Merge branch 'dev-kja' of https://github.com/OpenDendro/dplPy into de…

…v-kja

dplpy/smoothingspline.py

@@ -41,6 +41,9 @@ def get_param(amp, period):
 def spline(x, y, period=None):
     if period == None:
         period = len(x) * 0.67
+    elif period < 0:
+        # Use the absolute value of period as a percentage
+        period = len(x) * (abs(period) / 100.0)
 
     p = get_param(0.5, period)
     yi = csaps(x, y, x, smooth=p)

dplpy/transform.py

@@ -0,0 +1,72 @@
+__copyright__ = """
+   dplPy for tree ring width time series analyses
+   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
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+__license__ = "GNU GPLv3"
+
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+# Date: 12/15/2023
+# Author: Kevin Anchukaitis
+# Title: transform.py
+# Description: Transforms the raw series using one of several options.  
+#              Doing so gives the series better statistical properties
+#              prior to detrending and standardization
+# example usage:
+# >>> import dplpy as dpl 
+# >>> data = dpl.readers("../tests/data/csv/file.csv")
+# >>> dpl.transform(data,transform_type='power')
+
+from tkinter import Y
+import pandas as pd
+import numpy as np
+
+def transform_series(data: pd.DataFrame | pd.Series, transform_type='power'):
+    transformed_data = data.copy()
+
+    for col in transformed_data.columns:
+        series = transformed_data[col]
+
+        if transform_type == 'log':
+            series[series == 0] = 0.01
+            transformed_data[col] = np.log(series)
+
+        elif transform_type == 'log6':
+            series = series + (1/6)
+            series[series <= 0] = (1/6)
+            transformed_data[col] = np.log(series)
+
+        elif transform_type == 'inversehypersine':
+            valid_idx = ~series.isna()
+            transformed_data.loc[valid_idx, col] = np.log(series[valid_idx] + np.sqrt(series[valid_idx] + 1.0))
+
+        elif transform_type == 'power':
+            valid_idx = ~series.isna()
+            x = series[valid_idx]
+            original_mean = x.mean()
+            original_stdev = x.std()
+
+            M = (x[:-1] + x[1:]) / 2
+            S = np.abs(x[:-1] - x[1:])
+            b = np.polyfit(M, S, 1)
+
+            transformed_data.loc[valid_idx, col] = series[valid_idx] ** (1 - b[0])
+
+            # Optional: reset mean to original
+
+    return transformed_data

dplpy/writers.py

@@ -145,8 +145,8 @@ def write_rwl(data, file):
         file.write("\n")
 
 
-def write_crn(data, site_id, file, site_name="Unnamed object", species_code="UNKN", location="Unknown", species="Plantae", elevation="", lat_long="", investigator="", comp_date=""):
-    rwi_data = chron(detrend(data, fit="spline", method="residual", plot=False), prewhiten=True)
+def write_crn(data, site_id, file, site_name="Unnamed site", species_code="UNKN", location="Unknown", species="Plantae", elevation="", lat_long="", investigator="", comp_date=""):
+    rwi_data = data
     first = rwi_data.first_valid_index()
     last = rwi_data.last_valid_index()
 
@@ -159,8 +159,8 @@ def write_crn(data, site_id, file, site_name="Unnamed object", species_code="UNK
     file.write(str(rwi_data.first_valid_index()).rjust(4))
 
     for year in rwi_data.index.to_numpy():
-        file.write(str(round(rwi_data["Mean RWI"][year], 2)).replace(".", "").replace("0", "").rjust(4))
-        file.write(str(rwi_data["Sample depth"][year]).rjust(3))
+        file.write(str(round(rwi_data["Standard Chronology"][year], 2)).replace(".", "").replace("0", "").rjust(4))
+        file.write(str(rwi_data["Sample Depth"][year]).rjust(3))
 
         if year % 10 == 9:
             # write TRL ID#(optional) which takes columns 82-88