Revert "updated .py modules with additional help descriptions"

This reverts commit 2c1d1a5.
OpenDendro · Jan 1, 2024 · 84197e3 · 84197e3
1 parent 2c1d1a5
commit 84197e3
Show file tree

Hide file tree

Showing 16 changed files with 238 additions and 336 deletions.
diff --git a/dplpy/autoreg.py b/dplpy/autoreg.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -43,26 +43,6 @@
 import warnings
 
 def ar_func(data, max_lag=5):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#ar_func
-    
-    Description: Contains methods that fit series to autoregressive models and perform functions 
-                 related to AR modeling.
-  
-    NOTE: This function only accepts pandas series and dataframes as parameters. 
-   
-    **Required Inputs**
-        <data>["<series>"] -  a data file (.CSV or .RWL), or an array imported from dpl.readers()
-        <series> - an individual series within a chronology file
-        <lag> - years, default 5
-   
-    Example usage:
-        >>> import dplpy as dpl 
-        >>> data = dpl.readers("../tests/data/csv/file.csv")
-        >>> dpl.ar_func(data['series name']) -> returns residuals plus mean of best fit 
-                                                from AR models with max lag of either 5 
-                                                (default) or specified number
-    """
     if isinstance(data, pd.DataFrame):
         start_df = pd.DataFrame(index=pd.Index(data.index))
         to_concat = [start_df]
@@ -99,26 +79,6 @@ def ar_func_series(data, max_lag):
 # This method selects the best AR model with a specified maximum order
 # The best model is selected based on AIC value
 def autoreg(data: pd.Series, max_lag=5):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#autoreg
-    
-    Description: Contains methods that fit series to autoregressive models and perform functions 
-                 related to AR modeling.
-  
-    NOTE: This function only accepts pandas series and dataframes as parameters. 
-   
-    **Required Inputs**
-        <data>["<series>"] -  a data file (.CSV or .RWL), or an array imported from dpl.readers()
-        <series> - an individual series within a chronology file
-        <lag> - years, default 5
-   
-    Example usage:
-        >>> import dplpy as dpl 
-        >>> data = dpl.readers("../tests/data/csv/file.csv")
-        >>> dpl.autoreg(data['series name']) -> returns parameters of best fit AR model
-                                            with maxlag of 5 (default) or other 
-                                            specified number
-    """
     # validate data?
     if not isinstance(data, pd.Series):
         raise TypeError("Data argument should be pandas series. Received " + str(type(data)) + " instead.")

diff --git a/dplpy/chron.py b/dplpy/chron.py
@@ -2,7 +2,7 @@
 
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2021  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -48,29 +48,6 @@
 # Main function for creating chronology of series. Formats input, prewhitens if necessary
 # and produces output mean value chronology in a dataframe.
 def chron(rwi_data: pd.DataFrame, biweight=True, prewhiten=False, plot=True):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#chron
-    Description: Creates a mean value chronology for a dataset, typically the ring width 
-                 indices of a detrended series. Takes three optional arguments 'biweight', 
-                 'prewhiten', and 'plot'. They determine whether to find means using Tukey's 
-                 bi-weight robust mean (default 'True'), whether to prewhiten data by fitting 
-                 to an AR model (default 'False'), and whether to plot the results of the 
-                 chronology (default 'True').
-    
-    **Required Inputs**
-        <data> - a data frame loaded using readers
-        <prewhiten> - run pre-whitening on the time series, options: True or False, default is False  
-        <biweight> - use Tukey's bi-weight robust mean, default True
-        <plot> - plot the results, default True
-
-    Example Usage: 
-        >>> import dplpy as dpl 
-        >>> data = dpl.readers("../tests/data/csv/file.csv")
-        >>> rwi_data = dpl.detrend(data)
-        >>> dpl.chron(rwi_data)
-        >>> dpl.chron(rwi_data, prewhiten=True)
-        >>> chron_data = dpl.chron(rwi_data, biweight=False, plot=False)
-    """
     if not isinstance(rwi_data, pd.DataFrame):
         raise TypeError("Expected pandas dataframe as input, got " + str(type(rwi_data)) + " instead")
 

diff --git a/dplpy/curvefit.py b/dplpy/curvefit.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by

diff --git a/dplpy/detrend.py b/dplpy/detrend.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -41,33 +41,6 @@
 import curvefit
 
