added miovision data and another testing file

Miovision Data/example_script.py

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+import pandas as pd
+
+# Define the files we want to load
+travel_time_file = "Travel Time - train dataset - Raw.csv"
+tmc_file = "Dorchester Road and Huron Church Road_tmc_data_train.csv"
+
+# Load the TMC data into a dataframe
+tmc_df = pd.read_csv(tmc_file, parse_dates=["dt_bin"])
+tt_df = pd.read_csv(travel_time_file, parse_dates=["Start time", "End time"])
+
+# Some code to prepare and aggreagte the travel time data
+
+tt_agg_df = tt_df.groupby(
+    [
+        pd.Grouper(key="Start time", freq="15Min"),  # You can adjust the frequency here
+        "Source name",
+        "Source ID",
+        "Destination name",
+        "Destination ID",
+    ]
+).agg(["mean", "median", "count"])
+
+# Reset the index to make joining simpler
+tt_agg_df.reset_index()
+
+# And let's flatten the column hiearchy
+tt_agg_df.columns = [" ".join(col).strip() for col in tt_agg_df.columns.values]
+
+# We can also aggregate the TMC data
+tmc_agg_df = tmc_df.groupby(["Intersection Name", "Intersection ID", "dt_bin"]).sum()
+tmc_agg_df = tmc_agg_df.reset_index()
+
+# Here's how you can join the datasets together
+
+merged_df = pd.merge(
+    tmc_agg_df,
+    tt_agg_df,
+    how="left",
+    left_on=["dt_bin", "Intersection ID"],
+    right_on=["Start time", "Source ID"],
+)

asdf.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+from basic_tests import dorchester_arr
+
+testing = pd.read_csv("Miovision Data/Dorchester Road and Huron Church Road_tmc_data_test.csv")[["dt_bin", "Direction", "Movement Type",
+                                                                                  "Vehicle Classification",
+                                                                                  "Number of Vehicles"]]
+print(dorchester_arr[0])
+
+
+vehicles_aggregate = testing[["Vehicle Classification", "Number of Vehicles"]]
+
+vehicles = testing["Vehicle Classification"]
+unique_vehicles = []
+for vehicle in vehicles:
+    if vehicle not in unique_vehicles:
+        unique_vehicles.append(vehicle)
+
+#print(vehicles_aggregate)
+
+totals = {unique_vehicles[i]: 0 for i in range(0, len(unique_vehicles))}
+
+for i in range(0, len(vehicles_aggregate)):
+    totals[vehicles_aggregate["Vehicle Classification"][i]] += vehicles_aggregate["Number of Vehicles"][i]
+
+
+#print(totals)