Answers UCL-COMP0233-24-25/RSE-Classwork#10

earthquakes.py

@@ -1,3 +1,4 @@
+import json
 # The Python standard library includes some functionality for communicating
 # over the Internet.
 # However, we will use a more powerful and simpler library called requests.
@@ -21,37 +22,56 @@ def get_data():
             "orderby": "time-asc"}
     )
 
+
     # The response we get back is an object with several fields.
     # The actual contents we care about are in its text field:
     text = response.text
     # To understand the structure of this text, you may want to save it
     # to a file and open it in VS Code or a browser.
     # See the README file for more information.
-    ...
 
     # We need to interpret the text to get values that we can work with.
     # What format is the text in? How can we load the values?
-    return ...
+    return json.loads(text)
 
 def count_earthquakes(data):
     """Get the total number of earthquakes in the response."""
-    return ...
+    return data["metadata"]["count"]
 
 
 def get_magnitude(earthquake):
     """Retrive the magnitude of an earthquake item."""
-    return ...
+    return earthquake["properties"]["mag"]
 
 
 def get_location(earthquake):
     """Retrieve the latitude and longitude of an earthquake item."""
+    coordinates = earthquake["geometry"]["coordinates"]
     # There are three coordinates, but we don't care about the third (altitude)
-    return ...
+    return (coordinates[0], coordinates[1])
 
 
 def get_maximum(data):
     """Get the magnitude and location of the strongest earthquake in the data."""
-    ...
+    current_max_magnitude = get_magnitude(data["features"][0])
+    current_max_location = get_location(data["features"][0])
+    for item in data["features"]:
+        magnitude = get_magnitude(item)
+        # Note: what happens if there are two earthquakes with the same magnitude?
+        if magnitude > current_max_magnitude:
+            current_max_magnitude = magnitude
+            current_max_location = get_location(item)
+    return current_max_magnitude, current_max_location
+    # There are other ways of doing this too:
+    # biggest_earthquake = sorted(data["features"], key=get_magnitude)[0]
+    # return get_magnitude(biggest_earthquake), get_location(biggest_earthquake)
+    # Or...
+    # biggest_earthquake = max(
+    #     ({"mag": get_magnitude(item), "location": get_location(item)}
+    #     for item in data["features"]),
+    #     key=lambda x: x["mag"]
+    # )
+    # return biggest_earthquake["mag"], biggest_earthquake["location"]
 
 
 # With all the above functions defined, we can now call them and get the result

plot_earthquakenew.py

@@ -0,0 +1,111 @@
+from datetime import date
+import requests
+import matplotlib.pyplot as plt
+import json
+from collections import defaultdict
+import numpy as np
+
+def get_data():
+    """Retrieve the data we will be working with."""
+    response = 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": "2018-10-11",
+            "orderby": "time-asc"}
+    )
+
+
+    # The response we get back is an object with several fields.
+    # The actual contents we care about are in its text field:
+    text = response.text
+    # To understand the structure of this text, you may want to save it
+    # to a file and open it in VS Code or a browser.
+    # See the README file for more information.
+
+    # We need to interpret the text to get values that we can work with.
+    # What format is the text in? How can we load the values?
+    return json.loads(text)
+
+
+def get_year(earthquake):
+    """Extract the year in which an earthquake happened."""
+    timestamp = earthquake['properties']['time']
+    # The time is given in a strange-looking but commonly-used format.
+    # To understand it, we can look at the documentation of the source data:
+    # https://earthquake.usgs.gov/data/comcat/index.php#time
+    # Fortunately, Python provides a way of interpreting this timestamp:
+    # (Question for discussion: Why do we divide by 1000?)
+    year = date.fromtimestamp(timestamp/1000).year
+    return year
+
+
+def get_magnitude(earthquake):
+    """Retrive the magnitude of an earthquake item."""
+    return earthquake["properties"]["mag"]
+
+
+# This is function you may want to create to break down the computations,
+# although it is not necessary. You may also change it to something different.
+def get_magnitudes_per_year(earthquakes):
+    """Retrieve the magnitudes of all the earthquakes in a given year.
+    
+    Returns a dictionary with years as keys, and lists of magnitudes as values.
+    """
+    magnitudes_by_year = defaultdict(list)
+    for quake in earthquakes:
+        year = get_year(quake)
+        magnitude = get_magnitude(quake)
+        if magnitude:  # Skip earthquakes without magnitude data
+            magnitudes_by_year[year].append(magnitude)
+    return magnitudes_by_year
+
+
+def plot_average_magnitude_per_year(earthquakes):
+    counts_by_year = defaultdict(int)
+    for quake in earthquakes:
+        year = get_year(quake)
+        counts_by_year[year] += 1
+
+    years = sorted(counts_by_year.keys())
+    counts = [counts_by_year[year] for year in years]
+
+    plt.bar(years, counts, color='skyblue')
+    plt.xlabel('Year')
+    plt.ylabel('Number of Earthquakes')
+    plt.title('Number of Earthquakes Per Year')
+    plt.xticks(np.arange(min(years), max(years)+1, step=2), rotation=45)
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_number_per_year(earthquakes):
+    """Plot the average magnitude of earthquakes per year."""
+    magnitudes_by_year = get_magnitudes_per_year(earthquakes)
+
+    years = sorted(magnitudes_by_year.keys())
+    avg_magnitudes = [np.mean(magnitudes_by_year[year]) for year in years]
+
+    plt.plot(years, avg_magnitudes, marker='o', color='orange')
+    plt.xlabel('Year')
+    plt.ylabel('Average Magnitude')
+    plt.title('Average Earthquake Magnitude Per Year')
+    plt.xticks(np.arange(min(years), max(years)+1, step=2), rotation=45)
+    plt.tight_layout()
+    plt.show()
+
+
+
+# Get the data we will work with
+quakes = get_data()['features']
+
+# Plot the results - this is not perfect since the x axis is shown as real
+# numbers rather than integers, which is what we would prefer!
+plot_number_per_year(quakes)
+plt.clf()  # This clears the figure, so that we don't overlay the two plots
+plot_average_magnitude_per_year(quakes)

