PyIRI

https://pyiri.readthedocs.io/en/latest/
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PyIRI is a pure Python implementation of the International Reference Ionosphere (IRI) model that...
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### Install PyIRI System-Wide

Source: https://pyiri.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyIRI for all users on the system. This method requires root privileges.

```bash
sudo pip install .
```

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

### Install PyIRI with Documentation Requirements

Source: https://pyiri.readthedocs.io/en/latest/installation.html

Install PyIRI with additional optional dependencies needed for building the documentation locally.

```bash
python -m pip install PyIRI[doc]

```

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

### Install PyIRI at User Level

Source: https://pyiri.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyIRI for the current user only. This method does not require root privileges.

```bash
pip install --user .
```

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

### Install PyIRI with Test Requirements

Source: https://pyiri.readthedocs.io/en/latest/installation.html

Install PyIRI along with optional packages required for running the test suite and building documentation locally.

```bash
python -m pip install PyIRI[test]

```

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

### Install PyIRI in Editable Mode

Source: https://pyiri.readthedocs.io/en/latest/_sources/installation.rst.txt

Install PyIRI in editable mode for development. Changes made to the source code will be reflected immediately without needing reinstallation.

```bash
pip install --user -e .
```

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

### Clone PyIRI Git Repository

Source: https://pyiri.readthedocs.io/en/latest/_sources/installation.rst.txt

Clone the PyIRI repository from GitHub to your local machine. This is the first step before installing the library.

```bash
git clone https://github.com/victoriyaforsythe/PyIRI.git
```

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

### Plot Electron Density Profile (Sample)

Source: https://pyiri.readthedocs.io/en/latest/api.html

Plots the electron density profile for a single location for solar minimum and maximum conditions. Requires electron density data, time, and location parameters.

```python
PyIRI.plotting.PyIRI_EDP_sample(_EDP_ , _aUT_ , _alon_ , _alat_ , _lon_plot_ , _lat_plot_ , _aalt_ , _UT_ , _plot_dir_ , _plot_name ='PyIRI_EDP_sample.png'_)
```

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

### Plot Electron Density Profile (1 Day)

Source: https://pyiri.readthedocs.io/en/latest/api.html

Plots the electron density profile for a single location for a given day. Requires electron density data, time, and location parameters.

```python
PyIRI.plotting.PyIRI_EDP_sample_1day(_EDP_ , _aUT_ , _alon_ , _alat_ , _lon_plot_ , _lat_plot_ , _aalt_ , _UT_ , _plot_dir_ , _plot_name ='PyIRI_EDP_sample_1day.png'_)
```

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

### PyIRI_EDP_sample_1day()

Source: https://pyiri.readthedocs.io/en/latest/api.html

Provides a sample of the EDP data for a single day. This is a specialized sample function.

```APIDOC
## PyIRI_EDP_sample_1day()

### Description
Provides a sample of the EDP data for a single day. This is a specialized sample function.

### Method
Not specified (likely a Python function call)

### Endpoint
Not applicable (Python function)

### Parameters
(Specific parameters not detailed in the source)

### Request Example
(Not specified)

### Response
(Not specified)
```

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

### Run IRI for Daily Density Profiles

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/sh_library.html

Calculates daily ionospheric density profiles using specified parameters. Requires coefficient directory, time/spatial resolutions, altitude range, F107 index, and F2 region modeling choices.

```python
from PyIRI.sh_library import run_iri_reg_grid

# Example usage:
alon, alat, alon_2d, alat_2d, aalt, aUT, F2, F1, E, sun, mag, EDP = run_iri_reg_grid(
    year=2023, month=1, day=1, F107=150,
    hr_res=1, lat_res=5, lon_res=5, alt_res=20,
    alt_min=100, alt_max=1000, coord='GEO'
)
```

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

### PyIRI.edp_update.EDP_builder

Source: https://pyiri.readthedocs.io/en/latest/api.html

Constructs the vertical Electron Density Profile (EDP) using provided parameters. It takes a 11-parameter array, time, and horizontal grid information to generate a 3-D electron density output.

```APIDOC
## PyIRI.edp_update.EDP_builder

### Description
Constructs the vertical Electron Density Profile (EDP) from the provided parameters for all time frames, all vertical, and all horizontal points.

### Parameters
#### Path Parameters
- **x** (array-like) - Required - Array where 1st dimension indicates the parameter (total 11 parameters), second dimension is time, and third is horizontal grid [11, N_T, N_G].
- **aalt** (array-like) - Required - 1-D array of altitudes [N_V] in km.

### Returns
#### Success Response
- **density_out** (array-like) - 3-D electron density [N_T, N_V, N_G] in m-3.

### Notes
This function builds the EDP from the provided parameters for all time frames, all vertical and all horizontal points.

### References
Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.
```

