#!/usr/bin/env python3 import argparse from datetime import datetime, timedelta from importlib import import_module import io import json import os import re import shutil import sys import tempfile import threading import time import warnings try: from clint.textui import progress except ImportError: print("Warning: clint is not installed, no fancy progressbars in the terminal for you.") progress = None import numpy import yaml if sys.version_info[0] < 3: # OK, ancients, I will support Python 2, but you owe me a beer input = raw_input PB_MESSAGES = { "Burndown": "pb.pb_pb2.BurndownAnalysisResults", "Couples": "pb.pb_pb2.CouplesAnalysisResults", "Shotness": "pb.pb_pb2.ShotnessAnalysisResults", } def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("-o", "--output", default="", help="Path to the output file/directory (empty for display). " "If the extension is JSON, the data is saved instead of " "the real image.") parser.add_argument("-i", "--input", default="-", help="Path to the input file (- for stdin).") parser.add_argument("-f", "--input-format", default="auto", choices=["yaml", "pb", "auto"]) parser.add_argument("--text-size", default=12, type=int, help="Size of the labels and legend.") parser.add_argument("--backend", help="Matplotlib backend to use.") parser.add_argument("--style", choices=["black", "white"], default="black", help="Plot's general color scheme.") parser.add_argument("--size", help="Axes' size in inches, for example \"12,9\"") parser.add_argument("--relative", action="store_true", help="Occupy 100%% height for every measurement.") parser.add_argument("--couples-tmp-dir", help="Temporary directory to work with couples.") parser.add_argument("-m", "--mode", choices=["project", "file", "person", "churn_matrix", "ownership", "couples", "shotness", "all"], help="What to plot.") parser.add_argument( "--resample", default="year", help="The way to resample the time series. Possible values are: " "\"month\", \"year\", \"no\", \"raw\" and pandas offset aliases (" "http://pandas.pydata.org/pandas-docs/stable/timeseries.html" "#offset-aliases).") parser.add_argument("--disable-projector", action="store_true", help="Do not run Tensorflow Projector on couples.") parser.add_argument("--max-people", default=20, type=int, help="Maximum number of developers in churn matrix and people plots.") args = parser.parse_args() return args class Reader(object): def read(self, file): raise NotImplementedError def get_name(self): raise NotImplementedError def get_header(self): raise NotImplementedError def get_burndown_parameters(self): raise NotImplementedError def get_project_burndown(self): raise NotImplementedError def get_files_burndown(self): raise NotImplementedError def get_people_burndown(self): raise NotImplementedError def get_ownership_burndown(self): raise NotImplementedError def get_people_interaction(self): raise NotImplementedError def get_files_coocc(self): raise NotImplementedError def get_people_coocc(self): raise NotImplementedError def get_shotness_coocc(self): raise NotImplementedError def get_shotness(self): raise NotImplementedError class YamlReader(Reader): def read(self, file): yaml.reader.Reader.NON_PRINTABLE = re.compile(r"(?!x)x") try: loader = yaml.CLoader except AttributeError: print("Warning: failed to import yaml.CLoader, falling back to slow yaml.Loader") loader = yaml.Loader try: if file != "-": with open(file) as fin: data = yaml.load(fin, Loader=loader) else: data = yaml.load(sys.stdin, Loader=loader) except (UnicodeEncodeError, yaml.reader.ReaderError) as e: print("\nInvalid unicode in the input: %s\nPlease filter it through " "fix_yaml_unicode.py" % e) sys.exit(1) self.data = data def get_name(self): return self.data["hercules"]["repository"] def get_header(self): header = self.data["hercules"] return header["begin_unix_time"], header["end_unix_time"] def get_burndown_parameters(self): header = self.data["Burndown"] return header["sampling"], header["granularity"] def get_project_burndown(self): return self.data["hercules"]["repository"], \ self._parse_burndown_matrix(self.data["Burndown"]["project"]).T def get_files_burndown(self): return [(p[0], self._parse_burndown_matrix(p[1]).T) for p in