Menu
I'm trying to generate an equatorial coordinates plot that should look more or less like this one:
(The figure is taken from this article, and it shows the position of the Large and Small MCs in equatorial coordinates)
Important things to notice about this plot:
theta axis (ie: the right ascension) is in h:m:s (hours, minutes, seconds) as it is accustomed in astronomy, rather than in degrees as the default polar option does in matplotlib.r axis (ie: the declination) increases outward from -90º and the grid is centered in (0h, -90º).matplotlib does by default).Using the polar=True option in matplotlib, the closest plot I've managed to produce is this (MWE below, data file here; some points are not present compared to the image above since the data file is a bit smaller):
I also need to add a third column of data to the plot, which is why I add a colorbar and color each point accordingly to a z array:
So what I mostly need right now is a way to clip the plot. Based mostly on this question and this example @cphlewis came quite close with his answer, but several things are still missing (mentioned in his answer).
Any help and/or pointers with this issue will be greatly appreciated.
MWE
(Notice I use gridspec to position the subplot because I need to generate several of these in the same output image file)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
def skip_comments(f):
'''
Read lines that DO NOT start with a # symbol.
'''
for line in f:
if not line.strip().startswith('#'):
yield line
def get_data_bb():
'''RA, DEC data file.
'''
# Path to data file.
out_file = 'bb_cat.dat'
# Read data file
with open(out_file) as f:
ra, dec = [], []
for line in skip_comments(f):
ra.append(float(line.split()[0]))
dec.append(float(line.split()[1]))
return ra, dec
# Read RA, DEC data from file.
ra, dec = get_data_bb()
# Convert RA from decimal degrees to radians.
ra = [x / 180.0 * 3.141593 for x in ra]
# Make plot.
fig = plt.figure(figsize=(20, 20))
gs = gridspec.GridSpec(4, 2)
# Position plot in figure using gridspec.
ax = plt.subplot(gs[0], polar=True)
ax.set_ylim(-90, -55)
# Set x,y ticks
angs = np.array([330., 345., 0., 15., 30., 45., 60., 75., 90., 105., 120.])
plt.xticks(angs * np.pi / 180., fontsize=8)
plt.yticks(np.arange(-80, -59, 10), fontsize=8)
ax.set_rlabel_position(120)
ax.set_xticklabels(['$22^h$', '$23^h$', '$0^h$', '$1^h$', '$2^h$', '$3^h$',
'$4^h$', '$5^h$', '$6^h$', '$7^h$', '$8^h$'], fontsize=10)
ax.set_yticklabels(['$-80^{\circ}$', '$-70^{\circ}$', '$-60^{\circ}$'],
fontsize=10)
# Plot points.
ax.scatter(ra, dec, marker='o', c='k', s=1, lw=0.)
# Use this block to generate colored points with a colorbar.
#cm = plt.cm.get_cmap('RdYlBu_r')
#z = np.random.random((len(ra), 1)) # RGB values
#SC = ax.scatter(ra, dec, marker='o', c=z, s=10, lw=0., cmap=cm)
# Colorbar
#cbar = plt.colorbar(SC, shrink=1., pad=0.05)
#cbar.ax.tick_params(labelsize=8)
#cbar.set_label('colorbar', fontsize=8)
# Output png file.
fig.tight_layout()
plt.savefig(ra_dec_plot.png', dpi=300)
588
Getting the colorbar can be done with a merging of the OP code with @cphlewis's excellent answer. I've posted this as a turnkey solution on the request of the OP in chat. The first version of code simply adds a color bar, the final version (under EDIT 2) does an axes affine translation and corrects a few parameters / simplifies the code to suit OP spec exactly.
"""
An experimental support for curvilinear grid.
"""
import numpy as np
import mpl_toolkits.axisartist.angle_helper as angle_helper
import matplotlib.cm as cmap
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axisartist import SubplotHost
from mpl_toolkits.axisartist import GridHelperCurveLinear
def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1
# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(10, 60,
lon_cycle = 360,
lat_cycle = None,
lon_minmax = None,
lat_minmax = (0, np.inf),
)
grid_locator1 = angle_helper.LocatorHMS(12) #changes theta gridline count
tick_formatter1 = angle_helper.FormatterHMS()
grid_locator2 = angle_helper.LocatorDMS(6)
tick_formatter2 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1,
grid_locator2=grid_locator2,
tick_formatter2=tick_formatter2
)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
ax1.axis["bottom"].major_ticklabels.set_visible(True) #Turn off?
