Small clenup

README.md

@@ -23,7 +23,7 @@ Import the main function:
 
 Execute the function:
 
-`generate_market_heatmaps(csv_market_path='data/1894_market.csv)`
+`generate_market_heatmaps(market_path='data/1830_market.html')`
 
 ## Parameters
 

main.py

@@ -2,7 +2,7 @@
 from pathlib import Path
 
 from process_data import calculate_delta_map
-from importer import import_csv_market, import_html_market
+from market_import import import_csv_market, import_html_market
 from plot import generate_plot
 
 
@@ -60,7 +60,7 @@ def generate_market_heatmaps(
 
 
 if __name__ == '__main__':
-    market_path = f'data/1822_market.html'
+    market_path = 'data/1830_market.html'
 
     generate_market_heatmaps(market_path=market_path, show_market=True, delta_from_left_and_below=True,
                              delta_from_left=True, delta_from_below=True, color_scheme='gist_ncar')

importer.py → market_import.py

@@ -27,13 +27,13 @@ def is_alphanum_or_coma_or_newline(x: str) -> bool:
     return False
 
 
-def nan_or_int(x: str) -> int:
+def nan_or_int(x: str):
     if x in ['\n', 'nan']:
         return nan
     return int(x)
 
 
-def parse_html_txt_market(html_txt: str) -> list:
+def parse_html_txt_market(html_txt: str) -> list[list]:
     html_txt_junk_removed = ''.join([x for x in html_txt if is_alphanum_or_coma_or_newline(x)])
     html_txt_newlines_replaced = html_txt_junk_removed.replace('\n\n\n\n\n\n\n', ';').replace('\n\n\n\n\n', ',').strip().replace('\n', '\n,')
     rows = html_txt_newlines_replaced.split(';')

process_data.py

@@ -3,7 +3,7 @@
 from statistics import mean
 
 
-def get_cell_value(market: DataFrame, i: int, j: int) -> int:
+def get_cell_value(market: DataFrame, i: int, j: int):
     try:
         if i < 0 or j < 0:
             raise IndexError
@@ -13,7 +13,7 @@ def get_cell_value(market: DataFrame, i: int, j: int) -> int:
         return nan
 
 
-def calculate_delta(value: int, neighbor_values: list) -> float:
+def calculate_delta(value: int, neighbor_values: list):
     neighbor_values = [x for x in neighbor_values if not isna(x)]
     if not neighbor_values:
         return nan

requirements.txt

@@ -1,4 +1,5 @@
 matplotlib>=3.6
 numpy>=1.23
-pandas>=1.5.1
-seaborn>=0.12.1
+pandas>=1.5
+seaborn>=0.12
+beautifulsoup4>=4.1

test/test_market.csv

@@ -1,3 +1,3 @@
-125,140,155,170
+,,125,140,155,170
 110,120,130
 ,100

test/test_market.html

@@ -1,4 +1,6 @@
 <div style="width: max-content;">
+    <div style="position: relative; display: inline-block; padding: 4px; width: 40px; height: 40px; border: 1px solid rgba(0, 0, 0, 0); margin: 0px; vertical-align: top;"></div>
+    <div style="position: relative; display: inline-block; padding: 4px; width: 40px; height: 40px; border: 1px solid rgba(0, 0, 0, 0); margin: 0px; vertical-align: top;"></div>
     <div style="position: relative; display: inline-block; padding: 4px; width: 40px; height: 40px; border: 1px solid rgba(0, 0, 0, 0.2); margin: 0px; vertical-align: top; color: rgb(0, 0, 0); background-color: rgb(211, 211, 211);">
         <div class="xsmall_font">125</div>
         <div></div>

test/test_importer.py → test/test_market_import.py

@@ -3,13 +3,13 @@
 from pandas import DataFrame
 from numpy import nan
 
-from importer import import_csv_market, import_html_market
+from market_import import import_csv_market, import_html_market
 
 class TestImporter(unittest.TestCase):
     MARKET_CSV_PATH = 'test_market.csv'
     MARKET_HTML_PATH = 'test_market.html'
     MARKET = DataFrame([
-        [125, 140, 155, 170],
+        [nan, nan, 125, 140, 155, 170],
         [110, 120, 130],
         [nan, 100]
     ])