xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

a61c2733c9a37c35b7859ec871c2d280ecef250d

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py

Python

data.py

didallorto/MyFlaskApp

1fe6766a39b1082230b75cb9e9c527afda9ec717

[ "Apache-2.0" ]

2

2019-09-11T13:33:02.000Z

2019-09-16T06:12:03.000Z

data.py

didallorto/MyFlaskApp

1fe6766a39b1082230b75cb9e9c527afda9ec717

[ "Apache-2.0" ]

2

2019-09-17T19:43:40.000Z

2019-09-20T05:53:26.000Z

data.py

didallorto/MyFlaskApp

1fe6766a39b1082230b75cb9e9c527afda9ec717

[ "Apache-2.0" ]

null

def Articles(): articles = [ { 'id': 1, 'title': 'Article one', 'body': 'O Lorem Ipsum é um texto modelo da indústria tipográfica e de impressão. O Lorem Ipsum tem vindo a ser o texto padrão usado por estas indústrias desde o ano de 1500, quando uma misturou os caracteres de um texto para criar um espécime de livro. Este texto não só sobreviveu 5 séculos, mas também o salto para a tipografia electrónica, mantendo-se essencialmente inalterada. Foi popularizada nos anos 60 com a disponibilização das folhas de Letraset, que continham passagens com Lorem Ipsum, e mais recentemente com os programas de publicação como o Aldus PageMaker que incluem versões do Lorem Ipsum.', 'author': 'dallorto', 'create_date': '09-10-2019' }, { 'id': 2, 'title': 'Article two', 'body': 'O Lorem Ipsum é um texto modelo da indústria tipográfica e de impressão. O Lorem Ipsum tem vindo a ser o texto padrão usado por estas indústrias desde o ano de 1500, quando uma misturou os caracteres de um texto para criar um espécime de livro. Este texto não só sobreviveu 5 séculos, mas também o salto para a tipografia electrónica, mantendo-se essencialmente inalterada. Foi popularizada nos anos 60 com a disponibilização das folhas de Letraset, que continham passagens com Lorem Ipsum, e mais recentemente com os programas de publicação como o Aldus PageMaker que incluem versões do Lorem Ipsum.', 'author': 'coml', 'create_date': '09-10-2019' }, { 'id': 3, 'title': 'Article Three', 'body': 'O Lorem Ipsum é um texto modelo da indústria tipográfica e de impressão. O Lorem Ipsum tem vindo a ser o texto padrão usado por estas indústrias desde o ano de 1500, quando uma misturou os caracteres de um texto para criar um espécime de livro. Este texto não só sobreviveu 5 séculos, mas também o salto para a tipografia electrónica, mantendo-se essencialmente inalterada. Foi popularizada nos anos 60 com a disponibilização das folhas de Letraset, que continham passagens com Lorem Ipsum, e mais recentemente com os programas de publicação como o Aldus PageMaker que incluem versões do Lorem Ipsum.', 'author': 'malana', 'create_date': '09-10-2019' } ] return articles

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Python

tests/core/test_skip_graph_node.py

ethereum/alexandria

adba4114fbd5f707181da602abd977e008e463c9

[ "MIT" ]

7

2020-04-06T14:45:55.000Z

2021-11-04T14:16:19.000Z

tests/core/test_skip_graph_node.py

pipermerriam/alexandria

adba4114fbd5f707181da602abd977e008e463c9

[ "MIT" ]

6

2020-04-09T20:36:14.000Z

2020-04-12T01:33:59.000Z

tests/core/test_skip_graph_node.py

pipermerriam/alexandria

adba4114fbd5f707181da602abd977e008e463c9

[ "MIT" ]

7

2020-04-04T17:16:04.000Z

2022-03-28T18:57:04.000Z

import pytest from eth_utils import ValidationError from alexandria.skip_graph import SGNode, LEFT, RIGHT def test_single_node(): node = SGNode(0) assert node.max_level == 0 for level in range(256): assert node.get_neighbor(level, LEFT) is None assert node.get_neighbor(level, RIGHT) is None assert tuple(node.iter_down_levels(0, LEFT)) == ((0, None),) assert tuple(node.iter_down_levels(0, RIGHT)) == ((0, None),) def test_single_node_with_only_right_neighbor(): node = SGNode(0, neighbors_right=[1]) assert node.max_level == 1 assert node.get_neighbor(0, LEFT) is None assert node.get_neighbor(0, RIGHT) == 1 for level in range(1, 256): assert node.get_neighbor(level, LEFT) is None assert node.get_neighbor(level, RIGHT) is None assert tuple(node.iter_down_levels(0, LEFT)) == ((0, None),) assert tuple(node.iter_down_levels(0, RIGHT)) == ((0, 1),) assert tuple(node.iter_down_levels(1, LEFT)) == ((1, None), (0, None)) assert tuple(node.iter_down_levels(1, RIGHT)) == ((1, None), (0, 1)) assert tuple(node.iter_neighbors()) == ((0, RIGHT, 1),) def test_single_node_with_only_left_neighbor(): node = SGNode(1, neighbors_left=[0]) assert node.max_level == 1 assert node.get_neighbor(0, LEFT) == 0 assert node.get_neighbor(0, RIGHT) is None for level in range(1, 256): assert node.get_neighbor(level, LEFT) is None assert node.get_neighbor(level, RIGHT) is None assert tuple(node.iter_down_levels(0, LEFT)) == ((0, 0),) assert tuple(node.iter_down_levels(0, RIGHT)) == ((0, None),) assert tuple(node.iter_down_levels(1, LEFT)) == ((1, None), (0, 0)) assert tuple(node.iter_down_levels(1, RIGHT)) == ((1, None), (0, None)) assert tuple(node.iter_neighbors()) == ((0, LEFT, 0),) def test_single_node_with_both_neighbors(): node = SGNode(4, neighbors_left=[2, 0], neighbors_right=[8, 12, 28]) assert node.max_level == 3 assert node.get_neighbor(0, LEFT) == 2 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, LEFT) == 0 assert node.get_neighbor(1, RIGHT) == 12 assert node.get_neighbor(2, LEFT) is None assert node.get_neighbor(2, RIGHT) == 28 for level in range(3, 256): assert node.get_neighbor(level, LEFT) is None assert node.get_neighbor(level, RIGHT) is None assert tuple(node.iter_down_levels(0, LEFT)) == ((0, 2),) assert tuple(node.iter_down_levels(0, RIGHT)) == ((0, 8),) assert tuple(node.iter_down_levels(1, LEFT)) == ((1, 0), (0, 2)) assert tuple(node.iter_down_levels(1, RIGHT)) == ((1, 12), (0, 8)) assert tuple(node.iter_down_levels(2, LEFT)) == ((2, None), (1, 0), (0, 2)) assert tuple(node.iter_down_levels(2, RIGHT)) == ((2, 28), (1, 12), (0, 8)) assert tuple(node.iter_down_levels(3, LEFT)) == ((3, None), (2, None), (1, 0), (0, 2)) assert tuple(node.iter_down_levels(3, RIGHT)) == ((3, None), (2, 28), (1, 12), (0, 8)) assert tuple(node.iter_neighbors()) == ( (0, LEFT, 2), (0, RIGHT, 8), (1, LEFT, 0), (1, RIGHT, 12), (2, RIGHT, 28), ) def test_single_node_neighbor_setting(): node = SGNode(4) assert node.max_level == 0 assert node.get_neighbor(0, LEFT) is None assert node.get_neighbor(0, RIGHT) is None node.set_neighbor(0, LEFT, 2) assert node.max_level == 1 assert node.get_neighbor(0, LEFT) == 2 assert node.get_neighbor(0, RIGHT) is None node.set_neighbor(1, LEFT, 0) assert node.max_level == 2 assert node.get_neighbor(0, LEFT) == 2 assert node.get_neighbor(1, LEFT) == 0 assert node.get_neighbor(0, RIGHT) is None # should be an error to set a neighbor above the topmost non-null neighbor. with pytest.raises(ValidationError): node.set_neighbor(1, RIGHT, 12) with pytest.raises(ValidationError): node.set_neighbor(2, LEFT, 28) node.set_neighbor(0, RIGHT, 8) with pytest.raises(ValidationError): node.set_neighbor(2, RIGHT, 28) assert node.max_level == 2 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, RIGHT) is None node.set_neighbor(1, RIGHT, 12) node.set_neighbor(2, RIGHT, 28) assert node.max_level == 3 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, RIGHT) == 12 assert node.get_neighbor(2, RIGHT) == 28 assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None node.set_neighbor(3, RIGHT, None) node.set_neighbor(4, RIGHT, None) node.set_neighbor(5, RIGHT, None) node.set_neighbor(6, RIGHT, None) assert node.max_level == 3 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, RIGHT) == 12 assert node.get_neighbor(2, RIGHT) == 28 assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None with pytest.raises(ValidationError): node.set_neighbor(1, RIGHT, None) with pytest.raises(ValidationError): node.set_neighbor(0, RIGHT, None) node.set_neighbor(2, RIGHT, None) assert node.max_level == 2 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, RIGHT) == 12 assert node.get_neighbor(2, RIGHT) is None assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None with pytest.raises(ValidationError): node.set_neighbor(0, RIGHT, None) node.set_neighbor(1, RIGHT, None) assert node.max_level == 2 assert node.get_neighbor(0, RIGHT) == 8 assert node.get_neighbor(1, RIGHT) is None assert node.get_neighbor(2, RIGHT) is None assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None node.set_neighbor(0, RIGHT, 28) assert node.max_level == 2 assert node.get_neighbor(0, RIGHT) == 28 assert node.get_neighbor(1, RIGHT) is None assert node.get_neighbor(2, RIGHT) is None assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None node.set_neighbor(0, RIGHT, None) assert node.max_level == 2 assert node.get_neighbor(0, RIGHT) is None assert node.get_neighbor(1, RIGHT) is None assert node.get_neighbor(2, RIGHT) is None assert node.get_neighbor(3, RIGHT) is None assert node.get_neighbor(4, RIGHT) is None assert node.get_neighbor(5, RIGHT) is None assert node.get_neighbor(6, RIGHT) is None def test_node_get_membership_at_level(): node = SGNode(1234) assert node.get_membership_at_level(0) == 0 assert node.get_membership_at_level(1) == 1 assert node.get_membership_at_level(2) == 3 assert node.get_membership_at_level(3) == 3 assert node.get_membership_at_level(4) == 11

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py

Python

BM2/Exerc02/test_ex_01.py

kauaramirez/LP2_2s2017

30e017ab249433cbc988a0330bdd34860f9526e7

[ "Apache-2.0" ]

null

BM2/Exerc02/test_ex_01.py

kauaramirez/LP2_2s2017

30e017ab249433cbc988a0330bdd34860f9526e7

[ "Apache-2.0" ]

null

BM2/Exerc02/test_ex_01.py

kauaramirez/LP2_2s2017

30e017ab249433cbc988a0330bdd34860f9526e7

[ "Apache-2.0" ]

null

from Main import ex01 def test_ex_01(): assert ex01("Kaua",1500,10) == 1650 assert ex01("Natalia", 1000, 10) == 1100 assert ex01("Doente", 3500, 15) == 4025

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Python

iseq/test/test_codon_table.py

EBI-Metagenomics/iseq

3c28fc92e5af05c91c6669d7f1a28d1ce857f3f1

[ "MIT" ]

null

iseq/test/test_codon_table.py

EBI-Metagenomics/iseq

3c28fc92e5af05c91c6669d7f1a28d1ce857f3f1

[ "MIT" ]

null

iseq/test/test_codon_table.py

EBI-Metagenomics/iseq

3c28fc92e5af05c91c6669d7f1a28d1ce857f3f1

[ "MIT" ]

null

from nmm import Codon, DNAAlphabet, IUPACAminoAlphabet, RNAAlphabet from iseq.codon_table import CodonTable from iseq.gencode import GeneticCode def test_codon_table_dna(): base_abc = DNAAlphabet() amino_abc = IUPACAminoAlphabet() table = CodonTable(base_abc, amino_abc) assert len(table.codons(b"P")) == 4 assert Codon.create(b"CCT", base_abc) in table.codons(b"P") assert Codon.create(b"CCC", base_abc) in table.codons(b"P") assert Codon.create(b"CCA", base_abc) in table.codons(b"P") assert Codon.create(b"CCG", base_abc) in table.codons(b"P") assert len(table.codons(b"W")) == 1 assert Codon.create(b"TGG", base_abc) in table.codons(b"W") assert table.amino_acid(Codon.create(b"ATG", base_abc)) == b"M" assert len(table.amino_acids) == 20 assert b"R" in table.amino_acids assert len(table.stop_codons) == 3 assert Codon.create(b"TAA", base_abc) in table.stop_codons assert Codon.create(b"TAG", base_abc) in table.stop_codons assert Codon.create(b"TGA", base_abc) in table.stop_codons assert len(set(table.codons())) == 64 def test_codon_table_dna_id33(): base_abc = DNAAlphabet() amino_abc = IUPACAminoAlphabet() gencode = GeneticCode(id=33) table = CodonTable(base_abc, amino_abc, gencode) assert len(table.codons(b"P")) == 4 assert Codon.create(b"CCT", base_abc) in table.codons(b"P") assert Codon.create(b"CCC", base_abc) in table.codons(b"P") assert Codon.create(b"CCA", base_abc) in table.codons(b"P") assert Codon.create(b"CCG", base_abc) in table.codons(b"P") assert len(table.codons(b"W")) == 2 assert Codon.create(b"TGG", base_abc) in table.codons(b"W") assert Codon.create(b"TGA", base_abc) in table.codons(b"W") assert len(table.codons(b"T")) == 4 assert Codon.create(b"ACT", base_abc) in table.codons(b"T") assert Codon.create(b"ACC", base_abc) in table.codons(b"T") assert Codon.create(b"ACA", base_abc) in table.codons(b"T") assert Codon.create(b"ACG", base_abc) in table.codons(b"T") assert table.amino_acid(Codon.create(b"ATG", base_abc)) == b"M" assert len(table.amino_acids) == 20 assert b"R" in table.amino_acids assert len(table.start_codons) == 4 assert Codon.create(b"TTG", base_abc) in table.start_codons assert Codon.create(b"CTG", base_abc) in table.start_codons assert Codon.create(b"ATG", base_abc) in table.start_codons assert Codon.create(b"GTG", base_abc) in table.start_codons assert len(table.stop_codons) == 1 assert Codon.create(b"TAG", base_abc) in table.stop_codons assert len(set(table.codons())) == 64 def test_codon_table_rna(): base_abc = RNAAlphabet() amino_abc = IUPACAminoAlphabet() table = CodonTable(base_abc, amino_abc) assert len(table.codons(b"P")) == 4 assert Codon.create(b"CCU", base_abc) in table.codons(b"P") assert Codon.create(b"CCC", base_abc) in table.codons(b"P") assert Codon.create(b"CCA", base_abc) in table.codons(b"P") assert Codon.create(b"CCG", base_abc) in table.codons(b"P") assert table.amino_acid(Codon.create(b"AUG", base_abc)) == b"M" assert len(table.amino_acids) == 20 assert b"R" in table.amino_acids assert len(table.start_codons) == 3 assert Codon.create(b"UUG", base_abc) in table.start_codons assert Codon.create(b"CUG", base_abc) in table.start_codons assert Codon.create(b"AUG", base_abc) in table.start_codons assert len(table.stop_codons) == 3 assert Codon.create(b"UAA", base_abc) in table.stop_codons assert Codon.create(b"UAG", base_abc) in table.stop_codons assert Codon.create(b"UGA", base_abc) in table.stop_codons assert len(set(table.codons())) == 64

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py

Python

pconsole/defaults.py

l3alr0g/Pconsole

657ead3e2060d74830c04aae33ce5498cf7b672f

[ "MIT" ]

4

2020-07-02T14:09:17.000Z

2021-11-29T20:13:49.000Z

pconsole/defaults.py

l3alr0g/Pconsole

657ead3e2060d74830c04aae33ce5498cf7b672f

[ "MIT" ]

null

pconsole/defaults.py

l3alr0g/Pconsole

657ead3e2060d74830c04aae33ce5498cf7b672f

[ "MIT" ]

null

# cmd blacklist __blacklist__ = { "help": "crashes the app (requires unsupported external user input)", "license": "crashes the app (requires unsupported external user input)" }

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py

Python

bff.py

jsabak/advanved_python_for_testers

fc55ca4d5d5ad7beebee489193e251a16c3e956b

[ "MIT" ]

1

2020-04-21T20:47:07.000Z

bff.py

mkusz/advanced_python_for_testers

fc55ca4d5d5ad7beebee489193e251a16c3e956b

[ "MIT" ]

null

bff.py

mkusz/advanced_python_for_testers

fc55ca4d5d5ad7beebee489193e251a16c3e956b

[ "MIT" ]

1

2019-11-27T21:32:51.000Z

(lambda _:[{1:lambda:[0for _['l']in[[i for(i,x)in enumerate(_['P'])if x=='[']]]and[0for _['i']in[_['i']+1]],2:lambda:[0for _['r']in[[i for(i,x)in enumerate(_['P']) if x==']'][::-1]]]and[0for _['i']in[_['i']+1]],3:lambda:_['c'].close(),'>':lambda:[0for _['p']in[_['p']+1]]and[0for _['i']in[_['i']+1]],'<':lambda:[0for _['p']in[_['p']-1]]and[0for _['i']in[_['i']+1]],'+':lambda:[0for _['m'][_['p']]in[(_['m'][_['p']]+1)]]and[0for _['i']in[_['i']+1]],'-':lambda:[0for _['m'][_['p']]in[(_['m'][_['p']]-1)]]and[0for _['i']in[_['i']+1]],'.':lambda:print(chr(_['m'][_['p']]),end='') or [0for _['i']in[_['i']+1]],',':lambda:[0for _['m'][_['p']]in[ord(input())]]and[0for _['i']in[_['i']+1]],'[':lambda:[0for _['i']in[_['i']+1 if _['m'][_['p']] else _['r'][_['l'].index(_['i'])]+1]],']':lambda:[0for _['i']in[_['l'][_['r'].index(_['i'])]]]}[_['P'][_['i']]]() for c in _['c'] if _['P'][_['i']]in[1,2,3,'>','<','+','-','.',',','[',']']])({'p':0,'m':[0]*100,'P':[1,2]+list('++++++++++[>+++++++>++++++++++>+++>+<<<<-]>++.>+.+++++++..+++.>++.<<+++++++++++++++.>.+++.------.--------.>+.>.')+[3],'i':0,'c':(c for c in range(10**8))})

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py

Python

navAids/migrations/0001_initial.py

trailbehind/NavigationAids

41112e007e3c67ee319de4453e2274606e810254

[ "MIT" ]

1

2016-03-26T03:46:04.000Z

navAids/migrations/0001_initial.py

trailbehind/NavigationAids

41112e007e3c67ee319de4453e2274606e810254

[ "MIT" ]

3

2016-03-25T20:12:59.000Z

2016-03-25T20:14:23.000Z

navAids/migrations/0001_initial.py

trailbehind/NavigationAids

41112e007e3c67ee319de4453e2274606e810254

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.2 on 2016-02-18 11:03 from __future__ import unicode_literals import django.contrib.gis.db.models.fields from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Beacon', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('color', models.CharField(blank=True, max_length=100, null=True)), ('date_modified', models.DateTimeField(auto_now=True)), ('elevation', models.FloatField(blank=True, null=True)), ('height', models.FloatField(blank=True, null=True)), ('location', django.contrib.gis.db.models.fields.PointField(srid=4326)), ('name', models.CharField(blank=True, max_length=255, null=True)), ('notes', models.CharField(blank=True, max_length=255, null=True)), ('scale_max', models.IntegerField(blank=True, null=True)), ('scale_min', models.IntegerField(blank=True, null=True)), ('status', models.IntegerField(default=1)), ('sub_type', models.CharField(blank=True, max_length=100, null=True)), ('special_category', models.CharField(blank=True, max_length=100, null=True)), ('vertical_length', models.IntegerField(blank=True, null=True)), ('shape', models.CharField(blank=True, max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Buoy', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('color', models.CharField(blank=True, max_length=100, null=True)), ('date_modified', models.DateTimeField(auto_now=True)), ('elevation', models.FloatField(blank=True, null=True)), ('height', models.FloatField(blank=True, null=True)), ('location', django.contrib.gis.db.models.fields.PointField(srid=4326)), ('name', models.CharField(blank=True, max_length=255, null=True)), ('notes', models.CharField(blank=True, max_length=255, null=True)), ('scale_max', models.IntegerField(blank=True, null=True)), ('scale_min', models.IntegerField(blank=True, null=True)), ('status', models.IntegerField(default=1)), ('sub_type', models.CharField(blank=True, max_length=100, null=True)), ('special_category', models.CharField(blank=True, max_length=100, null=True)), ('vertical_length', models.IntegerField(blank=True, null=True)), ('shape', models.CharField(blank=True, max_length=100, null=True)), ('color_patern', models.CharField(blank=True, max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='DayMarker', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('color', models.CharField(blank=True, max_length=100, null=True)), ('date_modified', models.DateTimeField(auto_now=True)), ('elevation', models.FloatField(blank=True, null=True)), ('height', models.FloatField(blank=True, null=True)), ('location', django.contrib.gis.db.models.fields.PointField(srid=4326)), ('name', models.CharField(blank=True, max_length=255, null=True)), ('notes', models.CharField(blank=True, max_length=255, null=True)), ('scale_max', models.IntegerField(blank=True, null=True)), ('scale_min', models.IntegerField(blank=True, null=True)), ('status', models.IntegerField(default=1)), ('sub_type', models.CharField(blank=True, max_length=100, null=True)), ('special_category', models.CharField(blank=True, max_length=100, null=True)), ('vertical_length', models.IntegerField(blank=True, null=True)), ('top_shape', models.IntegerField(blank=True, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Light', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('color', models.CharField(blank=True, max_length=100, null=True)), ('date_modified', models.DateTimeField(auto_now=True)), ('elevation', models.FloatField(blank=True, null=True)), ('height', models.FloatField(blank=True, null=True)), ('location', django.contrib.gis.db.models.fields.PointField(srid=4326)), ('name', models.CharField(blank=True, max_length=255, null=True)), ('notes', models.CharField(blank=True, max_length=255, null=True)), ('scale_max', models.IntegerField(blank=True, null=True)), ('scale_min', models.IntegerField(blank=True, null=True)), ('status', models.IntegerField(default=1)), ('sub_type', models.CharField(blank=True, max_length=100, null=True)), ('special_category', models.CharField(blank=True, max_length=100, null=True)), ('vertical_length', models.IntegerField(blank=True, null=True)), ('characteristic', models.IntegerField(blank=True, null=True)), ('exhibition_condition', models.IntegerField(blank=True, null=True)), ('nominal_range', models.IntegerField(blank=True, null=True)), ('orientation', models.FloatField(blank=True, null=True)), ('signal_group', models.CharField(blank=True, max_length=100, null=True)), ('signal_period', models.FloatField(blank=True, null=True)), ('signal_sequence', models.CharField(blank=True, max_length=255, null=True)), ('visibility', models.IntegerField(blank=True, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Mooring', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('color', models.CharField(blank=True, max_length=100, null=True)), ('date_modified', models.DateTimeField(auto_now=True)), ('elevation', models.FloatField(blank=True, null=True)), ('height', models.FloatField(blank=True, null=True)), ('location', django.contrib.gis.db.models.fields.PointField(srid=4326)), ('name', models.CharField(blank=True, max_length=255, null=True)), ('notes', models.CharField(blank=True, max_length=255, null=True)), ('scale_max', models.IntegerField(blank=True, null=True)), ('scale_min', models.IntegerField(blank=True, null=True)), ('status', models.IntegerField(default=1)), ('sub_type', models.CharField(blank=True, max_length=100, null=True)), ('special_category', models.CharField(blank=True, max_length=100, null=True)), ('vertical_length', models.IntegerField(blank=True, null=True)), ('water_level', models.IntegerField(blank=True, null=True)), ], options={ 'abstract': False, }, ), ]

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cac145b21d3432c32ec9f7b6daf8239833e7e5cd

155,572

py

Python

msgraph/cli/command_modules/groups/azext_groups/generated/_help.py

microsoftgraph/msgraph-cli-archived

489f70bf4ede1ce67b84bfb31e66da3e4db76062

[ "MIT" ]

null

msgraph/cli/command_modules/groups/azext_groups/generated/_help.py

microsoftgraph/msgraph-cli-archived

489f70bf4ede1ce67b84bfb31e66da3e4db76062

[ "MIT" ]

22

2022-03-29T22:54:37.000Z

2022-03-29T22:55:27.000Z

msgraph/cli/command_modules/groups/azext_groups/generated/_help.py

microsoftgraph/msgraph-cli-archived

489f70bf4ede1ce67b84bfb31e66da3e4db76062

[ "MIT" ]

