = (data - data[0]) / (data[-1] - data[0])
normalized = np.absolute(normalized)
np.savetxt(join2(save_dir, save_data), normalized, delimiter=',')
p_guess = (np.median(concs_nums), np.median(normalized), 1.0, 1.0)
p, cov, infodict, mesg, ier = scipy.optimize.leastsq(residuals,
p_guess,
args=(concs_nums,
normalized),
full_output=1)
x0, y0, c, k = p
pxp = sigmoid(p, rangeconc)
ss_err = (infodict['fvec']**2).sum()
ss_tot = ((normalized - normalized.mean())**2).sum()
r_squared = 1 - (ss_err / ss_tot)
EC50 = x_for_known_y_sig(sigmoid, p)
ec50s_file.write(('%s Endpoint\t EC50 = ' % namedata + '%.3f' % EC50 +
'\tR-squared = ' + '%.3f' % r_squared + '\n'))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
plt.scatter(concs_nums, normalized, s=75, color='#756E71')
plt.ylabel('effect (0 to 1)')
plt.xlabel('concentration')
plt.plot(rangeconc, pxp, 'k', EC50, sigmoid(p, EC50), 'r<', ms=9)
ax.tick_params(axis='x')
ax.xaxis.tick_bottom()
ax.tick_params(axis='y')
ax.yaxis.tick_left()
plt.grid(True)
plt.savefig(join2(save_dir, save_fig))
plt.close()
# Convert list of tif images to jpg
import Image
import os
from os.path import join as join2
dir = os.getcwd()
tifs = [f for f in os.listdir(dir) if f.endswith('.png')]
for i in tifs:
Image.open( join2(dir, i)).save( join2(dir, i + '.jpg'), 'JPEG', quality=100)
# Convert list of tif images to jpg
import Image
import os
from os.path import join as join2
dir = os.getcwd()
tifs = [f for f in os.listdir(dir) if f.endswith('.png')]
for i in tifs:
Image.open(join2(dir, i)).save(join2(dir, i + '.jpg'), 'JPEG', quality=100)
