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The dplPy User Manual (Alpha)

Welcome to the dplPy manual.



Installation (Alpha)

Planned release information

DplPy is planned to be released as a pip and conda packages for easy installation (pip install dplpy or conda install -c conda-forge dplpy). As DplPy is still under development, the current installation process requires manual installation of specific packages.

Known Installation issues

  • Packages installing through pip, such as CSAPS or Jupyter Notebook, might return a DuplicateOptionError error upon installing. When running into said error, deactivate your conda enviroment and close and reopen your terminal/VScode.
  • Best practice: prior to creating an enviroment, ensure that you are outside of base by doing conda deactivate. This should be repeated at any give instance where the conda environment is shown as base.

  1. Clone the GitHub repository to your personal machine: git clone; move into the repository cd dplPy/
  2. Build conda environment: conda create -n dplpy3 python=3.8; Activate: conda activate dplpy3
  3. Install CSAPS: pip install -U csaps
  4. Update conda environment: conda env update -f environment.yml --prune

Access through Jupyter Notebook

Although dplPy is executable from the command line interface (CLI), e.g., BASH, ZSH, or a Cygwin terminal, The usage of Jupyter Notebook is suggested.

Accessing Jupyter Notebook on Linux, MacOS

  1. In your VScode terminal, activate the conda environment with conda activate dplpy3.
  2. From the terminal, execute jupyter notebook.
  3. If prompted to select a kernel, select dplpy3. This will automatically load the correct environment.

Accessing Jupyter Notebook on Windows

In VScode:

  1. In your VSCode terminal window, activate the conda environment with conda activate dplpy3.
  2. In the same terminal window, start a Jupyter Notebook with jupyter notebook. Jupyter will then return URLs that you can copy; Copy one of these URLs.
  3. Open a Jupyter Notebook (<file>.ipynb) and from the bottom right of the VSCode screen, click Jupyter Server; A dropdown menu will open from the top of the screen: select Existing and paste the URL you copied.
  4. Jupyter Notebook will now be able to access the environment created.


Obtain Git

Clone (and move into) the dplPy Git repository with:

$ git clone
$ cd dplPy


The dplPy Git repository contains:

  • source code (src/)
  • A jupyter notebook example (runnable_example.ipynb)
  • Test files in csv and rwl formats (tests/data/<format>/)

Import the dplpy library

dplPy currently exists as a python library; ensure you are in the correct folder prior to execution.

import os
directory = os.getcwd().split("/")
if directory[-1] != 'src':
import dplpy as dpl


Supported data types

dplPy currently supports csv and rwl data formats.

To load (read) data into a dataframe, do:

>>> data = dpl.readers("path/to/data.format")


>>> data  = dpl.readers("../tests/data/rwl/ca533.rwl")

Expected outputs:

  • A success/failure message;
  • A list of series within the data file.


The summary function generates a summary of each series recorded in rwl and csv format files.

>>> dpl.summary(<data>)

Expected outputs:

  • Table with count, mean, std, min, 25%, 50%, 75%, max for each series in data file.

General Statistics

Generates summary statistics for rwl and csv format files.

>>> dpl.stats(<data>)

Expected outputs:

  • Table with first, last, year, mean, median, stdev, skew, gini, ar1 for each series in data file.


Detrends a series by fitting to spline and calculating residuals.


Spline is the current detrent default, line graph shows residuals. The detrend funtion can modified to fit Hugershoff, modified negative exponential, linear and horizonal methods. These will become an option available in the short term future.

>>> dpl.detrend(data["<series>"])
Change <series> to the desired series to detrend.


>>> dpl.detrend(data["CAM191"])

Expected outputs:

  • A graph depicting the fitted curve;
  • A graph depicting residuals variability.

Autoregressive modeling

The current autoregressive modeling functions are called with

(1) >>> dpl.autoreg(<data>["<series>"], lag_number)
(2) >>> dpl.ar_func(<data>["<series>"])
As default, the max lag is set to 5; Adding a second parameter (integer) to change max lag (valid for function (1)).

The first (1) function calculates the autoregressive parameters, whilst th second (2) calculates the residual+mean (by choosing best AR model fit with the selected max lag.)


>>> dpl.autoreg(data["CAM191"], 10) #This changes the max lag to 10 instead of the default 5.
>>> dpl.ar_func(data["CAM191"])

Expected outputs:

  • (1) a table listing autoregressive paramenters for the specified series;
  • (2) an array of residual+mean for selected series.

Development & Future plans

We encourage community contributions through our GitHub, feel free to create Issues and Pull Requests.

Help menu

Echos the help menu on the CLI

$ python help
>>> import dplpy as dpl


Opens this webpage


$ python readme

Python Console:

>> import dplpy as dpl
>> dpl.readme()


Imports a .rwl or .csv format ring width series file and converts it to a dataframe (array).

full flag short flag Description
--input -i Input files come from the localhost using the --input parameter
--url -u Input file from any public URL using the --url parameter
--name -n name of the array created from the file


$ python dplpy reader --input=/home/user/directory/filename.rwl --name=dataset1  
$ python dplpy reader -i /home/user/directory/filename.rwl -n dataset1 
$ python dplpy reader /home/user/directory/filename.rwl dataset1
$ python dplpy reader --input=/home/user/directory/filename.csv --name=dataset2 
$ python dplpy reader csv /home/user/directory/filename.csv dataset2
$ python dplpy reader --url= --name=dataset3 
$ python dplpy reader dataset3

Python Console:

>> import dplpy as dpl
>> dataset1 = dpl.reader("/home/user/directory/filename.rwl")


Creates and prints the summary statistics for a ring width series dataframe

full flag short flag Description
--input -i Input files come from the localhost using the --input parameter or from any public URL using the --url parameter
--stats -s summary statistics to output all reports all stats


$ python summary --input=/home/user/directory/filename.rwl --stats=all
$ python summary /home/user/directory/filename.rwl all
$ python summary /home/user/directory/filename.rwl --stats=mean
$ python summary /home/user/directory/filename.rwl mean 

Python Console:

>> import dplpy as dpl
>> dataset1 = dpl.readers("/home/user/directory/filename.rwl")
>> dpl.summary(dataset1, all)
>> summary_dataset1 = dpl.summary("/home/user/directory/filename.rwl")