pyClimat package

Submodules

pyClimat.analysis module

Created on Thu Jul 29 18:49:19 2021

@author: dboateng Contains analysis routine required for calculation, extracting variables and domain, masking out areas and certian statistics.

pyClimat.analysis.EOF_analysis(data, maxlon, minlon, maxlat, minlat, return_variance=False, return_pcs=False, season=None, standardized=None, apply_coslat_weights=None, neofs=None, pcscaling=None, neigs=None, npcs=None, lev=None)[source]

# the projectField function can be used to generate corresponding set of pseudo-PCs using different data field

dataTYPE: datarray: DESCRIPTION. Dataset required for EOF analysis (eg. slp or geopoth500)
maxlonTYPE: float: DESCRIPTION.: Maximum longitude
minlonTYPE: float: DESCRIPTION: Minimum longitude
maxlatTYPE: float: DESCRIPTION:Maximum latitude
minlatTYPE: float: DESCRIPTION: Minimum latitude
return_varianceTYPE: Boolean, optional: DESCRIPTION. The default is False. If estimated varainces of the eofs are required as ouput
return_pcsTYPE: Boolean, optional: DESCRIPTION. The default is False. If the extracted pca series are required as ouput
seasonTYPE:STR, optional: DESCRIPTION. The default is None. Name of the season eg. DFJ, JJA
standardizedTYPE: Boolean, optional: DESCRIPTION. The default is None. True to standardized anomalies before EOF analysis
apply_coslat_weightsTYPE: Boolean, optional: DESCRIPTION. The default is None. True to apply coslat area weights before the EOF analysis
neofsTYPE: Float, optional: DESCRIPTION. The default is None. The no. of PCA to perform on the dataset
pcscalingTYPE: Int, optional: DESCRIPTION. The default is None. 0 : Unsclaed PCS, 1: Scaled to Unit variance, 2: PCs are multiplied by the square-root of their eigen values
neigsTYPE: Int, optional: DESCRIPTION. The default is None. the no. of eigenvalues to return fraction variance
npcsTYPE:Int, optional: DESCRIPTION. The default is None. The no. of pcs retrieve
levTYPE: float, optional: DESCRIPTION. The default is None. Vertical level if the dataset is on hybrid levels (eg. 500 for geopoth)

TYPE: DESCRIPTION. eofs: covariance matrix between the npcs time series and eofs input time series pcs: Principal Component time series var_frac: variance fraction of the estimated eigen values

pyClimat.analysis.compute_lterm_diff(data_control, data_main, time='annual', month=None, season=None, season_calendar=None)[source]

data_controlTYPE: datarray: DESCRIPTION. Reference or control data
data_mainTYPE: dataarray: DESCRIPTION. : Main module run data
timeTYPE: STR, optional: DESCRIPTION. The default is “annual”. But can be changed to season or month
monthTYPE:INT, optional: DESCRIPTION. The default is None. Define the specific month number to be computed or all will be eatimated
seasonTYPE: STR, optional: DESCRIPTION. The default is None. Define the specific season (eg. DJF, JJA, MAM, SON) number to be computed or all will be eatimated
season_calendarTYPE:STR, optional: DESCRIPTION. The default is None. Or Standard to consider the number of days in a month

data_ltmean_diffTYPE: datarray: DESCRIPTION. Long-tem difference

pyClimat.analysis.compute_lterm_mean(data, time='annual', month=None, season=None, season_calendar=None)[source]

dataTYPE: datarray: DESCRIPTION. The var_data extracted from dataset
timeTYPE: str, optional: DESCRIPTION. The default is “annual”. or season, month can be used for long-term estimates
monthTYPE, optional: DESCRIPTION. The default is None.
seasonTYPE, optional: DESCRIPTION. The default is None.
season_calendarTYPE, optional: DESCRIPTION. The default is None. Use standard if you want to consider the days of the month into consideration

data_ltmeanTYPE: datarray: DESCRIPTION. Long-term means

pyClimat.analysis.correlation(dataA, dataB, max_pvalue=0.1, use_spearmanr=False, use_pearsonr=False, return_pvalue=False, maxlon=None, minlon=None, maxlat=None, minlat=None)[source]

dataATYPE: Dataarray (3D): DESCRIPTION. Comparison data 1
dataBTYPE: Dataaray (3D): DESCRIPTION. Comparison data 2
max_pvalueTYPE: Float, optional: DESCRIPTION. The default is 0.1. The confidence interval for correlation estimation eg. 0.05 for 95%
use_spearmanrTYPE: Boolean, optional: DESCRIPTION. The default is False. True to use spearman correlation
use_pearsonrTYPE: Boolean, optional: DESCRIPTION. The default is False. True to use pearson correlation
return_pvalueTYPE: Boolean, optional: DESCRIPTION. The default is False. True to retrieve pvalue as an ouput variable
maxlonTYPE: float: DESCRIPTION.: Maximum longitude
minlonTYPE: float: DESCRIPTION: Minimum longitude
maxlatTYPE: float: DESCRIPTION:Maximum latitude
minlatTYPE: float: DESCRIPTION: Minimum latitude

