### Instance Setup Before Initialization

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.FilledContourPlot.html

This method is called before the instance's `__init__` method is executed, allowing for setup operations.

```python
setup_instance(_** kwargs: Any_) → None#
```

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

### Install MetPy from Source

Source: https://unidata.github.io/MetPy/latest/userguide/installguide.html

Builds and installs MetPy from the source code. Use this if you have cloned the repository and want to install locally.

```bash
pip install .
```

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

### Install MetPy with pip

Source: https://unidata.github.io/MetPy/latest/userguide/installguide.html

The recommended method for installing MetPy for most users. This command installs the latest stable release.

```bash
pip install metpy
```

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

### Instance Setup

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.FilledContourPlot.html

This method is called before the instance's __init__ method.

```APIDOC
## setup_instance

### Description
This is called **before** self.__init__ is called.

### Parameters

#### kwargs
- **kwargs** (_Any_) – Additional keyword arguments for setup.
```

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

### get_product

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.remote.NEXRADLevel3Archive.html

Get a product from the archive.

```APIDOC
## get_product(site, prod_id, dt=None)

### Description
Get a product from the archive.

### Parameters
* **site** (str) - The site to search for data
* **prod_id** (str) - The product ID to search for data
* **dt** (datetime.datetime, optional) - The desired date/time for the model run; the one closest matching in time will be returned. This should have the proper timezone included; if not specified, UTC will be assumed. If None, defaults to the current UTC date/time.

### See Also
`product_ids`, `sites`, `get_range`

```

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

### Install MetPy in Editable Mode

Source: https://unidata.github.io/MetPy/latest/devel/CONTRIBUTING.html

Create an editable install of MetPy. This allows your changes in the source code to be reflected immediately without reinstalling.

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

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

### Example Output

Source: https://unidata.github.io/MetPy/latest/examples/calculations/Thickness_Hydrostatic.html

The result of the hydrostatic thickness calculation, displayed in meters.

```text
5332.4331211279705 meter
```

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

### Get Product

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.remote.GOESArchive.html

Retrieve a specific product from the GOES archive for a given date and optional mode/band.

```APIDOC
## GOESArchive.get_product(product, dt=None, mode=None, band=None)

### Description
Get a product from the archive.

### Parameters
* **product** (str) - The site to search for data
* **dt** (datetime.datetime, optional) - The desired date/time for the model run; the one closest matching in time will be returned. This should have the proper timezone included; if not specified, UTC will be assumed. If `None`, defaults to the current UTC date/time.
* **mode** (str or int, optional) - The particular mode to select. If not given, the query will try to select an appropriate mode based on data available.
* **band** (str or int, optional) - The particular band (or channel) to select. Not all products have multiple bands. If not given, the query will try to select an appropriate band, but may error giving the channels available if multiple bands are available.

### See Also
`product_ids`, `get_range`
```

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

### Import MetPy and Libraries

Source: https://unidata.github.io/MetPy/latest/_downloads/324acb7faa1ec1d6ac5849ea2223364d/Smoothing.ipynb

Imports necessary libraries for data manipulation, plotting, and MetPy calculations. Ensure these are installed.

```python
from itertools import product

import matplotlib.pyplot as plt
import numpy as np

import metpy.calc as mpcalc
```

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

### get_with_range

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.ColortableRegistry.html

Get a color table and a corresponding BoundaryNorm using a start and end range.

```APIDOC
## get_with_range

### Description
Get a color table from the registry with a corresponding norm. Builds a `matplotlib.colors.BoundaryNorm` using _start_ , _end_ , and the number of colors, based on the color table obtained from _name_.

### Parameters
* **name** (str) - The name under which the color table will be stored
* **start** (float) - The starting boundary
* **end** (float) - The ending boundary

### Returns
`matplotlib.colors.BoundaryNorm`, `matplotlib.colors.ListedColormap` - The boundary norm based on _start_ and _end_ with the number of colors from the number of entries matching the color table, and the color table itself.
```

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

### setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.MapPanel.html

This is called before self.__init__ is called.

```APIDOC
## setup_instance

### Description
This is called **before** self.__init__ is called.

### Method
`setup_instance(_** kwargs: Any_)`

### Parameters
- **kwargs** (Any) - Arbitrary keyword arguments.
```

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

### setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ImagePlot.html

This is called before self.__init__ is called.

```APIDOC
## setup_instance

### Description
This is called **before** self.__init__ is called.

### Method
`setup_instance(_** kwargs: Any_)`

### Parameters
* **kwargs** (Any) - Arbitrary keyword arguments.
```

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

### get_with_steps

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.ColortableRegistry.html

Get a color table and a corresponding BoundaryNorm using a start value and step size.

```APIDOC
## get_with_steps

### Description
Get a color table from the registry with a corresponding norm. Builds a `matplotlib.colors.BoundaryNorm` using _start_ , _step_ , and the number of colors, based on the color table obtained from _name_.

### Parameters
* **name** (str) - The name under which the color table will be stored
* **start** (float) - The starting boundary
* **step** (float) - The step between boundaries

### Returns
`matplotlib.colors.BoundaryNorm`, `matplotlib.colors.ListedColormap` - The boundary norm based on _start_ and _step_ with the number of colors from the number of entries matching the color table, and the color table itself.
```

