new charts

.gitignore

@@ -1,2 +1,3 @@
 *.jpg
-*.csv
+*.csv
+__pycache__

app.py

@@ -3,16 +3,8 @@ import pandas as pd
 import plotly.express as px
 from datasets import load_dataset
 import os
-
-# @st.cache()
-def bar_chart(counts_df):
-    fig = px.bar(counts_df, x = 'car', y = 'large_vehicle')
-
-    # fig_app_by_arc.update_layout(
-    #                             xaxis_title="Name",
-    #                             yaxis_title="",
-    #                             )
-    return fig
+from src.basic_plot import basic_chart
+from src.map_viz import calling_map_viz
 
 def daily_average(counts_df):
     
@@ -55,13 +47,15 @@ def main():
                 )
 
     # Select Plot Option
-    st.sidebar.markdown("Checkboxes")
-    checkbox_one = st.sidebar.checkbox('Lorem Ip', value = True) # rename as necessary
-    checkbox_two = st.sidebar.checkbox('Lorem Ipsum', value = False)
+    st.sidebar.markdown("Select Plots to show")
+    checkbox_one = st.sidebar.checkbox('Overall Traffic', value = True) # rename as necessary
+    checkbox_two = st.sidebar.checkbox('Traffic Map', value = True)
 
     if checkbox_one:
-        st.write("Simple Bar Chart")
-        st.plotly_chart(bar_chart(counts_df),use_container_width=True)
+        st.plotly_chart(basic_chart(counts_df),use_container_width=True)
+
+    if checkbox_two:
+        st.pyplot(calling_map_viz(counts_df))
 
 if __name__ == "__main__":
     main()

env.yml

@@ -6,4 +6,5 @@ dependencies:
     - datasets>=2.8.0
     - pandas=1.5.3
     - plotly=5.13.0
-    - streamlit=1.18.1
+    - streamlit=1.18.1
+    - svgpath2mpl=1.0.0

requirements.txt

@@ -1,3 +1,4 @@
 pandas
 plotly
 datasets
+svgpath2mpl

src/basic_plot.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from datasets import load_dataset
+import os
+
+    
+def basic_chart(counts_df):
+    # data processing
+    counts_df['traffic'] = counts_df['car'] + counts_df['motorcycle'] + counts_df['large_vehicle']
+    counts_df['datetime'] = pd.to_datetime(counts_df['date'] + ' ' + counts_df['time'])
+    counts_df['weekday'] = counts_df['datetime'].dt.strftime('%A')
+    counts_df['hour'] = counts_df['datetime'].dt.strftime('%H')
+    
+    # plot types
+    plot = st.sidebar.selectbox('Choose Plot', options=['Day','Hour','Raw'])
+    
+    # view types
+    view = st.sidebar.selectbox('Choose View', options=counts_df['view'].unique())
+    filtered_views = counts_df[counts_df['view'] == view]
+    
+    # conditional views
+    if plot == 'Day':    
+        fig = px.bar(filtered_views, x='weekday', y='traffic')
+    elif plot == 'Hour':
+        fig = px.bar(filtered_views, x='hour', y='traffic')
+    elif plot == 'Raw':
+        fig = px.bar(filtered_views, x='datetime', y='traffic')
+        
+    return fig

src/map_viz.py

@@ -0,0 +1,83 @@
+from __future__ import division, print_function
+from six import StringIO
+from svgpath2mpl import parse_path
+from collections import defaultdict
+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
+
+
+def calling_map_viz(counts_df):
+    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_df = pd.read_csv("counts_dataset.csv")
+    counts_df['total'] = counts_df['car'] + counts_df['motorcycle'] + counts_df['large_vehicle']
+    count_max = counts_df['total'].max()
+    count_min = counts_df['total'].min()
+    last_date = counts_df.iloc[-1:,0].values[0]
+    last_time = counts_df.iloc[-1:,1].values[0]
+
+
+    count_dict = {"woodlands_to_sg" :counts_df.loc[counts_df['view'].str.contains(r'''Woodlands([a-zA-Z0-9_.+-]+)sg''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(),
+                  "woodlands_to_jh" :counts_df.loc[counts_df['view'].str.contains(r'''Woodlands([a-zA-Z0-9_.+-]+)jh''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(),
+                  "tuas_to_sg" :counts_df.loc[counts_df['view'].str.contains(r'''Tuas([a-zA-Z0-9_.+-]+)sg''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(),
+                  "tuas_to_jh" :counts_df.loc[counts_df['view'].str.contains(r'''Tuas([a-zA-Z0-9_.+-]+)jh''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum()
+                  }
+
+    values = np.array([0., 0.5, 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():
+        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])
+    return fig