Metadata-Version: 2.4
Name: msds-comms-plotter
Version: 0.1.1
Summary: Interactive Altair charts and a matplotlib/Altair styling toolkit for the 2022 FIFA World Cup (MSDS 610 example).
Author-email: Tom Sasser <tjsasser@dons.usfca.edu>
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Project-URL: Homepage, https://github.com/tjsasser/msds-comms-plotter
Project-URL: Repository, https://github.com/tjsasser/msds-comms-plotter
Keywords: altair,visualization,matplotlib,football,world-cup,statsbomb
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: pyarrow
Requires-Dist: requests
Requires-Dist: matplotlib
Requires-Dist: altair
Provides-Extra: png
Requires-Dist: vl-convert-python; extra == "png"
Dynamic: license-file

# msds-comms-plotter

A small teaching library for the MSDS 610 (Communications for Analytics)
coursework. It bundles a tiny pandas data layer, a World Cup 2022 example
dataset with matplotlib figures, and **`chartkit`** — a styling toolkit that
gives **matplotlib** and **Altair** one shared visual identity.

## General

The package ships three things:

| Module | What it does |
| --- | --- |
| `msds_comms_plotter.plots` | Ready-made matplotlib figures for the World Cup 2022 dataset. |
| `msds_comms_plotter.altair_charts` | Interactive Altair explorations of the same data (bar, line, scatter, histogram, heatmap, two linked-brush charts, a point-paths chart, and a horizon graph), with a page-wide color-scheme picker in the gallery. |
| `msds_comms_plotter.chartkit` | Shared theming for matplotlib **and** Altair. |

### About `chartkit`

matplotlib and Altair are independent renderers: matplotlib draws static
figures in Python, while Altair emits [Vega-Lite](https://vega.github.io/vega-lite/)
JSON that renders in a browser. You can't stack one on the other. What
`chartkit` does instead is give both the **same** visual identity so a static
matplotlib figure and an interactive Altair chart look like siblings:

- **One typeface** — JetBrains Mono, with a monospace fallback stack.
- **One palette** — a calm, colorblind-aware 8-color categorical set, plus a
  sequential (`viridis`) and diverging (`redblue`) scheme for magnitude and
  polarity data.
- **Matching minimal chrome** — left-aligned bold titles, quiet horizontal-only
  gridlines, no top/right spines, tickless axes.

> **Font note:** JetBrains Mono must be installed for matplotlib to use it, and
> available to the browser (e.g. via a webfont) for Altair. Both fall back to a
> generic monospace stack otherwise — nothing breaks, the type just changes.
> Get the font at <https://www.jetbrains.com/lp/mono/>.

## Installation

Requires **Python ≥ 3.9**.

**Just want to use it?** Inside an activated virtual environment (see below),
the package installs straight from PyPI — the World Cup sample data is bundled,
so the charts work with no extra downloads:

```bash
python -m pip install msds-comms-plotter          # add "[png]" for static PNG export
python -c "from msds_comms_plotter import altair_charts as ac; ac.linked_scatter_passing().save('passing.html')"
```

The rest of this section covers a full **editable/dev** install from a clone.
Either way, install into a **virtual environment (venv)**. This is the recommended (and on
many systems, required) approach: a modern Python — including the Homebrew
Python on macOS — is "externally managed" and will refuse a plain
`pip install` with an `error: externally-managed-environment` (PEP 668). A venv
sidesteps that entirely by giving the project its own isolated Python and
`pip`, so you never touch the system install.

### 1. Get the code

```bash
git clone https://github.com/tjsasser/msds-comms-plotter.git
cd msds-comms-plotter
```

### 2. Create the venv

Run this once, from the repo root. It makes a `.venv/` folder holding a
private copy of Python (`.venv/` is already covered by `.gitignore`).

```bash
python3 -m venv .venv
```

### 3. Activate it

You must activate the venv in **each new terminal session** before installing
or running anything. Pick the line for your shell:

```bash
# macOS / Linux (bash, zsh)
source .venv/bin/activate

# Windows (PowerShell)
.venv\Scripts\Activate.ps1

# Windows (Command Prompt)
.venv\Scripts\activate.bat
```

Once active, your prompt is prefixed with `(.venv)`. Confirm pip now points
inside the venv (not the system Python):

```bash
which pip     # macOS / Linux  -> .../msds-comms-plotter/.venv/bin/pip
where pip     # Windows        -> ...\msds-comms-plotter\.venv\Scripts\pip.exe
```

### 4. Install the package

With the venv active, upgrade pip and install this project in editable mode
(`-e`, so your source edits take effect without reinstalling). Because you're
inside the venv, **no** `--break-system-packages` or `--user` flag is needed:

```bash
python -m pip install --upgrade pip
python -m pip install -e .
```

This pulls in every dependency automatically (see the table below).

### 5. Verify

```bash
python -c "from msds_comms_plotter import chartkit; print('chartkit OK', len(chartkit.PALETTE), 'colors')"
```

Expected output: `chartkit OK 8 colors`.

### 6. When you're done

Leave the venv with:

```bash
deactivate
```

Next time you work on the project, just re-activate (step 3) — you don't need
to recreate the venv or reinstall.

> **Note — do not use `--break-system-packages`.** That flag (or a global
> `pip install` outside a venv) writes into the system/Homebrew Python and can
> corrupt it. The venv above is the safe, standard fix for the
> `externally-managed-environment` error.

### Requirements

Installing the package pulls these in automatically (declared in
`pyproject.toml`):

| Dependency | Used by |
| --- | --- |
| `pandas` | core data helpers |
| `pyarrow` | Parquet I/O |
| `requests` | data fetching |
| `matplotlib` | `chartkit` matplotlib theming + `plots` |
| `altair` | `chartkit` Altair theming (Altair 5.x; both the ≥5.5 and legacy theme APIs are supported) |

`chartkit` imports matplotlib and Altair **lazily**, inside the functions that
need them — so importing `chartkit` never fails just because one renderer is
missing. Use the matplotlib half without Altair installed, or vice versa.

**Optional — static PNG export.** Altair charts save as interactive `.html` out
of the box. To also write `.png` files, install the extra:

```bash
python -m pip install -e ".[png]"   # adds vl-convert-python
```

To register the JetBrains Mono font without a system-wide install, see
`register_font` in the API below.

## API

Import it from the package:

```python
from msds_comms_plotter import chartkit
```

### Design tokens (module constants)

These are the shared values every function draws from. Read them, or reuse them
directly in your own plotting code.

| Name | Value | Meaning |
| --- | --- | --- |
| `FONT` | `"JetBrains Mono"` | Primary typeface. |
| `FONT_STACK` | `["JetBrains Mono", "DejaVu Sans Mono", "Menlo", "Consolas", "monospace"]` | Fallback chain. |
| `PALETTE` | 8 hex colors | Categorical palette (blue, orange, teal, amber, pink, violet, green, red). |
| `INK` | `"#2c2c2a"` | Primary text / axis domain. |
| `MUTED` | `"#6b6a66"` | Secondary text / tick labels. |
| `GRID` | `"#e1e0d9"` | Gridlines. |
| `SURFACE` | `"#ffffff"` | Chart background. |
| `SEQUENTIAL` | `"viridis"` | Scheme for magnitude scales (heatmaps, choropleths). |
| `DIVERGING` | `"redblue"` | Scheme for polarity scales (deltas vs. a baseline). |
| `SIZE_TITLE`, `SIZE_LABEL`, `SIZE_TICK`, `SIZE_LEGEND` | `15, 12, 10, 11` | Font sizes (pt). |

### matplotlib functions

#### `apply_matplotlib_theme() -> dict`
Applies the chartkit look to matplotlib globally via `rcParams` (font, palette
cycle, figure size/DPI, spines, grid, ticks, legend). Call once, near the top of
your script or notebook. Returns the dict of rcParams it set.

