Drag Area Coefficient

v4/drag_area_coefficient/PROJECT_TEMPLATE.md

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+# Project Template
+
+This `project_template` folder is designed to be copy-pasted and adapted for every new UBC Solar Race Strategy data analysis project.
+
+---
+
+As with any template, use it as you see fit. **Feel free to remove unnecessary directories**.
+The structure is in place to help guide new projects and to maintain consistency within this code repository.
+
+However, **all new data analysis projects must**:
+
+
+
+1. **Be completed in a new, standalone folder.**
+
+If your work depends on the results of another project and the new project is still clearly distinct, either re-derive the values or add the required data to the new project's `/data` folder. Be sure to document where any data comes from.
+
+2. **Use a project-specific [UV](https://docs.astral.sh/uv/) environment to manage dependencies**
+
+// TODO: Add guide for UV
+
+3. **Include documentation**
+
+Documentation is required so that anyone can understand and reproduce your work. For Jupyter data analysis projects, writing out your process and thoughts in markdown is a simple and effective way to document your work. Be sure to include **your name**, **the date** and the **project name**.

v4/drag_area_coefficient/data/DATA.md

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+# Data
+
+Use this folder to store any **project-specific** data such as `.csv`,
+`.npy`, `.npz`, or `.pkl` files.
+
+Ensure that large files (>50MB) are being tracked by Git LFS (Large File Storage),
+so that the repository does not grow unnecessarily.
+
+To list files under Git LFS, run:
+
+```
+git lfs ls-files
+```

v4/drag_area_coefficient/pyproject.toml

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+[project]
+name = "drag-area-coefficient"
+version = "0.1.0"
+requires-python = ">=3.14"
+dependencies = [
+    "numpy>=2.4.6",
+    "ubc-solar-data-tools>=2.0.0",
+]

v4/drag_area_coefficient/results/RESULTS.md

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+# Results
+
+Use this folder to store the results of your analysis,
+like PDF files, images, tables or other data.
+
+---
+
+Jupyter notebooks alone can be complete enough as results,
+but consider exporting them to PDF files to make the result more accessible.
+
+To export a Jupyter notebook to pdf, the quickest way is to use a web converter.
+https://onlineconvertfree.com/convert/ipynb/ works pretty well.
+
+A command-line tool can be helpful if you want more control over the output or need to convert many files.
+To convert to PDF on the command-line,
+
+1. Install `nbconvert`
+
+`uv add nbconvert`
+
+You'll also need to install [Pandoc](https://minrk-nbconvert.readthedocs.io/en/stable/install.html#installing-pandoc) to convert to PDF.
+
+2. Export the file
+
+`\your_project> nbconvert .\example_notebook.ipynb --to pdf`

v4/drag_area_coefficient/scripts/SCRIPTS.md

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+# Scripts
+
+This directory is for any scripts that are better suited in a distinct
+Python (or other type of) file rather than inline in a Jupyter notebook.

v4/drag_area_coefficient/scripts/data_query.py

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+"""
+Experimenting with querying data from InfluxDB. Not used in the final analysis.
+"""
+
+
+import numpy as np
+from data_tools.collections import TimeSeries
+from data_tools.query import DBClient
+from datetime import datetime
+
+PACK_CURRENT_THRESHOLD = 0.10682  # pack current never reads 0; its lowest value is this constant (constant is rounded up here)
+
+if __name__ == "__main__":
+    client = DBClient()
+
+    start_times = [datetime.fromisoformat("2026-04-02T20:02:30Z"),
+                   datetime.fromisoformat("2026-04-02T20:07:10Z"),
+                   datetime.fromisoformat("2026-04-02T20:26:05Z"),
+                   datetime.fromisoformat("2026-04-02T20:35:40Z"),
+                   datetime.fromisoformat("2026-04-02T21:05:25Z"),
+                   datetime.fromisoformat("2026-04-02T21:10:30Z"),]
+
+    stop_times = [datetime.fromisoformat("2026-04-02T20:03:10Z"),
+                  datetime.fromisoformat("2026-04-02T20:07:30Z"),
+                  datetime.fromisoformat("2026-04-02T20:26:24Z"),
+                  datetime.fromisoformat("2026-04-02T20:36:15Z"),
+                  datetime.fromisoformat("2026-04-02T21:05:40Z"),
+                  datetime.fromisoformat("2026-04-02T21:10:40Z"),]
+
+    for start, stop in zip(start_times, stop_times):
+        motor_rotating_speed: TimeSeries = client.query_time_series(
+            start=start,
+            stop=stop,
+            field="MotorRotatingSpeed",
+            units="km/h"
+        )
+
+        pack_current: TimeSeries = client.query_time_series(
+            start=start,
+            stop=stop,
+            field="PackCurrent",
+            units="A"
+        )
+
+        brake_pressed: TimeSeries = client.query_time_series(
+            start=start,
+            stop=stop,
+            field="BrakePressed",
+        )
+
+        cruise_start_index = np.where(pack_current <= PACK_CURRENT_THRESHOLD)[0][0]
+        cruise_end_index = np.where(brake_pressed > 0)[0][0]
+
+        cruise_start_time = pack_current.datetime_x_axis[cruise_start_index]
+        cruise_end_time = brake_pressed.datetime_x_axis[cruise_end_index]
+
+        cruise_speed: TimeSeries = motor_rotating_speed.slice(cruise_start_time, cruise_end_time)
+        cruise_speed.meta = motor_rotating_speed.meta
+        cruise_speed.plot()
+
+
+
+
+
+
+