So I just started coding, and I'm so glad I chose Python to start me off! I
really enjoy the clean layout, easy syntax, and power of the language. I'm
doing astronomy research and I just started teaching myself matplotlib
along with my general python work. I feel like I'm catching on quick, but I
also feel that this particular plot script is a little rough. The plot
looks correct, but the code seems really long for what I'm trying to do. So
any tricks to make this more efficient would be greatly appreciated!
A little bit about what's going on before I post the script. I'm using
np.genfromtext to upload a CSV file of a bunch of astronomy data. I need to
break this data down into multiple subsets. 4 mass separated subsets
(row['logM']) and each of those is divided into bulge and disk dominated.
And for each of those I need to separate quiescent and star forming. So I
have a total of 8 star forming subsets and 8 quiescent subsets. And all
sixteen different styles of object need a distinct marker on the plot. I
hope that with the comments you can see where I divided those up in the
code. I did it all with a ton of for loops and if statements filling in a
bunch of empty arrays. Hopefully this is enough info to make sense of what
I'm going for. My main question is can I shorten this code, especially the
for loops, to make it more efficient and clean? Here is the script:
# -*- coding: utf-8 -*-
#----- Zach Rizer
#----- UVJ plot
#----- data = n>2_qui_flag.csv
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patch
from matplotlib.path import Path
fig = plt.figure('UVJ')
ax = fig.add_subplot(111) #subplot for shaded region
##########################################################################
# ----- Data uploaded from preliminary TopCat CSV Files
data =
np.genfromtxt('/Users/ProtonLenny/Documents/Research/Catalog_Data/Catalog_3/n>2_qui_flag.csv',\
dtype=None,names=True,delimiter =",")
# ----- Define Quiesent Subset
qui_m1d_xval = []
qui_m1d_yval = []
qui_m2d_xval = []
qui_m2d_yval = []
qui_m3d_xval = []
qui_m3d_yval = []
qui_m4d_xval = []
qui_m4d_yval = []
qui_m1b_xval = []
qui_m1b_yval = []
qui_m2b_xval = []
qui_m2b_yval = []
qui_m3b_xval = []
qui_m3b_yval = []
qui_m4b_xval = []
qui_m4b_yval = []
for row in data:
if row['Dv'] > 0.65 and row['DSw'] > 0.65:
* # filling disk-dom arrays*
if row['UminV'] > 1.3 and row['VminJ'] < 1.6 and row['UminV'] >
0.88*(row['VminJ']+0.59):#quiescent criteria at particular z
if row['Z'] >= 0.6 and row['Z'] < 0.9:#redshift bin criteria
if row['logM'] >= 9.7 and row['logM'] < 10:#mass subsets
for shape in plots
qui_m1d_xval.append(row['VminJ'])
* # (x) fill the empty list with the data from the appropriate column.*
qui_m1d_yval.append(row['UminV'])
elif row['logM'] >= 10 and row['logM'] < 10.5:
qui_m2d_xval.append(row['VminJ'])
qui_m2d_yval.append(row['UminV'])
elif row['logM'] >= 10.5 and row['logM'] < 10.8:
qui_m3d_xval.append(row['VminJ'])
qui_m3d_yval.append(row['UminV'])
elif row['logM'] >= 10.8:
qui_m4d_xval.append(row['VminJ'])
qui_m4d_yval.append(row['UminV'])
if row['Dv'] < 0.35 and row['DSw'] > 0.65:
* # filling bulge-dom arrays*
if row['UminV'] > 1.3 and row['VminJ'] < 1.6 and row['UminV'] >
0.88*(row['VminJ']+0.59):#quiescent criteria at particular z
if row['Z'] >= 0.6 and row['Z'] < 0.9:#redshift bin criteria
if row['logM'] >= 9.7 and row['logM'] < 10:#mass subsets
for shape in plots
qui_m1b_xval.append(row['VminJ'])
qui_m1b_yval.append(row['UminV'])
elif row['logM'] >= 10 and row['logM'] < 10.5:
qui_m2b_xval.append(row['VminJ'])
qui_m2b_yval.append(row['UminV'])
elif row['logM'] >= 10.5 and row['logM'] < 10.8:
qui_m3b_xval.append(row['VminJ'])
qui_m3b_yval.append(row['UminV'])
elif row['logM'] >= 10.8:
qui_m4b_xval.append(row['VminJ'])
