Source code for

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
This module provides objects for extracting timing data from the ABINIT output files
It also provides tools to analye and to visualize the parallel efficiency.

import sys
import os
import collections
import numpy as np

from monty.string import is_string, list_strings
from pymatgen.util.num import minloc
from pymatgen.util.plotting import add_fig_kwargs, get_ax_fig_plt

import logging

logger = logging.getLogger(__name__)

[docs]def alternate(*iterables): """ [a[0], b[0], ... , a[1], b[1], ..., a[n], b[n] ...] >>> alternate([1,4], [2,5], [3,6]) [1, 2, 3, 4, 5, 6] """ items = [] for tup in zip(*iterables): items.extend([item for item in tup]) return items
[docs]class AbinitTimerParserError(Exception): """Errors raised by AbinitTimerParser"""
[docs]class AbinitTimerParser( """ Responsible for parsing a list of output files, extracting the timing results and analyzing the results. Assume the Abinit output files have been produced with `timopt -1`. Example: parser = AbinitTimerParser() parser.parse(list_of_files) To analyze all *.abo files withing top, use: parser, paths, okfiles = AbinitTimerParser.walk(top=".", ext=".abo") """ # The markers enclosing the data. BEGIN_TAG = "-<BEGIN_TIMER" END_TAG = "-<END_TIMER>" Error = AbinitTimerParserError # DEFAULT_MPI_RANK = "0"
[docs] @classmethod def walk(cls, top=".", ext=".abo"): """ Scan directory tree starting from top, look for files with extension `ext` and parse timing data. Return: (parser, paths, okfiles) where `parser` is the new object, `paths` is the list of files found and `okfiles` is the list of files that have been parsed successfully. (okfiles == paths) if all files have been parsed. """ paths = [] for root, dirs, files in os.walk(top): for f in files: if f.endswith(ext): paths.append(os.path.join(root, f)) parser = cls() okfiles = parser.parse(paths) return parser, paths, okfiles
def __init__(self): """Initialize object.""" # List of files that have been parsed. self._filenames = [] # timers[filename][mpi_rank] # contains the timer extracted from the file filename associated to the MPI rank mpi_rank. self._timers = collections.OrderedDict() def __iter__(self): return self._timers.__iter__() def __len__(self): return len(self._timers) @property def filenames(self): """List of files that have been parsed successfully.""" return self._filenames
[docs] def parse(self, filenames): """ Read and parse a filename or a list of filenames. Files that cannot be opened are ignored. A single filename may also be given. Return: list of successfully read files. """ filenames = list_strings(filenames) read_ok = [] for fname in filenames: try: fh = open(fname) except IOError: logger.warning("Cannot open file %s" % fname) continue try: self._read(fh, fname) read_ok.append(fname) except self.Error as e: logger.warning("exception while parsing file %s:\n%s" % (fname, str(e))) continue finally: fh.close() # Add read_ok to the list of files that have been parsed. self._filenames.extend(read_ok) return read_ok
def _read(self, fh, fname): """Parse the TIMER section""" if fname in self._timers: raise self.Error("Cannot overwrite timer associated to: %s " % fname) def parse_line(line): """Parse single line.""" name, vals = line[:25], line[25:].split() try: ctime, cfract, wtime, wfract, ncalls, gflops = vals except ValueError: # v8.3 Added two columns at the end [Speedup, Efficacity] ctime, cfract, wtime, wfract, ncalls, gflops, speedup, eff = vals return AbinitTimerSection(name, ctime, cfract, wtime, wfract, ncalls, gflops) data = {} inside, has_timer = 0, False for line in fh: # print(line.strip()) if line.startswith(self.BEGIN_TAG): has_timer = True sections = [] info = {} inside = 1 line = line[len(self.BEGIN_TAG):].strip()[:-1] info["fname"] = fname for tok in line.split(","): key, val = [s.strip() for s in tok.split("=")] info[key] = val elif line.startswith(self.END_TAG): inside = 0 timer = AbinitTimer(sections, info, cpu_time, wall_time) mpi_rank = info["mpi_rank"] data[mpi_rank] = timer elif inside: inside += 1 line = line[1:].strip() if inside == 2: d = dict() for tok in line.split(","): key, val = [s.strip() for s in tok.split("=")] d[key] = float(val) cpu_time, wall_time = d["cpu_time"], d["wall_time"] elif inside > 5: sections.append(parse_line(line)) else: try: parse_line(line) except Exception: parser_failed = True if not parser_failed: raise self.Error("line should be empty: " + str(inside) + line) if not has_timer: raise self.Error("%s: No timer section found" % fname) # Add it to the dict self._timers[fname] = data