ValueError: DESCRIPTION. If the required stats module for correlation analysis is not defined

stats_resultTYPE: datarray: DESCRIPTION. Contians correlation map distribution and corresponding pvalues

pyClimat.analysis.extract_profile(data, maxlon, minlon, maxlat, minlat, dim, to_pandas=True, sea_land_mask=False, minelev=None, maxelev=None, Dataset=None)[source]

dataTYPE: dataarray: DESCRIPTION: Data to extract transect from base on coordinates, elevation or land sea masks
maxlonTYPE: float: DESCRIPTION.: Maximum longitude
minlonTYPE: float: DESCRIPTION: Minimum longitude
maxlatTYPE: float: DESCRIPTION:Maximum latitude
minlatTYPE: float: DESCRIPTION: Minimum latitude
sea_land_maskTYPE: str, optional: DESCRIPTION. The default is None. Yes, means that the land mask will be selected and No means the sea nask points will be selected
minelevTYPE, optional: DESCRIPTION. The default is None. To select data points base on the minimum elevation value
maxelevTYPE. float, optional: DESCRIPTION. The default is None. To select data points base on the maximum elevation value
DatasetTYPE: float, optional: DESCRIPTION. The default is None. Dataset containing geosp and slm for masking out elevation condition and continental values
dimTYPE: str: DESCRIPTION. lat ot lon depending on the axis of the profile
to_pandasTYPE:str, optional (recommended for plotting): DESCRIPTION. The default is None. yes if you want the data to be stored in DataFrame or Pandas Series

data_profTYPE: datarray or DataFrame or Pandas Series: DESCRIPTION. Data extracted along a profile (lat or lon)

pyClimat.analysis.extract_transect(data, maxlon, minlon, maxlat, minlat, sea_land_mask=False, minelev=None, maxelev=None, Dataset=None)[source]

This function extract grid points base on coordinate extents or land sea masks or max, min elevations: it can be used to estimate the statistics of a selected domain like the Alps or Andes!

dataTYPE: dataarray: DESCRIPTION: Data to extract transect from base on coordinates, elevation or land sea masks
maxlonTYPE: float: DESCRIPTION.: Maximum longitude
minlonTYPE: float: DESCRIPTION: Minimum longitude
maxlatTYPE: float: DESCRIPTION:Maximum latitude
minlatTYPE: float: DESCRIPTION: Minimum latitude
sea_land_maskTYPE: str, optional: DESCRIPTION. The default is None. Yes, means that the land mask will be selected and No means the sea nask points will be selected
minelevTYPE, optional: DESCRIPTION. The default is None. To select data points base on the minimum elevation value
maxelevTYPE. float, optional: DESCRIPTION. The default is None. To select data points base on the maximum elevation value
DatasetTYPE: float, optional: DESCRIPTION. The default is None. Dataset containing geosp and slm for masking out elevation condition and continental values

data_extractTYPE: DESCRIPTION.

pyClimat.analysis.extract_var(Dataset, varname, units=None, Dataset_wiso=None, other_data=None, lev_units=None, lev=None)[source]

This function extracts some defined variables from a netCDF file. Moreover, if the variable require calculation or unit conversion, the user speficication can be pass to such task. For example, echam out put the differrent component of precipitation (convective, large scale, etc). Therefore extraction of total precipitation would require the calculation of the total precipitation. Example of the defined variables:

“temp2” – near surface temperature “prec” – total precipitation “d18op” – O^18 isotopic composition in precipitation “d18ov” – O^18 isotopic composition in vapour “relhum” – relative humidity “elev” – topography or elevation “slm” – mean sea level pressure “evap” – evaporation “u, v, omega” – zonal, meridoinal, and vertical velocity

and others

data = xr.open_dataset(path_to_data) temp = extract_var(data, varname= “temp2”, unit= “°C”,) —> extract the t2m variable and convert it from K to °C

