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thorwhalen merged 2 commits into from
Sep 22, 2026
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fix: migrate CI off legacy isee-based workflow onto wads-managed uv CI #11

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3b2538b
fix: migrate CI off legacy isee-based workflow onto wads-managed uv CI
thorwhalen Sep 22, 2026
ef5f4fa
fix: force MPLBACKEND=Agg in CI (Windows Tests hung on plt.show())
thorwhalen Sep 22, 2026
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427 changes: 368 additions & 59 deletions .github/workflows/ci.yml
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223 changes: 117 additions & 106 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -7,16 +7,17 @@

```python
import pandas as pd
from oplot import heatmap
from oplot import heatmap

d = pd.DataFrame(
[
{'A': 1, 'B': 3, 'C': 1},
{'A': 1, 'B': 3, 'C': 2},
{'A': 5, 'B': 5, 'C': 4},
{'A': 3, 'B': 2, 'C': 2},
{'A': 1, 'B': 3, 'C': 3},
{'A': 4, 'B': 3, 'C': 1},
{'A': 5, 'B': 1, 'C': 3},
{"A": 1, "B": 3, "C": 1},
{"A": 1, "B": 3, "C": 2},
{"A": 5, "B": 5, "C": 4},
{"A": 3, "B": 2, "C": 2},
{"A": 1, "B": 3, "C": 3},
{"A": 4, "B": 3, "C": 1},
{"A": 5, "B": 1, "C": 3},
]
)
heatmap(d)
Expand Down Expand Up @@ -48,16 +49,19 @@ For example:
def ax_func(ax):
ax.plot([1, 5, 3])

# with an iterable of functions like ax_func, ax_func_to_plot makes
# a simple grid plot. The parameter n_per_row control the number of plots

# with an iterable of functions like ax_func, ax_func_to_plot makes
# a simple grid plot. The parameter n_per_row control the number of plots
# per row
ax_func_to_plot([ax_func] * 6,
n_per_row=3,
width=5,
height_row=3,
x_labels='x axis name',
y_labels='y axis name',
outer_axis_labels_only=True)
ax_func_to_plot(
[ax_func] * 6,
n_per_row=3,
width=5,
height_row=3,
x_labels="x axis name",
y_labels="y axis name",
outer_axis_labels_only=True,
)
```

<img src="https://user-images.githubusercontent.com/1906276/127305797-948851fa-6cb0-4d19-aac1-6508ee7db04f.png" width="320">
Expand Down Expand Up @@ -137,8 +141,8 @@ Example of a data dict with data having two different distributions:

```python
data_dict = {
'Unicorn Heights': np.random.normal(loc=6, scale=1, size=1000),
'Dragon Wingspan': np.concatenate(
"Unicorn Heights": np.random.normal(loc=6, scale=1, size=1000),
"Dragon Wingspan": np.concatenate(
[
np.random.normal(loc=3, scale=0.5, size=500),
np.random.normal(loc=7, scale=0.5, size=500),
Expand Down Expand Up @@ -167,18 +171,18 @@ density_distribution(
data_dict,
ax=ax,
axvline_kwargs={
'Unicorn Heights': {'color': 'magenta', 'linestyle': ':'},
'Dragon Wingspan': {'color': 'cyan', 'linestyle': '-.'},
"Unicorn Heights": {"color": "magenta", "linestyle": ":"},
"Dragon Wingspan": {"color": "cyan", "linestyle": "-."},
},
line_width=2,
location_linestyle='-.',
colors=('magenta', 'cyan'),
location_linestyle="-.",
colors=("magenta", "cyan"),
density_plot_func=sns.histplot,
density_plot_kwargs={'fill': True},
text_kwargs={'x': 0.1, 'y': 0.9, 'bbox': dict(facecolor='yellow', alpha=0.5)},
mean_line_kwargs={'linewidth': 2},
density_plot_kwargs={"fill": True},
text_kwargs={"x": 0.1, "y": 0.9, "bbox": dict(facecolor="yellow", alpha=0.5)},
mean_line_kwargs={"linewidth": 2},
)
ax.set_title('Customized Density Plot')
ax.set_title("Customized Density Plot")
plt.show()
```

