def plotSpecSens(plot_norm=False, log=True):
'''
'''
Lnorm, Mnorm, Snorm = genLMS(spectrum, filters,
fundamental='neitz', LMSpeaks=[559, 530, 419])
L, M, S = genLMS(spectrum, filters, remove_filters=False,
fundamental='neitz', LMSpeaks=[559, 530, 419])
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], Nticks=[5, 5])
if not log:
ax.plot(spectrum, L, 'r-')
ax.plot(spectrum, M, 'g-')
ax.plot(spectrum, S, 'b-')
ax.set_ylim([-0.01, 1.01])
else:
ax.semilogy(spectrum, L, 'r-')
ax.semilogy(spectrum, M, 'g-')
ax.semilogy(spectrum, S, 'b-')
ax.set_ylim([10 ** -4, 10 ** -0])
if plot_norm:
ax.plot(spectrum, Snorm, 'b', linewidth=2)
ax.plot(spectrum, Mnorm, 'g', linewidth=2)
ax.plot(spectrum, Lnorm, 'r', linewidth=2)
ax.set_xlim([380, 781])
ax.set_xlabel('wavelength (nm)')
ax.set_ylabel('sensitivity')
plt.show()
def plotCompare(compare=['stockman', 'stockSpecSens', 'neitz'],
invert=False):
'''
'''
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], Nticks=[5, 5])
style = ['-', '--', '-.']
for i, condition in enumerate(compare):
Lnorm, Mnorm, Snorm = genLMS(spectrum, filters,
fundamental=condition, LMSpeaks=[559, 530, 419])
ax.plot(spectrum, Lnorm, 'r' + style[i], linewidth=2)
ax.plot(spectrum, Mnorm, 'g' + style[i], linewidth=2)
ax.plot(spectrum, Snorm, 'b' + style[i], linewidth=2)
#ax.set_ylim([-0.01, 1.01])
ax.set_xlim([380, 781])
ax.set_xlabel('wavelength (nm)')
ax.set_ylabel('sensitivity')
if invert:
pf.invert(ax, fig, bk_color='k')
plt.show()
def plotRelativeSens():
'''
'''
Lnorm, Mnorm, Snorm = genLMS(spectrum, filters,
fundamental='neitz', LMSpeaks=[559, 530, 419])
L, M, S = genLMS(spectrum, filters, remove_filters=False,
fundamental='neitz', LMSpeaks=[559, 530, 419])
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], Nticks=[5, 5])
ax.semilogy(spectrum, L / M, 'k')
ax.semilogy(spectrum, np.ones(spectrum.size), 'k--')
#ax.set_ylim([-0.01, 1.01])
ax.set_xlim([380, 781])
ax.set_xlabel('wavelength (nm)')
ax.set_ylabel('L/M sensitivity ratio')
plt.show()
def genfund(self, fundamental):
'''
'''
self.fund = fundamental.lower()
if self.fund in ['neitz', 'stockman']:
self.genStockmanFilter()
self.Lnorm, self.Mnorm, self.Snorm = genLMS(self.spectrum,
self.filters,
fundamental=self.fund,
LMSpeaks=self.param['LMSpeaks'])
elif self.fund[:12] == 'smithpokorny' or self.fund == 'sp':
sens, spectrum = ss.smithpokorny(minLambda=390,
maxLambda=720,
return_spect=True,
quanta=False)
# create Judd-Vos CMFs
JVm = ss.sp_to_JuddVosCIE()
cmf = np.dot(JVm, sens.T)
self.spectrum = spectrum
self.Lnorm = sens[:, 0]
self.Mnorm = sens[:, 1]
self.Snorm = sens[:, 2]
# normalize to peak at unity
self.Lnorm /= np.max(self.Lnorm)
self.Mnorm /= np.max(self.Mnorm)
self.Snorm /= np.max(self.Snorm)
# need to generate conversion matrix here
M = np.linalg.lstsq(cmf.T, sens)[0].T
self.convMatrix = M
else:
raise InputError('fundamentals not supported: must be neitz, \
stockman or smithpokorny')
def genLMS(self, fundamental, LMSpeaks=[559.0, 530.0, 421.0]):
'''
'''
fund = fundamental.lower()
self.Lnorm, self.Mnorm, self.Snorm = genLMS(self.spectrum,
self.filters, fundamental=fund, LMSpeaks=LMSpeaks)
#! /usr/bin/env python
import matplotlib.pylab as plt
import numpy as np
from base import optics as op
from base import plot as pf
from stockmanModel import genStockmanAnalysis
from genLMS import genLMS
maxLambda = 770
filters, spectrum = op.filters.stockman(minLambda=390,
maxLambda=maxLambda, RETURN_SPECTRUM=True,
resolution=1)
Lnorm, Mnorm, Snorm = genLMS(spectrum, filters,
fundamental='stockspecsens', LMSpeaks=[559, 530, 421])
stage2, stage3 = genStockmanAnalysis(spectrum, filters, Lnorm,
Mnorm, Snorm)
def plotStage2Stockman():
'''
'''
fig = plt.figure()
fig.set_tight_layout(True)
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], Nticks=[5, 5])
for key in stage2:
ax.plot(spectrum, stage2[key])