added charts

tools/statistics/analyse/age.py

@@ -0,0 +1,23 @@
+from collections import Counter
+
+import matplotlib.pyplot as plt
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    ages_data = list(map(extract.age.extract, datas))
+
+    counter = Counter(ages_data)
+    ages_x = list(counter.keys())
+    ages_y = list(counter.values())
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre de personne par âge")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/completion_per_age.py

@@ -0,0 +1,39 @@
+from collections import defaultdict
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    ages_completion: dict[int, int] = defaultdict(lambda: 0)
+    ages_count: dict[int, int] = defaultdict(lambda: 0)
+
+    for data in datas:
+        age = extract.age.extract(data)
+        ages_count[age] += 1
+
+        for survey, survey_data in data["surveys"].items():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            if extract.mission_completed.extract(data, survey):
+                ages_completion[age] += 1
+
+    ages_x = list(ages_completion.keys())
+    ages_y = (
+        np.array(list(ages_completion.values()))
+        / np.array(list(ages_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre moyen de mission complété par âge")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/completion_per_experience.py

@@ -0,0 +1,37 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    experience_completion: dict[str, int] = {"yes": 0, "mixed": 0, "no": 0}
+    experience_count: dict[str, int] = {"yes": 0, "mixed": 0, "no": 0}
+
+    for data in datas:
+        experience = extract.experience.extract(data)
+        experience_count[experience] += 1
+
+        for survey, survey_data in data["surveys"].items():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            if extract.mission_completed.extract(data, survey):
+                experience_completion[experience] += 1
+
+    ages_x = list(experience_completion.keys())
+    ages_y = (
+        np.array(list(experience_completion.values()))
+        / np.array(list(experience_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre moyen de mission complété par expérience")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/completion_per_mission.py

@@ -0,0 +1,44 @@
+import matplotlib.pyplot as plt
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    completions: dict[str] = {
+        "mission-language": 0,
+        "mission-price": 0,
+        "mission-community-hub": 0,
+        "mission-game-page": 0,
+        "mission-game-dlc": 0,
+        "mission-actuality-new": 0,
+        "mission-profile": 0,
+        "mission-game-discussion": 0,
+        "mission-gift-card": 0,
+        "mission-workshop": 0,
+    }
+
+    # NOTE : séparé avant / après grosse mise à jour pour carte cadeau ?
+
+    for data in datas:
+        for survey in data["surveys"].keys():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            if extract.mission_completed.extract(data, survey):
+                completions[survey] += 1
+
+    completions_x = list(completions.keys())
+    completions_y = list(completions.values())
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre de personne ayant réussi par mission")
+
+    # bar chart
+    axes.bar(completions_x, completions_y)
+    axes.set_xticks(completions_x)
+    axes.set_xticklabels(completions_x, rotation=45)
+
+    plt.show(block=True)

tools/statistics/analyse/completion_per_usage.py

@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    usage_completion: dict[str, int] = {"always": 0, "often": 0, "sometime": 0, "rarely": 0, "never": 0}
+    usage_count: dict[str, int] = {"always": 0, "often": 0, "sometime": 0, "rarely": 0, "never": 0}
+
+    for data in datas:
+        usage = next(filter(
+            lambda it: it[1]["checked"],
+            data["surveys"]["question-usage-steam"]["choices"].items()
+        ))[0]
+
+        usage_count[usage] += 1
+
+        for survey in data["surveys"].keys():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            if extract.mission_completed.extract(data, survey):
+                usage_completion[usage] += 1
+
+    usages_x = list(usage_completion.keys())
+    usages_y = (
+        np.array(list(usage_completion.values()))
+        / np.array(list(usage_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre moyen de mission complété par niveau")
+
+    # bar chart
+    axes.bar(usages_x, usages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/duration_per_age.py

@@ -0,0 +1,40 @@
+from collections import defaultdict
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    ages_duration: dict[int, int] = defaultdict(lambda: 0)
+    ages_count: dict[int, int] = defaultdict(lambda: 0)
+
+    # TODO: faire des tranches d'âges ?
+
+    for data in datas:
+        age = extract.age.extract(data)
+        ages_count[age] += 1
+
+        for survey in data["surveys"].keys():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            ages_duration[age] += extract.mission_duration.extract(data, survey)
+
+    ages_x = list(ages_duration.keys())
+    ages_y = (
+        np.array(list(ages_duration.values()))
+        / np.array(list(ages_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Temps moyen passé par âge")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/duration_per_experience.py

