Mais coisas, estatisticas

utilsv2/stats.py

@@ -0,0 +1,49 @@
+import time
+
+import numpy as np
+from pymongo import MongoClient
+
+
+def print_filters(filters):
+    _res = ""
+    for k, v in filters.items():
+        _res += f"{k}: {v}\n"
+
+
+def calculate_stats(data: list, filters):
+    _stats_txt = "Estatísticas\n"
+    _stats_txt += f"Número de eventos: {len(data)}\n"
+    _res = calc_mag(data)
+    _stats_txt += f"Magnitudes:\n\tMédia: {_res[0]} \u00b1 {_res[1]}\n\tMediana: {_res[2]}\n\tValor Mínimo: {_res[3]}\n\tValor Máximo: {_res[4]}\n"
+    _res = calc_depth(data)
+    _stats_txt += f"\nProfundidade:\n\tMédia: {_res[0]} \u00b1 {_res[1]}\n\tMediana: {_res[2]}\n\tValor Mínimo: {_res[3]}\n\tValor Máximo: {_res[4]}\n"
+
+    fname = f"stats-{time.time_ns()}.txt"
+    with open(fname, "wb") as fp:
+        fp.write(_stats_txt.encode("utf-8"))
+
+    print(_stats_txt)
+
+
+def get_data(client: MongoClient):
+    pass
+
+
+def calc_depth(data):
+    if len(data) == 0:
+        return 0
+    depths = np.array([v["Depth"] for v in data], dtype=float)
+    return (
+        np.average(depths),
+        np.std(depths),
+        np.median(depths),
+        np.min(depths),
+        np.max(depths),
+    )
+
+
+def calc_mag(data):
+    if len(data) == 0:
+        return 0
+    mags = np.array([v["Magnitudes"]["L"]["Magnitude"] for v in data], dtype=float)
+    return (np.average(mags), np.std(mags), np.median(mags), np.min(mags), np.max(mags))