Installation and Implementation of DLSuR Python Package

Installing the DLSuR Python package is very simple. It is recommended to do this within an Anaconda environment.

A short video tutorial of creating an environment, installing DLSuR in the environment, and implementing the package to analyze data can be found below. If you would like to read the instructions instead, keeping scrolling down.

Instructions for each step are linked here for easy access.

Any analysis that does not follow the format of these prepared functions can be done by piecing together a custom analysis script using the individual functions within the modules.

Creating an Anaconda Environment

To create an Anaconda environment, simple enter in the command line the following code.

$ conda create --name example

You can replace example with any name you choose to name your environment.

Installing DLSuR Python Package

Before installing DLSuR, enter your environment by entering into your command line the following code.

$ conda activate example

Again, example should be replaced with the name you have chosen for your created environment.

Next, we need to install a preliminary package first so that we have the Python package pip installed. For this, we will install the Python package scipy.

$ conda install scipy

Click through the prompts (i.e. it will ask whether you want to proceed, type y). Once scipy has been installed, you can double check that pip has also been installed by typing the following into your command line:

$ conda list

The package pip should show up in the list that appears.

Now, we install DLSuR by entering the following code.

$ pip install DLSuR

This can take some time. Please be patient. Once it is finished, congratulations! You now have DLSuR installed!

Importing and Implementing DLSuR

To actually use the functions within the DLSuR package, you need to import them into your code file. This can be done by adding the following line at the top of your code file:

import dlsmicro

If you want to import a specific function (i.e. analyze_conditions), then you can use the following syntax:

from dlsmicro import analyze_conditions

If the above line does now allow you to find the function, try the following code:

from dlsmicro.analyze_conditions import analyze_conditions

To use this function, define all of the inputs required, including file names, replicates list, temperature, particle radius. See below for an example of the inputs being defined:

csv_name = 'exported2.csv'
root_folder = 'example_data/condition_example'
condition_dir = {'condition1': 'cond1', 'condition2': 'cond2'}
replicate_dict = {'condition1': [1,2,3], 'condition2': [1]}
cond_color = {'condition1': 'r', 'condition2': 'b'}
T = {'condition1': 37. + 273.15, 'condition2': 25. + 273.15}
r = {'condition1': 500./2., 'condition2': 1000./2.}
erg = {'condition1': True, 'condition2': False}

The function analyze_conditions can now be used to analyze the data defined above:

analyze_conditions(csv_name, root_folder, condition_dir,
                   replicate_dict, T, r, erg, Laplace=True,
                   save_as_text=True, save_as_df=True,
                   plot_corr=True, plot_msd=True, plot_G=True)

The analyzed data is saved as a Pandas Dataframe in the root_folder and titled condition_data.pkl unless you have defined a different name using the input parameter df_file_name. This Dataframe is the data input for the function plot_conditions that allows you to see all conditions plotted together as the average of the replicates of each condition:

saved_df = root_folder + '/' + 'condition_data.pkl'

plot_conditions(saved_df, condition_dir, replicate_dict, plot_ci=True,
                cond_color=None, plot_scattering=True,
                add_scaling=True, scaling_frac=[3.,4.])

Please visit this package’s Github page to find the above code blocks in the file test_new.py and the example data referenced here to see how the file structure is set up.