mkrause-nps / artificial-muscle

Artificial Muscle Data Presentation

For any Python script to run install requirements (e.g., by using a virtual environment). Run

python -m artificial-muscle -h

to find out input requirements to run the different studies.

Conductivity Study

A Python 3 script to construct a plot of conductivity data. The conductivity study focuses on answering the question of how does adding a conductive material to the resin affect the cured resin's conductivity.

Usage

To run the Python script install requirements (e.g., by using a virtual environment). Then in the project directory execute

python -m artificial-muscle datafilename conductivity

where datafilename is the absolute path to an Excel file, e.g., C:\data.xlsx, or just the filename.

The Excel file needs to contain at least one sheet with the following columns:

Position	Column Header	Datatype	Meaning
1	label	str	Describes percentage of substance mixed to resin
2	x	float	X-values
3	filtered	bool	Is the mixture filtered or not
4	y	float	Y-values
5	err	float	The error (e.g., standard error of the mean) for each Y-value
6	n	integer	The number of samples of Y for a given X

Metadata for a given spreadsheet are specified in config.py. Change it according to your needs.

The output is a semilogarithmic plot of conductivity values, which is saved in PNG format to the same directory the spreadsheet was loaded from.

Channel Width Study

The channel width study compares how different orientations of printing the channels affects channel widths. Specifically, how does printing the channel parallel or perpendicular to the print direction affect the channel width before and after baking.

The code requires passing an Excel file that contains a tab with the following filenames:

• py_hp_sm_prior
• py_vp_sm_prior
• py_hp_sm_past
• py_vp_sm_past
• py_hp_br_prior
• py_vp_br_prior
• py_hp_br_past
• py_vp_br_past

where hp denotes perpendicular and vp parallel print direction, respectively; sm denotes sacrificial material, br denotes black resin, and prior and past denote whether the specimen has been baked or not.

The tabs need to be of the following format:

Position	Column Header	Datatype	Meaning
1	label	str	An Enum: sacrificial material or black resin
2	x	int	X-values
3	y	float	Y-values
4	err	float	The error (e.g., standard error of the mean) for each Y-value

Executing the code generates four different kinds of scatter plots, depending on the type parameter.

Type Parameter	Plot Content
hp_sm	Channels containing sacrificial material, printed perpendicular before and after baking
vp_sm	Channels containing sacrificial material, printed parallel before and after baking
hp_br	Channels containing black resin material, printed perpendicular before and after baking
vp_br	Channels containing black resin material, printed parallel before and after baking

In the root directory execute

python -m artificial-muscle <path-to-Excel-datafile> <Type Parameter>

Files containing the plots are written to the same directory where the Excel datafile resides. The path-to-Excel-datafile can be relative because the absolute path is a parameter in config.py.

Clearance Study

Two scripts can be run within the Clearance Study:

1. Clearance Length: A script to analyze and plot the fraction of channel lengths that could be cleared for a given channel size. It writes the plot to disk in PNG format.
2. Clearance Width: A script to analyze and write summary plots and CSV files to disk about the widths of the cleared channel.

Clearance Length

Specify data directory and filenames at the beginning of the script. Those are used for in- and output of data. Then navigate to src and run python -m clearance_plot. It generates a PNG file with the data in variable datafile in the directory data_dir and with filename figname.

Clearance Width

Data for that script are expected in JSON format. To get the data into JSON I copied each of the four the raw data with headers (i.e., for each channel size) into an empty text file, and used a CSV-to-JSON converter to construct the JSON.

Set values for data_dir, datafile, figname and labels in the top of the script and execute python -m clearance_widths.

That writes two spreadsheet summary files and two PNG figures into data_dir.