Instruments

import os
import sys

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.join(".."))

Meudon PDR code predictions

from infobs.model import MeudonPDR

meudonpdr = MeudonPDR()

df_params = pd.DataFrame(
    {
        "Av": [1, 5, 10, 15, 20],
        "G0": [1e2, 1e2, 1e2, 1e2, 1e2],
        "Pth": [1e5, 1e5, 1e5, 1e5, 1e5],
        "angle": [0, 0, 0, 0, 0],
    }
)

df_params

	Av	G0	Pth	angle
0	1	100.0	100000.0	0
1	5	100.0	100000.0	0
2	10	100.0	100000.0	0
3	15	100.0	100000.0	0
4	20	100.0	100000.0	0

meudonpdr.predict(df_params)

	Av	G0	Pth	angle	kappa	h2_v0_j2__v0_j0	h2_v0_j3__v0_j1	h2_v0_j4__v0_j2	h2_v0_j5__v0_j3	h2_v0_j6__v0_j4	...	shp_n10_j11_f11d5__n9_j10_f10d5	shp_n10_j10_f10d5__n9_j9_f9d5	shp_n10_j9_f9d5__n9_j8_f8d5	shp_n10_j9_f9d5__n9_j10_f10d5	shp_n10_j10_f10d5__n9_j10_f10d5	shp_n5_j4_f4d5__n2_j3_f3d5	shp_n6_j5_f5d5__n3_j4_f4d5	shp_n7_j6_f6d5__n4_j5_f5d5	shp_n8_j7_f7d5__n5_j6_f6d5	shp_n9_j8_f8d5__n6_j7_f7d5
0	1.0	100.0	100000.0	0.0	1.0	0.010243	0.002992	0.000057	0.000010	0.000002	...	2.991196e-12	2.645448e-12	2.420507e-12	3.334513e-17	2.685572e-14	7.130854e-14	1.255194e-14	2.563640e-15	5.562906e-16	1.247624e-16
1	5.0	100.0	100000.0	0.0	1.0	0.009836	0.003158	0.000060	0.000010	0.000002	...	2.927281e-12	2.588342e-12	2.376320e-12	3.258995e-17	2.626043e-14	7.143769e-14	1.253789e-14	2.521841e-15	5.556850e-16	1.238246e-16
2	10.0	100.0	100000.0	0.0	1.0	0.010026	0.003207	0.000059	0.000010	0.000002	...	2.915170e-12	2.580306e-12	2.359131e-12	3.258680e-17	2.623749e-14	7.176126e-14	1.255073e-14	2.530706e-15	5.523370e-16	1.229295e-16
3	15.0	100.0	100000.0	0.0	1.0	0.010062	0.003317	0.000059	0.000010	0.000002	...	2.913692e-12	2.583992e-12	2.372837e-12	3.241424e-17	2.622504e-14	7.204642e-14	1.255310e-14	2.542738e-15	5.539160e-16	1.230413e-16
4	20.0	100.0	100000.0	0.0	1.0	0.010102	0.003278	0.000058	0.000011	0.000002	...	2.893779e-12	2.571290e-12	2.359847e-12	3.224938e-17	2.617005e-14	7.181391e-14	1.253679e-14	2.545107e-15	5.531721e-16	1.225758e-16

5 rows × 5025 columns

df = meudonpdr.predict(
    df_params,
    lines=[
        "13c_o_j1__j0",
        "13c_o_j2__j1",
        "13c_o_j3__j2",
        "c_el3p_j1__el3p_j0",
        "c_el3p_j2__el3p_j1",
        "cp_el2p_j3_2__el2p_j1_2",
    ],
)

df

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2	c_el3p_j1__el3p_j0	c_el3p_j2__el3p_j1	cp_el2p_j3_2__el2p_j1_2
0	1.0	100.0	100000.0	0.0	1.0	0.000276	0.000318	0.000128	0.237290	0.168541	7.993103
1	5.0	100.0	100000.0	0.0	1.0	10.176692	11.708745	3.919423	22.388675	11.729995	10.794163
2	10.0	100.0	100000.0	0.0	1.0	22.873276	21.915024	8.102161	21.884921	11.508051	10.750909
3	15.0	100.0	100000.0	0.0	1.0	29.498773	25.964494	10.321277	22.693579	11.846299	10.839103
4	20.0	100.0	100000.0	0.0	1.0	34.912525	29.605632	12.635616	23.212814	12.121948	10.800269

Instruments

obstime = 1  # Minutes
linewidth = 10  # km/s

Ideal instrument

Note: By definition, the integration time obstime has no effect when using the ideal instrument.

from infobs.instruments import IdealInstrument

ideal = IdealInstrument()

ideal.measure(df, obstime)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2	c_el3p_j1__el3p_j0	c_el3p_j2__el3p_j1	cp_el2p_j3_2__el2p_j1_2
0	1.0	100.0	100000.0	0.0	1.0	0.000276	0.000318	0.000128	0.237290	0.168541	7.993103
1	5.0	100.0	100000.0	0.0	1.0	10.176692	11.708745	3.919423	22.388675	11.729995	10.794163
2	10.0	100.0	100000.0	0.0	1.0	22.873276	21.915024	8.102161	21.884921	11.508051	10.750909
3	15.0	100.0	100000.0	0.0	1.0	29.498773	25.964494	10.321277	22.693579	11.846299	10.839103
4	20.0	100.0	100000.0	0.0	1.0	34.912525	29.605632	12.635616	23.212814	12.121948	10.800269

IRAM 30m EMIR

from infobs.instruments import IRAM30mEMIR

emir = IRAM30mEMIR(linewidth)

emir.measure(emir.filter_lines(df), obstime)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2
0	1.0	100.0	100000.0	0.0	1.0	2.304100	1.186082	2.559229
1	5.0	100.0	100000.0	0.0	1.0	11.045154	12.614035	7.962028
2	10.0	100.0	100000.0	0.0	1.0	21.275261	20.809291	12.215755
3	15.0	100.0	100000.0	0.0	1.0	30.982845	27.994013	21.121263
4	20.0	100.0	100000.0	0.0	1.0	34.579477	24.472355	8.870162

Changing integration time

emir.measure(emir.filter_lines(df), 10 * obstime)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2
0	1.0	100.0	100000.0	0.0	1.0	1.007058	-0.061244	-0.406238
1	5.0	100.0	100000.0	0.0	1.0	10.733919	11.054353	1.201543
2	10.0	100.0	100000.0	0.0	1.0	22.532765	20.564771	9.082299
3	15.0	100.0	100000.0	0.0	1.0	27.842653	29.493280	8.562712
4	20.0	100.0	100000.0	0.0	1.0	31.244859	32.500944	11.883908

Changing line width

linewidth mandatory argument.

emir = IRAM30mEMIR(linewidth=2 * linewidth)

emir.measure(emir.filter_lines(df), obstime)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2
0	1.0	100.0	100000.0	0.0	1.0	-5.599257	0.029949	6.860503
1	5.0	100.0	100000.0	0.0	1.0	8.376963	15.003082	9.801187
2	10.0	100.0	100000.0	0.0	1.0	17.128225	18.203982	3.208072
3	15.0	100.0	100000.0	0.0	1.0	29.142781	33.710153	14.633763
4	20.0	100.0	100000.0	0.0	1.0	35.617939	34.560992	-0.637018

Integrated precipitable water vapor

ipwv optional argument. * Available values: 0 (excellent conditions), 1 (good conditions), 2 (normal conditions) * Default: 2

emir = IRAM30mEMIR(linewidth, ipwv=0)

emir.measure(emir.filter_lines(df), obstime)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2
0	1.0	100.0	100000.0	0.0	1.0	3.637652	-0.302471	1.208588
1	5.0	100.0	100000.0	0.0	1.0	13.113089	11.641636	-1.319127
2	10.0	100.0	100000.0	0.0	1.0	23.734262	27.891698	8.298691
3	15.0	100.0	100000.0	0.0	1.0	27.723701	27.294525	13.622968
4	20.0	100.0	100000.0	0.0	1.0	31.520066	29.115825	14.508134

Implementing other instruments

from infobs.instruments import StandardInstrument

Mount Fuji submillimeter-wave telescope for [CI] lines

class Fuji(StandardInstrument):
    """
    Obstime must be interpreted as a relative integration time compared with the one achieved in T. Oka et al, 2004 (ApJ).
    For simplicity sake, we assume the noise RMS to be identical for both lines.
    """

    def __init__(self, linewidth: float, kelvin: bool = True):
        super().__init__(
            ["c_el3p_j1__el3p_j0", "c_el3p_j2__el3p_j1"], linewidth, kelvin
        )

    @property
    def dv(self):
        return 1.0  # km/s

    @property
    def ref_obstime(self):
        return {"c_el3p_j1__el3p_j0": 1, "c_el3p_j2__el3p_j1": 1}

    @property
    def rms(self):
        return {"c_el3p_j1__el3p_j0": 0.5, "c_el3p_j2__el3p_j1": 0.5}

    @property
    def percent(self):
        return {"c_el3p_j1__el3p_j0": 20, "c_el3p_j2__el3p_j1": 20}

    def __str__(self):
        return "Mount Fuji telescope"

fuji = Fuji(linewidth)
fuji_obstime = 0.5

fuji.measure(fuji.filter_lines(df), fuji_obstime)

