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Copy pathbulk_watershedVhalo_analysis_plots.py
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bulk_watershedVhalo_analysis_plots.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 28 21:24:34 2021
@author: genevieve.hayes
Bulk Analysis
"""
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
SAVEIMG = 1
# In[]:
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 16})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(9.5, 5.5)
#Watershed Info
# #File:Sample4B.tiff
# w4Bkpgp = 400.0
# w4Bkngn = 526.0
# w4Bkpgn = 1.0
# w4Bkngp = 2358.0
# #File:Sample3I.tiff
# w3Ikpgp = 53.0
# w3Ikngn = 1510.0
# w3Ikpgn = 14.0
# w3Ikngp = 42.0
# #File:Sample1I.tiff
# w1Ikpgp = 2150.0
# w1Ikngn = 327.0
# w1Ikpgn = 243.0
# w1Ikngp = 358.0
# #File:Sample6B.tiff
# w6Bkpgp = 3509.0
# w6Bkngn = 1048.0
# w6Bkpgn = 80.0
# w6Bkngp = 2516.0
# #File:Sample2C.tiff
# w2Ckpgp = 1595.0
# w2Ckngn = 741.0
# w2Ckpgn = 6.0
# w2Ckngp = 4231.0
# #File:Sample2B.tiff
# w2Bkpgp = 160.0
# w2Bkngn = 474.0
# w2Bkpgn = 4.0
# w2Bkngp = 662.0
# #File:Sample6C.tiff
# w6Ckpgp = 1570.0
# w6Ckngn = 799.0
# w6Ckpgn = 10.0
# w6Ckngp = 4813.0
# #File:Sample1H.tiff
# w1Hkpgp = 1262.0
# w1Hkngn = 255.0
# w1Hkpgn = 19.0
# w1Hkngp = 122.0
# #File:Sample3H.tiff
# w3Hkpgp = 1122.0
# w3Hkngn = 1828.0
# w3Hkpgn = 29.0
# w3Hkngp = 3293.0
# #File:Sample4C.tiff
# w4Ckpgp = 2091.0
# w4Ckngn = 560.0
# w4Ckpgn = 17.0
# w4Ckngp = 4390.0
# #File:Sample6D.tiff
# w6Dkpgp = 1137.0
# w6Dkngn = 2485.0
# w6Dkpgn = 9.0
# w6Dkngp = 2765.0
# #File:Sample2E.tiff
# w2Ekpgp = 386.0
# w2Ekngn = 1080.0
# w2Ekpgn = 13.0
# w2Ekngp = 1677.0
# #File:Sample4D.tiff
# w4Dkpgp = 2710.0
# w4Dkngn = 859.0
# w4Dkpgn = 9.0
# w4Dkngp = 4865.0
# #File:Sample2I.tiff
# w2Ikpgp = 320.0
# w2Ikngn = 1052.0
# w2Ikpgn = 13.0
# w2Ikngp = 1428.0
# #File:Sample6I.tiff
# w6Ikpgp = 64.0
# w6Ikngn = 1127.0
# w6Ikpgn = 2.0
# w6Ikngp = 1175.0
# #File:Sample1B.tiff
# w1Bkpgp = 143.0
# w1Bkngn = 231.0
# w1Bkpgn = 9.0
# w1Bkngp = 2554.0
# #File:Sample4E.tiff
# w4Ekpgp = 839.0
# w4Ekngn = 2389.0
# w4Ekpgn = 97.0
# w4Ekngp = 1003.0
# #File:Sample5C.tiff
# w5Ckpgp = 1862.0
# w5Ckngn = 938.0
# w5Ckpgn = 2.0
# w5Ckngp = 5169.0
# #File:Sample6E.tiff
# w6Ekpgp = 723.0
# w6Ekngn = 609.0
# w6Ekpgn = 3.0
# w6Ekngp = 2604.0
# #File:Sample6F.tiff
# w6Fkpgp = 97.0
# w6Fkngn = 1010.0
# w6Fkpgn = 5.0
# w6Fkngp = 516.0
# #File:Sample6J.tiff
# w6Jkpgp = 1215.0
# w6Jkngn = 928.0
# w6Jkpgn = 15.0
# w6Jkngp = 3520.0
# #File:Sample1A.tiff
# w1Akpgp = 708.0
# w1Akngn = 156.0
# w1Akpgn = 9.0
# w1Akngp = 2718.0
# #File:Sample5A.tiff
# w5Akpgp = 332.0
# w5Akngn = 243.0
# w5Akpgn = 3.0
# w5Akngp = 1738.0
# #File:Sample6G.tiff
# w6Gkpgp = 5507.0
# w6Gkngn = 441.0
# w6Gkpgn = 1.0
# w6Gkngp = 503.0
# #File:Sample1G.tiff
# w1Gkpgp = 1981.0
# w1Gkngn = 129.0
# w1Gkpgn = 27.0
# w1Gkngp = 18.0
# #File:Sample2A.tiff
# w2Akpgp = 238.0
# w2Akngn = 552.0
# w2Akpgn = 4.0
# w2Akngp = 1811.0
# #File:Sample5J.tiff
# w5Jkpgp = 776.0
# w5Jkngn = 193.0
# w5Jkpgn = 8.0
# w5Jkngp = 556.0
# #File:Sample1J.tiff
# w1Jkpgp = 3821.0
# w1Jkngn = 203.0
# w1Jkpgn = 64.0
# w1Jkngp = 21.0
# #File:Sample6A.tiff
# w6Akpgp = 1853.0
# w6Akngn = 1296.0
# w6Akpgn = 17.0
# w6Akngp = 1164.0
# #File:Sample3F.tiff
# w3Fkpgp = 609.0
# w3Fkngn = 480.0
# w3Fkpgn = 10.0
# w3Fkngp = 203.0
#File:Sample4B.tiff
w4Bkpgp = 9.0
w4Bkngn = 174.0
w4Bkpgn = 0.0
w4Bkngp = 2237.0
#File:Sample3I.tiff
w3Ikpgp = 52.0
w3Ikngn = 103.0
w3Ikpgn = 6.0
w3Ikngp = 83.0
#File:Sample1I.tiff
w1Ikpgp = 1904.0
w1Ikngn = 501.0
w1Ikpgn = 40.0
w1Ikngp = 578.0
#File:Sample6B.tiff
w6Bkpgp = 2619.0
w6Bkngn = 1141.0
w6Bkpgn = 40.0
w6Bkngp = 2552.0
#File:Sample2C.tiff
w2Ckpgp = 502.0
w2Ckngn = 1137.0
w2Ckpgn = 0.0
w2Ckngp = 3861.0
#File:Sample2B.tiff
w2Bkpgp = 43.0
w2Bkngn = 248.0
w2Bkpgn = 4.0
w2Bkngp = 673.0
#File:Sample6C.tiff
w6Ckpgp = 76.0
w6Ckngn = 1037.0
w6Ckpgn = 0.0
w6Ckngp = 4674.0
#File:Sample1H.tiff
w1Hkpgp = 1123.0
w1Hkngn = 227.0
w1Hkpgn = 10.0
w1Hkngp = 132.0
#File:Sample3H.tiff
w3Hkpgp = 395.0
w3Hkngn = 1333.0
w3Hkpgn = 4.0
w3Hkngp = 3346.0
#File:Sample4C.tiff
w4Ckpgp = 269.0
w4Ckngn = 867.0
w4Ckpgn = 4.0
w4Ckngp = 3993.0
#File:Sample6D.tiff
w6Dkpgp = 218.0
w6Dkngn = 798.0
w6Dkpgn = 2.0
w6Dkngp = 2655.0
#File:Sample2E.tiff
w2Ekpgp = 282.0
w2Ekngn = 483.0
w2Ekpgn = 5.0
w2Ekngp = 2241.0
#File:Sample4D.tiff
w4Dkpgp = 1492.0
w4Dkngn = 1068.0
w4Dkpgn = 3.0
w4Dkngp = 4730.0
#File:Sample2I.tiff
w2Ikpgp = 105.0
w2Ikngn = 562.0
w2Ikpgn = 1.0
w2Ikngp = 1450.0
#File:Sample6I.tiff
w6Ikpgp = 8.0
w6Ikngn = 594.0
w6Ikpgn = 0.0
w6Ikngp = 1352.0
#File:Sample1B.tiff
w1Bkpgp = 3.0
w1Bkngn = 210.0
w1Bkpgn = 0.0
w1Bkngp = 2608.0
#File:Sample4E.tiff
w4Ekpgp = 519.0
w4Ekngn = 970.0
w4Ekpgn = 32.0
w4Ekngp = 1106.0
#File:Sample5C.tiff
w5Ckpgp = 87.0
w5Ckngn = 1441.0
