In [109]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
In [81]:
keep_fake = pd.read_csv('keep_fake.csv')
found_fake = pd.read_csv('found_fake.csv')
found_res = pd.read_csv('found_res.csv')
In [82]:
keep_fake.columns
Out[82]:
Index(['x', 'y', 'xv', 'yv', 'flux', 'mag', 'unmasked_obs', 'x_final',
'y_final', 'patch'],
dtype='object')
In [137]:
fig = plt.figure(figsize=(24, 10))
fig.add_subplot(1,2,1)
n, bins, _ = plt.hist(keep_fake['mag'], bins=12, range=(20, 26), label='Total Simulated Objects')
n2, bins2, _ = plt.hist(found_fake['mag'], bins=bins, range=(20, 26),
histtype='stepfilled', lw=6, label='Recovered Simulated Objects')
plt.xlabel('Simulated Object Magnitudes', size=28)
plt.ylabel('Simulated Object Counts', size=28)
plt.legend(fontsize=23)
plt.xticks(size=28)
plt.yticks(size=28)
fig.add_subplot(1,2,2)
plt.plot(bins[:-1]+0.25, n2/n, '-o', markersize=20, lw=8)
plt.xlabel('Simulated Object Magnitudes', size=28)
plt.ylabel('Fraction of recovered simulated objects', size=28)
plt.legend(fontsize=22)
plt.xticks(size=28)
plt.yticks(size=28)
plt.suptitle('Simulated Object Recovery', size=32)
plt.subplots_adjust(top=0.92)
In [138]:
fig = plt.figure(figsize=(24, 10))
fig.add_subplot(1,2,1)
n, bins, _ = plt.hist(keep_fake['mag'], bins=24, range=(20, 26), label='Total Simulated Objects')
n2, bins2, _ = plt.hist(found_fake['mag'], bins=bins, range=(20, 26),
histtype='stepfilled', lw=6, label='Recovered Simulated Objects')
plt.xlabel('Simulated Object Magnitudes', size=28)
plt.ylabel('Simulated Object Counts', size=28)
plt.legend(fontsize=23)
plt.xticks(size=28)
plt.yticks(size=28)
fig.add_subplot(1,2,2)
plt.plot(bins[:-1]+0.125, n2/n, '-o', markersize=20, lw=8)
plt.xlabel('Simulated Object Magnitudes', size=28)
plt.ylabel('Fraction of recovered simulated objects', size=28)
plt.legend(fontsize=22)
plt.xticks(size=28)
plt.yticks(size=28)
plt.suptitle('Simulated Object Recovery', size=32)
plt.subplots_adjust(top=0.92)
In [114]:
plt.figure(figsize=(10, 10))
plt.plot(bins[:-1]+0.25, n2/n, '-o', markersize=20)
plt.xlabel('Simulated Object Magnitudes', size=22)
plt.ylabel('Fraction of recovered simulated objects', size=22)
plt.legend(fontsize=22)
plt.title('Simulated Object Recovery', size=28)
plt.xticks(size=22)
plt.yticks(size=22)
Out[114]:
(array([-0.2, 0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2]),
<a list of 8 Text yticklabel objects>)
In [99]:
n2/n
Out[99]:
array([ 0.62790698, 0.875 , 0.875 , 0.90243902, 0.96666667,
0.97727273, 0.8974359 , 0.93333333, 0.40909091, 0. ,
0. , 0. ])
In [38]:
keep_fake
Out[38]:
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yv
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In [39]:
found_fake
Out[39]:
x
y
xv
yv
flux
mag
unmasked_obs
x_final
y_final
patch
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9
3620
2084
3,2
393 rows × 10 columns
In [76]:
y = np.array([x for x in '2,4tempExp'])
In [77]:
int(y[0])*10 + int(y[2])
Out[77]:
24
In [ ]:
Content source: DiracInstitute/kbmod
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