Merge branch 'main' of github.com:twemyss/rse-classwork-2020 into mai…

…n. Answers UCL-RITS#90

week04/quakes.py

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+
+import json
+import requests
+import numpy as np
+import pandas as pd
+import inflect
+import matplotlib.pyplot as plt
+from datetime import datetime
+from scipy import stats
+
+# We use inflect to convert 1 into "1st", 2 into "2nd" etc
+integer_engine = inflect.engine()
+
+
+###################################################################
+# This code is split into 3 sections
+#    1. Download data and parse it
+#    2. Find and print strongest earthquakes
+#    3. Plot details of earthquake size and mean magnitude by year
+###################################################################
+
+###################################################################
+## Section 1 - load data and parse it into a dataframe
+###################################################################
+
+# Load the data from the USGS earthquake service
+quakes = requests.get("http://earthquake.usgs.gov/fdsnws/event/1/query.geojson",
+                      params={
+                          'starttime': "2000-01-01",
+                          "maxlatitude": "58.723",
+                          "minlatitude": "50.008",
+                          "maxlongitude": "1.67",
+                          "minlongitude": "-9.756",
+                          "minmagnitude": "1",
+                          "endtime": datetime.today().strftime('%Y-%m-%d'),
+                          "orderby": "time-asc"}
+                      )
+
+# Convert the result into a dictionary
+quakes_object = json.loads(quakes.text)
+
+# Now convert all the earthquakes into a pandas dataframe
+quakes_dataframe = pd.json_normalize(quakes_object['features'])
+
+###################################################################
+## Section 2 - find and print strongest earthquakes
+###################################################################
+
+# Get the row in the dataframe that corresponds to the strongest quake
+max_quake = quakes_dataframe[quakes_dataframe['properties.mag'] == quakes_dataframe['properties.mag'].max()]
+
+# Reset the index so the row numbers start from 0
+max_quake = max_quake.reset_index(drop=True)
+
+# Print all the strongest earthquakes
+for index, quake in max_quake.iterrows():
+    # Print the strongest earthquake
+    print(f"The maximum magnitude is {quake['properties.mag']} "
+          f"and it occured for the {integer_engine.ordinal(index + 1)} time at ({', '.join(str(x) for x in quake['geometry.coordinates'][0:2])}) at {quake['geometry.coordinates'][2]} kilometres deep.")
+
+###################################################################
+## Section 3 - plot by year, the number and mean quake magnitude
+###################################################################
+
+# Convert milisecond unix timestamp to datetime
+quakes_dataframe['properties.time'] = quakes_dataframe['properties.time'].apply(lambda x: datetime.fromtimestamp(x/1000))
+
+# Group by year and get the mean magnitude and number of magnitudes
+yearly_quakes = quakes_dataframe['properties.mag'].groupby(quakes_dataframe['properties.time'].dt.year).agg(['mean', 'count', stats.sem])
+
+# Get list of years
+years = yearly_quakes.index.tolist();
+
+# Take every 5th year for ticks
+xtick_years = np.append(np.arange(np.min(years), np.max(years), step=5), np.max(years))
+
+# Plot mean magnitude of quakes each year
+f = plt.figure()
+plt.errorbar(years, yearly_quakes['mean'], yearly_quakes['sem'], capsize=3)
+
+# Configure axes
+plt.xticks(xtick_years)
+plt.xlabel('Year', fontsize=18)
+plt.ylabel('Mean magnitude (Richter)', fontsize=16)
+
+# Show, and save as PDF
+plt.show()
+f.savefig("quake_magnitude_by_year.pdf", bbox_inches='tight')
+
+# Plot number of quakes per year
+f = plt.figure()
+plt.plot(years, yearly_quakes['count'])
+
+# Configure axes
+plt.xticks(xtick_years)
+plt.xlabel('Year', fontsize=18)
+plt.ylabel('Number of quakes', fontsize=16)
+
+# Show, and save as PDF
+plt.show()
+f.savefig("quake_count_by_year.pdf", bbox_inches='tight')

week04/requirements.txt

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+numpy
+scipy
+pandas
+inflect
+datetime