#### `style_axes(ax, title=None, xlabel=None, ylabel=None, ygrid_only=True) -> Axes`
Per-axes finishing touches the global rcParams can't express: left-aligned
title, axis labels, hidden tick marks (labels kept), and horizontal-only grid
when `ygrid_only=True`. Returns the same `ax` for chaining.

#### `ensure_font(name="JetBrains Mono") -> bool`
Returns `True` if the font is available to matplotlib; otherwise emits a
`UserWarning` explaining the fallback and returns `False`. Handy as a
pre-flight check.

#### `register_font(path) -> str`
Registers a `.ttf`/`.otf` with matplotlib at runtime (no system install needed)
and returns the resolved font family name.

### Altair functions

#### `altair_theme() -> dict`
Returns the chartkit Vega-Lite config as a plain dict — useful if you want to
merge or inspect it rather than register it.

#### `enable_altair_theme(name="chartkit") -> str`
Registers **and** enables the theme, transparently handling both the Altair
≥ 5.5 decorator API and the legacy `alt.themes` registry. Returns the theme
name. Call once per session.

#### `alt_line(data, x, y, color=None, title="") -> alt.Chart`
#### `alt_bar(data, x, y, color=None, title="") -> alt.Chart`
#### `alt_scatter(data, x, y, color=None, title="") -> alt.Chart`
Thin styled chart builders. Channel arguments use Altair shorthand
(`"date:T"`, `"price:Q"`, `"symbol:N"`). They return a normal `alt.Chart`, so
you can keep chaining Altair methods (`.properties(...)`, `.interactive()`, …).

## Examples

### matplotlib

```python
import matplotlib.pyplot as plt
from msds_comms_plotter import chartkit

chartkit.ensure_font()            # warns if JetBrains Mono isn't installed
chartkit.apply_matplotlib_theme() # set the global look once

x = range(1, 13)
y = [3, 5, 4, 7, 8, 6, 9, 11, 10, 13, 12, 15]

fig, ax = plt.subplots()
ax.plot(x, y, marker="o")
chartkit.style_axes(ax, title="Monthly revenue", xlabel="Month", ylabel="USD (000s)")
fig.savefig("revenue.png")        # 200 DPI, tight bbox, white background
```

If JetBrains Mono lives in a local file rather than a system install:

```python
family = chartkit.register_font("fonts/JetBrainsMono-Regular.ttf")
chartkit.apply_matplotlib_theme()
```

### Altair

```python
import pandas as pd
from msds_comms_plotter import chartkit

chartkit.enable_altair_theme()    # register + enable once

df = pd.DataFrame({
    "date":   pd.date_range("2024-01-01", periods=6, freq="MS").tolist() * 2,
    "price":  [10, 12, 11, 14, 13, 16, 8, 9, 9, 11, 10, 12],
    "symbol": ["AAA"] * 6 + ["BBB"] * 6,
})

chart = chartkit.alt_line(df, x="date:T", y="price:Q", color="symbol:N",
                          title="Price by symbol")
chart.save("price.html")          # or chart.interactive() in a notebook
```

Both figures now share the same font, palette, and chrome.

### Running the World Cup example charts

The package ships a worked example on real data: `altair_charts` explores the
2022 World Cup with the five most common Altair chart types (bar, line,
scatter, histogram, heatmap) plus two linked-brush charts, a point-paths chart,
and a horizon graph of shots-per-minute, and `plots` renders matplotlib figures
of the same data.

**Interactive — open the whole gallery in your browser (recommended).** The
example script `examples/show_wc2022_charts.py` builds every chart, stacks them
into one page, and opens it in your default web browser. Every chart has **hover
tooltips**; the **line and scatter charts also support zoom and pan**
(scroll/drag) — the bar, histogram, and heatmap have categorical or binned axes
where zoom isn't meaningful, so they're tooltip-only. The **linked brush** charts
let you drag a box on a scatter so the bars below recount only the players you
selected; the **passing** chart adds a player-name **search box**; and the
**point-paths** chart adds a match-number slider, hover-to-trace team paths, and
a team search box.

Two **color pickers are pinned to the top** of the gallery: a **Category** scheme
(15 categorical schemes) that recolors *every* chart at once — including the
single-hue bar/line/scatter/histogram, each taking a distinct slot of the scheme
— and a **Heatmap** ramp (15 sequential schemes) for the heatmap.

```bash
python examples/show_wc2022_charts.py
```

The example uses the World Cup data **bundled inside the installed package**, so
it works from any folder with no raw data and no network. It writes a
**self-contained** HTML gallery to `./wc2022_altair_gallery.html` in whatever
directory you run it from (the Vega libraries are embedded, so it stays
interactive offline — no server, no internet). Re-open that file any time
without re-running. On a headless machine the script skips the browser and just
prints the file path.

Two more drop-in example scripts ship alongside it:

```bash
python examples/save_png.py           # -> ./passing_chart.png  (needs the [png] extra)
python examples/use_your_own_data.py  # feed the chart your own DataFrame
```

**Quickest — just look at the pre-rendered charts.** Individual charts are
committed in `reports/figures/`. Open any `alt_*.html` in a browser for the
interactive version, or the matching `.png` for a static image:

```bash
open reports/figures/alt_scatter_xg_vs_goals.html    # macOS
xdg-open reports/figures/alt_scatter_xg_vs_goals.html # Linux
```

**Regenerate them yourself.** With the package installed (steps above), run the
module — it uses the bundled World Cup data, so no raw-data unpack is needed:

```bash
python -m msds_comms_plotter.altair_charts   # writes alt_*.html + alt_*.png
python -m msds_comms_plotter.plots            # the matplotlib figures
```

Both write to `reports/figures/`. (`.html` always; `.png` needs the `[png]`
extra above.)

**In a notebook**, call any builder to display one chart inline — no need to
run the whole module:

```python
from msds_comms_plotter import altair_charts as ac
ac.scatter_xg_vs_goals()            # returns an alt.Chart; renders in Jupyter / VS Code
ac.bar_goals_by_team()              # bar / line / scatter / histogram / heatmap
ac.linked_scatter_position_counts() # brush a box on the scatter → bars below recount
ac.scatter_point_paths_hover()      # hover to trace a team's path; slider + search box
ac.linked_scatter_passing()         # brush + player search + color-scheme dropdown (15 schemes)
```

See the [**chart cookbook**](#chart-cookbook) below for a picture and the core
Altair code for each chart type, and [`docs/wc2022_data.md`](docs/wc2022_data.md)
for the full chart list and the dataset columns.

## Chart cookbook

One recipe per chart type used in this project: what it looks like, and the
core Altair code to build it. The snippets are trimmed to the essential
`mark` + `encode` so the technique is clear — the full themed, interactive
versions live in
[`msds_comms_plotter.altair_charts`](src/msds_comms_plotter/altair_charts.py)
(call `ac.bar_goals_by_team()`, `ac.line_cumulative_goals()`, …). Every chart
first picks up the shared look with:

```python
import altair as alt
from msds_comms_plotter import chartkit
chartkit.enable_altair_theme()   # JetBrains Mono, shared palette, minimal chrome
```

Data comes from the bundled sample tables (`worldcup.sample_goals()`,
`worldcup.sample_player_match_stats()`, `worldcup.sample_shots()`); see
[`docs/wc2022_data.md`](docs/wc2022_data.md) for columns.

### 1. Bar chart — `mark_bar`

Magnitude across a category. One measure, one categorical axis, single hue;
rank is carried by length and the sort order.

