future stellar network

import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib matplotlib.style.use('ggplot') from scipy.spatial.distance import pdist, squareform import networkx as nx

stars=pd.read_csv('assets/stars.csv')

stars.sort_values('dist', ascending=True)

#Nameobject

Sun0%

Gl-5510%

2221 others99.9%

x-posfloat64

-70.9303 - 73.3303

Sun

Gl-551

2.9331

Gl-559A

3.1203

Gl-559B

3.1203

1078

Gl-699

4.9811

1141

Gl-406

-1.8978

1722

Gl-411

-3.4014

636

Gl-65A

-2.1037

657

Gl-244A

-5.7538

656

Gl-244B

-5.7538

stars['dist'].sort_values()

fig, ax = plt.subplots() plotstarshist=stars['dist'].plot.hist(color='k', alpha=0.5, bins=75, range=[0, 75], ax=ax)

squareform( pdist(stars[['x-pos','y-pos','z-pos']].values, metric='euclidean') ).flatten()

dist_mat = pd.DataFrame( pd.DataFrame( squareform( pdist(stars[['x-pos','y-pos','z-pos']].values, metric='euclidean') ).flatten() ).values.flatten() )

dist_mat.plot.hist(color='k', alpha=0.5, bins=150)

print( dist_mat.values.flatten().shape[0] / stars['#Name'].values.shape[0], stars['#Name'].values.shape[0] )

2223.0 2223

sourceCol = np.transpose(\ np.array( [ stars['#Name'].values for __ in range( stars['#Name'].values.shape[0] )\ ])\ ).flatten()

targetCol = np.array( [ stars['#Name'].values for __ in range( stars['#Name'].values.shape[0] )\ ]).flatten()

print( sourceCol.shape, targetCol.shape, dist_mat.values.shape )

(4941729,) (4941729,) (4941729, 1)

dist_mat.insert(1,'source',sourceCol) dist_mat.insert(2,'target',targetCol)

dist_mat.columns = ['distance', 'source', 'target']

dist_mat

distancefloat64

sourceobject

Sun

57.62606369

Sun

56.235066

Sun

56.33217694

Sun

74.12799908

Sun

52.60701013

Sun

50.72523783

Sun

60.28896872

Sun

42.46913781

Sun

66.56398959

Sun

#StarGraph = nx.from_pandas_dataframe(dist_mat, 'source', 'target', 'distance') StarGraph = nx.from_pandas_edgelist(dist_mat, 'source', 'target', 'distance')

StarGraph['Sun']['Sun']['distance']

print( [p for p in nx.all_shortest_paths(StarGraph, source='Sun', target='GJ-2108', weight='distance')] )

[['Sun', 'GJ-2108'], ['Sun', 'GJ-2108']]

dist10df = dist_mat[dist_mat['distance']<10]

dist10df

distancefloat64

0.0 - 9.99992184119456

sourceobject

Gl-663B0.1%

Gl-663A0.1%

2221 others99.7%

Sun

4.226040594

Sun

4.354670333

Sun

4.354670333

Sun

538

9.562127133

Sun

636

8.567428861

Sun

656

8.574223912

Sun

657

8.574223912

Sun

1078

5.981391004

Sun

1141

7.797337766

Sun

dist10df.to_csv('dist10df.csv', sep=',',index=False)

StarGraph10 = nx.from_pandas_edgelist(dist10df, 'source', 'target', 'distance')

nx.shortest_path(StarGraph10, source='Sun', target='Gl-305', weight='distance')

print( [p for p in nx.all_shortest_paths(StarGraph10, source='Sun', target='Gl-305', weight='distance')] )

[['Sun', 'Gl-551', 'Gl-845', 'Gl-780', 'Gl-19', 'Gl-902', 'Gl-865', 'Gl-842', 'GJ-1279', 'Gl-855.1A', 'Gl-808', 'Gl-820.1', 'Gl-67.1', 'Wo-9189', 'Gl-305'], ['Sun', 'Gl-551', 'Gl-845', 'Gl-780', 'Gl-19', 'Gl-902', 'Gl-865', 'Gl-842', 'GJ-1279', 'Gl-855.1A', 'Gl-808', 'Gl-820.1', 'Gl-67.1', 'Wo-9189', 'Gl-305']]

print( nx.dijkstra_path(StarGraph10,'Sun','Gl-305'), '\n' , nx.dijkstra_path(StarGraph10,'Sun','Gl-305',weight='distance') )

