Analyse the Results

The Analysis is tightly bound with the Measurement because this will determine how the data is shaped and which features you want to extract from it. Therefore, the base class should be extended as explained in this tutorial:

• Implement an Analysis

However, in the following example, the base class will be used to show the basic functionality.

Since the MA8X8 interface was used in the previous examples, the dataset should be filled with data accordingly. If you already have an HDF5 file from following the basic usage example (“Test.h5”), then this script should do the job:

import numpy as np
from cohesivm.database import Database

# load existing data and corresponding metadata
db = Database('Test.h5')
dataset = db.filter_by_sample_id('test_sample_42')[0]
metadata = db.load_metadata(dataset)

# create a new data to not interfere with previous examples
dataset = db.initialize_dataset(metadata)

# iterate over contact_ids and save data arrays
for contact_id in metadata.contact_ids:
    db.save_data(np.array(range(10), dtype=[('Voltage (V)', float)]), dataset, contact_id)

# load the data
data, metadata = db.load_dataset(dataset)

This time, the save_data() method was used correctly (contrary to the previous examples) because the provided data should always be a structured array.

Next, functions and plots must be defined:

>>> def maximum(contact_id: str) -> float:
...     return max(data[contact_id]['Voltage (V)'])
>>> functions = {'Maximum': maximum}
>>> plots = {}  # will be spared for simplicity (refer to the tutorial instead)

This approach seems too complex for what the function does, but it makes sense if you consider that this should be implemented in a separate Analysis class. There, the data is stored as a property and the functions (i.e., methods) have direct access to it. Due to the use of structured arrays (which facilitate to store the quantity and the unit alongside the data), the label also needs to be stated explicitly. But, again, this will normally be available as a property.

In the following, the class is initialized with and without using the Metadata from the dataset. The former approach has the advantage that all available fields could be accessed by the functions, e.g., values that are stored in the measurement_settings.

>>> from cohesivm.analysis import Analysis
# without metadata, the contact_position_dict must be provided
>>> analysis = Analysis(functions, plots, data, metadata.contact_position_dict)
# with metadata, additional metadata fields can be used in the analysis
>>> analysis = Analysis(functions, plots, (data, metadata))
>>> analysis.metadata.measurement_settings['illuminated']
True

The main usage of the Analysis, besides providing the framework for the Analysis GUI, is to quickly generate maps of analysis results:

>>> analysis.generate_result_maps('Maximum')[0]
array([[9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.],
       [9., 9., 9., 9., 9., 9., 9., 9.]])

As expected, the maximum value of the generated data is placed in a 2D numpy array on locations corresponding to the contact_positions.