DatasetTYPE: Dataset: DESCRIPTION. Processed output containing all variables or certain ones
varnameTYPE: STR: DESCRIPTION: Name of variable (eg. temp2:Tempeature, prec:Total precipitation, d18op: d18o in precipitation)
unitsTYPE, optional: DESCRIPTION. The default is None. Units of the variable for eg. °C for temperature or mm/month for precipitation
Dataset_wisoTYPE, optional: DESCRIPTION. The default is None. Wiso Dataset is required for calcullating d18op and d18ov
other_data: TYPE: datarray, optional: DESCRIPTION: Additional data required for calculating units eg. Pa/s–> m/s require temperature data on pressure levels (in K)
lev_units: TYPE: str, optional: DESCRIPTION: Units of vertical levels eg. hPa
lev: TYPE: int, optional: DESCRIPTION: The vertical height required for the variable, eg. 500 hPa for winds

var_dataTYPE: datarray: DESCRIPTION: returns the variable for etracted or calculated from the Dataset (notmally monthly long-term climatologies)

pyClimat.analysis.extract_vertical_section(data, maxlon, minlon, maxlat, minlat, dim, sea_land_mask=False, minelev=None, maxelev=None, Dataset=None, season=None, month=None)[source]

pyClimat.analysis.linregression(data_x, data_y, season=None, month=None, return_yhat=True)[source]

data_xTYPE: datarray: DESCRIPTION. The x-axis data for fitting
data_yTYPE: datarray: DESCRIPTION. The y-axis data for fitting
seasonTYPE, optional (of If specific season the data is required for fitting): DESCRIPTION. The default is None. Date must be in seasonal coordinates for time
monthTYPE, optional (of If specific month the data is required for fitting): DESCRIPTION. The default is None. Date must be in monthly coordinates for time
return_yhatTYPE, optional or if DataFrame containing all the fitting data and predictions are required: DESCRIPTION. The default is None.

TYPE: Scipy.stats output or plus DataFrame: DESCRIPTION.

pyClimat.analysis.student_t_test_btn_datasets(dataA, dataB, max_pvalue=0.1, return_pvalue=False, maxlon=None, minlon=None, maxlat=None, minlat=None)[source]

dataATYPE: Dataarray (3D)

DESCRIPTION. Comparison data 1

dataBTYPE: Dataaray (3D): DESCRIPTION. Comparison data 2
max_pvalueTYPE: Float, optional: DESCRIPTION. The default is 0.1. The confidence interval for correlation estimation eg. 0.05 for 95%
return_pvalueTYPE: Boolean, optional: DESCRIPTION. The default is False. True to retrieve pvalue as an ouput variable
maxlonTYPE: float: DESCRIPTION.: Maximum longitude
minlonTYPE: float: DESCRIPTION: Minimum longitude
maxlatTYPE: float: DESCRIPTION:Maximum latitude
minlatTYPE: float: DESCRIPTION: Minimum latitude

stats_resultTYPE: DESCRIPTION.

pyClimat.data module

Created on Wed Jul 28 16:55:45 2021

@author: Daniel Boateng

Reading data routine for Climat (required user declarations of paths to datasets eg. Reanalysis: , ECHAM, DWD stations and Gtopo files etc)

Note: All User specifications must be declared in the control script which will import all the functions defined here

pyClimat.data.read_ECHAM_input(main_path, exp_name, filename, read_var=False, varname=None)[source]

main_pathTYPE: str: DESCRIPTION. Path containing all module outputs
exp_nameTYPE:str: DESCRIPTION. Name of the experiment
varnameTYPE: str: DESCRIPTION. Name of the input file (jan_surf file)

dataTYPE: dataset: DESCRIPTION.

pyClimat.data.read_ECHAM_processed(main_path, exp_name, years='1003_1017', period='1m', add_name=None, read_wiso=True)[source]

Reads output processed from ECHAM

main_pathTYPE: STR: DESCRIPTION. directory to the main path for all module output (eg. esd02–>ESD, or local path)
exp_nameTYPESTR: DESCRIPTION. Name of experiment output (eg. a003_hpc-bw_e5w2.3_t159_PI_Alps_east_300_t159l31.6h)
yearsTYPE, optional: DESCRIPTION. The default is “1003_1017”. or range of year you have processed
periodTYPE, optional: DESCRIPTION. The default is “1m”. or 1d, 1y if implemted here!
add_nameTYPE, optional: DESCRIPTION. The default is None. or eg. _msl (for particular variable)

dataTYPE: Dataset: DESCRIPTION. Dataset of echam ouput will some or all variables
data_wisoTYPE: Dataset: DESCRIPTION. Dataset of echam wiso ouput will some or all variables

pyClimat.data.read_ERA_processed(path, varname)[source]

pathTYPE: STR: DESCRIPTION. path of the ERA dataset
varnameTYPE:STR: DESCRIPTION. Variable name for ERA (eg. t2m for temperature, tp:precipitation)

dataTYPE: datarray: DESCRIPTION.

