Page MenuHomec4science
Files F120135905

view_AMP_measurement.py
No OneTemporary
Actions

File Metadata

Created
Wed, Jul 2, 04:44

view_AMP_measurement.py
View Options

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 are 2 collections associated with the ampicillin measurements:
# 1) tseries_AMPexp1_meas
# 2) tseries_AMPexp1_analysis
# The first one contains the raw data and the second one contains meta-information about the analysis of the
# raw data.
# ==================================================================================================================
# Let's see how the raw data looks like. We load the first measurement of run 19 of the 'tseries_AMPexp1_meas'
# collection:
query = {'meas_num': '1', 'run': 19}
docs = get_docs_VM(db_name='Optical_Trapping_ML_DB', coll_name='tseries_AMPexp1_meas', 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())
transmission = doc.get('data')['transmission']
# For these measurements the time sequence must be created from the time step
dt = doc.get('dt')
time = np.arange(0, len(transmission) * dt, dt)
# We plot the raw data in Fig.1
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()
# Each measurement has been analyzed. Let's get the corresponding analyzed document:
analyzed_doc = get_docs_VM(db_name='Optical_Trapping_ML_DB', coll_name='tseries_AMPexp1_analysis',
query={'_id': doc['_id']})[0]
# analyzed_doc = get_docs_VM(db_name='Optical_TrappingDB', coll_name='tseries_AMPexp1_analysis',
# query=query)[0]
print(doc['_id'], analyzed_doc['_id'])
# The analysis of the time series amounts in classifying each part of the trace in OFF / ON / TRAPPING segments
classification = analyzed_doc['classification']
off_idx = [x['indices'] for x in classification if x['type'] == 'OFF']
on_idx = [x['indices'] for x in classification if x['type'] == 'ON']
trap_idx = [x['indices'] for x in classification if x['type'] == 'trapping']
# In Fig.2 we plot the different segments
fig2, ax2 = plt.subplots(figsize=(7, 5.8))
ax2.plot(time, transmission, 'k')
for k, idx in enumerate(off_idx):
if k == 0:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'r', label='Cavity OFF')
else:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'r')
for k, idx in enumerate(on_idx):
if k == 0:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'b', label='Cavity ON')
else:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'b')
for k, idx in enumerate(trap_idx):
if k == 0:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'g', label='Trapping')
else:
ax2.plot(time[idx[0]:idx[1]], transmission[idx[0]:idx[1]], 'g')
ax2.set_xlabel('Time (sec)', size=20)
ax2.set_ylabel('Transmission (V)', size=20)
ax2.yaxis.set_ticks_position('both')
ax2.xaxis.set_ticks_position('both')
ax2.tick_params(axis='both', which='both', direction='in', labelsize=20)
ax2.set_title('Raw data with classification', size=20)
fig2.tight_layout()
# In order to compare many measurements, we must carry out a normalization:
norm_factor = np.median(transmission[on_idx[0][0]:on_idx[0][1]])
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()

Event Timeline

c4science · Help