More complex examples
of using prodimopy slab libraries
Author: Aditya M Arabhavi (arabhavi@astro.rug.nl) Last Updated: July 24, 2023 Read the docs: prodimopy
Table of contents
2. Slicing, indexing and retrieving data from the read models
-
8. 1D Models with ProDiMo and prodimopy
0. Importing libraries and downloading example output
Import prodimopy slab library
[1]:
import prodimopy.run_slab as runs # required to run slab models on python, can be skipped if you run
# models on ProDiMo FORTRAN package
import prodimopy.read_slab as rs # required to read the slab output files
import prodimopy.plot_slab as ps # required to produce plots from the slab data read
import prodimopy.hitran as ht # useful to digest hitran data if needed for visualization (not required
# for reading and plotting prodimo slab models)
import matplotlib.pyplot as plt
OMP: Warning #182: OMP_STACKSIZE: ignored because GOMP_STACKSIZE has been defined
Download example output (Needs to be done only once) Contains slab model output with three temperatures 200K, 500K, 900K for two molecules C2H2 and HCN
[2]:
%%bash
wget https://raw.githubusercontent.com/adityamarabhavi/prodimopy/main/example/output_files/SlabResults.out
--2025-02-06 12:04:43-- https://raw.githubusercontent.com/adityamarabhavi/prodimopy/main/example/output_files/SlabResults.out
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 14183573 (14M) [text/plain]
Saving to: ‘SlabResults.out.2’
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13850K . 100% 2185G=0.2s
2025-02-06 12:04:43 (80.8 MB/s) - ‘SlabResults.out.2’ saved [14183573/14183573]
1. Reading slab models
NOTE: The below describes reading and plotting of slab models that calculate the line fluxes line-by-line (default). This does not account for opacity overlap between the lines, which becomes important where the lines are close together and/or the line opacity is large. At the end of this notebook, you can find description of methods of reading and running slab models that account for opacity overlap, in which case the model output only contains the spectra and no line fluxes. 1D models are also included at the end. Read routine to read all the models in single output file Note that slab models can be set up to produce one file per model. See ProDiMo slab wiki.
Syntax for single output file: read_slab(‘path/to/file’,verbose=True) verbose argument is True by default Syntax for multiple output files: read_slab(List_of_path_strings,verbose=True)Hint: glob library can be used to make a list of all available output files, check documentation
[3]:
data = rs.read_slab()
Reading slab model output from: SlabResults.out
Printing the models provides a simple overview of the contents. show() method provides a detailed overview of the contents of the data read
[4]:
print(data)
Info:
NModels: 6
Directory: SlabResults.out
[5]:
data.show()
Info:
NModels: 6
Directory: SlabResults.out
Info Model:
species_number= 1
model_number = 1
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 200.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 144496.0
nlevels = 13228
nlines = 6614
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.500000 1.0 1.000000 0.0 4.328270e-12
1 39052100.0 ... 0.5 0.500000 1.0 1.000000 0.0 1.231300e-11
2 17318400.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.943780e-11
3 39058700.0 ... 0.5 0.500000 1.0 1.000000 0.0 9.307870e-12
4 17327500.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.186830e-11
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.499716 1.0 0.999944 0.0 3.853570e-11
6610 1681760.0 ... 0.5 0.499999 1.0 1.000000 0.0 4.168440e-14
6611 1368980.0 ... 0.5 0.499987 1.0 0.999998 0.0 1.266870e-12
6612 1992760.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.261780e-14
6613 1264110.0 ... 0.5 0.499999 1.0 1.000000 0.0 6.814520e-14
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 1.009210e-20 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 3.000390e-21 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 8.980130e-21 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 2.721410e-21 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 4.734020e-21 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 1
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 200.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 144332.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.500000 1.0 1.000000 0.0 2.569190e-19
1 48870.0 ... 0.5 0.500000 1.0 1.000000 0.0 8.419100e-19
2 56423.6 ... 0.5 0.500000 1.0 1.000000 0.0 2.703290e-18
3 65150.5 ... 0.5 0.500000 1.0 1.000000 0.0 8.495400e-18
4 75252.3 ... 0.5 0.500000 1.0 1.000000 0.0 2.613550e-17
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.353368 1.0 0.892607 0.0 5.308110e-08
2420 2666820.0 ... 0.5 0.498730 1.0 0.999699 0.0 2.078870e-10
2421 2655050.0 ... 0.5 0.499999 1.0 1.000000 0.0 9.430030e-14
2422 2673930.0 ... 0.5 0.499999 1.0 1.000000 0.0 1.061540e-13
2423 2654600.0 ... 0.5 0.499999 1.0 1.000000 0.0 9.365050e-14
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 1.581780e-24 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 1.475990e-24 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 1.475990e-24 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 6.272920e-25 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 5.827240e-25 1.0 1.0 1.0
[4848 rows x 8 columns]
Info Model:
species_number= 1
model_number = 2
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 500.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 150990.0
nlevels = 13228
nlines = 6614
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.499906 1.0 0.999983 0.0 0.000071
1 39052100.0 ... 0.5 0.499611 1.0 0.999920 0.0 0.000343
2 17318400.0 ... 0.5 0.499660 1.0 0.999931 0.0 0.000295
3 39058700.0 ... 0.5 0.499808 1.0 0.999963 0.0 0.000158
4 17327500.0 ... 0.5 0.499833 1.0 0.999969 0.0 0.000135
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.492649 1.0 0.997707 0.0 0.002887
6610 1681760.0 ... 0.5 0.499577 1.0 0.999913 0.0 0.000111
6611 1368980.0 ... 0.5 0.498622 1.0 0.999670 0.0 0.000418
6612 1992760.0 ... 0.5 0.499628 1.0 0.999924 0.0 0.000096
6613 1264110.0 ... 0.5 0.499567 1.0 0.999910 0.0 0.000114
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 2.355520e-09 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 7.403340e-10 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 2.218930e-09 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 7.119910e-10 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 1.762510e-09 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 2