week05-testing/test_times.py

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+import times
+import pytest
+
+class TestTimeOverlap:
+    """TestTimeOverlap class contains all functions involved in
+    testing the overlap of two times based on example UCL RITS code"""
+
+    def test_given_input(self):
+        """Tests hardcoded input given as part of the example problem"""
+        result = times.compute_overlap_time(times.large, times.short)
+        expected = [('2010-01-12 10:30:00', '2010-01-12 10:37:00'), ('2010-01-12 10:38:00', '2010-01-12 10:45:00')]
+        assert result == expected
+
+    def test_non_overlapping_times(self):
+        """Tests two time ranges that do not overlap"""
+        t1 = times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00")
+        t2 = times.time_range("2010-01-12 12:30:00", "2010-01-12 12:45:00")
+        result = times.compute_overlap_time(t1, t2)
+        expected = []
+        assert result == expected
+
+    def test_several_intervals(self):
+        """Tests intervals where two intervals overlap"""
+        t1 = times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00", 2, 0)
+        t2 = times.time_range("2010-01-12 10:30:00", "2010-01-12 11:30:00", 2, 0)
+        result = times.compute_overlap_time(t1, t2)
+        expected = [('2010-01-12 10:30:00', '2010-01-12 11:00:00'),('2010-01-12 11:00:00', '2010-01-12 11:30:00')]
+        assert result == expected
+
+    def test_touching_times(self):
+        """Tests intervals where one finishes at the same time another starts"""
+        t1 = times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00")
+        t2 = times.time_range("2010-01-12 12:00:00", "2010-01-12 12:30:00")
+        result = times.compute_overlap_time(t1, t2)
+        expected = []
+        assert result == expected
+
+    def test_negative_time_interval(self):
+        """Tests a time range which goes backwards"""
+        t1 = times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00")
+        t2 = times.time_range("2010-01-12 11:00:00", "2010-01-12 10:30:00")
+        with pytest.raises(ValueError):
+            times.compute_overlap_time(t1, t2)

week05-testing/test_times_parameterised.py

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+import times
+import pytest
+
+class TestTimeOverlap:
+    """TestTimeOverlap class contains all functions involved in
+    testing the overlap of two times based on example UCL RITS code"""
+
+    @pytest.mark.parametrize("time_range_1, time_range_2, expected", [
+        # Test hardcoded input from example problem
+        (times.large, times.short, [('2010-01-12 10:30:00', '2010-01-12 10:37:00'), ('2010-01-12 10:38:00', '2010-01-12 10:45:00')]),
+        # Tests two time ranges that do not overlap
+        (times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00"), times.time_range("2010-01-12 12:30:00", "2010-01-12 12:45:00"), []),
+        # Tests intervals where each has several intervals and two sub-intervals overlap
+        (times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00", 2, 0), times.time_range("2010-01-12 10:30:00", "2010-01-12 11:30:00", 2, 0), [('2010-01-12 10:30:00', '2010-01-12 11:00:00'),('2010-01-12 11:00:00', '2010-01-12 11:30:00')]),
+        # Tests two intervals where one finishes at exactly the same time another started
+        (times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00"), times.time_range("2010-01-12 12:00:00", "2010-01-12 12:30:00"), [])
+                              ])
+    def test_given_input(self, time_range_1, time_range_2, expected):
+        """Tests hardcoded input given as part of the example problem"""
+        result = times.compute_overlap_time(time_range_1, time_range_2)
+        assert result == expected
+
+    def test_negative_time_interval(self):
+        """Tests a time range which goes backwards"""
+        t1 = times.time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00")
+        t2 = times.time_range("2010-01-12 11:00:00", "2010-01-12 10:30:00")
+        with pytest.raises(ValueError):
+            times.compute_overlap_time(t1, t2)

week05-testing/times.py

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+import datetime
+
+
+def time_range(start_time, end_time, number_of_intervals=1, gap_between_intervals_s=0):
+    start_time_s = datetime.datetime.strptime(start_time, "%Y-%m-%d %H:%M:%S")
+    end_time_s = datetime.datetime.strptime(end_time, "%Y-%m-%d %H:%M:%S")
+    d = (end_time_s - start_time_s).total_seconds() / number_of_intervals + gap_between_intervals_s * (1 / number_of_intervals - 1)
+    sec_range = [(start_time_s + datetime.timedelta(seconds=i * d + i * gap_between_intervals_s),
+                  start_time_s + datetime.timedelta(seconds=(i + 1) * d + i * gap_between_intervals_s))
+                 for i in range(number_of_intervals)]
+    return [(ta.strftime("%Y-%m-%d %H:%M:%S"), tb.strftime("%Y-%m-%d %H:%M:%S")) for ta, tb in sec_range]
+
+
+def compute_overlap_time(range1, range2):
+    overlap_time = []
+    for start1, end1 in range1:
+        for start2, end2 in range2:
+            if start1 > end1 or start2 > end2:
+                raise ValueError("Start time cannot be bigger than end time")
+            low = max(start1, start2)
+            high = min(end1, end2)
+            if low != high and low < high:
+                overlap_time.append((low, high))
+    return overlap_time
+
+# Define two time ranges
+large = time_range("2010-01-12 10:00:00", "2010-01-12 12:00:00")
+short = time_range("2010-01-12 10:30:00", "2010-01-12 10:45:00", 2, 60)
+
+if __name__ == "__main__":
+    print(compute_overlap_time(large, short))