394 lines
10 KiB
Python
394 lines
10 KiB
Python
import json
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import numpy as np
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import analyzers
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from util.geo import calc_distance
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def time_distribution(store):
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# json.dump(store.serializable(), open("new.json", "w"), indent=1)
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keys = [
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"simu",
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"question",
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"image",
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"audio",
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"video",
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"other",
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"map"
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]
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import matplotlib.pyplot as plt
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# results = []
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places = defaultdict(list)
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for log in store.get_all():
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result = defaultdict(lambda: 0)
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for i in log.get()['track']:
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duration = i['properties']['end_timestamp'] - i['properties']['start_timestamp']
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result[i['properties']['activity_type']] += duration
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print(json.dumps(result, indent=4))
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total = sum(result.values())
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print(total)
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percentage = defaultdict(lambda: 0)
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minutes = defaultdict(lambda: 0)
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for i in result:
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percentage[i] = result[i] / total
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minutes[i] = result[i] / 60_000
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print(json.dumps(percentage, indent=4))
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if not 'error' in result:
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# places[log.get()['instance']].append(percentage)
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places[log.get()['instance']].append(minutes)
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for place in places:
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places[place] = sorted(places[place], key=lambda item: item['map'])
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dummy = [0] * len(keys)
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results = []
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sites = []
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from util.meta_temp import CONFIG_NAMES
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for i in places:
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for j in places[i]:
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ordered = []
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for k in keys:
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ordered.append(j[k])
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results.append(ordered)
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results.append(dummy)
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sites.append(CONFIG_NAMES[i] if i in CONFIG_NAMES else "---")
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size = len(results)
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ind = np.arange(size)
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width = 0.9
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print(results)
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data = list(zip(*results))
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print(data)
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lines = []
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bottom = [0] * len(results)
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for i in range(0, len(data)):
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lines.append(plt.bar(ind, data[i], bottom=bottom, width=width)[0])
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for k, x in enumerate(data[i]):
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bottom[k] += x
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plt.legend(lines, keys)
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plt.title(", ".join(sites))
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plt.show()
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# size = len(results)
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# ind = np.arange(size)
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# width = 0.9
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# print(results)
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# data = list(zip(*results))
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# print(data)
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# lines = []
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# bottom = [0] * len(results)
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# for i in range(0, len(data)):
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# lines.append(plt.bar(ind, data[i], bottom=bottom, width=width)[0])
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# for k, x in enumerate(data[i]):
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# bottom[k] += x
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# plt.legend(lines, keys)
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# plt.title("Zwei Spiele in Filderstadt (t1=237min; t2=67min)")
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# plt.show()
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# json.dump(store.serializable(), open("new.json", "w"), indent=1)
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from collections import defaultdict
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import matplotlib.pyplot as plt
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from util.meta_temp import CONFIG_NAMES
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keys = [
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"simu",
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"question",
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"image",
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"audio",
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"video",
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"other",
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"map",
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# "error"
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]
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loc_keys = [
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"question",
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"image",
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"audio",
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"video"
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]
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def get_data(store, relative_values=True, sort=True, show_errors=False):
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places = defaultdict(list)
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for log in store.get_all():
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if not log.analysis() == analyzers.ActivityMapper:
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continue
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result = defaultdict(lambda: 0)
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for i in log.get()['track']:
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duration = i['properties']['end_timestamp'] - i['properties']['start_timestamp']
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result[i['properties']['activity_type']] += duration
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print(json.dumps(result, indent=4))
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total = sum(result.values())
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print(total)
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percentage = defaultdict(lambda: 0)
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minutes = defaultdict(lambda: 0)
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for i in result:
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percentage[i] = result[i] / total
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minutes[i] = result[i] / 60_000
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print(json.dumps(percentage, indent=4))
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if not 'error' in result or show_errors:
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if relative_values:
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places[log.get()['instance']].append(percentage)
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else:
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places[log.get()['instance']].append(minutes)
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if sort:
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for place in places:
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places[place] = sorted(places[place], key=lambda item: item['map'])
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return places
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whitelist = ['16fc3117-61db-4f50-b84f-81de6310206f', '5e64ce07-1c16-4d50-ac4e-b3117847ea43',
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'90278021-4c57-464e-90b1-d603799d07eb', 'ff8f1e8f-6cf5-4a7b-835b-5e2226c1e771']
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def get_data_distance(store, relative_values=True, sort=True, show_errors=False):
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places = defaultdict(list)
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for log in store.get_all():
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if not log.analysis() == analyzers.ActivityMapper:
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continue
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result = defaultdict(lambda: 0)
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for i in log.get()['track']:
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coords = i['coordinates']
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if len(coords) > 1:
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distance = calc_distance(coords)
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result[i['properties']['activity_type']] += distance
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total = sum(result.values())
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percentage = defaultdict(lambda: 0)
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for i in result:
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if not total == 0:
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percentage[i] = result[i] / total
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if not 'error' in result or show_errors:
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if relative_values:
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places[log.get()['instance']].append(percentage)
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else:
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places[log.get()['instance']].append(result)
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if sort:
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for place in places:
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places[place] = sorted(places[place], key=lambda item: item['map'])
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return places
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def get_all_data(store, sort=False, relative=True):
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places = defaultdict(list)
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simu_distribution = defaultdict(lambda: 0)
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# divisiors = {"time":60_000, "space":1000000}
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for log in store.get_all():
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if not log.analysis() == analyzers.ActivityMapper:
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continue
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result = defaultdict(lambda: defaultdict(lambda: 0))
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for i in log.get()['track']:
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coords = i['coordinates']
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if len(coords) > 1:
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distance = calc_distance(coords)
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else:
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distance = 0.0
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result["space"][i['properties']['activity_type']] += distance
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duration = i['properties']['end_timestamp'] - i['properties']['start_timestamp']
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result["time"][i['properties']['activity_type']] += duration
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total_space = sum(result["space"].values())
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total_time = sum(result["time"].values())
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percentage = defaultdict(lambda: defaultdict(lambda: 0))
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total = defaultdict(lambda: defaultdict(lambda: 0))
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for i in result["space"]:
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if not total_space == 0:
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percentage[i]["space"] = result["space"][i] / total_space
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else:
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percentage[i]["space"] = 0
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if not total_time == 0:
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percentage[i]["time"] = result["time"][i] / total_time
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else:
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percentage[i]["time"] = 0
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for t in ("space", "time"):
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# total[i][t] += (result[t][i] / divisiors[t])
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total[i][t] += result[t][i]
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print(percentage)
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if not 'error' in result:
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if relative:
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value = percentage
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else:
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value = total
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places[log.get()['instance']].append(value)
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simus = defaultdict(lambda: 0)
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for item in log.get()['boards']:
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if item["extra_data"]["activity_type"] == "simu":
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simus[item["board_id"]] += 1
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simu_distribution[len(simus)] += 1
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if sort:
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for place in places:
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places[place] = sorted(places[place], key=lambda item: item['map']['time'])
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print(simu_distribution)
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return places
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def stack_data(keys, places, type="space"):
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divisiors = {"time": 60_000, "space": 1000}
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# divisiors = {"time": 1, "space": 1}
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dummy = [0] * len(keys)
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results = []
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sites = []
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for i in sorted(places):
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if not i in whitelist:
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continue
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place = sorted(places[i], key=lambda item: item['map'][type])
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for j in place:
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ordered = []
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for k in keys:
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if k in j:
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ordered.append(j[k][type] / divisiors[type])
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else:
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ordered.append(0)
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print(sum(ordered))
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# if sum(ordered) > 0.9 and sum(ordered) < 4000 and sum(ordered)>10:
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if sum(ordered) > 0.9 and sum(ordered) < 100:
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# print(sum(ordered), 1-sum(ordered))
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# if sum(ordered)<1:
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# ordered[-2] = 1-sum(ordered[:-2], ordered[-1])
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results.append(ordered)
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results.append(dummy)