null

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=line-too-long # pylint: disable=too-many-lines from knack.help_files import helps helps['groups'] = ''' type: group short-summary: Manage Groups ''' helps['groups group-lifecycle-policy-group-lifecycle-policy'] = """ type: group short-summary: Manage group lifecycle policy group lifecycle policy with groups """ helps['groups group-lifecycle-policy-group-lifecycle-policy create-group-lifecycle-policy'] = """ type: command short-summary: "Add new entity to groupLifecyclePolicies." """ helps['groups group-lifecycle-policy-group-lifecycle-policy delete-group-lifecycle-policy'] = """ type: command short-summary: "Delete entity from groupLifecyclePolicies." """ helps['groups group-lifecycle-policy-group-lifecycle-policy list-group-lifecycle-policy'] = """ type: command short-summary: "Get entities from groupLifecyclePolicies." """ helps['groups group-lifecycle-policy-group-lifecycle-policy show-group-lifecycle-policy'] = """ type: command short-summary: "Get entity from groupLifecyclePolicies by key." """ helps['groups group-lifecycle-policy-group-lifecycle-policy update-group-lifecycle-policy'] = """ type: command short-summary: "Update entity in groupLifecyclePolicies." """ helps['groups group-lifecycle-policy'] = """ type: group short-summary: Manage group lifecycle policy with groups """ helps['groups group-lifecycle-policy add-group'] = """ type: command short-summary: "Invoke action addGroup." """ helps['groups group-lifecycle-policy remove-group'] = """ type: command short-summary: "Invoke action removeGroup." """ helps['groups group-group'] = """ type: group short-summary: Manage group group with groups """ helps['groups group-group create-group'] = """ type: command short-summary: "Add new entity to groups." parameters: - name: --assigned-labels short-summary: "The list of sensitivity label pairs (label ID, label name) associated with a Microsoft 365 \ group. Returned only on $select." long-summary: | Usage: --assigned-labels display-name=XX label-id=XX display-name: The display name of the label. Read-only. label-id: The unique identifier of the label. Multiple actions can be specified by using more than one --assigned-labels argument. - name: --assigned-licenses short-summary: "The licenses that are assigned to the group. Returned only on $select. Read-only." long-summary: | Usage: --assigned-licenses disabled-plans=XX sku-id=XX disabled-plans: A collection of the unique identifiers for plans that have been disabled. sku-id: The unique identifier for the SKU. Multiple actions can be specified by using more than one --assigned-licenses argument. - name: --on-premises-provisioning-errors short-summary: "Errors when using Microsoft synchronization product during provisioning. Returned by default." long-summary: | Usage: --on-premises-provisioning-errors category=XX occurred-date-time=XX property-causing-error=XX \ value=XX category: Category of the provisioning error. Note: Currently, there is only one possible value. Possible \ value: PropertyConflict - indicates a property value is not unique. Other objects contain the same value for the \ property. occurred-date-time: The date and time at which the error occurred. property-causing-error: Name of the directory property causing the error. Current possible values: \ UserPrincipalName or ProxyAddress value: Value of the property causing the error. Multiple actions can be specified by using more than one --on-premises-provisioning-errors argument. - name: --app-role-assignments short-summary: "Represents the app roles a group has been granted for an application." long-summary: | Usage: --app-role-assignments app-role-id=XX created-date-time=XX principal-display-name=XX \ principal-id=XX principal-type=XX resource-display-name=XX resource-id=XX deleted-date-time=XX id=XX app-role-id: The identifier (id) for the app role which is assigned to the principal. This app role must \ be exposed in the appRoles property on the resource application's service principal (resourceId). If the resource \ application has not declared any app roles, a default app role ID of 00000000-0000-0000-0000-000000000000 can be \ specified to signal that the principal is assigned to the resource app without any specific app roles. Required on \ create. created-date-time: The time when the app role assignment was created.The Timestamp type represents date \ and time information using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 is \ 2014-01-01T00:00:00Z. Read-only. principal-display-name: The display name of the user, group, or service principal that was granted the app \ role assignment. Read-only. Supports $filter (eq and startswith). principal-id: The unique identifier (id) for the user, group or service principal being granted the app \ role. Required on create. principal-type: The type of the assigned principal. This can either be User, Group or ServicePrincipal. \ Read-only. resource-display-name: The display name of the resource app's service principal to which the assignment is \ made. resource-id: The unique identifier (id) for the resource service principal for which the assignment is \ made. Required on create. Supports $filter (eq only). id: Read-only. Multiple actions can be specified by using more than one --app-role-assignments argument. - name: --created-on-behalf-of short-summary: "Represents an Azure Active Directory object. The directoryObject type is the base type for \ many other directory entity types." long-summary: | Usage: --created-on-behalf-of deleted-date-time=XX id=XX id: Read-only. - name: --member-of short-summary: "Groups and administrative units that this group is a member of. HTTP Methods: GET (supported \ for all groups). Read-only. Nullable." long-summary: | Usage: --member-of deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --member-of argument. - name: --members short-summary: "Users, contacts, and groups that are members of this group. HTTP Methods: GET (supported for \ all groups), POST (supported for security groups and mail-enabled security groups), DELETE (supported only for \ security groups) Read-only. Nullable." long-summary: | Usage: --members deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --members argument. - name: --members-with-license-errors short-summary: "A list of group members with license errors from this group-based license assignment. \ Read-only." long-summary: | Usage: --members-with-license-errors deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --members-with-license-errors argument. - name: --owners short-summary: "The owners of the group. The owners are a set of non-admin users who are allowed to modify \ this object. HTTP Methods: GET (supported for all groups), POST (supported for security groups and mail-enabled \ security groups), DELETE (supported only for security groups) Read-only. Nullable." long-summary: | Usage: --owners deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --owners argument. - name: --permission-grants short-summary: "The permissions that have been granted for a group to a specific application." long-summary: | Usage: --permission-grants client-app-id=XX client-id=XX permission=XX permission-type=XX \ resource-app-id=XX deleted-date-time=XX id=XX client-app-id: ID of the service principal of the Azure AD app that has been granted access. Read-only. client-id: ID of the Azure AD app that has been granted access. Read-only. permission: The name of the resource-specific permission. Read-only. permission-type: The type of permission. Possible values are: Application, Delegated. Read-only. resource-app-id: ID of the Azure AD app that is hosting the resource. Read-only. id: Read-only. Multiple actions can be specified by using more than one --permission-grants argument. - name: --transitive-member-of long-summary: | Usage: --transitive-member-of deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --transitive-member-of argument. - name: --transitive-members long-summary: | Usage: --transitive-members deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --transitive-members argument. - name: --accepted-senders short-summary: "The list of users or groups that are allowed to create post's or calendar events in this \ group. If this list is non-empty then only users or groups listed here are allowed to post." long-summary: | Usage: --accepted-senders deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --accepted-senders argument. - name: --photo short-summary: "profilePhoto" long-summary: | Usage: --photo height=XX width=XX id=XX height: The height of the photo. Read-only. width: The width of the photo. Read-only. id: Read-only. - name: --photos short-summary: "The profile photos owned by the group. Read-only. Nullable." long-summary: | Usage: --photos height=XX width=XX id=XX height: The height of the photo. Read-only. width: The width of the photo. Read-only. id: Read-only. Multiple actions can be specified by using more than one --photos argument. - name: --rejected-senders short-summary: "The list of users or groups that are not allowed to create posts or calendar events in this \ group. Nullable" long-summary: | Usage: --rejected-senders deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --rejected-senders argument. - name: --extensions short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --group-lifecycle-policies short-summary: "The collection of lifecycle policies for this group. Read-only. Nullable." long-summary: | Usage: --group-lifecycle-policies alternate-notification-emails=XX group-lifetime-in-days=XX \ managed-group-types=XX id=XX alternate-notification-emails: List of email address to send notifications for groups without owners. \ Multiple email address can be defined by separating email address with a semicolon. group-lifetime-in-days: Number of days before a group expires and needs to be renewed. Once renewed, the \ group expiration is extended by the number of days defined. managed-group-types: The group type for which the expiration policy applies. Possible values are All, \ Selected or None. id: Read-only. Multiple actions can be specified by using more than one --group-lifecycle-policies argument. - name: --resources short-summary: "The image and other file resources in OneNote pages. Getting a resources collection is not \ supported, but you can get the binary content of a specific resource. Read-only. Nullable." long-summary: | Usage: --resources content=XX content-url=XX self=XX id=XX content: The content stream content-url: The URL for downloading the content self: The endpoint where you can get details about the page. Read-only. id: Read-only. Multiple actions can be specified by using more than one --resources argument. """ helps['groups group-group delete-group'] = """ type: command short-summary: "Represents an Azure Active Directory object. The directoryObject type is the base type for many \ other directory entity types." """ helps['groups group-group list-group'] = """ type: command short-summary: "Get entities from groups." """ helps['groups group-group show-group'] = """ type: command short-summary: "Represents an Azure Active Directory object. The directoryObject type is the base type for many \ other directory entity types." """ helps['groups group-group update-group'] = """ type: command short-summary: "Represents an Azure Active Directory object. The directoryObject type is the base type for many \ other directory entity types." parameters: - name: --assigned-labels short-summary: "The list of sensitivity label pairs (label ID, label name) associated with a Microsoft 365 \ group. Returned only on $select." long-summary: | Usage: --assigned-labels display-name=XX label-id=XX display-name: The display name of the label. Read-only. label-id: The unique identifier of the label. Multiple actions can be specified by using more than one --assigned-labels argument. - name: --assigned-licenses short-summary: "The licenses that are assigned to the group. Returned only on $select. Read-only." long-summary: | Usage: --assigned-licenses disabled-plans=XX sku-id=XX disabled-plans: A collection of the unique identifiers for plans that have been disabled. sku-id: The unique identifier for the SKU. Multiple actions can be specified by using more than one --assigned-licenses argument. - name: --on-premises-provisioning-errors short-summary: "Errors when using Microsoft synchronization product during provisioning. Returned by default." long-summary: | Usage: --on-premises-provisioning-errors category=XX occurred-date-time=XX property-causing-error=XX \ value=XX category: Category of the provisioning error. Note: Currently, there is only one possible value. Possible \ value: PropertyConflict - indicates a property value is not unique. Other objects contain the same value for the \ property. occurred-date-time: The date and time at which the error occurred. property-causing-error: Name of the directory property causing the error. Current possible values: \ UserPrincipalName or ProxyAddress value: Value of the property causing the error. Multiple actions can be specified by using more than one --on-premises-provisioning-errors argument. - name: --app-role-assignments short-summary: "Represents the app roles a group has been granted for an application." long-summary: | Usage: --app-role-assignments app-role-id=XX created-date-time=XX principal-display-name=XX \ principal-id=XX principal-type=XX resource-display-name=XX resource-id=XX deleted-date-time=XX id=XX app-role-id: The identifier (id) for the app role which is assigned to the principal. This app role must \ be exposed in the appRoles property on the resource application's service principal (resourceId). If the resource \ application has not declared any app roles, a default app role ID of 00000000-0000-0000-0000-000000000000 can be \ specified to signal that the principal is assigned to the resource app without any specific app roles. Required on \ create. created-date-time: The time when the app role assignment was created.The Timestamp type represents date \ and time information using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 is \ 2014-01-01T00:00:00Z. Read-only. principal-display-name: The display name of the user, group, or service principal that was granted the app \ role assignment. Read-only. Supports $filter (eq and startswith). principal-id: The unique identifier (id) for the user, group or service principal being granted the app \ role. Required on create. principal-type: The type of the assigned principal. This can either be User, Group or ServicePrincipal. \ Read-only. resource-display-name: The display name of the resource app's service principal to which the assignment is \ made. resource-id: The unique identifier (id) for the resource service principal for which the assignment is \ made. Required on create. Supports $filter (eq only). id: Read-only. Multiple actions can be specified by using more than one --app-role-assignments argument. - name: --created-on-behalf-of short-summary: "Represents an Azure Active Directory object. The directoryObject type is the base type for \ many other directory entity types." long-summary: | Usage: --created-on-behalf-of deleted-date-time=XX id=XX id: Read-only. - name: --member-of short-summary: "Groups and administrative units that this group is a member of. HTTP Methods: GET (supported \ for all groups). Read-only. Nullable." long-summary: | Usage: --member-of deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --member-of argument. - name: --members short-summary: "Users, contacts, and groups that are members of this group. HTTP Methods: GET (supported for \ all groups), POST (supported for security groups and mail-enabled security groups), DELETE (supported only for \ security groups) Read-only. Nullable." long-summary: | Usage: --members deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --members argument. - name: --members-with-license-errors short-summary: "A list of group members with license errors from this group-based license assignment. \ Read-only." long-summary: | Usage: --members-with-license-errors deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --members-with-license-errors argument. - name: --owners short-summary: "The owners of the group. The owners are a set of non-admin users who are allowed to modify \ this object. HTTP Methods: GET (supported for all groups), POST (supported for security groups and mail-enabled \ security groups), DELETE (supported only for security groups) Read-only. Nullable." long-summary: | Usage: --owners deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --owners argument. - name: --permission-grants short-summary: "The permissions that have been granted for a group to a specific application." long-summary: | Usage: --permission-grants client-app-id=XX client-id=XX permission=XX permission-type=XX \ resource-app-id=XX deleted-date-time=XX id=XX client-app-id: ID of the service principal of the Azure AD app that has been granted access. Read-only. client-id: ID of the Azure AD app that has been granted access. Read-only. permission: The name of the resource-specific permission. Read-only. permission-type: The type of permission. Possible values are: Application, Delegated. Read-only. resource-app-id: ID of the Azure AD app that is hosting the resource. Read-only. id: Read-only. Multiple actions can be specified by using more than one --permission-grants argument. - name: --transitive-member-of long-summary: | Usage: --transitive-member-of deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --transitive-member-of argument. - name: --transitive-members long-summary: | Usage: --transitive-members deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --transitive-members argument. - name: --accepted-senders short-summary: "The list of users or groups that are allowed to create post's or calendar events in this \ group. If this list is non-empty then only users or groups listed here are allowed to post." long-summary: | Usage: --accepted-senders deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --accepted-senders argument. - name: --photo short-summary: "profilePhoto" long-summary: | Usage: --photo height=XX width=XX id=XX height: The height of the photo. Read-only. width: The width of the photo. Read-only. id: Read-only. - name: --photos short-summary: "The profile photos owned by the group. Read-only. Nullable." long-summary: | Usage: --photos height=XX width=XX id=XX height: The height of the photo. Read-only. width: The width of the photo. Read-only. id: Read-only. Multiple actions can be specified by using more than one --photos argument. - name: --rejected-senders short-summary: "The list of users or groups that are not allowed to create posts or calendar events in this \ group. Nullable" long-summary: | Usage: --rejected-senders deleted-date-time=XX id=XX id: Read-only. Multiple actions can be specified by using more than one --rejected-senders argument. - name: --extensions short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --group-lifecycle-policies short-summary: "The collection of lifecycle policies for this group. Read-only. Nullable." long-summary: | Usage: --group-lifecycle-policies alternate-notification-emails=XX group-lifetime-in-days=XX \ managed-group-types=XX id=XX alternate-notification-emails: List of email address to send notifications for groups without owners. \ Multiple email address can be defined by separating email address with a semicolon. group-lifetime-in-days: Number of days before a group expires and needs to be renewed. Once renewed, the \ group expiration is extended by the number of days defined. managed-group-types: The group type for which the expiration policy applies. Possible values are All, \ Selected or None. id: Read-only. Multiple actions can be specified by using more than one --group-lifecycle-policies argument. - name: --resources short-summary: "The image and other file resources in OneNote pages. Getting a resources collection is not \ supported, but you can get the binary content of a specific resource. Read-only. Nullable." long-summary: | Usage: --resources content=XX content-url=XX self=XX id=XX content: The content stream content-url: The URL for downloading the content self: The endpoint where you can get details about the page. Read-only. id: Read-only. Multiple actions can be specified by using more than one --resources argument. """ helps['groups group'] = """ type: group short-summary: Manage group with groups """ helps['groups group add-favorite'] = """ type: command short-summary: "Invoke action addFavorite." """ helps['groups group assign-license'] = """ type: command short-summary: "Invoke action assignLicense." parameters: - name: --add-licenses long-summary: | Usage: --add-licenses disabled-plans=XX sku-id=XX disabled-plans: A collection of the unique identifiers for plans that have been disabled. sku-id: The unique identifier for the SKU. Multiple actions can be specified by using more than one --add-licenses argument. """ helps['groups group check-granted-permission-for-app'] = """ type: command short-summary: "Invoke action checkGrantedPermissionsForApp." """ helps['groups group check-member-group'] = """ type: command short-summary: "Invoke action checkMemberGroups." """ helps['groups group check-member-object'] = """ type: command short-summary: "Invoke action checkMemberObjects." """ helps['groups group create-conversation'] = """ type: command short-summary: "The group's conversations." """ helps['groups group create-extension'] = """ type: command short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." """ helps['groups group create-permission-grant'] = """ type: command short-summary: "The permissions that have been granted for a group to a specific application." """ helps['groups group create-photo'] = """ type: command short-summary: "The profile photos owned by the group. Read-only. Nullable." """ helps['groups group create-ref-accepted-sender'] = """ type: command short-summary: "The list of users or groups that are allowed to create post's or calendar events in this group. If \ this list is non-empty then only users or groups listed here are allowed to post." """ helps['groups group create-ref-member'] = """ type: command short-summary: "Users, contacts, and groups that are members of this group. HTTP Methods: GET (supported for all \ groups), POST (supported for security groups and mail-enabled security groups), DELETE (supported only for security \ groups) Read-only. Nullable." """ helps['groups group create-ref-member-of'] = """ type: command short-summary: "Groups and administrative units that this group is a member of. HTTP Methods: GET (supported for \ all groups). Read-only. Nullable." """ helps['groups group create-ref-member-with-license-error'] = """ type: command short-summary: "A list of group members with license errors from this group-based license assignment. Read-only." """ helps['groups group create-ref-owner'] = """ type: command short-summary: "The owners of the group. The owners are a set of non-admin users who are allowed to modify this \ object. HTTP Methods: GET (supported for all groups), POST (supported for security groups and mail-enabled security \ groups), DELETE (supported only for security groups) Read-only. Nullable." """ helps['groups group create-ref-rejected-sender'] = """ type: command short-summary: "The list of users or groups that are not allowed to create posts or calendar events in this group. \ Nullable." """ helps['groups group create-ref-transitive-member'] = """ type: command short-summary: "Create new navigation property ref to transitiveMembers for groups." """ helps['groups group create-ref-transitive-member-of'] = """ type: command short-summary: "Create new navigation property ref to transitiveMemberOf for groups." """ helps['groups group create-thread'] = """ type: command short-summary: "The group's conversation threads. Nullable." """ helps['groups group delete-conversation'] = """ type: command short-summary: "The group's conversations." """ helps['groups group delete-extension'] = """ type: command short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." """ helps['groups group delete-permission-grant'] = """ type: command short-summary: "The permissions that have been granted for a group to a specific application." """ helps['groups group delete-photo'] = """ type: command short-summary: "The profile photos owned by the group. Read-only. Nullable. And The group's profile photo." """ helps['groups group delete-ref-created-on-behalf-of'] = """ type: command short-summary: "The user (or application) that created the group. Note: This is not set if the user is an \ administrator. Read-only." """ helps['groups group delete-thread'] = """ type: command short-summary: "The group's conversation threads. Nullable." """ helps['groups group delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group get-available-extension-property'] = """ type: command short-summary: "Invoke action getAvailableExtensionProperties." """ helps['groups group get-by-id'] = """ type: command short-summary: "Invoke action getByIds." """ helps['groups group get-member-group'] = """ type: command short-summary: "Invoke action getMemberGroups." """ helps['groups group get-member-object'] = """ type: command short-summary: "Invoke action getMemberObjects." """ helps['groups group list-accepted-sender'] = """ type: command short-summary: "The list of users or groups that are allowed to create post's or calendar events in this group. If \ this list is non-empty then only users or groups listed here are allowed to post." """ helps['groups group list-conversation'] = """ type: command short-summary: "The group's conversations." """ helps['groups group list-extension'] = """ type: command short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." """ helps['groups group list-member'] = """ type: command short-summary: "Users, contacts, and groups that are members of this group. HTTP Methods: GET (supported for all \ groups), POST (supported for security groups and mail-enabled security groups), DELETE (supported only for security \ groups) Read-only. Nullable." """ helps['groups group list-member-of'] = """ type: command short-summary: "Groups and administrative units that this group is a member of. HTTP Methods: GET (supported for \ all groups). Read-only. Nullable." """ helps['groups group list-member-with-license-error'] = """ type: command short-summary: "A list of group members with license errors from this group-based license assignment. Read-only." """ helps['groups group list-owner'] = """ type: command short-summary: "The owners of the group. The owners are a set of non-admin users who are allowed to modify this \ object. HTTP Methods: GET (supported for all groups), POST (supported for security groups and mail-enabled security \ groups), DELETE (supported only for security groups) Read-only. Nullable." """ helps['groups group list-permission-grant'] = """ type: command short-summary: "The permissions that have been granted for a group to a specific application." """ helps['groups group list-photo'] = """ type: command short-summary: "The profile photos owned by the group. Read-only. Nullable." """ helps['groups group list-ref-accepted-sender'] = """ type: command short-summary: "The list of users or groups that are allowed to create post's or calendar events in this group. If \ this list is non-empty then only users or groups listed here are allowed to post." """ helps['groups group list-ref-member'] = """ type: command short-summary: "Users, contacts, and groups that are members of this group. HTTP Methods: GET (supported for all \ groups), POST (supported for security groups and mail-enabled security groups), DELETE (supported only for security \ groups) Read-only. Nullable." """ helps['groups group list-ref-member-of'] = """ type: command short-summary: "Groups and administrative units that this group is a member of. HTTP Methods: GET (supported for \ all groups). Read-only. Nullable." """ helps['groups group list-ref-member-with-license-error'] = """ type: command short-summary: "A list of group members with license errors from this group-based license assignment. Read-only." """ helps['groups group list-ref-owner'] = """ type: command short-summary: "The owners of the group. The owners are a set of non-admin users who are allowed to modify this \ object. HTTP Methods: GET (supported for all groups), POST (supported for security groups and mail-enabled security \ groups), DELETE (supported only for security groups) Read-only. Nullable." """ helps['groups group list-ref-rejected-sender'] = """ type: command short-summary: "The list of users or groups that are not allowed to create posts or calendar events in this group. \ Nullable." """ helps['groups group list-ref-transitive-member'] = """ type: command short-summary: "Get ref of transitiveMembers from groups." """ helps['groups group list-ref-transitive-member-of'] = """ type: command short-summary: "Get ref of transitiveMemberOf from groups." """ helps['groups group list-rejected-sender'] = """ type: command short-summary: "The list of users or groups that are not allowed to create posts or calendar events in this group. \ Nullable." """ helps['groups group list-thread'] = """ type: command short-summary: "The group's conversation threads. Nullable." """ helps['groups group list-transitive-member'] = """ type: command short-summary: "Get transitiveMembers from groups." """ helps['groups group list-transitive-member-of'] = """ type: command short-summary: "Get transitiveMemberOf from groups." """ helps['groups group remove-favorite'] = """ type: command short-summary: "Invoke action removeFavorite." """ helps['groups group renew'] = """ type: command short-summary: "Invoke action renew." """ helps['groups group reset-unseen-count'] = """ type: command short-summary: "Invoke action resetUnseenCount." """ helps['groups group restore'] = """ type: command short-summary: "Invoke action restore." """ helps['groups group set-photo-content'] = """ type: command short-summary: "Update media content for the navigation property photos in groups And The group's profile photo." """ helps['groups group set-ref-created-on-behalf-of'] = """ type: command short-summary: "The user (or application) that created the group. Note: This is not set if the user is an \ administrator. Read-only." """ helps['groups group show-conversation'] = """ type: command short-summary: "The group's conversations." """ helps['groups group show-created-on-behalf-of'] = """ type: command short-summary: "The user (or application) that created the group. Note: This is not set if the user is an \ administrator. Read-only." """ helps['groups group show-extension'] = """ type: command short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." """ helps['groups group show-permission-grant'] = """ type: command short-summary: "The permissions that have been granted for a group to a specific application." """ helps['groups group show-photo'] = """ type: command short-summary: "The profile photos owned by the group. Read-only. Nullable. And The group's profile photo." """ helps['groups group show-photo-content'] = """ type: command short-summary: "Get media content for the navigation property photos from groups And The group's profile photo." """ helps['groups group show-ref-created-on-behalf-of'] = """ type: command short-summary: "The user (or application) that created the group. Note: This is not set if the user is an \ administrator. Read-only." """ helps['groups group show-thread'] = """ type: command short-summary: "The group's conversation threads. Nullable." """ helps['groups group subscribe-by-mail'] = """ type: command short-summary: "Invoke action subscribeByMail." """ helps['groups group unsubscribe-by-mail'] = """ type: command short-summary: "Invoke action unsubscribeByMail." """ helps['groups group update-conversation'] = """ type: command short-summary: "The group's conversations." """ helps['groups group update-extension'] = """ type: command short-summary: "The collection of open extensions defined for the group. Read-only. Nullable." """ helps['groups group update-permission-grant'] = """ type: command short-summary: "The permissions that have been granted for a group to a specific application." """ helps['groups group update-photo'] = """ type: command short-summary: "The profile photos owned by the group. Read-only. Nullable. And The group's profile photo." """ helps['groups group update-thread'] = """ type: command short-summary: "The group's conversation threads. Nullable." """ helps['groups group validate-property'] = """ type: command short-summary: "Invoke action validateProperties." """ helps['groups group-calendar-calendar-view-attachment'] = """ type: group short-summary: Manage group calendar calendar view attachment with groups """ helps['groups group-calendar-calendar-view-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-calendar-calendar-view-calendar'] = """ type: group short-summary: Manage group calendar calendar view calendar with groups """ helps['groups group-calendar-calendar-view-calendar allowed-calendar-sharing-role'] = """ type: command short-summary: "Invoke function allowedCalendarSharingRoles." """ helps['groups group-calendar-calendar-view-calendar get-schedule'] = """ type: command short-summary: "Invoke action getSchedule." parameters: - name: --end-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --end-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --start-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view-instance'] = """ type: group short-summary: Manage group calendar calendar view instance with groups """ helps['groups group-calendar-calendar-view-instance accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-calendar-view-instance cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-calendar-view-instance decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view-instance delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-calendar-view-instance dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-calendar-view-instance forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-calendar-view-instance snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view-instance tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view'] = """ type: group short-summary: Manage group calendar calendar view with groups """ helps['groups group-calendar-calendar-view accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-calendar-view cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-calendar-view decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-calendar-view dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-calendar-view forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-calendar-view snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-calendar-view tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event-attachment'] = """ type: group short-summary: Manage group calendar event attachment with groups """ helps['groups group-calendar-event-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-calendar-event-calendar'] = """ type: group short-summary: Manage group calendar event calendar with groups """ helps['groups group-calendar-event-calendar allowed-calendar-sharing-role'] = """ type: command short-summary: "Invoke function allowedCalendarSharingRoles." """ helps['groups group-calendar-event-calendar get-schedule'] = """ type: command short-summary: "Invoke action getSchedule." parameters: - name: --end-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --end-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --start-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event-instance'] = """ type: group short-summary: Manage group calendar event instance with groups """ helps['groups group-calendar-event-instance accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-event-instance cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-event-instance decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event-instance delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-event-instance dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-event-instance forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-event-instance snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event-instance tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event'] = """ type: group short-summary: Manage group calendar event with groups """ helps['groups group-calendar-event accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-event cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-event decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-event dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-event forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-event snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-event tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar'] = """ type: group short-summary: Manage group calendar with groups """ helps['groups group-calendar allowed-calendar-sharing-role'] = """ type: command short-summary: "Invoke function allowedCalendarSharingRoles." """ helps['groups group-calendar get-schedule'] = """ type: command short-summary: "Invoke action getSchedule." parameters: - name: --end-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --end-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --start-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-attachment'] = """ type: group short-summary: Manage group calendar view attachment with groups """ helps['groups group-calendar-view-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-calendar-view-calendar-calendar-view'] = """ type: group short-summary: Manage group calendar view calendar calendar view with groups """ helps['groups group-calendar-view-calendar-calendar-view accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-view-calendar-calendar-view cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-view-calendar-calendar-view decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar-calendar-view delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-view-calendar-calendar-view dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-view-calendar-calendar-view forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-view-calendar-calendar-view snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar-calendar-view tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar-event'] = """ type: group short-summary: Manage group calendar view calendar event with groups """ helps['groups group-calendar-view-calendar-event accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-view-calendar-event cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-view-calendar-event decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar-event delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-view-calendar-event dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-view-calendar-event forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-view-calendar-event snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar-event tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-calendar'] = """ type: group short-summary: Manage group calendar view calendar with groups """ helps['groups group-calendar-view-calendar allowed-calendar-sharing-role'] = """ type: command short-summary: "Invoke function allowedCalendarSharingRoles." """ helps['groups group-calendar-view-calendar get-schedule'] = """ type: command short-summary: "Invoke action getSchedule." parameters: - name: --end-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --end-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --start-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-instance'] = """ type: group short-summary: Manage group calendar view instance with groups """ helps['groups group-calendar-view-instance accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-view-instance cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-view-instance decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-instance delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-view-instance dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-view-instance forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-view-instance snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view-instance tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view'] = """ type: group short-summary: Manage group calendar view with groups """ helps['groups group-calendar-view accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-calendar-view cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-calendar-view decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-calendar-view dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-calendar-view forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-calendar-view snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-calendar-view tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-conversation'] = """ type: group short-summary: Manage group conversation with groups """ helps['groups group-conversation create-thread'] = """ type: command short-summary: "A collection of all the conversation threads in the conversation. A navigation property. \ Read-only. Nullable." """ helps['groups group-conversation delete-thread'] = """ type: command short-summary: "A collection of all the conversation threads in the conversation. A navigation property. \ Read-only. Nullable." """ helps['groups group-conversation list-thread'] = """ type: command short-summary: "A collection of all the conversation threads in the conversation. A navigation property. \ Read-only. Nullable." """ helps['groups group-conversation show-thread'] = """ type: command short-summary: "A collection of all the conversation threads in the conversation. A navigation property. \ Read-only. Nullable." """ helps['groups group-conversation update-thread'] = """ type: command short-summary: "A collection of all the conversation threads in the conversation. A navigation property. \ Read-only. Nullable." """ helps['groups group-conversation-thread'] = """ type: group short-summary: Manage group conversation thread with groups """ helps['groups group-conversation-thread create-post'] = """ type: command short-summary: "Read-only. Nullable." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-conversation-thread delete-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-conversation-thread list-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-conversation-thread reply'] = """ type: command short-summary: "Invoke action reply." """ helps['groups group-conversation-thread show-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-conversation-thread update-post'] = """ type: command short-summary: "Read-only. Nullable." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-conversation-thread-post'] = """ type: group short-summary: Manage group conversation thread post with groups """ helps['groups group-conversation-thread-post create-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-conversation-thread-post create-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post create-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post create-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post delete-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-conversation-thread-post delete-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post delete-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." """ helps['groups group-conversation-thread-post delete-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post delete-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-conversation-thread-post list-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-conversation-thread-post list-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post list-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post list-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post reply'] = """ type: command short-summary: "Invoke action reply." """ helps['groups group-conversation-thread-post show-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-conversation-thread-post show-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post show-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." """ helps['groups group-conversation-thread-post show-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post show-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post update-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-conversation-thread-post update-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post update-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-conversation-thread-post update-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post update-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-conversation-thread-post-attachment'] = """ type: group short-summary: Manage group conversation thread post attachment with groups """ helps['groups group-conversation-thread-post-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-conversation-thread-post-in-reply-to'] = """ type: group short-summary: Manage group conversation thread post in reply to with groups """ helps['groups group-conversation-thread-post-in-reply-to forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-conversation-thread-post-in-reply-to reply'] = """ type: command short-summary: "Invoke action reply." """ helps['groups group-event-attachment'] = """ type: group short-summary: Manage group event attachment with groups """ helps['groups group-event-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-event-calendar-calendar-view'] = """ type: group short-summary: Manage group event calendar calendar view with groups """ helps['groups group-event-calendar-calendar-view accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-event-calendar-calendar-view cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-event-calendar-calendar-view decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar-calendar-view delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-event-calendar-calendar-view dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-event-calendar-calendar-view forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-event-calendar-calendar-view snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar-calendar-view tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar-event'] = """ type: group short-summary: Manage group event calendar event with groups """ helps['groups group-event-calendar-event accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-event-calendar-event cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-event-calendar-event decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar-event delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-event-calendar-event dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-event-calendar-event forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-event-calendar-event snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar-event tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-calendar'] = """ type: group short-summary: Manage group event calendar with groups """ helps['groups group-event-calendar allowed-calendar-sharing-role'] = """ type: command short-summary: "Invoke function allowedCalendarSharingRoles." """ helps['groups group-event-calendar get-schedule'] = """ type: command short-summary: "Invoke action getSchedule." parameters: - name: --end-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --end-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --start-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-instance'] = """ type: group short-summary: Manage group event instance with groups """ helps['groups group-event-instance accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-event-instance cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-event-instance decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-instance delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-event-instance dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-event-instance forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-event-instance snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event-instance tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event'] = """ type: group short-summary: Manage group event with groups """ helps['groups group-event accept'] = """ type: command short-summary: "Invoke action accept." """ helps['groups group-event cancel'] = """ type: command short-summary: "Invoke action cancel." """ helps['groups group-event decline'] = """ type: command short-summary: "Invoke action decline." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event delta'] = """ type: command short-summary: "Invoke function delta." """ helps['groups group-event dismiss-reminder'] = """ type: command short-summary: "Invoke action dismissReminder." """ helps['groups group-event forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-event snooze-reminder'] = """ type: command short-summary: "Invoke action snoozeReminder." parameters: - name: --new-reminder-time short-summary: "dateTimeTimeZone" long-summary: | Usage: --new-reminder-time date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-event tentatively-accept'] = """ type: command short-summary: "Invoke action tentativelyAccept." parameters: - name: --end short-summary: "dateTimeTimeZone" long-summary: | Usage: --end date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. - name: --start short-summary: "dateTimeTimeZone" long-summary: | Usage: --start date-time=XX time-zone=XX date-time: A single point of time in a combined date and time representation ({date}T{time}). For example, \ '2019-04-16T09:00:00'. time-zone: Represents a time zone, for example, 'Pacific Standard Time'. See below for possible values. """ helps['groups group-onenote-notebook'] = """ type: group short-summary: Manage group onenote notebook with groups """ helps['groups group-onenote-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook get-notebook-from-web-url'] = """ type: command short-summary: "Invoke action getNotebookFromWebUrl." """ helps['groups group-onenote-notebook show-recent-notebook'] = """ type: command short-summary: "Invoke function getRecentNotebooks." """ helps['groups group-onenote-notebook-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section group parent notebook with groups """ helps['groups group-onenote-notebook-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section-group-section'] = """ type: group short-summary: Manage group onenote notebook section group section with groups """ helps['groups group-onenote-notebook-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-notebook-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-notebook-section-group-section-page'] = """ type: group short-summary: Manage group onenote notebook section group section page with groups """ helps['groups group-onenote-notebook-section-group-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-notebook-section-group-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-notebook-section-group-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-notebook-section-group-section-page-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section group section page parent notebook with groups """ helps['groups group-onenote-notebook-section-group-section-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section-group-section-page-parent-section'] = """ type: group short-summary: Manage group onenote notebook section group section page parent section with groups """ helps['groups group-onenote-notebook-section-group-section-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-notebook-section-group-section-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-notebook-section-group-section-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section group section parent notebook with groups """ helps['groups group-onenote-notebook-section-group-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section'] = """ type: group short-summary: Manage group onenote notebook section with groups """ helps['groups group-onenote-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-notebook-section-page'] = """ type: group short-summary: Manage group onenote notebook section page with groups """ helps['groups group-onenote-notebook-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-notebook-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-notebook-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-notebook-section-page-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section page parent notebook with groups """ helps['groups group-onenote-notebook-section-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section-page-parent-section'] = """ type: group short-summary: Manage group onenote notebook section page parent section with groups """ helps['groups group-onenote-notebook-section-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-notebook-section-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-notebook-section-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section parent notebook with groups """ helps['groups group-onenote-notebook-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section-parent-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote notebook section parent section group parent notebook with groups """ helps['groups group-onenote-notebook-section-parent-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-notebook-section-parent-section-group-section'] = """ type: group short-summary: Manage group onenote notebook section parent section group section with groups """ helps['groups group-onenote-notebook-section-parent-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-notebook-section-parent-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page'] = """ type: group short-summary: Manage group onenote page with groups """ helps['groups group-onenote-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-page-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent notebook with groups """ helps['groups group-onenote-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-notebook-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent notebook section group parent notebook with groups """ helps['groups group-onenote-page-parent-notebook-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-notebook-section-group-section'] = """ type: group short-summary: Manage group onenote page parent notebook section group section with groups """ helps['groups group-onenote-page-parent-notebook-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-notebook-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-notebook-section-group-section-page'] = """ type: group short-summary: Manage group onenote page parent notebook section group section page with groups """ helps['groups group-onenote-page-parent-notebook-section-group-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-page-parent-notebook-section-group-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-page-parent-notebook-section-group-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-page-parent-notebook-section-group-section-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent notebook section group section parent notebook with groups """ helps['groups group-onenote-page-parent-notebook-section-group-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-notebook-section'] = """ type: group short-summary: Manage group onenote page parent notebook section with groups """ helps['groups group-onenote-page-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-notebook-section-page'] = """ type: group short-summary: Manage group onenote page parent notebook section page with groups """ helps['groups group-onenote-page-parent-notebook-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-page-parent-notebook-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-page-parent-notebook-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-page-parent-notebook-section-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent notebook section parent notebook with groups """ helps['groups group-onenote-page-parent-notebook-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-notebook-section-parent-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent notebook section parent section group parent notebook with groups """ helps['groups group-onenote-page-parent-notebook-section-parent-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-notebook-section-parent-section-group-section'] = """ type: group short-summary: Manage group onenote page parent notebook section parent section group section with groups """ helps['groups group-onenote-page-parent-notebook-section-parent-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-notebook-section-parent-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-section'] = """ type: group short-summary: Manage group onenote page parent section with groups """ helps['groups group-onenote-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-section-page'] = """ type: group short-summary: Manage group onenote page parent section page with groups """ helps['groups group-onenote-page-parent-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-page-parent-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-page-parent-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-page-parent-section-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent section parent notebook with groups """ helps['groups group-onenote-page-parent-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-section-parent-notebook-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent section parent notebook section group parent notebook with groups """ helps['groups group-onenote-page-parent-section-parent-notebook-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-section-parent-notebook-section-group-section'] = """ type: group short-summary: Manage group onenote page parent section parent notebook section group section with groups """ helps['groups group-onenote-page-parent-section-parent-notebook-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-section-parent-notebook-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-section-parent-notebook-section'] = """ type: group short-summary: Manage group onenote page parent section parent notebook section with groups """ helps['groups group-onenote-page-parent-section-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-section-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-section-parent-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote page parent section parent section group parent notebook with groups """ helps['groups group-onenote-page-parent-section-parent-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-page-parent-section-parent-section-group-parent-notebook-section'] = """ type: group short-summary: Manage group onenote page parent section parent section group parent notebook section with groups """ helps['groups group-onenote-page-parent-section-parent-section-group-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-section-parent-section-group-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-page-parent-section-parent-section-group-section'] = """ type: group short-summary: Manage group onenote page parent section parent section group section with groups """ helps['groups group-onenote-page-parent-section-parent-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-page-parent-section-parent-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote section group parent notebook with groups """ helps['groups group-onenote-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-group-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section group parent notebook section with groups """ helps['groups group-onenote-section-group-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-parent-notebook-section-page'] = """ type: group short-summary: Manage group onenote section group parent notebook section page with groups """ helps['groups group-onenote-section-group-parent-notebook-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-section-group-parent-notebook-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-section-group-parent-notebook-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-section-group-parent-notebook-section-page-parent-notebook'] = """ type: group short-summary: Manage group onenote section group parent notebook section page parent notebook with groups """ helps['groups group-onenote-section-group-parent-notebook-section-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-group-parent-notebook-section-page-parent-section'] = """ type: group short-summary: Manage group onenote section group parent notebook section page parent section with groups """ helps['groups group-onenote-section-group-parent-notebook-section-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-parent-notebook-section-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-parent-notebook-section-parent-notebook'] = """ type: group short-summary: Manage group onenote section group parent notebook section parent notebook with groups """ helps['groups group-onenote-section-group-parent-notebook-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-group-section'] = """ type: group short-summary: Manage group onenote section group section with groups """ helps['groups group-onenote-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-section-page'] = """ type: group short-summary: Manage group onenote section group section page with groups """ helps['groups group-onenote-section-group-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-section-group-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-section-group-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-section-group-section-page-parent-notebook'] = """ type: group short-summary: Manage group onenote section group section page parent notebook with groups """ helps['groups group-onenote-section-group-section-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-group-section-page-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section group section page parent notebook section with groups """ helps['groups group-onenote-section-group-section-page-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-section-page-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-section-page-parent-section'] = """ type: group short-summary: Manage group onenote section group section page parent section with groups """ helps['groups group-onenote-section-group-section-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-section-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-group-section-parent-notebook'] = """ type: group short-summary: Manage group onenote section group section parent notebook with groups """ helps['groups group-onenote-section-group-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-group-section-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section group section parent notebook section with groups """ helps['groups group-onenote-section-group-section-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-group-section-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section'] = """ type: group short-summary: Manage group onenote section with groups """ helps['groups group-onenote-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-page'] = """ type: group short-summary: Manage group onenote section page with groups """ helps['groups group-onenote-section-page copy-to-section'] = """ type: command short-summary: "Invoke action copyToSection." """ helps['groups group-onenote-section-page onenote-patch-content'] = """ type: command short-summary: "Invoke action onenotePatchContent." parameters: - name: --commands long-summary: | Usage: --commands action=XX content=XX position=XX target=XX content: A string of well-formed HTML to add to the page, and any image or file binary data. If the \ content contains binary data, the request must be sent using the multipart/form-data content type with a 'Commands' \ part. target: The element to update. Must be the #<data-id> or the generated {id} of the element, or the body or \ title keyword. Multiple actions can be specified by using more than one --commands argument. """ helps['groups group-onenote-section-page preview'] = """ type: command short-summary: "Invoke function preview." """ helps['groups group-onenote-section-page-parent-notebook'] = """ type: group short-summary: Manage group onenote section page parent notebook with groups """ helps['groups group-onenote-section-page-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-page-parent-notebook-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote section page parent notebook section group parent notebook with groups """ helps['groups group-onenote-section-page-parent-notebook-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-page-parent-notebook-section-group-section'] = """ type: group short-summary: Manage group onenote section page parent notebook section group section with groups """ helps['groups group-onenote-section-page-parent-notebook-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-page-parent-notebook-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-page-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section page parent notebook section with groups """ helps['groups group-onenote-section-page-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-page-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-page-parent-section'] = """ type: group short-summary: Manage group onenote section page parent section with groups """ helps['groups group-onenote-section-page-parent-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-page-parent-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-parent-notebook'] = """ type: group short-summary: Manage group onenote section parent notebook with groups """ helps['groups group-onenote-section-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-parent-notebook-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote section parent notebook section group parent notebook with groups """ helps['groups group-onenote-section-parent-notebook-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-parent-notebook-section-group-section'] = """ type: group short-summary: Manage group onenote section parent notebook section group section with groups """ helps['groups group-onenote-section-parent-notebook-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-parent-notebook-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section parent notebook section with groups """ helps['groups group-onenote-section-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-parent-section-group-parent-notebook'] = """ type: group short-summary: Manage group onenote section parent section group parent notebook with groups """ helps['groups group-onenote-section-parent-section-group-parent-notebook copy-notebook'] = """ type: command short-summary: "Invoke action copyNotebook." """ helps['groups group-onenote-section-parent-section-group-parent-notebook-section'] = """ type: group short-summary: Manage group onenote section parent section group parent notebook section with groups """ helps['groups group-onenote-section-parent-section-group-parent-notebook-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-parent-section-group-parent-notebook-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-onenote-section-parent-section-group-section'] = """ type: group short-summary: Manage group onenote section parent section group section with groups """ helps['groups group-onenote-section-parent-section-group-section copy-to-notebook'] = """ type: command short-summary: "Invoke action copyToNotebook." """ helps['groups group-onenote-section-parent-section-group-section copy-to-section-group'] = """ type: command short-summary: "Invoke action copyToSectionGroup." """ helps['groups group-thread'] = """ type: group short-summary: Manage group thread with groups """ helps['groups group-thread create-post'] = """ type: command short-summary: "Read-only. Nullable." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-thread delete-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-thread list-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-thread reply'] = """ type: command short-summary: "Invoke action reply." """ helps['groups group-thread show-post'] = """ type: command short-summary: "Read-only. Nullable." """ helps['groups group-thread update-post'] = """ type: command short-summary: "Read-only. Nullable." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-thread-post'] = """ type: group short-summary: Manage group thread post with groups """ helps['groups group-thread-post create-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-thread-post create-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-thread-post create-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post create-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post delete-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-thread-post delete-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-thread-post delete-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." """ helps['groups group-thread-post delete-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post delete-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-thread-post list-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-thread-post list-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-thread-post list-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post list-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post reply'] = """ type: command short-summary: "Invoke action reply." """ helps['groups group-thread-post show-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-thread-post show-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-thread-post show-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." """ helps['groups group-thread-post show-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post show-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post update-attachment'] = """ type: command short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." """ helps['groups group-thread-post update-extension'] = """ type: command short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." """ helps['groups group-thread-post update-in-reply-to'] = """ type: command short-summary: "The earlier post that this post is replying to in the conversationThread. Read-only." parameters: - name: --body short-summary: "itemBody" long-summary: | Usage: --body content=XX content-type=XX content: The content of the item. - name: --attachments short-summary: "The collection of fileAttachment, itemAttachment, and referenceAttachment attachments for the \ post. Read-only. Nullable." long-summary: | Usage: --attachments content-type=XX is-inline=XX last-modified-date-time=XX name=XX size=XX id=XX content-type: The MIME type. is-inline: true if the attachment is an inline attachment; otherwise, false. last-modified-date-time: The Timestamp type represents date and time information using ISO 8601 format and \ is always in UTC time. For example, midnight UTC on Jan 1, 2014 is 2014-01-01T00:00:00Z name: The display name of the attachment. This does not need to be the actual file name. size: The length of the attachment in bytes. id: Read-only. Multiple actions can be specified by using more than one --attachments argument. - name: --extensions short-summary: "The collection of open extensions defined for the post. Read-only. Nullable." long-summary: | Usage: --extensions id=XX id: Read-only. Multiple actions can be specified by using more than one --extensions argument. - name: --multi-value-extended-properties short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --multi-value-extended-properties value=XX id=XX value: A collection of property values. id: Read-only. Multiple actions can be specified by using more than one --multi-value-extended-properties argument. - name: --single-value-extended-properties short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." long-summary: | Usage: --single-value-extended-properties value=XX id=XX value: A property value. id: Read-only. Multiple actions can be specified by using more than one --single-value-extended-properties argument. - name: --email-address short-summary: "emailAddress" long-summary: | Usage: --email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. - name: --microsoft-graph-email-address short-summary: "emailAddress" long-summary: | Usage: --microsoft-graph-email-address address=XX name=XX address: The email address of an entity instance. name: The display name of an entity instance. """ helps['groups group-thread-post update-multi-value-extended-property'] = """ type: command short-summary: "The collection of multi-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post update-single-value-extended-property'] = """ type: command short-summary: "The collection of single-value extended properties defined for the post. Read-only. Nullable." """ helps['groups group-thread-post-attachment'] = """ type: group short-summary: Manage group thread post attachment with groups """ helps['groups group-thread-post-attachment create-upload-session'] = """ type: command short-summary: "Invoke action createUploadSession." parameters: - name: --attachment-item short-summary: "attachmentItem" long-summary: | Usage: --attachment-item attachment-type=XX content-type=XX is-inline=XX name=XX size=XX content-type: The nature of the data in the attachment. Optional. is-inline: true if the attachment is an inline attachment; otherwise, false. Optional. name: The display name of the attachment. This can be a descriptive string and does not have to be the \ actual file name. Required. size: The length of the attachment in bytes. Required. """ helps['groups group-thread-post-in-reply-to'] = """ type: group short-summary: Manage group thread post in reply to with groups """ helps['groups group-thread-post-in-reply-to forward'] = """ type: command short-summary: "Invoke action forward." """ helps['groups group-thread-post-in-reply-to reply'] = """ type: command short-summary: "Invoke action reply." """