pyClimat.data.read_GNIP_data(path, filename)[source]

pathTYPE: str: DESCRIPTION. The directory holding all the data
filenameTYPE: str: DESCRIPTION. The name of the file

dfTYPE: DataFrame: DESCRIPTION. Data containing lat, lon and d18op

pyClimat.data.read_Gtopo(path, tile_name, extract_var=None)[source]

pathTYPE: str: DESCRIPTION. directrory to all the tile files (or path to tiles)
tile_nameTYPE: str: DESCRIPTION. Which tile to use for modification (check the image in the original files folder)
extract_varTYPE, optionalor yes: DESCRIPTION. The default is None or To extract only the values to datarray

TYPE: Dataset or dataarray: DESCRIPTION. It reads a particular tile file

pyClimat.gtopo module

Created on Mon Aug 2 19:27:52 2021

@author: dboateng

pyClimat.gtopo.extract_alps(path, tile_name, minelev, extract_var=None, east_alps=None, west_alps=None, west_buffer=None, central_buffer=None, east_buffer=None)[source]

pathTYPE: str: DESCRIPTION. The directrory to all the tile files
tile_nameTYPE: str: DESCRIPTION. : hich tile to use for modification (check the image in the original files folder)
minelevTYPE: float: DESCRIPTION. Minimum elevation to extaract the Alps
extract_varTYPE, optional or yes: DESCRIPTION. The default is None or To extract only the values to datarray
east_alpsTYPE, optional or yes: DESCRIPTION. The default is None. to mask out the eastern part
west_alpsTYPE, optional or yes: DESCRIPTION. The default is None. Or mask ou the west part of the Alps
west_bufferTYPE, optional: DESCRIPTION. The default is None. Or yes to apply buffer gradient
central_bufferTYPE, optional: DESCRIPTION. The default is None. or yes to apply buffer gradient at the central Alps
east_bufferTYPE, optional: DESCRIPTION. The default is None. or to apply buffer gradient at the eastern side after modification

TYPE: DESCRIPTION.

pyClimat.gtopo.modify_Gtopo(factor, part_of_alps, path_to_store, path, tile_name, minelev, extract_var=None, east_alps=None, west_alps=None, west_buffer=None, central_buffer=None, east_buffer=None)[source]

factorTYPE: float
DESCRIPTION. The factor to multiply for changing the original topography

part_of_alpsTYPE: str
DESCRIPTION. Eg. full or whole, East_alps, West_alps

path_to_storeTYPE:str
DESCRIPTION. The directory to save the output

pathTYPE: str
DESCRIPTION. The directory to load the original paths

tile_nameTYPE: str

DESCRIPTION. : hich tile to use for modification (check the image in the original files folder)

minelevTYPE: float: DESCRIPTION. Minimum elevation to extaract the Alps
extract_varTYPE, optional or yes: DESCRIPTION. The default is None or To extract only the values to datarray
east_alpsTYPE, optional or yes: DESCRIPTION. The default is None. to mask out the eastern part
west_alpsTYPE, optional or yes: DESCRIPTION. The default is None. Or mask ou the west part of the Alps
west_bufferTYPE, optional: DESCRIPTION. The default is None. Or yes to apply buffer gradient
central_bufferTYPE, optional: DESCRIPTION. The default is None. or yes to apply buffer gradient at the central Alps
east_bufferTYPE, optional: DESCRIPTION. The default is None. or to apply buffer gradient at the eastern side after modification

DatasetTYPE: Dataset: DESCRIPTION. Returns Dataset with similar headers like the original file from Gtopo

pyClimat.gtopo.visualise_topo(data, path_to_store, name_of_plot)[source]

The function to visualise the modified topo file

dataTYPE: Dataset: DESCRIPTION. The Dataset containing the modified topography
path_to_storeTYPE: str: DESCRIPTION. The Dataset to store the figure
name_of_plotTYPE:str: DESCRIPTION. The name of the file

None.

pyClimat.plot_utils module

Created on Thu Jul 29 18:50:11 2021

@author: dboateng This module contains all the utilities used in the Climat_plots

class pyClimat.plot_utils.FixedPointNormalized(vmin=None, vmax=None, sealevel=0, color_val=0.21875, clip=False)[source]: Bases: Normalize

class pyClimat.plot_utils.MidpointNormalize(vmin=None, vmax=None, midpoint=None, clip=False)[source]

Bases: Normalize

At the moment its a bug to use divergence colormap and set the colorbar range midpoint to zero if both vmax and vmin has different magnitude. This might be possible in future development in matplotlib through colors.offsetNorm(). This class was original developed by Joe Kingto and modified by Daniel Boateng. It sets the divergence color bar to a scale of 0-1 by dividing the midpoint to 0.5 Use this class at your own risk since its non-standard practice for quantitative data.

pyClimat.plot_utils.apply_style(fontsize=20, style=None, linewidth=2)[source]

fontsizeTYPE, optional: DESCRIPTION. The default is 10.
styleTYPE, optional: DESCRIPTION. The default is “bmh”. [“seaborn”, “fivethirtyeight”,]

None.

pyClimat.plot_utils.plot_background(p, domain=None, use_AlbersEqualArea=None, ax=None, left_labels=True, bottom_labels=True)[source]

This funtion defines the plotting domain and also specifies the background. It requires the plot handle from xarray.plot.imshow and other optional arguments Parameters ————-

p: TYPE: plot handle DESCRIPTION: the plot handle after plotting with xarray.plot.imshow

domian = TYPE:str DESCRIPTION: defines the domain size, eg. “Europe”, “Asia”, “Africa”

“South America”, “Alaska”, “Tibet Plateau” or “Himalaya”, “Eurosia”, “New Zealand”, default: global

pyClimat.plots module

Created on Thu Jul 29 18:49:46 2021

@author: dboateng This module contains all the functions required for generating annual, seasonal and monthly plots. It also contains all the analysis plots like isotopic profile plots, lapse rate scatter plots

pyClimat.plots.plot_annual_mean(variable, data_alt, cmap, units, ax=None, vmax=None, vmin=None, levels=None, domain=None, center=True, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, data_v10=None, data_u10=None, GNIP_data=None, left_labels=True, bottom_labels=True, add_colorbar=True, plot_stats=False, compare_data1=None, compare_data2=None, max_pvalue=None, hatches=None, fig=None, cbar_pos=None, use_colorbar_default=False)[source]

variableTYPE: str: DESCRIPTION. The variable to be plotted. Note, it will be display as colorbar name
data_altTYPE: datarray: DESCRIPTION. The processed data to be visualized
cmapTYPE: plt.cmap: DESCRIPTION. Color map handle from matplotlib
unitsTYPE: str: DESCRIPTION. The unit of the dataset to be visualized
axTYPE: GeoAxis using Matplotlib, optional or defined in control script if subplots are required for different variables: DESCRIPTION. The default is None. Figure handle to contain plot
vmaxTYPE: float, optional: DESCRIPTION. The default is None. maximum value limit of the variable to be ploted
vminTYPE: float, optional: DESCRIPTION. The default is None. minimum value limit of the variable to be ploted
levelsTYPE: float, optional: DESCRIPTION. The default is None. the number of levels for colorbar scale
domainTYPE: str, optional: DESCRIPTION. The default is None. eg. Africa, Asia, Europe
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: str, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data

data_v10 = datarray (required for ploting winds) data_u10 = datarray (required for ploting winds)

GNIP_data = DataFrame with lon, lat and d18Op for plotting a scatter circles with filled colormap left_labels: TYPE: Boolean, Default is True

DESCRIPTION. To add lat coordinates on the left of the plots, optioanl

bottom_labels: TYPE: Boolean, Default is True: DESCRIPTION. To add lon coordinates on the bottom of the plots, optioanl
add_colorbar: TYPE: Boolean, Default is True: DESCRIPTION. To add colormap to the plot
plot_stats: TYPE: Boolean, Default: DESCRIPTION: plot the statiscal difference between two varied datasets
compare_data1: TYPE: datarray: DESCRIPTION: dataset 1 if plot_stats == true
compare_data2: TYPE: datarray: DESCRIPTION: dataset 2 if plot_stats == true

center: TYPE: Boolean, True to apply norm for centering zero

max_pvalue: TYPE: float, optional: DESCRIPTION: pvalue for the student t-test significance testing
hatches: TYPE: str, optional:: DESCRIPTION: hatches from matplotlib

None.

pyClimat.plots.plot_echam_topo(variable, data, cmap, units, ax=None, vmax=None, vmin=None, levels=None, domain=None, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, cbar=None, cbar_orientation=None, cbar_position=None, fig=None, left_labels=True, bottom_labels=True)[source]