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

### PyIRI.main_library.run_iri_reg_grid

Source: https://pyiri.readthedocs.io/en/latest/api.html

Runs the IRI model for a single day on a regular grid with specified resolutions and boundaries.

```APIDOC
## PyIRI.main_library.run_iri_reg_grid

### Description
Runs the IRI model for a single day on a regular grid with specified resolutions and boundaries.

### Method
(Not specified, likely a function call)

### Parameters
#### Path Parameters
(None specified)

#### Query Parameters
(None specified)

#### Request Body
(None specified)

### Parameters
- **year** (int) - Four digit year in C.E.
- **month** (int) - Integer month (range 1-12).
- **day** (int) - Integer day of month (range 1-31).
- **f107** (int or float) - F10.7 index for the given day.
- **hr_res** (int or float) - Time resolution in hours (default=1).
- **lat_res** (int or float) - Latitude resolution.
- **lon_res** (int or float) - Longitude resolution.
- **alt_res** (int or float) - Altitude resolution (default=10).
- **alt_min** (int or float) - Minimum altitude (default=0).
- **alt_max** (int or float) - Maximum altitude (default=700).
- **ccir_or_ursi** (int) - Specifies CCIR or URSI model (default=0).
```

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

### Plot foE for Solar Min/Max

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/plotting.html

Generates plots of foE (minimum and maximum electron density) for solar minimum and maximum conditions. Requires pre-calculated IRI data and solar position information.

```python
import matplotlib.pyplot as plt
import numpy as np
import os


def PyIRI_plot_foE_min_max(E, aUT, alon, alat, alon_2d, alat_2d, sun,
                           UT, plot_dir, plot_name='PyIRI_foE_min_max.png'):
    """Plot foE for solar min and max.

    Parameters
    ----------
    E : dict
        Dictionary output of IRI_monthly_mean_parameters.
    aUT : array-like
        Array of universal times in hours used in PyIRI.
    alon : array-like
        Flattened array of geo longitudes in degrees.
    alat : array-like
        Flattened array of geo latitudes in degrees.
    alon_2d : array-like
        2-D array of geo longitudes in degrees.
    alat_2d : array-like
        2-D array of geo latitudes in degrees.
    sun : dict
        Dictionary output of IRI_monthly_mean_parameters.
    UT : float
        UT time frame from array aUT to plot.
    plot_dir : str
        Direction where to save the figure.
    plot_name : str
        Name for the output figure, without directory
        (default='PyIRI_foE_min_max.png')

    """
    ind_time = np.where(aUT == UT)
    ind_grid = np.where(np.isfinite(alon))
    figname = os.path.join(plot_dir, plot_name)
    fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(8, 3),
                           constrained_layout=True)
    plt.xlim([-180, 180])
    plt.ylim([-90, 90])
    plt.xticks(np.arange(-180, 180 + 45, 90))
    plt.yticks(np.arange(-90, 90 + 45, 45))
    abc = ['(a)', '(b)']
    atitle = ['Solar Min', 'Solar Max']
    for isol in range(0, 2):
        ax[isol].set_facecolor('grey')
        ax[isol].set_xlabel('Geo Lon (°)')
        ax[isol].set_ylabel('Geo Lat (°)')
        if isol == 1:
            ax[1].set_ylabel(' ')
        z = np.reshape(E['fo'][ind_time, ind_grid, isol], alon_2d.shape)
        mesh = ax[isol].pcolormesh(alon_2d, alat_2d, z)
        cbar = fig.colorbar(mesh, ax=ax[isol])
        ax[isol].text(140, 70, abc[isol], c='white')
        ax[isol].title.set_text(atitle[isol])
        ax[isol].scatter(sun['lon'][ind_time], sun['lat'][ind_time], c='red',
                         s=20, edgecolors="black", linewidths=0.5)
    cbar.set_label('$fo$E (MHz)')
    plt.savefig(figname, format='pdf', bbox_inches='tight')
    return

```

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

### PyIRI_EDP_sample()

Source: https://pyiri.readthedocs.io/en/latest/api.html

Provides a sample of the Empirical Dynamic Profile (EDP) data. This is likely for demonstration or testing purposes.

```APIDOC
## PyIRI_EDP_sample()

### Description
Provides a sample of the Empirical Dynamic Profile (EDP) data. This is likely for demonstration or testing purposes.

### Method
Not specified (likely a Python function call)

### Endpoint
Not applicable (Python function)

### Parameters
(Specific parameters not detailed in the source)

### Request Example
(Not specified)

### Response
(Not specified)
```