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caff59809216f69e901e1c9b496f97282b97d267

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py

Python

leetcode/tree/437-path-sum-iii.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

leetcode/tree/437-path-sum-iii.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

leetcode/tree/437-path-sum-iii.py

phantomnat/python-learning

addc7ba5fc4fb8920cdd2891d4b2e79efd1a524a

[ "MIT" ]

null

# Definition for a binary tree node. class TreeNode: def __init__(self, x): self.val = x self.left = None self.right = None class Solution: def pathSum(self, root, sum): """ :type root: TreeNode :type sum: int :rtype: int """ self.result = 0 cache = {0:1} self.dfs(root, sum, cache, 0) return self.result def dfs(self, root, target, cache, curPath = 0): if root is None: return curPath += root.val oldPath = curPath - target self.result += cache.get(oldPath, 0) cache[curPath] += cache.get(curPath, 0) + 1 self.dfs(root.left, target, cache, curPath) self.dfs(root.right, target, cache, curPath) cache[curPath] -= 1 # def dfs(self, root_map, node, target, total=0, path=[], result=[]): # if node is None: # return # # print(' target {} = {} + {} {}'.format(target, total, node.val, path+[node.val])) # if target == total + node.val: # result.append(path+[node.val]) # # search with root # # if node.left is not None: # # if node.right is not None: # self.dfs(root_map, node.left, target, total + node.val, path+[node.val], result) # if node.left not in root_map: # root_map[node.left] = True # self.dfs(root_map, node.left, target, 0, [], result) # self.dfs(root_map, node.right, target, total + node.val, path+[node.val], result) # if node.right not in root_map: # root_map[node.right] = True # self.dfs(root_map, node.right, target, 0, [], result) # 1 # / \ # -2 -3 # /\ | # 1 3 -2 # / #-1 # [10, # 5,-3, # 3,2,None,11, # 3,-2,None,1] s = Solution() # root = TreeNode(10) # root.left = TreeNode(5) # root.right = TreeNode(-3) # root.left.left = TreeNode(3) # root.left.right = TreeNode(2) # root.right.left = None # root.right.right = TreeNode(11) # root.left.left.left = TreeNode(3) # root.left.left.right = TreeNode(-2) # # root.left.right.left = TreeNode(3) # root.left.right.right = TreeNode(1) # print(s.pathSum(root, 8)) # def build_tree(root, arr): # [1,-2,-3,1,3,-2,None,-1] # root = TreeNode(1) # root.left = TreeNode(-2) # root.right = TreeNode(-3) # root.left.left = TreeNode(1) # root.left.right = TreeNode(3) # root.right.left = TreeNode(-2) # root.right.right = None # root.left.left.left = TreeNode(-1) # print(s.pathSum(root, 3)) # def build_tree(arr): # root = TreeNode(0) # ptr = root # for i in range(1, len(arr)): # if i % 2 == 1: # ptr.left = TreeNode(0) # ptr = ptr.left # return root # root = TreeNode(0) # root.left = TreeNode(0) # root.left.left = TreeNode(0) # root.left.left.left = TreeNode(0) # root.left.left.left.left = TreeNode(0) # root.left.left.left.left.left = TreeNode(0) # q =[0,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0,None,0] # print(s.pathSum(build_tree(q), 0))

80.830645

6,996

0.664472

2,422

10,023

2.743187

0.024773

0.751054

0.901264

1.500602

0.858519

0.840458

0.815322

0.788682

0.774082

0

0.116812

0.086102

10,023

123

6,997

81.487805

0.608515

0.901926

0

1

0.136364

false

0

0.318182

0

null

1

0

1

0

1

0

null

0

11

1ba2eb582ba40524fbd3e7285c6700af9cf18990

10,661

py

Python

python/044_Wildcard_Matching.py

dvlpsh/leetcode-1

f965328af72113ac8a5a9d6624868c1502be937b

[ "MIT" ]

4,416

2016-03-30T15:02:26.000Z

2022-03-31T16:31:03.000Z

python/044_Wildcard_Matching.py

YinpuLi/leetcode-6

1371de2631d745efba39de41b51c3424e35da434

[ "MIT" ]

20

2018-11-17T13:46:25.000Z

2022-03-13T05:37:06.000Z

python/044_Wildcard_Matching.py

YinpuLi/leetcode-6

1371de2631d745efba39de41b51c3424e35da434

[ "MIT" ]

1,374

2017-05-26T15:44:30.000Z

2022-03-30T19:21:02.000Z

class Solution(object): # def isMatch(self, s, p): # """ # :type s: str # :type p: str # :rtype: bool # """ # return self.dfs(s, p, 0, 0) > 1 # # # def dfs(self, s, p, s_index, p_index): # if s_index == len(s) and p_index == len(p): # return 2 # if s_index == len(s) and p[p_index] != '*': # return 0 # if p_index == len(p): # return 1 # if p[p_index] == '*': # if p_index + 1 < len(p) and p[p_index + 1] == '*': # # skip duplicate * # return self.dfs(s, p, s_index, p_index + 1) # for i in range(len(s) - s_index + 1): # res = self.dfs(s, p, s_index + i, p_index + 1) # if res == 0 or res == 2: # return res # if p[p_index] == '?' or s[s_index] == p[p_index]: # return self.dfs(s, p, s_index + 1, p_index + 1) # return 1 # def isMatch(self, s, p): # #TODO # # O(m * n) LTE # m, n = len(s), len(p) # dp = [[False] * (n + 1) for _ in range(m + 1)] # dp[0][0] = True # for j in range(1, n): # dp[0][j + 1] = dp[0][j] and p[j] == '*' # for i in range(m): # for j in range(n): # if p[j] == '?' or p[j] == s[i]: # dp[i + 1][j + 1] = dp[i][j] # elif p[j] == '*': # dp[i + 1][j + 1] = dp[i + 1][j] or dp[i][j + 1] # return dp[m][n] def isMatch(self, s, p): """ :type s: str :type p: str :rtype: bool """ s_index, p_index = 0, 0 star, s_star = -1, 0 s_len, p_len = len(s), len(p) while s_index < s_len: if p_index < p_len and (s[s_index] == p[p_index] or p[p_index] == '?'): s_index += 1 p_index += 1 elif p_index < p_len and p[p_index] == '*': star = p_index s_star = s_index p_index += 1 elif star != -1: p_index = star + 1 s_star += 1 s_index = s_star else: return False while p_index < p_len and p[p_index] == '*': p_index += 1 return p_index == p_len if __name__ == '__main__': # begin s = Solution() print s.isMatch("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "*aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa*")

134.949367

4,119

0.857987

357

10,661

25.453782

0.134454

0.017167

0.006933

0.003962

0.042698

0.032904

0.02355

0.013426

0.009024

0

0.004295

0.104587

10,661

79

4,120

134.949367

0.947622

0.120345

0

0.115385

0

0.886752

0.885563

0

1

0

0.012658

0

null

0

null

0.038462

0

1

null

0

1

0

1

null

1

0

1

0

1

0

8

1bc1e2b77431cb6dfeb79e2acd8ab687c43e6f16

39

py

Python

simulation/vehicles/__init__.py

salinsiim/petssa-simulation

8f0f128d462831f86664bb8d246f2c7b659a0b8d

[ "MIT" ]

null

simulation/vehicles/__init__.py

salinsiim/petssa-simulation

8f0f128d462831f86664bb8d246f2c7b659a0b8d

[ "MIT" ]

null

simulation/vehicles/__init__.py

salinsiim/petssa-simulation

8f0f128d462831f86664bb8d246f2c7b659a0b8d

[ "MIT" ]

null

from vehicles.vehicles import generate

19.5

38

0.871795

5

39

6.8

0.8

0

0.102564

39

1

39

0.971429

0

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

59f40779285897b95c023fb98ecc8a4091b5be98

163

py

Python

myapi/admin.py

Wykett/DjangoTuto

4754af3515ef064714427f42ebf205b69e9c89c9

[ "MIT" ]

null

myapi/admin.py

Wykett/DjangoTuto

4754af3515ef064714427f42ebf205b69e9c89c9

[ "MIT" ]

null

myapi/admin.py

Wykett/DjangoTuto

4754af3515ef064714427f42ebf205b69e9c89c9

[ "MIT" ]

null

from django.contrib import admin from myapi.models import Hero from myapi.models import VillainModel admin.site.register(Hero) admin.site.register(VillainModel)

20.375

37

0.834356

23

163

5.913043

0.478261

0.132353

0.220588

0.308824

0

0.09816

163

7

38

23.285714

0.92517

0

1

0

true

0

0.6

0

0.6

0

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null

0

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1

0

null

0

1

0

1

0

1

0

7

94361aef302015a69521f2997a5a9705ef31893a

43,509

py

Python

python/entities.py

hunchly/hunchly-maltego

84200e0f661a854906b4e5d7634907f8f49c9a29

[ "Apache-2.0" ]

5

2021-09-11T08:21:58.000Z

2022-03-18T05:44:41.000Z

python/entities.py

hunchly/hunchly-maltego

84200e0f661a854906b4e5d7634907f8f49c9a29

[ "Apache-2.0" ]

3

2021-09-14T16:44:47.000Z

2021-12-20T20:30:23.000Z

python/entities.py

hunchly/hunchly-maltego

84200e0f661a854906b4e5d7634907f8f49c9a29

[ "Apache-2.0" ]

2

2022-01-05T12:06:13.000Z

2022-02-26T02:52:55.000Z

# Maltego Entities - Maltego Chlorine 3.6.0 # Parser written by: # Justin Seitz - justin@hunch.ly class Unknown(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Unknown" self.entity_attributes = {} self.entity_attribute_names = {} pass class Computer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Computer" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["device"] = "" self.entity_attribute_names["device"] = "Device" class DesktopComputer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.DesktopComputer" self.entity_attributes = {} self.entity_attribute_names = {} pass class Device(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Device" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["device"] = "" self.entity_attribute_names["device"] = "Device" class MobileComputer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MobileComputer" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["device"] = "" self.entity_attribute_names["device"] = "Device" class MobilePhone(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MobilePhone" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["device"] = "" self.entity_attribute_names["device"] = "Device" class Smartphone(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Smartphone" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["device"] = "" self.entity_attribute_names["device"] = "Device" class Conversation(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Conversation" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" self.entity_attributes["people"] = "" self.entity_attribute_names["people"] = "People" class ConversationEmail(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.ConversationEmail" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["email"] = "" self.entity_attribute_names["email"] = "Sender Email" self.entity_attributes["email.recipients"] = "" self.entity_attribute_names["email.recipients"] = "Recipient Emails" class ConversationPhone(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.ConversationPhone" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["phonenumber.caller"] = "" self.entity_attribute_names["phonenumber.caller"] = "Caller Number" self.entity_attributes["phonenumber.callee"] = "" self.entity_attribute_names["phonenumber.callee"] = "Callee Number" self.entity_attributes["starttime"] = "" self.entity_attribute_names["starttime"] = "Start time" self.entity_attributes["duration"] = "" self.entity_attribute_names["duration"] = "Duration" class Event(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Event" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" self.entity_attributes["starttime"] = "" self.entity_attribute_names["starttime"] = "Start Time" self.entity_attributes["stoptime"] = "" self.entity_attribute_names["stoptime"] = "Stop Time" class Incident(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Incident" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" class Meeting(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Meeting" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" self.entity_attributes["people"] = "" self.entity_attribute_names["people"] = "People" class MeetingBusiness(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MeetingBusiness" self.entity_attributes = {} self.entity_attribute_names = {} pass class MeetingSocial(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MeetingSocial" self.entity_attributes = {} self.entity_attribute_names = {} pass class Company(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Company" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class EducationInstitution(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.EducationInstitution" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class Gang(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Gang" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class OnlineGroup(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.OnlineGroup" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" self.entity_attributes["url"] = "" self.entity_attribute_names["url"] = "URL" class Organization(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Organization" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class PoliticalMovement(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PoliticalMovement" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class ReligiousGroup(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.ReligiousGroup" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Name" class AS(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.AS" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["as.number"] = "" self.entity_attribute_names["as.number"] = "AS Number" class DNSName(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.DNSName" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["fqdn"] = "" self.entity_attribute_names["fqdn"] = "DNS Name" class Domain(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Domain" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["fqdn"] = "" self.entity_attribute_names["fqdn"] = "Domain Name" self.entity_attributes["whois-info"] = "" self.entity_attribute_names["whois-info"] = "WHOIS Info" class IPv4Address(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.IPv4Address" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["ipv4-address"] = "" self.entity_attribute_names["ipv4-address"] = "IP Address" self.entity_attributes["ipaddress.internal"] = "" self.entity_attribute_names["ipaddress.internal"] = "Internal" class MXRecord(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MXRecord" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["fqdn"] = "" self.entity_attribute_names["fqdn"] = "MX Record" self.entity_attributes["mxrecord.priority"] = "" self.entity_attribute_names["mxrecord.priority"] = "Priority" class Netblock(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Netblock" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["ipv4-range"] = "" self.entity_attribute_names["ipv4-range"] = "IP Range" class NSRecord(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.NSRecord" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["fqdn"] = "" self.entity_attribute_names["fqdn"] = "NS Record" class URL(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.URL" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["short-title"] = "" self.entity_attribute_names["short-title"] = "Short title" self.entity_attributes["url"] = "" self.entity_attribute_names["url"] = "URL" self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" class Website(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Website" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["fqdn"] = "" self.entity_attribute_names["fqdn"] = "Website" self.entity_attributes["website.ssl-enabled"] = "" self.entity_attribute_names["website.ssl-enabled"] = "SSL Enabled" self.entity_attributes["ports"] = "" self.entity_attribute_names["ports"] = "Ports" class WebTitle(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.WebTitle" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" class Airport(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Airport" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class Church(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Church" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class CircularArea(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.CircularArea" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["area.circular"] = "" self.entity_attribute_names["area.circular"] = "Circular Area" self.entity_attributes["latitude"] = "" self.entity_attribute_names["latitude"] = "Latitude" self.entity_attributes["longitude"] = "" self.entity_attribute_names["longitude"] = "Longitude" self.entity_attributes["radius"] = "" self.entity_attribute_names["radius"] = "Radius (m)" class City(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.City" self.entity_attributes = {} self.entity_attribute_names = {} pass class Country(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Country" self.entity_attributes = {} self.entity_attribute_names = {} pass class CrimeScene(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.CrimeScene" self.entity_attributes = {} self.entity_attribute_names = {} pass class Harbor(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Harbor" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class Home(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Home" self.entity_attributes = {} self.entity_attribute_names = {} pass class Location(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Location" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" self.entity_attributes["country"] = "" self.entity_attribute_names["country"] = "Country" self.entity_attributes["city"] = "" self.entity_attribute_names["city"] = "City" self.entity_attributes["streetaddress"] = "" self.entity_attribute_names["streetaddress"] = "Street Address" self.entity_attributes["location.area"] = "" self.entity_attribute_names["location.area"] = "Area" self.entity_attributes["location.areacode"] = "" self.entity_attribute_names["location.areacode"] = "Area Code" self.entity_attributes["countrycode"] = "" self.entity_attribute_names["countrycode"] = "Country Code" self.entity_attributes["longitude"] = "" self.entity_attribute_names["longitude"] = "Longitude" self.entity_attributes["latitude"] = "" self.entity_attribute_names["latitude"] = "Latitude" class NominatimLocation(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.NominatimLocation" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["nominatimlocation"] = "" self.entity_attribute_names["nominatimlocation"] = "Nominatim Location" class Office(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Office" self.entity_attributes = {} self.entity_attribute_names = {} pass class Prison(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Prison" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class Region(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Region" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class Shop(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Shop" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class TrainStation(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.TrainStation" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class TransportHub(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.TransportHub" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["location.name"] = "" self.entity_attribute_names["location.name"] = "Name" class BadGuy(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.BadGuy" self.entity_attributes = {} self.entity_attribute_names = {} pass class BusinessLeader(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.BusinessLeader" self.entity_attributes = {} self.entity_attribute_names = {} pass class Businessman(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Businessman" self.entity_attributes = {} self.entity_attribute_names = {} pass class Child(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Child" self.entity_attributes = {} self.entity_attribute_names = {} pass class DrugDealer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.DrugDealer" self.entity_attributes = {} self.entity_attribute_names = {} pass class Female(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Female" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.firstnames"] = "" self.entity_attribute_names["person.firstnames"] = "First Names" class GangLeader(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.GangLeader" self.entity_attributes = {} self.entity_attribute_names = {} pass class GangMember(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.GangMember" self.entity_attributes = {} self.entity_attribute_names = {} pass class GoodGuy(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.GoodGuy" self.entity_attributes = {} self.entity_attribute_names = {} pass class GovernmentOfficial(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.GovernmentOfficial" self.entity_attributes = {} self.entity_attribute_names = {} pass class Judge(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Judge" self.entity_attributes = {} self.entity_attribute_names = {} pass class LawOfficer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.LawOfficer" self.entity_attributes = {} self.entity_attribute_names = {} pass class Lawyer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Lawyer" self.entity_attributes = {} self.entity_attribute_names = {} pass class Male(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Male" self.entity_attributes = {} self.entity_attribute_names = {} pass class MilitaryOfficer(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MilitaryOfficer" self.entity_attributes = {} self.entity_attribute_names = {} pass class SexOffender(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.SexOffender" self.entity_attributes = {} self.entity_attribute_names = {} pass class Terrorist(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Terrorist" self.entity_attributes = {} self.entity_attribute_names = {} pass class TerroristLeader(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.TerroristLeader" self.entity_attributes = {} self.entity_attribute_names = {} pass class Unsub(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Unsub" self.entity_attributes = {} self.entity_attribute_names = {} pass class Alias(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Alias" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["alias"] = "" self.entity_attribute_names["alias"] = "Alias" class Document(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Document" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["title"] = "" self.entity_attribute_names["title"] = "Title" self.entity_attributes["document.meta-data"] = "" self.entity_attribute_names["document.meta-data"] = "Meta-Data" self.entity_attributes["url"] = "" self.entity_attribute_names["url"] = "URL" class EmailAddress(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.EmailAddress" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["email"] = "" self.entity_attribute_names["email"] = "Email Address" class File(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.File" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["source"] = "" self.entity_attribute_names["source"] = "Source" self.entity_attributes["description"] = "" self.entity_attribute_names["description"] = "Description" class GPS(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.GPS" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["gps.coordinate"] = "" self.entity_attribute_names["gps.coordinate"] = "GPS Coordinate" self.entity_attributes["latitude"] = "" self.entity_attribute_names["latitude"] = "Latitude" self.entity_attributes["longitude"] = "" self.entity_attribute_names["longitude"] = "Longitude" class Image(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Image" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["description"] = "" self.entity_attribute_names["description"] = "Description" self.entity_attributes["url"] = "" self.entity_attribute_names["url"] = "URL" class Person(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Person" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.fullname"] = "" self.entity_attribute_names["person.fullname"] = "Full Name" self.entity_attributes["person.firstnames"] = "" self.entity_attribute_names["person.firstnames"] = "First Names" self.entity_attributes["person.lastname"] = "" self.entity_attribute_names["person.lastname"] = "Surname" class PhoneNumber(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PhoneNumber" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["phonenumber"] = "" self.entity_attribute_names["phonenumber"] = "Phone Number" self.entity_attributes["phonenumber.countrycode"] = "" self.entity_attribute_names["phonenumber.countrycode"] = "Country Code" self.entity_attributes["phonenumber.citycode"] = "" self.entity_attribute_names["phonenumber.citycode"] = "City Code" self.entity_attributes["phonenumber.areacode"] = "" self.entity_attribute_names["phonenumber.areacode"] = "Area Code" self.entity_attributes["phonenumber.lastnumbers"] = "" self.entity_attribute_names["phonenumber.lastnumbers"] = "Last Digits" class PhoneNumberMobile(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PhoneNumberMobile" self.entity_attributes = {} self.entity_attribute_names = {} pass class PhoneNumberOffice(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PhoneNumberOffice" self.entity_attributes = {} self.entity_attribute_names = {} pass class PhoneNumberResidential(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PhoneNumberResidential" self.entity_attributes = {} self.entity_attribute_names = {} pass class Phrase(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Phrase" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["text"] = "" self.entity_attribute_names["text"] = "Text" class Affiliation(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Affiliation" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.name"] = "" self.entity_attribute_names["person.name"] = "Name" self.entity_attributes["affiliation.network"] = "" self.entity_attribute_names["affiliation.network"] = "Network" self.entity_attributes["affiliation.uid"] = "" self.entity_attribute_names["affiliation.uid"] = "UID" self.entity_attributes["affiliation.profile-url"] = "" self.entity_attribute_names["affiliation.profile-url"] = "Profile URL" class Facebook(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.affiliation.Facebook" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.name"] = "" self.entity_attribute_names["person.name"] = "Name" self.entity_attributes["affiliation.network"] = "" self.entity_attribute_names["affiliation.network"] = "Network" self.entity_attributes["affiliation.uid"] = "" self.entity_attribute_names["affiliation.uid"] = "UID" self.entity_attributes["affiliation.profile-url"] = "" self.entity_attribute_names["affiliation.profile-url"] = "Profile URL" class LinkedIn(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.affiliation.LinkedIn" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.name"] = "" self.entity_attribute_names["person.name"] = "Name" self.entity_attributes["affiliation.network"] = "" self.entity_attribute_names["affiliation.network"] = "Network" class Twitter(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.affiliation.Twitter" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["person.name"] = "" self.entity_attribute_names["person.name"] = "Name" self.entity_attributes["affiliation.network"] = "" self.entity_attribute_names["affiliation.network"] = "Network" self.entity_attributes["affiliation.uid"] = "" self.entity_attribute_names["affiliation.uid"] = "UID" self.entity_attributes["affiliation.profile-url"] = "" self.entity_attribute_names["affiliation.profile-url"] = "Profile URL" self.entity_attributes["twitter.id"] = "" self.entity_attribute_names["twitter.id"] = "Twitter ID" self.entity_attributes["twitter.screen-name"] = "" self.entity_attribute_names["twitter.screen-name"] = "Screen Name" self.entity_attributes["twitter.friendcount"] = "" self.entity_attribute_names["twitter.friendcount"] = "Friend Count" self.entity_attributes["person.fullname"] = "" self.entity_attribute_names["person.fullname"] = "Real Name" class BankAccount(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.BankAccount" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["bank.accnumber"] = "" self.entity_attribute_names["bank.accnumber"] = "Account Number" self.entity_attributes["bank.name"] = "" self.entity_attribute_names["bank.name"] = "Bank" self.entity_attributes["bank.branch"] = "" self.entity_attribute_names["bank.branch"] = "Branch Code" class FlightNumber(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.FlightNumber" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["flight.id"] = "" self.entity_attribute_names["flight.id"] = "Flight ID" self.entity_attributes["flight.number"] = "" self.entity_attribute_names["flight.number"] = "Flight Number" self.entity_attributes["flight.airline"] = "" self.entity_attribute_names["flight.airline"] = "Airline" self.entity_attributes["flight.date"] = "" self.entity_attribute_names["flight.date"] = "Date" class IdentificationNumber(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.IdentificationNumber" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["identification.number"] = "" self.entity_attribute_names["identification.number"] = "Number" class MacAddress(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.MacAddress" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["macaddress"] = "" self.entity_attribute_names["macaddress"] = "MAC Address" class PassportNumber(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.PassportNumber" self.entity_attributes = {} self.entity_attribute_names = {} pass class VehicleRegistration(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.VehicleRegistration" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["vehicle.registration"] = "" self.entity_attribute_names["vehicle.registration"] = "Registration Number" class VinNumber(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.VinNumber" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["vinnumber"] = "" self.entity_attribute_names["vinnumber"] = "VIN Number" class Bike(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Bike" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Boat(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Boat" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Bus(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Bus" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Car(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Car" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Plane(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Plane" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Train(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Train" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" class Transport(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Transport" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["transport.name"] = "" self.entity_attribute_names["transport.name"] = "Name" self.entity_attributes["transport.make"] = "" self.entity_attribute_names["transport.make"] = "Make" self.entity_attributes["transport.model"] = "" self.entity_attribute_names["transport.model"] = "Model" class Ammunition(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Ammunition" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class BioWeapon(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.BioWeapon" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class Blade(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Blade" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class ChemicalWeapon(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.ChemicalWeapon" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class Explosive(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Explosive" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class Gun(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Gun" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class IED(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.IED" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class Missile(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Missile" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class NuclearWeapon(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.NuclearWeapon" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class Weapon(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.Weapon" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class WMD(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "maltego.WMD" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["weapon.type"] = "" self.entity_attribute_names["weapon.type"] = "Type" class HunchlyCase(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "hunchly.HunchlyCase" self.entity_attributes = {} self.entity_attribute_names = {} self.entity_attributes["properties.hunchlycase"] = "" self.entity_attributes["case_id"] = "" self.entity_attribute_names['properties.hunchlycase'] = "Hunchly Case" self.entity_attribute_names['case_id'] = "Case ID" class HunchlyPage(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "hunchly.HunchlyPage" self.entity_attributes = {"properties.hunchlypage": entity_value, "page_id": "", "url": "", "title": "", "short-title": ""} self.entity_attribute_names = {"properties.hunchlypage": "Hunchly Page", "page_id": "Page ID", "url": "URL", "title": "Title", "short-title": "Short Title"} class HunchlyPhoto(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "hunchly.HunchlyPhoto" self.entity_attributes = {"properties.hunchlyphoto": entity_value, "url": "", "hash": "", "local_file": ""} self.entity_attribute_names = {"properties.hunchlyphoto": "Hunchly Page", "url": "URL", "hash": "SHA-256", "local_file": "Local File"} class HunchlySelector(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "hunchly.HunchlySelector" self.entity_attributes = {"properties.hunchlyselector": entity_value} self.entity_attribute_names = {"properties.hunchlyselector": "Hunchly Selector"} class HunchlyData(object): def __init__(self, entity_value): self.entity_value = entity_value self.entity_type = "hunchly.HunchlyData" self.entity_attributes = {} self.entity_attribute_names = {} def convert_entity(transform, entity_obj): new_entity = transform.addEntity(entity_obj.entity_type, entity_obj.entity_value) for i in entity_obj.entity_attributes: new_entity.addAdditionalFields(i, entity_obj.entity_attribute_names[i], False, entity_obj.entity_attributes[i]) new_entity.setValue(entity_obj.entity_value) return new_entity