variableTYPE: str: DESCRIPTION. The variable to be plotted. Note, it will be display as colorbar name
dataTYPE: datarray: DESCRIPTION. The processed data to be visualized (Eg. topo input file or can be retrieved from model output)
cmapTYPE: plt.cmap: DESCRIPTION. Color map handle from matplotlib
unitsTYPE: str: DESCRIPTION. The unit of the dataset to be visualized
axTYPE: matplotlib ax handle, optional: DESCRIPTION. The default is None.
figTYPE: Matplotlib figure handle, optional: DESCRIPTION. The default is None.
vmaxTYPE: float, optional: DESCRIPTION. The default is None. maximum value limit of the variable to be ploted
vminTYPE: float, optional: DESCRIPTION. The default is None. minimum value limit of the variable to be ploted
levelsTYPE: float, optional: DESCRIPTION. The default is None. the number of levels for colorbar scale
domainTYPE: str, optional: DESCRIPTION. The default is None. eg. Africa, Asia, Europe
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: str, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data
cbarTYPE: Boolean, optional: DESCRIPTION. The default is None. True is the plot require colobar axis
cbar_orientationTYPE: , optional: DESCRIPTION.
cbar_positionTYPE: list, optional: DESCRIPTION. The default is None. The default is None. the list defing the position of the color bar eg. [0.90, 0.30, 0.02, 0.40]
left labels, right_labels, bottom_labels,TYPE: Bol, optional: DESCRIPTION. To set the left, right, and bottom axis label to None

None.

pyClimat.plots.plot_eofsAsCovariance(variable, data, mode_var=None, cmap=None, levels=None, units=None, ax=None, domain=None, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, cbar=None, cbar_orientation=None, cbar_position=None, fig=None, use_AlberEqualArea=None, vmax=None, vmin=None, left_labels=True, bottom_labels=True)[source]

variableTYPE: str: DESCRIPTION. The variable to be plotted. Note, it will be display as colorbar name
dataTYPE: datarray: DESCRIPTION. The processed data to be visualized (Eg. topo input file or can be retrieved from model output)
cmapTYPE: plt.cmap: DESCRIPTION. Color map handle from matplotlib
unitsTYPE: str: DESCRIPTION. The unit of the dataset to be visualized
axTYPE: matplotlib ax handle, optional: DESCRIPTION. The default is None.
figTYPE: Matplotlib figure handle, optional: DESCRIPTION. The default is None.
vmaxTYPE: float, optional: DESCRIPTION. The default is None. maximum value limit of the variable to be ploted
vminTYPE: float, optional: DESCRIPTION. The default is None. minimum value limit of the variable to be ploted
levelsTYPE: float, optional: DESCRIPTION. The default is None. the number of levels for colorbar scale
domainTYPE: str, optional: DESCRIPTION. The default is None. eg. Africa, Asia, Europe
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: str, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data
cbarTYPE: Boolean, optional: DESCRIPTION. The default is None. True is the plot require colobar axis
cbar_orientationTYPE: , optional: DESCRIPTION.
cbar_positionTYPE: list, optional: DESCRIPTION. The default is None. The default is None. the list defing the position of the color bar eg. [0.90, 0.30, 0.02, 0.40]
mode_varTYPE: float, optional: DESCRIPTION. The default is None. The explained variance estimated from the EOF analysis
use_AlberEqualAreaTYPE: Boolean, optional: DESCRIPTION. The default is None. To use ccrs.AlberEqualArea() as geoaxis projection
left labels, right_labels, bottom_labels,TYPE: Bol, optional: DESCRIPTION. To set the left, right, and bottom axis label to None

None.

pyClimat.plots.plot_iso_profiles(df_iso, df_geosp, dim, iso_color, iso_label, ax=None, season=None, month=None, xmax=None, xmin=None, ymax=None, ymin=None, ax_legend=None, isomax=None, isomin=None, output_name=None, output_format=None, title=None, path_to_store=None, left_labels=True, bottom_labels=True, right_labels=True, shade_color=None, shade_alpha=None, edgecolor='dimgrey')[source]

df_isoTYPE: DataFrame: DESCRIPTION. The output from extract_profile functions for isotope
df_geospTYPE: DataFrame: DESCRIPTION. The output from extract_profile functions for elevation
dimTYPE: str: DESCRIPTION. The direction of the profile line (whether lat or lon)
iso_colorTYPE: Matplotlib color handle: DESCRIPTION. Color for a specific isotopic profile
iso_labelTYPE: str: DESCRIPTION. The lable for module experiment used for constructing isotopic profile
axTYPE: plt axes handle, optional: DESCRIPTION. The default is None. This must be defined in the control script if multiple experiments are used
seasonTYPE: str, optional: DESCRIPTION. The default is None. Must be defined if specific season is required
monthTYPE: int, optional: DESCRIPTION. The default is None. he default is None. Must be defined if specific month is required
xmaxTYPE: float, optional: DESCRIPTION. The default is None. The maximum limit of coordinates
xminTYPE:float, optional: DESCRIPTION. The default is None. The minimun limit of cordinates
ymaxTYPE: float, optional: DESCRIPTION. The default is None. The maximum limit of elevation axis
yminTYPE: float, optional: DESCRIPTION. The default is None. The minimum limit of elevation axis
ax_legendTYPE: Boolean, optional: DESCRIPTION. The default is None. True if you want to show legend. Can also be defined as fig.lenged if mutiple data are used in the control script. Check the example script
isomaxTYPE: float, optional: DESCRIPTION. The default is None. The maximum limit of the iso values
isominTYPE:float, optional: DESCRIPTION. The default is None. The minimum limit of the iso values
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: str, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data
left labels, right_labels, bottom_labels,TYPE: Bol, optional: DESCRIPTION. To set the left, right, and bottom axis label to None
shade_color: TYPE: STR: DESCRIPTION: shade color for plotting fill_between
shade_alpha: TYPE: float, optional: DESCRIPTION: shade factor for plotting fill_between

