Merge pull request #1 from davidnabergoj/dev

Dev

README.md

@@ -25,7 +25,7 @@ Supported image output formats include popular formats such as JPG, PNG, BMP. Su
 pip install bootplot
 ```
 
-Alternatively, you can install **bootplot** without PyPI:
+Alternatively, you can install **bootplot** using:
 
 ```
 git clone https://github.com/davidnabergoj/bootplot
@@ -59,6 +59,8 @@ def plot_regression(data_subset, data_full, ax):
     bbox_kwargs = dict(facecolor='none', edgecolor='black', pad=10.0)
     ax.text(x=0, y=-8, s=f'RMSE: {rmse:.4f}', fontsize=12, ha='center', bbox=bbox_kwargs)
 
+    ax.set_xlim(-10, 10)
+    ax.set_ylim(-10, 10)
 
 if __name__ == '__main__':
     np.random.seed(0)
@@ -74,8 +76,6 @@ if __name__ == '__main__':
         dataset,
         output_image_path='demo_image.png',
         output_animation_path='demo_animation.gif',
-        xlim=(-10, 10),
-        ylim=(-10, 10),
         verbose=True
     )
 ```

bootplot/backend/base.py

@@ -0,0 +1,160 @@
+from abc import abstractmethod, ABC
+from typing import Tuple, Union
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from tqdm import tqdm
+
+import bootplot.backend.matplotlib
+
+
+class Backend(ABC):
+    def __init__(self,
+                 f: callable,
+                 data: Union[np.ndarray, pd.DataFrame],
+                 m: int,
+                 output_size_px: Tuple[int, int]):
+        self.output_size_px = output_size_px
+        self.f = f
+        self.data = data
+        self.m = m
+
+    @abstractmethod
+    def create_figure(self):
+        raise NotImplemented
+
+    def plot(self):
+        indices = np.random.randint(low=0, high=len(self.data), size=len(self.data))
+        if isinstance(self.data, pd.DataFrame):
+            return self.f(self.data.iloc[indices], self.data, *self.plot_args)
+        elif isinstance(self.data, np.ndarray):
+            return self.f(self.data[indices], self.data, *self.plot_args)
+
+    @abstractmethod
+    def plot_to_array(self) -> np.ndarray:
+        raise NotImplemented
+
+    @abstractmethod
+    def clear_figure(self):
+        raise NotImplemented
+
+    @abstractmethod
+    def close_figure(self):
+        raise NotImplemented
+
+    @property
+    @abstractmethod
+    def plot_args(self):
+        raise NotImplemented
+
+
+class Basic(Backend):
+    def __init__(self, f: callable, data: Union[np.ndarray, pd.DataFrame], m: int, output_size_px: Tuple[int, int]):
+        super().__init__(f, data, m, output_size_px)
+        self.cached_image = None
+
+    def plot(self):
+        self.cached_image = super().plot()
+
+    def create_figure(self):
+        pass
+
+    def plot_to_array(self) -> np.ndarray:
+        return self.cached_image
+
+    def clear_figure(self):
+        pass
+
+    def close_figure(self):
+        pass
+
+    @property
+    def plot_args(self):
+        return []
+
+
+class Matplotlib(Backend):
+    def __init__(self,
+                 f: callable,
+                 data: Union[np.ndarray, pd.DataFrame],
+                 m: int,
+                 output_size_px: Tuple[int, int] = (512, 512)):
+        self.fig = None
+        self.ax = None
+        super().__init__(f, data, m, output_size_px)
+
+    def create_figure(self):
+        self.fig, self.ax = bootplot.backend.matplotlib.create_figure(self.output_size_px)
+
+    def plot_to_array(self) -> np.ndarray:
+        return bootplot.backend.matplotlib.plot_to_array(self.fig)
+
+    def clear_figure(self):
+        bootplot.backend.matplotlib.clear_figure(self.ax)
+
+    def close_figure(self):
+        bootplot.backend.matplotlib.close_figure(None)
+
+    @property
+    def plot_args(self):
+        return [self.ax]
+
+
+class GGPlot2(Backend):
+    def __init__(self,
+                 f: callable,
+                 data: Union[np.ndarray, pd.DataFrame],
+                 m: int,
+                 output_size_px: Tuple[int, int] = (512, 512)):
+        super().__init__(f, data, m, output_size_px)
+
+    def create_figure(self):
+        raise NotImplemented
+
+    def plot_to_array(self) -> np.ndarray:
+        raise NotImplemented
+
+    def clear_figure(self):
+        raise NotImplemented
+
+    def close_figure(self):
+        raise NotImplemented
+
+    @property
+    def plot_args(self):
+        raise NotImplemented
+
+
+class Plotly(Backend):
+    def __init__(self,
+                 f: callable,
+                 data: Union[np.ndarray, pd.DataFrame],
+                 m: int,
+                 output_size_px: Tuple[int, int] = (512, 512)):
+        super().__init__(f, data, m, output_size_px)
+
+    def create_figure(self):
+        raise NotImplemented
+
+    def plot_to_array(self) -> np.ndarray:
+        raise NotImplemented
+
+    def clear_figure(self):
+        raise NotImplemented
+
+    def close_figure(self):
+        raise NotImplemented
+
+    @property
+    def plot_args(self):
+        raise NotImplemented
+
+
+def create_backend(backend_string, f, data, m, **kwargs):
+    if backend_string == 'matplotlib':
+        return Matplotlib(f=f, data=data, m=m, **kwargs)
+    elif backend_string == 'basic':
+        return Basic(f=f, data=data, m=m, **kwargs)
+    else:
+        raise NotImplemented

bootplot/backend/matplotlib.py

@@ -0,0 +1,31 @@
+import io
+from typing import Tuple
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def plot_to_array(fig):
+    with io.BytesIO() as buff:
+        fig.savefig(buff, format='raw')
+        buff.seek(0)
+        data = np.frombuffer(buff.getvalue(), dtype=np.uint8)
+    w, h = fig.canvas.get_width_height()
+    im = data.reshape((int(h), int(w), -1))
+    return im
+
+
+def create_figure(output_size_px: Tuple[int, int]):
+    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)
+    )
+    return fig, ax
+
+
+def clear_figure(ax):
+    ax.cla()
+
+
+def close_figure(fig):
+    plt.close(fig)