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FilterDCA
Commit adfad9b7 by Maureen MUSCAT
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parent 994652a1
Showing with 37 additions and 36 deletions
.ipynb_checkpoints/FilterDCA-checkpoint.ipynb
......@@ -26,7 +26,6 @@
"source": [
"import pandas\n",
"import numpy as np\n",
"import sys\n",
"import scipy.stats\n",
"import matplotlib.pyplot as plt\n",
"import pickle\n",
......@@ -35,7 +34,7 @@
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......@@ -138,16 +137,16 @@
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......@@ -216,16 +215,16 @@
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{
......@@ -259,7 +258,7 @@
"Text(0.5, 1.0, 'Predicted contact map')"
]
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......@@ -284,7 +283,7 @@
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......
FilterDCA.ipynb
......@@ -26,7 +26,6 @@
"source": [
"import pandas\n",
"import numpy as np\n",
"import sys\n",
"import scipy.stats\n",
"import matplotlib.pyplot as plt\n",
"import pickle\n",
......@@ -35,7 +34,7 @@
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......@@ -92,7 +91,7 @@
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......@@ -128,7 +127,7 @@
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......@@ -138,16 +137,16 @@
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......@@ -204,7 +203,7 @@
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......@@ -216,16 +215,16 @@
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......@@ -250,7 +249,7 @@
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"execution_count": 19,
"execution_count": 11,
"metadata": {},
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{
......@@ -259,7 +258,7 @@
"Text(0.5, 1.0, 'Predicted contact map')"
]
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"execution_count": 19,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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......@@ -284,7 +283,7 @@
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"source": [
......
read_me.md
......@@ -12,10 +12,13 @@ To use the script you need:
- and to set the size of the M effictive ('medium' if under 200 and 'big' otherwise).
In the 2 folders you can find:
- the 6 maps (3 corresponding to helix-helix contact, and 3 for strand-strand contacts) for each of the possibles size (5, 13, 21, 37, 45 or 69)
- the classifier and the 'min'/'max' values to normalise the correlation/ pattern score
- the 6 maps (3 corresponding to helix-helix contact, and 3 for strand-strand contacts) for each of the possible sizes (5, 13, 21, 37, 45 or 69)
- the classifier (logistic regression) and the 'min'/'max' values to normalise the correlation/ pattern score
You can then produice :
- the pattern score: the best correlation score matrix
- the matrix of probabililty of contact
- and finaly the predicted contact map
The code is a iPython3 notebook, severals package are needed: pandas, numpy, scipy, matplotlib, pickle and sklearn.
results.dat 0 → 100644
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