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 500.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 150597.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.500000 1.0 1.000000 0.0 4.166920e-09
1 48870.0 ... 0.5 0.500000 1.0 1.000000 0.0 7.349970e-09
2 56423.6 ... 0.5 0.500000 1.0 1.000000 0.0 1.286150e-08
3 65150.5 ... 0.5 0.500000 1.0 1.000000 0.0 2.230220e-08
4 75252.3 ... 0.5 0.500000 1.0 1.000000 0.0 3.833080e-08
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.423582 1.0 0.957125 0.0 4.748030e-02
2420 2666820.0 ... 0.5 0.483367 1.0 0.993888 0.0 7.601090e-03
2421 2655050.0 ... 0.5 0.498488 1.0 0.999633 0.0 4.639270e-04
2422 2673930.0 ... 0.5 0.498418 1.0 0.999613 0.0 4.884880e-04
2423 2654600.0 ... 0.5 0.498491 1.0 0.999634 0.0 4.626630e-04
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 8.843560e-11 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 8.602060e-11 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 8.602060e-11 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 6.190810e-11 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 6.010970e-11 1.0 1.0 1.0
[4848 rows x 8 columns]
Info Model:
species_number= 1
model_number = 3
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 900.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 159236.0
nlevels = 13228
nlines = 6614
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.499048 1.0 0.999783 0.0 0.002829
1 39052100.0 ... 0.5 0.495719 1.0 0.998784 0.0 0.015871
2 17318400.0 ... 0.5 0.497106 1.0 0.999228 0.0 0.010118
3 39058700.0 ... 0.5 0.498088 1.0 0.999520 0.0 0.006338
4 17327500.0 ... 0.5 0.498721 1.0 0.999696 0.0 0.004020
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.493722 1.0 0.998097 0.0 0.180507
6610 1681760.0 ... 0.5 0.499083 1.0 0.999792 0.0 0.019865
6611 1368980.0 ... 0.5 0.498282 1.0 0.999575 0.0 0.040455
6612 1992760.0 ... 0.5 0.499086 1.0 0.999792 0.0 0.019791
6613 1264110.0 ... 0.5 0.499170 1.0 0.999814 0.0 0.017751
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 1.590700e-06 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 5.082560e-07 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 1.523980e-06 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 4.973520e-07 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 1.366840e-06 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 3
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 900.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 158566.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.499998 1.0 1.000000 0.0 0.000002
1 48870.0 ... 0.5 0.499997 1.0 1.000000 0.0 0.000003
2 56423.6 ... 0.5 0.499996 1.0 1.000000 0.0 0.000004
3 65150.5 ... 0.5 0.499995 1.0 0.999999 0.0 0.000006
4 75252.3 ... 0.5 0.499993 1.0 0.999999 0.0 0.000009
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.466714 1.0 0.985529 0.0 1.314090
2420 2666820.0 ... 0.5 0.482335 1.0 0.993422 0.0 0.612505
2421 2655050.0 ... 0.5 0.494369 1.0 0.998325 0.0 0.158292
2422 2673930.0 ... 0.5 0.494214 1.0 0.998271 0.0 0.163387
2423 2654600.0 ... 0.5 0.494376 1.0 0.998328 0.0 0.158057
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 4.822100e-07 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 4.748490e-07 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 4.748490e-07 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 3.994670e-07 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 3.929780e-07 1.0 1.0 1.0
[4848 rows x 8 columns]
2. Slicing, indexing and retrieving data from the read models
The slab models read are store in a class ‘slab_data’. This class object is very flexible to retrieve data and to select models by slicing or indexing. The simplest way of selecting would be by a single index number or by slicing as shown below. The above output file read has 24 models, i.e., 3 species (C2H2, CO2, HCN) across 8 temperatures from 100K to 800K.
[6]:
data[5] # single index number selection, this retrieves the 6th model
data[2:5:2]; # slicing selection, this retrieves every alternate model starting from the third model upto 6th model
The next way would be, which is more useful, by defining the parameters to slice. The syntax is: slab_data[‘parameter’:single_value] or slab_data[‘parameter’:lower_limit:upper_limit] or slab_data[‘parameter’:[list of allowed values]]
The following table summarises the parameters available:
Parameter | prodimopy keyword | values |
|---|---|---|
Species | species_name | String of species name or list ‘C2H2_H’ or [‘C2H2_H’,’CO2_H’,’HCN_H’] |
Species | species_number | Index/number, range or list 2 or 4:14:2 or [5,24,6,3] |
Upper model number | model_number | Index/number, range or list 2 or 4:14:2 or [5,24,6,3] |
Total gas column density | NH | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Collision partners abundance | nColl | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Electron abundance | ne | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Helium abundance | nHe | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
HII abundance | nHII | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
HI abundance | nHI | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
H2 abundance | nH2 | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Dust to gas ratio | dust_to_gas | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Turbulent broadening | vturb | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Gas temperature | Tg | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Dust temperature | Td | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
ProDiMo species index | species_index | Index/number, range or list 2 or 4:14:2 or [5,24,6,3] |
Species abundance | abundance | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Total broadening | dv | Single value, range or list 1e21 or 1e16:1e30 or [1e16,1.5e16,3e17] |
Number of energy levels | nlevels | Index/number, range or list 2 or 4:14:2 or [5,24,6,3] |
Number of lines | nlines | Index/number, range or list 2 or 4:14:2 or [5,24,6,3] |
[7]:
data['Tg':500] # selecting all slabs that have gas temperature of 500K
data['species_name':['C2H2_H','HCN_H']] # selecting all slabs for C2H2_H and HCN_H
data['NH':1e16:1e20]; # selecting all slabs with total gas column density between 1e16 and 1e20
With the same prodimopy keywords, the data from the slab models can be obtained.
[8]:
print(data[0].Tg) # gas temperature of the first model
print(data[2:4].species_name) # species name of 13th model
data[1].linedata # line data containing line frequencies, fluxes, einstein A coefficients, etc.