[docs] def timers(self, filename=None, mpi_rank="0"): """ Return the list of timers associated to the given `filename` and MPI rank mpi_rank. """ if filename is not None: return [self._timers[filename][mpi_rank]] else: return [self._timers[filename][mpi_rank] for filename in self._filenames]
[docs] def section_names(self, ordkey="wall_time"): """ Return the names of sections ordered by ordkey. For the time being, the values are taken from the first timer. """ section_names = [] # FIXME this is not trivial for idx, timer in enumerate(self.timers()): if idx == 0: section_names = [ for s in timer.order_sections(ordkey)] # check = section_names # else: # new_set = set( [ for s in timer.order_sections(ordkey)]) # section_names.intersection_update(new_set) # check = check.union(new_set) # if check != section_names: # print("sections", section_names) # print("check",check) return section_names
[docs] def get_sections(self, section_name): """ Return the list of sections stored in self.timers() given `section_name` A fake section is returned if the timer does not have section_name. """ sections = [] for timer in self.timers(): for sect in timer.sections: if == section_name: sections.append(sect) break else: sections.append(AbinitTimerSection.fake()) return sections
[docs] def pefficiency(self): """ Analyze the parallel efficiency. Return: :class:`ParallelEfficiency` object. """ timers = self.timers() # Number of CPUs employed in each calculation. ncpus = [timer.ncpus for timer in timers] # Find the minimum number of cpus used and its index in timers. min_idx = minloc(ncpus) min_ncpus = ncpus[min_idx] # Reference timer ref_t = timers[min_idx] # Compute the parallel efficiency (total and section efficiency) peff = {} ctime_peff = [(min_ncpus * ref_t.wall_time) / (t.wall_time * ncp) for (t, ncp) in zip(timers, ncpus)] wtime_peff = [(min_ncpus * ref_t.cpu_time) / (t.cpu_time * ncp) for (t, ncp) in zip(timers, ncpus)] n = len(timers) peff["total"] = {} peff["total"]["cpu_time"] = ctime_peff peff["total"]["wall_time"] = wtime_peff peff["total"]["cpu_fract"] = n * [100] peff["total"]["wall_fract"] = n * [100] for sect_name in self.section_names(): # print(sect_name) ref_sect = ref_t.get_section(sect_name) sects = [t.get_section(sect_name) for t in timers] try: ctime_peff = [(min_ncpus * ref_sect.cpu_time) / (s.cpu_time * ncp) for (s, ncp) in zip(sects, ncpus)] wtime_peff = [(min_ncpus * ref_sect.wall_time) / (s.wall_time * ncp) for (s, ncp) in zip(sects, ncpus)] except ZeroDivisionError: ctime_peff = n * [-1] wtime_peff = n * [-1] assert sect_name not in peff peff[sect_name] = {} peff[sect_name]["cpu_time"] = ctime_peff peff[sect_name]["wall_time"] = wtime_peff peff[sect_name]["cpu_fract"] = [s.cpu_fract for s in sects] peff[sect_name]["wall_fract"] = [s.wall_fract for s in sects] return ParallelEfficiency(self._filenames, min_idx, peff)
[docs] def summarize(self, **kwargs): """ Return pandas DataFrame with the most important results stored in the timers. """ import pandas as pd colnames = ["fname", "wall_time", "cpu_time", "mpi_nprocs", "omp_nthreads", "mpi_rank"] frame = pd.DataFrame(columns=colnames) for i, timer in enumerate(self.timers()): frame = frame.append({k: getattr(timer, k) for k in colnames}, ignore_index=True) frame["tot_ncpus"] = frame["mpi_nprocs"] * frame["omp_nthreads"] # Compute parallel efficiency (use the run with min number of cpus to normalize). i = frame["tot_ncpus"].values.argmin() ref_wtime = frame.iloc[i]["wall_time"] ref_ncpus = frame.iloc[i]["tot_ncpus"] frame["peff"] = (ref_ncpus * ref_wtime) / (frame["wall_time"] * frame["tot_ncpus"]) return frame
