Source code for ase.visualize.primiplotter

# flake8: noqa
"""An experimental package for making plots during a simulation.

A PrimiPlotter can plot a list of atoms on one or more output devices.

import collections
import os
import sys
import time
import weakref
from math import pi
from typing import Optional

import numpy as np

from ase.utils import warn_legacy
from ase.visualize.colortable import color_table

class PrimiPlotterBase:
    "Base class for PrimiPlotter and Povrayplotter."
    #def set_dimensions(self, dims):
    #    "Set the size of the canvas (a 2-tuple)."
    #    self.dims = dims

    def set_rotation(self, rotation):
        "Set the rotation angles (in degrees)."
        self.angles[:] = np.array(rotation) * (pi/180)

    def set_radii(self, radii):
        """Set the atomic radii.  Give an array or a single number."""
        self.radius = radii

    def set_colors(self, colors):
        """Explicitly set the colors of the atoms.

        The colors can either be a dictionary mapping tags to colors
        or an array of colors, one per atom.

        Each color is specified as a greyscale value from 0.0 to 1.0
        or as three RGB values from 0.0 to 1.0.
        self.colors = colors

    def set_color_function(self, colors):
        """Set a color function, to be used to color the atoms."""
        if isinstance(colors, collections.Callable):
            self.colorfunction = colors
            raise TypeError("The color function is not callable.")

    def set_invisible(self, inv):
        """Choose invisible atoms."""
        self.invisible = inv

    def set_invisibility_function(self, invfunc):
        """Set an invisibility function."""
        if isinstance(invfunc, collections.Callable):
            self.invisibilityfunction = invfunc
            raise TypeError("The invisibility function is not callable.")

    def set_cut(self, xmin=None, xmax=None, ymin=None, ymax=None,
                zmin=None, zmax=None):
        self.cut = {"xmin":xmin, "xmax":xmax, "ymin":ymin, "ymax":ymax,
                    "zmin":zmin, "zmax":zmax}

    def update(self, newatoms = None):
        """Cause a plot (respecting the interval setting).

        update causes a plot to be made.  If the interval variable was
        specified when the plotter was create, it will only produce a
        plot with that interval.  update takes an optional argument,
        newatoms, which can be used to replace the list of atoms with
        a new one.
        if newatoms is not None:
            self.atoms = newatoms
        if self.skipnext <= 0:
            self.skipnext = self.interval
        self.skipnext -= 1

    def set_log(self, log):
        """Sets a file for logging.

        log may be an open file or a filename.
        if hasattr(log, "write"):
            self.logfile = log
            self.ownlogfile = False
            self.logfile = open(log, "w")
            self.ownlogfile = True

    def log(self, message):
        """logs a message to the file set by set_log."""
        if self.logfile is not None:

    def _verb(self, txt):
        if self.verbose:

    def _starttimer(self):
        self.starttime = time.time()

    def _stoptimer(self):
        elapsedtime = time.time() - self.starttime
        self.totaltime = self.totaltime + elapsedtime
        print("plotting time %s sec (total %s sec)" % (elapsedtime,

    def _getpositions(self):
        return self.atoms.get_positions()

    def _getradii(self):
        if self.radius is not None:
            if hasattr(self.radius, "shape"):
                return self.radius   # User has specified an array
                return self.radius * np.ones(len(self.atoms), float)
        # No radii specified.  Try getting them from the atoms.
            return self.atoms.get_atomic_radii()
        except AttributeError:
                z = self._getatomicnumbers()
            except AttributeError:
        # No radius available.  Defaulting to 1.0
        return np.ones(len(self.atoms), float)

    def _getatomicnumbers(self):
        return self.atoms.get_atomic_numbers()

