def WaitingtimesDistributions(self,waiting_times,rates,trajectory_index,linestyle,linewidth, marker,colors,title,xlabel,ylabel,is_legend,legend_location):
"""
Plots the waiting times for each reaction in the model.
Makes use of ObtainWaitingtimes to derive the waiting times out of the SSA output.
Input:
- *waiting_times* (dict)
- *rates* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewith* (float)
- *marker* (string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
plt.figure(self.plotnum)
if len(rates) == 1:
j = trajectory_index
else:
j=0
L_legend_names = []
for r_id in rates:
L_waiting_times = waiting_times[r_id] # get list of waiting times for a given reaction
if len(L_waiting_times) > 1: # At least 2 waiting times are necessary per reaction
(x,y,nbins) = LogBin(L_waiting_times,1.5) # Create logarithmic bins (HARDCODED 1.5)
if x != None:
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
if colors == None:
plt.loglog(x,y,marker,ls = linestyle,lw=linewidth,color = self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.loglog(x,y,marker,ls = linestyle,lw=linewidth,color = colors[j])
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
plt.loglog(x,y,marker,ls = linestyle,lw=linewidth,color = self.colors[j])
colors = None
L_legend_names.append(r_id)
j+=1
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if is_legend:
plt.legend(L_legend_names,numpoints=1,frameon=True,loc=legend_location)
def TimeSeries(self,data,npoints,datatype,labels,trajectory_index,linestyle,linewidth,marker,colors,title,xlabel,ylabel,is_legend,legend_location):
"""
Tracks the propensities and/or species over time.
Input:
- *data* (array)
- *npoints* (integer)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
data = getDataForTimeSimPlot(data,npoints,self.quiet)
Arr_time = data[:,0]
if len(datatype) == 1:
j = trajectory_index
else:
j=0
for i in datatype_indices:
y = data[:,i+1]
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
if colors == None:
plt.plot(Arr_time,y,marker,ls = linestyle,lw = linewidth,color = self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.plot(Arr_time,y,marker,ls = linestyle,lw = linewidth,color = colors[j])
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
plt.plot(Arr_time,y,marker,ls = linestyle,lw = linewidth,color = self.colors[j])
colors = None
j+=1
if is_legend:
plt.legend(datatype,numpoints=1,frameon=True,loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def AverageTimeSeries(self,means,stds,time,nstd,datatype,labels,linestyle,linewidth,marker,ms,colors,title,xlabel,ylabel,is_legend,legend_location):
"""
Plots the average and standard deviation of datatype on a regular grid.
Input:
- *means* (array)
- *stds* (array)
- *time* (array)
- *nstd* (float)
- *datatype* (list)
- *labels* (list)
- *linestyle* (string)
- *linewidth* (float)
- *marker* (string)
- *ms* (float)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
assert nstd > 0, "Error: The number of STDs must be a value larger than zero"
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
j=0
for i in datatype_indices:
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
# plot with y-axis error bars
if colors == None:
plt.errorbar(time,means[:,i],yerr = nstd*np.array(stds[:,i]),color = self.colors[j],ls = linestyle,lw=linewidth,marker = marker,ms=ms,label = labels[i])
else:
if clr.is_color_like(colors[j]):
plt.errorbar(time,means[:,i],yerr = nstd*np.array(stds[:,i]),color = colors[j],ls = linestyle,lw=linewidth,marker = marker,ms=ms,label = labels[i])
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
plt.errorbar(time,means[:,i],yerr = nstd*np.array(stds[:,i]),color = self.colors[j],ls = linestyle,lw=linewidth,marker = marker,ms=ms,label = labels[i])
colors = None
j+=1
if is_legend:
plt.legend(numpoints=1,frameon=True,loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def Autocorrelations(self,lags,data,datatype,labels,trajectory_index,linestyle,linewidth,marker,colors,title,xlabel,ylabel,is_legend,legend_location):
"""
Input:
- *lags*
- *data* (array)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *marker* string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
if len(datatype) == 1:
j = trajectory_index
else:
j=0
for i in datatype_indices:
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
y = data[i][0:len(lags)]
if colors == None:
plt.plot(lags,y,marker,ls = linestyle,lw = linewidth, color = self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.plot(lags,y,marker,ls = linestyle,lw = linewidth, color = colors[j])
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
plt.plot(lags,y,marker,ls = linestyle,lw = linewidth, color = self.colors[j])
colors = None
j+=1
if is_legend:
plt.legend(datatype,numpoints=1,frameon=True,loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def AverageDistributionsCI(self, means, stds, nstd, datatype, labels,
colors, title, xlabel, ylabel, is_legend,
legend_location):
"""
Plots the average and standard deviation.