w5Ckpgn = 0.0
w5Ckngp = 4602.0
#File:Sample6E.tiff
w6Ekpgp = 125.0
w6Ekngn = 349.0
w6Ekpgn = 0.0
w6Ekngp = 2417.0
#File:Sample6F.tiff
w6Fkpgp = 11.0
w6Fkngn = 164.0
w6Fkpgn = 0.0
w6Fkngp = 530.0
#File:Sample6J.tiff
w6Jkpgp = 457.0
w6Jkngn = 849.0
w6Jkpgn = 1.0
w6Jkngp = 3510.0
#File:Sample1A.tiff
w1Akpgp = 22.0
w1Akngn = 276.0
w1Akpgn = 0.0
w1Akngp = 2673.0
#File:Sample5A.tiff
w5Akpgp = 67.0
w5Akngn = 260.0
w5Akpgn = 2.0
w5Akngp = 1658.0
#File:Sample6G.tiff
w6Gkpgp = 4916.0
w6Gkngn = 606.0
w6Gkpgn = 1.0
w6Gkngp = 973.0
#File:Sample1G.tiff
w1Gkpgp = 1880.0
w1Gkngn = 161.0
w1Gkpgn = 15.0
w1Gkngp = 53.0
#File:Sample2A.tiff
w2Akpgp = 75.0
w2Akngn = 431.0
w2Akpgn = 1.0
w2Akngp = 1888.0
#File:Sample5J.tiff
w5Jkpgp = 570.0
w5Jkngn = 253.0
w5Jkpgn = 3.0
w5Jkngp = 574.0
#File:Sample1J.tiff
w1Jkpgp = 3593.0
w1Jkngn = 345.0
w1Jkpgn = 31.0
w1Jkngp = 78.0
#File:Sample6A.tiff
w6Akpgp = 1242.0
w6Akngn = 1107.0
w6Akpgn = 5.0
w6Akngp = 1276.0
#File:Sample3F.tiff
w3Fkpgp = 535.0
w3Fkngn = 248.0
w3Fkpgn = 6.0
w3Fkngp = 242.0
# In[]: HALO Info
stain1_4E_h = 4228
stain2_4E_h = 1558
h_4E_kpgp = 669
h_4E_kngn = 2115
h_4E_kpgn = stain1_4E_h - h_4E_kpgp
h_4E_kngp = stain1_4E_h - h_4E_kpgp
stain1_6I_h = 2010
stain2_6I_h = 31
h_6I_kpgp = 23
h_6I_kngn = 602
h_6I_kpgn = stain1_6I_h - h_6I_kpgp
h_6I_kngp = stain1_6I_h - h_6I_kpgp
stain1_4D_h = 7316
stain2_4D_h = 1879
h_4D_kpgp = 1758
h_4D_kngn = 376
h_4D_kpgn = stain1_4D_h - h_4D_kpgp
h_4D_kngp = stain1_4D_h - h_4D_kpgp
stain1_6D_h = 4570
stain2_6D_h = 387
h_6D_kpgp = 284
h_6D_kngn = 1894
h_6D_kpgn = stain1_6D_h - h_6D_kpgp
h_6D_kngp = stain1_6D_h - h_6D_kpgp
stain1_2E_h = 3784
stain2_2E_h = 672
h_2E_kpgp = 346
h_2E_kngn = 987
h_2E_kpgn = stain1_2E_h - h_2E_kpgp
h_2E_kngp = stain1_2E_h - h_2E_kpgp
stain1_4B_h = 2461
stain2_4B_h = 13
h_4B_kpgp = 9
h_4B_kngn = 425
h_4B_kpgn = stain1_4B_h - h_4B_kpgp
h_4B_kngp = stain1_4B_h - h_4B_kpgp
stain1_4C_h = 2461
stain2_4C_h = 13
h_4C_kpgp = 9
h_4C_kngn = 425
h_4C_kpgn = stain1_4C_h - h_4C_kpgp
h_4C_kngp = stain1_4C_h - h_4C_kpgp
stain1_6B_h = 7087
stain2_6B_h = 3918
h_6B_kpgp = 3439
h_6B_kngn = 747
h_6B_kpgn = stain1_6B_h - h_6B_kpgp
h_6B_kngp = stain1_6B_h - h_6B_kpgp
stain1_3I_h = 372
stain2_3I_h = 140
h_3I_kpgp = 63
h_3I_kngn = 2225
h_3I_kpgn = stain1_3I_h - h_3I_kpgp
h_3I_kngp = stain1_3I_h - h_3I_kpgp
stain1_1I_h = 3191
stain2_1I_h = 3937
h_1I_kpgp = 2034
h_1I_kngn = 677
h_1I_kpgn = stain1_1I_h - h_1I_kpgp
h_1I_kngp = stain1_1I_h - h_1I_kpgp
stain1_2C_h = 5992
stain2_2C_h = 646
h_2C_kpgp = 613
h_2C_kngn = 259
h_2C_kpgn = stain1_2C_h - h_2C_kpgp
h_2C_kngp = stain1_2C_h - h_2C_kpgp
stain1_2B_h = 1289
stain2_2B_h = 122
h_2B_kpgp = 71
h_2B_kngn = 614
h_2B_kpgn = stain1_2B_h - h_2B_kpgp
h_2B_kngp = stain1_2B_h - h_2B_kpgp
stain1_6C_h = 6333
stain2_6C_h = 31
h_6C_kpgp = 26
h_6C_kngn = 258
h_6C_kpgn = stain1_6C_h - h_6C_kpgp
h_6C_kngp = stain1_6C_h - h_6C_kpgp
stain1_1H_h = 1556
stain2_1H_h = 1968
h_1H_kpgp = 1213
h_1H_kngn = 223
h_1H_kpgn = stain1_1H_h - h_1H_kpgp
h_1H_kngp = stain1_1H_h - h_1H_kpgp
stain1_3H_h = 5201
stain2_3H_h = 728
h_3H_kpgp = 612
h_3H_kngn = 952
h_3H_kpgn = stain1_3H_h - h_3H_kpgp
h_3H_kngp = stain1_3H_h - h_3H_kpgp
stain1_2I_h = 2178
stain2_2I_h = 219
h_2I_kpgp = 172
h_2I_kngn = 903
h_2I_kpgn = stain1_2I_h - h_2I_kpgp
h_2I_kngp = stain1_2I_h - h_2I_kpgp
stain1_1B_h = 3529
stain2_1B_h = 3
h_1B_kpgp = 2
h_1B_kngn = 233
h_1B_kpgn = stain1_1B_h - h_1B_kpgp
h_1B_kngp = stain1_1B_h - h_1B_kpgp
stain1_3H_h = 5201
stain2_3H_h = 728
h_3H_kpgp = 612
h_2H_kngn = 952
h_3H_kpgn = stain1_3H_h - h_3H_kpgp
h_3H_kngp = stain1_3H_h - h_3H_kpgp
stain1_5C_h = 5924
stain2_5C_h = 27
h_5C_kpgp = 23
h_5C_kngn = 512
h_5C_kpgn = stain1_5C_h - h_5C_kpgp
h_5C_kngp = stain1_5C_h - h_5C_kpgp
stain1_6E_h = 2913
stain2_6E_h = 206
h_6E_kpgp = 138
h_6E_kngn = 370
h_6E_kpgn = stain1_6E_h - h_6E_kpgp
h_6E_kngp = stain1_6E_h - h_6E_kpgp
stain1_6F_h = 953
stain2_6F_h = 30
h_6F_kpgp = 19
h_6F_kngn = 871
h_6F_kpgn = stain1_6F_h - h_6F_kpgp
h_6F_kngp = stain1_6F_h - h_6F_kpgp
stain1_6J_h = 5119
stain2_6J_h = 781
h_6J_kpgp = 687
h_6J_kngn = 398
h_6J_kpgn = stain1_6J_h - h_6J_kpgp
h_6J_kngp = stain1_6J_h - h_6J_kpgp
stain1_1A_h = 3201
stain2_1A_h = 7
h_1A_kpgp = 6
h_1A_kngn = 42
h_1A_kpgn = stain1_1A_h - h_1A_kpgp
h_1A_kngp = stain1_1A_h - h_1A_kpgp
stain1_5A_h = 2128
stain2_5A_h = 118
h_5A_kpgp = 100
h_5A_kngn = 278
h_5A_kpgn = stain1_5A_h - h_5A_kpgp
h_5A_kngp = stain1_5A_h - h_5A_kpgp
stain1_6G_h = 6819
stain2_6G_h = 5800
h_6G_kpgp = 5400
h_6G_kngn = 340
h_6G_kpgn = stain1_6G_h - h_6G_kpgp
h_6G_kngp = stain1_6G_h - h_6G_kpgp
stain1_1G_h = 2198
stain2_1G_h = 2673
h_1G_kpgp = 1975
h_1G_kngn = 307
h_1G_kpgn = stain1_1G_h - h_1G_kpgp
h_1G_kngp = stain1_1G_h - h_1G_kpgp
stain1_2A_h = 2389
stain2_2A_h = 172
h_2A_kpgp = 133
h_2A_kngn = 777
h_2A_kpgn = stain1_2A_h - h_2A_kpgp
