In [1]:
from assaytools import grant
from glob import glob
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


Couldn't import dot_parser, loading of dot files will not be possible.

In [2]:
SrcColumns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer']
AblColumns = ['A - Abl','B - Buffer','C - Abl','D - Buffer', 'E - Abl','F - Buffer','G - Abl','H - Buffer']

In [3]:
file_ABL_GEF= "../competition/data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml"
file_ABL_GEF_IMA= "../competition/data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate_1.xml"

In [4]:
AblGef_df = grant.file2df(file_ABL_GEF,AblColumns)
AblGefIma_df = grant.file2df(file_ABL_GEF_IMA,AblColumns)

In [5]:
AblGef_df


Out[5]:
A - Abl B - Buffer C - Abl D - Buffer E - Abl F - Buffer G - Abl H - Buffer
0 62213 19784 62890 19797 62821 20594 63317 17635
1 62497 17332 62936 18205 62927 18102 63343 14681
2 63194 12950 63056 13223 62794 13049 63251 10586
3 63017 10782 62743 11030 62841 10622 63045 9235
4 56558 10186 62107 10257 61824 9884 55471 8785
5 34259 9837 38003 9884 38786 9620 35771 8796
6 22106 9861 24868 9716 24637 9757 23488 8789
7 14589 9659 16880 10010 17193 9518 17755 8612
8 11475 9601 12311 10035 13272 9758 12312 8625
9 10505 9669 11388 9891 11702 9649 10925 8513
10 9771 9722 10457 10630 10585 9889 10062 8840
11 8652 9757 9712 9396 9653 9578 9429 8671

In [6]:
sns.set_palette("Paired", 10)

plt.plot(AblGefIma_df[:].values, 'r');
plt.plot(AblGef_df[:].values, 'k');
plt.text(8,60000,'Gefitinib (ABL)',fontsize=15)
plt.text(8,55000,'Imatinib + Gefitinib (ABL)',fontsize=15,color='red')
plt.savefig('Abl_Gef_Ima_Jan2016_repeat.png',dpi=1000)



In [7]:
ligand_conc = np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0001e-6], np.float64) # ligand concentration, M

In [8]:
sns.set_palette("Paired", 10)

plt.semilogx(ligand_conc*12,AblGefIma_df[:].values, 'r');
plt.semilogx(ligand_conc*12,AblGef_df[:].values, 'k');
plt.text(4e-9,60000,'Gefitinib (ABL)',fontsize=15)
plt.text(4e-9,55000,'Imatinib + Gefitinib (ABL)',fontsize=15,color='red')
plt.xlabel('Ligand Concentration (M)')
plt.ylabel('Relative Fluorescence')
plt.xlim(1e-9,3e-4)
plt.tight_layout();



In [9]:
plt.figure(figsize=(2.5,2.5))

plt.semilogx(ligand_conc*12,AblGefIma_df[['A - Abl','C - Abl','E - Abl','G - Abl']].values, 'r');
plt.semilogx(ligand_conc*12,AblGefIma_df[['B - Buffer','D - Buffer','F - Buffer','H - Buffer']].values, 'r', linestyle=':');
plt.semilogx(ligand_conc*12,AblGef_df[['A - Abl','C - Abl','E - Abl','G - Abl']].values, 'k');
plt.semilogx(ligand_conc*12,AblGef_df[['B - Buffer','D - Buffer','F - Buffer','H - Buffer']].values, 'k', linestyle=':');
plt.text(1.5e-9,63000,'1 $\mu$M Abl + gefitinib',fontsize=7)
plt.text(1.5e-9,52000,'1 $\mu$M Abl + 10 $\mu$M imatinib \n    + gefitinib',fontsize=7, color='red')
plt.xlabel('[gefitinib] (M)',fontsize=9)
plt.ylabel('relative fluorescence',fontsize=9)
plt.title('excitation 280 nm/ emission 480 nm',fontsize=9)
plt.xlim(1e-9,3e-4)
plt.yticks([])
plt.xticks(fontsize=8)
plt.tight_layout();
plt.savefig('Abl_Gef_Ima_grant.eps',type='eps',dpi=1000)



In [10]:
AblGefIma_df[['A - Abl','C - Abl']]


Out[10]:
A - Abl C - Abl
0 53250 63141
1 43872 52725
2 31303 38690
3 24462 29540
4 21709 23561
5 18941 20785
6 17939 19159
7 17258 18313
8 17135 17703
9 17250 17936
10 16964 17938
11 17185 17280

In [11]:
AblGefIma_df[:].values


Out[11]:
array([[ 53250.,  12894.,  63141.,  13739.,  63400.,  13953.,  63432.,
         16807.],
       [ 43872.,  12797.,  52725.,  12068.,  53561.,  12777.,  49829.,
         11596.],
       [ 31303.,  11876.,  38690.,  12203.,  38596.,  12048.,  37127.,
         11445.],
       [ 24462.,  11368.,  29540.,  11254.,  29405.,  11867.,  27706.,
         10914.],
       [ 21709.,  10305.,  23561.,  10359.,  23947.,  10513.,  22149.,
          9179.],
       [ 18941.,   9813.,  20785.,  10048.,  20759.,   9671.,  20375.,
          8845.],
       [ 17939.,   9907.,  19159.,   9782.,  20094.,   9803.,  18045.,
          8713.],
       [ 17258.,   9639.,  18313.,   9788.,  18623.,   9717.,  17424.,
          8804.],
       [ 17135.,   9746.,  17703.,   9823.,  18595.,  10231.,  17284.,
          8846.],
       [ 17250.,   9698.,  17936.,  10016.,  18799.,   9771.,  17618.,
          8657.],
       [ 16964.,   9965.,  17938.,   9939.,  18223.,   9851.,  17518.,
          9041.],
       [ 17185.,   9933.,  17280.,   9746.,  17532.,   9652.,  17583.,
          8669.]])

In [ ]: