python类suptitle()的实例源码

active_inference_naoqi.py 文件源码 项目:actinf 作者: x75 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def plotstuff():
    X__ = np.load("tm_X.npy")
    S_pred = np.load("tm_S_pred.npy")
    E_pred = np.load("tm_E_pred.npy")
    M = np.load("tm_M.npy")

    pl.ioff()
    pl.suptitle("mode: %s (X: FM input, state pred: FM output)" % ("bluib"))
    pl.subplot(511)
    pl.title("X[goals]")
    pl.plot(X__[10:,0:4], "-x")
    pl.subplot(512)
    pl.title("X[prediction error]")
    pl.plot(X__[10:,4:], "-x")
    pl.subplot(513)
    pl.title("state pred")
    pl.plot(S_pred)
    pl.subplot(514)
    pl.title("error state - goal")
    pl.plot(E_pred)
    pl.subplot(515)
    pl.title("state")
    pl.plot(M)
    pl.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 22 收藏 0 点赞 0 评论 0
def plot_scattermatrix(df, title = "plot_scattermatrix"):
    """plot a scattermatrix of dataframe df"""
    if df is None:
        print "plot_scattermatrix: no data passed"
        return

    from pandas.tools.plotting import scatter_matrix
    # df = pd.DataFrame(X, columns=['x1_t', 'x2_t', 'x1_tptau', 'x2_tptau', 'u_t'])
    # scatter_data_raw = np.hstack((np.array(Xs), np.array(Ys)))
    # scatter_data_raw = np.hstack((Xs, Ys))
    # print "scatter_data_raw", scatter_data_raw.shape

    pl.ioff()
    # df = pd.DataFrame(scatter_data_raw, columns=["x_%d" % i for i in range(scatter_data_raw.shape[1])])
    sm = scatter_matrix(df, alpha=0.2, figsize=(10, 10), diagonal='hist')
    fig = sm[0,0].get_figure()
    fig.suptitle(title)
    if SAVEPLOTS:
        fig.savefig("fig_%03d_scattermatrix.pdf" % (fig.number), dpi=300)
    fig.show()
    # pl.show()
astrom_common.py 文件源码 项目:astromalign 作者: dstndstn 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def plotaffinegrid(affines, exag=1e3, affineOnly=True, R=0.025, tpre='', bboxes=None):
    import pylab as plt
    NR = 3
    NC = int(ceil(len(affines)/3.))
    #R = 0.025 # 1.5 arcmin
    #for (exag,affonly) in [(1e2, False), (1e3, True), (1e4, True)]:
    plt.clf()
    for i,aff in enumerate(affines):
        plt.subplot(NR, NC, i+1)
        dl = aff.refdec - R
        dh = aff.refdec + R
        rl = aff.refra  - R / aff.rascale
        rh = aff.refra  + R / aff.rascale
        RR,DD = np.meshgrid(np.linspace(rl, rh, 11),
                            np.linspace(dl, dh, 11))
        plotaffine(aff, RR.ravel(), DD.ravel(), exag=exag, affineOnly=affineOnly,
                   doclf=False,
                   units='dots', width=2, headwidth=2.5, headlength=3, headaxislength=3)
        if bboxes is not None:
            for bb in bboxes:
                plt.plot(*bb, linestyle='-', color='0.5')
            plt.plot(*bboxes[i], linestyle='-', color='k')
        setRadecAxes(rl,rh,dl,dh)
        plt.xlabel('')
        plt.ylabel('')
        plt.xticks([])
        plt.yticks([])
        plt.title('field %i' % (i+1))
    plt.subplots_adjust(left=0.05, right=0.95, wspace=0.1)
    if affineOnly:
        tt = tpre + 'Affine part of transformations'
    else:
        tt = tpre + 'Transformations'
    plt.suptitle(tt + ' (x %g)' % exag)
sunkarautil.py 文件源码 项目:PyME 作者: vikramsunkara 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def plot_marginals(state_space,p,name,t,labels = False):
    import matplotlib
    #matplotlib.use("PDF")
    #matplotlib.rcParams['figure.figsize'] = 5,10
    import matplotlib.pyplot as pl
    pl.suptitle("time: "+ str(t)+" units")
    print("time : "+ str(t))

    D = state_space.shape[1]

    for i in range(D):
        marg_X = np.unique(state_space[:,i])
        A = np.where(marg_X[:,np.newaxis] == state_space[:,i].T[np.newaxis,:],1,0)
        marg_p = np.dot(A,p)
        pl.subplot(int(D/2)+1,2,i+1)
        pl.plot(marg_X,marg_p)
        pl.axvline(np.sum(marg_X*marg_p),color= 'r')
        pl.axvline(marg_X[np.argmax(marg_p)],color='g')
        if labels == False:
            pl.xlabel("Specie: " + str(i+1))
        else:
            pl.xlabel(labels[i])
    #pl.savefig("Visuals/marginal_"+name+".pdf",format='pdf')
    pl.show()
    pl.clf()

##Simple Compress : best N-term approximation under the ell_1 norm
#@param state_space the state space shape: (Number of Species X Number of states) 
#@param p probability vector
#@param eps the ell_1 error to remove
#@return -Compressed state space
#       -Compressed Probs
plotters.py 文件源码 项目:PyME 作者: vikramsunkara 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def plot_marginals(state_space,p,name,t,labels = False,interactive = False):
    import matplotlib

    import matplotlib.pyplot as pl
    if interactive == True: 
        pl.ion()
    pl.clf()
    pl.suptitle("time: "+ str(t)+" units")
    #print("time : "+ str(t))
    D = state_space.shape[1]

    for i in range(D):
        marg_X = np.unique(state_space[:,i])
        A = np.where(marg_X[:,np.newaxis] == state_space[:,i].T[np.newaxis,:],1,0)
        marg_p = np.dot(A,p)
        pl.subplot(int(D/2)+1,2,i+1)
        pl.plot(marg_X,marg_p)
        pl.yticks(np.linspace(np.amin(marg_p), np.amax(marg_p), num=3))
        pl.axvline(np.sum(marg_X*marg_p),color= 'r')
        pl.axvline(marg_X[np.argmax(marg_p)],color='g')
        if labels == False:
            pl.xlabel("Specie: " + str(i+1))
        else:
            pl.xlabel(labels[i])
    if interactive == True:
        pl.draw()
    else:
        pl.tight_layout()
        pl.show()
plotting.py 文件源码 项目:ugali 作者: DarkEnergySurvey 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def plot6(self, filename, title=None):
        fig = plt.figure('summary', figsize=(11, 6))
        fig.subplots_adjust(wspace=0.4, hspace=0.25)
        fdg = r'{.}\!^\circ'
        coordstring = ('%.2f, %.2f'%(self.ra, self.dec)).replace('.',fdg)
        if title is None:
            #title = r'%s; ($\alpha_{2000}$, $\delta_{2000}$, $m-M$) = (%s, %.2f)'%(self.source.name, coordstring, self.isochrone.distance_modulus)
            title = r'$(\alpha_{2000}, \delta_{2000}, m-M) = (%s, %.1f)$'%(coordstring, self.isochrone.distance_modulus)

        if title: 
            plt.suptitle(title, fontsize=14)

        logger.debug("Drawing smooth stars...")
        plt.subplot(2, 3, 1)
        self.drawSmoothStars()

        logger.debug("Drawing density profile...")
        pylab.subplot(2, 3, 2)
        self.drawDensityProfile()

        logger.debug("Drawing spatial distribution of members...")
        pylab.subplot(2, 3, 3)
        self.drawMembersSpatial(filename)

        logger.debug("Drawing smooth galaxies...")
        plt.subplot(2, 3, 4)
        self.drawSmoothGalaxies()

        logger.debug("Drawing Hess diagram...")         
        plt.subplot(2,3,5)
        self.drawHessDiagram()

        logger.debug("Drawing CMD of members...")                  
        pylab.subplot(2, 3, 6)
        self.drawMembersCMD(filename)
active_inference_basic.py 文件源码 项目:actinf 作者: x75 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def rh_e2p_sample_plot(self):
        # intro checks
        if not self.attr_check(["y_samples"]):
            return

        pl.ioff()
        # 2a. plot sampling results
        pl.suptitle("%s step 1 + 2: learning proprio, then learning e2p" % (self.mode,))
        ax = pl.subplot(211)
        pl.title("Exteroceptive state S_e, extero to proprio mapping p2e")
        self.S_ext = ax.plot(self.logs["S_ext"], "k-", alpha=0.8, label="S_e")
        p2e   = ax.plot(self.logs["P2E_pred"], "r-", alpha=0.8, label="p2e")
        handles, labels = ax.get_legend_handles_labels()
        ax.legend(handles=[handles[i] for i in [0, 2]],
                  labels=[labels[i] for i in [0, 2]])
        ax2 = pl.subplot(212)
        pl.title("Proprioceptive state S_p, proprio to extero mapping e2p")
        ax2.plot(self.logs["M_prop_pred"], "k-", label="S_p")
        # pl.plot(self.logs["E2P_pred"], "y-", label="E2P knn")
        ax2.plot(self.y_samples, "g-", label="E2P gmm cond", alpha=0.8, linewidth=2)
        ax2.plot(self.logs["X__"][:,:3], "r-", label="goal goal")
        for _ in self.y_samples_:
            plausibility = _ - self.logs["X__"][:,:3]
            # print "_.shape = %s, plausibility.shape = %s, %d" % (_.shape, plausibility.shape, 0)
            # print "_", np.sum(_), _ - self.logs["X__"][:,:3]
            plausibility_norm = np.linalg.norm(plausibility, 2, axis=1)
            print "plausibility = %f" % (np.mean(plausibility_norm))
            if np.mean(plausibility_norm) < 0.8: # FIXME: what is that for, for thinning out the number of samples?
                ax2.plot(_, "b.", label="E2P gmm samples", alpha=0.2)
        handles, labels = ax2.get_legend_handles_labels()
        print "handles, labels", handles, labels
        legidx = slice(0, 12, 3)
        ax2.legend(handles[legidx], labels[legidx])
        # ax.legend(handles=[handles[i] for i in [0, 2]],
        #           labels=[labels[i] for i in [0, 2]])
        pl.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 27 收藏 0 点赞 0 评论 0
def rh_e2p_sample_and_drive_plot(self):
        # e2pidx = slice(self.numsteps,self.numsteps*2)
        e2pidx = slice(0, self.numsteps)
        pl.suptitle("%s top: extero goal and extero state, bottom: error_e = |g_e - s_e|^2" % (self.mode,))
        pl.subplot(211)
        pl.plot(self.logs["goal_ext"][e2pidx])
        pl.plot(self.logs["S_ext"][e2pidx])
        pl.subplot(212)
        pl.plot(np.linalg.norm(self.logs["E_pred_e"][e2pidx], 2, axis=1))
        pl.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 25 收藏 0 点赞 0 评论 0
def plot_scattermatrix_reduced(df, title = "plot_scattermatrix_reduced"):
    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

    gs = gridspec.GridSpec(Ys.shape[1], Xs.shape[1])
    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(Xs.shape[1]):
        for j in range(Ys.shape[1]):
            # print "i, j", i, j, Xs, Ys
            ax = fig.add_subplot(gs[j, i])
            ax.plot(Xs.as_matrix()[:,i], Ys.as_matrix()[:,j], "ko", alpha = alpha)
            ax.set_xlabel(input_cols[i])
            ax.set_ylabel(output_cols[j])
    if SAVEPLOTS:
        fig.savefig("fig_%03d_scattermatrix_reduced.pdf" % (fig.number), dpi=300)
    fig.show()
prepare.py 文件源码 项目:facade-segmentation 作者: jfemiani 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def process_files(files, basedir='./data', debug=False, rectify=False,
                  outdir='./data/for-labelme', **kwargs):
    attempts = 0
    n = len(files)

    print "Rectify is set to", rectify

    try:
        os.makedirs(outdir)
    except OSError as e:
        pass

    if debug:
        try:
            os.makedirs(os.path.join(outdir, 'debug'))
        except OSError as e:
            # Directory already exists
            pass

    for i, f in enumerate(files):
        try:
            newbasename = rename_file(f, basedir)
            newname = os.path.join(outdir, newbasename)
            print i + 1, 'of', n, newname

            image = imread(f)

            if rectify:
                try:
                    meta = {}
                    rectified = rectify_building(image, meta)
                    if debug:
                        import pylab as pl
                        h = meta['homography']
                        pl.suptitle('u:{} d:{} l:{} r:{}'.format(h.du, h.dd, h.dl, h.dr))
                        pl.subplot(221)
                        pl.imshow(image)
                        pl.axis('off')
                        pl.subplot(222)
                        pl.imshow(meta['building'])
                        pl.axis('off')
                        pl.subplot(223)
                        h.plot_original()
                        pl.subplot(224)
                        h.plot_rectified()
                        pl.savefig(os.path.join(outdir, 'debug', newbasename))
                    imsave(newname, rectified)
                except Exception as e:
                    print e
                    pass
            else:
                imsave(newname, image)
        except Exception as e:
            print e
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 plotTriangle(srcfile,samples,burn=0,**kwargs):
    #import triangle
    import corner
    import ugali.analysis.source
    import ugali.analysis.mcmc
    #matplotlib.rcParams.update({'text.usetex': True})

    source = ugali.analysis.source.Source()
    source.load(srcfile,section='source')
    params = source.get_params()
    results = yaml.load(open(srcfile))['results']
    samples = ugali.analysis.mcmc.Samples(samples)

    names = samples.names
    labels = names 
    truths = [params[n] for n in names]
    chain = samples.get(burn=burn,clip=5)

    ### Triangle plot
    #extents = [[0,15e3],[323.6,323.8],[-59.8,-59.7],[0,0.1],[19.5,20.5]]

    kwargs.setdefault('extents',None)
    kwargs.setdefault('plot_contours',True)
    kwargs.setdefault('plot_datapoints',True)
    kwargs.setdefault('verbose',False)
    kwargs.setdefault('quantiles',[0.16,0.84])

    if len(names) > 1:
        fig = corner.corner(chain,labels=labels,truths=truths,**kwargs)
    else:
        fig = plt.figure()
        plt.hist(chain,bins=100)
        plt.xlabel(names[0])

    try:
        text  = 'RA,DEC = (%.2f,%.2f)\n'%(results['ra'][0],results['dec'][0])
        text += '(m-M,D) = (%.1f, %.0f kpc)\n'%(results['distance_modulus'][0],results['distance'][0])
        text += r'$r_h$ = %.1f arcmin'%(results['extension_arcmin'][0])+'\n'
        text += 'TS = %.1f\n'%results['ts'][0]
        text += 'NSamples = %i\n'%(len(chain))
        #plt.figtext(0.65,0.90,text,ha='left',va='top')
    except KeyError as e:
        logger.warning(str(e))
        pass

    label = map(str.capitalize,source.name.split('_'))
    label[-1] = label[-1].upper()
    title = '%s'%' '.join(label)
    plt.suptitle(title)


############################################################


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