h_2A_kngp = stain1_2A_h - h_2A_kpgp
stain1_5J_h = 1704
stain2_5J_h = 921
h_5J_kpgp = 827
h_5J_kngn = 131
h_5J_kpgn = stain1_5J_h - h_5J_kpgp
h_5J_kngp = stain1_5J_h - h_5J_kpgp
stain1_1J_h = 4162
stain2_1J_h = 5598
h_1J_kpgp = 4015
h_1J_kngn = 626
h_1J_kpgn = stain1_1J_h - h_1J_kpgp
h_1J_kngp = stain1_1J_h - h_1J_kpgp
stain1_6A_h = 3954
stain2_6A_h = 1759
h_6A_kpgp = 1575
h_6A_kngn = 596
h_6A_kpgn = stain1_6A_h - h_6A_kpgp
h_6A_kngp = stain1_6A_h - h_6A_kpgp
stain1_3F_h = 1288
stain2_3F_h = 681
h_3F_kpgp = 559
h_3F_kngn = 603
h_3F_kpgn = stain1_3F_h - h_3F_kpgp
h_3F_kngp = stain1_3F_h - h_3F_kpgp
# In[]: Make the plots
wp1 = np.array([w4Bkngp,
w3Ikngp,
w1Ikngp,
w6Bkngp,
w2Ckngp,
w2Bkngp,
w6Ckngp,
w1Hkngp,
w3Hkngp,
w4Ckngp,
w6Dkngp,
w2Ekngp,
w4Dkngp,
w2Ikngp,
w6Ikngp,
w1Bkngp,
w4Ekngp,
w5Ckngp,
w6Ekngp,
w6Fkngp,
w6Jkngp,
w1Akngp,
w5Akngp,
w6Gkngp,
w1Gkngp,
w2Akngp,
w5Jkngp,
w1Jkngp,
w6Akngp,
w3Fkngp])
hp1 = np.array([h_4B_kpgp,
h_3I_kngp,
h_1I_kngp,
h_6B_kngp,
h_2C_kngp,
h_2B_kngp,
h_6C_kngp,
h_1H_kngp,
h_3H_kngp,
h_4C_kngp,
h_6D_kngp,
h_2E_kngp,
h_4D_kngp,
h_2I_kngp,
h_6I_kngp,
h_1B_kngp,
h_4E_kngp,
h_5C_kngp,
h_6E_kngp,
h_6F_kngp,
h_6J_kngp,
h_1A_kngp,
h_5A_kngp,
h_6G_kngp,
h_1G_kngp,
h_2A_kngp,
h_5J_kngp,
h_1J_kngp,
h_6A_kngp,
h_3F_kngp])
p1 = plt.scatter(wp1, hp1, marker='*', color='red')
wp2 = np.array([w4Bkpgn,
w3Ikpgn,
w1Ikpgn,
w6Bkpgn,
w2Ckpgn,
w2Bkpgn,
w6Ckpgn,
w1Hkpgn,
w3Hkpgn,
w4Ckpgn,
w6Dkpgn,
w2Ekpgn,
w4Dkpgn,
w2Ikpgn,
w6Ikpgn,
w1Bkpgn,
w4Ekpgn,
w5Ckpgn,
w6Ekpgn,
w6Fkpgn,
w6Jkpgn,
w1Akpgn,
w5Akpgn,
w6Gkpgn,
w1Gkpgn,
w2Akpgn,
w5Jkpgn,
w1Jkpgn,
w6Akpgn,
w3Fkpgn])
hp2 = np.array([h_4B_kpgn,
h_3I_kpgn,
h_1I_kpgn,
h_6B_kpgn,
h_2C_kpgn,
h_2B_kpgn,
h_6C_kpgn,
h_1H_kpgn,
h_3H_kpgn,
h_4C_kpgn,
h_6D_kpgn,
h_2E_kpgn,
h_4D_kpgn,
h_2I_kpgn,
h_6I_kpgn,
h_1B_kpgn,
h_4E_kpgn,
h_5C_kpgn,
h_6E_kpgn,
h_6F_kpgn,
h_6J_kpgn,
h_1A_kpgn,
h_5A_kpgn,
h_6G_kpgn,
h_1G_kpgn,
h_2A_kpgn,
h_5J_kpgn,
h_1J_kpgn,
h_6A_kpgn,
h_3F_kpgn])
p2 = plt.scatter(wp2, hp2, marker='d', color='blue')
wp3 = np.array([w4Bkpgp,
w3Ikpgp,
w1Ikpgp,
w6Bkpgp,
w2Ckpgp,
w2Bkpgp,
w6Ckpgp,
w1Hkpgp,
w3Hkpgp,
w4Ckpgp,
w6Dkpgp,
w2Ekpgp,
w4Dkpgp,
w2Ikpgp,
w6Ikpgp,
w1Bkpgp,
w4Ekpgp,
w5Ckpgp,
w6Ekpgp,
w6Fkpgp,
w6Jkpgp,
w1Akpgp,
w5Akpgp,
w6Gkpgp,
w1Gkpgp,
w2Akpgp,
w5Jkpgp,
w1Jkpgp,
w6Akpgp,
w3Fkpgp])
hp3 = np.array([h_4B_kpgp,
h_3I_kpgp,
h_1I_kpgp,
h_6B_kpgp,
h_2C_kpgp,
h_2B_kpgp,
h_6C_kpgp,
h_1H_kpgp,