Input:
- *means* (nested list)
- *stds* (nested list)
- *nstd* (float)
- *labels* (list)
- *linestyle* (string)
- *linewidth* (float)
- *marker* (string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
assert nstd > 0, "Error: The number of STDs must be a value larger than zero"
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
for i in datatype_indices:
L_s_amount = copy.copy(means[i][0])
L_mu = copy.copy(means[i][1])
L_sigma = copy.copy(stds[i][1])
# Add an additional value
L_s_amount.append(L_s_amount[-1] + 1)
L_mu.append(L_mu[-1])
L_sigma.append(L_sigma[-1])
X_i = []
Y_i = []
L_errors = []
for j in range(len(L_s_amount)):
if (not L_s_amount[j] == L_s_amount[0]) and (
not L_s_amount[j] == L_s_amount[-1]):
X_i.append(L_s_amount[j])
Y_i.append(L_mu[j - 1])
L_errors.append(L_sigma[j - 1])
X_i.append(L_s_amount[j])
Y_i.append(L_mu[j])
L_errors.append(L_sigma[j])
X_e = np.concatenate([X_i, X_i[::-1]])
Y_e = np.concatenate([
np.array(Y_i) - nstd * np.array(L_errors),
(np.array(Y_i) + nstd * np.array(L_errors))[::-1]
])
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
if colors == None:
plt.fill(X_e - 0.5,
Y_e,
alpha=.25,
ec='None',
label='{0} STD confidence interval'.format(nstd),
color=self.colors[j])
plt.plot(np.array(X_i) - 0.5,
np.array(Y_i),
color=self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.fill(X_e - 0.5,
Y_e,
alpha=.25,
ec='None',
label='{0} STD confidence interval'.format(nstd),
color=colors[j])
plt.plot(np.array(X_i) - 0.5,
np.array(Y_i),
color=colors[j])
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
plt.fill(X_e - 0.5,
Y_e,
alpha=.25,
ec='None',
label='{0} STD confidence interval'.format(nstd),
color=self.colors[j])
plt.plot(np.array(X_i) - 0.5,
np.array(Y_i),
color=self.colors[j])
colors = None
if is_legend:
plt.legend(numpoints=1, frameon=True, loc=legend_location)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
def AverageDistributions(self, means, stds, nstd, datatype, labels,
linestyle, linewidth, marker, colors, title,
xlabel, ylabel, is_legend, legend_location):
"""
Plots the average and standard deviation.
Input:
- *means* (nested list)
- *stds* (nested list)
- *nstd* (float)
- *labels* (list)
- *linestyle* (string)
- *linewidth* (float)
- *marker* (string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
assert nstd > 0, "Error: The number of STDs must be a value larger than zero"
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
j = 0
for i in datatype_indices:
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
if colors == None:
plt.errorbar(means[i][0],
means[i][1],
yerr=nstd * np.array(stds[i][1]),
color=self.colors[j],
ls=linestyle,
lw=linewidth,
marker=marker,
label=labels[i]) # plot with y-axis error bars
else:
if clr.is_color_like(colors[j]):
plt.errorbar(means[i][0],
means[i][1],
yerr=nstd * np.array(stds[i][1]),
color=colors[j],
ls=linestyle,
lw=linewidth,
marker=marker,
label=labels[i])
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
plt.errorbar(means[i][0],
means[i][1],
yerr=nstd * np.array(stds[i][1]),
color=self.colors[j],
ls=linestyle,
lw=linewidth,
marker=marker,
label=labels[i])
colors = None
j += 1
if is_legend:
plt.legend(numpoints=1, frameon=True, loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def WaitingtimesDistributions(self, waiting_times, rates, trajectory_index,
linestyle, linewidth, marker, colors, title,
xlabel, ylabel, is_legend, legend_location):
"""
Plots the waiting times for each reaction in the model.
Makes use of ObtainWaitingtimes to derive the waiting times out of the SSA output.
Input:
- *waiting_times* (dict)
- *rates* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewith* (float)
- *marker* (string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
plt.figure(self.plotnum)
if len(rates) == 1:
j = trajectory_index
else:
j = 0
L_legend_names = []
for r_id in rates:
L_waiting_times = waiting_times[
r_id] # get list of waiting times for a given reaction
if len(
L_waiting_times
) > 1: # At least 2 waiting times are necessary per reaction
(x, y, nbins) = LogBin(
L_waiting_times,
1.5) # Create logarithmic bins (HARDCODED 1.5)
if x != None:
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
if colors == None:
plt.loglog(x,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.loglog(x,
y,
marker,
ls=linestyle,
lw=linewidth,
color=colors[j])
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
plt.loglog(x,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
colors = None
L_legend_names.append(r_id)
j += 1
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if is_legend:
plt.legend(L_legend_names,
numpoints=1,
frameon=True,
loc=legend_location)
def Distributions(self,
distributions,
datatype,
labels,
trajectory_index,
linestyle,
linewidth,
colors,
title,
xlabel,
ylabel,
is_legend=True,
legend_location='upper right',
bin_size=1,
histtype='step',
orientation='vertical',
multiplotting=False):
"""
Plots the distributions of species and/or propensities
Normed=False because the total probability is determined by summation not by integration.