# let right and bottom axis show ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks=0
ax1.axis["bottom"].get_helper().nth_coord_ticks=0
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
ax1.set_aspect(1.)
ax1.set_xlim(-4,15) # moves the origin left-right in ax1
ax1.set_ylim(-3, 20) # moves the origin up-down
ax1.set_ylabel('90$^\circ$ + Declination')
ax1.set_xlabel('Ascension')
ax1.grid(True)
#ax1.grid(linestyle='--', which='x') # either keyword applies to both
#ax1.grid(linestyle=':', which='y') # sets of gridlines
return tr
import matplotlib.pyplot as plt
fig = plt.figure(1, figsize=(5, 5))
fig.clf()
tr = curvelinear_test2(fig) # tr.transform_point((x, 0)) is always (0,0)
# => (theta, r) in but (r, theta) out...
r_test = [0, 1.2, 2.8, 3.8, 5, 8, 10, 13.3, 17] # distance from origin
deg_test = [0, -7, 12, 28, 45, 70, 79, 90, 100] # degrees ascension
out_test = tr.transform(zip(deg_test, r_test))
sizes = [40, 30, 10, 30, 80, 33, 12, 48, 45]
#hues = [.9, .3, .2, .8, .6, .1, .4, .5,.7] # Oddly, floats-to-colormap worked for a while.
hues = np.random.random((9,3)) #RGB values
# Use this block to generate colored points with a colorbar.
cm = plt.cm.get_cmap('RdYlBu_r')
z = np.random.random((len(r_test), 1)) # RGB values
SC = ax1.scatter(out_test[:,0], #ax1 is a global
out_test[:,1],
s=sizes,
c=z,
cmap=cm,
zorder=9) #on top of gridlines
# Colorbar
cbar = plt.colorbar(SC, shrink=1., pad=0.05)
cbar.ax.tick_params(labelsize=8)
cbar.set_label('colorbar', fontsize=8)
plt.show()
EDIT
Bit of tidying parameters, adding in OP data, removing redundancy yields the following plot. Still need to centre the data on -90 instead of 0 - at the moment this is hacked, but I'm sure curvelinear_test2() can be changed to account for it...
EDIT 2
Following OP comment on intermediate version in this answer, a final version as below gives the plot at the very end of the post - with -90 on the dec axis and subplot demo
"""
An experimental support for curvilinear grid.
"""
import numpy as np
import mpl_toolkits.axisartist.angle_helper as angle_helper
import matplotlib.cm as cmap
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axisartist import SubplotHost
from mpl_toolkits.axisartist import GridHelperCurveLinear
def curvelinear_test2(fig, rect=111):
"""
polar projection, but in a rectangular box.
"""
# see demo_curvelinear_grid.py for details
tr = Affine2D().translate(0,90) + Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(10, 60,
lon_cycle = 360,
lat_cycle = None,
lon_minmax = None,
lat_minmax = (-90, np.inf),
)
grid_locator1 = angle_helper.LocatorHMS(12) #changes theta gridline count
tick_formatter1 = angle_helper.FormatterHMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1
)
ax1 = SubplotHost(fig, rect, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
ax1.axis["bottom"].major_ticklabels.set_visible(True) #Turn off?
# let right and bottom axis show ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks=0
ax1.axis["bottom"].get_helper().nth_coord_ticks=0
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
# You may or may not need these - they set the view window explicitly rather than using the
# default as determined by matplotlib with extreme finder.
ax1.set_aspect(1.)
ax1.set_xlim(-4,25) # moves the origin left-right in ax1
ax1.set_ylim(-3, 30) # moves the origin up-down
ax1.set_ylabel('Declination')
ax1.set_xlabel('Ascension')
ax1.grid(True)
#ax1.grid(linestyle='--', which='x') # either keyword applies to both
#ax1.grid(linestyle=':', which='y') # sets of gridlines
return ax1,tr
def skip_comments(f):
'''
Read lines that DO NOT start with a # symbol.
'''
for line in f:
if not line.strip().startswith('#'):
yield line
def get_data_bb():
'''RA, DEC data file.
