graficos, estatitsticas e filtros

utils/stats.py

@@ -1,13 +1,15 @@
 # pyright: basic
 
+import datetime
 import os
 import sys
 
-import pandas as pd
 import numpy as np
+import pandas as pd
+import utils
 
-STAT_HEADER ="""=== Terramotos ===
- == Estatísticas == 
+STAT_HEADER = """=== Terramotos ===
+ == Estatísticas ==
 """
 
 STAT_MENU = """[1] Média
@@ -26,7 +28,7 @@ FILTER_CHOICES = """[1] Magnitudes
 
 """
 
-CHOICE = {"1": "Magnitudes", "2": "Distancia","3": "Prof"}
+CHOICE = {"1": "Magnitudes", "2": "Distancia", "3": "Prof"}
 
 
 def filter_submenu(type: str):
@@ -124,7 +126,7 @@ def stat_menu(df: pd.DataFrame):
 
 
 def average(df: pd.DataFrame, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -136,7 +138,7 @@ def average(df: pd.DataFrame, filter_by):
 
 
 def variance(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -149,12 +151,12 @@ def variance(df, filter_by):
 
 
 def std_dev(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
-    
+
     try:
         return np.std(values)
     except:
@@ -162,27 +164,27 @@ def std_dev(df, filter_by):
 
 
 def max_v(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
-    
+
     return np.max(values)
 
 
 def min_v(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
-    
+
     return np.min(values)
 
 
 def moda(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = df.drop_duplicates(subset="ID", keep="first")
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -191,7 +193,7 @@ def moda(df, filter_by):
     uniques, count = np.unique(values, return_counts=True)
     uniques_list = list(zip(uniques, count))
 
-    return sorted(uniques_list, reverse=True ,key=lambda x: x[1])[0][0]
+    return sorted(uniques_list, reverse=True, key=lambda x: x[1])[0][0]
 
 
 def _unpack_mags(arr: np.ndarray):
@@ -201,3 +203,128 @@ def _unpack_mags(arr: np.ndarray):
             newVals = np.append(newVals, float(m["Magnitude"]))
     return newVals
 
+
+def filter_mags(data, more_than=None, less_than=None):
+    """Filters by magnitudes a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :param more_than(optional): Filter for magnitudes above threshold
+    :param after(optional): Filters for dates after set date
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data.drop_duplicates(subset="ID", keep="first")
+    _dict = {"Data": [], "MagL": []}
+    for idx, c in v.iterrows():
+        _dict["Data"].append(str(c.Data))
+        _dict["MagL"].append(utils.extract_mag_l(c.Magnitudes))
+
+    _df = pd.DataFrame.from_dict(_dict)
+    if more_than:
+        _df = _df[_df["MagL"] >= more_than]
+
+    if less_than:
+        _df = _df[_df["MagL"] <= less_than]
+    return _df
+
+
+def filter_date(
+    data: pd.DataFrame,
+    before: datetime.datetime | None = None,
+    after: datetime.datetime | None = None,
+) -> pd.DataFrame:
+    """Filters by date a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :param before(optional): Filter for dates before set date
+    :param after(optional): Filters for dates after set date
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data
+    for idx, c in v.iterrows():
+        v.at[idx, "Data"] = datetime.datetime.fromisoformat(c.Data)
+
+    if after:
+        v = v[v["Data"] >= after]
+
+    if before:
+        v = v[v["Data"] >= before]
+    return v
+
+
+def filter_depth(
+    data: pd.DataFrame,
+    less_than: float | None = None,
+    more_than: float | None = None,
+) -> pd.DataFrame:
+    """Filters by the depth a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :param less_than(optional): Filter for depths below the threshold
+    :param after(optional): Filters for depths deeper than threshold
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data.drop_duplicates(subset="ID", keep="first")
+
+    if more_than:
+        v = v[v["Profundidade"] >= more_than]
+
+    if less_than:
+        v = v[v["Profundidade"] >= less_than]
+    return v
+
+
+def filter_gap(
+    data: pd.DataFrame,
+    threshold: int,
+) -> pd.DataFrame:
+    """Filters by the depth a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :param threshold: Filter for GAPS below the threshold
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data.drop_duplicates(subset="ID", keep="first")
+    v = v[v["Gap"] <= threshold]
+    return v
+
+
+def filter_sz(
+    data: pd.DataFrame,
+) -> pd.DataFrame:
+    """Filters by SZ plane a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data[data["SZ"].notna()]
+    return v
+
+
+def filter_vz(
+    data: pd.DataFrame,
+) -> pd.DataFrame:
+    """Filters by VZ plane a DataFrame into a new Dataframe
+
+    :param data: Raw pandas DataFrame
+    :returns: Returns a filtered pandas DataFrame
+    """
+    v = data[data["VZ"].notna()]
+    return v
+
+
+def _preprare_days(data):
+    c = data.Data.to_list()
+    for idx, d in enumerate(c):
+        aux = datetime.datetime.fromisoformat(d)
+        c[idx] = datetime.datetime.strftime(aux, "%Y-%m-%d")
+
+    return c
+
+
+def _preprare_months(data):
+    c = data.Data.to_list()
+    for idx, d in enumerate(c):
+        aux = datetime.datetime.fromisoformat(d)
+        c[idx] = datetime.datetime.strftime(aux, "%Y-%m")
+
+    return c