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437 lines
16 KiB
437 lines
16 KiB
#!/usr/bin/env python3
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import traceback
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import sys
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import json
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from collections import defaultdict
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import matplotlib.pyplot as plt
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import matplotlib.path as mpath
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import numpy as np
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from graphviz import Digraph
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PLOT_WIDTH = 10 # inches
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PLOT_HEIGHT = 7 # inches
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PLOT_SIZE_zipf = 20
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data = json.load(sys.stdin)
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DATA_records = []
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DATA_records_by_session = defaultdict(list)
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for user in data["UsersRecords"]:
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for device in user["Devices"]:
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for record in device["Records"]:
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if "invalid" in record and record["invalid"]:
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continue
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DATA_records.append(record)
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DATA_records_by_session[record["seqSessionId"]].append(record)
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DATA_records = list(sorted(DATA_records, key=lambda x: x["realtimeAfterLocal"]))
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for pid, session in DATA_records_by_session.items():
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session = list(sorted(session, key=lambda x: x["realtimeAfterLocal"]))
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# TODO: this should be a cmdline option
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async_draw = True
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# for strategy in data["Strategies"]:
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# print(json.dumps(strategy))
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def zipf(length):
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return list(map(lambda x: 1/2**x, range(0, length)))
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def trim(text, length, add_elipse=True):
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if add_elipse and len(text) > length:
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return text[:length-1] + "…"
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return text[:length]
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# Figure 3.1. The normalized command frequency, compared with Zipf.
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def plot_cmdLineFrq_rank(plotSize=PLOT_SIZE_zipf, show_labels=False):
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cmdLine_count = defaultdict(int)
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for record in DATA_records:
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cmdLine_count[record["cmdLine"]] += 1
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tmp = sorted(cmdLine_count.items(), key=lambda x: x[1], reverse=True)[:plotSize]
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cmdLineFrq = list(map(lambda x: x[1] / tmp[0][1], tmp))
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labels = list(map(lambda x: trim(x[0], 7), tmp))
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ranks = range(1, len(cmdLineFrq)+1)
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.plot(ranks, zipf(len(ranks)), '-')
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plt.plot(ranks, cmdLineFrq, 'o-')
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plt.title("Commandline frequency / rank")
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plt.ylabel("Normalized commandline frequency")
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plt.xlabel("Commandline rank")
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plt.legend(("Zipf", "Commandline"), loc="best")
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if show_labels:
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plt.xticks(ranks, labels, rotation=-60)
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# TODO: make xticks integral
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if async_draw:
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plt.draw()
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else:
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plt.show()
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# similar to ~ Figure 3.1. The normalized command frequency, compared with Zipf.
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def plot_cmdFrq_rank(plotSize=PLOT_SIZE_zipf, show_labels=False):
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cmd_count = defaultdict(int)
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for record in DATA_records:
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cmd = record["firstWord"]
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if cmd == "":
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continue
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cmd_count[cmd] += 1
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tmp = sorted(cmd_count.items(), key=lambda x: x[1], reverse=True)[:plotSize]
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cmdFrq = list(map(lambda x: x[1] / tmp[0][1], tmp))
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labels = list(map(lambda x: trim(x[0], 7), tmp))
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ranks = range(1, len(cmdFrq)+1)
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.plot(ranks, zipf(len(ranks)), 'o-')
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plt.plot(ranks, cmdFrq, 'o-')
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plt.title("Command frequency / rank")
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plt.ylabel("Normalized command frequency")
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plt.xlabel("Command rank")
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plt.legend(("Zipf", "Command"), loc="best")
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if show_labels:
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plt.xticks(ranks, labels, rotation=-60)
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# TODO: make xticks integral
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if async_draw:
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plt.draw()
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else:
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plt.show()
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# Figure 3.2. Command vocabulary size vs. the number of command lines entered for four individuals.
