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.dockerignore 0 → 100644
View file @ dab489de
/etl/credentials/db_credentials*.yml
/etl/stg/archive/*
/etl/prs/fits/cubes/*
/etl/prs/fits/moments/*
/etl/prs/fits/ratios/*
/etl/prs/fits/grids/*
/etl/mdl/radmc_files/*
/etl/mdl/scratches/*
/etl/prs/output/*
/etl/*.sif
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/etl/tests/test_files/config.yml
/etl/trn/trn_convert_grid_to_polaris.py
/etl/posteriors.yml
/etl/run_id.txt
/etl/test_sak.html
/etl/prs/output/
/etl/prs/fits/
/etl/stg/archive/
/etl/assets/internal_files/
/etl/credentials/db_credentials.yml
/etl/credentials/db_credentials_local.yml
/etl/mdl/polaris_input_file.cmd
/etl/mdl/radmc3d_postprocessing.sh
/etl/mdl/scratches/
Dockerfile 0 → 100644
View file @ dab489de
FROM python:3.9-slim
# STEP 1) Setting up environment
# apt packages
RUN apt-get update && \
apt-get --yes upgrade && \
apt-get install --yes libgomp1 && \
apt-get install --yes postgresql-client && \
apt-get install --yes --reinstall build-essential && \
apt-get -y install curl vim less nano git && \
apt-get -y install zip unzip && \
apt-get clean
WORKDIR /usr/src/etl
RUN python -m pip install --upgrade pip
COPY etl/requirements.txt .
RUN pip install -r requirements.txt
RUN mkdir /sak_user
# RUN /usr/sbin/usermod -d /sak_user root
RUN HOME=/sak_user
COPY documentation/radmc3d_install.sh .
RUN chmod 755 radmc3d_install.sh
RUN HOME=/sak_user;./radmc3d_install.sh
ENV PATH="/sak_user/bin:${PATH}"
ENV PYTHONPATH="/sak_user/bin/python:${PYTHONPATH}"
RUN chmod 755 /sak_user/bin/radmc3d
# STEP 2) Bundling app
COPY etl .
CMD python main.py
\ No newline at end of file
README.md 0 → 100644
View file @ dab489de
The Swiss Army Knife project has the objective to assess whether CH3OH can be used as an effective volume density probe and in which regime.
Important resources are listed in the [documentation.md file](documentation/documentation.md).
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apptainer.def 0 → 100644
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Bootstrap: docker
From: postgres:14.1-alpine
%environment
export POSTGRES_DB=$DB_NAME
export POSTGRES_USER=$DB_USER
export POSTGRES_PASSWORD=$DB_PASS
create_apptainer_image_file.sh 0 → 100644
View file @ dab489de
rm swiss_army_knife_latest.sif
singularity pull --disable-cache docker://git.ia2.inaf.it:5050/andrea.giannetti/swiss_army_knife
docker-compose.yaml 0 → 100644
View file @ dab489de
version: '3.8'
services:
db:
image: postgres:14.1-alpine
container_name: db_container_sak
restart: always
environment:
- POSTGRES_DB=$DB_NAME
- POSTGRES_USER=$DB_USER
- POSTGRES_PASSWORD=$DB_PASS
ports:
- '31000:5432'
volumes:
- db:/var/lib/postgresql/data
networks:
- sak_network
etl:
build:
dockerfile: Dockerfile
container_name: 'etl_sak'
image: 'sak_etl'
depends_on:
- db
stdin_open: true
tty: true
networks:
- sak_network
volumes:
- ./etl:/usr/src/etl
volumes:
db:
driver: local
networks:
sak_network:
name: 'sak_app_network'
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documentation/documentation.md 0 → 100644
View file @ dab489de
# SAK documentation
This file contains the overview of the SAK tool for inferring volume densities, described in the
paper https://git.overleaf.com/6373bb408e4040043398e495.
## Repositories
The project remote repository for the code is:
https://www.ict.inaf.it/gitlab/andrea.giannetti/swiss_army_knife_stable/-/tree/main
The first paper is here:
https://git.overleaf.com/6373bb408e4040043398e495
## Pipeline
The SAK non-LTE, toy model pipeline uses three main layers:
1. **Staging layer:** In the staging layer (`etl/stg`), the `stg_radmc_input_generator.py` file takes care of preparing
the input files for RADMC, and saves them in the `etl/mdl/radmc_files` folder.
The `etl/stg/config/config.yml` file contains the default values of the parameters used to prepare the RADMC files
for the postprocessing. All of them are described in detail in the following.
2. **Model layer:** The model layer (`etl/mdl`) takes care of preparing and executing the RADMC command according to the
configuration in the `etl/mdl/config/config.yml` file. This is done by the `mdl_execute_radmc_command.py` script,
that also creates `radmc3d_postprocessing.sh`.
The results are then converted to fits cubes, which are saved by default into `prs/fits/cubes`, for later
processing.
3. **Presentation layer:** In the presentation layer (`etl/prs`) moment-0 maps and line-ratio maps are computed
executing the `prs_compute_integrated_fluxes_and_ratios.py` script. At the moment, the integration limits cannot be
specified, and the entire cube is collapsed. *WARNING:* Pay attention to the presence of secondary modeled lines in
the simulated spectra.
The script `prs_inspect_results.py` reduces the ratio maps to single points and produces an image of the ratio values
as a function of gas number density and temperature. Be aware that at the moment the query is hardcoded and works for
SAK-generated models only.
The script `prs_prepare_backup.py` compressed and copies the output of a model run for sharing.
The scripts `prs_ml_training.py`, `prs_ml_training_ratio.py` and `prs_expand_modelling_with_ml.py` `prs_expand_ratio_modelling_with_ml.py` can be run to perform ML-assisted emulation of
the modelled data, in
order to expand the grid performed with actual RT computation. These scripts rely on
the `etl/config/ml_modelling.yml` to perform training and evaluation of the emulation model, and to actually produce
emulated data, which are saved to the `etl/inferred_data.csv`. This file is used by the `prs_density_inference.py` to
concatenate these data to those from the formal computation to perform the density inference (see below). In our
case, XGBoost worked best, and we used this model to perform emulation.
There is a final script `prs_density_inference.py`, that is used for preparing the KDE model and to perform density
inference, given the measured ratios. It uses the YAML file `etl/config/density_inference_input.yml` to provide the
needed input for the script. It produces the `output/run_type/{provided_run_type}/density_pdf*.png` output file,
where `provided_run_type` is defined in the global configuration file and the wildcard represents the
source name.
The entire ETL pipeline is executed by the `main.py` script, where it is possible to define overrides for the default
values in the specific stage configuration file (so that it's possible to specify an entire grid of models). These
overrides are included into the `etl/config/config.yml` configuration file.
4. **Additional files**:
The script `prs_analytical_representations.py` provides a convenient way of checking the analytical representations of the ratio vs. density curves.
The file `prs_check_biases_poc_sample.py` checks for biases in the massive clump sample used in the proof-of-concept.
The scripts `prs_poc_figures.py`, and `prs_poc_latex_table.py` can be used to reproduce the content of the paper, regarding the POC.
### Running the pipeline
The pipeline is now dockerized. To run it clone the repository and in bash run:
`docker compose up --build`
from the root project directory. Docker compose will bring up a local database for your runs, with a persistent storage,
so that all the results can be found and inspected. Similarly, a local volume is mounted, so that intermediate files (
radmc files, cubes, moment zero- and ratio images) can be found in the project directory structure, after a run.
Remember to set the environment variables:
* DB_USER;
* DB_PASS;
* DB_HOST;
* DB_NAME;
so that the database is correctly initialized.
The main routine accepts the following parameters as input:
* --run_id: the run id of the grid to process; if not provided, generates a new run_id;
* --cleanup_scratches: whether to empty the mld/scratches directory;
* --distributed: whether the grid is processed in a distributed environment, in which case it uses a queue from the
database to process all models in the grid, otherwise, executes with multiprocessing.
To reset the database, use the command:
`docker volume rm swiss_army_knife_db`.
Be aware that at the moment the first run (also after DB reset) fails due to an issue in the DB initialization. Run the
docker compose command once more to start the pipeline.
The pipeline produces a series of files that are used to produce the final output.
* It compresses and archives in `stg/archive` all the input files for radmc in a zip file, whose name is also used as a
primary key part in the database. This file can be used to inspect run results and/or to repeat a run manually if
needed.
* It saves the H2 volume density, dust temperature grids, and the volume density of the species included in the model as
fits files in `prs/fits/grids`.
* Converts the `image.out` file to a fits cube in `prs/fits/cubes`.
* Computes the moment-zero maps by integrating the full cube, persisting them in `prs/fits/moments`.
* Determines the line ratios pixel-by-pixel and saves the maps in `prs/fits/ratios`.
After the last model is completed, the results are summarized in a series of figures, that are saved in
the `prs/output/run_type/{provided_run_type}/figures`
folder.
The files named `ratio_grid_lines_*.png` show the average ratio across the entire clump, as a function of the
characteristic temperature and volume density.
The pixel-by-pixel ratios as a function of the average line-of-sight H2 volume density is shown
in `ratio_vs_avg_density_los_*.png`. The KDE-smoothed data are computed and save
in `ratio_vs_avg_density_los_kde_*.png`.
To check the opacity of the lines, we include a plot showing the integrated emission px-by-px as a function of the
molecular colum density in that pixel (`coldens_moments_lines_*.png`)).
If density inference is performed, the posterior PDF is saved as `density_inference.png`.
### Configuration files parameters
In this paragraph we describe in more detail the parameters that can be set in the different configuration files, and
their meaning.
#### The staging configuration file (`etl/stg/config/config.yml`)
The staging config file has three main categories:
* **grid:**
* grid_type: regular or spherical
* coordinate_system: cartesian or polar
* central_density: the central gas number density of the toy model. If you are using a power-law distribution, it
corresponds also to the maximum possible density in the grid.
* density_unit: the units in which the number density is expressed, e.g. 'cm^-3'
* density_powerlaw_idx: the power-law index of the density distribution
* density_at_reference: the gas number density at a reference value, e.g. at 1 pc
* distance_reference: the reference radius for scaling the power-law
* distance_reference_unit: the units of the reference radius, e.g. 'pc', 'AU', 'cm'
* dust_temperature: the dust temperature value
* dust_temperature_unit: the dust temperature unit
* dust_temperature_powerlaw_idx: the dust temperature power-law index
* microturbulence: the unresolved turbulent velocity of the gas
* microturbulence_unit: the units of the microturbulence value, e.g. 'km/s'
* dimN: a dictionary-like parameter, it should include the 'size', 'size_unit', 'shape', and 'refpix' keys. E.g. {"
size":1, "size_units": "pc", "shape": 3, "refpix": 1}. If only dim1 is provided, the grid is cubic.
* size: the grid size in physical units
* size_unit: the units in which the grid size is expressed, e.g. 'pc'
* shape: the number of cells in the grid for this axis
* refpix: the reference pixel that corresponds to the grid "centre" from which the distance are computed for
power-law models, for instance
* velocity_field: velocity field to apply to the gas, it can only be 'solid' at the moment (the gas is assumed to
rotate around the y axis). Power-law is in principle also supported, but the wiring is still to be implemented.
* velocity_gradient: the value of the velocity gradient for solid-body roation.
* velocity_gradient_unit: the unit of the velocity gradient, e.g. "km/s/pc"
* **stars:**
Adding the star configuration section makes the program compute the dust temperature distribution given the properties
of the stars included. _Caveat:_ be careful, test runs show unexpectedtly low dust temperatures using a blackbody.
* nstars: the number of stars to include
* rstars: the radius of the star in cm; it is normally ignored, unless the parameter `istar_sphere` is set to 1
* mstars: the mass in grams; not used in the current version of RADMC
* star_positions: x, y, z coordinates of the star in cm, expressed as a list of lists
* star_fluxes: stellar fluxes at each of the computed lambdas in erg cm$^-2$ s$-1$ Hz$-1$; if negative, interpreted
as the peak temperature of a blackbody
* nlambdas: number of wavelengths to compute
* spacing: log or linear
* lambdas_micron_limits: the limits in wavelength to consider in the run, expressed in micron
* **lines:**
* species_to_include: the list of molecular species to include in the RADMC postprocessing, e.g. ['e-ch3oh']
* molecular_abundances: a dict-like parameter, containing the species name and the corresponding fractional
abundance, e.g. {"e-ch3oh": 1e-8, "p-h2": 1}
* lines_mode: the line transfer mode. It can be 'lte', 'lvg', 'optically_thin_non_lte', 'user_defined_populations' (
see the RADMC documentation if in doubt)
* collision_partners: the list of collision partners to be used; it must appear in the correct order as in the
molecule_{molname}.inp file of the molecule to be simulated, e.g. ['p-h2']
#### The model configuration file (`etl/mdl/config/config.yml`)
The model configuration file has two categories:
* **radmc_postprocessing:**
* nphotons: the number of photons to use for the postprocessing
* scattering_mode_max: override the scattering settings in the dust opacity files; 0 excludes scattering, 1 treats
it in an isotropic way (if defined), 2 includes anisotropic scattering (if defined)
* iranfreqmode: 1
* tgas_eq_tdust: whether the gas temperature is equal to the dust temperature (if not, it must be specified or
computed separately!)
* **radmc_observation:**
* inclination: the inclination wrt. the line-of-sight in degrees
* position_angle: the position angle wrt. the observer
* imolspec: the index of the species to be modeled; defaults to the first
* iline: the line identifier for the line to be modeled, according to the molecule_{molname}.inp
* width_kms: the range in km/s to be modeled around the line
* nchannels: the number of channels to be considered
* npix: the number of pixels in the final image; WARNING: it must be a multiple of the grid shape
* threads: number of threads to be used by radmc
* image_size_pc: the size of the image to produce; it is useful to get a good alignment
#### The global configuration file (`etl/config/config.yml`)
The global configuration file, in addition to the run_type name, has two categories, "computation" and "overrides":
* run_type: name of the folder where the results are grouped and saved in the prs step
* **computation:**
* threads: number of threads to include in the multiprocessing pool
* **overrides:**
* dust_temperature_grid_type: the spacing in the dust temperature grid, can be: 'linear', 'log'
* dust_temperature_limits: the limits in the dust temperature grid, e.g. [10, 30]; the last point is excluded
* dust_temperature_step: the step size in the grid; if the spacing is logarithmic, it represents the increase
factor,
e.g. 10 means steps of one order of magnitude
* gas_density_grid_type: the spacing in the gas number density grid, can be: 'linear', 'log'
* gas_density_limits: the limits in the gas number density grid, e.g. [1e4, 1e8]; the last point is excluded
* gas_density_step: the step size in the grid; if the spacing is logarithmic, it represents the increase factor,
e.g. 10
means steps of one order of magnitude
* gas_density_unit: the units in which the gas number density is expressed, e.g. 'cm^-3'
* lines_to_process: the list of line identifiers to process, according to the molecule_{molname}.inp file,
e.g. [['87', '86'], ['88', '87']]. The parameter is gives as a list of lists, so that the program knows which
ratios
to compute.
#### The density inference input file (`etl/config/density_inference_input.yml`)
This file contains the measured ratios, their uncertainties, and a few other parameters to perform the inference.
* use_model_for_inference: the name of the folder where the model results to be used are stored.
* integrated_intensities_uncertainties: a (potentially nested) dictionary of measured integrated intensities. The keys
are the line id and the source name that will be concatenated to the output plot names. A subset can be given,
depending on what is available from observations. Each key can contain both a float representing the integrated
intensity or a dictionary of sources, each with their own data.
* measured_integrated_intensities: the rms uncertainty associated to the integrated intensity measurement. It has the
same structure as the integrated intensity dictionary.
* ratios_to_include: a list of strings representing the ratios to consider in the density determination, in the form
of 'transition_id_1-transition_id_2'.
* simulated_ratio_realizations: the number of simulated ratios to generate
* recompute_kde: True - retrain the model, False - unpickles it
* probability_threshold: the probability threshold to be used to compute the highest probability density interval (this
is the probability in the wings!).
* limit_rows [optional]: limits the number of rows to extract from the data to reduce computational time for tests.
* points_per_axis [optional]: the number of points on the density and ratio axes, to compute the probability
distributions.
* nthreads [optional]: the number of threads to be used for computation.
#### The ML-emulation input file (`etl/config/ml_modelling.yml`)
This file determines what tasks are performed as part of the `prs_ml_training.py` `prs_expand_modelling_with_ml.py`
scripts.
* retrain: boolean to specify whether the model should be retrained from scratch (`prs_ml_training.py`) or reloaded to
perform inference (`prs_expand_modelling_with_ml.py`).
* overrides: same as in the global configuration file.
* model_type: the kind of model to use to perform emulation (relevant only if retrain is true); admitted values are '
XGBoost', 'XGBoost_gridsearch' (XGBoost with gridsearch on the hyperparameters and CV), 'RandomForest', 'auto_skl' (
AutoSklearnRegressor).
* model_parameters: depending on model_type, a dictionary of the parameters to pass to the model. For example, XGBoost
supports `n_estimators`, `max_depth`, `learning_rate`, etc; compare the documentation of the xgboost, sklearn, and
auto-sklearn packages for a comprehensive list. For the XGBoost with grid search, it is a nested dictionary with two
keys:
* param_grid: a dictionary of the hyperparameters to explore, each with a corresponding list of [start, stop, step]
values to pass to numpy.arange.
* param_gridsearch: a dictionary of parameters for the GridSearchCV object; verbosity is put to 3 by default.
## Database
The database is an instance of PostgreSQL, created via docker compose or with the `apptainer.def` singularity definition
file.
The database ER diagram is shown in `publications/6373bb408e4040043398e495/figures/sak_database.png`. In the following
we document the tables and their relationships.
### Description of the tables
The tables and their column are described in detail in this section.
#### grid_parameters
The `grid_parameters` table contains the metadata of the models grid computed in the SAK run. The primary key is
composed of the `run_id` and `zipped_grid_name` columns.
| Column name | Type | PK | FK | FK Table | Description |
|---------------------------------|--------------|-------|-------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | True | | | The name of the compressed grid file containing all the input used to generate the model |
| grid_type | text | False | | | The type of the grid to be constructed (to date regular or spherical) |
| coordinate_system | text | False | | | The coordinate system to use in the grid (to date cartesian or polar) |
| central_density | Float | False | | | The maximum density above which the grid is cut and filled with; if 1e8 cm^-3, every point above this threshold is set to 1e8 cm^-3 instead; expressed in cm^-3 |
| density_powerlaw_index | Float | False | | | The power-law index used to construct the volume density grid |
| density_at_reference | Float | False | | | The value of the volume density at the reference radius; expressed in cm^-3 |
| dust_temperature | Float | False | | | The maximum value of the dust temperature above which the grid is cut and filled with; if 2000K, every point above this threshold is set to 2000K instead; expressed in K |
| dust_temperature_powerlaw_index | Float | False | | | The power-law index used to construct the dust temperature grid |
| dust_temperature_at_reference | Float | False | | | The dust temperature value at the reference radius; expressed in K |
| microturbulence | Float | False | | | The microturbulence value (standard deviation) to add to the thermal broadening of the lines; expressed in km/s |
| velocity_field | text | False | | | The type of velocity field to use to construct the velocity grid (to date 'solid' for solid-body rotation |
| velocity_gradient | text | False | | | The value of the velocity gradient to use for the velocity field construction; expressed in km/s/pc for solid-body rotation |
| velocity_powerlaw_index | Float | False | | | The index of the power-law used to construct the velocity grid |
| velocity_at_reference | Float | False | | | The value of the velocity at the reference radius; expressed in km/s |
| distance_reference | Float | False | | | The value of the reference radius; expressed in pc |
| maximum_radius | Float | False | | | Grid cutoff radius, the external radius of the fragment |
| grid_size_1 | Float | False | | | The dimension of the grid along the x axis; expressed in pc |
| grid_shape_1 | Float | False | | | The number of pixels along the x axis |
| grid_refpix_1 | Float | False | | | The reference pixel for the x axis |
| grid_size_2 | Float | False | | | The dimension of the grid along the y axis; expressed in pc |
| grid_shape_2 | Float | False | | | The number of pixels along the y axis |
| grid_refpix_2 | Float | False | | | The reference pixel for the y axis |
| grid_size_3 | Float | False | | | The dimension of the grid along the z axis; expressed in pc |
| grid_shape_3 | Float | False | | | The reference pixel for the z axis |
| grid_refpix_3 | Float | False | | | The reference pixel for the z axis |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | | | The id of the run |
#### grid_files
This table contains the name of the fits files where the physical grids have been saved.
| Column name | Type | PK | FK | FK Table | Description |
|------------------|--------------|-------|-------|-----------------|------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | True | True | grid_parameters | The name of the compressed grid file containing all the input used to generate the model |
| quantity | varchar(30) | True | False | | The quantity saved in the fits file e.g. temperature, volume density, etc. |
| fits_grid_name | text | False | False | | The name of the fits file with the physical grid |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | grid_parameters | The id of the run |
#### stars_parameters
In this table there are the parameters used to describe the (potential) distribution of stars in the grid.
| Column name | Type | PK | FK | FK Table | Description |
|-----------------------|--------------|-------|-------|-----------------|------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | True | True | grid_parameters | The name of the compressed grid file containing all the input used to generate the model |
| nstars | integer | False | False | | The number of stars to include in the grid |
| rstars | Array(float) | False | False | | The radiuses of the stars in the grid (as described in the RADMC3D docs) |
| mstars | Array(float) | False | False | | The masses of the stars in the grid (as described in the RADMC3D docs) |
| star_positions | Array(float) | False | False | | The array of stellar positions |
| star_fluxes | Array(float) | False | False | | The arrays of the stellar fluxes, for each star |
| nlamdbas | integer | False | False | | The number of wavelengths to consider for the stellar spectrum |
| spacing | text | False | False | | |
| lambdas_micron_limits | Array(float) | False | False | | The spectrum limits array, expressed in microns |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | grid_parameters | The id of the run |
#### lines_parameters
The `lines_parameters` table contains the mode used by RADMC to perform the radiative transfer.
| Column name | Type | PK | FK | FK Table | Description |
|------------------|--------------|-------|-------|-----------------|------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | True | True | grid_parameters | The name of the compressed grid file containing all the input used to generate the model |
| lines_mode | varchar(20) | False | False | | The mode used by RADMC to perform the radiative transfer |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | grid_parameters | The id of the run |
#### species_and_partners
This table contains the species and their partners that are used in the radiative transfer computation.
| Column name | Type | PK | FK | FK Table | Description |
|---------------------------------------|--------------|-------|-------|-----------------|------------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | True | True | grid_parameters | The name of the compressed grid file containing all the input used to generate the model |
| species_to_include | varchar(100) | True | False | | A molecular species for which RT was performed |
| molecular_abundance | float | False | False | | The base abundance of the molecule |
| threshold | float | False | False | | The temperature threshold above which the temperature is assumed to evaporate from dust grains |
| abundance_jump | float | False | False | | The factor by which the base abundance is scaled after evaporation |
| collision_partner | varchar(100) | True | False | | A collision partner used for excitation |
| molecular_abundance_collision_partner | float | False | False | | The molecular abundance of the collision partner |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | grid_parameters | The id of the run |
#### model_pars
This table includes the configuration parameters used for RADMC.
| Column name | Type | PK | FK | FK Table | Description |
|---------------------|--------------|-------|-------|-----------------|------------------------------------------------------------------------------------------|
| zipped_grid_name | varchar(150) | False | True | grid_parameters | The name of the compressed grid file containing all the input used to generate the model |
| fits_cube_name | varchar(100) | True | False | | A molecular species for which RT was performed |
| nphotons | float | False | False | | The number of photons used in the RT computation |
| scattering_mode_max | integer | False | False | | The type of scattering considered in RADMC |
| iranfreqmode | integer | False | False | | |
| tgas_eq_tdust | integer | False | False | | Whether or not the gas and dust temperatures are assumed equal |
| inclination | float | False | False | | The model inclination |
| position_angle | float | False | False | | The model position angle |
| imolspec | float | False | False | | The species identifier |
| iline | float | False | False | | The line identifier, according to the molecular transitions descriptor file |
| width_kms | float | False | False | | The width of the spectrum to be computed |
| nchannels | float | False | False | | The number of channel into which the spectrum will be divided |
| npix | float | False | False | | The number of pixels used for the output image |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | grid_parameters | The id of the run |
#### moment_zero_maps
This table contains the information on the moment-zero images and their computation.
| Column name | Type | PK | FK | FK Table | Description |
|------------------------|--------------|-------|-------|------------------|---------------------------------------------------------------------------------------------------|
| mom_zero_name | varchar(150) | True | False | | The name of the moment-zero fits file |
| fits_cube_name | varchar(150) | False | True | model_parameters | The name of the fits cube from which the moment zero was computed |
| integration_limit_low | float | False | False | | The lower integration limit for the computation of the moment zero; if null includes all channels |
| integration_limit_high | float | False | False | | The upper integration limit for the computation of the moment zero; if null includes all channels |
| aggregated_moment_zero | float | False | False | | The moment zero computed for the entire cube, according to aggregation_function |
| aggregation_function | varchar(20) | False | False | | The function used to aggregate the pixel-values of the moment-zero maps |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | moment_zero_maps | The id of the run |
#### ratio_maps
This table includes the information about the ratio computation.
| Column name | Type | PK | FK | FK Table | Description |
|----------------------|--------------|-------|-------|------------------|---------------------------------------------------------------------------|
| ratio_map_name | varchar(150) | True | False | | The name of the fits file that contains the ratio maps |
| mom_zero_name_1 | varchar(150) | False | True | moment_zero_maps | The name of the moment-zero map constituting the numerator of the ratio |
| mom_zero_name_2 | varchar(150) | False | True | moment_zero_maps | The name of the moment-zero map constituting the denominator of the ratio |
| aggregated_ratio | float | False | False | | The value of the ratio computed over the full simulated cubes |
| aggregation_function | varchar(20) | False | False | | The function used to compute the aggregated ratio |
| created_on | DateTime | False | False | | The timestamp of the record creation |
| run_id | text | True | True | moment_zero_maps | The id of the run |
#### tmp_execution_queue
An auxiliary table used to efficiently parallelize computation in a distributed environment.
| Column name | Type | PK | FK | FK Table | Description |
|--------------------------|---------|-------|-------|----------|------------------------------------------------------------------------------------------------------|
| row_id | integer | False | False | | A row counter |
| run_id | text | True | False | | The id of the run |
| dust_temperature | float | True | False | | The characteristic dust temperature of the model |
| density | float | True | False | | The characteristic number density temperature of the model |
| line | integer | True | False | | The line ID |
| density_keyword | text | True | False | | The column used in the query to select the input (central_density or density_at_reference) |
| dust_temperature_keyword | text | True | False | | The column used in the query to select the input (dust_temperature or dust_temperature_at_reference) |
| fits_cube_name | text | False | False | | The name of the associated fits cube |
| done | bool | False | False | | Whether the image was processed already |
\ No newline at end of file
documentation/radmc3d_install.sh 0 → 100644
View file @ dab489de
#!/bin/bash
# Set PYCHARM_HOME, INSTALL_DEPENDENCIES environment variable
PYCHARM_HOME=${PYCHARM_HOME:=$HOME}
INSTALL_DEPENDENCIES="${INSTALL_DEPENDENCIES:=true}"
echo $PYCHARM_HOME
cd $PYCHARM_HOME
export RADMC_HOME=$PYCHARM_HOME/radmc3d-2.0
if [[ ! -d "$RADMC_HOME" ]]
then
echo RADMC not found, cloning into "$(pwd)"
git clone https://github.com/dullemond/radmc3d-2.0.git
fi
if $INSTALL_DEPENDENCIES
then
apt-get install -y gfortran
fi
cd $RADMC_HOME/src
make
echo "y\n y\n"| make install
# Add this to the .bashrc configuration file (or equivalent)
export PATH=$HOME/bin:$PATH
export PYTHONPATH=$HOME/bin/python:$PYTHONPATH
cd $RADMC_HOME/python/radmc3dPy
python setup.py install
etl/assets/commons/__init__.py 0 → 100644
View file @ dab489de
import glob
import logging
import sys
import yaml
import numpy as np
import shutil
import os
import urllib.request
import json
import pandas as pd
from hashlib import sha1
from typing import (List,
Union,
Tuple)
from astropy import units as u
from assets.constants import (leiden_url_mapping)
def setup_logger(name: str,
log_level: str = None) -> logging.Logger:
"""
Configure default logger
:param name: Logger name
:param log_level: Logging levels, as defined by logging
:return: the logger object
"""
log_level = 'DEBUG' if log_level is None else log_level
"""general logger configurator"""
logger = logging.getLogger(name)
logger.setLevel(log_level)
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(log_level)
formatter = logging.Formatter("%(asctime)s - %(name)-7s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
def validate_parameter(param_to_validate,
default):
"""
Substitute default value if the parameter is set to None
:param param_to_validate: input to validate
:param default: default value
:return: default value if parameter is set to None, else the passed value
"""
return param_to_validate if param_to_validate is not None else default
def load_config_file(config_file_path: str,
override_config: Union[dict, None] = None) -> dict:
"""
Load the information in the YAML configuration file into a python dictionary
:param config_file_path: path to the configuration file
:param override_config: parameters of the input file to override (e.g. for grid creation)
:return: a dictionary with the parsed information
"""
_override_config = validate_parameter(override_config, default={})
with open(config_file_path) as config_file:
config = yaml.load(config_file, Loader=yaml.FullLoader)
for key in _override_config:
if key not in config.keys():
config[key] = {}
for subkey in _override_config[key]:
try:
config[key][subkey] = _override_config[key][subkey]
except KeyError:
config[key] = {subkey: _override_config[key][subkey]}
try:
if config['density_powerlaw_idx'] == 0:
config['central_density'] = config['density_at_reference']
config['density_at_reference'] = None
except KeyError:
pass
try:
if config['dust_temperature_powerlaw_idx'] == 0:
config['dust_temperature'] = config['dust_temperature_at_reference']
config['dust_temperature_at_reference'] = None
except KeyError:
pass
return config
def get_moldata(species_names: list,
logger: logging.Logger,
path: Union[str, None] = None,
use_cache: bool = False):
"""
Downloads the molecular data from the Leiden molecular database; check whether the molecule in mapped in
leiden_url_mapping
:param species_names: the names of the species for which to download data
:param logger: logger for output printing
:param path: path where the files must be saved
:param use_cache: use molecular input files in cache, if possible
"""
_path = validate_parameter(path, default=os.path.join('mdl', 'radmc_files'))
for species in species_names:
molecular_file_name = f'molecule_{species}.inp'
if use_cache is True:
shutil.copy(os.path.join('mdl', 'data', molecular_file_name),
os.path.join(_path, molecular_file_name))
if not os.path.isfile(os.path.join(_path, molecular_file_name)):
logger.info(f'Downloading file molecule_{species}.inp...')
data = urllib.request.urlopen(leiden_url_mapping[species]).read().decode()
with open(os.path.join(_path, f'molecule_{species}.inp'), 'w') as outfile:
outfile.writelines(data)
else:
logger.info(f'File molecule_{species}.inp found, skipping download...')
def compute_unique_hash_filename(config: dict) -> str:
"""
Compute a unique and reproducible filename given a configuration dictionary
:param config: the configuration dictionary to hash
:return: a unique hash of the dictionary, to use as a key and filename
"""
hashed_dict = sha1(json.dumps(config, sort_keys=True).encode())
return f'{hashed_dict.hexdigest()}'
def make_archive(output_filename: str,
source_dir: str,
archive_path: Union[str, None] = None):
"""
Compresses all files in a directory into a .zip file
:param output_filename: the output filename
:param source_dir: the directory to compress
:param archive_path: path to store the compressed-grid archives
"""
_archive_path = validate_parameter(archive_path,
default=os.path.join('stg', 'archive'))
filename_root, filename_format = output_filename.rsplit('.', maxsplit=1)
try:
os.remove(os.path.join(_archive_path, output_filename))
except FileNotFoundError:
pass
shutil.make_archive(base_name=os.path.join(_archive_path, filename_root),
format=filename_format,
root_dir=source_dir)
def cleanup_directory(directory: str,
logger: logging.Logger):
"""
Removes all files from the specified directory.
:param directory: directory to clean up
:param logger: logger to use
"""
if os.path.isdir(directory):
file_list = glob.glob(f'{directory}/*')
for filename in file_list:
if os.path.isfile(filename) is True:
logger.debug(f'Removing file {filename}')
os.remove(filename)
else:
logger.debug(f'Skipping {filename}')
def convert_frequency_to_wavelength(frequency: u.Quantity,
output_units: u.Unit):
return frequency.to(output_units, equivalencies=u.spectral())
def get_value_if_specified(parameters_dict: dict,
key: str):
try:
return parameters_dict[key]
except KeyError:
return None
def get_postprocessed_data(limit_rows: Union[None, int] = None,
use_model_for_inference: Union[None, str] = None) -> Tuple[List[List[str]], pd.DataFrame]:
"""
Retrieve data and line pairs from the main config file
:param use_model_for_inference: the prs/output/run_type folder from which to get the data from inference;
defaults to fiducial model (constant_abundance_p15_q05)
:param limit_rows: the number of rows to use from the original dataset; useful to run tests and limit
computation time
:return: the line pairs list and the dataset
"""
_use_model_for_inference = validate_parameter(
use_model_for_inference,
default='constant_abundance_p15_q05'
)
_data_file = os.path.join('prs', 'output', 'run_type', _use_model_for_inference, 'data', 'full_dataset.csv')
config = load_config_file(os.path.join('config', 'config.yml'))
line_pairs = config['overrides']['lines_to_process']
results_df_dict = {
'ratios_and_los_averages': add_px_index_column(_data_file)
}
if limit_rows is not None:
data = results_df_dict['ratios_and_los_averages'].head(limit_rows)
else:
data = results_df_dict['ratios_and_los_averages']
return line_pairs, data
def prepare_matrix(filename: str,
columns: list,
use_model_for_inference: Union[None, str] = None) -> pd.DataFrame:
"""
Retrieve and prepare the data matrix from a specified file and columns.
:param filename: The name of the file to read the data from.
:param columns: The list of columns to extract from the dataframe.
:param use_model_for_inference: The folder within prs/output/run_type to get the data for inference;
defaults to the fiducial model ('constant_abundance_p15_q05') if None is provided.
:return: A pandas DataFrame containing the specified columns from the file with 'nh2' and 'tdust' columns
rounded to one decimal place and converted to string type.
"""
_use_model_for_inference = validate_parameter(
use_model_for_inference,
default='constant_abundance_p15_q05'
)
df = pd.read_csv(os.path.join('prs', 'output', 'run_type', _use_model_for_inference, 'data', filename))
df['nh2'] = pd.Series(df['nh2'].round(1), dtype='string')
df['tdust'] = pd.Series(df['tdust'].round(1), dtype='string')
return df[columns]
def get_data(limit_rows: Union[int, None] = None,
use_model_for_inference: Union[None, str] = None,
log_columns: Union[None, List] = None):
"""
Retrieve and preprocess dataset.
:param limit_rows: The number of rows to use from the original dataset; useful for running tests and limiting
computation time. Defaults to None, which uses all rows.
:param use_model_for_inference: The folder within prs/output/run_type to get the data for inference;
defaults to the fiducial model ('constant_abundance_p15_q05') if None is provided.
:param log_columns: The list of columns to apply a logarithmic transformation to. Defaults to
['log_nh2', 'log_tdust', 'avg_nh2', 'avg_tdust', 'molecule_column_density', 'std_nh2'] if None is provided.
:return: A pandas DataFrame containing the merged and processed data from multiple sources, with specified
columns logarithmically transformed.
"""
_use_model_for_inference = validate_parameter(
use_model_for_inference,
default='constant_abundance_p15_q05'
)
_log_columns = validate_parameter(
log_columns,
default=['log_nh2', 'log_tdust', 'avg_nh2', 'avg_tdust', 'molecule_column_density', 'std_nh2']
)
_, data = get_postprocessed_data(limit_rows=limit_rows,
use_model_for_inference=_use_model_for_inference)
data['nh2'] = pd.Series(data['nh2'].round(1), dtype='string')
data['tdust'] = pd.Series(data['tdust'].round(1), dtype='string')
df87 = prepare_matrix(filename='full_dataset_NH2_lines_87-86.csv',
columns=['px_index', 'nh2', 'tdust', 'mom_zero_87', 'mom_zero_86', 'molecule_column_density'],
use_model_for_inference=_use_model_for_inference)
df88 = prepare_matrix(filename='full_dataset_NH2_lines_88-86.csv',
columns=['px_index', 'nh2', 'tdust', 'mom_zero_88'],
use_model_for_inference=_use_model_for_inference)
df257 = prepare_matrix(filename='full_dataset_NH2_lines_257-256.csv',
columns=['px_index', 'nh2', 'tdust', 'mom_zero_257', 'mom_zero_256'],
use_model_for_inference=_use_model_for_inference)
df381 = prepare_matrix(filename='full_dataset_NH2_lines_381-380.csv',
columns=['px_index', 'nh2', 'tdust', 'mom_zero_381', 'mom_zero_380'],
use_model_for_inference=_use_model_for_inference)
merged = data.merge(df87, on=['px_index', 'nh2', 'tdust'])
merged = merged.merge(df88, on=['px_index', 'nh2', 'tdust'])
merged = merged.merge(df257, on=['px_index', 'nh2', 'tdust'])
merged = merged.merge(df381, on=['px_index', 'nh2', 'tdust'])
# These are transformed later on, to allow splitting
merged['nh2'] = pd.to_numeric(merged['nh2'])
merged['tdust'] = pd.to_numeric(merged['tdust'])
merged['log_nh2'] = pd.to_numeric(merged['nh2'])
merged['log_tdust'] = pd.to_numeric(merged['tdust'])
for column in _log_columns:
merged[column] = np.log10(merged[column])
npixels = 101
refpix = 50
merged['px_distance'] = np.sqrt(
((merged['px_index'] / npixels).astype(int) - refpix) ** 2 + ((merged['px_index'] % npixels) - refpix) ** 2)
merged['core_radius_px'] = (1e8 / merged['nh2']) ** -(2 / 3) * 0.5 * 101 / 2
return merged
def add_px_index_column(filename: str) -> pd.DataFrame:
"""
Reset index to add pixel index column to data
:param filename: The filename of the csv file that contains the data
:return: a pandas dataframe with the px_index column
"""
df = pd.read_csv(filename, index_col=0)
if 'px_index' not in df.columns:
df = df.reset_index().rename(columns={'index': 'px_index'})
return df
etl/assets/commons/db_utils.py 0 → 100644
View file @ dab489de
import base64
import logging
import os
from contextlib import closing
from typing import Union, List
import sqlalchemy as sqla
import yaml
from sqlalchemy.dialects.postgresql import insert
from sqlalchemy.orm import Session
from assets.commons import validate_parameter
def get_credentials(logger: logging.Logger,
credentials_filename: Union[None, str] = None,
set_envs: bool = False) -> Union[None, dict]:
"""
Retrieves credentials, if available, for logging in to the requested service.
:param domain: the name of the service, as it appears in the credentials file (case sensitive!).
:param logger: the logger to use.
:param credentials_filename: the name of the credentials file to be used. If None, uses the default.
:param set_envs: whether to set the credentials as environment variables.
:return: a dictionary with username and password, as retrieved from the credentials file or None.
"""
_credentials_filename = credentials_filename if credentials_filename is not None \
else os.path.join('credentials', 'credentials.yml')
try:
with open(_credentials_filename) as credentials_file:
credentials = yaml.load(credentials_file, Loader=yaml.FullLoader)
credentials['DB_USER'] = base64.b64decode(credentials['DB_USER'].encode()).decode()
credentials['DB_PASS'] = base64.b64decode(credentials['DB_PASS'].encode()).decode()
if set_envs is True:
for key in credentials:
os.environ[key] = str(credentials[key])
return credentials
except FileNotFoundError:
logger.info('Credentials not found!')
return None
def upsert(table_object: sqla.orm.decl_api.DeclarativeMeta,
row_dict: dict,
conflict_keys: List[sqla.Column],
engine: sqla.engine):
"""
Upsert the row into the specified table, according to the indicated conflict columns
:param table_object: the table into which the row must be inserted
:param row_dict: the dictionary representing the row to insert
:param conflict_keys: the conflict columns list, representing the primary key of the table
:param engine: the SQLAlchemy engine to use
"""
statement = insert(table_object).values(row_dict)
statement = statement.on_conflict_do_update(
index_elements=conflict_keys, set_=row_dict)
with closing(Session(engine)) as session:
session.execute(statement)
session.commit()
def get_pg_engine(logger: logging.Logger, engine_kwargs: Union[dict, None] = None) -> sqla.engine.Engine:
"""
Return the SQLAlchemy engine, given the credentials in the external file
:param logger: the logger to use
:return: the SQLAlchemy engine
"""
_kwargs = validate_parameter(engine_kwargs, default={})
credentials = get_credentials(logger=logger,
credentials_filename=os.path.join('credentials', 'db_credentials.yml'))
url = f'postgresql://{credentials["DB_USER"]}:{credentials["DB_PASS"]}@{credentials["DB_HOST"]}:{credentials["DB_PORT"]}/{credentials["DB_NAME"]}'
engine = sqla.create_engine(url, **_kwargs)
return engine
etl/assets/commons/grid_utils.py 0 → 100644
View file @ dab489de
import logging
import os
from typing import Union, List
import numpy as np
from astropy import units as u
from assets.commons import validate_parameter, load_config_file, setup_logger
from assets.commons.parsing import get_grid_properties
from assets.constants import mean_molecular_mass, radmc_grid_map, radmc_coord_system_map
def compute_power_law_radial_profile(
central_value: float,
power_law_index: float,
distance_matrix: np.array,
maximum_radius: Union[float, None] = None,
value_at_reference: Union[float, None] = None,
distance_reference: Union[float, u.Quantity] = 1.0,
fill_reference_pixel: bool = True) -> np.array:
"""
Compute a power law distribution over the specified grid
:param central_value: value in the center of the grid
:param power_law_index: index of the power law used to scale the profile
:param distance_matrix: the matrix of distances from the reference point
:param maximum_radius: the maximum radius to populate with gas within the grid in the same unit as the distance
matrix and distance reference
:param value_at_reference: value of the profile at the reference distance; defaults to central value if not provided
:param distance_reference: value of the reference distance
:param fill_reference_pixel: whether to fill the reference point with central_value and set this to the maximum in
the grid
:return: the distance matrix
"""
_value_at_reference = validate_parameter(value_at_reference, default=central_value)
_distance_matrix = np.where(distance_matrix == 0, np.nanmin(distance_matrix[distance_matrix > 0]),
distance_matrix) if fill_reference_pixel is True else distance_matrix
try:
_distance_reference = distance_reference.to(u.cm).value
except AttributeError:
_distance_reference = distance_reference
profile = _value_at_reference * (_distance_matrix / _distance_reference) ** power_law_index
# If the routine fills the 0-distance point (the centre), it fixes the profile making the central value the maximum
if (fill_reference_pixel is True) and (power_law_index != 0):
profile = np.where(profile > central_value, central_value, profile)
if maximum_radius is not None:
profile = np.where(distance_matrix > maximum_radius + maximum_radius / 1e5, 0, profile)
return profile
def compute_cartesian_coordinate_grid(indices: np.array,
physical_px_size: np.array) -> np.array:
"""
Compute the physical coordinate grid, for a regular grid
:param indices: the indices of the grid pixels
:param physical_px_size: the array of physical pixel sizes or the pixel size, as astropy.Quantities
:return: the numpy.array with the physical grid coordinates
"""
try:
_physical_px_size_cm = [px_size.to(u.cm).value for px_size in physical_px_size]
except TypeError:
_physical_px_size_cm = physical_px_size.to(u.cm).value
return (indices.T * _physical_px_size_cm).T
def get_centered_indices(grid_metadata: dict) -> np.array:
"""
Recompute the indices using as reference the reference pixel in the configuration file
:param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape and the reference pixel
:return: a numpy array of indices, wrt. the reference
"""
return (np.indices(grid_metadata['grid_shape']).T - grid_metadata['grid_refpix']).T
def get_grid_edges(grid_metadata: dict) -> np.array:
"""
Compute the physical coordinates at the grid boundaries
:param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, the reference pixel, and the
physical pixel size
:return: a numpy array with the coordinates of the edges, per axis
"""
grid_edges = []
for axis_idx in range(len(grid_metadata['grid_shape'])):
indices = np.arange(grid_metadata['grid_shape'][axis_idx] + 1) - grid_metadata['grid_refpix'][axis_idx] - 0.5
# valid for regular grid
grid_edges.append(compute_cartesian_coordinate_grid(indices=indices,
physical_px_size=grid_metadata['physical_px_size'][
axis_idx]))
# Transposed for consistent flattening
return np.array(grid_edges).T
def get_distance_matrix(grid_metadata: dict,
indices: np.array) -> np.array:
"""
Compute physical distance from the reference pixel, in cm
:param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, and the physical pixel size
:param indices: numpy array of the grid pixel indices
:return: a numpy array with the euclidean distance from the reference pixel
"""
distance_matrix = np.zeros(grid_metadata['grid_shape'])
for axis_idx in range(len(grid_metadata['grid_shape'])):
distance_matrix += (indices[axis_idx, ...] * grid_metadata['physical_px_size'][axis_idx].to(u.cm).value) ** 2
return np.sqrt(distance_matrix)
def get_physical_px_size(grid_metadata: dict) -> List[u.Quantity]:
"""
Compute the physical pixel size
:param grid_metadata: the dictionary with the grid metadata, to obtain the grid shape, the grid size, and the grid
size units
:return: a list of physical pixel sizes, per axis
"""
physical_px_size = []
for axis_idx in range(len(grid_metadata['grid_shape'])):
physical_px_size.append(
(grid_metadata['grid_size'][axis_idx] * u.Unit(grid_metadata['grid_size_units'][axis_idx])).to(u.cm) /
grid_metadata['grid_shape'][axis_idx])
return physical_px_size
def compute_analytic_profile(central_value: float,
power_law_index: float,
maximum_radius: float,
value_at_reference: float,
distance_reference: float) -> np.array:
"""
Compute the analytic radial profile based on a power-law model.
:param central_value (float): Central value of the profile, where the power-law could be undefined.
:param power_law_index (float): Power-law index determining the shape of the profile.
:param maximum_radius (float): Maximum radius within which to compute the profile; values outside this radius are
set to zero.
:param value_at_reference (float): Value of the profile at the reference distance.
:param distance_reference (float): Reference distance at which the profile value is specified.
:return: np.array: Computed radial profile.
Example:
# Compute an analytic profile with given parameters
profile = compute_analytic_profile(central_value=10.0,
power_law_index=-1.5,
maximum_radius=5.0,
value_at_reference=5.0,
distance_reference=1.0)
"""
config = load_config_file(config_file_path=os.path.join('stg', 'config', 'config.yml'))
grid_metadata = extract_grid_metadata(config=config)
distance_matrix = get_distance_matrix(grid_metadata=grid_metadata,
indices=get_centered_indices(grid_metadata=grid_metadata))
profile = compute_power_law_radial_profile(central_value=central_value,
power_law_index=power_law_index,
distance_matrix=distance_matrix,
maximum_radius=maximum_radius,
value_at_reference=value_at_reference,
distance_reference=distance_reference)
return profile
def compute_los_average_weighted_profile(profile: np.array,
weights: Union[float, np.array]) -> np.array:
"""
Compute the line-of-sight (LOS) average weighted profile.
This function computes the weighted average profile along the LOS, where each profile is weighted
according to the provided weights. The computation is performed by summing the products of each
profile value and its corresponding weight across all LOS, and then dividing by the sum of weights
to obtain the average.
Note:
- If a profile value is 0, it is treated as missing data and not included in the computation.
- If a weight is 0, the corresponding profile is effectively excluded from the computation.
:param profile: (np.array) Array containing the profiles along different lines of sight (LOS).
:param weights: (Union[float, np.array]) Array containing the weights corresponding to each profile in the input array,
or a single weight value if uniform weighting is desired.
:return: (np.array) LOS average weighted profile.
"""
return np.nansum(np.where(profile == 0, np.nan, profile * weights), axis=2) / \
np.nansum(np.where(profile == 0, np.nan, weights), axis=2)
def compute_density_and_temperature_avg_profiles(density_profile: np.array,
temperature_profile: np.array,
molecular_abundance: float,
threshold: float,
abundance_jump: Union[float, int],
logger: Union[logging.Logger, None] = None) -> tuple:
"""
Compute the average density and temperature profiles along the line-of-sight along with related quantities.
This function computes the average density and temperature profiles along the LOS, along with related quantities
such as molecular column density and the standard deviation of the species' number density. It first computes the
molecular number density grid using the given density, temperature, abundance, temperature threshold, and abundance
jump for simulating hot core-like abundance profiles. Then, it computes the average density and temperature profiles
using the molecular number density grid as weights along the LOS. Finally, it calculates the
molecular column density and its standard deviation.
:param density_profile (np.array): Array containing the density profiles.
:param temperature_profile (np.array): Array containing the temperature profiles.
:param molecular_abundance (float): Molecular abundance for computing molecular number density.
:param threshold (float): Threshold for adjusting the abundance in hot cores, where the molecule is potentially
evaporated from dust grains.
:param abundance_jump (Union[float, int]): Abundance jump in hot cores.
:param logger (Union[logging.Logger, None], optional): Logger object for logging messages. If not specified, creates
a generic logger for reporting the total mass.
:return: Tuple[np.array, np.array, np.array, np.array]
- Average density profile.
- Average temperature profile.
- Molecular column density.
- Standard deviation of molecular number density.
