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 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 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)
