Metadata-Version: 1.1
Name: numpngw
Version: 0.0.3
Summary: Write numpy array(s) to a PNG or animated PNG file.
Home-page: https://github.com/WarrenWeckesser/numpngw
Author: Warren Weckesser
Author-email: UNKNOWN
License: BSD
Description: numpngw
        =======
        
        This python package defines the function `write_png` that writes a
        numpy array to a PNG file, and the function `write_apng` that writes
        a sequence of arrays to an animated PNG (APNG) file.  Also included
        is the class `AnimatedPNGWriter` that can be used to save a Matplotlib
        animation as an animated PNG file; see Example 8 for an example.
        
        Capabilities of `write_png` include:
        
        * creation of 8-bit and 16-bit RGB files;
        * creation of 1-bit, 2-bit, 4-bit, 8-bit and 16-bit grayscale files;
        * creation of RGB and grayscale images with an alpha channel;
        * setting a transparent color;
        * automatic creation of a palette for an indexed PNG file;
        * inclusion of `tEXt`, `tIME`, `bKGD` and `gAMA` chunks.
        
        This is prototype-quality software.  The documentation is sparse, and the API
        will likely change.
        
        This software is released under the BSD 2-clause license.
        
        For packages with more features (including functions for *reading* PNG files),
        take a look at:
        
        * `pypng` (https://pypi.python.org/pypi/pypng) or
        * `imageio` (https://pypi.python.org/pypi/imageio).
        
        
        Example 1
        ---------
        
        The following script creates this PNG file, an 8-bit RGB image.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example1.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_png
        
        
            # Example 1
            #
            # Create an 8-bit RGB image.
        
            img = np.zeros((80, 128, 3), dtype=np.uint8)
        
            grad = np.linspace(0, 255, img.shape[1])
        
            img[:16, :, :] = 127
            img[16:32, :, 0] = grad
            img[32:48, :, 1] = grad[::-1]
            img[48:64, :, 2] = grad
            img[64:, :, :] = 127
        
            write_png('example1.png', img)
        
        
        Example 2
        ---------
        
        The following script creates this PNG file, a 1-bit grayscale image.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example2.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_png
        
            # Example 2
            #
            # Create a 1-bit grayscale image.
        
            mask = np.zeros((48, 48), dtype=np.uint8)
            mask[:2, :] = 1
            mask[:, -2:] = 1
            mask[4:6, :-4] = 1
            mask[4:, -6:-4] = 1
            mask[-16:, :16] = 1
            mask[-32:-16, 16:32] = 1
        
            write_png('example2.png', mask, bitdepth=1)
        
        
        Example 3
        ---------
        
        The following script creates this PNG file, a 16-bit RGB file in which
        the value (0, 0, 0) is transparent.  It might not be obvious, but the
        two squares are transparent.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example3.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_png
        
            # Example 3
            #
            # Create a 16-bit RGB image, with (0, 0, 0) indicating a transparent pixel.
        
            # Create some interesting data.
            w = 32
            nrows = 3*w
            ncols = 5*w
            kernel = np.exp(-np.linspace(-2, 2, 35)**2)
            kernel = kernel/kernel.sum()
            np.random.seed(123)
            x = np.random.randn(nrows, ncols, 3)
            x = np.apply_along_axis(lambda z: np.convolve(z, kernel, mode='same'), 0, x)
            x = np.apply_along_axis(lambda z: np.convolve(z, kernel, mode='same'), 1, x)
        
            # Convert to 16 bit unsigned integers.
            z = (65535*((x - x.max())/x.ptp())).astype(np.uint16)
        
            # Create two squares containing (0, 0, 0).
            z[w:2*w, w:2*w] = 0
            z[w:2*w, -2*w:-w] = 0
        
            # Write the PNG file, and indicate that (0, 0, 0) should be transparent.
            write_png('example3.png', z, transparent=(0, 0, 0))
        
        
        Example 4
        ---------
        
        The following script uses the option `use_palette=True` to create this 8-bit
        indexed RGB file.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example4.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_png
        
