python类colorbar()的实例源码

figrc.py 文件源码 项目:tap 作者: mfouesneau 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def plot_density_map(x, y, xbins, ybins, Nlevels=4, cbar=True, weights=None):

    Z = np.histogram2d(x, y, bins=(xbins, ybins), weights=weights)[0].astype(float).T

    # central values
    lt = get_centers_from_bins(xbins)
    lm = get_centers_from_bins(ybins)
    cX, cY = np.meshgrid(lt, lm)
    X, Y = np.meshgrid(xbins, ybins)

    im = plt.pcolor(X, Y, Z, cmap=plt.cm.Blues)
    plt.contour(cX, cY, Z, levels=nice_levels(Z, Nlevels), cmap=plt.cm.Greys_r)

    if cbar:
        cb = plt.colorbar(im)
    else:
        cb = None
    plt.xlim(xbins[0], xbins[-1])
    plt.ylim(ybins[0], ybins[-1])

    try:
        plt.tight_layout()
    except Exception as e:
        print(e)
    return plt.gca(), cb
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 38 收藏 0 点赞 0 评论 0
def twoDimensionalScatter(title, title_x, title_y,
                          x, y,
                          lim_x = None, lim_y = None,
                          color = 'b', size = 20, alpha=None):
    """
    Create a two-dimensional scatter plot.

    INPUTS
    """
    pylab.figure()

    pylab.scatter(x, y, c=color, s=size, alpha=alpha, edgecolors='none')

    pylab.xlabel(title_x)
    pylab.ylabel(title_y)
    pylab.title(title)
    if type(color) is not str:
        pylab.colorbar()

    if lim_x:
        pylab.xlim(lim_x[0], lim_x[1])
    if lim_y:
        pylab.ylim(lim_y[0], lim_y[1])

############################################################
pathfinder.py 文件源码 项目:AdK_analysis 作者: orbeckst 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def plot(self,**kwargs):
        """plot landscape (kwargs are passed on to imshow()

        Use interpolation='bilinear' or 'bicubic' for a smooth
        surface. Default is 'nearest', which shows exact bin
        boundaries.
        """
        import pylab

        kwargs.setdefault('interpolation','nearest')
        pylab.clf()
        pylab.xlabel('x')
        pylab.ylabel('y')
        pylab.imshow(self.pmf_masked.T,**kwargs)
        pylab.colorbar()
        pylab.show()
plot.py 文件源码 项目:pyrsss 作者: butala 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def add_colorbar(ax, im, side='right', size='5%', pad=0.1, **kwds):
    """
    Add colorbar to the axes *ax* with colors corresponding to the
    color mappable object *im*. Place the colorbar at the *side* of
    *ax* (options are `'right'`, `'left'`, `'top'`, or
    `'bottom'`). The width (or height) of the colorbar is specified by
    *size* and is relative to *ax*. Add space *pad* between *ax* and
    the colorbar. The remaining keyword arguments *kwds* are passed to
    the call to :func:`colorbar`. Return the colorbar instance.

    Reference: http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html
    """
    divider = make_axes_locatable(ax)
    cax = divider.append_axes(side, size=size, pad=pad)
    cb = PL.colorbar(im, cax=cax, **kwds)
    PL.axes(ax)
    return cb
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def drawStellarDensity(self,ax=None):
        if not ax: ax = plt.gca()
        # Stellar Catalog
        self._create_catalog()
        catalog = self.catalog
        #catalog=ugali.observation.catalog.Catalog(self.config,roi=self.roi)
        pix = ang2pix(self.nside, catalog.lon, catalog.lat)
        counts = collections.Counter(pix)
        pixels, number = numpy.array(sorted(counts.items())).T
        star_map = healpy.UNSEEN * numpy.ones(healpy.nside2npix(self.nside))
        star_map[pixels] = number
        star_map = numpy.where(star_map == 0, healpy.UNSEEN, star_map)