[docs] @add_fig_kwargs def plot_efficiency(self, key="wall_time", what="good+bad", nmax=5, ax=None, **kwargs): """ Plot the parallel efficiency Args: key: Parallel efficiency is computed using the wall_time. what: Specifies what to plot: `good` for sections with good parallel efficiency. `bad` for sections with bad efficiency. Options can be concatenated with `+`. nmax: Maximum number of entries in plot ax: matplotlib :class:`Axes` or None if a new figure should be created. ================ ==================================================== kwargs Meaning ================ ==================================================== linewidth matplotlib linewidth. Default: 2.0 markersize matplotlib markersize. Default: 10 ================ ==================================================== Returns: `matplotlib` figure """ ax, fig, plt = get_ax_fig_plt(ax=ax) lw = kwargs.pop("linewidth", 2.0) msize = kwargs.pop("markersize", 10) what = what.split("+") timers = self.timers() peff = self.pefficiency() n = len(timers) xx = np.arange(n) # ax.set_color_cycle(['g', 'b', 'c', 'm', 'y', 'k']) ax.set_prop_cycle(color=['g', 'b', 'c', 'm', 'y', 'k']) lines, legend_entries = [], [] # Plot sections with good efficiency. if "good" in what: good = peff.good_sections(key=key, nmax=nmax) for g in good: # print(g, peff[g]) yy = peff[g][key] line, = ax.plot(xx, yy, "-->", linewidth=lw, markersize=msize) lines.append(line) legend_entries.append(g) # Plot sections with bad efficiency. if "bad" in what: bad = peff.bad_sections(key=key, nmax=nmax) for b in bad: # print(b, peff[b]) yy = peff[b][key] line, = ax.plot(xx, yy, "-.<", linewidth=lw, markersize=msize) lines.append(line) legend_entries.append(b) # Add total if not already done if "total" not in legend_entries: yy = peff["total"][key] total_line, = ax.plot(xx, yy, "r", linewidth=lw, markersize=msize) lines.append(total_line) legend_entries.append("total") ax.legend(lines, legend_entries, loc="best", shadow=True) # ax.set_title(title) ax.set_xlabel('Total_NCPUs') ax.set_ylabel('Efficiency') ax.grid(True) # Set xticks and labels. labels = ["MPI=%d, OMP=%d" % (t.mpi_nprocs, t.omp_nthreads) for t in timers] ax.set_xticks(xx) ax.set_xticklabels(labels, fontdict=None, minor=False, rotation=15) return fig
[docs] @add_fig_kwargs def plot_pie(self, key="wall_time", minfract=0.05, **kwargs): """ Plot pie charts of the different timers. Args: key: Keyword used to extract data from timers. minfract: Don't show sections whose relative weight is less that minfract. Returns: `matplotlib` figure """ timers = self.timers() n = len(timers) # Make square figures and axes import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec fig = plt.gcf() gspec = GridSpec(n, 1) for idx, timer in enumerate(timers): ax = plt.subplot(gspec[idx, 0]) ax.set_title(str(timer)) timer.pie(ax=ax, key=key, minfract=minfract, show=False) return fig
[docs] @add_fig_kwargs def plot_stacked_hist(self, key="wall_time", nmax=5, ax=None, **kwargs): """ Plot stacked histogram of the different timers. Args: key: Keyword used to extract data from the timers. Only the first `nmax` sections with largest value are show. mmax: Maximum nuber of sections to show. Other entries are grouped together in the `others` section. ax: matplotlib :class:`Axes` or None if a new figure should be created. Returns: `matplotlib` figure """ ax, fig, plt = get_ax_fig_plt(ax=ax) mpi_rank = "0" timers = self.timers(mpi_rank=mpi_rank) n = len(timers) names, values = [], [] rest = np.zeros(n) for idx, sname in enumerate(self.section_names(ordkey=key)): sections = self.get_sections(sname) svals = np.asarray([s.__dict__[key] for s in sections]) if idx < nmax: names.append(sname) values.append(svals) else: rest += svals names.append("others (nmax=%d)" % nmax) values.append(rest) # The dataset is stored in values. Now create the stacked histogram. ind = np.arange(n) # the locations for the groups width = 0.35 # the width of the bars colors = nmax * ['r', 'g', 'b', 'c', 'k', 'y', 'm'] bars = [] bottom = np.zeros(n) for idx, vals in enumerate(values): color = colors[idx] bar =, vals, width, color=color, bottom=bottom) bars.append(bar) bottom += vals ax.set_ylabel(key) ax.set_title("Stacked histogram with the %d most important sections" % nmax) ticks = ind + width / 2.0 labels = ["MPI=%d, OMP=%d" % (t.mpi_nprocs, t.omp_nthreads) for t in timers] ax.set_xticks(ticks) ax.set_xticklabels(labels, rotation=15) # Add legend. ax.legend([bar[0] for bar in bars], names, loc="best") return fig
