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assignments/a3/a3_part4.py

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+"""CSC110 Fall 2021 Assignment 3, Part 4: Forest Fires (SOLUTIONS)
+
+Instructions (READ THIS FIRST!)
+===============================
+Implement each of the functions in this file. As usual, do not change any function headers
+or preconditions. You do NOT need to add doctests.
+
+Copyright and Usage Information
+===============================
+
+This file is provided solely for the personal and private use of students
+taking CSC110 at the University of Toronto St. George campus. All forms of
+distribution of this code, whether as given or with any changes, are
+expressly prohibited. For more information on copyright for CSC110 materials,
+please consult our Course Syllabus.
+
+This file is Copyright (c) 2021 Mario Badr and Tom Fairgrieve.
+"""
+import csv
+import plotly.graph_objects as go
+
+from a3_ffwi_system import WeatherMetrics, FfwiOutput
+import a3_ffwi_system as ffwi
+
+
+def load_data(filename: str) -> tuple[list[WeatherMetrics], list[FfwiOutput]]:
+    """Return a tuple of two parallel lists based on the data in filename. The first list contains
+    WeatherMetrics. The second list contains the corresponding FfwiOutput.
+
+    The data in filename is in a csv format with 12 columns. The first six columns correspond to
+    the month, day, temperature, relative humidity, wind speed, and precipitation, in that order.
+    The last six columns correspond to the FFMC, DMC, DC, ISI, BUI, and FWI values that would be
+    calculated based on the first six columns and the previous day's values.
+    """
+    # ACCUMULATOR inputs_so_far: The WeatherMetrics parsed from filename so far
+    inputs_so_far = []
+    # ACCUMULATOR outputs_so_far: The FfwiOutputs parsed from filename so far
+    outputs_so_far = []
+
+    with open(filename) as f:
+        reader = csv.reader(f, delimiter=',')
+        next(reader)  # skip the header
+
+        for row in reader:
+            assert len(row) == 12, 'Expected every row to contain 12 elements.'
+            # row is a list of strings
+            # Your task is to extract the relevant data from row and add it
+            # to the accumulator.
+
+    return inputs_so_far, outputs_so_far
+
+
+def calculate_ffwi_outputs(readings: list[WeatherMetrics]) -> dict[tuple[int, int], FfwiOutput]:
+    """Return a dictionary mapping (month, day) tuples to their corresponding FfwiOutput based on
+    the daily weather measurements found in readings.
+
+    Use the functions in a3_ffwi_system for initial FFMC, DMC, and DC values and to calculate each
+    attribute needed for FfwiOutput.
+
+    Preconditions:
+        - Every reading in readings has a unique (month, day) pair
+    """
+
+
+def get_xy_data(outputs: dict[tuple[int, int], FfwiOutput], attribute: str) -> \
+        tuple[list[str], list[float]]:
+    """Return a tuple of two parallel lists. The first list contains the keys of outputs as
+    strings in the format 'month, day'. The second list contains the corresponding value of
+    the attribute of FfwiOutput.
+
+    You can access an attribute from a data class using the getattr built-in function. For example,
+        >>> output = FfwiOutput(2.0, 3.0, 4.0, 5.0, 6.0, 7.0)
+        >>> getattr(output, 'ffmc')
+        2.0
+    """
+
+
+def plot_ffwi_attribute(outputs: dict[tuple[int, int], FfwiOutput], attribute: str) -> None:
+    """Plot an attribute from FfwiOutput as a time series.
+
+    Preconditions:
+        - attribute in {'ffmc', 'dmc', 'dc', 'isi', 'bui', 'fwi'}
+        - outputs != {}
+    """
+    # Convert the outputs into parallel x and y lists
+    x_data, y_data = get_xy_data(outputs, attribute)
+
+    # Create the figure
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=x_data, y=y_data, name=attribute))
+
+    # Configure the figure
+    fig.update_layout(title=f'Time Series of {attribute}',
+                      xaxis_title='(Month, Day)',
+                      yaxis_title=f'Calculated {attribute}')
+
+    # Show the figure in the browser
+    fig.show()
+    # Is the above not working for you? Comment it out, and uncomment the following:
+    # fig.write_html('my_figure.html')
+    # You will need to manually open the my_figure.html file created above.
+
+
+if __name__ == '__main__':
+    import python_ta
+    import python_ta.contracts
+    python_ta.contracts.DEBUG_CONTRACTS = False
+    python_ta.contracts.check_all_contracts()
+
+    # When you are ready to check your work with python_ta, uncomment the following lines.
+    # (Delete the "#" and space before each line.)
+    # IMPORTANT: keep this code indented inside the "if __name__ == '__main__'" block
+    # python_ta.check_all(config={
+    #     'allowed-io': ['load_data'],
+    #     'extra-imports': ['python_ta.contracts', 'csv', 'plotly.graph_objects', 'a3_ffwi_system'],
+    #     'max-line-length': 100,
+    #     'max-args': 6,
+    #     'max-locals': 25,
+    #     'disable': ['R1705'],
+    # })