Mercurial > repos > astroteam > hess_astro_tool

annotate Spectrum.py @ 1:593c4b45eda5 draft default tip

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planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
author astroteam
date Thu, 18 Apr 2024 09:26:15 +0000
parents 02e4bb4fa10c
children
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02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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1 #!/usr/bin/env python
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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2 # coding: utf-8
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4 # flake8: noqa
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6 import json
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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7 import os
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8 import shutil
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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9
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10 import matplotlib.pyplot as plt
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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11 import numpy as np
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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12 from astropy.coordinates import SkyCoord
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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13 from astropy.io import fits
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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14 from astropy.time import Time
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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15 from numpy import log10, sqrt
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16 from oda_api.data_products import ODAAstropyTable, PictureProduct
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17 from oda_api.json import CustomJSONEncoder
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19 if os.path.exists("hess_dl3_dr1.tar.gz") == False:
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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20 get_ipython().system( # noqa: F821
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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21 "wget https://zenodo.org/record/1421099/files/hess_dl3_dr1.tar.gz"
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22 )
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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23 get_ipython().system("tar -zxvf hess_dl3_dr1.tar.gz") # noqa: F821
1
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24 from oda_api.api import ProgressReporter
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26 pr = ProgressReporter()
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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27 pr.report_progress(stage="Progress", progress=0.0)
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29 # src_name='Crab' #http://odahub.io/ontology#AstrophysicalObject
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30 # RA = 83.628700 # http://odahub.io/ontology#PointOfInterestRA
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31 # DEC = 22.014700 # http://odahub.io/ontology#PointOfInterestDEC
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32 src_name = "PKS 2155-304" # http://odahub.io/ontology#AstrophysicalObject
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33 RA = 329.716938 # http://odahub.io/ontology#PointOfInterestRA
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34 DEC = -30.225588 # http://odahub.io/ontology#PointOfInterestDEC
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36 T1 = "2000-10-09T13:16:00.0" # http://odahub.io/ontology#StartTime
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37 T2 = "2022-10-10T13:16:00.0" # http://odahub.io/ontology#EndTime
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38 Radius = 2.5 # http://odahub.io/ontology#AngleDegrees
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39 R_s = 0.2 # http://odahub.io/ontology#AngleDegrees
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40
1
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41 Emin = 0.1 # http://odahub.io/ontology#Energy_TeV
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42 Emax = 100.0 # http://odahub.io/ontology#Energy_TeV
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43 NEbins = 30 # http://odahub.io/ontology#Integer
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44
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45 Efit_min = 0.2 # http://odahub.io/ontology#Energy_TeV
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46 Efit_max = 10.0 # http://odahub.io/ontology#Energy_TeV
0
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47
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48 _galaxy_wd = os.getcwd()
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49
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50 with open("inputs.json", "r") as fd:
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51 inp_dic = json.load(fd)
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52 if "_data_product" in inp_dic.keys():
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53 inp_pdic = inp_dic["_data_product"]
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54 else:
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55 inp_pdic = inp_dic
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56
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57 for vn, vv in inp_pdic.items():
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58 if vn != "_selector":
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59 globals()[vn] = type(globals()[vn])(vv)
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60
1
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61 E0 = 1.0
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62
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63 def model_dNdE(E, N, Gam):
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64 return N * (E / E0) ** (Gam)
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65
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66 def model_rate(E1, E2, N, Gam):
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67 dEE = E2 - E1
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68 EE = sqrt(E1 * E2)
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69 return model_dNdE(EE, N, Gam) * dEE
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70
0
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71 Ebins = np.logspace(log10(Emin), log10(Emax), NEbins + 1)
1
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72 Emins = Ebins[:-1]
