prereise.gather.winddata package

Subpackages

• prereise.gather.winddata.hrrr package
  • Subpackages
    • prereise.gather.winddata.hrrr.tests package
      • Submodules
      • prereise.gather.winddata.hrrr.tests.test_calculations module
      • prereise.gather.winddata.hrrr.tests.test_grib module
      • prereise.gather.winddata.hrrr.tests.test_helpers module
      • prereise.gather.winddata.hrrr.tests.test_hrrr_api module
      • Module contents
  • Submodules
  • prereise.gather.winddata.hrrr.calculations module
    • calculate_pout_blended()
    • calculate_pout_individual()
    • extract_wind_speed()
    • find_closest_wind_grids()
    • get_wind_data_lat_long()
  • prereise.gather.winddata.hrrr.constants module
  • prereise.gather.winddata.hrrr.downloader module
    • Downloader
      • Downloader.download()
  • prereise.gather.winddata.hrrr.grib module
    • GribRecordInfo
      • GribRecordInfo.beginning_byte
      • GribRecordInfo.byte_range_header_string()
      • GribRecordInfo.ending_byte
      • GribRecordInfo.forecast
      • GribRecordInfo.from_string()
      • GribRecordInfo.full_file()
      • GribRecordInfo.generate_grib_record_information_list()
      • GribRecordInfo.initialization_date
      • GribRecordInfo.level
      • GribRecordInfo.message_number
      • GribRecordInfo.variable
  • prereise.gather.winddata.hrrr.helpers module
    • formatted_filename()
    • get_indices_that_contain_selector()
  • prereise.gather.winddata.hrrr.hrrr module
    • retrieve_data()
  • prereise.gather.winddata.hrrr.hrrr_api module
    • HrrrApi
      • HrrrApi.U_COMPONENT_FILTER
      • HrrrApi.V_COMPONENT_FILTER
      • HrrrApi.download_meteorological_data()
      • HrrrApi.download_wind_data()
  • Module contents
• prereise.gather.winddata.rap package
  • Subpackages
    • prereise.gather.winddata.rap.tests package
      • Submodules
      • prereise.gather.winddata.rap.tests.test_noaa_api module
      • Module contents
  • Submodules
  • prereise.gather.winddata.rap.helpers module
    • to_reise()
  • prereise.gather.winddata.rap.noaa_api module
    • NoaaApi
      • NoaaApi.base_url
      • NoaaApi.build_url()
      • NoaaApi.fallback_url
      • NoaaApi.get_hourly_data()
      • NoaaApi.get_path_list()
      • NoaaApi.iter_hours()
      • NoaaApi.var_u
      • NoaaApi.var_v
  • prereise.gather.winddata.rap.rap module
    • retrieve_data()
  • Module contents

Submodules

prereise.gather.winddata.const module

prereise.gather.winddata.impute module

prereise.gather.winddata.impute.gaussian(data, wind_farm, inplace=True, curve='state')

Impute missing data using gaussian distributions of U & V. For each missing entry, sample U & V based on mean and covariance of non-missing entries that have the same location, same month, and same hour.

Parameters:

• data (pandas.DataFrame) – data frame as returned by prereise.gather.winddata.rap.rap.retrieve_data().

• wind_farm (pandas.DataFrame) – data frame of wind farms.

• inplace (bool) – should the imputation be done in place.

• curve (str) – ‘state’ to use the state average, otherwise named curve.

Returns:

(pandas.DataFrame) – data frame with missing entries imputed.

prereise.gather.winddata.impute.linear(data, inplace=True)

Given a 2D array, linearly interpolate any missing values column-wise.

Parameters:

• data (numpy.array/pandas.DataFrame) – data to interpolate.

• inplace (bool) – whether to modify the data inplace or return a modified copy.

Returns:

(None/pandas.DataFrame) – if inplace is False, data frame with missing entries imputed.

prereise.gather.winddata.impute.simple(data, wind_farm, inplace=True, curve='state')

Impute missing data using a simple procedure. For each missing entry, the extrema of the U and V components of the wind speed of all non missing entries that have the same location, same month, same hour are first found for each missing entry. Then, a U and V value are randomly generated between the respective derived ranges.

Parameters:

• data (pandas.DataFrame) – data frame as returned by prereise.gather.winddata.rap.rap.retrieve_data().

• wind_farm (pandas.DataFrame) – data frame of wind farms.

• inplace (bool) – should the imputation be done in place.

• curve (str) – ‘state’ to use the state average, otherwise named curve.

Returns:

(pandas.DataFrame) – data frame with missing entries imputed.

prereise.gather.winddata.power_curves module

prereise.gather.winddata.power_curves.build_state_curves(form_860, power_curves, maxspd=30, default='IEC class 2', rsd=0)

Parse Form 860 and turbine curves to obtain average state curves.

Parameters:

• form_860 (pandas.DataFrame) – EIA Form 860 data.

• power_curves (pandas.DataFrame) – turbine power curves.

• maxspd (float) – maximum x value for state curves.

• default (str) – turbine curve name for turbines not in power_curves.

• rsd (float) – relative standard deviation for spatiotemporal smoothing.

Returns:

(pandas.DataFrame) - DataFrame of state curves.

prereise.gather.winddata.power_curves.get_form_860(data_dir, year=2016)

Read data for EIA Form 860.

Parameters:

• data_dir (str) – data directory.

• year (int) – EIA data year to get.

Returns:

(pandas.DataFrame) – dataframe with Form 860 data.

prereise.gather.winddata.power_curves.get_power(power_curves, state_power_curves, wspd, turbine, default='IEC class 2')

Convert wind speed to power using NREL turbine power curves.

Parameters:

• power_curves (pandas.DataFrame) – turbine power curves data.

• state_power_curves (pandas.DataFrame) – state average power curves data.

• wspd (float) – wind speed (in m/s).

• turbine (str) – turbine name, IEC class, or state code for average.

• default (str) – default turbine name.

Returns:

(float) – normalized power.

prereise.gather.winddata.power_curves.get_state_power_curves(filename='StatePowerCurves.csv', rsd=0.4)

Load state power curves from csv, if the csv is present. Otherwise, construct them from EIA form 860 data and turbine curves.

Parameters:

• filename (str) – filename (not path) of csv file to read from.

• rsd (float) – relative standard deviation, for wind speed distribution.

Returns:

(pandas.DataFrame) – normalized state power curves.

prereise.gather.winddata.power_curves.get_turbine_power_curves(filename='PowerCurves.csv')

Load turbine power curves from csv.

Parameters:

filename (str) – filename (not path) of csv file to read from.

Returns:

(pandas.DataFrame) – normalized turbine power curves.

prereise.gather.winddata.power_curves.shift_turbine_curve(turbine_curve, hub_height, maxspd, new_curve_res)

Shift a turbine curve based on a given hub height.

Parameters:

• turbine_curve (pandas.Series) – power curve data, wind speed index.

• hub_height (float) – height to shift power curve to.

• maxspd (float) – Extent of new curve (m/s).

• new_curve_res (float) – Resolution of new curve (m/s).

Module contents