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

### Plotting Electron Density Profile (EDP) Sample

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/plotting.html

Plots a single electron density profile for a specific location and time, comparing solar minimum and maximum conditions. Requires EDP data, time/location arrays, and altitude information. The plot is saved as a PDF.

```python
figname = os.path.join(plot_dir, plot_name)
fig, ax = plt.subplots(1, 1, sharex=True, sharey=True, figsize=(5, 5),
                           constrained_layout=True)
ax.set_xlabel('Electron Density (m$^{-3}$)')
ax.set_ylabel('Altitude (km)')
ax.set_facecolor("grey")
ind_grid = np.where((alon == lon_plot) & (alat == lat_plot))
ind_time = np.where(aUT == UT)
ind_vert = np.where(aalt >= 0)
ind_min = 0, ind_time, ind_vert, ind_grid
x = np.reshape(EDP[ind_min], aalt.shape)
ax.plot(x, aalt, c='black', label='Sol min', linewidth=1)
ind_max = 1, ind_time, ind_vert, ind_grid
x = np.reshape(EDP[ind_max], aalt.shape)
ax.plot(x, aalt, c='white', label='Sol max', linewidth=1)
ax.legend(loc='upper right', prop={'size': 8})
plt.title(str(lon_plot) + ' Lon, ' + str(lat_plot) + ' Lat')
plt.savefig(figname, format='pdf', bbox_inches='tight')
return
```

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

### Organize Solar Parameters

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/edp_update.html

Collects solar position parameters (longitude and latitude) into a dictionary.

```python
sun = {'lon': slon,
           'lat': slat}
```

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

### PyIRI.main_library.EDP_builder

Source: https://pyiri.readthedocs.io/en/latest/api.html

Constructs the vertical Electron Density Profile (EDP) from provided parameters for all time frames, vertical, and horizontal points.

```APIDOC
## PyIRI.main_library.EDP_builder

### Description
Constructs the vertical Electron Density Profile (EDP) from provided parameters.

### Parameters
#### Path Parameters
- **x** (array-like) - Required - Array where 1st dimension indicates the parameter (total 11 parameters), second dimension is time, and third is horizontal grid [11, N_T, N_G].
- **aalt** (array-like) - Required - 1-D array of altitudes [N_V] in km.

### Returns
#### Success Response
- **density_out** (array-like) - 3-D electron density [N_T, N_V, N_G] in m-3.
```

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

### PyIRI.sh_library.EDP_builder_continuous

Source: https://pyiri.readthedocs.io/en/latest/api.html

Constructs vertical Electron Density Profiles (EDP) with a continuous F1 layer using provided parameters for F2, F1, and E layers.

```APIDOC
## PyIRI.sh_library.EDP_builder_continuous(_F2_ , _F1_ , _E_ , _aalt_)

### Description
Construct vertical EDP with continuous F1 layer.

### Parameters

#### Path Parameters
* **F2** (dict) - Dictionary of parameters for the F2 layer. Shape (N_T, N_G).
* **F1** (dict) - Dictionary of parameters for the F1 layer. Shape (N_T, N_G).
* **E** (dict) - Dictionary of parameters for the E layer. Shape (N_T, N_G).
* **aalt** (array-like) - 1-D array of altitudes [km]. Shape (N_V,)

### Returns
* **density** (numpy.ndarray) - 3-D electron density profiles [m-3]. Shape (N_T, N_V, N_G)

### Notes
This function builds the EDP from the provided parameters for all time frames, all vertical and all horizontal points.

### References
Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.
```

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

### Organize F1 Layer Parameters

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/edp_update.html

Collects F1 layer parameters into a dictionary.

```python
F1 = {'Nm': NmF1,
          'fo': foF1,
          'P': P_F1,
          'hm': hmF1,
          'B_bot': B_F1_bot}
```

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

### Plot F1 Bottom Side Thickness (Solar Min/Max)

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/plotting.html

Generates plots for the thickness of the F1 bottom side for solar minimum and maximum conditions. Requires IRI_monthly_mean_parameters output and plotting configurations.

```python
def PyIRI_plot_B_F1_bot_min_max(F1, aUT, alon, alat, alon_2d, alat_2d, sun,
                                UT, plot_dir,
                                plot_name='PyIRI_B_F1_bot_min_max.png'):
    """Plot thickness of F1 bottom side for solar min and max.

    Parameters
    ----------
    F1 : dict
        Dictionary output of IRI_monthly_mean_parameters.
    aUT : array-like
        Array of universal times in hours used in PyIRI
    alon : array-like
        Flattened array of geo longitudes in degrees.
    alat : array-like
        Flattened array of geo latitudes in degrees.
    alon_2d : array-like
        2-D array of geo longitudes in degrees.
    alat_2d : array-like
        2-D array of geo latitudes in degrees.
    sun : dict
        Dictionary output of IRI_monthly_mean_parameters.
    UT : float
        UT time frame from array aUT to plot.
    plot_dir : str
        Direction where to save the figure.
    plot_name : str
        Name for the output figure, without directory
        (default='PyIRI_B_F1_bot_min_max.png')

    """
    ind_time = np.where(aUT == UT)
    ind_grid = np.where(np.isfinite(alon))
    figname = os.path.join(plot_dir, plot_name)
    fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(8, 3),
                           constrained_layout=True)
    plt.xlim([-180, 180])
    plt.ylim([-90, 90])
    plt.xticks(np.arange(-180, 180 + 45, 90))
    plt.yticks(np.arange(-90, 90 + 45, 45))
    abc = ['(a)', '(b)']
    atitle = ['Solar Min', 'Solar Max']
    for isol in range(0, 2):
        ax[isol].set_facecolor('grey')
        ax[isol].set_xlabel('Geo Lon (°)')
        ax[isol].set_ylabel('Geo Lat (°)')
        if isol == 1:
            ax[1].set_ylabel(' ')
        z = np.reshape(F1['B_bot'][ind_time, ind_grid, isol], alon_2d.shape)
        mesh = ax[isol].pcolormesh(alon_2d, alat_2d, z)
        cbar = fig.colorbar(mesh, ax=ax[isol])
        ax[isol].text(140, 70, abc[isol], c='white')
        ax[isol].title.set_text(atitle[isol])
        ax[isol].scatter(sun['lon'][ind_time], sun['lat'][ind_time], c='red',
                         s=20, edgecolors="black", linewidths=0.5)
    cbar.set_label('$B^{F1}_{bot}$ (km)')
    plt.savefig(figname, format='pdf', bbox_inches='tight')
    return