ValueError: DESCRIPTION.

None.

pyClimat.plots.plot_monthly_mean(variable, data_mlt, cmap, units, months, axes=None, fig=None, vmax=None, vmin=None, levels=None, domain=None, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, data_v10=None, data_u10=None, left_labels=True, bottom_labels=True)[source]

variableTYPE: str: DESCRIPTION. The variable to be plotted. Note, it will be display as colorbar name
data_sltTYPE: datarray: DESCRIPTION. The processed data to be visualized (must contain the season time coordinate)
cmapTYPE: plt.cmap: DESCRIPTION. Color map handle from matplotlib
unitsTYPE: str: DESCRIPTION. The unit of the dataset to be visualized
monthsTYPE: str: DESCRIPTION. The range of months to visualise eg. Jan-Jun or Ju-Dec
axesTYPE, optional: DESCRIPTION. The default is None.
figTYPE, optional: DESCRIPTION. The default is None.
vmaxTYPE: float, optional: DESCRIPTION. The default is None. maximum value limit of the variable to be ploted
vminTYPE: float, optional: DESCRIPTION. The default is None. minimum value limit of the variable to be ploted
levelsTYPE: float, optional: DESCRIPTION. The default is None. the number of levels for colorbar scale
domainTYPE: str, optional: DESCRIPTION. The default is None. eg. Africa, Asia, Europe
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: str, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data

ValueError: DESCRIPTION.

None.

pyClimat.plots.plot_seasonal_mean(variable, data_slt, cmap, units, seasons, axes=None, fig=None, vmax=None, vmin=None, levels=None, domain=None, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, data_v=None, plot_winds_pattern=False, plot_winds_streamline=False, data_u=None, cbar_pos=None, fig_title=None, season_label=None, plot_stats=False, compare_data1=None, compare_data2=None, max_pvalue=None, hatches=None, add_colorbar=True, left_labels=True, bottom_labels=True, show_arrow_scale=True, center=True)[source]

variableTYPE: str: DESCRIPTION. The variable to be plotted. Note, it will be display as colorbar name
data_sltTYPE: datarray: DESCRIPTION. The processed data to be visualized (must contain the season time coordinate)
cmapTYPE: plt.cmap: DESCRIPTION. Color map handle from matplotlib
unitsTYPE: str: DESCRIPTION. The unit of the dataset to be visualized
seasonsTYPE: List containing str: DESCRIPTION.List of seasons to be plotted eg. [“JJA”, “DJF”] or [“JJA] or list of all seasons
axesTYPE, optional: DESCRIPTION. The default is None.
figTYPE, optional: DESCRIPTION. The default is None.
vmaxTYPE: float, optional: DESCRIPTION. The default is None. maximum value limit of the variable to be ploted
vminTYPE: float, optional: DESCRIPTION. The default is None. minimum value limit of the variable to be ploted
levelsTYPE: float, optional: DESCRIPTION. The default is None. the number of levels for colorbar scale
domainTYPE: str, optional: DESCRIPTION. The default is None. eg. Africa, Asia, Europe
output_nameTYPE: str, optional: DESCRIPTION. The default is None. Filename of generated figures
output_formatTYPE: str, optional: DESCRIPTION. The default is Notime=”season”, season_calendar=”standard”ne. Format to save figure eg. pdf, svg, tiff
level_ticksTYPE: float, optional: DESCRIPTION. The default is None. Interval of ticks for colorbar
titleTYPE: Bolean, optional: DESCRIPTION. The default is None. Title of plots
path_to_storeTYPE: str, optional: DESCRIPTION. The default is None. Directory to store data
cbar_posTYPE: list, optional: DESCRIPTION. The default is None. the list defing the position of the color bar eg. [0.90, 0.30, 0.02, 0.40]

fig_title = None seasonal_label: str (fro the label of which season) plot_stats: TYPE: Boolean, optional