[docs] def plot_all(self, show=True, **kwargs): """ Call all plot methods provided by the parser. """ figs = [] app = figs.append app(self.plot_stacked_hist(show=show)) app(self.plot_efficiency(show=show)) app(self.plot_pie(show=show)) return figs
[docs]class ParallelEfficiency(dict): """ Store results concerning the parallel efficiency of the job. """ def __init__(self, filenames, ref_idx, *args, **kwargs): """ Args: filennames: List of filenames ref_idx: Index of the Reference time (calculation done with the smallest number of cpus) """ self.update(*args, **kwargs) self.filenames = filenames self._ref_idx = ref_idx def _order_by_peff(self, key, criterion, reverse=True): self.estimator = { "min": min, "max": max, "mean": lambda items: sum(items) / len(items), }[criterion] data = [] for (sect_name, peff) in self.items(): # Ignore values where we had a division by zero. if all([v != -1 for v in peff[key]]): values = peff[key][:] # print(sect_name, values) if len(values) > 1: ref_value = values.pop(self._ref_idx) assert ref_value == 1.0 data.append((sect_name, self.estimator(values))) data.sort(key=lambda t: t[1], reverse=reverse) return tuple([sect_name for (sect_name, e) in data])
[docs] def totable(self, stop=None, reverse=True): """ Return table (list of lists) with timing results. Args: stop: Include results up to stop. None for all reverse: Put items with highest wall_time in first positions if True. """ osects = self._order_by_peff("wall_time", criterion="mean", reverse=reverse) if stop is not None: osects = osects[:stop] n = len(self.filenames) table = [["AbinitTimerSection"] + alternate(self.filenames, n * ["%"])] for sect_name in osects: peff = self[sect_name]["wall_time"] fract = self[sect_name]["wall_fract"] vals = alternate(peff, fract) table.append([sect_name] + ["%.2f" % val for val in vals]) return table
[docs] def good_sections(self, key="wall_time", criterion="mean", nmax=5): """ Return first `nmax` sections with best value of key `key` using criterion `criterion`. """ good_sections = self._order_by_peff(key, criterion=criterion) return good_sections[:nmax]
[docs] def bad_sections(self, key="wall_time", criterion="mean", nmax=5): """ Return first `nmax` sections with worst value of key `key` using criterion `criterion`. """ bad_sections = self._order_by_peff(key, criterion=criterion, reverse=False) return bad_sections[:nmax]
[docs]class AbinitTimerSection: """Record with the timing results associated to a section of code.""" STR_FIELDS = [ "name" ] NUMERIC_FIELDS = [ "wall_time", "wall_fract", "cpu_time", "cpu_fract", "ncalls", "gflops", ] FIELDS = tuple(STR_FIELDS + NUMERIC_FIELDS)
[docs] @classmethod def fake(cls): """Return a fake section. Mainly used to fill missing entries if needed.""" return AbinitTimerSection("fake", 0.0, 0.0, 0.0, 0.0, -1, 0.0)
def __init__(self, name, cpu_time, cpu_fract, wall_time, wall_fract, ncalls, gflops): """ Args: name: Name of the sections. cpu_time: CPU time in seconds. cpu_fract: Percentage of CPU time. wall_time: Wall-time in seconds. wall_fract: Percentage of wall-time. ncalls: Number of calls gflops: Gigaflops. """ = name.strip() self.cpu_time = float(cpu_time) self.cpu_fract = float(cpu_fract) self.wall_time = float(wall_time) self.wall_fract = float(wall_fract) self.ncalls = int(ncalls) self.gflops = float(gflops)
[docs] def to_tuple(self): """Convert object to tuple.""" return tuple([self.__dict__[at] for at in AbinitTimerSection.FIELDS])