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73 Emaxs = Ebins[1:]
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74 Emeans = sqrt(Emins * Emaxs)
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75 lgEmeans = log10(Emeans)
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76 dE = Ebins[1:] - Ebins[:-1]
0
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77
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78 T1 = Time(T1, format="isot", scale="utc").mjd
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79 T2 = Time(T2, format="isot", scale="utc").mjd
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80 message = ""
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81 RA_pnts = []
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82 DEC_pnts = []
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83 DL3_files = []
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84 OBSIDs = []
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85 Tstart = []
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86 Tstop = []
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87 flist = os.listdir("data")
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88 for f in flist:
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89 if f[-7:] == "fits.gz":
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90 DL3_files.append(f)
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91 OBSIDs.append(int(f[20:26]))
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92 hdul = fits.open("data/" + f)
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93 RA_pnts.append(float(hdul[1].header["RA_PNT"]))
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94 DEC_pnts.append(float(hdul[1].header["DEC_PNT"]))
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95 Tstart.append(
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96 Time(
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97 hdul[1].header["DATE-OBS"] + "T" + hdul[1].header["TIME-OBS"],
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98 format="isot",
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99 scale="utc",
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100 ).mjd
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101 )
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102 Tstop.append(
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103 Time(
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104 hdul[1].header["DATE-END"] + "T" + hdul[1].header["TIME-END"],
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105 format="isot",
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106 scale="utc",
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107 ).mjd
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108 )
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109 hdul.close()
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110
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111 Coords_s = SkyCoord(RA, DEC, unit="degree")
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112 COORDS_pnts = SkyCoord(RA_pnts, DEC_pnts, unit="degree")
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113 seps = COORDS_pnts.separation(Coords_s).deg
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114
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115 mask = np.where((seps < Radius) & (Tstart > T1) & (Tstop < T2))[0]
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116 OBSlist = []
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117 for i in mask:
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118 OBSlist.append(DL3_files[i])
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119 if len(OBSlist) == 0:
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120 message = "No data found"
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121 raise RuntimeError("No data found")
1
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122 offaxis = seps[mask]
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123 Tstart = np.array(Tstart)[mask]
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124 print("Found", len(Tstart), "pointings")
0
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125
1
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126 ind = 0
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127 pointing = OBSlist[ind]
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128 hdul = fits.open("data/" + pointing)
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129 RMF = hdul["EDISP"].data
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130 ENERG_LO = RMF["ENERG_LO"][0]
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131 ENERG_HI = RMF["ENERG_HI"][0]
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132 MIGRA_LO = RMF["MIGRA_LO"][0] # MIGRA_bins=np.linspace(0.2,5,161)
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133 MIGRA_HI = RMF["MIGRA_HI"][0]
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134 MIGRA = (MIGRA_LO + MIGRA_HI) / 2.0
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135 ENERG = sqrt(ENERG_LO * ENERG_HI)
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136 dENERG = ENERG_HI - ENERG_LO
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137
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138 cts_s = []
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139 cts_b = []
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140 Eff_area = []
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141 Eff_area_interp = []
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142 Texp = []
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143 RMFs = []
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144 for ind in range(len(OBSlist)):
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145 pointing = OBSlist[ind]
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146 hdul = fits.open("data/" + pointing)
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147
0
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148 RA_pnt = hdul[1].header["RA_PNT"]
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149 DEC_pnt = hdul[1].header["DEC_PNT"]
1
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150 Texp.append(hdul[1].header["LIVETIME"])
0
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151 dRA = RA - RA_pnt
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152 dDEC = DEC - DEC_pnt
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153 RA_b = RA_pnt - dRA
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154 DEC_b = DEC_pnt - dDEC
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155 Coords_b = SkyCoord(RA_b, DEC_b, unit="degree")
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156 Coords_pnt = SkyCoord(RA_pnt, DEC_pnt, unit="degree")
