Python matplotlib.pylab.ioff() Examples

The following are 11 code examples of matplotlib.pylab.ioff(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module matplotlib.pylab , or try the search function .
Example #1
Source File: prod_basis.py    From pyscf with Apache License 2.0 6 votes vote down vote up 
def generate_png_chess_dp_vertex(self):
    """Produces pictures of the dominant product vertex a chessboard convention"""
    import matplotlib.pylab as plt
    plt.ioff()
    dab2v = self.get_dp_vertex_doubly_sparse()
    for i, ab in enumerate(dab2v): 
        fname = "chess-v-{:06d}.png".format(i)
        print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname)
        if type(ab) != 'numpy.ndarray': ab = ab.toarray()
        fig = plt.figure()
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean)
        plt.colorbar()
        plt.savefig(fname)
        plt.close(fig)
Example #2
Source File: testfuncs.py    From Computable with MIT License 6 votes vote down vote up 
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.func_name)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.func_name)
    pl.ion()
Example #3
Source File: testfuncs.py    From matplotlib-4-abaqus with MIT License 6 votes vote down vote up 
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.func_name)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.func_name)
    pl.ion()
Example #4
Source File: testfuncs.py    From neural-network-animation with MIT License 6 votes vote down vote up 
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.__name__)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.__name__)
    pl.ion()
Example #5
Source File: testfuncs.py    From ImageFusion with MIT License 6 votes vote down vote up 
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.__name__)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.__name__)
    pl.ion()
Example #6
Source File: prod_basis.py    From pyscf with Apache License 2.0 5 votes vote down vote up 
def generate_png_spy_dp_vertex(self):
    """Produces pictures of the dominant product vertex in a common black-and-white way"""
    import matplotlib.pyplot as plt
    plt.ioff()
    dab2v = self.get_dp_vertex_doubly_sparse()
    for i,ab2v in enumerate(dab2v): 
      plt.spy(ab2v.toarray())
      fname = "spy-v-{:06d}.png".format(i)
      print(fname)
      plt.savefig(fname, bbox_inches='tight')
      plt.close()
    return 0
Example #7
Source File: speech_utils.py    From python-dlpy with Apache License 2.0 5 votes vote down vote up 
def convert_one_audio_file_to_specgram(local_audio_file, converted_local_png_file):
    '''
    Convert a local audio file into a png format with spectrogram.

    Parameters
    ----------
    local_audio_file : string
        Local location to the audio file to be converted.

    converted_local_png_file : string
        Local location to store the converted audio file

    Returns
    -------
    None

    Raises
    ------
    DLPyError
        If anything goes wrong, it complains and prints the appropriate message.

    '''

    try:
        import soundfile as sf
        import matplotlib.pylab as plt
    except (ModuleNotFoundError, ImportError):
        raise DLPyError('cannot import soundfile')

    data, sampling_rate = sf.read(local_audio_file)

    fig, ax = plt.subplots(1)
    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)
    ax.axis('off')
    ax.specgram(x=data, Fs=sampling_rate)
    ax.axis('off')
    fig.savefig(converted_local_png_file, dpi=300, frameon='false')
    # this is the key to avoid mem leaking in notebook
    plt.ioff()
    plt.close(fig)
Example #8
Source File: testfuncs.py    From Computable with MIT License 4 votes vote down vote up 
def plot(self, func, interp=True, plotter='imshow'):
        import matplotlib as mpl
        from matplotlib import pylab as pl
        if interp:
            lpi = self.interpolator(func)
            z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                    self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
        else:
            y, x = np.mgrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            z = func(x, y)

        z = np.where(np.isinf(z), 0.0, z)

        extent = (self.xrange[0], self.xrange[1],
            self.yrange[0], self.yrange[1])
        pl.ioff()
        pl.clf()
        pl.hot()  # Some like it hot
        if plotter == 'imshow':
            pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent,
                      origin='lower')
        elif plotter == 'contour':
            Y, X = np.ogrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            pl.contour(np.ravel(X), np.ravel(Y), z, 20)
        x = self.x
        y = self.y
        lc = mpl.collections.LineCollection(
            np.array([((x[i], y[i]), (x[j], y[j]))
                      for i, j in self.tri.edge_db]),
            colors=[(0, 0, 0, 0.2)])
        ax = pl.gca()
        ax.add_collection(lc)

