Added docstrings to several undocumented functions.

pdf/create_index.py

@@ -13,6 +13,13 @@ from collections import defaultdict
 
 
 def main():
+    """
+    This function reads the file index.html and extracts all headings from it.
+    It then creates a dictionary with the first letter of each heading as key,
+    and for each key, a dictionary containing all headings starting with that letter as value.
+    The second level of dictionaries contain the actual heading
+    text as keys and their ID's (which are also stored in another list) as values.
+    """
     html = read_file('index.html')
     doc  = BeautifulSoup(''.join(html), 'html.parser')
     hhh = defaultdict(lambda: defaultdict(list))
@@ -26,6 +33,14 @@ def main():
 
 
 def print_hhh(hhh):
+    """
+    Prints a table of contents for the commands in the given dictionary.
+
+    The keys of `hhh` are letters and each letter is mapped to another dictionary
+    that maps command names to lists of links. The first link in each list is used as the heading for that command name, so it should be unique among all
+    commands (and ideally short). All other links should be unique among all lists but not necessarily short. The order of letters and commands within a
+    letter will match their order in `hhh`.
+    """
     letters = hhh.keys()
     for letter in sorted(letters):
         hh = hhh[letter]

pdf/remove_links.py

@@ -25,6 +25,18 @@ MATCHES = {
 
 
 def main():
+    """
+    Replaces all occurrences of `from_` with `to_` in the file at the given path.
+
+    Args:
+        from_ (str): The string to be replaced.
+        to_ (str): The
+    replacement string.
+
+        index_path (Path): Path object representing the file that is being modified by this function call.  This parameter is not
+    optional, and it has a default value of None because we are required to pass it as an argument but we have no use for it in our code since we don't
+    need access to its attributes or methods while calling this function; therefore, there's no sensible default value for us to provide here.
+    """
     index_path = Path('..', 'index.html')
     lines = read_file(index_path)
     out = ''.join(lines)

web/convert_table.py

@@ -1,7 +1,39 @@
 #!/usr/bin/env python3
 
 def convert_table(lines):
+    """
+    Convert a table from ASCII art to Unicode box drawing characters or vice versa.
+
+    :param lines: A list of strings representing the lines of the table.
+    The first line is assumed to be the top border, and all other lines are assumed to be inside it (i.e., not on any side). All borders must use ``+``
+    for corners, ``-`` for horizontal bars, and ``|`` for vertical bars; no spaces are allowed between symbols in a single border symbol (except if there
+    is one space between two vertical bar symbols when converting from ASCII art). If there is only one line in `lines`, then it will be used as both the
+    top and bottom borders with no interior rows or columns; this can also happen if `lines` contains an empty string at some point. This function does
+    not check that each row has exactly as many columns as there are horizontal bar symbols in either its own border or that of its neighbor above/below
+    it (if applicable), but such checking could easily be added by modifying how column widths are calculated below.
+    #  :type lines: list(str)
+    #  :returns
+    out: The converted table represented by a single string containing newline characters at appropriate places so that when printed
+    """
     def from_ascii():
+        """
+        Convert a list of lines from an ASCII table to a reStructuredText grid table.
+
+        :param lines: A list of strings representing the rows in the ASCII
+        table.
+        :returns: A string containing the equivalent reStructuredText grid table.
+
+            The first line is assumed to be a header row and will be used as
+        such in the resulting grid table, with each column separated by ``|`` characters (ASCII 124).  The second line is assumed to contain column widths and
+        will be used as such in the resulting grid table, with each width separated by ``-`` characters (ASCII 45).  All subsequent lines are treated as data
+        rows which will have their contents centered within their columns, surrounded on either side by ``|`` characters (ASCII 124) and padded on either side
+        by blank spaces so that all data rows have an equal number of cells.  Data values are converted from plain text into bold typeface using double
+        asterisks for emphasis before being inserted into cells; this allows you to use plain text formatting within your tables without them being overridden
+        when converting back into ASCII tables later on.
+
+            For example, given these three lines...
+        +-----+--------+-------+------------------+-------------+-----------
+        """
         out = []
         first, header, third, *body, last = lines
         first = first.translate(str.maketrans({'-': '━', '+': '┯'}))
@@ -18,6 +50,13 @@ def convert_table(lines):
         out.append(f'┗{last[1:-1]}┛')
         return '\n'.join(out)
     def from_unicode():
+        """
+        Convert a Unicode box-drawing character string to ASCII.
+
+        :param str lines: A string of Unicode box-drawing characters.
+        :returns str out: The same
+        text with all the Unicode box drawing characters replaced by ASCII ones.
+        """
         out = []
         for line in lines:
             line = line.translate(str.maketrans('┏┓┗┛┠┼┨┯┷━─┃│', '+++++++++--||'))

web/update_plots.py

@@ -12,6 +12,11 @@ import re
 
 
 def main():
+    """
+    This function scrapes the data from the web and wrangles it into a pandas DataFrame.
+    It then creates an interactive plotly line graph of covid cases
+    in New York State.
+    """
     print('Updating covid deaths...')
     update_covid_deaths()
     print('Updating covid cases...')
@@ -19,6 +24,11 @@ def main():
 