        
            # Example 4
            #
            # Create an 8-bit indexed RGB image that uses a palette.
        
            img_width = 300
            img_height = 200
            img = np.zeros((img_height, img_width, 3), dtype=np.uint8)
        
            np.random.seed(222)
            for _ in range(40):
                width = np.random.randint(5, img_width // 5)
                height = np.random.randint(5, img_height // 5)
                row = np.random.randint(5, img_height - height - 5)
                col = np.random.randint(5, img_width - width - 5)
                color = np.random.randint(80, 256, size=2)
                img[row:row+height, col:col+width, 1:] = color
        
            write_png('example4.png', img, use_palette=True)
        
        
        Example 5
        ---------
        
        This animated PNG file is created by the following script.
        As in the other examples, most of script is code that generates
        the data to be saved.  The line that creates the PNG file is
        simply
        
            write_apng("example5.png", seq, delay=50, use_palette=True)
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example5.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_apng
        
            # Example 5
            #
            # Create an 8-bit RGB animated PNG file.
        
            height = 20
            width = 200
            t = np.linspace(0, 10*np.pi, width)
            seq = []
            for phase in np.linspace(0, 2*np.pi, 25, endpoint=False):
                y = 150*0.5*(1 + np.sin(t - phase))
                a = np.zeros((height, width, 3), dtype=np.uint8)
                a[:, :, 0] = y
                a[:, :, 2] = y
                seq.append(a)
        
            write_apng("example5.png", seq, delay=50, use_palette=True)
        
        
        Example 6
        ---------
        
        Another animated RGB PNG. In this example, the argument `seq`
        that is passed to `write_apng` is a numpy array with shape
        `(num_frames, height, width, 3)`.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example6.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_apng
        
            # Example 6
            #
            # Create an 8-bit RGB animated PNG file.
        
            def smoother(w):
                # Return the periodic convolution of w with a 3-d Gaussian kernel.
                r = np.linspace(-3, 3, 21)
                X, Y, Z = np.meshgrid(r, r, r)
                kernel = np.exp(-0.25*(X*X + Y*Y + Z*Z)**2)
                fw = np.fft.fftn(w)
                fkernel = np.fft.fftn(kernel, w.shape)
                v = np.fft.ifftn(fw*fkernel).real
                return v
        
            height = 40
            width = 250
            num_frames = 30
            np.random.seed(12345)
            w = np.random.randn(num_frames, height, width, 3)
            for k in range(3):
                w[..., k] = smoother(w[..., k])
        
            seq = (255*(w - w.min())/w.ptp()).astype(np.uint8)
        
            write_apng("example6.png", seq, delay=40)
        
        
        Example 7
        ---------
        
        Create an animated PNG with different display times for each frame.
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example7.png)
        
        The script::
        
            import numpy as np
            from numpngw import write_apng
        
            # Example 7
            #
            # Create an animated PNG file with nonuniform display times
            # of the frames.
        
            bits1 = np.array([
                [0,0,1,0,0],
                [0,1,1,0,0],
                [0,0,1,0,0],
                [0,0,1,0,0],
                [0,0,1,0,0],
                [0,0,1,0,0],
                [0,1,1,1,0],
                ])
        
            bits2 = np.array([
                [0,1,1,1,0],
                [1,0,0,0,1],
                [0,0,0,0,1],
                [0,1,1,1,0],
                [1,0,0,0,0],
                [1,0,0,0,0],
                [1,1,1,1,1],
                ])
        
            bits3 = np.array([
                [0,1,1,1,0],
                [1,0,0,0,1],
                [0,0,0,0,1],
                [0,0,1,1,0],
                [0,0,0,0,1],
                [1,0,0,0,1],
                [0,1,1,1,0],
                ])
        
            bits_box1 = np.array([
                [0,0,0,0,0],
                [1,1,1,1,1],
                [1,0,0,0,1],
                [1,0,0,0,1],
                [1,0,0,0,1],
                [1,1,1,1,1],
                [0,0,0,0,0],
                ])
        
            bits_box2 = np.array([
                [0,0,0,0,0],
                [0,0,0,0,0],
                [0,1,1,1,0],
                [0,1,0,1,0],
                [0,1,1,1,0],
                [0,0,0,0,0],
                [0,0,0,0,0],
                ])
        