200.0
['C2H2_H', 'HCN_H']
[8]:
| i | u | l | e | v | J | gu | Eu | A | B | ... | bNLTE | bLTE | pNLTE | pLTE | FNLTE | FLTE | global_u | global_l | local_u | local_l | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.0 | 4651.0 | 4486.0 | 1.0 | 1.0 | 1.0 | 594.0 | 8230.99 | 0.002285 | 42377.8 | ... | 0.5 | 0.500000 | 1.0 | 1.000000 | 0.0 | 2.569190e-19 | 0 3 1 0 | 0 2 2 0 | P 50e | |
| 1 | 2.0 | 4609.0 | 4402.0 | 1.0 | 1.0 | 1.0 | 582.0 | 8024.52 | 0.002700 | 48870.0 | ... | 0.5 | 0.500000 | 1.0 | 1.000000 | 0.0 | 8.419100e-19 | 0 3 1 0 | 0 2 2 0 | P 49e | |
| 2 | 3.0 | 4563.0 | 4309.0 | 1.0 | 1.0 | 1.0 | 570.0 | 7822.19 | 0.003193 | 56423.6 | ... | 0.5 | 0.500000 | 1.0 | 1.000000 | 0.0 | 2.703290e-18 | 0 3 1 0 | 0 2 2 0 | P 48e | |
| 3 | 4.0 | 4515.0 | 4199.0 | 1.0 | 1.0 | 1.0 | 558.0 | 7623.98 | 0.003775 | 65150.5 | ... | 0.5 | 0.500000 | 1.0 | 1.000000 | 0.0 | 8.495400e-18 | 0 3 1 0 | 0 2 2 0 | P 47e | |
| 4 | 5.0 | 4444.0 | 4096.0 | 1.0 | 1.0 | 1.0 | 546.0 | 7429.92 | 0.004463 | 75252.3 | ... | 0.5 | 0.500000 | 1.0 | 1.000000 | 0.0 | 2.613550e-17 | 0 3 1 0 | 0 2 2 0 | P 46e | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2419 | 2420.0 | 3177.0 | 70.0 | 1.0 | 1.0 | 1.0 | 90.0 | 4882.70 | 38.450000 | 2617200.0 | ... | 0.5 | 0.353368 | 1.0 | 0.892607 | 0.0 | 5.308110e-08 | 1 0 0 0 | 0 0 0 0 | R 6 | |
| 2420 | 2421.0 | 3884.0 | 557.0 | 1.0 | 1.0 | 1.0 | 174.0 | 6206.64 | 39.180000 | 2666820.0 | ... | 0.5 | 0.498730 | 1.0 | 0.999699 | 0.0 | 2.078870e-10 | 1 1 1 0 | 0 1 1 0 | R 13f | |
| 2421 | 2422.0 | 4573.0 | 1708.0 | 1.0 | 1.0 | 1.0 | 270.0 | 7833.80 | 39.050000 | 2655050.0 | ... | 0.5 | 0.499999 | 1.0 | 1.000000 | 0.0 | 9.430030e-14 | 1 2 2 0 | 0 2 2 0 | R 21f | |
| 2422 | 2423.0 | 4561.0 | 1688.0 | 1.0 | 1.0 | 1.0 | 270.0 | 7811.56 | 39.330000 | 2673930.0 | ... | 0.5 | 0.499999 | 1.0 | 1.000000 | 0.0 | 1.061540e-13 | 1 2 0 0 | 0 2 0 0 | R 21 | |
| 2423 | 2424.0 | 4576.0 | 1711.0 | 1.0 | 1.0 | 1.0 | 270.0 | 7835.20 | 39.050000 | 2654600.0 | ... | 0.5 | 0.499999 | 1.0 | 1.000000 | 0.0 | 9.365050e-14 | 1 2 2 0 | 0 2 2 0 | R 21e |
2424 rows × 27 columns
The parameters of all the models can be obtained by simply using the parameter over the slab_data class object
[9]:
data.dust_to_gas # returns list of all dust to gas ratios of all the models
data.Tg # returns list of all gas temperatures of all the models
data['species_name':'C2H2_H'].Tg # returns list of all gas temperatures of all the models whose species is C2H2_H
[9]:
[200.0, 500.0, 900.0]
The individual models (of class slab) are stores in the .models list of the slab_data class. So one can directly access individual models by slab_data.models[index or slice]
[10]:
data.models[0]
[10]:
<prodimopy.read_slab.slab at 0x154bdeff96d0>
Individual models can be added to the slab_data object by using .add_model(slab object) method. This way models of different slab model runs can be combined into a single slab_data object.
[11]:
new_slab = data.models[0] # creating a duplicate slab class object from existing slab model
data.add_model(new_slab) # adding the new slab object to existing slab_data object
data.nmodels
[11]:
7
Similar to adding slab models they can also be removed using .remove_model(index) method.
[12]:
data.remove_model(-1) # removing the last slab object from the existing slab_data object
data.nmodels
[12]:
6
3. Convolving the slab models at a spectral resolution
The models can be convolved individually or as a group. This can be done using .convolve() method. The possible arguments are:
Argument | Datatype | Description | Default value |
|---|---|---|---|
R | int or float | Spectral resolving power | 1 |
lambda_0 | float | Lower wavelength bound | minimum wavelength*1e-0.05 |
lambda_n | float | Upper wavelength bound | maximum wavelength*1e0.05 |
NLTE | logical | Convolve NLTE or LTE line fluxes | False |
conv_type | int (1 or 2) | Type of convolution to be applied | 1 |
freq_bins | array of float | Frequency bins for convolution type 2 (optional) | None |
vr | float | Width in cm/s | 1300 |
Convolving the first slab :
[13]:
data[0].convolve(R=2000,lambda_0=4,lambda_n=30)
1 ; Model number 1 ; Species number 1
[14]:
data.show()
Info:
NModels: 6
Directory: SlabResults.out
Info Model:
species_number= 1
model_number = 1
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 200.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 144496.0
nlevels = 13228
nlines = 6614
convType = 1
convR = 2000
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.500000 1.0 1.000000 0.0 4.328270e-12
1 39052100.0 ... 0.5 0.500000 1.0 1.000000 0.0 1.231300e-11
2 17318400.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.943780e-11
3 39058700.0 ... 0.5 0.500000 1.0 1.000000 0.0 9.307870e-12
4 17327500.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.186830e-11
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.499716 1.0 0.999944 0.0 3.853570e-11
6610 1681760.0 ... 0.5 0.499999 1.0 1.000000 0.0 4.168440e-14
6611 1368980.0 ... 0.5 0.499987 1.0 0.999998 0.0 1.266870e-12
6612 1992760.0 ... 0.5 0.500000 1.0 1.000000 0.0 2.261780e-14
6613 1264110.0 ... 0.5 0.499999 1.0 1.000000 0.0 6.814520e-14
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 4.064360e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 1.009210e-20 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 3.000390e-21 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 8.980130e-21 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 2.721410e-21 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 4.734020e-21 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 1
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 200.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 144332.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.500000 1.0 1.000000 0.0 2.569190e-19
1 48870.0 ... 0.5 0.500000 1.0 1.000000 0.0 8.419100e-19
2 56423.6 ... 0.5 0.500000 1.0 1.000000 0.0 2.703290e-18
3 65150.5 ... 0.5 0.500000 1.0 1.000000 0.0 8.495400e-18
4 75252.3 ... 0.5 0.500000 1.0 1.000000 0.0 2.613550e-17
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.353368 1.0 0.892607 0.0 5.308110e-08
2420 2666820.0 ... 0.5 0.498730 1.0 0.999699 0.0 2.078870e-10
2421 2655050.0 ... 0.5 0.499999 1.0 1.000000 0.0 9.430030e-14
2422 2673930.0 ... 0.5 0.499999 1.0 1.000000 0.0 1.061540e-13
2423 2654600.0 ... 0.5 0.499999 1.0 1.000000 0.0 9.365050e-14
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 1.044450e-02 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 1.581780e-24 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 1.475990e-24 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 1.475990e-24 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 6.272920e-25 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 5.827240e-25 1.0 1.0 1.0
