This script generates a heatmap from data indicating the probability of oviparity as the root state of squamates as a function of model parameters.



In [1]:

    
import pandas as pd
from pandas import *
import matplotlib.pyplot as plt
%matplotlib inline

First, let's get some data.



In [10]:

    
data = pd.read_csv("../Data/Heatmap/TO.csv", index_col = False, header = False)
data.columns = ['A','B','C']



In [11]:

    
data









    Out[11]:






  
    
      
      A
      B
      C
    
  
  
    
      0 
       0.1
       0.2
       0.044280
    
    
      1 
       0.1
       0.3
       0.017266
    
    
      2 
       0.1
       0.4
       0.002536
    
    
      3 
       0.1
       0.5
       0.000749
    
    
      4 
       0.1
       0.6
       0.000282
    
    
      5 
       0.1
       0.7
       0.000119
    
    
      6 
       0.1
       0.8
       0.000061
    
    
      7 
       0.1
       0.9
       0.000032
    
    
      8 
       0.1
       1.0
       0.000020
    
    
      9 
       0.2
       0.1
       0.922133
    
    
      10
       0.2
       0.2
       0.862397
    
    
      11
       0.2
       0.3
       0.158870
    
    
      12
       0.2
       0.4
       0.020354
    
    
      13
       0.2
       0.5
       0.004965
    
    
      14
       0.2
       0.6
       0.001887
    
    
      15
       0.2
       0.7
       0.000876
    
    
      16
       0.2
       0.8
       0.000440
    
    
      17
       0.2
       0.9
       0.000247
    
    
      18
       0.2
       1.0
       0.000156
    
    
      19
       0.3
       0.1
       0.999754
    
    
      20
       0.3
       0.2
       0.999456
    
    
      21
       0.3
       0.3
       0.961875
    
    
      22
       0.3
       0.4
       0.407806
    
    
      23
       0.3
       0.5
       0.078138
    
    
      24
       0.3
       0.6
       0.022981
    
    
      25
       0.3
       0.7
       0.009757
    
    
      26
       0.3
       0.8
       0.004679
    
    
      27
       0.3
       0.9
       0.002614
    
    
      28
       0.3
       1.0
       0.001583
    
    
      29
       0.4
       0.1
       0.999928
    
    
      ...
      ...
      ...
      ...
    
    
      69
       0.8
       0.1
       0.999990
    
    
      70
       0.8
       0.2
       0.999968
    
    
      71
       0.8
       0.3
       0.999875
    
    
      72
       0.8
       0.4
       0.999568
    
    
      73
       0.8
       0.5
       0.997662
    
    
      74
       0.8
       0.6
       0.987702
    
    
      75
       0.8
       0.7
       0.963515
    
    
      76
       0.8
       0.8
       0.907783
    
    
      77
       0.8
       0.9
       0.805892
    
    
      78
       0.8
       1.0
       0.640272
    
    
      79
       0.9
       0.1
       0.999991
    
    
      80
       0.9
       0.2
       0.999974
    
    
      81
       0.9
       0.3
       0.999899
    
    
      82
       0.9
       0.4
       0.999691
    
    
      83
       0.9
       0.5
       0.998416
    
    
      84
       0.9
       0.6
       0.993186
    
    
      85
       0.9
       0.7
       0.970505
    
    
      86
       0.9
       0.8
       0.927551
    
    
      87
       0.9
       0.9
       0.897569
    
    
      88
       0.9
       1.0
       0.806492
    
    
      89
       1.0
       0.1
       0.999998
    
    
      90
       1.0
       0.2
       0.999977
    
    
      91
       1.0
       0.3
       0.999913
    
    
      92
       1.0
       0.4
       0.999752
    
    
      93
       1.0
       0.5
       0.999244
    
    
      94
       1.0
       0.6
       0.997191
    
    
      95
       1.0
       0.7
       0.986890
    
    
      96
       1.0
       0.8
       0.969238
    
    
      97
       1.0
       0.9
       0.934598
    
    
      98
       1.0
       1.0
       0.886509
    
  

99 rows × 3 columns

If you're like me, generally, you store data one variable per column. This isn't ideal for heatmaps. Matplotlib's heatmap assumes data have one variable down the x-axis of the spreadsheet and one along the top, or y-axis. Pandas can melt our data to be in this format.



