### Install Cubedsphere (Pre-packaged)

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Install the cubedsphere package from the conda-forge channel.

```bash
conda install -c conda-forge cubedsphere
```

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

### Install Cubedsphere (Development Version)

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Install the cubedsphere package in editable mode from the local repository.

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

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

### Install Development Dependencies

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Install the necessary dependencies for the development version of cubedsphere using conda.

```bash
conda install -c conda-forge xesmf esmpy xgcm xmitgcm matplotlib-base xarray
```

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

### Update xmitgcm from GitHub

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Ensure you have the latest version of xmitgcm by installing it directly from its GitHub repository.

```bash
pip install git+https://github.com/MITgcm/xmitgcm.git
```

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

### Activate Conda Environment

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Activate the 'mitgcm' conda environment before proceeding with installations.

```bash
conda activate mitgcm
```

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

### Clone Cubedsphere Repository

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Clone the cubedsphere repository to your local machine for development installation.

```bash
git clone https://github.com/AaronDavidSchneider/cubedsphere.git
```

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

### Navigate to Cubedsphere Directory

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Change your current directory to the cloned cubedsphere repository.

```bash
cd cubedsphere
```

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

### Download and Prepare Data

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Downloads a gzipped tar file containing model data using wget and then extracts it. Specifies the output directory for the extracted data.

```python
!wget -O HD2.tar.gz https://figshare.com/ndownloader/files/36234516
!tar -xf HD2.tar.gz
outdir_ascii = 'HD2_test/run'   # path to data
```

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

### Load and Process Custom ASCII Dataset

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Loads a custom ASCII dataset using `cs.open_ascii_dataset`, specifying iterators, prefixes, and extra variables. It then regrids the dataset and applies post-processing for exorad.

```python
# open Dataset using xmitgcm (see docs for xmitgcm.open_mdsdataset for more details)
ds_ascii, grid = cs.open_ascii_dataset(outdir_ascii, iters=[41472000], prefix = ["EXOBFPla"], extra_variables=extra_variables)
# regrid dataset
regrid = cs.Regridder(ds_ascii, grid)
ds = regrid()
# (optional) converts wind, temperature and stuff
ds = cs.exorad_postprocessing(ds, outdir=outdir_ascii)
```

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

### Load and Regrid Dataset

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Opens an ASCII dataset using cubedsphere's open_ascii_dataset function and then regrids it to a specified grid. Includes optional post-processing steps.

```python
# open Dataset using xmitgcm (see docs for xmitgcm.open_mdsdataset for more details)
ds_ascii, grid = cs.open_ascii_dataset(outdir_ascii, iters=[41472000], prefix = ["T","U","V","W"])
# regrid dataset
regrid = cs.Regridder(ds_ascii, grid)
ds = regrid()
# (optional) converts wind, temperature and stuff
ds = cs.exorad_postprocessing(ds, outdir=outdir_ascii)
```

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

### Create Conda Environment

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/installation.md

Use this command to create a new conda environment named 'mitgcm'.

```bash
conda create -n mitgcm
```

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

### Perform Back Regridding

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Initializes a Regridder object and performs back regridding on a dataset. This is useful for transforming data to a different grid.

```python
regrid = cs.Regridder(ds, grid, input_type='ll')
ds_regback = regrid()
```

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

### Basic Temperature Plot with Winds

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Generates a basic plot of temperature data from a selected horizontal slice and time, with wind vectors overlaid. Requires matplotlib and cubedsphere.

```python
plt.figure()
# Select horizontal slice at latest time:
data = ds.isel(time=-1,Z=-20)
# Plot temperature:
data.T.plot()
# Overplot winds:
cs.overplot_wind(ds, data.U.values, data.V.values)
plt.show()
```

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

### Import Libraries

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Imports the required Python libraries for data manipulation, plotting, and cubed sphere operations. Cartopy is optional and used for enhanced map projections.

```python
import numpy as np
import cubedsphere as cs

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import cartopy.crs as ccrs  # optional, only needed for nicer projections
```

