Merge branch 'master' of https://git-wip-us.apache.org/repos/asf/climate
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2834ee6..9e73110 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -18,6 +18,7 @@
ocw/evaluation
ocw/metrics
ocw/plotter
+ ocw/statistical_downscaling
ocw/utils
data_source/data_sources
ui-backend/backend
diff --git a/docs/source/ocw/statistical_downscaling.rst b/docs/source/ocw/statistical_downscaling.rst
new file mode 100644
index 0000000..be63151
--- /dev/null
+++ b/docs/source/ocw/statistical_downscaling.rst
@@ -0,0 +1,8 @@
+Downscaling Module
+******************
+
+Downscaling
+===========
+.. autoclass:: statistical_downscaling.Downscaling
+ :members:
+
diff --git a/ocw/statistical_downscaling.py b/ocw/statistical_downscaling.py
index 57013a0..f3d701f 100755
--- a/ocw/statistical_downscaling.py
+++ b/ocw/statistical_downscaling.py
@@ -15,23 +15,36 @@
# specific language governing permissions and limitations
# under the License.
+"""
+Classes:
+ Downscaling - Container for applying statistical downscaling.
+"""
-import ocw.utils as utils
+
import numpy as np
-from scipy.stats import percentileofscore, linregress
+from scipy.stats import linregress, percentileofscore
-class Downscaling:
+class Downscaling(object):
+ """
+ Statistical downscaling infers higher resolution information from lower resolution data.
+ For example, data collected at a more coarse regional level applied to a more refined
+ local level.
+
+ Statistical downscaling establishes a relationship between different variables in the large scale
+ and the local scale and applies that relationship to the local scale.
+ """
def __init__(self, ref_dataset, model_present, model_future):
- '''
+ """ Default Downscaling constructor.
+
:param ref_dataset: The Dataset to use as the reference dataset (observation)
:type ref_dataset: Dataset
:param model_present: model simulation to be compared with observation
:type model_present: Dataset
:param model_future: model simulation to be calibrated for prediction
:type model_future: Dataset
- '''
+ """
self.ref_dataset = ref_dataset[~ref_dataset.mask].ravel()
self.model_present = model_present.ravel()
self.model_future = model_future.ravel()
@@ -39,11 +52,11 @@
description = "statistical downscaling methods"
def Delta_addition(self):
- '''Calculate the mean difference between future and present simulation,
+ """Calculate the mean difference between future and present simulation,
then add the difference to the observed distribution
:returns: downscaled model_present and model_future
- '''
+ """
ref = self.ref_dataset
model_present = self.model_present
model_future = self.model_future
@@ -51,23 +64,26 @@
return model_present, ref + np.mean(model_future - model_present)
def Delta_correction(self):
- '''Calculate the mean difference between observation and present simulation,
+ """Calculate the mean difference between observation and present simulation,
then add the difference to the future distribution
:returns: downscaled model_present and model_future
- '''
+ """
ref = self.ref_dataset
model_present = self.model_present
model_future = self.model_future
- return model_present + np.mean(ref) - np.mean(model_present), model_future + np.mean(ref) - np.mean(model_present)
+ return model_present + np.mean(ref) - np.mean(model_present), model_future + \
+ np.mean(ref) - np.mean(model_present)
def Quantile_mapping(self):
- '''Remove the biases for each quantile value
- Wood et al (2004) HYDROLOGIC IMPLICATIONS OF DYNAMICAL AND STATISTICAL APPROACHES TO DOWNSCALING CLIMATE MODEL OUTPUTS
+ """Remove the biases for each quantile value
+
+ Wood et al (2004) HYDROLOGIC IMPLICATIONS OF DYNAMICAL
+ AND STATISTICAL APPROACHES TO DOWNSCALING CLIMATE MODEL OUTPUTS
:returns: downscaled model_present and model_future
- '''
+ """
ref = self.ref_dataset
model_present = self.model_present
model_present_corrected = np.zeros(model_present.size)
@@ -86,11 +102,14 @@
return model_present_corrected, model_future_corrected
def Asynchronous_regression(self):
- '''Remove the biases by fitting a linear regression model with ordered observational and model datasets
- Stoner et al (2013) An asynchronous regional regression model for statistical downscaling of daily climate variables
+ """Remove the biases by fitting a linear regression model with ordered observational and
+ model datasets
+
+ Stoner et al (2013) An asynchronous regional regression model for statistical downscaling of
+ daily climate variables
:returns: downscaled model_present and model_future
- '''
+ """
ref_original = self.ref_dataset
model_present = self.model_present
