dataframe to dataset about pyimpspec HOT 2 OPEN

The value of path is used to document where the data came from, but it is not read. The first parameter of the pyimpspec.dataframe_to_data_sets function should be your pandas.DataFrame object that contains all of the data that you want to turn into one or more pyimpspec.DataSet objects. The value of path could be any string (even an empty string), but ideally it would be the path to the file that you parsed in order to get the data that you have in your pandas.DataFrame object. The value of path is also used to figure out a default value for the label parameter if you don't provide a value for label (e.g., path='/foo/bar/baz.txt' means that the default value for label is 'baz' if you don't provide another value for label).

Once you have your pyimpspec.DataSet objects, you can later on call the get_path method if you need to find out which file one of those objects corresponds to. Pyimpspec may also use the value of path in some cases to make it clear which file the analysis results correspond to. Below is an example where I used the command line interface of pyimspec via the Bash shell to fit a circuit consisting of one resistance to replicate measurements located in separate files by using the wildcard, *, to select all files that match the pattern C1*.xlsx (e.g., C1-1.xlsx and C1-2.xlsx) in the current folder. In this case, pyimpspec prints out the relative paths just before each set of results.

> pyimpspec fit "R" C1*.xlsx
C1-1.xlsx
CDC: [R]

| Element   | Parameter   |   Value |   Std. err. (%) | Unit   | Fixed   |
|:----------|:------------|--------:|----------------:|:-------|:--------|
| R_1       | R           | 575.739 |      0.00748923 | ohm    | No      |

| Label                          | Value               |
|:-------------------------------|:--------------------|
| Log pseudo chi-squared         | -5.533406855983064  |
| Log chi-squared                | -6.251845637980951  |
| Log chi-squared (reduced)      | -7.8646294947006865 |
| Akaike info. criterion         | -759.5891998959225  |
| Bayesian info. criterion       | -757.8515302776392  |
| Degrees of freedom             | 41                  |
| Number of data points          | 42                  |
| Number of function evaluations | 82                  |
| Method                         | cg                  |
| Weight                         | modulus             |

C1-2.xlsx
CDC: [R]

| Element   | Parameter   |   Value |   Std. err. (%) | Unit   | Fixed   |
|:----------|:------------|--------:|----------------:|:-------|:--------|
| R_1       | R           | 575.766 |       0.0357865 | ohm    | No      |

| Label                          | Value              |
|:-------------------------------|:-------------------|
| Log pseudo chi-squared         | -5.499914548531639 |
| Log chi-squared                | -5.811390468156221 |
| Log chi-squared (reduced)      | -7.424174324875956 |
| Akaike info. criterion         | -716.9934085527578 |
| Bayesian info. criterion       | -715.2557389344745 |
| Degrees of freedom             | 41                 |
| Number of data points          | 42                 |
| Number of function evaluations | 30                 |
| Method                         | least_squares      |
| Weight                         | modulus            |

If you have already parsed the data (frequencies and impedances) from a file, then you could also just use those to create a pyimpspec.DataSet object. The pyimpspec.dataframe_to_data_sets is mainly to reduce code duplication across the parsers for the various supported file formats. The function also helps with handling cases where a single file might contain multiple frequency sweeps by splitting such data into multiple pyimpspec.DataSet objects. Note that the path parameter is optional in the pyimpspec.DataSet constructor, but the value of path is used in the same way (i.e., just to help keep track of which file the data corresponds to).

from pyimpspec.

Hi, I've understand what you mean, Thanks a lot.
Best wishes!

from pyimpspec.