fixed bug in line smoothing

energy_demand/main.py

@@ -10,7 +10,7 @@
 # MAKE SIMLPLE TABLE FOR READING IN FUELS
 # correction factors on LAD level disaggregation? (load non-residential demand)
 # Improve plotting and processing (e.g. saisonal plots)
-
+# test if smoothing and plots
 # Weather station cleaning: Replace days with missing values
 
     Note

energy_demand/plotting/_scrap.py

@@ -1,34 +1,8 @@
-import pandas as pd
-import geopandas
-from shapely.geometry import Point
-import matplotlib.pyplot as plt
-
-df = pd.DataFrame(
-    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
-     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
-     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
-     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
-
-df['Coordinates'] = list(zip(df.Longitude, df.Latitude))
-
-
-df['Coordinates'] = df['Coordinates'].apply(Point)
-
-gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')
 
-world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
-
-
-# We restrict to South America.
-#ax = world[world.continent == 'South America'].plot(
-#    color='white', edgecolor='black')
-ax = world[world.name == "United Kingdom"].plot(
-    color='white', edgecolor='black')
-#uk =  world[world.name == "United Kingdom"]
-
-# We can now plot our GeoDataFrame.
-gdf.plot(ax=ax, color='red')
-
-plt.show()
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+import pylab
+from scipy.interpolate import interp1d
+from scipy.interpolate import spline
 
-print("aa")

energy_demand/plotting/basic_plot_functions.py

@@ -19,54 +19,93 @@ def cm2inch(*tupl):
     else:
         return tuple(i/inch for i in tupl)
 
-def smooth_data(x_list, y_list, num=500, spider=False):
+def simple_smooth(x_list, y_list, num=500, spider=False, interpol_kind='quadratic'):
+
+    nr_x_values = len(x_list)
+    min_x_val = min(x_list)
+    max_x_val = max(x_list)
+
+    x_values = np.linspace(min_x_val, max_x_val, num=nr_x_values, endpoint=True)
+
+    f2 = interp1d(x_values, y_list, kind=interpol_kind)
+
+    smoothed_data_x = np.linspace(
+        min_x_val,
+        max_x_val,
+        num=num,
+        endpoint=True)
+
+    smoothed_data_y = f2(smoothed_data_x)
+
+    return smoothed_data_x, smoothed_data_y
+
+def smooth_data(
+        x_list,
+        y_list,
+        num=500,
+        spider=False,
+        interpol_kind='quadratic'
+    ):
     """Smooth data
 
     x_list : list
         List with x values
     y_list : list
         List with y values
+    num : int
+        New number of interpolation points
     spider : bool
         Criteria whether spider plot or not
-    # https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
+
+    
 
     Note:
     ------
     - needs at least 4 entries in lists
+    - https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
+
     """
+
     if spider:
 
-        nr_x_values = len(x_list)
         min_x_val = min(x_list)
         max_x_val = math.pi * 2 #max is tow pi
 
-        x_values = np.linspace(min_x_val, max_x_val, num=nr_x_values, endpoint=True)
-
-        f2 = interp1d(x_values, y_list, kind='quadratic') #quadratic cubic
-
-        smoothed_data_x = np.linspace(
+        x_values = np.linspace(
             min_x_val,
             max_x_val,
-            num=num,
+            num=len(x_list),
             endpoint=True)
-    else:
-        nr_x_values = len(x_list)
-        min_x_val = min(x_list)
-        max_x_val = max(x_list)
-
-        x_values = np.linspace(min_x_val, max_x_val, num=nr_x_values, endpoint=True)
 
-        f2 = interp1d(x_values, y_list, kind='cubic')
+        f2 = interp1d(
+            x_values,
+            y_list,
+            kind='quadratic') #quadratic cubic
 
-        smoothed_data_x = np.linspace(
+        x_smooth = np.linspace(
             min_x_val,
             max_x_val,
             num=num,
             endpoint=True)
 
-    smoothed_data_y = f2(smoothed_data_x)
+        #y_smooth = f2(x_smooth)
 
-    return smoothed_data_x, smoothed_data_y
+    else:
+        # Smooth x data
+        x_smooth = np.linspace(
+            min(x_list),
+            max(x_list),
+            num=num)
+
+        f2 = interp1d(
+            x_list,
+            y_list,
+            kind=interpol_kind)
+
+    # smooth
+    y_smooth = f2(x_smooth)
+
+    return x_smooth, y_smooth
 
 def smooth_line(
         input_x_line_data,
@@ -78,6 +117,9 @@ def smooth_line(
     nr_line_points : int
         represents number of points to make between input_line_data.min and T.max
     """
+    input_x_line_data = np.array(input_x_line_data)
+    input_y_line_data = np.array(input_y_line_data)
+
     smooth_x_line_data = np.linspace(
         input_x_line_data.min(),
         input_x_line_data.max(),

energy_demand/plotting/fig_load_profile_dh_multiple.py

@@ -36,10 +36,8 @@ def run(
     """
     fig = plt.figure(
         figsize=basic_plot_functions.cm2inch(14, 25))
-
-    ax = fig.add_subplot(
-        nrows=4,
-        ncols=2)
+
+    ax = fig.add_subplot(nrows=4, ncols=2)
 
     plot_nr = 0
     row = -1
@@ -232,8 +230,6 @@ def run(
                 round(std_dev_abs, 2),
                 fontdict=font_additional_info))
             plt.title(title_info, loc='left', fontdict=font_additional_info)
-            #plt.ylabel("hours")
-            #plt.ylabel("average electricity [GW]")
 
     # ------------
     # Plot legend
@@ -358,14 +354,25 @@ def plot_radar_plot(
         linestyle='--',
         linewidth=0.5)
 
+    # plot data points
+    '''ax.scatter(angles, values, color="blue", s=10)'''
+
+    '''line_zeros = np.zeros(len(angles_smoothed))
+    ax.fill_between(
+        angles_smoothed, #zero coordinates
+        line_zeros, # line 1
+        values_smoothed, # line 2
+        color='blue',
+        alpha=0.1)'''
+
+    # Fill below
     ax.fill(
-        angles,
-        values,
+        angles_smoothed, #angles,
+        values_smoothed, #values,
         'blue', #b
         alpha=0.1)
 
     # Save fig
-    print("fig_name: " + str(fig_name))
     plt.savefig(fig_name)
 
     if plotshow: