Source code for pwem.viewers.plotter

# **************************************************************************
# *
# * Authors:     Josue Gomez Blanco (
# *
# * Unidad de  Bioinformatica of Centro Nacional de Biotecnologia , CSIC
# *
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License as published by
# * the Free Software Foundation; either version 3 of the License, or
# * (at your option) any later version.
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * GNU General Public License for more details.
# *
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307  USA
# *
# *  All comments concerning this program package may be sent to the
# *  e-mail address ''
# *
# **************************************************************************

from math import radians
import numpy as np
import as cm
from scipy.ndimage.filters import gaussian_filter

from pyworkflow.gui.plotter import Plotter, plt
import pwem.emlib.metadata as md
import numbers

[docs]class EmPlotter(Plotter): """ Class to create several plots. """ def __init__(self, x=1, y=1, mainTitle="", **kwargs): Plotter.__init__(self, x, y, mainTitle, **kwargs)
[docs] def plotAngularDistribution(self, title, rot, tilt, weight=[], max_p=40, min_p=5, color='blue'): """ Create an special type of subplot, representing the angular distribution of weight projections. """ if weight: max_w = max(weight) min_w = min(weight) a = self.createSubPlot(title, 'Min weight=%(min_w).2f, Max weight=%(max_w).2f' % locals(), '', projection='polar') for r, t, w in zip(rot, tilt, weight): pointsize = int((w - min_w) / (max_w - min_w + 0.001) * (max_p - min_p) + min_p) a.plot(r, t, markerfacecolor=color, marker='.', markersize=pointsize) else: a = self.createSubPlot(title, 'Empty plot', '', projection='polar') for r, t in zip(rot, tilt): a.plot(r, t, markerfacecolor=color, marker='.', markersize=10) return a
[docs] def plotAngularDistributionHistogram(self, title, rot, tilt): """ Create an special type of subplot, representing the angular distribution of weight projections. """ heatmap, xedges, yedges = np.histogram2d(rot, tilt, bins=100) sigma = min(max(xedges) - min(xedges), max(yedges) - min(yedges)) / 20 heatmap = gaussian_filter(heatmap, sigma=sigma) heatmapImage = heatmap.T extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] a = self.createSubPlot(title, 'Angular distribution', '') mappable = a.imshow(heatmapImage, extent=extent, origin='lower', cmap=cm.jet, aspect='auto') a.set_xlabel('Rotational angle') a.set_ylabel('Tilt angle') return mappable
[docs] def plotAngularDistributionFromMd(self, mdFile, title, **kwargs): """ Read the values of rot, tilt and weights from the metadata and plot the angular distribution. ANGLES are in DEGREES In the metadata: rot: MDL_ANGLE_ROT tilt: MDL_ANGLE_TILT weight: MDL_WEIGHT """ angMd = md.MetaData(mdFile) rot = [] tilt = [] if 'histogram' in kwargs: for row in md.iterRows(angMd): rot.append(row.getValue(md.MDL_ANGLE_ROT)) tilt.append(row.getValue(md.MDL_ANGLE_TILT)) return self.plotAngularDistributionHistogram(title, rot, tilt) else: weight = [] for row in md.iterRows(angMd): rot.append(radians(row.getValue(md.MDL_ANGLE_ROT))) tilt.append(row.getValue(md.MDL_ANGLE_TILT)) weight.append(row.getValue(md.MDL_WEIGHT)) return self.plotAngularDistribution(title, rot, tilt, weight, **kwargs)
[docs] def plotHist(self, yValues, nbins, color='blue', **kwargs): """ Create an histogram. """ # In some cases yValues is a generator, which cannot be indexed self.hist(list(yValues), nbins, facecolor=color, **kwargs)
[docs] def plotScatter(self, xValues, yValues, color='blue', **kwargs): """ Create an scatter plot. """ self.scatterP(xValues, yValues, c=color, **kwargs)