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def plot_cmdVocabularySize_cmdLinesEntered():
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cmd_vocabulary = set()
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y_cmd_count = [0]
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for record in DATA_records:
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cmd = record["firstWord"]
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if cmd in cmd_vocabulary:
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# repeat last value
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y_cmd_count.append(y_cmd_count[-1])
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else:
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cmd_vocabulary.add(cmd)
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# append last value +1
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y_cmd_count.append(y_cmd_count[-1] + 1)
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# print(cmd_vocabulary)
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x_cmds_entered = range(0, len(y_cmd_count))
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.plot(x_cmds_entered, y_cmd_count, '-')
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plt.title("Command vocabulary size vs. the number of command lines entered")
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plt.ylabel("Command vocabulary size")
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plt.xlabel("# of command lines entered")
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if async_draw:
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plt.draw()
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else:
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plt.show()
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# Figure 5.6. Command line vocabulary size vs. the number of commands entered for four typical individuals.
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def plot_cmdLineVocabularySize_cmdLinesEntered():
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cmdLine_vocabulary = set()
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y_cmdLine_count = [0]
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for record in DATA_records:
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cmdLine = record["cmdLine"]
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if cmdLine in cmdLine_vocabulary:
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# repeat last value
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y_cmdLine_count.append(y_cmdLine_count[-1])
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else:
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cmdLine_vocabulary.add(cmdLine)
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# append last value +1
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y_cmdLine_count.append(y_cmdLine_count[-1] + 1)
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# print(cmdLine_vocabulary)
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x_cmdLines_entered = range(0, len(y_cmdLine_count))
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.plot(x_cmdLines_entered, y_cmdLine_count, '-')
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plt.title("Command line vocabulary size vs. the number of command lines entered")
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plt.ylabel("Command line vocabulary size")
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plt.xlabel("# of command lines entered")
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if async_draw:
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plt.draw()
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else:
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plt.show()
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# Figure 3.3. Sequential structure of UNIX command usage, from Figure 4 in Hanson et al. (1984).
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# Ball diameters are proportional to stationary probability. Lines indicate significant dependencies,
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# solid ones being more probable (p < .0001) and dashed ones less probable (.005 < p < .0001).
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def graph_cmdSequences(node_count=33, edge_minValue=0.05):
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START_CMD = "_start_"
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cmd_count = defaultdict(int)
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cmdSeq_count = defaultdict(lambda: defaultdict(int))
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cmd_id = dict()
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x = 0
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cmd_id[START_CMD] = str(x)
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for pid, session in DATA_records_by_session.items():
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cmd_count[START_CMD] += 1
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prev_cmd = START_CMD
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for record in session:
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cmd = record["firstWord"]
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cmdSeq_count[prev_cmd][cmd] += 1
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cmd_count[cmd] += 1
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if cmd not in cmd_id:
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x += 1
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cmd_id[cmd] = str(x)
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prev_cmd = cmd
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# get `node_count` of largest nodes
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sorted_cmd_count = sorted(cmd_count.items(), key=lambda x: x[1], reverse=True)
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print(sorted_cmd_count)
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cmds_to_graph = list(map(lambda x: x[0], sorted_cmd_count))[:node_count]
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# use 3 biggest nodes as a reference point for scaling
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biggest_node = cmd_count[cmds_to_graph[0]]
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nd_biggest_node = cmd_count[cmds_to_graph[1]]
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rd_biggest_node = cmd_count[cmds_to_graph[1]]
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count2scale_coef = 3 / (biggest_node + nd_biggest_node + rd_biggest_node)
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# scaling constant
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# affects node size and node label
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base_scaling_factor = 21
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# extra scaling for experiments - not really useful imho
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# affects everything nodes, edges, node labels, treshold for turning label into xlabel, xlabel size, ...