plot_earthquakes.py

@@ -0,0 +1,110 @@
+from datetime import date
+import requests
+import matplotlib.pyplot as plt
+import json
+
+def get_data():
+    """Retrieve the data we will be working with."""
+    # With requests, we can ask the web service for the data.
+    # Can you understand the parameters we are passing here?
+    response = 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": "2018-10-11",
+            "orderby": "time-asc"}
+    )
+
+
+    # The response we get back is an object with several fields.
+    # The actual contents we care about are in its text field:
+    text = response.text
+    # To understand the structure of this text, you may want to save it
+    # to a file and open it in VS Code or a browser.
+    # See the README file for more information.
+
+    # We need to interpret the text to get values that we can work with.
+    # What format is the text in? How can we load the values?
+    return json.loads(text)
+
+    ...
+
+
+
+def get_year(earthquake):
+    """Extract the year in which an earthquake happened."""
+    timestamp = earthquake['properties']['time']
+    # The time is given in a strange-looking but commonly-used format.
+    # To understand it, we can look at the documentation of the source data:
+    # https://earthquake.usgs.gov/data/comcat/index.php#time
+    # Fortunately, Python provides a way of interpreting this timestamp:
+    # (Question for discussion: Why do we divide by 1000?)
+    year = date.fromtimestamp(timestamp/1000).year
+    return year
+
+
+def get_magnitude(earthquake):
+    """Retrive the magnitude of an earthquake item."""
+    return earthquake["properties"]["mag"]
+    ...
+
+
+# This is function you may want to create to break down the computations,
+# although it is not necessary. You may also change it to something different.
+def get_magnitudes_per_year(earthquakes):
+    """Retrieve the magnitudes of all the earthquakes in a given year.
+    
+    Returns a dictionary with years as keys, and lists of magnitudes as values.
+    """
+    magnitudes_per_year = {}
+    for quake in earthquakes:
+        year = get_year(quake)
+        magnitude = get_magnitude(quake)
+        if year not in magnitudes_per_year:
+            magnitudes_per_year[year] = []
+        magnitudes_per_year[year].append(magnitude)
+    return magnitudes_per_year
+
+def plot_average_magnitude_per_year(earthquakes):
+    """Plot the average magnitude of earthquakes per year."""
+    magnitudes_per_year = get_magnitudes_per_year(earthquakes)
+    years = sorted(magnitudes_per_year.keys())
+    average_magnitudes = [sum(magnitudes_per_year[year]) / len(magnitudes_per_year[year]) for year in years]
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(years, average_magnitudes, marker='o', color='b')
+    plt.title("Average Earthquake Magnitude per Year")
+    plt.xlabel("Year")
+    plt.ylabel("Average Magnitude")
+    plt.grid(True)
+    plt.show()
+
+
+def plot_number_per_year(earthquakes):
+    """Plot the number of earthquakes per year."""
+    magnitudes_per_year = get_magnitudes_per_year(earthquakes)
+    years = sorted(magnitudes_per_year.keys())
+    counts = [len(magnitudes_per_year[year]) for year in years]
+
+    plt.figure(figsize=(10, 6))
+    plt.bar(years, counts, color='g')
+    plt.title("Number of Earthquakes per Year")
+    plt.xlabel("Year")
+    plt.ylabel("Number of Earthquakes")
+    plt.grid(True)
+    plt.show()
+
+
+# Get the data we will work with
+quakes = get_data()['features']
+
+# Plot the results - this is not perfect since the x axis is shown as real
+# numbers rather than integers, which is what we would prefer!
+plot_number_per_year(quakes)
+plt.clf()  # This clears the figure, so that we don't overlay the two plots
+plot_average_magnitude_per_year(quakes)