h_3H_kpgp,
h_4C_kpgp,
h_6D_kpgp,
h_2E_kpgp,
h_4D_kpgp,
h_2I_kpgp,
h_6I_kpgp,
h_1B_kpgp,
h_4E_kpgp,
h_5C_kpgp,
h_6E_kpgp,
h_6F_kpgp,
h_6J_kpgp,
h_1A_kpgp,
h_5A_kpgp,
h_6G_kpgp,
h_1G_kpgp,
h_2A_kpgp,
h_5J_kpgp,
h_1J_kpgp,
h_6A_kpgp,
h_3F_kpgp])
p3 = plt.scatter(wp3, hp3, marker='^', color='green')
wp4 = np.array([w4Bkngn,
w3Ikngn,
w1Ikngn,
w6Bkngn,
w2Ckngn,
w2Bkngn,
w6Ckngn,
w1Hkngn,
w3Hkngn,
w4Ckngn,
w6Dkngn,
w2Ekngn,
w4Dkngn,
w2Ikngn,
w6Ikngn,
w1Bkngn,
w4Ekngn,
w5Ckngn,
w6Ekngn,
w6Fkngn,
w6Jkngn,
w1Akngn,
w5Akngn,
w6Gkngn,
w1Gkngn,
w2Akngn,
w5Jkngn,
w1Jkngn,
w6Akngn,
w3Fkngn])
hp4 = np.array([h_4B_kpgn,
h_3I_kngn,
h_1I_kngn,
h_6B_kngn,
h_2C_kngn,
h_2B_kngn,
h_6C_kngn,
h_1H_kngn,
h_3H_kngn,
h_4C_kngn,
h_6D_kngn,
h_2E_kngn,
h_4D_kngn,
h_2I_kngn,
h_6I_kngn,
h_1B_kngn,
h_4E_kngn,
h_5C_kngn,
h_6E_kngn,
h_6F_kngn,
h_6J_kngn,
h_1A_kngn,
h_5A_kngn,
h_6G_kngn,
h_1G_kngn,
h_2A_kngn,
h_5J_kngn,
h_1J_kngn,
h_6A_kngn,
h_3F_kngn])
p4 = plt.scatter(wp4, hp4, marker='o', color='cyan')
x = np.linspace(0, 6000, num=len(wp1))
# Add best fit lines
m1, b1 = np.polyfit(wp1,hp1, 1)
fit_p1 = plt.plot(x, m1*x+b1, '-r')
#m2, b2 = np.polyfit(wp2,hp2, 1)
#fit_p2 = plt.plot(x, m2*x+b2, '-b')
m3, b3 = np.polyfit(wp3,hp3, 1)
fit_p3 = plt.plot(x, m3*x+b3, '-g')
m4, b4 = np.polyfit(wp4,hp4, 1)
fit_p4 = plt.plot(x, m4*x+b4, '-c')
plt.xlabel('Watershed')
plt.ylabel('HALO')
plt.legend((p1, p2, p3, p4), ('KRT5-/GATA3+', 'KRT5+/GATA3-', 'KRT5+/GATA3+', 'KRT5-/GATA3-'), fontsize=10, ncol=1, framealpha=0, fancybox=True,bbox_to_anchor=(1.04,1))
plt.title('Watershed vs. HALO')
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/watershedVshalo.tiff"
plt.savefig(filename_output)
plt.show()
# In[]:
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 21})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10.5, 9.5)
plt.ylabel("Total Number of Cells Identified")
p1G_h = plt.bar("KRT5-/GATA3+", np.sum(wp1), width, color='g')
p2G_h = plt.bar("KRT5-/GATA3-", np.sum(wp4), width, color='c')
p3G_h = plt.bar("KRT5+/GATA3+", np.sum(wp3), width, color='r')
p4G_h = plt.bar("KRT5+/GATA3-", np.sum(wp2), width, color='b')
plt.title("Watershed Method",fontsize=25)
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/total_watershed_histogram.tiff"
plt.savefig(filename_output)
plt.show()
# In[]: Bland-Altman Plot
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 21})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10.5, 8.5)
def bland_altman_plot(data1, data2, *args, **kwargs):
data1 = np.asarray(data1)
data2 = np.asarray(data2)
mean = np.mean([data1, data2], axis=0)
diff = data1 - data2 # Difference between data1 and data2
md = np.mean(diff) # Mean of the difference
sd = np.std(diff, axis=0) # Standard deviation of the difference
lower = md - 1.96*sd
upper = md + 1.96*sd
plt.scatter(mean, diff, *args, **kwargs)
plt.axhline(md, color='red', linestyle='--')
plt.axhline(md + 1.96*sd, color='gray', linestyle='--')
plt.axhline(md - 1.96*sd, color='gray', linestyle='--')
#plt.text(data1.max()*0.85, upper * 0.85, " 1.96 SD", color = "grey", fontsize = "14")
#plt.text(data1.max()*0.85, lower * 1.2, "-1.96 SD", color = "grey", fontsize = "14")
ba_p4 = bland_altman_plot(wp4, hp4)
#ba_p4 = plotblandaltman(wp4, hp4, 'Bland-Altman Plot KRT5-/GATA3-',1000)
plt.title('Bland-Altman Plot KRT5-/GATA3-')
plt.xlabel("Mean of Watershed and HALO")
plt.ylabel("Difference of Watershed and HALO")
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/w_bland_altman_plot_kngn.tiff"
plt.savefig(filename_output)
plt.show()
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 21})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10.5, 8.5)
ba_p4 = bland_altman_plot(wp1, hp1)
plt.title('Bland-Altman Plot KRT5-/GATA3+')
plt.xlabel("Mean of Watershed and HALO")
plt.ylabel("Difference of Watershed and HALO")
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/w_bland_altman_plot_kngp.tiff"
plt.savefig(filename_output)
plt.show()
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 21})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10.5, 8.5)
ba_p4 = bland_altman_plot(wp3, hp3)
plt.title('Bland-Altman Plot KRT5+/GATA3+')
plt.xlabel("Mean of Watershed and HALO")
plt.ylabel("Difference of Watershed and HALO")
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/w_bland_altman_plot_kpgp.tiff"
plt.savefig(filename_output)
plt.show()
width = 0.7
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 21})
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(10.5, 8.5)
ba_p4 = bland_altman_plot(wp2, hp2)
plt.title('Bland-Altman Plot KRT5+/GATA3-')
plt.xlabel("Mean of Watershed and HALO")
plt.ylabel("Difference of Watershed and HALO")
plt.tight_layout()
if SAVEIMG == 1:
filename_output = "plots_30_analysis/w_bland_altman_plot_kpgn.tiff"
plt.savefig(filename_output)
plt.show()