def plot_all(velocity, width, name, name_width, path, log):
path_save = path
# Normal - W_50 for all
plt.figure(figsize=(12, 8))
plt.scatter(velocity, width, s=2, alpha=0.5, c=mass_galaxies)
plt.plot(velocity, velocity, linestyle=':', linewidth=0.2)
plt.nipy_spectral()
cbar = plt.colorbar()
cbar.ax.set_title('$(M_{*}$ $M_{0})$', size=10)
#plt.xlim(0, 100)
#plt.ylim(0,100)
#plt.xlim(0.5*10**2,0.5*10**3)
#plt.ylim(0.5*10**2,0.5*10**3)
if log == 1:
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$Velocity_{%s}$ km/s' % (name))
plt.ylabel('$%s/2$ km/s' % (name_width))
plt.title('$%s/2$ vs $Velocity_{%s}$' % (name_width, name))
#plt.savefig(path_save + name + name_width + 'log' + '.png')
plt.show()
else:
plt.xlabel('$Velocity_{%s}$ km/s' % (name))
plt.ylabel('$%s/2$ km/s' % (name_width))
plt.title('$%s/2$ vs $Velocity_{%s}$' % (name_width, name))
#plt.savefig(path_save + name + name_width + '.png')
plt.show()
def plot_mass_BT():
plt.figure(figsize=(12, 8))
plt.scatter(B_T_central[B_T_central > 0],
np.log10(mass_gas[B_T_central > 0]),
s=2,
alpha=0.5,
c=mass_galaxies[B_T_central > 0])
plt.nipy_spectral()
cbar = plt.colorbar()
cbar.ax.set_title('$M_{*}$ $M_{0}$', size=10)
plt.show()
for k in np.arange(h):
y = y1 + k * dy
for j in np.arange(w):
x = x1 + j * dx
C[k, j] = mandelbrot(x, y, maxit)
M = C
toc = time.time()
print('wall clock time: %8.2f seconds' % (toc-tic))
# eye candy (requires matplotlib)
if 1:
try:
from matplotlib import pyplot as plt
plt.imshow(M, aspect='equal')
try:
plt.nipy_spectral()
except AttributeError:
plt.spectral()
try:
import signal
def action(*args): raise SystemExit
signal.signal(signal.SIGALRM, action)
signal.alarm(2)
except:
pass
plt.show()
except:
pass
from numpy import abs, fft, zeros, shape, copy, loadtxt
#from cv2 import imread
import glob
import matplotlib.pyplot as plt
import scipy.ndimage as img
import argparse
parser = argparse.ArgumentParser(description='deconvolve images')
parser.add_argument('image', help='image number, ex. 0')
parser.add_argument('length', help='last image number, ex. 100')
parser.add_argument('width', help='last image number, ex. 20')
parser.add_argument('theta', help='last image number, ex. 50')
args = parser.parse_args()
plt.nipy_spectral()
def makePSF(length, width, theta, array):
""" Returns a linear point spread function
INPUT: length of x and y blurs, blurry image (to copy shape of array)
OUTPUT: array of same size as input that defines PSF """
# initialize gaussian array of same size as input array
psf = zeros(shape(array))
rows = len(psf[0]) # store dimensions
cols = len(psf)
psf[cols // 2 - width // 2:cols // 2 + width // 2,
rows // 2:rows // 2 + length] = 1. # make linear psf
psf = img.rotate(psf, theta, reshape=False) # rotate by theta
# transform to corners because of the weird fft indexing
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 3 19:19:55 2018
@author: verdu
"""
from numpy import abs, fft, zeros, shape, copy, savetxt
from cv2 import imread
import glob
from matplotlib.pyplot import figure, imshow, nipy_spectral, close, tick_params
from random import randint
import scipy.ndimage as img
nipy_spectral()
def makePSF(length, width, theta, array):
""" Returns a linear point spread function
INPUT: length of x and y blurs, blurry image (to copy shape of array)
OUTPUT: array of same size as input that defines PSF """
# initialize gaussian array of same size as input array
psf = zeros(shape(array))
rows = len(psf[0]) # store dimensions
cols = len(psf)
psf[cols // 2 - width // 2:cols // 2 + width // 2,
rows // 2:rows // 2 + length] = 1. # make linear psf
psf = img.rotate(psf, theta, reshape=False) # rotate by theta
if sys.argv[1:] == ['winter']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.winter())
elif sys.argv[1:] == ['cool']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.cool())
elif sys.argv[1:] == ['viridis']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.viridis())
elif sys.argv[1:] == ['plasma']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.plasma())
elif sys.argv[1:] == ['inferno']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.inferno())
elif sys.argv[1:] == ['jet']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.jet())
elif sys.argv[1:] == ['gist_ncar']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.gist_ncar())
elif sys.argv[1:] == ['rainbow']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.nipy_spectral())
else:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.nipy_spectral())
fig.colorbar(p)
ax2.set_xlabel('X1')
ax2.set_ylabel('X2')
ax2.set_zlabel('X3')
plt.show()
maxy = int(round(max(y)))
m = np.zeros((1, maxy))
for i in range(maxy):