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

### setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ContourPlot.html

This method is called before self.__init__ is called.

```APIDOC
## setup_instance

### Description
This is called **before** self.__init__ is called.

### Parameters

* **kwargs** (_Any_) – Additional keyword arguments.
```

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

### Calculate and Plot Stretching Deformation

Source: https://unidata.github.io/MetPy/latest/_downloads/fd475021e6195a260fca1e7e2b1c803e/Stretching_Deformation.ipynb

Loads example wind data, calculates stretching deformation using `metpy.calc.stretching_deformation`, and plots the result with wind barbs. Ensure MetPy and Matplotlib are installed.

```python
import matplotlib.pyplot as plt

import metpy.calc as mpcalc
from metpy.cbook import example_data

# load example data
ds = example_data()

# Calculate the stretching deformation of the flow
str_def = mpcalc.stretching_deformation(ds.uwind, ds.vwind)

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# plot stretching deformation and scale by 1e5
cf = ax.contourf(ds.lon, ds.lat, str_def * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
plt.colorbar(cf, pad=0, aspect=50)
ax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)
ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Stretching Deformation Calculation')

plt.show()
```

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

### setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.PlotObs.html

Initialization method called before the instance's __init__ method.

```APIDOC
## setup_instance

### Description

This is called **before** self.__init__ is called.

### Method

setup_instance(_** kwargs: Any_)
```

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

### Calculate and Plot Wind Speed

Source: https://unidata.github.io/MetPy/latest/_downloads/73ef2d00e1a399534eca354fa92c10ae/Wind_Speed.ipynb

Loads example wind data, calculates wind speed using `metpy.calc.wind_speed`, and plots the results with wind barbs. Ensure MetPy and Matplotlib are installed.

```python
import matplotlib.pyplot as plt

import metpy.calc as mpcalc
from metpy.cbook import example_data

# load example data
ds = example_data()

# Calculate the total deformation of the flow
wind_speed = mpcalc.wind_speed(ds.uwind, ds.vwind)

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# plot wind speed
cf = ax.contourf(ds.lon, ds.lat, wind_speed, range(5, 80, 5), cmap=plt.cm.BuPu)
plt.colorbar(cf, pad=0, aspect=50)
ax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)
ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Wind Speed Calculation')

plt.show()
```

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

### Calculate and Plot Absolute Vorticity

Source: https://unidata.github.io/MetPy/latest/_downloads/de54c6faa330a37497a365cfd608a074/Absolute_Vorticity.ipynb

Use this snippet to calculate absolute vorticity from u and v wind components and visualize it with contourf and wind barbs. Ensure MetPy and Matplotlib are installed and example data is available.

```python
import matplotlib.pyplot as plt

import metpy.calc as mpcalc
from metpy.cbook import example_data

# load example data
ds = example_data()

# Calculate the vertical vorticity of the flow
avor = mpcalc.absolute_vorticity(ds.uwind, ds.vwind)

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# plot and scale absolute vorticity by 1e5
cf = ax.contourf(ds.lon, ds.lat, avor * 1e5, range(-80, 81, 1), cmap=plt.cm.PuOr_r)
plt.colorbar(cf, pad=0, aspect=50)
ax.barbs(ds.lon.values, ds.lat.values, ds.uwind, ds.vwind, color='black', length=5, alpha=0.5)
ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Absolute Vorticity Calculation')

plt.show()
```

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

### setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.PlotGeometry.html

This method is called before self.__init__ is called during instance initialization.

```APIDOC
## setup_instance

### Description
This is called **before** self.__init__ is called.

### Parameters
#### kwargs
- **kwargs** (_Any_) – Arbitrary keyword arguments to be passed during instance setup.
```

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

### Calculate and Plot Q-Vectors

Source: https://unidata.github.io/MetPy/latest/_downloads/9c27973db96412dabcb4103ddee326d2/QVector.ipynb

This snippet calculates temperature advection and Q-vectors from example xarray data. It then plots isotherms, temperature advection, and the Q-vectors themselves using Matplotlib, including a key for vector scale. Ensure MetPy and Matplotlib are installed.

