Improving cloud field demo

demos/synthetic_clouds_demo.py

@@ -14,7 +14,7 @@
 
 # #### Load Timeseries Data
 
-datafn = "../../../demos/data/hope_melpitz_1s.h5"
+datafn = "data/hope_melpitz_1s.h5"
 twin = pd.date_range('2013-09-08 9:15:00', '2013-09-08 10:15:00', freq='1s', tz='UTC')
 data = pd.read_hdf(datafn, mode="r", key="data")
 data_i = data[40]

src/solarspatialtools/synthirrad/cloudfield.py

@@ -411,33 +411,35 @@ def get_timeseries_stats(kt_ts, clear_threshold=0.95, plot=False):
     return ktmean, kt_1_pct, ktmax, frac_clear, vs, weights, scales
 
 
-def space_to_time(pixres=1, cloud_speed=50):
-
-    # Existing code uses 1 pixel per meter, and divides by cloud speed to get X size
-    # does 3600 pixels, but assumes the clouds move by one full timestep.
-
-    # ECEF coordinate system (Earth Centered Earth Fixed) is used to convert lat/lon to x/y.
-    # This seems really weird. https://en.wikipedia.org/wiki/Geographic_coordinate_conversion
-
-    # [X, Y] = geod2ecef(Lat_to_Sim, Lon_to_Sim, zeros(size(Lat_to_Sim)));
-    # ynorm1 = Y - mean(Y);
-    # xnorm1 = X - mean(X);
-    # ynorm=round((ynorm1-min(ynorm1))./Cloud_Spd(qq))+1;
-    # xnorm=round((xnorm1-min(xnorm1))./Cloud_Spd(qq))+1;
-    # Xsize=60*60+max(xnorm);
-    # Ysize=max(ynorm);
-    # Xsize and Ysize are the pixel sizes generated
-
-    # Extracting a time series has us loop through the entire size of X pixels, and choose a window 3600 pixels wide, and multiply by GHI_cs
-    # GHI_syn(i,hour(datetime2(GHI_timestamp))==h1)=clouds_new{h1}(ynorm(i),xnorm(i):xnorm(i)+3600-1)'.*GHI_clrsky(hour(datetime2(GHI_timestamp))==h1);
-
-    pixjump = cloud_speed / pixres
-    # n space
-    # dx space
-    # velocity
-    # dt = dx / velocity
-    # max space = n * dx
-    # max time = max space / velocity
+def get_positional_ts(tgt_position, field, cloud_speed=50, duration=3600, pixres=1):
+    """
+    Get a time series of kt values for a target position in the field.
+
+    Parameters
+    ----------
+    tgt_position : tuple
+        The spatial target position in the field (in meters)
+    field : np.ndarray
+        The field of clouds generated by cloudfield_timeseries
+    cloud_speed : int
+        The speed of the clouds in meters per second
+    duration : int
+        The duration of the time series in seconds
+    pixres : int
+        The resolution of the field in seconds
+
+    Returns
+    -------
+    sim_kt : np.ndarray
+        The simulated time series of kt values for the point within the field
+    """
+
+    # Convert to the temporatal coordinate system
+    x = int(tgt_position[0]/cloud_speed)
+    y = int(tgt_position[1]/cloud_speed)
+    sim_kt = field[x:x + int(duration) + 1, y]
+
+    return sim_kt