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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 @@ ...@@ -26,7 +26,6 @@
"source": [ "source": [
"import pandas\n", "import pandas\n",
"import numpy as np\n", "import numpy as np\n",
"import sys\n",
"import scipy.stats\n", "import scipy.stats\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pickle\n", "import pickle\n",
...@@ -35,7 +34,7 @@ ...@@ -35,7 +34,7 @@
}, },
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...@@ -92,7 +91,7 @@ ...@@ -92,7 +91,7 @@
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{ {
...@@ -117,7 +116,7 @@ ...@@ -117,7 +116,7 @@
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...@@ -128,7 +127,7 @@ ...@@ -128,7 +127,7 @@
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...@@ -138,16 +137,16 @@ ...@@ -138,16 +137,16 @@
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{ {
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...@@ -194,7 +193,7 @@ ...@@ -194,7 +193,7 @@
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...@@ -204,7 +203,7 @@ ...@@ -204,7 +203,7 @@
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...@@ -216,16 +215,16 @@ ...@@ -216,16 +215,16 @@
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...@@ -250,7 +249,7 @@ ...@@ -250,7 +249,7 @@
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{ {
...@@ -259,7 +258,7 @@ ...@@ -259,7 +258,7 @@
"Text(0.5, 1.0, 'Predicted contact map')" "Text(0.5, 1.0, 'Predicted contact map')"
] ]
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...@@ -284,7 +283,7 @@ ...@@ -284,7 +283,7 @@
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......
FilterDCA.ipynb
...@@ -26,7 +26,6 @@ ...@@ -26,7 +26,6 @@
"source": [ "source": [
"import pandas\n", "import pandas\n",
"import numpy as np\n", "import numpy as np\n",
"import sys\n",
"import scipy.stats\n", "import scipy.stats\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pickle\n", "import pickle\n",
...@@ -35,7 +34,7 @@ ...@@ -35,7 +34,7 @@
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...@@ -92,7 +91,7 @@ ...@@ -92,7 +91,7 @@
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...@@ -117,7 +116,7 @@ ...@@ -117,7 +116,7 @@
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...@@ -128,7 +127,7 @@ ...@@ -128,7 +127,7 @@
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"metadata": {}, "metadata": {},
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"source": [ "source": [
...@@ -138,16 +137,16 @@ ...@@ -138,16 +137,16 @@
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{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 6,
"metadata": {}, "metadata": {},
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{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f8f6a289898>" "<matplotlib.colorbar.Colorbar at 0x7fdffc8074a8>"
] ]
}, },
"execution_count": 9, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -180,7 +179,7 @@ ...@@ -180,7 +179,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 7,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
...@@ -194,7 +193,7 @@ ...@@ -194,7 +193,7 @@
}, },
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...@@ -204,7 +203,7 @@ ...@@ -204,7 +203,7 @@
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...@@ -216,16 +215,16 @@ ...@@ -216,16 +215,16 @@
}, },
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"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 10,
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"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f8f04a54630>" "<matplotlib.colorbar.Colorbar at 0x7fdf7f652d30>"
] ]
}, },
"execution_count": 13, "execution_count": 10,
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"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -250,7 +249,7 @@ ...@@ -250,7 +249,7 @@
}, },
{ {
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"execution_count": 19, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -259,7 +258,7 @@ ...@@ -259,7 +258,7 @@
"Text(0.5, 1.0, 'Predicted contact map')" "Text(0.5, 1.0, 'Predicted contact map')"
] ]
}, },
"execution_count": 19, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -284,7 +283,7 @@ ...@@ -284,7 +283,7 @@
}, },
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"source": [ "source": [
......
read_me.md
...@@ -12,10 +12,13 @@ To use the script you need: ...@@ -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). - and to set the size of the M effictive ('medium' if under 200 and 'big' otherwise).
In the 2 folders you can find: 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 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 and the 'min'/'max' values to normalise the correlation/ pattern score - the classifier (logistic regression) and the 'min'/'max' values to normalise the correlation/ pattern score
You can then produice : You can then produice :
- the pattern score: the best correlation score matrix - the pattern score: the best correlation score matrix
- the matrix of probabililty of contact - the matrix of probabililty of contact
- and finaly the predicted contact map - 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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