easyclimate.core.extract¶

Obtain data within a specified time period

Functions¶

`get_specific_years_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer years.
`get_specific_months_data`(...)	Slicing and extracting the part of the data containing the specified year based on an array of given integer months.
`get_specific_days_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer days.
`get_specific_hours_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer hours.
`get_specific_minutes_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer minutes.
`get_specific_seconds_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer seconds.
`get_specific_microseconds_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer microseconds.
`get_specific_nanoseconds_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer nanoseconds.
`get_specific_dayofweek_data`(\| List[int], dim)	Slicing and extracting the part of the data containing the specified year based on an array of given integer dayofweek.
`get_yearmean_for_specific_months_data`(\| List[int], ...)	Get the annual average of certain months.
`get_year_exceed_index_upper_bound`(→ numpy.array)	Extract the years under the specified threshold (upper bound) in the annual average index (one-dimensional data with only a time dimension).
`get_year_exceed_index_lower_bound`(→ numpy.array)	Extract the years under the specified threshold (lower bound) in the annual average index (one-dimensional data with only a time dimension).
`get_time_exceed_index_upper_bound`(→ numpy.array)	Extract the time under the specified threshold (upper bound) in the annual average index (one-dimensional data with only a time dimension).
`get_time_exceed_index_lower_bound`(→ numpy.array)	Extract the time under the specified threshold (lower bound) in the annual average index (one-dimensional data with only a time dimension).

Module Contents¶

easyclimate.core.extract.get_specific_years_data(data_input: xarray.DataArray | xarray.Dataset, year_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer years.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
year_array: list[int]: Year(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_months_data(data_input: xarray.DataArray | xarray.Dataset, month_array: numpy.array, dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer months.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
month_array: list[int]: Month(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_days_data(data_input: xarray.DataArray | xarray.Dataset, day_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer days.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
day_array: list[int]: Days(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_hours_data(data_input: xarray.DataArray | xarray.Dataset, hour_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer hours.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
hour_array: list[int]: Hour(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_minutes_data(data_input: xarray.DataArray | xarray.Dataset, minute_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer minutes.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
minute_array: list[int]: Minute(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_seconds_data(data_input: xarray.DataArray | xarray.Dataset, second_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer seconds.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
second_array: list[int]: Second(s) to be extracted.
dimstr: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_microseconds_data(data_input: xarray.DataArray | xarray.Dataset, microsecond_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer microseconds.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
microsecond_array: list[int]: Microsecond(s) to be extracted.
dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_nanoseconds_data(data_input: xarray.DataArray | xarray.Dataset, nanosecond_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer nanoseconds.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
nanosecond_array: list[int]: Nanosecond(s) to be extracted.
dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_specific_dayofweek_data(data_input: xarray.DataArray | xarray.Dataset, dayofweek_array: np.array(int) | List[int], dim: str = 'time') → xarray.DataArray | xarray.Dataset¶

Slicing and extracting the part of the data containing the specified year based on an array of given integer dayofweek.

Parameters¶

data_inputxarray.DataArray

xarray.DataArray to be extracted.

dayofweek_array: list[int]

The days of the week to be extracted.

The integer numbers correspond to the days of the week as follows.

Day of the week	Integer numbers
Monday	0
Tuesday	1
Wednesday	2
Thursday	3
Friday	4
Saturday	5
Sunday	6

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_yearmean_for_specific_months_data(data_input: xarray.DataArray | xarray.Dataset, month_array: np.array(int) | List[int], dim: str = 'time', **kwargs) → xarray.DataArray | xarray.Dataset¶

Get the annual average of certain months.

Parameters¶

data_inputxarray.DataArray: xarray.DataArray to be extracted.
month_array: list[int]: Month(s) to be extracted.
dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.extract.get_year_exceed_index_upper_bound(data_input: xarray.DataArray, thresh: float, time_dim: str = 'time') → numpy.array¶

Extract the years under the specified threshold (upper bound) in the annual average index (one-dimensional data with only a time dimension).

Parameters¶

data_inputxarray.DataArray: The one-dimensional data with only a time dimension.
thresh: float.: The threshold value.
time_dim: str.: The time coordinate dimension name.

Returns¶

numpy.array.

easyclimate.core.extract.get_year_exceed_index_lower_bound(data_input: xarray.DataArray, thresh: float, time_dim: str = 'time') → numpy.array¶

Extract the years under the specified threshold (lower bound) in the annual average index (one-dimensional data with only a time dimension).

Parameters¶

data_inputxarray.DataArray: The one-dimensional data with only a time dimension.
thresh: float.: The threshold value.
time_dim: str.: The time coordinate dimension name.

Returns¶

numpy.array.

easyclimate.core.extract.get_time_exceed_index_upper_bound(data_input: xarray.DataArray, thresh: float, time_dim: str = 'time') → numpy.array¶

Extract the time under the specified threshold (upper bound) in the annual average index (one-dimensional data with only a time dimension).

Parameters¶

data_inputxarray.DataArray: The one-dimensional data with only a time dimension.
thresh: float.: The threshold value.
time_dim: str.: The time coordinate dimension name.

Returns¶

Time array.

easyclimate.core.extract.get_time_exceed_index_lower_bound(data_input: xarray.DataArray, thresh: float, time_dim: str = 'time') → numpy.array¶

Extract the time under the specified threshold (lower bound) in the annual average index (one-dimensional data with only a time dimension).

Parameters¶

data_inputxarray.DataArray: The one-dimensional data with only a time dimension.
thresh: float.: The threshold value.
time_dim: str.: The time coordinate dimension name.

Returns¶

Time array.