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fix: readme images
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@thorwhalen
thorwhalen committed Nov 4, 2024
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Showing 1 changed file with 13 additions and 13 deletions.
26 changes: 13 additions & 13 deletions README.md
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Expand Up @@ -41,7 +41,7 @@ taking an ax as input and drawing something on it.

For example:

![](https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_06.23.21.png)
![](https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_06.23.21.png)

```
from oplot import ax_func_to_plot
Expand Down Expand Up @@ -93,7 +93,7 @@ y = [sigmoid_map(i) for i in x]
plt.plot(x, y)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen Shot 2021-01-06 at 07.21.26.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen Shot 2021-01-06 at 07.21.26.png" width="320">


# outlier_scores.py
Expand All @@ -107,7 +107,7 @@ from oplot import plot_scores_and_zones
scores = np.random.random(200)
plot_scores_and_zones(scores, zones=[0, 0.25, 0.5, 0.9])
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_08.32.16.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_08.32.16.png" width="320">

find_prop_markers, get_confusion_zone_percentiles and get_confusion_zones_std provides tools
to find statistically meaningfull zones.
Expand All @@ -124,7 +124,7 @@ b) the aligned spectra

Parameters allows to add vertical markers to the plot like in the example below.

<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_09.08.55.png" width="800">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_09.08.55.png" width="800">


# plot_data_set.py
Expand Down Expand Up @@ -211,7 +211,7 @@ scatter_and_color_according_to_y(X, y,



<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_11.36.02.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_11.36.02.png" width="320">

```
from oplot import scatter_and_color_according_to_y
Expand All @@ -222,7 +222,7 @@ scatter_and_color_according_to_y(X, y,
```


<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_11.36.07.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_11.36.07.png" width="320">

There is also that little one, which I don't remeber ever using and needs some work:

Expand All @@ -232,7 +232,7 @@ from oplot import side_by_side_bar
side_by_side_bar([[1,2,3], [4,5,6]], list_names=['you', 'me'])
```

<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_11.56.42.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_11.56.42.png" width="320">


## plot_stats.py
Expand All @@ -257,7 +257,7 @@ y[:50] = (y[:50] + 1) % 4
plot_confusion_matrix(y, truth)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-06_at_12.59.52.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-06_at_12.59.52.png" width="320">

make_normal_outlier_timeline plots the scores with a color/legend given by
the aligned list truth
Expand All @@ -270,7 +270,7 @@ tags = np.array(['normal'] * 20 + ['outlier'] * 15 + ['crazy'] * (len(scores) -
make_normal_outlier_timeline(tags, scores)
```

<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-07_at_05.46.39.png" width="800">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-07_at_05.46.39.png" width="800">


make_tables_tn_fp_fn_tp is convenient to obtain True Positive and False Negative
Expand All @@ -283,7 +283,7 @@ scores = np.arange(-1, 3, 0.1)
truth = scores > 2.5
make_tables_tn_fp_fn_tp(truth, scores)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-07_at_05.51.23.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-07_at_05.51.23.png" width="320">

render_mpl_table takes any pandas dataframe and turn it into a pretty plot
which can then be saved as a pdf for example.
Expand All @@ -296,7 +296,7 @@ truth = scores > 2.5
df = make_tables_tn_fp_fn_tp(truth, scores)
render_mpl_table(df)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-07_at_05.54.23.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-07_at_05.54.23.png" width="320">

plot_outlier_metric_curve plots ROC type. You specify which pair of statistics
you want to display along with a list of scores and truth (0 for negative, 1 for positive).
Expand All @@ -313,7 +313,7 @@ pair_metrics={'x': 'recall', 'y': 'precision'}
plot_outlier_metric_curve(truth, scores,
pair_metrics=pair_metrics)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-07_at_06.04.52.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-07_at_06.04.52.png" width="320">


There are many choices for the statistics to display, some pairs making more or
Expand All @@ -326,7 +326,7 @@ pair_metrics={'x': 'false_positive_rate', 'y': 'false_negative_rate'}
plot_outlier_metric_curve(truth, scores,
pair_metrics=pair_metrics)
```
<img src="https://github.com/i2mint/oplot/readme_plots/Screen_Shot_2021-01-07_at_06.11.13.png" width="320">
<img src="https://github.com/i2mint/oplot/blob/master/readme_plots/Screen_Shot_2021-01-07_at_06.11.13.png" width="320">



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