Update docstring example code

pynssp/data.py

@@ -6,13 +6,13 @@ def load_simulated_ts():
     """Return a dataframe of simulated time series.
 
     Contains the following fields:
-         #   Column  Non-Null Count  Dtype
+        #   Column  Non-Null Count  Dtype
         ---  ------  --------------  -----
-         0   date    626 non-null    object
-         1   week    626 non-null    int64
-         2   year    626 non-null    int64
-         3   cases   626 non-null    int64
-         4   id      626 non-null    object
+        0   date    626 non-null    object
+        1   week    626 non-null    int64
+        2   year    626 non-null    int64
+        3   cases   626 non-null    int64
+        4   id      626 non-null    object
         dtypes: int64(3), object(2)
         memory usage: 24.6+ KB
     """

pynssp/detectors/ewma.py

@@ -171,6 +171,7 @@ def alert_ewma(df, t="date", y="count", B=28, g=2, w1=0.4, w2=0.9):
         Defaults to 0.9 to match ESSENCE implementation and approximate the C2 algorithm.
     :returns: Original pandas data frame with detection results.
     :examples:
+    
         >>> import pandas as pd
         >>> import numpy as np
         >>> from pynssp.detectors.ewma import *

pynssp/detectors/nbinom.py

@@ -127,6 +127,7 @@ def alert_nbinom(df, baseline_end, t="date", y="count", include_time=True):
         a binary alarm indicator field, and a binary indicator field of
         whether or not a time term was included.
     :examples:
+    
         >>> import pandas as pd
         >>> import numpy as np
         >>> df = pd.DataFrame({

pynssp/detectors/regression.py

@@ -158,6 +158,7 @@ def alert_regression(df, t="date", y="count", B=28, g=2):
             Defaults to 2.
     :returns: Original pandas data frame with detection results.
     :examples:
+    
         >>> import pandas as pd
         >>> import numpy as np
         >>> from pynssp.detectors.regression import *

pynssp/detectors/serfling.py

@@ -79,6 +79,7 @@ def alert_serfling(df, baseline_end, t="date", y="count"):
     :returns: Original pandas dataframe with model estimates, upper prediction interval bounds,
         a binary alarm indicator field, and a binary indicator
     :examples:
+    
         >>> import pandas as pd
         >>> import numpy as np
         >>> df = pd.DataFrame({

pynssp/detectors/switch.py

@@ -32,6 +32,7 @@ def alert_switch(df, t="date", y="count", B=28, g=2, w1=0.4, w2=0.9):
             Defaults to 0.9 to match NSSP-ESSENCE implementation and approximate the C2 algorithm.
     :returns: A dataframe containing the results of the analysis.
     :examples:
+    
         >>> import pandas as pd
         >>> import numpy as np
         >>> from pynssp.detectors.switch import *

pynssp/detectors/trend.py

@@ -1,45 +0,0 @@
-import pandas as pd
-import numpy as np
-import statsmodels.api as sm
-
-def classify_trend(df, t='date', data_count='dataCount', all_count='allCount', B=12):
-    t = df[t]
-    data_count = df[data_count]
-    all_count = df[all_count]
-
-    trend_analysis = pd.DataFrame({'t': t, 'data_count': data_count, 'all_count': all_count})\
-        .rolling(window=B)\
-        .apply(lambda x: np.nan \
-               if np.sum(x['data_count']) <= 10 \
-                else sm.GLM(np.column_stack([x['data_count'], x['all_count'] - x['data_count']]), 
-                            sm.add_constant(x['t']), 
-                            family=sm.families.Binomial()).fit().params[1],
-                            raw=True
-        )
-
-    p_value = pd.DataFrame({'t': t, 'data_count': data_count, 'all_count': all_count})\
-        .rolling(window=B)\
-        .apply(lambda x: np.nan \
-               if np.sum(x['data_count']) <= 10 \
-                else sm.GLM(np.column_stack([x['data_count'], x['all_count'] - x['data_count']]), 
-                            sm.add_constant(x['t']), 
-                            family=sm.families.Binomial()).fit().pvalues[1],
-                            raw=True
-        )
-
-    trend_classification = np.select(
-        condlist=[p_value < 0.01, p_value < 0.01, np.isnan(p_value)], 
-        choicelist=['Significant Increase', 'Significant Decrease', 'Insufficient Data'], 
-        default='Stable'
-    )
-
-    trend_classification = pd.Categorical(
-        trend_classification, 
-        categories=['Significant Increase', 'Significant Decrease', 'Stable', 'Insufficient Data']
-    )
-
-    return pd.DataFrame({
-        'trend_analysis': trend_analysis, 
-        'p.value': p_value, 
-        'trend_classification': trend_classification
-    })

pynssp/utils.py

@@ -14,6 +14,7 @@ def change_dates(url, start_date=None, end_date=None):
     :param end_date: str): A new end date to replace the existing end date in the URL. (Default value = None)
     :returns: The modified URL with the new start and end dates.
     :examples:
+    
         >>> from pynssp.utils import *
         >>> url = "https://example.com/data?startDate=01Jan2022&endDate=31Dec2022"
         >>> change_dates(url, start_date="01Jan2021", end_date="31Dec2021")