def plot_powerlaw_combined(data, data_inst, fig, units):
from powerlaw import plot_pdf, Fit, pdf
annotate_coord = (-.4, .95)
ax1 = fig.add_subplot(n_graphs,n_data,data_inst)
plot_pdf(data, ax=ax1, color='b', linewidth=2)
fit = Fit(data, xmin=1, discrete=True)
fit.power_law.plot_pdf(ax=ax1, linestyle=':', color='g')
p = fit.power_law.pdf()
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax1, linestyle='--', color='g')
from pylab import setp
setp( ax1.get_xticklabels(), visible=False)
if data_inst==1:
ax1.annotate("A", annotate_coord, xycoords="axes fraction", fontsize=14)
ax1.set_ylabel(r"$p(X)$")# (10^n)")
ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst)#, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')
fit.exponential.plot_pdf(ax=ax2, linestyle='--', color='r')
fit.plot_pdf(ax=ax2, color='b', linewidth=2)
ax2.set_ylim(ax1.get_ylim())
ax2.set_yticks(ax2.get_yticks()[::2])
ax2.set_xlim(ax1.get_xlim())
if data_inst==1:
ax2.annotate("B", annotate_coord, xycoords="axes fraction", fontsize=14)
ax2.set_xlabel(units)
def plot_basics(data, data_inst, fig, units):
'''
This function is the main plotting function. Adapted from Newman's powerlaw package.
'''
import pylab
pylab.rcParams['xtick.major.pad'] = '8'
pylab.rcParams['ytick.major.pad'] = '8'
pylab.rcParams['font.sans-serif'] = 'Arial'
from matplotlib import rc
rc('font', family='sans-serif')
rc('font', size=10.0)
rc('text', usetex=False)
from matplotlib.font_manager import FontProperties
panel_label_font = FontProperties().copy()
panel_label_font.set_weight("bold")
panel_label_font.set_size(12.0)
panel_label_font.set_family("sans-serif")
n_data = 1
n_graphs = 4
from powerlaw import plot_pdf, Fit, pdf
ax1 = fig.add_subplot(n_graphs, n_data, data_inst)
x, y = pdf(data, linear_bins=True)
ind = y > 0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5, label='data')
plot_pdf(data[data > 0], ax=ax1, color='b', linewidth=2, label='PDF')
from pylab import setp
setp(ax1.get_xticklabels(), visible=False)
plt.legend(loc='bestloc')
ax2 = fig.add_subplot(n_graphs, n_data, n_data + data_inst, sharex=ax1)
plot_pdf(data[data > 0], ax=ax2, color='b', linewidth=2, label='PDF')
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g', label='w/o xmin')
p = fit.power_law.pdf()
ax2.set_xlim(ax1.get_xlim())
fit = Fit(data, discrete=True, xmin=3)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g', label='w xmin')
from pylab import setp
setp(ax2.get_xticklabels(), visible=False)
plt.legend(loc='bestloc')
ax3 = fig.add_subplot(n_graphs, n_data,
n_data * 2 + data_inst) #, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g', label='powerlaw')
fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r', label='exp')
fit.plot_pdf(ax=ax3, color='b', linewidth=2)
ax3.set_ylim(ax2.get_ylim())
ax3.set_xlim(ax1.get_xlim())
plt.legend(loc='bestloc')
ax3.set_xlabel(units)
def plot_basics(data, data_inst, fig, units):
'''
This function is the main plotting function. Adapted from Newman's powerlaw package.