@@ -0,0 +1,37 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    experience_duration: dict[str, int] = {"yes": 0, "mixed": 0, "no": 0}
+    experience_count: dict[str, int] = {"yes": 0, "mixed": 0, "no": 0}
+
+    for data in datas:
+        experience = extract.experience.extract(data)
+        experience_count[experience] += 1
+
+        for survey, survey_data in data["surveys"].items():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            if extract.mission_completed.extract(data, survey):
+                experience_duration[experience] += extract.mission_duration.extract(data, survey)
+
+    ages_x = list(experience_duration.keys())
+    ages_y = (
+        np.array(list(experience_duration.values()))
+        / np.array(list(experience_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Temps moyen passé par expérience")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/duration_per_mission.py

@@ -0,0 +1,45 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    durations: dict[str] = {
+        "mission-language": 0,
+        "mission-price": 0,
+        "mission-community-hub": 0,
+        "mission-game-page": 0,
+        "mission-game-dlc": 0,
+        "mission-actuality-new": 0,
+        "mission-profile": 0,
+        "mission-game-discussion": 0,
+        "mission-gift-card": 0,
+        "mission-workshop": 0,
+    }
+
+    # NOTE : séparé avant / après grosse mise à jour pour carte cadeau ?
+    # NOTE : marqué en rouge la durée d'abandon ?
+
+    for data in datas:
+        for survey in data["surveys"].keys():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            durations[survey] += extract.mission_duration.extract(data, survey)
+
+    durations_x = list(durations.keys())
+    durations_y = np.array(list(durations.values())) / len(datas)
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Temps moyen passé par test")
+
+    # bar chart
+    axes.bar(durations_x, durations_y)
+    axes.set_xticks(durations_x)
+    axes.set_xticklabels(durations_x, rotation=45)
+
+    plt.show(block=True)

tools/statistics/analyse/duration_per_usage.py

@@ -0,0 +1,38 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    usage_completion: dict[str, int] = {"always": 0, "often": 0, "sometime": 0, "rarely": 0, "never": 0}
+    usage_count: dict[str, int] = {"always": 0, "often": 0, "sometime": 0, "rarely": 0, "never": 0}
+
+    # TODO: faire des tranches d'âges ?
+
+    for data in datas:
+        usage = extract.usage.extract(data)
+        usage_count[usage] += 1
+
+        for survey in data["surveys"].keys():
+            # only scan survey mission
+            if not survey.startswith("mission-"):
+                continue
+
+            usage_completion[usage] += extract.mission_duration.extract(data, survey)
+
+    ages_x = list(usage_completion.keys())
+    ages_y = (
+        np.array(list(usage_completion.values()))
+        / np.array(list(usage_count.values()))
+    )
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Temps moyen passé par niveau")
+
+    # bar chart
+    axes.bar(ages_x, ages_y)
+
+    plt.show(block=True)

tools/statistics/analyse/experience.py

@@ -0,0 +1,26 @@
+from collections import Counter
+
+import matplotlib.pyplot as plt
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    experiences: dict[str, int] = {"yes": 0, "mixed": 0, "no": 0}
+    for data in datas:
+        experience = extract.experience.extract(data)
+        experiences[experience] += 1
+
+    counter = Counter(experiences)
+    x = list(counter.keys())
+    y = list(counter.values())
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Nombre de personne par expérience")
+
+    # bar chart
+    axes.bar(x, y)
+
+    plt.show(block=True)

tools/statistics/analyse/hardest_mission.py

@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    missions = {
+        "mission-language": 0,
+        "mission-price": 0,
+        "mission-community-hub": 0,
+        "mission-game-page": 0,
+        "mission-game-dlc": 0,
+        "mission-actuality-new": 0,
+        "mission-profile": 0,
+        "mission-game-discussion": 0,
+        "mission-gift-card": 0,
+        "mission-workshop": 0
+    }
+
+    for data in datas:
+        missions[extract.hardest_mission.extract(data)] += 1
+
+    x = list(missions.keys())
+    y = list(missions.values())
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Mission la plus difficile des personnes sondées")
+
+    # bar chart
+    axes.bar(x, y)
+    axes.set_xticks(x)
+    axes.set_xticklabels(x, rotation=45)
+
+    plt.show(block=True)

tools/statistics/analyse/usage.py

@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+
+from tools.statistics import extract
+
+
+def analyse(datas: list[dict]):
+    usage_data = list(map(extract.usage.extract, datas))
+
+    usages: dict[str, int] = {"always": 0, "often": 0, "sometime": 0, "rarely": 0, "never": 0}
+    for usage in usage_data:
+        usages[usage] += 1
+
+    usages_x = list(usages.keys())
+    usages_y = list(usages.values())
+
+    # prepare plotting
+    figure: plt.Figure = plt.figure()
+    axes = figure.add_subplot(1, 1, 1)
+    axes.set_title("Expérience des personnes sondées")
+
+    # bar chart
+    axes.bar(usages_x, usages_y)
+
+    plt.show(block=True)

tools/statistics/extract/__init__.py

@@ -0,0 +1,6 @@
+from . import usage
+from . import age
+from . import mission_duration
+from . import mission_completed
+from . import experience
+from . import hardest_mission

tools/statistics/extract/age.py

@@ -0,0 +1,2 @@
+def extract(data: dict) -> int:
+    return data["surveys"]["question-age"]["value"]

tools/statistics/extract/experience.py

@@ -0,0 +1,5 @@
+def extract(data: dict) -> str:
+    return next(filter(
+        lambda it: it[1]["checked"],
+        data["surveys"]["question-experience"]["choices"].items()
+    ))[0]

tools/statistics/extract/hardest_mission.py

@@ -0,0 +1,5 @@
+def extract(data: dict) -> str:
+    return next(filter(
+        lambda it: it[1]["checked"],
+        data["surveys"]["question-hardest-mission"]["choices"].items()
+    ))[0]

tools/statistics/extract/mission_completed.py

@@ -0,0 +1,9 @@
+def extract(data: dict, mission: str) -> bool:
+    events = data["surveys"][mission]["event"]
+
+    try:
+        checks = next(filter(lambda event: event["type"] == "check", events))
+    except StopIteration:
+        return False
+    else:
+        return True

tools/statistics/extract/mission_duration.py

@@ -0,0 +1,2 @@
+def extract(data: dict, mission: str) -> float:
+    return data["surveys"][mission]["event"][-1]["time"]

tools/statistics/extract/usage.py

@@ -0,0 +1,5 @@
+def extract(data: dict) -> str:
+    return next(filter(
+        lambda it: it[1]["checked"],
+        data["surveys"]["question-usage-steam"]["choices"].items()
+    ))[0]

tools/statistics/main.py

@@ -1,38 +1,31 @@
 from pathlib import Path
 
+from tools.statistics.analyse import (age, usage, completion_per_mission, duration_per_mission, completion_per_age,
+                                      completion_per_usage, duration_per_age, duration_per_usage,
+                                      completion_per_experience, duration_per_experience, experience, hardest_mission)
+
 if __name__ == "__main__":
     from source.utils import compress
 
-    directory = Path(r"C:\Users\RC606\Downloads\résultats étude")
+    directory = Path(r"./sondage/")
 
     # read every people survey data
-    for file in directory.rglob("*.rsl"):
+    datas = [
-        # decompress the data
+        compress.uncompress_data(file.read_bytes())  # decompress the data
-        data = compress.uncompress_data(file.read_bytes())
+        for file in directory.rglob("*.rsl")
+    ]
 
+    # age.analyse(datas)
+    # usage.analyse(datas)
+    # experience.analyse(datas)
+    # hardest_mission.analyse(datas)  # !
 
+    # completion_per_mission.analyse(datas)
+    # completion_per_age.analyse(datas)
+    # completion_per_usage.analyse(datas)  # !
+    # completion_per_experience.analyse(datas)
 
-
+    # duration_per_mission.analyse(datas)
-    ages = sorted(list(map(
+    # duration_per_age.analyse(datas)
-        lambda data: data["surveys"]["question-age"]["value"],
+    # duration_per_usage.analyse(datas)  # !
-        datas
+    # duration_per_experience.analyse(datas)  # !
-    )))
-
-    print(ages)
-    print(sum(ages) / len(ages))
-    print(ages[len(ages) // 2])
-    print(min(ages))
-    print(max(ages))
-
-    print(datas[1]["surveys"]["question-usage-steam"])
-
-    usages = sorted(list(map(
-        lambda data: data["surveys"]["question-usage-steam"]["choices"],
-        datas
-    )))
-
-    print(usages)
-    print(sum(usages) / len(usages))
-    print(usages[len(usages) // 2])
-    print(min(usages))
-    print(max(usages))