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157 dist = Coords_pnt.separation(Coords_s).deg
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158
1
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159 RMF = hdul["EDISP"].data
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160 mask = RMF["THETA_LO"] < dist
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161 ind_th = len(RMF["THETA_LO"][mask]) - 1
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162 RMF_th = RMF["MATRIX"][0][ind_th]
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163 RMF_interp = np.zeros((len(Emeans), len(ENERG)))
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164 for k in range(len(ENERG)):
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165 dp_dErec = RMF_th[:, k] / ENERG[k]
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166 Erec = MIGRA * ENERG[k]
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167 dp_dErec_interp = (
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168 np.interp(Emeans, Erec, dp_dErec)
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169 * (Emeans > min(Erec))
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170 * (Emeans < max(Erec))
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171 )
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172 RMF_interp[:, k] = dp_dErec_interp
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173 RMFs.append(RMF_interp)
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174
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175 AEFF = hdul["AEFF"].data
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176 Eff_area.append(AEFF["EFFAREA"][0][ind_th])
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177 Eff_area_interp.append(np.interp(Emeans, ENERG, Eff_area[-1]))
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178
0
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179 ev = hdul["EVENTS"].data
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180 ev_ra = ev["RA"]
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181 ev_dec = ev["DEC"]
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182 ev_en = ev["ENERGY"]
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183 ev_time = ev["TIME"]
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184 ev_coords = SkyCoord(ev_ra, ev_dec, unit="degree")
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185 sep_s = ev_coords.separation(Coords_s).deg
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186 sep_b = ev_coords.separation(Coords_b).deg
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187
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188 mask = sep_s < R_s
1
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189 cts_s.append(np.histogram(ev_en[mask], bins=Ebins)[0])
0
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190 mask = sep_b < R_s
1
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191 cts_b.append(np.histogram(ev_en[mask], bins=Ebins)[0])
0
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192 hdul.close()
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193
1
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194 cts_s = np.array(cts_s)
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195 cts_b = np.array(cts_b)
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196 Eff_area = np.array(Eff_area)
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197 Eff_area_interp = np.array(Eff_area_interp)
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198 Texp = np.array(Texp)
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199
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200 cts_s_tot = sum(cts_s)
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201 cts_b_tot = sum(cts_b)
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202 src_tot = cts_s_tot - cts_b_tot
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203 src_tot_err = sqrt(cts_s_tot + cts_b_tot)
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204 Expos_tot_interp = sum(Eff_area_interp * np.outer(Texp, np.ones(NEbins))) * 1e4
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205 flux_tot = src_tot / (Expos_tot_interp + 1) / (Emaxs - Emins) * Emaxs * Emins
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206 flux_tot_err = (
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207 src_tot_err / (Expos_tot_interp + 1) / (Emaxs - Emins) * Emaxs * Emins
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208 )
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209 print(
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210 "Total source counts:",
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211 sum(cts_s_tot),
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212 "; background counts",
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213 sum(cts_b_tot),
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214 )
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215
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216 def model_cts_Erec(N, Gam):
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217 model_ENERG = model_rate(ENERG_LO, ENERG_HI, N, Gam)
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218 res = np.zeros(NEbins)
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219 for ind in range(len(OBSlist)):
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220 model_counts_ENERG = model_ENERG * Eff_area[ind] * 1e4 * Texp[ind]
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221 for k in range(len(ENERG)):
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222 res += model_counts_ENERG[k] * RMFs[ind][:, k] * dE
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223 return res
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224
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225 def chi2(p):
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226 N, slope = p
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227 counts = model_cts_Erec(N, slope)
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228 m = Emeans > Efit_min
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229 m &= Emeans < Efit_max
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230 m &= src_tot_err > 0.0
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231 chi2 = (((counts[m] - src_tot[m]) / src_tot_err[m]) ** 2).sum()
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232 dof = sum(m)
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233 # print(N,slope,chi2)
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234 return chi2, dof
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235
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236 plt.errorbar(
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237 Emeans,
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238 src_tot,