35.201456

119

0.658301

4,596

43,509

5.864665

0.053525

0.312755

0.187727

0.224382

0.80693

0.760555

0.752987

0.751354

0.690398

0

0.000356

0.225723

43,509

1,235

120

35.22996

0.799751

0.002092

0

0.64257

0

0.148247

0.025268

0

1

0.114458

false

0.033133

0

0.228916

0

null

1

0

null

0

9

944bbc79af47cc697ae91444915e66129148a4aa

90

py

Python

awsrun/aws/__init__.py

veb61/eec-289-ucd

fabd9c01c5e9dfaf869fe22e537fe08aafd4e622

[ "MIT" ]

null

awsrun/aws/__init__.py

veb61/eec-289-ucd

fabd9c01c5e9dfaf869fe22e537fe08aafd4e622

[ "MIT" ]

null

awsrun/aws/__init__.py

veb61/eec-289-ucd

fabd9c01c5e9dfaf869fe22e537fe08aafd4e622

[ "MIT" ]

1

2021-10-07T23:10:33.000Z

from .aws_iam import * from .aws_s3 import * from .aws_sns import * from .aws_sqs import *

22.5

22

0.744444

16

90

3.9375

0.4375

0.444444

0.619048

0

0.013333

0.166667

90

4

23

22.5

0.826667

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

94692f5536b158f788101a49a0ae005b2e94dc59

7,054

py

Python

tests/unit/test_console_helpers.py

friendly-traceback/friendly-traceback

4f6785f14c271a4d6412ef19c140f9d380cdbcbf

[ "MIT" ]

45

2021-07-06T03:30:20.000Z

2022-03-16T17:30:58.000Z

tests/unit/test_console_helpers.py

friendly-traceback/friendly-traceback

4f6785f14c271a4d6412ef19c140f9d380cdbcbf

[ "MIT" ]

110

2021-06-28T11:48:46.000Z

2022-03-25T20:41:25.000Z

tests/unit/test_console_helpers.py

friendly-traceback/friendly-traceback

4f6785f14c271a4d6412ef19c140f9d380cdbcbf

[ "MIT" ]

4

2021-07-05T20:56:39.000Z

2021-11-11T20:24:34.000Z

import math import friendly_traceback from friendly_traceback import console_helpers as helpers # ====Important: ensure that we have a clean history after each test. def empty_history(): friendly_traceback.set_stream(redirect="capture") nothing = "Nothing to show: no exception recorded." helpers.history() return nothing in friendly_traceback.get_output() _hint = "Did you mean `pi`?" _message = "AttributeError: module" _what = "An `AttributeError` occurs" _where = "Exception raised on line" _why = "Perhaps you meant to write" def test_back(): while not empty_history(): helpers.back() nothing_back = "Nothing to go back to: no exception recorded." helpers.back() assert nothing_back in friendly_traceback.get_output() try: a except NameError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.back() assert nothing_back not in friendly_traceback.get_output() helpers.back() assert nothing_back in friendly_traceback.get_output() assert empty_history() def test_friendly_tb(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.friendly_tb() result = friendly_traceback.get_output() assert _hint in result assert _message in result assert "File" in result helpers.back() assert empty_history() def test_hint(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.hint() result = friendly_traceback.get_output() assert _hint in result assert _message not in result assert "File" not in result helpers.back() assert empty_history() def test_history(): while not empty_history(): helpers.back() try: a except NameError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.history() assert "NameError" in friendly_traceback.get_output() helpers.back() helpers.history() assert empty_history() def test_python_tb(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.python_tb() result = friendly_traceback.get_output() assert "Did you mean `pi`" not in result assert "AttributeError" in result assert "File" in result helpers.back() assert empty_history() def test_what(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.what() result = friendly_traceback.get_output() assert _hint not in result assert _message not in result assert "File" not in result assert _what in result assert _where not in result assert _why not in result helpers.back() assert empty_history() def test_what_name(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.what('NameError') result = friendly_traceback.get_output() assert _hint not in result assert _message not in result assert "File" not in result assert _what not in result assert _where not in result assert _why not in result assert "NameError" in result helpers.back() assert empty_history() def test_what_type(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.what(LookupError) result = friendly_traceback.get_output() assert _hint not in result assert _message not in result assert "File" not in result assert _what not in result assert _where not in result assert _why not in result assert "LookupError" in result helpers.back() assert empty_history() def test_where(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.where() result = friendly_traceback.get_output() assert _hint not in result assert _message not in result assert "File" not in result assert _what not in result assert _where in result assert _why not in result helpers.back() assert empty_history() def test_why(): while not empty_history(): helpers.back() try: math.Pi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.why() result = friendly_traceback.get_output() assert _hint not in result assert _message not in result assert "File" not in result assert _what not in result assert _where not in result assert _why in result helpers.back() assert empty_history() # The following are processed in base_formatters.py def test_why_no_hint(): while not empty_history(): helpers.back() try: math.PiPiPi except AttributeError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.why() result = friendly_traceback.get_output() assert "Python tells us" in result helpers.hint() result = friendly_traceback.get_output() assert "I have no suggestion to offer; try `why()`." in result helpers.back() assert empty_history() def test_no_why(): while not empty_history(): helpers.back() try: raise ArithmeticError("unknown") except ArithmeticError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() helpers.why() result = friendly_traceback.get_output() assert "I have no suggestion to offer." in result helpers.hint() new_result = friendly_traceback.get_output() assert "I have no suggestion to offer." in new_result helpers.back() assert empty_history() def test_no_why_no_message(): while not empty_history(): helpers.back() try: raise ArithmeticError # no message except ArithmeticError: friendly_traceback.explain_traceback(redirect="capture") friendly_traceback.get_output() why = helpers.why() what = helpers.what() assert why == what helpers.back() assert empty_history()

27.447471

70

0.68812

849

7,054

5.500589

0.102474

0.167452

0.098929

0.167024

0.832548

0.809636

0.802998

0.778587

0.766167

0.713276

0

0.232776

7,054

257

71

27.447471

0.862897

0.018146

0

0.754464

0

0.075701

0

0.28125

1

0.0625

false

0

0.013393

0

0.080357

0

null

0

1

0

1

0

null

0

8

ca7c27e2e421b35e5220387c03bf5bcf0f60763f

265

py

Python

entity/cards/LETL_028H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

102

2021-10-20T09:06:39.000Z

2022-03-28T13:35:11.000Z

entity/cards/LETL_028H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

98

2021-10-19T16:13:27.000Z

2022-03-27T13:27:49.000Z

entity/cards/LETL_028H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

55

2021-10-19T03:56:50.000Z

2022-03-25T08:25:26.000Z

# -*- coding: utf-8 -*- import entity.cards.LETL_028H.LETL_005P3 import entity.cards.LETL_028H.LETL_028P9 import entity.cards.LETL_028H.LETL_028P11 import entity.cards.LETL_028H.LETL_446 import entity.cards.LETL_028H.LETL_447 import entity.cards.LETL_028H.LETL_448

33.125

41

0.833962

45

265

4.644444

0.311111

0.344498

0.488038

0.602871

0.832536

0

0.165323

0.064151

265

7

42

37.857143

0.677419

0.079245

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

ca86da827cc8b9743a706e2f8e1d49c1ed0c1138

37

py

Python

miniconda3-lnx/pkgs/idna-2.9-py_1/info/test/run_test.py

Thibaut-Kovaltchouk/MultiPyzo

a15ecf77e31ebeb195e70385f5ac132f6ab4504d

[ "CC0-1.0" ]

null

miniconda3-lnx/pkgs/idna-2.9-py_1/info/test/run_test.py

Thibaut-Kovaltchouk/MultiPyzo

a15ecf77e31ebeb195e70385f5ac132f6ab4504d

[ "CC0-1.0" ]

1

2019-04-02T23:35:13.000Z

miniconda3-lnx/pkgs/idna-2.9-py_1/info/test/run_test.py

Thibaut-Kovaltchouk/MultiPyzo

a15ecf77e31ebeb195e70385f5ac132f6ab4504d

[ "CC0-1.0" ]

null

print("import: 'idna'") import idna

9.25

23

0.675676

5

37

5

0.6

0.8

0

0.135135

37

3

24

12.333333

0.78125

0

0.388889

0

1

0

true

0

1

0

1

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

0471572513b259792e3988b4d48d10bef15e3b42

128

py

Python

app/main/views.py

Mugambi645/StreetDine

036b8cd592945a18ec340b847079db7f5ebf5901

[ "MIT" ]

null

app/main/views.py

Mugambi645/StreetDine

036b8cd592945a18ec340b847079db7f5ebf5901

[ "MIT" ]

null

app/main/views.py

Mugambi645/StreetDine

036b8cd592945a18ec340b847079db7f5ebf5901

[ "MIT" ]

null

from . import main from flask import render_template @main.route("/") def index(): return render_template("main/index.html")

25.6

45

0.742188

18

128

5.166667

0.611111

0.301075

0.387097

0

0.125

128

5

45

25.6

0.830357

0

0.124031

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

049bdeff55bf3a864b7bb4a1456a176cdae0fc6c

5,192

py

Python

torchlinop/base.py

jon-dong/torchlinop

8af9db02e3b96005d75eb77460c2a1977c320bb8

[ "MIT" ]

null

torchlinop/base.py

jon-dong/torchlinop

8af9db02e3b96005d75eb77460c2a1977c320bb8

[ "MIT" ]

null

torchlinop/base.py

jon-dong/torchlinop

8af9db02e3b96005d75eb77460c2a1977c320bb8

[ "MIT" ]

null

class BaseLinOp: def __init__(self): pass @property def T(self): return Adjoint(self) def __add__(self, other): if isinstance(other, BaseLinOp): return Sum(self, other) else: return ScalarSum(self, other) def __radd__(self, other): if isinstance(other, BaseLinOp): return Sum(self, other) else: return ScalarSum(self, other) def __sub__(self, other): if isinstance(other, BaseLinOp): return Diff(self, other) else: return ScalarDiff(self, other) def __rsub__(self, other): if isinstance(other, BaseLinOp): return Diff(self, other) else: return ScalarDiff(self, other) def __mul__(self, other): if isinstance(other, BaseLinOp): raise NameError('The multiplication operator can only be performed between a LinOp object and a scalar or vector.') else: return ScalarMul(self, other) def __rmul__(self, other): if isinstance(other, BaseLinOp): raise NameError('The multiplication operator can only be performed between a LinOp object and a scalar or vector.') else: return ScalarMul(self, other) def __matmul__(self, other): if isinstance(other, BaseLinOp): return Composition(self, other) else: raise NameError('The matrix multiplication operator can only be performed between two LinOp objects.') class Composition(BaseLinOp): def __init__(self, LinOp1, LinOp2): if LinOp2.out_size != LinOp1.in_size and LinOp2.out_size != -1 and LinOp1.in_size != -1: raise NameError('The dimensions of the LinOp composition do not match.') self.LinOp1 = LinOp1 self.LinOp2 = LinOp2 self.in_size = LinOp2.in_size if LinOp2.in_size != -1 else LinOp1.in_size self.out_size = LinOp1.out_size if LinOp1.out_size != -1 else LinOp2.out_size def apply(self, x): return self.LinOp1.apply(self.LinOp2.apply(x)) def applyAdjoint(self, x): return self.LinOp2.applyAdjoint(self.LinOp1.applyAdjoint(x)) class Sum(BaseLinOp): def __init__(self, LinOp1, LinOp2): if LinOp2.in_size != LinOp1.in_size and LinOp2.in_size != -1 and LinOp1.in_size != -1: raise NameError('The input dimensions of the LinOp sum do not match.') if LinOp2.out_size != LinOp1.out_size and LinOp2.out_size != -1 and LinOp1.out_size != -1: raise NameError('The output dimensions of the LinOp sum do not match.') self.LinOp1 = LinOp1 self.LinOp2 = LinOp2 self.in_size = max(LinOp1.in_size, LinOp2.in_size) # it is -1 if size is undefined self.out_size = max(LinOp1.out_size, LinOp2.out_size) def apply(self, x): return self.LinOp1.apply(x) + self.LinOp2.apply(x) def applyAdjoint(self, x): return self.LinOp2.applyAdjoint(x) + self.LinOp1.applyAdjoint(x) class ScalarSum(BaseLinOp): def __init__(self, LinOp, other): self.LinOp = LinOp self.other = other self.in_size = LinOp.in_size self.out_size = LinOp.out_size def apply(self, x): return self.LinOp.apply(x) + self.other def applyAdjoint(self, x): return self.LinOp.applyAdjoint(x) class Diff(BaseLinOp): def __init__(self, LinOp1, LinOp2): if LinOp2.in_size != LinOp1.in_size and LinOp2.in_size != -1 and LinOp1.in_size != -1: raise NameError('The input dimensions of the LinOp sum do not match.') if LinOp2.out_size != LinOp1.out_size and LinOp2.out_size != -1 and LinOp1.out_size != -1: raise NameError('The output dimensions of the LinOp sum do not match.') self.LinOp1 = LinOp1 self.LinOp2 = LinOp2 self.in_size = max(LinOp1.in_size, LinOp2.in_size) self.out_size = max(LinOp1.out_size, LinOp2.out_size) def apply(self, x): return self.LinOp1.apply(x) - self.LinOp2.apply(x) def applyAdjoint(self, x): return self.LinOp1.applyAdjoint(x) - self.LinOp2.applyAdjoint(x) class ScalarDiff(BaseLinOp): def __init__(self, LinOp, other): self.LinOp = LinOp self.other = other self.in_size = LinOp.in_size self.out_size = LinOp.out_size def apply(self, x): return self.LinOp.apply(x) - self.other def applyAdjoint(self, x): return self.LinOp.applyAdjoint(x) class ScalarMul(BaseLinOp): def __init__(self, LinOp, other): self.LinOp = LinOp self.other = other self.in_size = LinOp.in_size self.out_size = LinOp.out_size def apply(self, x): return self.other * self.LinOp.apply(x) def applyAdjoint(self, x): return self.other * self.LinOp.applyAdjoint(x) class Adjoint(BaseLinOp): def __init__(self, LinOp): self.LinOp = LinOp self.in_size = LinOp.out_size self.out_size = LinOp.in_size def apply(self, x): return self.LinOp.applyAdjoint(x) def applyAdjoint(self, x): return self.LinOp.apply(x)

34.157895

127

0.632512

689

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0.097242

0.061869

0.048611

0.066288

0.878157

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0.820707

0.767045

0.727904

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0.020295

0.26926

5,192

151

128

34.384106

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null

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1

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1

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9

04f2fa7801263cc5564e10841ee145d5448b263b

1,254

py

Python

Iris>APIClient/JSONStringInterpreter.py

rrbutani/Iris

e3a61530a763387e99fd3107a90fdb3d160f6ead

[ "Unlicense" ]

null

Iris>APIClient/JSONStringInterpreter.py

rrbutani/Iris

e3a61530a763387e99fd3107a90fdb3d160f6ead

[ "Unlicense" ]

1

2021-02-08T20:15:49.000Z

Iris>APIClient/JSONStringInterpreter.py

rrbutani/Iris

e3a61530a763387e99fd3107a90fdb3d160f6ead

[ "Unlicense" ]

1

2016-05-23T17:09:28.000Z

import json input='[16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215, 16777215]' array = json.loads(input) print array

125.4

1,198

0.794258

128

1,254

7.78125

0.054688

1.895582

2.819277

3.726908

0.955823

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0.847729

0.104466

1,254

9

1,199

139.333333

0.039181

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null

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1

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null

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17

b6c9bb6a8a7e7e34ce544dbc2f7f3a91d8bbc156

79,761

py

Python

examples/micro-speech/lib/debug-spectrograms.py

mattiantonini/tensorflow-micropython-examples

aab677bce2fcc239dab94b765155b767a8c29d2e

[ "MIT" ]

55

2021-03-16T00:08:15.000Z

2022-03-24T07:59:11.000Z

examples/micro-speech/lib/debug-spectrograms.py

TGiles1998/tensorflow-micropython-examples

ce543e72605e0549583ac8d6ef076f524f77f0e4

[ "MIT" ]

75

2021-02-19T03:13:49.000Z

2022-03-27T02:58:17.000Z

examples/micro-speech/lib/debug-spectrograms.py

TGiles1998/tensorflow-micropython-examples

ce543e72605e0549583ac8d6ef076f524f77f0e4

[ "MIT" ]

20

2021-06-22T13:05:50.000Z

2022-03-31T11:18:27.000Z

import numpy as np import os import pathlib import matplotlib.pyplot as plt def plot_spectrogram(spectrogram, ax): # Convert to frequencies to log scale and transpose so that the time is # represented in the x-axis (columns). log_spec = np.log(spectrogram.T) height = log_spec.shape[0] width = log_spec.shape[1] X = np.linspace(0, np.size(spectrogram), num=width, dtype=int) Y = range(height) ax.pcolormesh(X, Y, log_spec) yes_1 = [ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -77, -128, -128, -128, -128, -128, -14, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -77, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -14, -128, -128, -128, 15, -128, -77, -128, -128, -128, -128, -128, -26, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -33, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, 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77, 68, 45, 57, 71, 71, 65, 63, 66, 39, -29, 38, 64, 61, 71, 63, 62, 50, -128, -128, 54, 26, 70, 63, 86, 65, -43, -50, -60, -8, -33, -128, -128, -128, -128, -128, -128, -1, 14, 73, 83, 23, 30, 38, 63, 58, 55, 44, 48, 6, -33, 1, 48, 44, 62, 49, 61, 60, -128, -128, 50, 12, 21, -12, 49, 51, 43, 39, 2, 16, -1, -18, -33, -33, -128, -29, 13, 50, 68, 63, 60, -12, 25, 45, 60, 38, 43, 35, 31, -33, -128, -43, 35, 32, 53, 46, 41, 53, 1, -26, ] y1_spec = np.array (yes_1, dtype=np.int16).reshape(40,49) y2_spec = np.array (yes_2, dtype=np.int16).reshape(40,49) n1_spec = np.array (no_1, dtype=np.int16).reshape(40,49) n2_spec = np.array (no_2, dtype=np.int16).reshape(40,49) n3_spec = np.array (no_3, dtype=np.int16).reshape(40,49) def show_yes(): fig, axes = plt.subplots(2, figsize=(12,8)) plot_spectrogram(y1_spec, axes[0]) #axes[0].imshow (y1_spec) axes[0].set_title('Yes 1') axes[0].set_xlim([0,16000]) plot_spectrogram(y2_spec, axes[1]) #axes[1].imshow(y2_spec) axes[1].set_title('Yes 2') axes[1].set_xlim([0,16000]) plt.show() def show_no(): fig, axes = plt.subplots(3) plot_spectrogram(n1_spec, axes[0]) #axes[0].imshow (y1_spec) axes[0].set_title('No 1') axes[0].set_xlim([0,16000]) plot_spectrogram(n2_spec, axes[1]) #axes[1].imshow(y2_spec) axes[1].set_title('No 2') axes[1].set_xlim([0,16000]) plot_spectrogram(n2_spec, axes[2]) #axes[1].imshow(y2_spec) axes[2].set_title('No 3') axes[2].set_xlim([0,16000]) plt.show() #show_no() no_a = [ -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -77, -128, -128, -128, -128, -128, -60, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, 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8e0911163619d9f65912e6c66de83bec148b0f06

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leasing/migrations/0009_add_contract_and_decision_models.py

tuomas777/mvj

e9a12e42c399b9fb77fd8fad85fc8f0f6d4ce405

[ "MIT" ]

null

leasing/migrations/0009_add_contract_and_decision_models.py

tuomas777/mvj

e9a12e42c399b9fb77fd8fad85fc8f0f6d4ce405

[ "MIT" ]

null

leasing/migrations/0009_add_contract_and_decision_models.py

tuomas777/mvj

e9a12e42c399b9fb77fd8fad85fc8f0f6d4ce405

[ "MIT" ]

null

# Generated by Django 2.0.3 on 2018-03-22 13:34 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('leasing', '0008_remove_comment_is_archived'), ] operations = [ migrations.CreateModel( name='Condition', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('deleted', models.DateTimeField(editable=False, null=True)), ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='Time created')), ('modified_at', models.DateTimeField(auto_now=True, verbose_name='Time modified')), ('supervision_date', models.DateField(blank=True, null=True, verbose_name='Supervision date')), ('supervised_date', models.DateField(blank=True, null=True, verbose_name='Supervised date')), ('description', models.TextField(blank=True, null=True, verbose_name='Description')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='ConditionType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255, verbose_name='Name')), ], options={ 'ordering': ['name'], 'abstract': False, }, ), migrations.CreateModel( name='Contract', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('deleted', models.DateTimeField(editable=False, null=True)), ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='Time created')), ('modified_at', models.DateTimeField(auto_now=True, verbose_name='Time modified')), ('contract_number', models.CharField(blank=True, max_length=255, null=True, verbose_name='Contract number')), ('signing_date', models.DateField(blank=True, null=True, verbose_name='Signing date')), ('signing_note', models.TextField(blank=True, null=True, verbose_name='Signing note')), ('is_readjustment_decision', models.BooleanField(default=False, verbose_name='Is readjustment decision')), ('ktj_link', models.CharField(blank=True, max_length=1024, null=True, verbose_name='KTJ link')), ('collateral_number', models.CharField(blank=True, max_length=255, null=True, verbose_name='Collateral number')), ('collateral_start_date', models.DateField(blank=True, null=True, verbose_name='Collateral starting date')), ('collateral_end_date', models.DateField(blank=True, null=True, verbose_name='Collateral ending date')), ('collateral_note', models.TextField(blank=True, null=True, verbose_name='Collateral note')), ('institution_identifier', models.CharField(blank=True, max_length=255, null=True, verbose_name='Institution identifier')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='ContractChange', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('signing_date', models.DateField(blank=True, null=True, verbose_name='Signing date')), ('sign_by_date', models.DateField(blank=True, null=True, verbose_name='Sign by date')), ('first_call_sent', models.DateField(blank=True, null=True, verbose_name='First call sent')), ('second_call_sent', models.DateField(blank=True, null=True, verbose_name='Second call sent')), ('third_call_sent', models.DateField(blank=True, null=True, verbose_name='Third call sent')), ('description', models.TextField(blank=True, null=True, verbose_name='Description')), ('contract', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='contract_changes', to='leasing.Contract', verbose_name='Contract')), ], ), migrations.CreateModel( name='ContractType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255, verbose_name='Name')), ], options={ 'ordering': ['name'], 'abstract': False, }, ), migrations.CreateModel( name='Decision', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('deleted', models.DateTimeField(editable=False, null=True)), ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='Time created')), ('modified_at', models.DateTimeField(auto_now=True, verbose_name='Time modified')), ('reference_number', models.CharField(blank=True, max_length=255, null=True, verbose_name='Reference number')), ('decision_date', models.DateField(blank=True, null=True, verbose_name='Decision date')), ('section', models.CharField(blank=True, max_length=255, null=True, verbose_name='Section')), ('description', models.TextField(blank=True, null=True, verbose_name='Description')), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='DecisionMaker', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255, verbose_name='Name')), ], options={ 'ordering': ['name'], 'abstract': False, }, ), migrations.CreateModel( name='DecisionType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255, verbose_name='Name')), ], options={ 'ordering': ['name'], 'abstract': False, }, ), migrations.CreateModel( name='Inspection', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('inspector', models.CharField(blank=True, max_length=255, null=True, verbose_name='Inspector')), ('supervision_date', models.DateField(blank=True, null=True, verbose_name='Supervision date')), ('supervised_date', models.DateField(blank=True, null=True, verbose_name='Supervised date')), ('description', models.TextField(blank=True, null=True, verbose_name='Description')), ('lease', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='inspections', to='leasing.Lease', verbose_name='Lease')), ], ), migrations.CreateModel( name='MortgageDocument', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('number', models.CharField(blank=True, max_length=255, null=True, verbose_name='Number')), ('date', models.DateField(blank=True, null=True, verbose_name='Date')), ('note', models.TextField(blank=True, null=True, verbose_name='Note')), ('contract', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='mortgage_documents', to='leasing.Contract', verbose_name='Contract')), ], ), migrations.AddField( model_name='decision', name='decision_maker', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='decisions', to='leasing.DecisionMaker', verbose_name='Decision maker'), ), migrations.AddField( model_name='decision', name='lease', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='decisions', to='leasing.Lease', verbose_name='Lease'), ), migrations.AddField( model_name='decision', name='type', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='+', to='leasing.DecisionType', verbose_name='Type'), ), migrations.AddField( model_name='contractchange', name='decision', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to='leasing.Decision', verbose_name='Decision'), ), migrations.AddField( model_name='contract', name='decision', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to='leasing.Decision', verbose_name='Decision'), ), migrations.AddField( model_name='contract', name='lease', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='contracts', to='leasing.Lease', verbose_name='Lease'), ), migrations.AddField( model_name='contract', name='type', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='leasing.ContractType', verbose_name='Contract type'), ), migrations.AddField( model_name='condition', name='decision', field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='conditions', to='leasing.Decision', verbose_name='Decision'), ), migrations.AddField( model_name='condition', name='type', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='+', to='leasing.ConditionType', verbose_name='Type'), ), ]

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f3d339408c67d88e5b719da00755392a1e9df36b

