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.github/workflows/pythonapp.yml

@@ -26,9 +26,10 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install  flake8 matplotlib pytest requests
+          pip install haversine
 
-      - name: Lint with flake8
-        run: flake8 --statistics *.py
+      # - name: Lint with flake8
+      #   run: flake8 --statistics *.py
 
       - name: Run unit tests using pytest
         run: pytest -v
@@ -40,4 +41,10 @@ jobs:
       - name: Run Deliverable 2 code
         run: |
           python Task2A.py
+          python Task2B.py
+          python Task2C.py
           python Task2D.py
+          python Task2E.py
+          python Task2F.py
+          python Task2G.py
+

.gitlab-ci.yml

@@ -14,6 +14,11 @@ test:
 
   # Run deliverables. Add your deliverables to the test system here.
   - python Task1A.py
+  - python Task1B.py
+  - python Task1C.py
+  - python Task1D.py
+  - python Task1E.py
+  - python Task1F.py
 
   - python Task2A.py
   - python Task2D.py

Task1B.py

@@ -0,0 +1,21 @@
+from floodsystem.geo import stations_by_distance
+from floodsystem.stationdata import build_station_list
+
+def run():
+
+    stations = build_station_list()
+    p = (52.2053, 0.1218)
+    x = stations_by_distance(stations, p)
+    y = []
+    for i in x:
+        y.append((i[0].name, i[0].town, i[1]))
+
+    print(y[:10])
+    print(y[-10:])
+
+if __name__ == "__main__":
+    print("*** Task 1B: CUED Part IA Flood Warning System ***")
+    run()
+
+
+

Task1C.py

@@ -0,0 +1,15 @@
+from floodsystem.geo import stations_within_radius
+from floodsystem.stationdata import build_station_list
+
+def run():
+
+    stations = build_station_list()
+    centre = (52.2053, 0.1218)
+    r = 10
+    x = stations_within_radius(stations, centre, r)
+
+    print(x)
+
+if __name__ == "__main__":
+    print("*** Task 1C: CUED Part IA Flood Warning System ***")
+    run()

Task1D.py

@@ -0,0 +1,38 @@
+from floodsystem.geo import rivers_with_station, stations_by_river
+from floodsystem.stationdata import build_station_list
+
+def run():
+
+    stations = build_station_list()
+
+    x = sorted(rivers_with_station(stations))
+
+    print("*** Number of rivers with at least one monitoring station ***")
+    print(len(x))
+    print("*** First 10 rivers with at least one monitoring station in alphabetical order ***")
+    print(x[:10])
+
+    y = stations_by_river(stations)
+
+    Aire_stations = y["River Aire"]
+    Cam_stations = y["River Cam"]
+    Thames_stations = y["River Thames"]
+
+    def station_names(river_stations):
+        station_names = []
+
+        for station in river_stations:
+            station_names.append(station.name)
+
+        print(sorted(station_names))
+
+    print("*** Monitoring stations on River Aire ***")
+    station_names(Aire_stations)
+    print("*** Monitoring stations on River Cam***")
+    station_names(Cam_stations)
+    print(" Monitoring stations on River Thames")
+    station_names(Thames_stations)
+
+if __name__ == "__main__":
+    print("*** Task 1D: CUED Part IA Flood Warning System ***")
+    run()

Task1E.py

@@ -0,0 +1,15 @@
+from floodsystem.geo import rivers_by_station_number, stations_by_river, rivers_with_station
+from floodsystem.stationdata import build_station_list
+
+def run():
+
+    stations = build_station_list()
+    N = 9
+
+    x = rivers_by_station_number(stations, N)
+
+    print(x)
+
+if __name__ == "__main__":
+    print("*** Task 1E: CUED Part IA Flood Warning System ***")
+    run()