"""
_logger = validate_parameter(logger, setup_logger('GENERIC'))
config = load_config_file(config_file_path=os.path.join('stg', 'config', 'config.yml'))
grid_metadata = extract_grid_metadata(config=config)
molecule_grid = compute_molecular_number_density_hot_core(gas_number_density_profile=density_profile,
abundance=molecular_abundance,
temperature_profile=temperature_profile,
threshold=threshold,
abundance_jump=abundance_jump)
avg_density_profile = compute_los_average_weighted_profile(profile=density_profile,
weights=molecule_grid)
mass = (np.nansum(density_profile * u.cm ** -3 * grid_metadata['physical_px_size'][0]
* grid_metadata['physical_px_size'][1]
* grid_metadata['physical_px_size'][2]) * mean_molecular_mass * m_p).to(u.M_sun)
_logger.debug(f'Total mass: {mass}')
avg_temperature_profile = compute_los_average_weighted_profile(profile=temperature_profile,
weights=molecule_grid)
return (avg_density_profile,
avg_temperature_profile,
np.nansum(molecule_grid, axis=2) * grid_metadata['physical_px_size'][2].value,
np.nanstd(molecule_grid, axis=2))
def compute_molecular_number_density_hot_core(gas_number_density_profile: np.array,
abundance: float,
temperature_profile: np.array,
threshold: float,
abundance_jump: Union[float, int]) -> np.array:
"""
Compute the molecular number density, using a step function abundance, changing above a specific temperature to
simulate evaporation
:param gas_number_density_profile: the gas number density profile array
:param abundance: the gas abundance of the species
:param temperature_profile: the temperature profile of the source
:param threshold: the threshold at which the species evaporate
:param abundance_jump: the factor describing the abundance variation wrt the bas level
:return: the array of molecular gas density computed using a step function profile
"""
return np.where(temperature_profile < threshold,
gas_number_density_profile * abundance,
gas_number_density_profile * abundance * abundance_jump)
def extract_grid_metadata(config: dict) -> dict:
"""
Enrich grid metadata from the information in the configuration file
:param config: configuration dictionary
:return: a dictionary with the metadata
"""
grid_config = config['grid']
grid_metadata = grid_config.copy()
grid_metadata['grid_type'] = radmc_grid_map[grid_config['grid_type']]
grid_metadata['coordinate_system'] = radmc_coord_system_map[grid_config['coordinate_system']]
grid_properties_keywords = ['shape', 'refpix', 'size', 'size_units']
for key in grid_properties_keywords:
grid_metadata[f'grid_{key}'] = get_grid_properties(grid_config=grid_config,
keyword=key)
grid_metadata['physical_px_size'] = get_physical_px_size(grid_metadata)
grid_metadata['centered_indices'] = get_centered_indices(grid_metadata)
grid_metadata['coordinate_grid'] = compute_cartesian_coordinate_grid(indices=grid_metadata['centered_indices'],
physical_px_size=grid_metadata[
'physical_px_size'])
grid_metadata['distance_matrix'] = get_distance_matrix(grid_metadata, grid_metadata['centered_indices'])
grid_metadata['grid_edges'] = get_grid_edges(grid_metadata)
grid_metadata['continuum_lambdas'] = 100
return grid_metadata
etl/assets/commons/parsing.py 0 → 100644
View file @ dab489de
import os
from typing import Tuple
import numpy as np
from assets.commons import (load_config_file,
validate_parameter)
def parse_grid_overrides(par_name: str,
config: dict) -> np.array:
"""
Parse the global configuration for constructing the grid, computing the parameter values for which the grid has to
be computed.
:param par_name: parameter to parse
:param config: configuration dictionary to extract the grid properties
:return: the array of values to be considered in the grid
"""
config_overrides = config['overrides']
if config_overrides[f'{par_name}_grid_type'] == 'linear':
grid_values = np.arange(float(config_overrides[f'{par_name}_limits'][0]),
float(config_overrides[f'{par_name}_limits'][1]),
float(config_overrides[f'{par_name}_step']))
else:
_step = float(config_overrides[f'{par_name}_step']) if float(config_overrides[f'{par_name}_step']) > 1 else 10 ** float(config_overrides[f'{par_name}_step'])
grid_values = 10 ** np.arange(np.log10(float(config_overrides[f'{par_name}_limits'][0])),
np.log10(float(config_overrides[f'{par_name}_limits'][1])),
np.log10(_step))
return np.around(grid_values, 2)
def get_grid_properties(grid_config: dict,
keyword: str) -> list:
"""
Compute the grid axes properties using the grid configuration dictionary. If less than 3 axes are provided, fill
the missing ones with the first axis.
:param grid_config: the dictionary containing the grid configuration metadata
:param keyword: the keyword to read
:return: a list of the requested keyword values over the three spatial axes
"""
grid_property_list = [grid_config['dim1'][keyword]]
for axis_idx in range(2):
try:
grid_property_list.append(grid_config[f'dim{axis_idx + 2}'][keyword])
except KeyError:
grid_property_list.append(grid_config['dim1'][keyword])
return grid_property_list
def parse_input_main() -> Tuple[str, str, np.array, np.array, list, int, str]:
"""
Parse the stg and mdl configuration files, as well as the main one, to apply overrides, compute the arrays of
temperatures and number densities and the type of models defined in the grid (isothermal/uniform density vs.
power-law profiles)
:return: a tuple containing the dust profile type (isothermal/heated), the density profile (homogeneous/spherical),
the distinct values of dust temperatures and densities in the grid, the line pairs used to compute ratios,
the number of processes to be used in case of multiprocessing, and the model run_type string
"""
default_config_name = 'config.yml'
stg_config = load_config_file(os.path.join('stg', 'config', default_config_name))
pl_density_idx = float(stg_config['grid']['density_powerlaw_idx'])
pl_dust_temperature_idx = float(stg_config['grid']['dust_temperature_powerlaw_idx'])
_model_type = 'spherical' if pl_density_idx != 0 else 'homogeneous'
_tdust_model_type = 'heated' if pl_dust_temperature_idx != 0 else 'isothermal'
config = load_config_file(os.path.join('config', default_config_name))
# grid definition
dust_temperatures = parse_grid_overrides(par_name='dust_temperature',
config=config)
densities = parse_grid_overrides(par_name='gas_density',
config=config)
line_pairs = config['overrides']['lines_to_process']
n_processes = validate_parameter(config['computation']['threads'], default=10)
return _tdust_model_type, _model_type, dust_temperatures, densities, line_pairs, n_processes, config['run_type']
def read_abundance_variation_schema(line_config: dict) -> dict:
"""
Read and fill the abundance variation schema, defined as a step function
:param line_config: the dictionary of the line configurations, for the abundances
:return: the dictionary of the abundance variations per species, with the abundance jump factor and the temperature
threshold at which evaporation happens
"""
species_list = list(line_config['molecular_abundances'].keys())
results_dict = {}
for species in species_list:
results_dict[species] = {
'threshold': 20,
'abundance_jump': 1
}
if 'hot_core_specs' in line_config.keys():
if species in line_config['hot_core_specs']:
results_dict[species] = line_config['hot_core_specs'][species]
return results_dict
etl/assets/commons/training_utils.py 0 → 100644
View file @ dab489de
import logging
import os
import pickle
from typing import Union, List, Tuple
import numpy as np
import pandas as pd
from astropy import units as u
from matplotlib import pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.experimental import enable_halving_search_cv
from sklearn.model_selection import HalvingGridSearchCV
from sklearn.neighbors import KernelDensity
from sklearn.preprocessing import QuantileTransformer
from xgboost import XGBRegressor
from assets.commons import (validate_parameter,
get_data)
from assets.commons.grid_utils import (compute_analytic_profile,
compute_density_and_temperature_avg_profiles)
def compute_and_add_similarity_cols(average_features_per_target_bin: pd.DataFrame,
input_df: pd.DataFrame,
similarity_bins: int,
columns_to_drop: Union[list, None] = None) -> pd.DataFrame:
"""
Compute the cosine similarity between the input matrix and the average values split in bins of the target,
adding the columns to the input.
:param average_features_per_target_bin: the dataframe with the average values of the input per target bin
:param input_df: the input dataframe
:param columns_to_drop: the columns to drop from the input matrix before computing the cosine similarities
:param similarity_bins: number of bins used to group the target variable
:return: the input matrix with the similarity columns
"""
_columns_to_drop = validate_parameter(columns_to_drop, default=[])
clean_df = input_df.copy().drop(columns=_columns_to_drop)
ordered_columns = average_features_per_target_bin.columns
similarities = cosine_similarity(clean_df[ordered_columns], average_features_per_target_bin[ordered_columns])
similarities = pd.DataFrame(similarities,
columns=[f'sim_{str(bin_idx).zfill(2)}' for bin_idx in range(similarity_bins)])
similarities.index = clean_df.index
return pd.merge(input_df, similarities, left_index=True, right_index=True, how='inner', validate='1:1', on=None)
def plot_results(inferred_data: pd.DataFrame,
use_model_for_inference: str = None,
ratios_to_process: Union[List[List[str]], None] = None):
"""
Plot the results of inferred data against postprocessed data for specified line ratios, in oder to quickly check
the results.
:param inferred_data: The DataFrame containing the inferred data to plot.
:param use_model_for_inference: The folder within prs/output/run_type to get the data for inference;
defaults to the fiducial model ('constant_abundance_p15_q05') if None is provided.
:param ratios_to_process: The list of line ratios to plot, each specified as a list of two strings.
Defaults to [['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']] if None is provided.
:return: None. Saves the plot as a PNG file named according to the model used for inference.
"""
_use_model_for_inference = validate_parameter(
use_model_for_inference,
default='constant_abundance_p15_q05'
)
_ratios_to_process = validate_parameter(
ratios_to_process,
default=[['87', '86'], ['88', '87'], ['88', '86'], ['257', '256'], ['381', '380']]
)
postprocessed_data = get_data(limit_rows=None,
use_model_for_inference=_use_model_for_inference)
plt.clf()
_, subplots = plt.subplots(nrows=2, ncols=3)
subplots[-1][-1].axis('off')
for idx, line_pairs in enumerate(_ratios_to_process):
subplots[int(idx / 3)][idx % 3].semilogx()
subplots[int(idx / 3)][idx % 3].scatter(10 ** inferred_data['avg_nh2'],
inferred_data[f'ratio_{line_pairs[0]}-{line_pairs[1]}'],
alpha=0.1)
subplots[int(idx / 3)][idx % 3].scatter(10 ** postprocessed_data['avg_nh2'],
postprocessed_data[f'ratio_{line_pairs[0]}-{line_pairs[1]}'],
alpha=0.01)
plt.savefig(f'training_results_{_use_model_for_inference}.png')
def get_avg_profiles(
value_at_reference_density: float,
value_at_reference_temperature: float,
central_value_density: float = 1e8,
power_law_density: float = -1.5,
central_value_temperature: float = 2000,
power_law_temperature: float = -0.5,
maximum_radius: u.Quantity = 0.9 * u.pc,
distance_reference: u.Quantity = 0.5 * u.pc,
molecular_abundance: float = 1e-9,
threshold: float = 90,
abundance_jump: float = 1) -> Tuple[np.array, np.array, np.array, np.array]:
"""
Get the average density, temperature, and standard deviation profiles along the line-of-sight, as well as the
molecular column density.
This function computes the average density, temperature, and standard deviation profiles along the LOS, together
with the molecular column density.
It first computes the density and temperature profiles analytically based on the specified parameters.
Then, it calculates the average density and temperature profiles along with related quantities such as the
molecular column density and the standard deviation of the species' number density using the computed
profiles and specified parameters.
:param value_at_reference_density (float): Reference value of the density profile.
:param value_at_reference_temperature (float): Reference value of the temperature profile.
:param central_value_density (float, optional): Central value of the density profile, and ceiling for this
parameter. Defaults to 1e8 cm^-3.
:param power_law_density (float, optional): Power-law index for the density profile. Defaults to -1.5.
:param central_value_temperature (float, optional): Central value of the temperature profile, and ceiling for
this parameter. Defaults to 2000 K.
:param power_law_temperature (float, optional): Power-law index for the temperature profile. Defaults to -0.5.
:param maximum_radius (u.Quantity, optional): Maximum radius within which to compute the profiles, larger radii
are assumed to be devoid of material. Defaults to 0.9 pc.
:param distance_reference (u.Quantity, optional): Reference distance at which the profiles values are specified.
Defaults to 0.5 pc.
:param molecular_abundance (float, optional): Molecular abundance for computing molecular number density.
Defaults to 1e-9 for E-CH3OH.
:param threshold (float, optional): Threshold in temperature above which E-CH3OH is sublimated from grains in
hot cores. Defaults to 90 K.
:param abundance_jump (float, optional): Abundance jump in hot cores. Defaults to 1, i.e. no jump.
:return: Tuple[np.array, np.array, np.array, np.array]
- Average density profile.
- Average temperature profile.
- Molecular column density.
- Standard deviation of molecular number density profile.
"""
density_profile = compute_analytic_profile(central_value=central_value_density,
power_law_index=power_law_density,
maximum_radius=maximum_radius.to(u.cm).value,
value_at_reference=value_at_reference_density,
distance_reference=distance_reference.to(u.cm).value)
temperature_profile = compute_analytic_profile(central_value=central_value_temperature,
power_law_index=power_law_temperature,
maximum_radius=maximum_radius.to(u.cm).value,
value_at_reference=value_at_reference_temperature,
distance_reference=distance_reference.to(u.cm).value)
(avg_density_profile, avg_temperature_profile,
molecule_coldens, std_density) = compute_density_and_temperature_avg_profiles(
density_profile=density_profile,
temperature_profile=temperature_profile,
molecular_abundance=molecular_abundance,
threshold=threshold,
abundance_jump=abundance_jump
)
return (avg_density_profile.flatten('F'), avg_temperature_profile.flatten('F'),
molecule_coldens.flatten('F'), std_density.flatten('F'))
def get_weights(data: pd.Series,
show_weights: bool = False,
bandwidth: float = 0.1,
subsample_fraction: float = 0.5) -> np.array:
"""
Get weights for each data point using kernel density estimation (KDE).
:param data (pd.Series): Input data series for which weights are computed.
:param show_weights (bool, optional): Whether to interactively visualize the KDE and weights. Defaults to False.
:param bandwidth (float, optional): Bandwidth parameter for the KDE. Defaults to 0.1.
:param subsample_fraction (float, optional): Fraction of data to use for subsampling. Defaults to 0.5 to speed up
computation.
:return: np.array
Array of weights corresponding to each data point.
This function computes weights for each data point using kernel density estimation (KDE).
It fits a KDE model to the subsampled data and then evaluates the KDE at each data point
to obtain the weights. If specified, it also visualizes the KDE and weights.
"""
kde = KernelDensity(bandwidth=bandwidth, kernel='epanechnikov')
kde.fit(np.array(data.sample(frac=subsample_fraction, random_state=3)).reshape(-1, 1))
tgt_min = data.min()
tgt_max = data.max()
ymin_kde = tgt_min - 1
ymax_kde = tgt_max + 1
grid = np.linspace(ymin_kde, ymax_kde, 100)
z = np.exp(kde.score_samples(list(grid.reshape(-1, 1))))
z_points = np.exp(kde.score_samples(list(data.values.reshape(-1, 1))))
weights = 1 / z_points
finite_weights_mask = np.isfinite(weights)
padding_value = np.nanmax(weights[finite_weights_mask])
weights = np.where(finite_weights_mask,
weights,
padding_value)
if show_weights is True:
plt.clf()
plt.plot(grid, z)
plt.scatter(data, z_points, marker='+')
plt.scatter(data, weights / weights.max(), marker='+')
plt.show()
return weights
def split_data(merged: pd.DataFrame,
target_column: str,
predictor_columns: Union[list, None] = None,
test_models: Union[list, None] = None,
validation_models: Union[list, None] = None) -> Tuple[
np.array, np.array, np.array, np.array, np.array, np.array]:
"""
This function splits the data into training, validation, and test sets. It also performs some data processing,
such as taking the logarithm for the specified columns.
:param merged (pd.DataFrame): The DataFrame containing both predictor and target columns.
:param target_column (str): The name of the target column in the DataFrame.
:param predictor_columns (Union[list, None], optional): A list of predictor column names to be used in training.
Defaults to None.
:param test_models (Union[list, None], optional): A list of values for the characteristic number density,
identifying the test data. If not specified, it uses 27000 cm^-3.
:param validation_models (Union[list, None], optional): A list of values for the characteristic number density,
identifying the validation data. If not specified, it uses 2187000 cm^-3
:return: Tuple[np.array, np.array, np.array, np.array, np.array, np.array]
- x_test: Predictor variables for the test dataset.
- x_train: Predictor variables for the training dataset.
- x_validation: Predictor variables for the validation dataset.
- y_test: Target variable for the test dataset.
- y_train: Target variable for the training dataset.
- y_validation: Target variable for the validation dataset.
"""
_test_models = validate_parameter(test_models, default=[27000.0])
_validation_models = validate_parameter(validation_models, default=[2187000.0])
_predictor_columns = validate_parameter(
predictor_columns,
default=['nh2', 'tdust', 'avg_tdust', 'avg_nh2', 'molecule_column_density', 'std_nh2']
)
condition_test = merged['nh2'].isin(_test_models)
condition_validation = merged['nh2'].isin(_validation_models)
y = np.log10(merged[target_column].copy())
X = merged[_predictor_columns].copy()
nh2_list = [
1.000e+03,
3.000e+03,
9.000e+03,
2.700e+04,
8.100e+04,
2.430e+05,
7.290e+05,
2.187e+06,
6.561e+06
]
subsample = merged[
merged['nh2'].isin(nh2_list) & (~merged['nh2'].isin(_test_models)) & (~merged['nh2'].isin(_validation_models))]
for nh2_test in _test_models:
assert nh2_test not in list(subsample['nh2'].round(1).unique())
for nh2_validation in _validation_models:
assert nh2_validation not in list(subsample['nh2'].round(1).unique())
y_sub = np.log10(subsample[target_column].copy())
x_sub = subsample[_predictor_columns].copy()
x_train = x_sub[(~condition_test) & (~condition_validation)].reset_index(drop=True)
y_train = y_sub[(~condition_test) & (~condition_validation)].reset_index(drop=True)
x_test = X[condition_test].reset_index(drop=True)
y_test = y[condition_test].reset_index(drop=True)
x_validation = X[condition_validation].reset_index(drop=True)
y_validation = y[condition_validation].reset_index(drop=True)
for x_df in (x_train, x_test, x_validation):
for column in ('nh2', 'tdust'):
x_df[column] = np.log10(x_df[column])
return x_test, x_train, x_validation, y_test, y_train, y_validation
def train_ml_model(model_type: str,
model_kwargs: Union[dict, None],
x_train: pd.DataFrame,
y_train: pd.Series,
use_validation: bool = False,
x_validation: Union[None, pd.DataFrame] = None,
y_validation: Union[None, pd.Series] = None) -> Union[XGBRegressor, RandomForestRegressor]:
"""
This function trains a machine learning model based on the specified model type and its corresponding parameters.
It supports training XGBoost, Random Forest, with hyperparamter optimization and potentially other models in the
future.
:param model_type (str): Type of the machine learning model to be trained. Allowed values are 'XGBoost',
'XGBoost_gridsearch', 'auto_skl', 'RandomForest'.
:param model_kwargs (Union[dict, None]): Dictionary containing keyword arguments for initializing the model.
Defaults to None.
:param x_train (pd.DataFrame): Predictor variables for the training dataset.
:param y_train (pd.Series): Target variable for the training dataset.
:param use_validation (bool, optional): Whether to use a validation dataset for training during cross-validation and
optimization. Defaults to False.
:param x_validation (Union[None, pd.DataFrame], optional): Predictor variables for the validation dataset.
Defaults to None.
:param y_validation (Union[None, pd.Series], optional): Target variable for the validation dataset.
Defaults to None.
:return: Union[XGBRegressor, RandomForestRegressor]
Trained machine learning model.
"""
allowed_model_types = ('XGBoost', 'XGBoost_gridsearch', 'auto_skl', 'RandomForest')
assert model_type in allowed_model_types
if model_type == 'XGBoost':
model = XGBRegressor(**model_kwargs)
elif model_type == 'XGBoost_gridsearch':
model = HalvingGridSearchCV(XGBRegressor(),
param_grid=model_kwargs['param_grid'],
**model_kwargs['param_gridsearch'],
refit=True,
verbose=0)
elif model_type == 'RandomForest':
model = RandomForestRegressor()
else:
raise RuntimeError('Model type requested not yet implemented')
if model_type in ('XGBoost', 'XGBoost_gridsearch', 'RandomForest'):
weights = get_weights(data=y_train, show_weights=False)
if use_validation is True:
model.fit(x_train, y_train, sample_weight=weights, eval_set=[(x_validation, y_validation)], verbose=0)
else:
model.fit(x_train, y_train, sample_weight=weights)
else:
model.fit(x_train, y_train)
return model
def create_similarity_columns(model_root_folder: str,
target_id: str,
similarity_bins: int,
x_dataframes: dict,
y_train: pd.Series) -> dict:
"""
This function evaluates and adds cosine-similarity columns based on the average features per target bin. It calculates
the average features per target bin using the training data, groups the data into bins based on target
values, and then computes similarity columns for each DataFrame in the input dictionary (train, test, validation).
Finally, it saves the average features per target bin to a pickle file and returns the updated DataFrame dictionary.
:param model_root_folder (str): The root folder where the trained model and other related files will be stored.
:param target_id (str): Identifier for the target variable.
:param similarity_bins (int): Number of bins for similarity calculation.
:param x_dataframes (dict): Dictionary containing DataFrame objects for training, validation, and test sets.
:param y_train (pd.Series): Target variable for the training dataset.
:return: dict
Dictionary containing updated DataFrame objects with added similarity columns.
"""
pickle_outfile_name = os.path.join(model_root_folder, 'trained_model',
f'average_features_per_target_bin_{target_id}.pickle')
_x_dataframes = x_dataframes.copy()
bins = pd.cut(y_train, similarity_bins)
columns_to_drop = ([column for column in _x_dataframes['x_train'].columns if column.startswith('mom_zero_')]
+ [column for column in _x_dataframes['x_train'].columns if column.startswith('ratio_')])
clean_train = _x_dataframes['x_train'].copy().drop(columns=columns_to_drop)
average_features_per_target_bin = clean_train.groupby(bins).mean()
for key in _x_dataframes.keys():
_x_dataframes[key] = compute_and_add_similarity_cols(
average_features_per_target_bin=average_features_per_target_bin,
columns_to_drop=columns_to_drop,
input_df=_x_dataframes[key],
similarity_bins=similarity_bins)
if os.path.isfile(pickle_outfile_name):
os.remove(pickle_outfile_name)
with open(pickle_outfile_name, 'wb') as outfile:
pickle.dump(average_features_per_target_bin, outfile)
return _x_dataframes
def train_models_wrapper(target_name: str,
model_type: str,
model_kwargs: dict,
logger: logging.Logger,
similarity_bins: Union[None, int] = 50,
limit_rows: int = None,
model_root_folder: Union[str, None] = None,
use_validation: bool = False):
"""
Train machine learning models for a specified target variable.
:param target_name (str): Name of the target variable in the training set.
:param model_type (str): Type of the machine learning model to be trained.
:param model_kwargs (dict): Dictionary containing keyword arguments for initializing the model.
:param similarity_bins (Union[None, int], optional): Number of bins for similarity calculation. Defaults to 50.
:param limit_rows (int, optional): Limit the number of rows for preprocessing. Defaults to None.
:param model_root_folder (Union[str, None], optional): Root folder where the trained model and other related files
will be stored. if not specified, the fiducial model is assumed, i.e. 'prs/output/run_type/constant_abundance_p15_q05'.
:param use_validation (bool, optional): Whether to use a validation dataset for training. Defaults to False.
:param logger (logging.Logger): Logger object for logging messages.
"""
_model_root_folder = validate_parameter(
model_root_folder,
default=os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05')
)
logger.info(f"Training for target {target_name}")
target_id = target_name.split('_')[-1]
x_dataframes, y_series = preprocess_data(model_root_folder=_model_root_folder,
similarity_bins=similarity_bins,
target_name=target_name,
limit_rows=limit_rows,
logger=logger)
model = train_ml_model(model_type=model_type,
model_kwargs=model_kwargs,
x_train=x_dataframes['x_train'],
y_train=y_series['y_train'],
x_validation=x_dataframes['x_test'],
y_validation=y_series['y_test'],
use_validation=use_validation)
with open(os.path.join(_model_root_folder, 'trained_model', f'ml_model_{target_id}.pickle'), 'wb') as outfile:
pickle.dump(model, outfile)
inspect_training(model=model,
x_dataframes=x_dataframes,
y_series=y_series,
logger=logger)
def inspect_training(model: Union[XGBRegressor, RandomForestRegressor],
x_dataframes: dict,
y_series: dict,
logger: logging.Logger):
"""
Inspect the training process results and evaluate the model.
:param model (Union[XGBRegressor, RandomForestRegressor]): Trained machine learning model.
:param x_dataframes (dict): Dictionary containing DataFrame objects for training, validation, and test sets.
:param y_series (dict): Dictionary containing target variable Series for training, validation, and test sets.
:param logger (logging.Logger): Logger object for logging messages.
"""
pred = model.predict(x_dataframes['x_test'])
pred_val = model.predict(x_dataframes['x_validation'])
pred_train = model.predict(x_dataframes['x_train'])
mse = mean_squared_error(y_series['y_test'], pred)
mse_validation = mean_squared_error(y_series['y_validation'], pred_val)
mse_train = mean_squared_error(y_series['y_train'], pred_train)
logger.info(f'MSE {mse}, MSE validation {mse_validation}, MSE train {mse_train}')
plt.clf()
plt.scatter(10 ** y_series['y_test'], 10 ** pred)
plt.scatter(10 ** y_series['y_validation'], 10 ** pred_val)
plt.scatter(10 ** y_series['y_train'], 10 ** pred_train)
plt.loglog()
y_low = np.min(10 ** y_series['y_train'])
y_high = np.max(10 ** y_series['y_train'])
plt.plot([y_low, y_high], [y_low, y_high], color='r')
plt.show()
def preprocess_data(model_root_folder: str,
similarity_bins: Union[None, int],
target_name: str,
logger: logging.Logger,
limit_rows: int = None) -> Tuple[dict, dict]:
"""
Preprocess the data for training machine learning models.
:param model_root_folder (str): Root folder where the trained model and other related files will be stored.
:param similarity_bins (Union[None, int]): Number of bins for similarity calculation.
:param target_name (str): Name of the target variable.
:param limit_rows (int, optional): Limit the number of rows for preprocessing. Defaults to None.
:param logger (logging.Logger): Logger object for logging messages.