['Sun', 'Gl-551', 'Gl-845', 'Gl-780', 'Gl-19', 'Gl-902', 'Gl-865', 'Gl-842', 'GJ-1279', 'NN-4340', 'Gl-808', 'Gl-820.1', 'Gl-67.1', 'Wo-9189', 'Gl-305'] 
 ['Sun', 'Gl-551', 'Gl-845', 'Gl-780', 'Gl-19', 'Gl-902', 'Gl-865', 'Gl-842', 'GJ-1279', 'Gl-855.1A', 'Gl-808', 'Gl-820.1', 'Gl-67.1', 'Wo-9189', 'Gl-305']

dist10df['source'].shape

dist10dfSC = dist10df['source'].value_counts()

dist10dfSC

for i in range( dist10dfSC[dist10dfSC > 15].values.shape[0] ): print( dist_mat[ (dist_mat['target'] == dist10dfSC.index[i]) & (dist_mat['source'] == 'Sun') ]['distance'].values, dist10dfSC[dist10dfSC > 15].values[i] )

[17.3861465] 21
[17.38611038] 21
[17.92108462] 19
[9.56212713] 18
[14.84581102] 18
[31.15222331] 18
[28.33734858] 18
[28.33742726] 18
[13.47226589] 17
[55.18839962] 17
[12.94812881] 17
[12.94812881] 17
[40.26706168] 17
[15.414186] 17
[36.11996607] 17
[42.91619987] 16
[41.54952551] 16
[19.75547698] 16
[18.30323682] 16
[10.33467594] 16
[44.07612787] 16
[35.29905833] 16
[35.76350073] 16
[14.56084008] 16
[35.84208863] 16
[13.3291135] 16
[7.79733777] 16

def DistanceMatrix(stars_dataframe): return pd.DataFrame( \ pd.DataFrame( \ squareform( \ pdist(stars_dataframe[['x-pos','y-pos','z-pos']].values, metric='euclidean') \ ).flatten() \ ).values.flatten() \ )

def SourceColumn(stars_dataframe): return np.transpose(\ np.array( [ stars_dataframe['#Name'].values for __ in range( stars_dataframe['#Name'].values.shape[0] )\ ])\ ).flatten()

def TargetColumn(stars_dataframe): return np.array( [ stars_dataframe['#Name'].values for __ in range( stars_dataframe['#Name'].values.shape[0] )\ ]).flatten()

stars_30 = stars[stars['dist']<30] stars_30.sort_values('dist', ascending=True)

#Nameobject

Sun0.4%

Gl-5510.4%

235 others99.2%

x-posfloat64

-26.7395 - 25.5534

Sun

Gl-551

2.9331

Gl-559A

3.1203

Gl-559B

3.1203

1078

Gl-699

4.9811

1141

Gl-406

-1.8978

1722

Gl-411

-3.4014

636

Gl-65A

-2.1037

656

Gl-244B

-5.7538

657

Gl-244A

-5.7538

dist_mat_30 = DistanceMatrix(stars_30) dist_mat_30

0float64

0.0 - 58.4389028601667

26.5173792

20.98863922

27.20296207

28.58571961

20.2585738

22.54063289

26.97792182

18.61660453

28.23923983

print( dist_mat_30.values.flatten().shape[0] / stars_30['#Name'].values.shape[0], stars_30['#Name'].values.shape[0] )

237.0 237

dist_mat_30.insert(1,'source',SourceColumn(stars_30)) dist_mat_30.insert(2,'target',TargetColumn(stars_30))

dist_mat_30.columns = ['distance', 'source', 'target']

dist_mat_30

distancefloat64

0.0 - 58.4389028601667

sourceobject

Sun

26.5173792

Sun

20.98863922

Sun

27.20296207

Sun

28.58571961

Sun

20.2585738

Sun

22.54063289

Sun

26.97792182

Sun

18.61660453

Sun

28.23923983

Sun

dist10df_30 = dist_mat_30[dist_mat_30['distance']<10]

dist10df_30.to_csv('dist10df_30.csv', sep=',',index=False)

dist10df_30_st = dist10df_30[['source','target']] dist10df_30_st.to_csv('dist10df_30_st.csv', sep=',',index=False) # only source and target column

def FindTargets_dist10df_30_st(source_name): return dist10df_30_st[ dist10df_30_st['source']==source_name ]['target'].values