![Bar chart of goals per team](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_bar_goals_by_team.png)

```python
# by_team: columns "team", "goals"
alt.Chart(by_team).mark_bar().encode(
    x=alt.X("goals:Q", title="Goals scored"),
    y=alt.Y("team:N", sort="-x", title=None),   # sort teams by the x value
)
```

### 2. Line chart — `mark_line`

Change over time. Here goals are summed per match-day and accumulated into a
monotone curve. `point=True` marks each observation.

![Line chart of cumulative goals](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_line_cumulative_goals.png)

```python
# daily: columns "match_date" (datetime), "cumulative_goals"
alt.Chart(daily).mark_line(point=True).encode(
    x=alt.X("match_date:T", title="Match date"),
    y=alt.Y("cumulative_goals:Q", title="Cumulative goals"),
)
```

### 3. Scatter plot — `mark_point`

Relationship between two continuous measures — one dot per record.

![Scatter of xG vs goals](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_scatter_xg_vs_goals.png)

```python
# players: columns "xg", "goals"
alt.Chart(players).mark_point(filled=True, size=70).encode(
    x=alt.X("xg:Q", title="Expected goals (xG)"),
    y=alt.Y("goals:Q", title="Goals scored"),
)
```

Add a `y = x` reference line by layering a second chart:

```python
diag = alt.Chart(alt.Data(values=[{"v": 0}, {"v": 9}])).mark_line(
    strokeDash=[4, 4]).encode(x="v:Q", y="v:Q")
chart = diag + points
```

### 4. Histogram — `mark_bar` with `bin`

Distribution of a *continuous* variable: bin it, then count. A histogram is a
bar chart whose x is binned.

![Histogram of goal minutes](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_hist_goal_minutes.png)

```python
# goals: one row per goal, column "minute"
alt.Chart(goals).mark_bar().encode(
    x=alt.X("minute:Q", bin=alt.Bin(step=5), title="Match minute"),
    y=alt.Y("count():Q", title="Goals scored"),   # count() aggregates per bin
)
```

### 5. Heatmap — `mark_rect`

Two categorical axes with a magnitude in each cell, shaded by a **sequential**
color ramp.

![Heatmap of goals by team and phase](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_heatmap_team_phase.png)

```python
# counts: columns "team", "phase", "goals"
alt.Chart(counts).mark_rect(stroke="white", strokeWidth=2).encode(
    x=alt.X("phase:N", title="Match phase"),
    y=alt.Y("team:N", title=None),
    color=alt.Color("goals:Q", scale=alt.Scale(scheme="viridis")),
)
```

### 6. Linked brush — `selection_interval` (crossfilter)

Altair's signature interaction: **drag a box** on the scatter and a second
view re-aggregates to only the selected points; the rest fade to grey. One
selection drives both marks.

![Brushable scatter linked to count bars](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_brush_position_counts.png)

```python
brush = alt.selection_interval()
color = alt.Color("position:N")

points = alt.Chart(players).mark_point(filled=True).encode(
    x="xg:Q", y="goals:Q",
    # selected points keep their category color; the rest go grey
    color=alt.when(brush).then(color).otherwise(alt.value("lightgray")),
).add_params(brush)

bars = alt.Chart(players).mark_bar().encode(
    y="position:N", x="count():Q", color=color,
).transform_filter(brush)          # only the brushed points are counted

points & bars                      # vertical concatenation
```

The **passing** chart (`ac.linked_scatter_passing`) is the same pattern with a
player-name search box and a live color-scheme dropdown added on top — see the
one-call shortcut at the end of this cookbook.

### 7. Point paths on hover — `mark_trail` + hover selection

Each entity is a trajectory over an ordered dimension. Hovering one traces its
whole path as a tapering trail; the rest stay dim. (Abbreviated — the full
builder adds a match-number slider and a team search box.)