:return: Tuple[dict, dict]
- Dictionary containing DataFrame objects for training, validation, and test sets.
- Dictionary containing target variable Series for training, validation, and test sets.
"""
target_id = target_name.split('_')[-1]
merged = get_data(limit_rows,
use_model_for_inference=model_root_folder.split(os.path.sep)[-1])
x_test, x_train, x_validation, y_test, y_train, y_validation = split_data(merged=merged,
target_column=target_name)
x_dataframes = {
'x_train': x_train,
'x_test': x_test,
'x_validation': x_validation
}
if similarity_bins is not None:
x_dataframes = create_similarity_columns(model_root_folder=model_root_folder,
target_id=target_id,
similarity_bins=similarity_bins,
x_dataframes=x_dataframes,
y_train=y_train)
columns = x_dataframes['x_train'].columns
logger.info(f'Feature used for training {columns}')
scaler = QuantileTransformer(output_distribution='uniform')
scaler.fit(X=x_dataframes['x_train'])
for key in x_dataframes.keys():
x_dataframes[key] = pd.DataFrame(scaler.transform(X=x_dataframes[key]), columns=columns)
logger.info(f'Train {len(x_train)}, test {len(x_test)}, validation {len(x_validation)}')
with open(os.path.join(model_root_folder, 'trained_model', f'ml_scaler_{target_id}.pickle'), 'wb') as outfile:
pickle.dump(scaler, outfile)
y_series = {
'y_train': y_train,
'y_test': y_test,
'y_validation': y_validation
}
return x_dataframes, y_series
etl/assets/constants/__init__.py 0 → 100644
View file @ dab489de
import numpy as np
from .physical_constants import mean_molecular_mass
polaris_grid_map = {
'spherical': 30
}
polaris_data_map = {
"gas_number_density": 0,
"dust_temperature": 2,
"gas_temperature": 3,
"molecular_abundance_mass_ratio": 17
}
radmc_grid_map = {
'regular': 0,
'spherical': 100
}
radmc_coord_system_map = {
'cartesian': 0,
'polar': 100
}
radmc_input_headers = {
'amr_grid.inp': ['iformat', 'grid_type', 'coordinate_system', 'grid_info', 'active_axes', 'ncells_per_axis'],
'dust_density.inp': ['iformat', 'ncells', 'dust_species'],
'dust_temperature.dat': ['iformat', 'ncells', 'dust_species'],
'microturbulence.inp': ['iformat', 'ncells'],
'gas_velocity.inp': ['iformat', 'ncells'],
'gas_temperature.inp': ['iformat', 'ncells'],
'wavelength_micron.inp': ['continuum_lambdas'],
'numberdens_mol.inp': ['iformat', 'ncells'],
'escprob_lengthscale.inp': ['iformat', 'ncells'],
'stars.inp': ['iformat', 'nstars', 'continuum_lambdas', 'stars_properties', 'lambdas']
}
leiden_url_mapping = {
'co': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/co.dat',
'e-ch3oh': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/e-ch3oh.dat',
'a-ch3oh': 'https://home.strw.leidenuniv.nl/~moldata/datafiles/ch3oh_a.dat'
}
radmc_lines_mode_mapping = {
'lte': 1,
'user_defined_populations': 2,
'lvg': 3,
'optically_thin_non_lte': 4,
}
radmc_options_mapping = {
'inclination': 'incl',
'position_angle': 'phi',
'imolspec': 'imolspec',
'iline': 'iline',
'width_kms': 'widthkms',
'nchannels': 'linenlam',
'npix': 'npix',
'image_size_pc': 'sizepc',
'threads': 'setthreads'
}
aggregation_function_mapping = {
'mean': np.nanmean,
'sum': np.nansum,
'median': np.nanmedian
}
line_ratio_mapping = {
'87-86': r'$I(2_{0}-1_{0})/I(2_{-1}-1_{-1})$',
'88-86': r'$I(2_{1}-1_{1})/I(2_{-1}-1_{-1})$',
'88-87': r'$I(2_{1}-1_{1})/I(2_{0}-1_{0})$',
'257-256': r'$I(5_{-1}-4_{-1})/I(5_{0}-4_{0})$',
'381-380': r'$I(7_{-1}-6_{-1})/I(7_{0}-6_{0})$'
}
config_overrides = {
'isothermal_p15': {
'grid': {
'power_law_temperature': 0
}
},
'isothermal_p12': {
'grid': {
'power_law_density': -1.2,
'power_law_temperature': 0
}
},
'isothermal_p18': {
'grid': {
'power_law_density': -1.8,
'power_law_temperature': 0
}
},
'constant_abundance_p15_q05': {},
'constant_abundance_p12_q05': {
'grid': {
'power_law_density': -1.2,
}
},
'constant_abundance_p18_q05': {
'grid': {
'power_law_density': -1.8,
}
},
'constant_abundance_p15_q05_x01': {
'lines': {
'molecular_abundances': {
"e-ch3oh": 1e-10
}
}
},
'constant_abundance_p15_q05_x10': {
'lines': {
'molecular_abundances': {
"e-ch3oh": 1e-8
}
}
},
'hot_core_p15_q05': {
'lines': {
'hot_core_specs': {
"e-ch3oh": {
"threshold": 90,
"abundance_jump": 100
}
}
}
},
'hot_core_p12_q05': {
'grid': {
'power_law_density': -1.2,
},
'lines': {
'hot_core_specs': {
"e-ch3oh": {
"threshold": 90,
"abundance_jump": 100
}
}
}
},
'hot_core_p18_q05': {
'grid': {
'power_law_density': -1.8,
},
'lines': {
'hot_core_specs': {
"e-ch3oh": {
"threshold": 90,
"abundance_jump": 100
}
}
}
},
'hot_core_x10': {
'lines': {
'molecular_abundances': {
"e-ch3oh": 1e-8
},
'hot_core_specs': {
"e-ch3oh": {
"threshold": 90,
"abundance_jump": 100
}
}
}
},
'hot_core_x01': {
'lines': {
'molecular_abundances': {
"e-ch3oh": 1e-10
},
'hot_core_specs': {
"e-ch3oh": {
"threshold": 90,
"abundance_jump": 100
}
}
}
},
'double_microturbulence': {},
}
etl/assets/constants/physical_constants.py 0 → 100644
View file @ dab489de
mean_molecular_mass = 2.34
speed_of_light = 2.99792458e10 #cm/s
etl/assets/data/ch3oh_data_top35.csv 0 → 100644
View file @ dab489de
source_name,mass,distance,diameter,class,spectrum_index,tpeak_86,tpeak_unc_86,rms_noise_86,linewidth_mhz_86,linewidth_mhz_unc_86,linewidth_86,linewidth_unc_86,tpeak_87,tpeak_unc_87,rms_noise_87,linewidth_mhz_87,linewidth_mhz_unc_87,linewidth_87,linewidth_unc_87,tpeak_88,tpeak_unc_88,rms_noise_88,linewidth_mhz_88,linewidth_mhz_unc_88,linewidth_88,linewidth_unc_88,ratio_87-86,ratio_88-87,ratio_88-86,area_86,area_unc_86,area_87,area_unc_87,area_88,area_unc_88,tpeak_256,tpeak_unc_256,rms_noise_256,linewidth_mhz_256,linewidth_mhz_unc_256,linewidth_256,linewidth_unc_256,tpeak_257,tpeak_unc_257,rms_noise_257,linewidth_mhz_257,linewidth_mhz_unc_257,linewidth_257,linewidth_unc_257,ratio_257-256,area_256,area_unc_256,area_257,area_unc_257,tpeak_380,tpeak_unc_380,rms_noise_380,linewidth_mhz_380,linewidth_mhz_unc_380,linewidth_380,linewidth_unc_380,tpeak_381,tpeak_unc_381,rms_noise_381,linewidth_mhz_381,linewidth_mhz_unc_381,linewidth_381,linewidth_unc_381,ratio_381-380,area_381,area_unc_381,area_380,area_unc_380,AGAL_name,apex_name
G34.26+0.15,800,1.6,67,HII,1,2.53,0.02365,0.09583,2.056,0.0,6.371484227215981,0.010451084955200635,1.74,0.02365,0.09583,2.056,0.0,6.371142617374448,0.010451084955200635,1.16,0.02365,0.09583,2.056,0.003373,6.370421188227841,0.010451084955200635,0.6877470355731226,0.6666666666666666,0.45849802371541504,17.148782015804713,0.16275305913281377,11.79339165343781,0.16145840413561002,7.861370828025846,0.1607951465382442,1.98,0.735,0.09576,5.665,0.354,7.026574916609798,0.4390834104995355,3.18,0.735,0.09576,5.665,0.0,7.0246254898627,0.4390834104995355,1.6060606060606062,14.800657803071703,5.571485634832507,23.76415857208871,5.689969110997285,3.08,0.292,0.125,7.87,0.0,6.97780470860188,0.12404792512892213,3.73,0.292,0.125,7.87,0.14,6.97326550546155,0.12404792512892213,1.211038961038961,27.670510995076153,2.2213861922198466,22.863445215418928,2.2053525649662755,AGAL034.258+00.154,AG034.26+0.1
G35.20-0.74,460,2.2,74,IRb,2,2.41,0.02772,0.111,1.44,0.0,4.462518135793293,0.009989415326287235,0.868,0.02772,0.111,1.44,0.0,4.462278875982103,0.009989415326287235,0.364,0.02772,0.111,1.44,0.003224,4.461773594867748,0.009989415326287235,0.36016597510373444,0.41935483870967744,0.15103734439834024,11.441136922618977,0.13406585068059934,4.120487302502623,0.13191266413937913,1.7277506260977236,0.13163170537476587,0.693,0.369,0.05819,3.89,0.283,4.82495612102597,0.3510186586761823,1.8,0.369,0.05819,3.89,0.0,4.8236175031890385,0.3510186586761823,2.597402597402598,3.5571219062457415,1.9116488388286446,9.236714367808798,2.009289929523078,0.75,0.06533,0.132,5.281,0.0,4.682310885149495,0.048741974009585756,1.65,0.06533,0.132,5.281,0.05501,4.679264947184555,0.048741974009585756,2.1999999999999997,8.213603364738846,0.33627512715504143,3.735886344534172,0.32773616425273094,AGAL035.197-00.742,AG035.20-0.7
G34.41+0.23,210,1.6,63,IRb,3,3.27,0.04614,0.249,1.591,0.0,4.930462745866063,0.013468979039507015,1.33,0.04614,0.249,1.591,0.0,4.930198397005227,0.013468979039507015,0.59,0.04614,0.249,1.591,0.004347,4.929640131551797,0.013468979039507015,0.4067278287461774,0.44360902255639095,0.18042813455657492,17.151716147853218,0.24650623714095088,6.975706242571226,0.24274851847385398,3.094135827250596,0.24211954560123883,0.966,0.441,0.111,4.157,0.333,5.156129201826466,0.41303608953769866,2.09,0.441,0.111,4.157,0.0,5.154698704564739,0.41303608953769866,2.1635610766045548,5.298745541451453,2.4559502373777042,11.460979034617344,2.586820094338446,1.43,0.103,0.161,5.615,0.0,4.978446434409092,0.05608738329043407,2.37,0.103,0.161,5.615,0.0633,4.975207854277841,0.05608738329043407,1.6573426573426575,12.543875121955834,0.5631980961359404,7.573594043835107,0.552143182260122,AGAL034.411+00.234,AG034.41+0.2
G28.56-0.24,5400,5.5,101,70w,4,1.13,0.02395,0.0912,2.072,0.0,6.4210677620581285,0.03817295186720185,0.293,0.02395,0.0912,2.072,0.0,6.420723493774249,0.03817295186720185,0.0558,0.02395,0.0912,2.072,0.01232,6.419996450393038,0.03817295186720185,0.2592920353982301,0.19044368600682596,0.04938053097345133,7.718943160772005,0.16991450153312224,2.00135317412325,0.16402397963826934,0.3811019169488633,0.1635890088005906,0.04884,0.108,0.03238,5.506,0.529,6.829359486470176,0.6561444185148425,0.369,0.108,0.03238,5.506,0.0,6.827464774436721,0.6561444185148425,7.555282555282555,0.3548360822544717,0.7853900725084517,2.68014308698633,0.8256372552866202,0.005316,0.04456,0.03898,8.778,0.0,7.782867818565096,0.9126383063056414,0.117,0.04456,0.03898,8.778,1.03,7.777804905583417,0.9126383063056414,22.00902934537246,0.9680884829289998,0.38580323815715173,0.04401460140797027,0.3689771528703325,AGAL028.564-00.236,AG028.56-0.2
G31.41+0.31,3000,4.9,59,HII,5,1.29,0.02672,0.09408,2.43,0.0,7.530499354151182,0.03522942067614326,0.953,0.02672,0.09408,2.43,0.0,7.5300956032198,0.03522942067614326,0.751,0.02672,0.09408,2.43,0.01137,7.529242941339326,0.03522942067614326,0.7387596899224805,0.7880377754459602,0.5821705426356589,10.334408688143643,0.21945028804739836,7.634235223264329,0.21700641625554018,6.015384520155143,0.21586554131344782,0.891,0.301,0.071,6.905,0.456,8.564607201975402,0.5655989694570287,1.17,0.301,0.071,6.905,0.0,8.562231069285428,0.5655989694570287,1.313131313131313,8.11815427336179,2.794406439049449,10.657245054323353,2.830674270184918,1.56,0.217,0.09764,8.48,0.0,7.518651071022101,0.2020209066385303,1.96,0.217,0.09764,8.48,0.228,7.513760036380426,0.2020209066385303,1.2564102564102564,15.666989012027273,1.7849749705573348,12.47776135190902,1.7677726204007878,AGAL031.412+00.307,AG031.41+0.3
G34.40+0.23,270,1.6,82,HII,6,2.53,0.0227,0.11,1.765,0.0,5.469683687274418,0.010553333933416354,0.841,0.0227,0.11,1.765,0.0,5.469390427853064,0.010553333933416354,0.225,0.0227,0.11,1.765,0.003406,5.468771107598316,0.010553333933416354,0.3324110671936759,0.267538644470868,0.08893280632411069,14.72159545617476,0.13510656723296366,4.893358882791944,0.1324170108062351,1.3090143608612992,0.13208916075208882,0.649,0.391,0.07817,4.655,0.368,5.773822813207168,0.45644829114076,1.77,0.391,0.07817,4.655,0.0,5.772220945332899,0.45644829114076,2.727272727272727,3.986394686990907,2.4222527304195376,10.868969226850245,2.5501966207773785,0.595,0.05278,0.06479,6.293,0.0,5.579583866738455,0.062475851434573564,1.49,0.05278,0.06479,6.293,0.07051,5.5759542345450495,0.062475851434573564,2.504201680672269,8.838480648374599,0.32837271632849785,3.531757873095086,0.3157737464671486,AGAL034.401+00.226,AG034.40+0.2
G23.21-0.38,1200,4.6,61,IRw,7,1.56,0.0231,0.15,2.059,0.0,6.380781139998884,0.019687575987353937,0.784,0.0231,0.15,2.059,0.0,6.38043903169941,0.019687575987353937,0.28,0.0231,0.15,2.059,0.006354,6.379716549883815,0.019687575987353937,0.5025641025641026,0.35714285714285715,0.1794871794871795,10.589381466381129,0.16017213897769164,5.3215576604812105,0.15765334842081596,1.900341099965392,0.1568877828888521,0.618,0.281,0.09017,6.289,0.493,7.800552453761521,0.6114918682945507,1.14,0.281,0.09017,6.289,0.0,7.798388297572201,0.6114918682945507,1.8446601941747571,5.128448315345341,2.366268748274346,9.457619850247136,2.4463347346958884,0.919,0.06241,0.08324,7.887,0.0,6.9928774760791645,0.08744492664980946,1.31,0.06241,0.08324,7.887,0.09869,6.988328467798634,0.08744492664980946,1.4254624591947769,9.739053502995969,0.47971744770208,6.836653617571014,0.4720878606853434,AGAL023.206-00.377,AG023.21-0.3
G49.49-0.39,11000,5.4,62,HII,9,4.64,0.03185,0.207,2.618,0.0,8.113105888546418,0.009874772532530218,3.55,0.03185,0.207,2.618,0.0,8.112670900917463,0.009874772532530218,2.26,0.03185,0.207,2.618,0.003187,8.111752271780391,0.009874772532530218,0.7650862068965517,0.6366197183098591,0.4870689655172414,40.047671620058914,0.27918426179969574,30.638278402401056,0.2773996831748604,19.50272354704647,0.2758738141000653,3.83,1.43,0.253,7.217,0.435,8.951595970551265,0.5395516484951919,5.66,1.43,0.253,7.217,0.0,8.949112473140177,0.5395516484951919,1.4778067885117494,36.47299209277803,13.79415939155848,53.88508148720575,13.996345324993044,6.3,0.671,0.356,10.05,0.0,8.910665479218412,0.16746469892404486,7.58,0.671,0.356,10.05,0.189,8.90486891104048,0.16746469892404486,1.2031746031746031,71.80734717626261,6.498419983328799,59.72041757348511,6.458962659096731,AGAL049.489-00.389,AG049.49-0.3
G59.78+0.07,250,2.2,79,IRb,10,1.08,0.02071,0.04479,0.8098,0.0,2.5095466571982006,0.013475175947277662,0.394,0.02071,0.04479,0.8098,0.0,2.5094121067849353,0.013475175947277662,0.171,0.02071,0.04479,0.8098,0.004349,2.509127956336043,0.013475175947277662,0.3648148148148148,0.434010152284264,0.15833333333333333,2.88330892529155,0.05741683671115239,1.051817414971558,0.05557490883168034,0.45644774524836534,0.05533521700055069,,,,,,,,,,,,,,,,,,,,0.319,0.06157,0.06229,3.308,0.0,2.932983224403433,0.11252918922409365,0.719,0.06157,0.06229,3.308,0.127,2.931075259474817,0.11252918922409365,2.2539184952978055,2.24196075698127,0.21039710577940018,0.9953421793454204,0.19586918342589876,AGAL059.782+00.066,AG059.78+0.0
G53.14+0.07,100,1.6,67,IRb,11,1.05,0.01983,0.09708,1.155,0.0,3.5793114214175374,0.012877174347409972,0.335,0.01983,0.09708,1.155,0.0,3.5791195151106456,0.012877174347409972,0.11,0.01983,0.09708,1.155,0.004156,3.578714237550173,0.012877174347409972,0.3190476190476191,0.3283582089552239,0.10476190476190476,3.998167013285548,0.07686609123531718,1.2755372740021984,0.07564353071760102,0.41878570864948833,0.0755106793427082,0.345,0.19,0.05544,3.888,0.284,4.8224754237915075,0.3522590072934126,0.952,0.19,0.05544,3.888,0.0,4.82113749418997,0.3522590072934126,2.7594202898550724,1.769951086391564,0.9832922403050368,4.882683930286012,1.037736129080711,0.474,0.05759,0.07747,4.836,0.0,4.287759030597039,0.08176530379212095,1.08,0.05759,0.07747,4.836,0.09228,4.284969756596196,0.08176530379212095,2.278481012658228,4.9231567416211615,0.27882524298769307,2.1621252984074433,0.2659096119306583,AGAL053.141+00.069,AG053.14+0.0
G37.55+0.20,1200,6.7,70,IRb,12,0.96,0.02248,0.07483,1.649,0.0,5.110203059668848,0.027161046758757412,0.435,0.02248,0.07483,1.649,0.0,5.109929073954506,0.027161046758757412,0.172,0.02248,0.07483,1.649,0.008766,5.1093504569006365,0.027161046758757412,0.453125,0.3954022988505747,0.17916666666666667,5.218930784342653,0.12531848950082622,2.3647012203938407,0.12284811129200904,0.934902424028627,0.1222906026734552,0.259,0.137,0.04511,4.252,0.388,5.273962320463347,0.4812552634853665,0.614,0.137,0.04511,4.252,0.0,5.272499132020512,0.4812552634853665,2.3706563706563704,1.45314493723405,0.7800057066048043,3.44395156070276,0.8302500355158099,0.382,0.06053,0.05076,5.962,0.0,5.286108217621908,0.20645118967884896,0.432,0.06053,0.05076,5.962,0.233,5.282669497275954,0.20645118967884896,1.1308900523560208,2.427780024280013,0.3531542428043672,2.1481844033314563,0.3505786313165484,AGAL037.554+00.201,AG037.55+0.2
G34.82+0.35,110,1.6,89,IRb,13,0.616,0.02224,0.0291,1.16,0.0,3.5948062760557087,0.024604822303364436,0.163,0.02224,0.0291,1.16,0.0,3.594613538985583,0.024604822303364436,0.04946,0.02224,0.0291,1.16,0.007941,3.5942065069767968,0.024604822303364436,0.26461038961038963,0.3034355828220859,0.08029220779220779,2.3557453894152305,0.0865664840020827,0.6233212838879257,0.08515398090886098,0.18911644025007698,0.08504725085289648,0.06802,0.05868,0.03046,2.501,0.227,3.1021118916930455,0.2815591361112841,0.315,0.05868,0.03046,2.501,0.0,3.1012512533356773,0.2815591361112841,4.630990885033814,0.2244740966733627,0.19471981880448871,1.0392490902135514,0.21536538345879122,0.06994,0.05121,0.04384,7.683,0.0,6.812004266351747,0.8346653247960331,0.126,0.05121,0.04384,7.683,0.942,6.807572919753633,0.8346653247960331,1.8015441807263368,0.912504455201019,0.38737613581295827,0.5068421046687672,0.3762696495121889,AGAL034.821+00.351,AG034.82+0.3
G30.85-0.08,1200,4.9,78,70w,14,0.52,0.02097,0.05365,2.1,0.0,6.507838948031887,0.053603252216119474,0.15,0.02097,0.05365,2.1,0.0,6.5074900274739,0.053603252216119474,0.034,0.02097,0.05365,2.1,0.0173,6.506753159182133,0.053603252216119474,0.28846153846153844,0.22666666666666668,0.06538461538461539,3.600081120187853,0.1481775324641313,1.0384292597032818,0.14542418896080203,0.23535064618318358,0.14516891999615622,0.07805,0.07779,0.03649,5.532,0.463,6.861608550518165,0.574281409777641,0.286,0.07779,0.03649,5.532,0.0,6.859704891424618,0.574281409777641,3.664317745035234,0.5697324971999391,0.5698331975250233,2.087101701007916,0.5939589107713851,0.07117,0.04949,0.04257,8.502,0.0,7.5381570054044715,0.6831496448171354,0.224,0.04949,0.04257,8.502,0.771,7.533253281757828,0.6831496448171354,3.1473935647042297,1.7951582288444188,0.4287274799425559,0.5707347171006769,0.40023218446566816,AGAL030.848-00.081,AG030.85-0.0
G28.86+0.07,1000,7.4,69,IRb,15,0.9,0.02452,0.05009,1.262,0.0,3.9109013106744004,0.019699969802895238,0.402,0.02452,0.05009,1.262,0.0,3.9106916260343154,0.019699969802895238,0.201,0.02452,0.05009,1.262,0.006358,3.910248803279929,0.019699969802895238,0.44666666666666666,0.5,0.22333333333333333,3.744479978305277,0.10374528151605596,1.6724447166657392,0.10235811340222353,0.8361276696375168,0.10208620319040439,,,,,,,,,,,,,,,,,,,,0.336,0.04941,0.06195,4.484,0.0,3.97566408047914,0.11961764208860348,0.646,0.04941,0.06195,4.484,0.135,3.9730778305577625,0.11961764208860348,1.9226190476190474,2.7304343388726755,0.22443693449275692,1.4210884372776502,0.21330536611171214,AGAL028.861+00.066,AG028.86+0.0
G22.37+0.45,620,4.0,65,IRw,16,1.86,0.03935,0.123,1.457,0.0,4.515200641563076,0.020945548264795815,0.465,0.03935,0.123,1.457,0.0,4.514958557156892,0.020945548264795815,0.101,0.03935,0.123,1.457,0.00676,4.5144473109182695,0.020945548264795815,0.25,0.2172043010752688,0.0543010752688172,8.934333184369493,0.19350455111823223,2.2334635415722928,0.18928764677077922,0.48506295574760133,0.18899584856499071,0.188,0.153,0.05118,4.133,0.383,5.126360835012938,0.475053520399215,0.675,0.153,0.05118,4.133,0.0,5.1249385965759116,0.475053520399215,3.5904255319148937,1.0252721670025875,0.8397889239422417,3.6801420773284477,0.9012219713131503,0.166,0.04502,0.03825,5.697,0.0,5.051150371652467,0.16126230266759875,0.5,0.04502,0.03825,5.697,0.182,5.047864496139066,0.16126230266759875,3.012048192771084,2.6850343064569504,0.2565267403612208,0.8920116613769251,0.24358830354218275,AGAL022.376+00.447,AG022.37+0.4
G19.88-0.54,790,3.7,67,IRb,17,1.68,0.02777,0.0794,1.327,0.0,4.112334420970625,0.013431797592883118,0.623,0.02777,0.0794,1.327,0.0,4.112113936408507,0.013431797592883118,0.215,0.02777,0.0794,1.327,0.004335,4.1116483058260425,0.013431797592883118,0.37083333333333335,0.3451043338683788,0.12797619047619047,7.349704071521969,0.1238378758928978,2.725369130194149,0.12180807911292109,0.940430197609148,0.121507432969849,0.658,0.284,0.06431,3.098,0.227,3.8426000161795506,0.2815591361112841,1.6,0.284,0.06431,3.098,0.0,3.8415339395577486,0.2815591361112841,2.43161094224924,2.689820011325686,1.1775666735052563,6.538781173715317,1.2556873263733366,1.23,0.06673,0.09744,4.683,0.0,4.152104123301474,0.032598022610664606,2.33,0.06673,0.09744,4.683,0.03679,4.149403095562445,0.032598022610664606,1.8943089430894309,10.285222566660103,0.3054447929243074,5.43307241666044,0.297825582498799,AGAL019.882-00.534,AG019.88-0.5