'''
import pylab
pylab.rcParams['xtick.major.pad']='8'
pylab.rcParams['ytick.major.pad']='8'
pylab.rcParams['font.sans-serif']='Arial'
from matplotlib import rc
rc('font', family='sans-serif')
rc('font', size=10.0)
rc('text', usetex=False)
from matplotlib.font_manager import FontProperties
panel_label_font = FontProperties().copy()
panel_label_font.set_weight("bold")
panel_label_font.set_size(12.0)
panel_label_font.set_family("sans-serif")
n_data = 1
n_graphs = 4
from powerlaw import plot_pdf, Fit, pdf
ax1 = fig.add_subplot(n_graphs,n_data,data_inst)
x, y = pdf(data, linear_bins=True)
ind = y>0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5, label='data')
plot_pdf(data[data>0], ax=ax1, color='b', linewidth=2, label='PDF')
from pylab import setp
setp( ax1.get_xticklabels(), visible=False)
plt.legend(loc = 'bestloc')
ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst, sharex=ax1)
plot_pdf(data[data>0], ax=ax2, color='b', linewidth=2, label='PDF')
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g',label='w/o xmin')
p = fit.power_law.pdf()
ax2.set_xlim(ax1.get_xlim())
fit = Fit(data, discrete=True,xmin=3)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g', label='w xmin')
from pylab import setp
setp(ax2.get_xticklabels(), visible=False)
plt.legend(loc = 'bestloc')
ax3 = fig.add_subplot(n_graphs,n_data,n_data*2+data_inst)#, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g',label='powerlaw')
fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r',label='exp')
fit.plot_pdf(ax=ax3, color='b', linewidth=2)
ax3.set_ylim(ax2.get_ylim())
ax3.set_xlim(ax1.get_xlim())
plt.legend(loc = 'bestloc')
ax3.set_xlabel(units)
def plot_basics(data, data_inst, fig, units):
from powerlaw import plot_pdf, Fit, pdf
annotate_coord = (-.4, .95)
ax1 = fig.add_subplot(n_graphs,n_data,data_inst)
x, y = pdf(data, linear_bins=True)
ind = y>0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5)
plot_pdf(data[data>0], ax=ax1, color='b', linewidth=2)
from pylab import setp
setp( ax1.get_xticklabels(), visible=False)
if data_inst==1:
ax1.annotate("A", annotate_coord, xycoords="axes fraction", fontproperties=panel_label_font)
from mpl_toolkits.axes_grid.inset_locator import inset_axes
ax1in = inset_axes(ax1, width = "30%", height = "30%", loc=3)
ax1in.hist(data, normed=True, color='b')
ax1in.set_xticks([])
ax1in.set_yticks([])
ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst, sharex=ax1)
plot_pdf(data, ax=ax2, color='b', linewidth=2)
fit = Fit(data, xmin=1, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g')
p = fit.power_law.pdf()
ax2.set_xlim(ax1.get_xlim())
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')
from pylab import setp
setp( ax2.get_xticklabels(), visible=False)
if data_inst==1:
ax2.annotate("B", annotate_coord, xycoords="axes fraction", fontproperties=panel_label_font)
ax2.set_ylabel(u"p(X)")# (10^n)")
ax3 = fig.add_subplot(n_graphs,n_data,n_data*2+data_inst)#, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g')
fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r')
fit.plot_pdf(ax=ax3, color='b', linewidth=2)
ax3.set_ylim(ax2.get_ylim())
ax3.set_xlim(ax1.get_xlim())
if data_inst==1:
ax3.annotate("C", annotate_coord, xycoords="axes fraction", fontproperties=panel_label_font)
ax3.set_xlabel(units)
def plot_basics(data, data_inst, fig, units):
from powerlaw import plot_pdf, Fit, pdf
annotate_coord = (-.1, .95)
# annotate_coord = (1.1, .95)
ax1 = fig.add_subplot(n_graphs, n_data, data_inst, visible=False)
x, y = pdf(data, linear_bins=True)
ind = y > 0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5)
plot_pdf(data[data > 0], ax=ax1, color='b', linewidth=2)
from pylab import setp
setp(ax1.get_xticklabels(), visible=False)
# ABC
# if data_inst == 1:
# ax1.annotate("A", annotate_coord, xycoords="axes fraction", fontproperties=panel_label_font)
# ax1.set_ylabel(u"p(X)")
# from mpl_toolkits.axes_grid.inset_locator import inset_axes
# ax1in = inset_axes(ax1, width="30%", height="30%", loc=3)
# ax1in.hist(data, density=True, color='b')
# ax1in.set_xticks([])
# ax1in.set_yticks([])
# ax1.set_xlabel(units)
ax2 = fig.add_subplot(n_graphs,
n_data,
n_data + data_inst,
sharex=ax1,
visible=False)
plot_pdf(data, ax=ax2, color='b', linewidth=2, label="pdf of data")
fit = Fit(data, xmin=1, discrete=True)
fit.power_law.plot_pdf(ax=ax2,
linestyle=':',
color='g',
label="power law fit")
p = fit.power_law.pdf()
ax2.set_xlim(ax1.get_xlim())
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax2,
linestyle='--',
color='g',
label="power law fit--opt xmin")
from pylab import setp
setp(ax2.get_xticklabels(), visible=True)
# if data_inst == 1:
ax2.annotate("B",
annotate_coord,
xycoords="axes fraction",
fontproperties=panel_label_font)
ax2.set_ylabel(u"p(X)") # (10^n)")
handles, labels = ax2.get_legend_handles_labels()
ax2.legend(handles, labels, loc=3)
ax2.set_xlabel(units)
ax3 = fig.add_subplot(n_graphs, n_data, n_data * 2 +
data_inst) # , sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3,
linestyle='--',
color='g',
label="power law fit\n(opt-min)")
fit.exponential.plot_pdf(ax=ax3,
linestyle='--',
color='r',
label="exponential fit\n(opt-min)")
fit.plot_pdf(ax=ax3, color='b', linewidth=2, label="PDF\n(opt-min)")
ax3.set_ylim(ax2.get_ylim())
ax3.set_xlim(ax1.get_xlim())
handles, labels = ax3.get_legend_handles_labels()
ax3.legend(handles, labels, loc=3, fontsize=12)
ax3.set_xlabel(units, fontsize=15)
# if data_inst == 1:
ax3.annotate("C",
annotate_coord,
xycoords="axes fraction",
fontproperties=panel_label_font)
ax3.set_ylabel(u"p(X)", fontsize=15)
def plot_basics(data, data_inst, fig, units):
### Setup ###
from powerlaw import plot_pdf, Fit, pdf
import pylab
pylab.rcParams['xtick.major.pad'] = '8'
pylab.rcParams['ytick.major.pad'] = '8'
#pylab.rcParams['font.sans-serif']='Arial'
from matplotlib.font_manager import FontProperties
panel_label_font = FontProperties().copy()
panel_label_font.set_weight("bold")
panel_label_font.set_size(30.0)
panel_label_font.set_family("sans-serif")
n_data = 2
n_graphs = 4
annotate_coord = (-.4, .95)
#############
ax1 = fig.add_subplot(n_graphs, n_data, data_inst)
x, y = pdf(data, linear_bins=True)
ind = y > 0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5)
plot_pdf(data[data > 0], ax=ax1, color='b', linewidth=2)
from pylab import setp
setp(ax1.get_xticklabels(), visible=False)
if data_inst == 1:
ax1.annotate("A",
annotate_coord,
xycoords="axes fraction",
fontproperties=panel_label_font)
ax2 = fig.add_subplot(n_graphs, n_data, n_data + data_inst, sharex=ax1)