DESCRIPTION. The default is False. True for ploting hatching for signifacne difference using student t-test or correlation with spearmanr cor

Compare_data1, compare_data2: TYPE: datarray (not optional if plot_stats is set True): DESCRIPTION. the datasets required for statistic computation
hatches: TYPE: str: DESCRIPTION. the hatche style require for plotting..must be list in matplotlib hatch handle
max_pvalue: TYPE: float: DESCRIPTION. The confidence interval range for statistics significance (eg. 0.05 for 95% CI)
plot_winds_pattern: TYPE: Boolean, optional: DESCRIPTION: It plots the winds pattern using arrows on the plot background
plot_winds_streamline: TYPE: Boolean, optional: DESCRIPTION: It plots the wind streamlines on the plot

data_v = datarray (required for ploting winds) data_u = datarray (required for ploting winds)

plot_stats: TYPE: Boolean, Default: DESCRIPTION: plot the statiscal difference between two varied datasets
compare_data1: TYPE: datarray: DESCRIPTION: dataset 1 if plot_stats == true
compare_data2: TYPE: datarray: DESCRIPTION: dataset 2 if plot_stats == true

center: TYPE: Boolean, True to apply norm for centering zero

max_pvalue: TYPE: float, optional: DESCRIPTION: pvalue for the student t-test significance testing
hatches: TYPE: str, optional:: DESCRIPTION: hatches from matplotlib

None.

pyClimat.plots.plot_vertical_section(variable, data, cmap, units, season=None, ax=None, fig=None, vmax=None, vmin=None, levels=None, output_name=None, output_format=None, level_ticks=None, title=None, path_to_store=None, plot_colorbar=True, cbar_pos=None, fig_title=None, season_label=None, geosp_data=None, dim=None, left_labels=True, bottom_labels=True, right_labels=True, use_norm=False, use_cbar_norm=False)[source]

pyClimat.plots.plot_vertical_winds(*args)[source]

pyClimat.plots.plot_wind_streamlines()[source]

pyClimat.plots.scatter_plot_laspe_rate(reg_params, df_x_y_yhat, color, marker, label, ylabel=None, xlabel=None, ax=None, ax_legend=None, output_name=None, output_format=None, title=None, path_to_store=None, xmax=None, xmin=None, ymax=None, ymin=None, left_labels=True, bottom_labels=True)[source]

reg_paramsTYPE: output from stats.linregress

DESCRIPTION.

df_x_y_yhatTYPE: DataFrame output from linear_regression module in Climat_analysis

DESCRIPTION.

colorTYPE: plt.color handle

DESCRIPTION.

markerTYPE: plt.marker handle for scatter

DESCRIPTION.

labelTYPE: str

DESCRIPTION. Additional lable for data aside the equation of line of fitting

ylabelTYPE: str

DESCRIPTION. Y-axis lable name

xlabelTYPE: str

DESCRIPTION. X-axis label name

axTYPE: plt axes handle, optional: DESCRIPTION. The default is None. This must be defined in the control script if multiple experiments are used

ax_legendTYPE: Boolean, optional

DESCRIPTION. The default is None. True if you want to show legend. Can also be defined as fig.lenged if mutiple data are used in the control script. Check the example script

output_nameTYPE: str, optional

DESCRIPTION. The default is None. Filename of generated figures

output_formatTYPE: str, optional

DESCRIPTION. The default is None. Format to save figure eg. pdf, svg, tiff

level_ticksTYPE: float, optional

DESCRIPTION. The default is None. Interval of ticks for colorbar

titleTYPE: str, optional

DESCRIPTION. The default is None. Title of plots

path_to_storeTYPE: str, optional

DESCRIPTION. The default is None. Directory to store data

left labels, right_labels, bottom_labels,TYPE: Bol, optional

DESCRIPTION. To set the left, right, and bottom axis label to None

None.

pyClimat.utils module

Created on Thu Jul 29 18:53:11 2021

@author: dboateng This routine contains funtional utilities required in the other modules

pyClimat.utils.vert_coord_convertion(data, units)[source]

dataTYPE: Dataarray (MxNxV): DESCRIPTION. Dataarray on vertical coordinates with Pa as default unit
unitsTYPE: str: DESCRIPTION. The required unit for conversion eg. hPa

ValueError: DESCRIPTION. When other units is defined aside hPa

dataTYPE: Datarray: DESCRIPTION. Data vertical units converted

pyClimat package

Submodules

pyClimat.analysis module

pyClimat.data module

pyClimat.gtopo module

pyClimat.plot_utils module

pyClimat.plots module

pyClimat.utils module

Module contents