Protein Absorbance Heatmap--- This makes a 2d density plot of your absorbance plot, attempting to show you what your data looks like. The first file visualizes the entire microwell plate, and the second file visualizes the entire plate AND experiemntal data/standards; making for better figures. BCA absorbance visualizer for assay.

1_BCA_Assay_abosrbance_heatmap.py

import matplotlib.pyplot as plt
import numpy as np
from numpy import genfromtxt
import os
path =  os.path.abspath("A3.csv")

def main():
    X = genfromtxt(path,delimiter=',')
    max = np.amax(X)
    min = np.amin(X)
    print(X)

    plt.imshow(X, cmap='gray_r',interpolation='nearest', vmin=0, vmax=2)
    # Choose vmin and vmax based on your data set min and max absorbance values
    # I found that generally going under both max/min of absorbance yielded better visual results
    plt.title("BCA_Assay_Visualization")
    plt.gcf().canvas.set_window_title('BCA_Assay_Visualization')
    plt.show()

main()

2_BCA_Assay_abosrbance_heatmap.py

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
from numpy import genfromtxt
import os

microwell_path =  os.path.abspath("A3.csv")
exp_axis = ['0','A','B','C','D','E','F','G','H']

def plot_absorb():

    data = genfromtxt(microwell_path,delimiter=',')
    microwell_data = data[0:6,0:11]
    standard_data = data[6:8,0:11]
    max, min, maxS, minS = range_of_arr(microwell_data,standard_data)
    print(data)

    fig, axs = plt.subplots(2)

    axs[0].set_title("Absorbance data for experimental samples")
    im1 = axs[0].imshow(microwell_data,cmap="gray_r")

    divider1 = make_axes_locatable(axs[0])
    cax1 = divider1.append_axes("right", size="20%", pad=0.05)
    cbar1 = plt.colorbar(im1, cax=cax1)
    axs[0].set_yticklabels(exp_axis)

    axs[1].set_title("Absorbance data for standards")
    im2  = axs[1].imshow(standard_data,cmap="gray_r")

    divider2 = make_axes_locatable(axs[1])
    cax2 = divider2.append_axes("right", size="20%", pad=0.05)
    cbar2 = plt.colorbar(im2, cax=cax2)
    axs[1].set_yticklabels(['0','G','H'])

    fig, axs = plt.subplots(1)

    axs.set_title("Full Visual Image of 96 well plate")
    im3  = axs.imshow(data,cmap="gray_r")
    divider3 = make_axes_locatable(axs)
    cax3 = divider3.append_axes("right", size="20%", pad=0.05)
    cbar3 = plt.colorbar(im3, cax=cax3)
    axs.set_yticklabels(exp_axis)


    plt.show()

def range_of_arr(arr1,arr2):
    return np.amax(arr1),np.amin(arr1),np.amax(arr2),np.amin(arr2)

if __name__ == "__main__":
    plot_absorb()

A3.csv

Figure 1. Experimental Data and Standards Visuallized Seperately



Figure 2. Full 96 well microplate visualization