def plot_planck_vs_2mass(
av_k09,
av_pl,
filename=None,
av_error=None,
contour_plot=True,
levels=10,
plot_median=True,
limits=None,
labels=['', ''],
scale=('linear', 'linear'),
title='',
fit_labels=['', ''],
gridsize=(100, 100),
):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import cm
from astroML.plotting import scatter_contour
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
import myplotting as myplt
# set up plot aesthetics
# ----------------------
plt.close
plt.clf()
# color map
myplt.set_color_cycle(num_colors=3)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
axes = AxesGrid(
fig,
(1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Drop the NaNs from the images
if type(av_error) is float or av_error is None:
indices = np.where((av_pl == av_pl) &\
(av_k09 == av_k09)
)
av_pl = av_pl[indices]
av_k09 = av_k09[indices]
# Create plot
ax = axes[0]
if limits is None:
xmin = np.min(av_k09)
ymin = np.min(av_k09)
xmax = np.max(av_pl)
ymax = np.max(av_pl)
xscalar = 0.15 * xmax
yscalar = 0.15 * ymax
limits = [
xmin - xscalar, xmax + xscalar, ymin - yscalar, ymax + yscalar
]
if contour_plot:
contour_range = ((limits[0], limits[1]), (limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(
av_k09,
av_pl,
threshold=3,
log_counts=1,
levels=levels,
ax=ax,
histogram2d_args=dict(bins=30, range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
else:
image = ax.errorbar(nhi_nonans.ravel(),
av_nonans.ravel(),
yerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3)
# Plot sensitivies
#av_limit = np.median(av_errors[0])
#ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
if 1:
x_fit = np.linspace(-5, 200, 1000)
p, V = \
np.polyfit(av_k09, av_pl, deg=1,
#w=np.abs(1.0/av_error_nonans.ravel()),
cov=True
)
y_poly_fit = p[0] * x_fit + p[1]
if 1:
ax.plot(x_fit,
y_poly_fit,
color='r',
linestyle='dashed',
linewidth=2,
label=\
'Poly fit: \n' + \
fit_labels[0] + ' = ' + '{0:.1f}'.format(p[0]) + \
r'$\times$ ' + fit_labels[1] + \
' + {0:.1f} mag'.format(p[1]),
alpha=0.7,
)
if 0:
from scipy.interpolate import UnivariateSpline
spl = UnivariateSpline(av_k09, av_pl)
ax.plot(x_fit, spl(x_fit), linewidth=3, alpha=0.5)
if 0:
print('Bootstrapping...')
# bootstrap conf intervals
import scipy as sp
bootindex = sp.random.random_integers
nboot = 20
x_fit = av_k09
y_fit = p[0] * x_fit + p[1]
r = av_pl - y_fit
for i in xrange(
nboot): # loop over n bootstrap samples from the resids
pc = sp.polyfit(av_k09, y_fit + r[bootindex(0,
len(r) - 1, len(r))],
1)
ax.plot(x_fit,
sp.polyval(pc, x_fit),
'k-',
linewidth=2,
alpha=1.0 / float(nboot))
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
#ax.set_xlabel(r'$A_{V,{\rm K+09}}$ [mag]')
#ax.set_ylabel(r'$A_{V,{\rm Planck}}$ [mag]')
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
ax.set_title(title)
ax.legend(loc='best')
if filename is not None:
plt.savefig(filename)
def plot_cluster_data(data, colors=None, filename=None, show=False,
labels=None, show_tick_labels=False, zlim=None, ylim=None, xlim=None):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from myplotting import scatter_contour
import matplotlib as mpl
n_components = data.shape[1]
colors = mpl.cm.rainbow(colors)
if labels is None:
labels = ["1st eigenvector", "2nd eigenvector", "3rd eigenvector"]
alpha = 1.0 / np.log10(data.shape[0])
plt.clf()
if n_components <= 2:
fig = plt.figure(1, figsize=(4,4))
ax = fig.add_subplot(111)
#data[:,0] += np.abs(np.min(data[:,0]) + 1.0)
ax.scatter(data[:, 0],
data[:, 1],
color=colors,
alpha=alpha)
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
if not show_tick_labels:
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
#ax.set_xscale('log')
if 0:
scatter_contour(data[:, 0],
data[:,1],
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=50,),
plot_args=dict(marker='o',
linestyle='none',
markeredgewidth=0,
color='black',
alpha=0.4,
markersize=2.5,
),
contour_args=dict(cmap=plt.cm.binary,)
)
elif n_components >= 3:
#plt.figure(2, figsize=(6, 6))
plt.clf()
# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=27, azim=-22)
ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colors, alpha=alpha)
#ax.set_title("First three PCA directions")
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
ax.set_zlabel(labels[2])
if zlim is not None:
ax.set_zlim(zlim)
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
if not show_tick_labels:
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
if filename is not None:
plt.savefig(filename,)# bbox_inches='tight')
if show:
plt.show()
def test_scatter_contour():
from astropy.io import fits
from myimage_analysis import calculate_nhi
import mygeometry as myg
from mycoords import make_velocity_axis
# Parameters
# ----------
levels = (0.99, 0.985, 0.7)
levels = (
0.999,
0.998,
0.96,
0.86,
0.58,
)
levels = 7
levels = np.logspace(np.log10(0.995), np.log10(0.50), 5)
log_counts = 0
limits = [1, 10, -3, 30]
limits = None
# Begin test
# ----------
data_dir = '/d/bip3/ezbc/perseus/data/'
av = fits.getdata(data_dir +
'av/perseus_av_planck_tau353_5arcmin.fits')
hi, hi_header = fits.getdata(data_dir + \
'hi/perseus_hi_galfa_cube_regrid_planckres.fits',
header=True)
hi_vel_axis = make_velocity_axis(hi_header)
nhi = calculate_nhi(
cube=hi,
velocity_axis=hi_vel_axis,
velocity_range=[0, 10],
)
# Drop the NaNs from the images
indices = np.where((av == av) &\
(nhi == nhi)
)
av_nonans = av[indices]
nhi_nonans = nhi[indices]
fig, ax = plt.subplots()
if limits is None:
xmin = np.min(nhi_nonans)
ymin = np.min(av_nonans)
xmax = np.max(nhi_nonans)
ymax = np.max(av_nonans)
xscalar = 0.25 * xmax
yscalar = 0.25 * ymax
limits = [
xmin - xscalar, xmax + xscalar, ymin - yscalar, ymax + yscalar
]
contour_range = ((limits[0], limits[1]), (limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(
nhi_nonans.ravel(),
av_nonans.ravel(),
threshold=3,
log_counts=log_counts,
levels=levels,
ax=ax,
histogram2d_args=dict(bins=30, range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
scale = ['linear', 'linear']
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
ax.set_xlabel(r'$N($H$\textsc{i}) \times\,10^{20}$ cm$^{-2}$')
ax.set_ylabel(r'$A_V$ [mag]')
#ax.set_title(core_names[i])
ax.legend(loc='lower right')
plt.savefig('test_plots/test_scatter_contour.png')
def plot_pv(x, y, x_label=None, y_label=None, filename=None, show=False):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from myplotting import scatter_contour
import matplotlib as mpl
n_components = data.shape[1]
colors = mpl.cm.rainbow(colors)
if labels is None:
labels = ["1st eigenvector", "2nd eigenvector", "3rd eigenvector"]
alpha = 1.0 / np.log10(data.shape[0])
plt.clf()
if n_components <= 2:
fig = plt.figure(1, figsize=(4, 4))
ax = fig.add_subplot(111)
#data[:,0] += np.abs(np.min(data[:,0]) + 1.0)
ax.scatter(data[:, 0], data[:, 1], color=colors, alpha=alpha)
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
if not show_tick_labels:
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
#ax.set_xscale('log')
if 0:
scatter_contour(data[:, 0],
data[:, 1],
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=50, ),
plot_args=dict(
marker='o',
linestyle='none',
markeredgewidth=0,
color='black',
alpha=0.4,
markersize=2.5,
),
contour_args=dict(cmap=plt.cm.binary, ))
elif n_components >= 3:
#plt.figure(2, figsize=(6, 6))
plt.clf()
# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=27, azim=-22)
ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=colors, alpha=alpha)
#ax.set_title("First three PCA directions")
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
ax.set_zlabel(labels[2])
if zlim is not None:
ax.set_zlim(zlim)
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
if not show_tick_labels:
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
if filename is not None:
plt.savefig(filename, ) # bbox_inches='tight')
if show:
plt.show()
def test_scatter_contour_log():
import myimage_analysis as myia
# Parameters
# ----------
levels = (0.99, 0.985, 0.7)
levels = (
0.999,
0.998,
0.96,
0.86,
0.58,
)
levels = 7
levels = np.logspace(np.log10(0.995), np.log10(0.50), 5)
log_counts = 0
limits = [1, 10, -3, 30]
limits = None
x = np.linspace(1, 100, 10000)
y = x**3 + np.random.normal(10, size=10000) + 100
y_real = x**3 + 100
x_nonans, y_nonans = myia.mask_nans((x, y))
scale = ['linear', 'log']
fig, ax = plt.subplots()
if limits is None:
xmin = np.min(x_nonans)
ymin = np.min(y_nonans)
xmax = np.max(x_nonans)
ymax = np.max(y_nonans)
if scale[0] == 'linear':
xscalar = 0.25 * xmax
xlims = xmin - xscalar, xmax + xscalar
elif scale[0] == 'log':
xscalar = 0.25
xlims = xmin * xscalar, xmax / xscalar
if scale[1] == 'linear':
yscalar = 0.25 * ymax
ylims = ymin - yscalar, ymax + yscalar
elif scale[1] == 'log':
yscalar = 0.25
ylims = ymin * yscalar, ymax / yscalar
limits = [xlims[0], xlims[1], ylims[0], ylims[1]]
if 1:
bins = [
30,
np.logspace(np.log10(limits[2]), np.log10(limits[3]), 30),
]
else:
bins = 40
print bins
contour_range = ((limits[0], limits[1]), (limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
l1 = myplt.scatter_contour(
x_nonans.ravel(),
y_nonans.ravel(),
threshold=3,
log_counts=log_counts,
levels=levels,
ax=ax,
extent=limits,
histogram2d_args=dict(
bins=bins,
range=contour_range,
),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
ax.plot(x, y_real, color='r', alpha=0.5, linewidth=2)
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
ax.set_xlabel(r'x')
ax.set_ylabel(r'y')
#ax.set_title(core_names[i])
ax.legend(loc='lower right')
plt.savefig('test_plots/test_scatter_contour_log.png')
def plot_av_vs_beta_grid(plot_dict,
filename=None, levels=7,
limits=None, poly_fit=False, contour=True,
scale=['linear','linear']):
''' Plots a heat map of likelihoodelation values as a function of velocity
width and velocity center.
Parameters
----------
cloud : cloudpy.Cloud
If provided, properties taken from cloud.props.
'''
# Import external modules
import numpy as np
import matplotlib.pyplot as plt
import myplotting as myplt
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
# ----------------------
plt.close;plt.clf()
font_scale = 9
params = {
'figure.figsize': (3.6, 8),
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
axes = AxesGrid(fig, (1,1,1),
nrows_ncols=(3, 1),
ngrids=3,
axes_pad=0.1,
aspect=False,
label_mode='L',
share_all=True)
for i, cloud_name in enumerate(plot_dict):
x = plot_dict[cloud_name]['av']
y = plot_dict[cloud_name]['beta']
ax = axes[i]
if 'log' not in scale:
ax.locator_params(nbins = 6)
# Drop the NaNs from the images
indices = np.where((x == x) &\
(y == y)
)
x_nonans = x[indices]
y_nonans = y[indices]
ax = axes[i]
if contour:
if i == 0:
if limits is None:
xmin = np.min(x_nonans)
ymin = np.min(y_nonans)
xmax = np.max(x_nonans)
ymax = np.max(y_nonans)
xscalar = 0.15 * xmax
yscalar = 0.15 * ymax
limits = [xmin - xscalar, xmax + xscalar,
ymin - yscalar, ymax + yscalar]
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(x_nonans.ravel(),
y_nonans.ravel(),
threshold=2,
log_counts=1,
levels=levels,
ax=ax,
#errors=x_error_nonans.ravel(),
histogram2d_args=dict(bins=40,
range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
else:
image = ax.errorbar(
x_nonans.ravel()[::100],
y_nonans.ravel()[::100],
yerr=(x_error_nonans.ravel()[::100]),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
c_cycle = myplt.set_color_cycle(num_colors=2, cmap_limits=[0.5, 0.7])
if poly_fit:
from scipy.optimize import curve_fit
p = np.polyfit(x_nonans, y_nonans, 1)
x_fit = np.linspace(-10, 100, 100)
y_poly_fit = p[0] * x_fit + p[1]
ax.plot(x_fit,
y_poly_fit,
color=c_cycle[1],
linestyle='-',
linewidth=2,
label=\
'Polynomial fit: \n' + \
r'$\beta$ = {0:.2f}'.format(p[0] * 100.0) + \
r'$\frac{{A_V}}{{100 \rm mag}}$ + {0:.2f}'.format(p[1]) + \
r'',
alpha=0.7,
)
# Annotations
#anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_ylabel(r'$\beta$')
ax.set_xlabel(r'$A_V$ [mag]')
if 1:
loc = 'lower right'
elif i == 0:
loc = 'upper left'
else:
loc = 'lower left'
ax.legend(loc=loc)
ax.annotate(cloud_name.capitalize(),
#xytext=(0.96, 0.9),
xy=(0.96, 0.96),
textcoords='axes fraction',
xycoords='axes fraction',
size=10,
color='k',
bbox=dict(boxstyle='square',
facecolor='w',
alpha=1),
horizontalalignment='right',
verticalalignment='top',
)
if filename is not None:
plt.draw()
plt.savefig(filename, bbox_inches='tight', dpi=400)
def test_scatter_contour():
from astropy.io import fits
from myimage_analysis import calculate_nhi
import mygeometry as myg
from mycoords import make_velocity_axis
# Parameters
# ----------
levels = (0.99, 0.985, 0.7)
levels = (0.999, 0.998, 0.96, 0.86, 0.58,)
levels = 7
levels = np.logspace(np.log10(0.995), np.log10(0.50), 5)
log_counts = 0
limits = [1, 10, -3, 30]
limits = None
# Begin test
# ----------
data_dir = '/d/bip3/ezbc/perseus/data/'
av = fits.getdata(data_dir + 'av/perseus_av_planck_tau353_5arcmin.fits')
hi, hi_header = fits.getdata(data_dir + \
'hi/perseus_hi_galfa_cube_regrid_planckres.fits',
header=True)
hi_vel_axis = make_velocity_axis(hi_header)
nhi = calculate_nhi(cube=hi,
velocity_axis=hi_vel_axis,
velocity_range=[0, 10],
)
# Drop the NaNs from the images
indices = np.where((av == av) &\
(nhi == nhi)
)
av_nonans = av[indices]
nhi_nonans = nhi[indices]
fig, ax = plt.subplots()
if limits is None:
xmin = np.min(nhi_nonans)
ymin = np.min(av_nonans)
xmax = np.max(nhi_nonans)
ymax = np.max(av_nonans)
xscalar = 0.25 * xmax
yscalar = 0.25 * ymax
limits = [xmin - xscalar, xmax + xscalar,
ymin - yscalar, ymax + yscalar]
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(nhi_nonans.ravel(),
av_nonans.ravel(),
threshold=3,
log_counts=log_counts,
levels=levels,
ax=ax,
histogram2d_args=dict(bins=30,
range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
scale = ['linear', 'linear']
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_xlabel(r'$N($H$\textsc{i}) \times\,10^{20}$ cm$^{-2}$')
ax.set_ylabel(r'$A_V$ [mag]')
#ax.set_title(core_names[i])
ax.legend(loc='lower right')
plt.savefig('test_plots/test_scatter_contour.png')
def plot_planck_vs_2mass(av_k09, av_pl, filename=None, av_error=None,
contour_plot=True, levels=10, plot_median=True, limits=None,
labels=['',''], scale=('linear','linear'), title = '',
fit_labels=['',''], gridsize=(100,100),):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import cm
from astroML.plotting import scatter_contour
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
import myplotting as myplt
# set up plot aesthetics
# ----------------------
plt.close;plt.clf()
# color map
myplt.set_color_cycle(num_colors=3)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
axes = AxesGrid(fig, (1,1,1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Drop the NaNs from the images
if type(av_error) is float or av_error is None:
