Bounds¶

class dataset.Bounds(lat_min, lat_max, lon_min, lon_max, start, end)¶

Container for holding spatial and temporal bounds information.

Certain operations require valid bounding information to be present for correct functioning. Bounds guarantees that a function receives well formed information without the need to do the validation manually.

Spatial and temporal bounds must follow the following guidelines.

• Latitude values must be in the range [-90, 90]
• Longitude values must be in the range [-180, 180]
• Lat/Lon Min values must be less than the corresponding Lat/Lon Max values.
• Temporal bounds must a valid datetime object

Default Bounds constructor

Parameters:	lat_min (float) – The minimum latitude bound. lat_max (float) – The maximum latitude bound. lon_min (float) – The minimum longitude bound. lon_max (float) – The maximum longitude bound. start (datetime) – The starting datetime bound. end (datetime) – The ending datetime bound.
Raises:	ValueError

Dataset¶

class dataset.Dataset(lats, lons, times, values, variable=None, name='')¶

Container for a dataset’s attributes and data.

Default Dataset constructor

Parameters:

lats (numpy array) – One dimensional numpy array of unique latitude values. lons (numpy array) – One dimensional numpy array of unique longitude values. times (numpy array) – One dimensional numpy array of unique python datetime objects. values (numpy array) – Three dimensional numpy array of parameter values with shape [timesLength, latsLength, lonsLength]. variable (string) – Name of the value variable. name – An optional string name for the Dataset.

Raises:

ValueError

spatial_boundaries()¶

Calculate the spatial boundaries.

Returns:	The Dataset’s bounding latitude and longitude values as a tuple in the form (min_lat, max_lat, min_lon, max_lon)
Return type:	(float, float, float, float)

spatial_resolution()¶

Calculate the latitudinal and longitudinal spatial resolution.

Warning

This only works with properly gridded data.

Returns:	The Dataset’s latitudinal and longitudinal spatial resolution as a tuple of the form (lat_resolution, lon_resolution).
Return type:	(float, float)

temporal_resolution()¶

Calculate the temporal resolution.

Raises ValueError:
	If timedelta.days as calculated from the sorted list of times is an unrecognized value a ValueError is raised.
Returns:	The temporal resolution.
Return type:	string

time_range()¶

Calculate the temporal range

Returns:	The start and end date of the Dataset’s temporal range as a tuple in the form (start_time, end_time).
Return type:	(datetime, datetime)

UnaryMetric¶

class metrics.UnaryMetric¶

Abstract Base Class from which all unary metrics inherit.

x.__init__(...) initializes x; see help(type(x)) for signature

run(target_dataset)¶

Run the metric for a given target dataset.

Parameters:	target_dataset – The dataset on which the current metric will be run.
Returns:	The result of evaluating the metric on the target_dataset.

BinaryMetric¶

class metrics.BinaryMetric¶

Abstract Base Class from which all binary metrics inherit.

x.__init__(...) initializes x; see help(type(x)) for signature

run(ref_dataset, target_dataset)¶

Run the metric for the given reference and target datasets.

Parameters:	ref_dataset (Dataset) – The Dataset to use as the reference dataset when running the evaluation. target_dataset – The Dataset to use as the target dataset when running the evaluation.
Returns:	The result of evaluation the metric on the reference and target dataset.

Bias¶

class metrics.Bias¶

Calculate the bias between a reference and target dataset.

x.__init__(...) initializes x; see help(type(x)) for signature

run(ref_dataset, target_dataset)¶

Calculate the bias between a reference and target dataset.

Note

Overrides BinaryMetric.run()

Parameters:	ref_dataset (Dataset.) – The reference dataset to use in this metric run. target_dataset (Dataset.) – The target dataset to evaluate against the reference dataset in this metric run.
Returns:	The difference between the reference and target datasets.
Return type:	Numpy Array

TemporalStdDev¶

class metrics.TemporalStdDev¶

Calculate the standard deviation over the time.

x.__init__(...) initializes x; see help(type(x)) for signature

run(target_dataset)¶

Calculate the temporal std. dev. for a datasets.

Note

Overrides UnaryMetric.run()

Parameters:	target_dataset (Dataset) – The target_dataset on which to calculate the temporal standard deviation.
Returns:	The temporal standard deviation of the target dataset
Return type:	Numpy Array

Local Module¶

RCMED Module¶

DAP Module¶