Change name

README.md

@@ -1,7 +1,7 @@
-# Bootstrap your plot
+# Bootplot: bootstrap your plot
 
-**Bootstrap your plot** is a library that lets you easily visualize uncertainty. You only need to provide a function
-that generates a plot from your data and pass it to `bootstrapped_plot`. This will generate a static image and an
+**Bootplot** is a library that lets you easily visualize uncertainty. You only need to provide a function
+that generates a plot from your data and pass it to `bootplot`. This will generate a static image and an
 animation of your data uncertainty.
 
 The method works by resampling the original dataset and plotting each bootstrapped sample.
@@ -14,7 +14,7 @@ line and visualize the uncertainty with the following code:
 import numpy as np
 from sklearn.linear_model import LinearRegression
 
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_linear_regression(data_subset, data_full, ax):
@@ -41,7 +41,7 @@ if __name__ == '__main__':
     dataset[:, 1] = dataset[:, 0] * 1.5 + 2 + noise
 
     # Create image and animation that show uncertainty
-    bootstrapped_plot(
+    bootplot(
         make_linear_regression,
         dataset,
         output_image_path='bootstrapped_linear_regression.png',

bootplot/__init__.py

@@ -0,0 +1,3 @@
+from bootplot.sorting import sort_images, PCASorter, LucasKanadeSorter, HorizontalMassSorter, TravelingSalesmanSorter, \
+    FarnebackSorter
+from bootplot.base import bootplot

src/base.py → bootplot/base.py

@@ -8,7 +8,7 @@
 from tqdm import tqdm
 from PIL import Image
 
-from src.sorting import sort_images
+from bootplot.sorting import sort_images
 
 
 def fig_to_array(fig, ax):
@@ -50,17 +50,17 @@ def decay_images(images, m: int, decay_length: int):
     return decayed_images
 
 
-def bootstrapped_plot(f: callable,
-                      data: np.ndarray,
-                      m: int = 100,
-                      output_size_px: Tuple[int, int] = (512, 512),
-                      output_image_path: Union[str, Path] = None,
-                      output_animation_path: Union[str, Path] = None,
-                      sort_type: str = 'tsp',
-                      sort_kwargs: dict = None,
-                      decay: int = 0,
-                      fps: int = 60,
-                      verbose: bool = False):
+def bootplot(f: callable,
+             data: np.ndarray,
+             m: int = 100,
+             output_size_px: Tuple[int, int] = (512, 512),
+             output_image_path: Union[str, Path] = None,
+             output_animation_path: Union[str, Path] = None,
+             sort_type: str = 'tsp',
+             sort_kwargs: dict = None,
+             decay: int = 0,
+             fps: int = 60,
+             verbose: bool = False):
     px_size_inches = 1 / plt.rcParams['figure.dpi']
     fig, ax = plt.subplots(figsize=(output_size_px[0] * px_size_inches, output_size_px[1] * px_size_inches))
     bootstrapped_matrices = np.stack([

src/sorting.py → bootplot/sorting.py

@@ -11,7 +11,7 @@
 import scipy
 import networkx as nx
 
-from src.image_features import compute_hog_features, compute_center_of_mass, compute_center_of_mass_per_component
+from bootplot.image_features import compute_hog_features, compute_center_of_mass, compute_center_of_mass_per_component
 
 
 class Sorter(abc.ABC):

demo/bar_chart.py

@@ -1,5 +1,5 @@
 import numpy as np
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_bar_chart(data_subset, data_full, ax):
@@ -21,7 +21,7 @@ def make_bar_chart(data_subset, data_full, ax):
     np.random.seed(0)
 
     dataset = np.random.binomial(20, 0.3, size=1000)
-    bootstrapped_plot(
+    bootplot(
         make_bar_chart,
         dataset,
         m=100,

demo/linear_regression.py

@@ -1,7 +1,7 @@
 import numpy as np
 from sklearn.linear_model import LinearRegression
 
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_linear_regression(data_subset, data_full, ax):
@@ -26,7 +26,7 @@ def make_linear_regression(data_subset, data_full, ax):
     noise = np.random.randn(len(dataset)) * 2.5
     dataset[:, 1] = dataset[:, 0] * 1.5 + 2 + noise
 
-    bootstrapped_plot(
+    bootplot(
         make_linear_regression,
         dataset,
         m=100,

demo/multiple_point_plot.py

@@ -1,5 +1,5 @@
 import numpy as np
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_point_plot(data_subset, data_full, ax):
@@ -27,7 +27,7 @@ def make_point_plot(data_subset, data_full, ax):
         j = float(np.random.randint(n_classes))
         dataset[i] = [j, np.random.randn() * j]
 
-    bootstrapped_plot(
+    bootplot(
         make_point_plot,
         dataset,
         m=100,

demo/pie_chart.py

@@ -1,5 +1,5 @@
 import numpy as np
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_pie_chart(data_subset, data_full, ax):
@@ -30,7 +30,7 @@ def make_pie_chart(data_subset, data_full, ax):
 
     dataset = np.random.binomial(7, 0.2, size=1000)
 
-    bootstrapped_plot(
+    bootplot(
         make_pie_chart,
         dataset,
         m=100,

demo/polynomial_regression.py

@@ -1,7 +1,7 @@
 import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import PolynomialFeatures
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_polynomial_regression(data_subset, data_full, ax):
@@ -30,7 +30,7 @@ def make_polynomial_regression(data_subset, data_full, ax):
     noise = np.random.randn(len(dataset)) * 2.5
     dataset[:, 1] = dataset[:, 0] * 1.5 + 2 + noise
 
-    bootstrapped_plot(
+    bootplot(
         make_polynomial_regression,
         dataset,
         m=100,

demo/single_point_plot.py

@@ -1,5 +1,5 @@
 import numpy as np
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_plot_demo_point_plot(data_subset, data_full, ax):
@@ -16,7 +16,7 @@ def make_plot_demo_point_plot(data_subset, data_full, ax):
     n_observations = 1000
     dataset = np.random.uniform(low=-5.5, high=5.5, size=(n_observations,))
 
-    mat = bootstrapped_plot(
+    mat = bootplot(
         make_plot_demo_point_plot,
         dataset,
         m=100,

demo/text.py

@@ -1,6 +1,6 @@
 import numpy as np
 from sklearn.linear_model import LinearRegression
-from src import bootstrapped_plot
+from bootplot import bootplot
 
 
 def make_plot_demo_text(data_subset, data_full, ax):
@@ -32,7 +32,7 @@ def make_plot_demo_text(data_subset, data_full, ax):
     noise = np.random.randn(len(dataset)) * 2.5
     dataset[:, 1] = (dataset[:, 0] * 1.5 + noise) / 5 + 2.551
 
-    bootstrapped_plot(
+    bootplot(
         make_plot_demo_text,
         dataset,
         m=100,