'''
# Path to data file.
out_file = 'bb_cat.dat'
# Read data file
with open(out_file) as f:
ra, dec = [], []
for line in skip_comments(f):
ra.append(float(line.split()[0]))
dec.append(float(line.split()[1]))
return ra, dec
import matplotlib.pyplot as plt
fig = plt.figure(1, figsize=(5, 5))
fig.clf()
ax1, tr = curvelinear_test2(fig,121) # tr.transform_point((x, 0)) is always (0,0)
# => (theta, r) in but (r, theta) out...
# Read RA, DEC data from file.
ra, dec = get_data_bb()
out_test = tr.transform(zip(ra, dec))
# Use this block to generate colored points with a colorbar.
cm = plt.cm.get_cmap('RdYlBu_r')
z = np.random.random((len(ra), 1)) # RGB values
SC = ax1.scatter(out_test[:,0], #ax1 is a global
out_test[:,1],
marker = 'o',
c=z,
cmap=cm,
lw = 0.,
zorder=9) #on top of gridlines
# Colorbar
cbar = plt.colorbar(SC, shrink=1., pad=0.1)
cbar.ax.tick_params(labelsize=8)
cbar.set_label('colorbar', fontsize=8)
ax2, tr = curvelinear_test2(fig,122) # tr.transform_point((x, 0)) is always (0,0)
# => (theta, r) in but (r, theta) out...
# Read RA, DEC data from file.
ra, dec = get_data_bb()
out_test = tr.transform(zip(ra, dec))
# Use this block to generate colored points with a colorbar.
cm = plt.cm.get_cmap('RdYlBu_r')
z = np.random.random((len(ra), 1)) # RGB values
SC = ax2.scatter(out_test[:,0], #ax1 is a global
out_test[:,1],
marker = 'o',
c=z,
cmap=cm,
lw = 0.,
zorder=9) #on top of gridlines
# Colorbar
cbar = plt.colorbar(SC, shrink=1., pad=0.1)
cbar.ax.tick_params(labelsize=8)
cbar.set_label('colorbar', fontsize=8)
plt.show()
Final plot:
74
Chewing on the AxisArtist example is actually pretty promising (this combines two AxisArtist examples -- I wouldn't be surprised if AxisArtist was written with RA plots in mind):
Still to do:
aesthetic:
anything else?
"""
An experimental support for curvilinear grid.
"""
import numpy as np
import mpl_toolkits.axisartist.angle_helper as angle_helper
import matplotlib.cm as cmap
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axisartist import SubplotHost
from mpl_toolkits.axisartist import GridHelperCurveLinear
def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1
# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(10, 60,
lon_cycle = 360,
lat_cycle = None,
lon_minmax = None,
lat_minmax = (0, np.inf),
)
grid_locator1 = angle_helper.LocatorHMS(12) #changes theta gridline count
tick_formatter1 = angle_helper.FormatterHMS()
grid_locator2 = angle_helper.LocatorDMS(6)
tick_formatter2 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1,
grid_locator2=grid_locator2,
tick_formatter2=tick_formatter2
)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
ax1.axis["bottom"].major_ticklabels.set_visible(True) #Turn off?
# let right and bottom axis show ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks=0
ax1.axis["bottom"].get_helper().nth_coord_ticks=0
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
ax1.set_aspect(1.)
ax1.set_xlim(-4,15) # moves the origin left-right in ax1
ax1.set_ylim(-3, 20) # moves the origin up-down
ax1.set_ylabel('90$^\circ$ + Declination')
ax1.set_xlabel('Ascension')
ax1.grid(True)
#ax1.grid(linestyle='--', which='x') # either keyword applies to both
#ax1.grid(linestyle=':', which='y') # sets of gridlines
return tr
import matplotlib.pyplot as plt
fig = plt.figure(1, figsize=(5, 5))
fig.clf()
tr = curvelinear_test2(fig) # tr.transform_point((x, 0)) is always (0,0)
# => (theta, r) in but (r, theta) out...
r_test = [0, 1.2, 2.8, 3.8, 5, 8, 10, 13.3, 17] # distance from origin
deg_test = [0, -7, 12, 28, 45, 70, 79, 90, 100] # degrees ascension
out_test = tr.transform(zip(deg_test, r_test))
sizes = [40, 30, 10, 30, 80, 33, 12, 48, 45]
#hues = [.9, .3, .2, .8, .6, .1, .4, .5,.7] # Oddly, floats-to-colormap worked for a while.
hues = np.random.random((9,3)) #RGB values
ax1.scatter(out_test[:,0], #ax1 is a global
out_test[:,1],
s=sizes,
c=hues,
#cmap=cmap.RdYlBu_r,
zorder=9) #on top of gridlines
plt.show()
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