```

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

### PyIRI.main_library.reconstruct_density_from_parameters_1level

Source: https://pyiri.readthedocs.io/en/latest/api.html

Constructs vertical Electron Density Profiles (EDP) for one level of solar activity using provided layer parameters.

```APIDOC
## PyIRI.main_library.reconstruct_density_from_parameters_1level

### Description
Constructs vertical Electron Density Profiles (EDP) for one level of solar activity using provided layer parameters.

### Method
(Not specified, likely a function call)

### Parameters
#### Path Parameters
(None specified)

#### Query Parameters
(None specified)

#### Request Body
(None specified)

### Parameters
- **F2** (dict) - Dictionary of parameters for F2 layer.
- **F1** (dict) - Dictionary of parameters for F1 layer.
- **E** (dict) - Dictionary of parameters for E layer.
- **alt** (array-like) - 1-D array of altitudes [N_V] in km.

### Returns
- **x_out** (array-like) - Electron density for two levels of solar activity [N_T, N_V, N_G] in m-3.

### Notes
This function calculates 3-D density from given dictionaries of the parameters for 1 level of solar activity.

### References
Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.
```

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

### Plot F2 Top Side Thickness (Solar Min/Max)

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/plotting.html

Generates plots for the thickness of the F2 top side for solar minimum and maximum conditions. Requires IRI_monthly_mean_parameters output and plotting configurations.

```python
def PyIRI_plot_B_F2_top_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun,
                                UT, plot_dir,
                                plot_name='PyIRI_B_F2_top_min_max.png'):
    """Plot thickness of F2 topside for solar min and max.

    Parameters
    ----------
    F2 : dict
        Dictionary output of IRI_monthly_mean_parameters.
    aUT : array-like
        Array of universal times in hours used in PyIRI.
    alon : array-like
        Flattened array of geo longitudes in degrees.
    alat : array-like
        Flattened array of geo latitudes in degrees.
    alon_2d : array-like
        2-D array of geo longitudes in degrees.
    alat_2d : array-like
        2-D array of geo latitudes in degrees.
    sun : dict
        Dictionary output of IRI_monthly_mean_parameters.
    UT : float
        UT time frame from array aUT to plot.
    plot_dir : str
        Direction where to save the figure.
    plot_name : str
        Name for the output figure, without directory
        (default='PyIRI_B_F2_top_min_max.png')

    """
    ind_time = np.where(aUT == UT)
    ind_grid = np.where(np.isfinite(alon))
    figname = os.path.join(plot_dir, plot_name)

```

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

### PyIRI.main_library.reconstruct_density_from_parameters

Source: https://pyiri.readthedocs.io/en/latest/api.html

Constructs vertical Electron Density Profiles (EDP) for two levels of solar activity using provided layer parameters.

```APIDOC
## PyIRI.main_library.reconstruct_density_from_parameters

### Description
Constructs vertical Electron Density Profiles (EDP) for two levels of solar activity using provided layer parameters.

### Method
(Not specified, likely a function call)

### Parameters
#### Path Parameters
(None specified)

#### Query Parameters
(None specified)

#### Request Body
(None specified)

### Parameters
- **F2** (dict) - Dictionary of parameters for F2 layer.
- **F1** (dict) - Dictionary of parameters for F1 layer.
- **E** (dict) - Dictionary of parameters for E layer.
- **alt** (array-like) - 1-D array of altitudes [N_V] in km.

### Returns
- **x_out** (array-like) - Electron density for two levels of solar activity [2, N_T, N_V, N_G] in m-3.

### Notes
This function calculates 3-D density from given dictionaries of the parameters for 2 levels of solar activity.

### References
Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.
```