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

import metpy.calc as mpcalc
from metpy.cbook import example_data
from metpy.units import units

# load example data
ds = example_data()

# Calculate the temperature advection of the flow
tadv = mpcalc.advection(ds.temperature, ds.uwind, ds.vwind)

# Calculate the q-vectors. Passing in a fixed value of static stability, but could also
# use `mpcalc.static_stability()`.
u_qvect, v_qvect = mpcalc.q_vector(ds.uwind, ds.vwind, ds.temperature, 850 * units.hPa,
                                   static_stability=2e-6 * units('J / kg / Pa^2'))

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# plot isotherms
cs = ax.contour(ds.lon, ds.lat, ds.temperature, range(4, 26, 2), colors='tab:red',
                linestyles='dashed', linewidths=3)
plt.clabel(cs, fmt='%d', fontsize=16)

# plot temperature advection in Kelvin per 3 hours
cf = ax.contourf(ds.lon, ds.lat, tadv.metpy.convert_units('kelvin/hour') * 3, range(-6, 7, 1),
                 cmap=plt.cm.bwr, alpha=0.75)
plt.colorbar(cf, pad=0, aspect=50)

# calculate a scale length quantity of our vectors based on their mean
scale = (np.hypot(u_qvect, v_qvect).mean() * np.sqrt(u_qvect.size)).data

# plot Q-vectors as arrows, every other arrow
qvec = ax.quiver(ds.lon.values[::2], ds.lat.values[::2],
                 u_qvect[::2, ::2], v_qvect[::2, ::2],
                 color='black', scale=scale.m, alpha=0.5, width=0.01)

# calculate representative arrow for key
key = scale / 10

# add key for arrow length
qk = ax.quiverkey(qvec, 0.65, 0.905, key.m, f'{key:0.2~P}', labelpos='E',
                  coordinates='figure')

ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Q-Vector Calculation', loc='left')

plt.show()
```

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

### Import Libraries and Setup

Source: https://unidata.github.io/MetPy/latest/examples/calculations/Smoothing.html

Imports necessary libraries for plotting and calculations, and sets up a random data grid with noise for demonstration.

```python
from itertools import product

import matplotlib.pyplot as plt
import numpy as np

import metpy.calc as mpcalc

```

```python
rng = np.random.default_rng(61461542)
size = 128
x, y = np.mgrid[:size, :size]
distance = np.sqrt((x - size / 2) ** 2 + (y - size / 2) ** 2)
raw_data = rng.random((size, size)) * 0.3 + distance / distance.max() * 0.7

fig, ax = plt.subplots(1, 1, figsize=(4, 4))
ax.set_title('Raw Data')
ax.imshow(raw_data, vmin=0, vmax=1)
ax.axis('off')
plt.show()

```

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

### Calculate and Plot Temperature Advection

Source: https://unidata.github.io/MetPy/latest/_downloads/4d4c25299feb8e3fe80f0a671f3ee7d3/Advection.ipynb

Use this snippet to compute temperature advection from wind and temperature data. It plots the isotherms, the calculated advection (converted to Kelvin per 3 hours), and wind barbs. Ensure MetPy and Matplotlib are installed and example data is available.

```python
import matplotlib.pyplot as plt

import metpy.calc as mpcalc
from metpy.cbook import example_data

# load example data
ds = example_data()

# Calculate the temperature advection of the flow
tadv = mpcalc.advection(ds.temperature, ds.uwind, ds.vwind)

# See the units that come back from the advection function
print(tadv.data.units)

# start figure and set axis
fig, ax = plt.subplots(figsize=(5, 5))

# plot isotherms
cs = ax.contour(ds.lon, ds.lat, ds.temperature, range(4, 26, 2), colors='tab:red',
                linestyles='dashed', linewidths=3)
plt.clabel(cs, fmt='%d', fontsize=16)

# plot temperature advection and convert units to Kelvin per 3 hours
cf = ax.contourf(ds.lon, ds.lat, tadv.metpy.convert_units('kelvin/hour') * 3, range(-6, 7, 1),
                 cmap=plt.cm.bwr, alpha=0.75)
plt.colorbar(cf, pad=0, aspect=50)
ax.barbs(ds.lon.values[::2], ds.lat.values[::2],
         ds.uwind[::2, ::2], ds.vwind[::2, ::2],
         color='black', length=6, alpha=0.5)
ax.set(xlim=(260, 270), ylim=(30, 40))
ax.set_title('Temperature Advection Calculation')

plt.show()
```

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

### ArrowPlot.setup_instance

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ArrowPlot.html

This method is called before self.__init__ is called.

```APIDOC
## ArrowPlot.setup_instance

### Description
This is called **before** self.__init__ is called.

### Method
setup_instance(_** kwargs: Any_) -> None
```

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

### metpy.units.setup_registry

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.units.html

Sets up a given registry with MetPy's default tweaks and settings.

```APIDOC
## `setup_registry(reg)`

### Description
Set up a given registry with MetPy's default tweaks and settings.

### Parameters
#### Path Parameters
- **reg** (pint.UnitRegistry) - Required - The registry to set up.
```

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

### Install MetPy with conda

Source: https://unidata.github.io/MetPy/latest/userguide/installguide.html

Installs MetPy and its dependencies from the conda-forge channel. This is an alternative for users who prefer the conda package manager.

```bash
conda install -c conda-forge metpy
```

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

### Instantiate GiniFile

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.io.GiniFile.html

Create an instance of GiniFile by providing the filename or a file-like object. Gzip and bzip2 compressed files are supported.

