pyPETaL

https://github.com/zstone19/pypetal
Admin
pyPETaL is a time-series data analysis pipeline for AGN reverberation mapping that combines multiple...
Tokens:60,561
Snippets:374
Trust Score:6.1
License:MIT
Update:8 months ago
Show doc for...
Tokens:
Raw
### Install pypetal-jav from source

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs the latest development version of pyPetal-jav by cloning the repository and installing it using pip. Ensure NumPy is installed prior to this step if JAVELIN requires it.

```bash
git clone https://github.com/Zstone19/pypetal-jav.git
cd pypetal-jav
pip install .
```

--------------------------------

### Install pyPetal from source

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs the latest development version of pyPetal by cloning the repository and installing it using pip. This method is useful for developers or users who need the most recent features.

```bash
git clone https://github.com/Zstone19/pypetal.git
cd pypetal
pip install .
```

--------------------------------

### Install pyPetal using pip

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs the pyPetal package from the Python Package Index (PyPI) using pip. This is the standard method for installing the latest stable release.

```bash
pip install pypetal
```

--------------------------------

### Start and Finish Sampling Process

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Initiates and completes the MCMC sampling process. This involves running parallel chains and collecting the resulting parameter estimates.

```Shell
start sampling
sampling finished

```

```Shell
start sampling
sampling finished

```

```Shell
start sampling
sampling finished

```

--------------------------------

### Initialize Pypetal Pipeline for MICA2

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/mica2/numcomp.ipynb

Imports the necessary pypetal pipeline module and sets up the environment for running MICA2. This is the initial setup for spectral line analysis.

```python
%matplotlib inline
import pypetal.pipeline as pl
```

--------------------------------

### Install pypetal-jav using pip

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs the pyPetal-jav package, a Python 2 compatible module for JAVELIN, from PyPI. It is recommended to install NumPy separately beforehand if needed.

```bash
pip install --no-deps pypetal-jav
pip install pypetal-jav
```

--------------------------------

### Install MICA2 dependencies with poetry

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs optional dependencies for the MICA2 module using the 'poetry' package manager. This command installs extra dependencies required for MICA2's functionality.

```bash
poetry install --with extra
```

--------------------------------

### Initialize pyPetal Pipeline Parameters

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Sets up initial parameters for the pyPetal pipeline, including input file paths, output directory, and line names. These variables are used in subsequent pipeline execution calls.

```python
import pypetal.pipeline as pl

main_dir = 'pypetal/examples/dat/javelin_'

filenames = [ main_dir + 'continuum.dat', main_dir + 'yelm.dat', main_dir + 'zing.dat' ]
output_dir = 'quickstart_output/'
line_names = ['Continuum', 'H-alpha', 'H-beta']
```

--------------------------------

### Create pyPetal-jav Virtual Environment

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/from_bash.ipynb

This code snippet shows the process of setting up a second virtual environment for pyPetal-jav, including installing necessary packages like numpy and pypetal-jav.

```bash
python -m virtualenv pypetal_env2
cd pypetal_env2
source activate
pip install numpy
pip install pypetal-jav
```

--------------------------------

### Install optional PLIKE module

Source: https://github.com/zstone19/pypetal/blob/main/docs/installation.rst

Installs the PLIKE algorithm, an optional component for pyPetal used for time lag estimation. This script compiles and installs PLIKE, assuming 'gfortran' is the available Fortran compiler.

```bash
sh build_plike.sh
```

--------------------------------

### PyCCF Module Configuration and Output Example

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/pyccf.ipynb

This snippet shows an example of configuring and running the pyCCF module, including parameters like time unit, light curve unit, file format, and lag bounds. It also illustrates the expected output format, including diagnostic messages and a placeholder for the resulting figure.

```python
plot=True,
time_unit='d',
lc_unit='mJy',
file_fmt='ascii',
lag_bounds=[-50,100])
```

```
Output:

Running pyCCF
-----------------
lag_bounds: [[-50, 100]]
interp: 2.0000000001
nsim: 2000
mcmode: 0
sigmode: 0.3
thres: 0.8
nbin: 110
-----------------
        
Failed centroids:  0
Failed peaks:  0

```

```
Result:
<Figure size 1200x700 with 5 Axes>
```
```

--------------------------------

### Analyze Acceptance Fractions

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Reports the acceptance fractions for all walkers after sampling. This metric indicates the efficiency of the MCMC sampler.

