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2522d9abf7f4fcc6d398258759e255fc064f6901 | 15,340 | py | Python | verification/tests.py | cielavenir/checkio-task-painting-wall | 3502ac1d9f795b4e5c8fa46739e9fa438d16e026 | [
"MIT"
] | null | null | null | verification/tests.py | cielavenir/checkio-task-painting-wall | 3502ac1d9f795b4e5c8fa46739e9fa438d16e026 | [
"MIT"
] | 1 | 2017-06-21T18:00:21.000Z | 2017-06-21T18:00:21.000Z | verification/tests.py | CheckIO-to-German/Painting-Wall | 737e07e1b3b2176ea3fa999b916c30992b397506 | [
"MIT"
] | 1 | 2017-06-21T17:57:21.000Z | 2017-06-21T17:57:21.000Z | #TESTS is a dict with all you tests.
#Keys for this will be categories' names.
#Each test is dict with
# "input" -- input data for user function
# "answer" -- your right answer
# "explanation" -- not necessary key, it's using for additional info in animation.
TESTS = {
"1. Small By Hand 1 (Example)": [
{
"input": [5, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 1,
"explanation": [25, [5]]
},
{
"input": [6, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 2,
"explanation": [25, [5, 10]]
},
{
"input": [11, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 3,
"explanation": [25, [5, 10, 15]]
},
{
"input": [16, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 4,
"explanation": [25, [5, 10, 15, 20]]
},
{
"input": [21, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": -1,
"explanation": [25, [5, 10, 15, 20]]
}
],
"2. Small By Hand 2": [
{
"input": [5, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 2,
"explanation": [30, [2, 13]]
},
{
"input": [15, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 4,
"explanation": [30, [2, 13, 13, 19]]
},
{
"input": [20, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 5,
"explanation": [30, [2, 13, 13, 19, 29]]
},
{
"input": [30, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 6,
"explanation": [30, [2, 13, 13, 19, 29, 30]]
},
{
"input": [35, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": -1,
"explanation": [30, [2, 13, 13, 19, 29, 30]]
}
],
"3. Small Generated 1": [
{
"input": [1000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 2
},
{
"input": [5000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 9
},
{
"input": [5400, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 11
},
{
"input": [5700, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 14
},
{
"input": [6000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 15
},
{
"input": [6500, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": -1
}
],
"4. Small Generated 2": [
{
"input": [30000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 6
},
{
"input": [57000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 11
},
{
"input": [68000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 20
},
{
"input": [70000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": -1
}
],
"5. Large By Hand": [
{
"input": [10000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 1
},
{
"input": [20000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 2
},
{
"input": [30000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 3
},
{
"input": [50000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 4
},
{
"input": [100000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": -1
}
],
"6. Large Generated 1": [
{
"input": [464578738600000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": 8
},
{
"input": [623032679900000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": 16
},
{
"input": [650000000000000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": -1
}
],
"7. Large Generated 2": [
{
"input": [409810512978000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": 10
},
{
"input": [612742616513000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": 19
},
{
"input": [620000000000000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": -1
}
],
}
| 59.688716 | 119 | 0.469296 | #TESTS is a dict with all you tests.
#Keys for this will be categories' names.
#Each test is dict with
# "input" -- input data for user function
# "answer" -- your right answer
# "explanation" -- not necessary key, it's using for additional info in animation.
TESTS = {
"1. Small By Hand 1 (Example)": [
{
"input": [5, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 1,
"explanation": [25, [5]]
},
{
"input": [6, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 2,
"explanation": [25, [5, 10]]
},
{
"input": [11, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 3,
"explanation": [25, [5, 10, 15]]
},
{
"input": [16, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": 4,
"explanation": [25, [5, 10, 15, 20]]
},
{
"input": [21, [[1, 5], [11, 15], [2, 14], [21, 25]]],
"answer": -1,
"explanation": [25, [5, 10, 15, 20]]
}
],
"2. Small By Hand 2": [
{
"input": [5, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 2,
"explanation": [30, [2, 13]]
},
{
"input": [15, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 4,
"explanation": [30, [2, 13, 13, 19]]
},
{
"input": [20, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 5,
"explanation": [30, [2, 13, 13, 19, 29]]
},
{
"input": [30, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": 6,
"explanation": [30, [2, 13, 13, 19, 29, 30]]
},
{
"input": [35, [[1, 2], [20, 30], [25, 28], [5, 10], [4, 21], [1, 6]]],
"answer": -1,
"explanation": [30, [2, 13, 13, 19, 29, 30]]
}
],
"3. Small Generated 1": [
{
"input": [1000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 2
},
{
"input": [5000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 9
},
{
"input": [5400, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 11
},
{
"input": [5700, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 14
},
{
"input": [6000, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": 15
},
{
"input": [6500, [[8598, 9442], [4221, 4432], [4864, 5415], [1315, 1960], [9577, 10482], [8147, 8346],
[6063, 6836],
[24, 606], [6170, 7131], [1397, 2020], [4690, 5651], [5267, 5464], [8422, 8886],
[5547, 5738],
[5722, 6511], [6605, 6905], [1321, 2242], [9335, 9993], [1626, 1887], [4699, 4926]]],
"answer": -1
}
],
"4. Small Generated 2": [
{
"input": [30000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 6
},
{
"input": [57000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 11
},
{
"input": [68000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": 20
},
{
"input": [70000,
[[53013, 58178], [66996, 70770], [46244, 50076], [69373, 79267], [3343, 9935], [80414, 82602],
[61293, 68007], [50771, 53974], [34296, 43518], [92413, 100031], [17305, 19487], [84654, 87021],
[17333, 21892], [93387, 99456], [92406, 97098], [37781, 42924], [98927, 100960], [86738, 89338],
[48177, 52067], [28524, 32583]]],
"answer": -1
}
],
"5. Large By Hand": [
{
"input": [10000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 1
},
{
"input": [20000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 2
},
{
"input": [30000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 3
},
{
"input": [50000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": 4
},
{
"input": [100000000000000, [[183456789012345, 193456789078479], [163456789034827, 173456789028737],
[103456789038198, 113456789073490], [123456789073249, 203456789073621]]],
"answer": -1
}
],
"6. Large Generated 1": [
{
"input": [464578738600000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": 8
},
{
"input": [623032679900000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": 16
},
{
"input": [650000000000000, [[618092831212219, 692920328043160], [784541961979258, 849250239149590],
[153212679000591, 170403813123184], [397402071388980, 494330805397679],
[947688880896887, 1031315325383029], [935301256930565, 986287435347942],
[15780649536663, 68473689252834], [526775205748614, 588991194047996],
[655862607385363, 673910180521194], [773094918999390, 841619268667280],
[155080217158458, 163523868832795], [619408007658049, 621341345909422],
[187242353118947, 253984595396399], [620306694433414, 710742874379812],
[268250344952342, 301251875692343], [493812097589401, 585287020679206],
[660549666775455, 686382635470395], [953288849899107, 981074697686018],
[752283881298002, 800681840076545], [628408208573475, 664503152611721]]],
"answer": -1
}
],
"7. Large Generated 2": [
{
"input": [409810512978000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": 10
},
{
"input": [612742616513000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": 19
},
{
"input": [620000000000000, [[858310018365524, 902063077244091], [932665378449117, 1028409672338264],
[882165278163239, 957945652291761], [155331862264691, 231608087199557],
[309323812898016, 328794059405147], [311727991597994, 391226174154816],
[826415306967097, 893972043882819], [170753995991478, 221100797836809],
[472995836315594, 478902758061898], [779003863306990, 822734502976504],
[539843675072188, 554844466580541], [977564633426502, 991018537238369],
[889461015856698, 901719104033374], [268288887276466, 292053591549963],
[87698520389374, 109261297832598], [650723837467456, 729926149124749],
[627448683684809, 644021001384284], [264317870081369, 322309330307873],
[238729907671924, 290743490959244], [938382837602825, 955450166170994]]],
"answer": -1
}
],
}
| 0 | 0 | 0 |
fb0a633bd86c4a2b25ef3b54465361faff3666bb | 1,749 | py | Python | homepage/migrations/0001_initial.py | chadrick-kwag/labeldat.a | 02a5fe06ee244ae5e724336a8ae3c9c28a76b115 | [
"Apache-2.0"
] | null | null | null | homepage/migrations/0001_initial.py | chadrick-kwag/labeldat.a | 02a5fe06ee244ae5e724336a8ae3c9c28a76b115 | [
"Apache-2.0"
] | null | null | null | homepage/migrations/0001_initial.py | chadrick-kwag/labeldat.a | 02a5fe06ee244ae5e724336a8ae3c9c28a76b115 | [
"Apache-2.0"
] | null | null | null | # Generated by Django 2.0 on 2018-02-24 15:05
from django.db import migrations, models
import django.db.models.deletion
| 38.866667 | 115 | 0.578045 | # Generated by Django 2.0 on 2018-02-24 15:05
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Dataset',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('dsid', models.IntegerField(unique=True)),
('title', models.CharField(help_text='enter title for dataset', max_length=200)),
('description', models.TextField()),
('thumbnail_filename', models.ImageField(null=True, upload_to='datasets/thumbnails/')),
('label_jsonfile', models.FileField(null=True, upload_to='datasets/labeljson/')),
],
),
migrations.CreateModel(
name='recent_activity',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('userid', models.IntegerField()),
('access_datetime', models.DateTimeField(auto_now=True)),
('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='homepage.Dataset')),
],
),
migrations.CreateModel(
name='save_progress',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('username', models.CharField(max_length=50)),
('dsid', models.IntegerField()),
('savefile', models.FileField(upload_to='saves')),
],
),
]
| 0 | 1,604 | 23 |
8cc5128fa7df88fc6132133d988d02139b2ec674 | 150 | py | Python | kekette/__init__.py | prashnts/kekette | 6f39107b8fe3a53f16334e1dd51612e13ec03241 | [
"MIT"
] | null | null | null | kekette/__init__.py | prashnts/kekette | 6f39107b8fe3a53f16334e1dd51612e13ec03241 | [
"MIT"
] | null | null | null | kekette/__init__.py | prashnts/kekette | 6f39107b8fe3a53f16334e1dd51612e13ec03241 | [
"MIT"
] | null | null | null | from .attrdict import AttrDict
from .magic import mproperty, magic_tuple
__version__ = (0, 1, 0)
__all__ = ('AttrDict', 'mproperty', 'magic_tuple')
| 21.428571 | 50 | 0.733333 | from .attrdict import AttrDict
from .magic import mproperty, magic_tuple
__version__ = (0, 1, 0)
__all__ = ('AttrDict', 'mproperty', 'magic_tuple')
| 0 | 0 | 0 |
99aa8acb8fad1fff2e2d1e7bfd8b5d2514c352c9 | 4,104 | py | Python | pysnmp/CISCO-WAN-ATM-CONN-STAT-CAPABILITY.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 11 | 2021-02-02T16:27:16.000Z | 2021-08-31T06:22:49.000Z | pysnmp/CISCO-WAN-ATM-CONN-STAT-CAPABILITY.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 75 | 2021-02-24T17:30:31.000Z | 2021-12-08T00:01:18.000Z | pysnmp/CISCO-WAN-ATM-CONN-STAT-CAPABILITY.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 10 | 2019-04-30T05:51:36.000Z | 2022-02-16T03:33:41.000Z | #
# PySNMP MIB module CISCO-WAN-ATM-CONN-STAT-CAPABILITY (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-WAN-ATM-CONN-STAT-CAPABILITY
# Produced by pysmi-0.3.4 at Mon Apr 29 18:03:46 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint, ValueRangeConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsUnion")
ciscoAgentCapability, = mibBuilder.importSymbols("CISCO-SMI", "ciscoAgentCapability")
NotificationGroup, AgentCapabilities, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "AgentCapabilities", "ModuleCompliance")
ModuleIdentity, Counter32, Unsigned32, Bits, MibIdentifier, iso, Counter64, TimeTicks, NotificationType, Gauge32, Integer32, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, ObjectIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "Counter32", "Unsigned32", "Bits", "MibIdentifier", "iso", "Counter64", "TimeTicks", "NotificationType", "Gauge32", "Integer32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "ObjectIdentity")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoWanAtmConnStatCapability = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 7, 9999))
ciscoWanAtmConnStatCapability.setRevisions(('2003-04-08 00:00', '2001-03-21 00:00',))
if mibBuilder.loadTexts: ciscoWanAtmConnStatCapability.setLastUpdated('200304080000Z')
if mibBuilder.loadTexts: ciscoWanAtmConnStatCapability.setOrganization('Cisco Systems, Inc.')
cwaConnStatCapabilityAxsmV21R60 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 1))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwaConnStatCapabilityAxsmV21R60 = cwaConnStatCapabilityAxsmV21R60.setProductRelease('MGX8850 Release 2.1.60')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwaConnStatCapabilityAxsmV21R60 = cwaConnStatCapabilityAxsmV21R60.setStatus('current')
cwAtmConnStatCapabilityAxsmeV2R0170 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 2))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityAxsmeV2R0170 = cwAtmConnStatCapabilityAxsmeV2R0170.setProductRelease('MGX8850 Release 2.1.70')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityAxsmeV2R0170 = cwAtmConnStatCapabilityAxsmeV2R0170.setStatus('current')
cwAtmConnStatCapabilityPxm1eV2R300 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 3))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityPxm1eV2R300 = cwAtmConnStatCapabilityPxm1eV2R300.setProductRelease('MGX8850 Release 3.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityPxm1eV2R300 = cwAtmConnStatCapabilityPxm1eV2R300.setStatus('current')
cwacsCapabilityAxsmxgV4R00 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 4))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwacsCapabilityAxsmxgV4R00 = cwacsCapabilityAxsmxgV4R00.setProductRelease('MGX8950 Release 4.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwacsCapabilityAxsmxgV4R00 = cwacsCapabilityAxsmxgV4R00.setStatus('current')
mibBuilder.exportSymbols("CISCO-WAN-ATM-CONN-STAT-CAPABILITY", ciscoWanAtmConnStatCapability=ciscoWanAtmConnStatCapability, cwacsCapabilityAxsmxgV4R00=cwacsCapabilityAxsmxgV4R00, cwAtmConnStatCapabilityAxsmeV2R0170=cwAtmConnStatCapabilityAxsmeV2R0170, PYSNMP_MODULE_ID=ciscoWanAtmConnStatCapability, cwaConnStatCapabilityAxsmV21R60=cwaConnStatCapabilityAxsmV21R60, cwAtmConnStatCapabilityPxm1eV2R300=cwAtmConnStatCapabilityPxm1eV2R300)
| 102.6 | 477 | 0.78655 | #
# PySNMP MIB module CISCO-WAN-ATM-CONN-STAT-CAPABILITY (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-WAN-ATM-CONN-STAT-CAPABILITY
# Produced by pysmi-0.3.4 at Mon Apr 29 18:03:46 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint, ValueRangeConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsUnion")
ciscoAgentCapability, = mibBuilder.importSymbols("CISCO-SMI", "ciscoAgentCapability")
NotificationGroup, AgentCapabilities, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "AgentCapabilities", "ModuleCompliance")
ModuleIdentity, Counter32, Unsigned32, Bits, MibIdentifier, iso, Counter64, TimeTicks, NotificationType, Gauge32, Integer32, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, ObjectIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "Counter32", "Unsigned32", "Bits", "MibIdentifier", "iso", "Counter64", "TimeTicks", "NotificationType", "Gauge32", "Integer32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "ObjectIdentity")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoWanAtmConnStatCapability = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 7, 9999))
ciscoWanAtmConnStatCapability.setRevisions(('2003-04-08 00:00', '2001-03-21 00:00',))
if mibBuilder.loadTexts: ciscoWanAtmConnStatCapability.setLastUpdated('200304080000Z')
if mibBuilder.loadTexts: ciscoWanAtmConnStatCapability.setOrganization('Cisco Systems, Inc.')
cwaConnStatCapabilityAxsmV21R60 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 1))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwaConnStatCapabilityAxsmV21R60 = cwaConnStatCapabilityAxsmV21R60.setProductRelease('MGX8850 Release 2.1.60')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwaConnStatCapabilityAxsmV21R60 = cwaConnStatCapabilityAxsmV21R60.setStatus('current')
cwAtmConnStatCapabilityAxsmeV2R0170 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 2))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityAxsmeV2R0170 = cwAtmConnStatCapabilityAxsmeV2R0170.setProductRelease('MGX8850 Release 2.1.70')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityAxsmeV2R0170 = cwAtmConnStatCapabilityAxsmeV2R0170.setStatus('current')
cwAtmConnStatCapabilityPxm1eV2R300 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 3))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityPxm1eV2R300 = cwAtmConnStatCapabilityPxm1eV2R300.setProductRelease('MGX8850 Release 3.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwAtmConnStatCapabilityPxm1eV2R300 = cwAtmConnStatCapabilityPxm1eV2R300.setStatus('current')
cwacsCapabilityAxsmxgV4R00 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 9999, 4))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwacsCapabilityAxsmxgV4R00 = cwacsCapabilityAxsmxgV4R00.setProductRelease('MGX8950 Release 4.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
cwacsCapabilityAxsmxgV4R00 = cwacsCapabilityAxsmxgV4R00.setStatus('current')
mibBuilder.exportSymbols("CISCO-WAN-ATM-CONN-STAT-CAPABILITY", ciscoWanAtmConnStatCapability=ciscoWanAtmConnStatCapability, cwacsCapabilityAxsmxgV4R00=cwacsCapabilityAxsmxgV4R00, cwAtmConnStatCapabilityAxsmeV2R0170=cwAtmConnStatCapabilityAxsmeV2R0170, PYSNMP_MODULE_ID=ciscoWanAtmConnStatCapability, cwaConnStatCapabilityAxsmV21R60=cwaConnStatCapabilityAxsmV21R60, cwAtmConnStatCapabilityPxm1eV2R300=cwAtmConnStatCapabilityPxm1eV2R300)
| 0 | 0 | 0 |
6a8f74e1f78f8a05b4a07946a323223213f3486d | 1,188 | py | Python | matching/tests/test_partition.py | centrefornetzero/epc-ppd-address-matching | eb48b6b0f408b568f0804436f081af70f59df571 | [
"MIT"
] | 2 | 2022-03-24T10:54:18.000Z | 2022-03-25T10:31:12.000Z | matching/tests/test_partition.py | centrefornetzero/epc-ppd-address-matching | eb48b6b0f408b568f0804436f081af70f59df571 | [
"MIT"
] | 3 | 2021-12-01T08:19:05.000Z | 2022-01-10T11:57:36.000Z | matching/tests/test_partition.py | centrefornetzero/epc-ppd-address-matching | eb48b6b0f408b568f0804436f081af70f59df571 | [
"MIT"
] | null | null | null | import os
import pathlib
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from matching.partition import get_args, partition_data
| 27.627907 | 72 | 0.657407 | import os
import pathlib
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from matching.partition import get_args, partition_data
def test_get_args() -> None:
args = get_args(["input.parquet", "output_path"])
assert args.input_path == "input.parquet"
assert args.output_path == "output_path"
def test_partition_data(tmp_path: pathlib.Path) -> None:
table = pa.Table.from_pydict(
{
"id": [1, 2, 3, 4],
"postcode": ["HG2 9LW", "HG2 9LW", "CF31 3PD", "RM14 3HR"],
}
)
input_path = str(tmp_path / "input.parquet")
pq.write_table(table, input_path)
output_path = str(tmp_path / "output")
num_partitions = 2
partition_data(input_path, output_path, num_partitions, "postcode")
partitioned_dataset = pq.ParquetDataset(output_path)
assert len(os.listdir(output_path)) == num_partitions
got = (
partitioned_dataset.read()
.to_pandas()
.drop("partition_key", axis=1)
.sort_values(by="id")
.reset_index(drop=True)
)
want = table.to_pandas().sort_values(by="id").reset_index(drop=True)
pd.testing.assert_frame_equal(got, want)
| 987 | 0 | 46 |
227f2116d1f99396958e9d7fe6b189375da9b28e | 117,740 | py | Python | Processor.py | mdorobek/authorship_analysis | 34221a56da229e5113b6d7bb2fb125cd50deb077 | [
"Unlicense"
] | null | null | null | Processor.py | mdorobek/authorship_analysis | 34221a56da229e5113b6d7bb2fb125cd50deb077 | [
"Unlicense"
] | null | null | null | Processor.py | mdorobek/authorship_analysis | 34221a56da229e5113b6d7bb2fb125cd50deb077 | [
"Unlicense"
] | null | null | null | import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from datetime import datetime
from os import listdir
from os.path import isfile, join
import sys
import math
from sklearn.metrics import accuracy_score, f1_score
import re
from Extractor import get_word_length_matrix, get_word_length_matrix_with_interval, get_average_word_length, \
get_word_length_matrix_with_margin, get_char_count, get_digits, get_sum_digits, get_word_n_grams, \
get_char_affix_n_grams, get_char_word_n_grams, get_char_punct_n_grams, get_pos_tags_n_grams, get_bow_matrix, \
get_yules_k, get_special_char_matrix, get_function_words, get_pos_tags, get_sentence_end_start, \
get_flesch_reading_ease_vector, get_sentence_count, get_word_count
from sklearn.preprocessing import StandardScaler, Normalizer
# Chapter 7.1.1. method to trim a feature with low sum e.g. ngrams lower then 5
# Chapter 7.1.1. method to trim feature with low occurrence over all article
# Chapter 7.1.1. Process of filtering the data with low occurrence and save the filtered features in a new file
# Chapter 7.1.2. method to filter words based on document frequency
# Chapter 7.1.2 Process of filtering the data with high document frequency and save the filtered features in a new file
# Chapter 7.1.4. get the relative frequency based on a length metric (char, word, sentence)
# Chapter 7.1.4. whole process of the chapter. Get the individual relative frequency of a feature and compare
# the correlation to the article length from the absolute and relative feature, save the feature with the estimated
# relative frequency in a new file
# Chapter 7.2.1 First step of the iterative filter: Rank the features
# Chapter 7.2.1 method to get the best percentile for GNB
# Chapter 7.2.1 method to get the best percentile for SVC
# Chapter 7.2.1 method to get the best percentile for KNN
# Chapter 7.2.1 Filter the feature based on the estimated best percentile and save it into a new file
# Chapter 7.2.1 Complete process of the iterative Filter
# Chapter 8.1. and later, basically the process of the iterative filter only with the svc classifier
# Chapter 7.2.1. Get the accuracy of the features before the iterative filter, results in table 18
# Chapter 7.2.1. Get the accuracy of the features after the iterative filter, results in table 18
# Get n article for a given number of authors. Required for setups with different numbers of authors and article
# Return indices for n article for a given number of authors. Required for setups with different
# numbers of authors and article
# Method to estimate the f1 score of the test data for GNB
# Method to estimate the f1 score of the test data for SVC
# Method to estimate the f1 score of the test data for KNN
# Method to estimate the accuracy of the test data for SVC
# Chapter 7.3.1. comparison of the word length feature alternatives
# Chapter 7.3.2. comparison of the digit feature alternatives
# Chapter 7.3.3. comparison of the word ngrams with n 4-6
# Chapter 7.3.3. comparison of the word ngrams with n 2-3
# Chapter 7.3.4. comparison of the different lengths of char ngrams
# Chapter 7.3.4. whole process of the comparison of the char-n-gram features
# Chapter 7.3.4. char-affix-ngrams
# Chapter 7.3.4. char-word-ngrams
# Chapter 7.3.4. char-punct-ngrams
# Chapter 7.3.4. Print the char-n-gram features in different files
# combined preprocessing steps of the pos-tag-n-grams
# combined preprocessing steps of the char-n-grams
# Feature selection with the iterative filter without printing the results in a file
# Chapter 7.3.5. function to compare the pos-tag-n-grams
# Chapter 7.3.5. complete process of the pos-tag-n-grams comparison
# Method to print all features for different counts of authors and texts
# Including all Preprocessing steps and filtering
# create a dataframe with the combined features for a specific number of authors and texts
# features can be excluded by name
# Chapter 8.4. comparison of normalization and standardization
# Chapter 8.5.1. Comparison of the individual features, data for table 21
# Chapter 8.5.2. Get the values of the difference functions, data for table 22
# Chapter 8.5.3. Comparison of the model with or without content features, picture 28
# Chapter 8.5.3. Get the difference functions without content features, table 23
# Global options to see all lines and columns of a printed DataFrame
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
# The following calculations were used in the chapters for the preprocessing and modeling of the data.
# They are all commented to be able to use them separate
# # Preprocessing
# Chapter 7.1.1. filter features with low occurrence
# filter_low_occurrence()
# Chapter 7.1.2. filter features with high document frequency
# filter_high_document_frequency()
# Chapter 7.1.4. individual relative frequency
# individual_relative_frequency()
# # Feature Selection
# Chapter 7.2.1. iterative filter process
"""iterative_filter_process('daten/5_iterative_filter/', pd.read_csv("musikreviews_balanced_authors.csv", sep=',',
encoding="utf-8", nrows=2500), "all", "all")"""
# Chapter 7.2.2. evaluation of the iterative filter
# get_accuracy_after_iterative_filter()).to_csv(f"daten/5_iterative_filter/results/accuracy_after_filter.csv", index=False)
# get_accuracy_before_iterative_filter().to_csv(f"daten/5_iterative_filter/results/accuracy_before_filter.csv", index=False)
# # Feature alternatives
# Chapter 7.3.1. alternatives word length
# compare_word_length_features().to_csv("daten/6_feature_analysis/results/compared_word_length.csv", index=False)
# Chapter 7.3.2. alternatives digit features
# compare_digit_features().to_csv("daten/6_feature_analysis/results/compared_digit_features.csv", index=False)
# Chapter 7.3.3. alternatives word-n-grams
# compare_word_4_6_grams().to_csv("daten/6_feature_analysis/results/compared_4_6_word_grams.csv", index=False)
# compare_word_2_3_grams().to_csv("daten/6_feature_analysis/results/compared_2_3_word_grams.csv", index=False)
# Chapter 7.3.4. alternatives char-n-grams
# compare_char_n_grams_process("daten/6_feature_analysis_char_n_grams/")
# Chapter 7.3.5. alternatives pos-tag-n-grams
# compare_pos_n_grams_process("daten/6_feature_analysis_pos_tag_n_grams/")
# # Modeling
# Get all Features for different count of authors and texts. Including all preprocessing and selection steps.
# Needed for further steps in chapter 8.
# print_all_features_svc("daten/7_modeling/", "musikreviews_balanced_authors.csv")
# Chapter 8.4. compare scaling methods
"""compare_normalization_standardization("musikreviews_balanced_authors.csv","daten/7_modeling/",
[5, 10, 15, 25, 50, 75, 100], [2, 3, 4, 5, 10, 15, 25])\
.to_csv("daten/7_modeling/results/compared_stand_normal.csv", index=False)"""
# Chapter 8.5.1. compare single features
"""compare_single_features("musikreviews_balanced_authors.csv", "daten/7_modeling/", [5, 10, 15, 25, 50, 75, 100],
[25]).to_csv("daten/7_modeling/results/compared_single_features.csv", index=False)"""
# Chapter 8.5.2. difference functions with content features
"""df_wo_feature, df_diff = \
get_feature_function_difference("musikreviews_balanced_authors.csv", "daten/7_modeling/",
[5, 10, 15, 25, 50, 75, 100], [25])
df_wo_feature.to_csv("daten/7_modeling/results/f1_without_feature.csv", index=False)
df_diff.to_csv("daten/7_modeling/results/f1_difference_function", index=False)"""
# Chapter 8.5.2. compare model with and without content features
"""compare_content_features("musikreviews_balanced_authors.csv", "daten/7_modeling/",[5, 10, 15, 25, 50], [15])\
.to_csv("daten/7_modeling/results/compare_content_feature.csv", index=False)"""
# Chapter 8.5.2. difference functions without content features
"""df_wo_feature, df_diff \
= get_feature_function_difference_without_content("musikreviews_balanced_authors.csv",
"daten/7_modeling/", [5, 10, 15, 25, 50, 75, 100], [25])
df_wo_feature.to_csv("daten/7_modeling/results/f1_without_feature_no_content.csv", index=False)
df_diff.to_csv("daten/7_modeling/results/f1_difference_function_no_content", index=False)"""
| 56.524244 | 125 | 0.630109 | import pandas as pd
import numpy as np
import copy
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV
from sklearn.feature_selection import mutual_info_classif, SelectKBest
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from datetime import datetime
from os import listdir
from os.path import isfile, join
import sys
import math
from sklearn.metrics import accuracy_score, f1_score
import re
from Extractor import get_word_length_matrix, get_word_length_matrix_with_interval, get_average_word_length, \
get_word_length_matrix_with_margin, get_char_count, get_digits, get_sum_digits, get_word_n_grams, \
get_char_affix_n_grams, get_char_word_n_grams, get_char_punct_n_grams, get_pos_tags_n_grams, get_bow_matrix, \
get_yules_k, get_special_char_matrix, get_function_words, get_pos_tags, get_sentence_end_start, \
get_flesch_reading_ease_vector, get_sentence_count, get_word_count
from sklearn.preprocessing import StandardScaler, Normalizer
# Chapter 7.1.1. method to trim a feature with low sum e.g. ngrams lower then 5
def trim_df_sum_feature(par_df, par_n):
par_df = par_df.fillna(value=0)
columns = par_df.columns.to_numpy()
data_array = par_df.to_numpy(dtype=float)
sum_arr = data_array.sum(axis=0)
# reduce n if 0 features would be returned
while len(par_df.columns) - len(np.where(sum_arr < par_n)[0]) == 0:
par_n -= 1
positions = list(np.where(sum_arr < par_n))
columns = np.delete(columns, positions)
data_array = np.delete(data_array, positions, axis=1)
return pd.DataFrame(data=data_array, columns=columns)
# Chapter 7.1.1. method to trim feature with low occurrence over all article
def trim_df_by_occurrence(par_df, n):
df_masked = par_df.notnull().astype('int')
word_rate = df_masked.sum()
columns = []
filtered_bow = pd.DataFrame()
for i in range(0, len(word_rate)):
if word_rate[i] > n:
columns.append(word_rate.index[i])
for c in columns:
filtered_bow[c] = par_df[c]
return filtered_bow
# Chapter 7.1.1. Process of filtering the data with low occurrence and save the filtered features in a new file
def filter_low_occurrence():
df_bow = pd.read_csv("daten/raw/bow.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"BOW before: {len(df_bow.columns)}")
df_bow = trim_df_by_occurrence(df_bow, 1)
print(f"BOW after: {len(df_bow.columns)}")
df_bow.to_csv(f"daten/2_filter_low_occurrence/bow.csv", index=False)
for n in range(2, 7):
word_n_gram = pd.read_csv(f"daten/raw/word_{n}_gram.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"Word_{n}_gram before: {len(word_n_gram.columns)}")
word_n_gram = trim_df_by_occurrence(word_n_gram, 1)
print(f"Word_{n}_gram after: {len(word_n_gram.columns)}")
word_n_gram.to_csv(f"daten/2_filter_low_occurrence/word_{n}_gram.csv", index=False)
for n in range(2, 6):
char_affix_n_gram = pd.read_csv(f"daten/trimmed_occ_greater_one/char_affix_{n}_gram_1.csv", sep=',',
encoding="utf-8", nrows=2500)
print(f"char_affix_{n}_gram before: {len(char_affix_n_gram.columns)}")
char_affix_n_gram = trim_df_sum_feature(char_affix_n_gram, 5)
print(f"char_affix_{n}_gram after: {len(char_affix_n_gram.columns)}")
char_affix_n_gram.to_csv(f"daten/2_filter_low_occurrence/char_affix_{n}_gram.csv", index=False)
char_word_n_gram = pd.read_csv(f"daten/trimmed_occ_greater_one/char_word_{n}_gram_1.csv", sep=',',
encoding="utf-8", nrows=2500)
print(f"char_word_{n}_gram before: {len(char_word_n_gram.columns)}")
char_word_n_gram = trim_df_sum_feature(char_word_n_gram, 5)
print(f"char_word_{n}_gram after: {len(char_word_n_gram.columns)}")
char_word_n_gram.to_csv(f"daten/2_filter_low_occurrence/char_word_{n}_gram.csv", index=False)
char_punct_n_gram = pd.read_csv(f"daten/trimmed_occ_greater_one/char_punct_{n}_gram_1.csv", sep=',',
encoding="utf-8", nrows=2500)
print(f"char_punct_{n}_gram before: {len(char_punct_n_gram.columns)}")
char_punct_n_gram = trim_df_sum_feature(char_punct_n_gram, 5)
print(f"char_punct_{n}_gram after: {len(char_punct_n_gram.columns)}")
char_punct_n_gram.to_csv(f"daten/2_filter_low_occurrence/char_punct_{n}_gram.csv", index=False)
df_f_word = pd.read_csv("daten/raw/function_words.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"Function Words before: {len(df_f_word.columns)}")
df_f_word = trim_df_by_occurrence(df_f_word, 1)
print(f"Function Words after: {len(df_f_word.columns)}")
df_f_word.to_csv(f"daten/2_filter_low_occurrence/function_words.csv", index=False)
for n in range(2, 6):
pos_tags_n_gram = pd.read_csv(f"daten/raw/pos_tag_{n}_gram.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"pos_tag_{n}_gram before: {len(pos_tags_n_gram.columns)}")
pos_tags_n_gram = trim_df_by_occurrence(pos_tags_n_gram, 1)
print(f"pos_tag_{n}_gram after: {len(pos_tags_n_gram.columns)}")
pos_tags_n_gram.to_csv(f"daten/2_filter_low_occurrence/pos_tag_{n}_gram.csv", index=False)
# Chapter 7.1.2. method to filter words based on document frequency
def trim_df_by_doc_freq(par_df, par_doc_freq):
df_masked = par_df.notnull().astype('int')
word_rate = df_masked.sum() / len(par_df)
columns = []
filtered_bow = pd.DataFrame()
for i in range(0, len(word_rate)):
if word_rate[i] < par_doc_freq:
columns.append(word_rate.index[i])
for c in columns:
filtered_bow[c] = par_df[c]
return filtered_bow
# Chapter 7.1.2 Process of filtering the data with high document frequency and save the filtered features in a new file
def filter_high_document_frequency():
# Filter words with high document frequency
df_bow = pd.read_csv("daten/2_filter_low_occurrence/bow.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"BOW before: {len(df_bow.columns)}")
df_bow = trim_df_by_doc_freq(df_bow, 0.5)
print(f"BOW after: {len(df_bow.columns)}")
df_bow.to_csv(f"daten/3_fiter_high_frequency/bow.csv", index=False)
df_f_word = pd.read_csv("daten/2_filter_low_occurrence/function_words.csv", sep=',', encoding="utf-8", nrows=2500)
print(f"Function Word before: {len(df_f_word.columns)}")
df_f_word = trim_df_by_doc_freq(df_f_word, 0.5)
print(f"Function Word after: {len(df_f_word.columns)}")
df_f_word.to_csv(f"daten/3_fiter_high_frequency/function_words.csv", index=False)
for n in range(2, 7):
word_n_gram = pd.read_csv(f"daten/2_filter_low_occurrence/word_{n}_gram.csv", sep=',', encoding="utf-8",
nrows=2500)
print(f"Word_{n}_gram before: {len(word_n_gram.columns)}")
word_n_gram = trim_df_by_doc_freq(word_n_gram, 0.5)
print(f"Word_{n}_gram after: {len(word_n_gram.columns)}")
word_n_gram.to_csv(f"daten/3_fiter_high_frequency/word_{n}_gram.csv", index=False)
# Chapter 7.1.4. get the relative frequency based on a length metric (char, word, sentence)
def get_rel_frequency(par_df_count, par_df_len_metric_vector):
df_rel_freq = pd.DataFrame(columns=par_df_count.columns)
for index, row in par_df_count.iterrows():
df_rel_freq = df_rel_freq.append(row.div(par_df_len_metric_vector[index]))
return df_rel_freq
# Chapter 7.1.4. whole process of the chapter. Get the individual relative frequency of a feature and compare
# the correlation to the article length from the absolute and relative feature, save the feature with the estimated
# relative frequency in a new file
def individual_relative_frequency():
df_len_metrics = pd.read_csv(f"daten/1_raw/length_metrics.csv", sep=',', encoding="utf-8", nrows=2500)
# different metrics for individual relative frequencies
metrics = ['word_count', 'char_count', 'sentence_count']
for m in metrics:
# The csv is placed in a folder based on the metric for the individual relative frequency
path = f'daten/4_relative_frequency/{m}'
files = [f for f in listdir(path) if isfile(join(path, f))]
for f in files:
x = pd.read_csv(f"daten/4_relative_frequency/{m}/{f}",
sep=',', encoding="utf-8", nrows=2500).fillna(value=0)
x_rel = get_rel_frequency(x, df_len_metrics[m])
# Save the CSV with relative frequency
x_rel.to_csv(
f"daten/4_relative_frequency/{f.split('.')[0]}"
f"_rel.csv", index=False)
# Correlation is always between the metrics and the word_count
x['word_count'] = df_len_metrics['word_count']
x_rel['word_count'] = df_len_metrics['word_count']
# only on the test data 60/40 split
x_train, x_test = train_test_split(x, test_size=0.4, random_state=42)
x_train_rel, x_test_rel = train_test_split(x_rel, test_size=0.4, random_state=42)
# Calculate the median correlation
print(f"{f}_abs: {x_train.corr(method='pearson', min_periods=1)['word_count'].iloc[:-1].mean()}")
print(f"{f}_rel: {x_train_rel.corr(method='pearson', min_periods=1)['word_count'].iloc[:-1].mean()}")
# Chapter 7.2.1 First step of the iterative filter: Rank the features
def sort_features_by_score(par_x, par_y, par_select_metric):
# Get a sorted ranking of all features by the selected metric
selector = SelectKBest(par_select_metric, k='all')
selector.fit(par_x, par_y)
# Sort the features by their score
return pd.DataFrame(dict(feature_names=par_x.columns, scores=selector.scores_)).sort_values('scores',
ascending=False)
# Chapter 7.2.1 method to get the best percentile for GNB
def get_best_percentile_gnb(par_x_train, par_y_train, par_iter, par_df_sorted_features, step):
result_list = []
gnb = GaussianNB()
best_perc_round = par_iter - 1 # If no other point is found, highest amount of features (-1 starts to count from 0)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if len(par_y_train.index) / len(np.unique(par_y_train.values).tolist()) < 10:
cv = int(len(par_y_train.index) / len(np.unique(par_y_train.values).tolist())) - 1
else:
cv = 10
for perc_features in np.arange(step, par_iter + 1, step):
start_time = datetime.now()
# 1%*i best features to keep and create new dataframe with those only
number_of_features = int(perc_features * (len(par_x_train.columns) / 100))
# minimum one feature
number_of_features = 1 if number_of_features < 1 else number_of_features
feature_list = par_df_sorted_features['feature_names'][: number_of_features].tolist()
x_new_training = copy.deepcopy(par_x_train[feature_list])
# GNB Training
result_list.append(
cross_val_score(gnb, x_new_training, par_y_train, cv=cv, n_jobs=-1, scoring='accuracy').mean())
# Compares the accuracy with the 5 following points => needs 6 points minimum
if len(result_list) > 5:
# list starts to count at 0, subtract one more from len
difference_list_p2p = [result_list[p + 1] - result_list[p] for p in
range(len(result_list) - 6, len(result_list) - 1)]
difference_list_1p = [result_list[p + 1] - result_list[len(result_list) - 6] for p in
range(len(result_list) - 6, len(result_list) - 1)]
# Find the best percent if 5 following points were lower then the point before or had a deviation <= 0.5%
# or all points are 2% lower then the first point
if all(point_y <= 0 for point_y in difference_list_p2p) or \
all(-0.005 <= point_y <= 0.005 for point_y in difference_list_1p) or \
all(point_y < -0.02 for point_y in difference_list_1p):
# the best perc is the results - 6 point in the result list
best_perc_round = len(result_list) - 6
break
# Console Output
print(f"GNB Round {perc_features / step}: {datetime.now() - start_time}")
# Optimization of the best percent
# If any point with a lower percent is higher, it is the new optimum
if any(point_y > result_list[best_perc_round] for point_y in result_list[:len(result_list) - 5]):
best_perc_round = result_list.index(max(result_list[:len(result_list) - 5]))
# Tradeoff of 0.5% accuracy for lesser percent of features
# As long as there is a lesser maximum with 1% lesser accuracy, which has a minimum of 2% less percent features
better_perc_exists = True
best_accuracy_tradeoff = result_list[best_perc_round] - 0.01
# If there are no 5% left for the tradeoff there is no better perc
if best_perc_round - int(2 / step) < 0:
better_perc_exists = False
while better_perc_exists:
earliest_pos = best_perc_round - int(2 / step)
# if its less then 0 it starts to count backside
earliest_pos = 0 if earliest_pos < 0 else earliest_pos
if any(point_y > best_accuracy_tradeoff for point_y in result_list[:earliest_pos]):
best_perc_round = result_list.index(max(result_list[:earliest_pos]))
else:
better_perc_exists = False
# the best percent of the features is calculated by the percent start plus the rounds * step
best_perc = step + step * best_perc_round
print(best_perc)
return best_perc, best_perc_round, result_list
# Chapter 7.2.1 method to get the best percentile for SVC
def get_best_percentile_svc(par_x_train, par_y_train, par_iter, par_df_sorted_features, step):
result_list = []
# Parameter for SVC
param_grid_svc = {'C': (0.001, 0.01, 0.1, 1, 10),
'kernel': ('linear', 'poly', 'rbf'),
'gamma': ('scale', 'auto')}
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if len(par_y_train.index) / len(np.unique(par_y_train.values).tolist()) < 10:
cv = int(len(par_y_train.index) / len(np.unique(par_y_train.values).tolist())) - 1
else:
cv = 10
best_perc_round = par_iter - 1 # If no other point is found, highest amount of features (-1 starts to count from 0)
for perc_features in np.arange(step, par_iter + 1, step):
start_time = datetime.now()
# 1%*i best features to keep and create new dataframe with those only
number_of_features = int(perc_features * (len(par_x_train.columns) / 100))
# minimum one feature
number_of_features = 1 if number_of_features < 1 else number_of_features
feature_list = par_df_sorted_features['feature_names'][: number_of_features].tolist()
x_new_training = copy.deepcopy(par_x_train[feature_list])
# SVC Test
grid_search = GridSearchCV(svm.SVC(), param_grid_svc, cv=cv, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_new_training, par_y_train)
result_list.append(grid_results.best_score_)
# Compares the accuracy with the 5 following points => needs 6 points minimum
if len(result_list) > 5:
# list starts to count at 0, subtract one more from len
difference_list_p2p = [result_list[p + 1] - result_list[p] for p in
range(len(result_list) - 6, len(result_list) - 1)]
difference_list_1p = [result_list[p + 1] - result_list[len(result_list) - 6] for p in
range(len(result_list) - 6, len(result_list) - 1)]
# Find the best percent if 5 following points were lower then the point before or had a deviation <= 0.5%
# or all points are 2% lower then the first point
if all(point_y <= 0 for point_y in difference_list_p2p) or \
all(-0.005 <= point_y <= 0.005 for point_y in difference_list_1p) or \
all(point_y < -0.02 for point_y in difference_list_1p):
# the best perc is the results - 6 point in the result list
best_perc_round = len(result_list) - 6
break
# Console Output
print(f"SVC Round {perc_features / step}: {datetime.now() - start_time}")
# Optimization of the best percent
# If any point with a lower percent is higher, it is the new optimum
if any(point_y > result_list[best_perc_round] for point_y in result_list[:len(result_list) - 5]):
best_perc_round = result_list.index(max(result_list[:len(result_list) - 5]))
# Tradeoff of 1% accuracy for lesser percent of features
# As long as there is a lesser maximum with 1% lesser accuracy, which has a minimum of 2% less percent features
better_perc_exists = True
best_accuracy_tradeoff = result_list[best_perc_round] - 0.01
# If there are no 5% left for the tradeoff there is no better perc
if best_perc_round - int(2 / step) < 0:
better_perc_exists = False
while better_perc_exists:
earliest_pos = best_perc_round - int(2 / step)
# if its less then 0 it starts to count backside
earliest_pos = 0 if earliest_pos < 0 else earliest_pos
if any(point_y > best_accuracy_tradeoff for point_y in result_list[:earliest_pos]):
best_perc_round = result_list.index(max(result_list[:earliest_pos]))
else:
better_perc_exists = False
# the best percent of the features is calculated by the percent start plus the rounds * step
best_perc = step + step * best_perc_round
print(best_perc)
return best_perc, best_perc_round, result_list
# Chapter 7.2.1 method to get the best percentile for KNN
def get_best_percentile_knn(par_x_train, par_y_train, par_iter, par_df_sorted_features, step):
result_list = []
best_perc_round = par_iter - 1 # If no other point is found, highest amount of features (-1 starts to count from 0)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if len(par_y_train.index) / len(np.unique(par_y_train.values).tolist()) < 10:
cv = int(len(par_y_train.index) / len(np.unique(par_y_train.values).tolist())) - 1
else:
cv = 10
for perc_features in np.arange(step, par_iter + 1, step):
start_time = datetime.now()
# 1%*i best features to keep and create new dataframe with those only
number_of_features = int(perc_features * (len(par_x_train.columns) / 100))
# minimum one feature
number_of_features = 1 if number_of_features < 1 else number_of_features
feature_list = par_df_sorted_features['feature_names'][: number_of_features].tolist()
x_new_training = copy.deepcopy(par_x_train[feature_list])
# Parameter for KNN
# Some Values from 3 to square of samples
neighbors = [i for i in range(3, int(math.sqrt(len(x_new_training.index))), 13)]
neighbors += [1, 3, 5, 11, 19, 36]
if int(math.sqrt(len(feature_list))) not in neighbors:
neighbors.append(int(math.sqrt(len(x_new_training.index))))
# Not more neighbors then samples-2
neighbors = [x for x in neighbors if x < len(x_new_training.index) - 2]
# remove duplicates
neighbors = list(set(neighbors))
param_grid_knn = {'n_neighbors': neighbors,
'weights': ['uniform', 'distance'],
'metric': ['euclidean', 'manhattan']}
# KNN Training
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=cv, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_new_training, par_y_train)
result_list.append(grid_results.best_score_)
# Compares the accuracy with the 5 following points => needs 6 points minimum
if len(result_list) > 5:
# list starts to count at 0, subtract one more from len
difference_list_p2p = [result_list[p + 1] - result_list[p] for p in
range(len(result_list) - 6, len(result_list) - 1)]
difference_list_1p = [result_list[p + 1] - result_list[len(result_list) - 6] for p in
range(len(result_list) - 6, len(result_list) - 1)]
# Find the best percent if 5 following points were lower then the point before or had a deviation <= 0.5%
# or all points are 2% lower then the first point
if all(point_y <= 0 for point_y in difference_list_p2p) or \
all(-0.005 <= point_y <= 0.005 for point_y in difference_list_1p) or \
all(point_y < -0.02 for point_y in difference_list_1p):
# the best perc is the results - 6 point in the result list
best_perc_round = len(result_list) - 6
break
# Console Output
print(f"KNN Round {perc_features / step}: {datetime.now() - start_time}")
# Optimization of the best percent
# If any point with a lower percent is higher, it is the new optimum
if any(point_y > result_list[best_perc_round] for point_y in result_list[:len(result_list) - 5]):
best_perc_round = result_list.index(max(result_list[:len(result_list) - 5]))
# Tradeoff of 1% accuracy for lesser percent of features
# As long as there is a lesser maximum with 1% lesser accuracy, which has a minimum of 2% less percent features
better_perc_exists = True
best_accuracy_tradeoff = result_list[best_perc_round] - 0.01
# If there are no 5% left for the tradeoff there is no better perc
if best_perc_round - int(2 / step) < 0:
better_perc_exists = False
while better_perc_exists:
earliest_pos = best_perc_round - int(2 / step)
# if its less then 0 it starts to count backside
earliest_pos = 0 if earliest_pos < 0 else earliest_pos
if any(point_y >= best_accuracy_tradeoff for point_y in result_list[:earliest_pos]):
best_perc_round = result_list.index(max(result_list[:earliest_pos]))
else:
better_perc_exists = False
# the best percent of the features is calculated by the percent start plus the rounds * step
best_perc = step + step * best_perc_round
print(best_perc)
return best_perc, best_perc_round, result_list
# Chapter 7.2.1 Filter the feature based on the estimated best percentile and save it into a new file
def print_filter_feature_percentile(par_path, par_df_sorted_features, par_percent, par_x, par_file_name):
# select the 1 percent of the features (len/100) multiplied by par_best_percent
number_features = round(par_percent * (len(par_x.columns) / 100))
# If the 1st percent is less then 1
number_features = 1 if number_features < 1 else number_features
feature_list = par_df_sorted_features['feature_names'][:number_features].tolist()
# print the name of the features in a file
original_stdout = sys.stdout
with open(f'{par_path}selected_features/{par_file_name}_filtered.txt', 'w', encoding="utf-8") as f:
sys.stdout = f
print(f"Features: {len(feature_list)}")
print(f"{feature_list}")
sys.stdout = original_stdout
# select the best features from the original dataset
par_x[feature_list].to_csv(f"{par_path}csv_after_filter/{par_file_name}_filtered.csv", index=False)
# Chapter 7.2.1 Complete process of the iterative Filter
def iterative_filter_process(par_path, par_df, par_num_texts, par_num_authors):
y = par_df['label_encoded']
path = f'{par_path}csv_before_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
# Filter the files for author and text numbers if 'all' is not set.
if par_num_authors != "all":
r = re.compile(f"a{par_num_authors}_")
files = list(filter(r.match, files))
if par_num_authors != "all":
r = re.compile(f".*t{par_num_texts}_")
files = list(filter(r.match, files))
step_perc = 1.0
for f in files:
filename = f.split(".")[0]
print(f)
x = pd.read_csv(f"{par_path}csv_before_filter/{f}", sep=',', encoding="utf-8", nrows=2500)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
# Get sorted features
df_sorted_features = sort_features_by_score(x_train, y_train, mutual_info_classif)
# Calculate the best percentiles of the data for the different classifier
best_perc_gnb, best_round_gnb, result_list_gnb = get_best_percentile_gnb(x_train, y_train, 50,
df_sorted_features, step_perc)
best_perc_svc, best_round_svc, result_list_svc = get_best_percentile_svc(x_train, y_train, 50,
df_sorted_features, step_perc)
best_perc_knn, best_round_knn, result_list_knn = get_best_percentile_knn(x_train, y_train, 50,
df_sorted_features, step_perc)
# select the beast features from the original dataset
print_filter_feature_percentile(par_path, df_sorted_features, best_perc_gnb, x, "gnb_" + filename)
print_filter_feature_percentile(par_path, df_sorted_features, best_perc_svc, x, "svc_" + filename)
print_filter_feature_percentile(par_path, df_sorted_features, best_perc_knn, x, "knn_" + filename)
# print best perc to a file
original_stdout = sys.stdout
with open(f'{par_path}best_perc/{filename}.txt', 'w') as f:
sys.stdout = f
print(f"best_perc_gnb: ({best_perc_gnb}|{result_list_gnb[best_round_gnb]})\n"
f"best_perc_svc: ({best_perc_svc}|{result_list_svc[best_round_svc]})\n"
f"best_perc_knn: ({best_perc_knn}|{result_list_knn[best_round_knn]})")
sys.stdout = original_stdout
# draw diagram
len_list = [len(result_list_gnb), len(result_list_svc), len(result_list_knn)]
plt.plot([i * step_perc for i in range(1, len(result_list_gnb) + 1)], result_list_gnb, 'r-', label="gnb")
plt.plot(best_perc_gnb, result_list_gnb[best_round_gnb], 'rx')
plt.plot([i * step_perc for i in range(1, len(result_list_svc) + 1)], result_list_svc, 'g-', label="svc")
plt.plot(best_perc_svc, result_list_svc[best_round_svc], 'gx')
plt.plot([i * step_perc for i in range(1, len(result_list_knn) + 1)], result_list_knn, 'b-', label="knn")
plt.plot(best_perc_knn, result_list_knn[best_round_knn], 'bx')
plt.axis([step_perc, (max(len_list) + 1) * step_perc, 0, 1])
plt.xlabel('Daten in %')
plt.ylabel('Genauigkeit')
plt.legend()
plt.savefig(f"{par_path}/diagrams/{filename}")
plt.cla()
# print accuracy to file
df_percent = pd.DataFrame(data=[i * step_perc for i in range(1, max(len_list) + 1)], columns=['percent'])
df_gnb = pd.DataFrame(data=result_list_gnb, columns=['gnb'])
df_svc = pd.DataFrame(data=result_list_svc, columns=['svc'])
df_knn = pd.DataFrame(data=result_list_knn, columns=['knn'])
df_accuracy = pd.concat([df_percent, df_gnb, df_svc, df_knn], axis=1)
df_accuracy = df_accuracy.fillna(value="")
df_accuracy.to_csv(f'{par_path}accuracy/{filename}_filtered.csv', index=False)
# Chapter 8.1. and later, basically the process of the iterative filter only with the svc classifier
def iterative_filter_process_svm(par_path, par_df, par_num_texts, par_num_authors):
y = par_df['label_encoded']
path = f'{par_path}csv_before_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
# Filter the files for author and text numbers if 'all' is not set.
if par_num_authors != "all":
r = re.compile(f"a{par_num_authors}_")
files = list(filter(r.match, files))
if par_num_authors != "all":
r = re.compile(f".*t{par_num_texts}_")
files = list(filter(r.match, files))
step_perc = 1.0
for f in files:
filename = f.split(".")[0]
print(f)
x = pd.read_csv(f"{par_path}csv_before_filter/{f}", sep=',', encoding="utf-8", nrows=2500)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
# Get sorted features
df_sorted_features = sort_features_by_score(x_train, y_train, mutual_info_classif)
# Calculate the best percentiles of the data for svc
best_perc_svc, best_round_svc, result_list_svc = get_best_percentile_svc(x_train, y_train, 50,
df_sorted_features, step_perc)
# select the beast features from the original dataset
print_filter_feature_percentile(par_path, df_sorted_features, best_perc_svc, x, filename)
# print best perc to a file
original_stdout = sys.stdout
with open(f'{par_path}best_perc/{filename}.txt', 'w') as out_f:
sys.stdout = out_f
print(f"best_perc_svc: ({best_perc_svc}|{result_list_svc[best_round_svc]})\n")
sys.stdout = original_stdout
# draw diagram
plt.plot([i * step_perc for i in range(1, len(result_list_svc) + 1)], result_list_svc, 'g-', label="svc")
plt.plot(best_perc_svc, result_list_svc[best_round_svc], 'gx')
plt.axis([step_perc, (len(result_list_svc) + 1) * step_perc, 0, 1])
plt.xlabel('Daten in %')
plt.ylabel('Genauigkeit')
plt.legend()
plt.savefig(f"{par_path}/diagrams/{filename}")
plt.cla()
# print accuracy to file
df_percent = pd.DataFrame(data=[i * step_perc for i in range(1, len(result_list_svc) + 1)], columns=['percent'])
df_svc = pd.DataFrame(data=result_list_svc, columns=['svc'])
df_accuracy = pd.concat([df_percent, df_svc], axis=1)
df_accuracy = df_accuracy.fillna(value="")
df_accuracy.to_csv(f'{par_path}accuracy/{filename}_filtered.csv', index=False)
# Chapter 7.2.1. Get the accuracy of the features before the iterative filter, results in table 18
def get_accuracy_before_iterative_filter():
gnb_result_list, svc_result_list, knn_result_list, gnb_time_list, svc_time_list, knn_time_list \
= [], [], [], [], [], []
y = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8", nrows=2500)['label_encoded']
path = f'daten/5_iterative_filter/csv_before_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
gnb = GaussianNB()
param_grid_svc = {'C': (0.001, 0.01, 0.1, 1, 10),
'kernel': ('linear', 'poly', 'rbf'),
'gamma': ('scale', 'auto')}
# Get the feature names for the table
feature_list = [re.search("(.+?(?=_rel))", f).group(1) for f in files]
for f in files:
print(f)
x = pd.read_csv(f"daten/5_iterative_filter/csv_before_filter/{f}", sep=',', encoding="utf-8", nrows=2500)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42)
# GNB fit
start_time = datetime.now()
gnb.fit(x_train, y_train)
# score on test data
score = accuracy_score(gnb.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"GNB test score for {f}: {score}")
print(f"GNB time for {f}: {time_taken}")
gnb_result_list.append(score)
gnb_time_list.append(time_taken)
# SVC parameter optimization
grid_search = GridSearchCV(svm.SVC(), param_grid_svc, cv=10, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_train, y_train)
svc = svm.SVC(C=grid_results.best_params_['C'], gamma=grid_results.best_params_['gamma'],
kernel=grid_results.best_params_['kernel'])
start_time = datetime.now()
# fit on train data
svc.fit(x_train, y_train)
# predict test data
score = accuracy_score(svc.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"SVC test score for {f}: {score}")
print(f"SVC time for {f}: {time_taken}")
svc_result_list.append(score)
svc_time_list.append(time_taken)
# Parameter for KNN
# Some Values from 3 to square of k
neighbors = [i for i in range(3, int(math.sqrt(len(x.columns))), 13)]
neighbors += [5, 11, 19, 36]
if int(math.sqrt(len(x.columns))) not in neighbors:
neighbors.append(int(math.sqrt(len(x.columns))))
param_grid_knn = {'n_neighbors': neighbors,
'weights': ['uniform', 'distance'],
'metric': ['euclidean', 'manhattan']}
# KNN parameter optimization
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=10, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_train, y_train)
knn = KNeighborsClassifier(n_neighbors=grid_results.best_params_['n_neighbors'],
metric=grid_results.best_params_['metric'],
weights=grid_results.best_params_['weights'])
# fit on train data
knn.fit(x_train, y_train)
# KNN predict test data
start_time = datetime.now()
# predict test data
score = accuracy_score(knn.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"KNN test score for {f}: {score}")
print(f"KNN time for {f}: {time_taken}")
knn_result_list.append(score)
knn_time_list.append(time_taken)
# create dataframe with the scores and times
df_results = pd.DataFrame()
df_results['feature'] = feature_list
df_results['score_gnb'] = gnb_result_list
df_results['time_gnb'] = gnb_time_list
df_results['score_svc'] = svc_result_list
df_results['time_svc'] = svc_time_list
df_results['score_knn'] = knn_result_list
df_results['time_knn'] = knn_time_list
return df_results
# Chapter 7.2.1. Get the accuracy of the features after the iterative filter, results in table 18
def get_accuracy_after_iterative_filter():
df_gnb_result = pd.DataFrame(columns=['feature', 'score_gnb', 'time_gnb'])
df_svc_result = pd.DataFrame(columns=['feature', 'score_svc', 'time_svc'])
df_knn_result = pd.DataFrame(columns=['feature', 'score_knn', 'time_knn'])
y = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8", nrows=2500)['label_encoded']
# path = f'daten/5_iterative_filter/csv_after_filter'
path = f'daten/5_iterative_filter/5_iterative_filter/csv_after_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
gnb = GaussianNB()
param_grid_svc = {'C': (0.001, 0.01, 0.1, 1, 10),
'kernel': ('linear', 'poly', 'rbf'),
'gamma': ('scale', 'auto')}
for f in files:
print(f)
# Get the feature name for the table
feature = re.search(".{4}(.+?(?=_rel))", f).group(1)
# x = pd.read_csv(f"daten/5_iterative_filter/csv_after_filter/{f}", sep=',', encoding="utf-8", nrows=2500)
x = pd.read_csv(f"daten/5_iterative_filter/5_iterative_filter/csv_after_filter/{f}", sep=',', encoding="utf-8",
nrows=2500)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42)
# Select the classifier by the start of the filename
if f.split("_")[0] == "gnb":
# GNB fit
start_time = datetime.now()
gnb.fit(x_train, y_train)
# score on test data
score = accuracy_score(gnb.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"GNB test score for {f}: {score}")
print(f"GNB time for {f}: {time_taken}")
df_gnb_result = df_gnb_result.append(pd.DataFrame(data={'feature': [feature], 'score_gnb': [score],
'time_gnb': [time_taken]}), ignore_index=True)
elif f.split("_")[0] == "svc":
# SVC parameter optimization
grid_search = GridSearchCV(svm.SVC(), param_grid_svc, cv=10, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_train, y_train)
svc = svm.SVC(C=grid_results.best_params_['C'], gamma=grid_results.best_params_['gamma'],
kernel=grid_results.best_params_['kernel'])
start_time = datetime.now()
# fit on train data
svc.fit(x_train, y_train)
# predict test data
score = accuracy_score(svc.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"SVC test score for {f}: {score}")
print(f"SVC training time for {f}: {time_taken}")
df_svc_result = df_svc_result.append(pd.DataFrame(data={'feature': [feature], 'score_svc': [score],
'time_svc': [time_taken]}), ignore_index=True)
elif f.split("_")[0] == "knn":
# Parameter for KNN
# Some Values from 3 to square of k
neighbors = [i for i in range(3, int(math.sqrt(len(x.columns))), 13)]
neighbors += [5, 11, 19, 36]
if int(math.sqrt(len(x.columns))) not in neighbors:
neighbors.append(int(math.sqrt(len(x.columns))))
param_grid_knn = {'n_neighbors': neighbors,
'weights': ['uniform', 'distance'],
'metric': ['euclidean', 'manhattan']}
# KNN parameter optimization
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=10, n_jobs=-1, scoring='accuracy')
grid_results = grid_search.fit(x_train, y_train)
knn = KNeighborsClassifier(n_neighbors=grid_results.best_params_['n_neighbors'],
metric=grid_results.best_params_['metric'],
weights=grid_results.best_params_['weights'])
start_time = datetime.now()
# fit on train data
knn.fit(x_train, y_train)
# KNN predict test data
start_time = datetime.now()
# predict test data
score = accuracy_score(knn.predict(x_test), y_test)
time_taken = datetime.now() - start_time
print(f"KNN test score for {f}: {score}")
print(f"KNN test time for {f}: {time_taken}")
df_knn_result = df_knn_result.append(pd.DataFrame(data={'feature': [feature], 'score_knn': [score],
'time_knn': [time_taken]}), ignore_index=True)
df_merge = pd.merge(df_gnb_result, df_knn_result, on="feature", how='outer')
df_merge = pd.merge(df_merge, df_svc_result, on="feature", how='outer')
return df_merge
# Get n article for a given number of authors. Required for setups with different numbers of authors and article
def get_n_article_by_author(par_df, par_label_count, par_article_count):
df_articles = pd.DataFrame(columns=['label_encoded', 'text'])
# only keep entries of the "par_label_count" first labels
par_df = par_df.where(par_df['label_encoded'] <= par_label_count).dropna()
labels = np.unique(par_df['label_encoded'].values).tolist()
list_article_count = [par_article_count for i in labels]
for index, row in par_df.iterrows():
if list_article_count[labels.index(row['label_encoded'])] != 0:
d = {'label_encoded': [row['label_encoded']], 'text': [row['text']]}
df_articles = df_articles.append(pd.DataFrame.from_dict(d), ignore_index=True)
list_article_count[labels.index(row['label_encoded'])] -= 1
if sum(list_article_count) == 0:
break
return df_articles
# Return indices for n article for a given number of authors. Required for setups with different
# numbers of authors and article
def get_n_article_index_by_author(par_df, par_label_count, par_article_count):
index_list = []
# only keep entries of the "par_label_count" first labels
par_df = par_df.where(par_df['label_encoded'] <= par_label_count).dropna()
labels = np.unique(par_df['label_encoded'].values).tolist()
list_article_count = [par_article_count for i in labels]
for index, row in par_df.iterrows():
if row['label_encoded'] in labels:
if list_article_count[labels.index(row['label_encoded'])] != 0:
index_list.append(index)
list_article_count[labels.index(row['label_encoded'])] -= 1
if sum(list_article_count) == 0:
break
return index_list
# Method to estimate the f1 score of the test data for GNB
def get_f1_for_gnb(par_x_train, par_x_test, par_y_train, par_y_test):
gnb = GaussianNB()
# GNB fit
gnb.fit(par_x_train, par_y_train)
# score on test data
gnb_score = f1_score(gnb.predict(par_x_test), par_y_test, average='micro')
return gnb_score
# Method to estimate the f1 score of the test data for SVC
def get_f1_for_svc(par_x_train, par_x_test, par_y_train, par_y_test, par_cv):
# Param Grid SVC
param_grid_svc = {'C': (0.001, 0.01, 0.1, 1, 10),
'kernel': ('linear', 'poly', 'rbf'),
'gamma': ('scale', 'auto')}
# SVC parameter optimization
grid_search = GridSearchCV(svm.SVC(), param_grid_svc, cv=par_cv, n_jobs=-1, scoring='f1_micro')
grid_results = grid_search.fit(par_x_train, par_y_train)
svc = svm.SVC(C=grid_results.best_params_['C'], gamma=grid_results.best_params_['gamma'],
kernel=grid_results.best_params_['kernel'])
# fit on train data
svc.fit(par_x_train, par_y_train)
# predict test data
svc_score = f1_score(svc.predict(par_x_test), par_y_test, average='micro')
return svc_score
# Method to estimate the f1 score of the test data for KNN
def get_f1_for_knn(par_x_train, par_x_test, par_y_train, par_y_test, par_cv):
# define param grid for knn, neighbors has the be lower than samples
neighbors = [1, 3, 5, 11, 19, 36, 50]
# number of neighbors must be less than number of samples
neighbors = [x for x in neighbors if x < len(par_x_test)]
param_grid_knn = {'n_neighbors': neighbors,
'weights': ['uniform', 'distance'],
'metric': ['euclidean', 'manhattan']}
# KNN parameter optimization
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid_knn, cv=par_cv, n_jobs=-1, scoring='f1_micro')
grid_results = grid_search.fit(par_x_train, par_y_train)
knn = KNeighborsClassifier(n_neighbors=grid_results.best_params_['n_neighbors'],
metric=grid_results.best_params_['metric'],
weights=grid_results.best_params_['weights'])
# fit on train data
knn.fit(par_x_train, par_y_train)
# predict test data
knn_score = f1_score(knn.predict(par_x_test), par_y_test, average='micro')
return knn_score
# Method to estimate the accuracy of the test data for SVC
def get_accuracy_for_svc(par_x_train, par_x_test, par_y_train, par_y_test, par_cv):
# Param Grid SVC
param_grid_svc = {'C': (0.001, 0.01, 0.1, 1, 10),
'kernel': ('linear', 'poly', 'rbf'),
'gamma': ('scale', 'auto')}
# SVC parameter optimization
grid_search = GridSearchCV(svm.SVC(), param_grid_svc, cv=par_cv, n_jobs=-1, scoring='f1_micro')
grid_results = grid_search.fit(par_x_train, par_y_train)
svc = svm.SVC(C=grid_results.best_params_['C'], gamma=grid_results.best_params_['gamma'],
kernel=grid_results.best_params_['kernel'])
# fit on train data
svc.fit(par_x_train, par_y_train)
# predict test data
svc_score = accuracy_score(svc.predict(par_x_test), par_y_test)
return svc_score
# Chapter 7.3.1. comparison of the word length feature alternatives
def compare_word_length_features():
df_all_texts = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
# Different values for the texts by authors
list_author_texts = [10, 15, 25, 50, 75, 100]
# save the results in a dictionary
dic_f1_results = {'wl_matrix_gnb': [], 'wl_matrix_svc': [], 'wl_matrix_knn': [],
'wl_matrix_bins_20_30_gnb': [], 'wl_matrix_bins_20_30_svc': [], 'wl_matrix_bins_20_30_knn': [],
'wl_matrix_bins_10_20_gnb': [], 'wl_matrix_bins_10_20_svc': [], 'wl_matrix_bins_10_20_knn': [],
'wl_matrix_20_gnb': [], 'wl_matrix_20_svc': [], 'wl_matrix_20_knn': [],
'wl_avg_gnb': [], 'wl_avg_svc': [], 'wl_avg_knn': []}
for author_texts in list_author_texts:
# get article for n authors with number of author texts
df_article = get_n_article_by_author(df_all_texts, 25, author_texts)
# Get the word count for the individual relative frequency
word_count = get_word_count(df_article)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if author_texts * 0.4 < 10:
cv = int(author_texts * 0.4)
else:
cv = 10
# Get the scores for every feature
for feature in ["wl_matrix", "wl_matrix_bins_20_30", "wl_matrix_bins_10_20", "wl_avg", "wl_matrix_20"]:
# select the test/train data by the feature name and calculate the individual relative frequency
if feature == "wl_matrix":
x = get_rel_frequency(get_word_length_matrix(df_article).fillna(value=0), word_count['word_count'])
elif feature == "wl_matrix_bins_20_30":
x = get_rel_frequency(get_word_length_matrix_with_interval(df_article, 20, 30).fillna(value=0),
word_count['word_count'])
elif feature == "wl_matrix_bins_10_20":
x = get_rel_frequency(get_word_length_matrix_with_interval(df_article, 10, 20).fillna(value=0),
word_count['word_count'])
elif feature == "wl_avg":
x = get_average_word_length(df_article)
elif feature == "wl_matrix_20":
x = get_word_length_matrix_with_margin(df_article, 20)
# Scale the data, else high counter in wl_matrix can dominate and hyperparameter optimization for svc
# takes a while because of small differences from average
scaler = StandardScaler()
scaler.fit(x)
x = scaler.transform(x)
y = df_article['label_encoded']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
y = df_article['label_encoded']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
# calculate scores
gnb_score = get_f1_for_gnb(x_train, x_test, y_train, y_test)
svc_score = get_f1_for_svc(x_train, x_test, y_train, y_test, cv)
knn_score = get_f1_for_knn(x_train, x_test, y_train, y_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {author_texts}: {gnb_score}")
print(f"SVC-Score for {feature} with {author_texts}: {svc_score}")
print(f"KNN-Score for {feature} with {author_texts}: {knn_score}")
df_results = pd.DataFrame(dic_f1_results)
df_results['number_article'] = list_author_texts
return df_results
# Chapter 7.3.2. comparison of the digit feature alternatives
def compare_digit_features():
df_all_texts = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
# Different values for the texts by authors
list_author_texts = [10, 15, 25, 50, 75, 100]
# save the results in a dictionary
dic_f1_results = {'digit_sum_gnb': [], 'digit_sum_svc': [], 'digit_sum_knn': [],
'digits_gnb': [], 'digits_svc': [], 'digits_knn': []}
for author_texts in list_author_texts:
# get article for n authors with number of author texts
df_article = get_n_article_by_author(df_all_texts, 25, author_texts)
# Get the word count for the individual relative frequency
char_count = get_char_count(df_article)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if author_texts * 0.4 < 10:
cv = int(author_texts * 0.4)
else:
cv = 10
# Get the scores for every feature
for feature in ["digit_sum", "digits"]:
# select the test/train data by the feature name and calculate the individual relative frequency
if feature == "digit_sum":
x = get_rel_frequency(get_sum_digits(df_article).fillna(value=0), char_count['char_count'])
elif feature == "digits":
x = get_rel_frequency(get_digits(df_article).fillna(value=0), char_count['char_count'])
y = df_article['label_encoded']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
# calculate scores
gnb_score = get_f1_for_gnb(x_train, x_test, y_train, y_test)
svc_score = get_f1_for_svc(x_train, x_test, y_train, y_test, cv)
knn_score = get_f1_for_knn(x_train, x_test, y_train, y_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {author_texts}: {gnb_score}")
print(f"SVC-Score for {feature} with {author_texts}: {svc_score}")
print(f"KNN-Score for {feature} with {author_texts}: {knn_score}")
df_results = pd.DataFrame(dic_f1_results)
df_results['number_article'] = list_author_texts
return df_results
# Chapter 7.3.3. comparison of the word ngrams with n 4-6
def compare_word_4_6_grams():
df_all_texts = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
# Different values for the texts by authors
list_author_texts = [10, 15, 25, 50, 75, 100]
# save the results in a dictionary
dic_f1_results = {'w4g_gnb': [], 'w4g_svc': [], 'w4g_knn': [],
'w5g_gnb': [], 'w5g_svc': [], 'w5g_knn': [],
'w6g_gnb': [], 'w6g_svc': [], 'w6g_knn': []}
# load the data
df_w4g = pd.read_csv("daten/6_feature_analysis/input_data/word_4_gram_rel.csv", sep=',', encoding="utf-8")
df_w5g = pd.read_csv("daten/6_feature_analysis/input_data/word_5_gram_rel.csv", sep=',', encoding="utf-8")
df_w6g = pd.read_csv("daten/6_feature_analysis/input_data/word_6_gram_rel.csv", sep=',', encoding="utf-8")
for author_texts in list_author_texts:
# indices for article for n authors with m texts
index_list = get_n_article_index_by_author(df_all_texts, 25, author_texts)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if author_texts * 0.4 < 10:
cv = int(author_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
# Get the scores for every feature
for feature in ["w4g", "w5g", "w6g"]:
# select the indices from the article rows by the given indices
if feature == "w4g":
x = df_w4g.iloc[index_list]
elif feature == "w5g":
x = df_w5g.iloc[index_list]
elif feature == "w6g":
x = df_w6g.iloc[index_list]
# Delete features which only occur once
x = trim_df_by_occurrence(x, 1)
# reset the indices to have a order from 0 to authors * text per author - 1
x = x.reset_index(drop=True)
y = df_all_texts.iloc[index_list]['label_encoded']
y = y.reset_index(drop=True)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=42, stratify=y)
# calculate scores
gnb_score = get_f1_for_gnb(x_train, x_test, y_train, y_test)
svc_score = get_f1_for_svc(x_train, x_test, y_train, y_test, cv)
knn_score = get_f1_for_knn(x_train, x_test, y_train, y_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {author_texts}: {gnb_score}")
print(f"SVC-Score for {feature} with {author_texts}: {svc_score}")
print(f"KNN-Score for {feature} with {author_texts}: {knn_score}")
df_results = pd.DataFrame(dic_f1_results)
df_results['number_article'] = list_author_texts
return df_results
# Chapter 7.3.3. comparison of the word ngrams with n 2-3
def compare_word_2_3_grams():
df_all_texts = pd.read_csv("musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
# Different values for the texts by authors
list_author_texts = [10, 15, 25, 50, 75, 100]
# save the results in a dictionary
dic_f1_results = {'w2g_gnb': [], 'w2g_svc': [], 'w2g_knn': [],
'w3g_gnb': [], 'w3g_svc': [], 'w3g_knn': []}
for author_texts in list_author_texts:
print(f"Texte pro Autor: {author_texts}")
# indices for article for n authors with m texts
index_list = get_n_article_index_by_author(df_balanced, 25, author_texts)
# define the splits for the hyperparameter tuning, cannot be greater than the number of members in each class
if author_texts * 0.4 < 10:
cv = int(author_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
# select the indices from the article rows by the given indices
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
print(f"Artikel: {len(df_balanced.index)}")
# extract the features
df_w2g = get_word_n_grams(df_balanced, 2)
df_w3g = get_word_n_grams(df_balanced, 3)
# Preprocessing steps
word_count = get_word_count(df_balanced)
df_w2g = preprocessing_steps_pos_tag_n_grams(df_w2g, word_count['word_count'])
df_w3g = preprocessing_steps_pos_tag_n_grams(df_w3g, word_count['word_count'])