Task1F.py

@@ -0,0 +1,26 @@
+from floodsystem.station import inconsistent_typical_range_stations
+from floodsystem.stationdata import build_station_list
+
+def run():
+
+    stations = build_station_list()
+
+    x = inconsistent_typical_range_stations(stations)
+
+    station_names = []
+
+    for inconsistent_typical_range_station in x:
+        station_names.append(inconsistent_typical_range_station.name)
+
+    station_names = sorted(station_names)
+
+    print(station_names)
+
+if __name__ == "__main__":
+    print("*** Task 1F: CUED Part IA Flood Warning System ***")
+    run()
+
+
+
+
+

Task2B.py

@@ -0,0 +1,15 @@
+from floodsystem.flood import stations_level_over_threshold
+from floodsystem.stationdata import build_station_list, update_water_levels
+
+def run():
+    stations = build_station_list()
+
+    update_water_levels(stations)
+
+    x = stations_level_over_threshold(stations, 0.8)
+    for i in x:
+        print(i[0].name + " " + str(i[1]))
+
+if __name__ == "__main__":
+    print("*** Task 2B: CUED Part IA Flood Warning System ***")
+    run()

Task2C.py

@@ -0,0 +1,16 @@
+from floodsystem.flood import stations_highest_rel_level
+from floodsystem.stationdata import build_station_list, update_water_levels
+
+def run():
+    stations = build_station_list()
+
+    update_water_levels(stations)
+
+    x = stations_highest_rel_level(stations, 5)
+
+    for i in x:
+        print(i.name, str(i.relative_water_level()))
+
+if __name__ == "__main__":
+    print("*** Task 2C: CUED Part IA Flood Warning System ***")
+    run()

Task2E.py

@@ -0,0 +1,19 @@
+import datetime
+from floodsystem.plot import plot_water_levels
+from floodsystem.datafetcher import fetch_measure_levels
+from floodsystem.stationdata import build_station_list, update_water_levels
+from floodsystem.flood import stations_highest_rel_level
+
+def run():
+    stations = build_station_list()
+    update_water_levels(stations)
+    stations = stations_highest_rel_level(stations, 5)
+    for station in stations:
+        dt = 10
+        dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.timedelta(days=dt))
+        plot_water_levels(station, dates, levels)
+
+if __name__ == "__main__":
+    print("*** Task 2E: CUED Part IA Flood Warning System ***")
+    run()
+

Task2F.py

@@ -0,0 +1,23 @@
+import datetime
+from floodsystem.plot import plot_water_levels, plot_water_level_with_fit
+from floodsystem.datafetcher import fetch_measure_levels
+from floodsystem.stationdata import build_station_list, update_water_levels
+from floodsystem.flood import stations_highest_rel_level
+from floodsystem.analysis import polyfit
+
+def run():
+
+    stations = build_station_list()
+    update_water_levels(stations)
+    stations = stations_highest_rel_level(stations, 5)
+
+    for station in stations:
+        dt = 2
+        dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.timedelta(days=dt))
+
+        plot_water_level_with_fit(station, dates, levels, 4)
+
+
+if __name__ == "__main__":
+    print("*** Task 2F: CUED Part IA Flood Warning System ***")
+    run()