```Shell
acceptance fractions for all walkers are
0.69 0.68 0.67 0.70 0.67 0.72 0.65 0.73 0.66 0.74 0.77 0.70 0.67 0.71 0.73 0.71 0.67 0.73 0.79 0.65 0.73 0.65 0.72 0.69 0.62 0.68 0.63 0.67 0.66 0.72 0.62 0.73 0.70 0.75 0.64 0.68 0.66 0.74 0.64 0.63 0.79 0.68 0.73 0.70 0.69 0.72 0.70 0.68 0.71 0.65 0.75 0.75 0.59 0.71 0.75 0.69 0.69 0.74 0.77 0.70 0.73 0.63 0.77 0.68 0.74 0.77 0.73 0.69 0.79 0.74 0.66 0.74 0.65 0.72 0.69 0.75 0.71 0.65 0.66 0.74 0.73 0.71 0.79 0.70 0.67 0.79 0.75 0.62 0.69 0.78 0.72 0.65 0.63 0.73 0.68 0.59 0.63 0.81 0.68 0.76

```

```Shell
acceptance fractions are
0.08 0.02 0.00 0.09 0.06 0.04 0.09 0.09 0.03 0.02 0.12 0.02 0.15 0.07 0.06 0.03 0.06 0.07 0.03 0.10 0.02 0.08 0.06 0.12 0.19 0.11 0.10 0.01 0.10 0.07 0.08 0.06 0.09 0.09 0.00 0.13 0.12 0.03 0.10 0.07 0.09 0.02 0.10 0.13 0.12 0.00 0.05 0.09 0.08 0.03 0.03 0.06 0.04 0.07 0.11 0.04 0.05 0.16 0.06 0.09 0.06 0.15 0.06 0.14 0.06 0.03 0.10 0.06 0.05 0.09 0.14 0.04 0.00 0.06 0.10 0.04 0.14 0.02 0.00 0.14 0.09 0.08 0.10 0.00 0.12 0.07 0.04 0.07 0.08 0.09 0.04 0.06 0.14 0.16 0.05 0.09 0.03 0.19 0.06 0.03

```

```Shell
acceptance fractions for all walkers are
0.72 0.78 0.70 0.75 0.64 0.74 0.69 0.70 0.65 0.71 0.73 0.69 0.65 0.64 0.66 0.84 0.68 0.77 0.81 0.73 0.71 0.68 0.73 0.69 0.70 0.75 0.78 0.73 0.77 0.71 0.73 0.57 0.70 0.65 0.75 0.72 0.60 0.65 0.65 0.76 0.71 0.79 0.73 0.69 0.69 0.73 0.68 0.77 0.75 0.66 0.75 0.69 0.63 0.69 0.71 0.67 0.77 0.69 0.77 0.70 0.70 0.73 0.72 0.80 0.72 0.76 0.72 0.73 0.60 0.68 0.68 0.66 0.73 0.80 0.77 0.70 0.65 0.69 0.57 0.75 0.76 0.67 0.75 0.70 0.79 0.73 0.68 0.74 0.67 0.76 0.68 0.60 0.65 0.71 0.71 0.70 0.69 0.74 0.68 0.75

```

--------------------------------

### Create pyPetal Virtual Environment

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/from_bash.ipynb

This code snippet demonstrates how to create a Python virtual environment for pyPetal, install pyPetal within it, and activate the environment.

```bash
python -m venv pypetal_env1
cd pypetal_env1
source activate
pip install pypetal
```

--------------------------------

### Run Parallel Chains and Burn-in

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Configures and executes the MCMC sampling, including running a specified number of parallel chains and performing a burn-in phase to ensure convergence.

```Shell
run parallel chains of number 45 
start burn-in
using priors on sigma and tau from continuum fitting
[[  1.742 135.553]
 [  2.131 207.09 ]
 [  2.931 387.869]]
penalize lags longer than 0.30 of the baseline
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished

```

```Shell
run parallel chains of number 45 
start burn-in
using priors on sigma and tau from continuum fitting
[[  1.768 143.548]
 [  2.205 224.383]
 [  3.015 428.443]]
penalize lags longer than 0.30 of the baseline
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished

```

--------------------------------

### Run MICA2 with Two Gaussian Components

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/mica2/numcomp.ipynb

Configures and executes the MICA2 pipeline with two Gaussian components for fitting spectral lines. This demonstrates how to increase the complexity of the model.

