wolfhece.irm_qdf

Author: HECE - University of Liege, Pierre Archambeau Date: 2024

Copyright (c) 2024 University of Liege. All rights reserved.

This script and its content are protected by copyright law. Unauthorized copying or distribution of this file, via any medium, is strictly prohibited.

Module Contents

wolfhece.irm_qdf.Montana_a1 = 'a1'

wolfhece.irm_qdf.Montana_a2 = 'a2'

wolfhece.irm_qdf.Montana_a3 = 'a3'

wolfhece.irm_qdf.Montana_b1 = 'b1'

wolfhece.irm_qdf.Montana_b2 = 'b2'

wolfhece.irm_qdf.Montana_b3 = 'b3'

wolfhece.irm_qdf.RT2 = '2'

wolfhece.irm_qdf.RT5 = '5'

wolfhece.irm_qdf.RT10 = '10'

wolfhece.irm_qdf.RT15 = '15'

wolfhece.irm_qdf.RT20 = '20'

wolfhece.irm_qdf.RT25 = '25'

wolfhece.irm_qdf.RT30 = '30'

wolfhece.irm_qdf.RT40 = '40'

wolfhece.irm_qdf.RT50 = '50'

wolfhece.irm_qdf.RT75 = '75'

wolfhece.irm_qdf.RT100 = '100'

wolfhece.irm_qdf.RT200 = '200'

wolfhece.irm_qdf.RT

wolfhece.irm_qdf.freqdep

wolfhece.irm_qdf.freqndep

wolfhece.irm_qdf.dur10min = '10 min'

wolfhece.irm_qdf.dur20min = '20 min'

wolfhece.irm_qdf.dur30min = '30 min'

wolfhece.irm_qdf.dur1h = '1 h'

wolfhece.irm_qdf.dur2h = '2 h'

wolfhece.irm_qdf.dur3h = '3 h'

wolfhece.irm_qdf.dur6h = '6 h'

wolfhece.irm_qdf.dur12h = '12 h'

wolfhece.irm_qdf.dur1d = '1 d'

wolfhece.irm_qdf.dur2d = '2 d'

wolfhece.irm_qdf.dur3d = '3 d'

wolfhece.irm_qdf.dur4d = '4 d'

wolfhece.irm_qdf.dur5d = '5 d'

wolfhece.irm_qdf.dur7d = '7 d'

wolfhece.irm_qdf.dur10d = '10 d'

wolfhece.irm_qdf.dur15d = '15 d'

wolfhece.irm_qdf.dur20d = '20 d'

wolfhece.irm_qdf.dur25d = '25 d'

wolfhece.irm_qdf.dur30d = '30 d'

wolfhece.irm_qdf.durationstext

wolfhece.irm_qdf.durations

wolfhece.irm_qdf.durationsd

wolfhece.irm_qdf.durations

wolfhece.irm_qdf.durations_seconds

class wolfhece.irm_qdf.MontanaIRM(coeff: pandas.DataFrame, time_bounds=None)

Classe pour la gestion des relations de Montana pour les précipitations

coeff

get_ab(dur, T)

Get the Montana coefficients for a given duration and return period

Parameters:

• dur – the duration

• T – the return period

get_meanrain(dur, T, ab=None)

Get the mean rain for a given duration and return period

Parameters:

• dur – the duration

• T – the return period

• ab – the Montana coefficients

get_instantrain(dur, T, ab=None)

Get the instantaneous rain for a given duration and return period

Parameters:

• dur – the duration

• T – the return period

• ab – the Montana coefficients

get_Q(dur, T)

Get the quantity of rain for a given duration and return period

Parameters:

• dur – the duration

• T – the return period

get_hyeto(durmax, T, r=0.5)

Get the hyetogram for a given return period

Parameters:

• durmax – the maximum duration of the hyetogram

• T – the return period

• r – Decentration coefficient

plot_hyeto(durmax, T, r=0.5)

Plot the hyetogram for a given return period

Parameters:

• durmax – the maximum duration of the hyetogram

• T – the return period

• r – Decentration coefficient

plot_hyetos(durmax, r=0.5)

Plot the hyetograms for all return periods

Parameters:

• durmax – the maximum duration of the hyetograms

• r – Decentration coefficient

class wolfhece.irm_qdf.Qdf_IRM(store_path='irm', code: int = 0, name='', force_import=False, ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018, localities: wolfhece.ins.Localities = None, dataframe: pandas.DataFrame = None)

Gestion des relations QDF calculées par l’IRM

Exemple d’utilisation :

Pour importer les fichiers depuis le site web de l’IRM meteo.be from wolfhece.irm_qdf import Qdf_IRM qdf = Qdf_IRM(force_import=True) qdf =

Il est possible de spécifier le répertoire de stockage des fichiers Excel Par défaut, il s’agit d’un sous-répertoire ‘irm’ du répertoire courant qui sera créé s’il n’exsiste pas

Une fois importé/téléchargé, il est possible de charger une commune sur base de l’INS ou de son nom

myqdf = Qdf_IRM(name=’Jalhay’)

Les données sont ensuite disponibles dans les propriétés, qui sont des “dataframes” pandas (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) :

• qdf : les relation Quantité/durée/fréquence

• standarddev : l’écart-type de l’erreur

• confintlow : la valeur inférieure de l’intervalle de confiance (-2*stddev)

• confintup : la valeur supérieure de l’intervalle de confiance (+2*stddev)

• montanacoeff : les coeffciients de Montana

L’index est le temps (dur10min, dur30min, dur1h, … – durationstext) et les colonnes sont les périodes de retour (RT2, RT5, RT10, … – RT).

Il est par exemple possible d’accéder aux coefficients de Montana via l’une de ces lignes ou une combinaison :

display(myqdf.montanacoeff) rt = myqdf.montanacoeff.index display(myqdf.montanacoeff.loc[rt[0]]) display(myqdf.montanacoeff.iloc[0]) display(myqdf.get_Montanacoeff(qdf.RT2))

store

qdf = None

standarddev = None

confintlow = None

confintup = None

montanacoeff = None

montana = None

_code = None

_name = None

_qdf_image_table = None

_qdf_image_plot = None

has_data_for_locality() → bool

Has this instance been initialized with data from a locality ?

property name

property code

property code_name

property name_code

export_allmontana2xls()

Export all Montana coefficients to an Excel file

classmethod importfromwebsite(store_path: pathlib.Path = 'irm', verbose: bool = False, waitingtime: float = 0.01, ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018, ins_code: int = None)

Import Excel files for one or all municipalities from the IRM website

Parameters:

• store_path – Where to store the downloaded data. Directory will be created if it doesn’t exists.

• verbose – If True, will print some progress information. If False, will do nothing. If a callable, then will call it with a float in [0, 1]. 0 means nothing downloaded, 1 means everything downloaded.

• waitingtime – How long to wait (in seconds) betwenn the download of each station (will make sure we don’t overwhelm IRM’s website).

• ins – The year of the INS codes to use.

• code – Restricts the data download to a specific NIS code. None means full download.

_read_csv_or_excel(code='', name='')

Lecture des caractéristiques d’une commune depuis le fichier CSV ou Excel associé au code INS

Parameters:

• code – le code INS de la commune

• name – le nom de la commune

_read_csv_or_excel_Montana_only(code='', name='')

Lecture des caractéristiques d’une commune depuis le fichier CSV Excel associé au code INS

Parameters:

• code – le code INS de la commune

• name – le nom de la commune

classmethod convert_xls2csv(store_path='irm', ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018)

Convert all Excel files to CSV files

Parameters:

• store_path – Where to store the downloaded data. Directory will be created if it doesn’t exists.

• ins – The year of the INS codes to use.

plot_idf(T=None, which: Literal['All', 'Montana', 'QDFTable'] = 'All', color=[27.0 / 255.0, 136.0 / 255.0, 245.0 / 255.0])

Plot IDF relations on a new figure

:param T : the return period (based on RT constants) :param which : information to plot

• ‘Montana’

• ‘QDFTable’

• ‘All’

plot_qdf(T=None, which: Literal['All', 'Montana', 'QDFTable'] = 'All', color=[27.0 / 255.0, 136.0 / 255.0, 245.0 / 255.0])

Plot QDF relations on a new figure :param T : the return period (based on RT constants) :param which : information to plot

• ‘Montana’

• ‘QDFTable’

• ‘All’

plot_cdf(dur=None)

Plot the cdf of the QDF data for a given duration

fit_all()

Fit all durations with a Generalized Extreme Value distribution

save_fits_json()

Save the fits in a csv file

load_fits_json()

Load the fits from a json file

fit_cdf(dur=None, plot=False)

Fit the cdf of the QDF data with a Generalized Extreme Value distribution

Parameters:

• dur – the duration to fit

• plot – if True, will plot the cdf with the fit

get_Tfromrain(Q, dur=dur1h)

Get the return period for a given quantity of rain

Parameters:

• Q – the quantity of rain

• dur – the duration

get_rainfromT(T, dur=dur1h)

Get the quantity of rain for a given return period and duration

Parameters:

• T – the return period

• dur – the duration

get_MontanacoeffforT(return_period)

Get the Montana coefficients for a given return period

Parameters:

return_period – the return period

plot_hyeto(durmax, T, r=0.5)

Plot the hyetogram for a given return period

Parameters:

• durmax – the maximum duration of the hyetogram

• T – the return period

• r – the decentration coefficient

plot_hyetos(durmax, r=0.5)

Plot the hyetograms for all return periods

Parameters:

• durmax – the maximum duration of the hyetograms

• r – the decentration coefficient

make_image_qdf_plot(T=None, which: Literal['All', 'Montana', 'QDFTable'] = 'All', color=[27.0 / 255.0, 136.0 / 255.0, 245.0 / 255.0])

Create an image of the QDF plot.

We use the matplotlib library to create a PNG image of the QDF data. The image will be saved in the store path with the name <code>_qdf_plot.png.

Parameters:

• durmax – the maximum duration of the hyetograms

• r – Decentration coefficient

Returns:

a PNG image

make_image_qdf_table()

Create an image of the QDF data.

We use the dataframe_image library to create a PNG image of the QDF data. Added style to the DataFrame to make it more readable.

Returns:

a PNG image

make_images()

Create all images for the QDF data.

property path_image_plot

Get the path for the QDF plot image.

property path_image_table

Get the path for the QDF table image.

class wolfhece.irm_qdf.QDF_Belgium(store_path='irm', ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018, force_import: bool = False)

Class to manage all QDF data for Belgium

localities

store_path

all: dict[int, Qdf_IRM]

make_images()

Create all images for all QDF data.

wolfhece.irm_qdf.TRANSLATION_HEADER

wolfhece.irm_qdf.RE_REFERENCE

class wolfhece.irm_qdf.Climate_IRM(store_path='irm', ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018)

store_path

localities

_climate_data

classmethod importfromwebsite(store_path='irm', verbose: bool = False, waitingtime: float = 0.01, ins: Literal['2018', '2019', '2025', 2018, 2019, 2025] = 2018, ins_code: int = None, convert=False)

Import Excel files for one or all municipalities from the IRM website

Parameters:

• store_path – Where to store the downloaded data. Directory will be created if it doesn’t exists.

• verbose – If True, will print some progress information. If False, will do nothing. If a callable, then will call it with a float in [0, 1]. 0 means nothing downloaded, 1 means everything downloaded.

• waitingtime – How long to wait (in seconds) betwenn the download of each station (will make sure we don’t overwhelm IRM’s website).

• ins – The year of the INS codes to use.

• code – Restricts the data download to a specific NIS code. None means full download.

• convert – Converts the downloaded PDF to Excel files.

classmethod _scrap_table(t)

Helper method to transform a table represented as a list of list to a pandas DataFrame.

classmethod _convert_irm_file(pdf_file: str | pathlib.Path)

Scrap a PDF from IRM into two tables in a single Excel file with two sheets.

wolfhece.irm_qdf.PLUVIO_INI = 'pluvio.ini'

wolfhece.irm_qdf.MATCH_NUM_ZONE_SHAPEFILE_INS_INDEX = 'Match_num_zone_shapefile_INS_index.txt'

wolfhece.irm_qdf.EXTREME_PRECIP_COMMUNES = 'Extreme_rain_ins.txt'

wolfhece.irm_qdf.GEOMETRY_MUNICIPALITIES = 'PDS__COMMUNES.shp'

class wolfhece.irm_qdf.QDF_Hydrology(store_path=DATADIR / 'irm_qdf', ini_file: str = PLUVIO_INI, ins: Literal['2018', 2018] = 2018, geometry: str = GEOMETRY_MUNICIPALITIES)

Prepare data from IRM website for WOLF hydrology calculations.

We need : - pluvio.ini - Match_num_zone_shapefile_INS_index.txt

“pluvio.ini” contains the path to the rainfall data files for each locality: - Extreme_precip_communes.txt

store_path

_data: dict[int, Qdf_IRM]

_ins = 2018

_extreme_file = 'Extreme_rain_ins.txt'

_nb_lines_extreme_file = 0

_nb_cols_extreme_file = 0

localities

_ini_file = 'pluvio.ini'

_geometry = 'PDS__COMMUNES.shp'

_geometry_df = None

read_data()

Read the data from the ini file and the extreme precipitation file.

download_municipalities_2018(force: bool = False)

Download the municipalities shapefile from HECE.

Parameters:

force – If True, will download the file even if it already exists.

create_match_num_zone_shapefile()

Create the Match_num_zone_shapefile_INS_index.txt file.

This file contains the mapping between the INS codes and the shapefile indices.

create_default_data()

Create data from scratch for WOLF hydrology calculations.

create_extreme_precipitation_file()

Create the extreme precipitation file for all localities.

Each line of the file contains the following data: - INS code - Duration in seconds - Quantity for each return period (RT2, RT5, RT10, RT20, RT50, RT100)

create_ini_file()

Create a parameter file for the class

get_all_ins() → list[int]

Get a list of all INS codes.

class wolfhece.irm_qdf.QDF_Hydrology_Draw(store_path=DATADIR / 'irm_qdf', ins: Literal['2018', 2018] = 2018, idx: str = '', plotted: bool = True, mapviewer=None)

Bases: wolfhece.drawing_obj.Element_To_Draw

Class to draw the QDF hydrology data on a map.

This class is used to draw the QDF hydrology data on a map using the WOLF hydrology calculations.

_qdf_hydrology

_scale_factor = 1.0

_geometry_zones

_geometry_tables

_geometry_plots

_centroids

_show_table = False

_show_plot = False

_reload_images = True

_current_images = None

_get_vector_tables(ins: str | int) → wolfhece.PyVertexvectors.vector

Get the vector for a given INS code.

_get_vector_plots(ins: str | int) → wolfhece.PyVertexvectors.vector

Get the vector for a given INS code.

property store_path

Get the store path for the QDF hydrology data.

_prepare_image_location()

Set the default size for the images.

set_images_as_legend(plot_or_table: Literal['plot', 'table'] = 'plot', which: list = None)

Set all images in the collection as legend images.

hide_all_images()

Hide all images in the collection.

check_plot()

Generic function responding to check operation from mapviewer

find_nearest_centroid(x: float, y: float, bounds: tuple[float, float, float, float])

Pick the municipality at the given coordinates.

Parameters:

• x – The x coordinate.

• y – The y coordinate.

Returns:

The name of the municipality or an empty string if not found.

pick_municipality(x: float, y: float, bounds: tuple[float, float, float, float])

Activate plot for the nearest municipality to the given coordinates.

find_centroids_in_polygon(polygon: wolfhece.PyVertexvectors.Polygon) → list[tuple[wolfhece.PyVertexvectors.vector, str]]

Find all centroids in a given polygon.

Parameters:

polygon – A shapely Polygon object defining the area to search.

find_centroid_in_bounds(bounds: tuple[float, float, float, float]) → list[tuple[wolfhece.PyVertexvectors.vector, str]]

Find all centroids within the given bounds.

Parameters:

bounds – A tuple of (minx, miny, maxx, maxy) defining the bounding box.

property show_plot: bool

Check if the plot is shown.

property show_table: bool

Check if the table is shown.

scale_images(factor: float = 1.0)

Scale the images in the collection by a given factor.

Parameters:

factor – The scaling factor to apply to the images.

plot(sx=None, sy=None, xmin=None, ymin=None, xmax=None, ymax=None, size=None)

Plot the QDF hydrology data on the map.