Adding to sphinx

demos/synthetic_clouds_demo.py

@@ -204,4 +204,25 @@
 axs[1].set_aspect('equal')
 axs[1].set_xlabel('X Position')
 axs[1].set_ylabel('Y Position')
+plt.show()
+
+# It might also be interesting to compare the statistical distribution of the true and simulated timeseries. We can do this by comparing the histograms and CDFs of the two time series.
+# show histograms and CDFs
+fig, axs = plt.subplots(2, 2, figsize=(10, 8))
+axs[0,0].hist(kt[40], bins=100, alpha=0.5, label='True')
+axs[0,0].hist(sim_kt[40], bins=100, alpha=0.5, label='Simulated')
+axs[0,0].legend()
+axs[0,0].set_title('Hist - Sensor 40')
+axs[0,1].ecdf(kt[40], label='True')
+axs[0,1].ecdf(sim_kt[40], label='Simulated')
+axs[0,1].set_title('CDF - Sensor 40')
+axs[0,1].legend()
+axs[1,0].hist(kt.values.flatten(), bins=100, alpha=0.5, label='True')
+axs[1,0].hist(sim_kt.values.flatten(), bins=100, alpha=0.5, label='Simulated')
+axs[1,0].legend()
+axs[1,0].set_title('Hist - All Sensors')
+axs[1,1].ecdf(kt.values.flatten(), label='True')
+axs[1,1].ecdf(sim_kt.values.flatten(), label='Simulated')
+axs[1,1].set_title('CDF - All Sensors')
+axs[1,1].legend()
 plt.show()

docs/sphinx/source/demos/synthetic_clouds_demo.nblink

@@ -0,0 +1,3 @@
+{
+  "path":"../../../../demos/synthetic_clouds_demo.ipynb"
+}

docs/sphinx/source/examples.rst

@@ -21,6 +21,16 @@ Field Analysis Examples
    demos/field_demo_detailed
    demos/field_reassignment_demo
 
+.. _synthirrad-examples:
+
+Synthetic Irradiance Examples
+-----------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   demos/synthetic_clouds_demo
+
 Other Examples
 --------------
 

docs/sphinx/source/index.rst

@@ -26,6 +26,8 @@ The :mod:`solarspatialtools.cmv` module contains functions for calculating the c
 
 The :mod:`solarspatialtools.field` module contains functions for validating the layout of a PV plant or measurement network by calculating the relative delays between each sensor in the network subject to cloud motion.
 
+The :mod:`solarspatialtools.synthirrad` package contains functions for downscaling and generation of synthetic irradiance timeseries.
+
 The best starting point is to read through the :ref:`cmv-examples` and :ref:`field-examples` sections to see some sample Jupyter notebooks that demonstrate how these functions can be used in practice.
 
 
@@ -38,6 +40,7 @@ Contents
 
    cmv
    field
+   synthirrad
    othermods
 
 .. toctree::

docs/sphinx/source/synthirrad.rst

@@ -0,0 +1,25 @@
+.. currentmodule:: solarspatialtools
+
+Synthetic irradiance generation
+----------------------------------
+The `solarspatialtools.synthirrad` package contains tools for generating synthetic irradiance timeseries and performing downscaling of timeseries. The package implements the following approaches:
+
+cloudfield
+==========
+
+Generate a simulated field of clouds from which spatially distributed timeseries of kt can be extracted. The field distributions are based on the properties of a time series of kt values. This is an implementation of the method described by Lave et al [1]. Some aspects of the implementation diverge slightly from the initial paper to follow a subsequent code implementation of the method shared by the original authors.
+
+ [1] Matthew Lave, Matthew J. Reno, Robert J. Broderick, "Creation and Value of Synthetic High-Frequency Solar Inputs for Distribution System QSTS Simulations," 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), Washington, DC, USA, 2017, pp. 3031-3033, doi: https://dx.doi.org/10.1109/PVSC.2017.8366378.
+
+.. automodule:: solarspatialtools.synthirrad.cloudfield
+
+
+    .. rubric:: Functions
+
+    .. autosummary::
+        :toctree: generated/
+
+            get_timeseries_stats
+            cloudfield_timeseries
+
+

src/solarspatialtools/spatial.py

@@ -314,7 +314,7 @@ def rotate_vector(vector, theta):
     vector : (x, y) numeric
         A tuple (or numpy array) containing the input vector.
         To operate on multiple points, vector should be of the form:
-            ((x1, x2, x3, x4), (y1, y2, y3, y4))
+        ((x1, x2, x3, x4), (y1, y2, y3, y4))
 
     theta : numeric
         Angle of rotation in radians

src/solarspatialtools/synthirrad/cloudfield.py

@@ -570,9 +570,15 @@ def get_positional_ts(tgt_position, field, cloud_speed, duration=3600, pixres=1)
 
 
 
-def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1pct, edgesmoothing=3):
+def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1pct, scaling='original', edgesmoothing=3):
     """
-    Generate a time series of cloud fields based on the properties of a time series of kt values.
+    Generate a time series of cloud fields based on the properties of a time series of kt values. This is an
+    implementation of the method described by Lave et al [1]. Some aspects of the implementation diverge slightly from
+    the initial paper to follow a subsequent code implementation of the method shared by the original authors.
+
+    [1] Matthew Lave, Matthew J. Reno, Robert J. Broderick, "Creation and Value of Synthetic High-Frequency Solar Inputs
+    for Distribution System QSTS Simulations," 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC),
+    Washington, DC, USA, 2017, pp. 3031-3033, doi: https://dx.doi.org/10.1109/PVSC.2017.8366378.
 
     Parameters
     ----------
@@ -590,6 +596,8 @@ def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1p
         The maximum of the kt values
     kt1pct : float
         The 1st percentile of the kt values
+    scaling : str
+        The scaling method to use. Either 'original' or 'basic'
     edgesmoothing : int
         The size of the uniform filter for edge smoothing
 
@@ -604,5 +612,11 @@ def cloudfield_timeseries(weights, scales, size, frac_clear, ktmean, ktmax, kt1p
 
     edges, smoothed = _find_edges(clear_mask, edgesmoothing)
 
-    field_final = _scale_field_lave(cfield, clear_mask, smoothed, ktmean, ktmax, kt1pct, plot=False)
+    if scaling == 'original':
+        field_final = _scale_field_lave(cfield, clear_mask, edges, ktmean, ktmax, kt1pct, plot=False)
+    elif scaling == 'basic':
+        field_final = _scale_field_basic(cfield, clear_mask, smoothed, ktmean, ktmax, kt1pct, plot=False)
+    else:
+        raise ValueError("Scaling method must be either 'original' or 'basic'.")
+
     return field_final