# Scaler, else SVM need a lot of time with very low numbers.
scaler = StandardScaler()
df_w2g[df_w2g.columns] = scaler.fit_transform(df_w2g[df_w2g.columns])
df_w3g[df_w3g.columns] = scaler.fit_transform(df_w3g[df_w3g.columns])
label = df_balanced['label_encoded']
# Train/Test 60/40 split
df_w2g_train, df_w2g_test, df_w3g_train, df_w3g_test, label_train, label_test = \
train_test_split(df_w2g, df_w3g, label, test_size=0.4, random_state=42, stratify=label)
# Get the scores for every feature
for feature in ["w2g", "w3g"]:
# select the indices from the article rows by the given indices
# iterative filter
# returns df_x_train_gnb, df_x_test_gnb, df_x_train_svc, df_x_test_svc, df_x_train_knn, df_x_test_knn
if feature == "w2g":
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test = \
feature_selection_iterative_filter(df_w2g_train, df_w2g_test, label_train, 1.0, mutual_info_classif)
elif feature == "w3g":
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test = \
feature_selection_iterative_filter(df_w3g_train, df_w3g_test, label_train, 1.0, mutual_info_classif)
# Do not use iterative filter for gnb train caused by bad results
x_gnb_train, x_gnb_test, label_train, label_test = \
train_test_split(df_w3g, label, test_size=0.4, random_state=42, stratify=label)
print(f"cv: {cv}")
print(f"Train Labels: {label_train.value_counts()}")
print(f"Test Labels: {label_test.value_counts()}")
# calculate scores
gnb_score = get_f1_for_gnb(x_gnb_train, x_gnb_test, label_train, label_test)
svc_score = get_f1_for_svc(x_svc_train, x_svc_test, label_train, label_test, cv)
knn_score = get_f1_for_knn(x_knn_train, x_knn_test, label_train, label_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {author_texts}: {gnb_score}")
print(f"SVC-Score for {feature} with {author_texts}: {svc_score}")
print(f"KNN-Score for {feature} with {author_texts}: {knn_score}")
df_results = pd.DataFrame(dic_f1_results)
df_results['number_article'] = list_author_texts
return df_results
# Chapter 7.3.4. comparison of the different lengths of char ngrams
# Chapter 7.3.4. whole process of the comparison of the char-n-gram features
def compare_char_n_grams_process(par_base_path):
df_all_texts = pd.read_csv(f"musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
author_counts = [25]
text_counts = [10, 15, 25, 50, 75, 100]
for number_authors in author_counts:
for number_texts in text_counts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
extract_n_gram_features_to_csv(df_balanced, par_base_path, number_authors, number_texts)
iterative_filter_process(par_base_path, df_balanced, number_texts, number_authors)
compare_char_affix_ngrams(text_counts, author_counts, par_base_path, df_all_texts) \
.to_csv(f"{par_base_path}results/char_affix_n_grams.csv", index=False)
compare_char_word_ngrams(text_counts, author_counts, par_base_path, df_all_texts) \
.to_csv(f"{par_base_path}results/char_word_n_grams.csv", index=False)
compare_char_punct_ngrams(text_counts, author_counts, par_base_path, df_all_texts) \
.to_csv(f"{par_base_path}results/char_punct_n_grams.csv", index=False)
# Chapter 7.3.4. char-affix-ngrams
def compare_char_affix_ngrams(par_author_texts, par_authors, par_base_path, par_df):
# save the results in a dictionary
dic_f1_results = {'c_affix_2_gnb': [], 'c_affix_2_svc': [], 'c_affix_2_knn': [],
'c_affix_3_gnb': [], 'c_affix_3_svc': [], 'c_affix_3_knn': [],
'c_affix_4_gnb': [], 'c_affix_4_svc': [], 'c_affix_4_knn': [],
'c_affix_5_gnb': [], 'c_affix_5_svc': [], 'c_affix_5_knn': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(par_df, number_authors, number_texts)
df_balanced = par_df.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Get the scores for every feature
# Append authors and texts
dic_f1_results['number_authors'].append(number_authors)
dic_f1_results['number_texts'].append(number_texts)
for feature in ["c_affix_2", "c_affix_3", "c_affix_4", "c_affix_5"]:
# read the data based on n, texts and authors
if feature == "c_affix_2":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_affix_2_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_affix_2_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_affix_2_gram_filtered.csv")
elif feature == "c_affix_3":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_affix_3_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_affix_3_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_affix_3_gram_filtered.csv")
elif feature == "c_affix_4":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_affix_4_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_affix_4_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_affix_4_gram_filtered.csv")
elif feature == "c_affix_5":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_affix_5_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_affix_5_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_affix_5_gram_filtered.csv")
# Scaler, else SVM need a lot of time with very low numbers.
scaler = StandardScaler()
df_gnb[df_gnb.columns] = scaler.fit_transform(df_gnb[df_gnb.columns])
df_svc[df_svc.columns] = scaler.fit_transform(df_svc[df_svc.columns])
df_knn[df_knn.columns] = scaler.fit_transform(df_knn[df_knn.columns])
# Train/Test 60/40 split
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test, label_train, label_test = \
train_test_split(df_gnb, df_svc, df_knn, label, test_size=0.4, random_state=42, stratify=label)
# calculate scores
gnb_score = get_f1_for_gnb(x_gnb_train, x_gnb_test, label_train, label_test)
svc_score = get_f1_for_svc(x_svc_train, x_svc_test, label_train, label_test, cv)
knn_score = get_f1_for_knn(x_knn_train, x_knn_test, label_train, label_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {number_authors} authors and {number_texts} texts: {gnb_score}")
print(f"SVC-Score for {feature} with {number_authors} authors and {number_texts} texts: {svc_score}")
print(f"KNN-Score for {feature} with {number_authors} authors and {number_texts} texts: {knn_score}")
return pd.DataFrame(dic_f1_results)
# Chapter 7.3.4. char-word-ngrams
def compare_char_word_ngrams(par_author_texts, par_authors, par_base_path, par_df):
# save the results in a dictionary
dic_f1_results = {'c_word_2_gnb': [], 'c_word_2_svc': [], 'c_word_2_knn': [],
'c_word_3_gnb': [], 'c_word_3_svc': [], 'c_word_3_knn': [],
'c_word_4_gnb': [], 'c_word_4_svc': [], 'c_word_4_knn': [],
'c_word_5_gnb': [], 'c_word_5_svc': [], 'c_word_5_knn': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(par_df, number_authors, number_texts)
df_balanced = par_df.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Get the scores for every feature
# Append authors and texts
dic_f1_results['number_authors'].append(number_authors)
dic_f1_results['number_texts'].append(number_texts)
for feature in ["c_word_2", "c_word_3", "c_word_4", "c_word_5"]:
# read the data based on n, texts and authors
if feature == "c_word_2":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_word_2_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_word_2_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_word_2_gram_filtered.csv")
elif feature == "c_word_3":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_word_3_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_word_3_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_word_3_gram_filtered.csv")
elif feature == "c_word_4":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_word_4_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_word_4_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_word_4_gram_filtered.csv")
elif feature == "c_word_5":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_word_5_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_word_5_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_word_5_gram_filtered.csv")
# Scaler, else SVM need a lot of time with very low numbers.
scaler = StandardScaler()
df_gnb[df_gnb.columns] = scaler.fit_transform(df_gnb[df_gnb.columns])
df_svc[df_svc.columns] = scaler.fit_transform(df_svc[df_svc.columns])
df_knn[df_knn.columns] = scaler.fit_transform(df_knn[df_knn.columns])
# Train/Test 60/40 split
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test, label_train, label_test = \
train_test_split(df_gnb, df_svc, df_knn, label, test_size=0.4, random_state=42, stratify=label)
# calculate scores
gnb_score = get_f1_for_gnb(x_gnb_train, x_gnb_test, label_train, label_test)
svc_score = get_f1_for_svc(x_svc_train, x_svc_test, label_train, label_test, cv)
knn_score = get_f1_for_knn(x_knn_train, x_knn_test, label_train, label_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {number_authors} authors and {number_texts} texts: {gnb_score}")
print(f"SVC-Score for {feature} with {number_authors} authors and {number_texts} texts: {svc_score}")
print(f"KNN-Score for {feature} with {number_authors} authors and {number_texts} texts: {knn_score}")
return pd.DataFrame(dic_f1_results)
# Chapter 7.3.4. char-punct-ngrams
def compare_char_punct_ngrams(par_author_texts, par_authors, par_base_path, par_df):
# save the results in a dictionary
dic_f1_results = {'c_punct_2_gnb': [], 'c_punct_2_svc': [], 'c_punct_2_knn': [],
'c_punct_3_gnb': [], 'c_punct_3_svc': [], 'c_punct_3_knn': [],
'c_punct_4_gnb': [], 'c_punct_4_svc': [], 'c_punct_4_knn': [],
'c_punct_5_gnb': [], 'c_punct_5_svc': [], 'c_punct_5_knn': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(par_df, number_authors, number_texts)
df_balanced = par_df.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Get the scores for every feature
# Append authors and texts
dic_f1_results['number_authors'].append(number_authors)
dic_f1_results['number_texts'].append(number_texts)
for feature in ["c_punct_2", "c_punct_3", "c_punct_4", "c_punct_5"]:
# read the data based on n, texts and authors
if feature == "c_punct_2":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_punct_2_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_punct_2_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_punct_2_gram_filtered.csv")
elif feature == "c_punct_3":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_punct_3_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_punct_3_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_punct_3_gram_filtered.csv")
elif feature == "c_punct_4":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_punct_4_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_punct_4_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_punct_4_gram_filtered.csv")
elif feature == "c_punct_5":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_char_punct_5_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_char_punct_5_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_char_punct_5_gram_filtered.csv")
# Scaler, else SVM need a lot of time with very low numbers.
scaler = StandardScaler()
df_gnb[df_gnb.columns] = scaler.fit_transform(df_gnb[df_gnb.columns])
df_svc[df_svc.columns] = scaler.fit_transform(df_svc[df_svc.columns])
df_knn[df_knn.columns] = scaler.fit_transform(df_knn[df_knn.columns])
# Train/Test 60/40 split
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test, label_train, label_test = \
train_test_split(df_gnb, df_svc, df_knn, label, test_size=0.4, random_state=42, stratify=label)
# calculate scores
gnb_score = get_f1_for_gnb(x_gnb_train, x_gnb_test, label_train, label_test)
svc_score = get_f1_for_svc(x_svc_train, x_svc_test, label_train, label_test, cv)
knn_score = get_f1_for_knn(x_knn_train, x_knn_test, label_train, label_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {number_authors} authors and {number_texts} texts: {gnb_score}")
print(f"SVC-Score for {feature} with {number_authors} authors and {number_texts} texts: {svc_score}")
print(f"KNN-Score for {feature} with {number_authors} authors and {number_texts} texts: {knn_score}")
return pd.DataFrame(dic_f1_results)
# Chapter 7.3.4. Print the char-n-gram features in different files
def extract_n_gram_features_to_csv(par_df, par_base_path, par_number_authors, par_number_texts):
char_count = get_char_count(par_df)
# n from 2-5
for n in range(2, 6):
ca_ng = get_char_affix_n_grams(par_df, n)
preprocessing_steps_char_n_grams(ca_ng, char_count['char_count'])\
.to_csv(f"{par_base_path}csv_before_filter/a{par_number_authors}_t{par_number_texts}"
f"_char_affix_{n}_gram.csv", index=False)
cw_ng = get_char_word_n_grams(par_df, n)
preprocessing_steps_char_n_grams(cw_ng, char_count['char_count'])\
.to_csv(f"{par_base_path}csv_before_filter/a{par_number_authors}_t{par_number_texts}"
f"_char_word_{n}_gram.csv", index=False)
cp_ng = get_char_punct_n_grams(par_df, n)
preprocessing_steps_char_n_grams(cp_ng, char_count['char_count'])\
.to_csv(f"{par_base_path}csv_before_filter/a{par_number_authors}_t{par_number_texts}"
f"_char_punct_{n}_gram.csv", index=False)
print(f"Extraction Round {n - 1} done")
return True
# combined preprocessing steps of the pos-tag-n-grams
def preprocessing_steps_pos_tag_n_grams(par_feature, length_metric):
# Filter features which only occur once
par_feature = trim_df_by_occurrence(par_feature, 1)
# Individual relative frequency
par_feature = get_rel_frequency(par_feature.fillna(value=0), length_metric)
return par_feature
# combined preprocessing steps of the char-n-grams
def preprocessing_steps_char_n_grams(par_feature, length_metric):
# Filter features which only occur once
par_feature = trim_df_sum_feature(par_feature, 5)
# Individual relative frequency
par_feature = get_rel_frequency(par_feature.fillna(value=0), length_metric)
return par_feature
# Feature selection with the iterative filter without printing the results in a file
def feature_selection_iterative_filter(par_x_train, par_x_test, par_y_train, par_step, par_classif):
df_sorted_features = sort_features_by_score(par_x_train, par_y_train, par_classif)
# Calculate the best percentiles of the data for the different classifier
best_perc_gnb = get_best_percentile_gnb(par_x_train, par_y_train, 50, df_sorted_features, par_step)[0]
best_perc_svc = get_best_percentile_svc(par_x_train, par_y_train, 50, df_sorted_features, par_step)[0]
best_perc_knn = get_best_percentile_knn(par_x_train, par_y_train, 50, df_sorted_features, par_step)[0]
# select the 1 percent of the features (len/100) multiplied by par_best_percent
# select the best features from the original dataset
df_x_train_gnb = par_x_train[
df_sorted_features['feature_names'][: round(best_perc_gnb * (len(par_x_train.columns) / 100))].tolist()]
df_x_test_gnb = par_x_test[
df_sorted_features['feature_names'][: round(best_perc_gnb * (len(par_x_train.columns) / 100))].tolist()]
df_x_train_svc = par_x_train[
df_sorted_features['feature_names'][: round(best_perc_svc * (len(par_x_train.columns) / 100))].tolist()]
df_x_test_svc = par_x_test[
df_sorted_features['feature_names'][: round(best_perc_svc * (len(par_x_train.columns) / 100))].tolist()]
df_x_train_knn = par_x_train[
df_sorted_features['feature_names'][: round(best_perc_knn * (len(par_x_train.columns) / 100))].tolist()]
df_x_test_knn = par_x_test[
df_sorted_features['feature_names'][: round(best_perc_knn * (len(par_x_train.columns) / 100))].tolist()]
return df_x_train_gnb, df_x_test_gnb, df_x_train_svc, df_x_test_svc, df_x_train_knn, df_x_test_knn
# Chapter 7.3.5. function to compare the pos-tag-n-grams
def compare_pos_tag_ngrams(par_author_texts, par_authors, par_base_path, par_df):
# save the results in a dictionary
dic_f1_results = {'pos_2_gnb': [], 'pos_2_svc': [], 'pos_2_knn': [],
'pos_3_gnb': [], 'pos_3_svc': [], 'pos_3_knn': [],
'pos_4_gnb': [], 'pos_4_svc': [], 'pos_4_knn': [],
'pos_5_gnb': [], 'pos_5_svc': [], 'pos_5_knn': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(par_df, number_authors, number_texts)
df_balanced = par_df.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Get the scores for every feature
# Append authors and texts
dic_f1_results['number_authors'].append(number_authors)
dic_f1_results['number_texts'].append(number_texts)
for feature in ["pos_2", "pos_3", "pos_4", "pos_5"]:
# read the data based on n, texts and authors
if feature == "pos_2":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_pos_tag_2_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_pos_tag_2_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_pos_tag_2_gram_filtered.csv")
elif feature == "pos_3":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_pos_tag_3_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_pos_tag_3_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_pos_tag_3_gram_filtered.csv")
elif feature == "pos_4":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_pos_tag_4_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_pos_tag_4_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_pos_tag_4_gram_filtered.csv")
elif feature == "pos_5":
df_gnb = pd.read_csv(
f"{par_base_path}csv_after_filter/gnb_a{number_authors}_t{number_texts}"
f"_pos_tag_5_gram_filtered.csv")
df_svc = pd.read_csv(
f"{par_base_path}csv_after_filter/svc_a{number_authors}_t{number_texts}"
f"_pos_tag_5_gram_filtered.csv")
df_knn = pd.read_csv(
f"{par_base_path}csv_after_filter/knn_a{number_authors}_t{number_texts}"
f"_pos_tag_5_gram_filtered.csv")
# Scaler, else SVM need a lot of time with very low numbers.
scaler = StandardScaler()
df_gnb[df_gnb.columns] = scaler.fit_transform(df_gnb[df_gnb.columns])
df_svc[df_svc.columns] = scaler.fit_transform(df_svc[df_svc.columns])
df_knn[df_knn.columns] = scaler.fit_transform(df_knn[df_knn.columns])
# Train/Test 60/40 split
x_gnb_train, x_gnb_test, x_svc_train, x_svc_test, x_knn_train, x_knn_test, label_train, label_test = \
train_test_split(df_gnb, df_svc, df_knn, label, test_size=0.4, random_state=42, stratify=label)
# calculate scores
gnb_score = get_f1_for_gnb(x_gnb_train, x_gnb_test, label_train, label_test)
svc_score = get_f1_for_svc(x_svc_train, x_svc_test, label_train, label_test, cv)
knn_score = get_f1_for_knn(x_knn_train, x_knn_test, label_train, label_test, cv)
# Append scores to dictionary
dic_f1_results[f'{feature}_gnb'].append(gnb_score)
dic_f1_results[f'{feature}_svc'].append(svc_score)
dic_f1_results[f'{feature}_knn'].append(knn_score)
# Console output
print(f"GNB-Score for {feature} with {number_authors} authors and {number_texts} texts: {gnb_score}")
print(f"SVC-Score for {feature} with {number_authors} authors and {number_texts} texts: {svc_score}")
print(f"KNN-Score for {feature} with {number_authors} authors and {number_texts} texts: {knn_score}")
return pd.DataFrame(dic_f1_results)
# Chapter 7.3.5. complete process of the pos-tag-n-grams comparison
def compare_pos_n_grams_process(par_base_path):
df_all_texts = pd.read_csv(f"musikreviews_balanced_authors.csv", sep=',', encoding="utf-8")
author_counts = [25]
text_counts = [10, 15, 25, 50, 75, 100]
for number_authors in author_counts:
for number_texts in text_counts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
word_count = get_word_count(df_balanced)
# extract features and preprocessing
for n in range(2, 6):
pt_ng = get_pos_tags_n_grams(df_balanced, n)
preprocessing_steps_pos_tag_n_grams(pt_ng, word_count['word_count']) \
.to_csv(f"{par_base_path}csv_before_filter/"
f"a{number_authors}_t{number_texts}_pos_tag_{n}_gram.csv", index=False)
iterative_filter_process(par_base_path, df_balanced, number_texts, number_authors)
# 2 grams for svc get not filtered, overwrite unfiltered for svc
pt_ng = get_pos_tags_n_grams(df_balanced, 2)
preprocessing_steps_pos_tag_n_grams(pt_ng, word_count['word_count']) \
.to_csv(f"{par_base_path}csv_after_filter/"
f"svc_a{number_authors}_t{number_texts}_pos_tag_2_gram_filtered.csv", index=False)
compare_pos_tag_ngrams(text_counts, author_counts, par_base_path, df_all_texts) \
.to_csv(f"{par_base_path}results/pos_tag_n_grams.csv", index=False)
# Method to print all features for different counts of authors and texts
# Including all Preprocessing steps and filtering
def print_all_features_svc(par_base_path, par_article_path):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
author_counts = [2, 3, 4, 5, 10, 15, 25]
text_counts = [5, 10, 15, 25, 50, 75, 100]
for number_authors in author_counts:
for number_texts in text_counts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# get all the features
df_bow = get_bow_matrix(df_balanced)
df_word_2g = get_word_n_grams(df_balanced, 2)
df_word_count = get_word_count(df_balanced)
df_word_length = get_word_length_matrix_with_margin(df_balanced, 20)
df_yules_k = get_yules_k(df_balanced)
sc_label_vector = ["!", "„", "“", "§", "$", "%", "&", "/", "(", ")", "=", "?", "{", "}", "[", "]", "\\",
"@", "#",
"‚", "‘", "-", "_", "+", "*", ".", ",", ";"]
special_char_matrix = get_special_char_matrix(df_balanced, sc_label_vector)
sc_label_vector = ["s_char:" + sc for sc in sc_label_vector]
df_special_char = pd.DataFrame(data=special_char_matrix, columns=sc_label_vector)
df_char_affix_4g = get_char_affix_n_grams(df_balanced, 4)
df_char_word_3g = get_char_word_n_grams(df_balanced, 3)
df_char_punct_3g = get_char_punct_n_grams(df_balanced, 3)
df_digits = get_sum_digits(df_balanced)
df_fwords = get_function_words(df_balanced)
df_pos_tags = get_pos_tags(df_balanced)
df_pos_tag_2g = get_pos_tags_n_grams(df_balanced, 2)
df_start_pos, df_end_pos = get_sentence_end_start(df_balanced)
df_start_end_pos = pd.concat([df_start_pos, df_end_pos], axis=1)
df_fre = get_flesch_reading_ease_vector(df_balanced)
# 7.1.1 Remove low occurrence
df_bow = trim_df_by_occurrence(df_bow, 1)
df_word_2g = trim_df_by_occurrence(df_word_2g, 1)
df_fwords = trim_df_by_occurrence(df_fwords, 1)
df_pos_tag_2g = trim_df_by_occurrence(df_pos_tag_2g, 1)
df_char_affix_4g = trim_df_sum_feature(df_char_affix_4g, 5)
df_char_word_3g = trim_df_sum_feature(df_char_word_3g, 5)
df_char_punct_3g = trim_df_sum_feature(df_char_punct_3g, 5)
# 7.1.2 Remove high frequency
df_bow = trim_df_by_doc_freq(df_bow, 0.5)
df_word_2g = trim_df_by_doc_freq(df_word_2g, 0.5)
df_fwords = trim_df_by_doc_freq(df_fwords, 0.5)
# 7.1.4 individual relative frequency
df_len_metrics = pd.concat([get_char_count(df_balanced), get_sentence_count(df_balanced),
df_word_count], axis=1)
df_bow = get_rel_frequency(df_bow.fillna(value=0), df_len_metrics['word_count'])
df_word_2g = get_rel_frequency(df_word_2g.fillna(value=0), df_len_metrics['word_count'])
df_word_length = get_rel_frequency(df_word_length.fillna(value=0), df_len_metrics['word_count'])
df_special_char = get_rel_frequency(df_special_char.fillna(value=0), df_len_metrics['char_count'])
df_char_affix_4g = get_rel_frequency(df_char_affix_4g.fillna(value=0), df_len_metrics['char_count'])
df_char_word_3g = get_rel_frequency(df_char_word_3g.fillna(value=0), df_len_metrics['char_count'])
df_char_punct_3g = get_rel_frequency(df_char_punct_3g.fillna(value=0), df_len_metrics['char_count'])
df_digits = get_rel_frequency(df_digits.fillna(value=0), df_len_metrics['char_count'])
df_fwords = get_rel_frequency(df_fwords.fillna(value=0), df_len_metrics['word_count'])
df_pos_tags = get_rel_frequency(df_pos_tags.fillna(value=0), df_len_metrics['word_count'])
df_pos_tag_2g = get_rel_frequency(df_pos_tag_2g.fillna(value=0), df_len_metrics['word_count'])
df_start_end_pos = get_rel_frequency(df_start_end_pos.fillna(value=0), df_len_metrics['sentence_count'])
# Print to CSV
# Files for iterative filter
df_bow.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}_bow.csv", index=False)
df_word_2g.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}"
f"_word_2_gram.csv", index=False)
df_char_affix_4g.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}"
f"_char_affix_4_gram.csv", index=False)
df_char_word_3g.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}"
f"_char_word_3_gram.csv", index=False)
df_char_punct_3g.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}"
f"_char_punct_3_gram.csv", index=False)
df_fwords.to_csv(f"{par_base_path}csv_before_filter/a{number_authors}_t{number_texts}"
f"_function_words.csv", index=False)
# Files not for iterative filter directly in after filter folder
df_word_count.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_word_count.csv", index=False)
df_word_length.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_word_length.csv", index=False)
df_yules_k.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_yules_k.csv", index=False)
df_special_char.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_special_char.csv", index=False)
df_digits.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_digits.csv", index=False)
df_pos_tags.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_pos_tag.csv", index=False)
df_pos_tag_2g.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_pos_tag_2_gram.csv", index=False)
df_start_end_pos.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}"
f"_pos_tag_start_end.csv", index=False)
df_fre.to_csv(f"{par_base_path}csv_after_filter/a{number_authors}_t{number_texts}_fre.csv", index=False)
print(f"Extraction for {number_authors} authors with {number_texts} texts done. Starting iterative filter")
# Run the iterative filter
iterative_filter_process_svm(par_base_path, df_balanced, number_texts, number_authors)
# create a dataframe with the combined features for a specific number of authors and texts
# features can be excluded by name
def create_df_combined_features(par_path, par_num_texts, par_num_authors, par_exclude):
path = f'{par_path}csv_after_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
# Filter the files for author and text numbers
r = re.compile(f"a{par_num_authors}_")
files = list(filter(r.match, files))
r = re.compile(f".*t{par_num_texts}_")
files = list(filter(r.match, files))
# exclude a feature by regex
regex = re.compile(f'.*{par_exclude}')
files = [i for i in files if not regex.match(i)]
df_all = pd.DataFrame()
# combine all features
for feature in files:
df_feature = pd.read_csv(f"{par_path}csv_after_filter/{feature}", sep=',', encoding="utf-8")
df_all = pd.concat([df_all, df_feature], axis=1)
return df_all
# Chapter 8.4. comparison of normalization and standardization
def compare_normalization_standardization(par_article_path, par_feature_path, par_author_texts, par_authors):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
dic_f1_results = {'without': [], 'standard': [], 'normal': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Append authors and texts
dic_f1_results['number_authors'].append(number_authors)
dic_f1_results['number_texts'].append(number_texts)
df_features = create_df_combined_features(par_feature_path, number_texts, number_authors, "nothing")
# standardization of features
df_features_stand = copy.deepcopy(df_features)
scaler = StandardScaler()
df_features_stand[df_features_stand.columns] = \
scaler.fit_transform(df_features_stand[df_features_stand.columns])
# normalization of features
df_features_norm = copy.deepcopy(df_features)
normalizer = Normalizer()
df_features_norm[df_features_norm.columns] = \
normalizer.fit_transform(df_features_norm[df_features_norm.columns])
x_train, x_test, x_train_stand, x_test_stand, x_train_norm, x_test_norm, label_train, label_test = \
train_test_split(df_features, df_features_stand, df_features_norm, label,
test_size=0.4, random_state=42, stratify=label)
# append the results
dic_f1_results['without'].append(get_f1_for_svc(x_train, x_test, label_train, label_test, cv))
dic_f1_results['standard'].append(get_f1_for_svc(x_train_stand, x_test_stand, label_train,
label_test, cv))
dic_f1_results['normal'].append(get_f1_for_svc(x_train_norm, x_test_norm, label_train,
label_test, cv))
print(f"Scores for {number_authors} authors with {number_texts} texts created.")
return pd.DataFrame(dic_f1_results)
# Chapter 8.5.1. Comparison of the individual features, data for table 21
def compare_single_features(par_article_path, par_feature_path, par_author_texts, par_authors):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
dic_results = {'number_authors': [], 'number_texts': []}
path = f'{par_feature_path}csv_after_filter'
files = [f for f in listdir(path) if isfile(join(path, f))]
# get unique values for the list of the features
feature_list = list(set([re.search(r"a\d+_t\d+_(.+?(?=$))", f).group(1) for f in files]))
for feature in feature_list:
dic_results[feature] = []
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Append authors and texts
dic_results['number_authors'].append(number_authors)
dic_results['number_texts'].append(number_texts)
for feature in feature_list:
df_feature = pd.read_csv(
f"{par_feature_path}csv_after_filter/a{number_authors}_t{number_texts}_{feature}")
# standardization of features
scaler = StandardScaler()
df_feature[df_feature.columns] = \
scaler.fit_transform(df_feature[df_feature.columns])
x_train, x_test, label_train, label_test = \
train_test_split(df_feature, label, test_size=0.4, random_state=42, stratify=label)
dic_results[feature].append(
get_f1_for_svc(x_train, x_test, label_train, label_test, cv))
print(f"Scores for {number_authors} authors with {number_texts} texts created.")
return pd.DataFrame(dic_results)
# Chapter 8.5.2. Get the values of the difference functions, data for table 22
def get_feature_function_difference(par_article_path, par_feature_path, par_author_texts, par_authors):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
dic_f1_wo_feature = {'wo_bow': [], 'wo_word_2_gram': [], 'wo_word_count': [], 'wo_word_length': [],
'wo_yules_k': [], 'wo_special_char': [], 'wo_char_affix': [], 'wo_char_word': [],
'wo_char_punct': [], 'wo_digits': [], 'wo_function_words': [], 'wo_pos_tag.csv': [],
'wo_pos_tag_2_gram': [], 'wo_pos_tag_start_end': [], 'wo_fre': [], 'number_authors': [],
'number_texts': []}
dic_f1_diff_feature = {'diff_bow': [], 'diff_word_2_gram': [], 'diff_word_count': [], 'diff_word_length': [],
'diff_yules_k': [], 'diff_special_char': [], 'diff_char_affix': [], 'diff_char_word': [],
'diff_char_punct': [], 'diff_digits': [], 'diff_function_words': [], 'diff_pos_tag.csv': [],
'diff_pos_tag_2_gram': [], 'diff_pos_tag_start_end': [], 'diff_fre': [],
'number_authors': [],
'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Append authors and texts
dic_f1_wo_feature['number_authors'].append(number_authors)
dic_f1_wo_feature['number_texts'].append(number_texts)
dic_f1_diff_feature['number_authors'].append(number_authors)
dic_f1_diff_feature['number_texts'].append(number_texts)
# Read the f1 Score from the previous calculations
df_score_all = pd.read_csv(f"{par_feature_path}/results/compared_stand_normal.csv")
f1_score_all = df_score_all.loc[(df_score_all['number_authors'] == number_authors) &
(df_score_all['number_texts'] == number_texts)]['standard'].iloc[0]
for key in dic_f1_diff_feature:
if key != "number_authors" and key != "number_texts":
key = re.search(r'.+?(?=_)_(.*)', key).group(1)
# exclude the specific feature
df_features = create_df_combined_features(par_feature_path, number_texts, number_authors, key)
# standardization of features
scaler = StandardScaler()
df_features[df_features.columns] = \
scaler.fit_transform(df_features[df_features.columns])
x_train, x_test, label_train, label_test = \
train_test_split(df_features, label, test_size=0.4, random_state=42, stratify=label)
# append the results
score_wo = get_f1_for_svc(x_train, x_test, label_train, label_test, cv)
dic_f1_wo_feature[f'wo_{key}'].append(score_wo)
dic_f1_diff_feature[f'diff_{key}'].append(f1_score_all - score_wo)
print(f"{key} done for {number_authors} authors and {number_texts} texts.")
return pd.DataFrame(dic_f1_wo_feature), pd.DataFrame(dic_f1_diff_feature)
# Chapter 8.5.3. Comparison of the model with or without content features, picture 28
def compare_content_features(par_article_path, par_feature_path, par_author_texts, par_authors):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
dic_results = {'wo_content_features': [], 'with_content_features': [], 'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Append authors and texts
dic_results['number_authors'].append(number_authors)
dic_results['number_texts'].append(number_texts)
# calculate f1 with all features
df_all = create_df_combined_features(par_feature_path, number_texts, number_authors, "nothing")
# standardization of features
scaler = StandardScaler()
df_all[df_all.columns] = \
scaler.fit_transform(df_all[df_all.columns])
x_train, x_test, label_train, label_test = \
train_test_split(df_all, label, test_size=0.4, random_state=42, stratify=label)
dic_results['with_content_features'].append(get_f1_for_svc(x_train, x_test, label_train, label_test, cv))
# calculate f1 without content features
df_wo_content = create_df_combined_features(par_feature_path, number_texts, number_authors,
"(word_count|word_2_gram|char_word_3_gram|bow)")
# standardization of features
scaler = StandardScaler()
df_wo_content[df_wo_content.columns] = \
scaler.fit_transform(df_wo_content[df_wo_content.columns])
x_train, x_test, label_train, label_test = \
train_test_split(df_wo_content, label, test_size=0.4, random_state=42, stratify=label)
dic_results['wo_content_features'].append(
get_f1_for_svc(x_train, x_test, label_train, label_test, cv))
print(f"{number_authors} authors with {number_texts} texts compared.")
return pd.DataFrame(dic_results)
# Chapter 8.5.3. Get the difference functions without content features, table 23
def get_feature_function_difference_without_content(par_article_path, par_feature_path, par_author_texts, par_authors):
df_all_texts = pd.read_csv(f"{par_article_path}", sep=',', encoding="utf-8")
dic_f1_wo_feature = {'wo_word_length': [], 'wo_yules_k': [], 'wo_special_char': [], 'wo_char_affix': [],
'wo_char_punct': [], 'wo_digits': [], 'wo_function_words': [], 'wo_pos_tag.csv': [],
'wo_pos_tag_2_gram': [], 'wo_pos_tag_start_end': [], 'wo_fre': [], 'number_authors': [],
'number_texts': []}
dic_f1_diff_feature = {'diff_word_length': [], 'diff_yules_k': [], 'diff_special_char': [], 'diff_char_affix': [],
'diff_char_punct': [], 'diff_digits': [], 'diff_function_words': [], 'diff_pos_tag.csv': [],
'diff_pos_tag_2_gram': [], 'diff_pos_tag_start_end': [], 'diff_fre': [],
'number_authors': [], 'number_texts': []}
for number_authors in par_authors:
for number_texts in par_author_texts:
index_list = get_n_article_index_by_author(df_all_texts, number_authors, number_texts)
df_balanced = df_all_texts.iloc[index_list].reset_index(drop=True)
# define the splits for the hyperparameter tuning, cannot be greater than number of members in each class
if number_texts * 0.4 < 10:
cv = int(number_texts * 0.4)
# CV between 5 and 10 is unusual
cv = 5 if cv > 5 else cv
else:
cv = 10
label = df_balanced['label_encoded']
# Append authors and texts
dic_f1_wo_feature['number_authors'].append(number_authors)
dic_f1_wo_feature['number_texts'].append(number_texts)
dic_f1_diff_feature['number_authors'].append(number_authors)
dic_f1_diff_feature['number_texts'].append(number_texts)
# Read the f1 Score from the previous calculations
df_score_all = pd.read_csv(f"{par_feature_path}/results/compare_content_feature.csv")
f1_score_all = df_score_all.loc[(df_score_all['number_authors'] == number_authors) &
(df_score_all['number_texts'] == number_texts)]['wo_content_features'].iloc[0]
for key in dic_f1_diff_feature:
if key != "number_authors" and key != "number_texts":
key = re.search(r'.+?(?=_)_(.*)', key).group(1)
# exclude the specific feature and all content features
df_features = create_df_combined_features(par_feature_path, number_texts, number_authors,
f"({key}|word_count|word_2_gram|char_word_3_gram|bow)")
# standardization of features
scaler = StandardScaler()
df_features[df_features.columns] = \
scaler.fit_transform(df_features[df_features.columns])
x_train, x_test, label_train, label_test = \
train_test_split(df_features, label, test_size=0.4, random_state=42, stratify=label)
# append the results
score_wo = get_f1_for_svc(x_train, x_test, label_train, label_test, cv)
dic_f1_wo_feature[f'wo_{key}'].append(score_wo)
dic_f1_diff_feature[f'diff_{key}'].append(f1_score_all - score_wo)
print(f"{key} done for {number_authors} authors and {number_texts} texts.")
return pd.DataFrame(dic_f1_wo_feature), pd.DataFrame(dic_f1_diff_feature)
# Global options to see all lines and columns of a printed DataFrame
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
# The following calculations were used in the chapters for the preprocessing and modeling of the data.
# They are all commented to be able to use them separate
# # Preprocessing
# Chapter 7.1.1. filter features with low occurrence
# filter_low_occurrence()
# Chapter 7.1.2. filter features with high document frequency
# filter_high_document_frequency()
# Chapter 7.1.4. individual relative frequency
# individual_relative_frequency()
# # Feature Selection
# Chapter 7.2.1. iterative filter process
"""iterative_filter_process('daten/5_iterative_filter/', pd.read_csv("musikreviews_balanced_authors.csv", sep=',',
encoding="utf-8", nrows=2500), "all", "all")"""
# Chapter 7.2.2. evaluation of the iterative filter
# get_accuracy_after_iterative_filter()).to_csv(f"daten/5_iterative_filter/results/accuracy_after_filter.csv", index=False)
# get_accuracy_before_iterative_filter().to_csv(f"daten/5_iterative_filter/results/accuracy_before_filter.csv", index=False)
# # Feature alternatives
# Chapter 7.3.1. alternatives word length
# compare_word_length_features().to_csv("daten/6_feature_analysis/results/compared_word_length.csv", index=False)
# Chapter 7.3.2. alternatives digit features
# compare_digit_features().to_csv("daten/6_feature_analysis/results/compared_digit_features.csv", index=False)
# Chapter 7.3.3. alternatives word-n-grams
# compare_word_4_6_grams().to_csv("daten/6_feature_analysis/results/compared_4_6_word_grams.csv", index=False)
# compare_word_2_3_grams().to_csv("daten/6_feature_analysis/results/compared_2_3_word_grams.csv", index=False)
# Chapter 7.3.4. alternatives char-n-grams
# compare_char_n_grams_process("daten/6_feature_analysis_char_n_grams/")
# Chapter 7.3.5. alternatives pos-tag-n-grams
# compare_pos_n_grams_process("daten/6_feature_analysis_pos_tag_n_grams/")
# # Modeling
# Get all Features for different count of authors and texts. Including all preprocessing and selection steps.
# Needed for further steps in chapter 8.
# print_all_features_svc("daten/7_modeling/", "musikreviews_balanced_authors.csv")
# Chapter 8.4. compare scaling methods
"""compare_normalization_standardization("musikreviews_balanced_authors.csv","daten/7_modeling/",
[5, 10, 15, 25, 50, 75, 100], [2, 3, 4, 5, 10, 15, 25])\
.to_csv("daten/7_modeling/results/compared_stand_normal.csv", index=False)"""
# Chapter 8.5.1. compare single features
"""compare_single_features("musikreviews_balanced_authors.csv", "daten/7_modeling/", [5, 10, 15, 25, 50, 75, 100],
[25]).to_csv("daten/7_modeling/results/compared_single_features.csv", index=False)"""
# Chapter 8.5.2. difference functions with content features
"""df_wo_feature, df_diff = \
get_feature_function_difference("musikreviews_balanced_authors.csv", "daten/7_modeling/",
[5, 10, 15, 25, 50, 75, 100], [25])
df_wo_feature.to_csv("daten/7_modeling/results/f1_without_feature.csv", index=False)
df_diff.to_csv("daten/7_modeling/results/f1_difference_function", index=False)"""
# Chapter 8.5.2. compare model with and without content features
"""compare_content_features("musikreviews_balanced_authors.csv", "daten/7_modeling/",[5, 10, 15, 25, 50], [15])\
.to_csv("daten/7_modeling/results/compare_content_feature.csv", index=False)"""
# Chapter 8.5.2. difference functions without content features
"""df_wo_feature, df_diff \
= get_feature_function_difference_without_content("musikreviews_balanced_authors.csv",
"daten/7_modeling/", [5, 10, 15, 25, 50, 75, 100], [25])
df_wo_feature.to_csv("daten/7_modeling/results/f1_without_feature_no_content.csv", index=False)
df_diff.to_csv("daten/7_modeling/results/f1_difference_function_no_content", index=False)"""
| 107,813 | 0 | 989 |
c7698d4ce5cfa89219ea4a5af69575fe9fa793f4 | 1,386 | py | Python | climate_toolbox/io/io.py | ClimateImpactLab/climate_toolbox | 9cd3e952213ef938c5cdd6b0805ace06134fcedc | [
"MIT"
] | 2 | 2017-07-19T19:12:41.000Z | 2020-05-03T20:41:36.000Z | climate_toolbox/io/io.py | ClimateImpactLab/climate_toolbox | 9cd3e952213ef938c5cdd6b0805ace06134fcedc | [
"MIT"
] | 19 | 2017-07-25T01:09:19.000Z | 2021-11-15T17:47:36.000Z | climate_toolbox/io/io.py | ClimateImpactLab/climate_toolbox | 9cd3e952213ef938c5cdd6b0805ace06134fcedc | [
"MIT"
] | 2 | 2019-05-23T17:13:39.000Z | 2019-06-11T22:07:39.000Z | import xarray as xr
from climate_toolbox.utils.utils import *
def standardize_climate_data(ds):
"""
Read climate data and standardize units to:
- lon and lat,
- lon to -180 to 180 and
Parameters
----------
ds: xr.Dataset
Returns
-------
xr.Dataset
"""
ds = rename_coords_to_lon_and_lat(ds)
ds = convert_lons_split(ds, lon_name='lon')
return ds
def load_bcsd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
"""
Read and prepare climate data
After reading data, this method also fills NA values using linear
interpolation, and standardizes longitude to -180:180
Parameters
----------
fp: str
File path or dataset
varname: str
Variable name to be read
lon_name : str, optional
Name of the longitude dimension (defualt selects from ['lon' or
'longitude'])
Returns
-------
xr.Dataset
xarray dataset loaded into memory
"""
if lon_name is not None:
lon_names = [lon_name]
if hasattr(fp, 'sel_points'):
ds = fp
else:
with xr.open_dataset(fp) as ds:
ds.load()
return standardize_climate_data(ds)
| 19.25 | 71 | 0.609668 | import xarray as xr
from climate_toolbox.utils.utils import *
def standardize_climate_data(ds):
"""
Read climate data and standardize units to:
- lon and lat,
- lon to -180 to 180 and
Parameters
----------
ds: xr.Dataset
Returns
-------
xr.Dataset
"""
ds = rename_coords_to_lon_and_lat(ds)
ds = convert_lons_split(ds, lon_name='lon')
return ds
def load_bcsd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
"""
Read and prepare climate data
After reading data, this method also fills NA values using linear
interpolation, and standardizes longitude to -180:180
Parameters
----------
fp: str
File path or dataset
varname: str
Variable name to be read
lon_name : str, optional
Name of the longitude dimension (defualt selects from ['lon' or
'longitude'])
Returns
-------
xr.Dataset
xarray dataset loaded into memory
"""
if lon_name is not None:
lon_names = [lon_name]
if hasattr(fp, 'sel_points'):
ds = fp
else:
with xr.open_dataset(fp) as ds:
ds.load()
return standardize_climate_data(ds)
def load_gmfd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
pass
def load_best(fp, varname, lon_name='lon', broadcast_dims=('time',)):
pass
| 114 | 0 | 46 |
dc4986dd9e7eea6b3dc30c43bafdf44fa5592180 | 1,737 | py | Python | OverleafDownloader.py | mwelsch-tgm/AM-Matura2020 | 99899a3570cb24a894e005d90c01307b954b179e | [
"Beerware"
] | null | null | null | OverleafDownloader.py | mwelsch-tgm/AM-Matura2020 | 99899a3570cb24a894e005d90c01307b954b179e | [
"Beerware"
] | null | null | null | OverleafDownloader.py | mwelsch-tgm/AM-Matura2020 | 99899a3570cb24a894e005d90c01307b954b179e | [
"Beerware"
] | null | null | null | import re
from json import *
import requests
import StaticVars
from OverleafDownloaderException import OverleafDownloaderException
| 34.058824 | 99 | 0.655153 | import re
from json import *
import requests
import StaticVars
from OverleafDownloaderException import OverleafDownloaderException
class OverleafDownloader:
def __init__(self):
try:
with open("config.json") as config:
self.json = load(config)
if self.json["email"] == "":
raise OverleafDownloaderException("Email is missing in config.json")
if self.json["password"] == "":
raise OverleafDownloaderException("Password is missing in config.json")
if self.json["project_id"] == "":
raise OverleafDownloaderException("Project Id is missing in config.json")
self.session = requests.Session()
except IOError:
raise OverleafDownloaderException("Please check if config.json exists.")
except JSONDecodeError:
raise OverleafDownloaderException("Please check format of config.json.")
def get_email(self):
return self.json["email"]
def get_password(self):
return self.json["password"]
def get_project_id(self):
return self.json["project_id"]
def login(self):
csrf_req = self.session.get(StaticVars.OVERLEAF_LOGIN)
csrf_token = re.findall(StaticVars.OVERLEAF_CSRF_REGEX, csrf_req.text)[0]
payload = {"_csrf": csrf_token, "email": self.get_email(), "password": self.get_password()}
return self.session.post(StaticVars.OVERLEAF_LOGIN, payload)
def download(self):
project_id = self.get_project_id()
url = StaticVars.get_download_url(project_id)
download_request = self.session.get(url)
open(str(project_id) + ".zip", "wb").write(download_request.content)
| 1,416 | 4 | 184 |
427234c66c82320c22f22997132ac12163d9ee78 | 233 | py | Python | images/hub/canvasauthenticator/setup.py | STAT-89A/datahub | 2e257a2921a1d771f1bdaae0ab91fb15809e1964 | [
"BSD-3-Clause"
] | 1 | 2020-03-12T21:11:41.000Z | 2020-03-12T21:11:41.000Z | images/hub/canvasauthenticator/setup.py | STAT-89A/datahub | 2e257a2921a1d771f1bdaae0ab91fb15809e1964 | [
"BSD-3-Clause"
] | 7 | 2021-06-08T21:58:22.000Z | 2022-03-12T00:39:12.000Z | images/hub/canvasauthenticator/setup.py | STAT-89A/datahub | 2e257a2921a1d771f1bdaae0ab91fb15809e1964 | [
"BSD-3-Clause"
] | 1 | 2020-05-11T22:26:41.000Z | 2020-05-11T22:26:41.000Z | from setuptools import setup, find_packages
setup(
name='jupyterhub-canvasauthenticator',
version='0.1dev',
python_requires='>=3.5',
packages=find_packages(),
install_requires=[
'oauthenticator',
]
)
| 19.416667 | 43 | 0.669528 | from setuptools import setup, find_packages
setup(
name='jupyterhub-canvasauthenticator',
version='0.1dev',
python_requires='>=3.5',
packages=find_packages(),
install_requires=[
'oauthenticator',
]
)
| 0 | 0 | 0 |
53f8378d85207c764dfcae0953e3dee99ff4139d | 458 | py | Python | code/tests/unit/api/utils.py | CiscoSecurity/tr-05-serverless-gigamon-threatinsight | 2a4edf83037f9a5f2743aa9c0de6e09b2d4e6b9c | [
"MIT"
] | 2 | 2020-07-29T07:46:40.000Z | 2020-10-08T20:02:02.000Z | code/tests/unit/api/utils.py | CiscoSecurity/tr-05-serverless-gigamon-threatinsight | 2a4edf83037f9a5f2743aa9c0de6e09b2d4e6b9c | [
"MIT"
] | 6 | 2020-07-28T06:21:48.000Z | 2021-11-30T09:47:53.000Z | code/tests/unit/api/utils.py | CiscoSecurity/tr-05-gigamon-threatinsight | aeaa0bee55d2cdc8badf4c9ea0e830822acab24e | [
"MIT"
] | 1 | 2021-02-24T08:59:24.000Z | 2021-02-24T08:59:24.000Z | import json
import os
def load_fixture(path):
"""Load a JSON fixture given a relative path to it."""
if not path.endswith('.json'):
path += '.json'
# Build the absolute path to the fixture.
path = os.path.join(
os.path.dirname(__file__),
'fixtures',
path,
)
with open(path) as file:
return json.load(file)
| 19.083333 | 58 | 0.591703 | import json
import os
def headers(jwt, type_='Bearer'):
return {'Authorization': f'{type_} {jwt}'}
def load_fixture(path):
"""Load a JSON fixture given a relative path to it."""
if not path.endswith('.json'):
path += '.json'
# Build the absolute path to the fixture.
path = os.path.join(
os.path.dirname(__file__),
'fixtures',
path,
)
with open(path) as file:
return json.load(file)
| 59 | 0 | 23 |
7c2317edf72efe98a4f8a8b8871fb51dae9105d2 | 1,849 | py | Python | postgresqleu/elections/migrations/0001_initial.py | bradfordboyle/pgeu-system | bbe70e7a94092c10f11a0f74fda23079532bb018 | [
"MIT"
] | 11 | 2020-08-20T11:16:02.000Z | 2022-03-12T23:25:04.000Z | postgresqleu/elections/migrations/0001_initial.py | bradfordboyle/pgeu-system | bbe70e7a94092c10f11a0f74fda23079532bb018 | [
"MIT"
] | 71 | 2019-11-18T10:11:22.000Z | 2022-03-27T16:12:57.000Z | postgresqleu/elections/migrations/0001_initial.py | bradfordboyle/pgeu-system | bbe70e7a94092c10f11a0f74fda23079532bb018 | [
"MIT"
] | 18 | 2019-11-18T09:56:31.000Z | 2022-01-08T03:16:43.000Z | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
from postgresqleu.util.fields import LowercaseEmailField
| 38.520833 | 114 | 0.561385 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
from postgresqleu.util.fields import LowercaseEmailField
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='Candidate',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(max_length=100)),
('email', LowercaseEmailField(max_length=200)),
('presentation', models.TextField()),
],
),
migrations.CreateModel(
name='Election',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(max_length=100)),
('startdate', models.DateField()),
('enddate', models.DateField()),
('slots', models.IntegerField(default=1)),
('isopen', models.BooleanField(default=False, verbose_name="Voting open")),
('resultspublic', models.BooleanField(default=False, verbose_name="Results public")),
],
options={
'ordering': ('-startdate',),
},
),
migrations.CreateModel(
name='Vote',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('score', models.IntegerField()),
('candidate', models.ForeignKey(to='elections.Candidate', on_delete=models.CASCADE)),
('election', models.ForeignKey(to='elections.Election', on_delete=models.CASCADE)),
],
),
]
| 0 | 1,662 | 23 |
84ee1972673354726805c16f05a0d8ff2c653033 | 3,807 | py | Python | tests/unit/lms/validation/authentication/_helpers/_jwt_test.py | mattdricker/lms | 40b8a04f95e69258c6c0d7ada543f4b527918ecf | [
"BSD-2-Clause"
] | 38 | 2017-12-30T23:49:53.000Z | 2022-02-15T21:07:49.000Z | tests/unit/lms/validation/authentication/_helpers/_jwt_test.py | mattdricker/lms | 40b8a04f95e69258c6c0d7ada543f4b527918ecf | [
"BSD-2-Clause"
] | 1,733 | 2017-11-09T18:46:05.000Z | 2022-03-31T11:05:50.000Z | tests/unit/lms/validation/authentication/_helpers/_jwt_test.py | mattdricker/lms | 40b8a04f95e69258c6c0d7ada543f4b527918ecf | [
"BSD-2-Clause"
] | 10 | 2018-07-11T17:12:46.000Z | 2022-01-07T20:00:23.000Z | import copy
import datetime
import jwt
import pytest
from lms.validation.authentication._helpers import _jwt
| 34.609091 | 84 | 0.674284 | import copy
import datetime
import jwt
import pytest
from lms.validation.authentication._helpers import _jwt
class TestDecodeJWT:
def test_it_returns_the_decoded_payload(self):
original_payload = {"TEST_KEY": "TEST_VALUE"}
jwt_str = self.encode_jwt(original_payload)
decoded_payload = _jwt.decode_jwt(jwt_str, "test_secret")
assert decoded_payload == original_payload
def test_it_raises_if_the_jwt_is_encoded_with_the_wrong_secret(self):
jwt_str = self.encode_jwt({"TEST_KEY": "TEST_VALUE"}, secret="wrong_secret")
with pytest.raises(_jwt.InvalidJWTError):
_jwt.decode_jwt(jwt_str, "test_secret")
def test_it_raises_if_the_jwt_is_encoded_with_the_wrong_algorithm(self):
jwt_str = self.encode_jwt({"TEST_KEY": "TEST_VALUE"}, algorithm="HS384")
with pytest.raises(_jwt.InvalidJWTError):
_jwt.decode_jwt(jwt_str, "test_secret")
def test_it_raises_if_the_jwt_has_expired(self):
jwt_str = self.encode_jwt(
{
"TEST_KEY": "TEST_VALUE",
"exp": datetime.datetime.utcnow() - datetime.timedelta(hours=1),
}
)
with pytest.raises(_jwt.ExpiredJWTError):
_jwt.decode_jwt(jwt_str, "test_secret")
def test_it_raises_if_the_jwt_has_no_expiry_time(self):
jwt_str = self.encode_jwt({"TEST_KEY": "TEST_VALUE"}, omit_exp=True)
with pytest.raises(_jwt.InvalidJWTError):
_jwt.decode_jwt(jwt_str, "test_secret")
def test_it_can_decode_a_jwt_from_encode_jwt(self):
original_payload = {"TEST_KEY": "TEST_VALUE"}
jwt_str = _jwt.encode_jwt(original_payload, "test_secret")
decoded_payload = _jwt.decode_jwt(jwt_str, "test_secret")
assert decoded_payload == original_payload
def encode_jwt(self, payload, omit_exp=False, secret=None, algorithm=None):
"""Return payload encoded to a jwt with secret and algorithm."""
payload = copy.deepcopy(payload)
if not omit_exp:
# Insert an unexpired expiry time into the given payload dict, if
# it doesn't already contain an expiry time.
payload.setdefault(
"exp", datetime.datetime.utcnow() + datetime.timedelta(hours=1)
)
jwt_str = jwt.encode(
payload, secret or "test_secret", algorithm=algorithm or "HS256"
)
return jwt_str
class TestEncodeJWT:
def test_it_returns_the_encoded_jwt(self):
original_payload = {"TEST_KEY": "TEST_VALUE"}
jwt_str = _jwt.encode_jwt(original_payload, "test_secret")
decoded_payload = jwt.decode(jwt_str, "test_secret", algorithms=["HS256"])
del decoded_payload["exp"]
assert decoded_payload == original_payload
def test_it_returns_a_string_not_bytes(self):
jwt_str = _jwt.encode_jwt({"TEST_KEY": "TEST_VALUE"}, "test_secret")
assert isinstance(jwt_str, str)
def test_it_uses_one_hour_lifetime_by_default(self):
jwt_str = _jwt.encode_jwt({}, "test_secret")
decoded_payload = jwt.decode(jwt_str, "test_secret", algorithms=["HS256"])
lifetime_minutes = self._jwt_lifetime(decoded_payload) / 60
assert round(lifetime_minutes) == 60
def test_it_uses_custom_lifetime(self):
jwt_str = _jwt.encode_jwt(
{}, "test_secret", lifetime=datetime.timedelta(hours=24)
)
decoded_payload = jwt.decode(jwt_str, "test_secret", algorithms=["HS256"])
lifetime_hours = self._jwt_lifetime(decoded_payload) / (60 * 60)
assert round(lifetime_hours) == 24
@staticmethod
def _jwt_lifetime(payload):
now = datetime.datetime.now(datetime.timezone.utc).timestamp()
return payload["exp"] - now
| 2,713 | 935 | 46 |
1a73613aa5ec448cfe53f4a1324b1d24b0ef2770 | 1,755 | py | Python | test/test_main.py | Ondkloss/norwegian-numbers | 1661a8ba296973c83957dda20035a853652e4bb2 | [
"MIT"
] | null | null | null | test/test_main.py | Ondkloss/norwegian-numbers | 1661a8ba296973c83957dda20035a853652e4bb2 | [
"MIT"
] | null | null | null | test/test_main.py | Ondkloss/norwegian-numbers | 1661a8ba296973c83957dda20035a853652e4bb2 | [
"MIT"
] | null | null | null | import unittest
import norwegian_numbers.__main__ as internal
| 34.411765 | 67 | 0.640456 | import unittest
import norwegian_numbers.__main__ as internal
class TestMain(unittest.TestCase):
def test_argparser(self):
args = internal.argparser(['-m', 'kid10', '1234'])
self.assertEqual('kid10', args.make)
self.assertEqual(None, args.verify)
self.assertEqual('1234', args.value)
def test_main_make_kid10(self):
result = internal.main(['-m', 'kid10', '1234'])
self.assertEqual('12344', result)
def test_main_make_kid11(self):
result = internal.main(['-m', 'kid11', '1234'])
self.assertEqual('12343', result)
def test_main_make_organisation(self):
result = internal.main(['-m', 'organisation', '12345678'])
self.assertEqual('123456785', result)
def test_main_make_birth(self):
result = internal.main(['-m', 'birth', '311299567'])
self.assertEqual('31129956715', result)
def test_main_make_account(self):
result = internal.main(['-m', 'account', '1234567890'])
self.assertEqual('12345678903', result)
def test_main_verify_kid10(self):
result = internal.main(['-v', 'kid10', '12344'])
self.assertEqual(True, result)
def test_main_verify_kid11(self):
result = internal.main(['-v', 'kid11', '12343'])
self.assertEqual(True, result)
def test_main_verify_organisation(self):
result = internal.main(['-v', 'organisation', '123456785'])
self.assertEqual(True, result)
def test_main_verify_birth(self):
result = internal.main(['-v', 'birth', '31129956715'])
self.assertEqual(True, result)
def test_main_verify_account(self):
result = internal.main(['-v', 'account', '12345678903'])
self.assertEqual(True, result)
| 1,360 | 13 | 319 |
a83166858444291fe6269a5c374d4ed5ad3d7884 | 1,365 | py | Python | snomedscraper/snomedscraper/spiders/dicomorgspider.py | petrovanster/snomed-rt2ct | 755fe83da872ad6ddbcfbea08ffe7433adbbfac4 | [
"MIT"
] | null | null | null | snomedscraper/snomedscraper/spiders/dicomorgspider.py | petrovanster/snomed-rt2ct | 755fe83da872ad6ddbcfbea08ffe7433adbbfac4 | [
"MIT"
] | null | null | null | snomedscraper/snomedscraper/spiders/dicomorgspider.py | petrovanster/snomed-rt2ct | 755fe83da872ad6ddbcfbea08ffe7433adbbfac4 | [
"MIT"
] | null | null | null | import scrapy
from scrapy.http import request
| 41.363636 | 117 | 0.550183 | import scrapy
from scrapy.http import request
class DicomorgspiderSpider(scrapy.Spider):
name = 'dicomorgspider'
allowed_domains = ['dicom.nema.org']
start_urls = ['http://dicom.nema.org/medical/dicom/current/output/chtml/part16/PS3.16.html']
def parse(self, response):
cids = response.xpath("//a[contains(text(), 'CID')]")
for cid in cids:
yield response.follow(cid, callback=self.parse_cid) # scrapy.Request(cid.attrib['href'], self.parse_cid)
pass
def parse_cid(self, response):
all = response.xpath("//tbody/tr")
title = response.xpath("//head/title/text()").get()
cidnumber = response.xpath("//head/title/text()").re('CID (\d+)')
for line in all:
data = {
"CID": title,
"CID number": cidnumber,
"Coding Scheme Designator": "".join(line.xpath('td[1]//text()').getall()).strip(),
"Code Value": "".join(line.xpath('td[2]//text()').getall()).strip(),
"Code Meaning": "".join(line.xpath('td[3]//text()').getall()).strip(),
"SNOMED-RT ID": "".join(line.xpath('td[4]//text()').getall()).strip(),
"SNOMED URL": "".join(line.xpath('td[4]/p/a[@class="link"]/@href').getall()).strip()
}
yield data
pass
pass
| 1,054 | 241 | 23 |
7742e3f9b237f450a1868332795a43142a5b4576 | 4,745 | py | Python | tests/munki/test_munki_install_probe.py | arubdesu/zentral | ac0fe663f6e1c27f9a9f55a7500a87e6ac7d9190 | [
"Apache-2.0"
] | 634 | 2015-10-30T00:55:40.000Z | 2022-03-31T02:59:00.000Z | tests/munki/test_munki_install_probe.py | arubdesu/zentral | ac0fe663f6e1c27f9a9f55a7500a87e6ac7d9190 | [
"Apache-2.0"
] | 145 | 2015-11-06T00:17:33.000Z | 2022-03-16T13:30:31.000Z | tests/munki/test_munki_install_probe.py | arubdesu/zentral | ac0fe663f6e1c27f9a9f55a7500a87e6ac7d9190 | [
"Apache-2.0"
] | 103 | 2015-11-07T07:08:49.000Z | 2022-03-18T17:34:36.000Z | from django.test import TestCase
from zentral.core.events import event_types
from zentral.core.events.base import EventMetadata
from zentral.core.probes.base import PayloadFilter
from zentral.core.probes.models import ProbeSource
from tests.inventory.utils import MockMetaMachine
payload_template = {
"unattended": True,
"run_type": "auto",
"status": 0,
"duration_seconds": 16,
"basename": "ManagedInstallReport.plist",
"type": "install",
"applesus": False,
"munki_version": "2.8.0.2810",
"version": "50.0",
"display_name": "Mozilla Firefox",
"sha1sum": "8d08cb494d4a8ee11678d7734ea5ddb6aef50dad",
"download_kbytes_per_sec": 10513
}
| 41.26087 | 116 | 0.624447 | from django.test import TestCase
from zentral.core.events import event_types
from zentral.core.events.base import EventMetadata
from zentral.core.probes.base import PayloadFilter
from zentral.core.probes.models import ProbeSource
from tests.inventory.utils import MockMetaMachine
payload_template = {
"unattended": True,
"run_type": "auto",
"status": 0,
"duration_seconds": 16,
"basename": "ManagedInstallReport.plist",
"type": "install",
"applesus": False,
"munki_version": "2.8.0.2810",
"version": "50.0",
"display_name": "Mozilla Firefox",
"sha1sum": "8d08cb494d4a8ee11678d7734ea5ddb6aef50dad",
"download_kbytes_per_sec": 10513
}
def build_munki_event(name, install_type, unattended, machine_tag_id):
MunkiEvent = event_types["munki_event"]
payload = payload_template.copy()
payload["name"] = name
payload["display_name"] = name.capitalize()
payload["type"] = install_type
payload["unattended"] = unattended
event = MunkiEvent(EventMetadata(machine_serial_number="YO",
event_type=MunkiEvent.event_type),
payload)
if machine_tag_id:
# hack
event.metadata.machine = MockMetaMachine([], [machine_tag_id], None, None, serial_number="YO")
return event
class MunkiInstallProbeTestCase(TestCase):
def create_probe(self, install_types, installed_item_names=None, unattended_installs=None, machine_tag_id=None):
body = {"install_types": install_types}
if installed_item_names:
body["installed_item_names"] = installed_item_names
if unattended_installs is not None:
body["unattended_installs"] = unattended_installs
if machine_tag_id:
body["filters"] = {"inventory": [{"tag_ids": [machine_tag_id]}]}
ps = ProbeSource.objects.create(
model="MunkiInstallProbe",
name="munki install probe",
body=body
)
p = ps.load()
return ps, p
def test_metadata_filters(self):
ps, p = self.create_probe(install_types=["install"])
self.assertEqual(len(p.metadata_filters), 1)
mf = p.metadata_filters[0]
self.assertEqual(mf.event_types, set(["munki_event"]))
self.assertEqual(len(mf.event_tags), 0)
def test_probe_source(self):
ps, p = self.create_probe(install_types=["install"])
self.assertEqual(ps.event_types, ["munki_event"])
def test_payload_filters(self):
ps, p = self.create_probe(install_types=["removal", "install"])
self.assertEqual(len(p.payload_filters), 1)
pf = p.payload_filters[0]
self.assertEqual(len(pf.items), 1)
self.assertEqual(pf.items[0], ("type", PayloadFilter.IN, set(["removal", "install"])))
def test_events_batch_1(self):
ps, p = self.create_probe(install_types=["install"])
tests = (
("Firefox", "removal", None, None, False),
("Firefox", "install", None, None, True),
("Firefox", "install", True, None, True),
("Firefox", "install", False, None, True),
("Firefox", "install", None, 1, True),
)
for name, install_type, unattended, machine_tag_id, result in tests:
event = build_munki_event(name, install_type, unattended, machine_tag_id)
self.assertEqual(p.test_event(event), result)
def test_events_batch_2(self):
ps, p = self.create_probe(install_types=["install"], machine_tag_id=1)
tests = (
("Firefox", "install", None, None, False),
("Firefox", "install", None, 2, False),
("Firefox", "install", None, 1, True),
)
for name, install_type, unattended, machine_tag_id, result in tests:
event = build_munki_event(name, install_type, unattended, machine_tag_id)
self.assertEqual(p.test_event(event), result)
def test_events_batch_3(self):
ps, p = self.create_probe(installed_item_names=["Firefox", "Chrome"],
install_types=["removal"],
unattended_installs=True)
tests = (
("Firefox", "removal", None, None, False),
("Firefox", "removal", False, None, False),
("Firefox", "removal", True, None, True),
("Firefox", "install", True, 2, False),
("Chrome", "removal", True, 2, True),
("Yo", "removal", True, 2, False),
)
for name, install_type, unattended, machine_tag_id, result in tests:
event = build_munki_event(name, install_type, unattended, machine_tag_id)
self.assertEqual(p.test_event(event), result)
| 3,805 | 21 | 234 |
8bacd348f80b68cf75a8c348fe5eb0bcd378a489 | 3,076 | py | Python | tests/test_dispatcher.py | ConsolinnoEnergy/KNW-Opt | 1c48d2fcea9d6eeef8da225e97d6e5016cc3dfc0 | [
"MIT"
] | null | null | null | tests/test_dispatcher.py | ConsolinnoEnergy/KNW-Opt | 1c48d2fcea9d6eeef8da225e97d6e5016cc3dfc0 | [
"MIT"
] | null | null | null | tests/test_dispatcher.py | ConsolinnoEnergy/KNW-Opt | 1c48d2fcea9d6eeef8da225e97d6e5016cc3dfc0 | [
"MIT"
] | null | null | null | import unittest
from knwopt.dispatcher import dispatcher | 30.156863 | 85 | 0.451886 | import unittest
from knwopt.dispatcher import dispatcher
class DispatcherTest(unittest.TestCase):
def test_nothing_available(self):
expected_power = 5
portfolio = {
"hans":{
"available":False,
"power": -1.,
"max_power": -1.
},
"Wurscht":{
"available":False,
"power": 6.,
"max_power": 6.
},
}
self.assertTrue({}==dispatcher(expected_power, portfolio) )
def test_one_available_helping(self):
expected_power = 5
portfolio = {
"hans":{
"available":True,
"power": 5.,
"max_power": 5.
},
"Wurscht":{
"available":False,
"power": 0.,
"max_power": 0.
},
}
self.assertAlmostEqual(5.,dispatcher(expected_power, portfolio)["hans"] )
def test_one_available_not_helping(self):
expected_power = 5
portfolio = {
"hans":{
"available":True,
"power": 5.,
"max_power": 5.
},
"Wurscht":{
"available":False,
"power": 5.,
"max_power": 5.
},
}
self.assertAlmostEqual(0.,dispatcher(expected_power, portfolio)["hans"] )
def test_available_positive(self):
expected_power = 10
portfolio = {
"hans":{
"available":True,
"power": 5.,
"max_power": 5.
},
"Wurscht":{
"available":True,
"power": 5.,
"max_power": 5.
},
}
self.assertAlmostEqual(5.,dispatcher(expected_power, portfolio)["hans"] )
self.assertAlmostEqual(5.,dispatcher(expected_power, portfolio)["Wurscht"] )
def test_available_positive_negative(self):
expected_power = 10
portfolio = {
"hans":{
"available":True,
"power": 13.,
"max_power": 13.
},
"Wurscht":{
"available":True,
"power": -5.,
"max_power": -5.
},
}
self.assertAlmostEqual(13.,dispatcher(expected_power, portfolio)["hans"] )
self.assertAlmostEqual(-5.,dispatcher(expected_power, portfolio)["Wurscht"] )
def test_available_negative_negative(self):
expected_power = 10
portfolio = {
"hans":{
"available":True,
"power": -13.,
"max_power": -13.
},
"Wurscht":{
"available":True,
"power": -5.,
"max_power": -5.
},
}
self.assertAlmostEqual(0.,dispatcher(expected_power, portfolio)["hans"] )
self.assertAlmostEqual(0.,dispatcher(expected_power, portfolio)["Wurscht"] ) | 2,809 | 19 | 192 |
0e87d1c175bfbb6220bba4dd601eb79b60c2ba41 | 1,476 | py | Python | prj/setup_complex.py | asvetlov/optimization-kiev-2017 | 760c7af223d4c993beb367a15687f7dc99313431 | [
"Apache-2.0"
] | null | null | null | prj/setup_complex.py | asvetlov/optimization-kiev-2017 | 760c7af223d4c993beb367a15687f7dc99313431 | [
"Apache-2.0"
] | null | null | null | prj/setup_complex.py | asvetlov/optimization-kiev-2017 | 760c7af223d4c993beb367a15687f7dc99313431 | [
"Apache-2.0"
] | null | null | null | from distutils.command.build_ext import build_ext
from distutils.errors import (CCompilerError, DistutilsExecError,
DistutilsPlatformError)
from setuptools import Extension, setup
try:
from Cython.Build import cythonize
USE_CYTHON = True
except ImportError:
USE_CYTHON = False
ext = '.pyx' if USE_CYTHON else '.c'
extensions = [Extension('prj._mod', ['prj/_mod' + ext])]
if USE_CYTHON:
extensions = cythonize(extensions)
# This class allows C extension building to fail.
args = dict(
name='prj',
version='0.0.1',
packages=['prj'],
ext_modules=extensions,
cmdclass=dict(build_ext=ve_build_ext))
try:
setup(**args)
except BuildFailed:
print("************************************************************")
print("Cannot compile C accelerator module, use pure python version")
print("************************************************************")
del args['ext_modules']
del args['cmdclass']
setup(**args)
| 25.016949 | 73 | 0.604336 | from distutils.command.build_ext import build_ext
from distutils.errors import (CCompilerError, DistutilsExecError,
DistutilsPlatformError)
from setuptools import Extension, setup
try:
from Cython.Build import cythonize
USE_CYTHON = True
except ImportError:
USE_CYTHON = False
ext = '.pyx' if USE_CYTHON else '.c'
extensions = [Extension('prj._mod', ['prj/_mod' + ext])]
if USE_CYTHON:
extensions = cythonize(extensions)
class BuildFailed(Exception):
pass
class ve_build_ext(build_ext):
# This class allows C extension building to fail.
def run(self):
try:
build_ext.run(self)
except (DistutilsPlatformError, FileNotFoundError):
raise BuildFailed()
def build_extension(self, ext):
try:
build_ext.build_extension(self, ext)
except (CCompilerError, DistutilsExecError,
DistutilsPlatformError, ValueError):
raise BuildFailed()
args = dict(
name='prj',
version='0.0.1',
packages=['prj'],
ext_modules=extensions,
cmdclass=dict(build_ext=ve_build_ext))
try:
setup(**args)
except BuildFailed:
print("************************************************************")
print("Cannot compile C accelerator module, use pure python version")
print("************************************************************")
del args['ext_modules']
del args['cmdclass']
setup(**args)
| 339 | 26 | 100 |
6e39cafd38ccaf3686f22fe82f721f9fdcf4a7e2 | 2,833 | py | Python | ask-smapi-model/ask_smapi_model/v1/skill/manifest/custom/suppressed_interface.py | alexa-labs/alexa-apis-for-python | 52838be4f57ee1a2479402ea78b1247b56017942 | [
"Apache-2.0"
] | 90 | 2018-09-19T21:56:42.000Z | 2022-03-30T11:25:21.000Z | ask-smapi-model/ask_smapi_model/v1/skill/manifest/custom/suppressed_interface.py | ishitaojha/alexa-apis-for-python | a68f94b7a0e41f819595d6fe56e800403e8a4194 | [
"Apache-2.0"
] | 11 | 2018-09-23T12:16:48.000Z | 2021-06-10T19:49:45.000Z | ask-smapi-model/ask_smapi_model/v1/skill/manifest/custom/suppressed_interface.py | ishitaojha/alexa-apis-for-python | a68f94b7a0e41f819595d6fe56e800403e8a4194 | [
"Apache-2.0"
] | 28 | 2018-09-19T22:30:38.000Z | 2022-02-22T22:57:07.000Z | # coding: utf-8
#
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file
# except in compliance with the License. A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for
# the specific language governing permissions and limitations under the License.
#
import pprint
import re # noqa: F401
import six
import typing
from enum import Enum
if typing.TYPE_CHECKING:
from typing import Dict, List, Optional, Union, Any
from datetime import datetime
class SuppressedInterface(Enum):
"""
Allowed enum values: [AudioPlayer, PlaybackController, Display, VideoPlayer, GameEngine, GadgetController, CanHandleIntentRequest, CanFulfillIntentRequest, AlexaPresentationApl, AlexaPresentationHtml, AlexaDataStore, AlexaDataStorePackageManager, PhotoCaptureController, VideoCaptureController, UploadController, CustomInterface, AlexaAugmentationEffectsController]
"""
AudioPlayer = "AudioPlayer"
PlaybackController = "PlaybackController"
Display = "Display"
VideoPlayer = "VideoPlayer"
GameEngine = "GameEngine"
GadgetController = "GadgetController"
CanHandleIntentRequest = "CanHandleIntentRequest"
CanFulfillIntentRequest = "CanFulfillIntentRequest"
AlexaPresentationApl = "AlexaPresentationApl"
AlexaPresentationHtml = "AlexaPresentationHtml"
AlexaDataStore = "AlexaDataStore"
AlexaDataStorePackageManager = "AlexaDataStorePackageManager"
PhotoCaptureController = "PhotoCaptureController"
VideoCaptureController = "VideoCaptureController"
UploadController = "UploadController"
CustomInterface = "CustomInterface"
AlexaAugmentationEffectsController = "AlexaAugmentationEffectsController"
def to_dict(self):
# type: () -> Dict[str, Any]
"""Returns the model properties as a dict"""
result = {self.name: self.value}
return result
def to_str(self):
# type: () -> str
"""Returns the string representation of the model"""
return pprint.pformat(self.value)
def __repr__(self):
# type: () -> str
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
# type: (Any) -> bool
"""Returns true if both objects are equal"""
if not isinstance(other, SuppressedInterface):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
# type: (Any) -> bool
"""Returns true if both objects are not equal"""
return not self == other
| 35.4125 | 369 | 0.715143 | # coding: utf-8
#
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file
# except in compliance with the License. A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for
# the specific language governing permissions and limitations under the License.
#
import pprint
import re # noqa: F401
import six
import typing
from enum import Enum
if typing.TYPE_CHECKING:
from typing import Dict, List, Optional, Union, Any
from datetime import datetime
class SuppressedInterface(Enum):
"""
Allowed enum values: [AudioPlayer, PlaybackController, Display, VideoPlayer, GameEngine, GadgetController, CanHandleIntentRequest, CanFulfillIntentRequest, AlexaPresentationApl, AlexaPresentationHtml, AlexaDataStore, AlexaDataStorePackageManager, PhotoCaptureController, VideoCaptureController, UploadController, CustomInterface, AlexaAugmentationEffectsController]
"""
AudioPlayer = "AudioPlayer"
PlaybackController = "PlaybackController"
Display = "Display"
VideoPlayer = "VideoPlayer"
GameEngine = "GameEngine"
GadgetController = "GadgetController"
CanHandleIntentRequest = "CanHandleIntentRequest"
CanFulfillIntentRequest = "CanFulfillIntentRequest"
AlexaPresentationApl = "AlexaPresentationApl"
AlexaPresentationHtml = "AlexaPresentationHtml"
AlexaDataStore = "AlexaDataStore"
AlexaDataStorePackageManager = "AlexaDataStorePackageManager"
PhotoCaptureController = "PhotoCaptureController"
VideoCaptureController = "VideoCaptureController"
UploadController = "UploadController"
CustomInterface = "CustomInterface"
AlexaAugmentationEffectsController = "AlexaAugmentationEffectsController"
def to_dict(self):
# type: () -> Dict[str, Any]
"""Returns the model properties as a dict"""
result = {self.name: self.value}
return result
def to_str(self):
# type: () -> str
"""Returns the string representation of the model"""
return pprint.pformat(self.value)
def __repr__(self):
# type: () -> str
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
# type: (Any) -> bool
"""Returns true if both objects are equal"""
if not isinstance(other, SuppressedInterface):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
# type: (Any) -> bool
"""Returns true if both objects are not equal"""
return not self == other
| 0 | 0 | 0 |
6d2b8635261249ca7683308f227bee628e4dba54 | 4,150 | py | Python | mail.py | pokerG/Evernote2kindle | 13c25a72cb8b502720b1bc51121f8ff230467c73 | [
"MIT"
] | 3 | 2016-01-13T16:30:49.000Z | 2018-11-12T12:49:09.000Z | mail.py | pokerG/Evernote2kindle | 13c25a72cb8b502720b1bc51121f8ff230467c73 | [
"MIT"
] | 1 | 2015-01-16T09:43:44.000Z | 2015-01-16T14:36:14.000Z | mail.py | pokerG/Evernote2kindle | 13c25a72cb8b502720b1bc51121f8ff230467c73 | [
"MIT"
] | 2 | 2015-03-27T00:19:55.000Z | 2021-11-11T07:22:12.000Z | # -*- coding: utf-8 -*-
import httplib2
from apiclient.discovery import build
from oauth2client.client import flow_from_clientsecrets
from oauth2client.file import Storage
from oauth2client.tools import run
import base64
from email.mime.audio import MIMEAudio
from email.mime.base import MIMEBase
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import mimetypes
import os
from apiclient import errors
def CreateMessageWithAttachment(
sender, to, subject, message_text, file_dir, filename):
"""Create a message for an email.
Args:
sender: Email address of the sender.
to: Email address of the receiver.
subject: The subject of the email message.
message_text: The text of the email message.
file_dir: The directory containing the file to be attached.
filename: The name of the file to be attached.
Returns:
An object containing a base64url encoded email object.
"""
message = MIMEMultipart()
message['to'] = to
message['from'] = sender
message['subject'] = subject
msg = MIMEText(message_text)
message.attach(msg)
path = os.path.join(file_dir, filename)
content_type, encoding = mimetypes.guess_type(path)
if content_type is None or encoding is not None:
content_type = 'application/octet-stream'
main_type, sub_type = content_type.split('/', 1)
if main_type == 'text':
fp = open(path, 'rb')
msg = MIMEText(fp.read(), _subtype=sub_type)
fp.close()
elif main_type == 'image':
fp = open(path, 'rb')
msg = MIMEImage(fp.read(), _subtype=sub_type)
fp.close()
elif main_type == 'audio':
fp = open(path, 'rb')
msg = MIMEAudio(fp.read(), _subtype=sub_type)
fp.close()
else:
fp = open(path, 'rb')
msg = MIMEBase(main_type, sub_type)
msg.set_payload(fp.read())
fp.close()
msg.add_header('Content-Disposition', 'attachment', filename=filename)
message.attach(msg)
return {'raw': base64.urlsafe_b64encode(message.as_string())}
def SendMessage(service, user_id, message):
"""Send an email message.
Args:
service: Authorized Gmail API service instance.
user_id: User's email address. The special value "me"
can be used to indicate the authenticated user.
message: Message to be sent.
Returns:
Sent Message.
"""
try:
message = (service.users().messages().send(userId=user_id, body=message)
.execute())
print 'Message Id: %s' % message['id']
return message
except errors.HttpError, error:
print 'An error occurred: %s' % error
if __name__ == '__main__':
Run('pokerfacehlg@gmail.com','pokerfacehlg@gmail.com',os.getcwd(),'README.md')
| 33.467742 | 88 | 0.673253 | # -*- coding: utf-8 -*-
import httplib2
from apiclient.discovery import build
from oauth2client.client import flow_from_clientsecrets
from oauth2client.file import Storage
from oauth2client.tools import run
import base64
from email.mime.audio import MIMEAudio
from email.mime.base import MIMEBase
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import mimetypes
import os
from apiclient import errors
def CreateMessageWithAttachment(
sender, to, subject, message_text, file_dir, filename):
"""Create a message for an email.
Args:
sender: Email address of the sender.
to: Email address of the receiver.
subject: The subject of the email message.
message_text: The text of the email message.
file_dir: The directory containing the file to be attached.
filename: The name of the file to be attached.
Returns:
An object containing a base64url encoded email object.
"""
message = MIMEMultipart()
message['to'] = to
message['from'] = sender
message['subject'] = subject
msg = MIMEText(message_text)
message.attach(msg)
path = os.path.join(file_dir, filename)
content_type, encoding = mimetypes.guess_type(path)
if content_type is None or encoding is not None:
content_type = 'application/octet-stream'
main_type, sub_type = content_type.split('/', 1)
if main_type == 'text':
fp = open(path, 'rb')
msg = MIMEText(fp.read(), _subtype=sub_type)
fp.close()
elif main_type == 'image':
fp = open(path, 'rb')
msg = MIMEImage(fp.read(), _subtype=sub_type)
fp.close()
elif main_type == 'audio':
fp = open(path, 'rb')
msg = MIMEAudio(fp.read(), _subtype=sub_type)
fp.close()
else:
fp = open(path, 'rb')
msg = MIMEBase(main_type, sub_type)
msg.set_payload(fp.read())
fp.close()
msg.add_header('Content-Disposition', 'attachment', filename=filename)
message.attach(msg)
return {'raw': base64.urlsafe_b64encode(message.as_string())}
def SendMessage(service, user_id, message):
"""Send an email message.
Args:
service: Authorized Gmail API service instance.
user_id: User's email address. The special value "me"
can be used to indicate the authenticated user.
message: Message to be sent.
Returns:
Sent Message.
"""
try:
message = (service.users().messages().send(userId=user_id, body=message)
.execute())
print 'Message Id: %s' % message['id']
return message
except errors.HttpError, error:
print 'An error occurred: %s' % error
def Run(fr,to,path,filename):
# Path to the client_secret.json file downloaded from the Developer Console
CLIENT_SECRET_FILE = './client_secret.json'
# Check https://developers.google.com/gmail/api/auth/scopes for all available scopes
OAUTH_SCOPE = ['https://www.googleapis.com/auth/gmail.modify',
'https://www.googleapis.com/auth/gmail.readonly',
'https://www.googleapis.com/auth/gmail.compose',
'https://mail.google.com/']
# Location of the credentials storage file
STORAGE = Storage('gmail.storage')
# Start the OAuth flow to retrieve credentials
flow = flow_from_clientsecrets(CLIENT_SECRET_FILE, scope=OAUTH_SCOPE)
http = httplib2.Http()
# Try to retrieve credentials from storage or run the flow to generate them
credentials = STORAGE.get()
if credentials is None or credentials.invalid:
credentials = run(flow, STORAGE, http=http)
# Authorize the httplib2.Http object with our credentials
http = credentials.authorize(http)
# Build the Gmail service from discovery
gmail_service = build('gmail', 'v1', http=http)
print gmail_service
ma = CreateMessageWithAttachment(fr,to,'','',path,filename)
SendMessage(gmail_service,'me',ma)
if __name__ == '__main__':
Run('pokerfacehlg@gmail.com','pokerfacehlg@gmail.com',os.getcwd(),'README.md')
| 1,250 | 0 | 23 |
4ac218d7ea527b17208ec1d752b29b243c8114d3 | 4,281 | py | Python | scratch/rtsp/flir_videostream.py | johnnewto/FLIR-pubsub | 71eec3a5bf8fe5959cf997f7f12d9cfe0f58d8fc | [
"Apache-2.0"
] | null | null | null | scratch/rtsp/flir_videostream.py | johnnewto/FLIR-pubsub | 71eec3a5bf8fe5959cf997f7f12d9cfe0f58d8fc | [
"Apache-2.0"
] | 6 | 2021-05-20T12:36:50.000Z | 2022-02-26T06:25:11.000Z | scratch/rtsp/flir_videostream.py | johnnewto/FLIR-pubsub | 71eec3a5bf8fe5959cf997f7f12d9cfe0f58d8fc | [
"Apache-2.0"
] | 1 | 2020-03-24T02:07:21.000Z | 2020-03-24T02:07:21.000Z | # import the necessary packages
from threading import Thread
import cv2, time
from FLIR_pubsub.FLIR_client_utils import *
class Flir_VideoStream:
""" Maintain live FLIRCam feed without buffering. """
def __init__(self, src=0, name="FlirVideoStream", verbose = False):
"""
src: the path to an RTSP server. should start with "rtsp://"
name: give it a name
verbose: print log or not
"""
self.name = name # initialize the thread name
self.fps = 0.0 # measured fps
self._src = src
self._verbose = verbose
self._stream = None
self._frame = None # returned images from stream
# initialize the variable used to indicate if the thread should be stopped
self._stopped = False
self._fps = FPS()
def start(self):
"""start the thread to read frames from the video stream"""
if self._verbose:
print(f"[INFO] connecting to Cam: {self._src}")
self._stopped = False
self._thread = Thread(target=self.update, name=self.name, args=())
self._thread.daemon = True
self._thread.start()
self._fps.start()
return self
def update(self):
"""keep looping infinitely until the thread is stopped"""
while not self._stopped:
if self._stream is not None and self._stream.isOpened():
(self.grabbed, self._frame) = self._stream.read()
if self.grabbed:
self._fps.update()
self.last = datetime.datetime.now()
time.sleep(0.01)
else:
self.connect()
time.sleep(0.01)
# time.sleep(1)
if self._fps.elapsed() > 5:
self._fps.stop()
self.fps = self._fps.fps
print(self.fps)
if self._fps.numFrames == 0:
self.connect()
self._fps.start()
# Thread has stopped
if self._verbose:
print(f"[INFO] Connection closed Cam: {self._src}")
import datetime
class FPS:
'''Calculate the frames per second'''
# def fps(self):
# # compute the (approximate) frames per second, must be stopped first
# return self._numFrames / self.elapsed()
| 25.331361 | 92 | 0.683719 | # import the necessary packages
from threading import Thread
import cv2, time
from FLIR_pubsub.FLIR_client_utils import *
class Flir_VideoStream:
""" Maintain live FLIRCam feed without buffering. """
def __init__(self, src=0, name="FlirVideoStream", verbose = False):
"""
src: the path to an RTSP server. should start with "rtsp://"
name: give it a name
verbose: print log or not
"""
self.name = name # initialize the thread name
self.fps = 0.0 # measured fps
self._src = src
self._verbose = verbose
self._stream = None
self._frame = None # returned images from stream
# initialize the variable used to indicate if the thread should be stopped
self._stopped = False
self._fps = FPS()
def start(self):
"""start the thread to read frames from the video stream"""
if self._verbose:
print(f"[INFO] connecting to Cam: {self._src}")
self._stopped = False
self._thread = Thread(target=self.update, name=self.name, args=())
self._thread.daemon = True
self._thread.start()
self._fps.start()
return self
def connect(self):
if self.isOpened():
self._stream.release()
self._stream = cv2.VideoCapture(self._src)
if self._verbose:
if self._stream.isOpened():
print(f"[INFO] connected to Cam: {self._src}")
else:
print(f"[INFO] Failed to connect Cam: {self._src}")
time.sleep(1)
def update(self):
"""keep looping infinitely until the thread is stopped"""
while not self._stopped:
if self._stream is not None and self._stream.isOpened():
(self.grabbed, self._frame) = self._stream.read()
if self.grabbed:
self._fps.update()
self.last = datetime.datetime.now()
time.sleep(0.01)
else:
self.connect()
time.sleep(0.01)
# time.sleep(1)
if self._fps.elapsed() > 5:
self._fps.stop()
self.fps = self._fps.fps
print(self.fps)
if self._fps.numFrames == 0:
self.connect()
self._fps.start()
# Thread has stopped
if self._verbose:
print(f"[INFO] Connection closed Cam: {self._src}")
def read(self):
# return the frame most recently read
return self._frame
def stop(self):
# indicate that the thread should be stopped or closed
self._close()
def close(self):
# indicate that the thread should be stopped or closed
self._close()
def _close(self):
if self.isOpened():
self._stream.release()
self._stopped = True
# wait until stream resources are released (producer thread might be still grabbing frame)
# Todo this code does not always work, Thread is a daemon so closes anyhow
# if not self._thread._is_stopped:
# self._thread.join()
# else:
# pass
def isOpened(self):
try:
return self._stream is not None and self._stream.isOpened()
except:
return False
import datetime
class FPS:
'''Calculate the frames per second'''
def __init__(self):
# store the start time, end time, and total number of frames
# that were examined between the start and end intervals
self._start = None
self._end = None
self.numFrames = 0
self.fps = 0.0
def start(self):
# start the timer
self._start = datetime.datetime.now()
self._end = None
self.numFrames = 0
return self
def stop(self):
# stop the timer
self._end = datetime.datetime.now()
self.fps = self.numFrames / self.elapsed()
def update(self):
# increment the total number of frames examined during the
# start and end intervals
self.numFrames += 1
# return self._numFrames
def elapsed(self):
# return the total number of seconds between the start and
# end interval
# if self._end is None:
self._end = datetime.datetime.now()
# ret = (self._end - self._start).total_seconds()
# self._end = None
# else:
# ret = (self._end - self._start).total_seconds()
return (self._end - self._start).total_seconds()
def poll_fps(self):
# compute the (approximate) frames per second without stopping
# if self._end is not None:
# return self._numFrames / self.elapsed()
# else:
self.numFrames += 1
self._end = datetime.datetime.now()
self.fps = self.numFrames / self.elapsed()
return self.fps
# self._end = None
# def fps(self):
# # compute the (approximate) frames per second, must be stopped first
# return self._numFrames / self.elapsed()
| 2,045 | 0 | 287 |
d8745b38983055f152e550e1310e6937ffe1176b | 210 | py | Python | output/models/sun_data/ctype/p_substitutions/p_substitutions00101m/p_substitutions00101m_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 1 | 2021-08-14T17:59:21.000Z | 2021-08-14T17:59:21.000Z | output/models/sun_data/ctype/p_substitutions/p_substitutions00101m/p_substitutions00101m_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | 4 | 2020-02-12T21:30:44.000Z | 2020-04-15T20:06:46.000Z | output/models/sun_data/ctype/p_substitutions/p_substitutions00101m/p_substitutions00101m_xsd/__init__.py | tefra/xsdata-w3c-tests | b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f | [
"MIT"
] | null | null | null | from output.models.sun_data.ctype.p_substitutions.p_substitutions00101m.p_substitutions00101m_xsd.p_substitutions00101m import (
A,
B,
C,
E,
)
__all__ = [
"A",
"B",
"C",
"E",
]
| 15 | 128 | 0.628571 | from output.models.sun_data.ctype.p_substitutions.p_substitutions00101m.p_substitutions00101m_xsd.p_substitutions00101m import (
A,
B,
C,
E,
)
__all__ = [
"A",
"B",
"C",
"E",
]
| 0 | 0 | 0 |
2059aac1d6d0b0e8d8baf7f33f48f03a7991f817 | 1,972 | py | Python | problems/chapter16/mshibatatt/007_supple.py | tokuma09/algorithm_problems | 58534620df73b230afbeb12de126174362625a78 | [
"CC0-1.0"
] | 1 | 2021-07-07T15:46:58.000Z | 2021-07-07T15:46:58.000Z | problems/chapter16/mshibatatt/007_supple.py | tokuma09/algorithm_problems | 58534620df73b230afbeb12de126174362625a78 | [
"CC0-1.0"
] | 5 | 2021-06-05T14:16:41.000Z | 2021-07-10T07:08:28.000Z | problems/chapter16/mshibatatt/007_supple.py | tokuma09/algorithm_problems | 58534620df73b230afbeb12de126174362625a78 | [
"CC0-1.0"
] | null | null | null | # %%
import subprocess
import sys
from networkx.algorithms.bipartite.basic import color
reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])
installed_packages = [r.decode().split('==')[0] for r in reqs.split()]
if not 'matplotlib' in installed_packages:
subprocess.run('pip install matplotlib', shell=True)
import networkx as nx
import matplotlib.pyplot as plt
# %%
with open('/workspaces/algorithm_problems/input.txt', 'r') as f:
r, c = map(int, f.readline().split())
G = []
for _ in range(r):
G.append(f.readline())
# %%
ff = nx.DiGraph()
s, t = r*c*3, r*c*3 + 1
num_blank, num_adjacent = 0, 0
for i in range(r):
for j in range(c):
if G[i][j] == '#':
num_blank += 1
v = i*c + j
# if adjacent in row, cannot both 0
if i+1 < r and G[i+1][j] == '#':
num_adjacent += 1
newv = v + r*c
ff.add_edge(newv, t, capacity=1)
ff.add_edge(v, newv, capacity=float('inf'))
ff.add_edge(v+c, newv, capacity=float('inf'))
# if adjacent in col, cannot both 1
if j+1 < c and G[i][j+1] == '#':
num_adjacent += 1
newv = v + r*c*2
ff.add_edge(s, newv, capacity=1)
ff.add_edge(newv, v, capacity=float('inf'))
ff.add_edge(newv, v+1, capacity=float('inf'))
# %%
edges = ff.edges()
nodes = sorted(list(ff.nodes()))
pos = {s:[1, 1], t:[1, 0]}
counter = 0
for v in nodes[0:len(nodes)-2]:
counter = v % (r*c)
pos[v] = [0.33*(v//(r*c)), 1/(r*c)*counter]
colors = ['g' if ff[u][v]['capacity'] == 1 else 'r' for u,v in edges]
nx.draw_networkx(ff, pos)
capacity = nx.get_edge_attributes(ff,'capacity')
nx.draw(ff, pos, edges=edges, edge_color=colors)
print('red if capacity is inf, green if capacity is 1')
plt.show()
print(f'num_blank:{num_blank}, num_adjacent:{num_adjacent}')
# %%
| 30.8125 | 71 | 0.556795 | # %%
import subprocess
import sys
from networkx.algorithms.bipartite.basic import color
reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])
installed_packages = [r.decode().split('==')[0] for r in reqs.split()]
if not 'matplotlib' in installed_packages:
subprocess.run('pip install matplotlib', shell=True)
import networkx as nx
import matplotlib.pyplot as plt
# %%
with open('/workspaces/algorithm_problems/input.txt', 'r') as f:
r, c = map(int, f.readline().split())
G = []
for _ in range(r):
G.append(f.readline())
# %%
ff = nx.DiGraph()
s, t = r*c*3, r*c*3 + 1
num_blank, num_adjacent = 0, 0
for i in range(r):
for j in range(c):
if G[i][j] == '#':
num_blank += 1
v = i*c + j
# if adjacent in row, cannot both 0
if i+1 < r and G[i+1][j] == '#':
num_adjacent += 1
newv = v + r*c
ff.add_edge(newv, t, capacity=1)
ff.add_edge(v, newv, capacity=float('inf'))
ff.add_edge(v+c, newv, capacity=float('inf'))
# if adjacent in col, cannot both 1
if j+1 < c and G[i][j+1] == '#':
num_adjacent += 1
newv = v + r*c*2
ff.add_edge(s, newv, capacity=1)
ff.add_edge(newv, v, capacity=float('inf'))
ff.add_edge(newv, v+1, capacity=float('inf'))
# %%
edges = ff.edges()
nodes = sorted(list(ff.nodes()))
pos = {s:[1, 1], t:[1, 0]}
counter = 0
for v in nodes[0:len(nodes)-2]:
counter = v % (r*c)
pos[v] = [0.33*(v//(r*c)), 1/(r*c)*counter]
colors = ['g' if ff[u][v]['capacity'] == 1 else 'r' for u,v in edges]
nx.draw_networkx(ff, pos)
capacity = nx.get_edge_attributes(ff,'capacity')
nx.draw(ff, pos, edges=edges, edge_color=colors)
print('red if capacity is inf, green if capacity is 1')
plt.show()
print(f'num_blank:{num_blank}, num_adjacent:{num_adjacent}')
# %%
| 0 | 0 | 0 |
9b4f4d7160635a05e94c2e88c70e2e875c8ea756 | 2,256 | py | Python | 2-dl-container/Container-Root/job/resnet/compile_model-gpu.py | kamranjkhan/aws-reinvent21-inf1-workshop | d5c94dc4a53779e6b5d30581295fe98e4055c270 | [
"MIT-0"
] | 5 | 2021-11-29T22:01:15.000Z | 2022-03-08T00:55:49.000Z | 2-dl-container/Container-Root/job/resnet/compile_model-gpu.py | kamranjkhan/aws-reinvent21-inf1-workshop | d5c94dc4a53779e6b5d30581295fe98e4055c270 | [
"MIT-0"
] | null | null | null | 2-dl-container/Container-Root/job/resnet/compile_model-gpu.py | kamranjkhan/aws-reinvent21-inf1-workshop | d5c94dc4a53779e6b5d30581295fe98e4055c270 | [
"MIT-0"
] | 2 | 2021-11-16T22:49:37.000Z | 2021-11-23T23:19:29.000Z | ######################################################################
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# SPDX-License-Identifier: MIT-0 #
######################################################################
import os
import torch
import torchvision
from PIL import Image
from torchvision import transforms
from common_settings import default_image_size, default_batch_size, default_model_name
image_size = int(os.getenv('IMAGE_SIZE', default_image_size))
batch_size = int(os.getenv('BATCH_SIZE', default_batch_size))
print('Image Size: %d, Batch Size: %d'%(image_size, batch_size))
ts_model_file = '%s_gpu_%d_%d.pt'%(default_model_name,image_size, batch_size)
img_cat = Image.open("data/cat.png").convert('RGB')
#
# Create a preprocessing pipeline
#
preprocess = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)])
#
# Pass the image for preprocessing and the image preprocessed
#
img_cat_preprocessed = preprocess(img_cat)
img_cat_preprocessed_unsqueeze = torch.unsqueeze(img_cat_preprocessed, 0)
batch_img_cat_tensor = torch.cat([img_cat_preprocessed_unsqueeze] * batch_size)
batch_img_cat_tensor_gpu = batch_img_cat_tensor.cuda()
model_ft_gpu = torchvision.models.resnet50(pretrained=True).cuda()
model_ft_gpu.eval()
# REmove None Attributes
# https://forums.developer.nvidia.com/t/torchscripted-pytorch-lightning-module-fails-to-load/181002
remove_attributes = []
for key, value in vars(model_ft_gpu).items():
if value is None:
remove_attributes.append(key)
for key in remove_attributes:
delattr(model_ft_gpu, key)
out = model_ft_gpu(batch_img_cat_tensor_gpu)
print('Original Model Output:', out)
print('torch.jit.script cuda version')
ts_model = torch.jit.script(model_ft_gpu, (batch_img_cat_tensor_gpu))
out = ts_model(batch_img_cat_tensor_gpu)
ts_model.save(ts_model_file)
# Load the saved model and perform inference
ts_model_reloaded = torch.jit.load(ts_model_file)
ts_output = ts_model_reloaded(batch_img_cat_tensor_gpu)
print('Torchscript Model Output:', ts_output)
| 34.181818 | 99 | 0.717642 | ######################################################################
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# SPDX-License-Identifier: MIT-0 #
######################################################################
import os
import torch
import torchvision
from PIL import Image
from torchvision import transforms
from common_settings import default_image_size, default_batch_size, default_model_name
image_size = int(os.getenv('IMAGE_SIZE', default_image_size))
batch_size = int(os.getenv('BATCH_SIZE', default_batch_size))
print('Image Size: %d, Batch Size: %d'%(image_size, batch_size))
ts_model_file = '%s_gpu_%d_%d.pt'%(default_model_name,image_size, batch_size)
img_cat = Image.open("data/cat.png").convert('RGB')
#
# Create a preprocessing pipeline
#
preprocess = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)])
#
# Pass the image for preprocessing and the image preprocessed
#
img_cat_preprocessed = preprocess(img_cat)
img_cat_preprocessed_unsqueeze = torch.unsqueeze(img_cat_preprocessed, 0)
batch_img_cat_tensor = torch.cat([img_cat_preprocessed_unsqueeze] * batch_size)
batch_img_cat_tensor_gpu = batch_img_cat_tensor.cuda()
model_ft_gpu = torchvision.models.resnet50(pretrained=True).cuda()
model_ft_gpu.eval()
# REmove None Attributes
# https://forums.developer.nvidia.com/t/torchscripted-pytorch-lightning-module-fails-to-load/181002
remove_attributes = []
for key, value in vars(model_ft_gpu).items():
if value is None:
remove_attributes.append(key)
for key in remove_attributes:
delattr(model_ft_gpu, key)
out = model_ft_gpu(batch_img_cat_tensor_gpu)
print('Original Model Output:', out)
print('torch.jit.script cuda version')
ts_model = torch.jit.script(model_ft_gpu, (batch_img_cat_tensor_gpu))
out = ts_model(batch_img_cat_tensor_gpu)
ts_model.save(ts_model_file)
# Load the saved model and perform inference
ts_model_reloaded = torch.jit.load(ts_model_file)
ts_output = ts_model_reloaded(batch_img_cat_tensor_gpu)
print('Torchscript Model Output:', ts_output)
| 0 | 0 | 0 |
0a3981c85cdd123a552a2bd637dd23bb7d8c7572 | 10,305 | py | Python | otcextensions/osclient/cce/v1/cluster_node.py | zsoltn/python-otcextensions | 4c0fa22f095ebd5f9636ae72acbae5048096822c | [
"Apache-2.0"
] | 10 | 2018-03-03T17:59:59.000Z | 2020-01-08T10:03:00.000Z | otcextensions/osclient/cce/v1/cluster_node.py | zsoltn/python-otcextensions | 4c0fa22f095ebd5f9636ae72acbae5048096822c | [
"Apache-2.0"
] | 208 | 2020-02-10T08:27:46.000Z | 2022-03-29T15:24:21.000Z | otcextensions/osclient/cce/v1/cluster_node.py | zsoltn/python-otcextensions | 4c0fa22f095ebd5f9636ae72acbae5048096822c | [
"Apache-2.0"
] | 15 | 2020-04-01T20:45:54.000Z | 2022-03-23T12:45:43.000Z | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
'''CCE Cluster Nodes v1 action implementations'''
import argparse
import logging
from osc_lib import utils
from osc_lib.command import command
from otcextensions.i18n import _
LOG = logging.getLogger(__name__)
def _flatten_cluster_node(obj):
"""Flatten the structure of the cluster node into a single dict
"""
data = {
'id': obj.id,
'name': obj.name,
'cluster_uuid': obj.spec.cluster_uuid,
'private_ip': obj.spec.private_ip,
'public_ip': obj.spec.public_ip,
'status': obj.status,
'memory': obj.spec.memory,
'cpu': obj.spec.cpu,
'flavor': obj.spec.flavor,
'availability_zone': obj.spec.availability_zone,
'ssh_key': obj.spec.ssh_key,
'replica_count': obj.replica_count,
'volumes': ';'.join(
(x.disk_type + ':' + str(x.disk_size))
for x in obj.spec.volume
) if obj.spec.volume else '',
'conditions': ';'.join(
(cond['type'] + '=' + cond['status'])
for cond in obj.spec.status.conditions
) if obj.spec.status.conditions else ''
}
if obj.spec.status.capacity:
cap_max = {
'cpu_max': obj.spec.status.capacity.cpu,
'mem_max': obj.spec.status.capacity.memory,
'pods_max': obj.spec.status.capacity.pods,
}
data.update(cap_max)
if obj.spec.status.allocatable:
cap_alloc = {
'cpu_allocatable': obj.spec.status.allocatable.cpu,
'mem_allocatable': obj.spec.status.allocatable.memory,
'pods_allocatable': obj.spec.status.allocatable.pods,
}
data.update(cap_alloc)
return data
| 34.122517 | 79 | 0.545075 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
'''CCE Cluster Nodes v1 action implementations'''
import argparse
import logging
from osc_lib import utils
from osc_lib.command import command
from otcextensions.i18n import _
LOG = logging.getLogger(__name__)
def _flatten_cluster_node(obj):
"""Flatten the structure of the cluster node into a single dict
"""
data = {
'id': obj.id,
'name': obj.name,
'cluster_uuid': obj.spec.cluster_uuid,
'private_ip': obj.spec.private_ip,
'public_ip': obj.spec.public_ip,
'status': obj.status,
'memory': obj.spec.memory,
'cpu': obj.spec.cpu,
'flavor': obj.spec.flavor,
'availability_zone': obj.spec.availability_zone,
'ssh_key': obj.spec.ssh_key,
'replica_count': obj.replica_count,
'volumes': ';'.join(
(x.disk_type + ':' + str(x.disk_size))
for x in obj.spec.volume
) if obj.spec.volume else '',
'conditions': ';'.join(
(cond['type'] + '=' + cond['status'])
for cond in obj.spec.status.conditions
) if obj.spec.status.conditions else ''
}
if obj.spec.status.capacity:
cap_max = {
'cpu_max': obj.spec.status.capacity.cpu,
'mem_max': obj.spec.status.capacity.memory,
'pods_max': obj.spec.status.capacity.pods,
}
data.update(cap_max)
if obj.spec.status.allocatable:
cap_alloc = {
'cpu_allocatable': obj.spec.status.allocatable.cpu,
'mem_allocatable': obj.spec.status.allocatable.memory,
'pods_allocatable': obj.spec.status.allocatable.pods,
}
data.update(cap_alloc)
return data
class ListCCEClusterNode(command.Lister):
_description = _('List CCE Cluster Nodes')
columns = ('ID', 'name', 'private_ip', 'public_ip',
'availability_zone', 'status')
def get_parser(self, prog_name):
parser = super(ListCCEClusterNode, self).get_parser(prog_name)
parser.add_argument(
'cluster',
metavar='<cluster>',
help=_('The ID or name of the cluster.')
)
return parser
def take_action(self, parsed_args):
client = self.app.client_manager.cce
data = client.cluster_nodes(parsed_args.cluster)
table = (self.columns,
(utils.get_dict_properties(
_flatten_cluster_node(s), self.columns,
) for s in data))
return table
class ShowCCEClusterNode(command.ShowOne):
_description = _('Show single Cluster node details')
columns = ('ID', 'name', 'cluster_uuid',
'flavor', 'cpu', 'memory', 'volumes',
'cpu_max', 'mem_max', 'pods_max',
'cpu_allocatable', 'mem_allocatable', 'pods_allocatable',
'conditions',
'private_ip', 'public_ip', 'availability_zone', 'ssh_key',
'status', 'replica_count')
def get_parser(self, prog_name):
parser = super(ShowCCEClusterNode, self).get_parser(prog_name)
parser.add_argument(
'cluster',
metavar='<cluster>',
help=_('ID of the cluster.')
)
parser.add_argument(
'node',
metavar='<node>',
help=_('ID of the cluster node.')
)
return parser
def take_action(self, parsed_args):
client = self.app.client_manager.cce
obj = client.find_cluster_node(
cluster=parsed_args.cluster,
node=parsed_args.node
)
data = utils.get_dict_properties(
_flatten_cluster_node(obj), self.columns)
return (self.columns, data)
class DeleteCCEClusterNode(command.Command):
_description = _('Delete CCE Cluster nodes')
def get_parser(self, prog_name):
parser = super(DeleteCCEClusterNode, self).get_parser(prog_name)
parser.add_argument(
'cluster',
metavar='<cluster>',
help=_('ID of the cluster.')
)
parser.add_argument(
'node',
metavar='<node>',
nargs='+',
help=_('Name of the cluster node.')
)
return parser
def take_action(self, parsed_args):
if parsed_args.cluster and parsed_args.node:
client = self.app.client_manager.cce
client.delete_cluster_nodes(
cluster=parsed_args.cluster,
node_names=parsed_args.node)
class CreateCCEClusterNode(command.Command):
_description = _('Create CCE Cluster Node')
columns = ('ID', 'name', 'cluster_uuid',
'flavor', 'cpu', 'memory', 'volumes',
'cpu_max', 'mem_max', 'pods_max',
'cpu_allocatable', 'mem_allocatable', 'pods_allocatable',
'conditions',
'private_ip', 'public_ip', 'availability_zone', 'ssh_key',
'status', 'replica_count')
def get_parser(self, prog_name):
parser = super(CreateCCEClusterNode, self).get_parser(prog_name)
parser.add_argument(
'cluster',
metavar='<cluster>',
help=_('Name or ID of the cluster.')
)
parser.add_argument(
'--flavor',
metavar='<flavor>',
required=True,
help=_('The flavor for the server.')
)
parser.add_argument(
'--label',
metavar='<label>',
help=_('The label attached to the node.')
)
parser.add_argument(
'--volume',
metavar='<volume>',
action='append',
required=True,
help=_(
'Disk information to attach to the instance.\n'
'format = `DISK_TYPE`,`VOLUME_TYPE`,`SIZE`\n'
'`DISK_TYPE` can be in [SYS, DATA] and identifies '
'whether disk should be a system or data disk\n'
'`VOLUME_TYPE` can be in: \n'
'* `SATA` = Common I/O \n'
'* `SAS` = High I/O \n'
'* `SSD` = Ultra-High I/O \n'
'`SIZE` is size in Gb (40 hardcoded for root, [100..32768])\n'
'(Repeat multiple times for multiple disks).')
)
parser.add_argument(
'--ssh_key',
metavar='<ssh_key>',
required=True,
help=_('SSH Key used to create the node.')
)
parser.add_argument(
'--assign_floating_ip',
required=True,
action='store_true',
help=_('Whether to assign floating IP to the server or not.')
)
parser.add_argument(
'--availability_zone',
metavar='<availability_zone>',
help=_('Availability zone to place server in.')
)
parser.add_argument(
'--tag',
metavar='<tag>',
action='append',
help=_('Tag to assign to the server in KEY=VALUE format. '
'Repeat for multiple values.')
)
parser.add_argument(
'--replica_count',
metavar='[1..15]',
type=int,
choices=range(1, 15),
default=1,
help=_('Number of node instances to create '
'(max 15 in the cluster).')
)
return parser
def take_action(self, parsed_args):
attrs = {}
spec = {}
spec['flavor'] = parsed_args.flavor
spec['ssh_key'] = parsed_args.ssh_key
spec['availability_zone'] = parsed_args.availability_zone
if parsed_args.label:
spec['label'] = parsed_args.label
if parsed_args.assign_floating_ip:
spec['assign_floating_ip'] = parsed_args.assign_floating_ip
attrs['replica_count'] = parsed_args.replica_count
spec['volume'] = []
for disk in parsed_args.volume:
disk_parts = disk.split(',')
disk_data = {}
if 3 == len(disk_parts):
dt = disk_parts[0].upper()
# Disk type: SYS->root; DATA->data
if dt in ('SYS', 'DATA'):
disk_data['disk_type'] = 'data' if dt == 'DATA' else 'root'
else:
msg = _('Disk Type is not in (SYS, DATA)')
raise argparse.ArgumentTypeError(msg)
vt = disk_parts[1].upper()
if vt in ('SATA', 'SAS', 'SSD'):
disk_data['volume_type'] = vt
else:
msg = _('Volume Type is not in (SATA, SAS, SSD)')
raise argparse.ArgumentTypeError(msg)
if dt == 'DATA':
# Size only relevant for data disk
if disk_parts[2].isdigit:
disk_data['disk_size'] = disk_parts[2]
else:
msg = _('Volume SIZE is not a digit')
raise argparse.ArgumentTypeError(msg)
spec['volume'].append(disk_data)
else:
msg = _('Cannot parse disk information')
raise argparse.ArgumentTypeError(msg)
if parsed_args.tag:
tags = []
for tag in parsed_args.tag:
(k, v) = tag.split('=')
tags.append(k + '.' + v)
spec['tags'] = tags
attrs['spec'] = spec
client = self.app.client_manager.cce
obj = client.add_node(cluster=parsed_args.cluster, **attrs)
data = utils.get_dict_properties(
_flatten_cluster_node(obj),
self.columns)
return (self.columns, data)
| 6,614 | 1,337 | 92 |
704483bf9312a2405c5646a2a4e2c54386a2f901 | 7,638 | py | Python | omlet/lightning/checkpoint.py | LinxiFan/omlet | 9009ec2595cfa177271839f58683940e536e77a2 | [
"MIT"
] | null | null | null | omlet/lightning/checkpoint.py | LinxiFan/omlet | 9009ec2595cfa177271839f58683940e536e77a2 | [
"MIT"
] | null | null | null | omlet/lightning/checkpoint.py | LinxiFan/omlet | 9009ec2595cfa177271839f58683940e536e77a2 | [
"MIT"
] | null | null | null | import os
import re
from pytorch_lightning.utilities import rank_zero_warn, rank_zero_only
from pytorch_lightning.callbacks import ModelCheckpoint
import torch
import omlet.utils as U
from typing import Optional
from . import omlet_logger as _log
| 38.969388 | 108 | 0.595968 | import os
import re
from pytorch_lightning.utilities import rank_zero_warn, rank_zero_only
from pytorch_lightning.callbacks import ModelCheckpoint
import torch
import omlet.utils as U
from typing import Optional
from . import omlet_logger as _log
class ExtendedCheckpoint(ModelCheckpoint):
def __init__(self,
save_dir,
filename_template: str = '{epoch}',
best_filename_template: str = 'best/{epoch}',
monitor_metric: str = 'val/loss',
monitor_metric_mode: str = 'auto',
save_top_k: int = 1,
save_epoch_interval: int = 1,
always_save_last: bool = True,
):
"""
Periodic checkpoint save every `period`, in addition to standard `best` save
`epoch` is always actual epoch index + 1 (number of epochs so far)
Verbose is controlled by global logging level.
set logging level to INFOV (i.e. INFO - 2)
"""
self.save_dir = os.path.expanduser(save_dir)
if not filename_template:
filename_template = '{epoch}'
self.ckpt_path = os.path.join(self.save_dir, filename_template)
if not best_filename_template:
best_filename_template = 'best-{epoch}'
self.best_ckpt_path = os.path.join(self.save_dir, best_filename_template)
os.makedirs(self.save_dir, exist_ok=True)
self.always_save_last = always_save_last
super().__init__(
filepath=self.ckpt_path, monitor=monitor_metric, verbose=False,
save_top_k=save_top_k, save_weights_only=False,
mode=monitor_metric_mode, period=save_epoch_interval, prefix=''
)
def format_checkpoint_name(self, epoch, metrics, ver=None, ckpt_type='best'):
"""Override
Example::
>>> tmpdir = os.path.dirname(__file__)
>>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}'))
>>> os.path.basename(ckpt.format_checkpoint_name(0, {}))
'epoch=0.ckpt'
>>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch:03d}'))
>>> os.path.basename(ckpt.format_checkpoint_name(5, {}))
'epoch=005.ckpt'
>>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}-{val_loss:.2f}'))
>>> os.path.basename(ckpt.format_checkpoint_name(2, dict(val_loss=0.123456)))
'epoch=2-val_loss=0.12.ckpt'
>>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{missing:d}'))
>>> os.path.basename(ckpt.format_checkpoint_name(0, {}))
'missing=0.ckpt'
"""
if ckpt_type == 'best':
fpath = self.best_ckpt_path
else:
fpath = self.ckpt_path
# check if user passed in keys to the string
groups = re.findall(r'(\{.*?)[:\}]', fpath)
assert len(groups) > 0, \
f'INTERNAL ERROR: file name template {fpath} should have at least one {{...}}'
metrics = {k.replace('/', '_'): v for k, v in metrics.items()}
metrics['epoch'] = epoch + 1
for tmp in groups:
name = tmp[1:]
name = name.replace('/', '_')
fpath = fpath.replace(tmp, name + '={' + name)
if name not in metrics:
metrics[name] = 0
fpath = fpath.format(**metrics)
if fpath.endswith('.ckpt'):
fpath = fpath[:-len('.ckpt')]
str_ver = f'_v{ver}' if ver is not None else ''
return fpath + str_ver + '.ckpt'
def check_monitor_top_k(self, current):
"""
Override warning from super class
"""
less_than_k_models = len(self.best_k_models) < self.save_top_k
if less_than_k_models:
return True
if not isinstance(current, torch.Tensor):
current = torch.tensor(current)
monitor_op = {
"min": torch.lt,
"max": torch.gt,
}[self.mode]
return monitor_op(current, self.best_k_models[self.kth_best_model])
@rank_zero_only
def on_validation_end(self, trainer, pl_module):
"""
Mostly copied from pytorch_lightning.callbacks.ModelCheckpoint
"""
# only run on main process
if trainer.proc_rank != 0:
return
metrics = trainer.callback_metrics
epoch = trainer.current_epoch
_best_path = self._save_best(metrics, epoch)
_periodic_path = self._save_periodic(metrics, epoch, _best_path)
self._save_last(_periodic_path, _best_path)
def _save_best(self, metrics, epoch):
"""
Returns:
filepath if the best is saved, None if nothing saved
"""
if self.save_top_k <= 0:
# no best saved
return None
filepath = self.format_checkpoint_name(epoch, metrics, ckpt_type='best')
version_cnt = 1
while os.path.isfile(filepath):
filepath = self.format_checkpoint_name(epoch, metrics, ver=version_cnt, ckpt_type='best')
# this epoch called before
version_cnt += 1
current = metrics.get(self.monitor)
if current is None:
rank_zero_warn(
f'Can save best model only with {self.monitor} available, skipping.', RuntimeWarning
)
elif self.check_monitor_top_k(current):
self._do_check_save(filepath, current, epoch)
if version_cnt > 1:
_log.warn(f'best ckpt filepath exists, saving to a different version: {filepath}')
_log.infov(
f'\nEpoch {epoch:05d}: {self.monitor} reached'
f' {current:0.5f} (best {self.best:0.5f}), saving model to'
f' {filepath} as top {self.save_top_k}')
return filepath
_log.infov(f'\nEpoch {epoch:03d}: {self.monitor} was not in top {self.save_top_k}')
return None
def _save_periodic(self, metrics, epoch, _best_save_path):
"""
Don't duplicate save if the same ckpt has already been saved as a best model
"""
if self.period <= 0 or not ((epoch + 1) % self.period) == 0:
# skip if not this period
return None
filepath = self.format_checkpoint_name(epoch, metrics, ckpt_type='periodic')
version_cnt = 1
while os.path.isfile(filepath):
filepath = self.format_checkpoint_name(epoch, metrics, ver=version_cnt, ckpt_type='periodic')
# this epoch called before
version_cnt += 1
if version_cnt > 1:
_log.warn(f'ckpt filepath exists, saving to a different version: {filepath}')
_log.infov(f'\nEpoch {epoch:03d}: periodic saving model to {filepath}')
if _best_save_path:
# the destination shouldn't exist tho
_log.debug2(f'\nEpoch {epoch:03d}: periodic saving copies from the best ckpt {_best_save_path}')
U.f_copy(_best_save_path, filepath, exists_ok=True)
else:
self._save_model(filepath)
return filepath
def _save_last(self, _periodic_save_path, _best_save_path):
if not self.always_save_last:
return
# always save a copy to the special `last.ckpt`
last_path = os.path.join(self.save_dir, 'last.ckpt')
if _periodic_save_path:
U.f_copy(_periodic_save_path, last_path, exists_ok=True)
elif _best_save_path:
U.f_copy(_best_save_path, last_path, exists_ok=True)
else:
# neither best or periodic saved
self._save_model(last_path)
| 508 | 6,859 | 23 |
ecb327fcb8ea5565310991e7e5a2418452557c97 | 4,199 | py | Python | ext5.py | ccvv804/FairyTaleExtractor | 797351308f72704410515b2ee37454d01e71bfc7 | [
"MIT"
] | null | null | null | ext5.py | ccvv804/FairyTaleExtractor | 797351308f72704410515b2ee37454d01e71bfc7 | [
"MIT"
] | null | null | null | ext5.py | ccvv804/FairyTaleExtractor | 797351308f72704410515b2ee37454d01e71bfc7 | [
"MIT"
] | null | null | null | import zlib
import os
import sys
import re
'''
동화나라 침공 계획
동화나라 추출기
5판 2021년 04월 30일
'''
print('동화나라 추출기 5판')
print('2021년 04월 30일')
입력받은파일경로 = sys.argv[1]
메인프로그램(입력받은파일경로)
'''
def search(dirname):
for (path, dir, files) in os.walk(dirname):
for filename in files:
ext = os.path.splitext(filename)[-1]
if ext == '.pkg':
메인프로그램(path+"/"+filename)
elif ext == '.PKG':
메인프로그램(path+"/"+filename)
search("./in")
''' | 32.804688 | 95 | 0.508931 | import zlib
import os
import sys
import re
'''
동화나라 침공 계획
동화나라 추출기
5판 2021년 04월 30일
'''
def 메인프로그램(파일경로):
오픈파일=open(파일경로,'rb')
읽은데이터=오픈파일.read()
오픈파일.close()
#현재폴더위치=os.getcwd()
신파일이름 = os.path.splitext(os.path.basename(파일경로))[0]
try:
os.mkdir(신파일이름)
except FileExistsError:
print('폴더가 이미 있습니다! 프로그램이 잘못 작동될 가능성이 있습니다!')
os.chdir(신파일이름)
이름위치=int.from_bytes(읽은데이터[20:24], byteorder='little')
고정이름위치=이름위치
이름헤더=읽은데이터[이름위치:이름위치+12]
인포이름헤더=이름헤더[0:4]
압축푼이름크기=이름헤더[4:8]
압축이름크기=이름헤더[8:12]
압축이름크기숫자=int.from_bytes(압축이름크기, byteorder='little')
이름카운터=0
브레이크=False
while True:
이름파일크기=읽은데이터[이름위치+12:이름위치+16]
if 이름파일크기 == b'':
break
이름파일크기숫자=int.from_bytes(이름파일크기, byteorder='little')
이름파일데이터=읽은데이터[이름위치+16:이름위치+16+이름파일크기숫자]
압축풀린이름데이터=zlib.decompress(이름파일데이터)
드디어이름이나왔다=re.sub(b'\x00', b'', 압축풀린이름데이터[:1024]).decode('EUC-KR', "replace")
#print(드디어이름이나왔다)
뉴파일주소=int.from_bytes(압축풀린이름데이터[1044:1048], byteorder='little')
파일헤더=읽은데이터[뉴파일주소:20+뉴파일주소]
인포파일헤더=파일헤더[0:4]
압축푼파일크기=파일헤더[4:8]
압축파일크기=파일헤더[8:12]
모르는뒤헤더1=파일헤더[12:16]
압축여부=파일헤더[16:20]
압축파일크기숫자=int.from_bytes(압축파일크기, byteorder='little')
파일데이터=읽은데이터[20+뉴파일주소:20+뉴파일주소+압축파일크기숫자]
if 압축여부 == b'\x00\x00\x00\x00':
압축풀린데이터=파일데이터
elif 압축여부 == b'\x01\x00\x00\x00':
압축풀린데이터=zlib.decompress(파일데이터)
elif 압축여부 == b'\x02\x00\x00\x00':
print(드디어이름이나왔다+'는 암호화된 데이터입니다')
압축풀린데이터=파일데이터
브레이크=True
elif 압축여부 == b'\x03\x00\x00\x00':
print(드디어이름이나왔다+'는 압축 암호화된 데이터입니다')
압축풀린데이터=zlib.decompress(파일데이터)
브레이크=True
else :
print(드디어이름이나왔다+'는 미확인 타입 데이터입니다')
압축풀린데이터=파일데이터
print(압축여부)
print(드디어이름이나왔다)
브레이크=True
기존압축풀린데이터=압축풀린데이터
반복크기보정=0
if 뉴파일주소+20+압축파일크기숫자 < 이름위치 and not 브레이크 :
while True:
#print(뉴파일주소+20+압축파일크기숫자+반복크기보정)
다음파일헤더=읽은데이터[뉴파일주소+20+압축파일크기숫자+반복크기보정:뉴파일주소+40+압축파일크기숫자+반복크기보정]
#print(다음파일헤더)
다음인포파일헤더=다음파일헤더[0:4]
다음압축푼파일크기=다음파일헤더[4:8]
다음압축파일크기=다음파일헤더[8:12]
다음압축파일크기숫자=int.from_bytes(다음압축파일크기, byteorder='little')
다음모르는뒤헤더1=다음파일헤더[12:16]
다음압축여부=다음파일헤더[16:20]
if 다음인포파일헤더 == b'\x00\x00\x00\x00' :
break
else:
뉴파일데이터=읽은데이터[뉴파일주소+40+압축파일크기숫자+반복크기보정:뉴파일주소+40+압축파일크기숫자+다음압축파일크기숫자+반복크기보정]
if 다음압축여부 == b'\x00\x00\x00\x00':
뉴압축풀린데이터=뉴파일데이터
elif 다음압축여부 == b'\x01\x00\x00\x00':
뉴압축풀린데이터=zlib.decompress(뉴파일데이터)
else :
print('해독할 수 없는 타입입니다?')
뉴압축풀린데이터=뉴파일데이터
print(압축여부)
기존압축풀린데이터=기존압축풀린데이터+뉴압축풀린데이터
반복크기보정=반복크기보정+다음압축파일크기숫자+20
#print(반복크기보정)
if not 브레이크:
파일없는경로=os.path.split(드디어이름이나왔다)[0]
if not 파일없는경로 == '':
os.makedirs(파일없는경로, exist_ok=True)
파일저장=open(드디어이름이나왔다,'bw')
파일저장.write(기존압축풀린데이터)
파일저장.close
else :
브레이크=False
'''
파일저장=open(str(이름카운터)+'.name','bw')
파일저장.write(압축풀린이름데이터)
파일저장.close
'''
이름위치=이름위치+4+이름파일크기숫자
이름카운터=이름카운터+1
#os.chdir(현재폴더위치)
print('동화나라 추출기 5판')
print('2021년 04월 30일')
입력받은파일경로 = sys.argv[1]
메인프로그램(입력받은파일경로)
'''
def search(dirname):
for (path, dir, files) in os.walk(dirname):
for filename in files:
ext = os.path.splitext(filename)[-1]
if ext == '.pkg':
메인프로그램(path+"/"+filename)
elif ext == '.PKG':
메인프로그램(path+"/"+filename)
search("./in")
''' | 5,821 | 0 | 22 |
8ab04722582af4caed7c1c2acccc0756654425bc | 8,636 | py | Python | src/_cffi_src/openssl/x509.py | jonathanslenders/cryptography | 2535e557872ac2f94355eb8a91a598f5cfc8afcf | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause"
] | 4,492 | 2015-01-02T23:02:52.000Z | 2022-03-31T12:59:57.000Z | src/_cffi_src/openssl/x509.py | jonathanslenders/cryptography | 2535e557872ac2f94355eb8a91a598f5cfc8afcf | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause"
] | 3,692 | 2015-01-01T03:16:56.000Z | 2022-03-31T19:20:25.000Z | src/_cffi_src/openssl/x509.py | jonathanslenders/cryptography | 2535e557872ac2f94355eb8a91a598f5cfc8afcf | [
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause"
] | 1,155 | 2015-01-09T00:48:05.000Z | 2022-03-31T23:46:43.000Z | # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
INCLUDES = """
#include <openssl/ssl.h>
/*
* This is part of a work-around for the difficulty cffi has in dealing with
* `STACK_OF(foo)` as the name of a type. We invent a new, simpler name that
* will be an alias for this type and use the alias throughout. This works
* together with another opaque typedef for the same name in the TYPES section.
* Note that the result is an opaque type.
*/
typedef STACK_OF(X509) Cryptography_STACK_OF_X509;
typedef STACK_OF(X509_CRL) Cryptography_STACK_OF_X509_CRL;
typedef STACK_OF(X509_REVOKED) Cryptography_STACK_OF_X509_REVOKED;
"""
TYPES = """
typedef ... Cryptography_STACK_OF_X509;
typedef ... Cryptography_STACK_OF_X509_CRL;
typedef ... Cryptography_STACK_OF_X509_REVOKED;
typedef struct {
ASN1_OBJECT *algorithm;
...;
} X509_ALGOR;
typedef ... X509_ATTRIBUTE;
typedef ... X509_EXTENSION;
typedef ... X509_EXTENSIONS;
typedef ... X509_REQ;
typedef ... X509_REVOKED;
typedef ... X509_CRL;
typedef ... X509;
typedef ... NETSCAPE_SPKI;
typedef ... PKCS8_PRIV_KEY_INFO;
typedef void (*sk_X509_EXTENSION_freefunc)(X509_EXTENSION *);
"""
FUNCTIONS = """
X509 *X509_new(void);
void X509_free(X509 *);
X509 *X509_dup(X509 *);
int X509_cmp(const X509 *, const X509 *);
int X509_up_ref(X509 *);
int X509_print_ex(BIO *, X509 *, unsigned long, unsigned long);
int X509_set_version(X509 *, long);
EVP_PKEY *X509_get_pubkey(X509 *);
int X509_set_pubkey(X509 *, EVP_PKEY *);
unsigned char *X509_alias_get0(X509 *, int *);
int X509_sign(X509 *, EVP_PKEY *, const EVP_MD *);
int X509_digest(const X509 *, const EVP_MD *, unsigned char *, unsigned int *);
ASN1_TIME *X509_gmtime_adj(ASN1_TIME *, long);
unsigned long X509_subject_name_hash(X509 *);
int X509_set_subject_name(X509 *, X509_NAME *);
int X509_set_issuer_name(X509 *, X509_NAME *);
int X509_add_ext(X509 *, X509_EXTENSION *, int);
X509_EXTENSION *X509_EXTENSION_dup(X509_EXTENSION *);
ASN1_OBJECT *X509_EXTENSION_get_object(X509_EXTENSION *);
void X509_EXTENSION_free(X509_EXTENSION *);
int X509_REQ_set_version(X509_REQ *, long);
X509_REQ *X509_REQ_new(void);
void X509_REQ_free(X509_REQ *);
int X509_REQ_set_pubkey(X509_REQ *, EVP_PKEY *);
int X509_REQ_set_subject_name(X509_REQ *, X509_NAME *);
int X509_REQ_sign(X509_REQ *, EVP_PKEY *, const EVP_MD *);
int X509_REQ_verify(X509_REQ *, EVP_PKEY *);
EVP_PKEY *X509_REQ_get_pubkey(X509_REQ *);
int X509_REQ_print_ex(BIO *, X509_REQ *, unsigned long, unsigned long);
int X509_REQ_add_extensions(X509_REQ *, X509_EXTENSIONS *);
X509_EXTENSIONS *X509_REQ_get_extensions(X509_REQ *);
int X509_REQ_add1_attr_by_OBJ(X509_REQ *, const ASN1_OBJECT *,
int, const unsigned char *, int);
int X509V3_EXT_print(BIO *, X509_EXTENSION *, unsigned long, int);
ASN1_OCTET_STRING *X509_EXTENSION_get_data(X509_EXTENSION *);
X509_REVOKED *X509_REVOKED_new(void);
void X509_REVOKED_free(X509_REVOKED *);
int X509_REVOKED_set_serialNumber(X509_REVOKED *, ASN1_INTEGER *);
int X509_REVOKED_add_ext(X509_REVOKED *, X509_EXTENSION*, int);
int X509_REVOKED_add1_ext_i2d(X509_REVOKED *, int, void *, int, unsigned long);
X509_EXTENSION *X509_REVOKED_delete_ext(X509_REVOKED *, int);
int X509_REVOKED_set_revocationDate(X509_REVOKED *, ASN1_TIME *);
X509_CRL *X509_CRL_new(void);
X509_CRL *X509_CRL_dup(X509_CRL *);
X509_CRL *d2i_X509_CRL_bio(BIO *, X509_CRL **);
int X509_CRL_add0_revoked(X509_CRL *, X509_REVOKED *);
int X509_CRL_add_ext(X509_CRL *, X509_EXTENSION *, int);
int X509_CRL_cmp(const X509_CRL *, const X509_CRL *);
int X509_CRL_print(BIO *, X509_CRL *);
int X509_CRL_set_issuer_name(X509_CRL *, X509_NAME *);
int X509_CRL_set_version(X509_CRL *, long);
int X509_CRL_sign(X509_CRL *, EVP_PKEY *, const EVP_MD *);
int X509_CRL_sort(X509_CRL *);
int X509_CRL_verify(X509_CRL *, EVP_PKEY *);
int i2d_X509_CRL_bio(BIO *, X509_CRL *);
void X509_CRL_free(X509_CRL *);
int NETSCAPE_SPKI_verify(NETSCAPE_SPKI *, EVP_PKEY *);
int NETSCAPE_SPKI_sign(NETSCAPE_SPKI *, EVP_PKEY *, const EVP_MD *);
char *NETSCAPE_SPKI_b64_encode(NETSCAPE_SPKI *);
NETSCAPE_SPKI *NETSCAPE_SPKI_b64_decode(const char *, int);
EVP_PKEY *NETSCAPE_SPKI_get_pubkey(NETSCAPE_SPKI *);
int NETSCAPE_SPKI_set_pubkey(NETSCAPE_SPKI *, EVP_PKEY *);
NETSCAPE_SPKI *NETSCAPE_SPKI_new(void);
void NETSCAPE_SPKI_free(NETSCAPE_SPKI *);
/* ASN1 serialization */
int i2d_X509_bio(BIO *, X509 *);
X509 *d2i_X509_bio(BIO *, X509 **);
int i2d_X509_REQ_bio(BIO *, X509_REQ *);
X509_REQ *d2i_X509_REQ_bio(BIO *, X509_REQ **);
int i2d_PrivateKey_bio(BIO *, EVP_PKEY *);
EVP_PKEY *d2i_PrivateKey_bio(BIO *, EVP_PKEY **);
int i2d_PUBKEY_bio(BIO *, EVP_PKEY *);
EVP_PKEY *d2i_PUBKEY_bio(BIO *, EVP_PKEY **);
ASN1_INTEGER *X509_get_serialNumber(X509 *);
int X509_set_serialNumber(X509 *, ASN1_INTEGER *);
const char *X509_verify_cert_error_string(long);
const char *X509_get_default_cert_dir(void);
const char *X509_get_default_cert_file(void);
const char *X509_get_default_cert_dir_env(void);
const char *X509_get_default_cert_file_env(void);
int i2d_RSAPrivateKey_bio(BIO *, RSA *);
RSA *d2i_RSAPublicKey_bio(BIO *, RSA **);
int i2d_RSAPublicKey_bio(BIO *, RSA *);
int i2d_DSAPrivateKey_bio(BIO *, DSA *);
/* These became const X509 in 1.1.0 */
int X509_get_ext_count(X509 *);
X509_EXTENSION *X509_get_ext(X509 *, int);
X509_NAME *X509_get_subject_name(X509 *);
X509_NAME *X509_get_issuer_name(X509 *);
/* This became const ASN1_OBJECT * in 1.1.0 */
X509_EXTENSION *X509_EXTENSION_create_by_OBJ(X509_EXTENSION **,
ASN1_OBJECT *, int,
ASN1_OCTET_STRING *);
/* This became const X509_EXTENSION * in 1.1.0 */
int X509_EXTENSION_get_critical(X509_EXTENSION *);
/* This became const X509_REVOKED * in 1.1.0 */
int X509_REVOKED_get_ext_count(X509_REVOKED *);
X509_EXTENSION *X509_REVOKED_get_ext(X509_REVOKED *, int);
X509_REVOKED *X509_REVOKED_dup(X509_REVOKED *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
X509_REVOKED *Cryptography_X509_REVOKED_dup(X509_REVOKED *);
const X509_ALGOR *X509_get0_tbs_sigalg(const X509 *);
long X509_get_version(X509 *);
ASN1_TIME *X509_getm_notBefore(const X509 *);
ASN1_TIME *X509_getm_notAfter(const X509 *);
long X509_REQ_get_version(X509_REQ *);
X509_NAME *X509_REQ_get_subject_name(X509_REQ *);
Cryptography_STACK_OF_X509 *sk_X509_new_null(void);
void sk_X509_free(Cryptography_STACK_OF_X509 *);
int sk_X509_num(Cryptography_STACK_OF_X509 *);
int sk_X509_push(Cryptography_STACK_OF_X509 *, X509 *);
X509 *sk_X509_value(Cryptography_STACK_OF_X509 *, int);
X509_EXTENSIONS *sk_X509_EXTENSION_new_null(void);
int sk_X509_EXTENSION_num(X509_EXTENSIONS *);
X509_EXTENSION *sk_X509_EXTENSION_value(X509_EXTENSIONS *, int);
int sk_X509_EXTENSION_push(X509_EXTENSIONS *, X509_EXTENSION *);
int sk_X509_EXTENSION_insert(X509_EXTENSIONS *, X509_EXTENSION *, int);
X509_EXTENSION *sk_X509_EXTENSION_delete(X509_EXTENSIONS *, int);
void sk_X509_EXTENSION_free(X509_EXTENSIONS *);
void sk_X509_EXTENSION_pop_free(X509_EXTENSIONS *, sk_X509_EXTENSION_freefunc);
int sk_X509_REVOKED_num(Cryptography_STACK_OF_X509_REVOKED *);
X509_REVOKED *sk_X509_REVOKED_value(Cryptography_STACK_OF_X509_REVOKED *, int);
long X509_CRL_get_version(X509_CRL *);
ASN1_TIME *X509_CRL_get_lastUpdate(X509_CRL *);
ASN1_TIME *X509_CRL_get_nextUpdate(X509_CRL *);
const ASN1_TIME *X509_CRL_get0_lastUpdate(const X509_CRL *);
const ASN1_TIME *X509_CRL_get0_nextUpdate(const X509_CRL *);
X509_NAME *X509_CRL_get_issuer(X509_CRL *);
Cryptography_STACK_OF_X509_REVOKED *X509_CRL_get_REVOKED(X509_CRL *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
int X509_CRL_set_lastUpdate(X509_CRL *, ASN1_TIME *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
int X509_CRL_set_nextUpdate(X509_CRL *, ASN1_TIME *);
int X509_CRL_set1_lastUpdate(X509_CRL *, const ASN1_TIME *);
int X509_CRL_set1_nextUpdate(X509_CRL *, const ASN1_TIME *);
EC_KEY *d2i_EC_PUBKEY_bio(BIO *, EC_KEY **);
int i2d_EC_PUBKEY_bio(BIO *, EC_KEY *);
EC_KEY *d2i_ECPrivateKey_bio(BIO *, EC_KEY **);
int i2d_ECPrivateKey_bio(BIO *, EC_KEY *);
/* these functions were added in 1.1.0 */
const ASN1_INTEGER *X509_REVOKED_get0_serialNumber(const X509_REVOKED *);
const ASN1_TIME *X509_REVOKED_get0_revocationDate(const X509_REVOKED *);
"""
CUSTOMIZATIONS = """
/* Being kept around for pyOpenSSL */
X509_REVOKED *Cryptography_X509_REVOKED_dup(X509_REVOKED *rev) {
return X509_REVOKED_dup(rev);
}
"""
| 35.834025 | 79 | 0.769222 | # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
INCLUDES = """
#include <openssl/ssl.h>
/*
* This is part of a work-around for the difficulty cffi has in dealing with
* `STACK_OF(foo)` as the name of a type. We invent a new, simpler name that
* will be an alias for this type and use the alias throughout. This works
* together with another opaque typedef for the same name in the TYPES section.
* Note that the result is an opaque type.
*/
typedef STACK_OF(X509) Cryptography_STACK_OF_X509;
typedef STACK_OF(X509_CRL) Cryptography_STACK_OF_X509_CRL;
typedef STACK_OF(X509_REVOKED) Cryptography_STACK_OF_X509_REVOKED;
"""
TYPES = """
typedef ... Cryptography_STACK_OF_X509;
typedef ... Cryptography_STACK_OF_X509_CRL;
typedef ... Cryptography_STACK_OF_X509_REVOKED;
typedef struct {
ASN1_OBJECT *algorithm;
...;
} X509_ALGOR;
typedef ... X509_ATTRIBUTE;
typedef ... X509_EXTENSION;
typedef ... X509_EXTENSIONS;
typedef ... X509_REQ;
typedef ... X509_REVOKED;
typedef ... X509_CRL;
typedef ... X509;
typedef ... NETSCAPE_SPKI;
typedef ... PKCS8_PRIV_KEY_INFO;
typedef void (*sk_X509_EXTENSION_freefunc)(X509_EXTENSION *);
"""
FUNCTIONS = """
X509 *X509_new(void);
void X509_free(X509 *);
X509 *X509_dup(X509 *);
int X509_cmp(const X509 *, const X509 *);
int X509_up_ref(X509 *);
int X509_print_ex(BIO *, X509 *, unsigned long, unsigned long);
int X509_set_version(X509 *, long);
EVP_PKEY *X509_get_pubkey(X509 *);
int X509_set_pubkey(X509 *, EVP_PKEY *);
unsigned char *X509_alias_get0(X509 *, int *);
int X509_sign(X509 *, EVP_PKEY *, const EVP_MD *);
int X509_digest(const X509 *, const EVP_MD *, unsigned char *, unsigned int *);
ASN1_TIME *X509_gmtime_adj(ASN1_TIME *, long);
unsigned long X509_subject_name_hash(X509 *);
int X509_set_subject_name(X509 *, X509_NAME *);
int X509_set_issuer_name(X509 *, X509_NAME *);
int X509_add_ext(X509 *, X509_EXTENSION *, int);
X509_EXTENSION *X509_EXTENSION_dup(X509_EXTENSION *);
ASN1_OBJECT *X509_EXTENSION_get_object(X509_EXTENSION *);
void X509_EXTENSION_free(X509_EXTENSION *);
int X509_REQ_set_version(X509_REQ *, long);
X509_REQ *X509_REQ_new(void);
void X509_REQ_free(X509_REQ *);
int X509_REQ_set_pubkey(X509_REQ *, EVP_PKEY *);
int X509_REQ_set_subject_name(X509_REQ *, X509_NAME *);
int X509_REQ_sign(X509_REQ *, EVP_PKEY *, const EVP_MD *);
int X509_REQ_verify(X509_REQ *, EVP_PKEY *);
EVP_PKEY *X509_REQ_get_pubkey(X509_REQ *);
int X509_REQ_print_ex(BIO *, X509_REQ *, unsigned long, unsigned long);
int X509_REQ_add_extensions(X509_REQ *, X509_EXTENSIONS *);
X509_EXTENSIONS *X509_REQ_get_extensions(X509_REQ *);
int X509_REQ_add1_attr_by_OBJ(X509_REQ *, const ASN1_OBJECT *,
int, const unsigned char *, int);
int X509V3_EXT_print(BIO *, X509_EXTENSION *, unsigned long, int);
ASN1_OCTET_STRING *X509_EXTENSION_get_data(X509_EXTENSION *);
X509_REVOKED *X509_REVOKED_new(void);
void X509_REVOKED_free(X509_REVOKED *);
int X509_REVOKED_set_serialNumber(X509_REVOKED *, ASN1_INTEGER *);
int X509_REVOKED_add_ext(X509_REVOKED *, X509_EXTENSION*, int);
int X509_REVOKED_add1_ext_i2d(X509_REVOKED *, int, void *, int, unsigned long);
X509_EXTENSION *X509_REVOKED_delete_ext(X509_REVOKED *, int);
int X509_REVOKED_set_revocationDate(X509_REVOKED *, ASN1_TIME *);
X509_CRL *X509_CRL_new(void);
X509_CRL *X509_CRL_dup(X509_CRL *);
X509_CRL *d2i_X509_CRL_bio(BIO *, X509_CRL **);
int X509_CRL_add0_revoked(X509_CRL *, X509_REVOKED *);
int X509_CRL_add_ext(X509_CRL *, X509_EXTENSION *, int);
int X509_CRL_cmp(const X509_CRL *, const X509_CRL *);
int X509_CRL_print(BIO *, X509_CRL *);
int X509_CRL_set_issuer_name(X509_CRL *, X509_NAME *);
int X509_CRL_set_version(X509_CRL *, long);
int X509_CRL_sign(X509_CRL *, EVP_PKEY *, const EVP_MD *);
int X509_CRL_sort(X509_CRL *);
int X509_CRL_verify(X509_CRL *, EVP_PKEY *);
int i2d_X509_CRL_bio(BIO *, X509_CRL *);
void X509_CRL_free(X509_CRL *);
int NETSCAPE_SPKI_verify(NETSCAPE_SPKI *, EVP_PKEY *);
int NETSCAPE_SPKI_sign(NETSCAPE_SPKI *, EVP_PKEY *, const EVP_MD *);
char *NETSCAPE_SPKI_b64_encode(NETSCAPE_SPKI *);
NETSCAPE_SPKI *NETSCAPE_SPKI_b64_decode(const char *, int);
EVP_PKEY *NETSCAPE_SPKI_get_pubkey(NETSCAPE_SPKI *);
int NETSCAPE_SPKI_set_pubkey(NETSCAPE_SPKI *, EVP_PKEY *);
NETSCAPE_SPKI *NETSCAPE_SPKI_new(void);
void NETSCAPE_SPKI_free(NETSCAPE_SPKI *);
/* ASN1 serialization */
int i2d_X509_bio(BIO *, X509 *);
X509 *d2i_X509_bio(BIO *, X509 **);
int i2d_X509_REQ_bio(BIO *, X509_REQ *);
X509_REQ *d2i_X509_REQ_bio(BIO *, X509_REQ **);
int i2d_PrivateKey_bio(BIO *, EVP_PKEY *);
EVP_PKEY *d2i_PrivateKey_bio(BIO *, EVP_PKEY **);
int i2d_PUBKEY_bio(BIO *, EVP_PKEY *);
EVP_PKEY *d2i_PUBKEY_bio(BIO *, EVP_PKEY **);
ASN1_INTEGER *X509_get_serialNumber(X509 *);
int X509_set_serialNumber(X509 *, ASN1_INTEGER *);
const char *X509_verify_cert_error_string(long);
const char *X509_get_default_cert_dir(void);
const char *X509_get_default_cert_file(void);
const char *X509_get_default_cert_dir_env(void);
const char *X509_get_default_cert_file_env(void);
int i2d_RSAPrivateKey_bio(BIO *, RSA *);
RSA *d2i_RSAPublicKey_bio(BIO *, RSA **);
int i2d_RSAPublicKey_bio(BIO *, RSA *);
int i2d_DSAPrivateKey_bio(BIO *, DSA *);
/* These became const X509 in 1.1.0 */
int X509_get_ext_count(X509 *);
X509_EXTENSION *X509_get_ext(X509 *, int);
X509_NAME *X509_get_subject_name(X509 *);
X509_NAME *X509_get_issuer_name(X509 *);
/* This became const ASN1_OBJECT * in 1.1.0 */
X509_EXTENSION *X509_EXTENSION_create_by_OBJ(X509_EXTENSION **,
ASN1_OBJECT *, int,
ASN1_OCTET_STRING *);
/* This became const X509_EXTENSION * in 1.1.0 */
int X509_EXTENSION_get_critical(X509_EXTENSION *);
/* This became const X509_REVOKED * in 1.1.0 */
int X509_REVOKED_get_ext_count(X509_REVOKED *);
X509_EXTENSION *X509_REVOKED_get_ext(X509_REVOKED *, int);
X509_REVOKED *X509_REVOKED_dup(X509_REVOKED *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
X509_REVOKED *Cryptography_X509_REVOKED_dup(X509_REVOKED *);
const X509_ALGOR *X509_get0_tbs_sigalg(const X509 *);
long X509_get_version(X509 *);
ASN1_TIME *X509_getm_notBefore(const X509 *);
ASN1_TIME *X509_getm_notAfter(const X509 *);
long X509_REQ_get_version(X509_REQ *);
X509_NAME *X509_REQ_get_subject_name(X509_REQ *);
Cryptography_STACK_OF_X509 *sk_X509_new_null(void);
void sk_X509_free(Cryptography_STACK_OF_X509 *);
int sk_X509_num(Cryptography_STACK_OF_X509 *);
int sk_X509_push(Cryptography_STACK_OF_X509 *, X509 *);
X509 *sk_X509_value(Cryptography_STACK_OF_X509 *, int);
X509_EXTENSIONS *sk_X509_EXTENSION_new_null(void);
int sk_X509_EXTENSION_num(X509_EXTENSIONS *);
X509_EXTENSION *sk_X509_EXTENSION_value(X509_EXTENSIONS *, int);
int sk_X509_EXTENSION_push(X509_EXTENSIONS *, X509_EXTENSION *);
int sk_X509_EXTENSION_insert(X509_EXTENSIONS *, X509_EXTENSION *, int);
X509_EXTENSION *sk_X509_EXTENSION_delete(X509_EXTENSIONS *, int);
void sk_X509_EXTENSION_free(X509_EXTENSIONS *);
void sk_X509_EXTENSION_pop_free(X509_EXTENSIONS *, sk_X509_EXTENSION_freefunc);
int sk_X509_REVOKED_num(Cryptography_STACK_OF_X509_REVOKED *);
X509_REVOKED *sk_X509_REVOKED_value(Cryptography_STACK_OF_X509_REVOKED *, int);
long X509_CRL_get_version(X509_CRL *);
ASN1_TIME *X509_CRL_get_lastUpdate(X509_CRL *);
ASN1_TIME *X509_CRL_get_nextUpdate(X509_CRL *);
const ASN1_TIME *X509_CRL_get0_lastUpdate(const X509_CRL *);
const ASN1_TIME *X509_CRL_get0_nextUpdate(const X509_CRL *);
X509_NAME *X509_CRL_get_issuer(X509_CRL *);
Cryptography_STACK_OF_X509_REVOKED *X509_CRL_get_REVOKED(X509_CRL *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
int X509_CRL_set_lastUpdate(X509_CRL *, ASN1_TIME *);
/* This function is no longer used by pyOpenSSL >= 21.1 */
int X509_CRL_set_nextUpdate(X509_CRL *, ASN1_TIME *);
int X509_CRL_set1_lastUpdate(X509_CRL *, const ASN1_TIME *);
int X509_CRL_set1_nextUpdate(X509_CRL *, const ASN1_TIME *);
EC_KEY *d2i_EC_PUBKEY_bio(BIO *, EC_KEY **);
int i2d_EC_PUBKEY_bio(BIO *, EC_KEY *);
EC_KEY *d2i_ECPrivateKey_bio(BIO *, EC_KEY **);
int i2d_ECPrivateKey_bio(BIO *, EC_KEY *);
/* these functions were added in 1.1.0 */
const ASN1_INTEGER *X509_REVOKED_get0_serialNumber(const X509_REVOKED *);
const ASN1_TIME *X509_REVOKED_get0_revocationDate(const X509_REVOKED *);
"""
CUSTOMIZATIONS = """
/* Being kept around for pyOpenSSL */
X509_REVOKED *Cryptography_X509_REVOKED_dup(X509_REVOKED *rev) {
return X509_REVOKED_dup(rev);
}
"""
| 0 | 0 | 0 |
25f5b4b5e2f8574183c401cac6b187d76f453379 | 2,689 | py | Python | scripts/run_ann.py | lconaboy/seren3 | 5a2ec80adf0d69664d2ee874f5ba12cc02d6c337 | [
"CNRI-Python"
] | 1 | 2017-09-21T14:58:23.000Z | 2017-09-21T14:58:23.000Z | scripts/run_ann.py | lconaboy/seren3 | 5a2ec80adf0d69664d2ee874f5ba12cc02d6c337 | [
"CNRI-Python"
] | 1 | 2020-09-09T08:52:43.000Z | 2020-09-09T08:52:43.000Z | scripts/run_ann.py | lconaboy/seren3 | 5a2ec80adf0d69664d2ee874f5ba12cc02d6c337 | [
"CNRI-Python"
] | 1 | 2019-01-21T10:57:41.000Z | 2019-01-21T10:57:41.000Z |
if __name__ == "__main__":
import sys, os
ioutputs = [42, 48, 60, 70, 80, 90, 100, 106]
NN = int(sys.argv[1])
weights_fname = "/lustre/scratch/astro/ds381/simulations/baryfrac/bc03_fesc2_nohm/neural-net2/NN%i.weights" % NN
# weights_fname = "/lustre/scratch/astro/ds381/simulations/baryfrac/bc03_fesc2_nohm/neural-net2/106_90_60_NN40_niter100_fb_scaled_ftidal_scaled_logall.weights"
# ioutputs = [int(i) for i in sys.argv[1:-2]]
# weights_fname = sys.argv[-2]
# NN = int(sys.argv[-1])
# paths = ["bc03_fesc1.5_nohm/", "bc03_fesc2_nohm/", "bc03_fesc5_nohm/"]
# paths = ["bc03_fesc2_nohm/", "bc03_fesc5_nohm/"]
paths = ["bc03_fesc2_nohm/"]
lustre_path = "/lustre/scratch/astro/ds381/simulations/baryfrac/"
full_paths = ["%s/%s/neural-net2/" % (lustre_path, p) for p in paths]
print "NN = ", NN
print "Using weights file: ", weights_fname
print ioutputs
for fp in full_paths:
os.chdir(fp)
print "**************************************************"
print os.getcwd()
print "**************************************************"
main(ioutputs, weights_fname, NN)
print "Done" | 38.971014 | 163 | 0.645593 | def _get_predict_fname(out_dir, ioutput, weight, pdf_sampling):
if pdf_sampling:
return "%s/fb_%05i_%s_ftidal_pdf_sampling.predict" % (out_dir, ioutput, weight)
return "%s/fb_%05i_%s.predict" % (out_dir, ioutput, weight)
def _get_results_fname(out_dir, ioutput, weight, NN, pdf_sampling):
if pdf_sampling:
return "%s/fb_%05i_NN%i_%s_ftidal_pdf_sampling.results" % (out_dir, ioutput, NN, weight)
return "%s/fb_%05i_NN%i_%s.results" % (out_dir, ioutput, NN, weight)
def main(ioutputs, weights_fname, NN):
import os
from seren3.utils import which
_BIN = which("neural-net")
_CWD = os.getcwd()
_WEIGHT = "mw"
def _run(weights_fname, predict_fname, results_fname):
exe = "%s -l %s -p %s -o %s" % (_BIN, weights_fname, predict_fname, results_fname)
# print exe
os.system(exe)
for ioutput in ioutputs:
dir_name = "./%i_final/" % (ioutput)
# Run the standard and tidal force PDF sampled models
predict_fname = _get_predict_fname(dir_name, ioutput, _WEIGHT, False)
predict_pdf_sampled_fname = _get_predict_fname(dir_name, ioutput, _WEIGHT, True)
results_fname = _get_results_fname(dir_name, ioutput, _WEIGHT, NN, False)
results_pdf_sampled_fname = _get_results_fname(dir_name, ioutput, _WEIGHT, NN, True)
for pf, rf in zip([predict_fname, predict_pdf_sampled_fname], [results_fname, results_pdf_sampled_fname]):
_run(weights_fname, pf, rf)
if __name__ == "__main__":
import sys, os
ioutputs = [42, 48, 60, 70, 80, 90, 100, 106]
NN = int(sys.argv[1])
weights_fname = "/lustre/scratch/astro/ds381/simulations/baryfrac/bc03_fesc2_nohm/neural-net2/NN%i.weights" % NN
# weights_fname = "/lustre/scratch/astro/ds381/simulations/baryfrac/bc03_fesc2_nohm/neural-net2/106_90_60_NN40_niter100_fb_scaled_ftidal_scaled_logall.weights"
# ioutputs = [int(i) for i in sys.argv[1:-2]]
# weights_fname = sys.argv[-2]
# NN = int(sys.argv[-1])
# paths = ["bc03_fesc1.5_nohm/", "bc03_fesc2_nohm/", "bc03_fesc5_nohm/"]
# paths = ["bc03_fesc2_nohm/", "bc03_fesc5_nohm/"]
paths = ["bc03_fesc2_nohm/"]
lustre_path = "/lustre/scratch/astro/ds381/simulations/baryfrac/"
full_paths = ["%s/%s/neural-net2/" % (lustre_path, p) for p in paths]
print "NN = ", NN
print "Using weights file: ", weights_fname
print ioutputs
for fp in full_paths:
os.chdir(fp)
print "**************************************************"
print os.getcwd()
print "**************************************************"
main(ioutputs, weights_fname, NN)
print "Done" | 1,427 | 0 | 68 |
1b618726cdccd77b79583613b2080b6d3103becb | 381 | py | Python | src/definitions.py | FriendsOfGalaxy/galaxy-integration-epic | 370bd3be60c94482a143b51ed3b22f9781625cdb | [
"MIT"
] | 34 | 2019-06-27T07:04:00.000Z | 2022-03-08T13:50:14.000Z | src/definitions.py | FriendsOfGalaxy/galaxy-integration-epic | 370bd3be60c94482a143b51ed3b22f9781625cdb | [
"MIT"
] | 11 | 2019-09-07T12:42:41.000Z | 2020-07-24T12:24:27.000Z | src/definitions.py | FriendsOfGalaxy/galaxy-integration-epic | 370bd3be60c94482a143b51ed3b22f9781625cdb | [
"MIT"
] | 4 | 2020-02-09T00:10:31.000Z | 2022-02-02T14:14:04.000Z | from collections import namedtuple
import dataclasses
Asset = namedtuple("Asset", ["namespace", "app_name", "catalog_id"])
CatalogItem = namedtuple("CatalogItem", ["id", "title", "categories"])
@dataclasses.dataclass
@dataclasses.dataclass
| 20.052632 | 70 | 0.716535 | from collections import namedtuple
import dataclasses
Asset = namedtuple("Asset", ["namespace", "app_name", "catalog_id"])
CatalogItem = namedtuple("CatalogItem", ["id", "title", "categories"])
@dataclasses.dataclass
class GameInfo:
namespace: str
app_name: str
title: str
@dataclasses.dataclass
class EpicDlc:
parent_id: str
dlc_id: str
dlc_title: str
| 0 | 93 | 44 |
7096a10f3edad1c6a9a070b71183ae416eb95c7f | 1,594 | py | Python | src/ny_map/time_series_trend.py | graveszhang/2019-nCoV-Diffusion-Visualization | dd452994b2c43cf723950f8538cc913968efb836 | [
"MIT"
] | 2 | 2020-01-27T09:23:48.000Z | 2020-02-07T18:24:30.000Z | src/ny_map/time_series_trend.py | graveszhang/Beat-COVID-19-using-SIR-and-Graph-Neural-Networks | dd452994b2c43cf723950f8538cc913968efb836 | [
"MIT"
] | null | null | null | src/ny_map/time_series_trend.py | graveszhang/Beat-COVID-19-using-SIR-and-Graph-Neural-Networks | dd452994b2c43cf723950f8538cc913968efb836 | [
"MIT"
] | 3 | 2020-01-27T09:02:50.000Z | 2020-01-29T14:04:01.000Z | import csv
import matplotlib.pyplot as plt
import datetime
import numpy as np
plt.style.use('ggplot')
age_data = './coronavirus-data-master/archive/case-hosp-death.csv'
txt = []
with open(age_data, 'r', newline='') as file:
reader = csv.reader(file)
for line in reader:
txt.append(line)
header = txt[0]
dates = []
for i in txt[1::]:
dates.append(i[0])
fig,axs = plt.subplots(2,1,figsize=(12,9))
cii = 0
for jj in range(1,len(txt[0])):
vals = []
for ii in range(0,len(txt[1:])):
val = (txt[1:])[ii][jj]
if val=='':
val = np.nan
else:
val = float(val)
vals.append(val)
axs[0].scatter(dates,vals,label=txt[0][jj].replace('_',' '),color=plt.cm.tab10(cii),linewidth=3.0)
axs[1].plot(dates,np.nancumsum(vals),label=(txt[0][jj]).replace('_',' ').replace('NEW','TOTAL'),
linewidth=6.0,color=plt.cm.tab10(cii),linestyle=':')
cii+=1
axs[0].legend(title='New Case Counts')
axs[0].tick_params(axis='x', rotation=15)
axs[0].set_ylabel('New Count',fontsize=16)
axs[0].set_yscale('log')
axs[0].tick_params('both',labelsize=16)
axs[0].set_xticklabels([])
axs[1].legend(title='Total Case Counts')
axs[1].set_yscale('log')
axs[1].tick_params(axis='x', rotation=15)
axs[1].set_xlabel('Date [Year-Month-Day]',fontsize=18,labelpad=10)
axs[1].set_ylabel('Total Count',fontsize=18)
axs[1].tick_params('both',labelsize=16)
fig.subplots_adjust(hspace=0.1)
fig.savefig(header[0]+'_in_NYC_COVID19.png',dpi=300,facecolor='#FCFCFC')
plt.show()
| 30.075472 | 116 | 0.620452 | import csv
import matplotlib.pyplot as plt
import datetime
import numpy as np
plt.style.use('ggplot')
age_data = './coronavirus-data-master/archive/case-hosp-death.csv'
txt = []
with open(age_data, 'r', newline='') as file:
reader = csv.reader(file)
for line in reader:
txt.append(line)
header = txt[0]
dates = []
for i in txt[1::]:
dates.append(i[0])
fig,axs = plt.subplots(2,1,figsize=(12,9))
cii = 0
for jj in range(1,len(txt[0])):
vals = []
for ii in range(0,len(txt[1:])):
val = (txt[1:])[ii][jj]
if val=='':
val = np.nan
else:
val = float(val)
vals.append(val)
axs[0].scatter(dates,vals,label=txt[0][jj].replace('_',' '),color=plt.cm.tab10(cii),linewidth=3.0)
axs[1].plot(dates,np.nancumsum(vals),label=(txt[0][jj]).replace('_',' ').replace('NEW','TOTAL'),
linewidth=6.0,color=plt.cm.tab10(cii),linestyle=':')
cii+=1
axs[0].legend(title='New Case Counts')
axs[0].tick_params(axis='x', rotation=15)
axs[0].set_ylabel('New Count',fontsize=16)
axs[0].set_yscale('log')
axs[0].tick_params('both',labelsize=16)
axs[0].set_xticklabels([])
axs[1].legend(title='Total Case Counts')
axs[1].set_yscale('log')
axs[1].tick_params(axis='x', rotation=15)
axs[1].set_xlabel('Date [Year-Month-Day]',fontsize=18,labelpad=10)
axs[1].set_ylabel('Total Count',fontsize=18)
axs[1].tick_params('both',labelsize=16)
fig.subplots_adjust(hspace=0.1)
fig.savefig(header[0]+'_in_NYC_COVID19.png',dpi=300,facecolor='#FCFCFC')
plt.show()
| 0 | 0 | 0 |
9b1b53ca7b1b6ed85ae9411fccc117c1e84b36bc | 1,804 | py | Python | graphgallery/nn/models/tensorflow/edgeconv.py | kisekizzz/GraphGallery | fd4a1f474c244f774397460ae95935638ef48f5b | [
"MIT"
] | null | null | null | graphgallery/nn/models/tensorflow/edgeconv.py | kisekizzz/GraphGallery | fd4a1f474c244f774397460ae95935638ef48f5b | [
"MIT"
] | null | null | null | graphgallery/nn/models/tensorflow/edgeconv.py | kisekizzz/GraphGallery | fd4a1f474c244f774397460ae95935638ef48f5b | [
"MIT"
] | null | null | null | from tensorflow.keras import Input
from tensorflow.keras.layers import Dropout, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import regularizers
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from graphgallery.nn.layers.tensorflow import GraphEdgeConvolution, Gather
from graphgallery import floatx, intx
from graphgallery.nn.models import TFKeras
| 41 | 116 | 0.631929 | from tensorflow.keras import Input
from tensorflow.keras.layers import Dropout, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import regularizers
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from graphgallery.nn.layers.tensorflow import GraphEdgeConvolution, Gather
from graphgallery import floatx, intx
from graphgallery.nn.models import TFKeras
class EdgeGCN(TFKeras):
def __init__(self, in_channels, out_channels,
hiddens=[16], activations=['relu'], dropout=0.5,
weight_decay=5e-4, lr=0.01, use_bias=False):
_intx = intx()
_floatx = floatx()
x = Input(batch_shape=[None, in_channels],
dtype=_floatx, name='node_attr')
edge_index = Input(batch_shape=[None, 2], dtype=_intx,
name='edge_index')
edge_weight = Input(batch_shape=[None], dtype=_floatx,
name='edge_weight')
index = Input(batch_shape=[None],
dtype=_intx, name='node_index')
h = x
for hidden, activation in zip(hiddens, activations):
h = GraphEdgeConvolution(hidden, use_bias=use_bias,
activation=activation,
kernel_regularizer=regularizers.l2(weight_decay))([h, edge_index, edge_weight])
h = Dropout(rate=dropout)(h)
h = GraphEdgeConvolution(out_channels, use_bias=use_bias)(
[h, edge_index, edge_weight])
output = Gather()([h, index])
super().__init__(inputs=[x, edge_index, edge_weight, index], outputs=output)
self.compile(loss=SparseCategoricalCrossentropy(from_logits=True),
optimizer=Adam(lr=lr), metrics=['accuracy'])
| 1,355 | 2 | 50 |
ada5783324023798ea2f9aaac554503f82d4da36 | 373 | py | Python | code/test_dec.py | briansune/python_timer_destinator | 01beffa46f36db3e5aa9ff21c474b75aba6635ff | [
"MIT"
] | 1 | 2022-03-30T20:37:30.000Z | 2022-03-30T20:37:30.000Z | code/test_dec.py | briansune/python_timer_destinator | 01beffa46f36db3e5aa9ff21c474b75aba6635ff | [
"MIT"
] | null | null | null | code/test_dec.py | briansune/python_timer_destinator | 01beffa46f36db3e5aa9ff21c474b75aba6635ff | [
"MIT"
] | null | null | null | import logging
import test_wrap
@test_wrap.timeCountWrapper
@test_wrap.setupConfigsWrapper
@test_wrap.loopAntennaWrapper
@test_wrap.loopPowerWrapper
@test_wrap.loopChannelWrapper
logging.basicConfig(level=logging.INFO)
logging.info('Start~')
res = method_run(logging)
print(len(res))
print(res)
| 18.65 | 42 | 0.788204 | import logging
import test_wrap
@test_wrap.timeCountWrapper
@test_wrap.setupConfigsWrapper
@test_wrap.loopAntennaWrapper
@test_wrap.loopPowerWrapper
@test_wrap.loopChannelWrapper
def method_run(olog):
# olog.info('Inputs: {}'.format(None))
pass
logging.basicConfig(level=logging.INFO)
logging.info('Start~')
res = method_run(logging)
print(len(res))
print(res)
| 52 | 0 | 22 |
36632b6d1a76fdbaf2cab5da191fcf5cf6e8103c | 655 | py | Python | unpredictableArray.py | DhruvSalot/MyPy | 02b215b27cc1c3c865aa98d822ccc3a9842f030f | [
"MIT"
] | 1 | 2021-03-20T12:46:35.000Z | 2021-03-20T12:46:35.000Z | unpredictableArray.py | DhruvSalot/MyPy | 02b215b27cc1c3c865aa98d822ccc3a9842f030f | [
"MIT"
] | null | null | null | unpredictableArray.py | DhruvSalot/MyPy | 02b215b27cc1c3c865aa98d822ccc3a9842f030f | [
"MIT"
] | null | null | null | # ZUBSPOIL
# https://www.codechef.com/problems/ZUBSPOIL
testCase = input()
line2 = [int(i) for i in input().split()]
array = [int(i) for i in input().split()]
testList = []
for noOfTest in range(line2[1]):
testList.append([int(i) for i in input().split()])
print ("Case: " + testCase)
for k in range(len(testList)):
print(valueOfArray(repList(array, testList[k])))
array = repList(array, testList[k]) | 24.259259 | 67 | 0.671756 | # ZUBSPOIL
# https://www.codechef.com/problems/ZUBSPOIL
def valueOfArray(listOfInt):
value = 0
for i in range(len(listOfInt)-1):
value += abs(listOfInt[i]-listOfInt[i+1])
return(value)
def repList(listToRep, repWhat):
return ([repWhat[1] if l == repWhat[0] else l for l in listToRep])
testCase = input()
line2 = [int(i) for i in input().split()]
array = [int(i) for i in input().split()]
testList = []
for noOfTest in range(line2[1]):
testList.append([int(i) for i in input().split()])
print ("Case: " + testCase)
for k in range(len(testList)):
print(valueOfArray(repList(array, testList[k])))
array = repList(array, testList[k]) | 196 | 0 | 46 |
718845e1e39c7608372ee21d67ad5c63110eecb7 | 187 | py | Python | 01-Python/07-django/mysite/TestModel/admin.py | Jerrywx/Python_Down | 361d6bb8a5f7768c7064e97c40e4f485ece14a27 | [
"Apache-2.0"
] | null | null | null | 01-Python/07-django/mysite/TestModel/admin.py | Jerrywx/Python_Down | 361d6bb8a5f7768c7064e97c40e4f485ece14a27 | [
"Apache-2.0"
] | null | null | null | 01-Python/07-django/mysite/TestModel/admin.py | Jerrywx/Python_Down | 361d6bb8a5f7768c7064e97c40e4f485ece14a27 | [
"Apache-2.0"
] | null | null | null | from django.contrib import admin
# Register your models here.
from django.contrib import admin
from TestModel.models import Test
# Register your models here.
admin.site.register(Test) | 18.7 | 33 | 0.802139 | from django.contrib import admin
# Register your models here.
from django.contrib import admin
from TestModel.models import Test
# Register your models here.
admin.site.register(Test) | 0 | 0 | 0 |
c21d48a5c9622935e4842f542a60c0ef8093873b | 5,489 | py | Python | amplify/agent/objects/nginx/binary.py | dp92987/nginx-amplify-agent | 1b2eed6eab52a82f35974928d75044451b4bedaf | [
"BSD-2-Clause"
] | 308 | 2015-11-17T13:15:33.000Z | 2022-03-24T12:03:40.000Z | amplify/agent/objects/nginx/binary.py | dp92987/nginx-amplify-agent | 1b2eed6eab52a82f35974928d75044451b4bedaf | [
"BSD-2-Clause"
] | 211 | 2015-11-16T15:27:41.000Z | 2022-03-28T16:20:15.000Z | amplify/agent/objects/nginx/binary.py | dp92987/nginx-amplify-agent | 1b2eed6eab52a82f35974928d75044451b4bedaf | [
"BSD-2-Clause"
] | 80 | 2015-11-16T18:20:30.000Z | 2022-03-02T12:47:56.000Z | # -*- coding: utf-8 -*-
import re
from amplify.agent.common.util import subp
from amplify.agent.common.context import context
__author__ = "Mike Belov"
__copyright__ = "Copyright (C) Nginx, Inc. All rights reserved."
__license__ = ""
__maintainer__ = "Mike Belov"
__email__ = "dedm@nginx.com"
DEFAULT_PREFIX = '/usr/local/nginx'
DEFAULT_CONFPATH = 'conf/nginx.conf'
_SSL_LIB_CAPTURE_GROUPS = r'(\S+) +(\S+)(?: +(\d{1,2} +\w{3,} +\d{4}))?'
BUILT_WITH_RE = re.compile('^built with ' + _SSL_LIB_CAPTURE_GROUPS)
RUNNING_WITH_RE = re.compile('\(running with ' + _SSL_LIB_CAPTURE_GROUPS + '\)$')
RUN_WITH_RE = re.compile('^run with ' + _SSL_LIB_CAPTURE_GROUPS)
def nginx_v(bin_path):
"""
call -V and parse results
:param bin_path str - path to binary
:return {} - see result
"""
result = {
'version': None,
'plus': {'enabled': False, 'release': None},
'ssl': {'built': None, 'run': None},
'configure': {}
}
_, nginx_v_err = subp.call("%s -V" % bin_path)
for line in nginx_v_err:
# SSL stuff
try:
if line.lower().startswith('built with') and 'ssl' in line.lower():
match = BUILT_WITH_RE.search(line)
result['ssl']['built'] = list(match.groups())
# example: "built with OpenSSL 1.0.2g-fips 1 Mar 2016 (running with OpenSSL 1.0.2g 1 Mar 2016)"
match = RUNNING_WITH_RE.search(line) or match
result['ssl']['run'] = list(match.groups())
elif line.lower().startswith('run with') and 'ssl' in line.lower():
match = RUN_WITH_RE.search(line)
result['ssl']['run'] = list(match.groups())
except:
context.log.error('Failed to determine ssl library from "%s"' % line, exc_info=True)
parts = line.split(':', 1)
if len(parts) < 2:
continue
# parse version
key, value = parts
if key == 'nginx version':
# parse major version
major_parsed = re.match('.*/([\d\w\.]+)', value)
result['version'] = major_parsed.group(1) if major_parsed else value.lstrip()
# parse plus version
if 'plus' in value:
plus_parsed = re.match('.*\(([\w\-]+)\).*', value)
if plus_parsed:
result['plus']['enabled'] = True
result['plus']['release'] = plus_parsed.group(1)
# parse configure
elif key == 'configure arguments':
arguments = _parse_arguments(value)
result['configure'] = arguments
return result
def get_prefix_and_conf_path(cmd, configure=None):
"""
Finds prefix and path to config based on running cmd and optional configure args
:param running_binary_cmd: full cmd from ps
:param configure: parsed configure args from nginx -V
:return: prefix, conf_path
"""
cmd = cmd.replace('nginx: master process ', '')
params = iter(cmd.split())
# find bin path
bin_path = next(params)
prefix = None
conf_path = None
# try to find config and prefix
for param in params:
if param == '-c':
conf_path = next(params, None)
elif param == '-p':
prefix = next(params, None)
# parse nginx -V
parsed_v = nginx_v(bin_path)
if configure is None:
configure = parsed_v['configure']
# if prefix was not found in cmd - try to read it from configure args
# if there is no key "prefix" in args, then use default
if not prefix:
prefix = configure.get('prefix', DEFAULT_PREFIX)
if not conf_path:
# if there is a conf_path get it from the config file
if context.app_config.get('nginx', {}).get('configfile'):
conf_path = context.app_config['nginx']['configfile']
if not conf_path.startswith('/'):
conf_path = '/' + conf_path
# else get it from the parsed information/DEFAULT
else:
conf_path = configure.get('conf-path', DEFAULT_CONFPATH)
# remove trailing slashes from prefix
prefix = prefix.rstrip('/')
# start processing conf_path
# if it has not an absolutely path, then we should add prefix to it
if not conf_path.startswith('/'):
conf_path = '%s/%s' % (prefix, conf_path)
return bin_path, prefix, conf_path, parsed_v['version']
def _parse_arguments(argstring):
"""
Parses argstring from nginx -V
:param argstring: configure string
:return: {} of parsed string
"""
if argstring.startswith('configure arguments:'):
__, argstring = argstring.split(':', 1)
arg_parts = iter(filter(len, argstring.split(' --')))
arguments = {}
for part in arg_parts:
# if the argument is a simple switch, add it and move on
if '=' not in part:
arguments[part] = True
continue
key, value = part.split('=', 1)
# this fixes quoted argument values that broke from the ' --' split
if value.startswith("'"):
while not value.endswith("'"):
value += ' --' + next(arg_parts)
# if a key is set multiple times, values are stored as a list
if key not in arguments:
arguments[key] = value
elif not isinstance(arguments[key], list):
arguments[key] = [arguments[key], value]
else:
arguments[key].append(value)
return arguments
| 32.672619 | 111 | 0.587903 | # -*- coding: utf-8 -*-
import re
from amplify.agent.common.util import subp
from amplify.agent.common.context import context
__author__ = "Mike Belov"
__copyright__ = "Copyright (C) Nginx, Inc. All rights reserved."
__license__ = ""
__maintainer__ = "Mike Belov"
__email__ = "dedm@nginx.com"
DEFAULT_PREFIX = '/usr/local/nginx'
DEFAULT_CONFPATH = 'conf/nginx.conf'
_SSL_LIB_CAPTURE_GROUPS = r'(\S+) +(\S+)(?: +(\d{1,2} +\w{3,} +\d{4}))?'
BUILT_WITH_RE = re.compile('^built with ' + _SSL_LIB_CAPTURE_GROUPS)
RUNNING_WITH_RE = re.compile('\(running with ' + _SSL_LIB_CAPTURE_GROUPS + '\)$')
RUN_WITH_RE = re.compile('^run with ' + _SSL_LIB_CAPTURE_GROUPS)
def nginx_v(bin_path):
"""
call -V and parse results
:param bin_path str - path to binary
:return {} - see result
"""
result = {
'version': None,
'plus': {'enabled': False, 'release': None},
'ssl': {'built': None, 'run': None},
'configure': {}
}
_, nginx_v_err = subp.call("%s -V" % bin_path)
for line in nginx_v_err:
# SSL stuff
try:
if line.lower().startswith('built with') and 'ssl' in line.lower():
match = BUILT_WITH_RE.search(line)
result['ssl']['built'] = list(match.groups())
# example: "built with OpenSSL 1.0.2g-fips 1 Mar 2016 (running with OpenSSL 1.0.2g 1 Mar 2016)"
match = RUNNING_WITH_RE.search(line) or match
result['ssl']['run'] = list(match.groups())
elif line.lower().startswith('run with') and 'ssl' in line.lower():
match = RUN_WITH_RE.search(line)
result['ssl']['run'] = list(match.groups())
except:
context.log.error('Failed to determine ssl library from "%s"' % line, exc_info=True)
parts = line.split(':', 1)
if len(parts) < 2:
continue
# parse version
key, value = parts
if key == 'nginx version':
# parse major version
major_parsed = re.match('.*/([\d\w\.]+)', value)
result['version'] = major_parsed.group(1) if major_parsed else value.lstrip()
# parse plus version
if 'plus' in value:
plus_parsed = re.match('.*\(([\w\-]+)\).*', value)
if plus_parsed:
result['plus']['enabled'] = True
result['plus']['release'] = plus_parsed.group(1)
# parse configure
elif key == 'configure arguments':
arguments = _parse_arguments(value)
result['configure'] = arguments
return result
def get_prefix_and_conf_path(cmd, configure=None):
"""
Finds prefix and path to config based on running cmd and optional configure args
:param running_binary_cmd: full cmd from ps
:param configure: parsed configure args from nginx -V
:return: prefix, conf_path
"""
cmd = cmd.replace('nginx: master process ', '')
params = iter(cmd.split())
# find bin path
bin_path = next(params)
prefix = None
conf_path = None
# try to find config and prefix
for param in params:
if param == '-c':
conf_path = next(params, None)
elif param == '-p':
prefix = next(params, None)
# parse nginx -V
parsed_v = nginx_v(bin_path)
if configure is None:
configure = parsed_v['configure']
# if prefix was not found in cmd - try to read it from configure args
# if there is no key "prefix" in args, then use default
if not prefix:
prefix = configure.get('prefix', DEFAULT_PREFIX)
if not conf_path:
# if there is a conf_path get it from the config file
if context.app_config.get('nginx', {}).get('configfile'):
conf_path = context.app_config['nginx']['configfile']
if not conf_path.startswith('/'):
conf_path = '/' + conf_path
# else get it from the parsed information/DEFAULT
else:
conf_path = configure.get('conf-path', DEFAULT_CONFPATH)
# remove trailing slashes from prefix
prefix = prefix.rstrip('/')
# start processing conf_path
# if it has not an absolutely path, then we should add prefix to it
if not conf_path.startswith('/'):
conf_path = '%s/%s' % (prefix, conf_path)
return bin_path, prefix, conf_path, parsed_v['version']
def _parse_arguments(argstring):
"""
Parses argstring from nginx -V
:param argstring: configure string
:return: {} of parsed string
"""
if argstring.startswith('configure arguments:'):
__, argstring = argstring.split(':', 1)
arg_parts = iter(filter(len, argstring.split(' --')))
arguments = {}
for part in arg_parts:
# if the argument is a simple switch, add it and move on
if '=' not in part:
arguments[part] = True
continue
key, value = part.split('=', 1)
# this fixes quoted argument values that broke from the ' --' split
if value.startswith("'"):
while not value.endswith("'"):
value += ' --' + next(arg_parts)
# if a key is set multiple times, values are stored as a list
if key not in arguments:
arguments[key] = value
elif not isinstance(arguments[key], list):
arguments[key] = [arguments[key], value]
else:
arguments[key].append(value)
return arguments
| 0 | 0 | 0 |
6b0db90ad30a55308c43060a9860790f34a7eb4c | 75 | py | Python | passagens/models/__init__.py | ollyvergithub/AppViagensDjangoForm | 1b18f19fec2b072e0bb6f21f1ffc17550596da32 | [
"MIT"
] | null | null | null | passagens/models/__init__.py | ollyvergithub/AppViagensDjangoForm | 1b18f19fec2b072e0bb6f21f1ffc17550596da32 | [
"MIT"
] | 5 | 2021-03-30T13:43:44.000Z | 2021-09-22T19:20:06.000Z | passagens/models/__init__.py | ollyvergithub/AppViagensDjangoForm | 1b18f19fec2b072e0bb6f21f1ffc17550596da32 | [
"MIT"
] | null | null | null | from .classe_viagem import *
from .passagem import *
from .pessoa import *
| 18.75 | 28 | 0.76 | from .classe_viagem import *
from .passagem import *
from .pessoa import *
| 0 | 0 | 0 |
2371ee3c7adc9526ab87c102cf9e8fa26f249e6b | 655 | py | Python | 2loadconstruct.py | 21kmegerusca/MyProject | 1be72f40ff9b2927b824a979f6f5fb3a93efa1f9 | [
"MIT"
] | null | null | null | 2loadconstruct.py | 21kmegerusca/MyProject | 1be72f40ff9b2927b824a979f6f5fb3a93efa1f9 | [
"MIT"
] | null | null | null | 2loadconstruct.py | 21kmegerusca/MyProject | 1be72f40ff9b2927b824a979f6f5fb3a93efa1f9 | [
"MIT"
] | null | null | null | from mcpi.minecraft import Minecraft
mc = Minecraft.create()
import pickle
# Открываем файл и загружаем трехмерный список structure
myFile = open("myFile.txt", "rb")
structure = pickle.load(myFile)
myFile.close()
pos = mc.player.getTilePos()
x = pos.x
y = pos.y
z = pos.z
buildStructure(x, y, z, structure)
| 25.192308 | 59 | 0.560305 | from mcpi.minecraft import Minecraft
mc = Minecraft.create()
import pickle
def buildStructure(x, y, z, structure):
xStart = x
zStart = z
for row in structure:
for column in row:
for block in column:
mc.setBlock(x, y, z, block.id, block.data)
z += 1
x += 1
z = zStart
y += 1
x = xStart
# Открываем файл и загружаем трехмерный список structure
myFile = open("myFile.txt", "rb")
structure = pickle.load(myFile)
myFile.close()
pos = mc.player.getTilePos()
x = pos.x
y = pos.y
z = pos.z
buildStructure(x, y, z, structure)
| 303 | 0 | 23 |
c1b3446c4b039f3fafcef279b1fa9e624900ad0f | 10,379 | py | Python | RunThis.py | VisweshK/Jashmup | ca0cf639000734c5aea8583d9477af9a387f6d46 | [
"MIT"
] | null | null | null | RunThis.py | VisweshK/Jashmup | ca0cf639000734c5aea8583d9477af9a387f6d46 | [
"MIT"
] | null | null | null | RunThis.py | VisweshK/Jashmup | ca0cf639000734c5aea8583d9477af9a387f6d46 | [
"MIT"
] | null | null | null | '''
Controls:
To move your ship up and down, press and hold the up and down keys respectively.
To fire a laser at enemy ships, press the space key. (Only one may be on screen at a time.)
When the game has ended, press R to Restart the game.
Your ship begins with 3 shields. After the 3 are depleted, one hit will kill you.
Enemy lasers, as well as collisions with enemy ships, will damage your ship.
Each time you hit an enemy, you gain 100 points.
The weak enemies will sometimes drop a Bonus Gem.
If you collect this Gem, you get 1000 points.
You are able to destroy enemy lasers with your laser, but it can be difficult.
'''
import pygame, random, os
import shield, scorebar, spaceship, laser, customBackground, enemy, bonusItem
# init constants
good = weak = 1
bad = strong = 2
if __name__ == "__main__":
main()
| 45.722467 | 141 | 0.614606 | '''
Controls:
To move your ship up and down, press and hold the up and down keys respectively.
To fire a laser at enemy ships, press the space key. (Only one may be on screen at a time.)
When the game has ended, press R to Restart the game.
Your ship begins with 3 shields. After the 3 are depleted, one hit will kill you.
Enemy lasers, as well as collisions with enemy ships, will damage your ship.
Each time you hit an enemy, you gain 100 points.
The weak enemies will sometimes drop a Bonus Gem.
If you collect this Gem, you get 1000 points.
You are able to destroy enemy lasers with your laser, but it can be difficult.
'''
import pygame, random, os
import shield, scorebar, spaceship, laser, customBackground, enemy, bonusItem
# init constants
good = weak = 1
bad = strong = 2
def main():
# Initialize pygame and the window display
pygame.init()
pygame.mixer.init()
screen = pygame.display.set_mode((1000, 800))
pygame.display.set_caption("Jashmup : Just Another Shoot 'em Up")
# Fill in a background color
background = pygame.Surface(screen.get_size())
background = background.convert()
background.fill((255,255,255))
screen.blit(background, (0,0))
# Initialize instants of all sprites.
backgroundSprite = customBackground.Background(screen)
protagonist = spaceship.Spaceship(screen)
protLaser1 = laser.Laser(screen, good)
enemyLaser1 = laser.Laser(screen, bad)
enemyLaser2 = laser.Laser(screen, bad)
weakEnemy = enemy.Enemy(screen, weak)
strongEnemy = enemy.Enemy(screen, strong)
bonus = bonusItem.BonusItem(screen)
gameScorebar = scorebar.Scorebar(screen)
shield1 = shield.Shield(screen)
# Create useful sprite groups.
goodSprites = pygame.sprite.Group(protagonist, protLaser1)
badSprites = pygame.sprite.Group(weakEnemy, strongEnemy, enemyLaser1, enemyLaser2)
backgroundSprites = pygame.sprite.Group(backgroundSprite, gameScorebar)
allSprites = pygame.sprite.Group(protagonist, protLaser1, enemyLaser1, enemyLaser2, weakEnemy, strongEnemy, bonus, gameScorebar, shield1)
# Initialize the sound files.
playerdie = pygame.mixer.Sound(os.path.join("sounds", "playerdie.ogg"))
enemyhurt = pygame.mixer.Sound(os.path.join("sounds", "enemyhurt.ogg"))
enemydie = pygame.mixer.Sound(os.path.join("sounds", "enemydie.ogg"))
shielddeflect = pygame.mixer.Sound(os.path.join("sounds", "shielddeflect.ogg"))
playerlaser = pygame.mixer.Sound(os.path.join("sounds", "playerlaser.ogg"))
enemylaser = pygame.mixer.Sound(os.path.join("sounds", "enemylaser.ogg"))
bonusnoise = pygame.mixer.Sound(os.path.join("sounds", "bonusnoise.ogg"))
clock = pygame.time.Clock()
keepGoing = True
gameOver = False
while keepGoing:
clock.tick(30)
for event in pygame.event.get():
# Cause the game to quit.
if event.type == pygame.QUIT:
keepGoing = False
elif event.type == pygame.KEYDOWN:
# Used to make protagonist move up on screen.
if event.key == pygame.K_UP:
protagonist.upKey = True
# Used to make protagonist move down on screen.
elif event.key == pygame.K_DOWN:
protagonist.downKey = True
# Used to make protagonist fire a laser.
elif event.key == pygame.K_SPACE:
if protLaser1.moving == False:
playerlaser.play()
protLaser1.spaceKey = True
protLaser1.rect.left = protagonist.rect.right
protLaser1.rect.centery = protagonist.rect.centery
elif event.type == pygame.KEYUP:
# These allow the user to constantly move up, or down, if the appropriate key is held.
if protagonist.upKey == True:
protagonist.upKey = False
if protagonist.downKey == True:
protagonist.downKey = False
# If the protagonist touches the bonus item, points are added to the score and a sound is played.
if protagonist.rect.colliderect(bonus.rect):
bonusnoise.play()
gameScorebar.score += 1000
bonus.reset()
# If the protagonist touches enemy lasers or enemy sprites
# protagonist's shields decrease,
# a shield is displayed in protagonist's position briefly, and
# a sound plays.
if pygame.sprite.spritecollideany(protagonist, badSprites):
shield1.hit = True
shield1.currentx = protagonist.rect.centerx
shield1.currenty = protagonist.rect.centery
shielddeflect.play()
gameScorebar.shields -=1
# Enemy lasers are destroyed if touched.
if protagonist.rect.colliderect(enemyLaser1.rect):
enemyLaser1.reset()
if protagonist.rect.colliderect(enemyLaser2.rect):
enemyLaser2.reset()
# Enemy sprites are destroyed if touched.
if protagonist.rect.colliderect(weakEnemy.rect):
weakEnemy.reset()
if protagonist.rect.colliderect(strongEnemy.rect):
strongEnemy.reset()
# If the protagonist's laser touches enemy lasers or enemy sprites, the protagonist's laser is destroyed.
if pygame.sprite.spritecollideany(protLaser1, badSprites):
# If the collision is with a weak enemy, the enemy is destroyed, a sound is played, and points are added.
if protLaser1.rect.colliderect(weakEnemy.rect):
# There is a 1/10 chance that, upon destroying a weak enemy, it will drop a Bonus Item.
if random.randrange(1, 10) == 1:
if bonus.moving == False:
bonus.rect.centerx = weakEnemy.rect.centerx
bonus.rect.centery = weakEnemy.rect.centery
bonus.moving = True
weakEnemy.hp -= 1
enemydie.play()
gameScorebar.score += 100
# If the collision is with a strong enemy, the enemy's hp is decreased, a sound is played, and points are added.
if protLaser1.rect.colliderect(strongEnemy.rect):
strongEnemy.hp -= 1
if strongEnemy.hp == 0:
enemydie.play()
else:
enemyhurt.play()
gameScorebar.score += 100
# If the colllision is with an enemy laser, the enemy laser is destroyed.
if protLaser1.rect.colliderect(enemyLaser1.rect):
enemyLaser1.reset()
if protLaser1.rect.colliderect(enemyLaser2.rect):
enemyLaser2.reset()
protLaser1.reset()
# If the protagonist's shields are decreased to less than zero, a sound is played and the game ends.
if gameScorebar.shields < 0:
gameScorebar.shields = 0
playerdie.play()
protagonist.rect.centerx = 1000
shield1.rect.centerx = 1000
protagonist.kill()
shield1.kill()
keepGoing = False
gameOver = True
# This causes a random number to be generated.
# There is a 1/10 chance that, if a Weak Enemy isn't already on screen, one will appear on the far end of the screen.
enemyappear = random.randrange(1, 20)
if enemyappear == 1 or enemyappear == 2:
weakEnemy.moving = True
# There is a 1/20 chance that, if a Strong Enemy isn't already on screen, one will appear on the far end of the screen.
elif enemyappear == 3:
strongEnemy.moving = True
# This makes it so that, for example, immediately upon destroying a weak enemy,
# a second weak enemy does not always instantly appear on the far end of the screen.
# This causes a random number to be generated.
enemyattack = random.randrange(1, 25)
# If a weak enemy is on screen and doesn't have a laser firing,
# there is a 1/25 chance that it will fire a laser.
if enemyattack == weak:
if weakEnemy.moving == True:
if enemyLaser1.moving == False:
enemylaser.play()
enemyLaser1.rect.right = weakEnemy.rect.left
enemyLaser1.rect.centery = weakEnemy.rect.centery
enemyLaser1.moving = True
# If a strong enemy is on screen and doesn't have a laser firing,
# there is a 1/25 chance that it will fire a laser.
elif enemyattack == strong:
if strongEnemy.moving == True:
if enemyLaser2.moving == False:
enemylaser.play()
enemyLaser2.rect.right = strongEnemy.rect.left
enemyLaser2.rect.centery = strongEnemy.rect.centery
enemyLaser2.moving = True
# This makes it so that the enemies are not ceaselessly firing,
# but are firing often enough to be challenging.
# The sprites are cleared, updated, and drawn to the screen again.
backgroundSprites.clear(screen, background)
allSprites.clear(screen, background)
backgroundSprites.update()
allSprites.update()
backgroundSprites.draw(screen)
allSprites.draw(screen)
pygame.display.flip()
# When the loop is exited (game has ended), the sprites (except for the background and scorebar) are removed.
allSprites.clear(screen, background)
while gameOver == True:
clock.tick(30)
# The background continues to scroll.
backgroundSprites.update()
backgroundSprites.draw(screen)
pygame.display.flip()
for event in pygame.event.get():
# The user is able to quit.
if event.type == pygame.QUIT:
gameOver = False
# The user is able to restart the game by pressing the R key.
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_r:
keepGoing = True
gameOver = False
main()
pygame.quit()
if __name__ == "__main__":
main()
| 9,485 | 0 | 23 |
fbd1dee91196c264ee44850acb08c9dec4ced670 | 1,919 | py | Python | apps/controllerx/cx_core/action_type/call_service_action_type.py | xaviml/z2m_ikea_controller | e612af5a913e8b4784dcaa23ea5319115427d083 | [
"MIT"
] | 19 | 2019-11-21T19:51:40.000Z | 2020-01-14T09:24:33.000Z | apps/controllerx/cx_core/action_type/call_service_action_type.py | xaviml/z2m_ikea_controller | e612af5a913e8b4784dcaa23ea5319115427d083 | [
"MIT"
] | 11 | 2019-11-20T16:43:35.000Z | 2020-01-17T16:23:06.000Z | apps/controllerx/cx_core/action_type/call_service_action_type.py | xaviml/z2m_ikea_controller | e612af5a913e8b4784dcaa23ea5319115427d083 | [
"MIT"
] | 5 | 2019-12-20T21:31:07.000Z | 2020-01-06T18:49:52.000Z | from typing import TYPE_CHECKING, Any, Dict, Optional, cast
from cx_core.action_type.base import ActionType
from cx_core.integration import EventData
if TYPE_CHECKING:
from cx_core.type_controller import Entity, TypeController
| 36.207547 | 81 | 0.654508 | from typing import TYPE_CHECKING, Any, Dict, Optional, cast
from cx_core.action_type.base import ActionType
from cx_core.integration import EventData
if TYPE_CHECKING:
from cx_core.type_controller import Entity, TypeController
class CallServiceActionType(ActionType):
service: str
# Priority order for entity_id:
# - Inside data
# - In the same level as "service"
# - From the main config if the domain matches
entity_id: Optional[str]
data: Dict[str, Any]
def initialize(self, **kwargs: Any) -> None:
self.service = kwargs["service"]
self.data = kwargs.get("data", {})
self.entity_id = self.data.get("entity_id") or kwargs.get("entity_id")
if (
self.entity_id is None
and self._check_controller_isinstance_type_controller()
):
type_controller = cast("TypeController[Entity]", self.controller)
if self._get_service_domain(self.service) in type_controller.domains:
self.entity_id = type_controller.entity.name
if "entity_id" in self.data:
del self.data["entity_id"]
def _check_controller_isinstance_type_controller(self) -> bool:
# This is checked dynamically without the isinstance to avoid
# circular dependency
class_names = [c.__name__ for c in type(self.controller).mro()]
return "TypeController" in class_names
def _get_service_domain(self, service: str) -> str:
return service.replace(".", "/").split("/")[0]
async def run(self, extra: Optional[EventData] = None) -> None:
if self.entity_id:
await self.controller.call_service(
self.service, entity_id=self.entity_id, **self.data
)
else:
await self.controller.call_service(self.service, **self.data)
def __str__(self) -> str:
return f"Service ({self.service})"
| 1,291 | 371 | 23 |
31f5519335f0dbeb8a9abfe2868d419663a404d2 | 80 | py | Python | twitoff/__init__.py | fwenchino/TwitOff | 715325264fb114591b9febe06833c43ac43e7cfe | [
"MIT"
] | 1 | 2019-09-04T15:24:04.000Z | 2019-09-04T15:24:04.000Z | twitoff/__init__.py | fwenchino/TwitOff | 715325264fb114591b9febe06833c43ac43e7cfe | [
"MIT"
] | null | null | null | twitoff/__init__.py | fwenchino/TwitOff | 715325264fb114591b9febe06833c43ac43e7cfe | [
"MIT"
] | null | null | null | """ Entrt point for TwitOff"""
from .app import create_app
APP = create_app()
| 13.333333 | 30 | 0.7 | """ Entrt point for TwitOff"""
from .app import create_app
APP = create_app()
| 0 | 0 | 0 |
9b5b476527666fb1fafca593ae071b1c88fae6a5 | 115 | py | Python | src/pandatex/__init__.py | Marcelo-Kenji-Noda/PandaTexModule | 2e116c1d15f217b21d3b47e1148e03ee1d6c6b7d | [
"MIT"
] | null | null | null | src/pandatex/__init__.py | Marcelo-Kenji-Noda/PandaTexModule | 2e116c1d15f217b21d3b47e1148e03ee1d6c6b7d | [
"MIT"
] | null | null | null | src/pandatex/__init__.py | Marcelo-Kenji-Noda/PandaTexModule | 2e116c1d15f217b21d3b47e1148e03ee1d6c6b7d | [
"MIT"
] | null | null | null | from utils._texbasics import *
from utils._validate import _validate_options
from pandatex.pandatex import TexTable | 38.333333 | 45 | 0.869565 | from utils._texbasics import *
from utils._validate import _validate_options
from pandatex.pandatex import TexTable | 0 | 0 | 0 |
59625ad5bfe6849da5d03fdab7d1c1735059a8db | 3,176 | py | Python | routes/main_routes.py | yoonhoelee/twitter_application | 0e1fdab26a972002ddeaaa83ce4fb039d3d6dcca | [
"MIT"
] | null | null | null | routes/main_routes.py | yoonhoelee/twitter_application | 0e1fdab26a972002ddeaaa83ce4fb039d3d6dcca | [
"MIT"
] | null | null | null | routes/main_routes.py | yoonhoelee/twitter_application | 0e1fdab26a972002ddeaaa83ce4fb039d3d6dcca | [
"MIT"
] | null | null | null | from flask import Flask, render_template, request, Blueprint
from twit_app.models import User, Tweet, en, db
from sklearn.linear_model import LogisticRegression
import os, pickle
import numpy as np
from dotenv import load_dotenv
load_dotenv()
import tweepy
apikey= os.environ.get('apikey')
apipw = os.environ.get('apipw')
accesstoken = os.environ.get('accesstoken')
accesspw = os.environ.get('accesspw')
auth=tweepy.OAuthHandler(apikey, apipw)
auth.set_access_token(accesstoken, accesspw)
api = tweepy.API(auth)
main_routes = Blueprint('main_routes', __name__)
@main_routes.route('/')
@main_routes.route('/users/')
@main_routes.route('/add', methods=["GET", "POST"])
@main_routes.route('/delete')
@main_routes.route('/update/', methods=["GET", "POST"])
@main_routes.route('/compare', methods=["GET", "POST"])
| 29.962264 | 164 | 0.644521 | from flask import Flask, render_template, request, Blueprint
from twit_app.models import User, Tweet, en, db
from sklearn.linear_model import LogisticRegression
import os, pickle
import numpy as np
from dotenv import load_dotenv
load_dotenv()
import tweepy
apikey= os.environ.get('apikey')
apipw = os.environ.get('apipw')
accesstoken = os.environ.get('accesstoken')
accesspw = os.environ.get('accesspw')
auth=tweepy.OAuthHandler(apikey, apipw)
auth.set_access_token(accesstoken, accesspw)
api = tweepy.API(auth)
main_routes = Blueprint('main_routes', __name__)
@main_routes.route('/')
def welcome():
return render_template("index.html")
@main_routes.route('/users/')
def users():
data = User.query.all()
return render_template("users.html", data = data)
@main_routes.route('/add', methods=["GET", "POST"])
def add():
if request.method == "POST":
try:
db.create_all()
x = request.form.get("username")
newuser = api.get_user(screen_name=x)
user = User(id = newuser.id, username = newuser.screen_name, full_name = newuser.name, followers = newuser.followers_count, location = newuser.location)
db.session.add(user)
db.session.commit()
newtweet = api.user_timeline(screen_name=x)[0]
tweet = Tweet(text = newtweet.text, embedding = en.encode(texts=[newtweet.text]), user_id=user.id)
db.session.add(tweet)
db.session.commit()
except Exception as e:
db.session.rollback()
print("Failed to add user")
print(e)
users = User.query.all()
return render_template("add.html", users = users)
@main_routes.route('/delete')
def delete():
db.drop_all()
db.create_all()
return 'DB deleted'
@main_routes.route('/update/', methods=["GET", "POST"])
def update():
if request.method == "POST":
print(dict(request.form))
result = request.form
db.session.query(User).filter(User.username == result['username']).update({'username': result['change_name']})
db.session.commit()
return render_template('update.html')
@main_routes.route('/compare', methods=["GET", "POST"])
def compare():
users=User.query.all()
prediction=""
input_tweet=""
if request.method == "POST":
tweet_user1=request.form["user_1"]
tweet_user2=request.form["user_2"]
X=[]
y=[]
user_1=User.query.filter_by(username=tweet_user1).one()
user_2=User.query.filter_by(username=tweet_user2).one()
for tweet1 in user_1.tweets:
X.append(tweet1.embedding.flatten())
y.append(user_1.username)
for tweet2 in user_2.tweets:
X.append(tweet2.embedding.flatten())
y.append(user_2.username)
model= LogisticRegression()
model.fit(X,y)
input_tweet=request.form["input_tweet"]
prediction=model.predict(en.encode(texts=[input_tweet]))
print(f"Prediction Reuslts [prediction]")
return render_template('compare.html', users=users, prediction=prediction, input_tweet=input_tweet)
| 2,218 | 0 | 132 |
acc1b35a6d7619664a1fc082cacb3b94d8fc83ec | 1,040 | py | Python | zorrom/mb8623x.py | DanDresser/zorrom | 0fa0a10d8b7a762a5f29be1868e6c03769ebd7a6 | [
"BSD-2-Clause"
] | null | null | null | zorrom/mb8623x.py | DanDresser/zorrom | 0fa0a10d8b7a762a5f29be1868e6c03769ebd7a6 | [
"BSD-2-Clause"
] | null | null | null | zorrom/mb8623x.py | DanDresser/zorrom | 0fa0a10d8b7a762a5f29be1868e6c03769ebd7a6 | [
"BSD-2-Clause"
] | null | null | null | from zorrom import mrom
'''
The row and column decode logic should be up and right
Reference
https://siliconpr0n.org/map/sega/315-5571/mz_mit20x/
Rotate 90 CW to get expected rotation
Think MB86233/MB86234 is the same layout
'''
| 24.761905 | 71 | 0.592308 | from zorrom import mrom
'''
The row and column decode logic should be up and right
Reference
https://siliconpr0n.org/map/sega/315-5571/mz_mit20x/
Rotate 90 CW to get expected rotation
Think MB86233/MB86234 is the same layout
'''
class MB8623x(mrom.MaskROM):
@staticmethod
def desc(self):
return 'Fujitsu MB86233/MB86234'
@staticmethod
def txtwh():
return (32 * 8, 32 * 8)
@staticmethod
def txtgroups():
# Use monkey convention breaking on groups of 8
# Actual has no col breaks and every second row break is larger
return range(8, 32 * 8, 8), range(8, 32 * 8, 8)
@staticmethod
def invert():
return True
def oi2cr(self, offset, maski):
biti = offset * 8 + maski
#print biti
# Each column has 16 bytes
# Actually starts from right of image
col = (32 * 8 - 1) - biti // (8 * 32)
# 0, 8, 16, ... 239, 247, 255
row = (biti % 32) * 8 + (biti // 32) % 8
#print row
return (col, row)
| 572 | 213 | 23 |
8e568463de51361853b5ababd32cfbfd8d2f24ae | 1,975 | py | Python | Server/WebApp.py | OnurYigitArpali/ML-Algorithms-Visualization-and-Positioning | d6ef79e83c3e7c57bdae5211f8cf5c372edc5e3e | [
"MIT"
] | null | null | null | Server/WebApp.py | OnurYigitArpali/ML-Algorithms-Visualization-and-Positioning | d6ef79e83c3e7c57bdae5211f8cf5c372edc5e3e | [
"MIT"
] | null | null | null | Server/WebApp.py | OnurYigitArpali/ML-Algorithms-Visualization-and-Positioning | d6ef79e83c3e7c57bdae5211f8cf5c372edc5e3e | [
"MIT"
] | null | null | null | from flask import Flask, render_template, request
from threading import Thread
from bokeh.embed import server_document
from bokeh.server.server import Server
from tornado.ioloop import IOLoop
import sys
sys.path.append("..")
from Bokeh.Decision_Tree.Plot.plot_decision_tree import create_figure
from Bokeh.K_Means.kmeans_cluestering import create_figure as create_figure_k_means
from bokeh.embed import components
app = Flask(__name__)
@app.route('/q', methods=['GET'])
@app.route('/', methods=['GET'])
@app.route('/k_means', methods=['GET'])
@app.route('/karpuzkavun')
Thread(target=bk_worker).start()
if __name__ == '__main__':
print('Opening single process Flask app with embedded Bokeh application on http://localhost:8000/')
print()
print('Multiple connections may block the Bokeh app in this configuration!')
print('See "flask_gunicorn_embed.py" for one way to run multi-process')
app.run(port=8000)
| 29.924242 | 128 | 0.730633 | from flask import Flask, render_template, request
from threading import Thread
from bokeh.embed import server_document
from bokeh.server.server import Server
from tornado.ioloop import IOLoop
import sys
sys.path.append("..")
from Bokeh.Decision_Tree.Plot.plot_decision_tree import create_figure
from Bokeh.K_Means.kmeans_cluestering import create_figure as create_figure_k_means
from bokeh.embed import components
app = Flask(__name__)
def modify_doc(doc):
plot = create_figure()
doc.add_root(plot)
def modify_doc2(doc):
plot = create_figure_k_means()
doc.add_root(plot)
@app.route('/q', methods=['GET'])
def shut():
func = request.environ.get('werkzeug.server.shutdown')
if func is None:
raise RuntimeError('Not running with the Werkzeug Server')
func()
@app.route('/', methods=['GET'])
def bkapp_page():
script = server_document('http://localhost:5006/bkapp')
return render_template("index.html", script=script, template="Flask")
@app.route('/k_means', methods=['GET'])
def bkapp_page2():
script = server_document('http://localhost:5006/bkapp2')
return render_template("index2.html", script=script, template="Flask")
@app.route('/karpuzkavun')
def karpuz():
return render_template("karpuzkavun.html", template="Flask")
def bk_worker():
# Can't pass num_procs > 1 in this configuration. If you need to run multiple
# processes, see e.g. flask_gunicorn_embed.py
server = Server({'/bkapp': modify_doc, '/bkapp2': modify_doc2}, io_loop=IOLoop(), allow_websocket_origin=["localhost:8000"])
server.start()
server.io_loop.start()
Thread(target=bk_worker).start()
if __name__ == '__main__':
print('Opening single process Flask app with embedded Bokeh application on http://localhost:8000/')
print()
print('Multiple connections may block the Bokeh app in this configuration!')
print('See "flask_gunicorn_embed.py" for one way to run multi-process')
app.run(port=8000)
| 877 | 0 | 157 |
be4078b7c305db105cf15638ee2f23b391d4f76f | 31 | py | Python | arnparse/__init__.py | mikedlr/arnparse | 626662f8a6ffb31d6f514e2f3c3e2785d2a170ef | [
"MIT"
] | 30 | 2018-05-22T23:03:58.000Z | 2022-03-19T18:43:56.000Z | arnparse/__init__.py | mikedlr/arnparse | 626662f8a6ffb31d6f514e2f3c3e2785d2a170ef | [
"MIT"
] | 7 | 2018-05-25T18:18:12.000Z | 2020-11-12T22:49:52.000Z | arnparse/__init__.py | mikedlr/arnparse | 626662f8a6ffb31d6f514e2f3c3e2785d2a170ef | [
"MIT"
] | 7 | 2018-05-23T00:48:27.000Z | 2021-02-18T11:49:45.000Z | from .arnparse import arnparse
| 15.5 | 30 | 0.83871 | from .arnparse import arnparse
| 0 | 0 | 0 |
c84b458a3ec86af06a8780b7d6f62a876c098970 | 5,927 | py | Python | main.py | wintechis/RDeF | 459b26acefa30f42f3c3a237efb3f737c3038d7b | [
"MIT"
] | null | null | null | main.py | wintechis/RDeF | 459b26acefa30f42f3c3a237efb3f737c3038d7b | [
"MIT"
] | null | null | null | main.py | wintechis/RDeF | 459b26acefa30f42f3c3a237efb3f737c3038d7b | [
"MIT"
] | null | null | null | #####################################
## Set Variable for Logger Path #####
from dataclasses import dataclass, field, asdict
import os
import json
from typing import Iterator, List, Dict
os.environ['KIVY_HOME'] = os.getcwd()
import kivy.logger
###################################
## Kivy Import
import kivy
kivy.require('2.0.0')
#### Update standard font, if defined
#from kivy.config import Config
#Config.set('kivy', 'default_font', ['Roboto', 'data/fonts/Roboto-Regular.ttf', 'data/fonts/Roboto-Italic.ttf', 'data/fonts/Roboto-Bold.ttf', 'data/fonts/Roboto-BoldItalic.ttf'])
from kivy.app import App
from kivy.uix.screenmanager import ScreenManager
from kivy.clock import Clock
from kivy.properties import ListProperty, DictProperty
####################################
####################################
###################################
## Scene Import
kivy.resources.resource_add_path(os.path.join(os.getcwd(), 'templates'))
import sys
sys.path.append(os.path.join(os.getcwd(), 'templates'))
#from chapter import Chapter
from templates.story import Story
####################################
####################################
from string import Template
import rdflib
from templates.sparqlManager import SparqlManager
################################################################
# Main Loop
@dataclass
@dataclass
###########################################################
######## Main
##########################################################
if __name__ == '__main__':
Clock.max_iteration = 50
MainApp().run()
| 31.865591 | 178 | 0.540071 | #####################################
## Set Variable for Logger Path #####
from dataclasses import dataclass, field, asdict
import os
import json
from typing import Iterator, List, Dict
os.environ['KIVY_HOME'] = os.getcwd()
import kivy.logger
###################################
## Kivy Import
import kivy
kivy.require('2.0.0')
#### Update standard font, if defined
#from kivy.config import Config
#Config.set('kivy', 'default_font', ['Roboto', 'data/fonts/Roboto-Regular.ttf', 'data/fonts/Roboto-Italic.ttf', 'data/fonts/Roboto-Bold.ttf', 'data/fonts/Roboto-BoldItalic.ttf'])
from kivy.app import App
from kivy.uix.screenmanager import ScreenManager
from kivy.clock import Clock
from kivy.properties import ListProperty, DictProperty
####################################
####################################
###################################
## Scene Import
kivy.resources.resource_add_path(os.path.join(os.getcwd(), 'templates'))
import sys
sys.path.append(os.path.join(os.getcwd(), 'templates'))
#from chapter import Chapter
from templates.story import Story
####################################
####################################
from string import Template
import rdflib
from templates.sparqlManager import SparqlManager
################################################################
# Main Loop
@dataclass
class StoryInfo:
title: str = ''
media_source: str =''
authors: List[str] = field(default_factory=lambda: [])
tags: List[str] = field(default_factory=lambda: [])
desc: str = ''
@dataclass
class DesignInfo:
font_size: int = 20
fg: list[float] = field(default_factory=lambda:[151/255, 27/255, 47/255, 1])
bg: list[float] = field(default_factory=lambda:[0.9, 0.9, 0.9, 1])
hl: list[float] = field(default_factory=lambda: [209/255, 231/255, 224/255, 1] ) #[200/255, 15/255, 46/255, 1]
class RDeFManager(ScreenManager):
cur_chapter = ListProperty([0]) #next episode by cur_story[0] += 1
cur_episode = ListProperty([0])
def __init__(self, story_names: Dict[str,str], **kw):
super().__init__(**kw)
self.chapters = []
self.episodes = []
self.sparql = SparqlManager()
self.switch_to(Story(story_names))
class MainApp(App):
fg = ListProperty()
bg = ListProperty()
hl = ListProperty()
story_info = DictProperty()
def build(self):
## Load Design / Stories
self.sparql = SparqlManager()
self.title = 'RDeF - RDF Training and Demonstration Framework'
story_path = self.select_story()
if story_path == 'exit':
self.stop()
return
self.story_info = asdict(StoryInfo())
self.paths_resources = []
###
try:
self.load_story_info(story=story_path)
except FileNotFoundError:
di = DesignInfo()
self.fg = di.fg
self.bg = di.bg
self.hl = di.hl
self.font_size = 20
return RDeFManager(story_names=story_path)
def select_story(self):
story_dir = os.path.join(os.getcwd(),'stories')
story_names = list(self.get_stories(story_dir).keys())
if len(story_names) == 1:
return os.path.join(story_dir, story_names[0])
s = ''
for i, name in enumerate(story_names):
s += f'[{i+1}] {name} '
s += f'[{len(story_names)+ 1}] exit'
print(f'Select Game: {s}')
story = input()
if story in story_names:
return os.path.join(story_dir, story)
elif story.isnumeric() and 0 < int(story) <= len(story_names):
return os.path.join(story_dir, story_names[int(story)-1])
else:
return 'exit'
def get_stories(self, story_dir: str) -> list:
return {story: os.path.join(story_dir, story) for story in next(os.walk(story_dir))[1]}
def load_story_info(self, story):
p = os.path.join(story, 'info.ttl')
g = rdflib.ConjunctiveGraph().parse(p.replace('\\','/'))
g.parse(os.path.join(story,'db', 'people.ttl').replace('\\','/'))
info = self.sparql.execute('get_info', g, dict())[0]
authors = self.sparql.execute('get_authors', g, dict())
self.story_info = {
'title': info['title'].__str__(),
'media_source': info['trailer'].__str__(),
'authors': [author['author'].__str__() for author in authors],
'tags': info['tags'].__str__().split(','),
'desc': info['desc'].__str__()
}
colors = [list(map(float, info['bg'].__str__().split(','))),
list(map(float, info['fg'].__str__().split(','))),
list(map(float, info['hl'].__str__().split(',')))
]
self.fg, self.bg, self.hl = map(self.convert_color, colors)
def convert_color(self, rgb: List[int]):
if len(rgb) == 3: rgb.append(255)
return [val/255 for val in rgb]
#def load_resources(self):
# #<-- not used -->
# templ_dir = os.path.join(self.base_dir, 'templates')
# for base, dir, files in os.walk(templ_dir):
# for f in files:
# if f.endswith('.kv'):
# kivy.lang.Builder.load_file(os.path.join(base, f))
# if f.endswith('.py'):
# x = os.path.join(base, f)
# #from x import *
#def exist_base_dirs(self) -> bool:
# for name in ['stories', 'resources', 'templates']:
# if name not in next(os.walk(self.base_dir))[1]:
# return False
# return True
###########################################################
######## Main
##########################################################
if __name__ == '__main__':
Clock.max_iteration = 50
MainApp().run()
| 2,755 | 1,518 | 90 |
6edea2709166fa4f2e012d33caf1ef89888887ac | 1,393 | py | Python | omnifig/modes.py | felixludos/omni-f | 0bbe0bc362c332483877eff3a19266b60c175ad4 | [
"MIT"
] | 1 | 2021-04-13T14:01:48.000Z | 2021-04-13T14:01:48.000Z | omnifig/modes.py | felixludos/omni-f | 0bbe0bc362c332483877eff3a19266b60c175ad4 | [
"MIT"
] | null | null | null | omnifig/modes.py | felixludos/omni-f | 0bbe0bc362c332483877eff3a19266b60c175ad4 | [
"MIT"
] | 1 | 2021-11-24T13:36:41.000Z | 2021-11-24T13:36:41.000Z |
from .errors import MissingScriptError
# from .registry import Component, get_script
from .top import find_script
from .decorators import Component
from .util import autofill_args
from omnibelt import get_printer
prt = get_printer(__name__)
@Component('run_mode/default')
class Run_Mode:
'''
Run modes are used to actually execute the script specified by the user in the run command.
It is recommended to register all run_modes with a ``run_mode/`` prefix, but not required.
'''
@staticmethod
def get_script_info(script_name):
'''Given the name of the registered script, this returns the corresponding entry in the registry'''
return find_script(script_name)
def run(self, meta, config):
'''
When called this should actually execute the registered script whose name is specified under meta.script_name.
:param meta: meta config - contains script_name
:param config: config for script
:return: output of script
'''
script_name = meta.pull('script_name', silent=self.silent)
script_info = self.get_script_info(script_name)
if script_info is None:
raise MissingScriptError(script_name)
script_fn = script_info.fn
if script_info.use_config:
return script_fn(config)
return autofill_args(script_fn, config)
| 28.428571 | 113 | 0.731515 |
from .errors import MissingScriptError
# from .registry import Component, get_script
from .top import find_script
from .decorators import Component
from .util import autofill_args
from omnibelt import get_printer
prt = get_printer(__name__)
@Component('run_mode/default')
class Run_Mode:
'''
Run modes are used to actually execute the script specified by the user in the run command.
It is recommended to register all run_modes with a ``run_mode/`` prefix, but not required.
'''
def __init__(self, A):
self.silent = A.pull('silent', True, silent=True)
@staticmethod
def get_script_info(script_name):
'''Given the name of the registered script, this returns the corresponding entry in the registry'''
return find_script(script_name)
def run(self, meta, config):
'''
When called this should actually execute the registered script whose name is specified under meta.script_name.
:param meta: meta config - contains script_name
:param config: config for script
:return: output of script
'''
script_name = meta.pull('script_name', silent=self.silent)
script_info = self.get_script_info(script_name)
if script_info is None:
raise MissingScriptError(script_name)
script_fn = script_info.fn
if script_info.use_config:
return script_fn(config)
return autofill_args(script_fn, config)
| 54 | 0 | 24 |
307e167f40b4313a1e56a2da1d964893d273e387 | 3,255 | py | Python | scripts/new_patient_pipeline/new_pt_qc_script.py | MELDProject/meld_classifier | 487a6a0df2e7c359101a7a58100f438c5aac6a60 | [
"MIT"
] | 1 | 2022-03-16T15:01:07.000Z | 2022-03-16T15:01:07.000Z | scripts/new_patient_pipeline/new_pt_qc_script.py | MELDProject/meld_classifier | 487a6a0df2e7c359101a7a58100f438c5aac6a60 | [
"MIT"
] | 1 | 2022-02-14T13:46:21.000Z | 2022-02-14T13:46:21.000Z | scripts/new_patient_pipeline/new_pt_qc_script.py | MELDProject/meld_classifier | 487a6a0df2e7c359101a7a58100f438c5aac6a60 | [
"MIT"
] | null | null | null | ## This script open freeview with MRI images, MELD predictions and surfaces for quality check of segmentation
## To run : python new_pt_qc_script.py -id <sub_id>
import os
import sys
import argparse
import subprocess as sub
import glob
from meld_classifier.paths import MELD_DATA_PATH, FS_SUBJECTS_PATH
if __name__ == '__main__':
#parse commandline arguments
parser = argparse.ArgumentParser(description='perform cortical parcellation using recon-all from freesurfer')
parser.add_argument('-id','--id_subj',
help='Subject ID.',
required=True,)
args = parser.parse_args()
subject=str(args.id_subj)
# get subject folder and fs folder
subject_dir = os.path.join(MELD_DATA_PATH,'input',subject)
subject_fs_folder = os.path.join(FS_SUBJECTS_PATH, subject)
#initialise freesurfer variable environment
ini_freesurfer = format("$FREESURFER_HOME/SetUpFreeSurfer.sh")
# Find inputs T1 and FLAIR if exists
if not os.path.isdir(subject_fs_folder):
print(f'Freesurfer outputs does not exist for this subject. Unable to perform qc')
else :
#select inputs files T1 and FLAIR
T1_file = return_file(os.path.join(subject_dir, 'T1', '*T1*.nii*'), 'T1')
FLAIR_file = return_file(os.path.join(subject_dir, 'FLAIR', '*FLAIR*.nii*'), 'FLAIR')
#select predictions files
pred_lh_file = return_file(os.path.join(subject_dir, 'predictions', 'lh.prediction.nii*'), 'lh_prediction')
pred_rh_file = return_file(os.path.join(subject_dir, 'predictions', 'rh.prediction.nii*'), 'rh_prediction')
#setup cortical segmentation command
file_text = os.path.join(MELD_DATA_PATH, 'temp1.txt')
if T1_file:
#create txt file with freeview commands
with open(file_text, 'w') as f:
f.write(f'-v {T1_file}:colormap=grayscale -layout 2 \n')
if FLAIR_file:
f.write(f'-v {FLAIR_file}:colormap=grayscale \n')
if (pred_lh_file!=None) & (pred_rh_file!=None):
f.write(f'-v {pred_lh_file}:colormap=lut \n')
f.write(f'-v {pred_rh_file}:colormap=lut \n')
f.write(f'-f {subject_fs_folder}/surf/lh.white:edgecolor=yellow {subject_fs_folder}/surf/lh.pial:edgecolor=red {subject_fs_folder}/surf/rh.white:edgecolor=yellow {subject_fs_folder}/surf/rh.pial:edgecolor=red \n')
#launch freeview
freeview = format(f"freeview -cmd {file_text}")
command = ini_freesurfer + ';' + freeview
print(f"INFO : Open freeview")
sub.check_call(command, shell=True)
os.remove(file_text)
else:
print('Could not find either T1 volume')
pass
| 41.202532 | 229 | 0.640553 | ## This script open freeview with MRI images, MELD predictions and surfaces for quality check of segmentation
## To run : python new_pt_qc_script.py -id <sub_id>
import os
import sys
import argparse
import subprocess as sub
import glob
from meld_classifier.paths import MELD_DATA_PATH, FS_SUBJECTS_PATH
def return_file(path, file_name):
files = glob.glob(path)
if len(files)>1 :
print(f'Find too much volumes for {file_name}. Check and remove the additional volumes with same key name')
return None
elif not files:
print(f'Could not find {file_name} volume. Check if name follow the right nomenclature')
return None
else:
return files[0]
if __name__ == '__main__':
#parse commandline arguments
parser = argparse.ArgumentParser(description='perform cortical parcellation using recon-all from freesurfer')
parser.add_argument('-id','--id_subj',
help='Subject ID.',
required=True,)
args = parser.parse_args()
subject=str(args.id_subj)
# get subject folder and fs folder
subject_dir = os.path.join(MELD_DATA_PATH,'input',subject)
subject_fs_folder = os.path.join(FS_SUBJECTS_PATH, subject)
#initialise freesurfer variable environment
ini_freesurfer = format("$FREESURFER_HOME/SetUpFreeSurfer.sh")
# Find inputs T1 and FLAIR if exists
if not os.path.isdir(subject_fs_folder):
print(f'Freesurfer outputs does not exist for this subject. Unable to perform qc')
else :
#select inputs files T1 and FLAIR
T1_file = return_file(os.path.join(subject_dir, 'T1', '*T1*.nii*'), 'T1')
FLAIR_file = return_file(os.path.join(subject_dir, 'FLAIR', '*FLAIR*.nii*'), 'FLAIR')
#select predictions files
pred_lh_file = return_file(os.path.join(subject_dir, 'predictions', 'lh.prediction.nii*'), 'lh_prediction')
pred_rh_file = return_file(os.path.join(subject_dir, 'predictions', 'rh.prediction.nii*'), 'rh_prediction')
#setup cortical segmentation command
file_text = os.path.join(MELD_DATA_PATH, 'temp1.txt')
if T1_file:
#create txt file with freeview commands
with open(file_text, 'w') as f:
f.write(f'-v {T1_file}:colormap=grayscale -layout 2 \n')
if FLAIR_file:
f.write(f'-v {FLAIR_file}:colormap=grayscale \n')
if (pred_lh_file!=None) & (pred_rh_file!=None):
f.write(f'-v {pred_lh_file}:colormap=lut \n')
f.write(f'-v {pred_rh_file}:colormap=lut \n')
f.write(f'-f {subject_fs_folder}/surf/lh.white:edgecolor=yellow {subject_fs_folder}/surf/lh.pial:edgecolor=red {subject_fs_folder}/surf/rh.white:edgecolor=yellow {subject_fs_folder}/surf/rh.pial:edgecolor=red \n')
#launch freeview
freeview = format(f"freeview -cmd {file_text}")
command = ini_freesurfer + ';' + freeview
print(f"INFO : Open freeview")
sub.check_call(command, shell=True)
os.remove(file_text)
else:
print('Could not find either T1 volume')
pass
| 370 | 0 | 23 |
dc97e001fefbe37271405841d29fb05df667c08e | 2,001 | py | Python | chapter_4/4_6_successor.py | elishaking/CTCi | 6a91fd67e8765e5abef72c5b247f4d5444945438 | [
"MIT"
] | 1 | 2020-07-03T09:47:34.000Z | 2020-07-03T09:47:34.000Z | chapter_4/4_6_successor.py | elishaking/CTCi | 6a91fd67e8765e5abef72c5b247f4d5444945438 | [
"MIT"
] | null | null | null | chapter_4/4_6_successor.py | elishaking/CTCi | 6a91fd67e8765e5abef72c5b247f4d5444945438 | [
"MIT"
] | null | null | null | import unittest
# Time complexity: O(log(N))
# Space complexity: O(1)
if __name__ == "__main__":
unittest.main()
| 27.791667 | 70 | 0.593203 | import unittest
class BSTNodeP:
def __init__(self, value=0, parent=None, left=None, right=None):
self.value = value
self.left = left
self.right = right
self.parent = parent
def __str__(self):
return str(self.value)
class BSTP:
def __init__(self, root_value=0):
self.root = BSTNodeP(root_value)
def insert(self, value=0):
current_node = self.root
while current_node:
if current_node.value > value:
if current_node.left:
current_node = current_node.left
else:
current_node.left = BSTNodeP(value, current_node)
return self
else:
if current_node.right:
current_node = current_node.right
else:
current_node.right = BSTNodeP(value, current_node)
return self
# Time complexity: O(log(N))
# Space complexity: O(1)
def successor(node: BSTNodeP):
result: BSTNodeP = None
if node.right:
# get left-most child of right sub-tree
result = node.right
while result.left:
result = node.left
elif node.parent:
# get next-right ancestor of input node
current_node = node
while current_node.parent and not result:
if current_node.parent.left == current_node:
result = current_node.parent
current_node = current_node.parent
return result
class Tests(unittest.TestCase):
def test_successor(self):
bstp = BSTP(10).insert(5).insert(12).insert(6).insert(4)
test_node_1 = bstp.root.left
test_node_2 = bstp.root.left.right
test_node_3 = bstp.root.right
self.assertEqual(successor(test_node_1).value, 6)
self.assertEqual(successor(test_node_2).value, 10)
self.assertEqual(successor(test_node_3), None)
if __name__ == "__main__":
unittest.main()
| 1,660 | -6 | 223 |
8a85f3036c67974130bf8da629d90eecc39be1ee | 32,156 | py | Python | python/mirtex_benchmark/summarizeResults.py | mjoppich/miRExplore | 32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1 | [
"Apache-2.0"
] | null | null | null | python/mirtex_benchmark/summarizeResults.py | mjoppich/miRExplore | 32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1 | [
"Apache-2.0"
] | null | null | null | python/mirtex_benchmark/summarizeResults.py | mjoppich/miRExplore | 32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1 | [
"Apache-2.0"
] | null | null | null | import os,sys
sys.path.insert(0, "/mnt/f/dev/git/miRExplore/python/")
import time
from textdb.MiGenRelDB import MiGenRelDB
from textdb.SentenceDB import SentenceDB
from collections import defaultdict
from natsort import natsorted
sentDB, _ = SentenceDB.loadFromFile("./test/", "./development/pmid2sent", returnAll=True, redoPmid2Sent=True)
mmuDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.mmu.pmid", ltype="mirna", rtype="gene")
hsaDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.hsa.pmid", ltype="mirna", rtype="gene")
referenceSolution = [
('16831872','miR-9','ONECUT2','MIR_GENE'),
('16831872','miR-9','SYTL4','MIR_GENE'),
#('17438130','miR-17-92','MYC','GENE_MIR'), # not a miRNA (miR-17-92 cluster)
('17438130','let-7c','MYC','MIR_GENE'),
('17438130','let-7c','MIR17HG','MIR_GENE'), #The PPARalpha-mediated induction of c-myc via let-7C subsequently increased expression of the oncogenic mir-17-92 cluster; these events did not occur in Pparalpha-null mice.
('18185580','miR-335','SOX4','MIR_GENE'),
('18185580','miR-335','TNC','MIR_GENE'),
('18755897','miR-34','TP53','GENE_MIR'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a.
('18755897','miR-34a','TP53','GENE_MIR'),
('18755897','miR-34a','SIRT1','MIR_GENE'),
('18755897','miR-34','SIRT1','MIR_GENE'),
('18755897','miR-34','TP53','MIR_GENE'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a.
('18755897','miR-34','CDKN1A','MIR_GENE'), #p21 #miR-34a
('18755897','miR-34','TPT1','MIR_GENE'), # p21
('18755897','miR-34','NSG1','MIR_GENE'), # p21
('18755897','miR-34','H3F3AP6','MIR_GENE'), # p21
('18755897','miR-34','TCEAL1','MIR_GENE'), # p21
('18755897','miR-34','BBC3','MIR_GENE'), #34a, has syn PUMA
('19059913','miR-223','SPI1','GENE_MIR'),
('19059913','miR-223','NFIA','MIR_GENE'),
('19059913','miR-223','NFIC','MIR_GENE'), # TM error, means NFIA
('19059913','miR-223','CSF1R','MIR_GENE'),
('19073597','miR-133a','MYOD1','GENE_MIR'),
('19073597','miR-133a','UCP2','MIR_GENE'),
('19073597','miR-133a','BMIQ4','MIR_GENE'), # has UCP-2 as syn
('19073597','miR-133a-mediated','UCP2','MIR_GENE'),
('19073597','miR-133a-mediated','BMIQ4','MIR_GENE'), # has UCP-2 as syn
('22066022', 'miR-21', 'GPT', 'MIR_GENE'), # missing mirtex: Serum miR-21 levels correlated with histological activity index (HAI) in the liver, alanine aminotransferase (ALT), aspartate aminotransferase , bilirubin, international normalized ratio and gamma-glutamyltransferase.
('19158092','miR-21','PDCD4','MIR_GENE'),
('19158092','miR-21-mediated','PDCD4','MIR_GENE'),
#('19378336','miR-145','KRT7','MIR_GENE'), #no interaction
('19378336','miR-30','KRT7','MIR_GENE'),
('19378336','miR-133a','KRT7','MIR_GENE'),
#('19378336','miR-133b','KRT7','MIR_GENE'), #no interaction in text
#('19378336','miR-195','KRT7','MIR_GENE'), #no interaction
#('19378336','miR-125b','KRT7','MIR_GENE'), #no interaction in text
('19378336','miR-199a','KRT7','MIR_GENE'),
('19524507','miR-31','RHOA','MIR_GENE'),
('19544458','miR-92b','CORO1A','MIR_GENE'), # was p57
('19625769','miR-101','EZH2','MIR_GENE'),
('19723773','miR-290','CDKN2A','MIR_GENE'), # p16
('19839716','miR-205','ERBB3','MIR_GENE'),
('19839716','miR-205','ZEB1','MIR_GENE'),
('19956414','miR-29b','COL1A1','MIR_GENE'),
('19956414','miR-29b','OI4','MIR_GENE'), #COL1A1
('19956414','miR-29b','COL1A2','MIR_GENE'),
('19956414','miR-29b','COL4A1','MIR_GENE'),
('19956414','miR-29b','COL5A1','MIR_GENE'),
('19956414','miR-29b','COL5A2','MIR_GENE'),
('19956414','miR-29b','COL3A1','MIR_GENE'),
('19956414','miR-29b','EDS4A','MIR_GENE'), #COL3A1
('19956414','miR-29b','LAMC1','MIR_GENE'),
('19956414','miR-29b','FBN1','MIR_GENE'),
('19956414','miR-29b','SPARC','MIR_GENE'),
('19956414','miR-29b','ON','MIR_GENE'), #osteonectin
('19956414','miR-29b','BMP1','MIR_GENE'),
('19956414','miR-29b','PCOLC','MIR_GENE'), #BMP1
('19956414','miR-29b','ADAM12','MIR_GENE'),
('19956414','miR-29b','NKIRAS2','MIR_GENE'),
('20012062','miR-221','PSMD9','MIR_GENE'), #p27
('20012062','miR-222','PSMD9','MIR_GENE'),
('20012062','miR-221','SSSCA1','MIR_GENE'), #p27
('20012062','miR-222','SSSCA1','MIR_GENE'),
('20017139','miR-146a','CNTN2','GENE_MIR'),
('20017139','miR-146a','NFKB1','GENE_MIR'),
#('20046097', 'miR-449', 'CDK', 'MIR_GENE'), # CDK not a gene symbol, not in mirtex
('20046097', 'miR-449', 'E2F1', 'MIR_GENE'), #not in mirtex :miR-449 regulates CDK-Rb-E2F1 through an auto-regulatory feedback circuit.
('20046097', 'miR-449', 'RB1', 'MIR_GENE'), #not in mirtex
('20103675','miR-222','PPP2R2A','MIR_GENE'),
('20143188','miR-21','PDCD4','MIR_GENE'),
('20299489','miR-34a','ERK','GENE_MIR'),
('20299489','miR-34a','EPHB2','GENE_MIR'),# ERK syn
('20299489','miR-34a','MAPK1','GENE_MIR'),# ERK syn
('20299489','miR-34a','MAP2K1','MIR_GENE'),
('20299489','miR-221','FOS','MIR_GENE'),
('20299489','miR-222','FOS','MIR_GENE'),
('20299489','miR-34a','FOSB','GENE_MIR'), #mirtex missing: induced miR-34a expression by transactivation via the activator protein-1 binding site in the upstream region of the miR-34a gene.
('20299489','miR-34a','JUND','GENE_MIR'), # activator protein 1 syn
('20299489','miR-34a','JUN','GENE_MIR'), # induced miR-34a expression by transactivation via the activator protein-1 binding site
('20462046','miR-21','PDCD4','MIR_GENE'),
('20478254','miR-183','SLC1A1','MIR_GENE'),
('20478254','miR-96','SLC1A1','MIR_GENE'),
('20478254','miR-182','SLC1A1','MIR_GENE'),
('20498046','miR-200b','ATP2A2','MIR_GENE'),
('20498046','miR-214','ATP2A2','MIR_GENE'),
('20603081','miR-150','MYB','MIR_GENE'),
('20606648', 'miR-34a', 'BIRC5', 'MIR_GENE'), # missing in mirtex, miRNA-34a (miR-34a) induced apoptosis, inhibited survivin expression, and downregulated MAPK pathway in B16F10 cells.
('20620960','miR-200c','FAP','MIR_GENE'),
('20620960','miR-200','FAP','MIR_GENE'),
('20620960','miR-200c','GLMN','MIR_GENE'), # has FAP as syn
('20620960','miR-200','GLMN','MIR_GENE'), # has FAP as syn
('20620960','miR-200','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1.
('20620960','miR-200c','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1.
('20620960','miR-200','ZEB1','MIR_GENE'), # 200c
('20620960','miR-200','ZEB2','MIR_GENE'), # 200c
('20620960','miR-200','PPCD3','MIR_GENE'), # ZEB1
('20676061','miR-29c','WNT5A','MIR_GENE'),
('20676061','miR-130b','WNT5A','MIR_GENE'),
('20676061','miR-101','WNT5A','MIR_GENE'),
('20676061','miR-30b','WNT5A','MIR_GENE'),
('20676061','miR-140','WNT5A','MIR_GENE'),
('20676061','miR-29c','ZIC1','MIR_GENE'),
('20676061','miR-130b','ZIC1','MIR_GENE'),
('20676061','miR-101','ZIC1','MIR_GENE'),
('20676061','miR-30b','ZIC1','MIR_GENE'),
('20676061','miR-140','ZIC1','MIR_GENE'),
('20676061','miR-29c','TGFB1','MIR_GENE'),
('20676061','miR-130b','TGFB1','MIR_GENE'),
('20676061','miR-101','TGFB1','MIR_GENE'),
('20676061','miR-30b','TGFB1','MIR_GENE'),
('20676061','miR-140','TGFB1','MIR_GENE'),
('20676061','miR-29c','DPD1','MIR_GENE'), # has TGFB1 as syn
('20676061','miR-130b','DPD1','MIR_GENE'),
('20676061','miR-101','DPD1','MIR_GENE'),
('20676061','miR-30b','DPD1','MIR_GENE'),
('20676061','miR-140','DPD1','MIR_GENE'),
('20736365','miR-196','HOXC8','MIR_GENE'),
('20736365','miR-196','HOX3A','MIR_GENE'), # has syn HOXC8
('20859756', 'miR-126', 'TMEM8B', 'GENE_MIR'), # missing mirtex: In particular, miR-126, miR-142-3p, miR-155, miR-552, and miR-630 were all upregulated, whereas miR-146a, miR-152, miR-205, miR-365, miR-449, miR-518c, miR-584, miR-615, and miR-622 were downregulated after NGX6 transfection.
('20859756', 'miR-142', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-155', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-552', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-630', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-146a', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-152', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-205', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-365', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-449', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-518c', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-584', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-615', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-622', 'TMEM8B', 'GENE_MIR'),
('20945501', 'miR-141', 'AR', 'MIR_GENE'), # missing mirtex, inhibition of miR-141 by anti-miR-141 suppressed the growth of the LNCaP subline overexpressing AR.
('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'DHTR', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B3', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B7', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B8', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AREG', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'FDXR', 'MIR_GENE'), # has AR as syn
('20947507','miR-155','NFKB1','GENE_MIR'),
('20947507','miR-155','CARD11','GENE_MIR'),
('20947507','miR-155','SPI1','MIR_GENE'), # PU.1
#('20947507','miR-155','CD10','MIR_GENE'),
('20947507','miR-155','MME','MIR_GENE'), # CD10
('21088996','miR-21','PDCD4','MIR_GENE'),
('21276775','miR-145','ROBO2','MIR_GENE'),
('21276775','miR-145','SRGAP2','MIR_GENE'),
('21276775','miR-145','SRGAP3','MIR_GENE'), # has SRGAP2 syn
('21276775','miR-214','ROBO2','MIR_GENE'),
('21276775','miR-214','SRGAP2','MIR_GENE'),
('21276775','miR-214','SRGAP3','MIR_GENE'),# has SRGAP2 syn
('21285947','miR-24','INS','MIR_GENE'),
('21285947','miR-26','INS','MIR_GENE'),
('21285947','miR-182','INS','MIR_GENE'),
('21285947','miR-148','INS','MIR_GENE'),
#('21347332','miR-21','serum','GENE_MIR'), # not a gene
('21347332','miR-21','FGF2','GENE_MIR'),
('21347332','miR-21','RHOB','MIR_GENE'),
('21415212','miR-486','OLFM4','MIR_GENE'),
('21454627','mmu-miR-183','mSEL-1L','MIR_GENE'),
('21609717','miR-98-mediated','IL10','MIR_GENE'),
('21609717','miR-98','IL10','MIR_GENE'),
('21609717','miR-98','PTGS2','MIR_GENE'), #COX-2
('21609717','miR-98', 'LPS', 'GENE_MIR'),
('21609717','miR-98', 'IRF6', 'GENE_MIR'), #missing mirtex, MicroRNA-98 negatively regulates IL-10 production and endotoxin tolerance in macrophages after LPS stimulation.
('21666774','miR-21','LH (luteinizing hormone)','GENE_MIR'),
('21666774','miR-132','LH (luteinizing hormone)','GENE_MIR'),
('21666774','miR-212','LH (luteinizing hormone)','GENE_MIR'),
('21685392','miR-143','NOTCH1','MIR_GENE'), #should be N1ICD, TM issue
('21685392','miR-145','NOTCH1','MIR_GENE'),
('21685392','miR-143','TAN1','MIR_GENE'), #should be N1ICD, TM issue
('21685392','miR-145','TAN1','MIR_GENE'),
('21685392','miR-143','RBPJ','GENE_MIR'), #We also identified N1ICD complex binding to CBF1 sites within the endogenous human miR-143/145 promoter.
('21685392','miR-145','RBPJ','GENE_MIR'),
('21685392','miR-143','JAG1','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF.
('21685392','miR-145','JAG1','GENE_MIR'),
('21685392','miR-143','SRF','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF.
('21685392','miR-145','SRF','GENE_MIR'),
('21685392','miR-145','MYOCD','GENE_MIR'), #The serum response factor (SRF)/myocardin complex binds to CArG sequences to activate miR-143/145 transcription
('21685392','miR-143','MYOCD','GENE_MIR'),
('21693621','miR-21','MYC','GENE_MIR'),
('21693621','miR-29a','MYC','GENE_MIR'),
('21898400','miR-520c','MTOR','MIR_GENE'),
('21898400','miR-373','MTOR','MIR_GENE'),
('21898400','miR-520c','SIRT1','MIR_GENE'),
('21898400','miR-373','SIRT1','MIR_GENE'),
('21898400','miR-520c','MMP9','MIR_GENE'),
('21898400','miR-373','MMP9','MIR_GENE'),
('21898400','miR-520c','CLG4B','MIR_GENE'), # MMP-9
('21898400','miR-373','CLG4B','MIR_GENE'),
('22123611','miR-195','BCL2','MIR_GENE'),
('22123611','miR-195','CASP3','MIR_GENE'),
('22123611','miR-195','WT1','MIR_GENE'), #missing mirtex: miR-195-treated podocytes underwent actin rearrangement and failed to synthesize sufficient levels of WT-1 and synaptopodin proteins, which suggests that the cells had suffered injuries similar to those observed in diabetic nephropathy in both humans and animal models.
('22123611','miR-195','SYNPO','MIR_GENE'),
('22123611','miR-195','GUD','MIR_GENE'),
('22139444','miR-30c','MTA1','MIR_GENE'),
('22249219','miR-214','ADORA2A','MIR_GENE'),
('22249219','miR-15','ADORA2A','MIR_GENE'),
('22249219','miR-16','ADORA2A','MIR_GENE'),
('22269326','miR-29b','COL1A1','MIR_GENE'),
('22269326','miR-29b','COL3A1','MIR_GENE'),
('22269326','miR-29b','EDS4A','MIR_GENE'), #COL3A1 syn
('22269326','miR-29b','COL5A1','MIR_GENE'),
('22269326','miR-29b','ELN','MIR_GENE'),
('22286762','miR-21','NF-kappaB','GENE_MIR'),
('22286762','miR-10b','NF-kappaB','GENE_MIR'),
('22286762','miR-17','NF-kappaB','GENE_MIR'),
('22286762','miR-9','NF-kappaB','GENE_MIR'),
('22569260','miR-223','FOXO1','MIR_GENE'),
('22569260','miR-223','FKHR','MIR_GENE'), # FOXO1 syn
('22634495','miR-10a','CHL1','MIR_GENE'),
('22634495','miR-10a','DDX11','MIR_GENE'), # CHL1 syn
('22698995','let-7','BACH1','MIR_GENE'),
('22698995','let-7b','BACH1','MIR_GENE'),
('22698995','let-7c','BACH1','MIR_GENE'),
('22698995','miR-98','BACH1','MIR_GENE'),
#same gene symbol
('22698995','let-7','BRIP1','MIR_GENE'),
('22698995','let-7b','BRIP1','MIR_GENE'),
('22698995','let-7c','BRIP1','MIR_GENE'),
('22698995','miR-98','BRIP1','MIR_GENE'),
('22698995','let-7','HMOX1','MIR_GENE'),
('22761336','miR-96','REV1','MIR_GENE'),
('22761336','miR-96','RAD51','MIR_GENE'),
('22761336','miR-96','RECA','MIR_GENE'), #RAD51
('22761336','miR-96','RAD51A','MIR_GENE'),
('22847613','miR-130b','TP53','GENE_MIR'),
('22847613','miR-130b','ZEB1','MIR_GENE'),
('22847613','miR-130b','PPCD3','MIR_GENE'), # zeb1
('22891274','miR-146a','NFKB1','GENE_MIR'),
('22891274','miR-146a','NFKB1','MIR_GENE'),
('22891274','miR-146a','TRAF6','MIR_GENE'),
('22891274','miR-146a','IRAK1','MIR_GENE'),
('22925189','miR-30c','ERBB2','MIR_GENE'), #Her-2
('22925189','miR-30d','ERBB2','MIR_GENE'),
('22925189','miR-30e','ERBB2','MIR_GENE'),
('22925189','miR-532','ERBB2','MIR_GENE'),
('22955854','miR-144','ZFX','MIR_GENE'),
('22956424','miR-21','PTEN','MIR_GENE'),
('22956424','miR-21','MHAM','MIR_GENE'), # has PTEN syn
('22956424','miR-21','BZS','MIR_GENE'), # has PTEN syn
('22982443','miR-200c','BMI1','MIR_GENE'),
('22982443','miR-200c','ABCG2','MIR_GENE'),
('22982443','miR-200c','ABCG5','MIR_GENE'),
('22982443','miR-200c','MDR1','MIR_GENE'),
('22982443','miR-200c','TBC1D9','MIR_GENE'), # has syn MDR1
('22982443','miR-200c','ABCB1','MIR_GENE'), # has syn MDR1
('22982443','miR-200c','CDH1','MIR_GENE'),
('23010597','miR-134','FOXM1','MIR_GENE'),
('23010597','miR-134','FKHL16','MIR_GENE'), # has FOXM1 as syn
('23010597','miR-134','ITK','MIR_GENE'), # has EMT as syn; mirtex missing: Functional assays demonstrated that miR-134 inhibited EMT in NSCLC cells.
('23010597','miR-134','SLC22A3','MIR_GENE'), # has EMT as syn
('23041385','miR-21','CRP','MIR_GENE'),
#('23041385','miR-21','fibrinogen','MIR_GENE'), # not a gene
('23041385','miR-21','TGFB2','MIR_GENE'),
('23097316','miR-34c','RARg','MIR_GENE'),
('23113351','miR-29','TP53','MIR_GENE'), # missing in mirtex: While miRNA-29 members induced apoptosis through p53 gene activation, the effect of miRNA-29a on osteoblastic cells was independent on p53 expression level.
('23113351','miR-29a','TP53','MIR_GENE'),
('23113351','miR-29','BCL2','MIR_GENE'),
('23113351','miR-29','MCL1','MIR_GENE'),
('23113351','miR-29','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl
('23113351','miR-29a','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl
#('23113351','miR-29','E2F1','MIR_GENE'),
#('23113351','miR-29','E2F3','MIR_GENE'),
('23113351','miR-29a','BCL2','MIR_GENE'),
('23113351','miR-29a','MCL1','MIR_GENE'),
('23113351','miR-29a','E2F1','MIR_GENE'),
('23113351','miR-29a','E2F3','MIR_GENE'),
('23113351','miR-29a','E2F1','MIR_GENE'), # possibly too
('23113351','miR-29a','E2F3','MIR_GENE'), # possibly too
('23148210','miR-210','HIF1A','GENE_MIR'), # was actived in ...-dependant
('23169590','miR-451','IL6','GENE_MIR'),
('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN
('23169590','miR-451','YWHAZ','MIR_GENE'),
('23169590','miR-451','YWHAD','MIR_GENE'), # has syn YWHAZ
#('23169590','miR-451','14-3-3zeta','MIR_GENE'), # is YWHAZ
('23169590','miR-451','ZFP36','MIR_GENE'),
#Three types of primary DCs treated with antisense RNA antagomirs directed against miR-451 secreted elevated levels of IL-6, TNF, CCL5/RANTES, and CCL3/MIP1alpha, and these results were confirmed using miR-451(null) cells.
#this suggests that miR-451 suppresses these genes normalls
('23169590','miR-451','IL6','MIR_GENE'),
('23169590','miR-451','CCL3','MIR_GENE'),
('23169590','miR-451','CCL5','MIR_GENE'),
('23169590','miR-451','IL6','MIR_GENE'),
('23169590','miR-451','IFNB2','MIR_GENE'), #IL6
('23169590','miR-451','TNF','MIR_GENE'),
('23169590','miR-451','TNFA','MIR_GENE'), #TNF
#miR-451 levels are themselves increased by IL-6 and type I IFN, potentially forming a regulatory loop.
('23169590','miR-451','IL6','GENE_MIR'), #IL6
('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN
('23169590','miR-451','IFNB2','GENE_MIR'), #IL6
('23190607','miR-203','RAN','MIR_GENE'),
('23190607','miR-203','RAPH1','MIR_GENE'),
('23190607','miR-203','ALS2CR18','MIR_GENE'), # synonym
('23190608','miR-29b','SP1','GENE_MIR'),
('23190608','miR-29b','SP1','MIR_GENE'), # mirtex missing: miR-29b sensitizes multiple myeloma cells to bortezomib-induced apoptosis through the activation of a feedback loop with the transcription factor Sp1.
('23190608','miR-29b','DAND5','GENE_MIR'), # DAND5 has SP1 as syn
('23190608','miR-29b','DAND5','MIR_GENE'),
('23206698','miR-7','IRS2','MIR_GENE'),
('23396109','miR-17','PTEN','MIR_GENE'), #miR-17~92
('23396109','miR-17','MHAM','MIR_GENE'), # MHAM syn is PTEN too
('23396109','miR-17','BZS','MIR_GENE'), # BZS syn is PTEN too
#('23396109','miR-17','BIM','MIR_GENE'), # miR-17~92 is pten
('23396109','miR-17','BCL2L11','MIR_GENE'), # BCL2L11 syn is BIM
('23472202','miR-183','TAOK1','MIR_GENE'),
('23516615','miR-143','ERK5','MIR_GENE'),
('23516615','miR-143','PPARg','MIR_GENE'),
('23516615','miR-204','ERK5','MIR_GENE'),
('23516615','miR-204','PPARg','MIR_GENE'),
('23519125','miR-125a','erbB2','MIR_GENE'),
('23519125','miR-125a','erbB3','MIR_GENE'),
('23519125','miR-125b','erbB2','MIR_GENE'),
('23519125','miR-125b','erbB3','MIR_GENE'),
('23519125','miR-205','erbB2','MIR_GENE'),
('23519125','miR-205','erbB3','MIR_GENE'),
# these are no genes!
#('23527070','miR-21','collagen I','MIR_GENE'),
#('23527070','miR-21','collagen III','MIR_GENE'),
('23527070','miR-21','ELN','MIR_GENE'),
('23527070','miR-21','SMAD7','MIR_GENE'),
('23527070','miR-21','SMAD5','MIR_GENE'),
('23527070','miR-21','SMAD2','MIR_GENE'),
#('23534973','miR-152','HLA-G','MIR_GENE'), not a specific miRNA: miR-152 family
('23579289','miR-214','SP7','MIR_GENE'),
('23583389','miR-96','IRS1','MIR_GENE'),
('23592910','miR-146a','IL1B','GENE_MIR'),
('23592910','miR-146a','IFNG','GENE_MIR'),
#('23592910','miR-146a','TNFA','GENE_MIR'), # is TNF
('23592910','miR-146b','IL1B','GENE_MIR'),
('23592910','miR-146b','IFNG','GENE_MIR'),
('23592910','miR-146b','TNF','GENE_MIR'),
('23592910','miR-146a','IRAK','MIR_GENE'),
('23592910','miR-146b','IRAK','MIR_GENE'),
('23611780','miR-106b','FBXW11','MIR_GENE'), #beta-TRCP2
#('23611780','miR-106b','SNAIL','MIR_GENE'), nope. means cluster + indirect: miR-106b-25 cluster may play an important role in the metastasis of human non-small cell lung cancer cells by directly suppressing the beta-TRCP2 gene expression with a consequent increase in the expression of Snail.
('23611780','miR-93','FBXW11','MIR_GENE'),
('23630358','miR-155','MSR1','MIR_GENE'), # SR-AI syn
('23643257','miR-424','FGR','MIR_GENE'),
('23643257','miR-424','MAP2K1','MIR_GENE'),
('23643257','miR-424','MAPK1','MIR_GENE'), #mitogen-activated protein kinase 1
('23667495','miR-224','DPYSL2','MIR_GENE'),
('23667495','miR-224','KRAS','MIR_GENE'),
('23667495','miR-452','DPYSL2','MIR_GENE'),
('23667495','miR-452','KRAS','MIR_GENE'),
('23667495','miR-181c','KRAS','MIR_GENE'),
('23667495','miR-340','MECP2','MIR_GENE'),
('23667495','miR-181c','MECP2','MIR_GENE'),
('23667495','miR-340','KRAS','MIR_GENE'),
('23759586','miR-34a','SIRT1','MIR_GENE'),
('23759586','miR-125b','TP53','MIR_GENE'),
('23759586','miR-125b','SIRT1','MIR_GENE'),
('23797704','miR-21','TIMP3','MIR_GENE'),
('23797704','miR-221','TIMP3','MIR_GENE'),
('23797704','miR-21','SFD','MIR_GENE'),#is TIMP3
('23797704','miR-221','SFD','MIR_GENE'),#is TIMP3
('23797704','miR-217','TIMP3','MIR_GENE'),
('23797704','miR-217','SFD','MIR_GENE'), #is TIMP3
('23797704','miR-217','SIRT1','MIR_GENE'),
('23836497','miR-20','STAT3','MIR_GENE'),
('23836497','miR-20','CCND1','MIR_GENE'),
('23836497','miR-106a','STAT3','MIR_GENE'),
('23836497','miR-106a','CCND1','MIR_GENE'),
# same genesymbols
('23846856','miR-875','PRDX3','MIR_GENE'),
('23846856','miR-875','PRX','MIR_GENE'),
('23851184','miR-200b','WNT1','MIR_GENE'),
('23851184','miR-22','WNT1','MIR_GENE'),
('23895517','mir-494','TNFSF14','MIR_GENE'),
('23895517','mir-197','TNFSF14','MIR_GENE'),
('23968734','miR-133a','PDLIM5','MIR_GENE'), # LIM
#('23968734','miR-133a','SH3 protein 1','MIR_GENE'),
('23968734','miR-133a','LASP1','MIR_GENE'), #SH3 protein 1
('24006456','miR-29b','IGF1','MIR_GENE'),
('24006456','miR-30c','IGF1','MIR_GENE'),
('24006456','miR-29b','LIF','MIR_GENE'),
('24006456','miR-30c','LIF','MIR_GENE'),
('24006456','miR-29b','PTX3','MIR_GENE'),
('24023867','miR-135a','NR3C2','MIR_GENE'),
('24023867','miR-124','NR3C2','MIR_GENE'),
('24145190','miR-203','SNAI1','GENE_MIR'),
('24145190','miR-203','CD44','MIR_GENE'), # new, not in mirtex: we found that the levels of several EMT activators and miR-203 were positively and negatively correlated with those of CD44, respectively.
('24145190','miR-203','MDU3','MIR_GENE'),
('24145190','miR-203','MIC4','MIR_GENE'),
('24145190','miR-203','MDU2','MIR_GENE'),
('24145190','miR-203','SRC','GENE_MIR'), # missing in mirtex: Finally, we discovered that c-Src kinase activity was required for the downregulation of miR-203
('24155920','miR-21','SPRY1','MIR_GENE'),
('24155920','miR-29a','MCL1','MIR_GENE'),
('24155920','miR-29b','MCL1','MIR_GENE'),
#('24219008','miR-21-5p','TGFBR3','MIR_GENE'),
('24219008','miR-21','TGFBR3','MIR_GENE'), #add
('24219008','hsa-miR-21','TGFBR3','MIR_GENE'), #add
#('24219008','miR-21-5p','PDGFD','MIR_GENE'),
('24219008','miR-21','PDGFD','MIR_GENE'),
('24219008','hsa-miR-21','PDGFD','MIR_GENE'), #add
#('24219008','miR-21-5p','PPM1L','MIR_GENE'),
('24219008','miR-21','PPM1L','MIR_GENE'),
('24219008','hsa-miR-21','PPM1L','MIR_GENE'), #add
#('24219008','miR-181a-5p','ROPN1L','MIR_GENE'),
('24219008','miR-181a','ROPN1L','MIR_GENE'),
('24219008','hsa-miR-181a','ROPN1L','MIR_GENE'),
#('24219008','miR-181a-5p','SLC37A3','MIR_GENE'),
('24219008','hsa-miR-181a','SLC37A3','MIR_GENE'),
('24219008','miR-181a','SLC37A3','MIR_GENE'),
#('24219008','miR-24-2-5p','MYC','MIR_GENE'),
('24219008','hsa-miR-24-2','MYC','MIR_GENE'),
('24219008','hsa-miR-24','MYC','MIR_GENE'),
('24219008','miR-24-2','MYC','MIR_GENE'),
('24219008','miR-24','MYC','MIR_GENE'),
#('24219008','miR-24-2-5p','KCNJ2','MIR_GENE'),
('24219008','hsa-miR-24-2','KCNJ2','MIR_GENE'),
('24219008','hsa-miR-24','KCNJ2','MIR_GENE'),
('24219008','miR-24','KCNJ2','MIR_GENE'),
('24219008','miR-24-2','KCNJ2','MIR_GENE'),
('24219349','miR-203','BMI1','MIR_GENE'),
('24220339','miR-490','FOS','MIR_GENE'),
('24223656','miR-31','RASA1','MIR_GENE'),
('24314216','miR-106','TP53','MIR_GENE'),
('24319262','miR-34a','TP53','GENE_MIR'),
('24319262','miR-145','TP53','GENE_MIR'),
('24319262','miR-155','MAF','MIR_GENE'),
('24319262','miR-34a','TWIST2','MIR_GENE'),
('24319262','miR-34a','MAF','MIR_GENE'),
('24319262','miR-145','TWIST2','MIR_GENE'),
('24319262','miR-145','MAF','MIR_GENE'),
('24330780','miR-124','FLOT1','MIR_GENE'),
('24330780','miR-124','FLOT1','MIR_GENE'),
('24376808','miR-146a','CRK','MIR_GENE'),
('24376808','miR-424','CRK','MIR_GENE'),
('24376808','miR-146a','EGFR','MIR_GENE'),
('24376808','miR-424','EGFR','MIR_GENE'),
('24376808','miR-146a','MAPK14','MIR_GENE'), #p38 / ERK
('24376808','miR-424','MAPK14','MIR_GENE'),
('24376808','miR-146a','AIMP2','MIR_GENE'),#p38 / ERK
('24376808','miR-424','AIMP2','MIR_GENE'),
('24376808','miR-146a','AHSA1','MIR_GENE'),#p38 / ERK
('24376808','miR-424','AHSA1','MIR_GENE'),
]
refDict = defaultdict(set)
for x in referenceSolution:
refDict[x[0]].add((x[1], x[2], x[3]))
tmRemoveTMErrors = {
('19956414','miR-29b','MMRN1'), # ECM, extracellular matrix
('21415212','miR-486','GC'), #gastric cancer
('21415212','miR-486','HTC2'), #Array-CGH
('21415212','miR-486','EAF2'), #TRAITS
('21415212','miR-486','NF2'), #SCH cell line
('21703983', 'miR-632', 'PAFAH1B1'), # Notably, hsa-miR-378, hsa-miR-632, and hsa-miR-636 demonstrated particularly high discrimination between MDS and normal controls. MDS here is myelodysplastic syndromes
('21703983', 'hsa-miR-378', 'PAFAH1B1'),
('21703983', 'miR-378', 'PAFAH1B1'),
('21703983', 'hsa-miR-632', 'PAFAH1B1'),
('21703983', 'hsa-miR-636', 'PAFAH1B1'),
('21703983', 'miR-636', 'PAFAH1B1'),
('22066022', 'miR-21', 'FAM126A'), # HCC refers to hepatocellular carcinoma
('22066022', 'miR-21', 'ST14'), # HAI refers to histological activity index (HAI)
('22066022', 'miR-21', 'SPINT1'), # HAI refers to histological activity index (HAI)
('23643257','miR-424','FGR'), # recognizes FGR
('23643257','miR-424','FGFR1'),
('23643257','miR-424','KAL2'),
('23643257','miR-424','FLT2'),
('24330780','miR-124','TENM1'), #tumor node metastasis (TNM)
('24330780','miR-124','TNM'),
('24223656', 'miR-31', "TPT1"), # RAS p21 GTPase activating protein 1 (RASA1) => p21
('24223656', 'miR-31', "CDKN1A"),
('24223656', 'miR-31', "H3F3AP6"),
('24223656', 'miR-31', "TCEAL1"),
('24223656', 'miR-31', "NSG1"),
('21609717','miR-98','MT-CO2'), # accepts COX-2
('21609717','miR-98','COX8A'),
('21609717','miR-98','CPOX'),
('21609717','miR-98','MT-CO2'),
('23527070', 'miR-21', 'SMAD5'), # SMAD2/5
('23190608','miR-29b','SUPT20H'), # SUPT20H has transcription-factor as syn
('23190608','miR-29b','SUPT20H'),
('18185580','miR-335', 'SUPT20H'),
('23113351','miR-29','RB1'), # RB1 syn: osteosarcoma
('23113351','miR-29a','RB1'), # RB1 syn: osteosarcoma
('23113351','miR-29b','RB1'), # RB1 syn: osteosarcoma
('22982443','miR-200c','CDH17'), # CDH17 syn for cadherin, found in E-cadherin ...
('20603081','miR-150','GLI2'), # THP-1 refers to cells
('22139444', 'miR-30c', 'NDC80'), # refers to HEC-1-B cells ...
('23041385', 'miR-21', 'CO'), # centenarian offspring (CO)
('23041385', 'miR-21', 'CALCR'), # CTR control
('19723773','miR-290','MEF'),#mouse embryo fibroblasts (MEF)
('19723773','miR-290','MEFV'),#mouse embryo fibroblasts (MEF)
('19723773','miR-290','ELF4'),#mouse embryo fibroblasts (MEF)
('19547998', 'miR-21', 'CALR'), # SSA
('19547998', 'miR-181b', 'CALR'),
('19547998', 'miR-21', 'HP'), # hyperplastic polyps
('19547998', 'miR-181b', 'HP'),
('20103675', 'miR-222', 'FAM126A'), # HCC cell lines
('24219349','miR-203','SP'), # side population
('24219349','miR-203','TFF2'), # SP
('21088996','miR-21','BLOC1S6'), # PDAC cells (MIA-Pa-Ca-2)
('21088996','miR-21','MIA'), # PDAC cells (MIA-Pa-Ca-2)
('21088996','miR-21','CAR2'), # PDAC cells (MIA-Pa-Ca-2)
('22847613','miR-130b','SLC22A3'), # epithelial-mesenchymal transition (EMT)
('22847613','miR-130b','ITK'), # epithelial-mesenchymal transition (EMT)
('22925189', 'miR-370', 'II'), #stage II <=> gene symbol
('22925189', 'miR-370', 'IV'), #stage IV <=> gene symbol
('22925189', 'miR-30a', 'II'), #stage II <=> gene symbol
('22925189', 'miR-30a', 'IV'), #stage IV <=> gene symbol
('23592910', 'miR-146a', 'IFNA1'), # TM mismatch with interferon in interfon gamma
('23592910', 'miR-146b', 'IFNA1'),
('24006456','miR-29b','INS'), # spurious hit with insulin-like growth factor
('24006456','miR-30c','INS'), # insulin-like
('20945501', 'miR-141', 'PC'), # matches PC / prostate cancer
('20945501', 'miR-141', 'PODXL'), # matches CRPC (castration-resitant prostate cancer)
}
# gene-mir: 36 F1: 0.88 *0.135 = 0,1188
# mir-gene: 230 F1: 0.94 *0.865 = 0,8131
# all: F1: 0.9319
sent2rels = defaultdict(set)
allSents = sentDB.get_all_sentences()
doc2Rels = defaultdict(set)
for mirID in mmuDB.ltype2rel:
for rel in mmuDB.ltype2rel[mirID]:
jel = rel.toJSON()
sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) )
docID = rel.assocSent.split(".")[0]
doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) )
for mirID in hsaDB.ltype2rel:
for rel in hsaDB.ltype2rel[mirID]:
jel = rel.toJSON()
sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) )
#print(rel.assocSent, rel.lid, rel.rid, rel.assocInt, rel.assocCat, relSent)
docID = rel.assocSent.split(".")[0]
doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) )
from collections import Counter
#TM, REF
elemCaseCounter = Counter()
with open("test_list.bydoc.tsv", "w") as fout:
print("doc", "lid", "rid", "assocInt", sep="\t", file=fout)
allDocIDs = set([x.split(".")[0] for x in allSents])
for docID in natsorted(doc2Rels):
for elems in doc2Rels[docID]:
print(docID, *elems, sep="\t", file=fout)
refOnly = refDict[docID].difference(doc2Rels[docID])
tmOnly = doc2Rels[docID].difference(refDict[docID])
tmOnly = [x for x in tmOnly if not (docID, x[0], x[1]) in tmRemoveTMErrors]
correct = refDict[docID].intersection(doc2Rels[docID])
for x in correct:
elemCaseCounter[(True, True)] += 1
for x in refOnly:
elemCaseCounter[(False, True)] += 1
for x in tmOnly:
elemCaseCounter[(True, False)] += 1
if len(doc2Rels[docID]) == 0 and len(refDict[docID]):
continue
if len(refOnly) == 0 and len(tmOnly) == 0:
continue
print(docID, len(correct), "REFONLY", refOnly)
print(docID, len(correct), "TMONLY", tmOnly)
print()
precision = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(True, False)])
recall = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(False, True)])
f1 = 2* precision * recall / (precision+recall)
#specificity = elemCaseCounter[(False, False)] / (elemCaseCounter[(True, False)] + elemCaseCounter[(False, False)])
print()
print()
print("True, True", elemCaseCounter[(True, True)])
print("TM Only", elemCaseCounter[(True, False)])
print("Ref Only", elemCaseCounter[(False, True)])
print()
print("precision", precision)
print("recall", recall)
#print("specificity", specificity)
print("f1", f1)
with open("test_list.tsv", "w") as fout:
print("lid", "rid", "assocInt", "assocCat", "lpos", "rpos", "int_eval", "cat_eval", "sentID", "sent", sep="\t", file=fout)
for sentID in natsorted([x for x in allSents]):
sent = allSents[sentID]
allElems = sent2rels.get(sentID, None)
if allElems == None:
allElems = [ ("", "", "", "", "", "") ]
for lid, rid, assocInt, assocCat, lpos, rpos in allElems:
print(lid, rid, assocInt, assocCat, lpos, rpos, "FALSE", "FALSE", sentID, sent, sep="\t", file=fout)
| 40.963057 | 327 | 0.645572 | import os,sys
sys.path.insert(0, "/mnt/f/dev/git/miRExplore/python/")
import time
from textdb.MiGenRelDB import MiGenRelDB
from textdb.SentenceDB import SentenceDB
from collections import defaultdict
from natsort import natsorted
sentDB, _ = SentenceDB.loadFromFile("./test/", "./development/pmid2sent", returnAll=True, redoPmid2Sent=True)
mmuDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.mmu.pmid", ltype="mirna", rtype="gene")
hsaDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.hsa.pmid", ltype="mirna", rtype="gene")
referenceSolution = [
('16831872','miR-9','ONECUT2','MIR_GENE'),
('16831872','miR-9','SYTL4','MIR_GENE'),
#('17438130','miR-17-92','MYC','GENE_MIR'), # not a miRNA (miR-17-92 cluster)
('17438130','let-7c','MYC','MIR_GENE'),
('17438130','let-7c','MIR17HG','MIR_GENE'), #The PPARalpha-mediated induction of c-myc via let-7C subsequently increased expression of the oncogenic mir-17-92 cluster; these events did not occur in Pparalpha-null mice.
('18185580','miR-335','SOX4','MIR_GENE'),
('18185580','miR-335','TNC','MIR_GENE'),
('18755897','miR-34','TP53','GENE_MIR'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a.
('18755897','miR-34a','TP53','GENE_MIR'),
('18755897','miR-34a','SIRT1','MIR_GENE'),
('18755897','miR-34','SIRT1','MIR_GENE'),
('18755897','miR-34','TP53','MIR_GENE'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a.
('18755897','miR-34','CDKN1A','MIR_GENE'), #p21 #miR-34a
('18755897','miR-34','TPT1','MIR_GENE'), # p21
('18755897','miR-34','NSG1','MIR_GENE'), # p21
('18755897','miR-34','H3F3AP6','MIR_GENE'), # p21
('18755897','miR-34','TCEAL1','MIR_GENE'), # p21
('18755897','miR-34','BBC3','MIR_GENE'), #34a, has syn PUMA
('19059913','miR-223','SPI1','GENE_MIR'),
('19059913','miR-223','NFIA','MIR_GENE'),
('19059913','miR-223','NFIC','MIR_GENE'), # TM error, means NFIA
('19059913','miR-223','CSF1R','MIR_GENE'),
('19073597','miR-133a','MYOD1','GENE_MIR'),
('19073597','miR-133a','UCP2','MIR_GENE'),
('19073597','miR-133a','BMIQ4','MIR_GENE'), # has UCP-2 as syn
('19073597','miR-133a-mediated','UCP2','MIR_GENE'),
('19073597','miR-133a-mediated','BMIQ4','MIR_GENE'), # has UCP-2 as syn
('22066022', 'miR-21', 'GPT', 'MIR_GENE'), # missing mirtex: Serum miR-21 levels correlated with histological activity index (HAI) in the liver, alanine aminotransferase (ALT), aspartate aminotransferase , bilirubin, international normalized ratio and gamma-glutamyltransferase.
('19158092','miR-21','PDCD4','MIR_GENE'),
('19158092','miR-21-mediated','PDCD4','MIR_GENE'),
#('19378336','miR-145','KRT7','MIR_GENE'), #no interaction
('19378336','miR-30','KRT7','MIR_GENE'),
('19378336','miR-133a','KRT7','MIR_GENE'),
#('19378336','miR-133b','KRT7','MIR_GENE'), #no interaction in text
#('19378336','miR-195','KRT7','MIR_GENE'), #no interaction
#('19378336','miR-125b','KRT7','MIR_GENE'), #no interaction in text
('19378336','miR-199a','KRT7','MIR_GENE'),
('19524507','miR-31','RHOA','MIR_GENE'),
('19544458','miR-92b','CORO1A','MIR_GENE'), # was p57
('19625769','miR-101','EZH2','MIR_GENE'),
('19723773','miR-290','CDKN2A','MIR_GENE'), # p16
('19839716','miR-205','ERBB3','MIR_GENE'),
('19839716','miR-205','ZEB1','MIR_GENE'),
('19956414','miR-29b','COL1A1','MIR_GENE'),
('19956414','miR-29b','OI4','MIR_GENE'), #COL1A1
('19956414','miR-29b','COL1A2','MIR_GENE'),
('19956414','miR-29b','COL4A1','MIR_GENE'),
('19956414','miR-29b','COL5A1','MIR_GENE'),
('19956414','miR-29b','COL5A2','MIR_GENE'),
('19956414','miR-29b','COL3A1','MIR_GENE'),
('19956414','miR-29b','EDS4A','MIR_GENE'), #COL3A1
('19956414','miR-29b','LAMC1','MIR_GENE'),
('19956414','miR-29b','FBN1','MIR_GENE'),
('19956414','miR-29b','SPARC','MIR_GENE'),
('19956414','miR-29b','ON','MIR_GENE'), #osteonectin
('19956414','miR-29b','BMP1','MIR_GENE'),
('19956414','miR-29b','PCOLC','MIR_GENE'), #BMP1
('19956414','miR-29b','ADAM12','MIR_GENE'),
('19956414','miR-29b','NKIRAS2','MIR_GENE'),
('20012062','miR-221','PSMD9','MIR_GENE'), #p27
('20012062','miR-222','PSMD9','MIR_GENE'),
('20012062','miR-221','SSSCA1','MIR_GENE'), #p27
('20012062','miR-222','SSSCA1','MIR_GENE'),
('20017139','miR-146a','CNTN2','GENE_MIR'),
('20017139','miR-146a','NFKB1','GENE_MIR'),
#('20046097', 'miR-449', 'CDK', 'MIR_GENE'), # CDK not a gene symbol, not in mirtex
('20046097', 'miR-449', 'E2F1', 'MIR_GENE'), #not in mirtex :miR-449 regulates CDK-Rb-E2F1 through an auto-regulatory feedback circuit.
('20046097', 'miR-449', 'RB1', 'MIR_GENE'), #not in mirtex
('20103675','miR-222','PPP2R2A','MIR_GENE'),
('20143188','miR-21','PDCD4','MIR_GENE'),
('20299489','miR-34a','ERK','GENE_MIR'),
('20299489','miR-34a','EPHB2','GENE_MIR'),# ERK syn
('20299489','miR-34a','MAPK1','GENE_MIR'),# ERK syn
('20299489','miR-34a','MAP2K1','MIR_GENE'),
('20299489','miR-221','FOS','MIR_GENE'),
('20299489','miR-222','FOS','MIR_GENE'),
('20299489','miR-34a','FOSB','GENE_MIR'), #mirtex missing: induced miR-34a expression by transactivation via the activator protein-1 binding site in the upstream region of the miR-34a gene.
('20299489','miR-34a','JUND','GENE_MIR'), # activator protein 1 syn
('20299489','miR-34a','JUN','GENE_MIR'), # induced miR-34a expression by transactivation via the activator protein-1 binding site
('20462046','miR-21','PDCD4','MIR_GENE'),
('20478254','miR-183','SLC1A1','MIR_GENE'),
('20478254','miR-96','SLC1A1','MIR_GENE'),
('20478254','miR-182','SLC1A1','MIR_GENE'),
('20498046','miR-200b','ATP2A2','MIR_GENE'),
('20498046','miR-214','ATP2A2','MIR_GENE'),
('20603081','miR-150','MYB','MIR_GENE'),
('20606648', 'miR-34a', 'BIRC5', 'MIR_GENE'), # missing in mirtex, miRNA-34a (miR-34a) induced apoptosis, inhibited survivin expression, and downregulated MAPK pathway in B16F10 cells.
('20620960','miR-200c','FAP','MIR_GENE'),
('20620960','miR-200','FAP','MIR_GENE'),
('20620960','miR-200c','GLMN','MIR_GENE'), # has FAP as syn
('20620960','miR-200','GLMN','MIR_GENE'), # has FAP as syn
('20620960','miR-200','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1.
('20620960','miR-200c','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1.
('20620960','miR-200','ZEB1','MIR_GENE'), # 200c
('20620960','miR-200','ZEB2','MIR_GENE'), # 200c
('20620960','miR-200','PPCD3','MIR_GENE'), # ZEB1
('20676061','miR-29c','WNT5A','MIR_GENE'),
('20676061','miR-130b','WNT5A','MIR_GENE'),
('20676061','miR-101','WNT5A','MIR_GENE'),
('20676061','miR-30b','WNT5A','MIR_GENE'),
('20676061','miR-140','WNT5A','MIR_GENE'),
('20676061','miR-29c','ZIC1','MIR_GENE'),
('20676061','miR-130b','ZIC1','MIR_GENE'),
('20676061','miR-101','ZIC1','MIR_GENE'),
('20676061','miR-30b','ZIC1','MIR_GENE'),
('20676061','miR-140','ZIC1','MIR_GENE'),
('20676061','miR-29c','TGFB1','MIR_GENE'),
('20676061','miR-130b','TGFB1','MIR_GENE'),
('20676061','miR-101','TGFB1','MIR_GENE'),
('20676061','miR-30b','TGFB1','MIR_GENE'),
('20676061','miR-140','TGFB1','MIR_GENE'),
('20676061','miR-29c','DPD1','MIR_GENE'), # has TGFB1 as syn
('20676061','miR-130b','DPD1','MIR_GENE'),
('20676061','miR-101','DPD1','MIR_GENE'),
('20676061','miR-30b','DPD1','MIR_GENE'),
('20676061','miR-140','DPD1','MIR_GENE'),
('20736365','miR-196','HOXC8','MIR_GENE'),
('20736365','miR-196','HOX3A','MIR_GENE'), # has syn HOXC8
('20859756', 'miR-126', 'TMEM8B', 'GENE_MIR'), # missing mirtex: In particular, miR-126, miR-142-3p, miR-155, miR-552, and miR-630 were all upregulated, whereas miR-146a, miR-152, miR-205, miR-365, miR-449, miR-518c, miR-584, miR-615, and miR-622 were downregulated after NGX6 transfection.
('20859756', 'miR-142', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-155', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-552', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-630', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-146a', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-152', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-205', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-365', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-449', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-518c', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-584', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-615', 'TMEM8B', 'GENE_MIR'),
('20859756', 'miR-622', 'TMEM8B', 'GENE_MIR'),
('20945501', 'miR-141', 'AR', 'MIR_GENE'), # missing mirtex, inhibition of miR-141 by anti-miR-141 suppressed the growth of the LNCaP subline overexpressing AR.
('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'DHTR', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B3', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B7', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AKR1B8', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'AREG', 'MIR_GENE'), # has AR as syn
('20945501', 'miR-141', 'FDXR', 'MIR_GENE'), # has AR as syn
('20947507','miR-155','NFKB1','GENE_MIR'),
('20947507','miR-155','CARD11','GENE_MIR'),
('20947507','miR-155','SPI1','MIR_GENE'), # PU.1
#('20947507','miR-155','CD10','MIR_GENE'),
('20947507','miR-155','MME','MIR_GENE'), # CD10
('21088996','miR-21','PDCD4','MIR_GENE'),
('21276775','miR-145','ROBO2','MIR_GENE'),
('21276775','miR-145','SRGAP2','MIR_GENE'),
('21276775','miR-145','SRGAP3','MIR_GENE'), # has SRGAP2 syn
('21276775','miR-214','ROBO2','MIR_GENE'),
('21276775','miR-214','SRGAP2','MIR_GENE'),
('21276775','miR-214','SRGAP3','MIR_GENE'),# has SRGAP2 syn
('21285947','miR-24','INS','MIR_GENE'),
('21285947','miR-26','INS','MIR_GENE'),
('21285947','miR-182','INS','MIR_GENE'),
('21285947','miR-148','INS','MIR_GENE'),
#('21347332','miR-21','serum','GENE_MIR'), # not a gene
('21347332','miR-21','FGF2','GENE_MIR'),
('21347332','miR-21','RHOB','MIR_GENE'),
('21415212','miR-486','OLFM4','MIR_GENE'),
('21454627','mmu-miR-183','mSEL-1L','MIR_GENE'),
('21609717','miR-98-mediated','IL10','MIR_GENE'),
('21609717','miR-98','IL10','MIR_GENE'),
('21609717','miR-98','PTGS2','MIR_GENE'), #COX-2
('21609717','miR-98', 'LPS', 'GENE_MIR'),
('21609717','miR-98', 'IRF6', 'GENE_MIR'), #missing mirtex, MicroRNA-98 negatively regulates IL-10 production and endotoxin tolerance in macrophages after LPS stimulation.
('21666774','miR-21','LH (luteinizing hormone)','GENE_MIR'),
('21666774','miR-132','LH (luteinizing hormone)','GENE_MIR'),
('21666774','miR-212','LH (luteinizing hormone)','GENE_MIR'),
('21685392','miR-143','NOTCH1','MIR_GENE'), #should be N1ICD, TM issue
('21685392','miR-145','NOTCH1','MIR_GENE'),
('21685392','miR-143','TAN1','MIR_GENE'), #should be N1ICD, TM issue
('21685392','miR-145','TAN1','MIR_GENE'),
('21685392','miR-143','RBPJ','GENE_MIR'), #We also identified N1ICD complex binding to CBF1 sites within the endogenous human miR-143/145 promoter.
('21685392','miR-145','RBPJ','GENE_MIR'),
('21685392','miR-143','JAG1','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF.
('21685392','miR-145','JAG1','GENE_MIR'),
('21685392','miR-143','SRF','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF.
('21685392','miR-145','SRF','GENE_MIR'),
('21685392','miR-145','MYOCD','GENE_MIR'), #The serum response factor (SRF)/myocardin complex binds to CArG sequences to activate miR-143/145 transcription
('21685392','miR-143','MYOCD','GENE_MIR'),
('21693621','miR-21','MYC','GENE_MIR'),
('21693621','miR-29a','MYC','GENE_MIR'),
('21898400','miR-520c','MTOR','MIR_GENE'),
('21898400','miR-373','MTOR','MIR_GENE'),
('21898400','miR-520c','SIRT1','MIR_GENE'),
('21898400','miR-373','SIRT1','MIR_GENE'),
('21898400','miR-520c','MMP9','MIR_GENE'),
('21898400','miR-373','MMP9','MIR_GENE'),
('21898400','miR-520c','CLG4B','MIR_GENE'), # MMP-9
('21898400','miR-373','CLG4B','MIR_GENE'),
('22123611','miR-195','BCL2','MIR_GENE'),
('22123611','miR-195','CASP3','MIR_GENE'),
('22123611','miR-195','WT1','MIR_GENE'), #missing mirtex: miR-195-treated podocytes underwent actin rearrangement and failed to synthesize sufficient levels of WT-1 and synaptopodin proteins, which suggests that the cells had suffered injuries similar to those observed in diabetic nephropathy in both humans and animal models.
('22123611','miR-195','SYNPO','MIR_GENE'),
('22123611','miR-195','GUD','MIR_GENE'),
('22139444','miR-30c','MTA1','MIR_GENE'),
('22249219','miR-214','ADORA2A','MIR_GENE'),
('22249219','miR-15','ADORA2A','MIR_GENE'),
('22249219','miR-16','ADORA2A','MIR_GENE'),
('22269326','miR-29b','COL1A1','MIR_GENE'),
('22269326','miR-29b','COL3A1','MIR_GENE'),
('22269326','miR-29b','EDS4A','MIR_GENE'), #COL3A1 syn
('22269326','miR-29b','COL5A1','MIR_GENE'),
('22269326','miR-29b','ELN','MIR_GENE'),
('22286762','miR-21','NF-kappaB','GENE_MIR'),
('22286762','miR-10b','NF-kappaB','GENE_MIR'),
('22286762','miR-17','NF-kappaB','GENE_MIR'),
('22286762','miR-9','NF-kappaB','GENE_MIR'),
('22569260','miR-223','FOXO1','MIR_GENE'),
('22569260','miR-223','FKHR','MIR_GENE'), # FOXO1 syn
('22634495','miR-10a','CHL1','MIR_GENE'),
('22634495','miR-10a','DDX11','MIR_GENE'), # CHL1 syn
('22698995','let-7','BACH1','MIR_GENE'),
('22698995','let-7b','BACH1','MIR_GENE'),
('22698995','let-7c','BACH1','MIR_GENE'),
('22698995','miR-98','BACH1','MIR_GENE'),
#same gene symbol
('22698995','let-7','BRIP1','MIR_GENE'),
('22698995','let-7b','BRIP1','MIR_GENE'),
('22698995','let-7c','BRIP1','MIR_GENE'),
('22698995','miR-98','BRIP1','MIR_GENE'),
('22698995','let-7','HMOX1','MIR_GENE'),
('22761336','miR-96','REV1','MIR_GENE'),
('22761336','miR-96','RAD51','MIR_GENE'),
('22761336','miR-96','RECA','MIR_GENE'), #RAD51
('22761336','miR-96','RAD51A','MIR_GENE'),
('22847613','miR-130b','TP53','GENE_MIR'),
('22847613','miR-130b','ZEB1','MIR_GENE'),
('22847613','miR-130b','PPCD3','MIR_GENE'), # zeb1
('22891274','miR-146a','NFKB1','GENE_MIR'),
('22891274','miR-146a','NFKB1','MIR_GENE'),
('22891274','miR-146a','TRAF6','MIR_GENE'),
('22891274','miR-146a','IRAK1','MIR_GENE'),
('22925189','miR-30c','ERBB2','MIR_GENE'), #Her-2
('22925189','miR-30d','ERBB2','MIR_GENE'),
('22925189','miR-30e','ERBB2','MIR_GENE'),
('22925189','miR-532','ERBB2','MIR_GENE'),
('22955854','miR-144','ZFX','MIR_GENE'),
('22956424','miR-21','PTEN','MIR_GENE'),
('22956424','miR-21','MHAM','MIR_GENE'), # has PTEN syn
('22956424','miR-21','BZS','MIR_GENE'), # has PTEN syn
('22982443','miR-200c','BMI1','MIR_GENE'),
('22982443','miR-200c','ABCG2','MIR_GENE'),
('22982443','miR-200c','ABCG5','MIR_GENE'),
('22982443','miR-200c','MDR1','MIR_GENE'),
('22982443','miR-200c','TBC1D9','MIR_GENE'), # has syn MDR1
('22982443','miR-200c','ABCB1','MIR_GENE'), # has syn MDR1
('22982443','miR-200c','CDH1','MIR_GENE'),
('23010597','miR-134','FOXM1','MIR_GENE'),
('23010597','miR-134','FKHL16','MIR_GENE'), # has FOXM1 as syn
('23010597','miR-134','ITK','MIR_GENE'), # has EMT as syn; mirtex missing: Functional assays demonstrated that miR-134 inhibited EMT in NSCLC cells.
('23010597','miR-134','SLC22A3','MIR_GENE'), # has EMT as syn
('23041385','miR-21','CRP','MIR_GENE'),
#('23041385','miR-21','fibrinogen','MIR_GENE'), # not a gene
('23041385','miR-21','TGFB2','MIR_GENE'),
('23097316','miR-34c','RARg','MIR_GENE'),
('23113351','miR-29','TP53','MIR_GENE'), # missing in mirtex: While miRNA-29 members induced apoptosis through p53 gene activation, the effect of miRNA-29a on osteoblastic cells was independent on p53 expression level.
('23113351','miR-29a','TP53','MIR_GENE'),
('23113351','miR-29','BCL2','MIR_GENE'),
('23113351','miR-29','MCL1','MIR_GENE'),
('23113351','miR-29','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl
('23113351','miR-29a','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl
#('23113351','miR-29','E2F1','MIR_GENE'),
#('23113351','miR-29','E2F3','MIR_GENE'),
('23113351','miR-29a','BCL2','MIR_GENE'),
('23113351','miR-29a','MCL1','MIR_GENE'),
('23113351','miR-29a','E2F1','MIR_GENE'),
('23113351','miR-29a','E2F3','MIR_GENE'),
('23113351','miR-29a','E2F1','MIR_GENE'), # possibly too
('23113351','miR-29a','E2F3','MIR_GENE'), # possibly too
('23148210','miR-210','HIF1A','GENE_MIR'), # was actived in ...-dependant
('23169590','miR-451','IL6','GENE_MIR'),
('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN
('23169590','miR-451','YWHAZ','MIR_GENE'),
('23169590','miR-451','YWHAD','MIR_GENE'), # has syn YWHAZ
#('23169590','miR-451','14-3-3zeta','MIR_GENE'), # is YWHAZ
('23169590','miR-451','ZFP36','MIR_GENE'),
#Three types of primary DCs treated with antisense RNA antagomirs directed against miR-451 secreted elevated levels of IL-6, TNF, CCL5/RANTES, and CCL3/MIP1alpha, and these results were confirmed using miR-451(null) cells.
#this suggests that miR-451 suppresses these genes normalls
('23169590','miR-451','IL6','MIR_GENE'),
('23169590','miR-451','CCL3','MIR_GENE'),
('23169590','miR-451','CCL5','MIR_GENE'),
('23169590','miR-451','IL6','MIR_GENE'),
('23169590','miR-451','IFNB2','MIR_GENE'), #IL6
('23169590','miR-451','TNF','MIR_GENE'),
('23169590','miR-451','TNFA','MIR_GENE'), #TNF
#miR-451 levels are themselves increased by IL-6 and type I IFN, potentially forming a regulatory loop.
('23169590','miR-451','IL6','GENE_MIR'), #IL6
('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN
('23169590','miR-451','IFNB2','GENE_MIR'), #IL6
('23190607','miR-203','RAN','MIR_GENE'),
('23190607','miR-203','RAPH1','MIR_GENE'),
('23190607','miR-203','ALS2CR18','MIR_GENE'), # synonym
('23190608','miR-29b','SP1','GENE_MIR'),
('23190608','miR-29b','SP1','MIR_GENE'), # mirtex missing: miR-29b sensitizes multiple myeloma cells to bortezomib-induced apoptosis through the activation of a feedback loop with the transcription factor Sp1.
('23190608','miR-29b','DAND5','GENE_MIR'), # DAND5 has SP1 as syn
('23190608','miR-29b','DAND5','MIR_GENE'),
('23206698','miR-7','IRS2','MIR_GENE'),
('23396109','miR-17','PTEN','MIR_GENE'), #miR-17~92
('23396109','miR-17','MHAM','MIR_GENE'), # MHAM syn is PTEN too
('23396109','miR-17','BZS','MIR_GENE'), # BZS syn is PTEN too
#('23396109','miR-17','BIM','MIR_GENE'), # miR-17~92 is pten
('23396109','miR-17','BCL2L11','MIR_GENE'), # BCL2L11 syn is BIM
('23472202','miR-183','TAOK1','MIR_GENE'),
('23516615','miR-143','ERK5','MIR_GENE'),
('23516615','miR-143','PPARg','MIR_GENE'),
('23516615','miR-204','ERK5','MIR_GENE'),
('23516615','miR-204','PPARg','MIR_GENE'),
('23519125','miR-125a','erbB2','MIR_GENE'),
('23519125','miR-125a','erbB3','MIR_GENE'),
('23519125','miR-125b','erbB2','MIR_GENE'),
('23519125','miR-125b','erbB3','MIR_GENE'),
('23519125','miR-205','erbB2','MIR_GENE'),
('23519125','miR-205','erbB3','MIR_GENE'),
# these are no genes!
#('23527070','miR-21','collagen I','MIR_GENE'),
#('23527070','miR-21','collagen III','MIR_GENE'),
('23527070','miR-21','ELN','MIR_GENE'),
('23527070','miR-21','SMAD7','MIR_GENE'),
('23527070','miR-21','SMAD5','MIR_GENE'),
('23527070','miR-21','SMAD2','MIR_GENE'),
#('23534973','miR-152','HLA-G','MIR_GENE'), not a specific miRNA: miR-152 family
('23579289','miR-214','SP7','MIR_GENE'),
('23583389','miR-96','IRS1','MIR_GENE'),
('23592910','miR-146a','IL1B','GENE_MIR'),
('23592910','miR-146a','IFNG','GENE_MIR'),
#('23592910','miR-146a','TNFA','GENE_MIR'), # is TNF
('23592910','miR-146b','IL1B','GENE_MIR'),
('23592910','miR-146b','IFNG','GENE_MIR'),
('23592910','miR-146b','TNF','GENE_MIR'),
('23592910','miR-146a','IRAK','MIR_GENE'),
('23592910','miR-146b','IRAK','MIR_GENE'),
('23611780','miR-106b','FBXW11','MIR_GENE'), #beta-TRCP2
#('23611780','miR-106b','SNAIL','MIR_GENE'), nope. means cluster + indirect: miR-106b-25 cluster may play an important role in the metastasis of human non-small cell lung cancer cells by directly suppressing the beta-TRCP2 gene expression with a consequent increase in the expression of Snail.
('23611780','miR-93','FBXW11','MIR_GENE'),
('23630358','miR-155','MSR1','MIR_GENE'), # SR-AI syn
('23643257','miR-424','FGR','MIR_GENE'),
('23643257','miR-424','MAP2K1','MIR_GENE'),
('23643257','miR-424','MAPK1','MIR_GENE'), #mitogen-activated protein kinase 1
('23667495','miR-224','DPYSL2','MIR_GENE'),
('23667495','miR-224','KRAS','MIR_GENE'),
('23667495','miR-452','DPYSL2','MIR_GENE'),
('23667495','miR-452','KRAS','MIR_GENE'),
('23667495','miR-181c','KRAS','MIR_GENE'),
('23667495','miR-340','MECP2','MIR_GENE'),
('23667495','miR-181c','MECP2','MIR_GENE'),
('23667495','miR-340','KRAS','MIR_GENE'),
('23759586','miR-34a','SIRT1','MIR_GENE'),
('23759586','miR-125b','TP53','MIR_GENE'),
('23759586','miR-125b','SIRT1','MIR_GENE'),
('23797704','miR-21','TIMP3','MIR_GENE'),
('23797704','miR-221','TIMP3','MIR_GENE'),
('23797704','miR-21','SFD','MIR_GENE'),#is TIMP3
('23797704','miR-221','SFD','MIR_GENE'),#is TIMP3
('23797704','miR-217','TIMP3','MIR_GENE'),
('23797704','miR-217','SFD','MIR_GENE'), #is TIMP3
('23797704','miR-217','SIRT1','MIR_GENE'),
('23836497','miR-20','STAT3','MIR_GENE'),
('23836497','miR-20','CCND1','MIR_GENE'),
('23836497','miR-106a','STAT3','MIR_GENE'),
('23836497','miR-106a','CCND1','MIR_GENE'),
# same genesymbols
('23846856','miR-875','PRDX3','MIR_GENE'),
('23846856','miR-875','PRX','MIR_GENE'),
('23851184','miR-200b','WNT1','MIR_GENE'),
('23851184','miR-22','WNT1','MIR_GENE'),
('23895517','mir-494','TNFSF14','MIR_GENE'),
('23895517','mir-197','TNFSF14','MIR_GENE'),
('23968734','miR-133a','PDLIM5','MIR_GENE'), # LIM
#('23968734','miR-133a','SH3 protein 1','MIR_GENE'),
('23968734','miR-133a','LASP1','MIR_GENE'), #SH3 protein 1
('24006456','miR-29b','IGF1','MIR_GENE'),
('24006456','miR-30c','IGF1','MIR_GENE'),
('24006456','miR-29b','LIF','MIR_GENE'),
('24006456','miR-30c','LIF','MIR_GENE'),
('24006456','miR-29b','PTX3','MIR_GENE'),
('24023867','miR-135a','NR3C2','MIR_GENE'),
('24023867','miR-124','NR3C2','MIR_GENE'),
('24145190','miR-203','SNAI1','GENE_MIR'),
('24145190','miR-203','CD44','MIR_GENE'), # new, not in mirtex: we found that the levels of several EMT activators and miR-203 were positively and negatively correlated with those of CD44, respectively.
('24145190','miR-203','MDU3','MIR_GENE'),
('24145190','miR-203','MIC4','MIR_GENE'),
('24145190','miR-203','MDU2','MIR_GENE'),
('24145190','miR-203','SRC','GENE_MIR'), # missing in mirtex: Finally, we discovered that c-Src kinase activity was required for the downregulation of miR-203
('24155920','miR-21','SPRY1','MIR_GENE'),
('24155920','miR-29a','MCL1','MIR_GENE'),
('24155920','miR-29b','MCL1','MIR_GENE'),
#('24219008','miR-21-5p','TGFBR3','MIR_GENE'),
('24219008','miR-21','TGFBR3','MIR_GENE'), #add
('24219008','hsa-miR-21','TGFBR3','MIR_GENE'), #add
#('24219008','miR-21-5p','PDGFD','MIR_GENE'),
('24219008','miR-21','PDGFD','MIR_GENE'),
('24219008','hsa-miR-21','PDGFD','MIR_GENE'), #add
#('24219008','miR-21-5p','PPM1L','MIR_GENE'),
('24219008','miR-21','PPM1L','MIR_GENE'),
('24219008','hsa-miR-21','PPM1L','MIR_GENE'), #add
#('24219008','miR-181a-5p','ROPN1L','MIR_GENE'),
('24219008','miR-181a','ROPN1L','MIR_GENE'),
('24219008','hsa-miR-181a','ROPN1L','MIR_GENE'),
#('24219008','miR-181a-5p','SLC37A3','MIR_GENE'),
('24219008','hsa-miR-181a','SLC37A3','MIR_GENE'),
('24219008','miR-181a','SLC37A3','MIR_GENE'),
#('24219008','miR-24-2-5p','MYC','MIR_GENE'),
('24219008','hsa-miR-24-2','MYC','MIR_GENE'),
('24219008','hsa-miR-24','MYC','MIR_GENE'),
('24219008','miR-24-2','MYC','MIR_GENE'),
('24219008','miR-24','MYC','MIR_GENE'),
#('24219008','miR-24-2-5p','KCNJ2','MIR_GENE'),
('24219008','hsa-miR-24-2','KCNJ2','MIR_GENE'),
('24219008','hsa-miR-24','KCNJ2','MIR_GENE'),
('24219008','miR-24','KCNJ2','MIR_GENE'),
('24219008','miR-24-2','KCNJ2','MIR_GENE'),
('24219349','miR-203','BMI1','MIR_GENE'),
('24220339','miR-490','FOS','MIR_GENE'),
('24223656','miR-31','RASA1','MIR_GENE'),
('24314216','miR-106','TP53','MIR_GENE'),
('24319262','miR-34a','TP53','GENE_MIR'),
('24319262','miR-145','TP53','GENE_MIR'),
('24319262','miR-155','MAF','MIR_GENE'),
('24319262','miR-34a','TWIST2','MIR_GENE'),
('24319262','miR-34a','MAF','MIR_GENE'),
('24319262','miR-145','TWIST2','MIR_GENE'),
('24319262','miR-145','MAF','MIR_GENE'),
('24330780','miR-124','FLOT1','MIR_GENE'),
('24330780','miR-124','FLOT1','MIR_GENE'),
('24376808','miR-146a','CRK','MIR_GENE'),
('24376808','miR-424','CRK','MIR_GENE'),
('24376808','miR-146a','EGFR','MIR_GENE'),
('24376808','miR-424','EGFR','MIR_GENE'),
('24376808','miR-146a','MAPK14','MIR_GENE'), #p38 / ERK
('24376808','miR-424','MAPK14','MIR_GENE'),
('24376808','miR-146a','AIMP2','MIR_GENE'),#p38 / ERK
('24376808','miR-424','AIMP2','MIR_GENE'),
('24376808','miR-146a','AHSA1','MIR_GENE'),#p38 / ERK
('24376808','miR-424','AHSA1','MIR_GENE'),
]
refDict = defaultdict(set)
for x in referenceSolution:
refDict[x[0]].add((x[1], x[2], x[3]))
tmRemoveTMErrors = {
('19956414','miR-29b','MMRN1'), # ECM, extracellular matrix
('21415212','miR-486','GC'), #gastric cancer
('21415212','miR-486','HTC2'), #Array-CGH
('21415212','miR-486','EAF2'), #TRAITS
('21415212','miR-486','NF2'), #SCH cell line
('21703983', 'miR-632', 'PAFAH1B1'), # Notably, hsa-miR-378, hsa-miR-632, and hsa-miR-636 demonstrated particularly high discrimination between MDS and normal controls. MDS here is myelodysplastic syndromes
('21703983', 'hsa-miR-378', 'PAFAH1B1'),
('21703983', 'miR-378', 'PAFAH1B1'),
('21703983', 'hsa-miR-632', 'PAFAH1B1'),
('21703983', 'hsa-miR-636', 'PAFAH1B1'),
('21703983', 'miR-636', 'PAFAH1B1'),
('22066022', 'miR-21', 'FAM126A'), # HCC refers to hepatocellular carcinoma
('22066022', 'miR-21', 'ST14'), # HAI refers to histological activity index (HAI)
('22066022', 'miR-21', 'SPINT1'), # HAI refers to histological activity index (HAI)
('23643257','miR-424','FGR'), # recognizes FGR
('23643257','miR-424','FGFR1'),
('23643257','miR-424','KAL2'),
('23643257','miR-424','FLT2'),
('24330780','miR-124','TENM1'), #tumor node metastasis (TNM)
('24330780','miR-124','TNM'),
('24223656', 'miR-31', "TPT1"), # RAS p21 GTPase activating protein 1 (RASA1) => p21
('24223656', 'miR-31', "CDKN1A"),
('24223656', 'miR-31', "H3F3AP6"),
('24223656', 'miR-31', "TCEAL1"),
('24223656', 'miR-31', "NSG1"),
('21609717','miR-98','MT-CO2'), # accepts COX-2
('21609717','miR-98','COX8A'),
('21609717','miR-98','CPOX'),
('21609717','miR-98','MT-CO2'),
('23527070', 'miR-21', 'SMAD5'), # SMAD2/5
('23190608','miR-29b','SUPT20H'), # SUPT20H has transcription-factor as syn
('23190608','miR-29b','SUPT20H'),
('18185580','miR-335', 'SUPT20H'),
('23113351','miR-29','RB1'), # RB1 syn: osteosarcoma
('23113351','miR-29a','RB1'), # RB1 syn: osteosarcoma
('23113351','miR-29b','RB1'), # RB1 syn: osteosarcoma
('22982443','miR-200c','CDH17'), # CDH17 syn for cadherin, found in E-cadherin ...
('20603081','miR-150','GLI2'), # THP-1 refers to cells
('22139444', 'miR-30c', 'NDC80'), # refers to HEC-1-B cells ...
('23041385', 'miR-21', 'CO'), # centenarian offspring (CO)
('23041385', 'miR-21', 'CALCR'), # CTR control
('19723773','miR-290','MEF'),#mouse embryo fibroblasts (MEF)
('19723773','miR-290','MEFV'),#mouse embryo fibroblasts (MEF)
('19723773','miR-290','ELF4'),#mouse embryo fibroblasts (MEF)
('19547998', 'miR-21', 'CALR'), # SSA
('19547998', 'miR-181b', 'CALR'),
('19547998', 'miR-21', 'HP'), # hyperplastic polyps
('19547998', 'miR-181b', 'HP'),
('20103675', 'miR-222', 'FAM126A'), # HCC cell lines
('24219349','miR-203','SP'), # side population
('24219349','miR-203','TFF2'), # SP
('21088996','miR-21','BLOC1S6'), # PDAC cells (MIA-Pa-Ca-2)
('21088996','miR-21','MIA'), # PDAC cells (MIA-Pa-Ca-2)
('21088996','miR-21','CAR2'), # PDAC cells (MIA-Pa-Ca-2)
('22847613','miR-130b','SLC22A3'), # epithelial-mesenchymal transition (EMT)
('22847613','miR-130b','ITK'), # epithelial-mesenchymal transition (EMT)
('22925189', 'miR-370', 'II'), #stage II <=> gene symbol
('22925189', 'miR-370', 'IV'), #stage IV <=> gene symbol
('22925189', 'miR-30a', 'II'), #stage II <=> gene symbol
('22925189', 'miR-30a', 'IV'), #stage IV <=> gene symbol
('23592910', 'miR-146a', 'IFNA1'), # TM mismatch with interferon in interfon gamma
('23592910', 'miR-146b', 'IFNA1'),
('24006456','miR-29b','INS'), # spurious hit with insulin-like growth factor
('24006456','miR-30c','INS'), # insulin-like
('20945501', 'miR-141', 'PC'), # matches PC / prostate cancer
('20945501', 'miR-141', 'PODXL'), # matches CRPC (castration-resitant prostate cancer)
}
# gene-mir: 36 F1: 0.88 *0.135 = 0,1188
# mir-gene: 230 F1: 0.94 *0.865 = 0,8131
# all: F1: 0.9319
sent2rels = defaultdict(set)
allSents = sentDB.get_all_sentences()
doc2Rels = defaultdict(set)
for mirID in mmuDB.ltype2rel:
for rel in mmuDB.ltype2rel[mirID]:
jel = rel.toJSON()
sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) )
docID = rel.assocSent.split(".")[0]
doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) )
for mirID in hsaDB.ltype2rel:
for rel in hsaDB.ltype2rel[mirID]:
jel = rel.toJSON()
sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) )
#print(rel.assocSent, rel.lid, rel.rid, rel.assocInt, rel.assocCat, relSent)
docID = rel.assocSent.split(".")[0]
doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) )
from collections import Counter
#TM, REF
elemCaseCounter = Counter()
with open("test_list.bydoc.tsv", "w") as fout:
print("doc", "lid", "rid", "assocInt", sep="\t", file=fout)
allDocIDs = set([x.split(".")[0] for x in allSents])
for docID in natsorted(doc2Rels):
for elems in doc2Rels[docID]:
print(docID, *elems, sep="\t", file=fout)
refOnly = refDict[docID].difference(doc2Rels[docID])
tmOnly = doc2Rels[docID].difference(refDict[docID])
tmOnly = [x for x in tmOnly if not (docID, x[0], x[1]) in tmRemoveTMErrors]
correct = refDict[docID].intersection(doc2Rels[docID])
for x in correct:
elemCaseCounter[(True, True)] += 1
for x in refOnly:
elemCaseCounter[(False, True)] += 1
for x in tmOnly:
elemCaseCounter[(True, False)] += 1
if len(doc2Rels[docID]) == 0 and len(refDict[docID]):
continue
if len(refOnly) == 0 and len(tmOnly) == 0:
continue
print(docID, len(correct), "REFONLY", refOnly)
print(docID, len(correct), "TMONLY", tmOnly)
print()
precision = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(True, False)])
recall = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(False, True)])
f1 = 2* precision * recall / (precision+recall)
#specificity = elemCaseCounter[(False, False)] / (elemCaseCounter[(True, False)] + elemCaseCounter[(False, False)])
print()
print()
print("True, True", elemCaseCounter[(True, True)])
print("TM Only", elemCaseCounter[(True, False)])
print("Ref Only", elemCaseCounter[(False, True)])
print()
print("precision", precision)
print("recall", recall)
#print("specificity", specificity)
print("f1", f1)
with open("test_list.tsv", "w") as fout:
print("lid", "rid", "assocInt", "assocCat", "lpos", "rpos", "int_eval", "cat_eval", "sentID", "sent", sep="\t", file=fout)
for sentID in natsorted([x for x in allSents]):
sent = allSents[sentID]
allElems = sent2rels.get(sentID, None)
if allElems == None:
allElems = [ ("", "", "", "", "", "") ]
for lid, rid, assocInt, assocCat, lpos, rpos in allElems:
print(lid, rid, assocInt, assocCat, lpos, rpos, "FALSE", "FALSE", sentID, sent, sep="\t", file=fout)
| 0 | 0 | 0 |
e3267be3ca3ccbb7412f02733ff2024c2d162104 | 1,033 | py | Python | contacts/handle_contacts.py | intelligent-soft-robots/pam_demos | d4fd3939456f51aeb63d4c22e681bb0930aeeb95 | [
"BSD-3-Clause"
] | null | null | null | contacts/handle_contacts.py | intelligent-soft-robots/pam_demos | d4fd3939456f51aeb63d4c22e681bb0930aeeb95 | [
"BSD-3-Clause"
] | null | null | null | contacts/handle_contacts.py | intelligent-soft-robots/pam_demos | d4fd3939456f51aeb63d4c22e681bb0930aeeb95 | [
"BSD-3-Clause"
] | null | null | null | import pam_mujoco
MUJOCO_ID = "pam_demos_contacts"
ROBOT_SEGMENT_ID = "robot"
BALL_SEGMENT_ID = "ball"
| 22.955556 | 75 | 0.723136 | import pam_mujoco
MUJOCO_ID = "pam_demos_contacts"
ROBOT_SEGMENT_ID = "robot"
BALL_SEGMENT_ID = "ball"
def get_handle(robot_contact):
global MUJOCO_ID, ROBOT_SEGMENT_ID, BALL_SEGMENT_ID
robot_control = pam_mujoco.MujocoRobot.JOINT_CONTROL
robot = pam_mujoco.MujocoRobot(ROBOT_SEGMENT_ID, control=robot_control)
ball_control = pam_mujoco.MujocoItem.CONSTANT_CONTROL
if robot_contact:
ball_contact = pam_mujoco.ContactTypes.racket1
else:
ball_contact = pam_mujoco.ContactTypes.table
ball = pam_mujoco.MujocoItem(
BALL_SEGMENT_ID, control=ball_control, contact_type=ball_contact
)
graphics = True
accelerated_time = False
handle = pam_mujoco.MujocoHandle(
MUJOCO_ID,
table=True,
robot1=robot,
balls=(ball,),
graphics=graphics,
accelerated_time=accelerated_time,
)
return handle
def get_robot_contact_handle():
return get_handle(True)
def get_table_contact_handle():
return get_handle(False)
| 857 | 0 | 69 |
1fb0b6ec77b3341a82e254a16a60dd5ae5d01a4e | 234 | py | Python | django_cbv_utils/forms/widgets/__init__.py | okwrtdsh/django_cbv_utils | 6858f53dda65c79e2201a67ba34d3885b3f11e22 | [
"MIT"
] | null | null | null | django_cbv_utils/forms/widgets/__init__.py | okwrtdsh/django_cbv_utils | 6858f53dda65c79e2201a67ba34d3885b3f11e22 | [
"MIT"
] | null | null | null | django_cbv_utils/forms/widgets/__init__.py | okwrtdsh/django_cbv_utils | 6858f53dda65c79e2201a67ba34d3885b3f11e22 | [
"MIT"
] | null | null | null | from .datetime_picker import (
DatePickerWidget, DateTimePickerWidget, TimePickerWidget,
)
from .file import BootstrapFileInputWidget
from .numeric import (
NumericIntegerWidget, NumericPositiveIntegerWidget, NumericWidget,
)
| 29.25 | 70 | 0.824786 | from .datetime_picker import (
DatePickerWidget, DateTimePickerWidget, TimePickerWidget,
)
from .file import BootstrapFileInputWidget
from .numeric import (
NumericIntegerWidget, NumericPositiveIntegerWidget, NumericWidget,
)
| 0 | 0 | 0 |
591e412704a3ebc443c6a2063e7b566975c4afd3 | 513 | py | Python | Dataset/Leetcode/valid/11/37.py | kkcookies99/UAST | fff81885aa07901786141a71e5600a08d7cb4868 | [
"MIT"
] | null | null | null | Dataset/Leetcode/valid/11/37.py | kkcookies99/UAST | fff81885aa07901786141a71e5600a08d7cb4868 | [
"MIT"
] | null | null | null | Dataset/Leetcode/valid/11/37.py | kkcookies99/UAST | fff81885aa07901786141a71e5600a08d7cb4868 | [
"MIT"
] | null | null | null |
undefined
for (i = 0; i < document.getElementsByTagName("code").length; i++) { console.log(document.getElementsByTagName("code")[i].innerText); }
| 27 | 139 | 0.516569 | class Solution(object):
def XXX(self, height):
"""
:type height: List[int]
:rtype: int
"""
a = 0
for index, X in enumerate(height):
for j in range(len(height)):
res = min(X, height[j])
location = index - j
a = max(a, res*location)
return a
undefined
for (i = 0; i < document.getElementsByTagName("code").length; i++) { console.log(document.getElementsByTagName("code")[i].innerText); }
| 0 | 339 | 22 |
6803063230b074a8261628bbd89a6a9dc198905b | 5,296 | py | Python | scripts/evaluate-clusters.py | gmcgoldr/theissues | 4e4c9eb66c543cdbcda4f1b96a7d2b163450368c | [
"MIT"
] | 1 | 2021-12-07T11:23:32.000Z | 2021-12-07T11:23:32.000Z | scripts/evaluate-clusters.py | gmcgoldr/theissues | 4e4c9eb66c543cdbcda4f1b96a7d2b163450368c | [
"MIT"
] | null | null | null | scripts/evaluate-clusters.py | gmcgoldr/theissues | 4e4c9eb66c543cdbcda4f1b96a7d2b163450368c | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
"""
Evaluate the proximity of statements in pre-defined clusters.
"""
import json
import logging
import sys
import warnings
from pathlib import Path
import numpy as np
import tokenizers as tk
import torch
from theissues import clustering, training, utils
def distances_euclid(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""
Calculate the pair-wise distance between each vector in `x` and `y`, where
the eucledian space is the last axis.
"""
return np.linalg.norm(x - y, axis=-1)
def distances_cosine(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""
Calculate the pair-wise cosine distance between each vector in `x` and `y`,
where the eucledian space is the last axis.
"""
dot_product = np.sum(x * y, axis=-1)
norm = np.linalg.norm(x, axis=-1) * np.linalg.norm(y, axis=-1)
return 1.0 - dot_product / norm
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("path_statements", type=Path)
parser.add_argument("path_sequences", type=Path)
parser.add_argument("path_embeddings", type=Path)
parser.add_argument("path_tokenizer", type=Path)
parser.add_argument("--path_log", type=Path)
main(**vars(parser.parse_args()))
| 32.292683 | 81 | 0.668429 | #!/usr/bin/env python3
"""
Evaluate the proximity of statements in pre-defined clusters.
"""
import json
import logging
import sys
import warnings
from pathlib import Path
import numpy as np
import tokenizers as tk
import torch
from theissues import clustering, training, utils
def distances_euclid(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""
Calculate the pair-wise distance between each vector in `x` and `y`, where
the eucledian space is the last axis.
"""
return np.linalg.norm(x - y, axis=-1)
def distances_cosine(x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""
Calculate the pair-wise cosine distance between each vector in `x` and `y`,
where the eucledian space is the last axis.
"""
dot_product = np.sum(x * y, axis=-1)
norm = np.linalg.norm(x, axis=-1) * np.linalg.norm(y, axis=-1)
return 1.0 - dot_product / norm
def main(
path_statements: Path,
path_sequences: Path,
path_embeddings: Path,
path_tokenizer: Path,
path_log: Path,
):
logging_handlers = [logging.StreamHandler(sys.stdout)]
if path_log is not None:
# clear the log file
with path_log.open("w"):
pass
logging_handlers.append(logging.FileHandler(path_log))
logging.basicConfig(
level=logging.INFO,
format="%(message)s",
handlers=logging_handlers,
)
tokenizer = tk.Tokenizer.from_file(str(path_tokenizer))
special_tokens = utils.SpecialTokens(tokenizer)
logging.info("Reading statements ...")
with path_statements.open("r") as fio:
statements = [utils.Statement(*json.loads(l)) for l in fio]
logging.info("Reading sequences ...")
with path_sequences.open("rb") as fio:
sequences = np.load(fio)
sequence_splits = training.build_sequences_splits(
torch.from_numpy(sequences), special_tokens.bos_id
).numpy()
with path_embeddings.open("rb") as fio:
embeddings: np.ndarray = np.load(fio)
num_statements = len(statements)
if sequence_splits.shape[0] != num_statements:
raise RuntimeError("sequences don't match statements")
if embeddings.shape[0] != num_statements:
raise RuntimeError("embeddings don't match statements")
logging.info("Building clusters ...")
topic_groups = clustering.build_topic_groups([s.text for s in statements])
logging.info("Cluster sizes:")
for topic, group in topic_groups.items():
logging.info("%s: %d", topic, len(group))
if not group:
warnings.warn(f"empty topic group: {topic}")
logging.info("Evaluating clusters ...")
rng: np.random.Generator = np.random.default_rng()
# NOTE: relying on dict insertion order
pairs_intra = clustering.build_intra_topic_pairs(
groups=topic_groups.values(),
embeddings=embeddings,
num_pairs=2 ** 15,
rng=rng,
)
pairs_inter = clustering.build_inter_topic_pairs(
groups=topic_groups.values(),
embeddings=embeddings,
num_pairs=2 ** 15,
rng=rng,
)
logging.info("Euclidean distances:")
distances_intra = distances_euclid(pairs_intra[:, 0], pairs_intra[:, 1])
distances_inter = distances_euclid(pairs_inter[:, 0], pairs_inter[:, 1])
intra_mu = np.mean(distances_intra)
intra_sig = np.std(distances_intra)
inter_mu = np.mean(distances_inter)
inter_sig = np.std(distances_inter)
separation = (inter_mu - intra_mu) / (inter_sig ** 2 + intra_sig ** 2) ** 0.5
logging.info("Intra distance: %.2e ± %.2e", intra_mu, intra_sig)
logging.info("Inter distance: %.2e ± %.2e", inter_mu, inter_sig)
logging.info("Separation: %+.2e", separation)
logging.info("Cosine distances:")
distances_intra = distances_cosine(pairs_intra[:, 0], pairs_intra[:, 1])
distances_inter = distances_cosine(pairs_inter[:, 0], pairs_inter[:, 1])
intra_mu = np.mean(distances_intra)
intra_sig = np.std(distances_intra)
inter_mu = np.mean(distances_inter)
inter_sig = np.std(distances_inter)
separation = (inter_mu - intra_mu) / (inter_sig ** 2 + intra_sig ** 2) ** 0.5
logging.info("Intra distance: %.2e ± %.2e", intra_mu, intra_sig)
logging.info("Inter distance: %.2e ± %.2e", inter_mu, inter_sig)
logging.info("Separation: %+.2e", separation)
logging.info("Cosine neighbours:")
# want to re-use the same examples everytime this is run
rng: np.random.Generator = np.random.default_rng(123)
for topic, group in topic_groups.items():
if not group:
continue
ref_idx = rng.choice(group)
ref_embedding = embeddings[ref_idx]
distances = distances_cosine(ref_embedding[np.newaxis, :], embeddings)
sort_idx = np.argsort(distances)
logging.info("Topic %s", topic)
for idx in sort_idx[:3]:
logging.info("> %s", statements[idx].text)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("path_statements", type=Path)
parser.add_argument("path_sequences", type=Path)
parser.add_argument("path_embeddings", type=Path)
parser.add_argument("path_tokenizer", type=Path)
parser.add_argument("--path_log", type=Path)
main(**vars(parser.parse_args()))
| 4,000 | 0 | 23 |
49a88a53c6e84b35803c714c51ea020c2ec9b5d8 | 1,361 | py | Python | tests/unit/conftest.py | Joeper214/blueoil | 5aeb6634770011753b8d5ae283b91b7a0050101e | [
"Apache-2.0"
] | 248 | 2018-10-19T01:48:42.000Z | 2022-01-31T02:34:24.000Z | tests/unit/conftest.py | Joeper214/blueoil | 5aeb6634770011753b8d5ae283b91b7a0050101e | [
"Apache-2.0"
] | 1,102 | 2018-10-19T04:50:34.000Z | 2021-08-02T04:22:10.000Z | tests/unit/conftest.py | Joeper214/blueoil | 5aeb6634770011753b8d5ae283b91b7a0050101e | [
"Apache-2.0"
] | 110 | 2018-10-19T01:49:02.000Z | 2022-01-31T02:34:26.000Z | # -*- coding: utf-8 -*-
# Copyright 2018 The Blueoil Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import pytest
import tensorflow as tf
from blueoil import environment
@pytest.fixture
def reset_default_graph():
"""Reset tensorflow default graph."""
print("reset_default_graph")
tf.reset_default_graph()
@pytest.fixture
def set_test_environment():
"""Set test environment"""
print("set test environment")
yield environment.setup_test_environment()
# By using a yield statement instead of return, all the code after the yield statement serves as the teardown code:
# See also: https://docs.pytest.org/en/latest/fixture.html#fixture-finalization-executing-teardown-code
environment.teardown_test_environment()
| 34.897436 | 119 | 0.710507 | # -*- coding: utf-8 -*-
# Copyright 2018 The Blueoil Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
import pytest
import tensorflow as tf
from blueoil import environment
@pytest.fixture
def reset_default_graph():
"""Reset tensorflow default graph."""
print("reset_default_graph")
tf.reset_default_graph()
@pytest.fixture
def set_test_environment():
"""Set test environment"""
print("set test environment")
yield environment.setup_test_environment()
# By using a yield statement instead of return, all the code after the yield statement serves as the teardown code:
# See also: https://docs.pytest.org/en/latest/fixture.html#fixture-finalization-executing-teardown-code
environment.teardown_test_environment()
| 0 | 0 | 0 |
448034f959c02c6a99d686799cb1dc0584474a86 | 2,485 | py | Python | observations/r/wage.py | hajime9652/observations | 2c8b1ac31025938cb17762e540f2f592e302d5de | [
"Apache-2.0"
] | 199 | 2017-07-24T01:34:27.000Z | 2022-01-29T00:50:55.000Z | observations/r/wage.py | hajime9652/observations | 2c8b1ac31025938cb17762e540f2f592e302d5de | [
"Apache-2.0"
] | 46 | 2017-09-05T19:27:20.000Z | 2019-01-07T09:47:26.000Z | observations/r/wage.py | hajime9652/observations | 2c8b1ac31025938cb17762e540f2f592e302d5de | [
"Apache-2.0"
] | 45 | 2017-07-26T00:10:44.000Z | 2022-03-16T20:44:59.000Z | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def wage(path):
"""Mid-Atlantic Wage Data
Wage and other data for a group of 3000 workers in the Mid-Atlantic
region.
A data frame with 3000 observations on the following 12 variables.
`year`
Year that wage information was recorded
`age`
Age of worker
`sex`
Gender
`maritl`
A factor with levels `1. Never Married` `2. Married`
`3. Widowed` `4. Divorced` and `5. Separated` indicating
marital status
`race`
A factor with levels `1. White` `2. Black` `3. Asian` and
`4. Other` indicating race
`education`
A factor with levels `1. < HS Grad` `2. HS Grad`
`3. Some College` `4. College Grad` and `5. Advanced Degree`
indicating education level
`region`
Region of the country (mid-atlantic only)
`jobclass`
A factor with levels `1. Industrial` and `2. Information`
indicating type of job
`health`
A factor with levels `1. <=Good` and `2. >=Very Good` indicating
health level of worker
`health_ins`
A factor with levels `1. Yes` and `2. No` indicating whether
worker has health insurance
`logwage`
Log of workers wage
`wage`
Workers raw wage
Data was manually assembled by Steve Miller, of Open BI
(www.openbi.com), from the March 2011 Supplement to Current Population
Survey data.
http://thedataweb.rm.census.gov/TheDataWeb
Args:
path: str.
Path to directory which either stores file or otherwise file will
be downloaded and extracted there.
Filename is `wage.csv`.
Returns:
Tuple of np.ndarray `x_train` with 3000 rows and 12 columns and
dictionary `metadata` of column headers (feature names).
"""
import pandas as pd
path = os.path.expanduser(path)
filename = 'wage.csv'
if not os.path.exists(os.path.join(path, filename)):
url = 'http://dustintran.com/data/r/ISLR/Wage.csv'
maybe_download_and_extract(path, url,
save_file_name='wage.csv',
resume=False)
data = pd.read_csv(os.path.join(path, filename), index_col=0,
parse_dates=True)
x_train = data.values
metadata = {'columns': data.columns}
return x_train, metadata
| 25.357143 | 72 | 0.663581 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import numpy as np
import os
import sys
from observations.util import maybe_download_and_extract
def wage(path):
"""Mid-Atlantic Wage Data
Wage and other data for a group of 3000 workers in the Mid-Atlantic
region.
A data frame with 3000 observations on the following 12 variables.
`year`
Year that wage information was recorded
`age`
Age of worker
`sex`
Gender
`maritl`
A factor with levels `1. Never Married` `2. Married`
`3. Widowed` `4. Divorced` and `5. Separated` indicating
marital status
`race`
A factor with levels `1. White` `2. Black` `3. Asian` and
`4. Other` indicating race
`education`
A factor with levels `1. < HS Grad` `2. HS Grad`
`3. Some College` `4. College Grad` and `5. Advanced Degree`
indicating education level
`region`
Region of the country (mid-atlantic only)
`jobclass`
A factor with levels `1. Industrial` and `2. Information`
indicating type of job
`health`
A factor with levels `1. <=Good` and `2. >=Very Good` indicating
health level of worker
`health_ins`
A factor with levels `1. Yes` and `2. No` indicating whether
worker has health insurance
`logwage`
Log of workers wage
`wage`
Workers raw wage
Data was manually assembled by Steve Miller, of Open BI
(www.openbi.com), from the March 2011 Supplement to Current Population
Survey data.
http://thedataweb.rm.census.gov/TheDataWeb
Args:
path: str.
Path to directory which either stores file or otherwise file will
be downloaded and extracted there.
Filename is `wage.csv`.
Returns:
Tuple of np.ndarray `x_train` with 3000 rows and 12 columns and
dictionary `metadata` of column headers (feature names).
"""
import pandas as pd
path = os.path.expanduser(path)
filename = 'wage.csv'
if not os.path.exists(os.path.join(path, filename)):
url = 'http://dustintran.com/data/r/ISLR/Wage.csv'
maybe_download_and_extract(path, url,
save_file_name='wage.csv',
resume=False)
data = pd.read_csv(os.path.join(path, filename), index_col=0,
parse_dates=True)
x_train = data.values
metadata = {'columns': data.columns}
return x_train, metadata
| 0 | 0 | 0 |
77487db3218aff34e9120776d952281b311e7b4d | 1,271 | py | Python | accounts/models.py | tony-joseph/crimson-publisher | a156d0b6f681c1b3ffba890d9581f3a3b78d0d1d | [
"BSD-3-Clause"
] | 1 | 2020-05-06T16:59:43.000Z | 2020-05-06T16:59:43.000Z | accounts/models.py | tony-joseph/crimson-publisher | a156d0b6f681c1b3ffba890d9581f3a3b78d0d1d | [
"BSD-3-Clause"
] | null | null | null | accounts/models.py | tony-joseph/crimson-publisher | a156d0b6f681c1b3ffba890d9581f3a3b78d0d1d | [
"BSD-3-Clause"
] | null | null | null | from django.db import models
from django.contrib.auth.models import User
GENDER_CHOICES = (
('Male', 'Male'),
('Female', 'Female'),
('Not specified', 'Not specified')
)
class UserProfile(models.Model):
""" Model to store the user profile.
"""
user = models.OneToOneField(User, on_delete=models.CASCADE)
gender = models.CharField(max_length=30, blank=True, default='Not specified', choices=GENDER_CHOICES)
about = models.TextField(blank=True, null=True)
phone = models.CharField(max_length=20, blank=True, null=True)
email = models.EmailField(blank=True, null=True)
home_page = models.CharField(max_length=256, blank=True, null=True)
twitter = models.CharField(max_length=256, blank=True, null=True)
facebook = models.CharField(max_length=256, blank=True, null=True)
google_plus = models.CharField(max_length=256, blank=True, null=True)
city = models.CharField(max_length=128, blank=True, null=True)
state = models.CharField(max_length=128, blank=True, null=True)
country = models.CharField(max_length=128, blank=True, null=True)
created_on = models.DateTimeField(auto_now_add=True)
updated_on = models.DateTimeField(auto_now=True)
| 37.382353 | 105 | 0.719906 | from django.db import models
from django.contrib.auth.models import User
GENDER_CHOICES = (
('Male', 'Male'),
('Female', 'Female'),
('Not specified', 'Not specified')
)
class UserProfile(models.Model):
""" Model to store the user profile.
"""
user = models.OneToOneField(User, on_delete=models.CASCADE)
gender = models.CharField(max_length=30, blank=True, default='Not specified', choices=GENDER_CHOICES)
about = models.TextField(blank=True, null=True)
phone = models.CharField(max_length=20, blank=True, null=True)
email = models.EmailField(blank=True, null=True)
home_page = models.CharField(max_length=256, blank=True, null=True)
twitter = models.CharField(max_length=256, blank=True, null=True)
facebook = models.CharField(max_length=256, blank=True, null=True)
google_plus = models.CharField(max_length=256, blank=True, null=True)
city = models.CharField(max_length=128, blank=True, null=True)
state = models.CharField(max_length=128, blank=True, null=True)
country = models.CharField(max_length=128, blank=True, null=True)
created_on = models.DateTimeField(auto_now_add=True)
updated_on = models.DateTimeField(auto_now=True)
def __str__(self):
return self.user.username
| 31 | 0 | 27 |
7decd07b5d1a2b477e3ef3c630f2faec37d8b26d | 2,132 | py | Python | tests/test_identifier_translation.py | ini-bdds/minid | 7cd1eea02ccf4274f1094406090eeaf28d1d3b5d | [
"Apache-2.0"
] | 6 | 2015-12-10T18:04:16.000Z | 2018-02-16T20:55:19.000Z | tests/test_identifier_translation.py | fair-research/minid | 08f29d293c72fabe3560af55246a9acb34432f1f | [
"Apache-2.0"
] | 33 | 2018-03-28T19:20:34.000Z | 2021-12-03T00:06:17.000Z | tests/test_identifier_translation.py | fair-research/minid | 08f29d293c72fabe3560af55246a9acb34432f1f | [
"Apache-2.0"
] | 6 | 2018-05-22T15:51:32.000Z | 2021-08-19T13:51:12.000Z | import pytest
import minid
PROD_HDL = 'hdl:20.500.12582/prod_ident_xyz'
TEST_HDL = 'hdl:20.500.12633/test_ident_xyz'
PROD_MINID = 'minid:prod_ident_xyz'
TEST_MINID = 'minid.test:test_ident_xyz'
# The ordering is ('identifier', 'to_type', 'expected_result')
VALID_MINID_TRANSLATIONS = [
(PROD_HDL, 'minid', PROD_MINID),
(PROD_HDL, 'hdl', PROD_HDL),
(TEST_HDL, 'minid', TEST_MINID),
(TEST_HDL, 'hdl', TEST_HDL),
(PROD_MINID, 'minid', PROD_MINID),
(PROD_MINID, 'hdl', PROD_HDL),
(TEST_MINID, 'minid', TEST_MINID),
(TEST_MINID, 'hdl', TEST_HDL),
]
INVALID_MINID_TRANSLATIONS = [
('invalid_ident:3.141.59/', 'minid', minid.exc.UnknownIdentifier),
('invalid_ident:3.141.59/', 'hdl', minid.exc.UnknownIdentifier),
(PROD_HDL, 'does-not-exist', minid.exc.UnknownIdentifier),
(TEST_MINID, 'does-not-exist', minid.exc.UnknownIdentifier),
]
@pytest.fixture(params=VALID_MINID_TRANSLATIONS)
@pytest.fixture(params=INVALID_MINID_TRANSLATIONS)
@pytest.fixture(params=[(PROD_HDL, PROD_MINID), (TEST_HDL, TEST_MINID)])
@pytest.fixture(params=[
(42, False),
(PROD_HDL, False),
(TEST_HDL, False),
(PROD_MINID, True),
(TEST_MINID, True),
])
| 27.333333 | 76 | 0.734053 | import pytest
import minid
PROD_HDL = 'hdl:20.500.12582/prod_ident_xyz'
TEST_HDL = 'hdl:20.500.12633/test_ident_xyz'
PROD_MINID = 'minid:prod_ident_xyz'
TEST_MINID = 'minid.test:test_ident_xyz'
# The ordering is ('identifier', 'to_type', 'expected_result')
VALID_MINID_TRANSLATIONS = [
(PROD_HDL, 'minid', PROD_MINID),
(PROD_HDL, 'hdl', PROD_HDL),
(TEST_HDL, 'minid', TEST_MINID),
(TEST_HDL, 'hdl', TEST_HDL),
(PROD_MINID, 'minid', PROD_MINID),
(PROD_MINID, 'hdl', PROD_HDL),
(TEST_MINID, 'minid', TEST_MINID),
(TEST_MINID, 'hdl', TEST_HDL),
]
INVALID_MINID_TRANSLATIONS = [
('invalid_ident:3.141.59/', 'minid', minid.exc.UnknownIdentifier),
('invalid_ident:3.141.59/', 'hdl', minid.exc.UnknownIdentifier),
(PROD_HDL, 'does-not-exist', minid.exc.UnknownIdentifier),
(TEST_MINID, 'does-not-exist', minid.exc.UnknownIdentifier),
]
@pytest.fixture(params=VALID_MINID_TRANSLATIONS)
def valid_minid_translation(request):
return request.param
@pytest.fixture(params=INVALID_MINID_TRANSLATIONS)
def invalid_minid_translation(request):
return request.param
@pytest.fixture(params=[(PROD_HDL, PROD_MINID), (TEST_HDL, TEST_MINID)])
def valid_to_minid(request):
return request.param
@pytest.fixture(params=[
(42, False),
(PROD_HDL, False),
(TEST_HDL, False),
(PROD_MINID, True),
(TEST_MINID, True),
])
def is_minid(request):
return request.param
def test_valid_minid_translation(valid_minid_translation):
ident, to_type, expected = valid_minid_translation
result = minid.MinidClient.to_identifier(ident, identifier_type=to_type)
assert result == expected
def test_invalid_minid_translation(invalid_minid_translation):
ident, to_type, exc = invalid_minid_translation
with pytest.raises(exc):
minid.MinidClient.to_identifier(ident, identifier_type=to_type)
def test_valid_to_minid(valid_to_minid):
ident, expected = valid_to_minid
assert minid.MinidClient.to_minid(ident) == expected
def test_is_minid(is_minid):
ident, expected = is_minid
assert minid.MinidClient().is_minid(ident) == expected
| 745 | 0 | 180 |
8c523ff21cce00bfb6508a566525191ce5e43acb | 1,713 | py | Python | webscraper.py | cdaconta/ufc_fan | 7039150d73f1288f5ceb9d21a32bbaed4d2ec988 | [
"MIT"
] | null | null | null | webscraper.py | cdaconta/ufc_fan | 7039150d73f1288f5ceb9d21a32bbaed4d2ec988 | [
"MIT"
] | null | null | null | webscraper.py | cdaconta/ufc_fan | 7039150d73f1288f5ceb9d21a32bbaed4d2ec988 | [
"MIT"
] | null | null | null | from selenium import webdriver
from selenium.webdriver.common.by import By
import os
# This can be tested at heroku.com in 'Run console' with command: python webscraper.py
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--disable-dev-shm-usage")
chrome_options.add_argument("--no-sandbox")
chrome_options.binary_location = os.environ.get("GOOGLE_CHROME_BIN")
driver = webdriver.Chrome(executable_path=os.environ.get("CHROMEDRIVER_PATH"), options=chrome_options)
driver.implicitly_wait(10) # seconds
driver.get("https://www.ufc.com/trending/all")
def get_elements():
#elements = driver.find_elements(By.XPATH, '//div[@class="view-items-wrp"]/a')
""" driver.find_elements(By.XPATH, '//div[@class="view-items-wrp"]/a') """
heading3 = driver.find_elements_by_tag_name("h3")
elements = driver.find_elements_by_tag_name("a")
updatedLength = len(heading3)
filteredList = []
for item in heading3:
print(item.get_attribute("textContent"))
filteredHREF = []
for item in elements:
filteredHREF.append(item.get_attribute("href"))
""" substring = ["news", "video"]
filteredList = [word for word in filteredHREF if substring[0] or substring[1] in word]
for item in filteredList:
print(item) """
newsList = []
for item in filteredHREF:
if "https://www.ufc.com/news/" or "https://www.ufc.com/video/" or "https://www.ufc.com/gallery/" in item:
newsList.append(item)
print(f'This is news: {item}')
try:
print("We made it here!")
get_elements()
except Exception as e:
print(f'Couldn\'t find it - {e}')
print("Finished!") | 34.959184 | 113 | 0.690601 | from selenium import webdriver
from selenium.webdriver.common.by import By
import os
# This can be tested at heroku.com in 'Run console' with command: python webscraper.py
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--disable-dev-shm-usage")
chrome_options.add_argument("--no-sandbox")
chrome_options.binary_location = os.environ.get("GOOGLE_CHROME_BIN")
driver = webdriver.Chrome(executable_path=os.environ.get("CHROMEDRIVER_PATH"), options=chrome_options)
driver.implicitly_wait(10) # seconds
driver.get("https://www.ufc.com/trending/all")
def get_elements():
#elements = driver.find_elements(By.XPATH, '//div[@class="view-items-wrp"]/a')
""" driver.find_elements(By.XPATH, '//div[@class="view-items-wrp"]/a') """
heading3 = driver.find_elements_by_tag_name("h3")
elements = driver.find_elements_by_tag_name("a")
updatedLength = len(heading3)
filteredList = []
for item in heading3:
print(item.get_attribute("textContent"))
filteredHREF = []
for item in elements:
filteredHREF.append(item.get_attribute("href"))
""" substring = ["news", "video"]
filteredList = [word for word in filteredHREF if substring[0] or substring[1] in word]
for item in filteredList:
print(item) """
newsList = []
for item in filteredHREF:
if "https://www.ufc.com/news/" or "https://www.ufc.com/video/" or "https://www.ufc.com/gallery/" in item:
newsList.append(item)
print(f'This is news: {item}')
try:
print("We made it here!")
get_elements()
except Exception as e:
print(f'Couldn\'t find it - {e}')
print("Finished!") | 0 | 0 | 0 |
d727da06f635bc5fc0091a3943521f05ad08ca22 | 974 | py | Python | submissions/continuous-subarray-sum/solution.py | Wattyyy/LeetCode | 13a9be056d0a0c38c2f8c8222b11dc02cb25a935 | [
"MIT"
] | null | null | null | submissions/continuous-subarray-sum/solution.py | Wattyyy/LeetCode | 13a9be056d0a0c38c2f8c8222b11dc02cb25a935 | [
"MIT"
] | 1 | 2022-03-04T20:24:32.000Z | 2022-03-04T20:31:58.000Z | submissions/continuous-subarray-sum/solution.py | Wattyyy/LeetCode | 13a9be056d0a0c38c2f8c8222b11dc02cb25a935 | [
"MIT"
] | null | null | null | # https://leetcode.com/problems/continuous-subarray-sum
from itertools import accumulate
from collections import defaultdict
| 30.4375 | 80 | 0.530801 | # https://leetcode.com/problems/continuous-subarray-sum
from itertools import accumulate
from collections import defaultdict
class Solution:
def checkSubarraySum(self, nums, k):
if len(nums) <= 1:
return False
if k == 0:
cum_sum = list(accumulate(nums))
cum_sum = [0] + cum_sum
sum2idx = defaultdict(list)
for i, v in enumerate(cum_sum):
sum2idx[v].append(i)
if 1 < len(sum2idx[v]) and 1 < (sum2idx[v][-1] - sum2idx[v][0]):
return True
return False
k = abs(k)
cum_mod = list(accumulate(nums))
cum_mod = [num % k for num in cum_mod]
cum_mod = [0] + cum_mod
mod2idx = defaultdict(list)
for i, v in enumerate(cum_mod):
mod2idx[v].append(i)
if (1 < len(mod2idx[v])) and (mod2idx[v][-1] - mod2idx[v][0] > 1):
return True
return False
| 804 | -6 | 49 |
86b5822d95b8149ce13290b83517f3ea31869ed9 | 56,405 | py | Python | plugins/recorded_future/komand_recorded_future/actions/search_domains/schema.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 46 | 2019-06-05T20:47:58.000Z | 2022-03-29T10:18:01.000Z | plugins/recorded_future/komand_recorded_future/actions/search_domains/schema.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 386 | 2019-06-07T20:20:39.000Z | 2022-03-30T17:35:01.000Z | plugins/recorded_future/komand_recorded_future/actions/search_domains/schema.py | lukaszlaszuk/insightconnect-plugins | 8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892 | [
"MIT"
] | 43 | 2019-07-09T14:13:58.000Z | 2022-03-28T12:04:46.000Z | # GENERATED BY KOMAND SDK - DO NOT EDIT
import insightconnect_plugin_runtime
import json
| 27.394366 | 97 | 0.338995 | # GENERATED BY KOMAND SDK - DO NOT EDIT
import insightconnect_plugin_runtime
import json
class Component:
DESCRIPTION = "This action is used to search for results related to a specific parent domain"
class Input:
DIRECTION = "direction"
FROM = "from"
LIMIT = "limit"
ORDERBY = "orderby"
PARENT = "parent"
RISKRULE = "riskRule"
RISKSCORE = "riskScore"
class Output:
DATA = "data"
class SearchDomainsInput(insightconnect_plugin_runtime.Input):
schema = json.loads("""
{
"type": "object",
"title": "Variables",
"properties": {
"direction": {
"type": "string",
"title": "Result Direction",
"description": "Sort results ascending/descending",
"default": "asc",
"enum": [
"asc",
"desc"
],
"order": 4
},
"from": {
"type": "number",
"title": "Offset",
"description": "Number of initial records to skip",
"default": 0,
"order": 2
},
"limit": {
"type": "number",
"title": "Limit",
"description": "Number of results to retrieve, up to 100",
"order": 1
},
"orderby": {
"type": "string",
"title": "Order By",
"description": "Which property to sort the results by",
"enum": [
"Created",
"Firstseen",
"Lastseen",
"Modified",
"Riskscore",
"Rules",
"Sevendayshits",
"Sixtydayshits",
"Totalhits"
],
"order": 3
},
"parent": {
"type": "string",
"title": "Parent",
"description": "Parent domain, if any",
"order": 5
},
"riskRule": {
"type": "string",
"title": "Risk Rule",
"description": "Filters the results by risk rule",
"enum": [
"Historically Reported by Insikt Group",
"Newly Registered Certificate With Potential for Abuse - DNS Sandwich",
"Newly Registered Certificate With Potential for Abuse - Typo or Homograph",
"C\\u0026C Nameserver",
"C\\u0026C DNS Name",
"C\\u0026C URL",
"Compromised URL",
"Historical COVID-19-Related Domain Lure",
"Recently Resolved to Host of Many DDNS Names",
"Historically Reported as a Defanged DNS Name",
"Historically Reported by DHS AIS",
"Recent Fast Flux DNS Name",
"Historically Reported Fraudulent Content",
"Historically Reported in Threat List",
"Historically Linked to Cyber Attack",
"Historical Malware Analysis DNS Name",
"Historically Detected Malware Operation",
"Historically Detected Cryptocurrency Mining Techniques",
"Blacklisted DNS Name",
"Historical Phishing Lure",
"Historically Detected Phishing Techniques",
"Active Phishing URL",
"Recorded Future Predictive Risk Model",
"Historical Punycode Domain",
"Ransomware Distribution URL",
"Ransomware Payment DNS Name",
"Recently Reported by Insikt Group",
"Recent COVID-19-Related Domain Lure - Malicious",
"Recent COVID-19-Related Domain Lure - Suspicious",
"Recently Reported as a Defanged DNS Name",
"Recently Reported by DHS AIS",
"Recently Reported Fraudulent Content",
"Recently Linked to Cyber Attack",
"Recent Malware Analysis DNS Name",
"Recently Detected Malware Operation",
"Recently Detected Cryptocurrency Mining Techniques",
"Recent Phishing Lure - Malicious",
"Recent Phishing Lure - Suspicious",
"Recently Detected Phishing Techniques",
"Recent Punycode Domain",
"Recently Referenced by Insikt Group",
"Recently Reported Spam or Unwanted Content",
"URL Recently Linked to Suspicious Content",
"Recent Threat Researcher",
"Recent Typosquat Similarity - DNS Sandwich",
"Recent Typosquat Similarity - Typo or Homograph",
"Recently Active Weaponized Domain",
"Recently Defaced Site",
"Historically Referenced by Insikt Group",
"Recently Resolved to Malicious IP",
"Recently Resolved to Suspicious IP",
"Recently Resolved to Unusual IP",
"Recently Resolved to Very Malicious IP",
"Trending in Recorded Future Analyst Community",
"Historically Reported Spam or Unwanted Content",
"URL Historically Linked to Suspicious Content",
"Historical Threat Researcher",
"Historical Typosquat Similarity - DNS Sandwich",
"Historical Typosquat Similarity - Typo or Homograph",
"Historically Active Weaponized Domain"
],
"order": 6
},
"riskScore": {
"type": "string",
"title": "Risk Score",
"description": "Filters the results by risk score",
"order": 7
}
},
"required": [
"direction",
"from",
"limit",
"orderby",
"parent"
]
}
""")
def __init__(self):
super(self.__class__, self).__init__(self.schema)
class SearchDomainsOutput(insightconnect_plugin_runtime.Output):
schema = json.loads("""
{
"type": "object",
"title": "Variables",
"properties": {
"data": {
"type": "array",
"title": "Data",
"description": "Data",
"items": {
"$ref": "#/definitions/domain_search_data"
},
"order": 1
}
},
"required": [
"data"
],
"definitions": {
"analystNote": {
"type": "object",
"title": "analystNote",
"properties": {
"attributes": {
"$ref": "#/definitions/attributes",
"title": "Attributes",
"description": "Attributes",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"source": {
"$ref": "#/definitions/labels",
"title": "Source",
"description": "Source",
"order": 3
}
},
"definitions": {
"attributes": {
"type": "object",
"title": "attributes",
"properties": {
"context_entities": {
"type": "array",
"title": "Context Entities",
"description": "Context entities",
"items": {
"$ref": "#/definitions/context_entities"
},
"order": 1
},
"labels": {
"type": "array",
"title": "Labels",
"description": "Labels",
"items": {
"$ref": "#/definitions/labels"
},
"order": 2
},
"note_entities": {
"type": "array",
"title": "Note Entities",
"description": "Note entities",
"items": {
"$ref": "#/definitions/labels"
},
"order": 3
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 4
},
"text": {
"type": "string",
"title": "Text",
"description": "Text",
"order": 5
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 6
},
"topic": {
"$ref": "#/definitions/context_entities",
"title": "Topic",
"description": "Topic",
"order": 7
},
"validated_on": {
"type": "string",
"title": "Validated On",
"description": "Validated on",
"order": 8
},
"validation_urls": {
"type": "array",
"title": "Validation URLs",
"description": "Validation URLs",
"items": {
"$ref": "#/definitions/labels"
},
"order": 9
}
},
"definitions": {
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"attributes": {
"type": "object",
"title": "attributes",
"properties": {
"context_entities": {
"type": "array",
"title": "Context Entities",
"description": "Context entities",
"items": {
"$ref": "#/definitions/context_entities"
},
"order": 1
},
"labels": {
"type": "array",
"title": "Labels",
"description": "Labels",
"items": {
"$ref": "#/definitions/labels"
},
"order": 2
},
"note_entities": {
"type": "array",
"title": "Note Entities",
"description": "Note entities",
"items": {
"$ref": "#/definitions/labels"
},
"order": 3
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 4
},
"text": {
"type": "string",
"title": "Text",
"description": "Text",
"order": 5
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 6
},
"topic": {
"$ref": "#/definitions/context_entities",
"title": "Topic",
"description": "Topic",
"order": 7
},
"validated_on": {
"type": "string",
"title": "Validated On",
"description": "Validated on",
"order": 8
},
"validation_urls": {
"type": "array",
"title": "Validation URLs",
"description": "Validation URLs",
"items": {
"$ref": "#/definitions/labels"
},
"order": 9
}
},
"definitions": {
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"counts": {
"type": "object",
"title": "counts",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"date": {
"type": "string",
"title": "Date",
"description": "Date",
"order": 2
}
}
},
"domain_search_data": {
"type": "object",
"title": "domain_search_data",
"properties": {
"analystNotes": {
"type": "array",
"title": "Analyst Notes",
"description": "Notes from an analyst",
"items": {
"$ref": "#/definitions/analystNote"
},
"order": 1
},
"counts": {
"type": "array",
"title": "Counts",
"description": "Counts",
"items": {
"$ref": "#/definitions/counts"
},
"order": 2
},
"enterpriseLists": {
"type": "array",
"title": "Enterprise Lists",
"description": "Enterprise lists",
"items": {
"$ref": "#/definitions/enterpriseLists"
},
"order": 3
},
"entity": {
"$ref": "#/definitions/entity",
"title": "Entity",
"description": "Entity",
"order": 4
},
"intelCard": {
"type": "string",
"title": "Intel Card",
"description": "Intel card",
"order": 5
},
"metrics": {
"type": "array",
"title": "Metrics",
"description": "Metrics",
"items": {
"$ref": "#/definitions/metrics"
},
"order": 6
},
"relatedEntities": {
"type": "array",
"title": "Related Entities",
"description": "Related entities",
"items": {
"$ref": "#/definitions/relatedEntities"
},
"order": 7
},
"risk": {
"$ref": "#/definitions/risk",
"title": "Risk",
"description": "Risk",
"order": 8
},
"sightings": {
"type": "array",
"title": "Sightings",
"description": "Sightings",
"items": {
"$ref": "#/definitions/sightings"
},
"order": 9
},
"threatLists": {
"type": "array",
"title": "Threat Lists",
"description": "Threat lists",
"items": {
"$ref": "#/definitions/threatLists"
},
"order": 10
},
"timestamps": {
"$ref": "#/definitions/timestamps",
"title": "Timestamps",
"description": "Timestamps",
"order": 11
}
},
"definitions": {
"analystNote": {
"type": "object",
"title": "analystNote",
"properties": {
"attributes": {
"$ref": "#/definitions/attributes",
"title": "Attributes",
"description": "Attributes",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"source": {
"$ref": "#/definitions/labels",
"title": "Source",
"description": "Source",
"order": 3
}
},
"definitions": {
"attributes": {
"type": "object",
"title": "attributes",
"properties": {
"context_entities": {
"type": "array",
"title": "Context Entities",
"description": "Context entities",
"items": {
"$ref": "#/definitions/context_entities"
},
"order": 1
},
"labels": {
"type": "array",
"title": "Labels",
"description": "Labels",
"items": {
"$ref": "#/definitions/labels"
},
"order": 2
},
"note_entities": {
"type": "array",
"title": "Note Entities",
"description": "Note entities",
"items": {
"$ref": "#/definitions/labels"
},
"order": 3
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 4
},
"text": {
"type": "string",
"title": "Text",
"description": "Text",
"order": 5
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 6
},
"topic": {
"$ref": "#/definitions/context_entities",
"title": "Topic",
"description": "Topic",
"order": 7
},
"validated_on": {
"type": "string",
"title": "Validated On",
"description": "Validated on",
"order": 8
},
"validation_urls": {
"type": "array",
"title": "Validation URLs",
"description": "Validation URLs",
"items": {
"$ref": "#/definitions/labels"
},
"order": 9
}
},
"definitions": {
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"attributes": {
"type": "object",
"title": "attributes",
"properties": {
"context_entities": {
"type": "array",
"title": "Context Entities",
"description": "Context entities",
"items": {
"$ref": "#/definitions/context_entities"
},
"order": 1
},
"labels": {
"type": "array",
"title": "Labels",
"description": "Labels",
"items": {
"$ref": "#/definitions/labels"
},
"order": 2
},
"note_entities": {
"type": "array",
"title": "Note Entities",
"description": "Note entities",
"items": {
"$ref": "#/definitions/labels"
},
"order": 3
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 4
},
"text": {
"type": "string",
"title": "Text",
"description": "Text",
"order": 5
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 6
},
"topic": {
"$ref": "#/definitions/context_entities",
"title": "Topic",
"description": "Topic",
"order": 7
},
"validated_on": {
"type": "string",
"title": "Validated On",
"description": "Validated on",
"order": 8
},
"validation_urls": {
"type": "array",
"title": "Validation URLs",
"description": "Validation URLs",
"items": {
"$ref": "#/definitions/labels"
},
"order": 9
}
},
"definitions": {
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"context_entities": {
"type": "object",
"title": "context_entities",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"counts": {
"type": "object",
"title": "counts",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"date": {
"type": "string",
"title": "Date",
"description": "Date",
"order": 2
}
}
},
"enterpriseLists": {
"type": "object",
"title": "enterpriseLists",
"properties": {
"added": {
"type": "string",
"title": "Added",
"description": "Added",
"order": 1
},
"list": {
"$ref": "#/definitions/list",
"title": "List",
"description": "List",
"order": 2
}
},
"definitions": {
"list": {
"type": "object",
"title": "list",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entities": {
"type": "object",
"title": "entities",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"entity": {
"$ref": "#/definitions/entity",
"title": "Entity",
"description": "Entity",
"order": 2
}
},
"definitions": {
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"evidenceDetails": {
"type": "object",
"title": "evidenceDetails",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceString": {
"type": "string",
"title": "Evidence String",
"description": "Evidence string",
"order": 3
},
"rule": {
"type": "string",
"title": "Rule",
"description": "Rule",
"order": 4
},
"timestamp": {
"type": "string",
"title": "Timestamp",
"description": "Timestamp",
"order": 5
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"list": {
"type": "object",
"title": "list",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"metrics": {
"type": "object",
"title": "metrics",
"properties": {
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 1
},
"value": {
"type": "number",
"title": "Value",
"description": "Value",
"order": 2
}
}
},
"relatedEntities": {
"type": "object",
"title": "relatedEntities",
"properties": {
"entities": {
"type": "array",
"title": "Entities",
"description": "Entities",
"items": {
"$ref": "#/definitions/entities"
},
"order": 1
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 2
}
},
"definitions": {
"entities": {
"type": "object",
"title": "entities",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"entity": {
"$ref": "#/definitions/entity",
"title": "Entity",
"description": "Entity",
"order": 2
}
},
"definitions": {
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"risk": {
"type": "object",
"title": "risk",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceDetails": {
"type": "array",
"title": "Evidence Details",
"description": "Evidence details",
"items": {
"$ref": "#/definitions/evidenceDetails"
},
"order": 3
},
"riskSummary": {
"type": "string",
"title": "Risk Summary",
"description": "Risk summary",
"order": 4
},
"rules": {
"type": "integer",
"title": "Rules",
"description": "Rules",
"order": 5
},
"score": {
"type": "integer",
"title": "Score",
"description": "Score",
"order": 6
}
},
"definitions": {
"evidenceDetails": {
"type": "object",
"title": "evidenceDetails",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceString": {
"type": "string",
"title": "Evidence String",
"description": "Evidence string",
"order": 3
},
"rule": {
"type": "string",
"title": "Rule",
"description": "Rule",
"order": 4
},
"timestamp": {
"type": "string",
"title": "Timestamp",
"description": "Timestamp",
"order": 5
}
}
}
}
},
"sightings": {
"type": "object",
"title": "sightings",
"properties": {
"fragment": {
"type": "string",
"title": "Fragment",
"description": "Fragment",
"order": 1
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 2
},
"source": {
"type": "string",
"title": "Source",
"description": "Source",
"order": 3
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 4
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 5
},
"url": {
"type": "string",
"title": "URL",
"description": "URL",
"order": 6
}
}
},
"threatLists": {
"type": "object",
"title": "threatLists",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"timestamps": {
"type": "object",
"title": "timestamps",
"properties": {
"firstSeen": {
"type": "string",
"title": "First Seen",
"description": "First seen",
"order": 1
},
"lastSeen": {
"type": "string",
"title": "Last Seen",
"description": "Last seen",
"order": 2
}
}
}
}
},
"enterpriseLists": {
"type": "object",
"title": "enterpriseLists",
"properties": {
"added": {
"type": "string",
"title": "Added",
"description": "Added",
"order": 1
},
"list": {
"$ref": "#/definitions/list",
"title": "List",
"description": "List",
"order": 2
}
},
"definitions": {
"list": {
"type": "object",
"title": "list",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entities": {
"type": "object",
"title": "entities",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"entity": {
"$ref": "#/definitions/entity",
"title": "Entity",
"description": "Entity",
"order": 2
}
},
"definitions": {
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"evidenceDetails": {
"type": "object",
"title": "evidenceDetails",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceString": {
"type": "string",
"title": "Evidence String",
"description": "Evidence string",
"order": 3
},
"rule": {
"type": "string",
"title": "Rule",
"description": "Rule",
"order": 4
},
"timestamp": {
"type": "string",
"title": "Timestamp",
"description": "Timestamp",
"order": 5
}
}
},
"labels": {
"type": "object",
"title": "labels",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"list": {
"type": "object",
"title": "list",
"properties": {
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
},
"metrics": {
"type": "object",
"title": "metrics",
"properties": {
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 1
},
"value": {
"type": "number",
"title": "Value",
"description": "Value",
"order": 2
}
}
},
"relatedEntities": {
"type": "object",
"title": "relatedEntities",
"properties": {
"entities": {
"type": "array",
"title": "Entities",
"description": "Entities",
"items": {
"$ref": "#/definitions/entities"
},
"order": 1
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 2
}
},
"definitions": {
"entities": {
"type": "object",
"title": "entities",
"properties": {
"count": {
"type": "integer",
"title": "Count",
"description": "Count",
"order": 1
},
"entity": {
"$ref": "#/definitions/entity",
"title": "Entity",
"description": "Entity",
"order": 2
}
},
"definitions": {
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"entity": {
"type": "object",
"title": "entity",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 4
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 1
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 2
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 3
}
}
}
}
},
"risk": {
"type": "object",
"title": "risk",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceDetails": {
"type": "array",
"title": "Evidence Details",
"description": "Evidence details",
"items": {
"$ref": "#/definitions/evidenceDetails"
},
"order": 3
},
"riskSummary": {
"type": "string",
"title": "Risk Summary",
"description": "Risk summary",
"order": 4
},
"rules": {
"type": "integer",
"title": "Rules",
"description": "Rules",
"order": 5
},
"score": {
"type": "integer",
"title": "Score",
"description": "Score",
"order": 6
}
},
"definitions": {
"evidenceDetails": {
"type": "object",
"title": "evidenceDetails",
"properties": {
"criticality": {
"type": "number",
"title": "Criticality",
"description": "Criticality",
"order": 1
},
"criticalityLabel": {
"type": "string",
"title": "Criticality Label",
"description": "Criticality label",
"order": 2
},
"evidenceString": {
"type": "string",
"title": "Evidence String",
"description": "Evidence string",
"order": 3
},
"rule": {
"type": "string",
"title": "Rule",
"description": "Rule",
"order": 4
},
"timestamp": {
"type": "string",
"title": "Timestamp",
"description": "Timestamp",
"order": 5
}
}
}
}
},
"sightings": {
"type": "object",
"title": "sightings",
"properties": {
"fragment": {
"type": "string",
"title": "Fragment",
"description": "Fragment",
"order": 1
},
"published": {
"type": "string",
"title": "Published",
"description": "Published",
"order": 2
},
"source": {
"type": "string",
"title": "Source",
"description": "Source",
"order": 3
},
"title": {
"type": "string",
"title": "Title",
"description": "Title",
"order": 4
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 5
},
"url": {
"type": "string",
"title": "URL",
"description": "URL",
"order": 6
}
}
},
"threatLists": {
"type": "object",
"title": "threatLists",
"properties": {
"description": {
"type": "string",
"title": "Description",
"description": "Description",
"order": 1
},
"id": {
"type": "string",
"title": "ID",
"description": "ID",
"order": 2
},
"name": {
"type": "string",
"title": "Name",
"description": "Name",
"order": 3
},
"type": {
"type": "string",
"title": "Type",
"description": "Type",
"order": 4
}
}
},
"timestamps": {
"type": "object",
"title": "timestamps",
"properties": {
"firstSeen": {
"type": "string",
"title": "First Seen",
"description": "First seen",
"order": 1
},
"lastSeen": {
"type": "string",
"title": "Last Seen",
"description": "Last seen",
"order": 2
}
}
}
}
}
""")
def __init__(self):
super(self.__class__, self).__init__(self.schema)
| 112 | 56,076 | 115 |
968fc9ee7c3afd380ee0778dfce3ba487abb9fa8 | 1,483 | py | Python | edd/notify/consumers.py | TeselaGen/jbei-edd | 92792fb30bbd504143b2f75bf08d05b141a7ef6f | [
"BSD-3-Clause-LBNL"
] | null | null | null | edd/notify/consumers.py | TeselaGen/jbei-edd | 92792fb30bbd504143b2f75bf08d05b141a7ef6f | [
"BSD-3-Clause-LBNL"
] | null | null | null | edd/notify/consumers.py | TeselaGen/jbei-edd | 92792fb30bbd504143b2f75bf08d05b141a7ef6f | [
"BSD-3-Clause-LBNL"
] | null | null | null | # -*- coding: utf-8 -*-
import logging
from channels import Group
from channels.generic.websockets import JsonWebsocketConsumer
from django.contrib import messages
from edd import utilities
from .backend import DefaultBroker
logger = logging.getLogger(__name__)
class NotifySubscribeConsumer(JsonWebsocketConsumer):
"""
Consumer only adds the reply_channel to a Group for the user notifications, and sends any
active messages.
"""
channel_session = True
http_user_and_session = True
@classmethod
@classmethod
| 29.66 | 93 | 0.706001 | # -*- coding: utf-8 -*-
import logging
from channels import Group
from channels.generic.websockets import JsonWebsocketConsumer
from django.contrib import messages
from edd import utilities
from .backend import DefaultBroker
logger = logging.getLogger(__name__)
class NotifySubscribeConsumer(JsonWebsocketConsumer):
"""
Consumer only adds the reply_channel to a Group for the user notifications, and sends any
active messages.
"""
channel_session = True
http_user_and_session = True
def __init__(self, message, **kwargs):
super(NotifySubscribeConsumer, self).__init__(message, **kwargs)
# parent __init__ will add user to message
self.broker = DefaultBroker(message.user)
@classmethod
def decode_json(cls, text):
return utilities.JSONDecoder.loads(text)
@classmethod
def encode_json(cls, content):
return utilities.JSONEncoder.dumps(content)
def connection_groups(self, **kwargs):
return self.broker.group_names()
def connect(self, message, **kwargs):
super(NotifySubscribeConsumer, self).connect(message, **kwargs)
# get all current notifications and send to reply_channel
message.reply_channel.send(list(self.broker))
def receive(self, content, **kwargs):
super(NotifySubscribeConsumer, self).receive(content, **kwargs)
# get message ID, mark as read
#if content['action'] == 'dismiss':
logger.info(content)
| 773 | 0 | 160 |
3545431f028e9eb49ec16cddb7c5e0538efd2cfe | 4,174 | py | Python | src/pyTiming/stringlength.py | mirofedurco/PyAstronomy | b0e5806a18bde647654e6c9de323327803722864 | [
"MIT"
] | 98 | 2015-01-01T12:46:05.000Z | 2022-02-13T14:17:36.000Z | src/pyTiming/stringlength.py | mirofedurco/PyAstronomy | b0e5806a18bde647654e6c9de323327803722864 | [
"MIT"
] | 46 | 2015-02-10T19:53:38.000Z | 2022-01-11T17:26:05.000Z | src/pyTiming/stringlength.py | mirofedurco/PyAstronomy | b0e5806a18bde647654e6c9de323327803722864 | [
"MIT"
] | 38 | 2015-01-08T17:00:34.000Z | 2022-03-04T05:15:22.000Z | import numpy as np
from PyAstronomy import pyasl
from PyAstronomy.pyaC import pyaErrors as PE
def stringlength_norm(m, norm):
"""
Normalize string length data set.
Parameters
----------
m : array
Data array
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
Returns
-------
ms : array
The normalized data
"""
if norm == "default":
mmin = np.min(m)
mmax = np.max(m)
# Dworetsky 1983, Eq. 3
ms = (m-mmin) / (2 * (mmax-mmin)) - 0.25
elif norm == "no":
# Do nothing wrt normalization
ms = m
else:
raise(PE.PyAValError("Unknown value for 'norm': " + str(norm), \
where="stringlength_norm", \
solution="Use 'default' or 'no'"))
return ms
def stringlength_pm(p, m, norm="default", closed=True):
"""
Compute the string length for phased data set.
Parameters
----------
p : array
The phase array (0-1). Sorted in ascending order.
m : array
Data array
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
closed : boolean, optional
If True (default), first and last point on the phase axis
will be connected (close the loop).
Returns
-------
sl : float
The string length
"""
if np.any(np.diff(p) < 0):
raise(PE.PyAValError("Phase array (p) is not sorted in ascending order.", \
where="stringlength_pm"))
ms = stringlength_norm(m, norm)
# String length
sl = np.sum(np.sqrt( np.diff(p)**2 + np.diff(ms)**2 ))
if closed:
# Close the loop ...
sl += np.sqrt( (p[0] - p[-1] + 1)**2 + (ms[0] - ms[-1])**2 )
return sl
def stringlength_dat(x, m, tps, norm='default', isFreq=False, closed=True):
"""
Compute string length for data set.
Parameters
----------
x, m : arrays
x and y coordinates of data points.
tps : tuple or array
The trial periods (or frequencies): Either a three-tuple specifying
(pmin, pmax, nperiods) used by numpy's linspace or an array of trial periods.
If isFreq is True, tps is interpreted as frequencies.
isFreq : boolean, optional
If True, the input tps will be assumed to refer to frequencies instead of periods.
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
closed : boolean, optional
If True (default), first and last point on the phase axis
will be connected (close the loop).
Returns
-------
Trial periods/frequencies : array
The tested periods (or frequencies if isFreq is True).
String length : array
Associated string lengths
"""
if isinstance(tps, tuple) and len(tps) == 3:
pers = np.linspace(*tps)
elif isinstance(tps, np.ndarray):
pers = tps
else:
raise(PE.PyAValError("Unrecognized specification of trial periods or frequencies (tps).", \
where="stringlength_dat"))
if isFreq:
# The input is frequencies. Still need the periods.
pers = 1.0/pers
ms = stringlength_norm(m, norm)
sls = np.zeros(len(pers))
for i, p in enumerate(pers):
phase = pyasl.foldAt(x, p)
indi = np.argsort(phase)
sls[i] = stringlength_pm(phase[indi], ms[indi], norm='no', closed=closed)
if isFreq:
return 1.0/pers, sls
return pers, sls
| 31.621212 | 99 | 0.56828 | import numpy as np
from PyAstronomy import pyasl
from PyAstronomy.pyaC import pyaErrors as PE
def stringlength_norm(m, norm):
"""
Normalize string length data set.
Parameters
----------
m : array
Data array
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
Returns
-------
ms : array
The normalized data
"""
if norm == "default":
mmin = np.min(m)
mmax = np.max(m)
# Dworetsky 1983, Eq. 3
ms = (m-mmin) / (2 * (mmax-mmin)) - 0.25
elif norm == "no":
# Do nothing wrt normalization
ms = m
else:
raise(PE.PyAValError("Unknown value for 'norm': " + str(norm), \
where="stringlength_norm", \
solution="Use 'default' or 'no'"))
return ms
def stringlength_pm(p, m, norm="default", closed=True):
"""
Compute the string length for phased data set.
Parameters
----------
p : array
The phase array (0-1). Sorted in ascending order.
m : array
Data array
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
closed : boolean, optional
If True (default), first and last point on the phase axis
will be connected (close the loop).
Returns
-------
sl : float
The string length
"""
if np.any(np.diff(p) < 0):
raise(PE.PyAValError("Phase array (p) is not sorted in ascending order.", \
where="stringlength_pm"))
ms = stringlength_norm(m, norm)
# String length
sl = np.sum(np.sqrt( np.diff(p)**2 + np.diff(ms)**2 ))
if closed:
# Close the loop ...
sl += np.sqrt( (p[0] - p[-1] + 1)**2 + (ms[0] - ms[-1])**2 )
return sl
def stringlength_dat(x, m, tps, norm='default', isFreq=False, closed=True):
"""
Compute string length for data set.
Parameters
----------
x, m : arrays
x and y coordinates of data points.
tps : tuple or array
The trial periods (or frequencies): Either a three-tuple specifying
(pmin, pmax, nperiods) used by numpy's linspace or an array of trial periods.
If isFreq is True, tps is interpreted as frequencies.
isFreq : boolean, optional
If True, the input tps will be assumed to refer to frequencies instead of periods.
norm : string, {default, no}
If 'default' (default), the data points (mi) will be renormalized according
to Eq. 3 in Dworetsky 1983, i.e., using mnew = (mi - min(m)) / (2*(max(m) - min(m))).
If 'no' is specified, the data will not be changed.
closed : boolean, optional
If True (default), first and last point on the phase axis
will be connected (close the loop).
Returns
-------
Trial periods/frequencies : array
The tested periods (or frequencies if isFreq is True).
String length : array
Associated string lengths
"""
if isinstance(tps, tuple) and len(tps) == 3:
pers = np.linspace(*tps)
elif isinstance(tps, np.ndarray):
pers = tps
else:
raise(PE.PyAValError("Unrecognized specification of trial periods or frequencies (tps).", \
where="stringlength_dat"))
if isFreq:
# The input is frequencies. Still need the periods.
pers = 1.0/pers
ms = stringlength_norm(m, norm)
sls = np.zeros(len(pers))
for i, p in enumerate(pers):
phase = pyasl.foldAt(x, p)
indi = np.argsort(phase)
sls[i] = stringlength_pm(phase[indi], ms[indi], norm='no', closed=closed)
if isFreq:
return 1.0/pers, sls
return pers, sls
| 0 | 0 | 0 |
0868a262e1105712c1b1459683a752f900db5d7c | 455 | py | Python | fab_helper/util.py | toracle/fab_helper | 50bdf0c750e72e6b66360e067e05ab2e80f2948d | [
"MIT"
] | null | null | null | fab_helper/util.py | toracle/fab_helper | 50bdf0c750e72e6b66360e067e05ab2e80f2948d | [
"MIT"
] | null | null | null | fab_helper/util.py | toracle/fab_helper | 50bdf0c750e72e6b66360e067e05ab2e80f2948d | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import fabric
from fabric.api import run
import fabtools
| 28.4375 | 136 | 0.738462 | # -*- coding: utf-8 -*-
import fabric
from fabric.api import run
import fabtools
def install_python_devel():
fabtools.rpm.groupinstall('Development Tools')
fabtools.rpm.install('python-devel')
def match_and_delete_n_lines(remote_path, pattern, num_lines):
if fabric.contrib.files.exists(remote_path):
run("sed -i -e '/{pattern}/,+{num_lines}d' {remote_path}".format(pattern=pattern, remote_path=remote_path, num_lines=num_lines))
| 325 | 0 | 46 |
1d1cf28f9a480197bd37ce0c43f7a7abc65c8070 | 815,413 | py | Python | pfxbrick/pfxtestdata.py | fx-bricks/pfx-brick-py | dacd3c04fcbbecef7a6a5d9603ed8cf9a2eb3469 | [
"MIT"
] | 11 | 2018-04-26T22:53:44.000Z | 2022-02-14T13:44:27.000Z | pfxbrick/pfxtestdata.py | fx-bricks/pfx-brick-py | dacd3c04fcbbecef7a6a5d9603ed8cf9a2eb3469 | [
"MIT"
] | 3 | 2021-01-14T18:48:48.000Z | 2022-02-16T04:06:06.000Z | pfxbrick/pfxtestdata.py | fx-bricks/pfx-brick-py | dacd3c04fcbbecef7a6a5d9603ed8cf9a2eb3469 | [
"MIT"
] | 2 | 2018-05-20T11:30:53.000Z | 2022-02-15T06:42:37.000Z | # fmt: off
PINKFILE = """
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
"""
SINFILE = """
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"""
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
"""
# fmt: on
| 50,963.3125 | 462,396 | 0.93215 | # fmt: off
PINKFILE = """
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
"""
SINFILE = """
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"""
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
"""
# fmt: on
| 0 | 0 | 0 |
1c156e5f122bf8807853c07fb26f3bbb780b1d00 | 852 | py | Python | tests/test_var.py | caterinaurban/apronpy | 8a7e08e6929beeeeb97a9da648499be8c5d18bff | [
"MIT"
] | 7 | 2019-02-19T18:55:13.000Z | 2019-10-08T10:32:40.000Z | tests/test_var.py | caterinaurban/apronpy | 8a7e08e6929beeeeb97a9da648499be8c5d18bff | [
"MIT"
] | 3 | 2020-05-26T21:08:29.000Z | 2020-08-28T13:10:47.000Z | tests/test_var.py | caterinaurban/apronpy | 8a7e08e6929beeeeb97a9da648499be8c5d18bff | [
"MIT"
] | 1 | 2022-03-29T15:01:27.000Z | 2022-03-29T15:01:27.000Z | """
APRON (String) Variables - Unit Tests
=====================================
:Author: Caterina Urban
"""
import unittest
from copy import deepcopy
from apronpy.var import PyVar
if __name__ == '__main__':
unittest.main()
| 23.666667 | 51 | 0.566901 | """
APRON (String) Variables - Unit Tests
=====================================
:Author: Caterina Urban
"""
import unittest
from copy import deepcopy
from apronpy.var import PyVar
class TestPyVar(unittest.TestCase):
def test_init(self):
self.assertEqual(str(PyVar('x0')), 'x0')
self.assertEqual(str(PyVar('X')), 'X')
def test_deepcopy(self):
x0 = PyVar('X')
x1 = deepcopy(x0)
x2 = x0
self.assertNotEqual(id(x0), id(x1))
self.assertEqual(id(x0), id(x2))
def test_cmp(self):
self.assertTrue(PyVar('X') < PyVar('x0'))
self.assertFalse(PyVar('X') == PyVar('x0'))
self.assertTrue(PyVar('x0') == PyVar('x0'))
self.assertFalse(PyVar('X') > PyVar('x0'))
self.assertTrue(PyVar('y') > PyVar('x0'))
if __name__ == '__main__':
unittest.main()
| 502 | 14 | 104 |
a41227fd0193d99680d0f36404faf90aaae3bc70 | 184 | py | Python | deep_learning_zero/ch1/pyplot_img_smpl.py | kaito0223/shakyou | 8d901b4da24fbf0c708e3eb429a57d194e9857c1 | [
"MIT"
] | null | null | null | deep_learning_zero/ch1/pyplot_img_smpl.py | kaito0223/shakyou | 8d901b4da24fbf0c708e3eb429a57d194e9857c1 | [
"MIT"
] | null | null | null | deep_learning_zero/ch1/pyplot_img_smpl.py | kaito0223/shakyou | 8d901b4da24fbf0c708e3eb429a57d194e9857c1 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from matplotlib.image import imread
#img= imread('suzume.jpg')
img= imread('../images/suzume.jpg')
plt.imshow(img)
plt.show()
| 18.4 | 35 | 0.701087 | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from matplotlib.image import imread
#img= imread('suzume.jpg')
img= imread('../images/suzume.jpg')
plt.imshow(img)
plt.show()
| 0 | 0 | 0 |
16767bf10a073eb4b835f38554354ba050908bd5 | 16,703 | py | Python | protac_lib.py | Limsande/PRosettaC | 9cc9e11d045c9961fbdb667e700a98a46723a45b | [
"MIT"
] | 14 | 2020-09-27T07:23:07.000Z | 2022-03-02T08:19:02.000Z | protac_lib.py | Limsande/PRosettaC | 9cc9e11d045c9961fbdb667e700a98a46723a45b | [
"MIT"
] | 5 | 2021-08-19T02:31:25.000Z | 2022-03-04T15:43:22.000Z | protac_lib.py | Limsande/PRosettaC | 9cc9e11d045c9961fbdb667e700a98a46723a45b | [
"MIT"
] | 14 | 2020-09-30T09:30:10.000Z | 2022-02-28T14:14:08.000Z | import sys
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import rdFMCS
from rdkit.Chem import rdMolAlign
from rdkit.Chem import rdMolTransforms
from rdkit.Geometry.rdGeometry import Point3D
from rdkit.Chem.rdchem import Atom
import utils
import numpy as np
import math
import glob
import random
import copy
def x_rotation(vector,theta):
"""Rotates 3-D vector around x-axis"""
R = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0, np.sin(theta), np.cos(theta)]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
def y_rotation(vector,theta):
"""Rotates 3-D vector around y-axis"""
R = np.array([[np.cos(theta),0,np.sin(theta)],[0,1,0],[-np.sin(theta), 0, np.cos(theta)]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
def z_rotation(vector,theta):
"""Rotates 3-D vector around z-axis"""
R = np.array([[np.cos(theta), -np.sin(theta),0],[np.sin(theta), np.cos(theta),0],[0,0,1]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
#rotate a molecule around the origin
#sample an array of distances between the anchor points
#generate n random conformations for each smile in linkers_file
#this is an old function and is not used in the final pipeline
#generate constrained conformations for each of the local docking solutions
#printing head RMSD, calculated between a modeled PROTAC, and the .sdf files of the two binders (headA, headB)
| 45.887363 | 173 | 0.639406 | import sys
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import rdFMCS
from rdkit.Chem import rdMolAlign
from rdkit.Chem import rdMolTransforms
from rdkit.Geometry.rdGeometry import Point3D
from rdkit.Chem.rdchem import Atom
import utils
import numpy as np
import math
import glob
import random
import copy
def get_mcs_sdf(old_sdf, new_sdf, protac):
utils.addH_sdf(old_sdf)
OldSdf = Chem.SDMolSupplier(old_sdf)[0]
if OldSdf == None:
return None
PROTAC = Chem.MolFromSmiles(protac)
print(Chem.MolToSmiles(OldSdf))
print(Chem.MolToSmiles(PROTAC))
mcs = rdFMCS.FindMCS([OldSdf, PROTAC], ringMatchesRingOnly=False, completeRingsOnly=False)
print(mcs.smartsString)
mcs_patt = Chem.MolFromSmarts(mcs.smartsString)
print(mcs_patt.GetNumHeavyAtoms())
print(OldSdf.GetNumHeavyAtoms())
if mcs_patt.GetNumHeavyAtoms() < OldSdf.GetNumHeavyAtoms() * 0.6:
return None
rwmol = Chem.RWMol(mcs_patt)
rwconf = Chem.Conformer(rwmol.GetNumAtoms())
Match = OldSdf.GetSubstructMatch(mcs_patt)
for i, m in enumerate(Match):
rwconf.SetAtomPosition(i, OldSdf.GetConformer().GetAtomPosition(m))
rwmol.AddConformer(rwconf)
writer = Chem.SDWriter(new_sdf)
writer.write(rwmol)
NewSdf = Chem.SDMolSupplier(new_sdf, sanitize=False)[0]
NewSdf = Chem.MolFromSmarts(Chem.MolToSmarts(NewSdf))
print(Chem.MolToSmiles(NewSdf))
Matches = NewSdf.GetSubstructMatches(NewSdf, uniquify=False)
print(Matches)
if len(Matches) == 0:
return None
elif len(Matches) == 1:
return 0
else:
for i in range(len(Matches[0])):
a = Matches[0][i]
allowed = True
for j in Matches[1:]:
if not a == j[i]:
allowed = False
break
if allowed:
return a
return None
def translate_anchors(old_sdf, new_sdf, old_anchor):
OldSdf = Chem.SDMolSupplier(old_sdf, sanitize=False)[0]
NewSdf = Chem.SDMolSupplier(new_sdf, sanitize=True)[0]
print(Chem.MolToSmiles(OldSdf))
print(Chem.MolToSmiles(NewSdf))
NewMatch = NewSdf.GetSubstructMatch(OldSdf)
if len(NewMatch) == 0:
return -1
print(NewMatch)
return NewMatch[old_anchor]
def rmsd(query, ref, q_match, r_match):
rmsd = 0
for i in range(len(q_match)):
rmsd += (query.GetConformer().GetAtomPosition(q_match[i])-ref.GetConformer().GetAtomPosition(r_match[i])).LengthSq()
rmsd = np.sqrt(rmsd/len(q_match))
return rmsd
def heads_rmsd(query, headA, headB, headA_sub, headB_sub, match_A, match_B):
rmsd = 0
for i in range(len(match_A)):
rmsd += (query.GetConformer().GetAtomPosition(match_A[i])-headA.GetConformer().GetAtomPosition(headA_sub[i])).LengthSq()
for i in range(len(match_B)):
rmsd += (query.GetConformer().GetAtomPosition(match_B[i])-headB.GetConformer().GetAtomPosition(headB_sub[i])).LengthSq()
rmsd = np.sqrt(rmsd/(len(match_A) + len(match_B)))
return rmsd
def MCS_AtomMap(query, ref):
mcs = rdFMCS.FindMCS([query, ref], ringMatchesRingOnly=True, completeRingsOnly=True)
mcs_patt = Chem.MolFromSmarts(mcs.smartsString)
refMatch = ref.GetSubstructMatch(mcs_patt)
queryMatch = query.GetSubstructMatch(mcs_patt)
amap = []
for i in range(len(refMatch)):
amap.append((queryMatch[i], refMatch[i]))
return amap
def SetCoordsForMatch(query, ref, ref_match):
for i, pos in enumerate(ref_match):
ref_pos = ref.GetConformer().GetAtomPosition(i)
query.GetConformer().SetAtomPosition(pos, ref_pos)
def translateMol(mol, pointA, pointB):
for i in range(mol.GetConformer().GetNumAtoms()):
mol.GetConformer().SetAtomPosition(i, mol.GetConformer().GetAtomPosition(i) + pointA - pointB)
def x_rotation(vector,theta):
"""Rotates 3-D vector around x-axis"""
R = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0, np.sin(theta), np.cos(theta)]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
def y_rotation(vector,theta):
"""Rotates 3-D vector around y-axis"""
R = np.array([[np.cos(theta),0,np.sin(theta)],[0,1,0],[-np.sin(theta), 0, np.cos(theta)]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
def z_rotation(vector,theta):
"""Rotates 3-D vector around z-axis"""
R = np.array([[np.cos(theta), -np.sin(theta),0],[np.sin(theta), np.cos(theta),0],[0,0,1]])
output = np.dot(R, [vector.x, vector.y, vector.z])
return Point3D(output[0], output[1], output[2])
#rotate a molecule around the origin
def rotateMol(mol, x, y, z):
for i in range(mol.GetConformer().GetNumAtoms()):
atom = mol.GetConformer().GetAtomPosition(i)
atom = x_rotation(atom, x)
atom = y_rotation(atom, y)
atom = z_rotation(atom, z)
mol.GetConformer().SetAtomPosition(i, atom)
def randomRotateMol(mol):
x = random.random()*np.pi*2
y = random.random()*np.pi*2
z = random.random()*np.pi*2
rotateMol(mol, x, y, z)
#sample an array of distances between the anchor points
def SampleDist(Heads, Anchors, Linkers, n = 200, output_hist="initial_distances.hist", hist_threshold = 0.75, min_margin = 2, homo_protac = False):
writer = Chem.SDWriter("random_sampling.sdf")
random.seed(0)
[HeadA_sdf, HeadB_sdf] = Heads
#linkers
with open(Linkers, 'r') as f:
linkers = [Chem.MolFromSmiles(f.readline().split()[0])]
#loading the heads sdf files
HeadA = Chem.SDMolSupplier(HeadA_sdf)[0]
HeadB = Chem.SDMolSupplier(HeadB_sdf)[0]
origin = Point3D(0,0,0)
anchor_a = HeadA.GetConformer().GetAtomPosition(Anchors[0])
translateMol(HeadA, origin, anchor_a)
anchor_b = HeadB.GetConformer().GetAtomPosition(Anchors[1])
translateMol(HeadB, origin, anchor_b)
for linker in linkers:
#homo protacs are protacs with the same binder twice, causing self degradation of an E3 ligase
if homo_protac:
head_A = linker.GetSubstructMatches(HeadA)[0]
head_B = linker.GetSubstructMatches(HeadB)[1]
else:
mcs_A = rdFMCS.FindMCS([linker, HeadA])
mcs_patt_A = Chem.MolFromSmarts(mcs_A.smartsString)
mcs_B = rdFMCS.FindMCS([linker, HeadB])
mcs_patt_B = Chem.MolFromSmarts(mcs_B.smartsString)
#head_A_list = linker.GetSubstructMatches(HeadA, uniquify=False)
head_A_list = linker.GetSubstructMatches(mcs_patt_A, uniquify=False)
head_A_inner = HeadA.GetSubstructMatch(mcs_patt_A)
#head_B_list = linker.GetSubstructMatches(HeadB, uniquify=False)
head_B_list = linker.GetSubstructMatches(mcs_patt_B, uniquify=False)
head_B_inner = HeadB.GetSubstructMatch(mcs_patt_B)
print(Chem.MolToSmiles(linker))
print(Chem.MolToSmiles(HeadB))
print(head_B_list)
if len(head_A_list) == 0 or len(head_B_list) == 0:
return (None, None)
histogram = {}
seed = 0
b = 1
while True:
b_counter = 0
for i in range(n):
head_A = random.choice(head_A_list)
head_B = random.choice(head_B_list)
seed += 1
NewA = copy.deepcopy(HeadA)
NewB = copy.deepcopy(HeadB)
randomRotateMol(NewA)
randomRotateMol(NewB)
translateMol(NewB, Point3D(b, 0, 0), origin)
#the constraints for the conformation generation using the two randomized heads
cmap = {head_A[i]:NewA.GetConformer().GetAtomPosition(head_A_inner[i]) for i in range(len(head_A))}
cmap.update({head_B[i]:NewB.GetConformer().GetAtomPosition(head_B_inner[i]) for i in range(len(head_B))})
#only half of the atoms are required to make the constrained embedding
#this is done because using all the atoms sometimes makes it impossible
#to find solutions, the half is chosen randomly for each generation
cmap_tag = random.sample(list(cmap), int(len(cmap)/2))
cmap_tag = {ctag:cmap[ctag] for ctag in cmap_tag}
if AllChem.EmbedMolecule(linker, coordMap=cmap_tag, randomSeed=seed, useBasicKnowledge=True, maxAttempts=1) == -1:
continue
if int(round(rdMolTransforms.GetBondLength(linker.GetConformer(), head_A[Anchors[0]], head_B[Anchors[1]]))) == b:
writer.write(linker)
b_counter += 1
histogram[b] = b_counter
if b >= 10 and b_counter == 0:
break
b += 1
with open(output_hist, 'w') as f:
for h in histogram:
f.write(str(h) + "\t" + str(histogram[h]) + '\n')
max_value = max([histogram[i] for i in histogram])
sum_mul = 0
sum_his = 0
for i in histogram:
sum_mul += i * histogram[i]
sum_his += histogram[i]
if sum_his == 0:
return (0,0)
else:
avg_index = 1.0 * sum_mul / sum_his
threshold = max_value * hist_threshold
high_values = [i for i in histogram if histogram[i] >= threshold]
return(min(min(high_values), avg_index - min_margin), max(max(high_values), avg_index + min_margin))
#generate n random conformations for each smile in linkers_file
#this is an old function and is not used in the final pipeline
def GenRandConf(Heads, Anchors, Linkers, n=1000, output_hist="initial_distances.hist", output_sdf="random_sampling.sdf"):
writer = Chem.SDWriter(output_sdf)
[HeadA_sdf, HeadB_sdf] = Heads
#linkers
with open(Linkers, 'r') as f:
linkers = [Chem.MolFromSmiles(f.readline().split()[0])]
#loading the heads sdf files
HeadA = Chem.SDMolSupplier(HeadA_sdf)[0]
HeadB = Chem.SDMolSupplier(HeadB_sdf)[0]
#anchor distances
X1Y1_dist = []
for linker in linkers:
Chem.AddHs(linker)
amapA = MCS_AtomMap(HeadA, linker)
amapB = MCS_AtomMap(HeadB, linker)
anchors = [[item[1] for item in amapA if item[0] == Anchors[0]][0], [item[1] for item in amapB if item[0] == Anchors[1]][0]]
i = 0
seed = 0
while i < n:
seed += 1
if Chem.rdDistGeom.EmbedMolecule(linker, randomSeed=seed, useBasicKnowledge=True, maxAttempts=10) == -1:
continue
X1Y1_dist.append(rdMolTransforms.GetBondLength(linker.GetConformer(), anchors[0], anchors[1]))
writer.write(linker)
i += 1
hist = np.histogram(np.array(X1Y1_dist), range=(math.floor(min(X1Y1_dist)), math.ceil(max(X1Y1_dist))), bins=2*int(math.ceil(max(X1Y1_dist)-math.floor(min(X1Y1_dist)))))
with open(output_hist, 'w') as f:
f.write("range is: " + str([min(X1Y1_dist), max(X1Y1_dist)]) + '\n')
for i in range(len(hist[0])):
f.write(str(hist[0][i]) + '\t' + str(hist[1][i]) + '\n')
return (min(X1Y1_dist), max(X1Y1_dist))
#generate constrained conformations for each of the local docking solutions
def GenConstConf(Heads, Docked_Heads, Head_Linkers, output_sdf, Anchor_A, v_atoms_sdf, n = 100, homo_protac = False):
writer = Chem.SDWriter(output_sdf)
with open(Head_Linkers, 'r') as f:
head_linkers = [Chem.MolFromSmiles(f.readline().split()[0])]
#loading the heads sdf files
HeadA = Chem.SDMolSupplier(Heads[0])[0]
HeadB = Chem.SDMolSupplier(Heads[1])[0]
docked_heads = Chem.SDMolSupplier(Docked_Heads)[0]
#virtual atoms around the center of mass for the neighbor atom alignment
num_atoms = docked_heads.GetConformer().GetNumAtoms()
x = []
y = []
z = []
for i in range(num_atoms):
x.append(docked_heads.GetConformer().GetAtomPosition(i).x)
y.append(docked_heads.GetConformer().GetAtomPosition(i).y)
z.append(docked_heads.GetConformer().GetAtomPosition(i).z)
v1 = Point3D(sum(x)/num_atoms, sum(y)/num_atoms, sum(z)/num_atoms)
v2 = Point3D(sum(x)/num_atoms + 1, sum(y)/num_atoms, sum(z)/num_atoms)
v3 = Point3D(sum(x)/num_atoms, sum(y)/num_atoms + 1, sum(z)/num_atoms)
virtual_atoms = Chem.MolFromSmarts('[#23][#23][#23]')
Chem.rdDistGeom.EmbedMolecule(virtual_atoms)
virtual_atoms.GetConformer().SetAtomPosition(1, v1)
virtual_atoms.GetConformer().SetAtomPosition(0, v2)
virtual_atoms.GetConformer().SetAtomPosition(2, v3)
v_writer = Chem.SDWriter(v_atoms_sdf)
v_writer.write(virtual_atoms)
#homo protacs are protacs with the same binder twice, causing self degradation of an E3 ligase
if homo_protac:
docked_A = docked_heads.GetSubstructMatches(HeadA)[0]
docked_B = docked_heads.GetSubstructMatches(HeadB)[1]
else:
docked_A = docked_heads.GetSubstructMatch(HeadA)
docked_B = docked_heads.GetSubstructMatch(HeadB)
for head_linker in head_linkers:
Chem.AddHs(head_linker)
if homo_protac:
head_A = head_linker.GetSubstructMatches(HeadA)[0]
head_B = head_linker.GetSubstructMatches(HeadB)[1]
else:
head_A_list = head_linker.GetSubstructMatches(HeadA, uniquify=False)
head_B_list = head_linker.GetSubstructMatches(HeadB, uniquify=False)
i = 0
seed = 0
while i < n:
if seed > 10 * n:
break
if seed > n and i == 0:
break
seed += 1
random.seed(seed)
head_A = random.choice(head_A_list)
head_B = random.choice(head_B_list)
#amap for final alignment
amap = []
for j in range(len(docked_A)):
amap.append((head_A[j], docked_A[j]))
for j in range(len(docked_B)):
amap.append((head_B[j], docked_B[j]))
#the constraints for the conformation generation using the two docked heads
cmap = {head_A[j]:docked_heads.GetConformer().GetAtomPosition(docked_A[j]) for j in range(len(docked_A))}
cmap.update({head_B[j]:docked_heads.GetConformer().GetAtomPosition(docked_B[j]) for j in range(len(docked_B))})
#only half of the atoms are required to make the constrained embedding
#this is done because using all the atoms sometimes makes it impossible
#to find solutions, the half is chosen randomly for each generation
cmap_tag = random.sample(list(cmap), int(len(cmap)/2))
cmap_tag = {ctag:cmap[ctag] for ctag in cmap_tag}
if AllChem.EmbedMolecule(head_linker, coordMap=cmap_tag, randomSeed=seed, useBasicKnowledge=True, maxAttempts=10) == -1:
continue
#final alignment to bring the new conformation to the position of the pose's heads
#this is needed because the constrained embedding only applies
#to distances and not to atoms position
rdMolAlign.AlignMol(head_linker, docked_heads, atomMap=amap)
#make sure the alignment is good enough for both heads (also to ensure the save isomer
#for ambiguous rings
if rmsd(head_linker, docked_heads, head_A, docked_A) < 0.5 and rmsd(head_linker, docked_heads, head_B, docked_B) < 0.5:
writer.write(head_linker)
i += 1
return head_A[int(Anchor_A)], v_atoms_sdf
#printing head RMSD, calculated between a modeled PROTAC, and the .sdf files of the two binders (headA, headB)
def print_rmsd(headA, headB, Head_Linkers):
#loading the heads sdf files
HeadA = Chem.SDMolSupplier(headA)[0]
HeadB = Chem.SDMolSupplier(headB)[0]
head_linkers = Chem.SDMolSupplier(Head_Linkers)
for head_linker in head_linkers:
mcsA = rdFMCS.FindMCS([HeadA, head_linker])
mcsB = rdFMCS.FindMCS([HeadB, head_linker])
pattA = Chem.MolFromSmarts(mcsA.smartsString)
pattB = Chem.MolFromSmarts(mcsB.smartsString)
HeadA_sub = HeadA.GetSubstructMatches(pattA, uniquify=False)
HeadB_sub = HeadB.GetSubstructMatch(pattB)
head_A = head_linker.GetSubstructMatches(pattA, uniquify=False)
head_B = head_linker.GetSubstructMatch(pattB)
for H in HeadA_sub:
for h in head_A:
print(heads_rmsd(head_linker, HeadA, HeadB, H, HeadB_sub, h, head_B))
| 14,838 | 0 | 294 |
083eadddfa7babd35de9b66741b00913170b7446 | 7,557 | py | Python | astronomer/providers/amazon/aws/hooks/redshift_cluster.py | astronomer/astronomer-providers | e19c656daab19f3e881f140495e2184c16eaafe0 | [
"Apache-2.0"
] | 27 | 2022-03-02T04:49:54.000Z | 2022-03-30T13:19:02.000Z | astronomer/providers/amazon/aws/hooks/redshift_cluster.py | astronomer/astronomer-providers | e19c656daab19f3e881f140495e2184c16eaafe0 | [
"Apache-2.0"
] | 92 | 2022-03-02T08:01:31.000Z | 2022-03-31T19:47:33.000Z | astronomer/providers/amazon/aws/hooks/redshift_cluster.py | astronomer/astronomer-providers | e19c656daab19f3e881f140495e2184c16eaafe0 | [
"Apache-2.0"
] | 2 | 2022-03-07T17:39:41.000Z | 2022-03-18T20:37:03.000Z | import asyncio
from typing import Any, Dict, Optional
import botocore.exceptions
from astronomer.providers.amazon.aws.hooks.base_aws_async import AwsBaseHookAsync
class RedshiftHookAsync(AwsBaseHookAsync):
"""Interact with AWS Redshift using aiobotocore python library"""
async def cluster_status(self, cluster_identifier: str, delete_operation: bool = False) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and get the status
and returns the status of the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param delete_operation: whether the method has been called as part of delete cluster operation
"""
async with await self.get_client_async() as client:
try:
response = await client.describe_clusters(ClusterIdentifier=cluster_identifier)
cluster_state = (
response["Clusters"][0]["ClusterStatus"] if response and response["Clusters"] else None
)
return {"status": "success", "cluster_state": cluster_state}
except botocore.exceptions.ClientError as error:
if delete_operation and error.response.get("Error", {}).get("Code", "") == "ClusterNotFound":
return {"status": "success", "cluster_state": "cluster_not_found"}
return {"status": "error", "message": str(error)}
async def delete_cluster(
self,
cluster_identifier: str,
skip_final_cluster_snapshot: bool = True,
final_cluster_snapshot_identifier: Optional[str] = None,
polling_period_seconds: float = 5.0,
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
deletes the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param skip_final_cluster_snapshot: determines cluster snapshot creation
:param final_cluster_snapshot_identifier: name of final cluster snapshot
:param polling_period_seconds: polling period in seconds to check for the status
"""
try:
final_cluster_snapshot_identifier = final_cluster_snapshot_identifier or ""
async with await self.get_client_async() as client:
response = await client.delete_cluster(
ClusterIdentifier=cluster_identifier,
SkipFinalClusterSnapshot=skip_final_cluster_snapshot,
FinalClusterSnapshotIdentifier=final_cluster_snapshot_identifier,
)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "deleting":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "cluster_not_found", flag, True)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def pause_cluster(
self, cluster_identifier: str, polling_period_seconds: float = 5.0
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
pause the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param polling_period_seconds: polling period in seconds to check for the status
"""
try:
async with await self.get_client_async() as client:
response = await client.pause_cluster(ClusterIdentifier=cluster_identifier)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "pausing":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "paused", flag)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def resume_cluster(
self,
cluster_identifier: str,
polling_period_seconds: float = 5.0,
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
resume the cluster for the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param polling_period_seconds: polling period in seconds to check for the status
"""
async with await self.get_client_async() as client:
try:
response = await client.resume_cluster(ClusterIdentifier=cluster_identifier)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "resuming":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "available", flag)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def get_cluster_status(
self,
cluster_identifier: str,
expected_state: str,
flag: asyncio.Event,
delete_operation: bool = False,
) -> Dict[str, Any]:
"""
Make call self.cluster_status to know the status and run till the expected_state is met and set the flag
:param cluster_identifier: unique identifier of a cluster
:param expected_state: expected_state example("available", "pausing", "paused"")
:param flag: asyncio even flag set true if success and if any error
:param delete_operation: whether the method has been called as part of delete cluster operation
"""
try:
response = await self.cluster_status(cluster_identifier, delete_operation=delete_operation)
if ("cluster_state" in response and response["cluster_state"] == expected_state) or response[
"status"
] == "error":
flag.set()
return response
except botocore.exceptions.ClientError as error:
flag.set()
return {"status": "error", "message": str(error)}
| 48.133758 | 112 | 0.612148 | import asyncio
from typing import Any, Dict, Optional
import botocore.exceptions
from astronomer.providers.amazon.aws.hooks.base_aws_async import AwsBaseHookAsync
class RedshiftHookAsync(AwsBaseHookAsync):
"""Interact with AWS Redshift using aiobotocore python library"""
def __init__(self, *args: Any, **kwargs: Any) -> None:
kwargs["client_type"] = "redshift"
kwargs["resource_type"] = "redshift"
super().__init__(*args, **kwargs)
async def cluster_status(self, cluster_identifier: str, delete_operation: bool = False) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and get the status
and returns the status of the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param delete_operation: whether the method has been called as part of delete cluster operation
"""
async with await self.get_client_async() as client:
try:
response = await client.describe_clusters(ClusterIdentifier=cluster_identifier)
cluster_state = (
response["Clusters"][0]["ClusterStatus"] if response and response["Clusters"] else None
)
return {"status": "success", "cluster_state": cluster_state}
except botocore.exceptions.ClientError as error:
if delete_operation and error.response.get("Error", {}).get("Code", "") == "ClusterNotFound":
return {"status": "success", "cluster_state": "cluster_not_found"}
return {"status": "error", "message": str(error)}
async def delete_cluster(
self,
cluster_identifier: str,
skip_final_cluster_snapshot: bool = True,
final_cluster_snapshot_identifier: Optional[str] = None,
polling_period_seconds: float = 5.0,
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
deletes the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param skip_final_cluster_snapshot: determines cluster snapshot creation
:param final_cluster_snapshot_identifier: name of final cluster snapshot
:param polling_period_seconds: polling period in seconds to check for the status
"""
try:
final_cluster_snapshot_identifier = final_cluster_snapshot_identifier or ""
async with await self.get_client_async() as client:
response = await client.delete_cluster(
ClusterIdentifier=cluster_identifier,
SkipFinalClusterSnapshot=skip_final_cluster_snapshot,
FinalClusterSnapshotIdentifier=final_cluster_snapshot_identifier,
)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "deleting":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "cluster_not_found", flag, True)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def pause_cluster(
self, cluster_identifier: str, polling_period_seconds: float = 5.0
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
pause the cluster based on the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param polling_period_seconds: polling period in seconds to check for the status
"""
try:
async with await self.get_client_async() as client:
response = await client.pause_cluster(ClusterIdentifier=cluster_identifier)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "pausing":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "paused", flag)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def resume_cluster(
self,
cluster_identifier: str,
polling_period_seconds: float = 5.0,
) -> Dict[str, Any]:
"""
Connects to the AWS redshift cluster via aiobotocore and
resume the cluster for the cluster_identifier passed
:param cluster_identifier: unique identifier of a cluster
:param polling_period_seconds: polling period in seconds to check for the status
"""
async with await self.get_client_async() as client:
try:
response = await client.resume_cluster(ClusterIdentifier=cluster_identifier)
status = response["Cluster"]["ClusterStatus"] if response and response["Cluster"] else None
if status == "resuming":
flag = asyncio.Event()
while True:
expected_response = await asyncio.create_task(
self.get_cluster_status(cluster_identifier, "available", flag)
)
await asyncio.sleep(polling_period_seconds)
if flag.is_set():
return expected_response
return {"status": "error", "cluster_state": status}
except botocore.exceptions.ClientError as error:
return {"status": "error", "message": str(error)}
async def get_cluster_status(
self,
cluster_identifier: str,
expected_state: str,
flag: asyncio.Event,
delete_operation: bool = False,
) -> Dict[str, Any]:
"""
Make call self.cluster_status to know the status and run till the expected_state is met and set the flag
:param cluster_identifier: unique identifier of a cluster
:param expected_state: expected_state example("available", "pausing", "paused"")
:param flag: asyncio even flag set true if success and if any error
:param delete_operation: whether the method has been called as part of delete cluster operation
"""
try:
response = await self.cluster_status(cluster_identifier, delete_operation=delete_operation)
if ("cluster_state" in response and response["cluster_state"] == expected_state) or response[
"status"
] == "error":
flag.set()
return response
except botocore.exceptions.ClientError as error:
flag.set()
return {"status": "error", "message": str(error)}
| 163 | 0 | 27 |
e69cb278ba7a30d01e1b6f00f9146f7425760094 | 279 | py | Python | api/crud/wallets.py | MrNaif2018/bitcart | 19191d21765a03292526e57d3bace23261c0d100 | [
"MIT"
] | 48 | 2019-03-06T09:40:44.000Z | 2020-09-08T23:30:07.000Z | api/crud/wallets.py | MrNaif2018/bitcart | 19191d21765a03292526e57d3bace23261c0d100 | [
"MIT"
] | 68 | 2019-05-09T13:16:23.000Z | 2020-09-07T14:28:32.000Z | api/crud/wallets.py | MrNaif2018/bitcart | 19191d21765a03292526e57d3bace23261c0d100 | [
"MIT"
] | 19 | 2019-03-07T09:19:40.000Z | 2020-08-09T18:03:43.000Z | from api import models, settings, utils
| 39.857143 | 106 | 0.767025 | from api import models, settings, utils
async def get_wallet_coin_by_id(model_id: str, user):
wallet = await utils.database.get_object(models.Wallet, model_id, user)
return settings.settings.get_coin(wallet.currency, {"xpub": wallet.xpub, "contract": wallet.contract})
| 215 | 0 | 23 |
cdeb3e0137ee70f18fbc7cc85f5b046edb6fe4bc | 5,787 | py | Python | exportucd.py | fsonner/blenderucdexport | bf7f35e54a13cacd46c9183edb3eacd9c900572e | [
"MIT"
] | null | null | null | exportucd.py | fsonner/blenderucdexport | bf7f35e54a13cacd46c9183edb3eacd9c900572e | [
"MIT"
] | null | null | null | exportucd.py | fsonner/blenderucdexport | bf7f35e54a13cacd46c9183edb3eacd9c900572e | [
"MIT"
] | null | null | null | bl_info = {
"name": "Export to AVS/UCD.ASCII (.inp)",
"author": "Florian Sonner",
"version": (1, 0),
"blender": (2, 7, 4),
"location": "File > Export > AVS/UCD.ASCII (.inp)",
"description": "Export 2d polygonials to AVS/UCD file format (ASCII variant) and assigns colors to edges. Ignores z coordinates.",
"warning": "",
"wiki_url": "...",
"tracker_url": "...dev..",
"category": "Import-Export"}
import mathutils, math, struct
import bpy
from bpy.props import *
from os import remove
import time
from bpy_extras.io_utils import ExportHelper
if __name__ == "__main__":
register()
| 27.29717 | 134 | 0.648523 | bl_info = {
"name": "Export to AVS/UCD.ASCII (.inp)",
"author": "Florian Sonner",
"version": (1, 0),
"blender": (2, 7, 4),
"location": "File > Export > AVS/UCD.ASCII (.inp)",
"description": "Export 2d polygonials to AVS/UCD file format (ASCII variant) and assigns colors to edges. Ignores z coordinates.",
"warning": "",
"wiki_url": "...",
"tracker_url": "...dev..",
"category": "Import-Export"}
import mathutils, math, struct
import bpy
from bpy.props import *
from os import remove
import time
from bpy_extras.io_utils import ExportHelper
def do_export(context, exporter, props, filepath):
mesh = None
for selected_object in bpy.context.selected_objects:
if selected_object.type.lower() == 'mesh':
if mesh != None:
exporter.report({'ERROR'}, "More than one mesh selected. Aborting export.")
return False
else:
mesh = selected_object
if mesh == None:
exporter.report({'ERROR'}, "No mesh selected in 'Object Mode'. Aborting export.")
return False
out = open(filepath, "w")
vertices = mesh.data.vertices
quads = mesh.data.polygons;
'''
First line of the .inp file requires us to know the number of boundary edges. As a
3d program blender has no such concept, so we need to determine boundary edge on our
own. We do this by counting the number of adjacent quads for each edge:
1 = boundary
2 = inner
3 = invalid mesh (3D)
'''
edge_numadj = { } # maps (v1, v2) to number of adjacent quads [v1 < v2 guaranteed for uniqueness]
for idx, quad in enumerate(quads):
for i in range(0, 4):
v1idx = quad.vertices[i]
v2idx = quad.vertices[(i + 1) % 4]
edge_key = (min(v1idx, v2idx), max(v1idx, v2idx))
if edge_key not in edge_numadj:
edge_numadj[edge_key] = 1
else:
edge_numadj[edge_key] += 1
boundary_edges = [ ]
for edge_key, count in edge_numadj.items():
if count == 1:
boundary_edges.append(edge_key)
elif count > 2:
exporter.report({'ERROR'}, "Invalid mesh: One edge has more than two neighboring cells. Aborting export.")
return False
boundary_edge_colors = [ ]
for edge in boundary_edges:
v1 = edge[0]
v2 = edge[1]
v1_groups = [ ]
v2_groups = [ ]
for group in vertices[v1].groups:
v1_groups.append(group.group)
for group in vertices[v2].groups:
v2_groups.append(group.group)
common_groups = list(set(v1_groups) & set(v2_groups))
if len(common_groups) > 1:
exporter.report({'ERROR'}, "Invalid mesh: Edge vertices belong to more than one group. Aborting export.")
return False
if len(common_groups) == 1:
boundary_edge_colors.append(common_groups[0]+1) # offset group idx by 1, zero are unassigned edges
else:
boundary_edge_colors.append(0)
'''
Begin output
'''
out.write('{numVertices} {numElements} 0 0 0\n'.format(numVertices = len(vertices), numElements = len(boundary_edges) + len(quads)))
# print vertices
for idx, v in enumerate(vertices):
out.write('{idx} {x} {y} 0\n'.format(idx = idx, x = v.co[0], y = v.co[1]))
# print quads and make vertices clockwise
for idx, quad in enumerate(quads):
# sum over edges to test if vertices are clockwise (see http://stackoverflow.com/a/1165943)
clockwise_sum = 0
for i in range(0, 4):
# sum for clockwise test
v1idx = quad.vertices[i]
v1 = vertices[v1idx]
v2idx = quad.vertices[(i + 1) % 4]
v2 = vertices[v2idx]
x1 = v1.co[0]
y1 = v1.co[1]
x2 = v2.co[0]
y2 = v2.co[1]
clockwise_sum += (x2 - x1) * (y2 + y1)
if clockwise_sum < 0: # counterclockwise: order ok
v0idx = quad.vertices[0]
v1idx = quad.vertices[1]
v2idx = quad.vertices[2]
v3idx = quad.vertices[3]
else: # clockwise: reverse order
v0idx = quad.vertices[3]
v1idx = quad.vertices[2]
v2idx = quad.vertices[1]
v3idx = quad.vertices[0]
out.write('{idx} 0 quad {v0} {v1} {v2} {v3}\n'.format(idx = idx, v0 = v0idx, v1 = v1idx, v2 = v2idx, v3 = v3idx))
# print boundary edges
for idx, edge in enumerate(boundary_edges):
out.write('{idx} {col} line {v0} {v1}\n'.format(idx = idx, col = boundary_edge_colors[idx], v0 = edge[0], v1 = edge[1]))
out.flush()
out.close()
return True
class Export_ucd(bpy.types.Operator, ExportHelper):
bl_idname = "export_shape.ucd"
bl_label = "Export AVS/UCD.ASCII (.inp)"
filename_ext = ".inp"
@classmethod
def poll(cls, context):
return context.active_object.type in {'MESH'}
def execute(self, context):
start_time = time.time()
print('\n_____START_____')
props = self.properties
filepath = self.filepath
filepath = bpy.path.ensure_ext(filepath, self.filename_ext)
exported = do_export(context, self, props, filepath)
if exported:
print('finished export in %s seconds' %((time.time() - start_time)))
print(filepath)
return {'FINISHED'}
def invoke(self, context, event):
wm = context.window_manager
if True:
# File selector
wm.fileselect_add(self) # will run self.execute()
return {'RUNNING_MODAL'}
elif True:
# search the enum
wm.invoke_search_popup(self)
return {'RUNNING_MODAL'}
elif False:
# Redo popup
return wm.invoke_props_popup(self, event)
elif False:
return self.execute(context)
def menu_func(self, context):
self.layout.operator(Export_ucd.bl_idname, text="AVS/UCD.ASCII (.inp)")
def register():
bpy.utils.register_module(__name__)
bpy.types.INFO_MT_file_export.append(menu_func)
def unregister():
bpy.utils.unregister_module(__name__)
bpy.types.INFO_MT_file_export.remove(menu_func)
if __name__ == "__main__":
register()
| 4,838 | 226 | 115 |
7d1983c111802e041733d8571194da3cc7ff5e99 | 823 | py | Python | src/yellowdog_client/images/__init__.py | yellowdog/yellowdog-sdk-python-public | da69a7d6e45c92933e34fefcaef8b5d98dcd6036 | [
"Apache-2.0"
] | null | null | null | src/yellowdog_client/images/__init__.py | yellowdog/yellowdog-sdk-python-public | da69a7d6e45c92933e34fefcaef8b5d98dcd6036 | [
"Apache-2.0"
] | null | null | null | src/yellowdog_client/images/__init__.py | yellowdog/yellowdog-sdk-python-public | da69a7d6e45c92933e34fefcaef8b5d98dcd6036 | [
"Apache-2.0"
] | null | null | null | from typing import List
from typish import get_args
import jsons
from jsons import default_list_deserializer
from yellowdog_client.common.json import object_deserializer
from .images_client import ImagesClient
from .images_client_impl import ImagesClientImpl
from .images_service_proxy import ImagesServiceProxy
from .page import Page
from .pageable import Pageable
from .sort import Sort
jsons.set_deserializer(page_deserializer, Page)
__all__ = [
"ImagesClient",
"ImagesClientImpl",
"ImagesServiceProxy",
"Page",
"Pageable",
"Sort"
]
| 22.861111 | 74 | 0.749696 | from typing import List
from typish import get_args
import jsons
from jsons import default_list_deserializer
from yellowdog_client.common.json import object_deserializer
from .images_client import ImagesClient
from .images_client_impl import ImagesClientImpl
from .images_service_proxy import ImagesServiceProxy
from .page import Page
from .pageable import Pageable
from .sort import Sort
def page_deserializer(value: dict, cls: type, **kwargs) -> object:
p = object_deserializer(value, Page)
if "content" in value:
arg = get_args(cls)[0]
p.content = default_list_deserializer(value["content"], List[arg])
return p
jsons.set_deserializer(page_deserializer, Page)
__all__ = [
"ImagesClient",
"ImagesClientImpl",
"ImagesServiceProxy",
"Page",
"Pageable",
"Sort"
]
| 234 | 0 | 23 |
b442e843f4b91ccad8ec3ffb54dee40245978c18 | 229 | py | Python | objectModel/Python/cdm/objectmodel/spew_catcher.py | aaron-emde/CDM | 9472e9c7694821ac4a9bbe608557d2e65aabc73e | [
"CC-BY-4.0",
"MIT"
] | null | null | null | objectModel/Python/cdm/objectmodel/spew_catcher.py | aaron-emde/CDM | 9472e9c7694821ac4a9bbe608557d2e65aabc73e | [
"CC-BY-4.0",
"MIT"
] | 3 | 2021-05-11T23:57:12.000Z | 2021-08-04T05:03:05.000Z | objectModel/Python/cdm/objectmodel/spew_catcher.py | aaron-emde/CDM | 9472e9c7694821ac4a9bbe608557d2e65aabc73e | [
"CC-BY-4.0",
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
| 19.083333 | 43 | 0.672489 | from abc import ABC, abstractmethod
class SpewCatcher(ABC):
@abstractmethod
def clear(self) -> None:
raise NotImplementedError()
def spew_line(self, spew: str) -> None:
raise NotImplementedError()
| 93 | 76 | 23 |
8c6ab31581a9c7037911dce238b5276604ae1423 | 1,639 | py | Python | python/houghP.py | dennyzz/Pathfinder-v2 | e7853c1b86d0c76ebbf7c1b68aa76fadcf37f7fc | [
"MIT"
] | null | null | null | python/houghP.py | dennyzz/Pathfinder-v2 | e7853c1b86d0c76ebbf7c1b68aa76fadcf37f7fc | [
"MIT"
] | null | null | null | python/houghP.py | dennyzz/Pathfinder-v2 | e7853c1b86d0c76ebbf7c1b68aa76fadcf37f7fc | [
"MIT"
] | null | null | null | import time
import cv2
import numpy as np
cap = cv2.VideoCapture("0degree.mp4")
fps = cap.get(cv2.CAP_PROP_FPS)
print(fps)
count = 0
sum_x1 = 0
sum_y1 = 0
sum_x2 = 0
sum_y2 = 0
while(True):
ret, frame = cap.read()
ysize = frame.shape[0]
xsize = frame.shape[1]
left = frame[0:ysize, 0:int(xsize/2)]
right = frame[0:ysize, int(xsize/2):xsize]
grayright = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY)
grayleft = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY)
edgesl = cv2.Canny(grayleft,75,100,apertureSize = 3)
edgesr = cv2.Canny(grayright,75,100,apertureSize = 3)
minLineLength = 10
maxLineGap = 1
linesl = cv2.HoughLinesP(edgesl,35,np.pi/180,50,minLineLength,maxLineGap)
linesr = cv2.HoughLinesP(edgesr,35,np.pi/180,50,minLineLength,maxLineGap)
if linesr != None:
for i in range(0,len(linesr)):
for x1,y1,x2,y2 in linesr[i]:
cv2.line(right,(x1,y1),(x2,y2),(0,255,0),5,8)
count += 1
sum_x1 += x1
sum_y1 += y1
sum_x2 += x2
sum_y2 += y2
av_x1 = sum_x1 / count
av_x2 = sum_x2 / count
av_y1 = sum_y1 / count
av_y2 = sum_y2 / count
slope = (av_y2 - av_y1) / (av_x2 - av_x1)
print(slope)
if linesl != None:
for k in range(0,len(linesl)):
for x1,y1,x2,y2 in linesl[k]:
cv2.line(left,(x1,y1),(x2,y2),(0,255,0),5,8)
#cv2.imshow('cannyl',edgesl)
#cv2.imshow('cannyr',edgesr)
cv2.imshow('left', left)
cv2.imshow('right', right)
# show the frame
#cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
#rawCapture.truncate(0)
#if the `q` key was pressed, break from the loop
if key == ord("q"):
break
cap.release() | 19.987805 | 74 | 0.66565 | import time
import cv2
import numpy as np
cap = cv2.VideoCapture("0degree.mp4")
fps = cap.get(cv2.CAP_PROP_FPS)
print(fps)
count = 0
sum_x1 = 0
sum_y1 = 0
sum_x2 = 0
sum_y2 = 0
while(True):
ret, frame = cap.read()
ysize = frame.shape[0]
xsize = frame.shape[1]
left = frame[0:ysize, 0:int(xsize/2)]
right = frame[0:ysize, int(xsize/2):xsize]
grayright = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY)
grayleft = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY)
edgesl = cv2.Canny(grayleft,75,100,apertureSize = 3)
edgesr = cv2.Canny(grayright,75,100,apertureSize = 3)
minLineLength = 10
maxLineGap = 1
linesl = cv2.HoughLinesP(edgesl,35,np.pi/180,50,minLineLength,maxLineGap)
linesr = cv2.HoughLinesP(edgesr,35,np.pi/180,50,minLineLength,maxLineGap)
if linesr != None:
for i in range(0,len(linesr)):
for x1,y1,x2,y2 in linesr[i]:
cv2.line(right,(x1,y1),(x2,y2),(0,255,0),5,8)
count += 1
sum_x1 += x1
sum_y1 += y1
sum_x2 += x2
sum_y2 += y2
av_x1 = sum_x1 / count
av_x2 = sum_x2 / count
av_y1 = sum_y1 / count
av_y2 = sum_y2 / count
slope = (av_y2 - av_y1) / (av_x2 - av_x1)
print(slope)
if linesl != None:
for k in range(0,len(linesl)):
for x1,y1,x2,y2 in linesl[k]:
cv2.line(left,(x1,y1),(x2,y2),(0,255,0),5,8)
#cv2.imshow('cannyl',edgesl)
#cv2.imshow('cannyr',edgesr)
cv2.imshow('left', left)
cv2.imshow('right', right)
# show the frame
#cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
#rawCapture.truncate(0)
#if the `q` key was pressed, break from the loop
if key == ord("q"):
break
cap.release() | 0 | 0 | 0 |
ac55f0cccf6e22b2c103658745fde781bbe7dd8d | 1,511 | py | Python | echoAI/Activation/Torch/aria2.py | ksasi/Echo | f1cc3a91a7fe4239b0160d7aa43224b941951809 | [
"MIT"
] | null | null | null | echoAI/Activation/Torch/aria2.py | ksasi/Echo | f1cc3a91a7fe4239b0160d7aa43224b941951809 | [
"MIT"
] | null | null | null | echoAI/Activation/Torch/aria2.py | ksasi/Echo | f1cc3a91a7fe4239b0160d7aa43224b941951809 | [
"MIT"
] | 1 | 2022-02-07T16:55:52.000Z | 2022-02-07T16:55:52.000Z | '''
Applies the Aria-2 function element-wise:
.. math::
Aria2(x, \\alpha, \\beta) = (1+e^{-\\beta*x})^{-\\alpha}
See Aria paper:
https://arxiv.org/abs/1805.08878
'''
# import pytorch
import torch
from torch import nn
# import activation functions
import echoAI.Activation.Torch.functional as Func
class Aria2(nn.Module):
'''
Applies the Aria-2 function element-wise:
.. math::
Aria2(x, \\alpha, \\beta) = (1+e^{-\\beta*x})^{-\\alpha}
Plot:
.. figure:: _static/aria2.png
:align: center
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Arguments:
- alpha: hyper-parameter which has a two-fold effect; it reduces the curvature in 3rd quadrant as well as increases the curvature in first quadrant while lowering the value of activation (default = 1)
- beta: the exponential growth rate (default = 0.5)
References:
- See Aria paper:
https://arxiv.org/abs/1805.08878
Examples:
>>> m = Aria2(beta=0.5, alpha=1)
>>> input = torch.randn(2)
>>> output = m(input)
'''
def __init__(self, beta = 1., alpha = 1.5):
'''
Init method.
'''
super().__init__()
self.beta = beta
self.alpha = alpha
def forward(self, input):
'''
Forward pass of the function.
'''
return Func.aria2(input, self.beta, self.alpha)
| 22.893939 | 208 | 0.576439 | '''
Applies the Aria-2 function element-wise:
.. math::
Aria2(x, \\alpha, \\beta) = (1+e^{-\\beta*x})^{-\\alpha}
See Aria paper:
https://arxiv.org/abs/1805.08878
'''
# import pytorch
import torch
from torch import nn
# import activation functions
import echoAI.Activation.Torch.functional as Func
class Aria2(nn.Module):
'''
Applies the Aria-2 function element-wise:
.. math::
Aria2(x, \\alpha, \\beta) = (1+e^{-\\beta*x})^{-\\alpha}
Plot:
.. figure:: _static/aria2.png
:align: center
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Arguments:
- alpha: hyper-parameter which has a two-fold effect; it reduces the curvature in 3rd quadrant as well as increases the curvature in first quadrant while lowering the value of activation (default = 1)
- beta: the exponential growth rate (default = 0.5)
References:
- See Aria paper:
https://arxiv.org/abs/1805.08878
Examples:
>>> m = Aria2(beta=0.5, alpha=1)
>>> input = torch.randn(2)
>>> output = m(input)
'''
def __init__(self, beta = 1., alpha = 1.5):
'''
Init method.
'''
super().__init__()
self.beta = beta
self.alpha = alpha
def forward(self, input):
'''
Forward pass of the function.
'''
return Func.aria2(input, self.beta, self.alpha)
| 0 | 0 | 0 |
51f4a856115a7e32dd277b06122ef1247d4663d1 | 7,892 | py | Python | Om_UtilitiesV2/Om_UtilitiesV2/cogs/images.py | opensourze/Om-Bot-V2 | 97780b8d264de62048dd9adf065bbd28a3d661a9 | [
"MIT"
] | null | null | null | Om_UtilitiesV2/Om_UtilitiesV2/cogs/images.py | opensourze/Om-Bot-V2 | 97780b8d264de62048dd9adf065bbd28a3d661a9 | [
"MIT"
] | null | null | null | Om_UtilitiesV2/Om_UtilitiesV2/cogs/images.py | opensourze/Om-Bot-V2 | 97780b8d264de62048dd9adf065bbd28a3d661a9 | [
"MIT"
] | null | null | null | from contextlib import suppress
from io import BytesIO
from urllib.parse import quote
import discord
import requests
from discord.ext import commands
from discord_slash import SlashContext, cog_ext
from discord_slash.utils.manage_commands import create_option
from PIL import Image
| 34.614035 | 175 | 0.608844 | from contextlib import suppress
from io import BytesIO
from urllib.parse import quote
import discord
import requests
from discord.ext import commands
from discord_slash import SlashContext, cog_ext
from discord_slash.utils.manage_commands import create_option
from PIL import Image
class Images(commands.Cog, description="Generate fun images!"):
def __init__(self, client):
self.client: commands.Bot = client
self.emoji = "<:fun:911380380089745408>"
# amogus
@commands.command(
help="Amogus, but with a member's profile picture", aliases=["sus", "amongus"]
)
async def amogus(self, ctx, *, member: commands.MemberConverter = None):
try:
if not ctx.message.attachments:
member = member or ctx.author
img = member.avatar_url_as(size=256)
else:
img = ctx.message.attachments[0]
except AttributeError:
member = member or ctx.author
img = member.avatar_url_as(size=256)
data = BytesIO(await img.read())
av = Image.open(data).resize((187, 187))
amogus = Image.open("amogus-template.png")
# Paste the avatar/attachment onto the amogus image
amogus.paste(av, (698, 209))
amogus.save("image-outputs/amogus.png")
await ctx.send(file=discord.File("image-outputs/amogus.png"))
@cog_ext.cog_slash(
name="amogus",
description="Amogus, but with a member's profile picture",
options=[
create_option(
name="member",
description="The member whose profile picture you want to use",
required=True,
option_type=6,
)
],
)
async def amogus_slash(self, ctx: SlashContext, member):
await self.amogus(ctx, member=member)
# Tweet
@commands.command(help="Generate an image of you tweeting something")
async def tweet(self, ctx, *, text: str):
if len(text) >= 1000:
return await ctx.send(
"❌ Your text is too long, please use text shorter than 1000 characters!"
)
avatar = ctx.author.avatar_url_as(format="png")
embed = discord.Embed(
title=f"{ctx.author.display_name}'s Tweet", colour=0x1DA1F2
)
embed.set_image(
url=f"https://some-random-api.ml/canvas/tweet?comment={quote(text)}&avatar={avatar}&username={quote(ctx.author.name)}&displayname={quote(ctx.author.display_name)}"
)
await ctx.send(embed=embed)
@cog_ext.cog_slash(
name="tweet", description="Generate an image of you tweeting something"
)
async def tweet_slash(self, ctx: SlashContext, *, text):
await self.tweet(ctx, text=text)
# YouTube comment
@commands.command(
help="Generate an image of a YouTube comment by you",
aliases=["ytcomment", "comment"],
)
async def youtubecomment(self, ctx, *, comment: str):
if len(comment) >= 1000:
return await ctx.send(
"❌ Your text is too long, please use text shorter than 1000 characters!"
)
avatar = ctx.author.avatar_url_as(format="png")
embed = discord.Embed(
title=f"{ctx.author.display_name}'s YouTube comment", colour=0xC4302B
)
embed.set_image(
url=f"https://some-random-api.ml/canvas/youtube-comment?comment={quote(comment)}&username={quote(ctx.author.display_name)}&avatar={avatar}"
)
await ctx.send(embed=embed)
@cog_ext.cog_slash(
name="youtubecomment",
description="Generate an image of a YouTube comment by you",
)
async def ytcomment_slash(self, ctx: SlashContext, *, comment: str):
await self.youtubecomment(ctx, comment=comment)
# Wasted
@commands.command(
help="Add a Wasted overlay to an avatar or an attached image",
brief="Add a Wasted overlay to an avatar or image",
)
@commands.cooldown(1, 5, commands.BucketType.guild)
async def wasted(
self, ctx: SlashContext, *, member: commands.MemberConverter = None
):
try:
if not ctx.message.attachments:
member = member or ctx.author
img = member.avatar_url_as(format="png")
else:
img = ctx.message.attachments[0].url
except AttributeError:
member = member or ctx.author
img = member.avatar_url_as(format="png")
embed = discord.Embed(title="wasted.", colour=self.client.colour)
embed.set_image(url=f"https://some-random-api.ml/canvas/wasted?avatar={img}")
await ctx.send(embed=embed)
@cog_ext.cog_slash(
name="wasted",
description="Add a Wasted overlay to someone's avatar",
options=[
create_option(
name="member",
description="The member whose avatar to add the overlay on",
required=False,
option_type=6,
)
],
)
@commands.cooldown(1, 5, commands.BucketType.guild)
async def wasted_slash(self, ctx: SlashContext, member):
await self.wasted(ctx, member=member)
# Triggered
@commands.command(help="Create a triggered gif with someone's avatar")
@commands.cooldown(1, 5, commands.BucketType.channel)
async def triggered(self, ctx, *, member: commands.MemberConverter = None):
with suppress(AttributeError):
await ctx.trigger_typing()
member = member or ctx.author
avatar = member.avatar_url_as(format="png")
url = f"https://some-random-api.ml/canvas/triggered?avatar={avatar}"
r = requests.get(url)
image = Image.open(BytesIO(r.content))
image.save("image-outputs/triggered.gif", save_all=True)
file = discord.File("image-outputs/triggered.gif")
await ctx.send(file=file)
@cog_ext.cog_slash(
name="triggered",
description="Create a triggered gif with someone's avatar",
options=[
create_option(
name="member",
description="The member whose avatar to add the triggered overlay to",
required=False,
option_type=6,
)
],
)
@commands.cooldown(1, 10, commands.BucketType.channel)
async def triggered_slash(self, ctx: SlashContext, member=None):
await ctx.defer()
await self.triggered(ctx, member=member)
# Blurple
@commands.command(help="Filter an avatar/image with Discord's blurple colour")
@commands.cooldown(1, 5, commands.BucketType.guild)
async def blurple(self, ctx, *, member: commands.MemberConverter = None):
embed = discord.Embed(colour=0x5865F2)
try:
if not ctx.message.attachments:
member = member or ctx.author
img = member.avatar_url_as(format="png")
embed.title = f"Blurple {member.display_name}"
else:
img = ctx.message.attachments[0].url
except AttributeError:
member = member or ctx.author
img = member.avatar_url_as(format="png")
embed.set_image(url=f"https://some-random-api.ml/canvas/blurple2?avatar={img}")
await ctx.send(embed=embed)
@cog_ext.cog_slash(
name="blurple",
description="Filter someone's avatar with Discord's blurple",
options=[
create_option(
name="member",
description="The member whose avatar to blurplify",
required=False,
option_type=6,
)
],
)
async def blurple_slash(self, ctx: SlashContext, member):
await self.blurple(ctx, member=member)
def setup(client):
client.add_cog(Images(client))
| 4,465 | 3,100 | 46 |
d149062275e73950d85cd4f9289855812a0cafa0 | 9,644 | py | Python | lambda.py | ldonayre/SmartLight | 105dc6b4fe245407083a501bf032de1180cabe21 | [
"Apache-2.0"
] | 5 | 2018-03-21T10:52:58.000Z | 2021-04-21T01:04:15.000Z | lambda.py | ldonayre/SmartLight | 105dc6b4fe245407083a501bf032de1180cabe21 | [
"Apache-2.0"
] | null | null | null | lambda.py | ldonayre/SmartLight | 105dc6b4fe245407083a501bf032de1180cabe21 | [
"Apache-2.0"
] | 3 | 2018-02-28T07:44:52.000Z | 2020-03-24T17:47:02.000Z | from __future__ import print_function
import requests
import json
import boto3
##############################
# Required Intents
##############################
##############################
# On Launch
##############################
##############################
# Routing
##############################
# Called when the session starts
if __name__ == '__main__':
'''
event = {'request': { 'intent': { 'slots': { 'onoff': { 'value': 'on' } } }}}
light_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'onoff': { 'value': 'off' } } }}}
light_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunrise' } } }}}
scene_intent(event, "")
'''
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' } } }}}
scene_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' }, 'amount': { 'value': '60' }, 'minutehour': { 'value': 'minutes' }, } }}}
scene_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' }, 'amount': { 'value': '2' }, 'minutehour': { 'value': 'hours' }, } }}}
scene_intent(event, "")
'''
#event['request']['intent']['slots']['brightness']['value']
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': '100'} } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': '500'} } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': 'brighter' } } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': 'dimmer' } } }}}
brightness_intent(event, "")
'''
| 32.802721 | 155 | 0.604314 | from __future__ import print_function
import requests
import json
import boto3
def build_response_payload( title, output, reprompt_text, should_end_session ):
return {
'outputSpeech': {
'type': 'PlainText',
'text': output
},
'card': {
'type': 'Simple',
'title': "SmartLight - " + title,
'content': "SmartLight - " + output
},
'reprompt': {
'outputSpeech': {
'type': 'PlainText',
'text': reprompt_text
}
},
'shouldEndSession': should_end_session
}
def build_response( session_attributes, speechlet_response ):
return {
'version': '1.0',
'sessionAttributes': session_attributes,
'response': speechlet_response
}
def publish_light(session_arg):
client = boto3.client('iot-data', region_name='us-east-1')
response = client.publish(
topic='SmartLight',
qos=1,
payload=json.dumps({"light": session_arg})
)
def publish_scene(session_arg):
client = boto3.client('iot-data', region_name='us-east-1')
response = client.publish(
topic='SmartLight',
qos=1,
payload=json.dumps({"scene": session_arg})
)
def publish_brightness(session_arg):
client = boto3.client('iot-data', region_name='us-east-1')
response = client.publish(
topic='SmartLight',
qos=1,
payload=json.dumps({"brightness": session_arg})
)
def light_intent(event, context):
print("=== LIGHT_INTENT ===")
print("EVENT", event)
session_attributes = event['request']['intent']['slots']['onoff']['value']
print("SESSION_ATTRIBUTES:" + session_attributes)
print ("=== session_attributes ===", session_attributes)
publish_light(session_attributes)
card_title = "Light" + session_attributes
print ("CARD:", card_title)
speech_output = "light " + session_attributes
should_end_session = False
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
def brightness_intent(event, context):
print("=== SCENE ===")
print("EVENT", event)
if event['request']['intent']['slots']['brightness'].get('value'):
session_attributes = event['request']['intent']['slots']['brightness']['value']
elif event['request']['intent']['slots']['brighterdimmer'].get('value'):
session_attributes = event['request']['intent']['slots']['brighterdimmer']['value']
try:
n = int(session_attributes)
if n > 100:
session_attributes = "100"
elif n < 0:
session_attributes = "0"
except Exception as e:
pass
if session_attributes in ["brighter", "higher"]:
session_attributes = "999"
elif session_attributes in ["dimmer", "lower"]:
session_attributes = "-1"
print("SESSION_ATTRIBUTES:" + session_attributes)
print ("=== session_attributes ===", session_attributes)
publish_brightness(session_attributes)
card_title = "Brightness" + session_attributes
print ("CARD:", card_title)
if session_attributes == "999":
speech_output = "brightness increased"
elif session_attributes == "-1":
speech_output = "brightness decreased"
else:
speech_output = "brightness " + session_attributes
should_end_session = False
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
def scene_intent(event, context):
print("=== SCENE ===")
print("EVENT", event)
scene = event['request']['intent']['slots']['scene']['value']
duration = 0
unit = ''
if event['request']['intent']['slots'].get('amount'):
if event['request']['intent']['slots']['amount'].get('value'):
duration = int(event['request']['intent']['slots']['amount']['value'])
if event['request']['intent']['slots'].get('minutehour'):
if event['request']['intent']['slots']['minutehour'].get('value'):
unit = event['request']['intent']['slots']['minutehour']['value']
if unit in ["hour", "hours"]:
duration = (duration * 60)
if duration:
session_attributes = json.dumps({scene: duration})
else:
session_attributes = event['request']['intent']['slots']['scene']['value']
print("SESSION_ATTRIBUTES:" + session_attributes)
print ("=== session_attributes ===", session_attributes)
publish_scene(session_attributes)
card_title = "Scene" + session_attributes
print ("CARD:", card_title)
if duration == 1:
speech_output = scene + " playing " + str(duration) + "minute"
elif duration < 60:
speech_output = scene + " playing " + str(duration) + "minutes"
elif duration >= 60:
duration = duration/60
if duration == 1:
speech_output = scene + " playing " + str(duration) + "hour"
else:
speech_output = scene + " playing " + str(duration) + "hours"
should_end_session = False
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
def say_hello_world():
session_attributes = {}
card_title = "SmartLight"
speech_output = "SmartLight loaded"
should_end_session = False
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
##############################
# Required Intents
##############################
def cancel_intent():
session_attributes = {}
card_title = "Canceling"
speech_output = "Canceling. Goodby."
should_end_session = True
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
def help_intent():
session_attributes = {}
card_title = "Help"
speech_output = "You can say turn on or off light,\
brighter or dimmer,\
play sunrise or sunset,\
set light to a percentage,\
stop,\
cancel"
should_end_session = False
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
def stop_intent():
session_attributes = {}
card_title = "Stopping."
speech_output = "Stopping. Goodby"
should_end_session = True
return build_response( session_attributes, build_response_payload( card_title, speech_output, speech_output, should_end_session ) )
##############################
# On Launch
##############################
def on_launch(event, context):
return statement("title", "body")
##############################
# Routing
##############################
def intent_router(event, context):
intent = event['request']['intent']['name']
# Custom Intents
if intent == "LightIntent":
return light_intent(event, context)
if intent == "SceneIntent":
return scene_intent (event, context)
if intent == "BrightnessIntent":
return brightness_intent (event, context)
# Required Intents
if intent == "CancelIntent":
return cancel_intent()
if intent == "HelpIntent":
return help_intent()
if intent == "StopIntent":
return stop_intent()
# Called when the session starts
def on_session_started(session_started_request, session):
print("on_session_started requestId=" + session_started_request['requestId'] + ", sessionId=" + session['sessionId'])
def lambda_handler( event, context ):
session_attributes = {}
print("event.session.application.applicationId=" + event['session']['application']['applicationId'])
if event['session']['new']:
on_session_started({'requestId': event['request']['requestId']}, event['session'])
if event['request']['type'] == "IntentRequest":
return intent_router(event, context)
else:
return say_hello_world()
if __name__ == '__main__':
'''
event = {'request': { 'intent': { 'slots': { 'onoff': { 'value': 'on' } } }}}
light_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'onoff': { 'value': 'off' } } }}}
light_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunrise' } } }}}
scene_intent(event, "")
'''
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' } } }}}
scene_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' }, 'amount': { 'value': '60' }, 'minutehour': { 'value': 'minutes' }, } }}}
scene_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'scene': { 'value': 'sunset' }, 'amount': { 'value': '2' }, 'minutehour': { 'value': 'hours' }, } }}}
scene_intent(event, "")
'''
#event['request']['intent']['slots']['brightness']['value']
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': '100'} } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': '500'} } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': 'brighter' } } }}}
brightness_intent(event, "")
event = {'request': { 'intent': { 'slots': { 'brightness': { 'value': 'dimmer' } } }}}
brightness_intent(event, "")
'''
| 7,481 | 0 | 367 |
468d3e640cfac79f2138feb9e894964ff89b3045 | 1,270 | py | Python | expansions/game/code.py | croot/blacksmith-2 | 3bb544139a18184a709ca7668f8e69f3ca361475 | [
"Apache-2.0"
] | null | null | null | expansions/game/code.py | croot/blacksmith-2 | 3bb544139a18184a709ca7668f8e69f3ca361475 | [
"Apache-2.0"
] | null | null | null | expansions/game/code.py | croot/blacksmith-2 | 3bb544139a18184a709ca7668f8e69f3ca361475 | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
# BlackSmith mark.2
# exp_name = "game" # /code.py v.x2
# Id: 26~2c
# Code © (2011) by WitcherGeralt [alkorgun@gmail.com]
del GameChrLS, GameRules
| 21.166667 | 77 | 0.619685 | # coding: utf-8
# BlackSmith mark.2
# exp_name = "game" # /code.py v.x2
# Id: 26~2c
# Code © (2011) by WitcherGeralt [alkorgun@gmail.com]
class expansion_temp(expansion):
def __init__(self, name):
expansion.__init__(self, name)
GameDesc = {
GameChrLS[0]: {
GameChrLS[1]: 9,
GameChrLS[3]: 2
},
GameChrLS[1]: {
GameChrLS[2]: 0,
GameChrLS[3]: 5
},
GameChrLS[2]: {
GameChrLS[0]: 1,
GameChrLS[4]: 7
},
GameChrLS[3]: {
GameChrLS[2]: 6,
GameChrLS[4]: 3
},
GameChrLS[4]: {
GameChrLS[0]: 8,
GameChrLS[1]: 4
}
}
GameRules = GameRules
def command_game(self, stype, source, Char, disp):
if Char:
Char = Char.lower()
if Char in self.GameDesc:
Char_2 = choice(self.GameDesc.keys())
Answer(Char_2, stype, source, disp)
sleep(3.2)
if Char == Char_2:
answer = self.AnsBase[0]
elif self.GameDesc[Char_2].has_key(Char):
answer = self.AnsBase[1] % (self.GameRules[self.GameDesc[Char_2][Char]])
else:
answer = self.AnsBase[2] % (self.GameRules[self.GameDesc[Char][Char_2]])
else:
answer = AnsBase[2]
else:
answer = str.join(chr(10), self.GameRules)
Answer(answer, stype, source, disp)
commands = ((command_game, "game", 2,),)
del GameChrLS, GameRules
| 616 | 463 | 23 |
f8a97a2a59447bb0a11fc4d20e93cb511048ed29 | 481 | py | Python | server/the_water_project/topics/migrations/0008_auto_20210718_1720.py | Subhra264/the-water-project | f9f7244a58d87cadc89dd501ca2cf602dac86877 | [
"Apache-2.0"
] | 1 | 2021-06-12T05:10:43.000Z | 2021-06-12T05:10:43.000Z | server/the_water_project/topics/migrations/0008_auto_20210718_1720.py | Abhra303/the-water-project | d05f854fbafe676f3ee51d10e78ca7fafec33b7d | [
"Apache-2.0"
] | 2 | 2021-07-05T10:15:48.000Z | 2022-01-02T14:52:00.000Z | server/the_water_project/topics/migrations/0008_auto_20210718_1720.py | Abhra303/the-water-project | d05f854fbafe676f3ee51d10e78ca7fafec33b7d | [
"Apache-2.0"
] | 1 | 2021-06-12T05:10:51.000Z | 2021-06-12T05:10:51.000Z | # Generated by Django 3.2.4 on 2021-07-18 11:50
from django.db import migrations
| 21.863636 | 60 | 0.555094 | # Generated by Django 3.2.4 on 2021-07-18 11:50
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
("topics", "0007_auto_20210717_1646"),
]
operations = [
migrations.AlterModelOptions(
name="issue",
options={"ordering": ("-date",)},
),
migrations.AlterModelOptions(
name="topic",
options={"ordering": ("-updated_on", "-stars")},
),
]
| 0 | 375 | 23 |
575d6551e01e79ddeea79868ff78dde487e68298 | 764 | py | Python | cracking_the_coding_interview/chapter_17/min_unsorted_seq1.py | yasserglez/programming-problems | 08cef1186b182430b231ed9772d8f92ec1d2365b | [
"MIT"
] | 2 | 2017-02-17T01:40:27.000Z | 2018-04-22T12:47:28.000Z | cracking_the_coding_interview/chapter_17/min_unsorted_seq1.py | yasserglez/programming-problems | 08cef1186b182430b231ed9772d8f92ec1d2365b | [
"MIT"
] | null | null | null | cracking_the_coding_interview/chapter_17/min_unsorted_seq1.py | yasserglez/programming-problems | 08cef1186b182430b231ed9772d8f92ec1d2365b | [
"MIT"
] | 1 | 2016-10-14T06:00:42.000Z | 2016-10-14T06:00:42.000Z | # Interview Question 17.6
# Time complexity: O(n^2)
# Auxiliary space complexity: O(n)
if __name__ == '__main__':
integers = []
print(min_unsorted_seq(integers))
integers = [1, 2, 4, 7, 10, 11, 7, 12, 6, 7, 16, 18, 19]
print(min_unsorted_seq(integers))
integers = list(range(13))
print(min_unsorted_seq(integers))
| 23.875 | 71 | 0.589005 | # Interview Question 17.6
# Time complexity: O(n^2)
# Auxiliary space complexity: O(n)
def min_unsorted_seq(intergers):
if not integers:
return 0, 0
N = len(integers)
farthest_less_than = [0] * N
for i in range(N):
for j in range(i, N):
if integers[j] < integers[i]:
farthest_less_than[i] = j
m = next((i for i, x in enumerate(farthest_less_than) if x > 0), 0)
n = max(farthest_less_than)
if n == 0:
n = N - 1
return m, n
if __name__ == '__main__':
integers = []
print(min_unsorted_seq(integers))
integers = [1, 2, 4, 7, 10, 11, 7, 12, 6, 7, 16, 18, 19]
print(min_unsorted_seq(integers))
integers = list(range(13))
print(min_unsorted_seq(integers))
| 397 | 0 | 23 |
370f3623edfeb82ef759bf9a646da3b3b874800c | 2,865 | py | Python | model/layers.py | zzzqzhou/Dual-Normalization | b9831b6e2662a950600ba37ada087ba8ce93f60c | [
"MIT"
] | 12 | 2022-03-10T09:24:41.000Z | 2022-03-30T03:36:51.000Z | model/layers.py | zzzqzhou/Dual-Normalization | b9831b6e2662a950600ba37ada087ba8ce93f60c | [
"MIT"
] | 1 | 2022-03-30T09:41:23.000Z | 2022-03-30T09:41:23.000Z | model/layers.py | zzzqzhou/Dual-Normalization | b9831b6e2662a950600ba37ada087ba8ce93f60c | [
"MIT"
] | null | null | null | """
Wrappers for the operations to take the meta-learning gradient
updates into account.
"""
import torch.autograd as autograd
import torch.nn.functional as F
from torch.autograd import Variable
"""
The following are the new methods for 2D-Unet:
Conv2d, batchnorm2d, GroupNorm, InstanceNorm2d, MaxPool2d, UpSample
"""
#as per the 2D Unet: kernel_size, stride, padding | 31.141304 | 116 | 0.672949 | """
Wrappers for the operations to take the meta-learning gradient
updates into account.
"""
import torch.autograd as autograd
import torch.nn.functional as F
from torch.autograd import Variable
def linear(inputs, weight, bias, meta_step_size=0.001, meta_loss=None, stop_gradient=False):
# inputs = inputs.cuda()
# weight = weight.cuda()
# bias = bias.cuda()
if meta_loss is not None:
if not stop_gradient:
grad_weight = autograd.grad(meta_loss, weight, create_graph=True)[0]
if bias is not None:
grad_bias = autograd.grad(meta_loss, bias, create_graph=True)[0]
bias_adapt = bias - grad_bias * meta_step_size
else:
bias_adapt = bias
else:
grad_weight = Variable(autograd.grad(meta_loss, weight, create_graph=True)[0].data, requires_grad=False)
if bias is not None:
grad_bias = Variable(autograd.grad(meta_loss, bias, create_graph=True)[0].data, requires_grad=False)
bias_adapt = bias - grad_bias * meta_step_size
else:
bias_adapt = bias
return F.linear(inputs,
weight - grad_weight * meta_step_size,
bias_adapt)
else:
return F.linear(inputs, weight, bias)
def conv2d(inputs, weight, bias, stride=1, padding=1, dilation=1, groups=1, kernel_size=3):
# inputs = inputs.cuda()
# weight = weight.cuda()
# bias = bias.cuda()
return F.conv2d(inputs, weight, bias, stride, padding, dilation, groups)
def deconv2d(inputs, weight, bias, stride=2, padding=0, dilation=0, groups=1, kernel_size=None):
# inputs = inputs.cuda()
# weight = weight.cuda()
# bias = bias.cuda()
return F.conv_transpose2d(inputs, weight, bias, stride, padding, dilation, groups)
def relu(inputs):
return F.relu(inputs, inplace=True)
def maxpool(inputs, kernel_size, stride=None, padding=0):
return F.max_pool2d(inputs, kernel_size, stride, padding=padding)
def dropout(inputs):
return F.dropout(inputs, p=0.5, training=False, inplace=False)
def batchnorm(inputs, running_mean, running_var):
return F.batch_norm(inputs, running_mean, running_var)
"""
The following are the new methods for 2D-Unet:
Conv2d, batchnorm2d, GroupNorm, InstanceNorm2d, MaxPool2d, UpSample
"""
#as per the 2D Unet: kernel_size, stride, padding
def instancenorm(input):
return F.instance_norm(input)
def groupnorm(input):
return F.group_norm(input)
def dropout2D(inputs):
return F.dropout2d(inputs, p=0.5, training=False, inplace=False)
def maxpool2D(inputs, kernel_size, stride=None, padding=0):
return F.max_pool2d(inputs, kernel_size, stride, padding=padding)
def upsample(input):
return F.upsample(input, scale_factor=2, mode='bilinear', align_corners=False) | 2,215 | 0 | 276 |
9e5eb2b827274a017acaf55b36d14238660ffe36 | 233 | py | Python | setup.py | nokludge/python-keypad | 16cb8d7a4fd5f2b6ab62e2a8f2878555cb6ae88e | [
"MIT"
] | null | null | null | setup.py | nokludge/python-keypad | 16cb8d7a4fd5f2b6ab62e2a8f2878555cb6ae88e | [
"MIT"
] | null | null | null | setup.py | nokludge/python-keypad | 16cb8d7a4fd5f2b6ab62e2a8f2878555cb6ae88e | [
"MIT"
] | null | null | null | from setuptools import setup
setup(
name='Keymatrix',
version='0.3',
packages=['keypad'],
license='MIT',
author='Ruslan Walch',
long_description=open('README.md').read(),
install_requires=['rpi.gpio'],
)
| 19.416667 | 46 | 0.635193 | from setuptools import setup
setup(
name='Keymatrix',
version='0.3',
packages=['keypad'],
license='MIT',
author='Ruslan Walch',
long_description=open('README.md').read(),
install_requires=['rpi.gpio'],
)
| 0 | 0 | 0 |
3a427d5b75fb6837eaf7ab57cffe291e82ee2fcf | 297 | py | Python | textomatic/app/lexer.py | dankilman/textomatic | 9e64a9afa3cc43de29a64be154b709e4ea81b2b9 | [
"MIT"
] | 26 | 2020-10-05T22:32:07.000Z | 2021-02-27T08:03:46.000Z | textomatic/app/lexer.py | dankilman/textomatic | 9e64a9afa3cc43de29a64be154b709e4ea81b2b9 | [
"MIT"
] | null | null | null | textomatic/app/lexer.py | dankilman/textomatic | 9e64a9afa3cc43de29a64be154b709e4ea81b2b9 | [
"MIT"
] | 1 | 2020-10-13T03:26:43.000Z | 2020-10-13T03:26:43.000Z | from pygments.lexer import RegexLexer, bygroups
from pygments.token import Text, Keyword, Operator
| 22.846154 | 57 | 0.542088 | from pygments.lexer import RegexLexer, bygroups
from pygments.token import Text, Keyword, Operator
class CommandLexer(RegexLexer):
tokens = {
"root": [
(r"([iosthrd])(:)", bygroups(Keyword, Text)),
(r";", Operator),
(r".", Text),
],
}
| 0 | 174 | 23 |
4f4961c01c421d11775c321b49638d004490d794 | 1,100 | py | Python | lmk/calculator/EntryPointCalculator.py | dyno/LMK | f2c40dbbf5eeaacd9db738e4f0cc1f3d326e39bf | [
"MIT"
] | 12 | 2016-07-08T02:46:59.000Z | 2022-03-03T02:41:47.000Z | lmk/calculator/EntryPointCalculator.py | dyno/LMK | f2c40dbbf5eeaacd9db738e4f0cc1f3d326e39bf | [
"MIT"
] | 4 | 2020-12-10T04:04:46.000Z | 2021-07-26T14:57:36.000Z | lmk/calculator/EntryPointCalculator.py | dyno/LMK | f2c40dbbf5eeaacd9db738e4f0cc1f3d326e39bf | [
"MIT"
] | 5 | 2018-09-01T00:08:53.000Z | 2021-08-13T15:18:25.000Z | """EntryPointCalculator
Entry/Buy: price > btm + atr/2
Exit/Sell: price < top - atr/2
"""
BUY = 1
SELL = 2
| 26.190476 | 94 | 0.551818 | """EntryPointCalculator
Entry/Buy: price > btm + atr/2
Exit/Sell: price < top - atr/2
"""
BUY = 1
SELL = 2
class EntryPointCalculator(object):
def __init__(self, trade_type=BUY, atr_factor=1.0):
self.trade_type = trade_type
self.atr_factor = atr_factor
self.pivot = None
self.wait_for_trade = False
def __call__(self, tick):
trade = False
atr = tick["ATR"] * self.atr_factor
if self.trade_type == BUY:
if tick["Btm"]:
self.pivot = tick["Close"]
self.wait_for_trade = True
if self.pivot and self.wait_for_trade and tick["Close"] >= self.pivot + atr / 2.0:
self.wait_for_trade = False
trade = True
elif self.trade_type == SELL:
if tick["Top"]:
self.pivot = tick["Close"]
self.wait_for_trade = True
if self.pivot and self.wait_for_trade and tick["Close"] <= self.pivot - atr / 2.0:
self.wait_for_trade = False
trade = True
return trade
| 900 | 14 | 76 |
17f86476d0946b6fc0ff6cb392a95b805716a250 | 1,838 | py | Python | web_scrape.py | OSAMAMOHAMED1234/python_projects | fb4bc7356847c3f46df690a9386cf970377a6f7c | [
"MIT"
] | null | null | null | web_scrape.py | OSAMAMOHAMED1234/python_projects | fb4bc7356847c3f46df690a9386cf970377a6f7c | [
"MIT"
] | null | null | null | web_scrape.py | OSAMAMOHAMED1234/python_projects | fb4bc7356847c3f46df690a9386cf970377a6f7c | [
"MIT"
] | null | null | null |
import datetime
import os
import pandas as pd
import requests
import sys
from requests_html import HTML
if __name__ == '__main__':
try:
start = int(sys.argv[1])
except:
start = None
try:
count = int(sys.argv[2])
except:
count = 0
run(start_year=start, years_ago=count) # python test.py 2021 5 | 25.527778 | 85 | 0.683896 |
import datetime
import os
import pandas as pd
import requests
import sys
from requests_html import HTML
def url_to_txt(url, save=False):
r = requests.get(url)
if r.status_code == 200:
html_text = r.text
return html_text
return None
def parse_and_extract(url, name='2021'):
html_text = url_to_txt(url)
if html_text == None:
return False
r_html = HTML(html=html_text)
table_class = '.imdb-scroll-table'
r_table = r_html.find(table_class)
header_names = []
table_data = []
if len(r_table) == 0:
return False
parsed_table = r_table[0]
rows = parsed_table.find('tr')
header_row = rows[0]
header_cols = header_row.find('th')
header_names = [x.text for x in header_cols]
for row in rows[1:]:
cols = row.find('td')
row_data = []
for _, col in enumerate(cols):
row_data.append(col.text)
table_data.append(row_data)
df = pd.DataFrame(table_data, columns=header_names)
path = os.path.join(os.path.dirname(__file__), 'data')
os.makedirs(path, exist_ok=True)
filepath = os.path.join(path, f'{name}.csv')
df.to_csv(filepath, index=False)
return True
def run(start_year=None, years_ago=0):
if start_year == None:
now = datetime.datetime.now()
start_year = now.year
assert isinstance(start_year, int)
assert isinstance(years_ago, int)
assert len(f'{start_year}') == 4
for _ in range(0, years_ago+1):
url = f'https://www.boxofficemojo.com/year/world/{start_year}'
finished = parse_and_extract(url, name=start_year)
print(f'{start_year} finished') if finished else print(f'{start_year} not found')
start_year -= 1
if __name__ == '__main__':
try:
start = int(sys.argv[1])
except:
start = None
try:
count = int(sys.argv[2])
except:
count = 0
run(start_year=start, years_ago=count) # python test.py 2021 5 | 1,444 | 0 | 69 |
98e1bc05ad66affd281a6b035ad9896bec2ad02a | 270 | py | Python | src/apps/surveys22/urls.py | COAStatistics/alss | e1b14cb13de7b8455fa6835587cb1ceb16e4595c | [
"MIT"
] | null | null | null | src/apps/surveys22/urls.py | COAStatistics/alss | e1b14cb13de7b8455fa6835587cb1ceb16e4595c | [
"MIT"
] | null | null | null | src/apps/surveys22/urls.py | COAStatistics/alss | e1b14cb13de7b8455fa6835587cb1ceb16e4595c | [
"MIT"
] | null | null | null | from django.urls import path
from django.conf.urls import include
from .api import api
from .views import Surveys2022Index
app_name = "surveys22"
urlpatterns = [
path("", Surveys2022Index.as_view(), name="surveys22_index"),
path("api/", include(api.urls)),
]
| 20.769231 | 65 | 0.72963 | from django.urls import path
from django.conf.urls import include
from .api import api
from .views import Surveys2022Index
app_name = "surveys22"
urlpatterns = [
path("", Surveys2022Index.as_view(), name="surveys22_index"),
path("api/", include(api.urls)),
]
| 0 | 0 | 0 |
a3193b7d564017b0ffb57b1c6fe1f381daf08ba0 | 10,125 | py | Python | tools/pyBAR_converter_old.py | jp-barron/Susi_Simulation | 2c4990ed8adbccb816e793228e5a6158a9b9a565 | [
"BSD-2-Clause"
] | null | null | null | tools/pyBAR_converter_old.py | jp-barron/Susi_Simulation | 2c4990ed8adbccb816e793228e5a6158a9b9a565 | [
"BSD-2-Clause"
] | null | null | null | tools/pyBAR_converter_old.py | jp-barron/Susi_Simulation | 2c4990ed8adbccb816e793228e5a6158a9b9a565 | [
"BSD-2-Clause"
] | null | null | null | """This script converts a hdf5 table into a CERN ROOT Ttree.
"""
import tables as tb
import numpy as np
import ctypes
import progressbar
import os
import math
import ROOT as r
from pybar.fei4 import register_utils
from pybar.analysis.RawDataConverter import data_struct
if __name__ == "__main__":
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
input_file_name = r'/home/davidlp/geant4/SourceSim-build/SourceSimulation'
plsr_dac_calibation_file = r'/home/davidlp/git/SourceSim/converter/calibration_data/plsr_dac_scan.cfg'
tdc_calibation_file = r'/home/davidlp/git/SourceSim/converter/calibration_data/hit_or_calibration_calibration.h5'
create_hit_table(input_file_name, tdc_calibation_file, plsr_dac_calibation_file)
# with tb.openFile(tdc_calibation_file, mode="r") as in_file_calibration_h5:
# tdc_calibration, tdc_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 3]
# tot_calibration, tot_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 0], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 2]
# tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
# global_config = register_utils.parse_global_config(plsr_dac_calibation_file)
# c_high, vcal_c0, vcal_c1 = global_config['C_Inj_High'], global_config['Vcal_Coeff_0'], global_config['Vcal_Coeff_1']
# charge_calibration_values = (vcal_c0 + vcal_c1 * tdc_calibration_values) * c_high / 0.16022
#
# column, row = 20, 30
# print charge_calibration_values, tdc_calibration[column, row]
# tdc_interpolation = interp1d(x=charge_calibration_values, y=tdc_calibration[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tdc_error_interpolation = interp1d(x=charge_calibration_values, y=tdc_error[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tot_interpolation = interp1d(x=charge_calibration_values, y=tot_calibration[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tot_error_interpolation = interp1d(x=charge_calibration_values, y=tot_error[column, row], kind='slinear', bounds_error=False, fill_value=0)
# plt.plot(np.arange(2000, 10000, 1), tdc_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tdc_error_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tot_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tot_error_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.show()
| 61.737805 | 209 | 0.697284 | """This script converts a hdf5 table into a CERN ROOT Ttree.
"""
import tables as tb
import numpy as np
import ctypes
import progressbar
import os
import math
import ROOT as r
from pybar.fei4 import register_utils
from pybar.analysis.RawDataConverter import data_struct
def get_charge_calibration(tdc_calibation_file, plsr_dac_calibation_file):
with tb.openFile(tdc_calibation_file, mode="r") as in_file_calibration_h5:
tdc_calibration, tdc_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 3]
tot_calibration, tot_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 0], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 2]
tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
global_config = register_utils.parse_global_config(plsr_dac_calibation_file)
c_high, vcal_c0, vcal_c1 = global_config['C_Inj_High'], global_config['Vcal_Coeff_0'], global_config['Vcal_Coeff_1']
charge_calibration_values = (vcal_c0 + vcal_c1 * tdc_calibration_values) * c_high / 0.16022
return charge_calibration_values, tdc_calibration, tdc_error, tot_calibration, tot_error
def create_hit_table(input_file_name, tdc_calibation_file, plsr_dac_calibation_file, n_sub_files=8): # loops over all root files and merges the data into a hdf5 file aligned at the event number
print 'Converting data from CERN ROOT TTree to hdf5 table'
charge_calibration_values, tdc_calibration, tdc_error, tot_calibration, tot_error = get_charge_calibration(tdc_calibation_file, plsr_dac_calibation_file)
# add all files that have the input_file_name praefix and load their data
input_file_names = [input_file_name + '_t%d.root' % index for index in range(n_sub_files) if os.path.isfile(input_file_name + '_t%d.root' % index)]
n_files = len(input_file_names)
input_files_root = [r.TFile(file_name, 'read') for file_name in input_file_names]
pixel_digits = [input_file_root.Get('EventData').Get('Pixel Digits') for input_file_root in input_files_root]
n_hits = [pixel_digit.GetEntries() for pixel_digit in pixel_digits] # total pixel hits to analyze
n_total_hits = sum(n_hits)
with tb.open_file(input_file_name + '_interpreted.h5', 'w') as out_file_h5:
hit_table = out_file_h5.create_table(out_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
# tmp data structures to be filles by ROOT
data = {}
for index, pixel_digit in enumerate(pixel_digits):
column_data = {}
for branch in pixel_digit.GetListOfBranches(): # loop over the branches
column_data[branch.GetName()] = np.zeros(shape=1, dtype=np.int32)
branch.SetAddress(column_data[branch.GetName()].data)
data[index] = column_data
# result data structur to be filles in the following loop
hits = np.zeros((n_total_hits,), dtype=tb.dtype_from_descr(data_struct.HitInfoTable))
# get file index with lowest event number
for pixel_digit in pixel_digits:
pixel_digit.GetEntry(0)
min_event_number = min([data[index]['event'][0] for index in range(n_files)])
actual_file_index = np.where(np.array([data[index]['event'][0] for index in range(n_files)]) == min_event_number)[0][0]
indices = [0] * n_files
table_index = 0
actual_data = data[actual_file_index]
actual_event_number = actual_data['event'][0]
last_valid_event_number = 0
last_tdc = 0
expected_event_number = actual_event_number
indices[actual_file_index] = 1
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=n_total_hits, term_width=80)
progress_bar.start()
def add_actual_data(actual_data, table_index):
if actual_data['column'] >= 0 and actual_data['column'] < 80 and actual_data['row'] >= 0 and actual_data['row'] < 336:
tdc_interpolation = interp1d(x=charge_calibration_values, y=tdc_calibration[actual_data['column'], actual_data['row']], kind='slinear', bounds_error=False, fill_value=0)
tdc = tdc_interpolation(actual_data['charge'])
tot_interpolation = interp1d(x=charge_calibration_values, y=tot_calibration[actual_data['column'], actual_data['row']], kind='slinear', bounds_error=False, fill_value=0)
tot = tot_interpolation(actual_data['charge'])
if math.isnan(tdc): # do not add hits where tdc is nan, these pixel have a very high threshold or do not work
return table_index
if tdc == 0 and actual_data['charge'] > 10000: # no calibration for TDC due to high charge, thus mark as TDC overflow event
hits[table_index]['event_status'] |= 0b0000010000000000
tdc = 4095
if tot == 0 and actual_data['charge'] > 10000: # no calibration for TOT due to high charge, thus set max tot
tot = 13
hits[table_index]['event_status'] |= 0b0000000100000000
hits[table_index]['event_number'] = actual_data['event'][0].astype(np.int64)
hits[table_index]['column'] = (actual_data['column'] + 1).astype(np.uint8)
hits[table_index]['row'] = (actual_data['row'] + 1).astype(np.uint16)
hits[table_index]['TDC'] = int(actual_data['charge'] / 300.)
hits[table_index]['tot'] = int(tot)
table_index += 1
return table_index
while True:
actual_event_number = actual_data['event'][0]
if (actual_event_number == expected_event_number or actual_event_number == expected_event_number - 1): # check if event number increases
actual_index, actual_digits, actual_data = indices[actual_file_index], pixel_digits[actual_file_index], data[actual_file_index]
table_index = add_actual_data(actual_data, table_index)
else: # event number does not increase, thus the events are in another file --> switch file or the event number is missing
file_event_numbers = [data[file_index]['event'][0] for file_index in range(n_files)] # all files actual event number
actual_file_index = np.where(file_event_numbers == min(file_event_numbers))[0][0]
actual_index, actual_digits, actual_data = indices[actual_file_index], pixel_digits[actual_file_index], data[actual_file_index]
actual_event_number = actual_data['event'][0]
table_index = add_actual_data(actual_data, table_index)
progress_bar.update(table_index)
expected_event_number = actual_event_number + 1
actual_digits.GetEntry(actual_index)
if indices[actual_file_index] < n_hits[actual_file_index]: # simply stop when the first file is fully iterated
indices[actual_file_index] += 1
else:
break
# Set missing data and store to file
hits[:table_index]['LVL1ID'] = hits[:table_index]['event_number'] % 255
hits[:table_index]['BCID'] = hits[:table_index]['LVL1ID']
hits[:table_index]['relative_BCID'] = 6
hit_table.append(hits[:table_index])
progress_bar.finish()
for input_file_root in input_files_root:
input_file_root.Close()
if __name__ == "__main__":
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
input_file_name = r'/home/davidlp/geant4/SourceSim-build/SourceSimulation'
plsr_dac_calibation_file = r'/home/davidlp/git/SourceSim/converter/calibration_data/plsr_dac_scan.cfg'
tdc_calibation_file = r'/home/davidlp/git/SourceSim/converter/calibration_data/hit_or_calibration_calibration.h5'
create_hit_table(input_file_name, tdc_calibation_file, plsr_dac_calibation_file)
# with tb.openFile(tdc_calibation_file, mode="r") as in_file_calibration_h5:
# tdc_calibration, tdc_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 1], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 3]
# tot_calibration, tot_error = in_file_calibration_h5.root.HitOrCalibration[:, :, :, 0], in_file_calibration_h5.root.HitOrCalibration[:, :, :, 2]
# tdc_calibration_values = in_file_calibration_h5.root.HitOrCalibration.attrs.scan_parameter_values[:]
# global_config = register_utils.parse_global_config(plsr_dac_calibation_file)
# c_high, vcal_c0, vcal_c1 = global_config['C_Inj_High'], global_config['Vcal_Coeff_0'], global_config['Vcal_Coeff_1']
# charge_calibration_values = (vcal_c0 + vcal_c1 * tdc_calibration_values) * c_high / 0.16022
#
# column, row = 20, 30
# print charge_calibration_values, tdc_calibration[column, row]
# tdc_interpolation = interp1d(x=charge_calibration_values, y=tdc_calibration[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tdc_error_interpolation = interp1d(x=charge_calibration_values, y=tdc_error[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tot_interpolation = interp1d(x=charge_calibration_values, y=tot_calibration[column, row], kind='slinear', bounds_error=False, fill_value=0)
# tot_error_interpolation = interp1d(x=charge_calibration_values, y=tot_error[column, row], kind='slinear', bounds_error=False, fill_value=0)
# plt.plot(np.arange(2000, 10000, 1), tdc_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tdc_error_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tot_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.plot(np.arange(2000, 10000, 1), tot_error_interpolation(np.arange(2000, 10000, 1)), '-')
# plt.show()
| 7,363 | 0 | 46 |
b88e429f2c73875e87dbd49011f9170925e16a53 | 504 | py | Python | base/ntp_client.py | vvvvcp/NeverMore | ec1e269460f222b89666a3e5593ffe18f928a82d | [
"Apache-2.0"
] | null | null | null | base/ntp_client.py | vvvvcp/NeverMore | ec1e269460f222b89666a3e5593ffe18f928a82d | [
"Apache-2.0"
] | null | null | null | base/ntp_client.py | vvvvcp/NeverMore | ec1e269460f222b89666a3e5593ffe18f928a82d | [
"Apache-2.0"
] | null | null | null | import socket
import struct
import sys
import time
NTP_SERVER = "0.uk.pool.ntp.org"
TIME1970 = 2208988800L
if __name__ =='__main__':
sntp_client() | 22.909091 | 60 | 0.660714 | import socket
import struct
import sys
import time
NTP_SERVER = "0.uk.pool.ntp.org"
TIME1970 = 2208988800L
def sntp_client():
client = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)
data ='\x1b'+47*'\0'
client.sendto(data,(NTP_SERVER,123))
data, address = client.recvfrom(1024)
if data:
print "Response received from : ", address
t =struct.unpack('!12I',data)[10]
t -= TIME1970
print '\tTime=%s' % time.ctime(t)
if __name__ =='__main__':
sntp_client() | 324 | 0 | 23 |
814536c9653bd63332edceb91144afb9025e14e8 | 506 | py | Python | lista2/Q1-Q14/Q10.py | AlexandrePeBrito/Python | 79a09b1fb8e705dc7b6859d977c8916a2d0dd4d0 | [
"MIT"
] | null | null | null | lista2/Q1-Q14/Q10.py | AlexandrePeBrito/Python | 79a09b1fb8e705dc7b6859d977c8916a2d0dd4d0 | [
"MIT"
] | null | null | null | lista2/Q1-Q14/Q10.py | AlexandrePeBrito/Python | 79a09b1fb8e705dc7b6859d977c8916a2d0dd4d0 | [
"MIT"
] | null | null | null | #Faça um Programa que pergunte em que turno você estuda. Peça para digitar M-matutino ou
# VVespertino ou N- Noturno. Imprima a mensagem "Bom Dia!", "Boa Tarde!" ou "Boa Noite!" ou
#"Valor Inválido!", conforme o caso
shift = input("Informe o Turno que voce estuda M-matutino ou V-Vespertino ou N- Noturno: ")
if(shift=="M" or shift=="m"):
print("Tenha um BOM DIA!")
elif(shift=="V" or shift=="v"):
print("Tenha uma BOA TARDE!")
elif(shift=="N" or shift=="n"):
print("Tenha uma BOA NOITE!") | 42.166667 | 93 | 0.671937 | #Faça um Programa que pergunte em que turno você estuda. Peça para digitar M-matutino ou
# VVespertino ou N- Noturno. Imprima a mensagem "Bom Dia!", "Boa Tarde!" ou "Boa Noite!" ou
#"Valor Inválido!", conforme o caso
shift = input("Informe o Turno que voce estuda M-matutino ou V-Vespertino ou N- Noturno: ")
if(shift=="M" or shift=="m"):
print("Tenha um BOM DIA!")
elif(shift=="V" or shift=="v"):
print("Tenha uma BOA TARDE!")
elif(shift=="N" or shift=="n"):
print("Tenha uma BOA NOITE!") | 0 | 0 | 0 |
85fa1b03363af1b5263db51b0c124c310492ceac | 6,037 | py | Python | CI/machinery/ci_task.py | siconos/siconos-deb | 2739a23f23d797dbfecec79d409e914e13c45c67 | [
"Apache-2.0"
] | null | null | null | CI/machinery/ci_task.py | siconos/siconos-deb | 2739a23f23d797dbfecec79d409e914e13c45c67 | [
"Apache-2.0"
] | null | null | null | CI/machinery/ci_task.py | siconos/siconos-deb | 2739a23f23d797dbfecec79d409e914e13c45c67 | [
"Apache-2.0"
] | null | null | null | import os
import shutil
from subprocess import check_call, CalledProcessError
import copy
| 34.301136 | 95 | 0.496273 | import os
import shutil
from subprocess import check_call, CalledProcessError
import copy
class CiTask():
def __init__(self,
mode='Continuous',
build_configuration='Release',
distrib=None,
ci_config=None,
fast=True,
pkgs=None,
srcs=None,
targets=None,
cmake_cmd=None):
self._fast = fast
self._distrib = distrib
self._mode = mode
self._build_configuration = build_configuration
self._ci_config = ci_config
self._pkgs = pkgs
self._srcs = srcs
self._targets = targets
self._cmake_cmd = cmake_cmd
def build_dir(self, src):
if isinstance(self._ci_config, str):
ci_config_name = self._ci_config
else:
ci_config_name = '-'.join(self._ci_config)
return src.replace('.', '_') + self._distrib.replace(':', '-') + '_' +\
ci_config_name
def templates(self):
# remove build-base, gnu-c++, gfortran, it is redundant
return ','.join(self._pkgs)
def copy(self):
def init(mode=self._mode,
build_configuration=self._build_configuration,
distrib=self._distrib,
ci_config=self._ci_config, fast=self._fast, pkgs=self._pkgs,
srcs=self._srcs, targets=self._targets,
cmake_cmd=self._cmake_cmd,
add_pkgs=None, remove_pkgs=None, add_srcs=None,
remove_srcs=None, add_targets=None, remove_targets=None,
with_examples=False):
# WARNING: remember that default arg are mutable in python
# http://docs.python-guide.org/en/latest/writing/gotchas/
if add_pkgs is not None:
pkgs = self._pkgs + add_pkgs
if remove_pkgs is not None:
pkgs = list(filter(lambda p: p not in remove_pkgs, pkgs))
if add_srcs is not None:
srcs = self._srcs + add_srcs
if remove_srcs is not None:
srcs = list(filter(lambda p: p not in remove_srcs, srcs))
if add_targets is not None:
targets = self._targets + add_targets
if remove_targets is not None:
targets = list(
filter(lambda p: p not in remove_targets, targets))
if with_examples:
if 'examples' not in srcs:
srcs = srcs + ['examples']
targets = copy.deepcopy(targets)
targets.update({'.': ['docker-build', 'docker-ctest',
'docker-make-install'],
'examples': ['docker-build', 'docker-ctest']})
else:
if 'examples' in srcs:
srcs.remove('examples')
if 'examples' in targets:
targets.pop('examples')
return CiTask(mode, build_configuration, distrib, ci_config, fast,
pkgs, srcs, targets, cmake_cmd)
return init
def run(self, targets_override=None):
return_code = 0
for src in self._srcs:
if targets_override is not None:
self._targets[src] = targets_override
bdir = self.build_dir(src)
if os.path.exists(bdir):
shutil.rmtree(bdir, ignore_errors=True)
os.makedirs(bdir)
redundants = [
'build-base', 'gfortran', 'gnu-c++', 'lpsolve', 'wget', 'xz',
'asan', 'cppunit_clang', 'python-env', 'profiling',
'python3-env', 'path', 'h5py3']
templ_list = [p.replace('+', 'x')
for p in self._pkgs if p not in redundants]
# special case for examples
if src is 'examples':
ci_config_args = 'examples'
else:
# hm python is so lovely
if isinstance(self._ci_config, str):
ci_config_args = self._ci_config
else:
ci_config_args = ','.join(self._ci_config)
cmake_args = ['-DMODE={0}'.format(self._mode),
'-DCI_CONFIG={0}'.format(ci_config_args),
'-DWITH_DOCKER=1',
'-DBUILD_CONFIGURATION={0}'.format(
self._build_configuration),
'-DDOCKER_DISTRIB={0}'.format(self._distrib),
'-DDOCKER_TEMPLATES={0}'.format(self.templates()),
'-DDOCKER_TEMPLATE={0}'.format('-'.join(templ_list))]
if self._cmake_cmd:
src_absolute_dir = os.path.normpath(
os.path.abspath(__file__) + '../../../..')
cmake_args += [
'-DDOCKER_CMAKE_WRAPPER={:}/{:}'.format(src_absolute_dir, self._cmake_cmd)]
try:
check_call(
['cmake'] + cmake_args + [os.path.join('..', '..', src)],
cwd=bdir)
for target in self._targets[src]:
check_call(['make'] + ['-ki'] + [target], cwd=bdir)
except CalledProcessError as error:
return_code = 1
print(error)
return return_code
def clean(self):
for src in self._srcs:
bdir = self.build_dir(src)
try:
check_call(['make'] + ['docker-clean-usr-local'],
cwd=bdir)
if not self._fast:
check_call(['make'] + ['docker-clean'],
cwd=bdir)
except CalledProcessError as error:
print(error)
def display(self):
for attr, value in self.__dict__.items():
print('\t{:} = {:}'.format(attr, value))
| 5,740 | -6 | 212 |
30f81ed7e89db7217b849ad6d0f78859e2b961af | 3,008 | py | Python | testing/ball_tether_test.py | konsou/autsball | 9252a104c0f107098352ec1857f4ff5bb71931ab | [
"MIT"
] | null | null | null | testing/ball_tether_test.py | konsou/autsball | 9252a104c0f107098352ec1857f4ff5bb71931ab | [
"MIT"
] | 3 | 2017-06-27T13:39:30.000Z | 2017-07-10T10:24:53.000Z | testing/ball_tether_test.py | konsou/autsball | 9252a104c0f107098352ec1857f4ff5bb71931ab | [
"MIT"
] | null | null | null | # -*- coding: utf8 -*-
import pygame, math, vector, game_object, game
center_point = (400,300)
yellow = (255, 255, 0)
red = (255, 0, 0)
green = (0, 255, 0)
pygame.init()
win = pygame.display.set_mode((800, 600))
pygame.display.set_caption("Angle finding test")
class EmptyLevel(pygame.sprite.Sprite):
""" Level-classi. Käytännössä vain taustakuva, logiikka tapahtuu muualla. """
def show_text(pos, text, color=(255, 255, 255), bgcolor=(0, 0, 0), fontSize=24):
""" Utility-metodi tekstin näyttämiseen ruudulla """
global win
font = pygame.font.Font(None, fontSize)
textimg = font.render(text, 1, color, bgcolor)
win.blit(textimg, pos)
level = EmptyLevel()
ball = TestBall(level=level)
player = TestPlayer(level=level)
ball.attached_player = player
player.attached_ball = ball
clock = pygame.time.Clock()
viewscreen_rect = 0, 0, 800, 600
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
mouse_position = pygame.mouse.get_pos()
player.x, player.y = mouse_position
player.update_rect()
distance_squared = ball.distance_squared(player)
distance = ball.distance(player)
if distance_squared >= 10000:
line_color = yellow
else:
line_color = green
win.fill(0)
game.BallGroup.update(viewscreen_rect)
game.PlayerGroup.update()
game.LevelGroup.draw(win)
game.BallGroup.draw(win)
game.PlayerGroup.draw(win)
pygame.draw.line(win, line_color, (ball.x, ball.y), (player.x, player.y), 2)
show_text((10,10), str(center_point) + " - " + str(mouse_position))
show_text((10, 30), "Dist squared: " + str(distance_squared))
show_text((10, 50), "Dist: " + str(distance))
pygame.display.flip()
clock.tick(30)
pygame.exit() | 30.08 | 117 | 0.674867 | # -*- coding: utf8 -*-
import pygame, math, vector, game_object, game
center_point = (400,300)
yellow = (255, 255, 0)
red = (255, 0, 0)
green = (0, 255, 0)
pygame.init()
win = pygame.display.set_mode((800, 600))
pygame.display.set_caption("Angle finding test")
class TestBall(game.BallSprite):
def __init__(self, level):
game.BallSprite.__init__(self, level=level, parent=DummyParent())
self.gravity_affects = 1
self.start_position = 400, 300
self.rect.center = self.start_position
self.attached_player_max_distance = 100 # "tetherin" pituus
self.attached_player_max_distance_squared = self.attached_player_max_distance**2 # distance-laskelmia varten
class TestPlayer(game.PlayerSprite):
def __init__(self, level):
game.PlayerSprite.__init__(self, level=level, parent=DummyParent())
self.gravity_affects = 0
self.is_centered_on_screen = 0
self.viewscreen_rect = 0, 0, 800, 600
class DummyParent():
screen_center_point = 400,300
gravity = 0.1
class EmptyLevel(pygame.sprite.Sprite):
""" Level-classi. Käytännössä vain taustakuva, logiikka tapahtuu muualla. """
def __init__(self):
pygame.sprite.Sprite.__init__(self, game.LevelGroup)
# self.image = pygame.image.load('gfx/level_test.png').convert_alpha()
self.image = pygame.Surface((800, 600))
self.size_x = self.image.get_width()
self.size_y = self.image.get_height()
self.rect = self.image.get_rect()
self.center_point = self.size_x // 2, self.size_y // 2
def show_text(pos, text, color=(255, 255, 255), bgcolor=(0, 0, 0), fontSize=24):
""" Utility-metodi tekstin näyttämiseen ruudulla """
global win
font = pygame.font.Font(None, fontSize)
textimg = font.render(text, 1, color, bgcolor)
win.blit(textimg, pos)
level = EmptyLevel()
ball = TestBall(level=level)
player = TestPlayer(level=level)
ball.attached_player = player
player.attached_ball = ball
clock = pygame.time.Clock()
viewscreen_rect = 0, 0, 800, 600
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
mouse_position = pygame.mouse.get_pos()
player.x, player.y = mouse_position
player.update_rect()
distance_squared = ball.distance_squared(player)
distance = ball.distance(player)
if distance_squared >= 10000:
line_color = yellow
else:
line_color = green
win.fill(0)
game.BallGroup.update(viewscreen_rect)
game.PlayerGroup.update()
game.LevelGroup.draw(win)
game.BallGroup.draw(win)
game.PlayerGroup.draw(win)
pygame.draw.line(win, line_color, (ball.x, ball.y), (player.x, player.y), 2)
show_text((10,10), str(center_point) + " - " + str(mouse_position))
show_text((10, 30), "Dist squared: " + str(distance_squared))
show_text((10, 50), "Dist: " + str(distance))
pygame.display.flip()
clock.tick(30)
pygame.exit() | 966 | 77 | 147 |
26efd4d930a69ce9a17c164364d07a6a1d2e240d | 887 | py | Python | Scripts/englishDictionary.py | Jorgee97/Python_Projects | 187893a47b71bb0a626f740a1e9423fc45f8736f | [
"MIT"
] | null | null | null | Scripts/englishDictionary.py | Jorgee97/Python_Projects | 187893a47b71bb0a626f740a1e9423fc45f8736f | [
"MIT"
] | null | null | null | Scripts/englishDictionary.py | Jorgee97/Python_Projects | 187893a47b71bb0a626f740a1e9423fc45f8736f | [
"MIT"
] | null | null | null | import json
from difflib import get_close_matches
data = json.load(open("/home/coreman/Downloads/dictionary.json"))
s = input()
result = retrieve_def(s)
if type(result) == list:
for item in result:
print("-", item)
else:
print("-", result)
| 24.638889 | 103 | 0.599775 | import json
from difflib import get_close_matches
data = json.load(open("/home/coreman/Downloads/dictionary.json"))
def retrieve_def(word):
word = word.lower()
if word in data:
return data[word]
elif word.title() in data:
return data[word.title()]
elif word.upper() in data:
return data[word.upper()]
elif len(get_close_matches(word, data.keys())) > 0:
action = input("Did you mean %s instead? [y or n]: " % get_close_matches(word, data.keys())[0])
if action == 'y':
return data[get_close_matches(word, data.keys())[0]]
elif action == 'n':
return "The word doesn't exist."
else:
return "We don't understand your entry. sorry"
s = input()
result = retrieve_def(s)
if type(result) == list:
for item in result:
print("-", item)
else:
print("-", result)
| 601 | 0 | 23 |
c33531d9237427911f7642814fa1ec4545e853e5 | 405 | py | Python | 14/test.py | AlecRosenbaum/adventofcode2017 | 9214a64db77492790d30bbd22e835535d05abb25 | [
"MIT"
] | null | null | null | 14/test.py | AlecRosenbaum/adventofcode2017 | 9214a64db77492790d30bbd22e835535d05abb25 | [
"MIT"
] | null | null | null | 14/test.py | AlecRosenbaum/adventofcode2017 | 9214a64db77492790d30bbd22e835535d05abb25 | [
"MIT"
] | null | null | null | import unittest
from solution import solution_part_one, solution_part_two
TEST_INPUT = """flqrgnkx"""
if __name__ == "__main__":
unittest.main()
| 20.25 | 61 | 0.740741 | import unittest
from solution import solution_part_one, solution_part_two
TEST_INPUT = """flqrgnkx"""
class TestPartOne(unittest.TestCase):
def test_one(self):
self.assertEqual(solution_part_one(TEST_INPUT), 8108)
class TestPartTwo(unittest.TestCase):
def test_one(self):
self.assertEqual(solution_part_two(TEST_INPUT), 1242)
if __name__ == "__main__":
unittest.main()
| 120 | 32 | 98 |
a317a11e103dcfcebd4e668a6345e88f5d936144 | 709 | py | Python | pyml_ensemble/model/model.py | anthonymorast/pyml-ensemble | a52e454f4c8d92412b3ee66140f78d19da32b53c | [
"MIT"
] | null | null | null | pyml_ensemble/model/model.py | anthonymorast/pyml-ensemble | a52e454f4c8d92412b3ee66140f78d19da32b53c | [
"MIT"
] | 5 | 2020-02-13T03:55:53.000Z | 2021-02-12T17:53:15.000Z | pyml_ensemble/model/model.py | anthonymorast/pyml-ensemble | a52e454f4c8d92412b3ee66140f78d19da32b53c | [
"MIT"
] | null | null | null | import abc
| 28.36 | 77 | 0.620592 | import abc
class Model(abc.ABC):
def __init__(self):
pass
@abc.abstractmethod
def train(self, x, y):
pass
@abc.abstractmethod
def get_prediction(self, x):
pass
def custom_stat(self, stat):
""" Used to define custom aggregators. This stat can be used to match
with a stat in the aggregator. For example, if this were the k-NN
aggregator this would store the mean/variance values (somehow) so
that they can be compared with the data chunk's mean/variance.
Need to think of the best way to handle custom aggregators. Could
also use child classes of the model too...
"""
pass
| 45 | 630 | 23 |
9da0f87f8d24e2a12adc4d3f3df3720c1e2db205 | 261 | py | Python | pycleanup/__init__.py | axju/pyclean | 16ad3f44ee6ce3f2b0b12eaae220633c897580b1 | [
"MIT"
] | 1 | 2018-11-23T22:45:07.000Z | 2018-11-23T22:45:07.000Z | pycleanup/__init__.py | axju/pyclean | 16ad3f44ee6ce3f2b0b12eaae220633c897580b1 | [
"MIT"
] | 2 | 2018-11-23T23:00:05.000Z | 2019-10-10T07:39:11.000Z | pycleanup/__init__.py | axju/pyclean | 16ad3f44ee6ce3f2b0b12eaae220633c897580b1 | [
"MIT"
] | 1 | 2019-09-23T06:43:29.000Z | 2019-09-23T06:43:29.000Z | VERSION = '0.0.1'
DIR_KIND = {
'cache': {'search': '__pycache__', 'help': 'Delete the pycache folders'},
'egg': {'search': 'egg-info', 'help': 'Delete the egg folders'},
}
FILE_KIND = {
'pyc': {'search': '.pyc', 'help': 'Delete the pyc files'},
}
| 23.727273 | 77 | 0.56705 | VERSION = '0.0.1'
DIR_KIND = {
'cache': {'search': '__pycache__', 'help': 'Delete the pycache folders'},
'egg': {'search': 'egg-info', 'help': 'Delete the egg folders'},
}
FILE_KIND = {
'pyc': {'search': '.pyc', 'help': 'Delete the pyc files'},
}
| 0 | 0 | 0 |
9871f89a5ce1451771af2f79157524e8f85304d8 | 217 | py | Python | tests/pep562_test/__init__.py | p7g/dd-trace-py | 141ac0ab6e9962e3b3bafc9de172076075289a19 | [
"Apache-2.0",
"BSD-3-Clause"
] | 308 | 2016-12-07T16:49:27.000Z | 2022-03-15T10:06:45.000Z | tests/pep562_test/__init__.py | p7g/dd-trace-py | 141ac0ab6e9962e3b3bafc9de172076075289a19 | [
"Apache-2.0",
"BSD-3-Clause"
] | 1,928 | 2016-11-28T17:13:18.000Z | 2022-03-31T21:43:19.000Z | tests/pep562_test/__init__.py | p7g/dd-trace-py | 141ac0ab6e9962e3b3bafc9de172076075289a19 | [
"Apache-2.0",
"BSD-3-Clause"
] | 311 | 2016-11-27T03:01:49.000Z | 2022-03-18T21:34:03.000Z | from ddtrace.internal.compat import ensure_pep562
ensure_pep562(__name__)
| 19.727273 | 51 | 0.732719 | from ddtrace.internal.compat import ensure_pep562
def __getattr__(name):
if name == "deprecated":
raise RuntimeError("bad module attribute!")
return "good module attribute"
ensure_pep562(__name__)
| 117 | 0 | 23 |
2090d111bf40cbb4abfec4717fd4887559b95e71 | 2,371 | py | Python | starter_templates/sqlite/python/app/main.py | knarkzel/languages | 96ea0055cd5edcd4c8658d29737f3285af4921ab | [
"MIT"
] | 8 | 2020-12-07T09:54:55.000Z | 2022-03-21T01:27:53.000Z | starter_templates/sqlite/python/app/main.py | knarkzel/languages | 96ea0055cd5edcd4c8658d29737f3285af4921ab | [
"MIT"
] | 31 | 2020-05-03T05:44:09.000Z | 2022-02-10T23:14:32.000Z | starter_templates/sqlite/python/app/main.py | knarkzel/languages | 96ea0055cd5edcd4c8658d29737f3285af4921ab | [
"MIT"
] | 14 | 2020-05-03T01:03:49.000Z | 2021-12-23T14:52:08.000Z | import sys
from dataclasses import dataclass
# import sqlparse - available if you need it!
from .record_parser import parse_record
from .varint_parser import parse_varint
database_file_path = sys.argv[1]
command = sys.argv[2]
@dataclass(init=False)
if command == ".dbinfo":
with open(database_file_path, "rb") as database_file:
database_file.seek(100) # Skip the header section
page_header = PageHeader.parse_from(database_file)
database_file.seek(100+8) # Skip the database header & b-tree page header, get to the cell pointer array
cell_pointers = [int.from_bytes(database_file.read(2), "big") for _ in range(page_header.number_of_cells)]
sqlite_schema_rows = []
# Each of these cells represents a row in the sqlite_schema table.
for cell_pointer in cell_pointers:
database_file.seek(cell_pointer)
_number_of_bytes_in_payload = parse_varint(database_file)
rowid = parse_varint(database_file)
record = parse_record(database_file, 5)
# Table contains columns: type, name, tbl_name, rootpage, sql
sqlite_schema_rows.append({
'type': record[0],
'name': record[1],
'tbl_name': record[2],
'rootpage': record[3],
'sql': record[4],
})
print("Your code goes here!")
# Uncomment this to pass the first stage
# print(f"number of tables: {len(sqlite_schema_rows)}")
else:
print(f"Invalid command: {command}")
| 33.394366 | 114 | 0.661324 | import sys
from dataclasses import dataclass
# import sqlparse - available if you need it!
from .record_parser import parse_record
from .varint_parser import parse_varint
database_file_path = sys.argv[1]
command = sys.argv[2]
@dataclass(init=False)
class PageHeader:
page_type: int
first_free_block_start: int
number_of_cells: int
start_of_content_area: int
fragmented_free_bytes: int
@classmethod
def parse_from(cls, database_file):
"""
Parses a page header as mentioned here: https://www.sqlite.org/fileformat2.html#b_tree_pages
"""
instance = cls()
instance.page_type = int.from_bytes(database_file.read(1), "big")
instance.first_free_block_start = int.from_bytes(database_file.read(2), "big")
instance.number_of_cells = int.from_bytes(database_file.read(2), "big")
instance.start_of_content_area = int.from_bytes(database_file.read(2), "big")
instance.fragmented_free_bytes = int.from_bytes(database_file.read(1), "big")
return instance
if command == ".dbinfo":
with open(database_file_path, "rb") as database_file:
database_file.seek(100) # Skip the header section
page_header = PageHeader.parse_from(database_file)
database_file.seek(100+8) # Skip the database header & b-tree page header, get to the cell pointer array
cell_pointers = [int.from_bytes(database_file.read(2), "big") for _ in range(page_header.number_of_cells)]
sqlite_schema_rows = []
# Each of these cells represents a row in the sqlite_schema table.
for cell_pointer in cell_pointers:
database_file.seek(cell_pointer)
_number_of_bytes_in_payload = parse_varint(database_file)
rowid = parse_varint(database_file)
record = parse_record(database_file, 5)
# Table contains columns: type, name, tbl_name, rootpage, sql
sqlite_schema_rows.append({
'type': record[0],
'name': record[1],
'tbl_name': record[2],
'rootpage': record[3],
'sql': record[4],
})
print("Your code goes here!")
# Uncomment this to pass the first stage
# print(f"number of tables: {len(sqlite_schema_rows)}")
else:
print(f"Invalid command: {command}")
| 0 | 781 | 22 |
2df2a22e16da8f572b579694c924b07be50eb4fc | 12,055 | py | Python | shetar/rotations.py | AppliedAcousticsChalmers/Spherical-Helmholtz-Translation-and-Rotation | 84c5b51dab5d7ee26886ece44945a5d887bff369 | [
"MIT"
] | null | null | null | shetar/rotations.py | AppliedAcousticsChalmers/Spherical-Helmholtz-Translation-and-Rotation | 84c5b51dab5d7ee26886ece44945a5d887bff369 | [
"MIT"
] | null | null | null | shetar/rotations.py | AppliedAcousticsChalmers/Spherical-Helmholtz-Translation-and-Rotation | 84c5b51dab5d7ee26886ece44945a5d887bff369 | [
"MIT"
] | null | null | null | import numpy as np
from . import coordinates, bases, expansions
| 52.641921 | 158 | 0.539278 | import numpy as np
from . import coordinates, bases, expansions
class ColatitudeRotation(coordinates.OwnerMixin):
def __init__(self, order, position=None, colatitude=None, defer_evaluation=False):
self.coordinate = coordinates.Rotation.parse_args(position=position, colatitude=colatitude)
self._order = order
num_unique = (self.order + 1) * (self.order + 2) * (2 * self.order + 3) // 6
self._data = np.zeros((num_unique,) + self.coordinate.shapes.colatitude, dtype=float)
if not defer_evaluation:
self.evaluate(self.coordinate)
@property
def order(self):
return self._order
@property
def shape(self):
return self.coordinate.shapes.colatitude
def copy(self, deep=False):
new_obj = super().copy(deep=deep)
new_obj._order = self._order
if deep:
new_obj._data = self._data.copy()
else:
new_obj._data = self._data
return new_obj
def _idx(self, order=None, mode_out=None, mode_in=None, index=None):
if index is None:
# Default mode, get the linear index of a component
if abs(mode_out) > order:
raise IndexError(f'Mode {mode_out} is out of bounds for order {order}')
if abs(mode_in) > order:
raise IndexError(f'Mode {mode_in} is out of bounds for order {order}')
if order < 0 or order > self.order:
raise IndexError(f'Order {order} is out of bounds for {self.__class__.__name__} with max order {self.order}')
if mode_out < 0:
raise IndexError(f'Component {(order, mode_out, mode_in)} not stored in {self.__class__.__name__}. Use getter or index the object directly.')
if abs(mode_in) > mode_out:
raise IndexError(f'Component {(order, mode_out, mode_in)} not stored in {self.__class__.__name__}. Use getter or index the object directly.')
return order * (order + 1) * (2 * order + 1) // 6 + mode_out ** 2 + mode_out + mode_in
else:
# Inverse mode, get the component indices of a linear index
order = 0
while (order + 1) * (order + 2) * (2 * order + 3) // 6 <= index:
order += 1
index -= order * (order + 1) * (2 * order + 1) // 6
mode_out = int(index ** 0.5)
mode_in = index - mode_out * (mode_out + 1)
return order, mode_out, mode_in
@property
def _component_indices(self):
out = []
for n in range(self.order + 1):
for p in range(n + 1):
for m in range(-p, p + 1):
out.append((n, p, m))
return out
def __getitem__(self, key):
n, p, m = key
if abs(p) < m:
p, m = m, p
sign = (-1)**(m + p)
elif abs(m) <= -p > 0:
p, m = -p, -m
sign = (-1)**(m + p)
elif abs(p) < -m:
p, m, = -m, -p
sign = 1
elif p >= 0 and abs(m) <= p:
# If don't end up here it means we missed a case.
# It would be obvious since the sign variable will not exist for the return statement.
sign = 1
return sign * self._data[self._idx(n, p, m)]
def evaluate(self, position=None, colatitude=None):
self.coordinate = coordinates.Rotation.parse_args(position=position, colatitude=colatitude)
cosine_colatitude = np.cos(self.coordinate.colatitude)
sine_colatitude = (1 - cosine_colatitude**2)**0.5
legendre = bases.AssociatedLegendrePolynomials(order=self.order, x=cosine_colatitude)
# n=0 and n=1 will be special cases since the p=n-1 and p=n special cases will not behave
self._data[self._idx(0, 0, 0)] = 1
if self.order > 0:
self._data[self._idx(1, 0, 0)] = cosine_colatitude
self._data[self._idx(1, 1, -1)] = (1 - cosine_colatitude) * 0.5
self._data[self._idx(1, 1, 0)] = sine_colatitude * 2**-0.5
self._data[self._idx(1, 1, 1)] = (1 + cosine_colatitude) * 0.5
for n in range(2, self.order + 1):
for p in range(0, n - 1):
for m in range(-p, 0):
# Use recurrence for decreasing m here
self._data[self._idx(n, p, m)] = (
self._data[self._idx(n - 1, p - 1, m + 1)] * 0.5 * (1 - cosine_colatitude) * ((n + p - 1) * (n + p))**0.5
+ self._data[self._idx(n - 1, p, m + 1)] * sine_colatitude * ((n + p) * (n - p))**0.5
+ self._data[self._idx(n - 1, p + 1, m + 1)] * 0.5 * (1 + cosine_colatitude) * ((n - p - 1) * (n - p))**0.5
) / ((n - m - 1) * (n - m))**0.5
# Assign the m=0 value here
self._data[self._idx(n, p, 0)] = legendre[n, p] * (-1)**p * (2 / (2 * n + 1))**0.5
for m in range(1, p + 1):
# Use recurrence for increasing m here
self._data[self._idx(n, p, m)] = (
self._data[self._idx(n - 1, p - 1, m - 1)] * 0.5 * (1 + cosine_colatitude) * ((n + p - 1) * (n + p))**0.5
- self._data[self._idx(n - 1, p, m - 1)] * sine_colatitude * ((n + p) * (n - p))**0.5
+ self._data[self._idx(n - 1, p + 1, m - 1)] * 0.5 * (1 - cosine_colatitude) * ((n - p - 1) * (n - p))**0.5
) / ((n + m - 1) * (n + m))**0.5
# p=n-1 and p=n are special cases since one or two values are missing in the recurrences
# p = n-1
for m in range(-(n - 1), 0):
self._data[self._idx(n, n - 1, m)] = (
self._data[self._idx(n - 1, n - 2, m + 1)] * 0.5 * (1 - cosine_colatitude) * (2 * (n - 1) * (2 * n - 1))**0.5
+ self._data[self._idx(n - 1, n - 1, m + 1)] * sine_colatitude * (2 * n - 1)**0.5
) / ((n - m - 1) * (n - m))**0.5
self._data[self._idx(n, n - 1, 0)] = legendre[n, n - 1] * (-1)**(n - 1) * (2 / (2 * n + 1))**0.5
for m in range(1, n):
self._data[self._idx(n, n - 1, m)] = (
self._data[self._idx(n - 1, n - 1 - 1, m - 1)] * 0.5 * (1 + cosine_colatitude) * (2 * (n - 1) * (2 * n - 1))**0.5
- self._data[self._idx(n - 1, n - 1, m - 1)] * sine_colatitude * (2 * n - 1)**0.5
) / ((n + m - 1) * (n + m))**0.5
# p = n
for m in range(-n, 0):
self._data[self._idx(n, n, m)] = (
self._data[self._idx(n - 1, n - 1, m + 1)] * 0.5 * (1 - cosine_colatitude) * ((2 * n - 1) * 2 * n)**0.5
) / ((n - m - 1) * (n - m))**0.5
self._data[self._idx(n, n, 0)] = legendre[n, n] * (-1)**n * (2 / (2 * n + 1))**0.5
for m in range(1, n + 1):
# Use recurrence for increasing m here
self._data[self._idx(n, n, m)] = (
self._data[self._idx(n - 1, n - 1, m - 1)] * 0.5 * (1 + cosine_colatitude) * ((2 * n - 1) * 2 * n)**0.5
) / ((n + m - 1) * (n + m))**0.5
return self
def apply(self, expansion, inverse=False, out=None):
N = min(self.order, expansion.order) # Allows the rotation coefficients to be used for lower order expansions
if out is None:
wavenumber = getattr(expansion, 'wavenumber', None)
self_shape = self.shape
expansion_shape = np.shape(expansion)
output_shape = np.broadcast(np.empty(self_shape, dtype=[]), np.empty(expansion_shape, dtype=[])).shape
if isinstance(expansion, expansions.Expansion):
out = type(expansion)(order=N, shape=output_shape, wavenumber=wavenumber)
else:
out = expansions.Expansion(order=N, shape=output_shape, wavenumber=wavenumber)
elif expansion is out:
raise NotImplementedError('Rotations cannot currently be applied in place')
if inverse:
for n in range(N + 1):
for p in range(-n, n + 1):
# Use a local temporary variable to accumulate the summed value.
# We might not always be able to add in place to the components of the output.
value = 0
for m in range(-n, n + 1):
value += expansion[n, m] * self[n, p, m]
out[n, p] = value
else:
for n in range(N + 1):
for m in range(-n, n + 1):
value = 0
for p in range(-n, n + 1):
# Conjugate will not do anything for colatitude rotations,
# but full rotations use this method as well.
# The method version of conjugate (as opposed to np.conjugate)
# will not create copies of real arrays.
# value += expansion[n, p] * self[n, p, m].conjugate()
# Alternate version doing the same thing but exploiting
# symmetries instead of actuallly conjugating.
# This seems to be a little bit faster.
value += expansion[n, p] * (-1)**(m + p) * self[n, -p, -m]
out[n, m] = value
return out
class Rotation(ColatitudeRotation):
# Subclass of ColatitudeRotation to get access to the `apply` method, which work the same for both types of rotation.
def __init__(self, order, position=None, colatitude=None, azimuth=None, secondary_azimuth=None, new_z_axis=None, old_z_axis=None, defer_evaluation=False):
coordinate = coordinates.Rotation.parse_args(
position=position, new_z_axis=new_z_axis, old_z_axis=old_z_axis,
colatitude=colatitude, azimuth=azimuth, secondary_azimuth=secondary_azimuth)
super().__init__(order=order, position=coordinate, defer_evaluation=defer_evaluation)
def evaluate(self, position=None, colatitude=None, azimuth=None, secondary_azimuth=None, new_z_axis=None, old_z_axis=None):
if (position is None) and (new_z_axis is None) and (old_z_axis is None) and (colatitude is None):
# Allows chaing the azimuth angles without reevaluating the colatitude rotation.
# This might be useful sometimes since changing the azimuth angles is much cheaper than
# changing the colatitude angle. If we anyhow change the colatitude roration, reevaluating the
# azimuth rotations will be very cheap so we won't bother with checking if we could skip it.
if azimuth is not None:
self._primary_phase = np.asarray(np.exp(1j * azimuth))
if secondary_azimuth is not None:
self._secondary_phase = np.asarray(np.exp(1j * secondary_azimuth))
else:
self.coordinate = coordinates.Rotation.parse_args(
position=position, new_z_axis=new_z_axis, old_z_axis=old_z_axis,
colatitude=colatitude, azimuth=azimuth, secondary_azimuth=secondary_azimuth)
super().evaluate(position=self.coordinate)
self._primary_phase = np.asarray(np.exp(1j * self.coordinate.azimuth))
self._secondary_phase = np.asarray(np.exp(1j * self.coordinate.secondary_azimuth))
return self
@property
def shape(self):
return self.coordinate.shape
def copy(self, deep=False):
new_obj = super().copy(deep=deep)
if deep:
new_obj._primary_phase = self._primary_phase.copy()
new_obj._secondary_phase = self._secondary_phase.copy()
else:
new_obj._primary_phase = self._primary_phase
new_obj._secondary_phase = self._secondary_phase
return new_obj
def __getitem__(self, key):
n, p, m = key
phase = self._primary_phase ** m * self._secondary_phase ** p
return super().__getitem__(key) * phase
| 11,347 | 596 | 46 |
395a720e00ee7c4fa4922ced15ccf6788b7a3932 | 1,232 | py | Python | Problems/Top Interview Questions/Hard/03_merge_k_sorted_lists.py | andor2718/LeetCode | 59874f49085818e6da751f1cc26867b31079d35d | [
"BSD-3-Clause"
] | 1 | 2022-01-17T19:51:15.000Z | 2022-01-17T19:51:15.000Z | Problems/Top Interview Questions/Hard/03_merge_k_sorted_lists.py | andor2718/LeetCode | 59874f49085818e6da751f1cc26867b31079d35d | [
"BSD-3-Clause"
] | null | null | null | Problems/Top Interview Questions/Hard/03_merge_k_sorted_lists.py | andor2718/LeetCode | 59874f49085818e6da751f1cc26867b31079d35d | [
"BSD-3-Clause"
] | null | null | null | # https://leetcode.com/problems/merge-k-sorted-lists/
import heapq
from typing import Optional
# Definition for singly-linked list.
# Wrapper class for ListNodes, so we can compare them by values.
| 29.333333 | 79 | 0.62013 | # https://leetcode.com/problems/merge-k-sorted-lists/
import heapq
from typing import Optional
# Definition for singly-linked list.
class ListNode:
def __init__(self, val=0, next=None):
self.val = val
self.next = next
# Wrapper class for ListNodes, so we can compare them by values.
class Entry:
def __init__(self, node: ListNode):
self.node = node
def __lt__(self, other) -> bool:
return self.node.val < other.node.val
class Solution:
def mergeKLists(
self, lists: list[Optional[ListNode]]) -> Optional[ListNode]:
result_guard = ListNode()
tail = result_guard
entry_min_heap = list()
for head in lists:
if head:
entry_min_heap.append(Entry(head))
heapq.heapify(entry_min_heap)
while entry_min_heap:
curr_entry = heapq.heappop(entry_min_heap)
tail.next = curr_entry.node
tail = tail.next
if curr_entry.node.next:
curr_entry.node = curr_entry.node.next
heapq.heappush(entry_min_heap, curr_entry)
tail.next = None # It's a nice touch, but completely optional.
return result_guard.next
| 878 | -21 | 172 |
c62e471845b93de70871f48bb391224564e10b9d | 2,537 | py | Python | morgana/Examples/python_example_scripts/old_version/02_create_ground_truth.py | Nikoula86/organoidSegment | b5d00256c15302ccd76b8b7a412852750476504b | [
"MIT"
] | 8 | 2021-09-08T10:49:53.000Z | 2022-02-25T13:28:03.000Z | morgana/Examples/python_example_scripts/old_version/02_create_ground_truth.py | Nikoula86/organoidSegment | b5d00256c15302ccd76b8b7a412852750476504b | [
"MIT"
] | null | null | null | morgana/Examples/python_example_scripts/old_version/02_create_ground_truth.py | Nikoula86/organoidSegment | b5d00256c15302ccd76b8b7a412852750476504b | [
"MIT"
] | 1 | 2021-11-24T08:10:41.000Z | 2021-11-24T08:10:41.000Z | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 24 12:26:12 2020
@author: gritti
"""
import tqdm
import os
# import sys
# sys.path.append(os.path.join('..'))
from orgseg.GUIs.manualmask import makeManualMask
from orgseg.DatasetTools import io
###############################################################################
model_folders = [
os.path.join('test_data','2020-09-22_conditions','model'),
]
###############################################################################
###############################################################################
if __name__ == '__main__':
### compute parent folder as absolute path
model_folders = [os.path.abspath(i) for i in model_folders]
for model_folder in tqdm.tqdm(model_folders):
create_GT_mask(model_folder)
| 34.283784 | 162 | 0.587308 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 24 12:26:12 2020
@author: gritti
"""
import tqdm
import os
# import sys
# sys.path.append(os.path.join('..'))
from orgseg.GUIs.manualmask import makeManualMask
from orgseg.DatasetTools import io
###############################################################################
model_folders = [
os.path.join('test_data','2020-09-22_conditions','model'),
]
###############################################################################
def create_GT_mask(model_folder):
### check that model and trainingset exist
if not os.path.exists(model_folder):
print('Warning!')
print(model_folder,':')
print('Model folder not created! Skipping this gastruloid.')
return
trainingset_folder = os.path.join(model_folder,'trainingset')
if not os.path.exists(trainingset_folder):
print('Warning!')
print(model_folder,':')
print('Trainingset images not found! Skipping this gastruloid.')
return
### load trainingset images and previously generated ground truth
flist_in = io.get_image_list(trainingset_folder, string_filter='_GT', mode_filter='exclude')
flist_in.sort()
flist_gt = io.get_image_list(trainingset_folder, string_filter='_GT', mode_filter='include')
flist_gt.sort()
### if no trainingset images in the folder, skip this gastruloid
if len(flist_in) == 0:
print('\n\nWarning, no trainingset!','Selected "'+model_folder+'" but no trainingset *data* detected. Transfer some images in the "trainingset" folder.')
return
### if there are more trainingset than ground truth, promptuse to make mask
if len(flist_in)!=len(flist_gt):
print('\n\nWarning, trainingset incomplete!','Selected "'+model_folder+'" but not all masks have been created.\nPlease provide manually annotated masks.')
for f in flist_in:
fn,ext = os.path.splitext(f)
mask_name = fn+'_GT'+ext
if not os.path.exists(mask_name):
m = makeManualMask(f,subfolder='',fn=fn+'_GT'+ext,wsize = (2000,2000))
m.show()
m.exec()
###############################################################################
if __name__ == '__main__':
### compute parent folder as absolute path
model_folders = [os.path.abspath(i) for i in model_folders]
for model_folder in tqdm.tqdm(model_folders):
create_GT_mask(model_folder)
| 1,673 | 0 | 23 |