qui_m4b_yval.append(row['UminV'])
# ----- Define Star-Forming Subset
sf_m1d_xval = []
sf_m1d_yval = []
sf_m2d_xval = []
sf_m2d_yval = []
sf_m3d_xval = []
sf_m3d_yval = []
sf_m4d_xval = []
sf_m4d_yval = []
sf_m1b_xval = []
sf_m1b_yval = []
sf_m2b_xval = []
sf_m2b_yval = []
sf_m3b_xval = []
sf_m3b_yval = []
sf_m4b_xval = []
sf_m4b_yval = []
for row in data:
if row['Dv'] > 0.65 and row['DSw'] > 0.65:
# filling disk-dom arrays
if row['UminV'] < 1.3 or row['VminJ'] > 1.6 or row['UminV'] <
0.88*(row['VminJ']+0.59):#star forming creteria at particular z
if row['Z'] >= 0.6 and row['Z'] < 0.9:#reshift bin criteria
if row['logM'] >= 9.7 and row['logM'] < 10:
sf_m1d_xval.append(row['VminJ'])
sf_m1d_yval.append(row['UminV'])
elif row['logM'] >= 10 and row['logM'] < 10.5:
sf_m2d_xval.append(row['VminJ'])
sf_m2d_yval.append(row['UminV'])
elif row['logM'] >= 10.5 and row['logM'] < 10.8:
sf_m3d_xval.append(row['VminJ'])
sf_m3d_yval.append(row['UminV'])
elif row['logM'] >= 10.8:
sf_m4d_xval.append(row['VminJ'])
sf_m4d_yval.append(row['UminV'])
if row['Dv'] < 0.35 and row['DSw'] > 0.65:
# filling disk-dom arrays
if row['UminV'] < 1.3 or row['VminJ'] > 1.6 or row['UminV'] <
0.88*(row['VminJ']+0.59):#star forming creteria at particular z
if row['Z'] >= 0.6 and row['Z'] < 0.9:#reshift bin criteria
if row['logM'] >= 9.7 and row['logM'] < 10:
sf_m1b_xval.append(row['VminJ'])
sf_m1b_yval.append(row['UminV'])
elif row['logM'] >= 10 and row['logM'] < 10.5:
sf_m2b_xval.append(row['VminJ'])
sf_m2b_yval.append(row['UminV'])
elif row['logM'] >= 10.5 and row['logM'] < 10.8:
sf_m3b_xval.append(row['VminJ'])
sf_m3b_yval.append(row['UminV'])
elif row['logM'] >= 10.8:
sf_m4b_xval.append(row['VminJ'])
sf_m4b_yval.append(row['UminV'])
# ----- plot the first column as x, and second column as y
plot_qui_m4d, = plt.plot(qui_m4d_xval, qui_m4d_yval, 'r^', ms=12)
plot_qui_m4b, = plt.plot(qui_m4b_xval, qui_m4b_yval, 'ro', ms=12)
plot_qui_m3d, = plt.plot(qui_m3d_xval, qui_m3d_yval, 'r^', ms=9)
plot_qui_m3b, = plt.plot(qui_m3b_xval, qui_m3b_yval, 'ro', ms=9)
plot_qui_m2d, = plt.plot(qui_m2d_xval, qui_m2d_yval, 'r^', ms=6)
plot_qui_m2b, = plt.plot(qui_m2b_xval, qui_m2b_yval, 'ro', ms=6)
plot_qui_m1d, = plt.plot(qui_m1d_xval, qui_m1d_yval, 'r^', ms=3)
plot_qui_m1b, = plt.plot(qui_m1b_xval, qui_m1b_yval, 'ro', ms=3)
plot_sf_m4d, = plt.plot(sf_m4d_xval, sf_m4d_yval, 'b^', ms=12)
plot_sf_m4b, = plt.plot(sf_m4b_xval, sf_m4b_yval, 'bo', ms=12)
plot_sf_m3d, = plt.plot(sf_m3d_xval, sf_m3d_yval, 'b^', ms=9)
plot_sf_m3b, = plt.plot(sf_m3b_xval, sf_m3b_yval, 'bo', ms=9)
plot_sf_m2d, = plt.plot(sf_m2d_xval, sf_m2d_yval, 'b^', ms=6)
plot_sf_m2b, = plt.plot(sf_m2b_xval, sf_m2b_yval, 'bo', ms=6)
plot_sf_m1d, = plt.plot(sf_m1d_xval, sf_m1d_yval, 'b^', ms=3)
plot_sf_m1b, = plt.plot(sf_m1b_xval, sf_m1b_yval, 'bo', ms=3)
##########################################################################
####### Shading, coloring, patches, etc...
#------ quiescent divided region
verts = [(-1.,1.3),
(0.8,1.3),
(1.6,2.0),
(1.6,2.5),
(-1.,2.5),
(-1.,1.3)]
codes = [Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.CLOSEPOLY]
path = Path(verts,codes)
patch = patch.PathPatch(path, facecolor='none', lw=1.5, alpha=0.5,
hatch='//')
ax.add_patch(patch)
##########################################################################
#----- titles and axis labels and axis limits
plt.title('UVJ Plot of n>2 population at 0.6<z<0.9')
plt.xlabel('V-J')
plt.ylabel('U-V')
plt.xlim(0.0, 2.5)
plt.ylim(0.0, 2.5)
plt.legend([plot_qui_m4d,plot_qui_m4b,plot_sf_m4d,plot_sf_m4b],["Qui-Disk
(size goes w/ Mass)",\
"Qui-Bulge (size goes w/ Mass)","SF-Disk (size goes w/ Mass)",\
"SF-Bulge (size goes w/ Mass)"], 'best', numpoints=1)
plt.show()
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