[4848 rows x 8 columns]
Info Model:
species_number= 1
model_number = 2
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 500.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 150990.0
nlevels = 13228
nlines = 6614
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.499906 1.0 0.999983 0.0 0.000071
1 39052100.0 ... 0.5 0.499611 1.0 0.999920 0.0 0.000343
2 17318400.0 ... 0.5 0.499660 1.0 0.999931 0.0 0.000295
3 39058700.0 ... 0.5 0.499808 1.0 0.999963 0.0 0.000158
4 17327500.0 ... 0.5 0.499833 1.0 0.999969 0.0 0.000135
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.492649 1.0 0.997707 0.0 0.002887
6610 1681760.0 ... 0.5 0.499577 1.0 0.999913 0.0 0.000111
6611 1368980.0 ... 0.5 0.498622 1.0 0.999670 0.0 0.000418
6612 1992760.0 ... 0.5 0.499628 1.0 0.999924 0.0 0.000096
6613 1264110.0 ... 0.5 0.499567 1.0 0.999910 0.0 0.000114
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 8.845640e-04 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 2.355520e-09 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 7.403340e-10 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 2.218930e-09 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 7.119910e-10 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 1.762510e-09 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 2
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 500.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 150597.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.500000 1.0 1.000000 0.0 4.166920e-09
1 48870.0 ... 0.5 0.500000 1.0 1.000000 0.0 7.349970e-09
2 56423.6 ... 0.5 0.500000 1.0 1.000000 0.0 1.286150e-08
3 65150.5 ... 0.5 0.500000 1.0 1.000000 0.0 2.230220e-08
4 75252.3 ... 0.5 0.500000 1.0 1.000000 0.0 3.833080e-08
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.423582 1.0 0.957125 0.0 4.748030e-02
2420 2666820.0 ... 0.5 0.483367 1.0 0.993888 0.0 7.601090e-03
2421 2655050.0 ... 0.5 0.498488 1.0 0.999633 0.0 4.639270e-04
2422 2673930.0 ... 0.5 0.498418 1.0 0.999613 0.0 4.884880e-04
2423 2654600.0 ... 0.5 0.498491 1.0 0.999634 0.0 4.626630e-04
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 3.198080e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 8.843560e-11 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 8.602060e-11 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 8.602060e-11 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 6.190810e-11 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 6.010970e-11 1.0 1.0 1.0
[4848 rows x 8 columns]
Info Model:
species_number= 1
model_number = 3
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 900.0
Td = 10.0
species_name = C2H2_H
abundance = 0.001
dv = 159236.0
nlevels = 13228
nlines = 6614
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 11611.0 10859.0 1.0 1.0 1.0 99.0 6047.00 1.521
1 2.0 11724.0 11011.0 1.0 1.0 1.0 297.0 6222.63 3.461
2 3.0 11474.0 10676.0 1.0 1.0 1.0 291.0 5882.32 1.539
3 4.0 11614.0 10879.0 1.0 1.0 1.0 97.0 6057.84 3.501
4 5.0 11288.0 10429.0 1.0 1.0 1.0 95.0 5720.94 1.557
... ... ... ... ... ... ... ... ... ...
6609 6610.0 11932.0 3317.0 1.0 1.0 1.0 129.0 6555.04 23.920
6610 6611.0 12948.0 6412.0 1.0 1.0 1.0 51.0 7742.07 24.740
6611 6612.0 12485.0 4935.0 1.0 1.0 1.0 59.0 7047.24 20.140
6612 6613.0 13011.0 6763.0 1.0 1.0 1.0 51.0 7898.33 29.320
6613 6614.0 12874.0 6054.0 1.0 1.0 1.0 51.0 7586.73 18.600
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 17307800.0 ... 0.5 0.499048 1.0 0.999783 0.0 0.002829
1 39052100.0 ... 0.5 0.495719 1.0 0.998784 0.0 0.015871
2 17318400.0 ... 0.5 0.497106 1.0 0.999228 0.0 0.010118
3 39058700.0 ... 0.5 0.498088 1.0 0.999520 0.0 0.006338
4 17327500.0 ... 0.5 0.498721 1.0 0.999696 0.0 0.004020
... ... ... ... ... ... ... ... ...
6609 1626050.0 ... 0.5 0.493722 1.0 0.998097 0.0 0.180507
6610 1681760.0 ... 0.5 0.499083 1.0 0.999792 0.0 0.019865
6611 1368980.0 ... 0.5 0.498282 1.0 0.999575 0.0 0.040455
6612 1992760.0 ... 0.5 0.499086 1.0 0.999792 0.0 0.019791
6613 1264110.0 ... 0.5 0.499170 1.0 0.999814 0.0 0.017751
global_u global_l local_u local_l
0 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 50e
1 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 50e
2 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 49e
3 0 0 0 0 2 0+ g 0 0 0 0 1 1 u P 49e
4 0 0 0 1 1 0+ u 0 0 0 1 0 1 g P 48e
... ... ... ... ...
6609 0 0 1 0 1 1 g 0 0 0 0 1 1 u R 20e
6610 0 0 1 1 1 0- g 0 0 0 1 1 0- u R 24f
6611 0 1 0 2 1 1 2u 0 0 0 1 0 1 g R 28e
6612 0 0 1 0 2 0+ u 0 0 0 0 2 0+ g R 24e
6613 0 0 1 2 0 0+ u 0 0 0 2 0 0+ g R 24e
[6614 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
1 2.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
2 3.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
3 4.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
4 5.0 1.0 0.0 0.0 1.628080e-04 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
13223 13224.0 279.0 9233.6 0.0 1.590700e-06 1.0 1.0 1.0
13224 13225.0 91.0 9252.2 0.0 5.082560e-07 1.0 1.0 1.0
13225 13226.0 273.0 9252.6 0.0 1.523980e-06 1.0 1.0 1.0
13226 13227.0 91.0 9271.7 0.0 4.973520e-07 1.0 1.0 1.0
13227 13228.0 285.0 9389.3 0.0 1.366840e-06 1.0 1.0 1.0
[13228 rows x 8 columns]
Info Model:
species_number= 2
model_number = 3
NH = 1e+19
nColl = 2100000000000.0
ne = 100000000.0
nHe = 100000000000.0
nHII = 10.0
nHI = 1000000000000.0
nH2 = 1000000000000.0
dust_to_gas = 1e-21
vturb = 1.4
Tg = 900.0
Td = 10.0
species_name = HCN_H
abundance = 0.001
dv = 158566.0
nlevels = 4848
nlines = 2424
convType = None
convR = None
lineData =
i u l e v J gu Eu A \
0 1.0 4651.0 4486.0 1.0 1.0 1.0 594.0 8230.99 0.002285
1 2.0 4609.0 4402.0 1.0 1.0 1.0 582.0 8024.52 0.002700
2 3.0 4563.0 4309.0 1.0 1.0 1.0 570.0 7822.19 0.003193
3 4.0 4515.0 4199.0 1.0 1.0 1.0 558.0 7623.98 0.003775
4 5.0 4444.0 4096.0 1.0 1.0 1.0 546.0 7429.92 0.004463
... ... ... ... ... ... ... ... ... ...
2419 2420.0 3177.0 70.0 1.0 1.0 1.0 90.0 4882.70 38.450000
2420 2421.0 3884.0 557.0 1.0 1.0 1.0 174.0 6206.64 39.180000
2421 2422.0 4573.0 1708.0 1.0 1.0 1.0 270.0 7833.80 39.050000
2422 2423.0 4561.0 1688.0 1.0 1.0 1.0 270.0 7811.56 39.330000
2423 2424.0 4576.0 1711.0 1.0 1.0 1.0 270.0 7835.20 39.050000
B ... bNLTE bLTE pNLTE pLTE FNLTE FLTE \
0 42377.8 ... 0.5 0.499998 1.0 1.000000 0.0 0.000002
1 48870.0 ... 0.5 0.499997 1.0 1.000000 0.0 0.000003
2 56423.6 ... 0.5 0.499996 1.0 1.000000 0.0 0.000004
3 65150.5 ... 0.5 0.499995 1.0 0.999999 0.0 0.000006
4 75252.3 ... 0.5 0.499993 1.0 0.999999 0.0 0.000009
... ... ... ... ... ... ... ... ...