Input:
- *distributions* (nested list)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
- *bin_size* (string) [default = 1]
- *histtype* (string)) [default = 'step']
- *orientation* (string) [default = 'vertical']
- *multiplotting* (boolean) [default = False]
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
if len(datatype) == 1:
j = trajectory_index
else:
j = 0
for i in datatype_indices:
dat_min = distributions[i][0].min()
dat_max = distributions[i][0].max()
n_bins = 1 + (dat_max - dat_min
) / bin_size # Just take one trajectory as reference
L_bin_edges = np.linspace(dat_min - bin_size / 2.0,
dat_max + bin_size / 2.0, n_bins + 1)
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
if colors == None:
output = plt.hist(distributions[i][0],
L_bin_edges,
weights=distributions[i][1],
ls=linestyle,
lw=linewidth,
color=self.colors[j],
histtype=histtype,
orientation=orientation,
normed=False)
else:
if clr.is_color_like(colors[j]):
output = plt.hist(distributions[i][0],
L_bin_edges,
weights=distributions[i][1],
ls=linestyle,
lw=linewidth,
color=colors[j],
histtype=histtype,
orientation=orientation,
normed=False)
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
output = plt.hist(distributions[i][0],
L_bin_edges,
weights=distributions[i][1],
ls=linestyle,
lw=linewidth,
color=self.colors[j],
histtype=histtype,
orientation=orientation,
normed=False)
colors = None
j += 1
if is_legend:
plt.legend(datatype,
numpoints=1,
frameon=True,
loc=legend_location)
plt.title(title)
if orientation.lower() == 'horizontal':
plt.xlabel(ylabel)
plt.ylabel(xlabel)
if multiplotting:
plt.xticks([0, max(output[0]) * 1.1])
else:
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if multiplotting:
plt.yticks([0, max(output[0]) * 1.1])
def Autocorrelations(self, lags, data, datatype, labels, trajectory_index,
linestyle, linewidth, marker, colors, title, xlabel,
ylabel, is_legend, legend_location):
"""
Input:
- *lags*
- *data* (array)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *marker* string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
if len(datatype) == 1:
j = trajectory_index
else:
j = 0
for i in datatype_indices:
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
y = data[i][0:len(lags)]
if colors == None:
plt.plot(lags,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.plot(lags,
y,
marker,
ls=linestyle,
lw=linewidth,
color=colors[j])
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
plt.plot(lags,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
colors = None
j += 1
if is_legend:
plt.legend(datatype,
numpoints=1,
frameon=True,
loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def TimeSeries(self, data, npoints, datatype, labels, trajectory_index,
linestyle, linewidth, marker, colors, title, xlabel, ylabel,
is_legend, legend_location):
"""
Tracks the propensities and/or species over time.
Input:
- *data* (array)
- *npoints* (integer)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
data = getDataForTimeSimPlot(data, npoints, self.quiet)
Arr_time = data[:, 0]
if len(datatype) == 1:
j = trajectory_index
else:
j = 0
for i in datatype_indices:
y = data[:, i + 1]
if colors == None:
if j >= len(self.colors):
j = 0
elif isinstance(colors, list):
if j >= len(colors):
j = 0
elif isinstance(colors, str):
colors = [colors]
j = 0
if colors == None:
plt.plot(Arr_time,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.plot(Arr_time,
y,
marker,
ls=linestyle,
lw=linewidth,
color=colors[j])
else:
print(
"*** WARNING ***: '{0}' is not recognized as a valid color code"
.format(colors[j]))
plt.plot(Arr_time,
y,
marker,
ls=linestyle,
lw=linewidth,
color=self.colors[j])
colors = None
j += 1
if is_legend:
plt.legend(datatype,
numpoints=1,
frameon=True,
loc=legend_location)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
def AverageDistributionsCI(self,means,stds,nstd,datatype,labels,colors,title,xlabel,ylabel,is_legend,legend_location):
"""
Plots the average and standard deviation.
