bluemath_tk.teslakit.util package

Submodules

bluemath_tk.teslakit.util.operations module

bluemath_tk.teslakit.util.operations.GetBestRowsCols(n)[source]

try to square number n, used at gridspec plots

bluemath_tk.teslakit.util.operations.GetDivisors(x)[source]
bluemath_tk.teslakit.util.operations.GetRepeatedValues(series)[source]

Find adyacent repeated values inside series. Return list of tuples

bluemath_tk.teslakit.util.operations.GetUniqueRows(np_array)[source]

bluemath_tk.teslakit.util.time_operations module

bluemath_tk.teslakit.util.time_operations.DateConverter_Mat2Py(datearray_matlab)[source]

Parses matlab datenum array to python datetime list

bluemath_tk.teslakit.util.time_operations.add_max_storms_mask(xds, times_max_storms, name_mask='max_storms')[source]

fast method for adding a “max_storms” mask to a hourly xarray.Dataset

bluemath_tk.teslakit.util.time_operations.date2datenum(d)[source]

Returns date d (any format) in datetime

bluemath_tk.teslakit.util.time_operations.date2yearfrac(d)[source]

Returns date d in fraction of the year

bluemath_tk.teslakit.util.time_operations.datematlab2datetime(datenum_matlab)[source]

Return python datetime for matlab datenum. Transform and adjust from matlab.

bluemath_tk.teslakit.util.time_operations.datevec2datetime(d_vec)[source]

Returns datetime list from a datevec matrix d_vec = [[y1 m1 d1 H1 M1],[y2 ,2 d2 H2 M2],..]

bluemath_tk.teslakit.util.time_operations.fast_reindex_hourly(xds_data)[source]

Fast and secure method to reindex (pad) xarray.Dataset to hourly data

xds_data - xarray.Dataset with time coordinate

bluemath_tk.teslakit.util.time_operations.fast_reindex_hourly_nsim(xds_data)[source]

Fast and secure method to reindex (pad) xarray.Dataset to hourly data

xds_data - xarray.Dataset with time, n_sim coordinates

bluemath_tk.teslakit.util.time_operations.generate_datetimes(t0, t1, dtype='datetime64[h]')[source]
bluemath_tk.teslakit.util.time_operations.get_years_months_days(time)[source]

Returns years, months, days of time in separete lists

(Used to avoid problems with dates type)

bluemath_tk.teslakit.util.time_operations.hours_since(base_date, target_dates)[source]

fast method for locating “target_dates” hours since “base_date”

bluemath_tk.teslakit.util.time_operations.npdt64todatetime(dt64)[source]

converts np.datetime64[ns] into datetime

bluemath_tk.teslakit.util.time_operations.repair_times_hourly(xds)[source]

ensures that xarray.Dataset time index is rounded to nearest hour and does not repeat values

bluemath_tk.teslakit.util.time_operations.xds2datetime(d64)[source]

converts xr.Dataset np.datetime64[ns] into datetime

bluemath_tk.teslakit.util.time_operations.xds_common_dates_daily(xds_list)[source]

returns daily datetime array between a list of xarray.Dataset comon date limits

bluemath_tk.teslakit.util.time_operations.xds_further_dates(xds_list)[source]

returns datetime further date limits between a list of xarray.Dataset

bluemath_tk.teslakit.util.time_operations.xds_limit_dates(xds_list)[source]

returns datetime common limits between a list of xarray.Dataset

bluemath_tk.teslakit.util.time_operations.xds_reindex_daily(xds_data, dt_lim1=None, dt_lim2=None)[source]

Reindex xarray.Dataset to daily data between optional limits

bluemath_tk.teslakit.util.time_operations.xds_reindex_monthly(xds_data)[source]

Reindex xarray.Dataset to monthly data

Module contents