![Team paths through the tournament](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_point_paths_hover.png)

```python
hover = alt.selection_point(on="mouseover", fields=["team"], empty=False)

# team_matches: columns "xg", "goals", "team", "match_num"
base = alt.Chart(team_matches).encode(x="xg:Q", y="goals:Q", detail="team:N")

points = base.mark_circle(size=110).add_params(hover)
trail = base.mark_trail().encode(
    order=alt.Order("match_num:Q"),                 # connect in match order
    size=alt.Size("match_num:Q", legend=None),      # taper over time
    opacity=alt.when(hover).then(alt.value(0.4)).otherwise(alt.value(0)),
)
trail + points
```

### 8. Horizon graph — layered clipped `mark_area`

Compresses a tall time series into a short band: fold it into N layers, each a
clipped area of the amount spilling past its threshold, so busier values stack
into darker color.

![Horizon graph of shots per minute](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_horizon_shots.png)

```python
import math
# per_min: columns "minute", "shots"
bands = 4
band = math.ceil(per_min["shots"].max() / bands)

layers = [
    alt.Chart(per_min)
       .transform_calculate(v=f"clamp(datum.shots - {k * band}, 0, {band})")
       .mark_area(clip=True, interpolate="monotone", opacity=0.5)
       .encode(x=alt.X("minute:Q", title="Match minute"),
               y=alt.Y("v:Q", scale=alt.Scale(domain=[0, band]), axis=None))
    for k in range(bands)
]
alt.layer(*layers)
```

### Saving a chart

Any chart is an `alt.Chart`; save it as interactive HTML or a static PNG:

```python
chart.save("chart.html")            # interactive; add inline=True for offline
chart.save("chart.png", ppi=200)    # needs vl-convert-python (the [png] extra)
```

To see them all together with the shared color pickers, run the gallery:

```bash
python examples/show_wc2022_charts.py
```

### Shortcut: just call the library

You don't have to reproduce any of the code above. Each chart already has a
one-call builder in `altair_charts`, and every builder defaults to the bundled
World Cup data — so a plain call works right after `pip install`. For example,
the full **passing** chart — brush, player-name search box, color-scheme
dropdown, and the linked count-by-position bars — is one function call:

![Passing: volume vs accuracy](https://raw.githubusercontent.com/tjsasser/msds-comms-plotter/main/reports/figures/alt_brush_passing.png)

```python
from msds_comms_plotter import altair_charts as ac

chart = ac.linked_scatter_passing()   # bundled data; returns an alt.Chart
chart.save("passing.html")             # or just `chart` in a notebook
```

The same one-call pattern works for every chart — `ac.bar_goals_by_team()`,
`ac.horizon_shots_per_minute()`, and so on. To use your own numbers, pass a
DataFrame (`ac.linked_scatter_passing(stats=my_df)`) and you get the full
themed, interactive chart back as an `alt.Chart`.

## Project layout

```
msds-comms-plotter/
├── src/msds_comms_plotter/
│   ├── __init__.py       # exposes chartkit
│   ├── chartkit.py       # matplotlib + Altair theming  ← documented above
│   ├── altair_charts.py  # World Cup 2022 Altair charts (run as a module)
│   ├── plots.py          # World Cup 2022 matplotlib figures
│   └── worldcup.py       # dataset loading/paths
├── examples/             # runnable examples (show_wc2022_charts.py opens a browser)
├── data/                 # example datasets
├── docs/                 # dataset notes (see docs/wc2022_data.md)
├── notebooks/            # exploratory notebooks
├── reports/figures/      # generated figures (alt_*.html/.png, plus matplotlib .png)
└── pyproject.toml
```

## Contributing

This is a course project, but if you're extending it:

1. Keep the shared design tokens (`PALETTE`, `INK`, `GRID`, …) as the single
   source of truth — style through them rather than hard-coding colors.
2. Keep renderer imports lazy (import matplotlib/altair inside functions) so the
   two halves stay independent.
3. Run a quick import check before committing:
   `python -c "from msds_comms_plotter import chartkit"`.

## License

See [LICENSE](LICENSE).