self.data["Burndown"]["files"].items()] def get_people_burndown(self): return [(p[0], self._parse_burndown_matrix(p[1]).T) for p in self.data["Burndown"]["people"].items()] def get_ownership_burndown(self): return self.data["Burndown"]["people_sequence"].copy(),\ {p[0]: self._parse_burndown_matrix(p[1]) for p in self.data["Burndown"]["people"].items()} def get_people_interaction(self): return self.data["Burndown"]["people_sequence"].copy(), \ self._parse_burndown_matrix(self.data["Burndown"]["people_interaction"]) def get_files_coocc(self): coocc = self.data["Couples"]["files_coocc"] return coocc["index"], self._parse_coocc_matrix(coocc["matrix"]) def get_people_coocc(self): coocc = self.data["Couples"]["people_coocc"] return coocc["index"], self._parse_coocc_matrix(coocc["matrix"]) def get_shotness_coocc(self): shotness = self.data["Shotness"] index = ["%s:%s" % (i["file"], i["name"]) for i in shotness] indptr = numpy.zeros(len(shotness) + 1, dtype=numpy.int64) indices = [] data = [] for i, record in enumerate(shotness): pairs = [(int(k), v) for k, v in record["counters"].items()] pairs.sort() indptr[i + 1] = indptr[i] + len(pairs) for k, v in pairs: indices.append(k) data.append(v) indices = numpy.array(indices, dtype=numpy.int32) data = numpy.array(data, dtype=numpy.int32) from scipy.sparse import csr_matrix return index, csr_matrix((data, indices, indptr), shape=(len(shotness),) * 2) def get_shotness(self): from munch import munchify obj = munchify(self.data["Shotness"]) # turn strings into ints for item in obj: item.counters = {int(k): v for k, v in item.counters.items()} if len(obj) == 0: raise KeyError return obj def _parse_burndown_matrix(self, matrix): return numpy.array([numpy.fromstring(line, dtype=int, sep=" ") for line in matrix.split("\n")]) def _parse_coocc_matrix(self, matrix): from scipy.sparse import csr_matrix data = [] indices = [] indptr = [0] for row in matrix: for k, v in sorted(row.items()): data.append(v) indices.append(k) indptr.append(indptr[-1] + len(row)) return csr_matrix((data, indices, indptr), shape=(len(matrix),) * 2) class ProtobufReader(Reader): def read(self, file): try: from pb.pb_pb2 import AnalysisResults except ImportError as e: print("\n\n>>> You need to generate pb/pb_pb2.py - run \"make\"\n", file=sys.stderr) raise e from None self.data = AnalysisResults() if file != "-": with open(file, "rb") as fin: self.data.ParseFromString(fin.read()) else: self.data.ParseFromString(sys.stdin.buffer.read()) self.contents = {} for key, val in self.data.contents.items(): try: mod, name = PB_MESSAGES[key].rsplit(".", 1) except KeyError: sys.stderr.write("Warning: there is no registered PB decoder for %s\n" % key) continue cls = getattr(import_module(mod), name) self.contents[key] = msg = cls() msg.ParseFromString(val) def get_name(self): return self.data.header.repository def get_header(self): header = self.data.header return header.begin_unix_time, header.end_unix_time def get_burndown_parameters(self): burndown = self.contents["Burndown"] return burndown.sampling, burndown.granularity def get_project_burndown(self): return self._parse_burndown_matrix(self.contents["Burndown"].project) def get_files_burndown(self): return [self._parse_burndown_matrix(i) for i in self.contents["Burndown"].files] def get_people_burndown(self): return [self._parse_burndown_matrix(i) for i in self.contents["Burndown"].people] def get_ownership_burndown(self): people = self.get_people_burndown() return [p[0] for p in people], {p[0]: p[1].T for p in people} def get_people_interaction(self): burndown = self.contents["Burndown"] return [i.name for i in burndown.people], \ self._parse_sparse_matrix(burndown.people_interaction).toarray() def get_files_coocc(self): node = self.contents["Couples"].file_couples return list(node.index), self._parse_sparse_matrix(node.matrix) def get_people_coocc(self): node = self.contents["Couples"].people_couples return list(node.index), self._parse_sparse_matrix(node.matrix) def get_shotness_coocc(self): shotness = self.get_shotness() index = ["%s:%s" % (i.file, i.name) for i in shotness] indptr = numpy.zeros(len(shotness) + 1, dtype=numpy.int32) indices = [] data = [] for i, record in enumerate(shotness): pairs = list(record.counters.items()) pairs.sort() indptr[i + 1] = indptr[i] + len(pairs) for k, v in pairs: indices.append(k) data.append(v) indices = numpy.array(indices, dtype=numpy.int32) data = numpy.array(data, dtype=numpy.int32) from scipy.sparse import csr_matrix return index, csr_matrix((data, indices, indptr), shape=(len(shotness),) * 2) def get_shotness(self): records = self.contents["Shotness"].records if len(records) == 0: raise KeyError return records def _parse_burndown_matrix(self, matrix): dense = numpy.zeros((matrix.number_of_rows, matrix.number_of_columns), dtype=int) for y, row in enumerate(matrix.rows): for x, col in enumerate(row.columns): dense[y, x] = col return matrix.name, dense.T def _parse_sparse_matrix(self, matrix): from scipy.sparse import csr_matrix return csr_matrix((list(matrix.data), list(matrix.indices), list(matrix.indptr)), shape=(matrix.number_of_rows, matrix.number_of_columns)) READERS = {"yaml": YamlReader, "yml": YamlReader, "pb": ProtobufReader} def read_input(args): sys.stdout.write("Reading the input... ") sys.stdout.flush() if args.input != "-": if args.input_format == "auto": args.input_format = args.input.rsplit(".", 1)[1] elif args.input_format == "auto": args.input_format = "yaml" reader = READERS[args.input_format]() reader.read(args.input) print("done") return reader def calculate_average_lifetime(matrix): lifetimes = numpy.zeros(matrix.shape[1] - 1) for band in matrix: start = 0 for i, line in enumerate(band): if i == 0 or band[i - 1] == 0: start += 1 continue lifetimes[i - start] = band[i - 1] - line lifetimes[i - start] = band[i - 1] return (lifetimes.dot(numpy.arange(1, matrix.shape[1], 1)) / (lifetimes.sum() * matrix.shape[1])) def interpolate_burndown_matrix(matrix, granularity, sampling): daily = numpy.zeros( (matrix.shape[0] * granularity, matrix.shape[1] * sampling), dtype=numpy.float32) """ ----------> samples, x | | | ⌄ bands, y """ for y in range(matrix.shape[0]): for x in range(matrix.shape[1]): if y * granularity > (x + 1) * sampling: # the future is zeros continue def decay(start_index: int, start_val: float): if start_val == 0: return k = matrix[y][x] / start_val # <= 1 scale = (x + 1) * sampling - start_index for i in range(y * granularity, (y + 1) * granularity): initial = daily[i][start_index - 1] for j in range(start_index, (x + 1) * sampling): daily[i][j] = initial * ( 1 + (k - 1) * (j - start_index + 1) / scale) def grow(finish_index: int, finish_val: float): initial = matrix[y][x - 1] if x > 0 else 0 start_index = x * sampling if start_index < y * granularity: start_index = y * granularity if finish_index == start_index: return avg = (finish_val - initial) / (finish_index - start_index) for j in range(x * sampling, finish_index): for i in range(start_index, j + 1): daily[i][j] = avg # copy [x*g..y*s) for j in range(x * sampling, finish_index): for i in range(y * granularity, x * sampling): daily[i][j] = daily[i][j - 1] if (y + 1) * granularity >= (x + 1) * sampling: # x*granularity <= (y+1)*sampling # 1. x*granularity <= y*sampling # y*sampling..(y+1)sampling # # x+1 # / # / # / y+1 -| # / | # / y -| # / # / x # # 2. x*granularity > y*sampling # x*granularity..(y+1)sampling # # x+1 # / # / # / y+1 -| # / | # / x -| # / # / y if y * granularity <= x * sampling: grow((x + 1) * sampling, matrix[y][x]) elif (x + 1) * sampling > y * granularity: grow((x + 1) * sampling, matrix[y][x]) avg = matrix[y][x] / ((x + 1) * sampling - y * granularity) for j in range(y * granularity, (x + 1) * sampling): for i in range(y * granularity, j + 1): daily[i][j] = avg elif (y + 1) * granularity >= x * sampling: # y*sampling <= (x+1)*granularity < (y+1)sampling # y*sampling..(x+1)*granularity # (x+1)*granularity..(y+1)sampling # x+1 # /\ # / \ # / \ # / y+1 # / # y v1 = matrix[y][x - 1] v2 = matrix[y][x] delta = (y + 1) * granularity - x * sampling previous = 0 if x > 0 and (x - 1) * sampling >= y * granularity: # x*g <= (y-1)*s <= y*s <= (x+1)*g <= (y+1)*s # |________|.......^ if x > 1: previous = matrix[y][x - 2] scale = sampling else: # (y-1)*s < x*g <= y*s <= (x+1)*g <= (y+1)*s # |______|.......