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239 src_tot_err,
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240 xerr=[Emeans - Emins, Emaxs - Emeans],
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241 linestyle="none",
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242 )
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243
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244 plt.axvline(Efit_min, color="black")
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245 plt.axvline(Efit_max, color="black")
0
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246 plt.xscale("log")
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247 plt.yscale("log")
1
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248 N = 4e-11
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249 Gam = -2.7
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250 plt.plot(Emeans, model_cts_Erec(N, Gam))
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251 chi2([N, Gam])[0]
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252
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253 # 1) find 90% confidence contour scanning over a wide parameter space
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254 Norm_max = 1e-10
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255 Norm_min = 1e-12
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256 Norm_bins = 100
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257 Gam_min = -1.0
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258 Gam_max = -5.0
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259 Gam_bins = 100
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260 Ns = np.linspace(Norm_min, Norm_max, Norm_bins)
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261 Gams = np.linspace(Gam_min, Gam_max, Gam_bins)
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262 chi2_map = np.zeros((Norm_bins, Gam_bins))
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263 Norm_best = Norm_min
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264 Gam_best = Gam_min
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265 chi2_best = 1e10
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266 for i, N in enumerate(Ns):
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267 pr.report_progress(stage="Progress", progress=5 + 45 * i / Norm_bins)
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268 for j, Gam in enumerate(Gams):
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269 chi2_map[i, j] = chi2([N, Gam])[0]
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270 if chi2_map[i, j] < chi2_best:
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271 Norm_best = N
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272 Gam_best = Gam
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273 chi2_best = chi2_map[i, j]
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274 print(Norm_best, Gam_best)
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275 # plt.imshow(chi2_map,vmax=np.amin(chi2_map)+4,origin='lower',extent=[Gams[0],Gams[-1],Ns[0],Ns[-1]],aspect=(Gams[-1]-Gams[0])/(Ns[-1]-Ns[0]))
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276
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277 # 68% contour from https://ui.adsabs.harvard.edu/abs/1976ApJ...208..177L/abstract for two-parameter fit
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278 # 90% contour from https://ui.adsabs.harvard.edu/abs/1976ApJ...208..177L/abstract for two-parameter fit
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279
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280 cnt = plt.contour(
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281 Gams, Ns, chi2_map, levels=[np.amin(chi2_map) + 4.61], colors="red"
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282 )
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283 plt.scatter([Gam_best], [Norm_best], marker="x", color="red")
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284 # plt.colorbar()
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285 print(np.amin(chi2_map))
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286
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287 cont = cnt.get_paths()[0].vertices
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288 gammas = cont[:, 0]
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289 norms = cont[:, 1]
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290
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291 # 2) refine with 68% contour calculation within the initial 90% countour
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292 Norm_max = max(1.1 * norms)
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293 Norm_min = min(0.9 * norms)
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294 Norm_bins = 50
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295 Gam_min = min(gammas - 0.2)
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296 Gam_max = max(gammas + 0.2)
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297 Gam_bins = 50
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298 Ns = np.linspace(Norm_min, Norm_max, Norm_bins)
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299 Gams = np.linspace(Gam_min, Gam_max, Gam_bins)
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300 chi2_map = np.zeros((Norm_bins, Gam_bins))
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301 Norm_best = Norm_min
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302 Gam_best = Gam_min
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303 chi2_best = 1e10
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304 for i, N in enumerate(Ns):
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305 pr.report_progress(stage="Progress", progress=50 + 50 * i / Norm_bins)
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306 for j, Gam in enumerate(Gams):
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307 chi2_map[i, j] = chi2([N, Gam])[0]
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308 if chi2_map[i, j] < chi2_best:
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309 Norm_best = N
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310 Gam_best = Gam
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311 chi2_best = chi2_map[i, j]
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312 print(Norm_best, Gam_best)
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313 # plt.imshow(chi2_map,vmax=np.amin(chi2_map)+4,origin='lower',extent=[Gams[0],Gams[-1],Ns[0],Ns[-1]],aspect=(Gams[-1]-Gams[0])/(Ns[-1]-Ns[0]))
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314
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315 # 68% contour from https://ui.adsabs.harvard.edu/abs/1976ApJ...208..177L/abstract for two-parameter fit