    def _getcolors(self):
        # Try any explicitly given colors
        if self.colors is not None:
            if isinstance(self.colors, type({})):
                self.log("Explicit colors dictionary")
                return _colorsfromdict(self.colors,
                self.log("Explicit colors")
                return self.colors
        # Try the color function, if given
        if self.colorfunction is not None:
            self.log("Calling color function.")
            return self.colorfunction(self.atoms)
        # Maybe the atoms know their own colors
            c = self.atoms.get_colors()
        except AttributeError:
            c = None
        if c is not None:
            if isinstance(c, type({})):
                self.log("Color dictionary from atoms.get_colors()")
                return _colorsfromdict(c, np.asarray(self.atoms.get_tags(),int))
                self.log("Colors from atoms.get_colors()")
                return c
        # Default to white atoms
        self.log("No colors: using white")
        return np.ones(len(self.atoms), float)

    def _getinvisible(self):
        if self.invisible is not None:
            inv = self.invisible
            inv = np.zeros(len(self.atoms))
        if self.invisibilityfunction:
            inv = np.logical_or(inv, self.invisibilityfunction(self.atoms))
        r = self._getpositions()
        if len(r) > len(inv):
            # This will happen in parallel simulations due to ghost atoms.
            # They are invisible.  Hmm, this may cause trouble.
            i2 = np.ones(len(r))
            i2[:len(inv)] = inv
            inv = i2
            del i2
        if self.cut["xmin"] is not None:
            inv = np.logical_or(inv, np.less(r[:,0], self.cut["xmin"]))
        if self.cut["xmax"] is not None:
            inv = np.logical_or(inv, np.greater(r[:,0], self.cut["xmax"]))
        if self.cut["ymin"] is not None:
            inv = np.logical_or(inv, np.less(r[:,1], self.cut["ymin"]))
        if self.cut["ymax"] is not None:
            inv = np.logical_or(inv, np.greater(r[:,1], self.cut["ymax"]))
        if self.cut["zmin"] is not None:
            inv = np.logical_or(inv, np.less(r[:,2], self.cut["zmin"]))
        if self.cut["zmax"] is not None:
            inv = np.logical_or(inv, np.greater(r[:,2], self.cut["zmax"]))
        return inv

    def __del__(self):
        if self.ownlogfile:

[docs]class PrimiPlotter(PrimiPlotterBase): """Primitive PostScript-based plots during a simulation. The PrimiPlotter plots atoms during simulations, extracting the relevant information from the list of atoms. It is created using the list of atoms as an argument to the constructor. Then one or more output devices must be attached using set_output(device). The list of supported output devices is at the end. The atoms are plotted as circles. The system is first rotated using the angles specified by set_rotation([vx, vy, vz]). The rotation is vx degrees around the x axis (positive from the y toward the z axis), then vy degrees around the y axis (from x toward z), then vz degrees around the z axis (from x toward y). The rotation matrix is the same as the one used by RasMol. Per default, the system is scaled so it fits within the canvas (autoscale mode). Autoscale mode is enabled and disables using autoscale("on") or autoscale("off"). A manual scale factor can be set with set_scale(scale), this implies autoscale("off"). The scale factor (from the last autoscale event or from set_scale) can be obtained with get_scale(). Finally, an explicit autoscaling can be triggered with autoscale("now"), this is mainly useful before calling get_scale or before disabling further autoscaling. Finally, a relative scaling factor can be set with SetRelativeScaling(), it is multiplied to the usual scale factor (from autoscale or from set_scale). This is probably only useful in connection with autoscaling. The radii of the atoms are obtained from the first of the following methods which work: 1. If the radii are specified using PrimiPlotter.set_radii(r), they are used. Must be an array, or a single number. 2. If the atoms has a get_atomic_radii() method, it is used. This is unlikely. 3. If the atoms has a get_atomic_numbers() method, the corresponding covalent radii are extracted from the ASE.ChemicalElements module. 4. If all else fails, the radius is set to 1.0 Angstrom. The atoms are colored using the first of the following methods which work. 1. If colors are explicitly set using PrimiPlotter.set_colors(), they are used. 2. If these colors are specified as a dictionary, the tags (from atoms.get_tags()) are used as an index into the dictionary to get the actual colors of the atoms. 3. If a color function has been set using PrimiPlotter.set_color_function(), it is called with the atoms as an argument, and is expected to return an array of colors. 4. If the atoms have a get_colors() method, it is used to get the colors. 5. If these colors are specified as a dictionary, the tags (from atoms.get_tags()) are used as an index into the dictionary to get the actual colors of the atoms. 6. If all else fails, the atoms will be white. The colors are specified as an array of colors, one color per atom. Each color is either a real number from 0.0 to 1.0, specifying a grayscale (0.0 = black, 1.0 = white), or an array of three numbers from 0.0 to 1.0, specifying RGB values. The colors of all atoms are thus a Numerical Python N-vector or a 3xN matrix. In cases 1a and 3a above, the keys of the dictionary are integers, and the values are either numbers (grayscales) or 3-vectors (RGB values), or strings with X11 color names, which are then translated to RGB values. Only in case 1a and 3a are strings recognized as colors. Some atoms may be invisible, and thus left out of the plot. Invisible atoms are determined from the following algorithm. Unlike the radius or the coloring, all points below are tried and if an atom is invisible by any criterion, it is left out of the plot. 1. All atoms are visible. 2. If PrimiPlotter.set_invisible() has be used to specify invisible atoms, any atoms for which the value is non-zero becomes invisible. 