G18.89-0.47,2800,4.7,82,IRw,18,2.23,0.04458,0.16,1.891,0.0,5.860154024156332,0.026253199770357238,0.622,0.04458,0.16,1.891,0.0,5.859839829501498,0.026253199770357238,0.199,0.04458,0.16,1.891,0.008473,5.859176297149244,0.026253199770357238,0.2789237668161435,0.319935691318328,0.08923766816143498,13.902280291349596,0.284814022368405,3.8774684829254595,0.2784484436939946,1.240400088439042,0.27793012917232396,0.327,0.197,0.04993,4.739,0.367,5.878012097054516,0.4552079425235297,0.922,0.197,0.04993,4.739,0.0,5.876381323293792,0.4552079425235297,2.819571865443425,2.0447978252519436,1.2420175138135696,5.763854872422209,1.30997858860221,0.222,0.06106,0.08294,5.647,0.0,5.006818702601628,0.19050217073370182,0.72,0.06106,0.08294,5.647,0.215,5.00356166573588,0.19050217073370182,3.243243243243243,3.8325153184359935,0.3562705793927619,1.1824614382740017,0.32832733849500356,AGAL018.888-00.474,AG018.89-0.4
G18.73-0.23,7900,12.5,74,IRw,19,1.77,0.02653,0.0734,1.73,0.0,5.36121970480722,0.016477577762157417,0.692,0.02653,0.0734,1.73,0.0,5.360932260728499,0.016477577762157417,0.251,0.02653,0.0734,1.73,0.005318,5.360325221611947,0.016477577762157417,0.3909604519774011,0.3627167630057804,0.14180790960451978,10.095062635647638,0.15446019745191475,3.9465586430043844,0.15178923388940002,1.43132088364319,0.15135059269248557,0.286,0.197,0.03875,4.557,0.365,5.6522686487185965,0.4527272452890691,0.824,0.197,0.03875,4.557,0.0,5.650700504378522,0.4527272452890691,2.8811188811188813,1.7197328016314029,1.1925529389006952,4.953380016604151,1.2489705007902534,0.375,0.06313,0.06494,6.62,0.0,5.869512982330933,0.1444272271143879,0.766,0.06313,0.06494,6.62,0.163,5.865694745381889,0.1444272271143879,2.042666666666667,4.779917207406943,0.4111428033705079,2.341561030185213,0.3983825447868095,AGAL018.734-00.226,AG018.73-0.2
G18.61-0.07,870,4.3,75,IRw,20,1.01,0.02695,0.06391,1.479,0.0,4.583378001971029,0.022516464384655502,0.298,0.02695,0.06391,1.479,0.0,4.583132262206619,0.022516464384655502,0.04943,0.02695,0.06391,1.479,0.007267,4.582613296395417,0.022516464384655502,0.29504950495049503,0.16587248322147652,0.04894059405940594,4.924693385096532,0.13361496878643883,1.4529504405718856,0.13159312358515932,0.24097720770300585,0.13138983333672116,0.139,0.09774,0.05296,3.455,0.34,4.285404472530777,0.42171852985831093,0.465,0.09774,0.05296,3.455,0.0,4.284215545891549,0.42171852985831093,3.3453237410071943,0.6336927890231682,0.449933406945426,2.119319392382522,0.49189607844341887,0.129,0.05685,0.05772,5.744,0.0,5.092822140560254,0.28442417118845714,0.345,0.05685,0.05772,5.744,0.321,5.089509156718061,0.28442417118845714,2.6744186046511627,1.8679581479443947,0.32502672748966266,0.6989085703534818,0.31047076694386866,AGAL018.606-00.074,AG018.61-0.0
G15.03-0.67,1100,2.0,99,IRb,21,0.839,0.03087,0.04564,1.468,0.0,4.549289321767052,0.031201430625220992,0.405,0.03087,0.04564,1.468,0.0,4.549045409681755,0.031201430625220992,0.196,0.03087,0.04564,1.468,0.01007,4.548530303656843,0.031201430625220992,0.48271752085816455,0.4839506172839506,0.23361144219308702,4.060482703151656,0.1519740165468335,1.9599610541713945,0.14999621283291287,0.9484169569327037,0.1495172793656858,0.498,0.254,0.0375,3.9,0.282,4.837359607198272,0.349778310058952,1.26,0.254,0.0375,3.9,0.0,4.836017548184383,0.349778310058952,2.5301204819277108,2.5627713663667446,1.3201864103978675,6.482321394374812,1.3883181061379544,0.369,0.07326,0.05491,6.077,0.0,5.388071056438835,0.1231618685208584,1.02,0.07326,0.05491,6.077,0.139,5.384566007203283,0.1231618685208584,2.7642276422764227,5.842826943986542,0.4404189229260346,2.1151044891765216,0.4226996779770496,AGAL015.029-00.669,AG015.03-0.6
G14.63-0.58,250,1.8,84,IRb,22,1.86,0.02943,0.0442,0.9171,0.0,2.8420662377333534,0.011984819628436422,0.581,0.02943,0.0442,0.9171,0.0,2.841913859141102,0.011984819628436422,0.206,0.02943,0.0442,0.9171,0.003868,2.841592058231397,0.011984819628436422,0.3123655913978494,0.35456110154905335,0.11075268817204299,5.623662981046849,0.09208679562408917,1.7565446299584897,0.08928391653611226,0.6227318765911359,0.0890047760093749,0.4,0.209,0.03239,2.428,0.176,3.0115664426352318,0.21830135663253739,1.32,0.209,0.03239,2.428,0.0,3.0107309248696623,0.21830135663253739,3.3,1.2815176351639286,0.6760059683722098,4.22783491577442,0.7362604233371383,0.219,0.05913,0.05544,3.466,0.0,3.0730712986040807,0.0831298309685391,0.921,0.05913,0.05544,3.466,0.09382,3.0710722035488867,0.0831298309685391,4.205479452054795,3.008997339860133,0.20965184775081325,0.7159602280790358,0.19427704436733984,AGAL014.632-00.577,AG014.63-0.5
G14.49-0.14,1900,3.9,89,70w,23,1.15,0.03402,0.102,1.679,0.0,5.2031721874978745,0.035353358831556256,0.351,0.03402,0.102,1.679,0.0,5.2028932172041324,0.035353358831556256,0.03107,0.03402,0.102,1.679,0.01141,5.202304073460382,0.035353358831556256,0.30521739130434783,0.08851851851851852,0.02701739130434783,6.365582995343145,0.1932137455379076,1.9427824672751601,0.188762628907307,0.1719527527259724,0.18828275873133013,0.128,0.08905,0.0404,4.416,0.401,5.477379493689121,0.49737979550936084,0.414,0.08905,0.0404,4.416,0.0,5.475859869944164,0.49737979550936084,3.2343749999999996,0.7458559310555399,0.5232957299744114,2.4117084959115784,0.5631062582040194,0.07617,0.05799,0.0578,7.242,0.0,6.4209989453233565,0.4891032476511787,0.211,0.05799,0.0578,7.242,0.552,6.416821955597529,0.4891032476511787,2.7701194696074567,1.440371736841573,0.410805519891472,0.5203058400694469,0.39809874373412035,AGAL014.492-00.139,AG014.49-0.1
G14.19-0.19,820,3.9,72,IRw,24,2.66,0.04234,0.214,1.796,0.0,5.565751786031081,0.023145450523376444,1.01,0.04234,0.214,1.796,0.0,5.565453375877679,0.023145450523376444,0.232,0.04234,0.214,1.796,0.00747,5.564823178043385,0.023145450523376444,0.37969924812030076,0.2297029702970297,0.08721804511278196,15.749893351960294,0.2591102799195194,5.9799020315281455,0.2519127822081297,1.3734457205383674,0.2507189304794556,0.47,0.302,0.09003,4.967,0.423,6.16081158178303,0.5246674650884279,1.32,0.302,0.09003,4.967,0.0,6.159102349187649,0.5246674650884279,2.808510638297873,3.080405790891515,1.9966333535808138,8.648952235029466,2.1114871424317903,0.525,0.06245,0.09786,6.243,0.0,5.535252197687616,0.0992383401031377,1.24,0.06245,0.09786,6.243,0.112,5.531651404141863,0.0992383401031377,2.361904761904762,7.297072065038203,0.39012176592387576,3.0914972380702115,0.37189436179225493,AGAL014.194-00.194,AG014.19-0.1
G14.11-0.57,350,2.6,81,IRw,25,1.11,0.02986,0.05211,0.9884,0.0,3.0630228648736746,0.016328851975661828,0.337,0.02986,0.05211,0.9884,0.0,3.062858639597716,0.016328851975661828,0.07368,0.02986,0.05211,0.9884,0.00527,3.062511820255057,0.016328851975661828,0.3036036036036036,0.21863501483679523,0.06637837837837837,3.616973808521042,0.09919201016902335,1.0980674058983302,0.09747059316615404,0.24004879884722616,0.09729203927521679,0.131,0.122,0.03082,2.435,0.189,3.020248882955845,0.23442588865653163,0.725,0.122,0.03082,2.435,0.0,3.019410956366403,0.23442588865653163,5.534351145038167,0.4209070251778891,0.3933488163719442,2.328801003580471,0.4315807681683414,0.152,0.05723,0.05307,2.939,0.0,2.60581550680825,0.14974356676277029,0.506,0.05723,0.05307,2.939,0.169,2.604120371099301,0.14974356676277029,3.3289473684210527,1.4017924550811132,0.17786077971657932,0.42136591173920634,0.1604869867792384,AGAL014.114-00.574,AG014.11-0.5
G13.66-0.60,560,4.5,67,IRb,26,1.61,0.03453,0.148,1.608,0.0,4.983145251635845,0.025209020811002774,0.748,0.03453,0.148,1.608,0.0,4.982878078180016,0.025209020811002774,0.262,0.03453,0.148,1.608,0.008136,4.982313847602319,0.025209020811002774,0.46459627329192543,0.3502673796791444,0.16273291925465838,8.534961548014586,0.1880743486996585,3.965098726041119,0.18413718398606285,1.388687476672136,0.1831553534762108,0.332,0.169,0.07207,5.127,0.474,6.359267360539882,0.5879252445671745,0.754,0.169,0.07207,5.127,0.0,6.357503069113163,0.5879252445671745,2.2710843373493974,2.2460391103183417,1.1620189309770295,5.099529057565239,1.2364636458899043,0.297,0.05548,0.06308,7.321,0.0,6.49104298242368,0.22417232184012356,0.519,0.05548,0.06308,7.321,0.253,6.486820427634563,0.22417232184012356,1.7474747474747476,3.5815529807897217,0.40236991280836626,2.050893367850886,0.3896020198637059,AGAL013.658-00.599,AG013.66-0.6
G13.18+0.06,360,2.4,88,IRw,27,2.25,0.02928,0.13,1.465,0.0,4.5399924089841495,0.01237522481798735,0.828,0.02928,0.13,1.465,0.0,4.539748995356793,0.01237522481798735,0.216,0.02928,0.13,1.465,0.003994,4.539234942000869,0.01237522481798735,0.368,0.2608695652173913,0.096,10.867003106610996,0.1444849558699236,3.998842732080239,0.14182788103779068,1.043058241991689,0.14142093236987918,0.416,0.262,0.06283,4.11,0.35,5.0978328168166405,0.4341220160306142,1.22,0.262,0.06283,4.11,0.0,5.096418493086619,0.4341220160306142,2.932692307692308,2.2560621827614065,1.4338151830644401,6.614500597410293,1.528153771626887,0.323,0.05752,0.06747,5.72,0.0,5.071542939415852,0.11961764208860348,0.947,0.05752,0.06747,5.72,0.135,5.068243798124532,0.11961764208860348,2.9318885448916405,5.105986039174396,0.33272360105670684,1.7426684781184258,0.3130453732259515,AGAL013.178+00.059,AG013.18+0.0
G12.81-0.20,1800,2.4,84,HII,28,1.28,0.03161,0.06405,1.998,0.0,6.1917439134131955,0.02934855520179674,0.613,0.03161,0.06405,1.998,0.0,6.1914119404251675,0.02934855520179674,0.34,0.03161,0.06405,1.998,0.009472,6.1907108628789995,0.02934855520179674,0.47890625,0.5546492659053834,0.265625,8.431310860817968,0.2120144583210657,4.037590978170881,0.20908051769016978,2.2391932908285748,0.20844959195786034,0.729,0.328,0.06065,4.776,0.313,5.923904995892038,0.3882291171930921,1.52,0.328,0.06065,4.776,0.0,5.9222614897765675,0.3882291171930921,2.085048010973937,4.594177385112016,2.088877254036277,9.576422834532323,2.159742388795141,0.659,0.08476,0.06396,6.078,0.0,5.388957689819851,0.12582003834504957,1.11,0.08476,0.06396,6.078,0.142,5.385452063811348,0.12582003834504957,1.6843702579666162,6.359416798755954,0.5078276831867471,3.77800331658647,0.49386459697606344,AGAL012.804-00.199,AG012.81-0.2
G10.62-0.38,3700,5.0,65,HII,29,2.08,0.03,0.05695,2.515,0.0,7.793911883000093,0.02408118359674454,1.25,0.03,0.05695,2.515,0.0,7.7934940090937435,0.02408118359674454,0.795,0.03,0.05695,2.515,0.007772,7.792611521591935,0.02408118359674454,0.6009615384615384,0.636,0.3822115384615385,17.24610289004276,0.2543853719744313,10.363688841879979,0.2507814699869695,6.590559744325095,0.2495329007627918,1.58,0.637,0.102,6.728,0.405,8.345065496725635,0.5023411899782821,2.65,0.637,0.102,6.728,0.0,8.342750272867828,0.5023411899782821,1.6772151898734176,14.026812217900536,5.717801509967671,23.519455556489092,5.828217319934533,1.93,0.224,0.104,9.245,0.0,8.19692560749992,0.13556666103375062,3.02,0.224,0.104,9.245,0.153,8.19159334154918,0.13556666103375062,1.5647668393782384,26.31767222497715,2.000038955895338,16.829857896249838,1.9730421303059003,AGAL010.624-00.384,AG010.62-0.3
G10.47+0.03,10000,8.6,55,HII,30,2.53,0.03094,0.13,2.352,0.0,7.288779621795713,0.015761834914647384,1.89,0.03094,0.13,2.352,0.0,7.288388830770769,0.015761834914647384,1.37,0.03094,0.13,2.352,0.005087,7.287563538283988,0.015761834914647384,0.7470355731225297,0.724867724867725,0.5415019762845851,19.61767281185442,0.24363131553143652,14.654313712932716,0.24198076977736857,10.621236220690493,0.2409668716291754,1.32,0.475,0.103,7.338,0.467,9.101678153236133,0.5792428042465623,1.85,0.475,0.103,7.338,0.0,9.099153017583848,0.5792428042465623,1.4015151515151516,12.781079959863508,4.67062615566759,17.90790753460651,4.737191542189759,1.61,0.232,0.107,11.33,0.0,10.04555620691986,0.27556360510781985,1.7,0.232,0.107,11.33,0.311,10.039021369362054,0.27556360510781985,1.0559006211180124,18.155676944590947,2.5273382331342082,17.205686694830824,2.5238526974413102,AGAL010.472+00.027,AG010.47+0.0
G10.45-0.02,1600,8.6,65,IRw,31,0.608,0.02514,0.05804,1.789,0.0,5.54405898953764,0.04672468459069836,0.318,0.02514,0.05804,1.789,0.0,5.543761742452765,0.04672468459069836,0.09589,0.02514,0.05804,1.789,0.01508,5.543134000846112,0.04672468459069836,0.5230263157894737,0.30154088050314465,0.1577138157894737,3.585944537913708,0.15132273434195886,1.8754428022340206,0.14910635364494462,0.5654586375969508,0.14832595328337872,0.101,0.05316,0.02782,4.865,0.314,6.0342960228255365,0.38946946581032243,0.252,0.05316,0.02782,4.865,0.0,6.032621890235134,0.38946946581032243,2.495049504950495,0.6483658492610417,0.3438149090944644,1.6172560812119723,0.3567836016634692,0.06068,0.05091,0.04699,6.912,0.0,6.128409929587827,0.7115034562751749,0.142,0.05091,0.04699,6.912,0.803,6.124423274936498,0.7115034562751749,2.340145023071852,0.9251788351499816,0.34867580326888103,0.39560841970998867,0.33507487684960785,AGAL010.444-00.017,AG010.45-0.0
G08.71-0.41,1600,4.8,92,IRw,32,1.32,0.02558,0.06756,1.326,0.0,4.109235450042991,0.016269981351840653,0.299,0.02558,0.06756,1.326,0.0,4.10901513163352,0.016269981351840653,0.05847,0.02558,0.06756,1.326,0.005251,4.108549851940718,0.016269981351840653,0.2265151515151515,0.19555183946488294,0.04429545454545455,5.770415738358244,0.11413380926129593,1.3070165152749178,0.11193736538875601,0.25556054238614234,0.11180958574546757,0.0432,0.07308,0.02721,3.905,0.396,4.843561350284424,0.49117805242320917,0.328,0.07308,0.02721,3.905,0.0,4.842217570682056,0.49117805242320917,7.592592592592593,0.2225977131194544,0.3772371124870143,1.6896248544507602,0.4136351969963581,0.05405,0.05218,0.04433,3.462,0.0,3.069524765080014,0.3969533604125508,0.206,0.05218,0.04433,3.462,0.448,3.0675279771166317,0.3969533604125508,3.8112858464384827,0.6722454928574747,0.19121458811501119,0.1764976739921008,0.17191323474250284,AGAL008.706-00.414,AG008.71-0.4
etl/assets/data/tab_top100_carsten_sort.csv 0 → 100644
View file @ dab489de
timea_name,gc_name,sex_name,glon,glat,FWHM,D_app,d,d_ref,vlsr,dv_co32,lum_1kpc,lum_bol,lum_bol_err,M,M_err,r_src,r_fwhm,M_vir,M_vir_fwhm,a_vir,a_vir_csc,M_csc,f_870_csc,f_870_csc_peak,f_70_peak,f_160_peak,f_870_peak,N_H2,N_H2_peak,glon_peak,glat_peak,d_offset,class,fint_70,bg_70,f_70,f_err_70,isSat_70,fint_160,bg_160,f_160,f_err_160,isSat_160,fint_250,bg_250,f_250,f_err_250,isSat_250,fint_350,bg_350,f_350,f_err_350,isSat_350,fint_500,bg_500,f_500,f_err_500,isSat_500,fint_870,bg_870,f_870,f_err_870,isSat_870,fint_8,bg_8,f_8,f_err_8,isSat_8,fint_12,bg_12,f_12,f_err_12,isSat_12,fint_14,bg_14,f_14,f_err_14,isSat_14,fint_21,bg_21,f_21,f_err_21,isSat_21,fint_w1,bg_w1,f_w1,f_err_w1,isSat_w1,fint_w2,bg_w2,f_w2,f_err_w2,isSat_w2,fint_w3,bg_w3,f_w3,f_err_w3,isSat_w3,fint_w4,bg_w4,f_w4,f_err_w4,isSat_w4,T,T_err,beta,beta_err,tau,tau_err,T_hot,T_hot_err,Omega_hot,Omega_hot_err,bf_T,bf_T_err,bf_beta,bf_beta_err,bf_tau,bf_tau_err,bf_T_hot,bf_T_hot_err,bf_Omega_hot,bf_Omega_hot_err
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AG305.80-0.1,G305.7949-0.0965,AGAL305.794-00.096,305.7949,-0.0965,33.23,84.6688902707,3.8,davies2012,-40.82,2.3,68.15,980.6998653797,519.1856158098,594.5588018564,180.2830546499,0.7799241019,0.2498315214,701.3857448709,224.6734872314,1.1796743109,0.5001366326,449.2242171619,4.02,1.34,4.3185329626,12.3285553302,1.4883069143,9.63487320264818E+021,4.71887392643491E+022,305.7939466421,-0.0958088066,4.2392038664,24d,126.3648822099,116.5652496627,9.7996325472,1.9621387873,0,321.0690612879,212.6289889582,108.4400723297,21.6930458537,0,242.3570481801,140.2854072099,102.0716409702,20.4156383597,0,129.4988066503,69.8716953392,59.6271113111,11.9324925659,0,47.9485147403,25.776479175,22.1720355653,16.6305344353,0,6.0491604998,0.7285960864,5.3205644134,0.7981528558,0,3.3441073795,3.7631764075,\N,\N,0,6.7294802759,7.9792819021,\N,\N,0,3.0841176555,3.9419553999,\N,\N,0,7.6155412792,9.5603421988,\N,\N,0,0.9490802615,1.0513362045,\N,\N,0,1.3266523228,1.4695887223,\N,\N,0,1.4246013403,1.5780910656,\N,\N,0,13.8192478623,14.4679998734,\N,\N,0,16.037157193,0.1369815265,1.75,0,0.0073468582,0.0002175004,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG309.38-0.1,G309.3826-0.1332,AGAL309.384-00.134,309.3826,-0.1332,29.41,74.9356624394,5.34,green2011,-51.28,3.1,560.65,15932.9193638425,8571.1336351966,1213.1523431131,305.6954555709,0.9700065879,0.2883777906,1584.6997626701,471.1227965269,1.3062660858,0.5233554081,900.196671703,7.41,3.16,159.0358711063,196.2094810577,2.9305377846,1.27093062523244E+022,6.12614716477419E+022,309.3826416697,-0.1341409098,3.3905954383,IRb,329.4150924066,71.910679355,257.5044130517,51.5010244053,0,651.5050709891,218.336300922,433.1687700671,86.6351688147,0,401.7559378192,170.0594550271,231.6964827921,46.3398062053,0,202.2890829896,83.6325815566,118.6565014329,23.7321415747,0,69.6925943056,33.0291597014,36.6634346042,27.4989289704,0,10.7158751285,0.7297697084,9.9861054201,1.4979759676,0,4.5967388051,4.4862093281,0.1105294769,0.0567249297,0,6.756688682,6.5371373334,0.2195513486,0.06899815,0,3.8426122521,3.2600848315,0.5825274206,0.1751454782,0,8.0065177639,5.1880875616,2.8184302022,0.8460671572,0,0.2133465893,0.1644589354,0.0488876539,0.0146667932,0,0.2350396705,0.1715799798,0.0634596907,0.0190381243,0,3.8052759869,4.2699115011,\N,\N,0,11.7450692533,9.9784230849,1.7666461684,0.5299948765,0,23.9735846916,1.3331821013,1.75,0,0.0075794148,0.0017818671,161.917837309,23.8966139648,0.031709801,0.0155274014,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG310.01+0.3,G310.0135+0.3877,AGAL310.014+00.387,310.0135,0.3877,30.06,76.59183995,3.61,giannetti2014,-41.31,3,3821.5,49632.8148664747,30228.9485304523,415.3563467882,132.0448012003,0.6702465399,0.2024017159,1025.4772061061,309.6746252548,2.4689094413,0.9234382646,335.3495703195,8.94,3.65,860.4906333509,464.3216874683,3.8570041766,9.11396929916559E+021,4.7808196187591E+022,310.0108452179,0.3891465004,10.8838134719,IRb,1787.2133210676,74.3014585204,1712.9118625473,342.5823991858,0,1483.4979733301,223.3071647022,1260.190808628,252.0405470898,0,623.4221217092,191.5806365222,431.841485187,86.3694837759,0,256.378569618,97.948111743,158.430457875,31.6912270786,0,88.5870678853,40.88337288,47.7036950053,35.7783159797,0,12.7816028395,1.7087222726,11.0728805668,1.6610116725,0,66.0897409466,3.1299977664,62.9597431803,31.4798720633,0,116.4476837179,5.2063742087,111.2413095092,33.3723989318,0,143.1384242524,4.448378764,138.6900454884,41.6070181691,0,240.8064080813,10.5678104464,230.2385976349,69.0715983148,0,1.4621266342,0.1252517801,1.3368748541,0.4010624725,0,3.4065157574,0.32544574,3.0810700175,0.9243210314,0,9.3800841722,4.2811214092,5.098962763,1.5296889436,0,31.9618226457,13.4530381531,18.5087844926,5.5526356609,0,32.2048903984,1.0772438508,1.75,0,0.0057976449,0.0006326091,250.6744141971,24.000844645,0.1560255814,0.0326885437,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG313.58+0.3,G313.5763+0.3243,AGAL313.576+00.324,313.5763,0.3243,22.67,57.7623756376,3.78,green2011,-46.94,3.5,661.12,9414.1755444692,6280.4407605536,182.5525249268,57.5479208064,0.5292754137,0.1104467368,1102.2160491344,230.0053293839,6.0378022686,1.3627466821,168.7806929925,3.61,2.59,327.6799416086,251.7146678788,2.6541612256,6.42362634849123E+021,3.85651824327405E+022,313.5758332891,0.3258295355,5.7569593639,IRb,402.233781001,17.5178107358,384.7159702651,76.9431990271,0,505.5263354129,64.383348162,441.1429872508,88.2290127102,0,227.3457932781,66.4091545625,160.9366387156,32.18747096,0,101.4252563386,32.3519230144,69.0733333243,13.8159096248,0,27.2101950994,12.8915700121,14.3186250873,10.7392718045,0,4.000881351,0.0963197374,3.9045616137,0.5857373679,0,2.1431116568,1.1529662622,0.9901453946,0.4950896009,0,3.5895699474,2.4542509877,1.1353189598,0.340922885,0,3.2521051612,1.0849322739,2.1671728873,0.6502644528,0,12.3076503854,2.5084660071,9.7991843782,2.9398896113,0,0.0861556286,0.0558759927,0.030279636,0.0090839692,0,0.1554967585,0.0597703827,0.0957263758,0.0287179241,0,2.4674670792,1.5978356374,0.8696314418,0.2608895063,0,11.8117134534,5.3390089662,6.4727044872,1.9418114602,0,29.1669364429,1.8473097359,1.75,0,0.0047052234,0.0010512732,191.1371052051,29.8633455462,0.0406018836,0.0180161353,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG316.64-0.0,G316.6403-0.0877,AGAL316.641-00.087,316.6403,-0.0877,24.41,62.1958354351,1.19,urquhart2014a,-17.66,3.1,704,993.5445573965,1709.88802411,18.3392458928,15.7207334939,0.1794126833,0.0434814219,293.1065007478,71.0355989714,15.9824729141,4.4034628971,16.1317582621,3.71,2.3,325.695402877,239.242628011,2.3431268353,5.61605120090843E+021,3.21370028227891E+022,316.639166393,