Update data, pre-commit

.pre-commit-config.yaml

@@ -6,7 +6,7 @@
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
     -   id: trailing-whitespace
     -   id: check-yaml
@@ -31,26 +31,26 @@ repos:
          - toml-sort==0.23.1
 
 -   repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: 'v0.0.282'
+    rev: 'v0.1.0'
     hooks:
     -   id: ruff
         args: ['--fix', '--config', 'pyproject.toml']
 
 -   repo: https://github.com/psf/black
-    rev: 23.7.0
+    rev: 23.10.0
     hooks:
     -   id: black
         language_version: python3.10
         args: ['--config', 'pyproject.toml']
         verbose: true
 
 -   repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.5.0
+    rev: v1.6.1
     hooks:
     -   id: mypy
         additional_dependencies: [types-python-dateutil]
 
 -   repo: https://github.com/shellcheck-py/shellcheck-py
-    rev: v0.9.0.5
+    rev: v0.9.0.6
     hooks:
     -   id: shellcheck

dte/__init__.py

@@ -1,2 +1,2 @@
 # __init__.py
-from .dte import dte_calculator  # noqa
+from .dte import dte_calculator

dte/dte.py

@@ -1,12 +1,9 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 # Copyright 2020 by Jiuguang Wang (www.robo.guru)
 # All rights reserved.
 # This file is part of DTE Calculator and is released under the  MIT License.
 # Please see the LICENSE file that should have been included as part of
 # this package.
 
-from typing import Tuple
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -70,29 +67,34 @@ def compute_delivery_charges(kwh_monthly: Series) -> Series:
     return total
 
 
-def compute_ToD_rate(data_raw: DataFrame) -> Tuple[Series, Series, Series]:
+def compute_tod_rate(data_raw: DataFrame) -> tuple[Series, Series, Series]:
     # Init
     data = data_raw
 
-    data["Cost_Cap"] = 0
-    data["Cost_NonCap"] = 0
+    data["Cost_Cap"] = 0.0
+    data["Cost_NonCap"] = 0.0
 
     # Weekday filter
     index_weekday = data.index.weekday < 5
     index_weekend = data.index.weekday >= 5
 
     # Season filter
     index_summer = np.logical_and(
-        data.index.month >= 6, data.index.month <= 10
+        data.index.month >= 6,
+        data.index.month <= 10,
     )
     index_winter = np.logical_or(data.index.month >= 11, data.index.month <= 5)
 
     # Hour filter
     index_peak = np.logical_and(
-        data.index.hour >= 11, data.index.hour <= 18, index_weekday
+        data.index.hour >= 11,
+        data.index.hour <= 18,
+        index_weekday,
     )
     index_off_peak = np.logical_or(
-        data.index.hour < 11, data.index.hour > 18, index_weekend
+        data.index.hour < 11,
+        data.index.hour > 18,
+        index_weekend,
     )
 
     # Combine filters
@@ -154,17 +156,18 @@ def compute_ToD_rate(data_raw: DataFrame) -> Tuple[Series, Series, Series]:
     return total, consumption_peak, consumption_offpeak
 
 
-def compute_RES_rate(data_raw: DataFrame) -> Series:
+def compute_res_rate(data_raw: DataFrame) -> Series:
     # Compute daily total consumption
     data = data_raw["Total"].resample("D").sum().to_frame()
 
     data["Cost_CAP_17"] = RES_CUTOFF * RES_CAP_RATE_17KWH
     data["Cost_NON_CAP_17"] = RES_CUTOFF * RES_NON_CAP_RATE_17KWH
 
-    data["Cost_CAP_ADD"] = 0
-    data["Cost_NON_CAP_ADD"] = 0
+    data["Cost_CAP_ADD"] = 0.0
+    data["Cost_NON_CAP_ADD"] = 0.0
 