2419 2617200.0 ... 0.5 0.466714 1.0 0.985529 0.0 1.314090
2420 2666820.0 ... 0.5 0.482335 1.0 0.993422 0.0 0.612505
2421 2655050.0 ... 0.5 0.494369 1.0 0.998325 0.0 0.158292
2422 2673930.0 ... 0.5 0.494214 1.0 0.998271 0.0 0.163387
2423 2654600.0 ... 0.5 0.494376 1.0 0.998328 0.0 0.158057
global_u global_l local_u local_l
0 0 3 1 0 0 2 2 0 P 50e
1 0 3 1 0 0 2 2 0 P 49e
2 0 3 1 0 0 2 2 0 P 48e
3 0 3 1 0 0 2 2 0 P 47e
4 0 3 1 0 0 2 2 0 P 46e
... ... ... ... ...
2419 1 0 0 0 0 0 0 0 R 6
2420 1 1 1 0 0 1 1 0 R 13f
2421 1 2 2 0 0 2 2 0 R 21f
2422 1 2 0 0 0 2 0 0 R 21
2423 1 2 2 0 0 2 2 0 R 21e
[2424 rows x 27 columns]
levelData =
i g E pop ltepop e v J
0 1.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
1 2.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
2 3.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
3 4.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
4 5.0 6.0 0.0 0.0 1.037230e-03 1.0 1.0 1.0
... ... ... ... ... ... ... ... ...
4843 4844.0 534.0 10946.1 0.0 4.822100e-07 1.0 1.0 1.0
4844 4845.0 534.0 10959.9 0.0 4.748490e-07 1.0 1.0 1.0
4845 4846.0 534.0 10959.9 0.0 4.748490e-07 1.0 1.0 1.0
4846 4847.0 546.0 11135.5 0.0 3.994670e-07 1.0 1.0 1.0
4847 4848.0 546.0 11150.3 0.0 3.929780e-07 1.0 1.0 1.0
[4848 rows x 8 columns]
.convR or .convType can be used to check if the models are convolved.
[15]:
data['convType':1].nmodels
[15]:
1
[16]:
data[2:5].convolve(R=2000,lambda_0=4,lambda_n=30)
1 ; Model number 2 ; Species number 1
2 ; Model number 2 ; Species number 2
3 ; Model number 3 ; Species number 1
[17]:
data['convType':1].nmodels
[17]:
4
Convolving all the slabs together
[18]:
data.convolve(R=2000,lambda_0=4,lambda_n=30)
1 ; Model number 1 ; Species number 1
2 ; Model number 1 ; Species number 2
3 ; Model number 2 ; Species number 1
4 ; Model number 2 ; Species number 2
5 ; Model number 3 ; Species number 1
6 ; Model number 3 ; Species number 2
[19]:
data['convType':1].nmodels
[19]:
6
4. Plotting options
prodimopy.plot_slab provides several plotting options, such as Boltzmann diagram, energy level diagram, plotting the line fluxes, plotting the convolved slab spectra. Common arguments for all plotting routines: ax can be used to supply the axis in which the plot has to be made, fig the figure object in which the plot has to be made, figsize the figure size tuple (width,height), default:(7,5). The default figure size for all plots can be set before making any plot by plot_slab.set_default_figsize(width,height) funtion. plotBoltzmannDiagram(), plot_lines(), plot_spectra() can make the plots for multiple slab models at a time. For such functions, the colour c and label arguments can take in a list corresponding to each model. They also take in NLTE argument (logical) to make the plots using non LTE fluxes, which is by default set to False.if no fig or ax is passed, it returns new figure and axis objects; if only axis is passed it retrieves the figure object and returns both, if only fig is passed, then it creates a new axis object and returns that along with the figure object
[20]:
ps.set_default_figsize(16,5) # useful for line and spectra plots
4.1. Plotting Boltzmann diagram
plotBoltzmannDiagram(data,ax=None,fig=None,figsize=None,NLTE=False,label=None,s=0.1,c=’k’,set_axis_limits=True)
[21]:
fig,ax = ps.plotBoltzmannDiagram(data[0:3],
c=['r','g','b'],
label=[d.species_name+' '+str(d.Tg) for d in data[0:3]],
figsize=(10,8))
4.2. Plotting energy level diagram
plotLevelDiagram(data,ax=None,figsize=(10,18),seed=None,lambda_0=None,lambda_n=None,width_nlines=False):
This function takes in only 1 slab model at a time. The arrangement of the levels along the x axis is random, hence the function also returns the seed used to generate it (also prints). The seed can be passed as an argument to retrieve the same arrangement of levels again. The thickness of the line is proportional to the total flux that band is contributing. width_nlines can be set to True to make the line thickness proportional to the number of lines in that band. lambda_0 and lambda_n can be used to focus on certain spectral region to make the level diagram. This can be helpful to analyse which bands are contributing to a certain spectral feature.
[22]:
fig,ax,seed = ps.plotLevelDiagram(data[2]) # try this seed 7153029
Random seed = 7872905
4.3. Plotting the line fluxes
plot_lines(dat, normalise = False, fig=None, ax=None, overplot=False, c=None, cmap=None, colors=None, figsize=None, NLTE=False, label=’’, lw=1, scaling=1,offset=0):
Not passing any figure or axis objects will generate one figure and one axis object per slab model. This will be returned in two separate lists. normalise argument can be used to normalise the fluxes with peak flux. scaling argument can be used to scale the fluxes by any factor. Since it is a numpy multiplication, it can support either scalars or arrays of same size as number of lines offset argument can be used to offset the fluxes. Since it is a numpy multiplication, it can support either scalars or arrays of same size as number of lines. This can be useful for adding a background/continuum.
The colors can be specified as a single color c or list of colors with the argument colors or can as well pass the name of a color map (by default it uses ‘jet’ colormap for single figure and black color for separate figures)
NOTE: the Y axis of all the plots containing line fluxes and line spectra are in units [erg/s/cm2/sr] when not scaled or normalised
4.3.1. Plotting the line fluxes in separate figures
This can be done in two ways. First way to simply call the plotting function and pass the slab models. For example: [fig_array,ax_array] = ps.plot_lines(data,label=[d.species_name+’ ‘+str(d.Tg) for d in data]) This returns two arrays containing figures and axis objects respectively. Second way is to generate the figure and axes objects using matplotlib pyplot subplots and passing that to the function. For example: fig,ax = plt.subplots(data.nmodels,1) ps.plot_lines(data,fig=fig,ax=ax,label=[d.species_name+’ ‘+str(d.Tg) for d in data]);
[23]:
fig,ax = plt.subplots(data.nmodels,1,figsize=(16,10))
ps.plot_lines(data,
fig=fig,
ax=ax,
label=[d.species_name+' '+str(d.Tg) for d in data],
colors=['r','g','b','k','m','0.8']);
4.3.2. Plotting the line fluxes in single figure
This can be done in two ways. First way to pass overplot=True argument to the plotting function along with the slab models. For example: fig,ax = ps.plot_lines(data,label=[d.species_name+’ ‘+str(d.Tg) for d in data],overplot=True) This returns the figure and axis objects respectively. Second way is to generate the figure and axes objects using matplotlib pyplot subplots and passing that to the function. For example: fig,ax = plt.subplots(1,1) ps.plot_lines(data,fig=fig,ax=ax,label=[d.species_name+’ ‘+str(d.Tg) for d in data]);
[24]:
fig,ax = ps.plot_lines(data,
label=[d.species_name+' '+str(d.Tg) for d in data],
overplot=True,
cmap='jet_r')
4.4. Plotting the spectra
plot_spectra(dat, normalise = False, fig=None, ax=None, overplot=False, add=False, cmap=None, colors=None, style=’step’, figsize=None, NLTE=False, label=’’, lw=1, c=None, scaling=1,sampling=1,offset=0): To plot the spectra, the slab models have to first be convolved at a spectral resolving power by the user. See Sect. 3.
scaling, offset and normalise can be used to scale, offset and normalise the spectra. Like explained before, scaling and offset can be scalar or numpy arrays of size same as that of the convolved grid.
add=True can be used to add up the individual slab spectra to produce a composite slab spectra. See Sect 4.4.3.
The oversampling can be specified by sampling argument which is 1 by default.
4.4.1. Plotting the spectra in separate figures
This can be done in two ways. First way to simply call the plotting function and pass the slab models. For example: [fig_array,ax_array] = ps.plot_spectra(data,label=[d.species_name+’ ‘+str(d.Tg) for d in data]) This returns two arrays containing figures and axis objects respectively. Second way is to generate the figure and axes objects using matplotlib pyplot subplots and passing that to the function. For example: fig,ax = plt.subplots(data.nmodels,1) ps.plot_spectra(data,fig=fig,ax=ax,label=[d.species_name+’ ‘+str(d.Tg) for d in data]);
[25]:
fig,ax = plt.subplots(data.nmodels,1,figsize=(16,10))
ps.plot_spectra(data,
fig=fig,
ax=ax,
label=[d.species_name+' '+str(d.Tg) for d in data],
colors=['r','g','b','k','m','0.8'],
normalise=True);
4.4.2. Plotting the spectra in single figure
This can be done in two ways. First way to pass overplot=True argument to the plotting function along with the slab models. For example: fig,ax = ps.plot_spectra(data,label=[d.species_name+’ ‘+str(d.Tg) for d in data],overplot=True) This returns the figure and axis objects respectively. Second way is to generate the figure and axes objects using matplotlib pyplot subplots and passing that to the function. For example: fig,ax = plt.subplots(1,1) ps.plot_spectra(data,fig=fig,ax=ax,label=[d.species_name+’ ‘+str(d.Tg) for d in data]);
[26]:
fig,ax = ps.plot_spectra(data['Tg':200],
overplot=True,
label=[d.species_name[:-2]+'@'+str(d.Tg)+'K' for d in data['Tg':200]],
figsize=(16,5))
ax.set_xlim([6,17])
[26]:
(6.0, 17.0)
4.4.3. Plotting the combined spectra of multiple slab models
add=True can be used to add up the individual slab spectra to produce a composite slab spectra.
[27]:
fig,ax = ps.plot_spectra(data['Tg':500],
add=True,
label='Combined spectra at 500K',
figsize=(16,5),
sampling=2)
ax.set_xlim([6,17]);
5. Reading the HITRAN data
Data files from HITRAN database can be read using prodimopy.hitran read_hitran(filePath, moleculeName, isotopologue:list=[1], lowerLam:int=1, higherLam:int=30, sort_label=’lambda’, quanta=False, format:int=2020, verbose:bool=False) Older formats of HITRAN can be read by changing the format argument. The default is 2020.
The quantum numbers of the upper and lower global and local levels can also be decoded using quanta=True argument. However, this may fail for some molecules.
The read data is returned in a Pandas DataFrame
[28]:
file = '/home/rab/git/prodimo/data/HITRAN2020/C2H2.par'
hit_data = ht.read_hitran(file,moleculeName='C2H2')
hit_data
/home/rab/git/prodimopy/prodimopy/hitran.py:503: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['line_mixing']=data['line_mixing'].fillna(False)
[28]:
| Molecule_ID | Isotopologue_ID | lambda | nu | S | A | gamma_air | gamma_self | E_u | E_l | ... | del_air | global_u | global_l | local_u | local_l | err_ind | References | line_mixing | g_u | g_l | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 83436 | 26 | 1 | 1.003637 | 9963.76240 | 9.871000e-27 | 0.000546 | 0.0524 | 0.098 | 10918.12720 | 954.3648 | ... | -0.001 | 111 0 2 0 0+ u | 000 0 0 0 0+ g | NaN | R 28e | 356643 | 2219 2 1 1 1 | False | 59.0 | 57.0 |
| 83435 | 26 | 1 | 1.003766 | 9962.47770 | 3.929000e-26 | 0.000545 | 0.0541 | 0.101 | 10851.09300 | 888.6153 | ... | -0.001 | 111 0 2 0 0+ u | 000 0 0 0 0+ g | NaN | R 27e | 356643 | 2219 2 1 1 1 | False | 171.0 | 165.0 |
| 83434 | 26 | 1 | 1.003901 | 9961.14370 | 1.716000e-26 | 0.000543 | 0.0560 | 0.104 | 10786.34810 | 825.2044 | ... | -0.001 | 111 0 2 0 0+ u | 000 0 0 0 0+ g | NaN | R 26e | 356643 | 2219 2 1 1 1 | False | 55.0 | 53.0 |
| 83433 | 26 | 1 | 1.004040 | 9959.76120 | 6.657000e-26 | 0.000542 | 0.0578 | 0.107 | 10723.89420 | 764.1330 | ... | -0.001 | 111 0 2 0 0+ u | 000 0 0 0 0+ g | NaN | R 25e | 356643 | 2219 2 1 1 1 | False | 159.0 | 153.0 |
| 83432 | 26 | 1 | 1.004077 | 9959.39620 | 8.536000e-27 | 0.000036 | 0.0733 | 0.132 | 10279.32360 | 319.9274 | ... | -0.001 | 130 0 1 0 1 u | 000 0 0 0 0+ g | NaN | R 16e | 356643 | 2219 2 1 1 1 | False | 35.0 | 33.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5523 | 26 | 1 | 25.165840 | 397.36404 | 3.665000e-28 | 0.000278 | 0.0450 | 0.081 | 3254.21434 | 2856.8503 | ... | -0.001 | 000 2 0 2 0 g | 000 0 1 0 1 u | NaN | P 42f | 345543 | 2320 2 1 1 1 | False | 83.0 | 85.0 |