-0.0858330159,7.8630909254,IRb,577.6956975435,118.7786901893,458.9170073542,91.7834396823,0,513.8555616845,274.8210543368,239.0345073477,47.8141603976,0,308.2082571806,150.9986915437,157.2095656369,31.4441502657,0,130.3143878742,74.1097580024,56.2046298718,11.2517933468,0,40.9370262176,26.7420029822,14.1950232355,10.6510945234,0,5.5246534292,1.3069729286,4.2176805006,0.6328699251,0,4.1059996695,4.428344343,\N,\N,0,7.2314205112,6.9573608503,0.2740596609,0.0834928316,0,4.430122169,3.2671952917,1.1629268773,0.3490629586,0,13.1163086359,5.3287137911,7.7875948448,2.3364510762,0,0.2269630723,0.1376955265,0.0892675459,0.0267802783,0,0.2610884625,0.1597647803,0.1013236822,0.0303971156,0,4.1346402364,3.81518788,0.3194523563,0.0958366249,0,12.8023738936,7.8411810103,4.9611928832,1.4883582658,0,30.626792788,1.854751046,1.75,0,0.0026964941,0.0006760523,93.2661647012,6.2495896617,0.3618400241,0.1229722788,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG317.87-0.1,G317.8680-0.1514,AGAL317.867-00.151,317.868,-0.1514,24.44,62.2722743971,2.95,giannetti2014,-40.19,3.7,189.22,1641.0699095667,1151.7110019265,362.428411477,135.216414173,0.4453091495,0.1081371456,1036.3679835381,251.6675789991,2.8595108736,0.7637565567,329.512822905,6.49,4,31.2554551033,155.9221409854,4.0820827193,1.80158545544762E+022,1.06195776352106E+023,317.8672404225,-0.1491238616,8.6383229873,24d,79.7488535669,20.7870986177,58.9617549491,11.7923599177,0,353.2091569128,70.9276505906,282.2815063221,56.4565041746,0,258.4379563175,83.0864788424,175.3514774751,35.0704046109,0,135.0356577537,42.0012770209,93.0343807328,18.608454592,0,43.7368619938,18.203365949,25.5334960448,19.1505031821,0,8.1906016804,1.0523046947,7.1382969857,1.0707930292,0,1.3658406039,1.4993241968,\N,\N,0,2.0888263567,2.141246845,\N,\N,0,1.0823495912,1.3959566529,\N,\N,0,2.774995678,2.055737915,0.7192577631,0.216980109,0,0.1610423924,0.0633932953,0.0976490972,0.0292949423,0,0.1755798301,0.0787534821,0.0968263481,0.0290482309,0,1.7585200469,1.8604725379,\N,\N,0,4.3501419197,1.7124052374,2.6377366823,0.7913267613,0,19.2562215344,0.5452740087,1.75,0,0.0133525761,0.0011215358,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG318.78-0.1,G318.7790-0.1376,AGAL318.779-00.137,318.779,-0.1376,33.96,86.5289050133,2.78,giannetti2014,-38.14,2.8,830.93,6399.884096101,5296.086214374,359.4502637565,143.2392786535,0.5831105263,0.1887666778,777.1697094818,251.5882282313,2.1621063826,1.1733645737,214.4160765385,6.87,2.2,58.8339319083,90.6435500823,2.4671738794,1.0420604622244E+022,4.04294058247162E+022,318.7775382918,-0.1358222806,8.2853803208,24d,439.3300821721,31.2709784949,408.0591036772,81.612415185,0,679.8161308548,184.6649939659,495.1511368889,99.0531628474,0,473.4946020812,189.0213914034,284.4732106778,56.9065215466,0,259.8376494215,105.0970727189,154.7405767026,30.9700997667,0,94.1037537665,44.4541476695,49.649606097,37.2428044067,0,13.8007794651,2.2838104399,11.5169690252,1.7278424752,0,7.6129505893,2.4282653831,5.1846852062,2.5924140033,0,11.4553406695,5.3140484045,6.141292265,1.8425416191,0,7.0926336143,3.1452182364,3.9474153779,1.184381773,0,21.0935761308,6.3631620173,14.7304141136,4.4198599743,0,0.2050737211,0.0979576439,0.1071160772,0.0321364663,0,0.3738104837,0.1300249307,0.243785553,0.0731360581,0,6.1848997162,3.3734187692,2.811480947,0.8434450066,0,22.3532197243,11.7080161901,10.6452035342,3.1935650727,0,24.9235609487,1.5129471382,1.75,0,0.0062705074,0.0015494073,273.9799177651,73.7095784014,0.0289047209,0.0156756061,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG320.88-0.4,G320.8803-0.3970,AGAL320.881-00.397,320.8803,-0.397,27.13,69.1263013254,9.97,giannetti2014,-45.3,2.1,67.05,6642.5917302774,3422.3988342721,2771.4205351523,590.2217684806,1.6706418395,0.4632418353,1252.4801870725,347.2924039122,0.4519271511,0.1698597392,2044.5834043404,3,1.5,1.7094061991,20.5768987974,1.6735815545,9.78794663917896E+021,4.68002508588613E+022,320.8789003902,-0.3957124158,6.8464208553,70d,111.609167141,111.5257941101,0.0833730309,0.4032998283,0,332.9240838702,226.7645541571,106.1595297131,21.5998095548,0,239.8849890827,140.8049571001,99.0800319826,19.8839559426,0,101.4674003516,67.659393847,33.8080065045,7.2284196934,0,41.3835799374,26.0552640969,15.3283158404,11.5433228148,0,5.5999561435,1.5334741463,4.0664819972,0.6132686223,0,4.1804970111,4.2530420716,\N,\N,0,6.4628308657,6.744170326,\N,\N,0,3.5559493336,3.7758350439,\N,\N,0,7.2199358857,7.4672216344,\N,\N,0,0.1534761799,0.1898647218,\N,\N,0,0.1866675028,0.2471211631,\N,\N,0,3.7150916925,3.7521184048,\N,\N,0,4.1457603424,5.1287024089,\N,\N,0,17.3166591081,1.249694823,1.75,0,0.0069806602,0.0016391004,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG326.66+0.5,G326.6607+0.5190,AGAL326.661+00.519,326.6607,0.519,29.11,74.1712728192,1.82,moises2011,-39.79,2.4,2253.97,7440.6735359741,8362.6252366476,125.1962297622,71.4640358861,0.3272291586,0.096532067,320.4227920807,94.5241999815,2.5593645487,1.0158977935,93.0449899475,8.28,3.6,329.5332477919,251.8165708429,3.8619994826,1.1525058279879E+022,5.55758409825066E+022,326.6606745386,0.5207121334,6.1643616239,IRb,1282.2042640338,408.6317732311,873.5724908027,174.7148416596,0,1227.5742774384,306.8184652621,920.7558121763,184.1549370506,0,527.0386931009,176.5572646124,350.4814284884,70.0972128225,0,230.2580206725,79.3971005881,150.8609200844,30.1750688393,0,67.2968189427,32.2669717698,35.0298471729,26.2735476892,0,11.7937371114,0.6526237315,11.1411133798,1.6712245533,0,29.5480052669,9.7662561438,19.781749123,9.8909140332,0,88.9687543229,25.7965701029,63.1721842199,18.9519066415,0,121.2168986756,25.2923478024,95.9245508733,28.7776830529,0,232.1664054452,78.2711097877,153.8952956574,46.1697396504,0,4.339479152,3.1339539321,1.2055252199,0.3616595768,0,6.0658516733,4.3807334681,1.6851182051,0.5055382724,0,6.5137039195,4.7041702508,1.8095336687,0.542863119,0,65.3034550194,53.1563799399,12.1470750794,3.6441769594,0,28.3765248462,0.8142572093,1.75,0,0.00704685,0.00072572,211.9507586735,16.2242255266,0.1765785015,0.0346801158,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG326.99-0.0,G326.9871-0.0317,AGAL326.987-00.032,326.9871,-0.0317,25,63.6991350216,3.95,giannetti2014,-58.63,4.8,73.55,1143.7005826576,1157.2741979334,443.8650290652,159.3904353657,0.6099236945,0.153308805,2388.9491265409,600.479927439,5.3821521636,1.4571246447,412.0992185746,4.07,2.4,22.8960532204,83.9774106805,2.465782068,1.17613124479121E+022,7.08741876600948E+022,326.9858333336,-0.0291666633,10.1964804955,24d,159.2499793428,119.0448603055,40.2051190373,8.0415014077,0,352.4214390801,228.7069452522,123.714493828,24.7499959772,0,285.7772651813,188.2968017857,97.4804633956,19.5067270032,0,130.7930782358,105.4815070274,25.3115712084,5.143368552,0,58.514743262,44.7294192932,13.7853239688,10.3524319924,0,7.7016669172,3.3179394388,4.3837274784,0.6586195014,0,1.9593437272,2.1479659331,\N,\N,0,4.0276592851,4.3744045802,\N,\N,0,3.2144763052,3.2083562219,0.0061200833,0.0210659179,0,12.6050449767,10.791700637,1.8133443398,0.5490228026,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,17.9455446267,2.4110229717,1.75,0,0.0073540181,0.0031083225,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG327.12+0.5,G327.1197+0.5099,AGAL327.119+00.509,327.1197,0.5099,26.57,67.699440701,5.51,green2011,-83.65,2.8,1937.94,58636.0608403893,25411.6309422812,670.6633252913,161.2864576016,0.9042355029,0.2453130603,1205.1650782527,326.9532468263,1.7969747753,0.5735653284,570.0366298795,6.43,3.36,724.4403246175,454.5864844581,3.5101910715,8.08532195401959E+021,4.46469229158709E+022,327.1194339094,0.5106715253,2.9380400424,IRb,1254.3134395223,40.0491654559,1214.2642740664,242.852855972,0,1126.7643737259,134.0091249837,992.7552487423,198.5512579689,0,506.1080584141,138.3445217492,367.7635366649,73.5529468498,0,216.2525641492,74.7095046454,141.5430595038,28.3103659492,0,63.6721823035,27.92187934,35.7503029635,26.8139825398,0,8.7874992685,1.2224324718,7.5650667967,1.1348184161,0,4.4132723765,2.3437153364,2.0695570401,1.0347808068,0,9.6369089782,4.4932447788,5.1436641995,1.5431587786,0,12.0676412644,2.4225380051,9.6451032593,2.8935515338,0,33.1742605322,4.9795166263,28.1947439059,8.4584722918,0,1.2157698132,0.6280642215,0.5877055918,0.1763116889,0,1.7285061964,0.9070743583,0.8214318381,0.2464295673,0,1.8561247749,0.9740452184,0.8820795565,0.264623884,0,32.8408635817,16.9655232172,15.8753403645,4.7626024166,0,31.8453421752,1.2154195077,1.75,0,0.0060206931,0.0007523972,187.2542518431,15.5924355827,0.0817057304,0.0197265106,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG327.29-0.5,G327.2921-0.5792,AGAL327.293-00.579,327.2921,-0.5792,23.09,58.832521106,3.14,wienen2014,-44.72,5.9,8495.37,83476.1043823626,67867.9547588882,2845.9644255913,1046.126959871,0.4478081377,0.0976265996,2649.9942165818,577.7249281908,0.9311410195,0.2377860115,2429.6001457786,71.17,49.21,1321.9564264826,1918.296916617,51.3897327931,1.39894735582E+023,7.75346034352486E+023,327.2912904509,-0.5776296452,6.3602780954,IRb,4884.392337794,572.0896580322,4312.3026797619,862.5745769299,0,7972.703101461,1290.4476994102,6682.2554020509,1337.3514889585,0,1766.7216535629,497.4655872417,1269.2560663213,254.0487351585,1,963.2844166993,310.0587694212,653.2256472781,131.1345616125,1,375.5948041131,119.0096206559,256.5851834572,192.7399289457,0,94.7059758902,11.3394645236,83.3665113666,12.5063023291,0,13.8849576421,10.9162455069,2.9687121353,1.4867146771,0,19.7039374277,17.021059486,2.6828779417,0.8133125756,0,17.1352000621,8.9779803042,8.1572197579,2.4483312743,0,83.8958117105,37.4268921374,46.4689195731,13.9442764628,0,2.4637761513,1.9245615789,0.5392145724,0.1617670709,0,3.4687167713,2.7148177939,0.7538989775,0.2261734655,0,3.7248180914,2.9152574739,0.8095606175,0.242872236,0,40.5770019721,21.3492114893,19.2277904828,5.76836418,0,27.9421265242,2.2306959589,1.75,0,0.2205993205,0.0843514957,170.0496928021,36.4194312387,0.1055197809,0.0718980231,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG327.39+0.2,G327.3928+0.1984,AGAL327.393+00.199,327.3928,0.1984,27.53,70.1454874858,5.92,green2011,-89.17,3,382.61,13363.6323532743,6077.1005693462,1161.5504784839,272.0624909888,1.0066217729,0.2831307044,1540.1313125938,433.189977731,1.3259271475,0.4288827334,1010.0429418018,6.47,3.15,84.0185413194,192.2098027441,3.2701390594,1.1299554428048E+022,6.3850802459374E+022,327.3909718577,0.2007948901,10.8464618241,IRb,199.3767397882,45.360795402,154.0159443862,30.8031902653,0,445.338278072,172.2074976445,273.1307804275,54.6265160806,0,346.9099557222,159.5568459508,187.3531097714,37.47098882,0,162.4127747481,75.7606437503,86.6521309979,17.3333489989,0,41.232988203,29.7232690644,11.5097191386,8.6466637968,0,8.2047682116,0.7642611948,7.4405070168,1.1161280499,0,4.0597559202,2.8093387055,1.2504172147,0.6252110382,0,7.0922981292,5.4269316553,1.6653664739,0.499777608,0,5.5806384924,2.7923453214,2.7882931711,0.8365487064,0,8.4811659184,4.1125164144,4.368649504,1.3108479931,0,1.0895187803,0.7501531988,0.3393655815,0.1018097046,0,1.5540595666,1.0799056066,0.47415396,0.1422462301,0,1.6687984506,1.1596367154,0.5091617352,0.1527485658,0,12.8407383459,10.3739878425,2.4667505034,0.7400251924,0,23.2177106698,1.0270825532,1.75,0,0.0067215335,0.0012918241,248.5066364468,47.1754866615,0.0213111341,0.0089303164,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG328.81+0.6,G328.8087+0.6328,AGAL328.809+00.632,328.8087,0.6328,25.41,64.743800836,2.99,green2011,-41.86,5,17689.55,157608.6278652,133117.793613992,1036.0700158407,396.1798056792,0.469260776,0.1206362897,1994.3582980712,512.7042311019,1.9249261803,0.5705828967,898.5622142806,40.64,23.19,2074.3836728455,2268.8479281623,23.9364744291,4.63784750965788E+022,2.60985564557486E+023,328.8075526017,0.6346315683,7.7806365544,HII,11379.2655750978,323.0482170657,11056.2173580321,2211.2435327184,0,9429.892313669,707.2505585297,8722.6417551393,1744.5336564896,0,1754.1041508507,381.9317876101,1372.1723632407,274.4380705838,1,887.9991292177,175.060144478,712.9389847397,142.5997421815,0,305.7918390165,63.2588931923,242.5329458241,181.9054048326,0,53.6290223375,6.7698457835,46.859176554,7.0289197374,0,6.3049015874,1.6575785576,4.6473230298,2.3236711047,0,17.3828255462,3.1069248618,14.2759006844,4.2828002172,0,34.1318812537,2.1762410131,31.9556402406,9.5867054917,0,246.9801213145,10.3350698846,236.64505143,70.9935466218,0,1.6389242801,0.4665928342,1.1723314459,0.3516994682,0,2.3191820284,0.6803942743,1.6387877541,0.4916363743,0,2.4904112258,0.730628983,1.7597822428,0.5279347245,0,35.7572297063,12.8478915363,22.9093381699,6.8728021269,0,36.3213208956,3.1779900544,1.75,0,0.0182249964,0.0051114784,149.1972232341,20.4195923611,0.3284511387,0.1636518876,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG329.03-0.2,G329.0303-0.2022,AGAL329.029-00.206,329.0303,-0.2022,35.06,89.3316669543,11.49,green2011,-43.49,4,1797.05,236440.003248789,94399.7295323702,10945.4467015971,2033.0057759718,2.4881144002,0.817064585,6767.6711684967,2222.4156712914,0.6183092708,0.1323268446,16794.8965922089,29.39,8.82,159.5986795465,476.4361770213,9.0638309039,1.74280451447996E+022,1.73728047131756E+023,329.0291516218,-0.1974907952,17.449932749,24d,614.7218398313,42.5525094695,572.1693303618,114.433866846,0,2545.2211884371,235.6028440663,2309.6183443708,461.923761432,0,1554.1719704593,276.4169705854,1277.7549998739,255.5512361895,0,779.756912611,167.9592541348,611.7976584763,122.361467787,0,276.6017354575,71.3525744507,205.2491610069,153.9378759972,0,23.5517322614,4.3978972382,19.1538350233,2.873764127,0,2.7253569817,3.4231883749,\N,\N,0,4.8048180484,6.6034561451,\N,\N,0,2.6942301228,3.6250086209,\N,\N,0,5.9206770662,5.5871213271,0.3335557391,0.1062504661,0,0.9104440152,0.9967640205,\N,\N,0,1.3250284884,1.4458903238,\N,\N,0,1.4228576187,1.5526429898,\N,\N,0,16.3337142877,15.6351101956,0.6986040921,0.2095849832,0,23.7004384748,1.1335020009,1.75,0,0.0191143959,0.0047170298,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG329.07-0.3,G329.0656-0.3076,AGAL329.066-00.307,329.0656,-0.3076,34.32,87.4461725577,11.58,green2011,-41.9,3.6,534,71363.3786054417,26671.183206853,9081.0888382172,1689.6448541457,2.454676374,0.7985229071,5408.1429873023,1759.3056689701,0.5955390464,0.2666126027,6598.7340856481,10.15,3.18,114.6218000654,144.7915242891,3.4421859592,1.48561170928164E+022,7.01565539339553E+022,329.0641484348,-0.3058156043,8.2808683422,IRb,218.9592017017,28.7874514916,190.1717502101,38.0343516819,0,499.8324524145,125.5401932217,374.2922591928,74.8586158465,0,453.3669557226,182.1765412555,271.1904144671,54.2383414941,0,259.8819420249,96.5949300012,163.2870120237,32.6587496395,0,98.6700224115,38.2243862272,60.4456361843,45.3351121889,0,15.8278524332,1.8595593467,13.9682930865,2.0952809014,0,3.0294395111,2.8734013181,0.156038193,0.078071801,0,5.6537774627,4.9043900674,0.7493873953,0.2251557523,0,5.4150993124,3.1924793103,2.2226200021,0.6668602841,0,12.3499571198,5.0590363683,7.2909207515,2.1873745643,0,1.0255002154,0.8723073494,0.1531928659,0.0459587372,0,1.4837401422,1.2694803992,0.214259743,0.0642791487,0,1.5932872246,1.363208272,0.2300789527,0.0690250021,0,20.2140035527,14.8797067294,5.3342968233,1.600289051,0,21.8655031546,1.0472779758,1.75,0,0.0085419515,0.0018597048,151.4498803833,19.1580187481,0.0678258962,0.0306184436,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG330.95-0.1,G330.9545-0.1828,AGAL330.954-00.182,330.9545,-0.1828,23.01,58.6286838739,9.32,urquhart2012,-91.16,8,15092.33,1306498.55475342,1124974.5720792,17306.3290772028,4377.838561657,1.3245578519,0.2865041701,14411.189429015,3117.1653707709,0.8327120884,0.1992922672,15641.1757154623,64.64,45.01,2610.4751349218,2173.0645251187,46.1555262839,9.72341813470925E+022,5.70569952993899E+023,330.9524611129,-0.181324448,9.0605030384,HII,10384.8017038958,503.5991052266,9881.2025986693,1976.241920676,0,8447.7673118199,1025.8415043115,7421.9258075084,1484.4252447536,0,1780.4416124713,466.2634147435,1314.1781977278,262.854463961,1,781.6644718216,197.1077580587,584.5567137629,116.9634592965,1,355.3530208458,79.1836787897,276.1693420561,207.1325959488,0,78.3043703288,6.7828215718,71.521548757,10.7283074319,0,5.1892510345,3.8220047345,1.3672463001,0.6841039746,0,7.4271439034,6.2884982464,1.1386456571,0.3433359594,0,6.79261026,3.5182963062,3.2743139538,0.9827501955,0,55.9118135843,7.6068875855,48.3049259988,14.4917209584,0,0.1401267821,0.1304812362,0.009645546,0.0028954424,0,0.3584961426,0.16947129,0.1890248526,0.05670771,0,4.6402922343,3.2957899886,1.3445022457,0.4033553784,0,26.3040359579,11.1289369951,15.1750989628,4.552535655,0,33.0421425466,4.5009293289,1.75,0,0.1482232588,0.0800457519,148.7155889539,37.5061535239,0.1199563202,0.1105463717,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG331.71+0.5,G331.7084+0.5834,AGAL331.709+00.582,331.7084,0.5834,28.33,72.1838598065,10.53,giannetti2014,-67.76,4.4,337.93,37342.6671493985,8031.9677485027,5111.8625959463,913.5322089674,1.8425248237,0.5317316834,6064.1176996637,1750.0353162849,1.186283392,0.4315812301,4054.9384314287,7.12,3.27,48.3365551964,186.6499978684,3.5117616571,1.48425440633291E+022,7.64291347549982E+022,331.7074990928,0.5841425959,4.2030403861,IRb,107.2903063071,17.3988910663,89.8914152408,17.9793069531,0,473.9321808628,92.4629082711,381.4692725917,76.3483125966,0,374.5291420211,129.9761300199,244.5530120012,48.9282330547,0,196.9634137474,67.2160095912,129.7474041561,26.0859600383,0,66.7400228165,29.4026253034,37.3373975132,28.058969306,0,10.9640696822,1.9882338468,8.9758358354,1.3472004113,0,2.0484656201,2.3601125503,\N,\N,0,3.8526454301,4.1589578344,\N,\N,0,2.409982821,2.1200400735,0.2899427475,0.0876359317,0,3.0301441033,2.7705211071,0.2596229963,0.0840485316,0,0.084743134,0.0941473595,\N,\N,0,0.2129271475,0.1139635097,0.0989636378,0.0296898643,0,2.7443712533,2.7941077525,\N,\N,0,10.6864897981,10.5422203487,0.1442694494,0.0432898008,0,21.0033913233,0.2872225033,1.75,0,0.0106757926,0.0007030995,290.5456641223,68.1425592187,0.0048191844,0.0017533771,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG332.09-0.4,G332.0946-0.4210,AGAL332.094-00.421,332.0946,-0.421,27.47,69.9926095617,3.6,moises2011,-56.94,3.5,4529.39,58501.2989246233,34108.4378643035,625.8449171251,198.3796513812,0.6108007503,0.1714427118,1271.9925625385,357.0294472582,2.03244051,0.6959908571,512.9800824814,13,6.35,711.6471499838,590.8463259338,6.608314698,1.65357339306482E+022,8.79811301654679E+022,332.0941453691,-0.4191366531,6.9048251692,IRb,2180.373816712,193.6629798635,1986.7108368485,397.3422491706,0,2347.8460296242,379.285540415,1968.5604892093,393.7130683636,0,890.2143456976,201.7795917123,688.4347539853,137.6886842708,0,336.0995266395,94.2370906077,241.8624360318,48.3761204676,0,108.5827779138,36.4118882575,72.1708896563,54.1288419432,0,19.4348603114,3.5746267459,15.8602335655,2.3790711094,0,88.202957308,7.459331469,80.743625839,40.3718181995,0,137.8603323266,13.8789399605,123.981392366,37.194427367,0,168.8941216855,7.7817814115,161.112340274,48.3337069764,0,202.5905755288,16.7541968949,185.8363786339,55.7509524692,0,1.7706185321,0.3147676763,1.4558508558,0.4367553119,0,3.9917537456,0.4893053888,3.5024483569,1.0507345308,0,12.8877334661,6.9162028305,5.9715306356,1.7914595083,0,44.5555871855,18.2575127898,26.2980743957,7.8894234624,0,30.8271368121,0.6808839831,1.75,0,0.011582263,0.0008652625,288.1672990089,23.3271339947,0.1204825685,0.018770915,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG332.82-0.5,G332.8262-0.5493,AGAL332.826-00.549,332.8262,-0.5493,24.96,63.5972164056,3.6,moises2011,-57.06,5.4,18733.16,241956.240341434,171380.075759059,1935.750648444,639.9239465081,0.5549904151,0.1391788188,2751.1984856692,689.9372403406,1.421256652,0.4091866594,1686.118607621,51.44,30.44,2141.9682729649,1705.47452518,31.2132112766,6.19490023120387E+022,3.50340600279027E+023,332.8242086164,-0.5491698074,7.184285846,HII,12238.7179990941,784.3440787121,11454.373920382,2290.8798868431,0,8921.3112415766,1291.3263298092,7629.9849117674,1526.1071576784,0,1872.3714214613,641.0628078059,1231.3086136554,246.320580579,1,880.5591296129,273.3476661694,607.2114634436,121.5883412805,0,312.1992594536,102.2001083935,209.9991510601,157.5543129779,0,71.6247055531,12.5689470163,59.0557