```python
main_dir = 'pypetal/examples/dat/rm160_'
line_names = ['cont', 'hb']
filenames = [ main_dir + x + '.dat' for x in line_names ]

output_dir = 'mica2_output2b/'



params = {
    'max_num_saves': 2000,
    'no_order': True,
    'number_component': [2,2]
}

res = pl.run_pipeline(output_dir, filenames, line_names,
                      run_mica2=True, mica2_params=params,
                      verbose=True, plot=True, time_unit='d',
                      file_fmt='ascii', lag_bounds=[-500, 500])
```

--------------------------------

### Burn-in Configuration and Completion

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Specifies the number of burn-in iterations and walkers, then indicates the completion of the burn-in phase. This output is typically generated by MCMC software.

```Shell
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished

```

```Shell
nburn: 100 nwalkers: 100 --> number of burn-in iterations: 10000
burn-in finished

```

--------------------------------

### Run MICA2 with One Gaussian Component

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/mica2/numcomp.ipynb

Configures and executes the MICA2 pipeline with a single Gaussian component for fitting spectral lines. It specifies input data files, output directory, and MICA2-specific parameters.

```python
main_dir = 'pypetal/examples/dat/rm160_'
line_names = ['cont', 'hb']
filenames = [ main_dir + x + '.dat' for x in line_names ]

output_dir = 'mica2_output2a/'



params = {
    'max_num_saves': 2000,
    'no_order': True,
    'number_component': [1,1]
}

res = pl.run_pipeline(output_dir, filenames, line_names,
                      run_mica2=True, mica2_params=params,
                      verbose=True, plot=True, time_unit='d',
                      file_fmt='ascii', lag_bounds=[-500, 500])
```

--------------------------------

### Visualize MICA2 Output (One Gaussian)

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/mica2/numcomp.ipynb

Loads and displays the PDF output generated by the MICA2 pipeline when using one Gaussian component. This allows for visual inspection of the fitting results.

```python
from wand.image import Image as WImage
WImage(filename='mica2_output2a/hb/mica2/data/fig_1.pdf', resolution=300)
```

--------------------------------

### Visualize MICA2 Output (Two Gaussians)

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/mica2/numcomp.ipynb

Loads and displays the PDF output generated by the MICA2 pipeline when using two Gaussian components. This facilitates comparison with the single Gaussian fit.

```python
WImage(filename='mica2_output2b/hb/mica2/data/fig_2.pdf', resolution=300)
```

--------------------------------

### Run pyzdcf pipeline in pyPetal

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

This snippet demonstrates how to run the pyzdcf module within the pyPetal pipeline. It configures parameters such as output directory, filenames, line names, and enables features like running pyzdcf, verbose output, and plotting. The file format, time unit, and light curve unit are also specified.

```python
res = pl.run_pipeline( output_dir, filenames, line_names,
                       run_pyzdcf=True, 
                       verbose=True,
                       plot=True,
                       file_fmt='ascii',
                       time_unit='d',
                       lc_unit='mag')
```

--------------------------------

### Install LinMix for pyPETaL detrending (pip)

Source: https://github.com/zstone19/pypetal/blob/main/README.md

Installs the LinMix algorithm, used for detrending light curves within pyPETaL, using pip. This command installs directly from the Git repository.

```bash
pip install "linmix @ git+https://github.com/jmeyers314/linmix.git"
```

--------------------------------

### Run PyROA pipeline in pyPetal

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

This code snippet shows how to execute the PyROA module for lag analysis in pyPetal. It involves setting the output directory, filenames, line names, and enabling PyROA functionality. Options for verbose output, plotting, file format, time unit, and light curve unit are also configurable.

```python
res = pl.run_pipeline( output_dir, filenames, line_names,
                       run_pyroa=True, 
                       verbose=True,
                       plot=True,
                       file_fmt='ascii',
                       time_unit='d',
                       lc_unit='mag')
```

--------------------------------

### Install pyPETaL-JAV with pip (no dependencies)

Source: https://github.com/zstone19/pypetal/blob/main/README.md

Installs the pyPETaL-JAV package, a Python 2 compatible module for JAVELIN, without installing its dependencies. This allows for manual dependency management.