indices = np.where((av_pl == av_pl) &\
(av_k09 == av_k09)
)
av_pl = av_pl[indices]
av_k09 = av_k09[indices]
# Create plot
ax = axes[0]
if limits is None:
xmin = np.min(av_k09)
ymin = np.min(av_k09)
xmax = np.max(av_pl)
ymax = np.max(av_pl)
xscalar = 0.15 * xmax
yscalar = 0.15 * ymax
limits = [xmin - xscalar, xmax + xscalar,
ymin - yscalar, ymax + yscalar]
if contour_plot:
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(av_k09,
av_pl,
threshold=3,
log_counts=1,
levels=levels,
ax=ax,
histogram2d_args=dict(bins=30,
range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
else:
image = ax.errorbar(nhi_nonans.ravel(),
av_nonans.ravel(),
yerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
# Plot sensitivies
#av_limit = np.median(av_errors[0])
#ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
if 1:
x_fit = np.linspace(-5, 200, 1000)
p, V = \
np.polyfit(av_k09, av_pl, deg=1,
#w=np.abs(1.0/av_error_nonans.ravel()),
cov=True
)
y_poly_fit = p[0] * x_fit + p[1]
if 1:
ax.plot(x_fit,
y_poly_fit,
color='r',
linestyle='dashed',
linewidth=2,
label=\
'Poly fit: \n' + \
fit_labels[0] + ' = ' + '{0:.1f}'.format(p[0]) + \
r'$\times$ ' + fit_labels[1] + \
' + {0:.1f} mag'.format(p[1]),
alpha=0.7,
)
if 0:
from scipy.interpolate import UnivariateSpline
spl = UnivariateSpline(av_k09, av_pl)
ax.plot(x_fit, spl(x_fit), linewidth=3, alpha=0.5)
if 0:
print('Bootstrapping...')
# bootstrap conf intervals
import scipy as sp
bootindex = sp.random.random_integers
nboot = 20
x_fit = av_k09
y_fit = p[0] * x_fit + p[1]
r = av_pl - y_fit
for i in xrange(nboot): # loop over n bootstrap samples from the resids
pc = sp.polyfit(av_k09, y_fit + r[bootindex(0, len(r)-1, len(r))], 1)
ax.plot(x_fit, sp.polyval(pc,x_fit), 'k-', linewidth=2, alpha=1.0/float(nboot))
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
#ax.set_xlabel(r'$A_{V,{\rm K+09}}$ [mag]')
#ax.set_ylabel(r'$A_{V,{\rm Planck}}$ [mag]')
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
ax.set_title(title)
ax.legend(loc='best')
if filename is not None:
plt.savefig(filename)
def test_scatter_contour_log():
import myimage_analysis as myia
# Parameters
# ----------
levels = (0.99, 0.985, 0.7)
levels = (0.999, 0.998, 0.96, 0.86, 0.58,)
levels = 7
levels = np.logspace(np.log10(0.995), np.log10(0.50), 5)
log_counts = 0
limits = [1, 10, -3, 30]
limits = None
x = np.linspace(1, 100, 10000)
y = x**3 + np.random.normal(10, size=10000) + 100
y_real = x**3 + 100
x_nonans, y_nonans = myia.mask_nans((x,y))
scale = ['linear', 'log']
fig, ax = plt.subplots()
if limits is None:
xmin = np.min(x_nonans)
ymin = np.min(y_nonans)
xmax = np.max(x_nonans)
ymax = np.max(y_nonans)
if scale[0] == 'linear':
xscalar = 0.25 * xmax
xlims = xmin - xscalar, xmax + xscalar
elif scale[0] == 'log':
xscalar = 0.25
xlims = xmin * xscalar, xmax / xscalar
if scale[1] == 'linear':
yscalar = 0.25 * ymax
ylims = ymin - yscalar, ymax + yscalar
elif scale[1] == 'log':
yscalar = 0.25
ylims = ymin * yscalar, ymax / yscalar
limits = [xlims[0], xlims[1], ylims[0], ylims[1]]
if 1:
bins = [30, np.logspace(np.log10(limits[2]),
np.log10(limits[3]),
30),
]
else:
bins = 40
print bins
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
l1 = myplt.scatter_contour(x_nonans.ravel(),
y_nonans.ravel(),
threshold=3,
log_counts=log_counts,
levels=levels,
ax=ax,
extent=limits,
histogram2d_args=dict(bins=bins,
range=contour_range,
),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
ax.plot(x, y_real, color='r', alpha=0.5, linewidth=2)
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_xlabel(r'x')
ax.set_ylabel(r'y')
#ax.set_title(core_names[i])
ax.legend(loc='lower right')
plt.savefig('test_plots/test_scatter_contour_log.png')