FindTargets_dist10df_30_st('Sun')

flarejsondf = pd.DataFrame(columns=['source','targets', 'bright', 'temp'])

dist10df_30_st['source'].value_counts().index

stars[stars['#Name']=='Sun']['bright']

i_flag = 0 for source in dist10df_30_st['source'].value_counts().index: flarejsondf.loc[i] = [source, FindTargets_dist10df_30_st(source), stars[stars['#Name']==source]['bright'].values[0], stars[stars['#Name']==source]['temp'].values[0]] i = i + 1

flarejsondf

sourceobject

Gl-663B0.4%

Gl-663A0.4%

235 others99.2%

targetsobject

['Gl-663B' 'Gl-663A' 'Gl-664' 'GJ-1224' 'Gl-628' 'Gl-644C' 'Gl-644A' 'Gl-643' 'Gl-581' 'Gl-701' 'Gl-673' 'Gl-702B' 'Gl-702A']1.3%

['Gl-191' 'GJ-1061' 'Gl-229' 'Gl-71' 'Gl-144' 'Gl-166C' 'Gl-166B' 'Gl-166A' 'Gl-223.2' 'Gl-205' 'Gl-234A' 'NN-3379' 'Gl-213']1.3%

188 others97.5%

Gl-663B

['Gl-674' 'Gl-754' 'Gl-682' 'Gl-588' 'Gl-618B' 'Gl-618A' 'Gl-667C' 'Gl-667A' 'Gl-663B' 'Gl-663A' '…

Gl-663A

['Gl-674' 'Gl-754' 'Gl-682' 'Gl-588' 'Gl-618B' 'Gl-618A' 'Gl-667C' 'Gl-667A' 'Gl-663B' 'Gl-663A' '…

Gl-664

['Gl-674' 'Gl-754' 'Gl-682' 'Gl-588' 'Gl-618B' 'Gl-618A' 'Gl-667C' 'Gl-667A' 'Gl-663B' 'Gl-663A' '…

Gl-674

['Gl-780' 'Gl-693' 'Gl-845' 'Gl-674' 'Gl-666B' 'Gl-666A' 'Gl-754' 'Gl-784' 'Gl-682' 'Gl-588' 'Gl-7…

Gl-729

['Sun' 'Gl-551' 'Gl-559B' 'Gl-559A' 'Gl-845' 'Gl-674' 'Gl-682' 'Gl-825' 'Gl-887' 'Gl-663B' 'Gl-663…

Gl-860B

['Gl-820B' 'Gl-820A' 'Gl-15B' 'Gl-15A' 'Gl-905' 'Gl-873' 'GJ-1245A' 'GJ-1245B' 'Gl-892' 'Gl-860B' …

Gl-860A

['Gl-820B' 'Gl-820A' 'Gl-15B' 'Gl-15A' 'Gl-905' 'Gl-873' 'GJ-1245A' 'GJ-1245B' 'Gl-892' 'Gl-860B' …

Gl-234A

['Gl-229' 'Gl-244B' 'Gl-244A' 'Gl-144' 'Gl-166C' 'Gl-166B' 'Gl-166A' 'Gl-223.2' 'Gl-205' 'Gl-234A'…

Gl-682

['Gl-780' 'Gl-693' 'Gl-674' 'Gl-666B' 'Gl-666A' 'Gl-754' 'Gl-784' 'Gl-682' 'Gl-588' 'Gl-783A' 'Gl-…

NN-3522

['Gl-244B' 'Gl-244A' 'NN-3622' 'Gl-234A' 'Gl-447' 'Gl-285' 'Gl-280A' 'Gl-273' 'Gl-406' 'NN-3522' '…

flarejsondf.reset_index().to_json('dist10df_30.json', orient='records')