predictive app

app.py

@@ -5,6 +5,7 @@ from datasets import load_dataset
 import os
 from src.basic_plot import basic_chart
 from src.map_viz import calling_map_viz
+from src.map_viz_pred import calling_pred_map_viz
 from src.data_ingestion import daily_average
 from src.heatmap import HeatMap
 from src.data_ingestion import remove_previous_view, merge_volumes
@@ -91,6 +92,9 @@ def main():
             st.plotly_chart(heatmap.update_day_bar_chart(day_choice))
 
     else: 
+
+        st.sidebar.markdown("Select Plots to show")
+        checkbox_two_pred = st.sidebar.checkbox('Predictive Traffic Map', value = True)
         
         figs = gen_fig()
         today = get_today()
@@ -119,5 +123,22 @@ def main():
         st.plotly_chart(predicted_figure(clf, starter_variables, figs))
         st.plotly_chart(pred_bars(d, final_table))
         
+
+        if checkbox_two_pred:
+            st.subheader("Predictive Traffic Map")
+
+
+            hour_choice = st.selectbox(
+                "Choose Hour",
+                options= hours,
+                key = "hour", index = hours.index("08:00")
+            )
+
+            days = ["Monday", "Tuesday", "Wednesday", 
+                    "Thursday", "Friday","Saturday", "Sunday"]
+            day_choice = st.selectbox("Choose Day of the Week", options = days, key = "day", index = days.index("Saturday"))
+
+            st.pyplot(calling_pred_map_viz(counts_df, day_choice, hour_choice))
+
 if __name__ == "__main__":
     main()

src/map_viz_pred.py

@@ -0,0 +1,108 @@
+from __future__ import division, print_function
+from six import StringIO
+from svgpath2mpl import parse_path
+from collections import defaultdict
+from src.pred_plot import hour_rounder
+import xml.etree.ElementTree as etree
+import re
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import requests
+import pandas as pd
+import datetime
+
+import warnings
+warnings.filterwarnings("ignore")
+
+def calling_pred_map_viz(counts_df1, input_day = None, input_hour = None):
+    r = "svg/snazzy-image-01.svg"
+    tree = etree.parse(r)
+    root = tree.getroot()
+    path_elems = root.findall('.//{http://www.w3.org/2000/svg}path')
+
+    paths = [parse_path(elem.attrib['d']) for elem in path_elems]
+    facecolors = []
+    edgecolors = []
+    linewidths = []
+    for elem in path_elems:
+        facecolors.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("fill", "none"))
+        edgecolors.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("stroke", "none"))
+        linewidths.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("stroke-width", "none").replace("px", ""))
+
+    path_id = defaultdict(int)
+    for i, elem in enumerate(path_elems):
+        try:
+            #print(i, elem.attrib['id'])
+            path_id[elem.attrib['id']] = i
+        except:
+            continue
+
+    counts_df1['total'] = counts_df1['car'] + counts_df1['motorcycle'] + counts_df1['large_vehicle']
+    counts_df1.loc[:,'date_time'] = pd.to_datetime(counts_df1.loc[:,'date'] + " "+ counts_df1.loc[:,'time'], format='%Y-%m-%d %H:%M:%S')
+    counts_df1.loc[:,'hour'] = counts_df1.loc[:,'date_time'].apply(hour_rounder)
+    counts_df1.loc[:,'day_name'] = counts_df1.loc[:,'date_time'].dt.day_name()
+
+    if (input_day != None) & (input_hour != None):
+        filtered_day = input_day
+        filtered_hour = input_hour
+    else:
+        filtered_date = counts_df1.iloc[-1]['date']
+        filtered_time = counts_df1.iloc[-1]['time']
+        filtered_day = counts_df1.iloc[-1]['day_name']
+        filtered_hour = counts_df1.iloc[-1]['hour']
+
+    
+    # filtered_date = counts_df1.iloc[-1]['date']
+    # filtered_time = counts_df1.iloc[-1]['time']
+    # filtered_day = counts_df1.iloc[-1]['day_name']
+    # filtered_hour = counts_df1.iloc[-1]['hour']
+
+
+    day_hour_view_group = counts_df1.groupby(by=['view', 'day_name', 'hour'])['total'].mean().reset_index()
+    count_max = day_hour_view_group['total'].max()
+    count_min = day_hour_view_group['total'].min()
+
+
+    count_dict = {"woodlands_to_sg" :day_hour_view_group.loc[(day_hour_view_group['view'] == 'Woodlands - to SG') & (day_hour_view_group['day_name'] == filtered_day) & (day_hour_view_group['hour'] == filtered_hour), "total" ].iloc[0],
+                  "woodlands_to_jh" :day_hour_view_group.loc[(day_hour_view_group['view'] == 'Woodlands - to Johor') & (day_hour_view_group['day_name'] == filtered_day) & (day_hour_view_group['hour'] == filtered_hour), "total" ].iloc[0],
+                  "tuas_to_sg" :day_hour_view_group.loc[(day_hour_view_group['view'] == 'Tuas - to SG') & (day_hour_view_group['day_name'] == filtered_day) & (day_hour_view_group['hour'] == filtered_hour), "total" ].iloc[0],
+                  "tuas_to_jh" :day_hour_view_group.loc[(day_hour_view_group['view'] == 'Tuas - to Johor') & (day_hour_view_group['day_name'] == filtered_day) & (day_hour_view_group['hour'] == filtered_hour), "total" ].iloc[0]
+                  }
+
+    values = np.array([0., 0.25, 1.])
+    values = np.sort(np.array(values))
+    values = np.interp(values, (values.min(), values.max()), (0., 1.))
+    colors = ["#539f6b", "#ffc835", "#bf0000"]
+    cmap = mpl.colors.LinearSegmentedColormap.from_list("custom", list(zip(values, colors)))
+
+    norm = mpl.colors.Normalize(vmin=count_min, vmax=count_max)
+
+
+
+    hex_dict = {k: mpl.colors.to_hex(cmap(norm(v))) for k, v in count_dict.items()}
+    color_dict = defaultdict(str)
+
+    for k, i in path_id.items():
+        #print(k, i)
+        color_dict[i] = hex_dict[k]
+
+    for k, i in color_dict.items():
+        #print(k,i)
+        facecolors[k] = i
+
+    collection = mpl.collections.PathCollection(paths, 
+                                          edgecolors=edgecolors, 
+                                          linewidths=[int(i)/100 for i in linewidths if i != 'none'],
+                                          facecolors=[i.strip() for i in facecolors])
+
+
+
+    fig = plt.figure(figsize=(10,10))
+    ax = fig.add_subplot(111)
+    collection.set_transform(ax.transData)
+    ax.add_artist(collection)
+    ax.set_xlim([100, 1900])
+    ax.set_ylim([1800, 0])
+    ax.set_title(filtered_day+ " | " + filtered_hour + " SGT", fontname = 'Georgia')
+    return fig