Expand All @@ -198,14 +202,11 @@ from sklearn.datasets import make_classification
```python
from oplot import scatter_and_color_according_to_y

X, y = make_classification(n_samples=500,
n_features=20,
n_classes=4,
n_clusters_per_class=1)
X, y = make_classification(
n_samples=500, n_features=20, n_classes=4, n_clusters_per_class=1
)

scatter_and_color_according_to_y(X, y,
projection='2d',
dim_reduct='PCA')
scatter_and_color_according_to_y(X, y, projection="2d", dim_reduct="PCA")
```


Expand All @@ -215,9 +216,7 @@ scatter_and_color_according_to_y(X, y,
```python
from oplot import scatter_and_color_according_to_y

scatter_and_color_according_to_y(X, y,
projection='3d',
dim_reduct='LDA')
scatter_and_color_according_to_y(X, y, projection="3d", dim_reduct="LDA")
```


Expand All @@ -228,7 +227,7 @@ There is also that little one, which I don't remeber ever using and needs some w
```python
from oplot import side_by_side_bar

side_by_side_bar([[1,2,3], [4,5,6]], list_names=['you', 'me'])
side_by_side_bar([[1, 2, 3], [4, 5, 6]], list_names=["you", "me"])
```

<img src="https://raw.githubusercontent.com/i2mint/oplot/master/readme_plots/Screen_Shot_2021-01-06_at_11.56.42.png" width="320">
Expand All @@ -245,11 +244,10 @@ sklearn function:
from oplot.plot_stats import plot_confusion_matrix
from sklearn.datasets import make_classification

X, truth = make_classification(n_samples=500,
n_features=20,
n_classes=4,
n_clusters_per_class=1)

X, truth = make_classification(
n_samples=500, n_features=20, n_classes=4, n_clusters_per_class=1
)

# making a copy of truth and messing with it
y = truth.copy()
y[:50] = (y[:50] + 1) % 4
Expand All @@ -266,7 +264,9 @@ the aligned list truth
from oplot.plot_stats import make_normal_outlier_timeline

scores = np.arange(-1, 3, 0.1)
tags = np.array(['normal'] * 20 + ['outlier'] * 15 + ['crazy'] * (len(scores) - 20 - 15))
tags = np.array(
["normal"] * 20 + ["outlier"] * 15 + ["crazy"] * (len(scores) - 20 - 15)
)
make_normal_outlier_timeline(tags, scores)
```

Expand Down Expand Up @@ -310,9 +310,8 @@ from oplot.plot_stats import plot_outlier_metric_curve
scores = np.concatenate([np.random.random(100), np.random.random(100) * 2])
truth = np.array([0] * 100 + [1] * 100)

pair_metrics={'x': 'recall', 'y': 'precision'}
plot_outlier_metric_curve(truth, scores,
pair_metrics=pair_metrics)
pair_metrics = {"x": "recall", "y": "precision"}
plot_outlier_metric_curve(truth, scores, pair_metrics=pair_metrics)
```

<img src="https://raw.githubusercontent.com/i2mint/oplot/master/readme_plots/Screen_Shot_2021-01-07_at_06.04.52.png" width="320">
Expand All @@ -324,9 +323,8 @@ less sense, some not at all.
```python
from oplot.plot_stats import plot_outlier_metric_curve

pair_metrics={'x': 'false_positive_rate', 'y': 'false_negative_rate'}
plot_outlier_metric_curve(truth, scores,
pair_metrics=pair_metrics)
pair_metrics = {"x": "false_positive_rate", "y": "false_negative_rate"}
plot_outlier_metric_curve(truth, scores, pair_metrics=pair_metrics)
```