Input:
- *means* (nested list)
- *stds* (nested list)
- *nstd* (float)
- *labels* (list)
- *linestyle* (string)
- *linewidth* (float)
- *marker* (string)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
"""
assert nstd > 0, "Error: The number of STDs must be a value larger than zero"
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
for i in datatype_indices:
L_s_amount = copy.copy(means[i][0])
L_mu = copy.copy(means[i][1])
L_sigma = copy.copy(stds[i][1])
# Add an additional value
L_s_amount.append(L_s_amount[-1]+1)
L_mu.append(L_mu[-1])
L_sigma.append(L_sigma[-1])
X_i = []
Y_i = []
L_errors = []
for j in range(len(L_s_amount)):
if (not L_s_amount[j] == L_s_amount[0]) and (not L_s_amount[j] == L_s_amount[-1]):
X_i.append(L_s_amount[j])
Y_i.append(L_mu[j-1])
L_errors.append(L_sigma[j-1])
X_i.append(L_s_amount[j])
Y_i.append(L_mu[j])
L_errors.append(L_sigma[j])
X_e = np.concatenate([X_i, X_i[::-1]])
Y_e = np.concatenate([np.array(Y_i) - nstd*np.array(L_errors) ,(np.array(Y_i) + nstd*np.array(L_errors))[::-1]])
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
if colors == None:
plt.fill(X_e-0.5,Y_e, alpha=.25, ec='None', label='{0} STD confidence interval'.format(nstd),color = self.colors[j])
plt.plot(np.array(X_i)-0.5,np.array(Y_i),color = self.colors[j])
else:
if clr.is_color_like(colors[j]):
plt.fill(X_e-0.5,Y_e, alpha=.25, ec='None', label='{0} STD confidence interval'.format(nstd),color = colors[j])
plt.plot(np.array(X_i)-0.5,np.array(Y_i),color = colors[j])
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
plt.fill(X_e-0.5,Y_e, alpha=.25, ec='None', label='{0} STD confidence interval'.format(nstd),color = self.colors[j])
plt.plot(np.array(X_i)-0.5,np.array(Y_i),color = self.colors[j])
colors = None
if is_legend:
plt.legend(numpoints=1,frameon=True,loc=legend_location)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
def Distributions(self,distributions,datatype,labels,trajectory_index,linestyle,linewidth,colors,title,xlabel,ylabel,is_legend=True,legend_location='upper right',bin_size=1,histtype = 'step',orientation='vertical',multiplotting=False):
"""
Plots the distributions of species and/or propensities
Normed=False because the total probability is determined by summation not by integration.
Input:
- *distributions* (nested list)
- *datatype* (list)
- *labels* (list)
- *trajectory_index* (integer)
- *linestyle* (string)
- *linewidth* (float)
- *colors* (list)
- *title* (string)
- *xlabel* (string)
- *ylabel* (string)
- *is_legend* (boolean)
- *legend_location* [default = 'upper right'] (string/integer)
- *bin_size* (string) [default = 1]
- *histtype* (string)) [default = 'step']
- *orientation* (string) [default = 'vertical']
- *multiplotting* (boolean) [default = False]
"""
plt.figure(self.plotnum)
datatype_indices = [labels.index(Id) for Id in datatype]
if len(datatype) == 1:
j = trajectory_index
else:
j=0
for i in datatype_indices:
dat_min = distributions[i][0].min()
dat_max = distributions[i][0].max()
n_bins = 1 + (dat_max-dat_min) /bin_size # Just take one trajectory as reference
L_bin_edges = np.linspace(dat_min-bin_size/2.0,dat_max+bin_size/2.0,n_bins+1)
if colors == None:
if j >= len(self.colors):
j=0
elif isinstance(colors,list):
if j >= len(colors):
j=0
elif isinstance(colors,str):
colors = [colors]
j=0
if colors == None:
output = plt.hist(distributions[i][0],L_bin_edges,weights = distributions[i][1],ls = linestyle,lw = linewidth,color = self.colors[j],histtype = histtype,orientation=orientation,normed=False)
else:
if clr.is_color_like(colors[j]):
output = plt.hist(distributions[i][0],L_bin_edges,weights = distributions[i][1],ls = linestyle,lw = linewidth,color = colors[j],histtype = histtype,orientation=orientation,normed=False)
else:
print("*** WARNING ***: '{0}' is not recognized as a valid color code".format(colors[j]) )
output = plt.hist(distributions[i][0],L_bin_edges,weights = distributions[i][1],ls = linestyle,lw = linewidth,color = self.colors[j],histtype = histtype,orientation=orientation,normed=False)
colors = None
j+=1
if is_legend:
plt.legend(datatype,numpoints=1,frameon=True,loc=legend_location)
plt.title(title)
if orientation.lower() == 'horizontal':
plt.xlabel(ylabel)
plt.ylabel(xlabel)
if multiplotting:
plt.xticks([0,max(output[0])*1.1])
else:
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if multiplotting:
plt.yticks([0,max(output[0])*1.1])