3. If an invisiblility function has been set with PrimiPlotter.set_invisibility_function(), it is called with the atoms as argument. It is expected to return an integer per atom, any non-zero value makes that atom invisible. 4. If a cut has been specified using set_cut, any atom outside the cut is made invisible. Note that invisible atoms are still included in the algorithm for positioning and scaling the plot. The following output devices are implemented. PostScriptFile(prefix): Create PS files names etc. PnmFile(prefix): Similar, but makes PNM files. GifFile(prefix): Similar, but makes GIF files. JpegFile(prefix): Similar, but makes JPEG files. X11Window(): Show the plot in an X11 window using ghostscript. Output devices writing to files take an extra optional argument to the constructor, compress, specifying if the output file should be gzipped. This is not allowed for some (already compressed) file formats. Instead of a filename prefix, a filename containing a % can be used. In that case the filename is expected to expand to a real filename when used with the Python string formatting operator (%) with the frame number as argument. Avoid generating spaces in the file names: use e.g. %03d instead of %3d. """ def __init__(self, atoms, verbose=0, timing=0, interval=1, initframe=0): """ Parameters to the constructor: atoms: The atoms to be plottet. verbose = 0: Write progress information to stderr. timing = 0: Collect timing information. interval = 1: If specified, a plot is only made every interval'th time update() is called. Deprecated, normally you should use the interval argument when attaching the plotter to e.g. the dynamics. initframe = 0: Initial frame number, i.e. the number of the first plot. """ warn_legacy('PrimiPlotter') self.atoms = atoms self.outputdevice = [] self.angles = np.zeros(3, float) self.dims = (512, 512) self.verbose = verbose self.timing = timing self.totaltime = 0.0 self.radius = None self.colors = None self.colorfunction = None self.n = initframe self.interval = interval self.skipnext = 0 # Number of calls to update before anything happens. self.a_scale = 1 self.relativescale = 1.0 self.invisible = None self.invisibilityfunction = None self.set_cut() # No cut self.isparallel = 0 self.logfile = None self.ownlogfile = False
[docs] def set_output(self, device): "Attach an output device to the plotter." self.outputdevice.append(device) device.set_dimensions(self.dims) device.set_owner(weakref.proxy(self))
[docs] def set_dimensions(self, dims): "Set the size of the canvas (a 2-tuple)." if self.outputdevice: raise RuntimeError("Cannot set dimensions after an output device has been specified.") self.dims = dims
def autoscale(self, mode): if mode == "on": self.a_scale = 1 elif mode == "off": self.a_scale = 0 elif mode == "now": coords = self._rotate(self.atoms.get_positions()) radii = self._getradii() self._autoscale(coords, radii) else: raise ValueError("Unknown autoscale mode: ").with_traceback(+str(mode)) def set_scale(self, scale): self.autoscale("off") self.scale = scale def get_scale(self): return self.scale def set_relative_scale(self, rscale = 1.0): self.relativescale = rscale
[docs] def plot(self): """Create a plot now. Does not respect the interval timer. This method makes a plot unconditionally. It does not look at the interval variable, nor is this plot taken into account in the counting done by the update() method if an interval variable was specified. """ if self.timing: self._starttimer() self.log("PrimiPlotter: Starting plot at " + time.strftime("%a, %d %b %Y %H:%M:%S")) colors = self._getcolors() invisible = self._getinvisible() coords = self._rotate(self._getpositions()) radii = self._getradii() if self.a_scale: self._autoscale(coords,radii) scale = self.scale * self.relativescale coords = scale * coords center = self._getcenter(coords) offset = np.array(self.dims + (0.0,))/2.0 - center coords = coords + offset self.log("Scale is %f and size is (%d, %d)" % (scale, self.dims[0], self.dims[1])) self.log("Physical size of plot is %f Angstrom times %f Angstrom" % (self.dims[0] / scale, self.dims[1] / scale)) self._verb("Sorting.") order = np.argsort(coords[:,2]) coords = coords[order] ### take(coords, order) radii = radii[order] ### take(radii, order) colors = colors[order] ### take(colors, order) invisible = invisible[order] ### take(invisible, order) if self.isparallel: id = np.arange(len(coords))[order] ### take(arange(len(coords)), order) else: id = None radii = radii * scale selector = self._computevisibility(coords, radii, invisible, id) coords = np.compress(selector, coords, 0) radii = np.compress(selector, radii) colors = np.compress(selector, colors, 0) self._makeoutput(scale, coords, radii, colors) self.log("PrimiPlotter: Finished plotting at " + time.strftime("%a, %d %b %Y %H:%M:%S")) self.log("\n\n") if self.timing: self._stoptimer()
def _computevisibility(self, coords, rad, invisible, id, zoom = 1): xy = coords[:,:2] typradius = sum(rad) / len(rad) if typradius < 4.0: self.log("Refining visibility check.") if zoom >= 16: raise RuntimeError("Cannot check visibility - too deep recursion.") return self._computevisibility(xy*2, rad*2, invisible, id, zoom*2) else: self.log("Visibility(r_typ = %.1f pixels)" % (typradius,)) dims = np.array(self.dims) * zoom maxr = int(np.ceil(max(rad))) + 2 canvas = np.zeros((dims[0] + 4*maxr, dims[1] + 4*maxr), np.int8) # Atoms are only invisible if they are within the canvas, or closer # to its edge than their radius visible = (np.greater(xy[:,0], -rad) * np.less(xy[:,0], dims[0]+rad) * np.greater(xy[:,1], -rad) * np.less(xy[:,1], dims[1]+rad) * np.logical_not(invisible)) # Atoms are visible if not hidden behind other atoms xy = np.floor(xy + 2*maxr + 0.5).astype(int) masks = {} for i in range(len(rad)-1, -1, -1): if (i % 100000) == 0 and i: self._verb(str(i)) if not visible[i]: continue x, y = xy[i] r = rad[i] try: mask, invmask, rn = masks[r] except KeyError: rn = int(np.ceil(r)) nmask = 2*rn+1 mask = (np.arange(nmask) - rn)**2 mask = np.less(mask[:,np.newaxis]+mask[np.newaxis,:], r*r).astype(np.int8) invmask = np.equal(mask, 0).astype(np.int8) masks[r] = (mask, invmask, rn) window = np.logical_or(canvas[x-rn:x+rn+1, y-rn:y+rn+1], invmask) hidden = np.alltrue(window.flat) if hidden: visible[i] = 0 else: canvas[x-rn:x+rn+1, y-rn:y+rn+1] = np.logical_or(canvas[x-rn:x+rn+1, y-rn:y+rn+1], mask) self.log("%d visible, %d hidden out of %d" % (sum(visible), len(visible) - sum(visible), len(visible))) return visible def _rotate(self, positions): self.log("Rotation angles: %f %f %f" % tuple(self.angles)) mat =[2], 2), _rot(self.angles[1], 1)), _rot(self.angles[0]+pi, 0)) return, mat) def _getcenter(self, coords): return np.array((max(coords[:,0]) + min(coords[:,0]), max(coords[:,1]) + min(coords[:,1]), 0.0)) / 2.0 def _autoscale(self, coords, radii): x = coords[:,0] y = coords[:,1] maxradius = max(radii) deltax = max(x) - min(x) + 2*maxradius deltay = max(y) - min(y) + 2*maxradius scalex = self.dims[0] / deltax scaley = self.dims[1] / deltay self.scale = 0.95 * min(scalex, scaley) self.log("Autoscale: %f" % self.scale) def _makeoutput(self, scale, coords, radii, colors): for device in self.outputdevice: device.inform_about_scale(scale) device.plot(self.n, coords, radii, colors) self.n = self.n + 1