<img src="https://raw.githubusercontent.com/i2mint/oplot/master/readme_plots/Screen_Shot_2021-01-07_at_06.11.13.png" width="320">
Expand All @@ -339,40 +337,53 @@ The full list of usable statistics along with synonymous:
```python
# all these scores except for MCC gives a score between 0 and 1.
# I normalized MMC into what I call NNMC in order to keep the same scale for all.
base_statistics_dict = {'TPR': lambda tn, fp, fn, tp: tp / (tp + fn),
# sensitivity, recall, hit rate, or true positive rate
'TNR': lambda tn, fp, fn, tp: tn / (tn + fp), # specificity, selectivity or true negative rate
'PPV': lambda tn, fp, fn, tp: tp / (tp + fp), # precision or positive predictive value
'NPV': lambda tn, fp, fn, tp: tn / (tn + fn), # negative predictive value
'FNR': lambda tn, fp, fn, tp: fn / (fn + tp), # miss rate or false negative rate
'FPR': lambda tn, fp, fn, tp: fp / (fp + tn), # fall-out or false positive rate
'FDR': lambda tn, fp, fn, tp: fp / (fp + tp), # false discovery rate
'FOR': lambda tn, fp, fn, tp: fn / (fn + tn), # false omission rate
'TS': lambda tn, fp, fn, tp: tp / (tp + fn + fp),
# threat score (TS) or Critical Success Index (CSI)
'ACC': lambda tn, fp, fn, tp: (tp + tn) / (tp + tn + fp + fn), # accuracy
'F1': lambda tn, fp, fn, tp: (2 * tp) / (2 * tp + fp + fn), # F1 score
'NMCC': lambda tn, fp, fn, tp: ((tp * tn - fp * fn) / (
(tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) ** 0.5 + 1) / 2,
# NORMALIZED TO BE BETWEEN 0 AND 1 Matthews correlation coefficient
'BM': lambda tn, fp, fn, tp: tp / (tp + fn) + tn / (tn + fp) - 1,
# Informedness or Bookmaker Informedness
'MK': lambda tn, fp, fn, tp: tp / (tp + fp) + tn / (tn + fn) - 1} # Markedness

synonyms = {'TPR': ['recall', 'sensitivity', 'true_positive_rate', 'hit_rate', 'tpr'],
'TNR': ['specificity', 'SPC', 'true_negative_rate', 'selectivity', 'tnr'],
'PPV': ['precision', 'positive_predictive_value', 'ppv'],
'NPV': ['negative_predictive_value', 'npv'],
'FNR': ['miss_rate', 'false_negative_rate', 'fnr'],
'FPR': ['fall_out', 'false_positive_rate', 'fpr'],
'FDR': ['false_discovery_rate', 'fdr'],
'FOR': ['false_omission_rate', 'for'],
'TS': ['threat_score', 'critical_success_index', 'CSI', 'csi', 'ts'],
'ACC': ['accuracy', 'acc'],
'F1': ['f1_score', 'f1', 'F1_score'],
'NMCC': ['normalized_Matthews_correlation_coefficient', 'nmcc'],
'BM': ['informedness', 'bookmaker_informedness', 'bi', 'BI', 'bm'],
'MK': ['markedness', 'mk']}
base_statistics_dict = {
"TPR": lambda tn, fp, fn, tp: tp / (tp + fn),
# sensitivity, recall, hit rate, or true positive rate
"TNR": lambda tn, fp, fn, tp: (
tn / (tn + fp)
), # specificity, selectivity or true negative rate
"PPV": lambda tn, fp, fn, tp: (
tp / (tp + fp)
), # precision or positive predictive value
"NPV": lambda tn, fp, fn, tp: tn / (tn + fn), # negative predictive value
"FNR": lambda tn, fp, fn, tp: fn / (fn + tp), # miss rate or false negative rate
"FPR": lambda tn, fp, fn, tp: fp / (fp + tn), # fall-out or false positive rate
"FDR": lambda tn, fp, fn, tp: fp / (fp + tp), # false discovery rate
"FOR": lambda tn, fp, fn, tp: fn / (fn + tn), # false omission rate
"TS": lambda tn, fp, fn, tp: tp / (tp + fn + fp),
# threat score (TS) or Critical Success Index (CSI)
"ACC": lambda tn, fp, fn, tp: (tp + tn) / (tp + tn + fp + fn), # accuracy
"F1": lambda tn, fp, fn, tp: (2 * tp) / (2 * tp + fp + fn), # F1 score
"NMCC": lambda tn, fp, fn, tp: (
(
(tp * tn - fp * fn) / ((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) ** 0.5
+ 1
)
/ 2
),
# NORMALIZED TO BE BETWEEN 0 AND 1 Matthews correlation coefficient
"BM": lambda tn, fp, fn, tp: tp / (tp + fn) + tn / (tn + fp) - 1,
# Informedness or Bookmaker Informedness
"MK": lambda tn, fp, fn, tp: tp / (tp + fp) + tn / (tn + fn) - 1,
} # Markedness

synonyms = {
"TPR": ["recall", "sensitivity", "true_positive_rate", "hit_rate", "tpr"],
"TNR": ["specificity", "SPC", "true_negative_rate", "selectivity", "tnr"],
"PPV": ["precision", "positive_predictive_value", "ppv"],
"NPV": ["negative_predictive_value", "npv"],
"FNR": ["miss_rate", "false_negative_rate", "fnr"],
"FPR": ["fall_out", "false_positive_rate", "fpr"],
"FDR": ["false_discovery_rate", "fdr"],
"FOR": ["false_omission_rate", "for"],
"TS": ["threat_score", "critical_success_index", "CSI", "csi", "ts"],
"ACC": ["accuracy", "acc"],
"F1": ["f1_score", "f1", "F1_score"],
"NMCC": ["normalized_Matthews_correlation_coefficient", "nmcc"],
"BM": ["informedness", "bookmaker_informedness", "bi", "BI", "bm"],
"MK": ["markedness", "mk"],
}
```