```bash
pip install --no-deps pypetal-jav
```

--------------------------------

### Initialize JAVELIN Pipeline Components in pypetal

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/all_together.ipynb

Imports the JAVELIN pipeline module from pypetal and initializes necessary variables like line names and output directory. This setup is a precursor to running the JAVELIN analysis within the pypetal framework.

```python
%matplotlib inline
import pypetal_jav.pipeline as plj
import numpy as np

line_names = ['cont', 'ha', 'hb', 'mg2']
output_dir = 'tot_output/'
```

--------------------------------

### Initialize pyPetal Pipeline for PLIKE Analysis

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/plike.ipynb

Initializes the pyPetal pipeline by importing necessary modules and defining input parameters for light curve files and line names. This setup is required before running the main pipeline function.

```python
import pypetal.pipeline as pl

main_dir = 'pypetal/examples/dat/pyzdcf_'
filenames = [main_dir + 'lc1.dat', main_dir + 'lc2.dat']

line_names = ['Continuum', 'H-alpha']
output_dir = 'plike_output/'
```

--------------------------------

### Start Burn-in Phase

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Initiates the burn-in phase of the MCMC sampling. This phase is crucial for allowing the chains to reach the stationary distribution before collecting samples.

```Shell
start burn-in

```

```Shell
start burn-in

```

--------------------------------

### Initialize pyCCF Pipeline Parameters

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/pyccf.ipynb

Sets up initial parameters for running the pyCCF pipeline, including the output directory, input filenames, and line names. This prepares the environment for the pyCCF analysis.

```python
import pypetal.pipeline as pl 

main_dir = 'pypetal/examples/dat/pyccf_'
filenames = [ main_dir + 'lc1.dat', main_dir + 'lc2.dat' ]

output_dir = 'pyccf_out/'
line_names = ['Continuum', 'H-alpha']
```

--------------------------------

### Save Log Probability of MCMC Chains

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Saves the log probabilities associated with the MCMC chains to a specified text file. This is useful for assessing the convergence and quality of the sampling.

```Shell
save logp of MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/logp_cont.txt

```

```Shell
save logp of MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/logp_rmap.txt

```

```Shell
save logp of MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/logp_cont.txt

```

```Shell
save logp of MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/logp_rmap.txt

```

--------------------------------

### Initialize pyZDCF Parameters and Run Pipeline

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/pyzdcf.ipynb

This snippet demonstrates how to set up parameters for the pyZDCF module and initiate the pyPetal pipeline. It specifies the number of simulations, minimum points per bin, and output prefix. The `run_pipeline` function is called with various options including time and light curve units, and file format.

```python
import pypetal.pipeline as pl

main_dir = 'pypetal/examples/dat/pyzdcf_'
filenames = [main_dir + 'lc1.dat', main_dir + 'lc2.dat']

line_names = ['Continuum', 'H-alpha']
output_dir = 'pyzdcf_output/'

params = {
    'nsim': 1000,
    'prefix': 'pyzdcf',
    'minpts': 14
}

res = pl.run_pipeline( output_dir, filenames, line_names,
                      run_pyzdcf=True,
                      pyzdcf_params=params,
                      verbose=True,
                      plot=True,
                      time_unit='d',
                      lc_unit='Jy',
                      file_fmt='csv')
```

--------------------------------

### Covariance Matrix Calculation and Decomposition

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Performs calculations related to the covariance matrix, including its decomposition and update using a matrix U. This is a common step in statistical modeling and optimization.

```Shell
covariance matrix calculated
covariance matrix decomposed and updated by U

```

--------------------------------

### Running MICA2 with MPI

Source: https://github.com/zstone19/pypetal/blob/main/docs/mica2_toc.rst

Demonstrates how to run MICA2 on multiple cores using mpiexec. This is useful for computationally intensive tasks but requires careful consideration when used with other pyPetal modules.

```bash
mpiexec -n 4 python mica2_script.py
```

--------------------------------

### Save MCMC Chains to File

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Saves the main MCMC chains after the burn-in phase to a specified text file. These chains represent the converged posterior distributions of the model parameters.

```Shell
save MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/chain_cont.txt

```

```Shell
save MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/chain_rmap.txt

```

```Shell
save MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/chain_cont.txt

```

```Shell
save MCMC chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/chain_rmap.txt

```

--------------------------------

### Save Burn-in Chains to File

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Saves the burn-in chains generated during the MCMC process to a specified text file. This is crucial for later analysis and visualization of the initial sampling phase.