     # Filter
+    data["Total"] = data["Total"].astype(float)
     index = data["Total"] > RES_CUTOFF
     data.loc[index, "Cost_CAP_ADD"] = (
         data["Total"] - RES_CUTOFF
@@ -186,9 +189,7 @@ def compute_RES_rate(data_raw: DataFrame) -> Series:
     )
     cost_monthly = data["Total Cost"].resample(SAMPLE_METHOD).sum()
     sales_tax = cost_monthly * RATE_SALES_TAX
-    total = cost_monthly + delivery_charges_monthly + sales_tax
-
-    return total
+    return cost_monthly + delivery_charges_monthly + sales_tax
 
 
 def format_plots(plot_object: Axes) -> None:
@@ -202,11 +203,9 @@ def format_plots(plot_object: Axes) -> None:
     plot_object.tick_params(labelsize=AXIS_FONT_SIZE)
 
     # Change tick spacing
-    plot_object.set_xticks(plot_object.get_xticks()[::1])
-    plot_object.xaxis.set_major_locator(
-        MonthLocator(range(1, 13), bymonthday=1, interval=2)
-    )
-    plot_object.xaxis.set_major_formatter(DateFormatter("%b"))
+    plot_object.xaxis.set_major_locator(MonthLocator(interval=6))
+    plot_object.xaxis.set_major_formatter(DateFormatter("%b %Y"))
+    plot_object.tick_params(axis="x", rotation=90)
 
 
 def dte_calculator(filename: str) -> None:
@@ -217,26 +216,37 @@ def dte_calculator(filename: str) -> None:
     f, axarr = plt.subplots(2, 2)
 
     # Import data
-    data = pd.read_csv(filename, parse_dates=[["Day", "Hour of Day"]])
-    data = data.set_index(data["Day_Hour of Day"])
-    data.index.rename("Date", inplace=True)
-    data.rename(columns={"Hourly Total": "Total"}, inplace=True)
+    data = pd.read_csv(
+        filename,
+        parse_dates={"Datetime": ["Day", "Hour of Day"]},
+    )
 
+    # If you want to convert the "Datetime" column to datetime dtype explicitly
+    data["Datetime"] = pd.to_datetime(data["Datetime"])
+
+    data = data.set_index(data["Datetime"])
+    data = data.rename_axis("Date")
+    data = data.rename(columns={"Hourly Total": "Total"})
+    data["Total"] = data["Total"].replace(
+        "No Data",
+        0,
+    )  # sometimes DTE has an invalid entry
+    data["Total"] = data["Total"].astype(float)
     # Compute cost for the Residential Electric Service Rate
-    cost_monthly_res = compute_RES_rate(data)
+    cost_monthly_res = compute_res_rate(data)
 
     # Compute cost for the Time of Day Service Rate
-    cost_monthly_ToD, consumption_peak, consumption_offpeak = compute_ToD_rate(
-        data
+    cost_monthly_tod, consumption_peak, consumption_offpeak = compute_tod_rate(
+        data,
     )
 
     # Compute consumption KWH by month
     kwh_monthly = data["Total"].resample(SAMPLE_METHOD).sum()
 
     # Compute savings
-    savings_monthly = cost_monthly_res - cost_monthly_ToD
+    savings_monthly = cost_monthly_res - cost_monthly_tod
     res_total = round(cost_monthly_res.sum(), 2)
-    tod_total = round(cost_monthly_ToD.sum(), 2)
+    tod_total = round(cost_monthly_tod.sum(), 2)
     savings_total = round(savings_monthly.sum(), 2)
 
     # Plot 1 - Consumption
@@ -256,7 +266,9 @@ def dte_calculator(filename: str) -> None:
     # Plot 2 - Peak vs Off Peak
     axarr[0, 1].plot(consumption_peak.index, consumption_peak, label="Peak")
     axarr[0, 1].plot(
-        consumption_offpeak.index, consumption_offpeak, label="Off Peak"
+        consumption_offpeak.index,
+        consumption_offpeak,
+        label="Off Peak",
     )
     axarr[0, 1].set_title("Consumption by Month, Peak vs Off Peak")
     axarr[0, 1].set_ylabel("Consumption (kWh)")
@@ -265,18 +277,22 @@ def dte_calculator(filename: str) -> None:
 