```python
f = GiniFile("my_gini_file.gini")
f = GiniFile("my_gini_file.gini.gz")
f = GiniFile(file_obj)
```

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

### Define Cross Section Start and End Points

Source: https://unidata.github.io/MetPy/latest/_downloads/7fd39302ff9f3fa4a7870d3c31b04722/cross_section.ipynb

Specifies the geographic coordinates (latitude, longitude) for the start and end of the desired cross-section.

```python
start = (37.0, -105.0)
end = (35.5, -65.0)
```

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

### GempakGrid.__init__

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.io.GempakGrid.html

Instantiate GempakFile object from file.

```APIDOC
## GempakGrid.__init__

### Description
Instantiate GempakFile object from file.

### Parameters
* `file`: Path to the GEMPAK grid file.
* `*args`: Variable length argument list.
* `**kwargs`: Arbitrary keyword arguments.
```

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

### Install MetPy Dependencies

Source: https://unidata.github.io/MetPy/latest/devel/CONTRIBUTING.html

Install all necessary MetPy dependency packages from conda-forge using the requirement files. Ensure you are in the 'metpy' directory when running this command.

```bash
conda install --file ci/requirements.txt --file ci/extra_requirements.txt --file ci/test_requirements.txt
```

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

### GempakSurface.__init__

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.io.GempakSurface.html

Instantiates a GempakSurface object from a file.

```APIDOC
## GempakSurface.__init__

### Description
Instantiate GempakFile object from file.

### Parameters
* **file** (str) - The path to the GEMPAK surface data file.
* **args** - Additional positional arguments.
* **kwargs** - Additional keyword arguments.
```

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

### ColdFront Class Initialization

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ColdFront.html

Initializes a ColdFront object to draw a path as a cold front with customizable pips/triangles along the path.

```APIDOC
## class metpy.plots.ColdFront

### Description
Draw a path as a cold front, with (default blue) pips/triangles along the path.
Initialize the front path effect.

### Parameters
* **color** (_str_ _or_ _tuple_ _[__float_ _]_) – Color to use for the effect.
* **size** (_int_ _or_ _float_) – The size of the markers to plot in points. Defaults to 10.
* **spacing** (_int_ _or_ _float_) – The spacing between markers in normalized coordinates. Defaults to 1.
* **flip** (_bool_) – Whether the symbol should be flipped to the other side of the path. Defaults to _False_.
* **filled** (_bool_) – Whether the symbol should be filled with the color. Defaults to _True_.
```

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

### MetPy Software Citation Example

Source: https://unidata.github.io/MetPy/latest/userguide/citing.html

Example citation for MetPy software in a common citation style. Includes authors, year, title, publisher, and DOI.

```text
May, R. M., Arms, S. C., Marsh, P., Bruning, E., Leeman, J. R., Goebbert, K., Thielen, J. E.,
    Bruick, Z., and Camron, M. D., 2025: MetPy: A Python Package for Meteorological Data.
    Unidata, Unidata/MetPy, doi:10.5065/D6WW7G29.

```

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

### metpy.calc.dry_lapse

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.dry_lapse.html

Calculate the temperature at a level assuming only dry processes. This function lifts a parcel starting at `temperature`, conserving potential temperature. The starting pressure can be given by `reference_pressure`.

```APIDOC
## dry_lapse

### Description
Calculate the temperature at a level assuming only dry processes. This function lifts a parcel starting at `temperature`, conserving potential temperature. The starting pressure can be given by `reference_pressure`.

### Parameters
* **pressure** (`pint.Quantity`) – Atmospheric pressure level(s) of interest
* **temperature** (`pint.Quantity`) – Starting temperature
* **reference_pressure** (`pint.Quantity`, optional) – Reference pressure; if not given, it defaults to the first element of the pressure array.

### Returns
`pint.Quantity` – The parcel’s resulting temperature at levels given by `pressure`

### Examples
```python
>>> from metpy.calc import dry_lapse
>>> from metpy.units import units
>>> plevs = [1000, 925, 850, 700] * units.hPa
>>> dry_lapse(plevs, 15 * units.degC).to('degC')
<Quantity([ 15.           8.65249458   1.92593808 -12.91786723], 'degree_Celsius')>
```

### See Also
`moist_lapse`, `parcel_profile`, `potential_temperature`

### Notes
Only reliably functions on 1D profiles (not higher-dimension vertical cross sections or grids) unless reference_pressure is specified.
Changed in version 1.0: Renamed `ref_pressure` parameter to `reference_pressure`
```

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

### Configure Map Panel and Show Figure

Source: https://unidata.github.io/MetPy/latest/_downloads/ebb1d490725ed27549701eaf2c36b8b4/declarative_tutorial.ipynb

Set up a MapPanel with specified area, projection, layers, title, and add the configured plots. Then, create a PanelContainer, set its size, add the panel, and display the figure.

```python
# Set the attributes for the map and add our data to the map
panel = MapPanel()
panel.area = [-125, -74, 20, 55]
panel.projection = 'lcc'
panel.layers = ['states', 'coastline', 'borders']
panel.title = f'{cfill.level.m}-hPa Heights and Wind Speed at {plot_time}'
panel.plots = [cfill, cntr2, barbs]

# Set the attributes for the panel and put the panel in the figure
pc = PanelContainer()
pc.size = (15, 15)
pc.panels = [panel]

# Show the figure
pc.show()
```

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

### moist_lapse

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.moist_lapse.html

Calculate the temperature at a level assuming liquid saturation processes. This function lifts a parcel starting at temperature. The starting pressure can be given by reference_pressure. Essentially, this function is calculating moist pseudo-adiabats.

```APIDOC
## moist_lapse

### Description
Calculate the temperature at a level assuming liquid saturation processes. This function lifts a parcel starting at temperature. The starting pressure can be given by reference_pressure. Essentially, this function is calculating moist pseudo-adiabats.

### Parameters
* **pressure** (`pint.Quantity`) – Atmospheric pressure level(s) of interest
* **temperature** (`pint.Quantity`) – Starting temperature
* **reference_pressure** (`pint.Quantity`, optional) – Reference pressure; if not given, it defaults to the first element of the pressure array.

### Returns
`pint.Quantity` – The resulting parcel temperature at levels given by pressure

### Examples
```python
>>> from metpy.calc import moist_lapse
>>> from metpy.units import units
>>> plevs = [925, 850, 700, 500, 300, 200] * units.hPa
>>> moist_lapse(plevs, 5 * units.degC).to('degC')
<Quantity([  5.           0.99635104  -8.88958079 -28.38862857 -60.12003999
-83.34321585], 'degree_Celsius')>
```

### See Also
`dry_lapse`, `parcel_profile`

### Notes
This function is implemented by integrating the following differential equation: dTdP=1PRdT+LvrsCpd+Lv2rsϵRdT2. This equation comes from [Bakhshaii2013]. Only reliably functions on 1D profiles (not higher-dimension vertical cross sections or grids).

Changed in version 1.0: Renamed `ref_pressure` parameter to `reference_pressure`
```

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

### Configure and Display a Map Panel

Source: https://unidata.github.io/MetPy/latest/tutorials/declarative_tutorial.html

Sets up a map panel with specified area, projection, layers, and title, then displays it using a PanelContainer. Useful for creating static map visualizations.

```python
panel = MapPanel()
panel.area = [-125, -74, 20, 55]
panel.projection = 'lcc'
panel.layers = ['states', 'coastline', 'borders']
panel.title = f'{cfill.level.m}-hPa Heights and Wind Speed at {plot_time}'
panel.plots = [cfill, cntr2, barbs]

pc = PanelContainer()
pc.size = (15, 15)
pc.panels = [panel]

pc.show()
```

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

### Basic Skew-T Plotting Setup

Source: https://unidata.github.io/MetPy/latest/_downloads/cdca3e0cb8a2930cccab0e29b97ef52a/upperair_soundings.ipynb

Initializes a Matplotlib figure and a SkewT object, then plots the observed pressure, temperature, and dewpoint data, along with wind barbs.

```python
# Create a figure.
fig = plt.figure(1, figsize=(8, 8))

# Create a SkewT object.
skew = SkewT(fig=fig)

# Plot the data using skew
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_wave_lines()
skew.plot_mixing_lines()
```

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

### class_traits

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.PlotScalar.html

Gets a dictionary of all the traits of this class.

```APIDOC
## _classmethod _class_traits

### Description
Get a `dict` of all the traits of this class. The dictionary is keyed on the name and the values are the TraitType objects.

### Parameters
* `**metadata`: Metadata to filter traits by.
```

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

### Build and Upload PyPI Packages

Source: https://unidata.github.io/MetPy/latest/devel/infrastructureguide.html

Commands to build Python packages and upload them to PyPI. Ensure you have the 'build' and 'twine' packages installed. The 'dist/' directory should only contain files for the current release.

```bash
python -m build
```

```bash
twine upload dist/*
```

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

### traits

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ImagePlot.html

Get a `dict` of all the traits of this class.

```APIDOC
## traits

### Description
Get a `dict` of all the traits of this class. The dictionary is keyed on the name and the values are the TraitType objects.
The TraitTypes returned don’t know anything about the values that the various HasTrait’s instances are holding.
The metadata kwargs allow functions to be passed in which filter traits based on metadata values. The functions should take a single value as an argument and return a boolean. If any function returns False, then the trait is not included in the output. If a metadata key doesn’t exist, None will be passed to the function.

### Method
`traits(_** metadata: Any_)`

### Parameters
* **metadata** (Any) - Metadata keyword arguments to filter traits.
```

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

### Unit Registry Setup

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.units.html

This function sets up the unit registry, which is essential for MetPy's unit handling capabilities.

```APIDOC
## setup_registry

### Description
Sets up the unit registry for MetPy.

### Parameters
None

### Returns
None
```

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

### traits

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.MapPanel.html

Get a `dict` of all the traits of this class.

```APIDOC
## traits

### Description
Get a `dict` of all the traits of this class. The dictionary is keyed on the name and the values are the TraitType objects.
The TraitTypes returned don’t know anything about the values that the various HasTrait’s instances are holding.
The metadata kwargs allow functions to be passed in which filter traits based on metadata values. The functions should take a single value as an argument and return a boolean. If any function returns False, then the trait is not included in the output. If a metadata key doesn’t exist, None will be passed to the function.

### Method
`traits(_** metadata: Any_)`

### Parameters
- **metadata** (Any) - Metadata keyword arguments to filter traits.
```

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

### unit_vectors_from_cross_section

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.cross_section_components.html

Get unit vectors for a cross-section.

```APIDOC
## unit_vectors_from_cross_section

### Description
Get the unit vectors corresponding to the axes of a cross-section.

### Parameters
*   **ds** (xarray.Dataset) - The Dataset containing the cross-section data.

### Returns
(xarray.DataArray, xarray.DataArray) - A tuple containing the unit vectors for the cross-section axes.

### Example
```python
# Assuming 'ds' is an xarray Dataset with cross-section data
unit_vec_x, unit_vec_y = metpy.calc.unit_vectors_from_cross_section(ds)
```
```

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

### Create and Configure Map Panel

Source: https://unidata.github.io/MetPy/latest/_downloads/ebb1d490725ed27549701eaf2c36b8b4/declarative_tutorial.ipynb

Set up a map panel for plotting weather data. Specify the layout, projection, area of interest, layers, title, and plots to be displayed.

```python
# Panel for plot with Map features
panel = MapPanel()
panel.layout = (1, 1, 1)
panel.projection = 'lcc'
panel.area = 'in'
panel.layers = ['states']
panel.title = f'Surface plot for {obs_time}'
panel.plots = [obs]

# Bringing it all together
pc = PanelContainer()
pc.size = (10, 10)
pc.panels = [panel]

pc.show()
```

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

### metpy.calc.lcl

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.lcl.html

Calculate the lifted condensation level (LCL) from the starting point. The starting state for the parcel is defined by temperature, dewpoint, and pressure. If these are arrays, this function will return a LCL for every index. This function does work with surface grids as a result.

```APIDOC
## metpy.calc.lcl

### Description
Calculate the lifted condensation level (LCL) from the starting point. The starting state for the parcel is defined by temperature, dewpoint, and pressure. If these are arrays, this function will return a LCL for every index. This function does work with surface grids as a result.

### Parameters
* **pressure** (`pint.Quantity`) – Starting atmospheric pressure
* **temperature** (`pint.Quantity`) – Starting temperature
* **dewpoint** (`pint.Quantity`) – Starting dewpoint
* **max_iters** (optional) – Maximum number of iterations to use in the calculation.
* **eps** (optional) – The desired precision for the calculation.

### Returns
* `pint.Quantity` – LCL pressure
* `pint.Quantity` – LCL temperature

### Examples
```python
>>> from metpy.calc import lcl
>>> from metpy.units import units
>>> lcl(943 * units.hPa, 33 * units.degC, 28 * units.degC)
(<Quantity(877.033549, 'hectopascal')>, <Quantity(26.7591908, 'degree_Celsius')>)
```

### Notes
From [Romps2017], this directly solves for the temperature at the LCL, Eq 22a,
TLCL=c[W−1(RHl1/acexp⁡c)]−1T
and the pressure at the LCL, Eq 22b,
pLCL=p(TLCLT)cpm/Rm
where a (Eq 22d), b (Eq 22e), and c (Eq 22f) are derived constants, and W−1 is the k=−1 branch of the Lambert W function.
Changed in version 1.0: Renamed `dewpt` parameter to `dewpoint`
```

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

### names

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlotLayout.html

Get the list of names used by the StationPlotLayout.

```APIDOC
## names()

### Description
Get the list of names used by the layout.
```

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

### Set up map, parse bulletin, and plot surface analysis

Source: https://unidata.github.io/MetPy/latest/_downloads/d0fb0bcc036dab38c2a49ad11eb0832b/Plotting_Surface_Analysis.ipynb

Configures a Lambert Conformal map projection, parses a WPC surface analysis bulletin using MetPy, plots the features using the defined function, and displays the map with a title and MetPy logo.

```python
# Set up a default figure and map
fig = plt.figure(figsize=(7, 7), dpi=150)
ax = fig.add_subplot(
    1, 1, 1, projection=ccrs.LambertConformal(central_longitude=-100)
)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.BORDERS)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.LAKES)

# Parse the bulletin and plot it
df = parse_wpc_surface_bulletin(get_test_data('WPC_sfc_fronts_20210628_1800.txt'))
plot_bulletin(ax, df)

ax.set_title(f'WPC Surface Analysis Valid {df.valid.dt.strftime("%HZ %d %b %Y")[0]}')
add_metpy_logo(fig, 275, 295, size='large')
plt.show()
```

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

### _classmethod_trait_events

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.PlotSurfaceAnalysis.html

Get a `dict` of all the event handlers of this class.

```APIDOC
## _classmethod_trait_events(name: str | None = None) -> dict[str, EventHandler]

### Description
Get a `dict` of all the event handlers of this class.

### Method
`_classmethod_trait_events`

### Parameters
#### Path Parameters
- **name** (str | None) - Optional - The name of a trait of this class. If name is `None` then all the event handlers of this class will be returned instead. Defaults to `None`.

### Returns
A `dict` of the event handlers associated with a trait name, or all event handlers.
```

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

### Import Necessary Libraries for Static Stability

Source: https://unidata.github.io/MetPy/latest/_downloads/1474899b96adbc9492d387961a72cf37/Static_Stability.ipynb

Import pandas for data manipulation, MetPy's `static_stability` and `get_test_data` for calculations and data retrieval, `Rd` for the gas constant, and `units` for handling physical quantities.

```python
import pandas as pd

from metpy.calc import static_stability
from metpy.cbook import get_test_data
from metpy.constants import Rd
from metpy.units import units
```

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

### trait_names

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ContourPlot.html

Gets a list of all the names of this class' traits.

```APIDOC
## trait_names

### Description
Get a list of all the names of this class' traits.

### Parameters

* **metadata** (_Any_) – Metadata keyword arguments to filter traits.

### Returns

A list of trait names.
```

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

### Initialize and Plot Station Data with MetPy

Source: https://unidata.github.io/MetPy/latest/_downloads/bb9caa5586d62e19ca46e30c02d29b43/Station_Plot.ipynb

This snippet shows how to set up a map projection, add map features, and then initialize a StationPlot object to display meteorological data. It includes plotting temperature, dew point, sea-level pressure with custom formatting, cloud cover symbols, current weather symbols, wind barbs, and station IDs.

```python
plt.rcParams['savefig.dpi'] = 255

# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1100, 300, size='large')
ax = fig.add_subplot(1, 1, 1, projection=proj)

# Add some various map elements to the plot to make it recognizable.
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
ax.add_feature(cfeature.LAKES)
ax.add_feature(cfeature.COASTLINE)
ax.add_feature(cfeature.STATES)
ax.add_feature(cfeature.BORDERS)

# Set plot bounds
ax.set_extent((-118, -73, 23, 50))

#
# Here's the actual station plot
#

# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, data['longitude'].values, data['latitude'].values,
                          clip_on=True, transform=ccrs.PlateCarree(), fontsize=12)

# Plot the temperature and dew point to the upper and lower left, respectively, of
# the center point. Each one uses a different color.
stationplot.plot_parameter('NW', data['air_temperature'].values, color='red')
stationplot.plot_parameter('SW', data['dew_point_temperature'].values,
                           color='darkgreen')

# A more complex example uses a custom formatter to control how the sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of the pressure value
# in tenths of millibars.
stationplot.plot_parameter('NE', data['air_pressure_at_sea_level'].values,
                           formatter=lambda v: format(10 * v, '.0f')[-3:])

# Plot the cloud cover symbols in the center location. This uses the codes made above and
# uses the `sky_cover` mapper to convert these values to font codes for the
# weather symbol font.
stationplot.plot_symbol('C', data['cloud_coverage'].values, sky_cover)

# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', data['current_wx1_symbol'].values, current_weather)

# Add wind barbs
stationplot.plot_barb(data['eastward_wind'].values, data['northward_wind'].values)

# Also plot the actual text of the station id. Instead of cardinal directions,
# plot further out by specifying a location of 2 increments in x and 0 in y.
stationplot.plot_text((2, 0), data['station_id'].values)

plt.show()
```

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

### trait_metadata

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ContourPlot.html

Gets metadata values for a trait by key.

```APIDOC
## trait_metadata

### Description
Get metadata values for trait by key.

### Parameters

* **traitname** (_str_) – The name of the trait.
* **key** (_str_) – The metadata key.
* **default** (_Any_) – The default value to return if the key is not found.

### Returns

The metadata value associated with the key.
```

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

### ArrowPlot.trait_names

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ArrowPlot.html

Get a list of all the names of this class' traits.

```APIDOC
## ArrowPlot.trait_names

### Description
Get a list of all the names of this class' traits.

### Method
trait_names(_** metadata: Any_) -> list[str]
```

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

### GOESArchive Initialization

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.remote.GOESArchive.html

Initialize the GOESArchive client for a specific GOES satellite.

```APIDOC
## GOESArchive.__init__(satellite)

### Description
Initialize the archive client.

### Parameters
* **satellite** (str or int) - The specific GOES satellite to access (e.g. 16, 17, 18).
```

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

### ArrowPlot.trait_metadata

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ArrowPlot.html

Get metadata values for a trait by key.

```APIDOC
## ArrowPlot.trait_metadata

### Description
Get metadata values for trait by key.

### Method
trait_metadata(_traitname : str_, _key : str_, _default : Any = None_) -> Any
```

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

### metpy.units.setup_registry

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.units.setup_registry.html

Initializes and configures the unit registry used by MetPy. This function ensures that all necessary units and their conversions are available for use in calculations and data processing.

```APIDOC
## setup_registry()

### Description
Initializes the MetPy unit registry. This function should be called before using MetPy's unit-aware functionalities to ensure all standard units and conversions are loaded.

### Parameters
This function does not take any parameters.

### Returns
None. The function modifies the global unit registry.

### Example
```python
import metpy.units as units

# Call setup_registry to initialize the unit registry
units.setup_registry()

# Now you can use MetPy units
wind_speed = 10 * units.knots
print(wind_speed)
```
```

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

### trait_metadata

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ImagePlot.html

Get metadata values for trait by key.

```APIDOC
## trait_metadata

### Description
Get metadata values for trait by key.

### Method
`trait_metadata(_traitname : str_, _key : str_, _default : Any = None_)`

### Parameters
* **traitname** (str) - The name of the trait.
* **key** (str) - The key of the metadata to retrieve.
* **default** (Any, optional) - The default value to return if the metadata is not found. Defaults to None.
```

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

### Calculate Equilibrium Level (EL)

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.el.html

This example demonstrates how to calculate the equilibrium level (EL) using MetPy. It involves defining pressure, temperature, and relative humidity profiles, calculating the dewpoint, computing a parcel profile, and finally calling the `el` function. The `el` function returns the pressure and temperature at the EL.

```python
from metpy.calc import el, dewpoint_from_relative_humidity, parcel_profile
from metpy.units import units
# pressure
p = [1008., 1000., 950., 900., 850., 800., 750., 700., 650., 600.,
     550., 500., 450., 400., 350., 300., 250., 200.,
     175., 150., 125., 100., 80., 70., 60., 50.,
     40., 30., 25., 20.] * units.hPa
# temperature
T = [29.3, 28.1, 23.5, 20.9, 18.4, 15.9, 13.1, 10.1, 6.7, 3.1,
     -0.5, -4.5, -9.0, -14.8, -21.5, -29.7, -40.0, -52.4,
     -59.2, -66.5, -74.1, -78.5, -76.0, -71.6, -66.7, -61.3,
     -56.3, -51.7, -50.7, -47.5] * units.degC
# relative humidity
rh = [.85, .65, .36, .39, .82, .72, .75, .86, .65, .22, .52,
      .66, .64, .20, .05, .75, .76, .45, .25, .48, .76, .88,
      .56, .88, .39, .67, .15, .04, .94, .35] * units.dimensionless
# calculate dewpoint
Td = dewpoint_from_relative_humidity(T, rh)
# compute parcel profile temperature
prof = parcel_profile(p, T[0], Td[0]).to('degC')
# calculate EL
el(p, T, Td, prof)
```

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

### trait_metadata

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.MapPanel.html

Get metadata values for trait by key.

```APIDOC
## trait_metadata

### Description
Get metadata values for trait by key.

### Method
`trait_metadata(_traitname : str_, _key : str_, _default : Any = None_)`

### Parameters
- **traitname** (str) - The name of the trait.
- **key** (str) - The metadata key.
- **default** (Any, optional) - The default value to return if the key is not found. Defaults to None.
```

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

### Level3File Initialization

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.io.Level3File.html

Instantiate a Level3File object by providing the path to the Level 3 radar file.

```APIDOC
## Level3File

### Description
Create instance of `Level3File`.

### Parameters
#### Path Parameters
- **filename** (str or file-like object) - Required - If str, the name of the file to be opened. If file-like object, this will be read from directly.
```

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

### Create Sample Data for Gradient Calculation

Source: https://unidata.github.io/MetPy/latest/_downloads/62a1acd718d4c5b9717787544d4cf09f/Gradient.ipynb

Generates sample temperature data along with corresponding x and y coordinate arrays for gradient computation.

```python
data = np.array([[23, 24, 23],
                 [25, 26, 25],
                 [27, 28, 27],
                 [24, 25, 24]]) * units.degC

# Create an array of x position data (the coordinates of our temperature data)
x = np.array([[1, 2, 3],
              [1, 2, 3],
              [1, 2, 3],
              [1, 2, 3]]) * units.kilometer

y = np.array([[1, 1, 1],
              [2, 2, 2],
              [3, 3, 3],
              [4, 4, 4]]) * units.kilometer
```

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

### trait_events

Source: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ImagePlot.html

Get a `dict` of all the event handlers of this class.

```APIDOC
## trait_events

### Description
Get a `dict` of all the event handlers of this class.

### Method
`trait_events(_name : str | None = None_)`

### Parameters
* **name** (str | None, optional) - The name of a trait of this class. If name is `None` then all the event handlers of this class will be returned instead. Defaults to None.

### Returns
The event handlers associated with a trait name, or all event handlers.
```