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sites.append(CONFIG_NAMES[i] if i in CONFIG_NAMES else "---")
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return results, sites
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def plot_data(places, keys):
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results, sites = stack_data(keys, places)
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dpi = 86.1
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plt.figure(figsize=(1280 / dpi, 720 / dpi))
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size = len(results)
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print("{} elements total".format(size))
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ind = np.arange(size)
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width = 1
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# print(results)
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data = list(zip(*results))
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# print(data)
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lines = []
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bottom = [0] * size
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plt.ticklabel_format(useMathText=False)
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for i in range(0, len(data)):
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lines.append(plt.bar(ind, data[i], bottom=bottom, width=width)[0])
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for k, x in enumerate(data[i]):
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bottom[k] += x
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plt.legend(lines, keys)
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plt.title(", ".join(sites))
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# plt.show()
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dpi = 86
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plt.savefig("space_abs_{}.png".format(size), dpi=dpi, bbox_inches="tight")
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colors = {
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"simu": "blue",
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"question": "orange",
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"image": "green",
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"audio": "red",
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"video": "purple",
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"other": "brown",
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"map": "violet",
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# "error":"grey",
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"tasks": "olive",
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}
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markers = [".", "o", "x", "s", "*", "D", "p", ",", "<", ">", "^", "v", "1", "2", "3", "4"]
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def plot_time_space(time_data, space_data, keys):
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# assuming time_data and space_data are in same order!
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marker = 0
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for id in time_data:
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for k in keys:
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for i in range(len(time_data[id])):
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print(time_data[id][i][k], space_data[id][i][k])
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plt.plot(time_data[id][i][k], space_data[id][i][k], color=colors[k], marker=markers[marker])
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marker += 1
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plt.show()
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# plt.cla()
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# plt.clf()
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# plt.close()
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def group_locationbased_tasks(data):
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for id in data:
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for log in data[id]:
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loc = {"space": 0, "time": 0}
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for k in log:
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if k in loc_keys:
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for i in ["space", "time"]:
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loc[i] += log[k][i]
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log["tasks"] = loc
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def plot_time_space_rel(combined, keys):
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groups = defaultdict(list)
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keys = list(keys)
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keys.remove("other")
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for i in loc_keys:
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keys.remove(i)
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keys.append("tasks")
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ids = []
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group_locationbased_tasks(combined)
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for k in keys:
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for id in sorted(combined):
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if id not in whitelist:
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continue
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if not id in ids:
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ids.append(id)
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group = 0.0
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count = 0
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for item in combined[id]:
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if k in item:
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time = item[k]["time"] / 1000
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distance = item[k]["space"]
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if time > 0:
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group += (distance / time)
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count += 1
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else:
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print("div by zero", distance, time)
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if count > 0:
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groups[k].append(group / count)
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else:
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groups[k].append(0.0)
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print(ids)
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ind = np.arange(len(ids))
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width = .7 / len(groups)
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print(ind)
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print(json.dumps(groups, indent=1))
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bars = []
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dpi = 200
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plt.figure(figsize=(1280 / dpi, 720 / dpi))
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fig, ax = plt.subplots()
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for k in groups:
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print(groups[k])
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if not len(groups[k]):
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groups[k].append(0)
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ind = ind + (width)
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bars.append(ax.bar((ind + width * len(groups) / 2), groups[k], width, color=colors[k]))
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ax.set_xticks(ind + width / 2)
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ax.set_xticklabels(list([CONFIG_NAMES[i] if i in CONFIG_NAMES else "---" for i in ids]))
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kmh = plt.hlines((1 / 3.6), 0.3, 4.2, linestyles="dashed", label="1 km/h", linewidths=1)
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plt.legend(bars + [kmh], keys + [kmh.get_label()])
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print(combined.keys(), ids)
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print([CONFIG_NAMES[i] if i in CONFIG_NAMES else "---" for i in ids])
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# plt.show()
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dpi = 200
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plt.savefig("speed2.png", dpi=dpi)
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# plot_time_space_rel(temporal_data_rel, spatial_data_rel, keys)
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# plot_data(combined, keys)
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# plot_data(get_data_distance(store,relative_values=False), keys)
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