Merge branch 'pag_viz' into merge_activity_pag
Clemens Klug
commit
e254667256
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@ -52,7 +52,7 @@ __MAPPING__ = {
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StoreRender
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],
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SimulationOrderAnalyzer: [
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JSONRender,
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#JSONRender,
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# SimulationOrderRender,
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SimulationGroupRender
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]
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@ -52,7 +52,7 @@ class ResultStore:
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:return:
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"""
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result = []
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for key in self.store:
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for key in sorted(self.store):
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result += self.store[key]
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return result
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@ -204,7 +204,7 @@ class ActivityMapper(Analyzer):
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board_data = get_board_data(self.settings.source, self.instance_config_id, entry["sequence_id"],
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entry["board_id"])
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entry["extra_data"] = board_data
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entry["extra_data"]["activity_type"] = self.classify_entry(entry)
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entry["extra_data"]["activity_type"] = self.last_board_type
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entry['coordinate'] = self.new_coordinate()
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self.timeline.append(entry)
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return False
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@ -293,8 +293,8 @@ class InstanceConfig(Analyzer):
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print(entry)
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self.store["instance_id"] = json_path(entry, self.settings.custom["instance_config_id"])
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def result(self, store: ResultStore):
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store.add(Result(type(self), dict(self.store)))
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def result(self, store: ResultStore, name=None):
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store.add(Result(type(self), dict(self.store), name=name))
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class SimulationOrderAnalyzer(Analyzer):
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@ -305,8 +305,8 @@ class SimulationOrderAnalyzer(Analyzer):
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self.store = defaultdict(lambda: -1) # TODO verify
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self.order = []
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def result(self, store: ResultStore) -> None:
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store.add(Result(type(self), [self.store[sim] for sim in self.order]))
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def result(self, store: ResultStore, name=None) -> None:
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store.add(Result(type(self), [self.store[sim] for sim in self.order], name=name))
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def process(self, entry: dict) -> bool:
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entry_type = entry[self.settings.type_field]
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@ -186,7 +186,13 @@ class SimulationOrderRender(Render):
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class SimulationGroupRender(Render):
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def render(self, results: List[Result], name=None):
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data = [r.get() for r in self.filter(results)]
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#data = [r.get() for r in self.filter(results)]
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data = []
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for r in self.filter(results):
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raw = r.get()
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if len(raw) < 6:
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raw = [0] + raw
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data.append(raw)
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print(name, len(data))
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# graph_fit(list(data), name=name)
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graph_plot(list(data), ylabel="simulation retries", title="sequential simulation retries", rotation=None,
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@ -1,8 +1,13 @@
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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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from collections import defaultdict
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import json
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import numpy as np
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keys = [
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"simu",
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@ -70,18 +75,319 @@ def time_distribution(store):
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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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# 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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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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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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||||
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||||
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||||
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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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|
|
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|
|
@ -13,9 +13,9 @@
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],
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||||
"analyzers": {
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||||
"analyzers": [
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||||
"BiogamesCategorizer",
|
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"ActivityMapper",
|
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"SimulationFlagsAnalyzer"
|
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"SimulationCategorizer",
|
||||
"SimulationOrderAnalyzer",
|
||||
"ActivityMapper"
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||||
]
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||||
},
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"dis":[
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|
|
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|
|
@ -2,17 +2,16 @@ import json
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|||
import logging
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||||
from typing import List
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||||
|
||||
import numpy as np
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||||
|
||||
import analyzers
|
||||
from analyzers import get_renderer, Analyzer, render, Store
|
||||
from analyzers import get_renderer, render
|
||||
from analyzers.analyzer import ResultStore
|
||||
from analyzers.analyzer.default import write_logentry_count_csv, write_simulation_flag_csv
|
||||
from analyzers.render import wip
|
||||
from analyzers.render.default import LogEntryCountCSV
|
||||
from analyzers.render.wip import time_distribution
|
||||
from analyzers.render.wip import time_distribution, plot_data
|
||||
from analyzers.settings import LogSettings, load_settings
|
||||
from loaders import LOADERS
|
||||
from util.processing import grep, run_analysis
|
||||
from util.processing import grep, run_analysis, src_file
|
||||
|
||||
logging.basicConfig(format='%(levelname)s %(name)s:%(message)s', level=logging.DEBUG)
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||||
log: logging.Logger = logging.getLogger(__name__)
|
||||
|
|
@ -36,6 +35,9 @@ if __name__ == '__main__':
|
|||
# "91abfd4b31a5562b1c66be37d9",
|
||||
# "597b704fe9ace475316c345903",
|
||||
# "e01a684aa29dff9ddd9705edf8",
|
||||
"597b704fe9ace475316c345903",
|
||||
"e01a684aa29dff9ddd9705edf8",
|
||||
"fbf9d64ae0bdad0de7efa3eec6",
|
||||
# "fbf9d64ae0bdad0de7efa3eec6",
|
||||
"fe1331481f85560681f86827ec", # urach
|
||||
# "fe1331481f85560681f86827ec"]
|
||||
|
|
@ -45,18 +47,23 @@ if __name__ == '__main__':
|
|||
log_ids_gf = grep(["9d11b749c78a57e786bf5c8d28", # filderstadt
|
||||
"a192ff420b8bdd899fd28573e2", # eichstätt
|
||||
"3a3d994c04b1b1d87168422309", # stadtökologie
|
||||
"fe1331481f85560681f86827ec", # urach
|
||||
"96f6d9cc556b42f3b2fec0a2cb7ed36e" # oberelsbach
|
||||
],
|
||||
"/home/clemens/git/ma/test/src",
|
||||
settings)
|
||||
store: ResultStore = run_analysis(log_ids_gf, settings, LOADERS)
|
||||
log_ids = src_file("/home/clemens/git/ma/test/filtered_5_actions")
|
||||
|
||||
#store: ResultStore = run_analysis(log_ids_gf, settings, LOADERS)
|
||||
#store: ResultStore = run_analysis(log_ids, settings, LOADERS)
|
||||
|
||||
if False:
|
||||
for r in get_renderer(analyzers.LocomotionActionAnalyzer):
|
||||
r().render(store.get_all())
|
||||
if False:
|
||||
render(analyzers.LocationAnalyzer, store.get_all())
|
||||
# print(json.dumps(store.serializable(), indent=1))
|
||||
if True:
|
||||
if False:
|
||||
for cat in store.get_categories():
|
||||
render(analyzers.ActivityMapper, store.get_category(cat), name=cat)
|
||||
# render(analyzers.ProgressAnalyzer, store.get_all())
|
||||
|
|
@ -75,9 +82,27 @@ if __name__ == '__main__':
|
|||
write_logentry_count_csv(LogEntryCountCSV, store, render, analyzers)
|
||||
if False:
|
||||
write_simulation_flag_csv(store)
|
||||
if False:
|
||||
time_distribution(store)
|
||||
|
||||
if True:
|
||||
time_distribution(store)
|
||||
# spatial_data = get_data_distance(store,relative_values=False)
|
||||
# temporal_data = get_data(store,relative_values=False)
|
||||
# spatial_data_rel = get_data_distance(store,relative_values=True)
|
||||
# temporal_data_rel = get_data(store,relative_values=True)
|
||||
# temporal_data_rel = json.load(open("temporal_rel.json"))
|
||||
# spatial_data_rel = json.load(open("spatial_rel.json"))
|
||||
# import IPython
|
||||
# IPython.embed()
|
||||
|
||||
# print(json.dumps(get_all_data(store)))
|
||||
# json.dump(get_all_data(store), open("combined.json", "w"))
|
||||
# combined = get_all_data(store, sort=True, relative=True)
|
||||
# json.dump(combined, open("combined_rel.json", "w"))
|
||||
# combined = json.load(open("combined_rel.json"))
|
||||
combined = json.load(open("combined_total.json"))
|
||||
# plot_time_space_rel(combined, keys)
|
||||
plot_data(combined, wip.keys)
|
||||
|
||||
|
||||
# for analyzers in analyzers:
|
||||
|
|
|
|||
|
|
@ -5,3 +5,4 @@ osmnx==0.6
|
|||
networkx==2.0
|
||||
pydot==1.2.3
|
||||
scipy==1.0.0
|
||||
ipython==6.2.1
|
||||
|
|
@ -55,3 +55,12 @@ def grep(log_ids, source, settings):
|
|||
if id in line:
|
||||
logs.append(line.strip())
|
||||
return logs
|
||||
|
||||
|
||||
def src_file(filename):
|
||||
log_ids = []
|
||||
with open(filename) as src:
|
||||
for line in src:
|
||||
line = line.strip()
|
||||
log_ids.append(line)
|
||||
return log_ids
|
||||
|
|
|
|||
Loading…
Reference in New Issue