        #im = healpy.gnomview(star_map,**self.gnom_kwargs)
        #healpy.graticule(dpar=1,dmer=1,color='0.5',verbose=False)
        #pylab.close()

        im = drawHealpixMap(star_map,self.glon,self.glat,self.radius,coord=self.coord)
        #im = ax.imshow(im,origin='bottom')
        try:    ax.cax.colorbar(im)
        except: pylab.colorbar(im,ax=ax)
        ax.annotate("Stars",**self.label_kwargs)
        return im
PVAnalysis.py 文件源码 项目:PyPeVoc 作者: goiosunsw 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def plot_time_freq(self, colors=True, ax=None):
        import pylab as pl

        if ax is None:
            fig, allax = pl.subplots(1)
            ax = allax

        # make time matrix same shape as others
        t = np.outer(self.t, np.ones(self.npeaks))
        f = self.f
        if colors:
            mag = 20*np.log10(self.mag)
            ax.scatter(t, f, s=6, c=mag, lw=0)
        else:
            mag = 100 + 20*np.log10(self.mag)
            ax.scatter(t, f, s=mag, lw=0)
        pl.xlabel('Time (s)')
        pl.ylabel('Frequency (Hz)')
        # if colors:
        # cs = pl.colorbar(ax=ax)
        # cs.set_label('Magnitude (dB)')
        # pl.show()
        return ax
PVAnalysis.py 文件源码 项目:PyPeVoc 作者: goiosunsw 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def plot_time_mag(self):
        import pylab as pl

        pl.figure()
        t = np.outer(self.t, np.ones(self.npeaks))
        # f = np.log2(self.f)
        f = self.f
        mag = 20*np.log10(self.mag)
        pl.scatter(t, mag, s=10, c=f, lw=0,
                   norm=pl.matplotlib.colors.LogNorm())
        pl.xlabel('Time (s)')
        pl.ylabel('Magnitude (dB)')
        cs = pl.colorbar()
        cs.set_label('Frequency (Hz)')
        # pl.show()
        return pl.gca()
TargetImage.py 文件源码 项目:hco-experiments 作者: zooniverse 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def visualiseNormObject(self):
        shape = (2*self.extent, 2*self.extent)
        pylab.ion()
        pylab.clf()
        #pylab.set_cmap("bone")
        pylab.hot()
        pylab.title("image: %s" % self.fitsFile)
        pylab.imshow(np.reshape(self.signPreserveNorm(), shape, order="F"), interpolation="nearest")
        pylab.plot(np.arange(0,2*self.extent), self.extent*np.ones((2*self.extent,)), "r--")
        pylab.plot(self.extent*np.ones((2*self.extent,)), np.arange(0,2*self.extent), "r--")
        pylab.colorbar()
        pylab.ylim(-1, 2*self.extent)
        pylab.xlim(-1, 2*self.extent)
        pylab.xlabel("Pixels")
        pylab.ylabel("Pixels")
        pylab.show()
TargetImage.py 文件源码 项目:hco-experiments 作者: zooniverse 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def visualiseNormObject(self):
        shape = (2*self.extent, 2*self.extent)
        pylab.ion()
        pylab.clf()
        #pylab.set_cmap("bone")
        pylab.hot()
        pylab.title("image: %s" % self.fitsFile)
        pylab.imshow(np.reshape(self.signPreserveNorm(), shape, order="F"), interpolation="nearest")
        pylab.plot(np.arange(0,2*self.extent), self.extent*np.ones((2*self.extent,)), "r--")
        pylab.plot(self.extent*np.ones((2*self.extent,)), np.arange(0,2*self.extent), "r--")
        pylab.colorbar()
        pylab.ylim(-1, 2*self.extent)
        pylab.xlim(-1, 2*self.extent)
        pylab.xlabel("Pixels")
        pylab.ylabel("Pixels")
        pylab.show()
analyzeAngle.py 文件源码 项目:livespin 作者: biocompibens 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def plotAgainstGFP_hist2d(self):
        fig1 = pylab.figure(figsize = (20, 15))
        print len(self.GFP)
        for i in xrange(min(len(data.cat), 4)):
            print len(self.GFP[self.categories == i])
            vect = []
            pylab.subplot(2,2,i+1)
            pop = self.GFP[self.categories == i]
            print "cat", i, "n pop", len(self.GFP[(self.categories == i) & (self.GFP > -np.log(12.5))])
            H, xedges, yedges = np.histogram2d(self.angles[self.categories == i], self.GFP[self.categories == i], bins = 10)
            hist = pylab.hist2d(self.GFP[self.categories == i], self.angles[self.categories == i], bins = 10, cmap = pylab.cm.Reds, normed = True)
            pylab.clim(0.,0.035)
            pylab.colorbar()
            pylab.title(data.cat[i])
            pylab.xlabel('GFP score')
            pylab.ylabel('Angle (degree)')
            pylab.xlim([-4.2, -1])
        pylab.show()
TensorFlowInterface.py 文件源码 项目:IntroToDeepLearning 作者: robb-brown 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01):
    # Receptive Fields Summary
    try:
        W = layer.W
    except:
        W = layer
    wp = W.eval().transpose();
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape) 
    else:           # Convolutional layer already has shape
        features, channels, iy, ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))