     # Plot 3 -  Services
     axarr[1, 0].plot(
-        cost_monthly_res.index, cost_monthly_res, label="Standard RES Service"
+        cost_monthly_res.index,
+        cost_monthly_res,
+        label="Standard RES Service",
     )
     axarr[1, 0].plot(
-        cost_monthly_ToD.index, cost_monthly_ToD, label="Time of Day Service"
+        cost_monthly_tod.index,
+        cost_monthly_tod,
+        label="Time of Day Service",
     )
     axarr[1, 0].set_title("Total Cost by Month")
     axarr[1, 0].set_ylabel("Cost in US Dollars")
     axarr[1, 0].legend()
     format_plots(axarr[1, 0])
 
     # Plot 4 - Savings
-    axarr[1, 1].plot(cost_monthly_ToD.index, savings_monthly)
+    axarr[1, 1].plot(cost_monthly_tod.index, savings_monthly)
     axarr[1, 1].set_title("Total Savings by Month")
     axarr[1, 1].set_ylabel("Cost in US Dollars")
 

dte_calculator.py

@@ -1,5 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 # Copyright 2020 by Jiuguang Wang (www.robo.guru)
 # All rights reserved.
 # This file is part of DTE Calculator and is released under the  MIT License.

pyproject.toml

@@ -19,10 +19,11 @@ classifiers = [
 ]
 dependencies = [
   "tomli>=2.0.1",
-  "matplotlib>=3.7",
-  "pandas>=2.0.3",
+  "matplotlib>=3.8",
+  "pandas>=2.1.0",
   "seaborn>=0.12.2",
   "numpy>=1.25.2",
+  "chart_studio>=1.1.0",
   "toml>=0.10.2"
 ]
 description = "Python based utility to compare electric service plans based on past consumption data."
@@ -70,34 +71,6 @@ strict_equality = true
 warn_redundant_casts = true
 warn_unreachable = true
 
-[[tool.mypy.overrides]]
-disallow_untyped_defs = true
-module = "mypy-scripts.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "matplotlib.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "setuptools.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "scipy.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "pandas.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "seaborn.*"
-
-[[tool.mypy.overrides]]
-ignore_missing_imports = true
-module = "chart_studio.*"
-
 [tool.ruff]
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
@@ -126,11 +99,11 @@ exclude = [
 ]
 # Allow autofix for all enabled rules (when `--fix`) is provided.
 fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
-ignore = []
+ignore = ["D", "PLR2004", "T201", "S101"]
 # Same as Black.
 line-length = 79
 # Enable pycodestyle (`E`), Pyflakes (`F`), and import sorting (`I`)
-select = ["E", "F", "I"]
+select = ["ALL"]
 # Assume Python 3.10.
 target-version = "py310"
 unfixable = []

test/test_dte.py

@@ -1,12 +1,10 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 # Copyright 2020 by Jiuguang Wang (www.robo.guru)
 # All rights reserved.
 # This file is part of DTE Calculator and is released under the  MIT License.
 # Please see the LICENSE file that should have been included as part of
 # this package.
 
-import os
+from pathlib import Path
 
 import dte
 
@@ -16,7 +14,8 @@ def test_dte() -> None:
     dte.dte_calculator("data/dte.csv")
 
     # Get the file size of the output PDF
-    file_size = os.path.getsize("DTE.pdf")
+    file_path = Path("DTE.pdf")
+    file_size = file_path.stat().st_size
 
     # Check the size is greater than 40 KB
     assert file_size > 20 * 1024, "Test failed"