Make map of buildout

Author: staadecker

switch_model/generators/core/build.py

@@ -893,4 +893,18 @@ def graph_buildout_per_tech(tools):
     # Set the y-axis to use percent
     ax.yaxis.set_major_formatter(tools.mplt.ticker.PercentFormatter(1.0))
     # Horizontal line at 100%
+<<<<<<< HEAD
     ax.axhline(y=1, linestyle="--", color="b")
+=======
+    ax.axhline(y=1, linestyle="--", color='b')
+
+@graph(
+    "buildout_map",
+    title="Map of online capacity per load zone."
+)
+def buildout_map(tools):
+    buildout = tools.get_dataframe("gen_cap.csv").rename({"GenCapacity": "value"}, axis=1)
+    buildout = tools.transform.gen_type(buildout)
+    buildout = buildout.groupby(["gen_type", "gen_load_zone"], as_index=False)["value"].sum()
+    tools.graph_map_pychart(buildout)
+>>>>>>> 292648b0 (Make map of buildout)

switch_model/tools/graph/main.py

@@ -962,6 +962,93 @@ def graph_matrix(self, df, value_column, ylabel, row_specifier, col_specifier):
         legend_pairs = legend.items()
         fig.legend([h for _, h in legend_pairs], [l for l, _ in legend_pairs])
 
+    def graph_map_pychart(self, df, max_size=2500):
+        """
+        Graphs the data from the dataframe to a map pychart.
+        The dataframe should have 3 columns, gen_load_zone, gen_type and value.
+        """
+        # Scale the dataframe so the pie charts have the right size
+        current_max_size = df.groupby("gen_load_zone")["value"].sum().max()
+        df["value"] *= max_size / current_max_size
+
+        try:
+            import geopandas
+        except ModuleNotFoundError:
+            raise Exception("Could not find package 'geopandas'. Make sure you install it through conda.")
+
+        # Read shape files
+        try:
+            wecc_lz = geopandas.read_file("inputs/maps/wecc_102009.shp", crs="ESRI:102009")
+            wecc_states = geopandas.read_file("inputs/maps/wecc_states_4326.shp")
+        except FileNotFoundError:
+            raise Exception("Can't create maps, shape files are missing. Try running switch get_inputs.")
+
+        # Find the center point
+        wecc_lz["Center_point"] = wecc_lz["geometry"].centroid
+
+        # Extract lat and lon from the centerpoint
+        wecc_lz["lat"] = wecc_lz.Center_point.map(lambda p: p.x)
+        wecc_lz["lng"] = wecc_lz.Center_point.map(lambda p: p.y)
+
+        # New dataframe with the centerpoint geometry
+        gdf = geopandas.GeoDataFrame(
+            wecc_lz[["LOAD_AREA", "lat", "lng"]],
+            geometry=geopandas.points_from_xy(x=wecc_lz.lat, y=wecc_lz.lng),
+            crs="ESRI:102009",
+        )
+
+        # REFERENC: https://tinyurl.com/app/myurls
+        def pie_plot(x, y, ratios, colors, size, ax=None):
+            # determine arches
+            start = 0.0
+            xy = []
+            s = []
+            for ratio in ratios:
+                x0 = [0] + np.cos(
+                    np.linspace(2 * np.pi * start, 2 * np.pi * (start + ratio), 30)
+                ).tolist()  # 30
+                y0 = [0] + np.sin(
+                    np.linspace(2 * np.pi * start, 2 * np.pi * (start + ratio), 30)
+                ).tolist()  # 30
+
+                xy1 = np.column_stack([x0, y0])
+                s1 = np.abs(xy1).max()
+
+                xy.append(xy1)
+                s.append(s1)
+                start += ratio
+
+            for xyi, si, c in zip(xy, s, colors):
+                ax.scatter(
+                    [x], [y], marker=xyi, s=size * si ** 2, c=c, edgecolor="k", zorder=10
+                )
+
+
+        projection = "EPSG:4326"
+
+        cap_by_lz = gdf.merge(df, right_on="gen_load_zone", left_on="LOAD_AREA").to_crs(
+            projection
+        )
+
+        ax = self.get_axes(size=(15, 10))
+        ax.set_aspect("equal")
+        wecc_states.plot(ax=ax, lw=0.5, edgecolor="white", color="#E5E5E5", zorder=-999)
+
+        assert not cap_by_lz["gen_type"].isnull().values.any()
+        colors = self.get_colors()
+        for index, group in cap_by_lz.groupby(["gen_load_zone"]):
+            x, y = group["geometry"].iloc[0].x, group["geometry"].iloc[0].y
+            group_sum = group.groupby("gen_type")["value"].sum().sort_values()
+            group_sum = group_sum[(group_sum != 0)].copy()
+
+            tech_color = [colors[tech] for tech in group_sum.index.values]
+            total_size = group_sum.sum()
+            ratios = (group_sum / total_size).values
+            pie_plot(x, y, ratios, tech_color, ax=ax, size=total_size)
+        ax.axis("off")
+
+
+
     @staticmethod
     def sort_build_years(x):
         def val(v):