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

### PyIRI.main_library.create_reg_grid

Source: https://pyiri.readthedocs.io/en/latest/api.html

Generates a regular grid for running the IRI model on a single day. It allows customization of time, latitude, longitude, and altitude resolutions and ranges.

```APIDOC
## PyIRI.main_library.create_reg_grid

### Description
Run IRI for a single day on a regular grid.

### Parameters
* **hr_res** (int or float) - Time resolution in hours (default=1).
* **lat_res** (int or float) - Latitude resolution in degrees (default=1).
* **lon_res** (int or float) - Longitude resolution in degrees (default=1).
* **alt_res** (int or float) - Altitude resolution in km (default=10).
* **alt_min** (int or float) - Altitude minimum in km (default=0).
* **alt_max** (int or float) - Altitude maximum in km (default=700).

### Returns
* **alon** (array-like) - 1D longitude grid.
* **alat** (array-like) - 1D latitude grid.
* **alon_2d** (array-like) - 2D longitude grid.
* **alat_2d** (array-like) - 2D latitude grid.
* **aalt** (array-like) - Altitude grid.
* **ahr** (array-like) - UT grid.

### Notes
This function creates a regular global grid in Geographic coordinates using given spatial resolution lon_res, lat_res. In case you want to run IRI in magnetic coordinates, you can obtain a regular grid in magnetic coordinates, then convert it to geographic coordinates using your own methods, and then use these 1-D alon and alat arrays. Similarly, if you are interested in regional grid, then use your own alon and alat 1-D arrays. alon and alat can also be irregular arrays. In case you need to run IRI for 1 grid point, define alon and alat as NumPy arrays that have only 1 element. Size of alon and alat is [N_G]. This function creates creates an array of altitudes for the vertical dimension of electron density profiles. Any 1-D Numpy array in [km] would work, regularly or irregularly spaced. This function also creates time array aUT using a given temporal resolution hr_res in hours. E.g. if hr_res = 0.25 it will lead to 15-minutes time resolution. You can define aUT your own way, just keep it 1-D and expressed in hours. It can be regularly or irregularly spaced array. If you want to run PyIRI for just 1 time frame, then define aUT as NumPy arrays that have only 1 element. Size of aUT is [N_T].
```

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

### Clone PyIRI Fork

Source: https://pyiri.readthedocs.io/en/latest/develop_guide/contributing.html

Clone your forked PyIRI repository locally to begin development.

```bash
git clone git@github.com:your_name_here/PyIRI.git

```

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

### Plot F2 Bottom Side Thickness (Solar Min/Max)

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/plotting.html

Generates plots for the thickness of the F2 bottom side for solar minimum and maximum conditions. Requires IRI_monthly_mean_parameters output and plotting configurations.

```python
def PyIRI_plot_B_F2_bot_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun,
                                UT, plot_dir,
                                plot_name='PyIRI_B_F2_bot_min_max.png'):
    """Plot thickness of F2 bottom side for solar min and max.

    Parameters
    ----------
    F2 : dict
        Dictionary output of IRI_monthly_mean_parameters.
    aUT : array-like
        Array of universal times in hours used in PyIRI
    alon : array-like
        Flattened array of geo longitudes in degrees.
    alat : array-like
        Flattened array of geo latitudes in degrees.
    alon_2d : array-like
        2-D array of geo longitudes in degrees.
    alat_2d : array-like
        2-D array of geo latitudes in degrees.
    sun : dict
        Dictionary output of IRI_monthly_mean_parameters.
    UT : float
        UT time frame from array aUT to plot.
    plot_dir : str
        Direction where to save the figure.
    plot_name : str
        Name for the output figure, without directory
        (default='PyIRI_B_F2_bot_min_max.png')

    """
    ind_time = np.where(aUT == UT)
    ind_grid = np.where(np.isfinite(alon))
    figname = os.path.join(plot_dir, plot_name)
    fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(8, 3),
                           constrained_layout=True)
    plt.xlim([-180, 180])
    plt.ylim([-90, 90])
    plt.xticks(np.arange(-180, 180 + 45, 90))
    plt.yticks(np.arange(-90, 90 + 45, 45))
    abc = ['(a)', '(b)']
    atitle = ['Solar Min', 'Solar Max']
    for isol in range(0, 2):
        ax[isol].set_facecolor('grey')
        ax[isol].set_xlabel('Geo Lon (°)')
        ax[isol].set_ylabel('Geo Lat (°)')
        if isol == 1:
            ax[1].set_ylabel(' ')
        z = np.reshape(F2['B_bot'][ind_time, ind_grid, isol], alon_2d.shape)
        mesh = ax[isol].pcolormesh(alon_2d, alat_2d, z)
        cbar = fig.colorbar(mesh, ax=ax[isol])
        ax[isol].text(140, 70, abc[isol], c='white')
        ax[isol].title.set_text(atitle[isol])
        ax[isol].scatter(sun['lon'][ind_time], sun['lat'][ind_time], c='red',
                         s=20, edgecolors="black", linewidths=0.5)
    cbar.set_label('$B^{F2}_{bot}$ (km)')
    plt.savefig(figname, format='pdf', bbox_inches='tight')
    return

```

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

### PyIRI.main_library.IRI_density_1day

Source: https://pyiri.readthedocs.io/en/latest/api.html

Outputs ionospheric parameters for a particular day.

```APIDOC
## PyIRI.main_library.IRI_density_1day

### Description
Output ionospheric parameters for a particular day.

### Parameters
#### Path Parameters
- **year** (int) - Required - Year.
- **mth** (int) - Required - Month of year.
- **day** (int) - Required - Day of month.
- **aUT** (array-like) - Required - Array of universal time (UT) in hours. Must be Numpy array of any size [N_T].
- **alon** (array-like) - Required - Flattened array of geographic longitudes in degrees. Must be Numpy array of any size [N_G].
- **alat** (array-like) - Required - Flattened array of geographic latitudes in degrees. Must be Numpy array of any size [N_G].
- **aalt** (array-like) - Required - Array of altitudes in km. Must be Numpy array of any size [N_V].
- **F107** (float) - Required - User provided F10.7 solar flux index in SFU.
- **coeff_dir** (str) - Required - Place where coefficients are located.
- **ccir_or_ursi** (int) - Optional - If 0 is given CCIR will be used for F2 critical frequency. If 1 then URSI coefficients. (default=0)

### Returns
#### Success Response
- **F2** (dict) - ‘Nm’ is peak density of F2 region in m-3. ‘fo’ is critical frequency of F2 region in MHz. ‘M3000’ is the obliquity factor for a distance of 3,000 km. Defined as refracted in the ionosphere, can be received at a distance of 3,000 km, unitless. ‘hm’ is height of the F2 peak in km. ‘B_topi is top thickness of the F2 region in km. ‘B_bot’ is bottom thickness of the F2 region in km. Shape [N_T, N_G, 2].
- **F1** (dict) -
```

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

### Run IRI for Monthly Mean Density Profiles

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/sh_library.html

Calculates monthly mean ionospheric density profiles on a regular grid. Allows selection of F2 region coefficients and models, and coordinate systems.

```python
from PyIRI.sh_library import run_seas_iri_reg_grid

# Example usage:
alon, alat, alon_2d, alat_2d, aalt, aUT = run_seas_iri_reg_grid(
    year=2023, month=1, hr_res=1, lat_res=1,
    lon_res=1, alt_res=10, alt_min=0, alt_max=700,
    foF2_coeff='URSI', hmF2_model='SHU2015', coord='GEO'
)
```

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

### Run IRI for Monthly Mean Parameters

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/edp_update.html

Calculates monthly mean ionospheric parameters on a regular grid. Requires year, month, and grid resolutions. Outputs include various ionospheric characteristics for min/max solar activity.

```python
def run_seas_iri_reg_grid(year, month, hr_res=1, lat_res=1, lon_res=1,
                          alt_res=10, alt_min=0, alt_max=700, ccir_or_ursi=0):
    """Run IRI for monthly mean parameters on a regular grid.

    Parameters
    ----------
    year : int
        Four digit year in C.E.
    month : int
        Integer month (range 1-12)
    f107 : int or float
        F10.7 index for the given day
    hr_res : int or float
        Time resolution in hours (default=1)
    lat_res : int or float
        Latitude resolution in degrees (default=1)
    lon_res : int or float
        Longitude resolution in degrees (default=1)
    alt_res : int or float
        Altitude resolution in km (default=10)
    alt_min : int or float
        Altitude minimum in km (default=0)
    alt_max : int or float
        Altitude maximum in km (default=700)
    ccir_or_ursi : int
        If 0 use CCIR coefficients, if 1 use URSI coefficients

    Returns
    -------
    alon : array-like
        1D longitude grid
    alat : array-like
        1D latitude grid
    alon_2d : array-like
        2D longitude grid
    alat_2d : array-like
        2D latitude grid
    aalt : array-like
        Altitude grid
    ahr : array-like
        UT grid
    f2 : array-like
        F2 peak
    f1 : array-like
        F1 peak
    epeak : array-like
        E peak
    es_peak : array-like
        Sporadic E (Es) peak
    sun : array-like
        Solar zenith angle in degrees
    mag : array-like
        Magnetic inclination in degrees
    edens_prof : array-like
        Electron density profile in per cubic m

    See Also
    --------
    create_reg_grid

    """
    # Define the grids
    alon, alat, alon_2d, alat_2d, aalt, ahr = main.create_reg_grid(
        hr_res=hr_res, lat_res=lat_res, lon_res=lon_res,
        alt_res=alt_res, alt_min=alt_min,
        alt_max=alt_max)

    # -------------------------------------------------------------------------
    # Monthly mean ionospheric parameters for min and max of solar activity:
    # -------------------------------------------------------------------------
    # This is how PyIRI needs to be called to find monthly mean values for all
    # ionospheric parameters, for min and max conditions of solar activity:
    # year and month should be integers, and ahr and alon, alat should be 1-D
    # NumPy arrays. alon and alat should have the same size. coeff_dir is the
    # coefficient directory. Matrix size for all the output parameters is
    # [N_T, N_G, 2], where 2 indicates min and max of the solar activity that

```

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

### Run IRI for a Single Day

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/edp_update.html

Calculates ionospheric parameters for a specific day using IRI model. Requires date, time, location grids, and solar activity index.

```python
def run_iri_1day(year, month, day, hr_res=1, lat_res=1, lon_res=1,
                   alt_res=10, alt_min=0, alt_max=700, ccir_or_ursi=0):
    """Run IRI for a single day.

    Parameters
    ----------
    year : int
        Four digit year in C.E.
    month : int
        Integer month (range 1-12)
    day : int
        Integer day (range 1-31)
    f107 : int or float
        F10.7 index for the given day
    hr_res : int or float
        Time resolution in hours (default=1)
    lat_res : int or float
        Latitude resolution in degrees (default=1)
    lon_res : int or float
        Longitude resolution in degrees (default=1)
    alt_res : int or float
        Altitude resolution in km (default=10)
    alt_min : int or float
        Altitude minimum in km (default=0)
    alt_max : int or float
        Altitude maximum in km (default=700)
    ccir_or_ursi : int
        If 0 use CCIR coefficients, if 1 use URSI coefficients

    Returns
    -------
    alon : array-like
        1D longitude grid
    alat : array-like
        1D latitude grid
    alon_2d : array-like
        2D longitude grid
    alat_2d : array-like
        2D latitude grid
    aalt : array-like
        Altitude grid
    ahr : array-like
        UT grid
    f2 : array-like
        F2 peak
    f1 : array-like
        F1 peak
    epeak : array-like
        E peak
    es_peak : array-like
        Sporadic E (Es) peak
    sun : array-like
        Solar zenith angle in degrees
    mag : array-like
        Magnetic inclination in degrees
    edens_prof : array-like
        Electron density profile in per cubic m

    See Also
    --------
    create_reg_grid

    """
    # Define the grids
    alon, alat, alon_2d, alat_2d, aalt, ahr = main.create_reg_grid(
        hr_res=hr_res, lat_res=lat_res, lon_res=lon_res,
        alt_res=alt_res, alt_min=alt_min,
        alt_max=alt_max)
    # -------------------------------------------------------------------------
    # Monthly mean density for min and max of solar activity:
    # -------------------------------------------------------------------------
    # The original IRI model further interpolates between 2 levels of solar
    # activity to estimate density for a particular level of F10.7.
    # Additionally, it interpolates between 2 consecutive months to make a
    # smooth seasonal transition. Here is an example of how this interpolation
    # can be done. If you need to run IRI for a particular day, you can just
    # use this function
    f2, f1, epeak, es_peak, sun, mag, edens_prof = IRI_density_1day(
        year, month, day, ahr, alon, alat, aalt, f107, PyIRI.coeff_dir,
        ccir_or_ursi)

    return (alon, alat, alon_2d, alat_2d, aalt, ahr, f2, f1, epeak,
            es_peak, sun, mag, edens_prof)

```

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

### Run IRI Model on Regular Grid

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/main_library.html

Executes the IRI model for a specified date, solar activity level, and grid parameters. It returns various ionospheric parameters and electron density profiles.

```python
def run_iri_reg_grid(year, month, day, f107, hr_res=1, lat_res=1, lon_res=1,
                     alt_res=10, alt_min=0, alt_max=700, ccir_or_ursi=0):
    """Run IRI for a single day on a regular grid.

    Parameters
    ----------
    year : int
        Four digit year in C.E.
    month : int
        Integer month (range 1-12)
    day : int
        Integer day of month (range 1-31)
    f107 : int or float
        F10.7 index for the given day
    hr_res : int or float
        Time resolution in hours (default=1)
    lat_res : int or float
        Latitude resolution in degrees (default=1)
    lon_res : int or float
        Longitude resolution in degrees (default=1)
    alt_res : int or float
        Altitude resolution in km (default=10)
    alt_min : int or float
        Altitude minimum in km (default=0)
    alt_max : int or float
        Altitude maximum in km (default=700)
    ccir_or_ursi : int
        If 0 use CCIR coefficients, if 1 use URSI coefficients

    Returns
    -------
    alon : array-like
        1D longitude grid
    alat : array-like
        1D latitude grid
    alon_2d : array-like
        2D longitude grid
    alat_2d : array-like
        2D latitude grid
    aalt : array-like
        Altitude grid
    ahr : array-like
        UT grid
    f2 : array-like
        F2 peak
    f1 : array-like
        F1 peak
    epeak : array-like
        E peak
    es_peak : array-like
        Sporadic E (Es) peak
    sun : array-like
        Solar zenith angle in degrees
    mag : array-like
        Magnetic inclination in degrees
    edens_prof : array-like
        Electron density profile in per cubic m

    See Also
    --------
    create_reg_grid

    """
    # Define the grids
    alon, alat, alon_2d, alat_2d, aalt, ahr = create_reg_grid(
        hr_res=hr_res, lat_res=lat_res, lon_res=lon_res,
        alt_res=alt_res, alt_min=alt_min,
        alt_max=alt_max)
    # -------------------------------------------------------------------------
    # Monthly mean density for min and max of solar activity:
    # -------------------------------------------------------------------------

```

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

### Create Regular Geographic Grid

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/main_library.html

Generates 1D and 2D longitude and latitude grids, along with altitude and UT grids, based on specified resolutions. Useful for setting up simulation domains.

```python
def create_reg_grid(hr_res=1, lat_res=1, lon_res=1, alt_res=10, alt_min=0,
                    alt_max=700):
    """Run IRI for a single day on a regular grid.

    Parameters
    ----------
    hr_res : int or float
        Time resolution in hours (default=1)
    lat_res : int or float
        Latitude resolution in degrees (default=1)
    lon_res : int or float
        Longitude resolution in degrees (default=1)
    alt_res : int or float
        Altitude resolution in km (default=10)
    alt_min : int or float
        Altitude minimum in km (default=0)
    alt_max : int or float
        Altitude maximum in km (default=700)

    Returns
    -------
    alon : array-like
        1D longitude grid
    alat : array-like
        1D latitude grid
    alon_2d : array-like
        2D longitude grid
    alat_2d : array-like
        2D latitude grid
    aalt : array-like
        Altitude grid
    ahr : array-like
        UT grid

    Notes
    -----
    This function creates a regular global grid in Geographic coordinates
    using given spatial resolution lon_res, lat_res. In case you want to run
    IRI in magnetic coordinates, you can obtain a regular grid in magnetic
    coordinates, then convert it to geographic coordinates using your own
    methods, and then use these 1-D alon and alat arrays. Similarly, if you
    are interested in regional grid, then use your own alon and alat 1-D
    arrays. alon and alat can also be irregular arrays. In case you need to
    run IRI for 1 grid point, define alon and alat as NumPy arrays that have
    only 1 element. Size of alon and alat is [N_G].
    This function creates creates an array of altitudes for the vertical
    dimension of electron density profiles. Any 1-D Numpy array in [km] would
    work, regularly or irregularly spaced.
    This function also creates time array aUT using a given temporal
    resolution hr_res in hours.
    E.g. if hr_res = 0.25 it will lead to 15-minutes time
    resolution. You can define aUT your own way, just keep it 1-D and
    expressed in hours. It can be regularly or irregularly spaced array. If
    you want to run PyIRI for just 1 time frame, then define aUT as NumPy
    arrays that have only 1 element. Size of aUT is [N_T].

    """
    alon, alat, alon_2d, alat_2d = set_geo_grid(lon_res, lat_res)

    aalt = np.arange(alt_min, alt_max, alt_res)

    ahr, _, _, _, _ = set_temporal_array(hr_res)

    return alon, alat, alon_2d, alat_2d, aalt, ahr

```

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

### PyIRI.plotting.PyIRI_EDP_sample

Source: https://pyiri.readthedocs.io/en/latest/api.html

Plots the Electron Density Profile (EDP) for a single location, comparing solar minimum and maximum conditions.

```APIDOC
## PyIRI.plotting.PyIRI_EDP_sample

### Description
Plot EDP for one location for solar min and max.

### Method
Not applicable (Python function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **EDP** (array-like) - 3-D electron density array output of IRI_monthly_mean_parameters.
- **aUT** (array-like) - Array of universal times in hours used in PyIRI
- **alon** (array-like) - Flattened array of geo longitudes in degrees.
- **alat** (array-like) - Flattened array of geo latitudes in degrees.
- **lon_plot** (float) - Longitude location for EDP.
- **lat_plot** (array-like) - Latitude location for EDP.
- **sun** (dict) - Dictionary output of IRI_monthly_mean_parameters.
- **UT** (float) - UT time frame from array aUT to plot.
- **plot_dir** (str) - Direction where to save the figure.
- **plot_name** (str) - Name for the output figure, without directory (default=’PyIRI_EDP_sample.png’)
```

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

### Create Local Development Branch

Source: https://pyiri.readthedocs.io/en/latest/develop_guide/contributing.html

Create a new branch for your bug fix or feature development.

```bash
git checkout -b name-of-your-bugfix-or-feature

```

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

### Organize F2 Layer Parameters

Source: https://pyiri.readthedocs.io/en/latest/_modules/PyIRI/edp_update.html

Collects F2 layer parameters into a dictionary.

```python
F2 = {'Nm': NmF2,
          'fo': foF2,
          'M3000': M3000,
          'hm': hmF2,
          'B_top': B_F2_top,
          'B_bot': B_F2_bot}
```
Tokens:
Raw