chrpa.main package

Submodules

chrpa.main.processing module

chrpa: climate hazards return period analysis

chrpa.main.processing.convert_preciprate_to_mm(precip_df, tint, wvar)

Converts precipitation rate (in [kg/m^2 s]) to total depth (in mm) when time interval in between timestamps is provided

Parameters

• precip_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: time,

  dtype: datetime64[ns], description: time stamps

  Name: precip,

  dtype: float64, description: precipitation rate in (kg/m^2-s)

• tint (float) – Time interval in between the timestamps in precipDF

• wvar (str) – Name given to preciptation column in precipitation data frame

Returns

• Pandas DataFrame similar to input but with precipitation rate

• converted to total depth in mm. Time stamps with NaNs in

• precipitation column are dropped from the data frame.

chrpa.main.processing.convert_to_daily(w_df, tvar, aggfun)

Converts a weather data frame with hourly time stamps to daily time stamps based on a user defined aggregation method

Parameters

• w_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: time,

  dtype: datetime64[ns], description: time stamps

  Name: any weather variable,

  dtype: float64, description: can be either precipitation (in mm) or

  wind speed (in m/s)

• tvar (str) – Name given to time column in weather data frame

• aggfun (string) – Aggregation function that is read in by the ‘aggregate’ method for pandas groupby objects (e.g., ‘sum’, ‘mean’, etc.)

Returns

• Pandas DataFrame similar to input but with daily time intervals

• instead of more frequent time intervals (e.g., hourly)

chrpa.main.processing.convert_to_mm_daily(precip_df, tint, tvar, wvar, aggfun)

Converts precipitation rate (in [kg/m^2 s]) to daily total depth (in mm) when time interval in between timestamps is provided

Parameters

• precip_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: time,

  dtype: datetime64[ns], description: time stamps

  Name: precip,

  dtype: float64, description: precipitation rate in (kg/m^2-s)

• tint (float) – Time interval in between the timestamps in precipDF

• tvar (str) – Name given to time column in weather data frame

• wvar (str) – Name given to preciptation column in precipitation data frame

• aggfun (string) – Aggregation function that is read in by the ‘aggregate’ method for pandas groupby objects (e.g., ‘sum’, ‘mean’, etc.)

Returns

• Pandas DataFrame similar to input but with precipitation rate

• converted to daily total depth (in mm)

chrpa.main.processing.convert_uasvas_to_wind(uas_df, vas_df, ucol, vcol, tvar, wndvar)

Converts u and v (east/west and north/south) components of surface wind into wind speed (in m/s). Any time stamps that result in NaNs for the output wind speed column are dropped from the output data frame.

Parameters

• uas_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: time,

  dtype: datetime64[ns], description: time stamps

  Name: uas,

  dtype: float64, description: u-component of surface wind (in m/s)

• vas_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: time,

  dtype: datetime64[ns], description: time stamps

  Name: uas,

  dtype: float64, description: v-component of surface wind (in m/s)

• ucol (str) – Name given to wind column in uasDF

• ucol – Name given to wind column in vasDF

• tvar (str) – Name given to time column in uasDF

• wndvar (str) – Name to be given to wind speed column in output data frame

Returns

• Pandas DataFrame with u and v components of wind converted to wind

• speed (in m/s)

chrpa.main.processing.find_rl_location(future_df, rt_lv)

Finds a return period corresponding to a specified return level in a data frame of return-periods/return-levels as output by pyextremes

Parameters

• future_df (pandas.DataFrame) –

  Index:

  return periods

  Columns:

  Name: return value,

  dtype: float64, description: return values of specified return

  period (index)

  Name: lower ci,

  dtype: float64, description: lower confidence interval for return

  values of specified return period (index)

  Name: upper ci,

  dtype: float64, description: lower confidence interval for return values of specified return period (index)

• rt_lv (float) – Return level of interest for which a corresponding return period should be identified.

Returns

• Return period in future_df that most closely approximates the

• specified return level

chrpa.main.processing.get_annual_max(daily_df, max_dict_rename, wmodel)

Takes in a pandas data frame for daily weather and outputs annual maximum for the years covered in the input data. Function also renames new data frame columns to user-defined preference.

Parameters

• daily_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: date,

  dtype: datetime.date object, description: historical dates

  Name: precip or wind,

  dtype: float64, description: any daily weather parameter (e.g., precipitation or wind)

• max_dict_rename (dict) – Dictionary used to rename weather data column in output data frame so as to distinguish from input data (e.g., from ‘precip’ in dailyDF to ‘MaxPrecip’ in output data frame).