	Av	G0	Pth	angle	kappa	c_el3p_j1__el3p_j0	c_el3p_j2__el3p_j1
0	1.0	100.0	100000.0	0.0	1.0	-0.353822	-0.180102
1	5.0	100.0	100000.0	0.0	1.0	26.541506	10.524991
2	10.0	100.0	100000.0	0.0	1.0	18.180540	7.015612
3	15.0	100.0	100000.0	0.0	1.0	16.307703	11.164658
4	20.0	100.0	100000.0	0.0	1.0	27.354132	10.895989

SOFIA (Stratospheric Observatory for Infrared Astronomy) for [CII] line

class SOFIA(StandardInstrument):
    """
    Obstime must be interpreted as a relative integration time compared with the one achieved in Pabst et al, 2017 (A&A).
    Calibration error is discussed in Risacher et al, 2016.
    """

    def __init__(self, linewidth: float, kelvin: bool = True):
        super().__init__(["cp_el2p_j3_2__el2p_j1_2"], linewidth, kelvin)

    @property
    def dv(self):
        return 0.193  # km/s

    @property
    def ref_obstime(self):
        return {"cp_el2p_j3_2__el2p_j1_2": 1}

    @property
    def rms(self):
        return {"cp_el2p_j3_2__el2p_j1_2": 2.25}

    @property
    def percent(self):
        return {"cp_el2p_j3_2__el2p_j1_2": 5}

    def __str__(self):
        return "SOFIA"

sofia = SOFIA(linewidth)
sofia_obstime = 0.25

sofia.measure(sofia.filter_lines(df), fuji_obstime)

	Av	G0	Pth	angle	kappa	cp_el2p_j3_2__el2p_j1_2
0	1.0	100.0	100000.0	0.0	1.0	7.173556
1	5.0	100.0	100000.0	0.0	1.0	10.857059
2	10.0	100.0	100000.0	0.0	1.0	8.986045
3	15.0	100.0	100000.0	0.0	1.0	12.189415
4	20.0	100.0	100000.0	0.0	1.0	11.791652

Mixing instruments

from infobs.instruments import MergedInstrument

merged = MergedInstrument([emir, fuji, sofia])

merged.measure(merged.filter_lines(df), [obstime, fuji_obstime, sofia_obstime])

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2	c_el3p_j1__el3p_j0	c_el3p_j2__el3p_j1	cp_el2p_j3_2__el2p_j1_2
0	1.0	100.0	100000.0	0.0	1.0	-1.394607	1.790921	1.990865	1.067935	0.169435	8.098664
1	5.0	100.0	100000.0	0.0	1.0	10.271918	8.804715	5.461707	20.382862	12.709262	10.241453
2	10.0	100.0	100000.0	0.0	1.0	21.093096	20.478475	8.369292	17.634216	9.516761	8.793674
3	15.0	100.0	100000.0	0.0	1.0	29.499523	24.465393	9.611141	22.491916	9.317297	10.023532
4	20.0	100.0	100000.0	0.0	1.0	33.713076	35.565098	13.903594	19.555128	12.418816	6.144656

You can use the same instrument with two different integration times with two instances of the same instrument using the restrict_lines method.

In the following example, the second transition of [CI] is integrated 10 times longer.

fuji1 = Fuji(linewidth)
fuji1.restrict_lines(["c_el3p_j1__el3p_j0"])
fuji2 = Fuji(linewidth)
fuji2.restrict_lines(["c_el3p_j2__el3p_j1"])

merged = MergedInstrument([emir, fuji1, fuji2, sofia])

merged.measure(
    merged.filter_lines(df), [obstime, fuji_obstime, 10 * fuji_obstime, sofia_obstime]
)

	Av	G0	Pth	angle	kappa	13c_o_j1__j0	13c_o_j2__j1	13c_o_j3__j2	c_el3p_j1__el3p_j0	c_el3p_j2__el3p_j1	cp_el2p_j3_2__el2p_j1_2
0	1.0	100.0	100000.0	0.0	1.0	0.429596	1.290377	3.208243	-0.546726	-0.145097	9.386241
1	5.0	100.0	100000.0	0.0	1.0	11.208933	11.345129	-2.303984	25.432893	14.693545	13.910911
2	10.0	100.0	100000.0	0.0	1.0	22.865378	19.874641	9.037263	24.292709	12.537440	11.606017
3	15.0	100.0	100000.0	0.0	1.0	25.463512	24.593912	12.059598	23.657297	18.576150	11.382027
4	20.0	100.0	100000.0	0.0	1.0	35.870254	32.683043	19.930144	29.086794	17.062069	11.930846

All these instruments can then be used to simulate observations (see simulator.ipynb example notebook).