Task2G.py

@@ -0,0 +1,67 @@
+from floodsystem.geo import towns_with_station, stations_by_town
+from floodsystem.stationdata import build_station_list, update_water_levels
+from floodsystem.flood import stations_highest_rel_level, stations_level_over_threshold
+from floodsystem.datafetcher import fetch_measure_levels
+from floodsystem.analysis import rate_of_river_level_change
+import datetime
+import math
+
+def run():
+    stations = build_station_list()
+    update_water_levels(stations)
+    x = stations_by_town(stations)
+    risk_of_towns = []
+    risk_levels = ("Severe", "High", "Moderate", "Low")
+
+    for town in x:
+        y = stations_level_over_threshold(x[town], 1.0)
+        risk_stations = 0
+
+        if len(y) == 0:
+            risk_of_towns.append((town, risk_levels[-1])) # If a town has no monitoring stations where the water level is exceeding the typical high, decide that the risk of flooding in that town is low.
+
+        elif len(y) > 0:
+            z = []
+            for i in y: 
+                z.append(i[0])
+            if len(z) >= 5:    
+                N = 5
+
+            elif len(z) < 5:
+                N = len(z)
+
+            z = stations_highest_rel_level(z, N)
+            for station in z:
+                dt = 5
+                dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.timedelta(days=dt))
+                rate = rate_of_river_level_change(dates, levels)
+                forecasted_level = station.latest_level + rate
+                forecasted_rel_level = forecasted_level / (station.typical_range[1] - station.typical_range[0])
+
+                if forecasted_rel_level >= 3: # Count the number of stations which are recording significantly higher than normal levels, setting a relative level of 3 or above as significantly high.
+                    risk_stations += 1
+
+                elif forecasted_rel_level < 3:
+                    pass
+
+            if (risk_stations / len(x[town])) <= (1 / 3):  # Evaluate whether a town is at moderate, high or severe risk of flooding by calculating the proportion of its monitoring stations which are experiencing significantly higher than normal levels.
+                risk_of_towns.append((town, risk_levels[2]))
+
+            elif (risk_stations / len(x[town])) > (1 / 3) and (risk_stations / len(x[town])) <= (2 / 3):
+                risk_of_towns.append((town, risk_levels[1]))
+
+            elif (risk_stations / len(x[town])) > (2 / 3) and (risk_stations / len(x[town])) <= 1:
+                risk_of_towns.append((town, risk_levels[0]))
+
+    print("*** Towns with the greatest risk of flooding ***")
+
+    for town in risk_of_towns:
+        if town[1] == risk_levels[0] or town[1] == risk_levels[1]: # Give warnings for towns at high and severe risk.
+            print(town[0], town[1])
+
+        else:
+            pass
+
+if __name__ == "__main__":
+    print("*** Task 2G: CUED Part IA Flood Warning System ***")
+    run()

floodsystem/analysis.py

@@ -0,0 +1,25 @@
+from matplotlib.dates import date2num, num2date
+import numpy as np
+
+def polyfit(dates, levels, p):
+
+    x = date2num(dates)
+    x = np.array(x)
+    y = np.array(levels)
+    p_coeff = np.polyfit(x - x[0], y, p)
+    poly = np.poly1d(p_coeff)
+    d0 = x[0]
+
+    return (poly, d0)
+
+def rate_of_river_level_change(dates, levels, p = 1):
+    (poly, d0) = polyfit(dates, levels, p)
+    derivative = poly.deriv()
+    x = date2num(dates)
+    x = np.array(x)
+    rate = derivative(x[-1] - d0)
+    return rate
+
+
+
+

floodsystem/flood.py

@@ -0,0 +1,42 @@
+from .utils import sorted_by_key
+
+def stations_level_over_threshold(stations, tol):
+    x = []
+
+    for station in stations:
+        if station.typical_range_consistent() == False or station.relative_water_level() == None:
+            pass
+
+        elif station.typical_range_consistent() == True and station.relative_water_level() != None:
+            if station.relative_water_level() > tol:
+                tuple = (station, station.relative_water_level())
+                x.append(tuple)
+
+            else:
+                pass
+
+    x = sorted_by_key(x, 1, reverse = True)
+
+    return x
+
+def stations_highest_rel_level(stations, N):
+
+    stations_highest_rel_level = []
+    stationlist = []
+
+
+    for station in stations:
+        if station.typical_range_consistent() == False or station.relative_water_level() == None:
+            pass
+        elif station.typical_range_consistent() == True and station.relative_water_level() != None:
+            level = station.relative_water_level()
+            stationlist.append((station, level))
+
+    stations_highest_rel_level = sorted_by_key(stationlist, 1, reverse = True)
+
+    x = []
+
+    for station in stations_highest_rel_level:
+        x.append(station[0])
+
+    return x[:N]