[docs] def to_dict(self): """Convert object to dictionary.""" return {at: self.__dict__[at] for at in AbinitTimerSection.FIELDS}
[docs] def to_csvline(self, with_header=False): """Return a string with data in CSV format. Add header if `with_header`""" string = "" if with_header: string += "# " + " ".join(at for at in AbinitTimerSection.FIELDS) + "\n" string += ", ".join(str(v) for v in self.to_tuple()) + "\n" return string
def __str__(self): """String representation.""" string = "" for a in AbinitTimerSection.FIELDS: string += a + " = " + self.__dict__[a] + "," return string[:-1]
[docs]class AbinitTimer: """Container class storing the timing results.""" def __init__(self, sections, info, cpu_time, wall_time): """ Args: sections: List of sections info: Dictionary with extra info. cpu_time: Cpu-time in seconds. wall_time: Wall-time in seconds. """ # Store sections and names self.sections = tuple(sections) self.section_names = tuple([ for s in self.sections]) = info self.cpu_time = float(cpu_time) self.wall_time = float(wall_time) self.mpi_nprocs = int(info["mpi_nprocs"]) self.omp_nthreads = int(info["omp_nthreads"]) self.mpi_rank = info["mpi_rank"].strip() self.fname = info["fname"].strip() def __str__(self): string = "file=%s, wall_time=%.1f, mpi_nprocs=%d, omp_nthreads=%d" % ( self.fname, self.wall_time, self.mpi_nprocs, self.omp_nthreads) # string += ", rank = " + self.mpi_rank return string def __cmp__(self, other): return cmp(self.wall_time, other.wall_time) @property def ncpus(self): """Total number of CPUs employed.""" return self.mpi_nprocs * self.omp_nthreads
[docs] def get_section(self, section_name): """Return section associated to `section_name`.""" try: idx = self.section_names.index(section_name) except Exception: raise sect = self.sections[idx] assert == section_name return sect
[docs] def to_csv(self, fileobj=sys.stdout): """Write data on file fileobj using CSV format.""" openclose = is_string(fileobj) if openclose: fileobj = open(fileobj, "w") for idx, section in enumerate(self.sections): fileobj.write(section.to_csvline(with_header=(idx == 0))) fileobj.flush() if openclose: fileobj.close()
[docs] def to_table(self, sort_key="wall_time", stop=None): """Return a table (list of lists) with timer data""" table = [list(AbinitTimerSection.FIELDS), ] ord_sections = self.order_sections(sort_key) if stop is not None: ord_sections = ord_sections[:stop] for osect in ord_sections: row = [str(item) for item in osect.to_tuple()] table.append(row) return table
# Maintain old API totable = to_table
[docs] def get_dataframe(self, sort_key="wall_time", **kwargs): """ Return a pandas DataFrame with entries sorted according to `sort_key`. """ import pandas as pd frame = pd.DataFrame(columns=AbinitTimerSection.FIELDS) for osect in self.order_sections(sort_key): frame = frame.append(osect.to_dict(), ignore_index=True) # Monkey patch = frame.cpu_time = self.cpu_time frame.wall_time = self.wall_time frame.mpi_nprocs = self.mpi_nprocs frame.omp_nthreads = self.omp_nthreads frame.mpi_rank = self.mpi_rank frame.fname = self.fname return frame
[docs] def get_values(self, keys): """ Return a list of values associated to a particular list of keys. """ if is_string(keys): return [s.__dict__[keys] for s in self.sections] else: values = [] for k in keys: values.append([s.__dict__[k] for s in self.sections]) return values
[docs] def names_and_values(self, key, minval=None, minfract=None, sorted=True): """ Select the entries whose value[key] is >= minval or whose fraction[key] is >= minfract Return the names of the sections and the corresponding values. """ values = self.get_values(key) names = self.get_values("name") new_names, new_values = [], [] other_val = 0.0 if minval is not None: assert minfract is None for n, v in zip(names, values): if v >= minval: new_names.append(n) new_values.append(v) else: other_val += v new_names.append("below minval " + str(minval)) new_values.append(other_val) elif minfract is not None: assert minval is None total = self.sum_sections(key) for n, v in zip(names, values): if v / total >= minfract: new_names.append(n) new_values.append(v) else: other_val += v new_names.append("below minfract " + str(minfract)) new_values.append(other_val) else: # all values new_names, new_values = names, values if sorted: # Sort new_values and rearrange new_names. nandv = [nv for nv in zip(new_names, new_values)] nandv.sort(key=lambda t: t[1]) new_names, new_values = [n[0] for n in nandv], [n[1] for n in nandv] return new_names, new_values
