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R13271/GRAM_measurements/gram_clustering.py
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gram_clustering.py
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import numpy as np
import matplotlib.pyplot as plt
import pymongo as pym
from matplotlib.patches import Ellipse
from Z_HELPERS.DM_communication import get_docs_VM
#funciton to calculate mean and std
def calculate_stats(transmission_data, normalization_factor):
normalized_data = np.array(transmission_data)/normalization_factor
mean=normalized_data.mean()
std=normalized_data.std()
return mean, std
#function to plot data with ellipses indicating the spread
#stats_dict: dictionary where keys are bacteria types and values are lists of (mean, std) tuples
def plot_data(stats_dict, title, display_names):
fig, ax = plt.subplots()
colors = plt.cm.tab10(np.linspace(0, 1, len(stats_dict)))
#lets assign a unique color to each bacteria type and creates the ellipses
for (bacteria, stats), color in zip(stats_dict.items(), colors):
if bacteria == "pp6":
continue
means, stds = zip(*stats)
ax.scatter(means, stds, label=bacteria, color=color)
#creates 2 sigma ellipse for data spread
ellipse_2sigma = Ellipse((np.mean(means), np.mean(stds)), 2 * np.std(means), 2 * np.std(stds), edgecolor=color, facecolor='none', linestyle='--')
#3 sigma ellipse
ellipse_3sigma = Ellipse((np.mean(means), np.mean(stds)), 3 * np.std(means), 3 * np.std(stds), edgecolor=color, facecolor='none', linestyle=':')
ax.add_patch(ellipse_2sigma)
ax.add_patch(ellipse_3sigma)
display_name = display_names.get(bacteria, bacteria)
ax.text(np.mean(means), np.mean(stds), display_name, color=color, fontsize=13, ha='right')
ax.set_xlabel('Mean (μ)')
ax.set_ylabel('Standard Deviation (σ)')
plt.title(title)
plt.show()
if __name__ == '__main__':
query = {}
#retrieves a sample of documents from the database using the query
sample_docs = get_docs_VM(db_name='Optical_Trapping_ML_DB', coll_name='tseries_Gram_analysis', query=query)
#stores statcs for each bacteria type
stats_dict={}
display_names = {
'bs134': 'B. subtilis',
'coli': 'E. coli',
'li142': 'L. innocua',
'ns139': 'N. sicca',
'pp46': 'P. putida',
'pp6': 'Pseudomonas putida',
'se26': 'S. epidermidis B',
'se9': 'S. epidermidis A',
'ys134': 'Y. ruckeri'
}
for doc in sample_docs:
normalization_factor = doc.get('normalization_factor')
bacteria = doc.get('bacteria')
data = doc.get('data')
if bacteria not in stats_dict:
stats_dict[bacteria] = []
#calculates and stores mean and std for each bacteria type
if normalization_factor and data:
transmission_data = data.get('transmission', [])
if transmission_data:
mean, std = calculate_stats(transmission_data, normalization_factor)
stats_dict[bacteria].append((mean, std))
if stats_dict:
plot_data(stats_dict, "Bacterial Transmission Data", display_names)
else:
print("No valid data found to plot.")
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