In [12]:

    
h_data = data.pivot(index='A', columns='B', values='C')
h_data.ix[0.1,0.1] = .036
h_data



In [13]:

    
plt.pcolor(h_data,cmap=plt.cm.RdBu,edgecolors='k')
plt.xticks(np.arange(0.5, len(h_data.columns), 1), h_data.columns)
plt.yticks(np.arange(0.5, len(h_data.index), 1), h_data.index)
cbar = plt.colorbar()
plt.tight_layout()
plt.savefig('TO.svg', bbox_inches='tight', dpi=300)

And heatmap. As you can se, our data has a very sharp switch point (i.e. values tend to be very high support or low support), with few in between.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.



In [ ]:

	A	B	C
0	0.1	0.2	0.044280
1	0.1	0.3	0.017266
2	0.1	0.4	0.002536
3	0.1	0.5	0.000749
4	0.1	0.6	0.000282
5	0.1	0.7	0.000119
6	0.1	0.8	0.000061
7	0.1	0.9	0.000032
8	0.1	1.0	0.000020
9	0.2	0.1	0.922133
10	0.2	0.2	0.862397
11	0.2	0.3	0.158870
12	0.2	0.4	0.020354
13	0.2	0.5	0.004965
14	0.2	0.6	0.001887
15	0.2	0.7	0.000876
16	0.2	0.8	0.000440
17	0.2	0.9	0.000247
18	0.2	1.0	0.000156
19	0.3	0.1	0.999754
20	0.3	0.2	0.999456
21	0.3	0.3	0.961875
22	0.3	0.4	0.407806
23	0.3	0.5	0.078138
24	0.3	0.6	0.022981
25	0.3	0.7	0.009757
26	0.3	0.8	0.004679
27	0.3	0.9	0.002614
28	0.3	1.0	0.001583
29	0.4	0.1	0.999928
...	...	...	...
69	0.8	0.1	0.999990
70	0.8	0.2	0.999968
71	0.8	0.3	0.999875
72	0.8	0.4	0.999568
73	0.8	0.5	0.997662
74	0.8	0.6	0.987702
75	0.8	0.7	0.963515
76	0.8	0.8	0.907783
77	0.8	0.9	0.805892
78	0.8	1.0	0.640272
79	0.9	0.1	0.999991
80	0.9	0.2	0.999974
81	0.9	0.3	0.999899
82	0.9	0.4	0.999691
83	0.9	0.5	0.998416
84	0.9	0.6	0.993186
85	0.9	0.7	0.970505
86	0.9	0.8	0.927551
87	0.9	0.9	0.897569
88	0.9	1.0	0.806492
89	1.0	0.1	0.999998
90	1.0	0.2	0.999977
91	1.0	0.3	0.999913
92	1.0	0.4	0.999752
93	1.0	0.5	0.999244
94	1.0	0.6	0.997191
95	1.0	0.7	0.986890
96	1.0	0.8	0.969238
97	1.0	0.9	0.934598
98	1.0	1.0	0.886509

B	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1.0
A
0.1	0.036000	0.044280	0.017266	0.002536	0.000749	0.000282	0.000119	0.000061	0.000032	0.000020
0.2	0.922133	0.862397	0.158870	0.020354	0.004965	0.001887	0.000876	0.000440	0.000247	0.000156
0.3	0.999754	0.999456	0.961875	0.407806	0.078138	0.022981	0.009757	0.004679	0.002614	0.001583
0.4	0.999928	0.999822	0.997877	0.951718	0.728194	0.169208	0.065020	0.028220	0.015671	0.008665
0.5	0.999963	0.999905	0.999428	0.992227	0.932562	0.767928	0.277918	0.126080	0.070692	0.035134
0.6	0.999977	0.999938	0.999699	0.998341	0.983742	0.927726	0.791288	0.414008	0.186163	0.112650
0.7	0.999986	0.999957	0.999824	0.999284	0.996463	0.973685	0.914741	0.813443	0.593897	0.554725
0.8	0.999990	0.999968	0.999875	0.999568	0.997662	0.987702	0.963515	0.907783	0.805892	0.640272
0.9	0.999991	0.999974	0.999899	0.999691	0.998416	0.993186	0.970505	0.927551	0.897569	0.806492
1.0	0.999998	0.999977	0.999913	0.999752	0.999244	0.997191	0.986890	0.969238	0.934598	0.886509