Produce a RA vs DEC equatorial coordinates plot with python

Question

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:

• The 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 .
• The r axis (ie: the declination) increases outward from -90º and the grid is centered in (0h, -90º).
• The plot is clipped , meaning only a portion of it shows as opposed to the entire circle (as 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)

Answer 1

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:

1. Declination should run from -90 at the origin to 0
2. Be able to use and add a colorbar
3. adjust limits if plotting outside them

aesthetic:

4. Serif font in axis labels
5. Dashed gridlines for ascension

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()

Answer 2

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:

Answer 3

I think it may be a problem with Python 3+ , now the line

out_test = tr.transform(zip(deg_test, r_test))

returns the error:

ValueError: cannot reshape array of size 1 into shape (2)

Changing the line to

out_test = tr.transform(list(zip(deg_test, r_test)))

fixes the problem and allows the plot to be generated correctly.