plot_pdf(data, ax=ax2, color='b', linewidth=2)
fit = Fit(data, xmin=1, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')
_ = fit.power_law.pdf()
ax2.set_xlim((1, max(x)))
setp(ax2.get_xticklabels(), visible=False)
if data_inst == 1:
ax2.annotate("B",
annotate_coord,
xycoords="axes fraction",
fontproperties=panel_label_font)
ax2.set_ylabel(u"p(X)") # (10^n)")
ax3 = fig.add_subplot(n_graphs, n_data,
n_data * 2 + data_inst) #, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g')
fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r')
fit.lognormal.plot_pdf(ax=ax3, linestyle=':', color='r')
fit.plot_pdf(ax=ax3, color='b', linewidth=2)
ax3.set_ylim(ax2.get_ylim())
ax3.set_xlim(ax1.get_xlim())
if data_inst == 1:
ax3.annotate("C",
annotate_coord,
xycoords="axes fraction",
fontproperties=panel_label_font)
ax3.set_xlabel(units)
def plot_basics(data, data_inst, fig, units):
from powerlaw import plot_pdf, Fit, pdf
annotate_coord = (-.4, .95)
ax1 = fig.add_subplot(n_graphs,n_data,data_inst)
plot_pdf(data[data>0], ax=ax1, linear_bins=True, color='r', linewidth=.5)
x, y = pdf(data, linear_bins=True)
ind = y>0
y = y[ind]
x = x[:-1]
x = x[ind]
ax1.scatter(x, y, color='r', s=.5)
plot_pdf(data[data>0], ax=ax1, color='b', linewidth=2)
from pylab import setp
setp( ax1.get_xticklabels(), visible=False)
#ax1.set_xticks(ax1.get_xticks()[::2])
ax1.set_yticks(ax1.get_yticks()[::2])
locs,labels = yticks()
#yticks(locs, map(lambda x: "%.0f" % x, log10(locs)))
if data_inst==1:
ax1.annotate("A", annotate_coord, xycoords="axes fraction", fontsize=14)
from mpl_toolkits.axes_grid.inset_locator import inset_axes
ax1in = inset_axes(ax1, width = "30%", height = "30%", loc=3)
ax1in.hist(data, normed=True, color='b')
ax1in.set_xticks([])
ax1in.set_yticks([])
ax2 = fig.add_subplot(n_graphs,n_data,n_data+data_inst, sharex=ax1)
plot_pdf(data, ax=ax2, color='b', linewidth=2)
fit = Fit(data, xmin=1, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle=':', color='g')
p = fit.power_law.pdf()
#ax2.set_ylim(min(p), max(p))
ax2.set_xlim(ax1.get_xlim())
fit = Fit(data, discrete=True)
fit.power_law.plot_pdf(ax=ax2, linestyle='--', color='g')
from pylab import setp
setp( ax2.get_xticklabels(), visible=False)
#ax2.set_xticks(ax2.get_xticks()[::2])
if ax2.get_ylim()[1] >1:
ax2.set_ylim(ax2.get_ylim()[0], 1)
ax2.set_yticks(ax2.get_yticks()[::2])
#locs,labels = yticks()
#yticks(locs, map(lambda x: "%.0f" % x, log10(locs)))
if data_inst==1:
ax2.annotate("B", annotate_coord, xycoords="axes fraction", fontsize=14)
ax2.set_ylabel(r"$p(X)$")# (10^n)")
ax3 = fig.add_subplot(n_graphs,n_data,n_data*2+data_inst)#, sharex=ax1)#, sharey=ax2)
fit.power_law.plot_pdf(ax=ax3, linestyle='--', color='g')
fit.exponential.plot_pdf(ax=ax3, linestyle='--', color='r')
fit.plot_pdf(ax=ax3, color='b', linewidth=2)
#p = fit.power_law.pdf()
ax3.set_ylim(ax2.get_ylim())
ax3.set_yticks(ax3.get_yticks()[::2])
ax3.set_xlim(ax1.get_xlim())
#locs,labels = yticks()
#yticks(locs, map(lambda x: "%.0f" % x, log10(locs)))
if data_inst==1:
ax3.annotate("C", annotate_coord, xycoords="axes fraction", fontsize=14)
#if ax2.get_xlim()!=ax3.get_xlim():
# zoom_effect01(ax2, ax3, ax3.get_xlim()[0], ax3.get_xlim()[1])
ax3.set_xlabel(units)