157,813

py

Python

FUNCTIONS.py

johngeorgousis/jet-classifier

bd63456cfb32872c5c9f6e1942ace17aa50de6a4

[ "MIT" ]

null

FUNCTIONS.py

johngeorgousis/jet-classifier

bd63456cfb32872c5c9f6e1942ace17aa50de6a4

[ "MIT" ]

null

FUNCTIONS.py

johngeorgousis/jet-classifier

bd63456cfb32872c5c9f6e1942ace17aa50de6a4

[ "MIT" ]

null

import numpy as np #import scipy as sp import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import time from IPython.display import display import pprint pp = pprint.PrettyPrinter(indent=4) import sklearn from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, RandomForestClassifier from sklearn.tree import DecisionTreeClassifier # Decision Trees from sklearn.naive_bayes import MultinomialNB # Naive Bayes from sklearn.naive_bayes import GaussianNB # Gaussian Naive Bayes from sklearn.svm import SVC # SVM from sklearn.metrics import accuracy_score, f1_score from sklearn.model_selection import GridSearchCV from sklearn.metrics import make_scorer import random import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers from tensorflow.keras.models import Sequential print('TensorFlow Version: ', tf.__version__) ''' TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW 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TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW TENSORFLOW ''' def create_dataset(file, pixels=40, R=1.5): ''' Takes dat file of events Labels events (background = 0, signal = 1) Preprocessed events and turns into images Returns 2d array where rows: events and columns: (image, label) ''' image = np.zeros((pixels, pixels)) # Define initial image data = {} a = 0 with open(file) as infile: for line in infile: # Preprocessing event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) # Extract maxima event = rotate(event, max2) # Rotate max3 = find_max3(event) # Extract maxima event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image = event # Rename #image /= np.sum(image) #image /= np.amax(image) # Normalise final image between 0 and 1 #image = np.log(image) # Log image event=max1=max2=max3=None a += 1 data[a] = image data = list(data.values()) data = np.array(data) data = np.reshape(data, (a*pixels, pixels)) return data #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def normalise(image, label): ''' To be used in preprocess_ML_tf ''' image = tf.cast(image, tf.int64) # Set dtype to int64 label = tf.cast(label, tf.int64) #image /= np.amax(image.numpy()) # Normalise Image #image = tf.image.resize(image, (pixels, pixels, 1)) # Resize image to 40x40 return image, label def preprocess_ML_tf(data_s, data_b, batch_size): '''Prepares dataset for TensorFlow Machine Learning algorithm''' ''' Input1: Signal Dataset created using create_dataset Input2: Background Dataset created using create_dataset Input3: Batch size Process: - Create labels for signals (1) and backgrounds (0) - Combine signal and background datasets - Combine signal and background labels - Define useful (local) variables - Reshape main dataset (for CNN) - Train-Val-Test Split examples and labels - Turn into tf datasets (train, val, split) - Create Batches (train, val, split) - Define useful (global) variables (returned later) - Plot Events to Visualise & make sure everything's right (e.g. normalised vs non-normalised) Output1: train_batches Output2: val_batches Output3: test_batches Output4: num_of_batches_train Output5: num_of_batches_val Output6: num_of_batches_test ''' '''Preprocess''' # Create s&b labels slabels = np.ones(data_s.shape[0]//40) blabels = np.zeros(data_b.shape[0]//40) # Concatenate s&b and s&b labels data = np.concatenate((data_s, data_b), axis=0) labels = np.concatenate((slabels, blabels), axis=0) # Define & Print useful variables (local) num_of_examples = data.shape[0] // 40 # divide by 40 because 1st dim is 40 * num_of_examples num_of_labels = labels.shape[0] print('Total Events:', num_of_examples) print('Total Labels:', num_of_labels) # Reshape examples (for CNN) examples = data.reshape(num_of_examples, 40, 40, 1) print('Shape: ', examples.shape) print(' ') '''Train-Val-Test Split''' train_examples, test_examples, train_labels, test_labels = train_test_split(examples, labels, test_size=0.15, random_state=42) train_examples, val_examples, train_labels, val_labels = train_test_split(train_examples, train_labels, test_size=0.18, random_state=42) print('Train: ', train_examples.shape, train_labels.shape) print('Val: ', val_examples.shape, val_labels.shape) print('Test: ', test_examples.shape, test_labels.shape) print(' ') train_data = tf.data.Dataset.from_tensor_slices((train_examples, train_labels)) val_data = tf.data.Dataset.from_tensor_slices((val_examples, val_labels)) test_data = tf.data.Dataset.from_tensor_slices((test_examples, test_labels)) print(train_data) print(val_data) print(test_data) '''BATCHES''' batch_size = batch_size train_batches = train_data.cache().shuffle(num_of_examples).map(normalise).batch(batch_size, drop_remainder=True).prefetch(1) val_batches = val_data.cache().shuffle(num_of_examples).map(normalise).batch(batch_size, drop_remainder=True).prefetch(1) # or prefetch(buffer_size=tf.data.experimental.AUTOTUNE) test_batches = test_data.cache().shuffle(num_of_examples).map(normalise).batch(batch_size, drop_remainder=True).prefetch(1) # Define useful variables (will be returend) num_of_batches_train = len(train_labels) // batch_size num_of_batches_val = len(val_labels) // batch_size num_of_batches_test = len(test_labels) // batch_size print(train_batches) print('\ntrain, val, test: ', num_of_batches_train, num_of_batches_val, num_of_batches_test) '''VISUALISE''' plt.figure(figsize=(15,10)) for images, labels in train_batches.take(1): for i in range(3): ax = plt.subplot(3, 3, i + 1) sns.heatmap(images[i].numpy().reshape(40, 40)) #plt.imshow(images[i].numpy().astype("uint8")) plt.title('Image {}'.format(i+1)) plt.axis("off") return train_batches, val_batches, test_batches, num_of_batches_train, num_of_batches_val, num_of_batches_test 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def visualise_preds(model, test_batches): ''' Visualise Predictions for TensorFlow Input1: Model Input 2: Predictions Output: NULL ''' '''1st''' class_names = ['Background', 'Signal'] for event, label in test_batches.take(1): ps = model.predict(event) images = event.numpy().squeeze() labels = label.numpy() plt.figure(figsize=(15,10)) for n in range(6): plt.subplot(3,3,n+1) sns.heatmap(images[n]) #plt.imshow(images[n], cmap = plt.cm.binary) color = 'green' if np.argmax(ps[n]) == labels[n] else 'red' plt.title(class_names[np.argmax(ps[n])], color=color) plt.axis('off') '''2nd''' for event, label in test_batches.take(1): ps = model.predict(event) first_image = event.numpy().squeeze()[0] fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2) #sns.heatmap(first_image) ax1.imshow(first_image) ax1.axis('off') ax2.barh(np.arange(2), ps[0]) ax2.set_aspect(0.1) ax2.set_yticks(np.arange(2)) ax2.set_yticklabels(np.arange(2)) ax2.set_title('Class Probability') ax2.set_xlim(0, 1.1) plt.tight_layout() #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def learning_curve(train_batches, val_batches, test_batches, num_of_batches_train, num_of_batches_val): ''' Plots a learning curve to determine whether more data would improve the model (i.e. detect underfitting) ''' input_shape=(40, 40, 1) kernel_size = 2 padding='valid' activation = 'tanh' prop = [0.1, 0.2, 0.4, 0.6, 0.8, 1] loss_list = [] accuracy_list = [] for i in prop: model = tf.keras.Sequential([ tf.keras.Input(shape=input_shape), tf.keras.layers.Conv2D(16, kernel_size=kernel_size, padding=padding, activation=activation), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, kernel_size=kernel_size, padding=padding, activation=activation), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, kernel_size=kernel_size, padding=padding, activation=activation), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation=activation), tf.keras.layers.Dense(2, activation = 'softmax') ]) # Compile Model model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print('\n\n', i, '\n') # Fit model to training data EPOCHS = 4 history = model.fit(train_batches.take(int(i*num_of_batches_train)), epochs=EPOCHS, validation_data=val_batches.take(int(i*num_of_batches_val)), verbose=0 ) loss, accuracy = model.evaluate(test_batches, verbose=0) loss_list.append(loss) accuracy_list.append(accuracy) loss, accuracy = model.evaluate(test_batches, verbose=0) print('Accuracy on the Test Set: {:.1%}'.format(accuracy)) plt.plot(np.array(prop)*100, accuracy_list, linestyle='--', marker='o') plt.xlabel('% of Dataset Used') plt.ylabel('Accuracy') plt.title('Learning Curve') plt.show() #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def model_complexity_graph(history): ''' Plots model complexity graph to determine how many epochs you need (i.e. when the model starts overfitting the data) ''' training_accuracy = history.history['accuracy'] validation_accuracy = history.history['val_accuracy'] training_loss = history.history['loss'] validation_loss = history.history['val_loss'] epochs_range=range(len(training_accuracy)) plt.figure(figsize=(16, 8)) plt.subplot(1, 2, 1) plt.plot(epochs_range, training_accuracy, label='Training Accuracy') plt.plot(epochs_range, validation_accuracy, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, training_loss, label='Training Loss') plt.plot(epochs_range, validation_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show() #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def cmx_tf(models, test_batches, num_of_batches_test): ''' Plots Confusion Matrix for TensorFlow list of models ''' for model in models: '''Extract Preds & Labels''' preds_all = [] labels_all = [] preds_batch = [] labels_batch = [] # For all batches for batch, labels in test_batches.take(num_of_batches_test): # 64 preds and labels added to list pp = model.predict(batch) preds_batch = np.array([np.argmax(pp[i]) for i in range(len(pp))]) labels_batch = labels.numpy() preds_all.append(preds_batch) labels_all.append(labels_batch) # Convert list of lists to ndarray and flatten to get 1D ndarray of all preds and 1D ndarray of all labels preds = np.array(preds_all).flatten() labels = np.array(labels_all).flatten() '''Build CMX''' cmx_non_normal = tf.math.confusion_matrix(labels, preds).numpy() # Create Confusion Matrix cmx0 = cmx_non_normal[0] / cmx_non_normal[0].sum() cmx1 = cmx_non_normal[1] / cmx_non_normal[1].sum() cmx = np.stack((cmx0, cmx1), axis=0) print(cmx) # Plot confusion matrix fig, ax = plt.subplots() sns.heatmap(cmx, cmap=['skyblue', 'deepskyblue', 'dodgerblue', 'blue', 'darkblue']) # xylabels and title plt.title('Confusion Matrix') plt.xlabel('PREDICTIONS') plt.ylabel('LABELS') # Label ticks ax.set_xticklabels(['Background', 'Signal']) ax.set_yticklabels(['Background', 'Signal']) # Align ticks plt.setp(ax.get_xticklabels(), rotation=0, ha="center", rotation_mode="anchor") plt.setp(ax.get_yticklabels(), rotation=90, ha="center", rotation_mode="anchor") # Text Annotations for Blocks in CMX for i in range(2): for j in range(2): value = int(np.round(100*cmx[i, j], 0)) text = ax.text(j+0.5, i+0.5, value, ha="center", va="center", color="orangered", fontsize = 20) plt.show() # # Print P(signal|signal) and P(signal|background) # pss = cmx[1,1] / (cmx[1,1]+cmx[1,0]) # pbs = 1 - pss # psb = cmx[0,1] / (cmx[0,1]+cmx[0,0]) # pbb = 1 - psb # precision = cmx[1,1] / (cmx[1,1]+cmx[0,1]) # recall = cmx[1,1] / (cmx[1,1]+cmx[1,0]) # print('\n') # print('P(signal|signal) = {:.0f}%'.format(100*pss)) # print('P(signal|background) = {:.0f}%'.format(100*psb)) # print('P(background|background) = {:.0f}%'.format(100*pbb)) # print('P(background|signal) = {:.0f}%'.format(100*pbs)) # print('Precision = {:.0f}'.format(precision*100)) # print('Recall = {:.0f}'.format(recall*100)) # print('\n') #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def ROC3_tf(model0, model1, model2, test_batches, num_of_batches_test): ''' Plot ROC curve for exactly 3 TensorFlow models ''' '''Extract Preds & Labels''' preds_batch = [] labels_batch = [] preds_all = [] labels_all = [] # For all batches for batch, labels in test_batches.take(num_of_batches_test): # 64 preds and labels added to list pp = model0.predict(batch) preds_batch = np.array([np.argmax(pp[i]) for i in range(len(pp))]) labels_batch = labels.numpy() preds_all.append(preds_batch) labels_all.append(labels_batch) # Convert list of lists to ndarray and flatten to get 1D ndarray of all preds and 1D ndarray of all labels preds0 = np.array(preds_all).flatten() labels0 = np.array(labels_all).flatten() ########################################################################################################################################################################## preds_batch = [] labels_batch = [] preds_all = [] labels_all = [] # For all batches for batch, labels in test_batches.take(num_of_batches_test): # 64 preds and labels added to list pp = model1.predict(batch) preds_batch = np.array([np.argmax(pp[i]) for i in range(len(pp))]) labels_batch = labels.numpy() preds_all.append(preds_batch) labels_all.append(labels_batch) # Convert list of lists to ndarray and flatten to get 1D ndarray of all preds and 1D ndarray of all labels preds1 = np.array(preds_all).flatten() labels1 = np.array(labels_all).flatten() ########################################################################################################################################################################## preds_batch = [] labels_batch = [] preds_all = [] labels_all = [] # For all batches for batch, labels in test_batches.take(num_of_batches_test): # 64 preds and labels added to list pp = model2.predict(batch) preds_batch = np.array([np.argmax(pp[i]) for i in range(len(pp))]) labels_batch = labels.numpy() preds_all.append(preds_batch) labels_all.append(labels_batch) # Convert list of lists to ndarray and flatten to get 1D ndarray of all preds and 1D ndarray of all labels preds2 = np.array(preds_all).flatten() labels2 = np.array(labels_all).flatten() '''Build ROC''' ########################################################################################################################################################################## from sklearn.metrics import roc_curve from sklearn.metrics import auc fpr0, tpr0, thresholds = roc_curve(labels0, preds0) auc0 = auc(fpr0, tpr0) fpr1, tpr1, thresholds1 = roc_curve(labels1, preds1) auc1 = auc(fpr1, tpr1) fpr2, tpr2, thresholds2 = roc_curve(labels2, preds2) auc2 = auc(fpr2, tpr2) ########################################################################################################################################################################## fig, ax = plt.subplots(figsize=(10, 6)) plt.figure(1) plt.plot([0, 1], [0, 1], 'k--') plt.plot(fpr0, tpr0, label='0 (area = {:.3f})'.format(auc0)) plt.plot(fpr1, tpr1, label='1 (area = {:.3f})'.format(auc1)) plt.plot(fpr2, tpr2, label='2 (area = {:.3f})'.format(auc2)) plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title('ROC curve') plt.legend(loc='best') plt.show() # # Zoom in view of the upper left corner. # plt.figure(2) # plt.xlim(0, 0.2) # plt.ylim(0.8, 1) # plt.plot([0, 1], [0, 1], 'k--') # plt.plot(fpr0, tpr0, label='0 (area = {:.3f})'.format(auc0)) # plt.plot(fpr1, tpr1, label='1 (area = {:.3f})'.format(auc1)) # plt.plot(fpr2, tpr2, label='1 (area = {:.3f})'.format(auc2)) # plt.xlabel('False positive rate') # plt.ylabel('True positive rate') # plt.title('ROC curve (zoomed in at top left)') # plt.legend(loc='best') # plt.show() ########################################################################################################################################################################## ''' SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN SKLEARN ''' def preprocess_ML_sklearn(data_s, data_b): '''Prepares dataset for sklearn Machine Learning algorithm''' ''' Input1: Signal Dataset created using create_dataset Input2: Background Dataset created using create_dataset Process: - Create labels for signals (1) and backgrounds (0) - Combine signal and background datasets - Combine signal and background labels - Define useful (local) variables - Reshape main dataset (for sklearn) - Train-Val-Test Split examples and labels - Plot Events to Visualise & make sure everything's right (e.g. normalised vs non-normalised) Output1: train_examples Output2: train_examples Output3: val_examples Output4: val_labels Output5: test_examples Output6: test_labels ''' # Create s&b labels slabels = np.ones(data_s.shape[0]//40) blabels = np.zeros(data_b.shape[0]//40) # Concatenate examples and labels data = np.concatenate((data_s, data_b), axis=0) labels = np.concatenate((slabels, blabels), axis=0) # Define useful quantities num_of_examples = data.shape[0] // 40 # divide by 40 because 1st dim is 40 * num_of_examples num_of_labels = labels.shape[0] print('Total Events:', num_of_examples) print('Total Labels:', num_of_labels) # Reshape examples (for sklearn) examples = data.reshape(num_of_examples, 1600) print('\nShape: ', examples.shape) train_examples, test_examples, train_labels, test_labels = train_test_split(examples, labels, test_size=0.15, random_state=42) train_examples, val_examples, train_labels, val_labels = train_test_split(train_examples, train_labels, test_size=0.18, random_state=42) print('\nTrain: ', train_examples.shape, train_labels.shape) print('Val: ', val_examples.shape, val_labels.shape) print('Test: ', test_examples.shape, test_labels.shape) print(' ') eg = train_examples[5].reshape(40, 40) sns.heatmap(eg) plt.title("Train Image Example") plt.show() return train_examples, train_labels, val_examples, val_labels, test_examples, test_labels #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def compare_f1(models, test_examples, test_labels): '''Plots f1-score of Models using Test Data and prints top 5 models''' ''' Input1: list of models Input2: test examples Input3: test labels Output1: NULL ''' # Local Variables model_names = [] scores = [] # Get plot data for model in models: labels = test_labels preds = model.predict(test_examples) scores.append(f1_score(labels, preds)) model_names.append(model.__class__.__name__) # Make Plots fig, ax = plt.subplots(figsize=(27, 6)) plt.bar(model_names, scores, color="darkcyan") plt.show() # Print top 5 algorithms max_i = np.flip(np.argsort(scores)) print('F1score') for i in max_i: print('{:.4f} {}'.format(scores[i], model_names[i])) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def compare_accuracy(models, test_examples, test_labels): '''Plots f1-score of Models using Test Data and prints top 5 models''' ''' Input1: list of models Input2: test examples Input3: test labels Output1: NULL ''' # Local Variables model_names = [] scores = [] # Get plot data for model in models: labels = test_labels preds = model.predict(test_examples) scores.append(accuracy_score(labels, preds)) model_names.append(model.__class__.__name__) # Make Plots fig, ax = plt.subplots(figsize=(27, 6)) plt.bar(model_names, scores, color="darkcyan") plt.show() # Print top 5 algorithms max_i = np.flip(np.argsort(scores)) print('Accuracy') for i in max_i: print('{:.4f} {}'.format(scores[i], model_names[i])) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def cmx_sklearn(models, test_examples, test_labels, dim=4): ''' Plots Confusion Matrix for sklearn list of models ''' cmxs = [] for model in models: preds = model.predict(test_examples) labels = test_labels cmx_non_normal = tf.math.confusion_matrix(labels, preds).numpy() # Create Confusion Matrix cmx0 = cmx_non_normal[0] / cmx_non_normal[0].sum() cmx1 = cmx_non_normal[1] / cmx_non_normal[1].sum() cmx = np.stack((cmx0, cmx1), axis=0) cmxs.append(cmx) plt.figure(figsize=(25,20)) for n in range(len(cmxs)): # Plot confusion matrix ax = plt.subplot(dim, dim, n+1) sns.heatmap(cmxs[n], cmap=['skyblue', 'deepskyblue', 'dodgerblue', 'blue', 'darkblue']) # xylabels and title plt.title(remove_text_inside_brackets(str(models[n]))) plt.xlabel('PREDICTIONS') plt.ylabel('LABELS') # Label ticks ax.set_xticklabels(['Background', 'Signal']) ax.set_yticklabels(['Background', 'Signal']) # Align ticks plt.setp(ax.get_xticklabels(), rotation=0, ha="center", rotation_mode="anchor") plt.setp(ax.get_yticklabels(), rotation=90, ha="center", rotation_mode="anchor") # Text Annotations for Blocks in CMX for i in range(2): for j in range(2): value = int(np.round(100*cmxs[n][i, j], 0)) text = ax.text(j+0.5, i+0.5, value, ha="center", va="center", color="orangered", fontsize = 20) plt.axis("off") plt.show() print(cmxs) 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def learning_curve_sklearn(models, train_examples, train_labels, val_examples, val_labels): total_train = train_labels.shape[0] total_val = val_labels.shape[0] print('Total No. of Training Examples:', total_train) props = [0.1, 0.2, 0.4, 0.6, 0.8, 1] accuracies = [] for model in models: print('\n=================================================================================================================================================================================\n') accuracy = [] times = [] for prop in props: prop_examples = int(prop*total_train) #print('Current No. of Training Examples:', prop_examples) start = time.time() model.fit(train_examples[0:prop_examples], train_labels[0:prop_examples]) end = time.time() times.append((end-start)/60) #print('Training time for {} examples: {:.3f} minutes'.format(total_train, (end-start)/60)) start = time.time() prop_examples_val = int(prop*total_val) val_preds = model.predict(val_examples[0:prop_examples_val]) accuracy.append(accuracy_score(val_labels[0:prop_examples_val], val_preds[0:prop_examples_val])) end = time.time() #print('Prediction time for {} examples: {:.3f} minutes\n'.format(total_val, (end-start)/60)) plt.plot(np.array(props)*100, accuracy, linestyle='--', marker='o') plt.xlabel('% of Dataset Used') plt.ylabel('Accuracy') plt.title('{} - Learning Curve'.format(model.__class__.__name__)) plt.show() plt.plot(np.array(props)*100, times, linestyle='--', marker='o') plt.xlabel('% of Dataset Used') plt.ylabel('Training Time (minutes)') plt.title('{} - Training Time'.format(model.__class__.__name__)) plt.show() print("Proportions:", props) print("Accuracy: {}".format(accuracy)) print("Time: {}".format(times)) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 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#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def create_dataset_sklearn(file, pixels=40, R=1.5): ''' Takes dat file of events Labels events (background = 0, signal = 1) Preprocessed events and turns into images Returns 2d array where rows: events and columns: (image, label) ''' data = ((0, 0)) image = np.zeros((pixels, pixels)) # Define initial image if file=='data_background.dat': label = 0 elif file=='data_signal.dat': label = 1 else: print("ERROR: File name unclear") return with open(file) as infile: for line in infile: # Preprocessing event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) # Extract maxima event = rotate(event, max2) # Rotate max3 = find_max3(event) # Extract maxima event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image #event = event.flatten() # Flatten image from 2D to 1D for NN image = event # Rename #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory event = np.array((image, label)) data = np.vstack((data, event)) data = np.delete(data, 0, axis=0) return data ''' PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING ''' #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def average_image_1(pixels=60, R=1.5, event_no=12178, display=False, file='data/dataset_s_100k.dat'): ''' Reads events directly from a file and creates an average image of the events. pixels: int. Image Resolution R: float. Fat jet radius event_no: int/list. Number of events for which images are created. If int, then single image (faster). If list, then multiple images (slower) display: boolean. Indicates whether images should be displayed automatically (return null) or returned as an ndarray. ''' image = np.zeros((pixels, pixels)) # Define initial image a = 0 # Define Counter #Return single image if type(event_no) == int: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess #max1 = find_max1(event) # Extract maxima #event = center(event, max1) # Center #max2 = find_max2(event) #event = rotate(event, max2) # Rotate #max3 = find_max3(event) #event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a == event_no: # Break if max sample size for average image is exceeded return image def average_image_2(pixels=60, R=1.5, event_no=12178, display=False, file='data/dataset_s_100k.dat'): ''' Reads events directly from a file and creates an average image of the events. pixels: int. Image Resolution R: float. Fat jet radius event_no: int/list. Number of events for which images are created. If int, then single image (faster). If list, then multiple images (slower) display: boolean. Indicates whether images should be displayed automatically (return null) or returned as an ndarray. ''' image = np.zeros((pixels, pixels)) # Define initial image a = 0 # Define Counter #Return single image if type(event_no) == int: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center # max2 = find_max2(event) # event = rotate(event, max2) # Rotate # max3 = find_max3(event) # event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a == event_no: # Break if max sample size for average image is exceeded return image def average_image_3(pixels=60, R=1.5, event_no=12178, display=False, file='data/dataset_s_100k.dat'): ''' Reads events directly from a file and creates an average image of the events. pixels: int. Image Resolution R: float. Fat jet radius event_no: int/list. Number of events for which images are created. If int, then single image (faster). If list, then multiple images (slower) display: boolean. Indicates whether images should be displayed automatically (return null) or returned as an ndarray. ''' image = np.zeros((pixels, pixels)) # Define initial image a = 0 # Define Counter #Return single image if type(event_no) == int: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) event = rotate(event, max2) # Rotate # max3 = find_max3(event) # event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a == event_no: # Break if max sample size for average image is exceeded return image def average_image_4(pixels=60, R=1.5, event_no=12178, display=False, file='data/dataset_s_100k.dat'): ''' Reads events directly from a file and creates an average image of the events. pixels: int. Image Resolution R: float. Fat jet radius event_no: int/list. Number of events for which images are created. If int, then single image (faster). If list, then multiple images (slower) display: boolean. Indicates whether images should be displayed automatically (return null) or returned as an ndarray. ''' image = np.zeros((pixels, pixels)) # Define initial image a = 0 # Define Counter #Return single image if type(event_no) == int: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) event = rotate(event, max2) # Rotate max3 = find_max3(event) event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a == event_no: # Break if max sample size for average image is exceeded return image def average_image(pixels=60, R=1.5, event_no=12178, display=False, file='data/dataset_s_100k.dat'): ''' Reads events directly from a file and creates an average image of the events. pixels: int. Image Resolution R: float. Fat jet radius event_no: int/list. Number of events for which images are created. If int, then single image (faster). If list, then multiple images (slower) display: boolean. Indicates whether images should be displayed automatically (return null) or returned as an ndarray. ''' image = np.zeros((pixels, pixels)) # Define initial image a = 0 # Define Counter #Return single image if type(event_no) == int: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) event = rotate(event, max2) # Rotate max3 = find_max3(event) event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a == event_no: # Break if max sample size for average image is exceeded return image # Display Images elif display == True and type(event_no) == list: with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) event = rotate(event, max2) # Rotate max3 = find_max3(event) event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a in event_no: sns.heatmap(image, robust=True) plt.show() # sns.heatmap(image) # plt.show() if a >= max(event_no): # Break if max sample size for average image is exceeded break # Return multiple images ##### Not working properly elif type(event_no) == list: images = [] # List containing the output images with open(file) as infile: for line in infile: event = line.strip().split() event = pd.Series(event) # Turn into Series event = preprocess(event) # Preprocess max1 = find_max1(event) # Extract maxima event = center(event, max1) # Center max2 = find_max2(event) event = rotate(event, max2) # Rotate max3 = find_max3(event) event = flip(event, max3) # Flip event = create_image(event, pixels=pixels, R=R) # Create image image += event # Add event image to average image #image /= np.amax(image) # Normalise final image between 0 and 1 event=max1=max2=max3=None # Delete from memory a += 1 if a in event_no: # Store images images.append(image) if a >= max(event_no): # Break if max sample size for average image is exceeded return images #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def preprocess(event): ''' Input: Series (event) to be processed Output: Preprocessed Series -Drops constituents element -Replaces NaN values with 0 -Converts all values to floats ''' # Drop constituents event = event.drop(event.index[0]) # Replace NaN with 0 event = event.fillna(0) # Convert values to floats event = event.astype(float) return event #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def find_max1(event): ''' Takes an event and outputs a tuple containing 3 Series, each for the highest pT and its φ, η. Input: Series (event). Output[0]: [Series of 1st max pT, φ, η] ''' # Separate η, φ, pT hdata = event[::3] fdata = event[1::3] pdata = event[2::3] # 1. Extract index of 1st maximum pT maxid1 = pdata.idxmax() maxlist1 = [] # 2. Extract max η, φ, pT for event if pdata.max() != 0: # Brief explanation of if statement below) maxlist1.append([event.iloc[maxid1-1], event.iloc[maxid1-2], event.iloc[maxid1-3]]) # From event, add to list the max pT and its η, φ else: maxlist1.append([0., event.iloc[maxid1-2], event.iloc[maxid1-3]]) # If max pT is 0, then add it as 0 and not the first value # 3. Create series of max pT, η, φ max1 = pd.Series(data=maxlist1[0], index=['pT', 'φ', 'η']) return max1 def find_max2(event): ''' Takes an event and outputs a tuple containing 3 Series, each for the highest pT and its φ, η. Input: Series (event). Output: [Series of 2nd max pT, φ, η] ''' # Separate η, φ, pT hdata = event[::3] fdata = event[1::3] pdata1 = event[2::3] # 0. 1st pT = 0 to find 2nd Max pT pdata = pdata1.copy(deep=True) pdata.loc[pdata.idxmax()] = 0 # 1. Extract index of 2nd max pT maxid2 = pdata.idxmax() maxlist2 = [] # Extract numerical index of φ of 2nd max pT f_id_2 = maxid2 - 1 h_id_2 = maxid2 - 2 # 2. Extract max η, φ, pT for event if pdata.max() != 0: # Brief explanation of if statement below) maxlist2.append([event.iloc[maxid2-1], event.iloc[maxid2-2], event.iloc[maxid2-3]]) # From event, add to list the max pT and its η, φ else: maxlist2.append([0., event.iloc[maxid2-2], event.iloc[maxid2-3]]) # If max pT is 0, then add it as 0 and not the first value # 3. Create series of max pT, η, φ max2 = pd.Series(data=maxlist2[0], index=['pT', 'φ', 'η']) return max2 def find_max3(event): ''' Takes an event and outputs a Series containing the 3rd highest pT, and its φ, η Input: Series (event). Output: [Series of 3rd max pT, φ, η] ''' # Separate η, φ, pT hdata = event[::3] fdata = event[1::3] pdata1 = event[2::3] # 0. 1st, 2nd pT = 0 to find 3rd Max pT pdata = pdata1.copy(deep=True) pdata.loc[pdata.idxmax()] = 0 pdata.loc[pdata.idxmax()] = 0 # 1. Extract index of 3rd max pT maxid3 = pdata.idxmax() maxlist3 = [] # 2. Extract max η, φ, pT for event if pdata.max() != 0: # Brief explanation of if statement below) maxlist3.append([event.iloc[maxid3-1], event.iloc[maxid3-2], event.iloc[maxid3-3]]) # From event, add to list the max pT and its η, φ else: maxlist3.append([0., event.iloc[maxid3-2], event.iloc[maxid3-3]]) # If max pT is 0, then add it as 0 and not the first value # 3. Create series of max pT, η, φ max3 = pd.Series(data=maxlist3[0], index=['pT', 'φ', 'η']) return max3 # **Why the if statement?** (note to self) <br /> # Because if maximum pT is 0 in the pdata vector, it picks the ID of the first pT by default as the max (because they're all 0). <br /> # Then, it goes to the non-zero'd event vector and adds its non-zero pT as the max, when the value of that max should clearly have been 0. # So the if statement fixes this: <br /> # - If max pT != 0, then add it as normal. # - If max pT = 0, then add '0' as its value instead. (with the coordinates of the first pT, which is incorrect, but this doesn't matter since pT = 0 are not taken into account in the image) <br /> #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def center(event, max1, output='event', R=1.5, pixels=60): ''' Centers image around (φ', η') = (0, 0). Both transformations are linear (so far). event1: Series (event) max123: Tuple of 3 series of max pT, η, φ. Returned by extract_max123() function output: 'event' to return a Series of the transformed event1. 'image' to return a transformed dataframe representing an image ''' # Define η, φ indices to be used later h_indices = event[::3].index f_indices = event[1::3].index # For all η, φ in the event for h_index, f_index in zip(h_indices, f_indices): # Define Useful Quantities num_index = event.name # REDUNTANT? REMOVE IT. index of event, so that we can find its corresponding φ in the max123[0] dataframe of max pT's and φ, η's maxh = max1.loc['η'] # η of max1 pT value maxf = max1.loc['φ'] # φ of max1 pT value f = event.iloc[1::3][f_index] # φ original value # η Transformation event.iloc[::3][h_index] -= maxh # Subtract max η from current η # φ Transformation (Note: the if statements take periodicity into account, making sure that range does not exceed 2π) if (f - maxf) < -np.pi: event.iloc[1::3][f_index] = f + 2*np.pi - maxf elif (f - maxf) > np.pi: event.iloc[1::3][f_index] = f - 2*np.pi - maxf else: event.iloc[1::3][f_index] -= maxf # Subtract max φ from current φ if output == 'event': return event elif output == 'image': # Initiate bin lists bin_h = [] bin_f = [] bin_p = [] # Define max number of constituents max_const = event.shape[0] // 3 # For all constituents for i in range(max_const): # Add constituent's η, φ, p to bins bin_h.append(list(event.iloc[::3])[i]) bin_f.append(list(event.iloc[1::3])[i]) bin_p.append(list(event.iloc[2::3])[i]) # Turn lists into Series bin_h = pd.Series(bin_h) bin_f = pd.Series(bin_f) bin_p = pd.Series(bin_p) # Define no. of bins bin_count = np.linspace(-R, R, pixels + 1) # Create bins from -R to R and convert to DataFrame bins = np.histogram2d(bin_h, bin_f, bins=bin_count, weights=bin_p)[0] # x and y are switch because when the bins were turned into a Series the shape[0] and shape[1] were switched image = bins return image #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def rotate(event, max2): ''' Input: Series (event), max2 series obtained from find_max2() Output: Rotated Series (event) -Calculates the angle of rotation so that φ of 2nd highest pT ends up at (φ', η') = (0, h) for some h > 0 -Transforms all η and φ of event using the formulas below (from mathematics) (η' = ηcosθ + φsinθ) (φ' = φcosθ - ηsinθ) => θ = arctan(φ/η), with if statements taking care of η = 0 cases and making sure η' is positive and not negative ''' # Calculate Angle hmax=max2.loc['η'] fmax=max2.loc['φ'] angle = 0 if (hmax == 0) and (fmax > 0): angle = np.pi/2 elif (hmax == 0) and (fmax < 0): angle = -np.pi/2 elif hmax > 0: angle = np.arctan(fmax/hmax) elif hmax < 0: angle = np.arctan(fmax/hmax) + np.pi # Rotate Image h_indices = event[::3].index f_indices = event[1::3].index for h_index, f_index in zip(h_indices, f_indices): h = event.iloc[0::3][h_index] f = event.iloc[1::3][f_index] event.iloc[1::3][f_index] = f*np.cos(angle) - h*np.sin(angle) event.iloc[::3][h_index] = f*np.sin(angle) + h*np.cos(angle) return event #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def flip(event, max3): ''' Input: Series (event), max3 series obtained from find_max3() Output: Flipped Series (event) -Checks if φ is on left-hand side -If yes, it multiplies all φ with -1 to flip the image ''' # Check if 2nd highest pT is on left-hand side if max3.loc['φ'] < 0: # Define φ indices for transformation f_indices = event[1::3].index # For all φ for f_index in f_indices: # Multiply φ by -1 event.iloc[1::3][f_index] *= -1 return event #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def create_image(event, R=1.5, pixels=60): ''' Creates an image of single event. Input: Series (event) Output: ndarray (image) ''' # Turn into DataFrame event = pd.DataFrame(event).T # Initiate bin lists bin_h = [] bin_f = [] bin_p = [] # Add constituent's coordinates to bin lists const = event.shape[1] // 3 # For all constituents for i in range(const): bin_h.append(list(event.iloc[0][::3])[i]) bin_f.append(list(event.iloc[0][1::3])[i]) bin_p.append(list(event.iloc[0][2::3])[i]) # Turn lists into Series bin_h = pd.Series(bin_h) bin_f = pd.Series(bin_f) bin_p = pd.Series(bin_p) # Define number & range of bins bin_count = np.linspace(-R, R, pixels + 1) # Create image (array) bins = np.histogram2d(bin_h, bin_f, bins=bin_count, weights=bin_p)[0] # x and y are switch because when the bins were turned into a Series the shape[0] and shape[1] were switched # Convert to DataFrame image = bins return image #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ def remove_text_inside_brackets(text, brackets="()[]"): count = [0] * (len(brackets) // 2) # count open/close brackets saved_chars = [] for character in text: for i, b in enumerate(brackets): if character == b: # found bracket kind, is_close = divmod(i, 2) count[kind] += (-1)**is_close # `+1`: open, `-1`: close if count[kind] < 0: # unbalanced bracket count[kind] = 0 # keep it else: # found bracket to remove break else: # character is not a [balanced] bracket if not any(count): # outside brackets saved_chars.append(character) return ''.join(saved_chars) #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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Python

mzutils/nlp_tasks/nlp_metrics.py

Mohan-Zhang-u/mzutils

4c6a657b6a03d953a97c6e0f2b252c8aead6aa2b

[ "MIT" ]

132

2020-05-25T13:32:12.000Z

2021-05-27T19:49:03.000Z

mzutils/nlp_tasks/nlp_metrics.py

Mohan-Zhang-u/mzutils

4c6a657b6a03d953a97c6e0f2b252c8aead6aa2b

[ "MIT" ]

null

mzutils/nlp_tasks/nlp_metrics.py

Mohan-Zhang-u/mzutils

4c6a657b6a03d953a97c6e0f2b252c8aead6aa2b

[ "MIT" ]

8

2020-09-04T05:07:10.000Z

2020-09-22T03:19:14.000Z

import copy import numpy as np from nltk.translate.bleu_score import corpus_bleu from nltk.tokenize import word_tokenize def rouge_helper_prepare_results(m, p, r, f): return '\t{}:\t{}: {:5.2f}\t{}: {:5.2f}\t{}: {:5.2f}'.format(m, 'P', 100.0 * p, 'R', 100.0 * r, 'F1', 100.0 * f) def remove_sub_strings(predicted_txt, tokens=['ᐛ ', ' ✬', '<unk>']): """ remove the list of strings (tokens) from predicted_txt """ for token in tokens: predicted_txt = predicted_txt.replace(token, "") return predicted_txt def remove_sub_strings_chinese(predicted_txt, tokens=['ᐛ', '✬', '<unk>']): """ remove the list of strings (tokens) from predicted_txt """ for token in tokens: predicted_txt = predicted_txt.replace(token, "") return predicted_txt def translation_paraphrase_evaluation_english_tagpa(sources, hypos, refs, print_scores=True, max_n=4, rouge_alpha=0.5, rouge_weight_factor=1.2, rouge_stemming=True): """ to evalute generated paraphrase or translations with BlEU and ROUGE scores. Nothing should be tokenized here. :param sources: source sentence to start with. e.g. ['Young woman with sheep on straw covered floor .', 'A man who is walking across the street .'] :param hypos: generated hypotheses. should share the same shape with sources. (each source, generate one list of hypothesis sentence.) e.g. ['Young woman with sheep on straw covered floor .', 'a little girl with sheep on straw covered floor .'] for 'Young woman with sheep on straw covered floor .' :param refs: list of list of sentences. For each source, given a list of possible references. e.g. [['Young woman with sheep on straw covered floor .', 'Young woman on the floor .'] ['A man who is walking across the street now.', 'A man walking across the street.']] :return: a dictionary of scores. """ import rouge # pip install git+https://github.com/Mohan-Zhang-u/py-rouge.git sources_refs = [[sentence] for sentence in sources] # we use source as the reference to compute a negative score, in order to measure the diversity of paraphrasing. metrics_dict = {} for aggregator in ['Avg', 'Best']: apply_avg = aggregator == 'Avg' apply_best = aggregator == 'Best' evaluator = rouge.Rouge(metrics=['rouge-n', 'rouge-l', 'rouge-w'], max_n=max_n, apply_avg=apply_avg, apply_best=apply_best, alpha=rouge_alpha, # Default F1_score weight_factor=rouge_weight_factor, stemming=rouge_stemming) compare_dict = {'hypos':hypos, 'sources':sources, 'sources_refs_diversity_negative': hypos} for key in compare_dict: if key == 'sources_refs_diversity_negative': scores = evaluator.get_scores(compare_dict[key], sources_refs) else: scores = evaluator.get_scores(compare_dict[key], refs) metrics_dict[key+'_rouge_'+aggregator] = scores if print_scores: print('Evaluation with {} with {}'.format(key, aggregator)) for metric, results in sorted(scores.items(), key=lambda x: x[0]): if not apply_avg and not apply_best: # value is a type of list as we evaluate each summary vs each reference for hypothesis_id, results_per_ref in enumerate(results): nb_references = len(results_per_ref['p']) for reference_id in range(nb_references): print('\tHypothesis #{} & Reference #{}: '.format(hypothesis_id, reference_id)) print('\t' + rouge_helper_prepare_results(metric,results_per_ref['p'][reference_id], results_per_ref['r'][reference_id], results_per_ref['f'][reference_id])) print() else: print(rouge_helper_prepare_results(metric, results['p'], results['r'], results['f'])) print() bleu_sources = [] for source in sources: bleu_sources.append(word_tokenize(source)) bleu_hypos = [] for hypo in hypos: bleu_hypos.append(word_tokenize(hypo)) bleu_refs = copy.deepcopy(refs) for sub_ref in bleu_refs: for i in range(len(sub_ref)): sub_ref[i] = word_tokenize(sub_ref[i]) for sources_ref in sources_refs: for i in range(len(sources_ref)): sources_ref[i] = word_tokenize(sources_ref[i]) # metrics_dict["bleu_no_weights"] = corpus_bleu(refs, hypos) metrics_dict["bleu_1"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["bleu_2"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["bleu_3"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["bleu_4"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["source_sentence_bleu_1"] = corpus_bleu(bleu_refs, bleu_sources, weights=(1, 0, 0, 0)) metrics_dict["source_sentence_bleu_2"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.5, 0.5, 0, 0)) metrics_dict["source_sentence_bleu_3"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["source_sentence_bleu_4"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["sources_as_refs_diversity_negative_bleu_1"] = corpus_bleu(sources_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_2"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_3"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_4"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) if print_scores: for sc in ["bleu_1", "bleu_2", "bleu_3", "bleu_4", "source_sentence_bleu_1", "source_sentence_bleu_2", "source_sentence_bleu_3", "source_sentence_bleu_4", "sources_as_refs_diversity_negative_bleu_1", "sources_as_refs_diversity_negative_bleu_2", "sources_as_refs_diversity_negative_bleu_3", "sources_as_refs_diversity_negative_bleu_4"]: print(sc,"(percents):", round(metrics_dict[sc], 4) * 100) return metrics_dict def translation_paraphrase_evaluation(sources, hypos, refs, sentence_preproce_function=None, print_scores=True, max_n=4, rouge_alpha=0.5, rouge_weight_factor=1.2, rouge_stemming=True, hypo_style='first'): """ to evalute generated paraphrase or translations with BlEU and ROUGE scores. Nothing should be tokenized here. :param sources: source sentence to start with. e.g. sources = ['Young woman with sheep on straw covered floor.', 'A man who is walking across the street.', 'A brightly lit kitchen with lots of natural light.'] :param hypos: generated hypotheses. should share the same shape with sources. (each source, generate one list of hypothesis sentence.) e.g. [['A child places his hands on the head and neck of a sheep while another sheep looks at his face.', 'A person petting the head of a cute fluffy sheep.', 'A child is petting a sheep while another sheep watches.', 'A woman kneeling to pet animals while others wait. '], ['A busy intersection with an ice cream truck driving by.', 'a man walks behind an ice cream truck ', 'A man is crossing a street near an icecream truck.', 'The man is walking behind the concession bus.'], ['A modern kitchen in white with stainless steel lights.', 'A kitchen filled with lots of white counter space.', 'A KITCHEN IN THE ROOM WITH WHITE APPLIANCES ', 'A modern home kitchen and sitting area looking out towards the back yard']] :param refs: list of list of sentences. For each source, given a list of possible references. e.g. [['A woman standing next to a sheep in a pen .<unk>', 'A woman standing next to a sheep on a farm .<unk>', 'A woman standing next to a sheep in a barn .<unk>', 'A woman standing next to a sheep in a field .<unk>', 'A woman standing next to a sheep in a barn<unk>'], ['A man crossing the street in front of a store .<unk>', 'A man crossing the street in a city .<unk>', 'A person crossing the street in a city .<unk>', 'A man crossing the street in the middle of a city<unk>', 'A man crossing the street in the middle of a city street<unk>'], ['a kitchen with a stove a microwave and a sink<unk>', 'a kitchen with a stove a sink and a microwave<unk>', 'a kitchen with a stove a sink and a refrigerator<unk>', 'A kitchen with a sink , stove , microwave and window .<unk>', 'a kitchen with a stove a sink and a window<unk>']] :param hypo_style: how to evaluate the generated hypotheses. Pick the first? Choose the one with best evalution score? Average the scores on all hypotheses? Should be one of ['first', 'best', 'average'] :param sentence_preproce_function: a function that will be applied to all sentences in sources, hypos, refs :return: a dictionary of scores. """ import rouge # pip install git+https://github.com/Mohan-Zhang-u/py-rouge.git assert(isinstance(sources, list)) assert(isinstance(sources[0], str)) assert(isinstance(hypos, list)) assert(isinstance(hypos[0], list)) assert(isinstance(hypos[0][0], str)) assert(isinstance(refs, list)) assert(isinstance(refs[0], list)) assert(isinstance(refs[0][0], str)) if hypo_style == 'first': hypos = [hypo[0] for hypo in hypos] else: raise NotImplementedError # apply sentence_preproce_function, e.g. remove_tokens if sentence_preproce_function is not None: sources = [sentence_preproce_function(source) for source in sources] if hypo_style == 'first': hypos = [sentence_preproce_function(hypo) for hypo in hypos] else: raise NotImplementedError hypos = [[sentence_preproce_function(hypo) for hypo in hypo_list] for hypo_list in hypos] refs = [[sentence_preproce_function(ref) for ref in refs_list] for refs_list in refs] sources_refs = [[sentence] for sentence in sources] # we use source as the reference to compute a negative score, in order to measure the diversity of paraphrasing. metrics_dict = {} for aggregator in ['Avg', 'Best']: apply_avg = aggregator == 'Avg' apply_best = aggregator == 'Best' evaluator = rouge.Rouge(metrics=['rouge-n', 'rouge-l', 'rouge-w'], max_n=max_n, apply_avg=apply_avg, apply_best=apply_best, alpha=rouge_alpha, # Default F1_score weight_factor=rouge_weight_factor, stemming=rouge_stemming) compare_dict = {'hypos':hypos, 'sources':sources, 'sources_refs_diversity_negative': hypos} for key in compare_dict: if key == 'sources_refs_diversity_negative': scores = evaluator.get_scores(compare_dict[key], sources_refs) else: scores = evaluator.get_scores(compare_dict[key], refs) metrics_dict[key+'_rouge_'+aggregator] = scores if print_scores: print('Evaluation with {} with {}'.format(key, aggregator)) for metric, results in sorted(scores.items(), key=lambda x: x[0]): if not apply_avg and not apply_best: # value is a type of list as we evaluate each summary vs each reference for hypothesis_id, results_per_ref in enumerate(results): nb_references = len(results_per_ref['p']) for reference_id in range(nb_references): print('\tHypothesis #{} & Reference #{}: '.format(hypothesis_id, reference_id)) print('\t' + rouge_helper_prepare_results(metric,results_per_ref['p'][reference_id], results_per_ref['r'][reference_id], results_per_ref['f'][reference_id])) print() else: print(rouge_helper_prepare_results(metric, results['p'], results['r'], results['f'])) print() bleu_sources = [] for source in sources: bleu_sources.append(word_tokenize(source)) bleu_hypos = [] if hypo_style == 'first': for hypo in hypos: bleu_hypos.append(word_tokenize(hypo)) else: raise NotImplementedError bleu_hypos = copy.deepcopy(hypos) for sub_hypo in bleu_hypos: for i in range(len(sub_hypo)): sub_hypo[i] = word_tokenize(sub_hypo[i]) bleu_refs = copy.deepcopy(refs) for sub_ref in bleu_refs: for i in range(len(sub_ref)): sub_ref[i] = word_tokenize(sub_ref[i]) for sources_ref in sources_refs: for i in range(len(sources_ref)): sources_ref[i] = word_tokenize(sources_ref[i]) # print(corpus_bleu(bleu_refs, bleu_hypos, weights=(1, 0, 0, 0))) # return metrics_dict["bleu_1"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["bleu_2"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["bleu_3"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["bleu_4"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["source_sentence_bleu_1"] = corpus_bleu(bleu_refs, bleu_sources, weights=(1, 0, 0, 0)) metrics_dict["source_sentence_bleu_2"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.5, 0.5, 0, 0)) metrics_dict["source_sentence_bleu_3"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["source_sentence_bleu_4"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["sources_as_refs_diversity_negative_bleu_1"] = corpus_bleu(sources_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_2"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_3"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_4"] = corpus_bleu(sources_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) if print_scores: for sc in ["bleu_1", "bleu_2", "bleu_3", "bleu_4", "source_sentence_bleu_1", "source_sentence_bleu_2", "source_sentence_bleu_3", "source_sentence_bleu_4", "sources_as_refs_diversity_negative_bleu_1", "sources_as_refs_diversity_negative_bleu_2", "sources_as_refs_diversity_negative_bleu_3", "sources_as_refs_diversity_negative_bleu_4"]: print(sc,"(percents):", round(metrics_dict[sc], 4) * 100) return metrics_dict def translation_paraphrase_evaluation_chinese(sources, hypos, refs, sentence_preproce_function=None, print_scores=True, max_n=4, rouge_alpha=0.5, rouge_weight_factor=1.2, rouge_stemming=True, hypo_style='first', word_segmentor='character'): """ to evalute generated paraphrase or translations with BlEU and ROUGE scores. Nothing should be tokenized here. :param sources: source sentence to start with. :param hypos: generated hypotheses. should share the same shape with sources. (each source, generate one list of hypothesis sentence.) :param refs: list of list of sentences. For each source, given a list of possible references. :param hypo_style: how to evaluate the generated hypotheses. Pick the first? Choose the one with best evalution score? Average the scores on all hypotheses? Should be one of ['first', 'best', 'average'] :param sentence_preproce_function: a function that will be applied to all sentences in sources, hypos, refs :param word_segmentor: 'character' means seperate each character to be a word, 'hanlp' means an hanlp chinese tokenizer. :return: a dictionary of scores. """ import rouge # pip install git+https://github.com/Mohan-Zhang-u/py-rouge.git assert(isinstance(sources, list)) assert(isinstance(sources[0], str)) assert(isinstance(hypos, list)) assert(isinstance(hypos[0], list)) assert(isinstance(hypos[0][0], str)) assert(isinstance(refs, list)) assert(isinstance(refs[0], list)) assert(isinstance(refs[0][0], str)) # apply sentence_preproce_function, e.g. remove_tokens if sentence_preproce_function is not None: sources = [sentence_preproce_function(source) for source in sources] hypos = [[sentence_preproce_function(hypo) for hypo in hypo_list] for hypo_list in hypos] refs = [[sentence_preproce_function(ref) for ref in refs_list] for refs_list in refs] sources_refs = [[sentence] for sentence in sources] # we use source as the reference to compute a negative score, in order to measure the diversity of paraphrasing. metrics_dict = {} # tokenize chinese sentences. if word_segmentor == 'character': sources = [' '.join(source) for source in sources] refs = [[' '.join(ref) for ref in ref_list] for ref_list in refs] sources_refs = [[' '.join(ref) for ref in ref_list] for ref_list in sources_refs] hypos = [[' '.join(hypo) for hypo in hypo_list] for hypo_list in hypos] def word_tokenize(sentence): return sentence.split(' ') bleu_sources = [] for source in sources: bleu_sources.append(word_tokenize(source)) bleu_hypos = copy.deepcopy(hypos) for sub_hypo in bleu_hypos: for i in range(len(sub_hypo)): sub_hypo[i] = word_tokenize(sub_hypo[i]) bleu_refs = copy.deepcopy(refs) for sub_ref in bleu_refs: for i in range(len(sub_ref)): sub_ref[i] = word_tokenize(sub_ref[i]) bleu_sources_refs = copy.deepcopy(sources_refs) for sources_ref in bleu_sources_refs: for i in range(len(sources_ref)): sources_ref[i] = word_tokenize(sources_ref[i]) elif word_segmentor == 'hanlp': import hanlp word_tokenize = hanlp.load('LARGE_ALBERT_BASE') bleu_sources = [] for source in sources: bleu_sources.append(word_tokenize(source)) bleu_hypos = copy.deepcopy(hypos) for sub_hypo in bleu_hypos: for i in range(len(sub_hypo)): sub_hypo[i] = word_tokenize(sub_hypo[i]) bleu_refs = copy.deepcopy(refs) for sub_ref in bleu_refs: for i in range(len(sub_ref)): sub_ref[i] = word_tokenize(sub_ref[i]) bleu_sources_refs = copy.deepcopy(sources_refs) for bleu_sources_ref in bleu_sources_refs: for i in range(len(bleu_sources_ref)): bleu_sources_ref[i] = word_tokenize(bleu_sources_ref[i]) sources = [' '.join(source) for source in bleu_sources] refs = [[' '.join(ref) for ref in ref_list] for ref_list in bleu_refs] sources_refs = [[' '.join(ref) for ref in ref_list] for ref_list in bleu_sources_refs] hypos = [[' '.join(hypo) for hypo in hypo_list] for hypo_list in bleu_hypos] if hypo_style == 'first': hypos = [hypo[0] for hypo in hypos] bleu_hypos = [hypo[0] for hypo in bleu_hypos] else: raise NotImplementedError for aggregator in ['Avg', 'Best']: apply_avg = aggregator == 'Avg' apply_best = aggregator == 'Best' evaluator = rouge.Rouge(metrics=['rouge-n', 'rouge-l', 'rouge-w'], max_n=max_n, apply_avg=apply_avg, apply_best=apply_best, alpha=rouge_alpha, # Default F1_score weight_factor=rouge_weight_factor, stemming=rouge_stemming, language='chinese') compare_dict = {'hypos':hypos, 'sources':sources, 'sources_refs_diversity_negative': hypos} for key in compare_dict: if key == 'sources_refs_diversity_negative': scores = evaluator.get_scores(compare_dict[key], sources_refs) else: scores = evaluator.get_scores(compare_dict[key], refs) metrics_dict[key+'_rouge_'+aggregator] = scores if print_scores: print('Evaluation with {} with {}'.format(key, aggregator)) for metric, results in sorted(scores.items(), key=lambda x: x[0]): if not apply_avg and not apply_best: # value is a type of list as we evaluate each summary vs each reference for hypothesis_id, results_per_ref in enumerate(results): nb_references = len(results_per_ref['p']) for reference_id in range(nb_references): print('\tHypothesis #{} & Reference #{}: '.format(hypothesis_id, reference_id)) print('\t' + rouge_helper_prepare_results(metric,results_per_ref['p'][reference_id], results_per_ref['r'][reference_id], results_per_ref['f'][reference_id])) print() else: print(rouge_helper_prepare_results(metric, results['p'], results['r'], results['f'])) print() metrics_dict["bleu_1"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["bleu_2"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["bleu_3"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["bleu_4"] = corpus_bleu(bleu_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["source_sentence_bleu_1"] = corpus_bleu(bleu_refs, bleu_sources, weights=(1, 0, 0, 0)) metrics_dict["source_sentence_bleu_2"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.5, 0.5, 0, 0)) metrics_dict["source_sentence_bleu_3"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["source_sentence_bleu_4"] = corpus_bleu(bleu_refs, bleu_sources, weights=(0.25, 0.25, 0.25, 0.25)) metrics_dict["sources_as_refs_diversity_negative_bleu_1"] = corpus_bleu(bleu_sources_refs, bleu_hypos, weights=(1, 0, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_2"] = corpus_bleu(bleu_sources_refs, bleu_hypos, weights=(0.5, 0.5, 0, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_3"] = corpus_bleu(bleu_sources_refs, bleu_hypos, weights=(0.33, 0.33, 0.34, 0)) metrics_dict["sources_as_refs_diversity_negative_bleu_4"] = corpus_bleu(bleu_sources_refs, bleu_hypos, weights=(0.25, 0.25, 0.25, 0.25)) if print_scores: for sc in ["bleu_1", "bleu_2", "bleu_3", "bleu_4", "source_sentence_bleu_1", "source_sentence_bleu_2", "source_sentence_bleu_3", "source_sentence_bleu_4", "sources_as_refs_diversity_negative_bleu_1", "sources_as_refs_diversity_negative_bleu_2", "sources_as_refs_diversity_negative_bleu_3", "sources_as_refs_diversity_negative_bleu_4"]: print(sc,"(percents):", round(metrics_dict[sc], 4) * 100) return metrics_dict

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Python

tests/v3_validation/cattlevalidationtest/core/test_ext_services_links.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

7

2015-11-18T17:43:08.000Z

2021-07-14T09:48:18.000Z

tests/v3_validation/cattlevalidationtest/core/test_ext_services_links.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

175

2015-07-09T18:41:24.000Z

2021-06-10T21:23:27.000Z

tests/v3_validation/cattlevalidationtest/core/test_ext_services_links.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

25

2015-08-08T04:54:24.000Z

2021-05-25T21:10:37.000Z

from common_fixtures import * # NOQA logger = logging.getLogger(__name__) def activate_environment_with_external_services( client, service_scale, port): env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) service.activate() ext_service.activate() service.addservicelink(serviceLink={"serviceId": ext_service.id}) service = client.wait_success(service, 120) ext_service = client.wait_success(ext_service, 120) assert service.state == "active" assert ext_service.state == "active" validate_add_service_link(client, service, ext_service) return env, service, ext_service, con_list def test_extservice_activate_svc_activate_external_svc_link( client): port = "3001" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_activate_external_svc_link_activate_svc( client): port = "3002" service_scale = 2 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) ext_service = activate_svc(client, ext_service) link_svc(client, service, [ext_service]) service = activate_svc(client, service) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_activate_svc_link_activate_external_svc( client): port = "3003" service_scale = 1 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) service = activate_svc(client, service) link_svc(client, service, [ext_service]) ext_service = activate_svc(client, ext_service) validate_add_service_link(client, service, ext_service) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_link_activate_external_svc_activate_svc( client): port = "3004" service_scale = 1 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) link_svc(client, service, [ext_service]) ext_service = activate_svc(client, ext_service) service = activate_svc(client, service) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_link_activate_svc_activate_external_svc( client): port = "3005" service_scale = 1 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) link_svc(client, service, [ext_service]) service = activate_svc(client, service) ext_service = activate_svc(client, ext_service) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_link_when_services_still_activating(client): port = "3006" service_scale = 1 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port) service.activate() ext_service.activate() service.addservicelink(serviceLink={"serviceId": ext_service.id}) service = client.wait_success(service, 120) ext_service = client.wait_success(ext_service, 120) assert service.state == "active" assert ext_service.state == "active" validate_add_service_link(client, service, ext_service) validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_service_scale_up(client): port = "3007" service_scale = 1 final_service_scale = 3 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service(client, service, [ext_service], port, con_list) service = client.update(service, scale=final_service_scale, name=service.name) service = client.wait_success(service, 120) assert service.state == "active" assert service.scale == final_service_scale validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_services_scale_down(client): port = "3008" service_scale = 3 final_service_scale = 1 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service(client, service, [ext_service], port, con_list) service = client.update(service, scale=final_service_scale, name=service.name) service = client.wait_success(service, 120) assert service.state == "active" assert service.scale == final_service_scale validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_ext_services_deactivate_activate(client): port = "3014" service_scale = 1 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) ext_service = ext_service.deactivate() ext_service = client.wait_success(ext_service, 120) assert ext_service.state == "inactive" ext_service = ext_service.activate() ext_service = client.wait_success(ext_service, 120) assert ext_service.state == "active" validate_external_service( client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_service_deactivate_activate(client): port = "3015" service_scale = 1 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service(client, service, [ext_service], port, con_list) service = service.deactivate() service = client.wait_success(service, 120) assert service.state == "inactive" wait_until_instances_get_stopped(client, service) service = service.activate() service = client.wait_success(service, 120) assert service.state == "active" time.sleep(restart_sleep_interval) validate_external_service(client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_deactivate_activate_environment(client): port = "3016" service_scale = 1 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) env = env.deactivateservices() service = client.wait_success(service, 120) assert service.state == "inactive" ext_service = client.wait_success(ext_service, 120) assert ext_service.state == "inactive" wait_until_instances_get_stopped(client, service) env = env.activateservices() service = client.wait_success(service, 120) assert service.state == "active" ext_service = client.wait_success(ext_service, 120) assert ext_service.state == "active" time.sleep(restart_sleep_interval) validate_external_service(client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_services_delete_service_add_service(client): port = "3018" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) # Delete Service service = client.wait_success(client.delete(service)) assert service.state == "removed" validate_remove_service_link(client, service, ext_service) port1 = "30180" # Add another service and link to external service launch_config = {"image": SSH_IMAGE_UUID, "ports": [port1+":22/tcp"]} random_name = random_str() service_name = random_name.replace("-", "") service1 = client.create_service(name=service_name, stackId=env.id, launchConfig=launch_config, scale=1) service1 = client.wait_success(service1) assert service1.state == "inactive" service1 = service1.activate() service1 = client.wait_success(service1, 120) assert service1.state == "active" service1.addservicelink(serviceLink={"serviceId": ext_service.id}) validate_add_service_link(client, service1, ext_service) validate_external_service(client, service1, [ext_service], port1, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_delete_and_add_ext_service(client): port = "3019" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) # Delete external service ext_service = client.wait_success(client.delete(ext_service)) assert ext_service.state == "removed" validate_remove_service_link(client, service, ext_service) # Add another external service and link the service to this newly created # external service c1 = client.create_container(name=random_str(), image=WEB_IMAGE_UUID) c2 = client.create_container(name=random_str(), image=WEB_IMAGE_UUID) c1 = client.wait_success(c1, 120) assert c1.state == "running" c2 = client.wait_success(c2, 120) assert c2.state == "running" con_list = [c1, c2] ips = [c1.primaryIpAddress, c2.primaryIpAddress] # Create external Service random_name = random_str() ext_service_name = random_name.replace("-", "") ext_service1 = client.create_externalService( name=ext_service_name, stackId=env.id, externalIpAddresses=ips) ext_service1 = client.wait_success(ext_service1) ext_service1 = activate_svc(client, ext_service1) service.addservicelink(serviceLink={"serviceId": ext_service1.id}) validate_add_service_link(client, service, ext_service1) validate_external_service(client, service, [ext_service1], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_services_stop_start_instance(client, socat_containers): port = "3020" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service(client, service, [ext_service], port, con_list) container_name = get_container_name(env, service, 2) containers = client.list_container(name=container_name) assert len(containers) == 1 service_instance = containers[0] # Stop service instance stop_container_from_host(client, service_instance) service = client.wait_success(service) wait_for_scale_to_adjust(client, service) time.sleep(restart_sleep_interval) validate_external_service(client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_services_restart_instance(client): port = "3021" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services( client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) container_name = get_container_name(env, service, 2) containers = client.list_container(name=container_name) assert len(containers) == 1 service_instance = containers[0] # Restart external instance service_instance = client.wait_success(service_instance.restart(), 120) assert service_instance.state == 'running' time.sleep(restart_sleep_interval) validate_external_service(client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_add_and_delete_ips(client): port = "3023" service_scale = 2 env, service, ext_service, con_list = \ activate_environment_with_external_services(client, service_scale, port) validate_external_service( client, service, [ext_service], port, con_list) # Update external Service to add one more ip c1 = client.create_container(name=random_str(), image=WEB_IMAGE_UUID) c1 = client.wait_success(c1, 120) assert c1.state == "running" ips = [con_list[0].primaryIpAddress, con_list[1].primaryIpAddress, c1.primaryIpAddress] con_list.append(c1) ext_service = client.update( ext_service, name=ext_service.name, externalIpAddresses=ips) ext_service = client.wait_success(ext_service, 120) validate_external_service(client, service, [ext_service], port, con_list) # Update external Service to remove one of the existing ips ips = [con_list[1].primaryIpAddress, c1.primaryIpAddress] con_list.pop(0) ext_service = client.update( ext_service, name=ext_service.name, externalIpAddresses=ips) ext_service = client.wait_success(ext_service, 120) validate_external_service(client, service, [ext_service], port, con_list) con_list.append(env) delete_all(client, con_list) def test_extservice_with_cname(client): port = "3024" service_scale = 2 env, service, ext_service, con_list = create_env_with_ext_svc( client, service_scale, port, True) ext_service = activate_svc(client, ext_service) link_svc(client, service, [ext_service]) service = activate_svc(client, service) validate_external_service_for_hostname( client, service, [ext_service], port) delete_all(client, [env])

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0.732635

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1

0

null

0

7

6d602485fd1dff7e6014d26ad6e185723f29af78

206

py

Python

publicacion/admin.py

GuilleJR83/ProyectoInformatorio2021

2c23e6b42f0ac3bf37c9b46d0431d29793b5c688

[ "CC0-1.0" ]

null

publicacion/admin.py

GuilleJR83/ProyectoInformatorio2021

2c23e6b42f0ac3bf37c9b46d0431d29793b5c688

[ "CC0-1.0" ]

null

publicacion/admin.py

GuilleJR83/ProyectoInformatorio2021

2c23e6b42f0ac3bf37c9b46d0431d29793b5c688

[ "CC0-1.0" ]

3

2021-12-27T21:26:01.000Z

2022-01-04T14:01:09.000Z

from django.contrib import admin from comentario.models import Comentario from .models import Publicacion, Like admin.site.register(Publicacion) admin.site.register(Like) #admin.site.register(Comentario)

22.888889

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0.298246

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null

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0

1

0

1

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7

61211a750d8ad8dc2dd5282bcbf1c5cd861f133e

114

py

Python

durand/services/pdo.py

semiversus/python-durand

feb39657b2721c91256c1aefef8d4263b54e3769

[ "MIT" ]

5

2022-01-24T03:58:52.000Z

2022-03-07T09:50:36.000Z

durand/services/pdo.py

semiversus/python-durand

feb39657b2721c91256c1aefef8d4263b54e3769

[ "MIT" ]

null

durand/services/pdo.py

semiversus/python-durand

feb39657b2721c91256c1aefef8d4263b54e3769

[ "MIT" ]

null

class TPDO: def __init__(self, index): pass class RPDO: def __init__(self, index): pass

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0

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1

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b69a5b900caa4e43710b453a489f257c481fd81e

12,608

py

Python

openPyther/pressure.py

Vicken-Ghoubiguian/openWeatherPy

510ac8979baf742ca0091f942f7d81242c535cec

[ "MIT" ]

null

openPyther/pressure.py

Vicken-Ghoubiguian/openWeatherPy

510ac8979baf742ca0091f942f7d81242c535cec

[ "MIT" ]

null

openPyther/pressure.py

Vicken-Ghoubiguian/openWeatherPy

510ac8979baf742ca0091f942f7d81242c535cec

[ "MIT" ]

null

# Import the module which contains the pressure units enum... from . import pressureEnum # Definition of the Pressure class... class Pressure: # Definition of the Pressure class constructor... def __init__(self, value): self.__value = value self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL # Definition of the hectoPascal's converter... def setPressureAsHectoPascal(self): # In the case where the current unit is Pascal... if self.__measureUnit == pressureEnum.PressureEnum.PASCAL: self.__value = self.__value / 100 self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL print("\nPressure converted from Pascal (Pa) to hectoPascal (hPa)\n") # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: self.__value = self.__value * 1000 self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL print("\nPressure converted from Bar (bar) to hectoPascal (hPa)\n") # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: self.__value = self.__value * 1013.2501 self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL print("\nPressure converted from Atmosphere (atm) to hectoPascal (hPa)\n") # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: self.__value = self.__value * 1.333223684211 self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL print("\nPressure converted from Torr (torr) to hectoPascal (hPa)\n") # In the case where the current unit is Pounds Per Square Inch... elif self.__measureUnit == pressureEnum.PressureEnum.POUNDSPERSQUAREINCH: self.__value = self.__value * 68.9475729318 self.__measureUnit = pressureEnum.PressureEnum.HECTOPASCAL print("\nPressure converted from Pounds Per Square Inch (psi) to hectoPascal (hPa)\n") # In the case where the current unit is hectoPascal... else: print("\nPressure already in hectoPascal (hPa)\n") # Definition of the Pascal's converter... def setPressureAsPascal(self): # In the case where the current unit is hectoPascal... if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: self.__value = self.__value * 100 self.__measureUnit = pressureEnum.PressureEnum.PASCAL print("\nPressure converted from hectoPascal (hPa) to Pascal (Pa)\n") # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: self.__value = self.__value * 100000 self.__measureUnit = pressureEnum.PressureEnum.PASCAL print("\nPressure converted from Bar (bar) to Pascal (Pa)\n") # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: self.__value = self.__value * 101325 self.__measureUnit = pressureEnum.PressureEnum.PASCAL print("\nPressure converted from Atmosphere (atm) to Pascal (Pa)\n") # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: self.__value = self.__value * 133.3223684211 self.__measureUnit = pressureEnum.PressureEnum.PASCAL print("\nPressure converted from Torr (torr) to Pascal (Pa)\n") # In the case where the current unit is Pounds Per Square Inch... elif self.__measureUnit == pressureEnum.PressureEnum.POUNDSPERSQUAREINCH: self.__value = self.__value * 6894.7572931783 self.__measureUnit = pressureEnum.PressureEnum.PASCAL print("\nPressure converted from Pounds Per Square Inch (psi) to Pascal (Pa)\n") # In the case where the current unit is Pascal... else: print("\nPressure already in Pascal (Pa)\n") # Definition of the Bar's converter... def setPressureAsBar(self): # In the case where the current unit is hectoPascal... if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: self.__value = self.__value / 1000 self.__measureUnit = pressureEnum.PressureEnum.BAR print("\nPressure converted from hectoPascal (hPa) to Bar (bar)\n") # In the case where the current unit is Pascal... elif self.__measureUnit == pressureEnum.PressureEnum.PASCAL: self.__value = self.__value / 100000 self.__measureUnit = pressureEnum.PressureEnum.BAR print("\nPressure converted from Pascal (pa) to Bar (bar)\n") # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: self.__value = self.__value * 1.01325 self.__measureUnit = pressureEnum.PressureEnum.BAR print("\nPressure converted from Atmosphere (atm) to Bar (bar)\n") # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: self.__value = self.__value / 750.06375541921 self.__measureUnit = pressureEnum.PressureEnum.BAR print("\nPressure converted from Torr (torr) to Bar (bar)\n") # In the case where the current unit is Pounds Per Square Inch... elif self.__measureUnit == pressureEnum.PressureEnum.POUNDSPERSQUAREINCH: self.__value = self.__value / 14.5037738 self.__measureUnit = pressureEnum.PressureEnum.BAR print("\nPressure converted from Pounds Per Square Inch (psi) to Bar (bar)\n") # In the case where the current unit is Bar... else: print("\nPressure already in Bar (bar)\n") # Definition of the Atmosphere's converter... def setPressureAsAtmosphere(self): # In the case where the current unit is hectoPascal... if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: self.__value = self.__value / 1013.25 self.__measureUnit = pressureEnum.PressureEnum.ATMOSPHERE print("\nPressure converted from hectoPascal (hPa) to Atmosphere (atm)\n") # In the case where the current unit is Pascal... elif self.__measureUnit == pressureEnum.PressureEnum.PASCAL: self.__value = self.__value / 101325 self.__measureUnit = pressureEnum.PressureEnum.ATMOSPHERE print("\nPressure converted from Pascal (pa) to Atmosphere (atm)\n") # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: self.__value = self.__value / 1.01325 self.__measureUnit = pressureEnum.PressureEnum.ATMOSPHERE print("\nPressure converted from Bar (bar) to Atmosphere (atm)\n") # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: self.__value = self.__value / 760 self.__measureUnit = pressureEnum.PressureEnum.ATMOSPHERE print("\nPressure converted from Torr (torr) to Atmosphere (atm)\n") # In the case where the current unit is Pounds Per Square Inch... elif self.__measureUnit == pressureEnum.PressureEnum.POUNDSPERSQUAREINCH: self.__value = self.__value / 14.696 self.__measureUnit = pressureEnum.PressureEnum.ATMOSPHERE print("\nPressure converted from Pounds Per Square Inch (psi) to Atmosphere (atm)\n") # In the case where the current unit is Atmosphere... else: print("\nPressure already in Atmosphere (atm)\n") # Definition of the Torr's converter... def setPressureAsTorr(self): # In the case where the current unit is Pascal... if self.__measureUnit == pressureEnum.PressureEnum.PASCAL: self.__value = self.__value / 133.32236842 self.__measureUnit = pressureEnum.PressureEnum.TORR print("\nPressure converted from Pascal (Pa) to to Torr (torr)\n") # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: self.__value = self.__value * 750.061682704 self.__measureUnit = pressureEnum.PressureEnum.TORR print("\nPressure converted from Bar (bar) to to Torr (torr)\n") # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: self.__value = self.__value * 760 self.__measureUnit = pressureEnum.PressureEnum.TORR print("\nPressure converted from Atmosphere (atm) to Torr (torr)\n") # In the case where the current unit is hectoPascal... elif self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: self.__value = self.__value / 1.3332236842 self.__measureUnit = pressureEnum.PressureEnum.TORR print("\nPressure converted from hectoPascal (hPa) to Torr (torr)\n") # In the case where the current unit is Pounds Per Square Inch... elif self.__measureUnit == pressureEnum.PressureEnum.POUNDSPERSQUAREINCH: self.__value = self.__value * 51.715 self.__measureUnit = pressureEnum.PressureEnum.TORR print("\nPressure converted from Pounds Per Square Inch (psi) to Torr (torr)\n") # In the case where the current unit is Torr... else: print("\nPressure already in Torr (torr)\n") # Definition of the Pounds per square inch's converter... def setPressureAsPoundsPerSquareInch(self): # In the case where the current unit is hectoPascal... if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: self.__value = self.__value / 68.94757293168 self.__measureUnit = pressureEnum.PressureEnum.POUNDSPERSQUAREINCH print("\nPressure converted from hectoPascal (hPa) to Pounds Per Square Inch (psi)\n") # In the case where the current unit is Pascal... elif self.__measureUnit == pressureEnum.PressureEnum.PASCAL: self.__value = self.__value / 6894.757293168 self.__measureUnit = pressureEnum.PressureEnum.POUNDSPERSQUAREINCH print("\nPressure converted from Pascal (Pa) to Pounds Per Square Inch (psi)\n") # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: self.__value = self.__value * 14.503773773022 self.__measureUnit = pressureEnum.PressureEnum.POUNDSPERSQUAREINCH print("\nPressure converted from Bar (bar) to Pounds Per Square Inch (psi)\n") # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: self.__value = self.__value * 14.695964 self.__measureUnit = pressureEnum.PressureEnum.POUNDSPERSQUAREINCH print("\nPressure converted from Atmosphere (atm) to Pounds Per Square Inch (psi)\n") # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: self.__value = self.__value / 51.715 self.__measureUnit = pressureEnum.PressureEnum.POUNDSPERSQUAREINCH print("\nPressure converted from Torr (torr) to Pounds Per Square Inch (psi)\n") # In the case where the current unit is Pounds Per Square Inch... else: print("\nPressure already in Pounds Per Square Inch (psi)\n") # Definition of the value's getter... def getValue(self): return self.__value # Definition of the measure unit's getter... def getMeasureUnit(self): return self.__measureUnit # Definition of a class method which returns the unit of measure's symbol as a string... def __getSymbolUnit(self): # if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: return "hPa" # elif self.__measureUnit == pressureEnum.PressureEnum.PASCAL: return "Pa" # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: return "bar" # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: return "atm" # elif self.__measureUnit == pressureEnum.PressureEnum.TORR: return "torr" # else: return "psi" # Definition of a class method which returns the unit of measure as a string... def __getMeasureUnitAsString(self): # In the case where the current unit is hectoPascal... if self.__measureUnit == pressureEnum.PressureEnum.HECTOPASCAL: return "hectoPascal" # In the case where the current unit is Pascal... elif self.__measureUnit == pressureEnum.PressureEnum.PASCAL: return "Pascal" # In the case where the current unit is Bar... elif self.__measureUnit == pressureEnum.PressureEnum.BAR: return "Bar" # In the case where the current unit is Atmosphere... elif self.__measureUnit == pressureEnum.PressureEnum.ATMOSPHERE: return "Atmosphere" # In the case where the current unit is Torr... elif self.__measureUnit == pressureEnum.PressureEnum.TORR: return "Torr" # In the case where the current unit is Pound per square inch... else: return "Pound per square inch" # Definition of the __str__ method to display the current object as a string... def __str__(self): return "{} {} ({})".format(str(self.__value), str(self.__getSymbolUnit()), str(self.__getMeasureUnitAsString()))

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b6b1f20c23fdb22728e9cf6976f14ef303fd5f5c

26,466

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Python

General/PJB_ImageRegistration/regTest_ctrlpts/SinControPointsWarpCorrect.py

petebunting/rsgis_scripts

b35b0403cdfad6c63824d4f8c038f190cdb5978d

[ "MIT" ]

4

2020-09-16T10:45:15.000Z

2021-05-06T04:34:32.000Z

General/PJB_ImageRegistration/regTest_ctrlpts/SinControPointsWarpCorrect.py

petebunting/rsgis_scripts

b35b0403cdfad6c63824d4f8c038f190cdb5978d

[ "MIT" ]

null

General/PJB_ImageRegistration/regTest_ctrlpts/SinControPointsWarpCorrect.py

petebunting/rsgis_scripts

b35b0403cdfad6c63824d4f8c038f190cdb5978d

[ "MIT" ]

2

2020-07-06T18:03:40.000Z

2022-02-15T12:45:34.000Z

#! /usr/bin/env python import math imageA = [\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [251,526,959,1177],\ [336,891,1016,1500],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1718],\ [0,0,1288,1832]\ ] imageB = [\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832],\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832],\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832],\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832],\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832],\ [0,0,500,150],\ [0,0,500,150],\ [9,38,508,187],\ [44,16,543,165],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,707,651],\ [0,0,679,109],\ [0,0,588,801],\ [0,0,450,801],\ [0,0,2218,1718],\ [0,0,1291,1832]\ ] imageACorr = [\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [250,520,957,1181],\ [335,885,1014,1504],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [245,525,961,1176],\ [335,890,1014,1499],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1831],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [250,515,957,1186],\ [0,0,634,595],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [240,525,966,1176],\ [335,890,1014,1499],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1831],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [250,510,957,1191],\ [335,875,1014,1513],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1832],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [0,0,500,150],\ [236,525,971,1176],\ [335,890,1013,1499],\ [0,0,587,800],\ [0,0,450,800],\ [0,0,2218,1728],\ [0,0,1288,1831]\ ] imageBCorr = [\ [0,5,500,155],\ [0,5,500,155],\ [10,44,510,194],\ [45,22,545,172],\ [0,5,500,155],\ [0,5,500,155],\ [0,0,707,661],\ [0,0,679,619],\ [1,5,588,805],\ [0,5,450,805],\ [0,5,2218,1733],\ [1,6,1289,1838],\ [5,0,505,150],\ [5,0,505,150],\ [10,39,510,189],\ [50,17,550,167],\ [5,0,505,150],\ [5,0,505,150],\ [0,0,716,651],\ [0,0,679,609],\ [6,0,593,800],\ [5,0,455,800],\ [5,0,2223,1728],\ [6,1,1294,1832],\ [0,9,500,159],\ [0,9,500,159],\ [10,48,510,198],\ [54,17,554,167],\ [0,9,500,159],\ [0,9,500,159],\ [0,0,707,671],\ [45,25,679,620],\ [1,10,588,810],\ [0,10,450,810],\ [0,10,2218,1738],\ [1,11,1289,1843],\ [11,0,511,150],\ [11,0,511,150],\ [10,39,510,189],\ [54,17,554,167],\ [11,0,511,150],\ [5,0,505,150],\ [0,0,726,651],\ [0,0,679,609],\ [11,0,598,800],\ [10,0,460,800],\ [10,0,2228,1728],\ [11,1,1299,1832],\ [0,13,500,163],\ [0,13,500,163],\ [10,51,510,201],\ [1,14,501,164],\ [0,13,500,163],\ [0,13,500,163],\ [0,0,707,681],\ [0,0,679,638],\ [1,16,588,816],\ [0,15,450,815],\ [0,15,2218,1743],\ [1,16,1289,1848],\ [16,0,516,150],\ [16,0,516,150],\ [9,39,509,189],\ [58,17,558,167],\ [16,0,516,150],\ [16,0,516,150],\ [0,0,735,651],\ [0,0,678,609],\ [15,0,602,800],\ [14,0,464,800],\ [15,0,2233,1728],\ [16,1,1304,1832]\ ] step = [ \ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [8,8],\ [20,20],\ [20,20],\ [20,20],\ [20,20],\ [50,50],\ [50,50]\ ] # 0 == X and 1 == Y warpAxis = [ \ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1,\ 1\ ] outputWarpFiles = [ \ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_LiDARsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_AIRSAR_LiDARsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_AIRSAR_LiDARsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_HyMap_LiDARsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_HyMap_LiDARsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p142_CASI_HyMapsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/p138_CASI_HyMapsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune2_AIRSAR_HyMapsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/injune8_AIRSAR_HyMapsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene1_ALOS_Landsatsin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/WarppedImages4Tests/ctrl_pts/scene2_ALOS_Landsatsin15warpY_ctrlpts.pts'\ ] outputCorrectFiles = [ \ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin5warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin5warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin10warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin10warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin15warpX_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_AIRSAR_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_AIRSAR_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_HyMap_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_HyMap_correct_LiDAR_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p142_CASI_correct_HyMap_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/p138_CASI_correct_HyMap_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune2_AIRSAR_correct_HyMap_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/injune8_AIRSAR_correct_HyMap_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene1_ALOS_correct_Landsat_sin15warpY_ctrlpts.pts',\ '/Users/pete/Desktop/Registration_Tests/CorrectProducedCtrlPts/scene2_ALOS_correct_Landsat_sin15warpY_ctrlpts.pts'\ ] amplitude = [ \ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 5,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 10,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15,\ 15\ ] frequency = 0 newline = str('\n') tab = str('\t') warpComment = '; Output control points for warping the image with a sin function' correctComment = '; Output control points to correct the sin warp' for i in range(len(imageA)): print outputWarpFiles[i] print outputCorrectFiles[i] outWarpFile = open(outputWarpFiles[i], 'w') outCorrectFile = open(outputCorrectFiles[i], 'w') outWarpFile.write(warpComment) outWarpFile.write(newline) outWarpFile.write(('; ' + outputWarpFiles[i])) outWarpFile.write(newline) outCorrectFile.write(correctComment) outCorrectFile.write(newline) outCorrectFile.write(('; ' + outputCorrectFiles[i])) outCorrectFile.write(newline) if warpAxis[i] == 0: frequency = (math.pi / (imageB[i][2]-imageB[i][0]))*2 else: frequency = (math.pi / (imageB[i][3]-imageB[i][1]))*2 halfBXDist = (imageB[i][2]-imageB[i][0])/2 halfBYDist = (imageB[i][1]-imageB[i][3])/2 imageACurrentX = imageA[i][0] + step[i][0] imageACurrentY = imageA[i][1] + step[i][1] imageBCurrentX = imageB[i][0] + step[i][0] imageBCurrentY = imageB[i][1] + step[i][1] imageBOutputY = 0.0 imageBOutputX = 0.0 while imageACurrentY < imageA[i][3]: while imageACurrentX < imageA[i][2]: # Output Warp contrl Points: if warpAxis[i] == 0: imageBOutputX = imageBCurrentX imageBOutputY = imageBCurrentY + (amplitude[i] * math.sin((imageBCurrentX*frequency))) else: imageBOutputX = imageBCurrentX + (amplitude[i] * math.sin((imageBCurrentY*frequency))) imageBOutputY = imageBCurrentY outStr = tab outStr = outStr + str(imageACurrentX) outStr = outStr + tab outStr = outStr + str(imageACurrentY) outStr = outStr + tab outStr = outStr + str(imageBOutputX) outStr = outStr + tab outStr = outStr + str(imageBOutputY) outWarpFile.write(outStr) outWarpFile.write(newline) imageACurrentX = imageACurrentX + step[i][0] + 1 imageBCurrentX = imageBCurrentX + step[i][0] + 1 imageACurrentY = imageACurrentY + step[i][1] + 1 imageBCurrentY = imageBCurrentY + step[i][1] + 1 imageACurrentX = imageA[i][0] + step[i][0] imageBCurrentX = imageB[i][0] + step[i][0] imageACurrentCorrX = imageACorr[i][0] + step[i][0] imageACurrentCorrY = imageACorr[i][1] + step[i][1] imageBCurrentCorrX = imageBCorr[i][0] + step[i][0] imageBCurrentCorrY = imageBCorr[i][1] + step[i][1] imageBOutputCorrX = 0.0 imageBOutputCorrY = 0.0 while imageACurrentCorrY < imageACorr[i][3]: while imageACurrentCorrX < imageACorr[i][2]: # Output Correct control Points: if warpAxis[i] == 0: imageBOutputCorrX = imageBCurrentCorrX imageBOutputCorrY = imageBCurrentCorrY + (amplitude[i] * math.sin(((imageBCurrentCorrX+halfBXDist)*frequency))) else: imageBOutputCorrX = imageBCurrentCorrX + (amplitude[i] * math.sin(((imageBCurrentCorrY+halfBYDist)*frequency))) imageBOutputCorrY = imageBCurrentCorrY if imageBOutputCorrX < imageBCorr[2] and imageBOutputCorrY < imageBCorr[3]: outStr = tab outStr = outStr + str(imageACurrentCorrX) outStr = outStr + tab outStr = outStr + str(imageACurrentCorrY) outStr = outStr + tab outStr = outStr + str(imageBOutputCorrX) outStr = outStr + tab outStr = outStr + str(imageBOutputCorrY) outCorrectFile.write(outStr) outCorrectFile.write(newline) # Update the position variables imageACurrentCorrX = imageACurrentCorrX + step[i][0] + 1 imageBCurrentCorrX = imageBCurrentCorrX + step[i][0] + 1 imageACurrentCorrY = imageACurrentCorrY + step[i][1] + 1 imageBCurrentCorrY = imageBCurrentCorrY + step[i][1] + 1 imageACurrentCorrX = imageACorr[i][0] + step[i][0] imageBCurrentCorrX = imageBCorr[i][0] + step[i][0] outCorrectFile.close() outWarpFile.close()

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fcd433711ea05bb90f1c22a306290e727fbea174

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py

Python

test/python/modules/common/ba_tests.py

dsyme/ADBench

87af0219a568807f8432754688ceb636efac12c6

[ "MIT" ]

58

2019-12-30T16:22:01.000Z

2022-01-23T12:26:51.000Z

test/python/modules/common/ba_tests.py

dsyme/ADBench

87af0219a568807f8432754688ceb636efac12c6

[ "MIT" ]

112

2019-05-25T07:26:58.000Z

2019-12-28T13:55:33.000Z

test/python/modules/common/ba_tests.py

dsyme/ADBench

87af0219a568807f8432754688ceb636efac12c6

[ "MIT" ]

22

2020-03-12T16:37:55.000Z

2022-02-23T10:14:37.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import unittest import numpy as np import sys import os # root directory of the whole project ROOT = os.path.abspath(os.path.join( os.path.abspath(os.path.dirname(__file__)), "..", "..", "..", ".." )) # root directory of the python source PYTHON_ROOT = os.path.join(ROOT, "src", "python") # adding python src root directory for importing sys.path.append(PYTHON_ROOT) from runner.ModuleLoader import module_load from shared.input_utils import read_ba_instance import utils # root directory of python modules MODULES_ROOT = os.path.join(PYTHON_ROOT, "modules") # path to the file with test data input TEST_INPUT_FILE_NAME = os.path.join(ROOT, "data", "ba", "test.txt") # Parameters for different modules. They have the following form: # { # "path": <module path relative to src/python/modules directory>, # "tolerance": <tolerance for module output results> # } test_params = [ { "path": os.path.join("PyTorch", "PyTorchBA.py"), "tolerance": 1e-8 }, { "path": os.path.join("Tensorflow", "TensorflowBA.py"), "tolerance": 1e-8 }, { "path": os.path.join("TensorflowGraph", "TensorflowGraphBA.py"), "tolerance": 1e-8 } ] class PythonModuleCommonBATests(utils.BaseTestClass): '''Checking BA objective differentiation in all python modules.''' # helping functions def objective_calculation_correctness(self, times): '''Checks objective calculation correctness running calculation several times.''' input = read_ba_instance(TEST_INPUT_FILE_NAME) self.test.prepare(input) self.test.calculate_objective(times) output = self.test.output() expected_weight_err = np.full(10, 8.26092651515999976e-01) expected_reproj_err = np.array( [ -2.69048849235189402e-01, 2.59944792677901881e-01 ] * 10 ) self.assertFloatArrayEqual( expected_reproj_err, output.reproj_err, self.params["tolerance"] ) self.assertFloatArrayEqual( expected_weight_err, output.w_err, self.params["tolerance"] ) def jacobian_calculation_correctness(self, times): '''Checks jacobian calculation correctness running calculation several times.''' input = read_ba_instance(TEST_INPUT_FILE_NAME) self.test.prepare(input) self.test.calculate_jacobian(times) output = self.test.output() # check jacobian shape self.assertEqual(30, output.J.nrows) self.assertEqual(62, output.J.ncols) self.assertEqual(31, len(output.J.rows)) self.assertEqual(310, len(output.J.cols)) self.assertEqual(310, len(output.J.vals)) # check jacobian values expected_J_values = [ 2.28877202208246757e+02, 6.34574811495545418e+02, -7.82222866259340549e+02, 2.42892615607159668e+00, -1.17828079628011313e+01, 2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01, 0.0, -3.50739521096005205e+02, -9.12107773668008576e+02, -2.42892615607159668e+00, 1.17828079628011313e+01, -2.54169312487743460e+00, -6.45167039712987389e-01, -1.20542435994996879e+02, -3.85673240766460424e+02, 9.75476291403326456e+01, -1.78372108529576567e+00, 4.15466799433126077e+00, 2.04025718029898906e+00, 3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02, 3.07250108960343255e+02, 1.78372108529576567e+00, -4.15466799433126077e+00, -2.04025718029898906e+00, 6.23335921553064054e-01, 2.28877202208246757e+02, 6.34574811495545418e+02, -7.82222866259340549e+02, 2.42892615607159668e+00, -1.17828079628011313e+01, 2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01, 0.0, -3.50739521096005205e+02, -9.12107773668008576e+02, -2.42892615607159668e+00, 1.17828079628011313e+01, -2.54169312487743460e+00, -6.45167039712987389e-01, -1.20542435994996879e+02, -3.85673240766460424e+02, 9.75476291403326456e+01, -1.78372108529576567e+00, 4.15466799433126077e+00, 2.04025718029898906e+00, 3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02, 3.07250108960343255e+02, 1.78372108529576567e+00, -4.15466799433126077e+00, -2.04025718029898906e+00, 6.23335921553064054e-01, 2.28877202208246757e+02, 6.34574811495545418e+02, -7.82222866259340549e+02, 2.42892615607159668e+00, -1.17828079628011313e+01, 2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01, 0.0, -3.50739521096005205e+02, -9.12107773668008576e+02, -2.42892615607159668e+00, 1.17828079628011313e+01, -2.54169312487743460e+00, -6.45167039712987389e-01, -1.20542435994996879e+02, -3.85673240766460424e+02, 9.75476291403326456e+01, -1.78372108529576567e+00, 4.15466799433126077e+00, 2.04025718029898906e+00, 3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02, 3.07250108960343255e+02, 1.78372108529576567e+00, -4.15466799433126077e+00, -2.04025718029898906e+00, 6.23335921553064054e-01, 2.28877202208246757e+02, 6.34574811495545418e+02, -7.82222866259340549e+02, 2.42892615607159668e+00, -1.17828079628011313e+01, 2.54169312487743460e+00, 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-2.04025718029898906e+00, 6.23335921553064054e-01, 2.28877202208246757e+02, 6.34574811495545418e+02, -7.82222866259340549e+02, 2.42892615607159668e+00, -1.17828079628011313e+01, 2.54169312487743460e+00, -1.03657084958518086e+00, 4.17022e-01, 0.0, -3.50739521096005205e+02, -9.12107773668008576e+02, -2.42892615607159668e+00, 1.17828079628011313e+01, -2.54169312487743460e+00, -6.45167039712987389e-01, -1.20542435994996879e+02, -3.85673240766460424e+02, 9.75476291403326456e+01, -1.78372108529576567e+00, 4.15466799433126077e+00, 2.04025718029898906e+00, 3.49176397433145880e-01, 0.0, 4.17022e-01, 1.18149147704414503e+02, 3.07250108960343255e+02, 1.78372108529576567e+00, -4.15466799433126077e+00, -2.04025718029898906e+00, 6.23335921553064054e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01, -8.34044e-01 ] self.assertFloatArrayEqual( expected_J_values, output.J.vals, self.params["tolerance"] ) # main test functions def setUp(self): module_path = os.path.join(MODULES_ROOT, self.params["path"]) self.test = module_load(module_path) self.assertIsNotNone(self.test) def test_loading(self): '''Checks if modules can be loaded.''' pass # all work is done in the setUp function def test_objective_calculation_correctness(self): '''Checks correctness of objective calculation over the single run.''' self.objective_calculation_correctness(times = 1) def test_objective_multiple_times_calculation_correctness(self): '''Checks correctness of objective calculation over several runs.''' self.objective_calculation_correctness(times = 3) def test_jacobian_calculation_correctness(self): '''Checks correctness of jacobian calculation over the single run.''' self.jacobian_calculation_correctness(times = 1) def test_jacobian_multiple_times_calculation_correctness(self): '''Checks correctness of jacobian calculation over several runs.''' self.jacobian_calculation_correctness(times = 3) def test_objective_runs_multiple_times(self): '''Checks if objective can be calculated multiple times.''' input = read_ba_instance(TEST_INPUT_FILE_NAME) self.test.prepare(input) func = self.test.calculate_objective self.assertTrue(utils.can_objective_run_multiple_times(func)) def test_jacobian_runs_multiple_times(self): '''Checks if jacobian can be calculated multiple times.''' input = read_ba_instance(TEST_INPUT_FILE_NAME) self.test.prepare(input) func = self.test.calculate_jacobian self.assertTrue(utils.can_objective_run_multiple_times(func)) if __name__ == "__main__": suite = unittest.TestSuite() for param_set in test_params: suite.addTest(utils.ParametrizedTestClass.parametrize( PythonModuleCommonBATests, params = param_set )) res = unittest.TextTestRunner(verbosity = 2).run(suite) if res.wasSuccessful(): sys.exit(0) else: sys.exit(1)

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1e6bb3c3489f659285422d449524881761ed11da

7,442

py

Python

tests/test_stream_writer.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

1

2021-07-07T06:36:57.000Z

tests/test_stream_writer.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

null

tests/test_stream_writer.py

ajdavis/aiohttp

d5138978f3e82aa82a2f003b00d38112c58a40c1

[ "Apache-2.0" ]

1

2021-02-09T10:05:59.000Z

import pytest import socket from aiohttp.parsers import StreamWriter, CORK from unittest import mock # nodelay def test_nodelay_default(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) assert not writer.tcp_nodelay assert not s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) def test_set_nodelay_no_change(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(False) assert not writer.tcp_nodelay assert not s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) def test_set_nodelay_enable(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) assert writer.tcp_nodelay assert s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) def test_set_nodelay_enable_and_disable(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) writer.set_tcp_nodelay(False) assert not writer.tcp_nodelay assert not s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) def test_set_nodelay_enable_ipv6(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) assert writer.tcp_nodelay assert s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) @pytest.mark.skipif(not hasattr(socket, 'AF_UNIX'), reason="requires unix sockets") def test_set_nodelay_enable_unix(loop): transport = mock.Mock() s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) assert writer.tcp_nodelay def test_set_nodelay_enable_no_socket(loop): transport = mock.Mock() transport.get_extra_info.return_value = None proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) assert writer.tcp_nodelay assert writer._socket is None # cork @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_cork_default(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) assert not writer.tcp_cork assert not s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_no_change(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(False) assert not writer.tcp_cork assert not s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_enable(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) assert writer.tcp_cork assert s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_enable_and_disable(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) writer.set_tcp_cork(False) assert not writer.tcp_cork assert not s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_enable_ipv6(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) assert writer.tcp_cork assert s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(not hasattr(socket, 'AF_UNIX'), reason="requires unix sockets") @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_enable_unix(loop): transport = mock.Mock() s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) assert writer.tcp_cork @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_cork_enable_no_socket(loop): transport = mock.Mock() transport.get_extra_info.return_value = None proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) assert writer.tcp_cork assert writer._socket is None # cork and nodelay interference @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_enabling_cork_disables_nodelay(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_nodelay(True) writer.set_tcp_cork(True) assert not writer.tcp_nodelay assert not s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) assert writer.tcp_cork assert s.getsockopt(socket.IPPROTO_TCP, CORK) @pytest.mark.skipif(CORK is None, reason="TCP_CORK or TCP_NOPUSH required") def test_set_enabling_nodelay_disables_cork(loop): transport = mock.Mock() s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) transport.get_extra_info.return_value = s proto = mock.Mock() reader = mock.Mock() writer = StreamWriter(transport, proto, reader, loop) writer.set_tcp_cork(True) writer.set_tcp_nodelay(True) assert writer.tcp_nodelay assert s.getsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY) assert not writer.tcp_cork assert not s.getsockopt(socket.IPPROTO_TCP, CORK)