    fig = mpl.figure(figOffset); mpl.clf()

    # Using image grid
    from mpl_toolkits.axes_grid1 import ImageGrid
    grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
    for i in range(0,np.shape(fields)[0]):
        im = grid[i].imshow(fields[i],cmap=cmap); 

    grid.cbar_axes[0].colorbar(im)
    mpl.title('%s Receptive Fields' % layer.name)

    # old way
    # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    # tiled = []
    # for i in range(0,perColumn*perRow,perColumn):
    #   tiled.append(np.hstack(fields2[i:i+perColumn]))
    # 
    # tiled = np.vstack(tiled)
    # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
    mpl.figure(figOffset+1); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()
TensorFlowInterface.py 文件源码 项目:IntroToDeepLearning 作者: robb-brown 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
    # Output summary
    try:
        W = layer.output
    except:
        W = layer
    wp = W.eval(feed_dict=feed_dict);
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
        fields = np.reshape(temp,[1]+fieldShape)
    else:           # Convolutional layer already has shape
        wp = np.rollaxis(wp,3,0)
        features, channels, iy,ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
    fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    tiled = []
    for i in range(0,perColumn*perRow,perColumn):
        tiled.append(np.hstack(fields2[i:i+perColumn]))

    tiled = np.vstack(tiled)
    if figOffset is not None:
        mpl.figure(figOffset); mpl.clf(); 

    mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar();
TensorFlowInterface.py 文件源码 项目:IntroToDeepLearning 作者: robb-brown 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def plotFields(layer,fieldShape=None,channel=None,maxFields=25,figName='ReceptiveFields',cmap=None,padding=0.01):
    # Receptive Fields Summary
    W = layer.W
    wp = W.eval().transpose();
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape)
    else:           # Convolutional layer already has shape
        features, channels, iy, ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    fieldsN = min(fields.shape[0],maxFields)
    perRow = int(math.floor(math.sqrt(fieldsN)))
    perColumn = int(math.ceil(fieldsN/float(perRow)))

    fig = mpl.figure(figName); mpl.clf()

    # Using image grid
    from mpl_toolkits.axes_grid1 import ImageGrid
    grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
    for i in range(0,fieldsN):
        im = grid[i].imshow(fields[i],cmap=cmap);

    grid.cbar_axes[0].colorbar(im)
    mpl.title('%s Receptive Fields' % layer.name)

    # old way
    # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    # tiled = []
    # for i in range(0,perColumn*perRow,perColumn):
    #   tiled.append(np.hstack(fields2[i:i+perColumn]))
    #
    # tiled = np.vstack(tiled)
    # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
    mpl.figure(figName+' Total'); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()
TensorFlowInterface.py 文件源码 项目:IntroToDeepLearning 作者: robb-brown 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
    # Output summary
    W = layer.output
    wp = W.eval(feed_dict=feed_dict);
    if len(np.shape(wp)) < 4:       # Fully connected layer, has no shape
        temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
        fields = np.reshape(temp,[1]+fieldShape)
    else:           # Convolutional layer already has shape
        wp = np.rollaxis(wp,3,0)
        features, channels, iy,ix = np.shape(wp)
        if channel is not None:
            fields = wp[:,channel,:,:]
        else:
            fields = np.reshape(wp,[features*channels,iy,ix])

    perRow = int(math.floor(math.sqrt(fields.shape[0])))
    perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
    fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
    tiled = []
    for i in range(0,perColumn*perRow,perColumn):
        tiled.append(np.hstack(fields2[i:i+perColumn]))

    tiled = np.vstack(tiled)
    if figOffset is not None:
        mpl.figure(figOffset); mpl.clf();

    mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar();
plot.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def view_whitening(data):
    pylab.subplot(121)
    pylab.imshow(data['spatial'], interpolation='nearest')
    pylab.title('Spatial')
    pylab.xlabel('# Electrode')
    pylab.ylabel('# Electrode')
    pylab.colorbar()
    pylab.subplot(122)
    pylab.title('Temporal')
    pylab.plot(data['temporal'])
    pylab.xlabel('Time [ms]')
    x, y = pylab.xticks()
    pylab.xticks(x, (x-x[-1]//2)//10)
    pylab.tight_layout()
test_whitening.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def get_performance(file_name, name):

    a, b            = os.path.splitext(os.path.basename(file_name))
    file_name, ext  = os.path.splitext(file_name)
    file_out        = os.path.join(os.path.abspath(file_name), a)
    data            = {}
    result          = h5py.File(file_out + '.basis.hdf5')
    data['spatial']  = result.get('spatial')[:]
    data['temporal'] = numpy.zeros(61) #result.get('temporal')[:]

    pylab.figure()
    pylab.subplot(121)
    pylab.imshow(data['spatial'], interpolation='nearest')
    pylab.title('Spatial')
    pylab.xlabel('# Electrode')
    pylab.ylabel('# Electrode')
    pylab.colorbar()
    pylab.subplot(122)
    pylab.title('Temporal')
    pylab.plot(data['temporal'])
    pylab.xlabel('Time [ms]')
    x, y = pylab.xticks()
    pylab.xticks(x, (x-x[-1]//2)//10)
    pylab.tight_layout()
    plot_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
    plot_path = os.path.join(plot_path, 'plots')
    plot_path = os.path.join(plot_path, 'whitening')
    if not os.path.exists(plot_path):
        os.makedirs(plot_path)
    output = os.path.join(plot_path, '%s.pdf' %name)
    pylab.savefig(output)

    return data
viewRecon.py 文件源码 项目:emc_and_dm 作者: eucall-software 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def make_panel_of_intensity_slices(fn, c_n=9):
    M.rcParams.update({'font.size': 13})
    intensList = read.extract_arr_from_h5(fn, "/history/intensities", n=c_n)
    quatList = read.extract_arr_from_h5(fn, "/history/quaternion", n=-1)
    P.ioff()
    intens_len  = len(intensList)
    sqrt_len    = int(N.sqrt(intens_len))
    intens_sh   = intensList[0].shape
    iter_labels = read.create_interval_labels(len(quatList), c_n)[:intens_len]
    to_plot     = intensList[:intens_len]
    quat_label  = quatList[N.array(iter_labels)-1][:intens_len]
    plot_titles = ["iter_%d, quat_%d"%(ii,jj) for ii,jj in zip(iter_labels, quat_label)]
    fig, ax     = P.subplots(sqrt_len, sqrt_len, sharex=True, sharey=True, figsize=(1.8*sqrt_len, 2.*sqrt_len))
    plt_counter = 0
    for r in range(sqrt_len):
        for c in range(sqrt_len):
            ax[r,c].set_title(plot_titles[plt_counter])
            curr_slice = to_plot[plt_counter][intens_sh[0]/2]
            curr_slice = curr_slice*(curr_slice>0.) + 1.E-8*(curr_slice<=0.)
            ax[r,c].set_title(plot_titles[plt_counter], fontsize=11.5)
            im = ax[r,c].imshow(N.log10(curr_slice), vmin=-6.5, vmax=-3.5, aspect='auto', cmap=P.cm.coolwarm)
            plt_counter += 1
    fig.subplots_adjust(wspace=0.01)
    (shx, shy) = curr_slice.shape
    (h_shx, h_shy) = (shx/2, shy/2)
    xt = N.linspace(0.5*h_shx, shx-.5*h_shx-1, 3).astype('int')
    xt_l = N.linspace(-0.5*h_shx, 0.5*h_shx, 3).astype('int')
    yt = N.linspace(0, shy-1, 3).astype('int')
    yt_l = N.linspace(-1*h_shy, h_shy, 3).astype('int')
    P.setp(ax, xticks=xt, xticklabels=xt_l, yticks=yt, yticklabels=yt_l)
    cbar_ax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
    fig.colorbar(im, cax=cbar_ax, label="log10(intensities)")
    img_name = "recon_series.pdf"
    P.savefig(img_name, bbox_inches='tight')
    print("Image has been saved as %s" % img_name)
    P.close(fig)
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 36 收藏 0 点赞 0 评论 0
def twoDimensionalHistogram(title, title_x, title_y,
                            z, bins_x, bins_y,
                            lim_x=None, lim_y=None,
                            vmin=None, vmax=None):
    """
    Create a two-dimension histogram plot or binned map.

    If using the outputs of numpy.histogram2d, remember to transpose the histogram.

    INPUTS
    """
    pylab.figure()

    mesh_x, mesh_y = numpy.meshgrid(bins_x, bins_y)

    if vmin != None and vmin == vmax:
        pylab.pcolor(mesh_x, mesh_y, z)
    else:
        pylab.pcolor(mesh_x, mesh_y, z, vmin=vmin, vmax=vmax)
    pylab.xlabel(title_x)
    pylab.ylabel(title_y)
    pylab.title(title)
    pylab.colorbar()

    if lim_x:
        pylab.xlim(lim_x[0], lim_x[1])
    if lim_y:
        pylab.ylim(lim_y[0], lim_y[1])

############################################################
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def drawTS(self,ax=None, filename=None, zidx=0):
        if not ax: ax = plt.gca()
        if not filename:
            #dirname = self.config.params['output2']['searchdir']
            #basename = self.config.params['output2']['mergefile']
            #filename = os.path.join(dirname,basename)
            filename = self.config.mergefile

        results=pyfits.open(filename)[1]
        pixels = results.data['PIXEL']
        values = 2*results.data['LOG_LIKELIHOOD']
        if values.ndim == 1: values = values.reshape(-1,1)
        ts_map = healpy.UNSEEN * numpy.ones(healpy.nside2npix(self.nside))
        # Sum through all distance_moduli
        #ts_map[pixels] = values.sum(axis=1)
        # Just at maximum slice from object

        ts_map[pixels] = values[:,zidx]

        #im = healpy.gnomview(ts_map,**self.gnom_kwargs)
        #healpy.graticule(dpar=1,dmer=1,color='0.5',verbose=False)
        #pylab.close()
        #im = ax.imshow(im,origin='bottom')

        im = drawHealpixMap(ts_map,self.glon,self.glat,self.radius,coord=self.coord)

        try: ax.cax.colorbar(im)
        except: pylab.colorbar(im)
        ax.annotate("TS",**self.label_kwargs)
        return im
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def drawCMD(self, ax=None, radius=None, zidx=None):
        if not ax: ax = plt.gca()
        import ugali.isochrone

        if zidx is not None:
            filename = self.config.mergefile
            logger.debug("Opening %s..."%filename)
            f = pyfits.open(filename)
            distance_modulus = f[2].data['DISTANCE_MODULUS'][zidx]

            iso = ugali.isochrone.Padova(age=12,z=0.0002,mod=distance_modulus)
            #drawIsochrone(iso,ls='',marker='.',ms=1,c='k')
            drawIsochrone(iso)