def _reduce_sections(self, keys, operator): return operator(self.get_values(keys))
[docs] def sum_sections(self, keys): """Sum value of keys.""" return self._reduce_sections(keys, sum)
[docs] def order_sections(self, key, reverse=True): """Sort sections according to the value of key.""" return sorted(self.sections, key=lambda s: s.__dict__[key], reverse=reverse)
[docs] @add_fig_kwargs def cpuwall_histogram(self, ax=None, **kwargs): """ Plot histogram with cpu- and wall-time on axis `ax`. Args: ax: matplotlib :class:`Axes` or None if a new figure should be created. Returns: `matplotlib` figure """ ax, fig, plt = get_ax_fig_plt(ax=ax) nk = len(self.sections) ind = np.arange(nk) # the x locations for the groups width = 0.35 # the width of the bars cpu_times = self.get_values("cpu_time") rects1 =, cpu_times, width, color='r') wall_times = self.get_values("wall_time") rects2 = + width, wall_times, width, color='y') # Add ylable and title ax.set_ylabel('Time (s)') # plt.title('CPU-time and Wall-time for the different sections of the code') ticks = self.get_values("name") ax.set_xticks(ind + width, ticks) ax.legend((rects1[0], rects2[0]), ('CPU', 'Wall'), loc="best") return fig
[docs] @add_fig_kwargs def pie(self, key="wall_time", minfract=0.05, ax=None, **kwargs): """ Plot pie chart for this timer. Args: key: Keyword used to extract data from the timer. minfract: Don't show sections whose relative weight is less that minfract. ax: matplotlib :class:`Axes` or None if a new figure should be created. Returns: `matplotlib` figure """ ax, fig, plt = get_ax_fig_plt(ax=ax) # Set aspect ratio to be equal so that pie is drawn as a circle. ax.axis("equal") # Don't show section whose value is less that minfract labels, vals = self.names_and_values(key, minfract=minfract) ax.pie(vals, explode=None, labels=labels, autopct='%1.1f%%', shadow=True) return fig
[docs] @add_fig_kwargs def scatter_hist(self, ax=None, **kwargs): """ Scatter plot + histogram. Args: ax: matplotlib :class:`Axes` or None if a new figure should be created. Returns: `matplotlib` figure """ from mpl_toolkits.axes_grid1 import make_axes_locatable ax, fig, plt = get_ax_fig_plt(ax=ax) x = np.asarray(self.get_values("cpu_time")) y = np.asarray(self.get_values("wall_time")) # the scatter plot: axScatter = plt.subplot(1, 1, 1) axScatter.scatter(x, y) axScatter.set_aspect("auto") # create new axes on the right and on the top of the current axes # The first argument of the new_vertical(new_horizontal) method is # the height (width) of the axes to be created in inches. divider = make_axes_locatable(axScatter) axHistx = divider.append_axes("top", 1.2, pad=0.1, sharex=axScatter) axHisty = divider.append_axes("right", 1.2, pad=0.1, sharey=axScatter) # make some labels invisible plt.setp(axHistx.get_xticklabels() + axHisty.get_yticklabels(), visible=False) # now determine nice limits by hand: binwidth = 0.25 xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))]) lim = (int(xymax / binwidth) + 1) * binwidth bins = np.arange(-lim, lim + binwidth, binwidth) axHistx.hist(x, bins=bins) axHisty.hist(y, bins=bins, orientation='horizontal') # the xaxis of axHistx and yaxis of axHisty are shared with axScatter, # thus there is no need to manually adjust the xlim and ylim of these axis. # axHistx.axis["bottom"].major_ticklabels.set_visible(False) for tl in axHistx.get_xticklabels(): tl.set_visible(False) axHistx.set_yticks([0, 50, 100]) # axHisty.axis["left"].major_ticklabels.set_visible(False) for tl in axHisty.get_yticklabels(): tl.set_visible(False) axHisty.set_xticks([0, 50, 100]) # plt.draw() return fig