^ scale = sampling if x == 0 else x * sampling - y * granularity peak = v1 + (v1 - previous) / scale * delta if v2 > peak: # we need to adjust the peak, it may not be less than the decayed value if x < matrix.shape[1] - 1: # y*s <= (x+1)*g <= (y+1)*s < (y+2)*s # ^.........|_________| k = (v2 - matrix[y][x + 1]) / sampling # > 0 peak = matrix[y][x] + k * ((x + 1) * sampling - (y + 1) * granularity) # peak > v2 > v1 else: peak = v2 # not enough data to interpolate; this is at least not restricted grow((y + 1) * granularity, peak) decay((y + 1) * granularity, peak) else: # (x+1)*granularity < y*sampling # y*sampling..(y+1)sampling decay(x * sampling, matrix[y][x - 1]) return daily def load_burndown(header, name, matrix, resample): import pandas start, last, sampling, granularity = header assert sampling > 0 assert granularity >= sampling start = datetime.fromtimestamp(start) last = datetime.fromtimestamp(last) print(name, "lifetime index:", calculate_average_lifetime(matrix)) finish = start + timedelta(days=matrix.shape[1] * sampling) if resample not in ("no", "raw"): # Interpolate the day x day matrix. # Each day brings equal weight in the granularity. # Sampling's interpolation is linear. daily = interpolate_burndown_matrix(matrix, granularity, sampling) daily[(last - start).days:] = 0 # Resample the bands aliases = { "year": "A", "month": "M" } resample = aliases.get(resample, resample) periods = 0 date_granularity_sampling = [start] while date_granularity_sampling[-1] < finish: periods += 1 date_granularity_sampling = pandas.date_range( start, periods=periods, freq=resample) date_range_sampling = pandas.date_range( date_granularity_sampling[0], periods=(finish - date_granularity_sampling[0]).days, freq="1D") # Fill the new square matrix matrix = numpy.zeros( (len(date_granularity_sampling), len(date_range_sampling)), dtype=numpy.float32) for i, gdt in enumerate(date_granularity_sampling): istart = (date_granularity_sampling[i - 1] - start).days \ if i > 0 else 0 ifinish = (gdt - start).days for j, sdt in enumerate(date_range_sampling): if (sdt - start).days >= istart: break matrix[i, j:] = \ daily[istart:ifinish, (sdt - start).days:].sum(axis=0) # Hardcode some cases to improve labels' readability if resample in ("year", "A"): labels = [dt.year for dt in date_granularity_sampling] elif resample in ("month", "M"): labels = [dt.strftime("%Y %B") for dt in date_granularity_sampling] else: labels = [dt.date() for dt in date_granularity_sampling] else: labels = [ "%s - %s" % ((start + timedelta(days=i * granularity)).date(), ( start + timedelta(days=(i + 1) * granularity)).date()) for i in range(matrix.shape[0])] if len(labels) > 18: warnings.warn("Too many labels - consider resampling.") resample = "M" # fake resampling type is checked while plotting date_range_sampling = pandas.date_range( start + timedelta(days=sampling), periods=matrix.shape[1], freq="%dD" % sampling) return name, matrix, date_range_sampling, labels, granularity, sampling, resample def load_ownership(header, sequence, contents, max_people): import pandas start, last, sampling, _ = header start = datetime.fromtimestamp(start) last = datetime.fromtimestamp(last) people = [] for name in sequence: people.append(contents[name].sum(axis=1)) people = numpy.array(people) date_range_sampling = pandas.date_range( start + timedelta(days=sampling), periods=people[0].shape[0], freq="%dD" % sampling) if people.shape[0] > max_people: order = numpy.argsort(-people.sum(axis=1)) people = people[order[:max_people]] sequence = [sequence[i] for i in order[:max_people]] print("Warning: truncated people to most owning %d" % max_people) for i, name in enumerate(sequence): if len(name) > 40: sequence[i] = name[:37] + "..." return sequence, people, date_range_sampling, last def load_churn_matrix(people, matrix, max_people): matrix = matrix.astype(float) if matrix.shape[0] > max_people: order = numpy.argsort(-matrix[:, 0]) matrix = matrix[order[:max_people]][:, [0, 1] + list(2 + order[:max_people])] people = [people[i] for i in order[:max_people]] print("Warning: truncated people to most productive %d" % max_people) zeros = matrix[:, 0] == 0 matrix[zeros, :] = 1 matrix /= matrix[:, 0][:, None] matrix = -matrix[:, 1:] matrix[zeros, :] = 0 for i, name in enumerate(people): if len(name) > 