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https://github.com/TREX-CoE/Sherman-Morrison.git
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171 lines
4.7 KiB
Python
171 lines
4.7 KiB
Python
# General helper functions for both compare_runs and compare_variables
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import calendar
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import time
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from itertools import compress
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import numpy as np
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# Magic numbers
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max_ticks = 15
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max_zscore = 3
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##########################################################################
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# From a timestamp, return the associated metadata as a Pandas serie
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def get_metadata(metadata, timestamp):
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return metadata.loc[timestamp]
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# Convert a metadata Pandas series to a JS readable dict
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def metadata_to_dict(metadata):
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dict = metadata.to_dict()
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# JS doesn't accept True for booleans, and Python doesn't accept true
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# (because of the caps) => using an integer is a portable solution
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dict["is_git_commit"] = 1 if dict["is_git_commit"] else 0
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dict["date"] = time.ctime(metadata.name)
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return dict
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# Return a string that indicates the elapsed time since the run, used as the
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# x-axis tick in "Compare runs" or when selecting run in "Inspect run"
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def get_run_name(timestamp, hash):
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gmt = time.gmtime()
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now = calendar.timegm(gmt)
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diff = now - timestamp
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# Special case : < 1 minute (return string directly)
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if diff < 60:
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str = "Less than a minute ago"
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if hash != "":
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str = str + " (%s)" % hash
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if str == get_run_name.previous:
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get_run_name.counter = get_run_name.counter + 1
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str = "%s (%s)" % (str, get_run_name.counter)
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else:
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get_run_name.counter = 0
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get_run_name.previous = str
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return str
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# < 1 hour
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if diff < 3600:
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n = int(diff / 60)
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str = "%s minute%s ago"
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# < 1 day
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elif diff < 86400:
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n = int(diff / 3600)
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str = "%s hour%s ago"
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# < 1 week
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elif diff < 604800:
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n = int(diff / 86400)
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str = "%s day%s ago"
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# < 1 month
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elif diff < 2592000:
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n = int(diff / 604800)
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str = "%s week%s ago"
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# > 1 month
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else:
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n = diff / 2592000
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str = "%s month%s ago"
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plural = ""
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if n != 1:
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plural = "s"
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str = str % (n, plural)
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# We might want to add the git hash
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if hash != "":
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str = str + " (%s)" % hash
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# Finally, check for duplicate with previously generated string
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if str == get_run_name.previous:
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# Increment the duplicate counter and add it to str
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get_run_name.counter = get_run_name.counter + 1
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str = "%s (%s)" % (str, get_run_name.counter)
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else:
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# No duplicate, reset both previously generated str and duplicate
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# counter
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get_run_name.counter = 0
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get_run_name.previous = str
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return str
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# These external variables will store data about the last generated string to
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# avoid duplicates (assuming the runs are sorted by time)
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get_run_name.counter = 0
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get_run_name.previous = ""
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def reset_run_strings():
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get_run_name.counter = 0
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get_run_name.previous = ""
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# Update all the x-ranges from a dict of plots
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def reset_x_range(plot, x_range):
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plot.x_range.factors = x_range
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if len(x_range) < max_ticks:
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plot.xaxis.major_tick_line_color = "#000000"
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plot.xaxis.minor_tick_line_color = "#000000"
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plot.xaxis.major_label_text_font_size = "8pt"
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else:
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plot.xaxis.major_tick_line_color = None
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plot.xaxis.minor_tick_line_color = None
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plot.xaxis.major_label_text_font_size = "0pt"
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# Return an array of booleans that indicate which elements are outliers
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# (True means element is not an outlier and must be kept)
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def detect_outliers(array, max_zscore=max_zscore):
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if len(array) <= 2:
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return [True] * len(array)
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median = np.median(array)
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std = np.std(array)
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if std == 0:
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return array
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distance = abs(array - median)
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# Array of booleans with elements to be filtered
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outliers_array = distance < max_zscore * std
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return outliers_array
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def remove_outliers(array, outliers):
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return list(compress(array, outliers))
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def remove_boxplot_outliers(dict, outliers, prefix):
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outliers = detect_outliers(dict["%s_max" % prefix])
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dict["%s_x" % prefix] = remove_outliers(dict["%s_x" % prefix], outliers)
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dict["%s_min" % prefix] = remove_outliers(
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dict["%s_min" % prefix], outliers)
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dict["%s_quantile25" % prefix] = remove_outliers(
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dict["%s_quantile25" % prefix], outliers)
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dict["%s_quantile50" % prefix] = remove_outliers(
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dict["%s_quantile50" % prefix], outliers)
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dict["%s_quantile75" % prefix] = remove_outliers(
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dict["%s_quantile75" % prefix], outliers)
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dict["%s_max" % prefix] = remove_outliers(
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dict["%s_max" % prefix], outliers)
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dict["%s_mu" % prefix] = remove_outliers(dict["%s_mu" % prefix], outliers)
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dict["nsamples"] = remove_outliers(dict["nsamples"], outliers)
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