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R13271/GRAM_measurements/view_GRAM_measurements.py
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view_GRAM_measurements.py
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import matplotlib.pyplot as plt
import numpy as np
from Z_HELPERS.DM_communication import get_docs_VM
if __name__ == '__main__':
# First, we must query the correct Mongo DB and collection. The DB is called 'Optical_Trapping_ML_DB'.
# There is 1 collection associated with the GRAM measurements:
# 1) tseries_Gram_analysis
# Each document contains the trapping data and the normalization factor
# ==================================================================================================================
# Let's see how the raw data looks like. We load the first measurement of run 19 of the 'tseries_AMPexp1_meas'
# collection:
query = {"name":"se9_p2_0"}
docs = get_docs_VM(db_name='Optical_Trapping_ML_DB', coll_name='tseries_Gram_analysis', query=query)
# If you want to query all the measurements of a given collection you set "query = {}".
# ==================================================================================================================
# We loop over all the documents printing each key-value pair of the document, and we plot the data
for doc in docs:
for k, v in doc.items():
# We do not show the data completely but rather the keys
if k != 'data':
print('Key: {} - Value: {}'.format(k, v))
else:
print('data keys: ', v.keys(), '\n\n')
# For these measurements the time was saved with the transmission
transmission = np.array(doc.get('data')['transmission'])
time = doc.get('data')['time']
# We plot the raw data in Fig.1, which contains only the trapping signal without the rest of the trace
fig, ax = plt.subplots(figsize=(7, 5.8))
ax.plot(time, transmission, 'k')
ax.set_xlabel('Time (sec)', size=20)
ax.set_ylabel('Transmission (V)', size=20)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.tick_params(axis='both', which='both', direction='in', labelsize=20)
ax.set_title('Raw data', size=20)
fig.tight_layout()
# In order to compare many measurements, we must carry out a normalization:
norm_factor = doc['normalization_factor']
transmission = transmission / norm_factor
fig3, ax3 = plt.subplots(figsize=(7, 5.8))
ax3.plot(time, transmission, 'b')
ax3.set_xlabel('Time (sec)', size=20)
ax3.set_ylabel('Normalized transmission', size=20)
ax3.yaxis.set_ticks_position('both')
ax3.xaxis.set_ticks_position('both')
ax3.tick_params(axis='both', which='both', direction='in', labelsize=20)
ax3.set_title('Normalized Data', size=20)
fig3.tight_layout()
plt.show()
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