trajplot#

class trajplot.Figure(var: str, root_folder, scenario: str = 'RCP 8.5', data_crs=None, coords='all', coords_crs=None, rolling_days: int = 1, period_years: int = 10, annuality='calendar', plot_type: str = None, repres: (None, <class 'str'>) = 'area', cumul: bool = False, relative: bool = False, language: str = 'fr', color='scale', plotsize='wide', filename: str = '', credit: (None, <class 'str'>) = 'auto', showlegend: bool = True, shadow: (None, <class 'str'>) = None, verbose: bool = False)[source]#

Bases: object

Main data available as Figure attributes#

Main variables	Description
`self.model_names_list`	List of model names. For example in EXPLORE2 it corresponds to the identifier of the climatic experiment: ‘Model1’, ‘Model2’, ‘Model3’…, ‘Model17’.
`self.original_data`	List of the pandas.Dataframes retrieved from the NetCDF data, for each model (climatic experiments). NetCDF data are converted into time series by considering the spatial average over the `coords` argument (mask).
`self.relative_ref`	Equivalent to `self.original_data`, but contains the time series used as reference (historic) to compute relative values (if user-chosen).
`self.rea_data`	Equivalent to `self.original_data`, but contains the reanalysis time series, which are added to the plots in order to provide a historic reference.
`self.all_res`	List of pd.Dataframes for each period (according to `period_years` argument), containing timeseries averaged over a year (365 days) for each model (climatic experiments) (one column per model). The year starts on the month defined by `annuality` argument.
`self.graph_result`	Results formated for the plots. Either in the form of a list of pd.Dataframes, one for each period, each pd.Dataframe containing the aggregated result (min, mean, sum…) from `self.all_res` [in case of `plot_type = 'temporality'`]. Or in the form of a single pd.Dataframe containing the aggregated values for each day [in case of `plot_type` is a metric].

Examples#

from watertrajectories_pytools.src.graphics import trajplot as tjp

mask = r"D:- Postdoc- Travaux- Veille- Donnees- Territoire Annecy\masque_zone_etude.shp"

F = tjp.Figure(
    var = 'T',
    root_folder = r"E:\Inputs\Climat",
    scenario = 'rcp8.5',
    coords = mask,
    rolling_days = 30,
    period_years = 10,
    annuality = 3,
    filename = 'Annecy',
    )

for m in mask_dict:
    for scenario in ['SIM2', 'rcp8.5']:
        figweb, _, _ = tjp.temporality(
            var = 'PRETOT', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], filename = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 3, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'DRAIN', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], filename = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 10, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'SWI', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], filename = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 3, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)
        figweb, _, _ = tjp.temporality(
            var = 'T', scenario = scenario, root_folder = r"E:\Inputs\Climat",
            coords = mask_dict[m], filename = m,
            period_years = 10, rolling_days = 30, cumul = False, 
            plot_type = 'temporality', annuality = 10, relative = False, 
            language = 'fr', plotsize = 'wide', verbose = True)

Parameters#

varstr

‘PRETOT’ | ‘PRENEI’ | ‘ETP’ | ‘EVAPC’ | ‘RUNOFFC’ | ‘DRAINC’ | ‘T’ | ‘SWI’ | …

scenariostr

‘SIM2’ | ‘historical’ | ‘RCP 4.5’ | ‘RCP 8.5’

root_folderstr, path

Path to the folder containing climatic data.: r”D:- Postdoc- Travaux- Veille- Donnees8- Meteo” (on my PC) r”E:InputsClimat” (on the external harddrive)

data_crsTYPE, optional

DESCRIPTION. The default is None.

coordsTYPE, optional

DESCRIPTION. The default is ‘all’.

coords_crsTYPE, optional

DESCRIPTION. The default is None.

languageTYPE, optional

DESCRIPTION. The default is ‘fr’.

plotsizeTYPE, optional

DESCRIPTION. The default is ‘wide’.

rolling_daysTYPE, optional

DESCRIPTION. The default is 1.

period_yearsTYPE, optional

DESCRIPTION. The default is 10.

cumulTYPE, optional

DESCRIPTION. The default is False.

plot_typeTYPE

DESCRIPTION.

repres{None, “area” or “bar”}, optional, default None

Only used when plot_type is a metric. Defines the type of graphical representation of the metric plot.