40: people[i] = name[:37] + "..." return people, matrix def apply_plot_style(figure, axes, legend, style, text_size, axes_size): if axes_size is None: axes_size = (12, 9) else: axes_size = tuple(float(p) for p in axes_size.split(",")) figure.set_size_inches(*axes_size) for side in ("bottom", "top", "left", "right"): axes.spines[side].set_color(style) for axis in (axes.xaxis, axes.yaxis): axis.label.update(dict(fontsize=text_size, color=style)) for axis in ("x", "y"): axes.tick_params(axis=axis, colors=style, labelsize=text_size) if legend is not None: frame = legend.get_frame() for setter in (frame.set_facecolor, frame.set_edgecolor): setter("black" if style == "white" else "white") for text in legend.get_texts(): text.set_color(style) def get_plot_path(base, name): root, ext = os.path.splitext(base) if not ext: ext = ".png" output = os.path.join(root, name + ext) os.makedirs(os.path.dirname(output), exist_ok=True) return output def deploy_plot(title, output, style): import matplotlib.pyplot as pyplot if not output: pyplot.gcf().canvas.set_window_title(title) pyplot.show() else: if title: pyplot.title(title, color=style) try: pyplot.tight_layout() except: print("Warning: failed to set the tight layout") pyplot.savefig(output, transparent=True) pyplot.clf() def default_json(x): if hasattr(x, "tolist"): return x.tolist() if hasattr(x, "isoformat"): return x.isoformat() return x def plot_burndown(args, target, name, matrix, date_range_sampling, labels, granularity, sampling, resample): if args.output and args.output.endswith(".json"): data = locals().copy() del data["args"] data["type"] = "burndown" if args.mode == "project" and target == "project": output = args.output else: if target == "project": name = "project" output = get_plot_path(args.output, name) with open(output, "w") as fout: json.dump(data, fout, sort_keys=True, default=default_json) return import matplotlib if args.backend: matplotlib.use(args.backend) import matplotlib.pyplot as pyplot pyplot.stackplot(date_range_sampling, matrix, labels=labels) if args.relative: for i in range(matrix.shape[1]): matrix[:, i] /= matrix[:, i].sum() pyplot.ylim(0, 1) legend_loc = 3 else: legend_loc = 2 legend = pyplot.legend(loc=legend_loc, fontsize=args.text_size) pyplot.ylabel("Lines of code") pyplot.xlabel("Time") apply_plot_style(pyplot.gcf(), pyplot.gca(), legend, args.style, args.text_size, args.size) pyplot.xlim(date_range_sampling[0], date_range_sampling[-1]) locator = pyplot.gca().xaxis.get_major_locator() # set the optimal xticks locator if "M" not in resample: pyplot.gca().xaxis.set_major_locator(matplotlib.dates.YearLocator()) locs = pyplot.gca().get_xticks().tolist() if len(locs) >= 16: pyplot.gca().xaxis.set_major_locator(matplotlib.dates.YearLocator()) locs = pyplot.gca().get_xticks().tolist() if len(locs) >= 16: pyplot.gca().xaxis.set_major_locator(locator) if locs[0] < pyplot.xlim()[0]: del locs[0] endindex = -1 if len(locs) >= 2 and \ pyplot.xlim()[1] - locs[-1] > (locs[-1] - locs[-2]) / 2: locs.append(pyplot.xlim()[1]) endindex = len(locs) - 1 startindex = -1 if len(locs) >= 2 and \ locs[0] - pyplot.xlim()[0] > (locs[1] - locs[0]) / 2: locs.append(pyplot.xlim()[0]) startindex = len(locs) - 1 pyplot.gca().set_xticks(locs) # hacking time! labels = pyplot.gca().get_xticklabels() if startindex >= 0: labels[startindex].set_text(date_range_sampling[0].date()) labels[startindex].set_text = lambda _: None labels[startindex].set_rotation(30) labels[startindex].set_ha("right") if endindex >= 0: labels[endindex].set_text(date_range_sampling[-1].date()) labels[endindex].set_text = lambda _: None labels[endindex].set_rotation(30) labels[endindex].set_ha("right") title = "%s %d x %d (granularity %d, sampling %d)" % \ ((name,) + matrix.shape + (granularity, sampling)) output = args.output if output: if args.mode == "project" and target == "project": output = args.output else: if target == "project": name = "project" output = get_plot_path(args.output, name) deploy_plot(title, output, args.style) def plot_many_burndown(args, target, header, parts): if not args.output: print("Warning: output not set, showing %d plots." % len(parts)) itercnt = progress.bar(parts, expected_size=len(parts)) \ if progress is not None else parts stdout = io.StringIO() for name, matrix in itercnt: backup = sys.stdout sys.stdout = stdout plot_burndown(args, target, *load_burndown(header, name, matrix, args.resample)) sys.stdout = backup sys.stdout.write(stdout.getvalue()) def plot_churn_matrix(args, repo, people, matrix): if args.output and args.output.endswith(".json"): data = locals().copy() del data["args"] data["type"] = "churn_matrix" if args.mode == "all": output = get_plot_path(args.output, "matrix") else: output = args.output with open(output, "w") as fout: json.dump(data, fout, sort_keys=True, default=default_json) return import matplotlib if args.backend: matplotlib.use(args.backend) import matplotlib.pyplot as pyplot s = 4 + matrix.shape[1] * 0.3 fig = pyplot.figure(figsize=(s, s)) ax = fig.add_subplot(111) ax.xaxis.set_label_position("top") ax.matshow(matrix, cmap=pyplot.cm.OrRd) ax.set_xticks(numpy.arange(0, matrix.shape[1])) ax.set_yticks(numpy.arange(0, matrix.shape[0])) ax.set_xticklabels(["Unidentified"] + people, rotation=90, ha="center") ax.set_yticklabels(people, va="center") ax.set_xticks(numpy.arange(0.5, matrix.shape[1] + 0.5), minor=True) ax.set_yticks(numpy.arange(0.5, matrix.shape[0] + 0.5), minor=True) ax.grid(which="minor") apply_plot_style(fig, ax, None, args.style, args.text_size, args.size) if not args.output: pos1 = ax.get_position() pos2 = (pos1.x0 + 0.245, pos1.y0 - 0.1, pos1.width * 0.9, pos1.height * 0.9) ax.set_position(pos2) if args.mode == "all": output = get_plot_path(args.output, "matrix") else: output = args.output title = "%s %d developers overwrite" % (repo, matrix.shape[0]) if args.output: # FIXME(vmarkovtsev): otherwise the title is screwed in savefig() title = "" deploy_plot(title, output, args.style) def plot_ownership(args, repo, names, people, date_range, last): if args.output and args.output.endswith(".json"): data = locals().copy() del data["args"] data["type"] = "ownership" if args.mode == "all": output = get_plot_path(args.output, "people") else: output = args.output with open(output, "w") as fout: json.dump(data, fout, sort_keys=True, default=default_json) return import matplotlib if args.backend: matplotlib.use(args.backend) import matplotlib.pyplot as pyplot pyplot.stackplot(date_range, people, labels=names) pyplot.xlim(date_range[0], last) if args.relative: for i in range(people.shape[1]): people[:, i] /= people[:, i].sum() pyplot.ylim(0, 1) legend_loc = 3 else: legend_loc = 2 legend = pyplot.legend(loc=legend_loc, fontsize=args.text_size) apply_plot_style(pyplot.gcf(), pyplot.gca(), legend, args.style, args.text_size, args.size) if args.mode == "all": output = get_plot_path(args.output, "people") else: output = args.output deploy_plot("%s code ownership through time" % repo, output, args.style) IDEAL_SHARD_SIZE = 4096 def train_embeddings(index, matrix, tmpdir, shard_size=IDEAL_SHARD_SIZE): try: from . import swivel except (SystemError, ImportError): import swivel import tensorflow as tf assert matrix.shape[0] == matrix.shape[1] assert len(index) <= matrix.shape[0] outlier_threshold = numpy.percentile(matrix.data, 99) matrix.data[matrix.data > outlier_threshold] = outlier_threshold nshards = len(index) // shard_size if nshards * shard_size < len(index): nshards += 1 shard_size = len(index) // nshards nshards = len(index) // shard_size remainder = len(index) - nshards * shard_size if remainder > 0: lengths = matrix.indptr[1:] - matrix.indptr[:-1] filtered = sorted(numpy.argsort(lengths)[remainder:]) else: filtered = list(range(len(index))) if len(filtered) < matrix.shape[0]: print("Truncating the sparse matrix...") matrix = matrix[filtered, :][:, filtered] meta_index = [] for i, j in enumerate(filtered): meta_index.append((index[j], matrix[i, i])) index = [mi[0] for mi in meta_index] with tempfile.TemporaryDirectory(prefix="hercules_labours_", dir=tmpdir or None) as tmproot: print("Writing Swivel metadata...") vocabulary = "\n".join(index) with open(os.path.join(tmproot, "row_vocab.txt"), "w") as out: out.write(vocabulary) with open(os.path.join(tmproot, "col_vocab.txt"), "w") as out: out.write(vocabulary) del vocabulary bool_sums = matrix.indptr[1:] - matrix.indptr[:-1] bool_sums_str = "\n".join(map(str, bool_sums.tolist())) with open(os.path.join(tmproot, "row_sums.txt"), "w") as out: out.write(bool_sums_str) with open(os.path.join(tmproot, "col_sums.txt"), "w") as out: out.write(bool_sums_str) del bool_sums_str reorder = numpy.argsort(-bool_sums) print("Writing Swivel shards...") for row in range(nshards): for col in range(nshards): def _int64s(xs): return tf.train.Feature( int64_list=tf.train.Int64List(value=list(xs))) def _floats(xs): return tf.train.Feature( float_list=tf.train.FloatList(value=list(xs))) indices_row = reorder[row::nshards] indices_col = reorder[col::nshards] shard = matrix[indices_row][:, indices_col].tocoo() example = tf.train.Example(features=tf.train.Features(feature={ "global_row": _int64s(indices_row), "global_col": _int64s(indices_col), "sparse_local_row": _int64s(shard.row), "sparse_local_col": _int64s(shard.col), "sparse_value": _floats(shard.data)})) with open(os.path.join(tmproot, "shard-%03d-%03d.pb" % (row, col)), "wb") as out: out.write(example.SerializeToString()) print("Training Swivel model...") swivel.FLAGS.submatrix_rows = shard_size swivel.FLAGS.submatrix_cols = shard_size if len(meta_index) <= IDEAL_SHARD_SIZE / 16: embedding_size = 50 num_epochs = 20000 elif len(meta_index) <= IDEAL_SHARD_SIZE: embedding_size = 50 num_epochs = 10000 elif len(meta_index) <= IDEAL_SHARD_SIZE * 2: embedding_size = 60 num_epochs = 5000 elif len(meta_index) <= IDEAL_SHARD_SIZE * 4: embedding_size = 70 num_epochs = 4000 elif len(meta_index) <= IDEAL_SHARD_SIZE * 10: embedding_size = 80 num_epochs = 2500 elif len(meta_index) <= IDEAL_SHARD_SIZE * 25: embedding_size = 100 num_epochs = 500 elif len(meta_index) <= IDEAL_SHARD_SIZE * 100: embedding_size = 200 num_epochs = 300 else: embedding_size = 300 num_epochs = 200 swivel.FLAGS.embedding_size = embedding_size swivel.FLAGS.input_base_path = tmproot swivel.FLAGS.output_base_path = tmproot swivel.FLAGS.loss_multiplier = 1.0 / shard_size swivel.FLAGS.num_epochs = num_epochs # Tensorflow 1.5 parses sys.argv unconditionally *applause* argv_backup = sys.argv[1:] del sys.argv[1:] swivel.main(None) sys.argv.extend(argv_backup) print("Reading Swivel embeddings...") embeddings = [] with open(os.path.join(tmproot, "row_embedding.tsv")) as frow: with open(os.path.join(tmproot, "col_embedding.tsv")) as fcol: for i, (lrow, lcol) in enumerate(zip(frow, fcol)): prow, pcol = (l.split("\t", 1) for l in (lrow, lcol)) assert prow[0] == pcol[0] erow, ecol = \ (numpy.fromstring(p[1], dtype=numpy.float32, sep="\t") for p in (prow, pcol)) embeddings.append((erow + ecol) / 2) return meta_index, embeddings class CORSWebServer(object): def __init__(self): self.thread = threading.Thread(target=self.serve) self.server = None def serve(self): outer = self try: from http.server import HTTPServer, SimpleHTTPRequestHandler, test except ImportError: # Python 2 from BaseHTTPServer import HTTPServer, test from SimpleHTTPServer import SimpleHTTPRequestHandler class ClojureServer(HTTPServer): def __init__(self, *args, **kwargs): HTTPServer.__init__(self, *args, **kwargs) outer.server = self class CORSRequestHandler(SimpleHTTPRequestHandler): def end_headers (self): self.send_header("Access-Control-Allow-Origin", "*") SimpleHTTPRequestHandler.end_headers(self) test(CORSRequestHandler, ClojureServer) def start(self): self.thread.start() def stop(self): if self.running: self.server.shutdown() self.thread.join() @property def running(self): return self.server is not None web_server = CORSWebServer() def write_embeddings(name, output, run_server, index, embeddings): print("Writing Tensorflow Projector files...") if not output: output = "couples_" + name if output.endswith(".json"): output = os.path.join(output[:-5], "couples") run_server = False metaf = "%s_%s_meta.tsv" % (output, name) with open(metaf, "w") as fout: fout.write("name\tcommits\n") for pair in index: fout.write("%s\t%s\n" % pair) print("Wrote", metaf) dataf = "%s_%s_data.tsv" % (output, name) with open(dataf, "w") as fout: for vec in embeddings: fout.write("\t".join(str(v) for v in vec)) fout.write("\n") print("Wrote", dataf) jsonf = "%s_%s.json" % (output, name) with open(jsonf, "w") as fout: fout.write("""{ "embeddings": [ { "tensorName": "%s %s coupling", "tensorShape": [%s, %s], "tensorPath": "http://0.0.0.0:8000/%s", "metadataPath": "http://0.0.0.0:8000/%s" } ] } """ % (output, name, len(embeddings), len(embeddings[0]), dataf, metaf)) print("Wrote %s" % jsonf) if run_server and not web_server.running: web_server.start() url = "http://projector.tensorflow.org/?config=http://0.0.0.0:8000/" + jsonf print(url) if run_server: if shutil.which("xdg-open") is not None: os.system("xdg-open " + url) else: browser = os.getenv("BROWSER", "") if browser: os.system(browser + " " + url) else: print("\t" + url) def show_shotness_stats(data): top = sorted(((r.counters[i], i) for i, r in enumerate(data)), reverse=True) for count, i in top: r = data[i] print("%8d %s:%s [%s]" % (count, r.file, r.name, r.internal_role)) def main(): args = parse_args() reader = read_input(args) header = reader.get_header() name = reader.get_name() burndown_warning = "Burndown stats were not collected. Re-run hercules with --burndown." burndown_files_warning = \ "Burndown stats for files were not collected. Re-run hercules with " \ "--burndown --burndown-files." burndown_people_warning = \ "Burndown stats for people were not collected. Re-run hercules with " \ "--burndown --burndown-people." couples_warning = "Coupling stats were not collected. Re-run hercules with --couples." shotness_warning = "Structural hotness stats were not collected. Re-run hercules with " \ "--shotness. Also check --languages - the output may be empty." def project_burndown(): try: full_header = header + reader.get_burndown_parameters() except KeyError: print("project: " + burndown_warning) return plot_burndown(args, "project", *load_burndown(full_header, *reader.get_project_burndown(), resample=args.resample)) def files_burndown(): try: full_header = header + reader.get_burndown_parameters() except KeyError: print(burndown_warning) return try: plot_many_burndown(args, "file", full_header, reader.get_files_burndown()) except KeyError: print("files: " + burndown_files_warning) def people_burndown(): try: full_header = header + reader.get_burndown_parameters() except KeyError: print(burndown_warning) return try: plot_many_burndown(args, "person", full_header, reader.get_people_burndown()) except KeyError: print("people: " + burndown_people_warning) def churn_matrix(): try: plot_churn_matrix(args, name, *load_churn_matrix( *reader.get_people_interaction(), max_people=args.max_people)) except KeyError: print("churn_matrix: " + burndown_people_warning) def ownership_burndown(): try: full_header = header + reader.get_burndown_parameters() except KeyError: print(burndown_warning) return try: plot_ownership(args, name, *load_ownership( full_header, *reader.get_ownership_burndown(), max_people=args.max_people)) except KeyError: print("ownership: " + burndown_people_warning) def couples(): try: write_embeddings("files", args.output, not args.disable_projector, *train_embeddings(*reader.get_files_coocc(), tmpdir=args.couples_tmp_dir)) write_embeddings("people", args.output, not args.disable_projector, *train_embeddings(*reader.get_people_coocc(), tmpdir=args.couples_tmp_dir)) except KeyError: print(couples_warning) try: write_embeddings("shotness", args.output, not args.disable_projector, *train_embeddings(*reader.get_shotness_coocc(), tmpdir=args.couples_tmp_dir)) except KeyError: print(shotness_warning) def shotness(): try: data = reader.get_shotness() except KeyError: print(shotness_warning) return show_shotness_stats(data) if args.mode == "project": project_burndown() elif args.mode == "file": files_burndown() elif args.mode == "person": people_burndown() elif args.mode == "churn_matrix": churn_matrix() elif args.mode == "ownership": ownership_burndown() elif args.mode == "couples": couples() elif args.mode == "shotness": shotness() elif args.mode == "all": project_burndown() files_burndown() people_burndown() churn_matrix() ownership_burndown() couples() shotness() if web_server.running: secs = int(os.getenv("COUPLES_SERVER_TIME", "60")) print("Sleeping for %d seconds, safe to Ctrl-C" % secs) sys.stdout.flush() try: time.sleep(secs) except KeyboardInterrupt: pass web_server.stop() if __name__ == "__main__": sys.exit(main())