fig_int_edges.suptitle('Edge Teeth Intercept vs. Beamformer Temperature')
pol_titles = ['XX', 'YY', 'XY', 'YX']
# Show bad obs on scatter plots
c = 30 * ['b']
# bad_obs_ind = [23, 24, 27, 28, 29]
# for k in bad_obs_ind:
# c[k] = 'r'
for m in range(4):
plot_lib.scatter_plot_2d(fig_slope_main,
ax_slope_main[m / 2][m % 2],
max_loc_array,
fit_coeff_array[:, 0, m],
title=pol_titles[m],
xlabel='Beamformer Temperature (C)',
ylabel='Main Slope',
c=c,
ylim=[
np.amin(fit_coeff_array[:, 0, m]),
np.amax(fit_coeff_array[:, 0, m])
])
plot_lib.scatter_plot_2d(fig_int_main,
ax_int_main[m / 2][m % 2],
max_loc_array,
fit_coeff_array[:, 1, m],
title=pol_titles[m],
xlabel='Beamformer Temperature (C)',
ylabel='Main Y-Intercept',
c=c)
plot_lib.scatter_plot_2d(fig_slope_centers,
ax_slope_centers[m / 2][m % 2],
use('Agg')
import matplotlib.pyplot as plt
import glob
import plot_lib as pl
flag_title = 'All'
rms_path = '/Users/mike_e_dubs/MWA/Temperatures/Golden_Set_RMS/arrs/%s/' % (
flag_title)
outpath = '/Users/mike_e_dubs/MWA/Temperatures/Golden_Set_RMS/figs/%s/' % (
flag_title)
rms_list = glob.glob('%s*.npym' % (rms_path))
rms_list.sort()
rms_arr = np.zeros([2, len(rms_list)])
xticks = [1061313496, 1061315320, 1061317152, 1061318984, 1061320688]
for k, rms in enumerate(rms_list):
rms_arr[0, k] = int(rms[len(rms_path):len(rms_path) + 10])
rms_arr[1, k] = np.load(rms)
fig, ax = plt.subplots(figsize=(14, 8))
pl.scatter_plot_2d(fig,
ax,
rms_arr[0],
rms_arr[1],
title='Golden Set RMS, Post-Flagging',
xlabel='Obsid',
ylabel='RMS (UNCALIB)',
xticks=xticks)
fig.savefig('%sGolden_Set_RMS_%s.png' % (outpath, flag_title))
bins_all_widths = np.diff(bins_all)
bins_unflagged_widths = np.diff(bins_unflagged)
bins_all_centers = bins_all[:-1] + 0.5 * bins_all_widths
bins_unflagged_centers = bins_unflagged[:-1] + 0.5 * bins_unflagged_widths
max_loc_all = bins_all_centers[hist_all.argmax()]
max_loc_unflagged = bins_unflagged_centers[hist_unflagged.argmax()]
max_locs.append([int(obs1), max_loc_unflagged, max_loc_all])
max_locs.sort()
max_locs = np.array(max_locs)
fig, ax = plt.subplots(figsize=(14, 8), nrows=2)
fig.suptitle('Golden Set Histogram Count Max Locations')
fig_titles = ['Unflagged', 'All']
xticks = [1061313496, 1061315320, 1061317152, 1061318984]
for m in range(2):
plot_lib.scatter_plot_2d(fig,
ax[m],
max_locs[:, 0],
max_locs[:, m + 1],
title=fig_titles[m],
xlabel='GPS Time',
ylabel='Max Location (UNCALIB)',
xticks=xticks)
np.save('%smax_locs.npy' % (outpath), max_locs)
fig.savefig('%smax_locs_day1.png' % (outpath))
import numpy as np
import plot_lib as pl
import matplotlib.pyplot as plt
from matplotlib import cm
ant_pos_arr = np.load(
'/Users/mike_e_dubs/python_stuff/MJW-MWA/Useful_Information/MWA_ant_pos.npy'
)
fig, ax = plt.subplots(figsize=(14, 8))
pl.scatter_plot_2d(fig,
ax,
ant_pos_arr[:, 0],
ant_pos_arr[:, 1],
title='Antennas by Color',
xlabel='X (m)',
ylabel='Y (m)',
c=np.arange(128).astype(float),
cmap=cm.plasma)
fig.savefig('/Users/mike_e_dubs/MWA/Test_Plots/MWA_Ant_by_color.png')
plot_lib.line_plot(
fig_line,
ax_line[m / 2][m % 2], [
mean[:, m], fit[:, m], fit[:, m] + fit_centers[:, m],
fit[:, m] + fit_edges[:, m]
],
title=pol_titles[m],
xticks=xticks,
xminors=xminors,
xticklabels=xticklabels,
zorder=[1, 2, 2, 2],
labels=['Template', 'Fit', 'Center Teeth Fit', 'Edge Teeth Fit'])
plot_lib.scatter_plot_2d(fig_scatter,
ax_scatter[m / 2][m % 2],
temps[:, m],
mean[:, m],
title=pol_titles[m],
xlabel='Fit Width',
ylabel='Template')
fig_exc.savefig('%s%s_Vis_Avg_Excess.png' % (plot_dir, obslist[n]))
fig_ratio.savefig('%s%s_Vis_Avg_Ratio.png' % (plot_dir, obslist[n]))
fig_line.savefig('%s%s_Vis_Avg_Template.png' % (plot_dir, obslist[n]))
fig_scatter.savefig('%s%s_Vis_Avg_Temperature.png' %
(plot_dir, obslist[n]))
plt.close(fig_exc)
plt.close(fig_ratio)
plt.close(fig_line)
plt.close(fig_scatter)
}
color_dict = {
'soft_grad': 'blue',
'hard_grad': 'purple',
'NB': 'black',
'TV6': 'red',
'TV7': 'orange',
'TV8': 'yellow',
'streaks': 'green'
}
base = '/Users/mike_e_dubs/MWA/INS/Long_Run'
fig, ax = plt.subplots(figsize=(14, 8))
for key in obs_dict:
lst = []
for obs in obs_dict[key]:
obs_lst = np.load('%s/time_arrs/%s_lst_arr.npy' % (base, obs))[0]
if obs_lst > np.pi:
obs_lst -= 2 * np.pi
obs_lst *= 23.9345 / (2 * np.pi)
lst.append(obs_lst)
pl.scatter_plot_2d(fig,
ax,
lst,
obs_dict[key],
c=color_dict[key],
xlabel='LST (hours)',
ylabel='GPS Time (s)')
fig.savefig('%s/LST_v_Obs_Scatter.png' % base)