| 5522 | 26 | 1 | 25.325368 | 394.86100 | 6.886000e-28 | 0.000275 | 0.0450 | 0.081 | 3352.75900 | 2957.8980 | ... | -0.001 | 000 2 0 2 0 g | 000 0 1 0 1 u | NaN | P 43f | 345543 | 2320 2 1 1 1 | False | 255.0 | 261.0 |
| 5521 | 26 | 1 | 25.460632 | 392.76322 | 1.511000e-28 | 0.000092 | 0.0450 | 0.081 | 3444.88402 | 3052.1208 | ... | -0.001 | 000 2 0 2 0 g | 000 0 1 0 1 u | NaN | P 44e | 345543 | 2320 2 1 1 1 | False | 261.0 | 267.0 |
| 5520 | 26 | 1 | 25.487112 | 392.35516 | 1.420000e-28 | 0.000272 | 0.0450 | 0.081 | 3453.62536 | 3061.2702 | ... | -0.001 | 000 2 0 2 0 g | 000 0 1 0 1 u | NaN | P 44f | 345543 | 2320 2 1 1 1 | False | 87.0 | 89.0 |
| 5519 | 26 | 1 | 25.651120 | 389.84653 | 2.606000e-28 | 0.000269 | 0.0450 | 0.081 | 3556.81153 | 3166.9650 | ... | -0.001 | 000 2 0 2 0 g | 000 0 1 0 1 u | NaN | P 45f | 345543 | 2320 2 1 1 1 | False | 267.0 | 273.0 |
68326 rows × 21 columns
The level format can be retrieved using getLevelFormat(molecule:str,format:int=2020,level:str=’global’)
[29]:
ht.getLevelFormat('CO2',
format=2020,
level='global')
[29]:
'v1 v2 l2 v3 r '
[30]:
ht.getLevelFormat('CO2',
format=2020,
level='local')
[30]:
'm F '
[31]:
ht.getLevelFormat('CO2',
format=2009,
level='local')
[31]:
'F '
6. Generating SlabInput.in file for a ProDiMo slab run
generate_slab_grid(directory=’.’, grid_parameters={}, combination=False, Ntot=None, Tg=None, nHplus=None, nH1=None, nH2=None, nHe=None, nelec=None, Td=None, vturb=None, dust_to_gas=None, species_list={}, output_filename=’SlabResults.out’, separate_op_files=True)
Argument | Datatype | Description | Default value |
|---|---|---|---|
directory | string | directory where the SlabInput.in file is to be created | ‘.’ |
grid_parameters | dict | Dictionary containing the varying grid parameters. Each parameter should be associated with the list of values corresponding to each slab model. The length of such lists for each grid parameter should be the same | {} |
combination | logical | Under development | False |
Ntot | int or float | The default value of gas column density across models | 1e15 |
Tg | int or float | The default value of gas temperature across models | 100 |
nHplus | int or float | The default value of H+ number density across models | 1e1 |
nH1 | int or float | The default value of atomic hydrogen number density across models | 1e12 |
nH2 | int or float | The default value of molecular hydrogen number density across models | 1e12 |
nHe | int or float | The default value of Helium number density across models | 1e11 |
nelec | int or float | The default value of electron number density across models | 1e8 |
Td | int or float | The default value of dust temperature across models | 10 |
vturb | int or float | The default value of gas turbulent velocity across models | 1.4 |
dust_to_gas | int or float | The default value of dust to gas ratio across models | 1e-21 |
species_list | dict | Dictionary containing molecule names as keys and their abundance as values. Cannot be empty | {} |
output_filename | string | Filename for storing the output of slab models. To include the directory use the following format: f‘“folder/filename”’ | None |
separate_op_files | logical | If true, one file per slab model is generated. The name of each file is output_filename followed by a number with leading zeros. | False |
Note: The column density of the spiecies is given by total gas column denisty times the species abundance (Ntot x species_abundance)
The following example produces the SlabInput.in file used in the example output file in this notebook
[33]:
parameter_space = {'Tg':[200,500,900]}
rs.generate_slab_grid(directory = '.',
grid_parameters = parameter_space,
species_list = {'C2H2_H':0.001,
'HCN_H':0.001},
Ntot=1e19,
separate_op_files=False,)
The following generates a 2x2 grid for gas temperature and total gas column density.
[34]:
parameter_space = {'Tg':[100,200,100,200],'Ntot':[1e16,1e16,1e20,1e20]}
rs.generate_slab_grid(directory = '.',
grid_parameters = parameter_space,
species_list = {'C2H2_H':0.001,
'HCN_H':0.001},
Ntot=1e19,
separate_op_files=False,)
7. Models with opacity overlap
7.1. Running the models
Visit readthedocs for more information:
Models with line opacity overlap are output in a different format. These models can be run via ProDiMo or with prodimopy. Check ProDiMo wiki for more info on how to run these models on ProDiMo FORTRAN package. The run commands/functions for prodimopy are available via prodimopy.run_slab, however are limited to one molecule per model only. To run the 0D opacity overlap slab models on python the following command can be used:run_0D_slab(Ng, Tg, vturb, molecule, mol_mass, HITRANfile, R_grid, QTpath, output_filename, isotopolog=[1], wave_mol=[4, 30], wave_spec=[4.9, 28])
Information about what goes into each argument can be found with help(run_0D_slab).
[35]:
help(runs.run_0D_slab)
Help on function run_0D_slab in module prodimopy.run_slab:
run_0D_slab(Ng, Tg, vturb, molecule, mol_mass, HITRANfile, QTpath, isotopolog=[1], wave_mol=[4, 30], wave_spec=[4.9, 28], R_grid=100000.0, output='both', output_filename='default.out', mode='both', verbose=True)
This is a python implementation of 0D slab models, based on the FORTRAN version of 0D slab models in ProDiMo (https://prodimo.iwf.oeaw.ac.at/)
This runs models with/without line overlap. More robust and efficient code is available on Fortran, please check the above website (especially for running large grids of models).
The Fortran code also supports OMP and supports including multiple species.
Ng : float
Gas column density (cm-2)
Tg: float
Gas temperature (K)
vturb : float
Turbulent velocity of the gas (km/s)
molecule : string
Molecule name, e.g. 'CO2'
mol_mass : float or int
Molecular mass in atomic mass units (amu)
HITRANfile : string
Path to the HITRAN '.par' file containing the line data downloaded from HITRAN
QTpath : string
Path pointing to directory containing the directory QTpy provided by HITRAN (see hitran.org)
output_filename: string
Filename(+path) for the fits output file
Use the extension '.fits.gz'
isotopolog : list
List containing isotopologue numbers (HITRAN format)
wave_mol : list
Wavelength limits in microns to select the lines from the HITRAN file
wave_spec : list
Wavelength limits in microns for calculating the spectra
R_grid : float
High resolution grid on which the spectra has to be calculated initially
Recommend minimum of 1e5
overlap : boolean
Whether or not to perform line overlap calculations
verbose : boolean
Whether or not to print progress log
The following demonstrates running a slab model with opacity overlap with parameters: gas column density = 1e18cm-2, gas temperature = 250K, turbulent velocity = 2km/s, species = CO2, molecular mass = 44 amu, HITRAN file of CO2 located at ‘path/to/file.par’, spectral resolving power = 1e5, path to directory containing the QTpy directory = ‘path/to/directory/’, output file = ‘CO2.fits.gz’, with spectral limits of 4-30 microns.