"area": curves representing rolling averages

"bar": rectangles representing period averages

filenamestr or pathlib.Path

Prefix and extension to add in the file name. Especially usefull to indicate the name of the site.

creditstr, optional, default ‘auto’

To display the acknowledgement for data and conception. If 'auto', the standard info about data source and conception package (geop4th) will be displayed. To remove all mention of credits, pass credit=''.

color‘scale’, ‘discrete’, <colormap> (str) or list of colors, optional, default ‘scale’

Colors for the plots (for now, only for temporality plots)

relativebool

Whether the values should be computed as absolute or relatively to the reference period.

showlegendbool, optional, default True

Whether to display the legend or not.

shadow{None, ‘last’, ‘first’, ‘firstlast’, ‘lastfirst’, ‘all’}, optional, default = None

Whether to display dialy values in grey shadows, and for which period.

annualityint or str

Type of year: ‘calendar’ | ‘meteorological’ | ‘meteo’ | ‘hydrological’ | ‘hydro’ 1 | 9 | 10

verbose: bool

Whether or not to display memory diagnotics.

agg_labels = {'decrease': ['decrease', 'diminution'], 'increase': ['increase', 'augmentation'], 'max': ['maximum', 'maximum'], 'mean': ['mean', 'moyenne'], 'min': ['minimum', 'minimum'], 'range': ['range', 'amplitude'], 'sum': ['sum', 'somme']}#

layout(*, plotsize=None, filename=None, credit=None, color=None, language=None, showlegend=None, shadow=None, repres=None, cumul=None)[source]#

load()[source]#

Results#

This method also update the instance attributes all_res and graph_result, which respectively store the whole results and the final results used for the plot.

Warning#

This method can lead to memory size issues. It seems to appear when the garbage collector is not doing its job fast enough in the xarray variable data from C:ProgramDataMiniconda3envscwatenvLibsite-packagesxarraycodingvariables.py.

To solve this, you might just need to open this folder on Windows.

Examples (pipeline)#

>>> import os
>>> mask_folder = r"D:\2- Postdoc\2- Travaux\1- Veille\4- Donnees\15- Territoire Pays Basque"
>>> mask_dict = dict()
>>> mask_dict['cotier'] = os.path.join(mask_folder, r"Sage-cotier-basque.shp")
>>> mask_dict['CAPB'] = os.path.join(mask_folder, r"zone_etude_fusion.shp")
>>> mask_dict['Nive-Nivelle'] = os.path.join(mask_folder, r"Nive-Nivelle.shp")
>>> for var in ['PRETOT', 'DRAIN', 'SWI', 'T']:
>>>     for scenario in ['SIM2', 'rcp8.5']:
>>>         for m in mask_dict:
>>>             F = tjp.Figure(
>>>                 var = var,
>>>                 root_folder = r"E:\Inputs\Climat",
>>>                 scenario = scenario,
>>>                 coords = mask_dict[m],
>>>                 filename = m,
>>>                 rolling_days = 30,
>>>                 period_years = 10,
>>>                 )
>>>             F.plot(plot_type = 'annual_sum')
>>>             F.plot(plot_type = 'annual_sum', period_years = 30)
>>>             F.plot(plot_type = 'annual_sum', period_years = 1)
>>>             F.plot(plot_type = 'annual_scatter', agg_rule = 'sum')
>>>             F.plot(plot_type = '15/11', period_years = 10)
>>>             F.plot(plot_type = '15/05', period_years = 10)
>>>             if var == 'DRAIN':
>>>                 F.plot(plot_type = '> 1.5')

metric_labels = {'annual': ['annual {}', '{} annuelle'], 'date': ['date of {}', 'date de {}']}#

metric_list = ['annual', 'date']#

month_abbrev = {1: 'JAN', 2: 'FEB', 3: 'MAR', 4: 'APR', 5: 'MAY', 6: 'JUN', 7: 'JUL', 8: 'AUG', 9: 'SEP', 10: 'OCT', 11: 'NOV', 12: 'DEC'}#

plot(*, rolling_days=None, period_years=None, annuality=None, plot_type=None, plotsize=None, filename=None, credit=None, color=None, language=None, showlegend=None, shadow=None, repres=None, cumul=None)[source]#

plot_typestr: ‘temporality’ | ‘annual_sum’

None.

