created plot_any fnc

basic_tests.py

@@ -11,8 +11,11 @@ def split_df(df): # returns an array where each element is one traffic type of t
     start = 0
     end = 5670
     while (end < 40344):
-        split_arr.append(df.iloc[start:end].copy())
-        convert_to_int(split_arr[-1])
+        new_df = df.iloc[start:end].copy()
+        convert_to_int(new_df)
+        new_df['Start Time'] = pd.to_datetime(new_df['Start Time'])
+        new_df.set_index(['Start Time'], inplace=True)
+        split_arr.append(new_df)
         start += 5764
         end = start + 5760
     return split_arr

day_aggregate.py

@@ -3,27 +3,60 @@
 import matplotlib.pyplot as plt
 from basic_tests import dorchester_arr, malden_arr, totten_arr
 
+plt.close('all')
+
 def plot_data(df): # plot data for one direction of traffic at an intersection
-    ax1 = df['Thru'].plot(label="Through")
-    ax2 = df['Right'].plot(label="Right")
-    ax3 = df['Left'].plot(label="Left")
-    ax4 = df['U-Turn'].plot(label="U-Turn")
+    df.plot()
+    plt.legend(loc="upper right")
+
+# arr = array of dfs
+# d = direction (N = 0, E = 1, S = 2, W = 3)
+# i = index of arr with the df you want to graph
+def plot_any(arr, d, i):
+    d_dict = {0: 'Southbound', 1: 'Westbound', 2: 'Northbound', 3: 'Eastbound'}
+    i_dict = {0: 'General Traffic', 1: 'Single-Unit Trucks', 2: 'Articulated Trucks', 3: 'Buses',
+              4: 'Work Vans', 5: 'Bicycles', 6: 'Pedestrians'}
+    graph_title= d_dict[d] + " " + i_dict[i]
+    if (d==0):
+        df = arr[i].iloc[:, :6]
+    else:
+        df = arr[i].iloc[:, np.r_[0, d*6+1:d*6+6]]
+    df_grouped = df.groupby([df.index.hour, df.index.minute]).mean()
+    ax = df_grouped.plot(title=graph_title)
+    ax.set_xlabel("Time of day (hour, minute)")
+    ax.set_ylabel("# of vehicles")
     plt.legend(loc="upper right")
-    plt.show()
 
+plot_any(dorchester_arr,3, 5)
+plt.show()
+
+##### MOST OF BELOW CAN NOW BE COMPLETED WITH PLOT_ANY #####
 ### Setup
-d_lights_n = dorchester_arr[0]
-d_lights_n['Start Time'] = pd.to_datetime(d_lights_n['Start Time'])
-d_lights_n.set_index(['Start Time'], inplace=True)
+# d_lights_n = dorchester_arr[0].iloc[:, :6]
+# d_lights_n['Start Time'] = pd.to_datetime(d_lights_n['Start Time'])
+# d_lights_n.set_index(['Start Time'], inplace=True)
+
+# d_sut_n = dorchester_arr[1].iloc[:,0:6]
+# print(d_sut_n)
+# d_lights_e = dorchester_arr[0].iloc[:, np.r_[0, 7:12]]
+# d_sut_n['Start Time'] = pd.to_datetime(d_sut_n['Start Time'])
+# d_sut_n.set_index(['Start Time'], inplace=True)
 
 ## Filter by day (monday = 0)
-d_lights_n_monday = d_lights_n[d_lights_n.index.weekday == 0]
+# d_lights_n_monday = d_lights_n[d_lights_n.index.weekday == 0]
 
 ## Resample: split data at different intervals (ex. every day or every 30 min instead of 15min)
 # dlnr = d_lights_n_monday.resample('D').mean()
 
 ## Group by day (view average data for one day)
 # dlnr = d_lights_n.groupby(lambda x: x.hour+x.minute, axis=0).mean() # doesn't work
-dlnr = d_lights_n.groupby([d_lights_n.index.hour, d_lights_n.index.minute]).mean()
-print(dlnr)
-plot_data(dlnr)
+# dlnr = d_sut_n.groupby([d_sut_n.index.hour, d_sut_n.index.minute]).mean()
+#
+# print(dlnr)
+# plot_data(dlnr)
+# plt.show()
+
+# dler = d_lights_e.groupby([d_lights_e.index.hour, d_lights_e.index.minute]).mean()
+# print(dler.info)
+# plot_data(dler)
+# plt.show()

plotting_data.py

@@ -3,7 +3,8 @@
 import matplotlib.pyplot as plt
 from basic_tests import dorchester_arr, malden_arr, totten_arr
 
-# Plots right, left &
+## Testing data plotting
+# Plots right, left, through, u-turn
 d_lights_n = dorchester_arr[0].iloc[:, :6]
 plt.ylabel('Amount of Traffic')
 plt.xlabel('Date/Time')