class ParallelPrimiPlotter(PrimiPlotter): """A version of PrimiPlotter for parallel ASAP simulations. Used like PrimiPlotter, but only the output devices on the master node are used. Most of the processing is distributed on the nodes, but the actual output is only done on the master. See the PrimiPlotter docstring for details. """ def __init__(self, *args, **kwargs): warn_legacy('PrimiPlotter') PrimiPlotter.__init__(self, *args, **kwargs) self.isparallel = 1 import ase.parallel self.mpi = if self.mpi is None: raise RuntimeError("MPI is not available.") self.master = self.mpi.rank == 0 self.mpitag = 42 # Reduce chance of collision with other modules. def set_output(self, device): if self.master: PrimiPlotter.set_output(self, device) def set_log(self, log): if self.master: PrimiPlotter.set_log(self, log) def _getpositions(self): realpos = self.atoms.get_positions() ghostpos = self.atoms.get_ghost_positions() self.numberofrealatoms = len(realpos) self.numberofghostatoms = len(ghostpos) return np.concatenate((realpos, ghostpos)) def _getatomicnumbers(self): realz = self.atoms.get_atomic_numbers() ghostz = self.atoms.get_ghost_atomic_numbers() return np.concatenate((realz, ghostz)) def _getradius(self): r = PrimiPlotter._getradius(self) if len(r) == self.numberofrealatoms + self.numberofghostatoms: # Must have calculated radii from atomic numbers return r else: assert len(r) == self.numberofrealatoms # Heuristic: use minimum r for the ghosts ghostr = min(r) * np.ones(self.numberofghostatoms, float) return np.concatenate((r, ghostr)) def _getcenter(self, coords): # max(x) and min(x) only works for rank-1 arrays in Numeric version 17. maximal = np.maximum.reduce(coords[:,0:2]) minimal = np.minimum.reduce(coords[:,0:2]) self.mpi.max(maximal) self.mpi.min(minimal) maxx, maxy = maximal minx, miny = minimal return np.array([maxx + minx, maxy + miny, 0.0]) / 2.0 def _computevisibility(self, xy, rad, invisible, id, zoom = 1): # Find visible atoms, allowing ghost atoms to hide real atoms. v = PrimiPlotter._computevisibility(self, xy, rad, invisible, id, zoom) # Then remove ghost atoms return v * np.less(id, self.numberofrealatoms) def _autoscale(self, coords, radii): self._verb("Autoscale") n = len(self.atoms) x = coords[:n,0] y = coords[:n,1] assert len(x) == len(self.atoms) maximal = np.array([max(x), max(y), max(radii[:n])]) minimal = np.array([min(x), min(y)]) self.mpi.max(maximal) self.mpi.min(minimal) maxx, maxy, maxradius = maximal minx, miny = minimal deltax = maxx - minx + 2*maxradius deltay = maxy - miny + 2*maxradius scalex = self.dims[0] / deltax scaley = self.dims[1] / deltay self.scale = 0.95 * min(scalex, scaley) self.log("Autoscale: %f" % self.scale) def _getcolors(self): col = PrimiPlotter._getcolors(self) nghost = len(self.atoms.get_ghost_positions()) newcolshape = (nghost + col.shape[0],) + col.shape[1:] newcol = np.zeros(newcolshape, col.dtype) newcol[:len(col)] = col return newcol def _makeoutput(self, scale, coords, radii, colors): if len(colors.shape) == 1: # Greyscales ncol = 1 else: ncol = colors.shape[1] # 1 or 3. assert ncol == 3 # RGB values # If one processor says RGB, all must convert ncolmax = self.mpi.max(ncol) if ncolmax > ncol: assert ncol == 1 colors = colors[:,np.newaxis] + np.zeros(ncolmax)[np.newaxis,:] ncol = ncolmax assert colors.shape == (len(coords), ncol) # Now send data from slaves to master data = np.zeros((len(coords), 4+ncol), float) data[:,:3] = coords data[:,3] = radii if ncol == 1: data[:,4] = colors else: data[:,4:] = colors if not self.master: datashape = np.array(data.shape) assert datashape.shape == (2,) self.mpi.send(datashape, 0, self.mpitag) self.mpi.send(data, 0, self.mpitag) else: total = [data] n = len(coords) colsmin = colsmax = 4+ncol for proc in range(1, self.mpi.size): self._verb("Receiving from processor "+str(proc)) datashape = np.zeros(2, int) self.mpi.receive(datashape, proc, self.mpitag) fdat = np.zeros(tuple(datashape)) self.mpi.receive(fdat, proc, self.mpitag) total.append(fdat) n = n + len(fdat) if fdat.shape[1] < colsmin: colsmin = fdat.shape[1] if fdat.shape[1] > colsmax: colsmax = fdat.shape[1] self._verb("Merging data") # Some processors may have only greyscales whereas others # may have RGB. That will cause difficulties. trouble = colsmax != colsmin data = np.zeros((n, colsmax), float) if trouble: assert data.shape[1] == 7 else: assert data.shape[1] == 7 or data.shape[1] == 5 i = 0 for d in total: if not trouble or d.shape[1] == 7: data[i:i+len(d)] = d else: assert d.shape[1] == 5 data[i:i+len(d), :5] = d data[i:i+len(d), 5] = d[4] data[i:i+len(d), 6] = d[4] i = i + len(d) assert i == len(data) # Now all data is on the master self._verb("Sorting merged data") order = np.argsort(data[:,2]) data = data[order] ### take(data, order) coords = data[:,:3] radii = data[:,3] if data.shape[1] == 5: colors = data[:,4] else: colors = data[:,4:] PrimiPlotter._makeoutput(self, scale, coords, radii, colors) class _PostScriptDevice: """PostScript based output device.""" offset = (0,0) # Will be changed by some classes def __init__(self): self.scale = 1 self.linewidth = 1 self.outline = 1 def set_dimensions(self, dims): self.dims = dims def set_owner(self, owner): self.owner = owner def inform_about_scale(self, scale): self.linewidth = 0.1 * scale def set_outline(self, value): self.outline = value return self # Can chain these calls in set_output() def plot(self, *args, **kargs): self.Doplot(self.PSplot, *args, **kargs) def plotArray(self, *args, **kargs): self.Doplot(self.PSplotArray, *args, **kargs) def PSplot(self, file, n, coords, r, colors, noshowpage=0): xy = coords[:,:2] assert(len(xy) == len(r) and len(xy) == len(colors)) if len(colors.shape) == 1: gray = 1 else: gray = 0 assert(colors.shape[1] == 3) file.write("%!PS-Adobe-2.0\n") file.write("%%Creator: Primiplot\n") file.write("%%Pages: 1\n") file.write("%%%%BoundingBox: %d %d %d %d\n" % (self.offset + (self.offset[0] + self.dims[0], self.offset[1] + self.dims[1]))) file.write("%%EndComments\n") file.write("\n") file.write("% Enforce BoundingBox\n") file.write("%d %d moveto %d 0 rlineto 0 %d rlineto -%d 0 rlineto\n" % ((self.offset + self.dims + (self.dims[0],)))) file.write("closepath clip newpath\n\n") file.write("%f %f scale\n" % (2*(1.0/self.scale,))) file.write("%d %d translate\n" % (self.scale * self.offset[0], self.scale * self.offset[1])) file.write("\n") if gray: if self.outline: file.write("/circ { 0 360 arc gsave setgray fill grestore stroke } def\n") else: file.write("/circ { 0 360 arc setgray fill } def\n") else: if self.outline: file.write("/circ { 0 360 arc gsave setrgbcolor fill grestore stroke } def\n") else: file.write("/circ { 0 360 arc setrgbcolor fill } def\n") file.write("%f setlinewidth 0.0 setgray\n" % (self.linewidth * self.scale,)) if gray: data = np.zeros((len(xy), 4), float) data[:,0] = colors data[:,1:3] = (self.scale * xy) data[:,3] = (self.scale * r) for point in data: file.write("%.3f %.2f %.2f %.2f circ\n" % tuple(point)) else: data = np.zeros((len(xy), 6), float) data[:,0:3] = colors data[:,3:5] = (self.scale * xy) data[:,5] = (self.scale * r) for point in data: file.write("%.3f %.3f %.3f %.2f %.2f %.2f circ\n" % tuple(point)) if not noshowpage: file.write("showpage\n") def PSplotArray(self, file, n, data, noshowpage=0): assert(len(data.shape) == 3) assert(data.shape[0] == self.dims[1] and data.shape[1] == self.dims[0]) data = np.clip((256*data).astype(int), 0, 255) file.write("%!PS-Adobe-2.0\n") file.write("%%Creator: Fieldplotter\n") file.write("%%Pages: 1\n") file.write("%%%%BoundingBox: %d %d %d %d\n" % (self.offset + (self.offset[0] + self.dims[0], self.offset[1] + self.dims[1]))) file.write("%%EndComments\n") file.write("\n") file.write("%d %d translate\n" % self.offset) file.write("%f %f scale\n" % self.dims) file.write("\n") file.write("% String holding a single line\n") file.write("/pictline %d string def\n" %(data.shape[1]*data.shape[2],)) file.write("\n") file.write("%d %d 8\n" % self.dims) file.write("[%d 0 0 %d 0 0]\n" % self.dims) file.write("{currentfile pictline readhexstring pop}\n") file.write("false %d colorimage\n" % (data.shape[2],)) file.write("\n") s = "" for d in data.flat: s += ("%02X" % d) if len(s) >= 72: file.write(s+"\n") s = "" file.write(s+"\n") file.write("\n") if not noshowpage: file.write("showpage\n") class _PostScriptToFile(_PostScriptDevice): """Output device for PS files.""" compr_suffix: Optional[str] = None def __init__(self, prefix, compress = 0): self.compress = compress if "'" in prefix: raise ValueError("Filename may not contain a quote ('): "+prefix) if "%" in prefix: # Assume the user knows what (s)he is doing self.filenames = prefix else: self.filenames = prefix + "%04d" + self.suffix if compress: if self.compr_suffix is None: raise RuntimeError("Compression not supported.") self.filenames = self.filenames + self.compr_suffix _PostScriptDevice.__init__(self) class PostScriptFile(_PostScriptToFile): suffix = ".ps" compr_suffix = ".gz" offset = (50,50) # Inherits __init__ def Doplot(self, plotmethod, n, *args, **kargs): filename = self.filenames % (n,) self.owner.log("Output to PostScript file "+filename) if self.compress: file = os.popen("gzip > '"+filename+"'", "w") else: file = open(filename, "w") plotmethod(*(file, n)+args, **kargs) file.close() class _PS_to_bitmap(_PostScriptToFile): gscmd = "gs -q -sDEVICE={0} -sOutputFile=- -dDEVICEWIDTH=%d -dDEVICEHEIGHT=%d - " # Inherits __init__ def Doplot(self, plotmethod, n, *args, **kargs): filename = self.filenames % (n,) self.owner.log("Output to bitmapped file " + filename) cmd = self.gscmd.format(self.devicename) if self.compress: cmd = cmd + "| gzip " cmd = (cmd+" > '%s'") % (self.dims[0], self.dims[1], filename) file = os.popen(cmd, "w") plotmethod(*(file, n)+args, **kargs) file.close() class PnmFile(_PS_to_bitmap): suffix = ".pnm" devicename = "pnmraw" compr_suffix = ".gz" #class GifFile(_PS_via_PnmFile): # suffix = ".gif" # converter = "| ppmquant -floyd 256 2>/dev/null | ppmtogif 2>/dev/null" class JpegFile(_PS_to_bitmap): suffix = ".jpeg" devicename = "jpeg" class PngFile(_PS_to_bitmap): suffix = ".png" devicename = "png16m" class Png256File(_PS_to_bitmap): suffix = ".png" devicename = "png256" class X11Window(_PostScriptDevice): """Shows the plot in an X11 window.""" #Inherits __init__ gscmd = "gs -q -sDEVICE=x11 -dDEVICEWIDTH=%d -dDEVICEHEIGHT=%d -r72x72 -" def Doplot(self, plotmethod, n, *args, **kargs): self.owner.log("Output to X11 window") try: file = self.pipe self.pipe.write("showpage\n") except AttributeError: filename = self.gscmd % tuple(self.dims) file = os.popen(filename, "w") self.pipe = file kargs["noshowpage"] = 1 plotmethod(*(file, n)+args, **kargs) file.write("flushpage\n") file.flush() # Helper functions def _rot(v, axis): ax1, ax2 = ((1, 2), (0, 2), (0, 1))[axis] c, s = np.cos(v), np.sin(v) m = np.zeros((3,3), float) m[axis,axis] = 1.0 m[ax1,ax1] = c m[ax2,ax2] = c m[ax1,ax2] = s m[ax2,ax1] = -s return m def _colorsfromdict(dict, cls): """Extract colors from dictionary using cls as key.""" assert(isinstance(dict, type({}))) # Allow local modifications, to replace strings with rgb values. dict = dict.copy() isgray, isrgb = 0, 0 for k in dict.keys(): v = dict[k] if isinstance(v, str): v = color_table[v] dict[k] = v try: if len(v) == 3: isrgb = 1 # Assume it is an RGB value if not hasattr(v, "shape"): dict[k] = np.array(v) # Convert to array else: raise RuntimeError("Unrecognized color object "+repr(v)) except TypeError: isgray = 1 # Assume it is a number if isgray and isrgb: # Convert all to RGB for k in dict.keys(): v = dict[k] if not hasattr(v, "shape"): dict[k] = v * np.ones(3, float) # Now the dictionary is ready if isrgb: colors = np.zeros((len(cls),3), float) else: colors = np.zeros((len(cls),), float) for i in range(len(cls)): colors[i] = dict[cls[i]] return colors