Fix dates on x axis

banking_breakdown/__main__.py

@@ -1,34 +1,48 @@
-import pandas as pd
 from banking_breakdown import document_builder
-import subprocess
-import os
-import shutil
-
 import pandas as pd
-
 from banking_breakdown import types
 import numpy as np
 
 
-def main():
+def _generate_dummy_data() -> types.ReportData:
     categories = ["A", "B", "C", "D", "E", "F", "G"]
     values = np.array([10, 12, 53, 12, 90, 23, 32])
     values = values / values.sum() * 100
-
-    total_value = np.random.normal(size=10) + 4
-    net_income = np.diff(total_value)
+    values = np.round(values, decimals=1)
+    values[-1] += 100 - np.sum(values)
 
     category_overview_df = pd.DataFrame(
-        {"category": categories, "value": values.astype('int32')})
-    t = np.linspace(0, total_value.size, total_value.size)
-    total_value_df = pd.DataFrame({"t": t, "value": total_value})
-    t = np.linspace(0, net_income.size, net_income.size)
+        {"category": categories, "value": values})
+
+    t = ["2023-01-01",
+         "2023-02-01",
+         "2023-03-01",
+         "2023-04-01",
+         "2023-05-01",
+         "2023-06-01",
+         "2023-07-01",
+         "2023-08-01",
+         "2023-09-01",
+         "2023-10-01",
+         "2023-11-01",
+         "2023-12-01"]
+
+    net_income = 200 * np.random.normal(size=len(t)) + 100
     net_income_df = pd.DataFrame({"t": t, "value": net_income})
 
+    total_value = np.cumsum(net_income)
+
+    total_value_df = pd.DataFrame({"t": t, "value": total_value})
+
     report_data = types.ReportData(category_overview=category_overview_df,
                                    net_income=net_income_df,
                                    total_value=total_value_df)
 
+    return report_data
+
+
+def main():
+    report_data = _generate_dummy_data()
     document_builder.build_document(report_data)