Reference Guide

Air Sounding

This module allows to plot an upper air sounding on a Skew-T diagramm. It can also try to detect interesting layers into the upper air sounding.

Classes

class air_sounding.AirSounding(variables: VariablesSet)

Allows to plot and analyse an upper air sounding

Attributes

nb_dataint

The length of the given VariablesSet.

datadict

The variables contained into the given VariablesSet.

Methods

show(layers: tuple = ())

Plots and displays the upper air sounding on a Skew-T diagramm and a hodograph.

Parameters

layerstuple, optionnal

By default: (). The layers to be represented on the Skew-T diagramm.

auto_layers(layer_size: int = 0, nb_layers: int = 10, show: bool = False)

This method attempts to find interesting layers in the upper air sounding and prints the characteristics of these layers.

Parameters

layer_sizeint, optionnal

By default: automatic size. The size of each layers in termes of indexes.

nb_layersint, optionnal

By default: 10. The maximum number of layers you want.

showbool, optionnal

By default: False. If set on True, once the layers have been analyzed, it will plot the layers on the Skew-T diagramm.

Variables

This module provide a class to manipulate variables with dimensions.

Classes

class variables.LockedWarning

Warns the user that he’s trying to modify a locked VariablesSet instance.

class variables.VariablesSet(filename: str, **kwargs)

This class provides a standard format for passing variables to an AirSounding instance. It also allows data to be split between ascending and descending profiles.

Attributes

datapd.DataFrame, private

The DataFrame that contains all the data from the upper air sounding.

variablesdict, private

Identified variables in data. You can only identify the variables required by AirSounding, in particular :

• the pressure;

• the temperature;

• the dewpoint;

• the easterly wind;

• the northerly wind.

lockedbool, private

False if the instance can still be edited.

Methods

add_press(vardata: tuple)

Adds pressure to variables.

Parameters

vardatatuple

This tuple must contains:

• the name of the pressure in data;

• the unit of the pressure in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to add a pressure when the VariablesSet is locked.

add_temp(vardata: tuple)

Adds temperature to variables.

Parameters

vardatatuple

This tuple must contains:

• the name of the temperature in data;

• the unit of the temperature in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to add a temperature when the VariablesSet is locked.

add_dewpoint(vardata: tuple)

Adds dewpoint to variables.

Parameters

vardatatuple

This tuple must contains:

• the name of the dewpoint in data;

• the unit of the dewpoint in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to add a dewpoint when the VariablesSet is locked.

add_windu(vardata: tuple)

Adds east wind component to variables.

Parameters

vardatatuple

This tuple must contains:

• the name of the east wind component in data;

• the unit of the east wind component in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to add an east component of the wind when the VariablesSet is locked.

add_windv(vardata: tuple)

Adds north wind component to variables.

Parameters

vardatatuple

This tuple must contains:

• the name of the north wind component in data;

• the unit of the north wind component in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to add a north component of the wind when the VariablesSet is locked.

compute_dewpoint(temperature: tuple, relative_humitidy: tuple)

Computes the dewpoint from the air temperature and the relative humidity.

Parameters

temperaturetuple

This tuple must contains:

• the name of the temperature in data;

• the unit of the temperature in data (cf. metpy.units).

relative_humiditytuple

This tuple must contains:

• the name of the relative humidity in data;

• the unit of the relative humidity in data (cf. metpy.units).

Warns

LockedWarning

This warning should appear if you try to compute the dewpoint when the VariablesSet is locked.

apply_threshold(threshold: float, ascending: bool = True, start_cut: bool = False, atol: int = 10)

This function cuts data by applying a threshold to the pressure. It will retain all data above this threshold. You can also request an ascending or descending profile for altitude sounding.

Warning

Applying a threshold will lock the VariablesSet instance.

Parameters

thresholdfloat

The threshold to apply on pressure, all pressure above this value will be retain.

ascendingbool, optionnal

By default: True. Indicates which profile you want to keep. If ascending is set on True it will keep the ascending profile, otherwise the descending profile.

start_cutbool, optionnal

By default: False If set on True it will cuts the data at the start.

atolint, optionnal

By default: 10 The absolute tolerance parameter.

Warns

LockedWarning

This warning should appear if you try to apply a threshold when the VariablesSet is locked.

Layers

This module provides functions for dividing a signal into layers by analyzing the strongest gradients into the input signal.

Functions

layers.check_indices_spacing(indices: list, index_to_check: int, min_dist: int)

Checks that the index to be checked is far enough away from all the others.

Parameters

indiceslist

All the indices, they’re considered as a reference.

index_to_checkint

The index to be checked.

min_distint

The minimum distance beetween two indices.

Returns

outbool

It equals True if index_to_check is far enough away from all the others, and False in the other case.

layers.get_layers(layer_size: int, nb_layers: int, *datas)

Extracts strongest gradients from input data. If you give several input datas, the result will be the concatenation of all the gradients found.

Parameters

layer_sizeint

The minimum size of each layer (in terms of index). The input datas will be divided into small layers of this size, and the sliding gradients will be computed on these layers.

nb_layersint

The numbers of layers you want.

datas

All the data on which you wish to obtain the strongest gradients.

Returns

outlist

The sorted list of the indices of all the strongest gradients.

Exemple

If you’re searching for small phenomena into an upper air sounding, you should pass a small layer_size. You also have two variables from the sounding (e.g. temperature and humidty) and you want 10 layers:

layers_indices = get_layers(10, 10, data_temp, data_relative_hum)

Files

This little module gives some functions to handle common file formats.

Functions

files.open_ftr(filename: str, **kwargs)

Opens and creates a VariablesSet instance from a ftr file.

Parameters

filenamestr

The name of the ftr file to be read.

kwargs

The keyword arguments to be given to pandas.read_csv.

Returns

v_setVariablesSet

The VariablesSet instance, ready to be given to AirSounding.

files.open_cor(filename: str, **kwargs)

Opens and creates a VariablesSet instance from a cor file.

Parameters

filenamestr

The name of the cor file to be read.

kwargs

The keyword arguments to be given to pandas.read_csv.

Returns

v_setVariablesSet

The VariablesSet instance, ready to be given to AirSounding.