Python matplotlib.pyplot.pcolor() Examples
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code examples of matplotlib.pyplot.pcolor().
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Example #1
Source File: NavierStokes.py From PINNs with MIT License | 7 votes |
def plot_solution(X_star, u_star, index): lb = X_star.min(0) ub = X_star.max(0) nn = 200 x = np.linspace(lb[0], ub[0], nn) y = np.linspace(lb[1], ub[1], nn) X, Y = np.meshgrid(x,y) U_star = griddata(X_star, u_star.flatten(), (X, Y), method='cubic') plt.figure(index) plt.pcolor(X,Y,U_star, cmap = 'jet') plt.colorbar()
Example #2
Source File: plotter.py From plastering with MIT License | 6 votes |
def plot_colormap_upgrade(data, figSizeIn, xlabel, ylabel, cbarlabel, cmapIn, ytickRange, ytickTag, xtickRange=None, xtickTag=None, title=None, xmin=None, xmax=None, xgran=None, ymin=None, ymax=None, ygran=None): if xmin != None: y, x = np.mgrid[slice(ymin, ymax + ygran, ygran), slice(xmin, xmax + xgran, xgran)] fig = plt.figure(figsize = figSizeIn) # plt.pcolor(data, cmap=cmapIn) plt.pcolormesh(x, y, data, cmap=cmapIn) plt.grid(which='major',axis='both') plt.axis([x.min(), x.max(), y.min(), y.max()]) else: plt.pcolor(data, cmap=cmapIn) cbar = plt.colorbar() cbar.set_label(cbarlabel, labelpad=-0.1) plt.xlabel(xlabel) plt.ylabel(ylabel) # if xtickTag: # plt.xticks(xtickRange, xtickTag, fontsize=10) # # plt.yticks(ytickRange, ytickTag, fontsize=10) plt.tight_layout() if title: plt.title(title) plt.show() return fig
Example #3
Source File: plotter.py From plastering with MIT License | 6 votes |
def plot_colormap(data, figSizeIn, xlabel, ylabel, cbarlabel, cmapIn, ytickRange, ytickTag, xtickRange=None, xtickTag=None, title=None): fig = plt.figure(figsize = figSizeIn) plt.pcolor(data, cmap=cmapIn) cbar = plt.colorbar() cbar.set_label(cbarlabel, labelpad=-0.1) plt.xlabel(xlabel) plt.ylabel(ylabel) if xtickTag: plt.xticks(xtickRange, xtickTag, fontsize=10) plt.yticks(ytickRange, ytickTag, fontsize=10) plt.tight_layout() if title: plt.title(title) plt.show() return fig
Example #4
Source File: plotter.py From plastering with MIT License | 6 votes |
def plot_colormap_upgrade(data, figSizeIn, xlabel, ylabel, cbarlabel, cmapIn, ytickRange, ytickTag, xtickRange=None, xtickTag=None, title=None, xmin=None, xmax=None, xgran=None, ymin=None, ymax=None, ygran=None): if xmin != None: y, x = np.mgrid[slice(ymin, ymax + ygran, ygran), slice(xmin, xmax + xgran, xgran)] fig = plt.figure(figsize = figSizeIn) # plt.pcolor(data, cmap=cmapIn) plt.pcolormesh(x, y, data, cmap=cmapIn) plt.grid(which='major',axis='both') plt.axis([x.min(), x.max(), y.min(), y.max()]) else: plt.pcolor(data, cmap=cmapIn) cbar = plt.colorbar() cbar.set_label(cbarlabel, labelpad=-0.1) plt.xlabel(xlabel) plt.ylabel(ylabel) # if xtickTag: # plt.xticks(xtickRange, xtickTag, fontsize=10) # # plt.yticks(ytickRange, ytickTag, fontsize=10) plt.tight_layout() if title: plt.title(title) plt.show() return fig
Example #5
Source File: plotter.py From plastering with MIT License | 6 votes |
def plot_colormap(data, figSizeIn, xlabel, ylabel, cbarlabel, cmapIn, ytickRange, ytickTag, xtickRange=None, xtickTag=None, title=None): fig = plt.figure(figsize = figSizeIn) plt.pcolor(data, cmap=cmapIn) cbar = plt.colorbar() cbar.set_label(cbarlabel, labelpad=-0.1) plt.xlabel(xlabel) plt.ylabel(ylabel) if xtickTag: plt.xticks(xtickRange, xtickTag, fontsize=10) plt.yticks(ytickRange, ytickTag, fontsize=10) plt.tight_layout() if title: plt.title(title) plt.show() return fig
Example #6
Source File: test_axes.py From coffeegrindsize with MIT License | 6 votes |
def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30)
Example #7
Source File: data_class.py From MIDI-VAE with MIT License | 6 votes |
def draw_pianoroll(pianoroll, name='Notes', show=False, save_path=''): cm = matplotlib.cm.get_cmap('Greys') notes_color = cm(1.0) notes_patch = mpatches.Patch(color=notes_color, label=name) plt.figure(figsize=(20.0, 10.0)) plt.title('Pianoroll Pitch-plot of ' + name, fontsize=10) plt.legend(handles=[notes_patch], loc='upper right', prop={'size': 8}) plt.pcolor(pianoroll, cmap='Greys', vmin=0, vmax=np.max(pianoroll)) if show: plt.show() if len(save_path) > 0: plt.savefig(save_path) tikz_save(save_path + ".tex", encoding='utf-8', show_info=False) plt.close()
Example #8
Source File: test_axes.py From ImageFusion with MIT License | 6 votes |
def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30)
Example #9
Source File: test_axes.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30)
Example #10
Source File: thinkplot.py From DataExploration with MIT License | 6 votes |
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options): """Makes a pseudocolor plot. xs: ys: zs: pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot options: keyword args passed to pyplot.pcolor and/or pyplot.contour """ _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) X, Y = np.meshgrid(xs, ys) Z = zs x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = pyplot.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: pyplot.pcolormesh(X, Y, Z, **options) if contour: cs = pyplot.contour(X, Y, Z, **options) pyplot.clabel(cs, inline=1, fontsize=10)
Example #11
Source File: thinkplot.py From Lie_to_me with MIT License | 6 votes |
def Pcolor(xs, ys, zs, pcolor=True, contour=False, **options): """Makes a pseudocolor plot. xs: ys: zs: pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot options: keyword args passed to plt.pcolor and/or plt.contour """ _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) X, Y = np.meshgrid(xs, ys) Z = zs x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = plt.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: plt.pcolormesh(X, Y, Z, **options) if contour: cs = plt.contour(X, Y, Z, **options) plt.clabel(cs, inline=1, fontsize=10)
Example #12
Source File: test_axes.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30)
Example #13
Source File: test_axes.py From neural-network-animation with MIT License | 6 votes |
def test_pcolor_datetime_axis(): fig = plt.figure() fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15) base = datetime.datetime(2013, 1, 1) x = np.array([base + datetime.timedelta(days=d) for d in range(21)]) y = np.arange(21) z1, z2 = np.meshgrid(np.arange(20), np.arange(20)) z = z1 * z2 plt.subplot(221) plt.pcolor(x[:-1], y[:-1], z) plt.subplot(222) plt.pcolor(x, y, z) x = np.repeat(x[np.newaxis], 21, axis=0) y = np.repeat(y[:, np.newaxis], 21, axis=1) plt.subplot(223) plt.pcolor(x[:-1, :-1], y[:-1, :-1], z) plt.subplot(224) plt.pcolor(x, y, z) for ax in fig.get_axes(): for label in ax.get_xticklabels(): label.set_ha('right') label.set_rotation(30)
Example #14
Source File: smokes_friends_cancer.py From logictensornetworks with MIT License | 5 votes |
def plt_heatmap(df): # display the result of a nxm pandas dataframe in a heatmap plt.pcolor(df) plt.yticks(np.arange(0.5, len(df.index), 1), df.index) plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns) plt.colorbar()
Example #15
Source File: giplt.py From geoist with MIT License | 5 votes |
def squaremesh(mesh, prop, cmap=pyplot.cm.jet, vmin=None, vmax=None): """ Make a pseudo-color plot of a mesh of squares Parameters: * mesh : :class:`geoist.mesher.SquareMesh` or compatible The mesh (a compatible mesh must implement the methods ``get_xs`` and ``get_ys``) * prop : str The physical property of the squares to use as the color scale. * cmap : colormap Color map to be used. (see pyplot.cm module) * vmin, vmax : float Saturation values of the colorbar. Returns: * axes : ``matplitlib.axes`` The axes element of the plot """ if prop not in mesh.props: raise ValueError("Can't plot because 'mesh' doesn't have property '%s'" % (prop)) xs = mesh.get_xs() ys = mesh.get_ys() X, Y = numpy.meshgrid(xs, ys) V = numpy.reshape(mesh.props[prop], mesh.shape) plot = pyplot.pcolor(X, Y, V, cmap=cmap, vmin=vmin, vmax=vmax, picker=True) pyplot.xlim(xs.min(), xs.max()) pyplot.ylim(ys.min(), ys.max()) return plot
Example #16
Source File: smokes_friends_cancer.py From logictensornetworks with MIT License | 5 votes |
def plt_heatmap(df): plt.pcolor(df) plt.yticks(np.arange(0.5, len(df.index), 1), df.index) plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns) plt.colorbar()
Example #17
Source File: test_axes.py From coffeegrindsize with MIT License | 5 votes |
def test_pcolorargs_5205(): # Smoketest to catch issue found in gh:5205 x = [-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5] y = [-1.5, -1.25, -1.0, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5] X, Y = np.meshgrid(x, y) Z = np.hypot(X, Y) plt.pcolor(Z) plt.pcolor(list(Z)) plt.pcolor(x, y, Z) plt.pcolor(X, Y, list(Z))
Example #18
Source File: maxent_objectworld.py From Inverse-Reinforcement-Learning with MIT License | 5 votes |
def main(grid_size, discount, n_objects, n_colours, n_trajectories, epochs, learning_rate): """ Run maximum entropy inverse reinforcement learning on the objectworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. n_objects: Number of objects. int. n_colours: Number of colours. int. n_trajectories: Number of sampled trajectories. int. epochs: Gradient descent iterations. int. learning_rate: Gradient descent learning rate. float. """ wind = 0.3 trajectory_length = 8 ow = objectworld.Objectworld(grid_size, n_objects, n_colours, wind, discount) ground_r = np.array([ow.reward(s) for s in range(ow.n_states)]) policy = find_policy(ow.n_states, ow.n_actions, ow.transition_probability, ground_r, ow.discount, stochastic=False) trajectories = ow.generate_trajectories(n_trajectories, trajectory_length, lambda s: policy[s]) feature_matrix = ow.feature_matrix(discrete=False) r = maxent.irl(feature_matrix, ow.n_actions, discount, ow.transition_probability, trajectories, epochs, learning_rate) plt.subplot(1, 2, 1) plt.pcolor(ground_r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Groundtruth reward") plt.subplot(1, 2, 2) plt.pcolor(r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Recovered reward") plt.show()
Example #19
Source File: maxent_gridworld.py From Inverse-Reinforcement-Learning with MIT License | 5 votes |
def main(grid_size, discount, n_trajectories, epochs, learning_rate): """ Run maximum entropy inverse reinforcement learning on the gridworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. n_trajectories: Number of sampled trajectories. int. epochs: Gradient descent iterations. int. learning_rate: Gradient descent learning rate. float. """ wind = 0.3 trajectory_length = 3*grid_size gw = gridworld.Gridworld(grid_size, wind, discount) trajectories = gw.generate_trajectories(n_trajectories, trajectory_length, gw.optimal_policy) feature_matrix = gw.feature_matrix() ground_r = np.array([gw.reward(s) for s in range(gw.n_states)]) r = maxent.irl(feature_matrix, gw.n_actions, discount, gw.transition_probability, trajectories, epochs, learning_rate) plt.subplot(1, 2, 1) plt.pcolor(ground_r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Groundtruth reward") plt.subplot(1, 2, 2) plt.pcolor(r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Recovered reward") plt.show()
Example #20
Source File: NavierStokes.py From DeepHPMs with MIT License | 5 votes |
def plot_solution(X_data, w_data, index): lb = X_data.min(0) ub = X_data.max(0) nn = 200 x = np.linspace(lb[0], ub[0], nn) y = np.linspace(lb[1], ub[1], nn) X, Y = np.meshgrid(x,y) W_data = griddata(X_data, w_data.flatten(), (X, Y), method='cubic') plt.figure(index) plt.pcolor(X,Y,W_data, cmap = 'jet') plt.colorbar()
Example #21
Source File: lp_large_gridworld.py From Inverse-Reinforcement-Learning with MIT License | 5 votes |
def main(grid_size, discount): """ Run large state space linear programming inverse reinforcement learning on the gridworld MDP. Plots the reward function. grid_size: Grid size. int. discount: MDP discount factor. float. """ wind = 0.3 trajectory_length = 3*grid_size gw = gridworld.Gridworld(grid_size, wind, discount) ground_r = np.array([gw.reward(s) for s in range(gw.n_states)]) policy = [gw.optimal_policy_deterministic(s) for s in range(gw.n_states)] # Need a value function for each basis function. feature_matrix = gw.feature_matrix() values = [] for dim in range(feature_matrix.shape[1]): reward = feature_matrix[:, dim] values.append(value(policy, gw.n_states, gw.transition_probability, reward, gw.discount)) values = np.array(values) r = linear_irl.large_irl(values, gw.transition_probability, feature_matrix, gw.n_states, gw.n_actions, policy) plt.subplot(1, 2, 1) plt.pcolor(ground_r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Groundtruth reward") plt.subplot(1, 2, 2) plt.pcolor(r.reshape((grid_size, grid_size))) plt.colorbar() plt.title("Recovered reward") plt.show()
Example #22
Source File: test_axes.py From coffeegrindsize with MIT License | 5 votes |
def test_axes_margins(): fig, ax = plt.subplots() ax.plot([0, 1, 2, 3]) assert ax.get_ybound()[0] != 0 fig, ax = plt.subplots() ax.bar([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.barh([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_xbound()[0] == 0 fig, ax = plt.subplots() ax.pcolor(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.pcolorfast(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.hist(np.arange(10)) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.imshow(np.zeros((10, 10))) assert ax.get_xbound() == (-0.5, 9.5) assert ax.get_ybound() == (-0.5, 9.5)
Example #23
Source File: test_backend_pgf.py From coffeegrindsize with MIT License | 5 votes |
def test_mixedmode(): rc_xelatex = {'font.family': 'serif', 'pgf.rcfonts': False} mpl.rcParams.update(rc_xelatex) Y, X = np.ogrid[-1:1:40j, -1:1:40j] plt.figure() plt.pcolor(X**2 + Y**2).set_rasterized(True) # test bbox_inches clipping
Example #24
Source File: test_colors.py From coffeegrindsize with MIT License | 5 votes |
def test_autoscale_masked(): # Test for #2336. Previously fully masked data would trigger a ValueError. data = np.ma.masked_all((12, 20)) plt.pcolor(data) plt.draw()
Example #25
Source File: vis_predictions.py From spatial-reasoning with MIT License | 5 votes |
def vis_value_map(pred, targ, save_path, title='prediction', share=True): # print 'in vis: ', pred.shape, targ.shape dim = int(math.sqrt(pred.size)) if share: vmin = min(pred.min(), targ.min()) vmax = max(pred.max(), targ.max()) else: vmin = None vmax = None plt.clf() fig, (ax0,ax1) = plt.subplots(1,2,sharey=True) heat0 = ax0.pcolor(pred.reshape(dim,dim), vmin=vmin, vmax=vmax, cmap=cm.jet) ax0.set_title(title, fontsize=5) if not share: fig.colorbar(heat0) heat1 = ax1.pcolor(targ.reshape(dim,dim), vmin=vmin, vmax=vmax, cmap=cm.jet) ax1.invert_yaxis() ax1.set_title('target') fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(heat1, cax=cbar_ax) # print 'saving to: ', fullpath plt.savefig(save_path, bbox_inches='tight') plt.close(fig) # print pred.shape, targ.shape
Example #26
Source File: vis_predictions.py From spatial-reasoning with MIT License | 5 votes |
def vis_fig(data, save_path, title=None, vmax=None, vmin=None, cmap=cm.jet): # print 'in vis: ', pred.shape, targ.shape dim = int(math.sqrt(data.size)) # if share: # vmin = min(pred.min(), targ.min()) # vmax = max(pred.max(), targ.max()) # else: # vmin = None # vmax = None plt.clf() # fig, (ax0,ax1) = plt.subplots(1,2,sharey=True) plt.pcolor(data.reshape(dim,dim), vmin=vmin, vmax=vmax, cmap=cmap) plt.xticks([]) plt.yticks([]) # ax0.set_title(title, fontsize=5) # if not share: # fig.colorbar(heat0) # heat1 = ax1.pcolor(targ.reshape(dim,dim), vmin=vmin, vmax=vmax, cmap=cm.jet) fig = plt.gcf() ax = plt.gca() if title: ax.set_title(title) ax.invert_yaxis() fig.set_size_inches(4,4) # ax1.invert_yaxis() # ax1.set_title('target') # fig.subplots_adjust(right=0.8) # cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) # fig.colorbar(heat1, cax=cbar_ax) # print 'saving to: ', fullpath plt.savefig(save_path, bbox_inches='tight', pad_inches=0.0) plt.close(fig) # print pred.shape, targ.shape
Example #27
Source File: visualization.py From spatial-reasoning with MIT License | 5 votes |
def visualize_values(mdp, values, policy, filename, title=None): states = mdp.states # print states plt.clf() m = max(states, key=lambda x: x[0])[0] + 1 n = max(states, key=lambda x: x[1])[1] + 1 data = np.zeros((m,n)) for i in range(m): for j in range(n): state = (i,j) if type(values) == dict: data[i][j] = values[state] else: # print values[i][j] data[i][j] = values[i][j] action = policy[state] ## if using all_reachable actions, pick the best one if type(action) == tuple: action = action[0] if action != None: x, y, w, h = arrow(i, j, action) plt.arrow(x,y,w,h,head_length=0.4,head_width=0.4,fc='k',ec='k') heatmap = plt.pcolor(data, cmap=plt.get_cmap('jet')) plt.colorbar() plt.gca().invert_yaxis() if title: plt.title(title) plt.savefig(filename + '.png') # print data
Example #28
Source File: thinkplot.py From DataExploration with MIT License | 5 votes |
def Contour(obj, pcolor=False, contour=True, imshow=False, **options): """Makes a contour plot. d: map from (x, y) to z, or object that provides GetDict pcolor: boolean, whether to make a pseudocolor plot contour: boolean, whether to make a contour plot imshow: boolean, whether to use pyplot.imshow options: keyword args passed to pyplot.pcolor and/or pyplot.contour """ try: d = obj.GetDict() except AttributeError: d = obj _Underride(options, linewidth=3, cmap=matplotlib.cm.Blues) xs, ys = zip(*d.keys()) xs = sorted(set(xs)) ys = sorted(set(ys)) X, Y = np.meshgrid(xs, ys) func = lambda x, y: d.get((x, y), 0) func = np.vectorize(func) Z = func(X, Y) x_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) axes = pyplot.gca() axes.xaxis.set_major_formatter(x_formatter) if pcolor: pyplot.pcolormesh(X, Y, Z, **options) if contour: cs = pyplot.contour(X, Y, Z, **options) pyplot.clabel(cs, inline=1, fontsize=10) if imshow: extent = xs[0], xs[-1], ys[0], ys[-1] pyplot.imshow(Z, extent=extent, **options)
Example #29
Source File: test_axes.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_pcolorargs_5205(): # Smoketest to catch issue found in gh:5205 x = [-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5] y = [-1.5, -1.25, -1.0, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5] X, Y = np.meshgrid(x, y) Z = np.hypot(X, Y) plt.pcolor(Z) plt.pcolor(list(Z)) plt.pcolor(x, y, Z) plt.pcolor(X, Y, list(Z))
Example #30
Source File: test_axes.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_axes_margins(): fig, ax = plt.subplots() ax.plot([0, 1, 2, 3]) assert ax.get_ybound()[0] != 0 fig, ax = plt.subplots() ax.bar([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.barh([0, 1, 2, 3], [1, 1, 1, 1]) assert ax.get_xbound()[0] == 0 fig, ax = plt.subplots() ax.pcolor(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.pcolorfast(np.zeros((10, 10))) assert ax.get_xbound() == (0, 10) assert ax.get_ybound() == (0, 10) fig, ax = plt.subplots() ax.hist(np.arange(10)) assert ax.get_ybound()[0] == 0 fig, ax = plt.subplots() ax.imshow(np.zeros((10, 10))) assert ax.get_xbound() == (-0.5, 9.5) assert ax.get_ybound() == (-0.5, 9.5)