 def detrend(data: pd.DataFrame | pd.Series, fit="spline", method="residual", plot=True, period=None):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#detrend
-     
-    Description: Detrends a given series or dataframe, first by fitting data to curve(s), 
-                  with 'spline' as the default, and then by calculating residuals 
-                  (default = 'residual') or differences ('difference') compared to the original data. 
-                  Other supported curve fitting methods are 'ModNegex' (modified negative exponential), 
-                  'Hugershoff', 'linear', 'horizontal'.
-    
-    **Required Inputs**
-        <data> - a data frame loaded using dpl.readers()
-        <fit> - fitting method of curves, e.g., 'horizontal', 'Hugershoff', 'linear', 'ModNegex' (modified negative exponential), and 'spline', default 'spline'
-        <method> - intercomparison method, options: 'difference' or 'residual', default 'residual'
-        <plot> - plot the results, default 'True'
-    
-    Example Usage:
-    
-        # Detrend the entire dataframe
-        >>> dpl.detrend(<data>)
-
-        # Detrending a series part of the dataframe
-        >>> dpl.detrend(<data>["<series>"])
-
-        # Detrending function and its options
-        >>> dpl.detrend(<data>["<series>"], fit="<fitting method>", method="<comparison method>", plot=<True/False>)    
-       
-    """
     if isinstance(data, pd.DataFrame):
         res = pd.DataFrame(index=pd.Index(data.index))
         to_add = [res]

diff --git a/dplpy/dplpy.py b/dplpy/dplpy.py
@@ -2,7 +2,7 @@
 
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2021  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -91,7 +91,7 @@ def help_from_parser(args):
 # Open the Website README (Manual documentation) 
 def readme():
     try:
-        a = webbrowser.open("https://opendendro.org/python/", new=2)
+        a = webbrowser.open("https://opendendro.github.io/opendendro/python/", new=2)
         print("Success: Check your web browser for a new tab")
         if a == False:
             print("Your computer does not use a monitor, and cannot display the webpages")

diff --git a/dplpy/plot.py b/dplpy/plot.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -41,24 +41,6 @@
 from stats import stats
 
 def plot(inp: pd.DataFrame | str, type="seg"):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#plot
-    
-    Description: Plots a given dataframe or series of a specific dataframe in either 
-                 line ('default'), spaghetti ('spag') or segment ('seg') plots.
-                 
-    **Reqired Inputs**
-        <data> - a data frame loaded using dpl.readers()
-        <series> - a single time series within the data array
-        <type> - type of plot to generate, e.g., line, spaghetti (spag), or segment (seg). default line
-    
-    Example Usage:
-        # Plot entire data
-        >>> dpl.plot(<data>)
-
-        # Plot series subset of dataframe with a specified plot type
-        >>> dpl.plot(<data>["<series>"], type=<plot type>)
-    """
     if isinstance(inp, pd.DataFrame):
         series_data = inp
     elif isinstance(inp, str):

diff --git a/dplpy/rbar.py b/dplpy/rbar.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2023  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by

diff --git a/dplpy/readers.py b/dplpy/readers.py
@@ -2,7 +2,7 @@
 
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -46,18 +46,8 @@
 
 def readers(filename: str, skip_lines=0, header=False):
     """
-    Online Documentation:  https:/opendendro.org/dplpy-man/#readers
-    
-    Description: This function imports common ring width data files (.csv, .rwls) into Python as arrays
-    
-    **Required Inputs**
-        <filename> - a file (.CSV or .RWL), or the full path and file name
-    
-    Example usages:
-        >>> import dplpy as dpl
-        >>> data = dpl.readers('../tests/data/csv/filename.csv')
-        >>> data = dpl.readers('../tests/data/rwl/filename.rwl'), header=True
-    
+    This function imports common ring width data files into Python as arrays
+    Accepted file types are CSV and RWL
     """
     FORMAT = "." + filename.split(".")[-1]
     print("\nAttempting to read input file: " + os.path.basename(filename) + " as " + FORMAT + " format\n")
@@ -69,23 +59,22 @@ def readers(filename: str, skip_lines=0, header=False):
         series_data = process_rwl_pandas(filename, skip_lines, header)
     else:
         errorMsg = """
-        Error: Unsupported file type '{file_extension}'.
-        
-        Unable to read file, please check that you're using a supported type
-        Accepted file types are .csv and .rwl
-
-        Example usages:
-        >>> import dplpy as dpl
-        >>> data = dpl.readers('../tests/data/csv/filename.csv')
-        >>> data = dpl.readers('../tests/data/rwl/filename.rwl'), header=True
-        """
+
+Unable to read file, please check that you're using a supported type
+Accepted file types are .csv and .rwl
+
+Example usages:
+>>> import dplpy as dpl
+>>> data = dpl.readers('../tests/data/csv/filename.csv')
+>>> data = dpl.readers('../tests/data/rwl/filename.rwl'), header=True
+"""
 
         raise ValueError(errorMsg)
 
     # If no data is returned, then an error was encountered when reading the file.
     if series_data is None:
         errorMsg = """
-        Error reading file contents. Check that file exists and file formatting is consistent with {format} format.
+        Error reading file. Check that file exists and that file formatting is consistent with {format} format.
         If your file contains headers, run dpl.headers(file_path, header=True)
         """.format(format=FORMAT)
         raise ValueError(errorMsg)

diff --git a/dplpy/report.py b/dplpy/report.py
@@ -3,7 +3,7 @@
 
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -43,26 +43,6 @@
 from statsmodels.tsa.ar_model import AutoReg
 
 def report(inp: pd.DataFrame | str):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#report
-    
-    Description: Generates a report about the input dataset that includes:
-                    Number of dated series
-                    Number of measurements
-                    Avg series length (years)
-                    Range (total years)
-                    Span (start-end year)
-                    Mean (Standard Deviation) series intercorrelation
-                    Mean (Standard Deviation) AR1
-                    Years with absent rings listed by series
-
-    **Required Inputs**
-        <data> - a data file (.CSV or .RWL), or an array imported from dpl.readers()   
-    
-    Example Usage:
-        >>> dpl.report(<data>)
-
-    """
     if isinstance(inp, pd.DataFrame):
         series_data = inp
     elif isinstance(inp, str):

diff --git a/dplpy/series_corr.py b/dplpy/series_corr.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2023  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -46,25 +46,6 @@
 
 # Analyzes the crossdating of one series compared to the master chronology
 def series_corr(data: pd.DataFrame, series_name: str, prewhiten=True, corr="Spearman", seg_length=50, bin_floor=100, p_val=0.05):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#series_corr
-    
-    Description: Crossdating function that focuses on the comparison of one series to the master chronology.
-    
-    **Required Inputs**
-        <data> - a data file (.CSV or .RWL), or an array imported from dpl.readers()
-        <series> - a series name from the <data> array
-        <prewhiten> - run pre-whitening on the time series, options: 'True' or 'False', default is 'False'
-        <corr> - select correlation type if 'prewhiten=True', options: 'Pearson' or 'Spearman', default is 'Pearson'
-        <seg_length> - segment length (years), default '50'
-        <bin_floor> - select bin size, default '100'  
-        <p_val> - select a p-value, e.g., '0.05', '0.01', '0.001', default '0.05'
-        <plot> - plot the output, default is 'True'
-    
-    Example Usage:
-        >>> dpl.series_corr(ca533, "CAM191", prewhiten=False, corr="Pearson", bin_floor=10)    
-    
-    """
     # Check types of inputs
     if not isinstance(data, pd.DataFrame):
         errorMsg = "Expected dataframe input, got " + str(type(data)) + " instead."

diff --git a/dplpy/stats.py b/dplpy/stats.py
@@ -1,6 +1,6 @@
 __copyright__ = """
    dplPy for tree ring width time series analyses
-   Copyright (C) 2024  OpenDendro
+   Copyright (C) 2022  OpenDendro
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -51,20 +51,6 @@
 from statsmodels.tsa.ar_model import AutoReg
 
 def stats(inp: pd.DataFrame | str):
-    """
-    Online Documentation: https:/opendendro.org/dplpy-man/#stats
-    
-    Description: Generates summary statistics for .RWL and .CSV format files. 
-                 It outputs a table with 'first', 'last', 'year', 'mean', 'median', 'stdev', 
-                 'skew', 'gini', 'ar1' for each series in data file.
-    
-    **Required Inputs**
-         <data> - a data file (.CSV or .RWL), or an array imported from dpl.readers()
-         
-    Example Usage:
-        >>> dpl.stats(<data>)
-
-    """
     if isinstance(inp, pd.DataFrame):
         series_data = inp
     elif isinstance(inp, str):