def modelfit(data, uncert, indparams, model, nbins=75,
fignum=-22, savefile=None, fmt="."):
"""
Plot the binned dataset with given uncertainties and model curves
as a function of indparams.
In a lower panel, plot the residuals bewteen the data and model.
Parameters
----------
data: 1D float ndarray
Input data set.
uncert: 1D float ndarray
One-sigma uncertainties of the data points.
indparams: 1D float ndarray
Independent variable (X axis) of the data points.
model: 1D float ndarray
Model of data.
nbins: Integer
Number of bins in the output plot.
fignum: Integer
The figure number.
savefile: Boolean
If not None, name of file to save the plot.
fmt: String
Format of the plotted markers.
"""
# Bin down array:
binsize = int((np.size(data)-1)/nbins + 1)
bindata, binuncert, binindp = ba.binarray(data, uncert, indparams, binsize)
binmodel = ba.weightedbin(model, binsize)
fs = 14 # Font-size
p = plt.figure(fignum, figsize=(8,6))
p = plt.clf()
# Residuals:
a = plt.axes([0.15, 0.1, 0.8, 0.2])
p = plt.errorbar(binindp, bindata-binmodel, binuncert, fmt='ko', ms=4)
p = plt.plot([indparams[0], indparams[-1]], [0,0],'k:',lw=1.5)
p = plt.xticks(size=fs)
p = plt.yticks(size=fs)
p = plt.xlabel("x", size=fs)
p = plt.ylabel('Residuals', size=fs)
# Data and Model:
a = plt.axes([0.15, 0.35, 0.8, 0.55])
p = plt.errorbar(binindp, bindata, binuncert, fmt='ko', ms=4,
label='Binned Data')
p = plt.plot(indparams, model, "b", lw=2, label='Best Fit')
p = plt.setp(a.get_xticklabels(), visible = False)
p = plt.yticks(size=13)
p = plt.ylabel('y', size=fs)
p = plt.legend(loc='best')
if savefile is not None:
p = plt.savefig(savefile)
def modelfit(data, uncert, indparams, model, nbins=75, title=None,
fignum=-22, savefile=None):
"""
Plot the model and (binned) data arrays, and their residuals.
Parameters
----------
data: 1D float ndarray
The data array.
uncert: 1D float ndarray
Uncertainties of the data-array values.
indparams: 1D float ndarray
X-axis values of the data-array values.
model: 1D ndarray
The model of data (evaluated at indparams values).
nbins: Integer
Output number of data binned values.
title: String
Plot title.
fignum: Integer
The figure number.
savefile: Boolean
If not None, name of file to save the plot.
"""
# Bin down array:
binsize = (np.size(data)-1)/nbins + 1
bindata, binuncert, binindp = ba.binarray(data, uncert, indparams, binsize)
binmodel = ba.weightedbin(model, binsize)
fs = 14 # Font-size
p = plt.figure(fignum, figsize=(8,6))
p = plt.clf()
# Residuals:
a = plt.axes([0.15, 0.1, 0.8, 0.2])
p = plt.errorbar(binindp, bindata-binmodel, binuncert, fmt='ko', ms=4)
p = plt.plot([indparams[0], indparams[-1]], [0,0],'k:',lw=1.5)
p = plt.xticks(size=fs)
p = plt.yticks(size=fs)
p = plt.xlabel("x", size=fs)
p = plt.ylabel('Residuals', size=fs)
# Data and Model:
a = plt.axes([0.15, 0.35, 0.8, 0.55])
if title is not None:
p = plt.title(title, size=fs)
p = plt.errorbar(binindp, bindata, binuncert, fmt='ko', ms=4,
label='Binned Data')
p = plt.plot(indparams, model, "b", lw=2, label='Best Fit')
p = plt.setp(a.get_xticklabels(), visible = False)
p = plt.yticks(size=13)
p = plt.ylabel('y', size=fs)
p = plt.legend(loc='best')
if savefile is not None:
p = plt.savefig(savefile)
def modelfit(data,
uncert,
indparams,
model,
nbins=75,
title=None,
fignum=-22,
savefile=None,
fmt="."):
"""
Doc me!
"""
# Bin down array:
binsize = (np.size(data) - 1) / nbins + 1
bindata, binuncert, binindp = ba.binarray(data, uncert, indparams, binsize)
binmodel = ba.weightedbin(model, binsize)
fs = 14 # Font-size
p = plt.figure(fignum, figsize=(8, 6))
p = plt.clf()
# Residuals:
a = plt.axes([0.15, 0.1, 0.8, 0.2])
p = plt.errorbar(binindp, bindata - binmodel, binuncert, fmt='ko', ms=4)
p = plt.plot([indparams[0], indparams[-1]], [0, 0], 'k:', lw=1.5)
p = plt.xticks(size=fs)
p = plt.yticks(size=fs)
p = plt.xlabel("x", size=fs)
p = plt.ylabel('Residuals', size=fs)
# Data and Model:
a = plt.axes([0.15, 0.35, 0.8, 0.55])
if title is not None:
p = plt.title(title, size=fs)
p = plt.errorbar(binindp,
bindata,
binuncert,
fmt='ko',
ms=4,
label='Binned Data')
p = plt.plot(indparams, model, "b", lw=2, label='Best Fit')
p = plt.setp(a.get_xticklabels(), visible=False)
p = plt.yticks(size=13)
p = plt.ylabel('y', size=fs)
p = plt.legend(loc='best')
if savefile is not None:
p = plt.savefig(savefile)
def modelfit(data,
uncert,
indparams,
model,
nbins=75,
title=None,
fignum=-22,
savefile=None):
"""
Plot the model and (binned) data arrays, and their residuals.
Parameters
----------
data: 1D float ndarray
The data array.
uncert: 1D float ndarray
Uncertainties of the data-array values.
indparams: 1D float ndarray
X-axis values of the data-array values.
model: 1D ndarray
The model of data (evaluated at indparams values).
nbins: Integer
Output number of data binned values.
title: String
Plot title.
fignum: Integer
The figure number.
savefile: Boolean
If not None, name of file to save the plot.
"""
# Bin down array:
binsize = (np.size(data) - 1) / nbins + 1
bindata, binuncert, binindp = ba.binarray(data, uncert, indparams, binsize)
binmodel = ba.weightedbin(model, binsize)
fs = 14 # Font-size
p = plt.figure(fignum, figsize=(8, 6))
p = plt.clf()
# Residuals:
a = plt.axes([0.15, 0.1, 0.8, 0.2])
p = plt.errorbar(binindp, bindata - binmodel, binuncert, fmt='ko', ms=4)
p = plt.plot([indparams[0], indparams[-1]], [0, 0], 'k:', lw=1.5)
p = plt.xticks(size=fs)
p = plt.yticks(size=fs)
p = plt.xlabel("x", size=fs)
p = plt.ylabel('Residuals', size=fs)
# Data and Model:
a = plt.axes([0.15, 0.35, 0.8, 0.55])
if title is not None:
p = plt.title(title, size=fs)
p = plt.errorbar(binindp,
bindata,
binuncert,
fmt='ko',
ms=4,
label='Binned Data')
p = plt.plot(indparams, model, "b", lw=2, label='Best Fit')
p = plt.setp(a.get_xticklabels(), visible=False)
p = plt.yticks(size=13)
p = plt.ylabel('y', size=fs)
p = plt.legend(loc='best')
if savefile is not None:
p = plt.savefig(savefile)