```Shell
save burn-in chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/burn_cont.txt

```

```Shell
save burn-in chains to /home/stone28/pypetal/quickstart_ouput/H-alpha/javelin/burn_rmap.txt

```

```Shell
save burn-in chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/burn_cont.txt

```

```Shell
save burn-in chains to /home/stone28/pypetal/quickstart_ouput/H-beta/javelin/burn_rmap.txt

```

--------------------------------

### Initialize PyROA Parameters and File Paths

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/pyroa/basic.ipynb

Sets up the necessary parameters and file paths for running the PyROA analysis. This includes defining the main directory, line names, filenames, and the output directory.

```python
%matplotlib inline
import pypetal.pipeline as pl

main_dir = 'pypetal/examples/dat/javelin_'
line_names = ['continuum', 'yelm', 'zing']
filenames = [ main_dir + x + '.dat' for x in line_names ]

output_dir = 'pyroa_output1/'
```

--------------------------------

### Analyze HPD of Additional Parameters

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Calculates and displays the Highest Probability Density (HPD) intervals for additional parameters such as 'lag_H-alpha', 'wid_H-alpha', and 'scale_H-alpha'. This provides insights into the uncertainty of these derived quantities.

```Shell
HPD of lag_H-alpha
low: -1932.897 med  101.282 hig 1924.632
HPD of wid_H-alpha
low:    0.270 med    0.442 hig    0.648
HPD of scale_H-alpha
low:    0.492 med    0.590 hig    0.684

```

--------------------------------

### Initialize PyPetal for Individual Light Curve Fitting

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/pyroa/together.ipynb

Sets up the necessary directories, line names, and filenames for PyROA analysis. This step prepares the data for the `run_pipeline` function.

```python
%matplotlib inline
import pypetal.pipeline as pl

main_dir = 'pypetal/examples/dat/javelin_'
line_names = ['continuum', 'yelm', 'zing']
filenames = [ main_dir + x + '.dat' for x in line_names ]

output_dir = 'pyroa_output2/'
```

--------------------------------

### Analyze HPD of Sigma and Tau

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Calculates and displays the Highest Probability Density (HPD) intervals for the 'sigma' and 'tau' parameters. This provides a range within which the true parameter value is likely to lie.

```Shell
HPD of sigma
low:    1.742 med    2.131 hig    2.931
HPD of tau
low:  135.553 med  207.090 hig  387.869

```

```Shell
HPD of sigma
low:    1.879 med    2.167 hig    2.563
HPD of tau
low:  157.070 med  195.034 hig  251.908

```

```Shell
HPD of sigma
low:    1.768 med    2.205 hig    3.015
HPD of tau
low:  143.548 med  224.383 hig  428.443

```

--------------------------------

### Run pyPetal with pyCCF Cross-Correlation

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/getting_started.ipynb

Executes the pyPetal pipeline with the pyCCF module enabled for cross-correlation analysis. This function calculates the cross-correlation function between light curves. It includes options for verbosity, plotting, file format, time units, light curve units, and the number of threads for multiprocessing.

```python
res = pl.run_pipeline( output_dir, filenames, line_names,
                       run_pyccf=True, 
                       verbose=True,
                       plot=True,
                       file_fmt='ascii',
                       time_unit='d',
                       lc_unit='mag',
                       threads=45)
```

--------------------------------

### Run JAVELIN Pipeline with pypetal

Source: https://github.com/zstone19/pypetal/blob/main/docs/notebooks/javelin/basic.ipynb

This Python code snippet demonstrates how to run the JAVELIN pipeline using the pypetal library. It initializes the pipeline with specified output directory, line names, JAVELIN parameters, and plotting options. The `lag_bounds` parameter is crucial for defining the search range for the time lag between different light curves.

```python
import pypetal_jav.pipeline as pl

output_dir = 'javelin_output1/'
line_names = ['Continuum', 'H-alpha', 'H-beta']

params = {
    'nwalker': 50,
    'nburn': 50,
    'nchain': 100,
    'lagtobaseline': 0.1,
    'nbin': 100
}

res = pl.run_pipeline( output_dir, line_names,
                 javelin_params=params,
                 verbose=True,
                 plot=True,
                 file_fmt='ascii',
                 lag_bounds=[[0,200],[0,500]])
```
Tokens:
Raw