Bo Zhang (NAOC, mailto:bozhang@nao.cas.cn) will have a few lessons on python.
python to process astronomical data.These lectures are organized as below:
website: http://matplotlib.org/
some examples: http://leejjoon.github.io/matplotlib_astronomy_gallery/
This is all about aesthetics :-):http://blog.olgabotvinnik.com/prettyplotlib/
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# import matplotlib.pyplot as plt
# import numpy as np
%pylab inline
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fig = plt.figure()
ax = fig.add_subplot(111)
x = np.linspace(0.,5.,100)
y_c = np.sin(2*np.pi*x)
y_s = np.cos(2*np.pi*x)
ax.plot(x,y_c)
ax.plot(x,y_s, 'ro')
# fig.show()
ax.set_xlabel('time (s)')
ax.set_ylabel('voltage (mV)')
ax.set_title('test plot')
ax.grid(True)
# fig.savefig("test.pdf")
fig.show()
For scientific or mathematical notations: use \mathrm{} with latex style input
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ax.set_xlabel(r'$\mathrm{\theta_{0}}$')
ax.set_ylabel(r'$\mathrm{\alpha^{1}}$')
fig
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In [4]:
from mpl_toolkits.axes_grid1 import ImageGrid
im = np.arange(100)
im.shape = 10, 10
fig = plt.figure(1, (4., 4.))
grid = ImageGrid(fig, 111,
nrows_ncols = (2, 2), # creates 2x2 grid of axes
axes_pad=0.1)
for i in range(4):
grid[i].imshow(im)
In [5]:
fig = figure(1)
for i in range(4):
ax = fig.add_subplot(1, 4, i+1)
ax.imshow(im)
plt.subplots_adjust(hspace=0.1,wspace=0.1)
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from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
fig, ax = plt.subplots() # create a new figure with a default 111 subplot
ax.plot(x, y_c)
axins = zoomed_inset_axes(ax, 3, loc=1) # zoom-factor: 2.5
axins.plot(x, y_c)
x1, x2, y1, y2 = 1, 1.4, 0.5, 0.75 # specify the limits
axins.set_xlim(x1, x2) # apply the x-limits
axins.set_ylim(y1, y2) # apply the y-limits
plt.yticks(visible=False)
plt.xticks(visible=False)
#fancy zoom-effect
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
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matplotlib gallery: http://matplotlib.org/examples/pylab_examples/contour_demo.html
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%pylab inline
In [8]:
from scipy.stats import binned_statistic_2d
x_ = np.random.randn(1000,)*0.2+0.
y_ = np.random.randn(1000,)*1.0+3.
theta = np.pi*0.1
x = x_*np.cos(theta) + y_*np.sin(theta)
y = -x_*np.sin(theta) + y_*np.cos(theta)
statistic, x_edges, y_edges, binnumber = \
binned_statistic_2d(x, y, x, statistic='count', bins=[np.arange(-1.0, 3.0, 0.15), np.arange(-1.0, 6.0, 0.3)])
statistic = statistic.T
x_centers = diff(x_edges)/2.+x_edges[:-1]
y_centers = diff(y_edges)/2.+y_edges[:-1]
fig = plt.figure()
plt.plot(x, y, 'k.')
plt.xlim(-1., 3.)
plt.ylim(-1., 6.)
fig = plt.figure()
plt.contour(x_centers, y_centers, statistic)
plt.xlim(-1., 3.)
plt.ylim(-1., 6.)
plt.colorbar()
fig = plt.figure()
statistic[statistic<5.] = np.nan
plt.plot(x, y, '.', c='k', ms=1, zorder=2)
# Define custom colormaps: Set pixels with no sources to white
cmap = plt.cm.gray_r
cmap.set_bad('w', 0.)
plt.imshow(statistic,
extent=(x_edges[0], x_edges[-1], y_edges[0], y_edges[-1]),
interpolation='nearest',
origin='lower',
cmap=cmap,
aspect='auto',
alpha=1.0,
vmin=3, vmax=30,zorder=3)
# plt.contour(x_centers, y_centers, statistic)
plt.colorbar()
plt.xlim(-1., 3.)
plt.ylim(-1., 6.)
# fig.tight_layout()
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%run ./gallery/contourf_demo.py
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%run ./gallery/contour_demo.py
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