585367,8.8585703717,0,38.358510048,12.1338516065,26.2246584414,13.1125183914,0,109.2915040935,20.9621056932,88.3293984003,26.4994038477,0,179.5336781139,13.9215973742,165.6120807397,49.6839840878,0,749.0360567219,62.9384149917,686.0976417302,205.8297814276,0,0.7942929304,0.3229568841,0.4713360463,0.1414010304,0,1.9154385692,0.4441278897,1.4713106794,0.4413933629,0,11.751,9.7731338466,1.9778627431,0.5933825453,1,47.3568,40.1155415927,7.2412470426,2.1724412645,1,35.6747025799,2.8004642465,1.75,0,0.0197201689,0.0051838045,164.4026079372,21.8719065931,0.5193539744,0.2273083339,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG333.13-0.4,G333.1341-0.4314,AGAL333.134-00.431,333.1341,-0.4314,30.37,77.3817092242,3.6,moises2011,-53.11,6.1,32479.62,419504.685196908,259698.206510363,2887.0247265269,927.7962224878,0.6752828113,0.2053446623,4271.6364795166,1298.9487304934,1.4795981622,0.7194033874,1805.5916238592,54.23,21.68,2095.9917786719,2012.0288528482,23.5414282317,6.24072412466618E+022,2.53453250408818E+023,333.1331482451,-0.4300917273,5.8242334943,HII,21517.5192917282,2663.1918520857,18854.3274396425,3770.8677847772,0,18552.1312562857,3587.7836390485,14964.3476172372,2992.9653195786,0,3958.5563347866,1575.0802021962,2383.4761325904,476.7330488266,1,1850.0548757364,740.4415898568,1109.6132858796,222.0821544648,1,614.4430678055,217.8857258864,396.5573419191,297.4551618052,0,110.4296996593,23.719422069,86.7102775903,13.0077746889,0,103.799785753,30.9804668404,72.8193189126,36.4098976315,0,293.0182671525,63.6715172349,229.3467499176,68.8054682175,0,398.2829667867,51.7865042948,346.4964624919,103.9504698842,0,1200.8499907886,164.770544227,1036.0794465616,310.8272839248,0,2.2612752078,0.6972701517,1.5640050561,0.4692017057,0,4.01819095,1.0530124567,2.9651784934,0.8895537874,0,7.55143,16.2782407503,\N,\N,1,50.1872,80.2282492837,\N,\N,1,35.2315559768,1.6899721672,1.75,0,0.104112833,0.0185285285,190.1033648252,18.7093189602,0.4857171297,0.1365001544,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG333.28-0.3,G333.2841-0.3868,AGAL333.284-00.387,333.2841,-0.3868,33.53,85.433279891,3.6,moises2011,-52.37,3.5,10000.79,129169.493396301,85212.8905262437,2077.2509500298,674.1262475018,0.7455460231,0.2398829348,1552.5995930978,499.5562116594,0.7474299593,0.3309662992,1509.3869460536,37.52,12.31,791.2520751929,1139.6904836589,13.4436070264,3.68379898994035E+022,1.73881938525249E+023,333.2841825227,-0.3874623963,2.4030609394,HII,7943.5640256304,2359.6013388908,5583.9626867396,1116.7998321441,0,10141.7348973588,2971.4854094814,7170.2494878774,1434.0886944154,0,2948.2271892759,1175.456143792,1772.7710454839,354.5649627951,1,1320.0567886505,519.0556975113,801.0010911392,160.2244713694,0,423.2321281247,177.0683459462,246.1637821785,184.6294496507,0,65.2324664816,13.5966312514,51.6358352303,7.7457068234,0,46.3357230619,38.0533319791,8.2823910828,4.1427527232,0,84.7384738025,62.4312194137,22.3072543888,6.6992763274,0,81.7689106412,37.0281418547,44.7407687865,13.4271132071,0,343.3160891271,131.8716432638,211.4444458633,63.4428832141,0,0.8377297233,0.8356489141,0.0020808092,0.0008379631,0,1.6083146743,1.188940545,0.4193741293,0.1258156281,0,24.5523,19.1374368799,5.414858805,1.6244739014,1,93.9060078114,73.4633987319,20.4426090795,6.1328393924,0,30.3709945871,1.7612726241,1.75,0,0.0150432538,0.0032298688,166.2768046689,20.224743044,0.2635986504,0.1043644678,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG333.31+0.1,G333.3139+0.1057,AGAL333.314+00.106,333.3139,0.1057,27.7,70.5786416039,3.6,moises2011,-46.54,3.9,831.02,10733.3438852976,7263.44537771,426.6576684156,139.0382430476,0.6159148448,0.1742381521,1592.5710141898,450.5275898826,3.7326670352,1.3226770359,340.6179873615,6.84,3.28,194.4460800677,276.5323490244,3.4333506371,1.10864903330581E+022,5.73513943152256E+022,333.3143359417,0.1058105533,1.6190684284,HII,461.9099980064,68.751801146,393.1581968603,78.63164241,0,884.3565511992,235.2804160571,649.0761351422,129.8153926384,0,525.6138717818,208.2016494911,317.4122222907,63.4825509772,0,216.7555726553,96.5909793077,120.1645933476,24.0364070627,0,67.7942534608,35.6974813678,32.096772093,24.0817584521,0,9.8958457038,1.3280701891,8.5677755146,1.2852026883,0,4.0846891985,3.6477335196,0.4369556788,0.2186118282,0,7.196361659,6.3425829925,0.8537786665,0.2566748067,0,7.1114489434,3.3892411959,3.7222077475,1.1167208261,0,23.767973447,4.8495353275,18.9184381195,5.67563094,0,0.1277045237,0.1444289786,\N,\N,0,0.3721844804,0.1819214819,0.1902629985,0.0570791944,0,4.3064682947,3.7746101169,0.5318581778,0.1595575587,0,19.1499121962,12.0391724227,7.1107397735,2.1332219684,0,25.8514653182,1.5436902013,1.75,0,0.0076668318,0.0017939437,146.5111968806,18.4983892957,0.101860082,0.0474251981,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG333.60-0.2,G333.6036-0.2130,AGAL333.604-00.212,333.6036,-0.213,33.45,85.2294426589,3.6,moises2011,-48.39,6.4,96134.81,1241671.09637484,788650.479625529,3460.7190935718,1118.8080317415,0.7437672076,0.2390308702,5178.9998199687,1664.4197551951,1.4965097368,0.6209007517,2680.6534708291,97.41,32.08,2725.1866159157,2230.8760204587,34.5477021596,6.16663395493121E+022,3.09977486812422E+023,333.6022956865,-0.2097510929,12.6034099953,HII,54650.7703889711,7195.7425677182,47455.0278212529,9491.0126929578,1,26448.915156187,4448.4220222951,22000.493133892,4400.170363584,0,4240.6975460731,1424.5679492895,2816.1295967837,563.2515296208,1,1929.8992798535,486.7858924099,1443.1133874436,288.6609667177,1,555.2856476927,165.9473471643,389.3383005284,292.0138260322,0,147.6732719988,21.9171268747,125.756145124,18.8636385722,0,412.1725410547,104.588811571,307.5837294837,153.7922208317,0,2923.3159807393,233.7295902034,2689.5863905358,806.8765566914,0,2594.6052623959,205.2329957214,2389.3722666745,716.8128109124,0,5904.1477012003,999.4636060072,4904.684095193,1471.4134199681,0,5.596590368,2.331420381,3.2651699869,0.9795514276,0,9.0107235424,3.5249942235,5.485729319,1.6457194436,0,-0.414058,-0.4138537692,\N,\N,1,-7.44841,26.3157552316,\N,\N,1,41.1175357495,2.3586333303,1.75,0,0.0862612674,0.0175550758,201.236757401,18.6331024733,1.0905173722,0.2718797807,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG335.79+0.1,G335.7896+0.1737,AGAL335.789+00.174,335.7896,0.1737,28.13,71.6742667263,3.67,green2011,-50.11,3.4,1534.19,20593.5705085535,12439.9864738919,1107.2850506834,349.2528423785,0.6376380109,0.182896299,1253.0862189455,359.4278068401,1.1316744665,0.3689575896,974.1710619787,17.69,8.25,191.1676182707,523.2485850388,8.5042936997,2.68452164736514E+022,1.53494519493117E+023,335.7889491819,0.17578829,7.8744759743,IRb,820.9782747115,165.0024702534,655.9758044582,131.1952649906,0,1847.6203071305,339.8551418201,1507.7651653104,301.5555350223,0,884.0787730952,228.7114441986,655.3673288966,131.0750513406,0,435.7755168639,102.6360608497,333.1394560142,66.628886215,0,136.3134899645,42.2997236547,94.0137663097,70.5107063785,0,23.5248941109,3.4176734069,20.1072207039,3.016137564,0,8.1706901103,5.6403927344,2.5302973759,1.265263937,0,12.3081004914,10.0504801731,2.2576203183,0.6780185908,0,6.4284021732,5.4619742591,0.9664279141,0.2905588289,0,11.4389875726,9.3159233005,2.1230642722,0.6390518856,0,0.3354348815,0.2929262145,0.042508667,0.0127559414,0,0.5283610355,0.30099021,0.2273708255,0.0682116654,0,6.405267752,5.6622669133,0.7430008388,0.2229034993,0,16.0442346185,14.6346032679,1.4096313507,0.4228965125,0,24.6998682569,0.9424342011,1.75,0,0.0197319474,0.0031433826,453.3477377971,147.6774566363,0.0065790759,0.0028155185,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG338.92+0.5,G338.9249+0.5539,AGAL338.926+00.554,338.9249,0.5539,35.3,89.9431786505,4.4,green2011,-61.57,5.3,4878.79,94132.106576273,49942.2528901331,5968.1366127347,1660.222535564,0.9593250529,0.3159429999,4581.0649251625,1508.7226076319,0.7675871419,0.3481159041,4333.9663311525,53.27,15.76,594.1012887369,890.5415889363,17.1191010234,6.39238698469381E+022,3.0138203322876E+023,338.923706909,0.558032251,15.4837521901,IRb,2259.3881943057,400.5789022826,1858.8092920231,371.7625957153,0,5380.1988509052,948.3591759255,4431.8396749797,886.3701109818,0,2357.3846348412,532.0158120197,1825.3688228215,365.0752816648,1,1266.2576521087,228.4206125435,1037.8370395652,207.5792654726,0,393.4960846239,92.0798703708,301.4162142531,226.0657031867,0,82.2861166894,8.9300698104,73.3560468791,11.0034425114,0,16.7215255737,11.209330076,5.5121954977,2.756352615,0,30.0355524283,18.6171827346,11.4183696937,3.4261088279,0,31.3141358711,10.4324701008,20.8816657703,6.2646213255,0,109.9666182868,24.7106730001,85.2559452867,25.5772021694,0,0.5921436927,0.4157564217,0.1763872711,0.0529170041,0,1.1361927879,0.5324182944,0.6037744935,0.1811325463,0,15.437424791,11.0613992182,4.3760255728,1.3128119796,0,15.9952237459,11.2305792468,4.7646444991,1.4294155747,0,24.2119615416,0.9973168879,1.75,0,0.0249953327,0.0047493756,180.4179599691,22.7606395715,0.1426584683,0.0540620294,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG339.62-0.1,G339.6225-0.1220,AGAL339.623-00.122,339.6225,-0.122,31.46,80.1589915112,3.01,green2011,-34.58,2.7,1660.77,14995.6150677962,10913.9269993867,319.7854098961,118.2427307478,0.5848757524,0.1818399047,724.8365199207,225.3541938903,2.2666341162,0.9367100322,240.5805277423,7.93,2.95,464.8642527928,335.3011295687,3.1489674456,9.21482907955027E+021,4.49510636481556E+022,339.6209023233,-0.1208189317,7.1525877929,IRb,1055.3866540097,120.7976725147,934.588981495,186.9178330166,0,1323.7748412083,340.8057954665,982.9690457418,196.5948661271,0,712.619038285,284.6570122149,427.9620260701,85.5927642316,0,308.9591527501,136.6214572457,172.3376955045,34.469884377,0,97.7660019953,50.8502391116,46.9157628837,35.1879763907,0,12.8279094334,2.2871631632,10.5407462702,1.5811816388,0,9.2242332052,3.8921515811,5.3320816241,2.6660499154,0,14.0454151371,7.0076235614,7.0377915757,2.1114222693,0,17.4900813751,4.4182880255,13.0717933496,3.9215579394,0,45.723373962,7.2807493358,38.4426246263,11.5328357789,0,1.9027875562,1.0819700782,0.820817478,0.2462453117,0,1.2330587536,0.2988264173,0.9342323363,0.2802697936,0,7.0998014468,4.6951755706,2.4046258762,0.721387904,0,32.1165495189,18.2571816824,13.8593678365,4.1578107567,0,28.6588256191,1.3500305427,1.75,0,0.0065024754,0.0010985852,206.400214683,26.5775206806,0.076339308,0.0259061057,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG340.37-0.3,G340.3736-0.3904,AGAL340.374-00.391,340.3736,-0.3904,28.34,72.2093394605,3.6,giannetti2014,-43.55,2.6,47.9,618.7289967361,352.85608266,693.2189517139,219.9415525931,0.6301453686,0.1819072711,724.1630575648,209.0478359783,1.044638286,0.4525833745,461.8990616291,3.88,1.78,1.2948082057,15.3267777675,1.9145251904,1.7208570702568E+022,7.44036116108233E+022,340.3725002105,-0.3891597186,5.9675740517,70d,67.6094415088,74.0492034612,\N,\N,0,388.891030484,314.8086011442,74.0824293398,14.823187586,0,330.121566921,239.4816309841,90.6399359369,18.1297910556,0,174.5313095091,119.4845871479,55.0467223612,11.0169317112,0,63.5280431184,45.4052917036,18.1227514148,13.5963322772,0,9.2784336656,3.4553226094,5.8231110563,0.8736109324,0,3.3141269862,3.7692277262,\N,\N,0,5.4583550061,6.1858209862,\N,\N,0,3.2450642577,3.717071967,\N,\N,0,4.0648991631,4.6289543833,\N,\N,0,0.7841825429,0.914973098,\N,\N,0,0.1610911285,0.2081953902,\N,\N,0,3.504171134,3.9527909845,\N,\N,0,13.0378328217,13.3588555201,\N,\N,0,14.4457994304,0.271477608,1.75,0,0.0130055628,0.0009328661,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG340.75-1.0,G340.7456-1.0014,AGAL340.746-01.001,340.7456,-1.0014,29.49,75.1394996715,2.76,giannetti2014,-29.42,2.8,1011.88,7681.8644833599,5591.7810130093,213.1266855335,83.6297325944,0.5027154781,0.1497550679,670.0191892481,199.5935544618,3.1437601893,1.2509306687,159.5560485082,5.8,2.46,294.3559686473,267.3049719933,2.6815745491,8.31283553639911E+021,4.04264758409027E+022,340.7441651886,-1.0007224739,5.7122430635,IRb,597.8736697216,99.1396007403,498.7340689813,99.746876204,0,924.4545382111,274.7827522861,649.671785925,129.9487313415,0,518.79725625,205.6637182008,313.1335380492,62.6354725404,0,236.2960255461,110.5666222288,125.7294033173,25.1934461783,0,79.3880767151,46.9557403906,32.4323363245,24.3667292693,0,11.5285308659,3.7811919988,7.7473388671,1.1623577424,0,3.0034558744,3.1665084463,\N,\N,0,6.3372852509,5.4113345985,0.9259506524,0.2784522429,0,6.7979878639,3.222743595,3.5752442688,1.0726336665,0,27.9396356861,8.3008710631,19.6387646229,5.8917358816,0,1.1388935222,1.0549474694,0.0839460528,0.0251844637,0,0.3259517905,0.1816145212,0.1443372694,0.0433015752,0,4.7350567558,4.4748061872,0.2602505686,0.0780765941,0,25.8313869964,17.238852457,8.5925345394,2.577760665,0,27.0716664815,0.2781522901,1.75,0,0.0055809861,0.0002237743,107.7458880413,2.2298475762,0.308247783,0.0286148307,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG340.78-0.1,G340.7848-0.0968,AGAL340.784-00.097,340.7848,-0.0968,24.09,61.3804865068,9.99,green2011,-101.46,3.7,724.65,72073.8788996864,16132.7168177184,2800.6609912704,508.1654514661,1.4864170866,0.3523372676,3459.3384855782,819.9945228897,1.2351864422,0.3619582068,2265.4397872133,6.01,3.82,236.5970193476,333.6100480314,3.9588858039,1.24949646722608E+022,6.56559515600562E+022,340.7841643367,-0.0958320653,4.1688020638,IRb,424.65939914,47.9234744864,376.7359246536,75.3471867603,0,762.797941811,199.8984556144,562.8994861966,112.5800337146,0,410.3594392123,154.9250838982,255.434355314,51.0870734888,0,195.8288440594,72.8217905476,123.0070535118,24.603584946,0,63.6081804242,26.3104608918,37.2977195324,27.9745811155,0,8.3311493061,0.90125748,7.4298918261,1.1145279263,0,2.4747758564,2.4929627825,\N,\N,0,4.1615731032,4.7111168365,\N,\N,0,2.596225148,2.4861565207,0.1100686273,0.0345396867,0,3.7423612333,2.4669881939,1.2753730394,0.3834925077,0,0.6206823441,0.6395938903,\N,\N,0,0.1380081141,0.1275908673,0.0104172468,0.0031254557,0,2.7713307156,2.8137734773,\N,\N,0,10.3519098265,9.7865337163,0.5653761102,0.1696129489,0,26.1821555067,0.3884154945,1.75,0,0.0086957842,0.0005090797,85.0671606771,4.072695803,0.1821831263,0.0428432917,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG341.22-0.2,G341.2179-0.2122,AGAL341.217-00.212,341.2179,-0.2122,25.75,65.6101090722,3.67,green2011,-43.55,3,1208.6,16223.1929179747,10160.039340955,489.6686812645,155.4170605471,0.5836892553,0.1526498682,893.0445606812,233.5542983641,1.8237730834,0.5991142075,389.8326820439,8,4.44,328.6665123258,397.4535335918,4.6603722039,1.41675514734404E+022,7.30975401333261E+022,341.2157960917,-0.2108159293,9.0660565081,IRb,767.0474932081,142.2248173282,624.82267588,124.967416125,0,1257.8569776068,318.7616346217,939.0953429851,187.8476545107,0,599.3340918398,200.4942379977,398.8398538422,79.7768974683,0,263.04027476,88.5811052773,174.4591694828,34.9157088202,0,84.5438764944,43.6461467455,40.897729749,30.6864614895,0,12.1912538968,2.1424569884,10.0487969084,1.5076811944,0,7.4716980708,5.2470011184,2.2246969524,1.1128622535,0,11.021260804,7.8479577854,3.1733030186,0.9540903549,0,8.0285284352,4.4444404481,3.5840879871,1.0766319199,0,22.0517408289,7.8272818995,14.2244589294,4.2704146463,0,1.558770118,1.1715006557,0.3872694623,0.1161832539,0,0.6544714425,0.2625422076,0.3919292349,0.1175792388,0,6.0950405248,4.6320156612,1.4630248636,0.4389179183,0,21.018081734,12.9051084028,8.1129733312,2.4339129089,0,27.0348942099,1.2761175099,1.75,0,0.0105275003,0.0018884183,217.5809594134,32.3250281382,0.0404106289,0.0150375961,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG342.48+0.1,G342.4836+0.1831,AGAL342.484+00.182,342.4836,0.1831,25.44,64.820239798,12.55,green2011,-41.49,2.1,408.68,64149.5646222923,16429.9173779217,4912.7209004668,857.0888743198,1.9719651427,0.507741878,1478.3822674631,380.654085936,0.3009294233,0.0952707014,3995.4999834814,5.83,3.32,67.7280235707,192.280487681,3.4767751509,1.24531578457289E+022,6.57380583345301E+022,342.4826069435,0.1824706122,4.2325501098,IRb or HII,214.7002416731,50.8891129583,163.8111287148,32.7622526055,0,607.6850240603,199.3918139036,408.2932101567,81.6597033929,0,376.2727252196,173.6562420119,202.6164832078,40.5236837422,0,172.7035992309,91.0383211055,81.6652781253,16.3347128059,0,59.6266017994,34.1649958353,25.4616059641,19.0971079398,0,8.1826777412,1.0143225996,7.1683551416,1.0753109211,0,3.2413750011,2.8830552098,0.3583197913,0.1795252246,0,5.0035214301,4.5290721429,0.4744492873,0.1434385787,0,2.7646239321,2.7316865224,0.0329374096,0.014755523,0,4.1729910881,3.2744405068,0.8985505814,0.270920012,0,0.1267985341,0.1061371164,0.0206614177,0.0061988686,0,0.2196589018,0.1450702054,0.0745886965,0.0223766849,0,3.3914617582,3.0251536371,0.3663081211,0.1098934037,0,11.6604627063,10.6214415004,1.039021206,0.3117068051,0,23.6111900218,0.6760847036,1.75,0,0.0080857116,0.0009949904,275.3081826309,38.0587953234,0.0087193847,0.0025025795,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG343.13-0.0,G343.1271-0.0632,AGAL343.128-00.062,343.1271,-0.0632,27.11,69.0753420174,3.04,giannetti2014,-30.58,4.8,7815.22,71979.5695771259,82249.6851963946,1153.5272969721,470.1573856919,0.5090278015,0.1410405807,1993.7600928574,552.4277463964,1.7284030452,0.5596429395,987.107506293,35.23,17.67,1246.5523709818,1774.1290083916,18.3405435879,4.38834476745915E+022,2.43783807357125E+023,343.1248942481,-0.0622208643,8.6879032554,HII,4628.6693820266,141.4523262602,4487.2170557664,897.4434201678,0,6053.3253549716,471.6434536912,5581.6819012805,1116.3381609811,0,1571.5001753673,291.1747191432,1280.3254562241,256.065939647,1,838.6348425644,167.6324523507,671.0023902138,134.2080189699,0,244.9281303177,53.9821505452,190.9459797725,143.2120654379,0,46.4178593457,5.2483145765,41.1695447692,6.1754668021,0,2.8767078528,2.7766033794,0.1001044734,0.0501458137,0,4.6700151512,5.0824361795,\N,\N,0,3.1047158053,2.8004769978,0.3042388075,0.0918229038,0,19.8078752646,4.2129383064,15.5949369582,4.6785473484,0,0.1472979465,0.1427923049,0.0045056416,0.0013926563,0,0.6108644118,0.1784269289,0.4324374829,0.1297318106,0,3.4760029099,3.4306163292,0.0453865807,0.0136191854,0,23.1474143453,12.7757244359,10.3716899094,3.1115069821,0,30.9288385182,4.5083823208,1.75,0,0.0178878414,0.0095449482,133.7860416435,35.0483713277,0.0821202759,0.0836861619,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG343.76-0.1,G343.7559-0.1640,AGAL343.756-00.164,343.7559,-0.164,23.9,60.8963730807,2.9,busfield2006,-27.8,3.4,1180.58,9894.8822379251,7693.9773380842,615.6200403894,236.8084410194,0.4280892277,0.1000530733,841.280950279,196.6242996535,1.3665587458,0.3475063017,565.81506204,16.1,10.39,337.4257490568,665.706495394,10.615438781,3.31131135176792E+022,1.97574932335729E+023,343.7558316331,-0.1624979444,5.4129983549,IRb,640.4528371287,31.8837407595,608.5690963692,121.7138204177,0,1503.5537487973,168.3471646467,1335.2065841505,267.041804132,0,711.5402233408,175.8922819556,535.6479413852,107.1300404145,0,349.5656997961,98.5742491868,250.9914506092,50.1998437077,0,116.7884526883,41.4544266515,75.3340260368,56.5026244463,0,21.452432856,3.9352557513,17.5171771047,2.6276066654,0,0.9545305021,1.1416943573,\N,\N,0,1.862420156,2.1069423343,\N,\N,0,1.2362796939,1.2028674643,0.0334122296,0.0132018937,0,3.4373501971,2.203647568,1.2337026291,0.370658774,0,0.0426926656,0.0383221943,0.0043704713,0.0013129593,0,0.0993609815,0.0774432007,0.0219177809,0.0065762499,0,1.9644070631,2.0914979241,\N,\N,0,9.9067522818,9.118173497,0.7885787849,0.2365736403,0,24.3109861401,1.428695624,1.75,0,0.0236544274,0.0034377864,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG344.23-0.5,G344.2275-0.5688,AGAL344.227-00.569,344.2275,-0.5688,24.73,63.0111843634,2.52,green2011,-22.26,3.9,1784.83,11295.8564874486,9066.0144283881,1062.172795002,451.325843629,0.3849134244,0.0952113185,995.2706415903,246.1879062805,0.9370138703,0.2971974895,828.3646899529,28.63,17.26,199.0397339332,790.2194063522,17.8527726716,7.06683459033156E+022,3.57852126249882E+023,344.2252945893,-0.5688246198,7.9399731494,IRb,615.8360444028,35.4131842863,580.4228601164,116.084682947,0,2292.1392094337,268.5247630291,2023.6144464045,404.7353861672,0,1098.2866998898,312.0186957568,786.2680041329,157.2647270799,1,672.5620843995,204.4374425938,468.1246418057,93.6574388587,0,210.8663446702,80.