        if interp:
            title = '%s Interpolant' % self.name
        else:
            title = 'Reference'
        if hasattr(func, 'title'):
            pl.title('%s: %s' % (func.title, title))
        else:
            pl.title(title)

        pl.show()
        pl.ion()
Example #9
Source File: testfuncs.py    From matplotlib-4-abaqus with MIT License 4 votes vote down vote up 
def plot(self, func, interp=True, plotter='imshow'):
        import matplotlib as mpl
        from matplotlib import pylab as pl
        if interp:
            lpi = self.interpolator(func)
            z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                    self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
        else:
            y, x = np.mgrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            z = func(x, y)

        z = np.where(np.isinf(z), 0.0, z)

        extent = (self.xrange[0], self.xrange[1],
            self.yrange[0], self.yrange[1])
        pl.ioff()
        pl.clf()
        pl.hot()  # Some like it hot
        if plotter == 'imshow':
            pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent,
                      origin='lower')
        elif plotter == 'contour':
            Y, X = np.ogrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            pl.contour(np.ravel(X), np.ravel(Y), z, 20)
        x = self.x
        y = self.y
        lc = mpl.collections.LineCollection(
            np.array([((x[i], y[i]), (x[j], y[j]))
                      for i, j in self.tri.edge_db]),
            colors=[(0, 0, 0, 0.2)])
        ax = pl.gca()
        ax.add_collection(lc)

        if interp:
            title = '%s Interpolant' % self.name
        else:
            title = 'Reference'
        if hasattr(func, 'title'):
            pl.title('%s: %s' % (func.title, title))
        else:
            pl.title(title)

        pl.show()
        pl.ion()
Example #10
Source File: testfuncs.py    From neural-network-animation with MIT License 4 votes vote down vote up 
def plot(self, func, interp=True, plotter='imshow'):
        import matplotlib as mpl
        from matplotlib import pylab as pl
        if interp:
            lpi = self.interpolator(func)
            z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                    self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
        else:
            y, x = np.mgrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            z = func(x, y)

        z = np.where(np.isinf(z), 0.0, z)

        extent = (self.xrange[0], self.xrange[1],
            self.yrange[0], self.yrange[1])
        pl.ioff()
        pl.clf()
        pl.hot()  # Some like it hot
        if plotter == 'imshow':
            pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent,
                      origin='lower')
        elif plotter == 'contour':
            Y, X = np.ogrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            pl.contour(np.ravel(X), np.ravel(Y), z, 20)
        x = self.x
        y = self.y
        lc = mpl.collections.LineCollection(
            np.array([((x[i], y[i]), (x[j], y[j]))
                      for i, j in self.tri.edge_db]),
            colors=[(0, 0, 0, 0.2)])
        ax = pl.gca()
        ax.add_collection(lc)

        if interp:
            title = '%s Interpolant' % self.name
        else:
            title = 'Reference'
        if hasattr(func, 'title'):
            pl.title('%s: %s' % (func.title, title))
        else:
            pl.title(title)

        pl.show()
        pl.ion()
Example #11
Source File: testfuncs.py    From ImageFusion with MIT License 4 votes vote down vote up 
def plot(self, func, interp=True, plotter='imshow'):
        import matplotlib as mpl
        from matplotlib import pylab as pl
        if interp:
            lpi = self.interpolator(func)
            z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                    self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
        else:
            y, x = np.mgrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            z = func(x, y)

        z = np.where(np.isinf(z), 0.0, z)

        extent = (self.xrange[0], self.xrange[1],
            self.yrange[0], self.yrange[1])
        pl.ioff()
        pl.clf()
        pl.hot()  # Some like it hot
        if plotter == 'imshow':
            pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent,
                      origin='lower')
        elif plotter == 'contour':
            Y, X = np.ogrid[
                self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
                self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
            pl.contour(np.ravel(X), np.ravel(Y), z, 20)
        x = self.x
        y = self.y
        lc = mpl.collections.LineCollection(
            np.array([((x[i], y[i]), (x[j], y[j]))
                      for i, j in self.tri.edge_db]),
            colors=[(0, 0, 0, 0.2)])
        ax = pl.gca()
        ax.add_collection(lc)

        if interp:
            title = '%s Interpolant' % self.name
        else:
            title = 'Reference'
        if hasattr(func, 'title'):
            pl.title('%s: %s' % (func.title, title))
        else:
            pl.title(title)

        pl.show()
        pl.ion()