Expand Down Expand Up @@ -414,7 +425,7 @@ x = np.concatenate([x1, x2])
y = np.concatenate([y1, y2])

# Create a DataFrame
data = pd.DataFrame({'x': x, 'y': y})
data = pd.DataFrame({"x": x, "y": y})
```

## Plot with Boundary Condition `y ≤ x`
Expand All @@ -429,17 +440,17 @@ boundary_condition = lambda X, Y: Y <= X
# Plot using the custom KDE function
ax = kdeplot_w_boundary_condition(
data=data,
x='x',
y='y',
x="x",
y="y",
boundary_condition=boundary_condition,
fill=True,
cmap='viridis',
cmap="viridis",
figsize=(8, 6),
levels=15 # Increased levels for better resolution
levels=15, # Increased levels for better resolution
)

# Add a title
ax.set_title('KDE Plot with Boundary Condition: y ≤ x')
ax.set_title("KDE Plot with Boundary Condition: y ≤ x")

# Show the plot
plt.show()
Expand All @@ -454,17 +465,17 @@ plt.show()
```python
ax = kdeplot_w_boundary_condition(
data=data,
x='x',
y='y',
x="x",
y="y",
boundary_condition=None, # No boundary condition
fill=True,
cmap='viridis',
cmap="viridis",
figsize=(8, 6),
levels=15
levels=15,
)

# Add a title
ax.set_title('KDE Plot without Boundary Condition')
ax.set_title("KDE Plot without Boundary Condition")

# Show the plot
plt.show()
Expand All @@ -491,17 +502,17 @@ boundary_condition = lambda X, Y: Y >= X
# Plot using the custom KDE function
ax = kdeplot_w_boundary_condition(
data=data,
x='x',
y='y',
x="x",
y="y",
boundary_condition=boundary_condition,
fill=True,
cmap='coolwarm',
cmap="coolwarm",
figsize=(8, 6),
levels=15
levels=15,
)

# Add a title
ax.set_title('KDE Plot with Boundary Condition: y ≥ x')
ax.set_title("KDE Plot with Boundary Condition: y ≥ x")

# Show the plot
plt.show()
Expand All @@ -516,22 +527,22 @@ plt.show()

```python
# Define a circular boundary condition function
boundary_condition = lambda X, Y: (X - 2)**2 + (Y - 2)**2 <= 3**2
boundary_condition = lambda X, Y: (X - 2) ** 2 + (Y - 2) ** 2 <= 3**2

# Plot using the custom KDE function
ax = kdeplot_w_boundary_condition(
data=data,
x='x',
y='y',
x="x",
y="y",
boundary_condition=boundary_condition,
fill=True,
cmap='plasma',
cmap="plasma",
figsize=(8, 6),
levels=15
levels=15,
)

# Add a title
ax.set_title('KDE Plot with Circular Boundary Condition')
ax.set_title("KDE Plot with Circular Boundary Condition")

# Show the plot
plt.show()
Expand Down
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