[36]:
runs.run_0D_slab(Ng = 1e18,
Tg = 250,
vturb = 2,
molecule = 'CO2',
mol_mass = 44,
HITRANfile = '/home/rab/git/prodimo/data/HITRAN2020/CO2.par',
R_grid = 1e5,
QTpath = '/home/rab/MODELS/HITRANTIPS/TIPS_2021_PYTHON/QTpy/',
output_filename = 'CO2',
isotopolog = [1],
wave_mol = [4,30],
wave_spec = [4.9,28])
/home/rab/git/prodimopy/prodimopy/hitran.py:503: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['line_mixing']=data['line_mixing'].fillna(False)
Output written to CO2.fits.gz
Output written to CO2.out
[36]:
<prodimopy.read_slab.slab_data at 0x154bd4bc72f0>
This produces a fits output file containing the column of frequency, flux and opacity. The speed on python is low, typically ~20 times slower than on FORTRAN.
7.2. Running 1D slab models
1D models are implemented in ProDiMo FORTRAN package. These models are vertical slab models, see ProDiMo wiki for more information on the code implementation and assumptions. However these can be converted into a horizontal/radial slab model with ease, shown later.
However, 1D radial models can be run also on python using prodimopy, with the same limitation of only one molecule and no dust opacity. This can be run with:run_1D_radial_slab(Ng, Tg, R, d, vturb, molecule, mol_mass, HITRANfile, R_grid, QTpath, output_filename, width=’area_given’, Rin_limit=0, Rout_limit=1e99, isotopolog=[1], wave_mol=[4,30], wave_spec=[4.9,28]) The description of arguments can be found by help command.
[37]:
help(runs.run_1D_radial_slab)
Help on function run_1D_radial_slab in module prodimopy.run_slab:
run_1D_radial_slab(Ng, Tg, R, d, vturb, molecule, mol_mass, HITRANfile, R_grid, QTpath, output_filename, width='area_given', Rin_limit=0, Rout_limit=1e+99, isotopolog=[1], wave_mol=[4, 30], wave_spec=[4.9, 28], verbose=False)
This is a python implementation of 1D radial slab models, based on the FORTRAN version of 1D slab models in ProDiMo (https://prodimo.iwf.oeaw.ac.at/)
This runs models with line overlap. More robust and efficient code (including vertical slab model) is available in Fortran, please check the above website (especially for running large grids of models).
The Fortran code also supports OMP and supports including multiple species.
Ng : numpy array or list
Gas column density (cm-2)
Tg : numpy array or list
Gas temperature (K)
R : numpy array or list
Radius (AU)
d : float
Distance of the disk (parsec)
vturb : float
Turbulent velocity of the gas (km/s)
molecule : string
Molecule name, e.g. 'CO2'
mol_mass : float or int
Molecular mass in atomic mass units (amu)
HITRANfile : string
Path to the HITRAN '.par' file containing the line data downloaded from HITRAN
R_grid : float
High resolution grid on which the spectra has to be calculated initially
Recommend minimum of 1e5
QTpath : string
Path pointing to directory containing the directory QTpy provided by HITRAN (see hitran.org)
output_filename : string
Filename(+path) for the fits output file
Use the extension '.fits.gz'
width : string or list or float
'area_given' - the provided radii array is used as the emitting area equivalent radius
'infer' - Assumes the provided radius as radial distance from the star and calculates the width of individual rings of slab grids
list - List containing emitting area equivalent radii
float - Constant emitting area equivalent radius, used for all grid points
Rin_limit : float
Inner radius limit of the disk (AU)
Rout_limit : float
Outer radius limit of the disk (AU)
isotopolog : list
List containing isotopologue numbers (HITRAN format)
wave_mol : list
Wavelength limits in microns to select the lines from the HITRAN file
wave_spec : list
Wavelength limits in microns for calculating the spectra
The following shows an example of running a radial slab with 5 grid points.
[38]:
runs.run_1D_radial_slab(Ng = [1e22,1e21,1e20,1e19,1e18],
Tg = [ 650, 450, 350, 300, 250],
R = [0.07, 0.1,0.15, 0.2, 0.5],
d = 190,
vturb = 2,
molecule = 'CO2',
mol_mass = 44,
HITRANfile = '/home/rab/git/prodimo/data/HITRAN2020/CO2.par',
R_grid = 1e5,
QTpath = '/home/rab/MODELS/HITRANTIPS/TIPS_2021_PYTHON/QTpy/',
output_filename = 'CO2_1D',
width = 'infer',
isotopolog = [1],
wave_mol = [4,30],
wave_spec = [4.9,28])
/home/rab/git/prodimopy/prodimopy/hitran.py:503: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['line_mixing']=data['line_mixing'].fillna(False)
Note that the computation speed of the model is very low, if large number of models are to be run, e.g. a grid, then running models with the FORTRAN version is highly recommended.
7.3. Reading the models
The model outputs can be read using the prodimopy.read_slab functions. The overlap 0D slab model can be read using the read_slab function that was introduced above, but need to pass overlap_model=True. Note that these models have minimum data in the output, so many features of prodimopy described above might be limited.
[40]:
CO2_0D = rs.read_slab('CO2.fits.gz')
Reading slab line overlap model output from: CO2.fits.gz
1D ProDiMo FORTRAN models can be read using read_1D_slab function. However, the python implementation is very trivial and does not store the source functions, optical depths, etc of individual grids, instead outputs a single spectra similar to the above overlap 0D slab models. Hence, the output format is also the same as the 0D overlap slab model.
[42]:
CO2_1D = rs.read_slab('CO2_1D.fits.gz')
Reading slab line overlap model output from: CO2_1D.fits.gz
[43]:
CO2_0D.convolve(R=3000,lambda_0=4,lambda_n=30,overlap=True)
CO2_1D.convolve(R=3000,lambda_0=4,lambda_n=30,overlap=True)
1 ; Model number 1 ; Species number None
1 ; Model number 1 ; Species number None
[44]:
[(fig,ax)] = ps.plot_spectra(CO2_0D,overlap=True)
ax.set_xlim([12.5,17.5])
[44]:
(12.5, 17.5)
[45]:
jansky = 1e23
fig,ax = plt.subplots(figsize=(10,5))
ps.plot_spectra(CO2_1D,overlap=True,scaling=jansky,fig=fig,ax=ax)
ax.set_xlim([10.5,19.5])
[45]:
(10.5, 19.5)