5407107544,130.3256339157,97.7880949737,0,47.4438987374,10.7330061925,36.710892545,5.5073447286,0,1.7173233881,1.8189299935,\N,\N,0,2.8917947179,3.3076946309,\N,\N,0,1.7273899207,1.5056100233,0.2217798975,0.0679154901,0,4.9020124613,3.1011735588,1.8008389026,0.5424175398,0,0.186924933,0.094715221,0.092209712,0.0276670328,0,0.221615588,0.1302277495,0.0913878385,0.027419731,0,2.4345431279,2.4693007441,\N,\N,0,11.327549912,10.1106692451,1.2168806669,0.3650675034,0,22.84665056,0.3884055633,1.75,0,0.0274497689,0.0022871813,95.317388806,7.4571114714,0.1399964662,0.048264994,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG345.49+0.3,G345.4871+0.3142,AGAL345.488+00.314,345.4871,0.3142,33.87,86.2995881273,2.22,green2011,-17.57,4.6,12445.61,61128.4075772597,58639.418889194,925.341117329,444.1011632473,0.4644153599,0.1501541871,1670.5949324702,540.1346416586,1.8053827947,0.7683601674,702.9706439159,46.6,14.97,1406.8581076161,1741.7557410492,16.3104949444,4.22906718666942E+022,2.08511871309617E+023,345.4867723019,0.316391749,7.9780009797,HII,7585.8106036195,525.526138674,7060.2844649455,1412.0573249183,0,9070.2186495664,1239.7424636352,7830.4761859312,1566.0999396471,0,2775.1831551013,686.0337554646,2089.1493996366,417.8344341527,1,1316.25319773,314.8223333677,1001.4308643623,200.3037252772,0,434.9101536633,130.642380497,304.2677731664,228.2081026794,0,74.817247197,13.4762844687,61.3409627283,9.2012993279,0,36.5577286733,5.6786313312,30.8790973422,15.4397128086,0,69.2951941823,11.015641251,58.2795529312,17.4840674499,0,84.5816256879,7.373499261,77.2081264269,23.1625280607,0,329.7634272389,21.119011782,308.6444154569,92.5935577429,0,0.7241198739,0.266541744,0.4575781299,0.1372736451,0,1.785059402,0.411362079,1.373697323,0.4121093383,0,19.6519,8.2016303561,11.4502899625,3.4350895022,1,75.6892962512,32.2184923679,43.4708038833,13.0412475792,0,30.7014800057,1.6664638296,1.75,0,0.0172170241,0.0034381301,198.693208806,27.3456989838,0.2248963989,0.0846408616,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG345.72+0.8,G345.7172+0.8176,AGAL345.718+00.817,345.7172,0.8176,37.24,94.8862315282,1.56,giannetti2014,-11.19,1.9,772.38,1873.2609256257,2517.1160667525,200.5228372609,133.0086572625,0.3588167232,0.1206649784,220.2058230519,74.0520972504,1.0981583248,0.5453735356,135.7823444195,11.65,3.1,77.7641287006,170.1142645402,3.4128413894,1.53523324459075E+022,6.75606060317388E+022,345.7158822898,0.8173679934,4.8167237644,IRb,272.4266064026,18.1505253322,254.2760810704,50.8552164799,0,851.7696592885,123.1711869588,728.5984723297,145.7198156132,0,656.7919293959,180.0002022062,476.7917271896,95.3584799115,0,327.3888157164,96.469100122,230.9197155943,46.1846265454,0,121.4156392065,39.9369133435,81.478725863,61.1096348228,0,20.5948780579,3.3902034438,17.2046746142,2.5807159425,0,2.6997826214,2.5113834319,0.1883991895,0.0942447934,0,4.9618555269,4.9643195279,\N,\N,0,3.1357590559,1.8930919694,1.2426670865,0.372990155,0,11.3543744529,4.7364550529,6.6179194,1.9855527277,0,0.1039387116,0.1196331517,\N,\N,0,0.552656335,0.1788931519,0.3737631831,0.1121302285,0,5.0876920831,4.8388783479,0.2488137352,0.0746443188,0,26.766514468,21.4455303355,5.3209841325,1.5962952409,0,22.0541109697,1.2572027428,1.75,0,0.0104331349,0.0027136398,148.2224036566,21.8999118884,0.0569446891,0.0307981341,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
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AG351.25+0.6,G351.2437+0.6687,AGAL351.244+00.669,351.2437,0.6687,38.84,98.9629761696,1.82,moises2011,-3.29,3.1,23562.56,77783.2642025682,91099.2962685484,885.5898514194,510.2785383222,0.4366053118,0.148953497,713.2820978636,243.3453280857,0.8054316529,0.3730273346,652.3525369972,69.26,16.92,660.3446568853,1285.347703074,18.7246807507,4.57942002792609E+022,2.18937983188717E+023,351.2450895924,0.6700309269,6.9269214242,IRb,18760.9769152685,4896.6244737864,13864.3524414822,2772.8738225447,0,18507.26459602,4920.2485053651,13587.016090655,2717.507575897,0,4899.743457597,1902.2968269294,2997.4466306676,599.5233614124,1,2135.7417160301,738.0425585015,1397.6991575286,279.682786113,0,674.915201801,262.5078178204,412.4073839805,309.3438525664,0,125.76561746,31.7429079115,94.0227095485,14.1046527808,0,130.4505056623,68.4717349515,61.9787707108,30.9896131611,0,219.317461165,104.3215794013,114.9958817637,34.4991308996,0,209.2386956221,70.2816672292,138.9570283928,41.6873321249,0,941.6772021707,280.1357898863,661.5414122844,198.4636418487,0,4.66184,6.1001082178,\N,\N,1,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,125.928,164.7785838065,\N,\N,1,32.513422099,2.2230040888,1.75,0,0.0180148181,0.0043232442,196.2141704814,31.4760605631,0.3263703785,0.1449561824,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.42+0.6,G351.4161+0.6464,AGAL351.416+00.646,351.4161,0.6464,23.39,59.5969107262,1.34,Bessel (2),-7.37,4.8,22272.67,39856.8274230312,60337.2510828803,462.8117952501,353.260927148,0.1935857657,0.0433920833,758.2367272845,169.9581117235,1.6383262809,0.4353141693,390.426325919,79.08,53.27,2177.6270465051,1863.0716896476,55.1759077772,1.21734435198906E+023,6.66513875824715E+023,351.4156850234,0.6451905014,4.6033464107,IRb,16264.324165864,2849.2317789299,13415.0923869341,2683.0259123836,0,13421.2667608467,4153.1413356278,9268.125425219,1853.7098567007,0,2499.2055992975,1640.0801071748,859.1254921227,171.8995132536,1,1220.7213447835,841.8998121278,378.8215326558,76.0424372216,1,575.1638507874,292.4977281158,282.6661226716,212.03180987,0,120.9967498779,27.255231085,93.7415187929,14.0622642032,0,52.0600075908,14.5906417262,37.4693658646,18.7353356415,0,164.9203585782,22.0502448939,142.8701136843,42.8616359414,0,269.0550429595,16.6011493936,252.4538935659,75.7365479808,0,831.0559234769,82.9872598001,748.0686636768,224.4234137037,0,1.5452936782,1.643092443,\N,\N,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,41.742094839,44.3838726877,\N,\N,0,33.3838973128,1.4882760503,1.75,0,0.1779122242,0.0320490674,176.5885694838,16.9269707622,0.4914235682,0.1445025511,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.44+0.6,G351.4441+0.6579,AGAL351.444+00.659,351.4441,0.6579,37.06,94.427597756,1.34,Bessel (2),-4.3,3.8,5342.26,9559.9288059938,14735.0077459859,1411.7138199915,1079.8579994932,0.3067246068,0.1029651512,752.9475648757,252.758853163,0.5333570829,0.2251788607,1122.4803799415,125.02,33.56,147.4532440606,865.2566218805,36.4894024231,1.47912645988783E+023,7.63616460393333E+023,351.4441195465,0.6594144459,5.4524593735,24d,2770.2952991125,1526.7726294432,1243.5226696692,248.8310930066,0,12045.4732594383,6290.0662414283,5755.4070180101,1151.481707163,0,5480.2571519984,3811.5164469221,1668.7407050763,333.8227872521,1,2981.5137869457,1652.5261555405,1328.9876314052,265.894312762,1,1156.203509145,559.2498635145,596.9536456305,447.7386538809,0,229.3792688251,72.1449287607,157.2343400644,23.5859989021,0,18.9420118808,16.1174483193,2.8245635615,1.4207718161,0,27.4215305153,23.6976014739,3.7239290413,1.1457637784,0,21.5696026525,15.717548831,5.8520538215,1.7739162908,0,83.2390583258,47.3040034365,35.9350548893,10.8438535749,0,2.0100925326,1.9749903669,0.0351021657,0.0105664362,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,54.2974285781,53.3492333185,0.9481952596,0.2854252568,0,21.3985210227,1.124751402,1.75,0,0.2267728076,0.0735112777,184.147452916,37.8909277521,0.0802481293,0.0485737203,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.51+0.7,G351.4937+0.6908,AGAL351.496+00.691,351.4937,0.6908,28.42,72.4131766926,1.34,\N,\N,\N,852.26,1525.121230911,2300.5157015406,62.3464168738,47.5579593814,0.2352162241,0.0680628023,\N,\N,\N,\N,28.4532681358,4.06,1.85,24.0849708646,58.1644103628,2.9751449057,1.1107922007865E+022,3.28573032925236E+022,351.4909376711,0.6906927414,9.951877936,24d,634.2487276785,109.5187430422,524.7299846363,104.9460526945,0,1248.1643450528,467.8919090042,780.2724360486,156.0756894977,0,664.5878124896,317.9066787811,346.6811337084,69.3562121082,0,348.3570037164,175.1998525347,173.1571511817,34.708134965,0,106.1078973896,50.2762446657,55.8316527239,41.9077504556,0,22.7620736584,13.8658564893,8.8962171691,1.3383498462,0,3.5635028855,3.8903632407,\N,\N,0,4.4882463004,5.6171962307,\N,\N,0,3.1668032045,3.2786951715,\N,\N,0,6.6190934088,6.8715327145,\N,\N,0,0.8987565413,0.6231415787,0.2756149627,0.0826844897,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,24.277573429,16.8325514689,7.4450219601,2.2335066123,0,25.555104206,0.9057441968,1.75,0,0.0087529327,0.0007353419,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.57+0.7,G351.5719+0.7631,AGAL351.571+00.762,351.5719,0.7631,42.32,107.8298957646,1.34,Bessel (2),-3.15,1.5,253.69,453.983568306,679.7885336931,160.1452429778,121.9577459359,0.3502586463,0.1225044173,133.9739321952,46.8579396286,0.8365776573,0.5301454934,88.3869432339,7.18,1.48,4.5280122065,25.3673422704,1.7687729781,1.28674412650788E+022,4.61720812157805E+022,351.5692485472,0.7623544024,9.9154469849,24d,354.3583815607,333.5721453643,20.7862361964,4.1585207151,0,1049.8935833574,593.9341942826,455.9593890749,91.1962006906,0,773.177558287,431.0020592174,342.1754990696,68.4368786583,0,369.3198391959,182.8731935262,186.4466456697,37.2940780646,0,131.2268463726,71.195613471,60.0312329016,45.02546451,0,15.7324043273,2.7232108592,13.0091934681,1.9514390824,0,9.0948637528,10.4912302431,\N,\N,0,13.7089674988,16.230270679,\N,\N,0,7.8226940563,8.3865825505,\N,\N,0,15.9890266075,16.6316720437,\N,\N,0,1.441112306,1.4358643238,0.0052479821,0.0015750102,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,38.9279056434,38.7861491952,0.1417564481,0.0425435635,0,17.3176656573,0.2572845687,1.75,0,0.0109106628,0.0009445783,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.58-0.3,G351.5815-0.3528,AGAL351.581-00.352,351.5815,-0.3528,25.75,65.6101090722,6.81,green2011,-95.91,4.1,5306.69,245266.917394058,169323.47684003,8738.0930584847,2144.7234223047,1.083085512,0.2832549326,3095.1334675445,809.4576207979,0.3542115479,0.1078363938,7506.3491283014,44.88,24.96,877.8758710228,1600.5925830822,25.8173319626,7.34255218026012E+022,4.09628279049462E+023,351.5809677151,-0.3519502919,3.6095832835,IRb,2659.9746704027,101.3974923937,2558.577178009,511.7156739527,0,5931.5750215909,527.1217866561,5404.4532349348,1080.892528152,0,1736.3850066463,349.3939257856,1386.9910808607,277.4018784881,1,905.1770031284,175.5359115301,729.6410915984,145.938724649,0,301.8622041917,76.2958533464,225.5663508453,169.1790967143,0,58.0492425486,5.8047213091,52.2445212395,7.8367391812,0,3.2136565564,2.2384590419,0.9751975146,0.4880978886,0,4.7494817147,4.280839356,0.4686423587,0.1428000114,0,3.1253121298,2.2765131766,0.8487989531,0.2554485091,0,12.5654445465,3.9351024354,8.6303421111,2.589777852,0,0.8045981497,0.4859498603,0.3186482894,0.0955945604,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,21.7341291253,13.1266727576,8.6074563677,2.5822388971,0,27.098991633,2.5801526985,1.75,0,0.0298494654,0.0120046495,186.8732592967,51.5844841775,0.0324225043,0.0259644307,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG351.77-0.5,G351.7747-0.5369,AGAL351.774-00.537,351.7747,-0.5369,24.85,63.3169402115,1,snell1990,-2.75,5.6,16525.6,16469.408401921,34754.9392003081,264.1363043284,269.6946285055,0.1534845955,0.0382421281,818.2570755713,203.8764332027,3.0978591817,0.852020393,239.285860853,81.76,48.81,1980.6668245783,2258.1617025918,50.0427941446,1.10523435891839E+023,6.50052786343054E+023,351.7747054107,-0.5350526835,6.6503680412,IRb,10706.9121892791,418.0042305357,10288.9079587434,2057.7817734841,0,11245.5976173888,1163.2833946703,10082.3142227184,2016.4701212627,0,2053.9537027449,610.9505848898,1443.003117855,288.6218886338,1,878.9717984407,344.7200147877,534.251783653,106.9291383631,1,484.8616195827,122.9332561623,361.9283634204,271.4516028873,0,105.5047780573,15.2537947151,90.2509833423,13.5379469765,0,4.2738685895,2.0541068704,2.2197617191,1.1099007764,0,6.7371109704,3.7553543197,2.9817566508,0.8946587939,0,7.7179007655,2.0008199932,5.7170807724,1.7151562287,0,84.3985566829,6.5376484429,77.86090824,23.3583595603,0,1.4603236389,0.5546187071,0.9057049318,0.2717114984,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,39.4468499329,14.9815841632,24.4652657697,7.339580239,0,31.7889197918,3.5076843099,1.75,0,0.1778113628,0.0817919886,152.6194309622,33.9258250165,0.1523601644,0.1201652694,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG353.07+0.4,G353.0670+0.4519,AGAL353.066+00.452,353.067,0.4519,28.31,72.1329004985,0.86,\N,1.7,\N,77.6,57.1955461372,133.0733195029,17.8799959492,20.9646422358,0.1503753747,0.0433705597,\N,\N,\N,\N,14.4960235168,2.98,1.37,3.3597581044,28.5953617431,1.4908190905,7.7941694152584E+021,4.10034073180201E+022,353.0658149394,0.4524685297,4.731767647,70d,234.5666170433,225.9812323647,8.5853846786,1.7194809572,0,593.4439273538,472.0284524156,121.4154749382,24.3039020635,0,379.8897317456,281.0509626556,98.83876909,19.7780379881,0,177.768328199,125.2709376107,52.4973905883,10.526927555,0,61.8427934306,46.3904790437,15.4523143868,11.6017999009,0,5.9848353092,2.3091798533,3.6756554559,0.5518119195,0,7.8336973611,8.1531440383,\N,\N,0,13.4378192239,12.9679437667,0.4698754572,0.1446456909,0,6.9698601124,6.6106628545,0.359197258,0.1092962725,0,10.65199034,11.7712510133,\N,\N,0,0.7556509727,0.7755684406,\N,\N,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,20.411948204,20.9499677325,\N,\N,0,17.789305008,0.1561321639,1.75,0,0.0063517064,0.0003129717,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG353.41-0.3,G353.4102-0.3611,AGAL353.409-00.361,353.4102,-0.3611,35.49,90.4272920767,3.44,wienen2014,-16.05,3.8,10785.84,127201.318737518,87485.8082506395,3482.6517809779,1171.2932254017,0.7540546916,0.2488959434,1851.0534569445,610.9897618014,0.5315069015,0.2212719034,2761.2622865454,68.42,20.03,631.2723648946,1236.2360252886,21.6445453307,6.03754298383371E+022,3.10851034877879E+023,353.4083683307,-0.3590612803,9.8664898496,IRb,6304.8809744695,571.8746318799,5733.0063425896,1146.6053941185,0,7506.94533316,1207.0394947831,6299.905838377,1260.0526179816,0,2899.2789181629,830.1807226304,2069.0981955326,413.8429209057,1,1468.6051618068,429.0773751117,1039.5277866951,207.9552842808,0,499.3504740244,144.086343391,355.2641306334,266.4601849031,0,107.1538036689,20.8588377703,86.2949658986,12.9445907784,0,37.9642936381,10.592402764,27.371890874,13.6866092271,0,89.5197774729,20.2816302836,69.2381471892,20.7728756707,0,88.5792629358,11.7329393584,76.8463235774,23.0547631576,0,425.6064216984,37.4862146486,388.1202070498,116.438367988,0,3.82729,1.7412438538,2.086047158,0.6258145927,1,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,103.384,47.0351824321,56.3491559468,16.9047588116,1,28.2635243907,1.671996712,1.75,0,0.0211061755,0.0049670862,187.6072727212,29.2057192551,0.2772673068,0.1247955417,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG353.42-0.0,G353.4173-0.0803,AGAL353.417-00.079,353.4173,-0.0803,36.78,93.7141674438,6.06,giannetti2014,-54.43,\N,123.19,4508.4972338228,1668.8320014219,1790.8091829175,405.7588677386,1.3766475115,0.4608327957,\N,\N,\N,\N,710.0260973325,2.77,0.75,5.6639802114,50.3423446121,1.4549456288,9.3145028764475E+021,2.38217805545916E+022,353.4157553474,-0.0791565048,6.9186839165,24d,231.0682707364,210.6134995749,20.4547711616,4.1179491004,0,883.0429596061,696.1261881646,186.9167714415,37.6595277745,0,625.3090660522,467.4271318392,157.881934213,31.6139539799,0,323.0790656008,222.8194975539,100.2595680468,20.1421007105,0,110.1197940066,75.1953759601,34.9244180465,26.2114993378,0,12.5376881398,5.5512668585,6.9864212813,1.048648372,0,6.4657472272,7.2452336383,\N,\N,0,10.3178631184,12.7996252664,\N,\N,0,6.0203615266,7.5735431401,\N,\N,0,8.8052421339,12.3598645494,\N,\N,0,1.066155767,1.1152394069,\N,\N,0,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,28.7994286772,30.1252946263,\N,\N,0,17.1190420568,0.4360239522,1.75,0,0.0074571942,0.0006203143,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
AG354.94-0.5,G354.9437-0.5381,AGAL354.944-00.537,354.9437,-0.5381,34.61,88.1850825239,1.91,giannetti2014,-5.36,2,133.11,483.9300321145,566.4653756259,146.5587528497,81.236404977,0.4082943505,0.1333248349,277.640158325,90.6608877193,1.8943949299,0.9169717205,98.8698840901,4.6,1.42,6.6395145862,14.0232200513,1.5386906576,8.66604408896705E+021,3.80707528550808E+022,354.9441841025,-0.5374589446,2.8919167054,IRb or HII,57.503932221,29.4698387772,28.0340934438,5.606996757,0,170.6648045274,62.403934221,108.2608703064,21.6549335591,0,211.917220088,118.084291562,93.832928526,18.7670199125,0,118.9233319576,56.6614462892,62.2618856684,12.4534970578,0,45.6210117928,21.583588922,24.0374228707,18.0286172404,0,7.5473769489,0.7286143973,6.8187625515,1.0228310829,0,4.474525897,3.7386963124,0.7358295845,0.3679954931,0,7.5370107462,8.0568898931,\N,\N,0,4.4299329393,4.281815751,0.1481171883,0.045701127,0,6.8151153319,5.8563790381,0.9587362938,0.2883939121,0,0.3997620378,0.4891761347,\N,\N,0,0.4212959698,0.4200433307,0.0012526391,0.0004620051,0,6.0886161216,5.7708049197,0.3178112018,0.0953448359,0,24.0405356698,23.7186875432,0.3218481266,0.0965551736,0,19.1301556394,1.3200310402,1.75,0,0.0046346055,0.0016678283,334.861933606,173.3417383759,0.0048335688,0.0042345974,\N,\N,\N,\N,\N,\N,\N,\N,\N,\N
etl/assets/data/top100_density.csv 0 → 100644
View file @ dab489de
# select
# agal_name
# , round("N_H2_peak" / (2 * r_fwhm * 3.0856e18)) as volume_density
# from top100_sed_data tsd
"agal_name","volume_density"
AGAL006.216-00.609,89795.0
AGAL008.671-00.356,190969.0
AGAL008.684-00.367,68138.0
AGAL008.706-00.414,27785.0
AGAL008.831-00.027,NaN
AGAL010.444-00.017,22907.0
AGAL010.472+00.027,366225.0
AGAL010.624-00.384,299726.0
AGAL012.804-00.199,375774.0
AGAL013.178+00.059,68826.0
AGAL013.658-00.599,52684.0
AGAL014.114-00.574,75908.0
AGAL014.194-00.194,78815.0
AGAL014.492-00.139,74017.0
AGAL014.632-00.577,127991.0
AGAL015.029-00.669,209966.0
AGAL018.606-00.074,43782.0
AGAL018.734-00.226,19630.0
AGAL018.888-00.474,56893.0
AGAL019.609-00.234,92787.0
AGAL019.882-00.534,136234.0
AGAL022.376+00.447,82445.0
AGAL023.206-00.377,163688.0
AGAL024.416+00.101,15957.0
AGAL024.629+00.172,15443.0
AGAL024.651-00.169,7869.0
AGAL024.789+00.082,108737.0
AGAL028.564-00.236,37289.0
AGAL028.861+00.066,19217.0
AGAL030.818-00.056,386655.0
AGAL030.848-00.081,23220.0
AGAL030.893+00.139,49987.0
AGAL031.412+00.307,389457.0
AGAL034.258+00.154,1700013.0
AGAL034.401+00.226,218025.0
AGAL034.411+00.234,495365.0
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etl/assets/utils.py 0 → 100644
View file @ dab489de
import os
import pandas as pd
import yaml
from assets.constants import config_overrides
from typing import (Union,
Tuple)
from assets.commons import validate_parameter
def get_poc_results(line_fit_filename: str) -> pd.DataFrame:
df_class = pd.read_csv(os.path.join('assets', 'data', line_fit_filename))
with open(os.path.join('prs', 'output', 'run_type', 'constant_abundance_p15_q05', 'volume_density_results.yml'),
'r') as infile:
fit_results = yaml.load(infile, Loader=yaml.FullLoader)
df_results = pd.DataFrame.from_dict(fit_results, orient='index')
df_full = (df_results.merge(df_class, left_index=True, right_on='source_name', how='inner', validate='1:1')
.dropna(subset='best_fit'))
df_full['class_phys'] = 'HII'
df_full.loc[df_full['class'] == '70w', 'class_phys'] = 'Quiescent'
df_full.loc[df_full['class'] == 'IRw', 'class_phys'] = 'Protostellar'
df_full.loc[df_full['class'] == 'IRb', 'class_phys'] = 'MYSOs'
return df_full
def get_profile_input(
model_root_folder: str,
species: Union[None, str] = None) -> Tuple[dict, dict]:
overrides = config_overrides[model_root_folder.split(os.path.sep)[-1]]
_species = validate_parameter(species, default='e-ch3oh')
try:
overrides_grid = overrides['grid']
except KeyError:
overrides_grid = {}
try:
overrides_lines = overrides['lines']
except KeyError:
overrides_lines = {}
overrides_abundance = {}
try:
overrides_abundance['molecular_abundance'] = overrides_lines['molecular_abundances'][_species]
except KeyError:
pass
for key in ['abundance_jump', 'threshold']:
try:
overrides_abundance[key] = overrides_lines['hot_core_specs'][_species][key]
except KeyError:
pass
return overrides_grid, overrides_abundance