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316 cnt = plt.contour(
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317 Gams, Ns, chi2_map, levels=[np.amin(chi2_map) + 2.3], colors="black"
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318 )
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319 cont = cnt.get_paths()[0].vertices
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320 gammas = cont[:, 0]
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321 norms = cont[:, 1]
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322
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323 plt.scatter([Gam_best], [Norm_best], marker="x", color="black")
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324 # plt.colorbar()
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325 print(chi2([Norm_best, Gam_best]))
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326 plt.xlabel("Slope")
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327 plt.ylabel(r"Norm, 1/(TeV cm$^2$ s)")
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328 plt.savefig("Contour.png", format="png", bbox_inches="tight")
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329
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330 plt.figure(figsize=(8, 6))
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331 x = np.logspace(log10(Efit_min), log10(Efit_max), 10)
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332 ymax = np.zeros(10)
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333 ymin = np.ones(10)
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334 for i in range(len(gammas)):
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335 y = model_dNdE(x, norms[i], gammas[i]) * x**2
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336 ymax = np.maximum(y, ymax)
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337 ymin = np.minimum(y, ymin)
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338 # plt.plot(x,y)
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339 plt.fill_between(x, ymin, ymax, alpha=0.2)
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340 plt.plot(x, model_dNdE(x, Norm_best, Gam_best) * x**2)
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341
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342 plt.errorbar(
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343 Emeans,
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344 flux_tot,
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345 flux_tot_err,
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346 xerr=[Emeans - Emins, Emaxs - Emeans],
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347 linestyle="none",
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348 color="black",
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349 linewidth=2,
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350 )
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351
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352 # plt.axvspan(0, Efit_min, alpha=0.2, color='black')
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353 # plt.axvspan(Efit_max, 1000, alpha=0.2, color='black')
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354
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355 plt.xscale("log")
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356 plt.yscale("log")
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357 plt.xlim(0.1, 100)
0
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358 plt.xlabel("$E$, TeV")
1
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359 plt.ylabel("$E^2 dN/dE$, TeV/(cm$^2$ s)")
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360 plt.savefig("Spectrum.png", format="png", bbox_inches="tight")
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361
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362 print("Here")
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363 new_hdul = fits.HDUList()
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364 form = str(len(ENERG)) + "E"
0
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365
1
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366 for i in range(len(OBSlist)):
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367 col1 = fits.Column(name="COUNTS", format="E", array=cts_s[i])
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368 col2 = fits.Column(name="BACKGROUND", format="E", array=cts_b[i])
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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369 hdu = fits.BinTableHDU.from_columns([col1, col2], name="COUNTS")
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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370 new_hdul.append(hdu)
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371 col = fits.Column(format=form, array=RMFs[i], name="RMF")
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372 hdu = fits.BinTableHDU.from_columns([col], name="RMF")
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373 new_hdul.append(hdu)
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374 col = fits.Column(format="E", array=Eff_area[i], name="ARF")
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375 hdu = fits.BinTableHDU.from_columns([col], name="ARF")
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376 new_hdul.append(hdu)
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377
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378 col1 = fits.Column(name="E_MIN", format="E", unit="TeV", array=Emins)
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379 col2 = fits.Column(name="E_MAX", format="E", unit="TeV", array=Emaxs)
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380 cols = fits.ColDefs([col1, col2])
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381 hdu = fits.BinTableHDU.from_columns(cols, name="Erec_BOUNDS")
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382 new_hdul.append(hdu)
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383 col1 = fits.Column(name="ENERG_LO", format="E", unit="TeV", array=ENERG_LO)
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384 col2 = fits.Column(name="ENERG_HI", format="E", unit="TeV", array=ENERG_HI)
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385 cols = fits.ColDefs([col1, col2])
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386 hdu = fits.BinTableHDU.from_columns(cols, name="Etrue_BOUNDS")
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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387 new_hdul.append(hdu)
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388 # new_hdul.writeto('Spectra.fits',overwrite=True)
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389
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390 print("and here")