            bits_dot = np.array([
                [0,0,0,0,0],
                [0,0,0,0,0],
                [0,0,0,0,0],
                [0,0,1,0,0],
                [0,0,0,0,0],
                [0,0,0,0,0],
                [0,0,0,0,0],
                ])
        
            bits_zeros = np.zeros((7, 5), dtype=bool)
            bits_ones = np.ones((7, 5), dtype=bool)
        
        
            def bits_to_image(bits, blocksize=32, color=None):
                bits = np.asarray(bits, dtype=np.bool)
                if color is None:
                    color = np.array([255, 0, 0], dtype=np.uint8)
                else:
                    color = np.asarray(color, dtype=np.uint8)
        
                x = np.linspace(-1, 1, blocksize)
                X, Y = np.meshgrid(x, x)
                Z = np.sqrt(np.maximum(1 - (X**2 + Y**2), 0))
                # The "on" image:
                img1 = (Z.reshape(blocksize, blocksize, 1)*color)
                # The "off" image:
                img0 = 0.2*img1
        
                data = np.where(bits[:, None, :, None, None],
                                img1[:, None, :], img0[:, None, :])
                img = data.reshape(bits.shape[0]*blocksize, bits.shape[1]*blocksize, 3)
                return img.astype(np.uint8)
        
            # Create `seq` and `delay`, the sequence of images and the
            # corresponding display times.
        
            color = np.array([32, 48, 255])
            blocksize = 24
            # Images...
            im3 = bits_to_image(bits3, blocksize=blocksize, color=color)
            im2 = bits_to_image(bits2, blocksize=blocksize, color=color)
            im1 = bits_to_image(bits1, blocksize=blocksize, color=color)
            im_all = bits_to_image(bits_ones, blocksize=blocksize, color=color)
            im_none = bits_to_image(bits_zeros, blocksize=blocksize, color=color)
            im_box1 = bits_to_image(bits_box1, blocksize=blocksize, color=color)
            im_box2 = bits_to_image(bits_box2, blocksize=blocksize, color=color)
            im_dot = bits_to_image(bits_dot, blocksize=blocksize, color=color)
        
            # The sequence of images:
            seq = [im3, im2, im1, im_all, im_none, im_all, im_none, im_all, im_none,
                   im_box1, im_box2, im_dot, im_none]
            # The time duration to display each image, in milliseconds:
            delay = [1000, 1000, 1000, 333, 250, 333, 250, 333, 500,
                     167, 167, 167, 1000]
        
            # Create the animated PNG file.
            write_apng("example7.png", seq, delay=delay, default_image=im_all,
                       use_palette=True)
        
        
        Example 8
        ---------
        
        This example shows how a Matplotlib animation can be saved as
        an animated PNG file with `numpngw.AnimatedPNGWriter`.  (Be careful
        with this class--it can easily create very large PNG files.)
        
        ![](https://github.com/WarrenWeckesser/numpngw/blob/master/examples/example8.png)
        
        The script::
        
            import numpy as np
            import matplotlib.pyplot as plt
            from matplotlib import animation
            from numpngw import AnimatedPNGWriter
        
        
            def update_line(num, data, line):
                line.set_data(data[:, :num+1])
                return line,
        
            fig = plt.figure(figsize=(5.75, 5.6))
            ax = fig.add_subplot(111, xlim=(-1, 1), ylim=(-1, 1),
                                 autoscale_on=False,  aspect='equal',
                                 title="Matplotlib Animation")
        
            num_frames = 20
        
            theta = np.linspace(0, 24*np.pi, num_frames)
            data = np.exp(1j*theta).view(np.float64).reshape(-1, 2).T
        
            lineplot, = ax.plot([], [], 'c-', linewidth=3)
        
            ani = animation.FuncAnimation(fig, update_line, frames=num_frames,
                                          fargs=(data, lineplot))
            writer = AnimatedPNGWriter(fps=2)
            ani.save('example8.png', dpi=50, writer=writer)
        
Keywords: numpy png matplotlib animation
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