[docs] def plotMatrix(self, img , matrix , vminData , vmaxData , cmap='jet' , xticksLablesMajor=None , yticksLablesMajor=None , rotationX=90. , rotationY=0. , **kwargs): interpolation = kwargs.get('interpolation', "none") plot = img.imshow(matrix, interpolation=interpolation, cmap=cmap, vmin=vminData, vmax=vmaxData) if xticksLablesMajor is not None: plt.xticks(range(len(xticksLablesMajor)), xticksLablesMajor[:len(xticksLablesMajor)], rotation=rotationX) if yticksLablesMajor is not None: plt.yticks(range(len(yticksLablesMajor)), yticksLablesMajor[:len(yticksLablesMajor)], rotation=rotationY) return plot
[docs] def plotData(self, xValues, yValues, color='blue', **kwargs): """ Shortcut function to plot some values. Params: xValues: list of values to show in x-axis yValues: list of values to show as values in y-axis color: color for the plot. **kwargs: keyword arguments that accepts: marker, linestyle """ self.plot(xValues, yValues, color, **kwargs)
[docs] def plotDataBar(self, xValues, yValues, width, color='blue', **kwargs): """ Shortcut function to plot some values. Params: xValues: list of values to show in x-axis yValues: list of values to show as values in y-axis color: color for the plot. **kwargs: keyword arguments that accepts: marker, linestyle """, yValues, width=width, color=color, **kwargs)
[docs] @classmethod def createFromFile(cls, dbName, dbPreffix, plotType, columnsStr, colorsStr, linesStr, markersStr, xcolumn, ylabel, xlabel, title, bins, orderColumn, orderDirection): columns = columnsStr.split() colors = colorsStr.split() lines = linesStr.split() markers = markersStr.split() data = PlotData(dbName, dbPreffix, orderColumn, orderDirection) plotter = Plotter(windowTitle=title) ax = plotter.createSubPlot(title, xlabel, ylabel) xvalues = data.getColumnValues(xcolumn) if xcolumn else range(0, data.getSize()) for i, col in enumerate(columns): yvalues = data.getColumnValues(col) color = colors[i] line = lines[i] colLabel = col if not col.startswith("_") else col[1:] if bins: yvalues = data._removeInfinites(yvalues) ax.hist(yvalues, bins=int(bins), color=color, linestyle=line, label=colLabel) else: if plotType == 'Plot': marker = (markers[i] if not markers[i] == 'none' else None) ax.plot(xvalues, yvalues, color, marker=marker, linestyle=line, label=colLabel) else: ax.scatter(xvalues, yvalues, c=color, label=col, alpha=0.5) ax.legend() return plotter
[docs]class PlotData: """ Small wrapper around table data such as: sqlite or metadata files. """ def __init__(self, fileName, tableName, orderColumn, orderDirection): self._orderColumn = orderColumn self._orderDirection = orderDirection if fileName.endswith(".db") or fileName.endswith(".sqlite"): self._table = self._loadSet(fileName, tableName) self.getColumnValues = self._getValuesFromSet self.getSize = self._table.getSize else: # assume a metadata file self._table = self._loadMd(fileName, tableName) self.getColumnValues = self._getValuesFromMd self.getSize = self._table.size def _loadSet(self, dbName, dbPreffix): from pyworkflow.mapper.sqlite import SqliteFlatDb db = SqliteFlatDb(dbName=dbName, tablePrefix=dbPreffix) if dbPreffix: setClassName = "SetOf%ss" % db.getSelfClassName() else: setClassName = db.getProperty('self') # get the set class name # FIXME: Check why the import is here from pwem import Domain setObj = Domain.getObjects()[setClassName](filename=dbName, prefix=dbPreffix) return setObj def _getValuesFromSet(self, columnName): return [self._getValue(obj, columnName) for obj in self._table.iterItems(orderBy=self._orderColumn, direction=self._orderDirection)] @staticmethod def _removeInfinites(values): newValues = [] for value in values: if isinstance(value, numbers.Number) and value < float("Inf"): newValues.append(value) return newValues def _loadMd(self, fileName, tableName): label = md.str2Label(self._orderColumn) tableMd = md.MetaData('%s@%s' % (tableName, fileName)) tableMd.sort(label) # FIXME: use order direction # TODO: sort metadata by self._orderColumn return tableMd def _getValuesFromMd(self, columnName): label = md.str2Label(columnName) return [self._table.getValue(label, objId) for objId in self._table] def _getValue(self, obj, column): if column == 'id': return obj.getObjId() return obj.getNestedValue(column)