>>> F = tjp.Figure(
>>>     var = 'DRAINC',
>>>     root_folder = r"E:\Inputs\Climat",
>>>     scenario = 'rcp8.5',
>>>     coords = r"C:

ile.shp”,

>>>     rolling_days = 30,
>>>     annuality = 10,
>>>     filename = 'myCatchment',
>>>     )

>>> F.plot(plot_type = '> 1.5', period_years = 10)
>>> F.plot(plot_type = 'annual_sum')
>>> F.plot(plot_type = 'annual_sum', period_years = 30)
>>> F.plot(plot_type = 'annual_scatter', period_years = 1)
>>> F.plot(plot_type = '15/11', period_years = 10)

static timeseries(*, data_type, var, scenario, season, domain='Pays-Basque', data_crs=None, coords='all', coords_crs=None, language='fr', plotsize='wide', rolling_window=1, plot_type='2series')[source]#

Examples#

import climatic_plot as tjp

tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024 SWIAV”,: scenario = ‘historical’, season = ‘JJA’, domain = ‘Pays Basque’, rolling_window = 10, plot_type = ‘2series’)

# —- Original and rolled series for SWIAV for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for scenario in [‘historical’, ‘rcp45’, ‘rcp85’]:

for domain in [‘Pays Basque’, ‘Annecy’]:

tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,
var = ‘SWIAV’, scenario = scenario, season = season, domain = domain, rolling_window = 10, plot_type = ‘2series’)

# —- Same for SSWI for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for scenario in [‘rcp45’, ‘rcp85’]:

for domain in [‘Pays Basque’, ‘Annecy’]:

tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,
var = ‘SSWI’, scenario = scenario, season = season, domain = domain, rolling_window = 10, plot_type = ‘2series’)

# —- All different colors with SIM2 tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,

var = ‘SWIAV’, scenario = ‘historical’, season = ‘JJA’, domain = ‘Pays Basque’, rolling_window = 10, plot_type = ‘all with sim2’)

# —- Narratifs for season in [‘JJA’, ‘SON’, ‘DJF’, ‘MAM’]:

for domain in [‘Pays Basque’, ‘Annecy’]:

tjp.timeseries(data_type = “Indicateurs DRIAS-Eau 2024”,
var = ‘SWIAV’, scenario = ‘rcp85’, season = season, domain = domain, rolling_window = 10, plot_type = ‘narratifs’)

Parameters#

: TYPE
DESCRIPTION.

root_folderTYPE

DESCRIPTION.

scenariostr

‘historical’ | ‘rcp45’ | ‘rcp85’

seasonstr

‘DJF’ | ‘MAM’ | ‘JJA’ | ‘SON’ | ‘NDJFMA’ | ‘MAMJJASO’ | ‘JJASO’ | ‘SONDJFM’

data_typeTYPE

DESCRIPTION.

data_crsTYPE, optional

DESCRIPTION. The default is None.

coordsTYPE, optional

DESCRIPTION. The default is ‘all’.

coords_crsTYPE, optional

DESCRIPTION. The default is None.

languageTYPE, optional

DESCRIPTION. The default is ‘fr’.

plotsizeTYPE, optional

DESCRIPTION. The default is ‘wide’.

rolling_windowTYPE, optional

DESCRIPTION. The default is 10.

plot_typeTYPE, optional

DESCRIPTION. The default is ‘2series’.

: TYPE: DESCRIPTION.

Returns#

None.

update(*, var=None, root_folder=None, scenario=None, data_crs=None, coords=None, coords_crs=None, relative=None, rolling_days=None, period_years=None, annuality=None, plot_type=None, repres=None, cumul=None, language=None, plotsize=None, color=None, filename=None, credit=None, showlegend=None, shadow=None, verbose=None)[source]#

classmethod update_verbose(verbose: bool)[source]#

verbose: bool = False#

trajplot#

Main data available as Figure attributes#

Examples#

Parameters#

Results#

Warning#

Examples (pipeline)#

Examples#

Parameters#

Returns#

This Page