def test_setup_vmin_vmax_warns(): """Test that _setup_vmin_vmax warns properly.""" expected_msg = r'\(min=0.0, max=1\) range.*minimum of data is -1' with pytest.warns(UserWarning, match=expected_msg): _setup_vmin_vmax(data=[-1, 0], vmin=None, vmax=None, norm=True)
def _plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, outlines='head', contours=6, image_interp='bilinear', show=True, head_pos=None, onselect=None, extrapolate='box', border=0): import matplotlib.pyplot as plt from matplotlib.widgets import RectangleSelector data = np.asarray(data) if isinstance(pos, Info): # infer pos from Info object picks = _pick_data_channels(pos) # pick only data channels pos = pick_info(pos, picks) # check if there is only 1 channel type, and n_chans matches the data ch_type = {channel_type(pos, idx) for idx, _ in enumerate(pos["chs"])} info_help = ("Pick Info with e.g. mne.pick_info and " "mne.io.pick.channel_indices_by_type.") if len(ch_type) > 1: raise ValueError("Multiple channel types in Info structure. " + info_help) elif len(pos["chs"]) != data.shape[0]: raise ValueError("Number of channels in the Info object and " "the data array does not match. " + info_help) else: ch_type = ch_type.pop() if any(type_ in ch_type for type_ in ('planar', 'grad')): # deal with grad pairs from mne.channels.layout import (_merge_grad_data, find_layout, _pair_grad_sensors) picks, pos = _pair_grad_sensors(pos, find_layout(pos)) data = _merge_grad_data(data[picks]).reshape(-1) else: picks = list(range(data.shape[0])) pos = _find_topomap_coords(pos, picks=picks) if data.ndim > 1: raise ValueError("Data needs to be array of shape (n_sensors,); got " "shape %s." % str(data.shape)) # Give a helpful error message for common mistakes regarding the position # matrix. pos_help = ("Electrode positions should be specified as a 2D array with " "shape (n_channels, 2). Each row in this matrix contains the " "(x, y) position of an electrode.") if pos.ndim != 2: error = ("{ndim}D array supplied as electrode positions, where a 2D " "array was expected").format(ndim=pos.ndim) raise ValueError(error + " " + pos_help) elif pos.shape[1] == 3: error = ("The supplied electrode positions matrix contains 3 columns. " "Are you trying to specify XYZ coordinates? Perhaps the " "mne.channels.create_eeg_layout function is useful for you.") raise ValueError(error + " " + pos_help) # No error is raised in case of pos.shape[1] == 4. In this case, it is # assumed the position matrix contains both (x, y) and (width, height) # values, such as Layout.pos. elif pos.shape[1] == 1 or pos.shape[1] > 4: raise ValueError(pos_help) if len(data) != len(pos): raise ValueError("Data and pos need to be of same length. Got data of " "length %s, pos of length %s" % (len(data), len(pos))) norm = min(data) >= 0 vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm) if cmap is None: cmap = 'Reds' if norm else 'RdBu_r' pos, outlines = _check_outlines(pos, outlines, head_pos) assert isinstance(outlines, dict) ax = axes if axes else plt.gca() _prepare_topomap(pos, ax) _use_default_outlines = any(k.startswith('head') for k in outlines) if _use_default_outlines: # prepare masking _autoshrink(outlines, pos, res) mask_params = _handle_default('mask_params', mask_params) # find mask limits xlim = np.inf, -np.inf, ylim = np.inf, -np.inf, mask_ = np.c_[outlines['mask_pos']] xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]), np.max(np.r_[xlim[1], mask_[:, 0]])) ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]), np.max(np.r_[ylim[1], mask_[:, 1]])) # interpolate the data, we multiply clip radius by 1.06 so that pixelated # edges of the interpolated image would appear under the mask head_radius = (None if extrapolate == 'local' else outlines['clip_radius'][0] * 1.06) xi = np.linspace(xmin, xmax, res) yi = np.linspace(ymin, ymax, res) Xi, Yi = np.meshgrid(xi, yi) interp = _GridData(pos, extrapolate, head_radius, border).set_values(data) Zi = interp.set_locations(Xi, Yi)() # plot outline patch_ = None if 'patch' in outlines: patch_ = outlines['patch'] patch_ = patch_() if callable(patch_) else patch_ patch_.set_clip_on(False) ax.add_patch(patch_) ax.set_transform(ax.transAxes) ax.set_clip_path(patch_) if _use_default_outlines: from matplotlib import patches patch_ = patches.Ellipse((0, 0), 2 * outlines['clip_radius'][0], 2 * outlines['clip_radius'][1], clip_on=True, transform=ax.transData) # plot interpolated map im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower', aspect='equal', extent=(xmin, xmax, ymin, ymax), interpolation=image_interp) # This tackles an incomprehensible matplotlib bug if no contours are # drawn. To avoid rescalings, we will always draw contours. # But if no contours are desired we only draw one and make it invisible. linewidth = mask_params['markeredgewidth'] no_contours = False if isinstance(contours, (np.ndarray, list)): pass # contours precomputed elif contours == 0: contours, no_contours = 1, True if (Zi == Zi[0, 0]).all(): cont = None # can't make contours for constant-valued functions else: with warnings.catch_warnings(record=True): warnings.simplefilter('ignore') cont = ax.contour(Xi, Yi, Zi, contours, colors='k', linewidths=linewidth / 2.) if no_contours and cont is not None: for col in cont.collections: col.set_visible(False) if patch_ is not None: im.set_clip_path(patch_) if cont is not None: for col in cont.collections: col.set_clip_path(patch_) pos_x, pos_y = pos.T if sensors is not False and mask is None: _plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax) elif sensors and mask is not None: idx = np.where(mask)[0] ax.plot(pos_x[idx], pos_y[idx], **mask_params) idx = np.where(~mask)[0] _plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax) elif not sensors and mask is not None: idx = np.where(mask)[0] ax.plot(pos_x[idx], pos_y[idx], **mask_params) if isinstance(outlines, dict): _draw_outlines(ax, outlines) if show_names: if names is None: raise ValueError("To show names, a list of names must be provided" " (see `names` keyword).") if show_names is True: def _show_names(x): return x else: _show_names = show_names show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0] for ii, (p, ch_id) in enumerate(zip(pos, names)): if ii not in show_idx: continue ch_id = _show_names(ch_id) ax.text(p[0], p[1], ch_id, horizontalalignment='center', verticalalignment='center', size='x-small') if onselect is not None: ax.RS = RectangleSelector(ax, onselect=onselect) plt_show(show) return im, cont, interp, patch_
def plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True, res=64, axes=None, names=None, show_names=False, mask=None, mask_params=None, outlines='head', image_mask=None, contours=6, image_interp='bilinear', show=True, head_pos=None, onselect=None, axis=None): ''' see the docstring for mne.viz.plot_topomap, which i've simply modified to return more objects ''' from matplotlib.widgets import RectangleSelector from mne.io.pick import (channel_type, pick_info, _pick_data_channels) from mne.utils import warn from mne.viz.utils import (_setup_vmin_vmax, plt_show) from mne.defaults import _handle_default from mne.channels.layout import _find_topomap_coords from mne.io.meas_info import Info from mne.viz.topomap import _check_outlines, _prepare_topomap, _griddata, _make_image_mask, _plot_sensors, \ _draw_outlines data = np.asarray(data) if isinstance(pos, Info): # infer pos from Info object picks = _pick_data_channels(pos) # pick only data channels pos = pick_info(pos, picks) # check if there is only 1 channel type, and n_chans matches the data ch_type = set(channel_type(pos, idx) for idx, _ in enumerate(pos["chs"])) info_help = ("Pick Info with e.g. mne.pick_info and " "mne.channels.channel_indices_by_type.") if len(ch_type) > 1: raise ValueError("Multiple channel types in Info structure. " + info_help) elif len(pos["chs"]) != data.shape[0]: raise ValueError("Number of channels in the Info object and " "the data array does not match. " + info_help) else: ch_type = ch_type.pop() if any(type_ in ch_type for type_ in ('planar', 'grad')): # deal with grad pairs from ..channels.layout import (_merge_grad_data, find_layout, _pair_grad_sensors) picks, pos = _pair_grad_sensors(pos, find_layout(pos)) data = _merge_grad_data(data[picks]).reshape(-1) else: picks = list(range(data.shape[0])) pos = _find_topomap_coords(pos, picks=picks) if data.ndim > 1: raise ValueError("Data needs to be array of shape (n_sensors,); got " "shape %s." % str(data.shape)) # Give a helpful error message for common mistakes regarding the position # matrix. pos_help = ("Electrode positions should be specified as a 2D array with " "shape (n_channels, 2). Each row in this matrix contains the " "(x, y) position of an electrode.") if pos.ndim != 2: error = ("{ndim}D array supplied as electrode positions, where a 2D " "array was expected").format(ndim=pos.ndim) raise ValueError(error + " " + pos_help) elif pos.shape[1] == 3: error = ("The supplied electrode positions matrix contains 3 columns. " "Are you trying to specify XYZ coordinates? Perhaps the " "mne.channels.create_eeg_layout function is useful for you.") raise ValueError(error + " " + pos_help) # No error is raised in case of pos.shape[1] == 4. In this case, it is # assumed the position matrix contains both (x, y) and (width, height) # values, such as Layout.pos. elif pos.shape[1] == 1 or pos.shape[1] > 4: raise ValueError(pos_help) if len(data) != len(pos): raise ValueError("Data and pos need to be of same length. Got data of " "length %s, pos of length %s" % (len(data), len(pos))) norm = min(data) >= 0 vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm) if cmap is None: cmap = 'Reds' if norm else 'RdBu_r' pos, outlines = _check_outlines(pos, outlines, head_pos) if axis is not None: axes = axis warn('axis parameter is deprecated and will be removed in 0.13. ' 'Use axes instead.', DeprecationWarning) ax = axes if axes else plt.gca() pos_x, pos_y = _prepare_topomap(pos, ax) if outlines is None: xmin, xmax = pos_x.min(), pos_x.max() ymin, ymax = pos_y.min(), pos_y.max() else: xlim = np.inf, -np.inf, ylim = np.inf, -np.inf, mask_ = np.c_[outlines['mask_pos']] xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]), np.max(np.r_[xlim[1], mask_[:, 0]])) ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]), np.max(np.r_[ylim[1], mask_[:, 1]])) # interpolate data xi = np.linspace(xmin, xmax, res) yi = np.linspace(ymin, ymax, res) Xi, Yi = np.meshgrid(xi, yi) Zi = _griddata(pos_x, pos_y, data, Xi, Yi) if outlines is None: _is_default_outlines = False elif isinstance(outlines, dict): _is_default_outlines = any(k.startswith('head') for k in outlines) if _is_default_outlines and image_mask is None: # prepare masking image_mask, pos = _make_image_mask(outlines, pos, res) mask_params = _handle_default('mask_params', mask_params) # plot outline linewidth = mask_params['markeredgewidth'] patch = None if 'patch' in outlines: patch = outlines['patch'] patch_ = patch() if callable(patch) else patch patch_.set_clip_on(False) ax.add_patch(patch_) ax.set_transform(ax.transAxes) ax.set_clip_path(patch_) # plot map and countour im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower', aspect='equal', extent=(xmin, xmax, ymin, ymax), interpolation=image_interp) # This tackles an incomprehensible matplotlib bug if no contours are # drawn. To avoid rescalings, we will always draw contours. # But if no contours are desired we only draw one and make it invisible . no_contours = False if contours in (False, None): contours, no_contours = 1, True cont = ax.contour(Xi, Yi, Zi, contours, colors='k', linewidths=linewidth) if no_contours is True: for col in cont.collections: col.set_visible(False) if _is_default_outlines: from matplotlib import patches patch_ = patches.Ellipse((0, 0), 2 * outlines['clip_radius'][0], 2 * outlines['clip_radius'][1], clip_on=True, transform=ax.transData) if _is_default_outlines or patch is not None: im.set_clip_path(patch_) if cont is not None: for col in cont.collections: col.set_clip_path(patch_) if sensors is not False and mask is None: _plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax) elif sensors and mask is not None: idx = np.where(mask)[0] ax.plot(pos_x[idx], pos_y[idx], **mask_params) idx = np.where(~mask)[0] _plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax) elif not sensors and mask is not None: idx = np.where(mask)[0] ax.plot(pos_x[idx], pos_y[idx], **mask_params) if isinstance(outlines, dict): _draw_outlines(ax, outlines) if show_names: if names is None: raise ValueError("To show names, a list of names must be provided" " (see `names` keyword).") if show_names is True: def _show_names(x): return x else: _show_names = show_names show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0] for ii, (p, ch_id) in enumerate(zip(pos, names)): if ii not in show_idx: continue ch_id = _show_names(ch_id) ax.text(p[0], p[1], ch_id, horizontalalignment='center', verticalalignment='center', size='x-small') plt.subplots_adjust(top=.95) if onselect is not None: ax.RS = RectangleSelector(ax, onselect=onselect) plt_show(show) return ax, im, cont, pos_x, pos_y