 
 def update_covid_deaths():
+    """
+    Update the plot of global COVID-19 deaths over time.
+
+    :param df: A pandas DataFrame with columns 'Continent', 'Date', and 'Total Deaths per Million'.
+    """
     covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', 
                         usecols=['iso_code', 'date', 'total_deaths', 'population'])
     continents = pd.read_csv('https://gist.githubusercontent.com/stevewithington/20a69c0b6d2ff'
@@ -41,7 +51,15 @@ def update_covid_deaths():
 
 
 def update_confirmed_cases():
+    """
+    Update the file covid_cases.js with a plot of total cases, gold price, bitcoin price and Dow Jones index.
+    """
     def main():
+    """
+    This function scrapes the data from the web and wrangles it into a pandas DataFrame.
+    It then creates an interactive plotly line graph of covid cases
+    in New York State.
+    """
         df = wrangle_data(*scrape_data())
         f = get_figure(df)
         update_file('covid_cases.js', f)
@@ -49,11 +67,29 @@ def update_confirmed_cases():
         write_to_png_file('covid_cases.png', f, width=960, height=315)
 
     def scrape_data():
+        """
+        This function scrapes data from the following sources:
+            1. Our World in Data (Total Cases)
+            2. Yahoo Finance (Bitcoin, Gold, Dow Jones)
+        The
+        function returns a list of pandas Series objects containing the scraped data.
+        """
         def scrape_covid():
+            """
+            This function scrapes the total number of covid cases from a csv file on the internet.
+            """
             url = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
             df = pd.read_csv(url, usecols=['location', 'date', 'total_cases'])
             return df[df.location == 'World'].set_index('date').total_cases
         def scrape_yahoo(slug):
+            """
+            Downloads historical stock price data from Yahoo Finance.
+
+            :param str slug: The ticker symbol of the desired security. Expected to be a valid argument
+            for the `yfinance` function `Ticker()`.
+            :returns pd.Series(float): A pandas Series with timestamps as indices and adjusted closing prices as values,
+            sorted by timestamp in ascending order.
+            """
             url = f'https://query1.finance.yahoo.com/v7/finance/download/{slug}' + \
                   '?period1=1579651200&period2=9999999999&interval=1d&events=history'
             df = pd.read_csv(url, usecols=['Date', 'Close'])
@@ -63,6 +99,14 @@ def update_confirmed_cases():
         return map(pd.Series.rename, out, ['Total Cases', 'Bitcoin', 'Gold', 'Dow Jones'])
 
     def wrangle_data(covid, bitcoin, gold, dow):
+        """
+        This function joins the Dow Jones, Gold and Bitcoin dataframes into a single one.
+        It then sorts them by date and interpolates missing values. It
+        discards rows before '2020-02-23'.
+        Finally it calculates percentages relative to day 1 of each series (Dow Jones, Gold, Bitcoin) 
+        and adds a column
+        with covid cases. The result is returned as a new dataframe sorted by date in descending order.
+        """
         df = pd.concat([dow, gold, bitcoin], axis=1)  # Joins columns on dates.
         df = df.sort_index().interpolate()            # Sorts by date and interpolates NaN-s.
         yesterday = str(datetime.date.today() - datetime.timedelta(1))
@@ -72,6 +116,11 @@ def update_confirmed_cases():
         return df.sort_values(df.index[-1], axis=1)   # Sorts columns by last day's value.
 
     def get_figure(df):
+        """
+        This function returns a plotly figure that shows the total cases of COVID-19 in the US and its economic 
+        indicators. The data is taken from [The New
+        York Times](#) and retrieved using [NYT API](#).
+        """
         figure = go.Figure()
         for col_name in reversed(df.columns):            
             yaxis = 'y1' if col_name == 'Total Cases' else 'y2'
@@ -97,6 +146,27 @@ def update_confirmed_cases():
 #
 
 def update_file(filename, figure):
+    """
+    Updates the file at `filename` with the plotly figure `figure`.
+
+    :param filename: The path to a JSON file containing a Plotly figure.
+    :type filename:
+    str, required.
+        The extension of the file must be .json or .js (for legacy reasons).
+
+        Note that if you are using JupyterLab and want to open
+    your updated 
+        HTML files in an external browser window then you should save your 
+        notebook as an HTML file instead of as a Jupyter notebook.
+    For more 
+        information see this guide on [using Jupyter with Google Colab](http://jupyter-
+    notebook.readthedocs.io/en/stable/examples/Notebook/Running%20Code.html#Running-code).
+
+         If you are not using JupyterLab then it is recommended
+    that you use .html for all types of notebooks so that they can be opened in any web browser, including Chrome, Firefox and Edge on Windows and macOS
+    without any extra configuration needed (see below for more details). This is because some browsers do not support JavaScript which is used by default
+    by Plotly's exporting functions to generate
+    """
     lines = read_file(filename)
     f_json = figure.to_json(pretty=True).replace('\n', '\n        ')
     out = lines[:6] + [f'        {f_json}\n', '    )\n', '};\n']