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

### Compare Original vs. Regridded Data

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Compares the original data with the regridded data using Cartopy projections. Plots temperature on both, with titles indicating the data source.

```python
data_orig = ds_ascii.isel(time=-1,Z=-20)

fig, ax = plt.subplots(2,1, subplot_kw={"projection":ccrs.Robinson()})

# Do the plots
cs.plotCS(data_orig.T, data_orig, transform=ccrs.PlateCarree(), ax = ax[0])
ax[1].pcolormesh(data.lon, data.lat, data.T, transform = ccrs.PlateCarree())

ax[0].set_title('original data')
ax[1].set_title('regridded')

plt.show()
```

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

### Plot Globally Averaged Temperature Profile

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Plots the globally averaged temperature-pressure profile on a log-log scale. This helps in analyzing the vertical structure of the atmosphere.

```python
plt.figure()
T_global = (ds.T.isel(time=-1)*ds.area_c).sum(dim=['lon','lat'])/ds.area_c.sum(dim=['lon','lat'])
plt.loglog(T_global, ds.Z)
plt.ylim(700,1e-4)
plt.title('globally averaged temperature pressure profile')
plt.ylabel('p / bar')
plt.xlabel('T / K')
plt.show()
```

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

### Plot Planetary Emission Data

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Plots the 'EXOBFPla' variable from a dataset onto a Robinson projection. Requires Cartopy and Matplotlib. The plot displays the emission at the last time step and a specific pressure level.

```python
plt.figure()
ax = plt.axes(projection=ccrs.Robinson())
ds.EXOBFPla.isel(time=-1, Zp1=-1).plot(transform = ccrs.PlateCarree(), ax=ax, cmap=plt.get_cmap('inferno'))
ax.set_title('planetary emission at {:.0f} d'.format(data.time.values))
plt.show()
```

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

### Add Grid Information to Dataset

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Adds longitude and latitude boundary information to the regridded dataset. This is a preparatory step for further regridding operations.

```python
# Add some info to the regridded dataset
reg_grid = regrid._build_output_grid(5, 4)
ds["lon_b"] = reg_grid["lon_b"]
ds["lat_b"] = reg_grid["lat_b"]

```

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

### Display Dataset Information

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Prints the structure and coordinates of an xarray Dataset. Useful for inspecting the data after operations like regridding.

```python
ds
```

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

### Plot Regridded Data on Cubed Sphere

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Visualizes regridded data (temperature) on a cubed sphere projection using Robinson projection. Requires matplotlib and cartopy.

```python
plt.figure()
data_reg_back = ds_regback.isel(time=-1, Z=-20)
ax = plt.axes(projection=ccrs.Robinson())
ax.set_title('time: {:.0f} d, Z={:.1e} bar'.format(data.time.values,data.Z.values))
cs.plotCS(data_reg_back.T, data_reg_back, transform=ccrs.PlateCarree(), ax = ax)
plt.show()
```

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

### Temperature Plot with Winds using Cartopy

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Creates a more visually appealing plot using Cartopy for projections. Overplots temperature and wind data on a Robinson projection, with a custom title.

```python
plt.figure()
ax = plt.axes(projection=ccrs.Robinson())
# Plot temperature:
data.T.plot(transform = ccrs.PlateCarree(), ax=ax)
# Overplot winds:
cs.overplot_wind(ds, data.U.values, data.V.values, ax=ax, transform=ccrs.PlateCarree(), stepsize=2)
ax.set_title('time: {:.0f} d, Z={:.1e} bar'.format(data.time.values,data.Z.values))
plt.show()
```

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

### Define Extra Variables for Custom Datasets

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Defines a dictionary of extra variables to be loaded from custom datasets, specifying their dimensions and attributes. This is used with `cs.open_ascii_dataset`.

```python
# Note: Not needed, since already part of cubedsphere package, this is shown only to demonstrate how it works
extra_variables = dict(EXOBFPla=dict(dims=['k_p1', 'j', 'i'],
                                            attrs=dict(standard_name='EXOBFPla', long_name='Bolometric Planetary Flux',
                                                       units='W/m2')))
```

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

### Zonal Mean Wind Plot

Source: https://github.com/aarondavidschneider/cubedsphere/blob/main/doc/source/notebooks/example.ipynb

Generates a log-scale plot of the zonal mean wind. Sets specific y-axis limits for better visualization.

```python
plt.figure()
zmean = ds.U.isel(time=-1).mean(dim='lon')
zmean.plot()
plt.yscale('log')
plt.ylim([700,1e-4])
plt.show()
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.