Re: [Tutor] Question in regards to loops and matplotlib
]),
(1.6,2.5),
(-1.,2.5),
(-1.,1.3)]
codes = [Path.MOVETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.LINETO,
Path.CLOSEPOLY]
return Path(verts,codes)
def titles_labels(labels, z_range):
creates axis labels, title, and legend
associating labels to these three specific plots
plt.title('UVJ Plot of n2 population at ' + z_range[5])
plt.xlabel('V-J')
plt.ylabel('U-V')
plt.xlim(0.5, 2.0)
plt.ylim(0.5, 2.5)
plt.legend([labels[0], labels[1], labels[2]],
[Qui-Bulge-Dom, SF-Bulge-Dom, Disk-Dom],
'best', numpoints=1, fontsize='small')
def main():
calls total script to create plot
to run, simply load csv in the first spot in the np.genfromtxt args
and exchange the zbin value to either z1, z2, z3, or z4
fig = plt.figure('UVJ')
n2_subset =
np.genfromtxt('/Users/ProtonLenny/Documents/Research/Catalog_Data/Catalog_4/n2.csv',
dtype=None, names=True, delimiter =,)
zbin = z4
z_data = redshift(n2_subset, zbin)
qui_data = (quiescence(z_data, zbin))[0]
sf_data = (quiescence(z_data, zbin))[1]
qui_disk = (disk_vs_bulge(qui_data))[0]
qui_bulge = (disk_vs_bulge(qui_data))[1]
sf_disk = (disk_vs_bulge(sf_data))[0]
sf_bulge = (disk_vs_bulge(sf_data))[1]
m_q_disk = make_mass_cuts(qui_disk)
m_q_bulge = make_mass_cuts(qui_bulge)
m_sf_disk = make_mass_cuts(sf_disk)
m_sf_bulge = make_mass_cuts(sf_bulge)
legend_labels = plots(m_q_disk, m_q_bulge, m_sf_disk, m_sf_bulge)
path = make_hash_region(zbin)
titles_labels(legend_labels, zbin)
hash_region = patch.PathPatch(path, facecolor='none', lw=1.5,
alpha=0.5, hatch='//')
ax = fig.add_subplot(111) #subplot for shaded region
ax.add_patch(hash_region)
plt.show()
if __name__ == '__main__':
main()
On Thu, Aug 8, 2013 at 8:38 AM, Oscar Benjamin
oscar.j.benja...@gmail.comwrote:
On 1 August 2013 06:21, Zachary Rizer protonle...@gmail.com wrote:
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!
Hi Zachary,
You can definitely make this shorter. I can't run your code since you
didn't provide any data. It's good to use dummy data in examples
posted to mailing lists so that others can run the code. Instead I'll
show an example with my own dummy data:
#!/usr/bin/env python
#
# Plot heights and weights of subjects from different groups.
from matplotlib import pyplot as plt
# Dummy data
subjects = [
# Group, height (m), weight (kg), diagnosis
{'type': 'patient', 'height': 1.8, 'weight': 90 , 'diag': 'acute' },
{'type': 'patient', 'height': 1.6, 'weight': 85 , 'diag': 'acute' },
{'type': 'patient', 'height': 1.9, 'weight': 120, 'diag': 'chronic'},
{'type': 'patient', 'height': 2.0, 'weight': 110, 'diag': 'chronic'},
{'type': 'control', 'height': 1.7, 'weight': 60 , 'diag': 'N/A'},
{'type': 'control', 'height': 2.1, 'weight': 100, 'diag': 'N/A'},
]
# Have just one place where we define the properties of each group
groups = {
'control': {
'indicator': lambda s: s['type'] == 'control',
'color': 'blue',
'marker': '*',
'label':'Controls'
},
'chronic': {
'indicator': lambda s: s['diag'] == 'chronic',
'color': 'magenta',
'marker': 's',
'label':'Chronic patients'
},
'acute' : {
'indicator': lambda s: s['diag'] == 'acute',
'color': 'red',
'marker': 'o',
'label':'Acute patients'
},
}
# Now we can reuse the same code for every subject
for groupdata in groups.values():
groupdata['subjects'] = []
# Distribute the subjects to each group
for subject in subjects:
for groupdata in groups.values():
isingroup = groupdata['indicator']
if isingroup(subject):
groupdata['subjects'].append(subject)
break
# Now create/format the figure
fig = plt.figure(figsize=(6, 5))
ax = fig.add_axes([0.15, 0.15, 0.70, 0.70])
ax.set_xlabel(r'Weight ($kg$)')
ax.set_ylabel(r'Height ($m$)')
ax.set_title('Height vs. weight by subject group')
ax.set_xlim([50, 150])
ax.set_ylim([1.5, 2.2])
# Plot each group separately with its own format settings
for group, groupdata in groups.items():
xdata = [s['weight'] for s in groupdata['subjects']]
ydata = [s['height'] for s in groupdata
[Tutor] Question in regards to loops and matplotlib
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 = n2_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/n2_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:
