Source code for pygimli.mplviewer.dataview

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Some data related viewer."""

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import PatchCollection
from matplotlib.colors import LogNorm, Normalize
from matplotlib.patches import Rectangle, Wedge

import pygimli as pg

from .utils import updateAxes as updateAxes_


[docs]def generateMatrix(xvec, yvec, vals, **kwargs): """Generate a data matrix from x/y and value vectors. Parameters ---------- xvec, yvec, vals : iterables (list, np.array, pg.RVector) of same length full: bool [False] generate a fully symmetric matrix containing all unique xvec+yvec otherwise A is squeezed to the individual unique vectors Returns ------- A : np.ndarray(2d) matrix containing the values sorted according to unique xvec/yvec xmap/ymap : dict {key: num} dictionaries for accessing matrix position (row/col number from x/y[i]) """ if kwargs.pop('full', False): xymap = {xy: ii for ii, xy in enumerate(np.unique(np.hstack((xvec, yvec))))} xmap = xymap ymap = xymap else: xu, yu = np.unique(xvec), np.unique(yvec) if kwargs.pop('fillx', False): print('filling x', len(xu)) dx = np.median(np.diff(xu)).round(1) xu = np.arange(0, xu[-1] - xu[0] + dx * 0.5, dx) + xu[0] print(len(xu)) if kwargs.pop('filly', False): dy = np.median(np.diff(yu)).round(1) yu = np.arange(0, yu[-1] - yu[0] + dy * 0.5, dy) + yu[0] xmap = {xx: ii for ii, xx in enumerate(xu)} ymap = {yy: ii for ii, yy in enumerate(yu)} A = np.zeros((len(ymap), len(xmap))) inot = [] nshow = min([len(xvec), len(yvec), len(vals)]) for i in range(nshow): xi, yi = xvec[i], yvec[i] if A[ymap[yi], xmap[xi]]: inot.append(i) A[ymap[yi], xmap[xi]] = vals[i] if len(inot) > 0: print(len(inot), "data of", nshow, "not shown") if len(inot) < 30: print(inot) return A, xmap, ymap
[docs]def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None, logScale=None, label=None, dx=1, dy=None, **kwargs): """Plot previously generated (generateVecMatrix) y map (category). Parameters ---------- vals : iterable Data values to show. xvec : dict {i:num} dict (must match vals.shape[0]) ymap : iterable vector for x axis (must match vals.shape[0]) ax : mpl.axis axis to plot, if not given a new figure is created cMin/cMax : float minimum/maximum color values logScale : bool logarithmic colour scale [min(vals)>0] label : string colorbar label ** kwargs: * circular : bool Plot in polar coordinates. """ if cMin is None: cMin = np.min(vals) if cMax is None: cMax = np.max(vals) if logScale is None: logScale = (cMin > 0.0) norm = None if logScale and cMin > 0: norm = LogNorm(vmin=cMin, vmax=cMax) else: norm = Normalize(vmin=cMin, vmax=cMax) if ax is None: ax = plt.subplots()[1] recs = [] circular = kwargs.pop('circular', False) if circular: recs = [None] * len(xvec) if dy is None: # map y values to unique ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))} xyMap = {} for i, y in enumerate(yvec): if y not in xyMap: xyMap[y] = [] xyMap[y].append(i) # maxR = max(ymap.values()) # what's that for? not used dR = 1 / (len(ymap.values())+1) # dOff = np.pi / 2 # what's that for? not used for y, xIds in xyMap.items(): r = 1. - dR*(ymap[y]+1) # ax.plot(r * np.cos(xvec[xIds]), # r * np.sin(xvec[xIds]), 'o') # print(y, ymap[y]) for i in xIds: phi = xvec[i] # x = r * np.cos(phi) # what's that for? not used y = r * np.sin(phi) dPhi = (xvec[1] - xvec[0]) recs[i] = Wedge((0., 0.), r + dR/1.5, (phi - dPhi)*360/(2*np.pi), (phi + dPhi)*360/(2*np.pi), width=dR, zorder=1+r) # if i < 5: # ax.text(x, y, str(i)) # pg.wait() else: raise("Implementme") else: if dy is None: # map y values to unique ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))} for i in range(len(vals)): recs.append(Rectangle((xvec[i] - dx / 2, ymap[yvec[i]] - 0.5), dx, 1)) else: for i in range(len(vals)): recs.append(Rectangle((xvec[i] - dx / 2, yvec[i] - dy / 2), dx, dy)) ax.set_xlim(min(xvec) - dx / 2, max(xvec) + dx / 2) ax.set_ylim(len(ymap) - 0.5, -0.5) pp = PatchCollection(recs) # ax.clear() col = ax.add_collection(pp) pp.set_edgecolor(None) pp.set_linewidths(0.0) if circular: pp.set_edgecolor('black') pp.set_linewidths(0.1) cmap = pg.mplviewer.cmapFromName(**kwargs) if kwargs.pop('markOutside', False): cmap.set_bad('grey') cmap.set_under('darkgrey') cmap.set_over('lightgrey') cmap.set_bad('black') pp.set_cmap(cmap) pp.set_norm(norm) pp.set_array(vals) pp.set_clim(cMin, cMax) updateAxes_(ax) cbar = kwargs.pop('colorBar', True) ori = kwargs.pop('orientation', 'horizontal') if cbar in ['horizontal', 'vertical']: ori = cbar cbar = True if cbar is True: # not for cbar=1, which is really confusing! cbar = pg.mplviewer.createColorBar(col, cMin=cMin, cMax=cMax, nLevs=5, label=label, orientation=ori) elif cbar is not False: # .. cbar is an already existing cbar .. so we update its values pg.mplviewer.updateColorBar(cbar, cMin=cMin, cMax=cMax, nLevs=5, label=label) updateAxes_(ax) return ax, cbar, ymap
[docs]def patchMatrix(mat, xmap=None, ymap=None, ax=None, cMin=None, cMax=None, logScale=None, label=None, dx=1, **kwargs): """Plot previously generated (generateVecMatrix) matrix. Parameters ---------- mat : numpy.array2d matrix to show xmap : dict {i:num} dict (must match A.shape[0]) ymap : iterable vector for x axis (must match A.shape[0]) ax : mpl.axis axis to plot, if not given a new figure is created cMin/cMax : float minimum/maximum color values logScale : bool logarithmic colour scale [min(A)>0] label : string colorbar label dx : float width of the matrix elements (by default 1) """ mat = np.ma.masked_where(mat == 0.0, mat, False) if cMin is None: cMin = np.min(mat) if cMax is None: cMax = np.max(mat) if logScale is None: logScale = (cMin > 0.0) if logScale: norm = LogNorm(vmin=cMin, vmax=cMax) else: norm = Normalize(vmin=cMin, vmax=cMax) if 'ax' is None: ax = plt.subplots()[1] iy, ix = np.nonzero(mat) # != 0) recs = [] vals = [] for i, _ in enumerate(ix): recs.append(Rectangle((ix[i] - dx / 2, iy[i] - 0.5), dx, 1)) vals.append(mat[iy[i], ix[i]]) pp = PatchCollection(recs) col = ax.add_collection(pp) pp.set_edgecolor(None) pp.set_linewidths(0.0) if 'cmap' in kwargs: pp.set_cmap(kwargs.pop('cmap')) if 'cMap' in kwargs: pp.set_cmap(kwargs.pop('cMap')) pp.set_norm(norm) pp.set_array(np.array(vals)) pp.set_clim(cMin, cMax) xval = [k for k in xmap.keys()] ax.set_xlim(min(xval) - dx / 2, max(xval) + dx / 2) ax.set_ylim(len(ymap) + 0.5, -0.5) updateAxes_(ax) cbar = None if kwargs.pop('colorBar', True): ori = kwargs.pop('orientation', 'horizontal') cbar = pg.mplviewer.createColorBar(col, cMin=cMin, cMax=cMax, nLevs=5, label=label, orientation=ori) return ax, cbar
[docs]def plotMatrix(mat, xmap=None, ymap=None, ax=None, cMin=None, cMax=None, logScale=None, label=None, **kwargs): """Plot previously generated (generateVecMatrix) matrix. Parameters ---------- mat : numpy.array2d matrix to show xmap : dict {i:num} dict (must match A.shape[0]) ymap : iterable vector for x axis (must match A.shape[0]) ax : mpl.axis axis to plot, if not given a new figure is created cMin/cMax : float minimum/maximum color values logScale : bool logarithmic colour scale [min(A)>0] label : string colorbar label Returns ------- ax : matplotlib axes object axes object cb : matplotlib colorbar colorbar object """ if xmap is None: xmap = {i: i for i in range(mat.shape[0])} if ymap is None: ymap = {i: i for i in range(mat.shape[1])} if isinstance(mat, np.ma.MaskedArray): mat_ = mat else: mat_ = np.ma.masked_where(mat == 0.0, mat, False) if cMin is None: cMin = np.min(mat_) if cMax is None: cMax = np.max(mat_) if logScale is None: logScale = (cMin > 0.0) if logScale: norm = LogNorm(vmin=cMin, vmax=cMax) else: norm = Normalize(vmin=cMin, vmax=cMax) if ax is None: ax = plt.subplots()[1] im = ax.imshow(mat_, norm=norm, interpolation='nearest') if 'cmap' in kwargs: im.set_cmap(kwargs.pop('cmap')) if 'cMap' in kwargs: im.set_cmap(kwargs.pop('cMap')) ax.set_aspect(kwargs.pop('aspect', 1)) cbar = None if kwargs.pop('colorBar', True): ori = kwargs.pop('orientation', 'horizontal') cbar = pg.mplviewer.createColorBar(im, cMin=cMin, cMax=cMax, nLevs=5, label=label, orientation=ori) ax.grid(True) xt = np.unique(ax.get_xticks().clip(0, len(xmap) - 1)) yt = np.unique(ax.get_xticks().clip(0, len(ymap) - 1)) if kwargs.pop('showally', False): yt = np.arange(len(ymap)) else: yt = np.round(np.linspace(0, len(ymap) - 1, 5)) xx = np.sort([k for k in xmap]) ax.set_xticks(xt) ax.set_xticklabels(['{:g}'.format(round(xx[int(ti)], 2)) for ti in xt]) yy = np.unique([k for k in ymap]) ax.set_yticks(yt) ax.set_yticklabels(['{:g}'.format(round(yy[int(ti)], 2)) for ti in yt]) return ax, cbar
[docs]def plotVecMatrix(xvec, yvec, vals, full=False, **kwargs): """DEPRECATED for nameing """ pg.deprecated('plotVecMatrix', 'showVecMatrix') return showVecMatrix(xvec, yvec, vals, full, **kwargs)
[docs]def showVecMatrix(xvec, yvec, vals, full=False, **kwargs): """Plot three vectors as matrix. Parameters ---------- xvec, yvec : iterable (e.g. list, np.array, pg.RVector) of identical length vectors defining the indices into the matrix vals : iterable of same length as xvec/yvec vector containing the values to show full: bool [False] use a fully symmetric matrix containing all unique xvec+yvec otherwise A is squeezed to the individual unique xvec/yvec values **kwargs: forwarded to plotMatrix * ax : mpl.axis Axis to plot, if not given a new figure is created * cMin/cMax : float Minimum/maximum color values * logScale : bool Lgarithmic colour scale [min(A)>0] * label : string Colorbar label Returns ------- ax : matplotlib axes object axes object cb : matplotlib colorbar colorbar object """ A, xmap, ymap = generateMatrix(xvec, yvec, vals, full=full) return plotMatrix(A, xmap, ymap, **kwargs)
[docs]def plotDataContainerAsMatrix(data, x=None, y=None, v=None, **kwargs): """Plot data container as matrix. for each x, y and v token strings or vectors should be given """ if isinstance(x, str): x = data(x) if isinstance(y, str): y = data(y) if isinstance(v, str): v = data(v) if x is None or y is None or v is None: raise Exception("Vectors or strings must be given") if len(x) != len(y) or len(x) != len(v): raise Exception("lengths x/y/v not matching: {:d}!={:d}!={:d}".format( len(x), len(y), len(v))) return showVecMatrix(x, y, v, **kwargs)
[docs]def drawSensorAsMarker(ax, data): """Draw Sensor marker, these marker are pickable.""" elecsX = [] elecsY = [] for i in range(len(data.sensorPositions())): elecsX.append(data.sensorPositions()[i][0]) elecsY.append(data.sensorPositions()[i][1]) electrodeMarker, = ax.plot(elecsX, elecsY, 'x', color='black', picker=5.) ax.set_xlim([data.sensorPositions()[0][0] - 1., data.sensorPositions()[data.sensorCount() - 1][0] + 1.]) return electrodeMarker


2019 - GIMLi Development Team
Created using Bootstrap, Sphinx and pyGIMLi 1.0.10+38.gde1bd3dd) on Mar 13, 2019.