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extra_scaling_factor = 1.0
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for x in range(0, 10):
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# graphviz is not the most reliable piece of software
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# -> retry on fail but scale nodes down by 1%
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scaling_factor = base_scaling_factor * (1 - x * 0.01)
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# overlap: scale -> solve overlap by scaling the graph
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# overlap_shrink -> try to shrink the graph a bit after you are done
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# splines -> don't draw edges over nodes
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# sep: 2.5 -> assume that nodes are 2.5 inches larger
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graph_attr={'overlap':'scale', 'overlap_shrink':'true',
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'splines':'true', 'sep':'0.25'}
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graph = Digraph(name='command_sequentiality', engine='neato', graph_attr=graph_attr)
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# iterate over all nodes
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for cmd in cmds_to_graph:
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seq = cmdSeq_count[cmd]
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count = cmd_count[cmd]
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# iterate over all "following" commands (for each node)
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for seq_entry in seq.items():
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cmd2, seq_count = seq_entry
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relative_seq_count = seq_count / count
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# check if "follow" command is supposed to be in the graph
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if cmd2 not in cmds_to_graph:
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continue
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# check if the edge value is high enough
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if relative_seq_count < edge_minValue:
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continue
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# create starting node and end node for the edge
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# duplicates don't matter
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for id_, cmd_ in ((cmd_id[cmd], cmd), (cmd_id[cmd2], cmd2)):
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count_ = cmd_count[cmd_]
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scale_ = count_ * count2scale_coef * scaling_factor * extra_scaling_factor
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width_ = 0.08 * scale_
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fontsize_ = 8.5 * scale_ / (len(cmd_) + 3)
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width_ = str(width_)
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if fontsize_ < 12 * extra_scaling_factor:
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graph.node(id_, ' ', shape='circle', fixedsize='true', fontname='monospace bold',
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width=width_, fontsize=str(12 * extra_scaling_factor), forcelabels='true', xlabel=cmd_)
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else:
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fontsize_ = str(fontsize_)
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graph.node(id_, cmd_, shape='circle', fixedsize='true', fontname='monospace bold',
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width=width_, fontsize=fontsize_, forcelabels='true', labelloc='c')
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# value of the edge (percentage) 1.0 is max
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scale_ = seq_count / cmd_count[cmd]
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penwidth_ = str((0.5 + 4.5 * scale_) * extra_scaling_factor)
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#penwidth_bold_ = str(8 * scale_)
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if scale_ > 0.5:
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graph.edge(cmd_id[cmd], cmd_id[cmd2], constraint='true', splines='curved',
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penwidth=penwidth_, style='bold')
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elif scale_ > 0.2:
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graph.edge(cmd_id[cmd], cmd_id[cmd2], constraint='true', splines='curved',
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penwidth=penwidth_, arrowhead='open')
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elif scale_ > 0.1:
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graph.edge(cmd_id[cmd], cmd_id[cmd2], constraint='true', splines='curved',
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penwidth=penwidth_, style='dashed', arrowhead='open')
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else:
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graph.edge(cmd_id[cmd], cmd_id[cmd2], constraint='false', splines='curved',
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penwidth=penwidth_, style='dotted', arrowhead='empty')
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# graphviz sometimes fails - see above
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try:
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# graph.view()
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graph.render('/tmp/resh-graphviz-cmdSeq-{}.gv'.format(x), view=True)
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break
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except Exception as e:
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trace = traceback.format_exc()
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print("GRAPHVIZ EXCEPTION: <{}>\nGRAPHVIZ TRACE: <{}>".format(str(e), trace))
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def plot_strategies_matches(plot_size=50, selected_strategies=[]):
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.title("Matches at distance")
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plt.ylabel('%' + " of matches")
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plt.xlabel("Distance")
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legend = []
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x_values = range(1, plot_size+1)
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saved_matches_total = None
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saved_dataPoint_count = None
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for strategy in data["Strategies"]:
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strategy_title = strategy["Title"]
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# strategy_description = strategy["Description"]
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if len(selected_strategies) and strategy_title not in selected_strategies:
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continue
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dataPoint_count = 0
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matches = [0] * plot_size
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matches_total = 0
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charsRecalled = [0] * plot_size
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charsRecalled_total = 0
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for match in strategy["Matches"]:
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dataPoint_count += 1
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if not match["Match"]:
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continue
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chars = match["CharsRecalled"]
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charsRecalled_total += chars
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matches_total += 1
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dist = match["Distance"]
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if dist > plot_size:
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continue