0
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391 bin_image = PictureProduct.from_file("Spectrum.png")
1
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392 bin_image1 = PictureProduct.from_file("Contour.png")
0
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393 from astropy.table import Table
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394
1
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395 data = [Emeans, Emins, Emaxs, flux_tot, flux_tot_err]
0
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396 names = (
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397 "Emean[TeV]",
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398 "Emin[TeV]",
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399 "Emax[TeV]",
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400 "Flux[TeV/cm2s]",
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401 "Flux_error[TeV/cm2s]",
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402 )
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403 spec = ODAAstropyTable(Table(data, names=names))
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404
1
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405 data = [
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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406 np.concatenate(([Gam_best], gammas)),
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407 np.concatenate(([Norm_best], norms)),
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408 ]
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409 names = ["Gamma", "Norm_1TeV[1/(TeV cm2 s)]"]
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410 error_ellipse = ODAAstropyTable(Table(data, names=names))
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411
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412 png = bin_image # http://odahub.io/ontology#ODAPictureProduct
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413 table_confidence_contour = (
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414 error_ellipse # http://odahub.io/ontology#ODAAstropyTable
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415 )
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416 table_spectrum = spec # http://odahub.io/ontology#ODAAstropyTable
0
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417
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418 # output gathering
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419 _galaxy_meta_data = {}
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420 _oda_outs = []
1
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421 _oda_outs.append(("out_Spectrum_png", "png_galaxy.output", png))
0
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422 _oda_outs.append(
1
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423 (
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424 "out_Spectrum_table_confidence_contour",
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425 "table_confidence_contour_galaxy.output",
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426 table_confidence_contour,
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427 )
0
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428 )
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429 _oda_outs.append(
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430 (
1
593c4b45eda5 planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2f23ec010eab8d33d2760f061286756e17015af7
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431 "out_Spectrum_table_spectrum",
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432 "table_spectrum_galaxy.output",
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433 table_spectrum,
0
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434 )
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435 )
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436
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437 for _outn, _outfn, _outv in _oda_outs:
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438 _galaxy_outfile_name = os.path.join(_galaxy_wd, _outfn)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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439 if isinstance(_outv, str) and os.path.isfile(_outv):
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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440 shutil.move(_outv, _galaxy_outfile_name)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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441 _galaxy_meta_data[_outn] = {"ext": "_sniff_"}
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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442 elif getattr(_outv, "write_fits_file", None):
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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443 _outv.write_fits_file(_galaxy_outfile_name)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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444 _galaxy_meta_data[_outn] = {"ext": "fits"}
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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445 elif getattr(_outv, "write_file", None):
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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446 _outv.write_file(_galaxy_outfile_name)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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447 _galaxy_meta_data[_outn] = {"ext": "_sniff_"}
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
astroteam
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448 else:
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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449 with open(_galaxy_outfile_name, "w") as fd:
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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450 json.dump(_outv, fd, cls=CustomJSONEncoder)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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451 _galaxy_meta_data[_outn] = {"ext": "json"}
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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parents:
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452
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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453 with open(os.path.join(_galaxy_wd, "galaxy.json"), "w") as fd:
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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454 json.dump(_galaxy_meta_data, fd)
02e4bb4fa10c planemo upload for repository https://github.com/esg-epfl-apc/tools-astro/tree/main/tools commit 2991f65b25d4e6d1b69458603fce917adff40f94
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455 print("*** Job finished successfully ***")