switch_model/wecc/get_inputs/get_inputs.py

@@ -876,9 +876,7 @@ def query_db(full_config, skip_cf):
     # Make graphing files
     graph_config = os.path.join(os.path.dirname(__file__), "graph_config")
     print("\tgraph_config files...")
-    for root, dirs, files in os.walk(graph_config):
-        for name in files:
-            shutil.copy(os.path.join(root, name), ".")
+    shutil.copytree(graph_config, ".", dirs_exist_ok=True)
 
 
 def write_wind_to_solar_ratio(wind_to_solar_ratio):

switch_model/wecc/get_inputs/graph_config/maps/wecc_102009.cpg

@@ -0,0 +1 @@
+UTF-8

switch_model/wecc/get_inputs/graph_config/maps/wecc_102009.prj

@@ -0,0 +1 @@
+PROJCS["North_America_Lambert_Conformal_Conic",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-96.0],PARAMETER["Standard_Parallel_1",20.0],PARAMETER["Standard_Parallel_2",60.0],PARAMETER["Latitude_Of_Origin",40.0],UNIT["Meter",1.0]]

switch_model/wecc/get_inputs/graph_config/maps/wecc_102009.qmd

@@ -0,0 +1,43 @@
+<!DOCTYPE qgis PUBLIC 'http://mrcc.com/qgis.dtd' 'SYSTEM'>
+<qgis version="3.20.0-Odense">
+  <identifier></identifier>
+  <parentidentifier></parentidentifier>
+  <language></language>
+  <type>dataset</type>
+  <title></title>
+  <abstract></abstract>
+  <contact>
+    <name></name>
+    <organization></organization>
+    <position></position>
+    <voice></voice>
+    <fax></fax>
+    <email></email>
+    <role></role>
+  </contact>
+  <links/>
+  <fees></fees>
+  <encoding></encoding>
+  <crs>
+    <spatialrefsys>
+      <wkt></wkt>
+      <proj4></proj4>
+      <srsid>0</srsid>
+      <srid>0</srid>
+      <authid></authid>
+      <description></description>
+      <projectionacronym></projectionacronym>
+      <ellipsoidacronym></ellipsoidacronym>
+      <geographicflag>false</geographicflag>
+    </spatialrefsys>
+  </crs>
+  <extent>
+    <spatial crs="" dimensions="2" minz="0" miny="0" maxy="0" minx="0" maxx="0" maxz="0"/>
+    <temporal>
+      <period>
+        <start></start>
+        <end></end>
+      </period>
+    </temporal>
+  </extent>
+</qgis>

switch_model/wecc/get_inputs/graph_config/maps/wecc_102009.shp

@@ -0,0 +1 @@
+UTF-8

switch_model/wecc/get_inputs/graph_config/maps/wecc_102009.shx

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]