• wmodel (str) – Name for the climate model used for weather data sets

Returns

• pandas.DataFrames with annual maximum weather for years covered in

• input data

chrpa.main.processing.get_closest_point(site_loc, ncfilepath, offset)

Finds the closest data point between location of interest (site_loc) and weather stations captured by .nc files (ncfilepath)

Parameters

• site_loc (tuple) – Latitude and longitude in the form of (lat, lon) for location of interest

• ncfilepath (str) – A filepath in the form of a string that points to the location of the netCDF file of interest

• offset (list) – Contains the lattitude and longitude offest in the form of [lat, lon]

Returns

• Dictionary containing lat/lon, distance to, and corresponding x/y

• indices in the netCDF file (climate data) to the weather station

• that is closest to the input site_loc

chrpa.main.processing.make_ordered_time_weather_lists(nc_files, wstat_indices, ncwvar)

Takes a list of .nc files corresponding to different time frames for a specific weather model and produces an ordered list of time and weather numpy arrays for an identified weather station location (wstat_loc)

Parameters

• nc_files (list) – A list of strings, where each string is a filepath that points to the locations of the netCDF files corresponding to different years of a climate model

• wstat_indices (tuple) – xc and yc indices corresponding to specified location (weather station of interest) in the form of (xc,yc)

• ncwvar (str) – Weather variable of interest to be extracted from you .nc file (e.g., ‘pr’, ‘uas’, or ‘vas’)

Returns

• time_list (list) – List of numpy arrays corresponding to time data

• weather_list (list) – List of numpy arrays corresponding to weather data

chrpa.main.processing.myextremes(ann_df, rp_array, wvar, alpha=None)

Uses pyextremes to fit extreme (maximum annual) weather data to a generalized extreme value distribution (GEVD). The GEVD fit is then used to calculate return values for user-defined return period and user-defined confidence intervals. Takes the output of get_annual_max for annDF

Parameters

• ann_df (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: year,

  dtype: string, float, or int description: historical dates

  Name: precip or wind,

  dtype: float64, description: any daily weather parameter (e.g., precipitation or wind)

• rp_array (numpy.ndarray) – Numpy array with return periods for which return values should be calculated with pyextremes

• wvar (str) – Column name given to weather variable of interest in annDF (e.g., ‘MaxPrecip’, ‘MaxWind’, etc)

• alpha (float) – Width of confidence interval (0,1). If None (default), return None for upper and lower confidence interval bounds.

Returns

• pandas.DataFrame providing return values for user-requested return

• periods. Upper and lower confidence bounds are also output if

• alpha is non-zero.

chrpa.main.processing.parse_data_orig(ncfilepath, offset)

Extracts latitude and longitude from a netCDF (network Common Data Form) format and returns them in a data frame

Parameters

• ncfilepath (str) – A filepath in the form of a string that points to the location of the netCDF file of interest

• offset (list) – Contains the lattitude and longitude offest in the form of [lat, lon]

Returns

Dictionary with latitude and longitude components corresponding to the input file

Return type

Dictionary

chrpa.main.processing.summary_stat_fun(daily_df_current, daily_df_future, wmodel, site)

Takes in a pandas data frame for daily weather and outputs statstical summaries for that data set (min, max, mean, and std. dev.)

Parameters

• daily_df_current (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: date,

  dtype: datetime.date object, description: historical dates

  Name: precip or wind,

  dtype: float64, description: any daily weather parameter

  (e.g., precipitation or wind)

• daily_df_future (pandas.DataFrame) –

  Index:

  RangeIndex

  Columns:

  Name: date,

  dtype: datetime.date object, description: future dates

  Name: precip or wind,

  dtype: float64, description: any daily weather parameter (e.g., precipitation or wind)

• wmodel (str) – Name for the climate model used for weather data sets

• site (str) – Name for the specific site whose weather being analyzed

Returns

• pandas.DataFrames with statistical summaries

• (min, max, mean, and std. dev.)

chrpa.main.processing.timedelta_to_continuous_w_weather(time_data_list, weather_data_list, wvar, start_time, start_time_format='%Y-%m-%d %H:%M:%S', td_unit='days')

Takes a list of time and weather numpy arrays that correspond to a specific location from a single climate model. Concatenates the time and weather lists into a single pandas data frame with continuous time and weather data as two separate columns of the data frame.

Parameters

• time_data_list (list) – List of numpy arrays corresponding to time data for a specific climate model

• weather_data_list (list) – List of numpy arrays corresponding to weather data for a specific climate model

• wvar (str) – Column name given to weather variable of interest in weather data frame (e.g., ‘precip’, ‘uas’, or ‘vas’)

• start_time (str) – Time stamp corresponding to the start time of the arrays in time_data_list (e.g., ‘1968-01-01 00:00:00’)

• start_time_format (str) – Format in which the start_time is input (Default is ‘%Y-%m-%d %H:%M:%S’)

• td_unit (str) – Unit in which the arrays in time_data_list are provided (e.g., ‘days’)

Returns

• Pandas DataFrame with continuous time and weather data as two

• separate columns of the data frame

chrpa.main.processing.weather_data_calcs(nc_filepath, wstat_indices, ncwvar)

Extracts the weather and time data from a .nc file (nc_filepath) for specified location(wstat_coords)

Parameters

• ncfilepath (str) – A filepath in the form of a string that points to the location of the netCDF file of interest

• wstat_indices (tuple) – xc and yc indices corresponding to specified location (weather station of interest) in the form of (xc,yc)

• ncwvar (str) – Weather variable of interest to be extracted from you .nc file (e.g., ‘pr’, ‘uas’, or ‘vas’)

Returns

• time_data (Numpy array) – Numpy array corresponding to time data

• weather_data (Numpy array) – Numpy array corresponding to weather data

Module contents

main module