        # Stellar Catalog
        self._create_catalog()
        if radius is not None:
            sep = ugali.utils.projector.angsep(self.glon,self.glat,self.catalog.lon,self.catalog.lat)
            cut = (sep < radius)
            catalog_cmd = self.catalog.applyCut(cut)
        else:
            catalog_cmd = self.catalog

        ax.scatter(catalog_cmd.color, catalog_cmd.mag,color='b',marker='.',s=1)
        ax.set_xlim(self.roi.bins_color[0],self.roi.bins_color[-1])
        ax.set_ylim(self.roi.bins_mag[-1],self.roi.bins_mag[0])
        ax.set_xlabel('Color (mag)')
        ax.set_ylabel('Magnitude (mag)')

        try:    ax.cax.colorbar(im)
        except: pass
        ax.annotate("Stars",**self.label_kwargs)
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 53 收藏 0 点赞 0 评论 0
def drawMembersSpatial(self,data):
        ax = plt.gca()
        if isinstance(data,basestring):
            filename = data
            data = pyfits.open(filename)[1].data

        xmin, xmax = -0.25,0.25
        ymin, ymax = -0.25,0.25
        xx,yy = np.meshgrid(np.linspace(xmin,xmax),np.linspace(ymin,ymax))

        x_prob, y_prob = sphere2image(self.ra, self.dec, data['RA'], data['DEC'])

        sel = (x_prob > xmin)&(x_prob < xmax) & (y_prob > ymin)&(y_prob < ymax)
        sel_prob = data['PROB'][sel] > 5.e-2
        index_sort = numpy.argsort(data['PROB'][sel][sel_prob])

        plt.scatter(x_prob[sel][~sel_prob], y_prob[sel][~sel_prob], 
                      marker='o', s=2, c='0.75', edgecolor='none')
        sc = plt.scatter(x_prob[sel][sel_prob][index_sort], 
                         y_prob[sel][sel_prob][index_sort], 
                         c=data['PROB'][sel][sel_prob][index_sort], 
                         marker='o', s=10, edgecolor='none', cmap='jet', vmin=0., vmax=1.) # Spectral_r

        drawProjImage(xx,yy,None,coord='C')

        #ax.set_xlim(xmax, xmin)
        #ax.set_ylim(ymin, ymax)
        #plt.xlabel(r'$\Delta \alpha_{2000}\,(\deg)$')
        #plt.ylabel(r'$\Delta \delta_{2000}\,(\deg)$')
        plt.xticks([-0.2, 0., 0.2])
        plt.yticks([-0.2, 0., 0.2])

        divider = make_axes_locatable(ax)
        ax_cb = divider.new_horizontal(size="7%", pad=0.1)
        plt.gcf().add_axes(ax_cb)
        pylab.colorbar(sc, cax=ax_cb, orientation='vertical', ticks=[0, 0.2, 0.4, 0.6, 0.8, 1.0], label='Membership Probability')
        ax_cb.yaxis.tick_right()
TargetImage.py 文件源码 项目:hco-experiments 作者: zooniverse 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def visualiseObject(self, cmap="hot"):
        pylab.ion()
        #pylab.set_cmap("gray")
        pylab.gray()
        pylab.title("image: %s" % self.fitsFile)
        pylab.imshow(self.getObject(), interpolation="nearest", cmap=cmap)
        pylab.colorbar()
        pylab.ylim(-1, 2*self.extent)
        pylab.xlim(-1, 2*self.extent)
        pylab.xlabel("Pixels")
        pylab.ylabel("Pixels")
        pylab.show()
TargetImage.py 文件源码 项目:hco-experiments 作者: zooniverse 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def visualiseObject(self, cmap="hot"):
        pylab.ion()
        #pylab.set_cmap("gray")
        pylab.gray()
        pylab.title("image: %s" % self.fitsFile)
        pylab.imshow(self.getObject(), interpolation="nearest", cmap=cmap)
        pylab.colorbar()
        pylab.ylim(-1, 2*self.extent)
        pylab.xlim(-1, 2*self.extent)
        pylab.xlabel("Pixels")
        pylab.ylabel("Pixels")
        pylab.show()
mnist_cnn_3layers_vis.py 文件源码 项目:dlcv05 作者: telecombcn-dl 项目源码 文件源码 阅读 35 收藏 0 点赞 0 评论 0
def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)
actinf_models.py 文件源码 项目:actinf 作者: x75 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def plot_hebbsom_links_distances_activations(X, Y, mdl, predictions, distances, activities):
    """plot the hebbian link matrix, and all node distances and activities for all inputs"""


    hebblink_log = np.log(mdl.hebblink_filter.T + 1.0)

    fig4 = pl.figure()
    fig4.suptitle("Debugging SOM: hebbian links, distances, activities (%s)" % (mdl.__class__.__name__))
    gs = gridspec.GridSpec(4, 1)
    # pl.plot(X, Y, "k.", alpha=0.5)
    # pl.subplot(numplots, 1, numplots-1)
    ax1 = fig4.add_subplot(gs[0])
    # im1 = ax1.imshow(mdl.hebblink_filter, interpolation="none", cmap=pl.get_cmap("gray"))
    im1 = ax1.pcolormesh(hebblink_log, cmap=pl.get_cmap("gray"))
    ax1.set_xlabel("in (e)")
    ax1.set_ylabel("out (p)")
    cbar = fig4.colorbar(mappable = im1, ax=ax1, orientation="horizontal")

    ax2 = fig4.add_subplot(gs[1])

    distarray = np.array(distances)
    print("distarray.shape", distarray.shape)
    pcm = ax2.pcolormesh(distarray.T)
    cbar = fig4.colorbar(mappable = pcm, ax=ax2, orientation="horizontal")

    # pl.subplot(numplots, 1, numplots)
    ax3 = fig4.add_subplot(gs[2])
    actarray = np.array(activities)
    print("actarray.shape", actarray.shape)
    pcm = ax3.pcolormesh(actarray.T)
    cbar = fig4.colorbar(mappable = pcm, ax=ax3, orientation="horizontal")

    ax4 = fig4.add_subplot(gs[3])
    ax4.plot(hebblink_log.flatten())

    print("hebblink_log", hebblink_log)

    fig4.show()
active_inference_basic.py 文件源码 项目:actinf 作者: x75 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def plot_colormeshmatrix_reduced(
        X, Y, ymin = None, ymax = None,
        title = "plot_colormeshmatrix_reduced"):

    print "plot_colormeshmatrix_reduced X.shape", X.shape, "Y.shape", Y.shape
    # input_cols  = [i for i in df.columns if i.startswith("X")]
    # output_cols = [i for i in df.columns if i.startswith("Y")]
    # Xs = df[input_cols]
    # Ys = df[output_cols]

    # numsamples = df.shape[0]
    # print "plot_scattermatrix_reduced: numsamples = %d" % numsamples

    # # numplots = Xs.shape[1] * Ys.shape[1]
    # # print "numplots = %d" % numplots

    cbar_orientation = "vertical" # "horizontal"
    gs = gridspec.GridSpec(Y.shape[2], X.shape[2]/2)
    pl.ioff()
    fig = pl.figure()
    fig.suptitle(title)
    # # alpha = 1.0 / np.power(numsamples, 1.0/(Xs.shape[1] - 0))
    # alpha = 0.2
    # print "alpha", alpha
    # cols = ["k", "b", "r", "g", "c", "m", "y"]
    for i in range(X.shape[2]/2):
        for j in range(Y.shape[2]):
            # print "i, j", i, j, Xs, Ys
            ax = fig.add_subplot(gs[j, i])
            pcm = ax.pcolormesh(X[:,:,i], X[:,:,X.shape[2]/2+i], Y[:,:,j], vmin = ymin, vmax = ymax)
            # ax.plot(Xs.as_matrix()[:,i], Ys.as_matrix()[:,j], "ko", alpha = alpha)
            ax.set_xlabel("goal")
            ax.set_ylabel("error")
            cbar = fig.colorbar(mappable = pcm, ax=ax, orientation=cbar_orientation)
            ax.set_aspect(1)
    if SAVEPLOTS:
        fig.savefig("fig_%03d_colormeshmatrix_reduced.pdf" % (fig.number), dpi=300)
    fig.show()
external_methods.py 文件源码 项目:SkinLesionNeuralNetwork 作者: Neurality 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    from mpl_toolkits.axes_grid1 import make_axes_locatable
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)
misc.py 文件源码 项目:deep-learning-experiments 作者: raghakot 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def nice_imshow(ax, data, vmin=None, vmax=None, cmap=None):
    """Wrapper around pl.imshow"""
    if cmap is None:
        cmap = cm.jet
    if vmin is None:
        vmin = data.min()
    if vmax is None:
        vmax = data.max()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    im = ax.imshow(data, vmin=vmin, vmax=vmax, interpolation='nearest', cmap=cmap)
    pl.colorbar(im, cax=cax)
mlcomp_sparse_document_classification.py 文件源码 项目:Parallel-SGD 作者: angadgill 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def benchmark(clf_class, params, name):
    print("parameters:", params)
    t0 = time()
    clf = clf_class(**params).fit(X_train, y_train)
    print("done in %fs" % (time() - t0))

    if hasattr(clf, 'coef_'):
        print("Percentage of non zeros coef: %f"
              % (np.mean(clf.coef_ != 0) * 100))
    print("Predicting the outcomes of the testing set")
    t0 = time()
    pred = clf.predict(X_test)
    print("done in %fs" % (time() - t0))

    print("Classification report on test set for classifier:")
    print(clf)
    print()
    print(classification_report(y_test, pred,
                                target_names=news_test.target_names))

    cm = confusion_matrix(y_test, pred)
    print("Confusion matrix:")
    print(cm)

    # Show confusion matrix
    pl.matshow(cm)
    pl.title('Confusion matrix of the %s classifier' % name)
    pl.colorbar()
plot.py 文件源码 项目:spyking-circus 作者: spyking-circus 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def view_classification(data_1, data_2, title=None, save=None):

    fig    = pylab.figure()
    count  = 0
    panels = [0, 2, 1, 3]
    for item in [data_1, data_2]:
        clf, cld, X, X_raw, y = item
        for mode in ['predict', 'decision_function']:
            ax = fig.add_subplot(2, 2, panels[count]+1)

            if mode == 'predict':
                c    = clf
                vmax = 1.0
                vmin = 0.0
            elif mode == 'decision_function':
                c    = cld
                vmax = max(abs(numpy.amin(c)), abs(numpy.amax(c)))
                vmin = - vmax

            from circus.validating.utils import Projection
            p = Projection()
            _ = p.fit(X_raw, y)
            X_raw_ = p.transform(X_raw)
            # Plot figure.
            sc = ax.scatter(X_raw_[:, 0], X_raw_[:, 1], c=c, s=5, lw=0.1, cmap='bwr',
                            vmin=vmin, vmax=vmax)
            cb = fig.colorbar(sc)
            ax.grid(True)
            if panels[count] in [0, 1]:
                if panels[count] == 0:
                    ax.set_title('Classification Before')
                    ax.set_ylabel("2nd component")
                if panels[count] == 1:
                    ax.set_title('Classification After')
                    cb.set_label('Prediction')
            elif panels[count] in [2, 3]:
                ax.set_xlabel("1st component")
                if panels[count] == 2:
                    ax.set_ylabel("2nd component")
                if panels[count] == 3:
                    cb.set_label('Decision function')
            count += 1

    if save is None:
        pylab.show()
    else:
        pylab.savefig(save)
        pylab.close(fig)
    return


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