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matches[dist-1] += 1
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charsRecalled[dist-1] += chars
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# recent is very simple strategy so we will believe
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# that there is no bug in it and we can use it to determine total
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if strategy_title == "recent":
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saved_matches_total = matches_total
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saved_dataPoint_count = dataPoint_count
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if len(selected_strategies) and strategy_title not in selected_strategies:
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continue
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acc = 0
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matches_cumulative = []
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for x in matches:
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acc += x
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matches_cumulative.append(acc)
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# matches_cumulative.append(matches_total)
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matches_percent = list(map(lambda x: 100 * x / dataPoint_count, matches_cumulative))
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plt.plot(x_values, matches_percent, 'o-')
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legend.append(strategy_title)
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assert(saved_matches_total is not None)
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assert(saved_dataPoint_count is not None)
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max_values = [100 * saved_matches_total / saved_dataPoint_count] * len(x_values)
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plt.plot(x_values, max_values, 'r-')
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legend.append("maximum possible")
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x_ticks = list(range(1, plot_size+1, 2))
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x_labels = x_ticks[:]
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plt.xticks(x_ticks, x_labels)
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plt.legend(legend, loc="best")
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if async_draw:
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plt.draw()
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else:
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plt.show()
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def plot_strategies_charsRecalled(plot_size=50, selected_strategies=[]):
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plt.figure(figsize=(PLOT_WIDTH, PLOT_HEIGHT))
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plt.title("Average characters recalled at distance")
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plt.ylabel("Average characters recalled")
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plt.xlabel("Distance")
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x_values = range(1, plot_size+1)
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legend = []
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saved_charsRecalled_total = None
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saved_dataPoint_count = None
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for strategy in data["Strategies"]:
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strategy_title = strategy["Title"]
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# strategy_description = strategy["Description"]
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dataPoint_count = 0
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matches = [0] * plot_size
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matches_total = 0
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charsRecalled = [0] * plot_size
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charsRecalled_total = 0
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for match in strategy["Matches"]:
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dataPoint_count += 1
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if not match["Match"]:
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continue
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chars = match["CharsRecalled"]
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charsRecalled_total += chars
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matches_total += 1
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dist = match["Distance"]
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if dist > plot_size:
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continue
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matches[dist-1] += 1
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charsRecalled[dist-1] += chars
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# recent is very simple strategy so we will believe
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# that there is no bug in it and we can use it to determine total
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if strategy_title == "recent":
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saved_charsRecalled_total = charsRecalled_total
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saved_dataPoint_count = dataPoint_count
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if len(selected_strategies) and strategy_title not in selected_strategies:
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continue
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acc = 0
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charsRecalled_cumulative = []
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for x in charsRecalled:
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acc += x
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charsRecalled_cumulative.append(acc)
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charsRecalled_average = list(map(lambda x: x / dataPoint_count, charsRecalled_cumulative))
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plt.plot(x_values, charsRecalled_average, 'o-')
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legend.append(strategy_title)
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assert(saved_charsRecalled_total is not None)
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assert(saved_dataPoint_count is not None)
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max_values = [saved_charsRecalled_total / saved_dataPoint_count] * len(x_values)
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plt.plot(x_values, max_values, 'r-')
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legend.append("maximum possible")
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x_ticks = list(range(1, plot_size+1, 2))
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x_labels = x_ticks[:]
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plt.xticks(x_ticks, x_labels)
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plt.legend(legend, loc="best")
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if async_draw:
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plt.draw()
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else:
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plt.show()
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# graph_cmdSequences(node_count=33, edge_minValue=0.05)
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graph_cmdSequences(node_count=28, edge_minValue=0.06)
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plot_cmdLineFrq_rank()
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plot_cmdFrq_rank()
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plot_cmdLineVocabularySize_cmdLinesEntered()
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plot_cmdVocabularySize_cmdLinesEntered()
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plot_strategies_matches(20)
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plot_strategies_charsRecalled(20)
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if async_draw:
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plt.show()
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# be careful and check if labels fit the display |