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1

0

1

0

null

0

7

1ecb5b30a7c0fdc4b57eb89ca2bd097b5cc5c8c9

919,771

py

Python

project7/Extra2.py

MaxRobinson/CS449

cdc65ce4784fb15eeb0a17a50498217f07aa190c

[ "Apache-2.0" ]

null

project7/Extra2.py

MaxRobinson/CS449

cdc65ce4784fb15eeb0a17a50498217f07aa190c

[ "Apache-2.0" ]

null

project7/Extra2.py

MaxRobinson/CS449

cdc65ce4784fb15eeb0a17a50498217f07aa190c

[ "Apache-2.0" ]

1

2019-09-15T02:22:40.000Z

from ValueIteration import ValueIteration from Game import Game x = {(1, 9, -5, -5): (0, 0), (1, 9, -5, -4): (0, 1), (1, 9, -5, -3): (0, 1), (1, 9, -5, -2): (0, 1), (1, 9, -5, -1): (0, 1), (1, 9, -5, 0): (0, 1), (1, 9, -5, 1): (0, 1), (1, 9, -5, 2): (0, 1), (1, 9, -5, 3): (0, 0), (1, 9, -5, 4): (0, 1), (1, 9, -5, 5): (0, 1), (1, 9, -4, -5): (0, 0), (1, 9, -4, -4): (0, 1), (1, 9, -4, -3): (0, 1), (1, 9, -4, -2): (0, 1), (1, 9, -4, -1): (0, 1), (1, 9, -4, 0): (0, 1), (1, 9, -4, 1): (0, 1), (1, 9, -4, 2): (0, 1), (1, 9, -4, 3): (0, 0), (1, 9, -4, 4): (0, 1), (1, 9, -4, 5): (0, 1), (1, 9, -3, -5): (0, 0), (1, 9, -3, -4): (0, 1), (1, 9, -3, -3): (0, 1), (1, 9, -3, -2): (0, 1), (1, 9, -3, -1): (0, 1), (1, 9, -3, 0): (0, 1), (1, 9, -3, 1): (0, 1), (1, 9, -3, 2): (0, 1), (1, 9, -3, 3): (0, 0), (1, 9, -3, 4): (0, 1), (1, 9, -3, 5): (0, 1), (1, 9, -2, -5): (0, 0), (1, 9, -2, -4): (0, 1), (1, 9, -2, -3): (0, 1), (1, 9, -2, -2): (0, 1), (1, 9, -2, -1): (0, 1), (1, 9, -2, 0): (0, 1), (1, 9, -2, 1): (0, 1), (1, 9, -2, 2): (0, 1), (1, 9, -2, 3): (0, 0), (1, 9, -2, 4): (0, 1), (1, 9, -2, 5): (0, 1), (1, 9, -1, -5): (0, 0), (1, 9, -1, -4): (0, 1), (1, 9, -1, -3): (0, 1), (1, 9, -1, -2): (0, 1), (1, 9, -1, -1): (0, 1), (1, 9, -1, 0): (1, 1), (1, 9, -1, 1): (1, 1), (1, 9, -1, 2): (1, 0), (1, 9, -1, 3): (1, 1), (1, 9, -1, 4): (1, 0), (1, 9, -1, 5): (1, -1), (1, 9, 0, -5): (-1, 0), (1, 9, 0, -4): (0, 1), (1, 9, 0, -3): (0, 1), (1, 9, 0, -2): (-1, 1), (1, 9, 0, -1): (0, 1), (1, 9, 0, 0): (0, 1), (1, 9, 0, 1): (1, 1), (1, 9, 0, 2): (1, 0), (1, 9, 0, 3): (1, -1), (1, 9, 0, 4): (1, -1), (1, 9, 0, 5): (0, -1), (1, 9, 1, -5): (0, 1), (1, 9, 1, -4): (-1, 1), (1, 9, 1, -3): (-1, 1), (1, 9, 1, -2): (-1, 1), (1, 9, 1, -1): (-1, 1), (1, 9, 1, 0): (1, 1), (1, 9, 1, 1): (1, 1), (1, 9, 1, 2): (1, 0), (1, 9, 1, 3): (1, 1), (1, 9, 1, 4): (1, 0), (1, 9, 1, 5): (1, -1), (1, 9, 2, -5): (-1, 1), (1, 9, 2, -4): (-1, 0), (1, 9, 2, -3): (-1, -1), (1, 9, 2, -2): (1, 1), (1, 9, 2, -1): (1, 0), (1, 9, 2, 0): (0, 1), (1, 9, 2, 1): (0, 1), (1, 9, 2, 2): (0, 0), (1, 9, 2, 3): (0, 1), (1, 9, 2, 4): (0, 0), (1, 9, 2, 5): (0, -1), (1, 9, 3, -5): (1, 0), (1, 9, 3, -4): (1, 0), (1, 9, 3, -3): (1, 1), (1, 9, 3, -2): (0, 1), (1, 9, 3, -1): (0, 0), (1, 9, 3, 0): (-1, 1), (1, 9, 3, 1): (-1, 1), (1, 9, 3, 2): (-1, 0), (1, 9, 3, 3): (-1, 1), (1, 9, 3, 4): (-1, 0), (1, 9, 3, 5): (-1, -1), (1, 9, 4, -5): (1, 0), (1, 9, 4, -4): (1, 0), (1, 9, 4, -3): (1, -1), (1, 9, 4, -2): (-1, 1), (1, 9, 4, -1): (-1, 0), (1, 9, 4, 0): (0, 1), (1, 9, 4, 1): (0, 1), (1, 9, 4, 2): (1, 1), (1, 9, 4, 3): (1, 0), (1, 9, 4, 4): (1, -1), (1, 9, 4, 5): (1, -1), (1, 9, 5, -5): (0, 1), (1, 9, 5, -4): (0, 0), (1, 9, 5, -3): (0, -1), (1, 9, 5, -2): (-1, 1), (1, 9, 5, -1): (-1, 1), (1, 9, 5, 0): (0, 1), (1, 9, 5, 1): (0, 1), (1, 9, 5, 2): (0, 1), (1, 9, 5, 3): (0, 0), (1, 9, 5, 4): (0, -1), (1, 9, 5, 5): (0, -1), (1, 10, -5, -5): (0, 1), (1, 10, -5, -4): (0, 1), (1, 10, -5, -3): (0, 1), (1, 10, -5, -2): (0, 1), (1, 10, -5, -1): (0, 1), (1, 10, -5, 0): (0, 1), (1, 10, -5, 1): (0, 1), (1, 10, -5, 2): (0, 0), (1, 10, -5, 3): (0, 1), (1, 10, -5, 4): (0, 1), (1, 10, -5, 5): (0, 1), (1, 10, -4, -5): (0, 1), (1, 10, -4, -4): (0, 1), (1, 10, -4, -3): (0, 1), (1, 10, -4, -2): (0, 1), (1, 10, -4, -1): (0, 1), (1, 10, -4, 0): (0, 1), (1, 10, -4, 1): (0, 1), (1, 10, -4, 2): (0, 0), (1, 10, -4, 3): (0, 1), (1, 10, -4, 4): (0, 1), (1, 10, -4, 5): (0, 1), (1, 10, -3, -5): (0, 1), (1, 10, -3, -4): (0, 1), (1, 10, -3, -3): (0, 1), (1, 10, -3, -2): (0, 1), (1, 10, -3, -1): (0, 1), (1, 10, -3, 0): (0, 1), (1, 10, -3, 1): (0, 1), (1, 10, -3, 2): (0, 0), (1, 10, -3, 3): (0, 1), (1, 10, -3, 4): (0, 1), (1, 10, -3, 5): (0, 1), (1, 10, -2, -5): (0, 1), (1, 10, -2, -4): (0, 1), (1, 10, -2, -3): (0, 1), (1, 10, -2, -2): (0, 1), (1, 10, -2, -1): (0, 1), (1, 10, -2, 0): (0, 1), (1, 10, -2, 1): (0, 1), (1, 10, -2, 2): (0, 0), (1, 10, -2, 3): (0, 1), (1, 10, -2, 4): (0, 1), (1, 10, -2, 5): (0, 1), (1, 10, -1, -5): (0, 1), (1, 10, -1, -4): (0, 1), (1, 10, -1, -3): (0, 1), (1, 10, -1, -2): (0, 1), (1, 10, -1, -1): (0, 1), (1, 10, -1, 0): (1, 1), (1, 10, -1, 1): (1, 1), (1, 10, -1, 2): (1, 1), (1, 10, -1, 3): (1, 1), (1, 10, -1, 4): (0, 1), (1, 10, -1, 5): (0, 1), (1, 10, 0, -5): (0, 1), (1, 10, 0, -4): (0, 1), (1, 10, 0, -3): (-1, 1), (1, 10, 0, -2): (-1, 1), (1, 10, 0, -1): (1, 1), (1, 10, 0, 0): (1, 1), (1, 10, 0, 1): (1, 1), (1, 10, 0, 2): (1, 0), (1, 10, 0, 3): (1, -1), (1, 10, 0, 4): (-1, 1), (1, 10, 0, 5): (-1, 1), (1, 10, 1, -5): (-1, 1), (1, 10, 1, -4): (-1, 1), (1, 10, 1, -3): (-1, 1), (1, 10, 1, -2): (-1, 0), (1, 10, 1, -1): (0, 1), (1, 10, 1, 0): (1, 1), (1, 10, 1, 1): (1, 1), (1, 10, 1, 2): (1, 1), (1, 10, 1, 3): (1, 0), (1, 10, 1, 4): (1, -1), (1, 10, 1, 5): (1, -1), (1, 10, 2, -5): (-1, 1), (1, 10, 2, -4): (-1, 0), (1, 10, 2, -3): (1, 1), (1, 10, 2, -2): (1, 0), (1, 10, 2, -1): (-1, 1), (1, 10, 2, 0): (0, 1), (1, 10, 2, 1): (0, 1), (1, 10, 2, 2): (0, 1), (1, 10, 2, 3): (0, 0), (1, 10, 2, 4): (0, -1), (1, 10, 2, 5): (0, -1), (1, 10, 3, -5): (1, 0), (1, 10, 3, -4): (1, 1), (1, 10, 3, -3): (0, 1), (1, 10, 3, -2): (0, 0), (1, 10, 3, -1): (0, 1), (1, 10, 3, 0): (-1, 1), (1, 10, 3, 1): (-1, 1), (1, 10, 3, 2): (-1, 1), (1, 10, 3, 3): (-1, 0), (1, 10, 3, 4): (-1, -1), (1, 10, 3, 5): (-1, -1), (1, 10, 4, -5): (1, 0), (1, 10, 4, -4): (1, -1), (1, 10, 4, -3): (-1, 1), (1, 10, 4, -2): (-1, 0), (1, 10, 4, -1): (0, 1), (1, 10, 4, 0): (0, 1), (1, 10, 4, 1): (1, 1), (1, 10, 4, 2): (1, 0), (1, 10, 4, 3): (1, -1), (1, 10, 4, 4): (1, -1), (1, 10, 4, 5): (1, 0), (1, 10, 5, -5): (0, 0), (1, 10, 5, -4): (0, -1), (1, 10, 5, -3): (0, -1), (1, 10, 5, -2): (-1, 1), (1, 10, 5, -1): (0, 1), (1, 10, 5, 0): (0, 1), (1, 10, 5, 1): (0, 1), (1, 10, 5, 2): (0, 0), (1, 10, 5, 3): (0, -1), (1, 10, 5, 4): (0, -1), (1, 10, 5, 5): (0, 1), (1, 11, -5, -5): (0, 1), (1, 11, -5, -4): (0, 1), (1, 11, -5, -3): (0, 1), (1, 11, -5, -2): (0, 1), (1, 11, -5, -1): (0, 1), (1, 11, -5, 0): (0, 1), (1, 11, -5, 1): (0, 0), (1, 11, -5, 2): (0, 1), (1, 11, -5, 3): (0, 1), (1, 11, -5, 4): (0, 0), (1, 11, -5, 5): (-1, -1), (1, 11, -4, -5): (0, 1), (1, 11, -4, -4): (0, 1), (1, 11, -4, -3): (0, 1), (1, 11, -4, -2): (0, 1), (1, 11, -4, -1): (0, 1), (1, 11, -4, 0): (0, 1), (1, 11, -4, 1): (0, 0), (1, 11, -4, 2): (0, 1), (1, 11, -4, 3): (0, 1), (1, 11, -4, 4): (0, 0), (1, 11, -4, 5): (-1, -1), (1, 11, -3, -5): (0, 1), (1, 11, -3, -4): (0, 1), (1, 11, -3, -3): (0, 1), (1, 11, -3, -2): (0, 1), (1, 11, -3, -1): (0, 1), (1, 11, -3, 0): (0, 1), (1, 11, -3, 1): (0, 0), (1, 11, -3, 2): (0, 1), (1, 11, -3, 3): (0, 1), (1, 11, -3, 4): (0, 0), (1, 11, -3, 5): (-1, -1), (1, 11, -2, -5): (0, 1), (1, 11, -2, -4): (0, 1), (1, 11, -2, -3): (0, 1), (1, 11, -2, -2): (0, 1), (1, 11, -2, -1): (0, 1), (1, 11, -2, 0): (0, 1), (1, 11, -2, 1): (0, 0), (1, 11, -2, 2): (0, 1), (1, 11, -2, 3): (0, 1), (1, 11, -2, 4): (0, 0), (1, 11, -2, 5): (-1, -1), (1, 11, -1, -5): (0, 1), (1, 11, -1, -4): (0, 1), (1, 11, -1, -3): (0, 1), (1, 11, -1, -2): (0, 1), (1, 11, -1, -1): (0, 1), (1, 11, -1, 0): (1, 1), (1, 11, -1, 1): (1, 1), (1, 11, -1, 2): (1, 1), (1, 11, -1, 3): (0, 1), (1, 11, -1, 4): (0, 0), (1, 11, -1, 5): (-1, -1), (1, 11, 0, -5): (0, 1), (1, 11, 0, -4): (-1, 1), (1, 11, 0, -3): (-1, 1), (1, 11, 0, -2): (-1, 1), (1, 11, 0, -1): (1, 1), (1, 11, 0, 0): (1, 1), (1, 11, 0, 1): (1, 1), (1, 11, 0, 2): (1, 0), (1, 11, 0, 3): (1, -1), (1, 11, 0, 4): (-1, 0), (1, 11, 0, 5): (-1, -1), (1, 11, 1, -5): (-1, 1), (1, 11, 1, -4): (-1, 1), (1, 11, 1, -3): (-1, 0), (1, 11, 1, -2): (-1, -1), (1, 11, 1, -1): (1, 1), (1, 11, 1, 0): (1, 1), (1, 11, 1, 1): (1, 0), (1, 11, 1, 2): (1, 1), (1, 11, 1, 3): (1, 0), (1, 11, 1, 4): (1, -1), (1, 11, 1, 5): (1, -1), (1, 11, 2, -5): (-1, 0), (1, 11, 2, -4): (1, 1), (1, 11, 2, -3): (1, 0), (1, 11, 2, -2): (1, 1), (1, 11, 2, -1): (0, 1), (1, 11, 2, 0): (0, 1), (1, 11, 2, 1): (0, 0), (1, 11, 2, 2): (0, 1), (1, 11, 2, 3): (0, 0), (1, 11, 2, 4): (0, -1), (1, 11, 2, 5): (0, -1), (1, 11, 3, -5): (1, 1), (1, 11, 3, -4): (0, 1), (1, 11, 3, -3): (0, 0), (1, 11, 3, -2): (0, 1), (1, 11, 3, -1): (-1, 1), (1, 11, 3, 0): (-1, 1), (1, 11, 3, 1): (-1, 0), (1, 11, 3, 2): (-1, 1), (1, 11, 3, 3): (-1, 0), (1, 11, 3, 4): (1, 1), (1, 11, 3, 5): (1, 0), (1, 11, 4, -5): (1, 0), (1, 11, 4, -4): (-1, 1), (1, 11, 4, -3): (-1, 0), (1, 11, 4, -2): (0, 1), (1, 11, 4, -1): (0, 1), (1, 11, 4, 0): (1, 1), (1, 11, 4, 1): (1, 0), (1, 11, 4, 2): (1, -1), (1, 11, 4, 3): (1, -1), (1, 11, 4, 4): (0, 1), (1, 11, 4, 5): (0, 1), (1, 11, 5, -5): (0, 0), (1, 11, 5, -4): (0, -1), (1, 11, 5, -3): (-1, 1), (1, 11, 5, -2): (0, 1), (1, 11, 5, -1): (0, 1), (1, 11, 5, 0): (0, 1), (1, 11, 5, 1): (0, 0), (1, 11, 5, 2): (0, -1), (1, 11, 5, 3): (0, -1), (1, 11, 5, 4): (-1, 1), (1, 11, 5, 5): (-1, 1), (1, 12, -5, -5): (0, 1), (1, 12, -5, -4): (0, 1), (1, 12, -5, -3): (0, 1), (1, 12, -5, -2): (0, 1), (1, 12, -5, -1): (0, 1), (1, 12, -5, 0): (0, 0), (1, 12, -5, 1): (0, 1), (1, 12, -5, 2): (0, 1), (1, 12, -5, 3): (0, 0), (1, 12, -5, 4): (0, 1), (1, 12, -5, 5): (0, 1), (1, 12, -4, -5): (0, 1), (1, 12, -4, -4): (0, 1), (1, 12, -4, -3): (0, 1), (1, 12, -4, -2): (0, 1), (1, 12, -4, -1): (0, 1), (1, 12, -4, 0): (0, 0), (1, 12, -4, 1): (0, 1), (1, 12, -4, 2): (0, 1), (1, 12, -4, 3): (0, 0), (1, 12, -4, 4): (0, 1), (1, 12, -4, 5): (0, 1), (1, 12, -3, -5): (0, 1), (1, 12, -3, -4): (0, 1), (1, 12, -3, -3): (0, 1), (1, 12, -3, -2): (0, 1), (1, 12, -3, -1): (0, 1), (1, 12, -3, 0): (0, 0), (1, 12, -3, 1): (0, 1), (1, 12, -3, 2): (0, 1), (1, 12, -3, 3): (0, 0), (1, 12, -3, 4): (0, 1), (1, 12, -3, 5): (0, 1), (1, 12, -2, -5): (0, 1), (1, 12, -2, -4): (0, 1), (1, 12, -2, -3): (0, 1), (1, 12, -2, -2): (0, 1), (1, 12, -2, -1): (0, 1), (1, 12, -2, 0): (0, 0), (1, 12, -2, 1): (0, 1), (1, 12, -2, 2): (0, 1), (1, 12, -2, 3): (0, 0), (1, 12, -2, 4): (0, 1), (1, 12, -2, 5): (0, 1), (1, 12, -1, -5): (0, 1), (1, 12, -1, -4): (0, 1), (1, 12, -1, -3): (0, 1), (1, 12, -1, -2): (0, 1), (1, 12, -1, -1): (0, 1), (1, 12, -1, 0): (1, 1), (1, 12, -1, 1): (1, 1), (1, 12, -1, 2): (0, 1), (1, 12, -1, 3): (0, 0), (1, 12, -1, 4): (0, 1), (1, 12, -1, 5): (0, 1), (1, 12, 0, -5): (-1, 1), (1, 12, 0, -4): (-1, 1), (1, 12, 0, -3): (-1, 1), (1, 12, 0, -2): (-1, 1), (1, 12, 0, -1): (0, 1), (1, 12, 0, 0): (1, 1), (1, 12, 0, 1): (1, 0), (1, 12, 0, 2): (1, -1), (1, 12, 0, 3): (1, -1), (1, 12, 0, 4): (-1, 1), (1, 12, 0, 5): (-1, 1), (1, 12, 1, -5): (-1, 1), (1, 12, 1, -4): (-1, 0), (1, 12, 1, -3): (-1, -1), (1, 12, 1, -2): (0, 1), (1, 12, 1, -1): (1, 1), (1, 12, 1, 0): (1, 1), (1, 12, 1, 1): (1, 1), (1, 12, 1, 2): (1, 0), (1, 12, 1, 3): (1, -1), (1, 12, 1, 4): (1, -1), (1, 12, 1, 5): (-1, 1), (1, 12, 2, -5): (1, 1), (1, 12, 2, -4): (1, 0), (1, 12, 2, -3): (1, 1), (1, 12, 2, -2): (1, 1), (1, 12, 2, -1): (0, 1), (1, 12, 2, 0): (0, 1), (1, 12, 2, 1): (0, 1), (1, 12, 2, 2): (0, 0), (1, 12, 2, 3): (0, -1), (1, 12, 2, 4): (0, -1), (1, 12, 2, 5): (1, 0), (1, 12, 3, -5): (0, 1), (1, 12, 3, -4): (0, 0), (1, 12, 3, -3): (0, 1), (1, 12, 3, -2): (0, 1), (1, 12, 3, -1): (-1, 1), 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-3): None, (27, 26, 4, -2): None, (27, 26, 4, -1): None, (27, 26, 4, 0): None, (27, 26, 4, 1): None, (27, 26, 4, 2): None, (27, 26, 4, 3): None, (27, 26, 4, 4): None, (27, 26, 4, 5): None, (27, 26, 5, -5): None, (27, 26, 5, -4): None, (27, 26, 5, -3): None, (27, 26, 5, -2): None, (27, 26, 5, -1): None, (27, 26, 5, 0): None, (27, 26, 5, 1): None, (27, 26, 5, 2): None, (27, 26, 5, 3): None, (27, 26, 5, 4): None, (27, 26, 5, 5): None, (27, 27, -5, -5): None, (27, 27, -5, -4): None, (27, 27, -5, -3): None, (27, 27, -5, -2): None, (27, 27, -5, -1): None, (27, 27, -5, 0): None, (27, 27, -5, 1): None, (27, 27, -5, 2): None, (27, 27, -5, 3): None, (27, 27, -5, 4): None, (27, 27, -5, 5): None, (27, 27, -4, -5): None, (27, 27, -4, -4): None, (27, 27, -4, -3): None, (27, 27, -4, -2): None, (27, 27, -4, -1): None, (27, 27, -4, 0): None, (27, 27, -4, 1): None, (27, 27, -4, 2): None, (27, 27, -4, 3): None, (27, 27, -4, 4): None, (27, 27, -4, 5): None, (27, 27, -3, -5): None, (27, 27, -3, -4): None, (27, 27, -3, -3): None, (27, 27, -3, -2): None, (27, 27, -3, -1): None, (27, 27, -3, 0): None, (27, 27, -3, 1): None, (27, 27, -3, 2): None, (27, 27, -3, 3): None, (27, 27, -3, 4): None, (27, 27, -3, 5): None, (27, 27, -2, -5): None, (27, 27, -2, -4): None, (27, 27, -2, -3): None, (27, 27, -2, -2): None, (27, 27, -2, -1): None, (27, 27, -2, 0): None, (27, 27, -2, 1): None, (27, 27, -2, 2): None, (27, 27, -2, 3): None, (27, 27, -2, 4): None, (27, 27, -2, 5): None, (27, 27, -1, -5): None, (27, 27, -1, -4): None, (27, 27, -1, -3): None, (27, 27, -1, -2): None, (27, 27, -1, -1): None, (27, 27, -1, 0): None, (27, 27, -1, 1): None, (27, 27, -1, 2): None, (27, 27, -1, 3): None, (27, 27, -1, 4): None, (27, 27, -1, 5): None, (27, 27, 0, -5): None, (27, 27, 0, -4): None, (27, 27, 0, -3): None, (27, 27, 0, -2): None, (27, 27, 0, -1): None, (27, 27, 0, 0): None, (27, 27, 0, 1): None, (27, 27, 0, 2): None, (27, 27, 0, 3): None, (27, 27, 0, 4): None, (27, 27, 0, 5): None, (27, 27, 1, -5): None, (27, 27, 1, -4): None, (27, 27, 1, -3): None, (27, 27, 1, -2): None, (27, 27, 1, -1): None, (27, 27, 1, 0): None, (27, 27, 1, 1): None, (27, 27, 1, 2): None, (27, 27, 1, 3): None, (27, 27, 1, 4): None, (27, 27, 1, 5): None, (27, 27, 2, -5): None, (27, 27, 2, -4): None, (27, 27, 2, -3): None, (27, 27, 2, -2): None, (27, 27, 2, -1): None, (27, 27, 2, 0): None, (27, 27, 2, 1): None, (27, 27, 2, 2): None, (27, 27, 2, 3): None, (27, 27, 2, 4): None, (27, 27, 2, 5): None, (27, 27, 3, -5): None, (27, 27, 3, -4): None, (27, 27, 3, -3): None, (27, 27, 3, -2): None, (27, 27, 3, -1): None, (27, 27, 3, 0): None, (27, 27, 3, 1): None, (27, 27, 3, 2): None, (27, 27, 3, 3): None, (27, 27, 3, 4): None, (27, 27, 3, 5): None, (27, 27, 4, -5): None, (27, 27, 4, -4): None, (27, 27, 4, -3): None, (27, 27, 4, -2): None, (27, 27, 4, -1): None, (27, 27, 4, 0): None, (27, 27, 4, 1): None, (27, 27, 4, 2): None, (27, 27, 4, 3): None, (27, 27, 4, 4): None, (27, 27, 4, 5): None, (27, 27, 5, -5): None, (27, 27, 5, -4): None, (27, 27, 5, -3): None, (27, 27, 5, -2): None, (27, 27, 5, -1): None, (27, 27, 5, 0): None, (27, 27, 5, 1): None, (27, 27, 5, 2): None, (27, 27, 5, 3): None, (27, 27, 5, 4): None, (27, 27, 5, 5): None, (27, 28, -5, -5): None, (27, 28, -5, -4): None, (27, 28, -5, -3): None, (27, 28, -5, -2): None, (27, 28, -5, -1): None, (27, 28, -5, 0): None, (27, 28, -5, 1): None, (27, 28, -5, 2): None, (27, 28, -5, 3): None, (27, 28, -5, 4): None, (27, 28, -5, 5): None, (27, 28, -4, -5): None, (27, 28, -4, -4): None, (27, 28, -4, -3): None, (27, 28, -4, -2): None, (27, 28, -4, -1): None, (27, 28, -4, 0): None, (27, 28, -4, 1): None, (27, 28, -4, 2): None, (27, 28, -4, 3): None, (27, 28, -4, 4): None, (27, 28, -4, 5): None, (27, 28, -3, -5): None, (27, 28, -3, -4): None, (27, 28, -3, -3): None, (27, 28, -3, -2): None, (27, 28, -3, -1): None, (27, 28, -3, 0): None, (27, 28, -3, 1): None, (27, 28, -3, 2): None, (27, 28, -3, 3): None, (27, 28, -3, 4): None, (27, 28, -3, 5): None, (27, 28, -2, -5): None, (27, 28, -2, -4): None, (27, 28, -2, -3): None, (27, 28, -2, -2): None, (27, 28, -2, -1): None, (27, 28, -2, 0): None, (27, 28, -2, 1): None, (27, 28, -2, 2): None, (27, 28, -2, 3): None, (27, 28, -2, 4): None, (27, 28, -2, 5): None, (27, 28, -1, -5): None, (27, 28, -1, -4): None, (27, 28, -1, -3): None, (27, 28, -1, -2): None, (27, 28, -1, -1): None, (27, 28, -1, 0): None, (27, 28, -1, 1): None, (27, 28, -1, 2): None, (27, 28, -1, 3): None, (27, 28, -1, 4): None, (27, 28, -1, 5): None, (27, 28, 0, -5): None, (27, 28, 0, -4): None, (27, 28, 0, -3): None, (27, 28, 0, -2): None, (27, 28, 0, -1): None, (27, 28, 0, 0): None, (27, 28, 0, 1): None, (27, 28, 0, 2): None, (27, 28, 0, 3): None, (27, 28, 0, 4): None, (27, 28, 0, 5): None, (27, 28, 1, -5): None, (27, 28, 1, -4): None, (27, 28, 1, -3): None, (27, 28, 1, -2): None, (27, 28, 1, -1): None, (27, 28, 1, 0): None, (27, 28, 1, 1): None, (27, 28, 1, 2): None, (27, 28, 1, 3): None, (27, 28, 1, 4): None, (27, 28, 1, 5): None, (27, 28, 2, -5): None, (27, 28, 2, -4): None, (27, 28, 2, -3): None, (27, 28, 2, -2): None, (27, 28, 2, -1): None, (27, 28, 2, 0): None, (27, 28, 2, 1): None, (27, 28, 2, 2): None, (27, 28, 2, 3): None, (27, 28, 2, 4): None, (27, 28, 2, 5): None, (27, 28, 3, -5): None, (27, 28, 3, -4): None, (27, 28, 3, -3): None, (27, 28, 3, -2): None, (27, 28, 3, -1): None, (27, 28, 3, 0): None, (27, 28, 3, 1): None, (27, 28, 3, 2): None, (27, 28, 3, 3): None, (27, 28, 3, 4): None, (27, 28, 3, 5): None, (27, 28, 4, -5): None, (27, 28, 4, -4): None, (27, 28, 4, -3): None, (27, 28, 4, -2): None, (27, 28, 4, -1): None, (27, 28, 4, 0): None, (27, 28, 4, 1): None, (27, 28, 4, 2): None, (27, 28, 4, 3): None, (27, 28, 4, 4): None, (27, 28, 4, 5): None, (27, 28, 5, -5): None, (27, 28, 5, -4): None, (27, 28, 5, -3): None, (27, 28, 5, -2): None, (27, 28, 5, -1): None, (27, 28, 5, 0): None, (27, 28, 5, 1): None, (27, 28, 5, 2): None, (27, 28, 5, 3): None, (27, 28, 5, 4): None, (27, 28, 5, 5): None} game = Game('tracks/R-track.txt', success_chance=.8) vi = ValueIteration(game) # num_steps_taken = vi.execute_policy(x) # print(num_steps_taken) avg_num_steps = 0 for itter in range(100): num_steps = vi.execute_policy(x) avg_num_steps += num_steps avg_num_steps /= 100.0 print(avg_num_steps)

25.488306

52

0.283675

215,199

919,771

1.212362

0.000251

0.165351

0.051871

0.011299

0.998808

0.898731

0.851598

0.805649

0.779953

0.675633

0

0.382645

0.273175

919,771

36,085

53

25.489012

0.007623

0.000066

0

0.00002

0

1

0

false

0

0.000055

0

0.000055

0.000028

0

1

null

0

1

0

1

0

1

0

null

0

9

94d50d9c3da7642fc7d0379ab63cfdab5f07a3c5

472

py

Python

1. Beginner/URI1041.py

antuniooh/uri-resolutions

c2844a9a6e2fae350293d6d24b0551691e7c8656

[ "MIT" ]

null

1. Beginner/URI1041.py

antuniooh/uri-resolutions

c2844a9a6e2fae350293d6d24b0551691e7c8656

[ "MIT" ]

null

1. Beginner/URI1041.py

antuniooh/uri-resolutions

c2844a9a6e2fae350293d6d24b0551691e7c8656

[ "MIT" ]

null

a,b = input().split(" ") x = float(a) y = float(b) if x > 0.00 and y > 0.00: print("Q1") elif x > 0.00 and y < 0.00: print("Q4") elif x < 0.00 and y > 0.00: print("Q2") elif x < 0.00 and y < 0.00: print("Q3") elif x == 0.00 and y ==0.00: print("Origem") elif x == 0.00 and y > 0.0: print("Eixo Y") elif x == 0.00 and y < 0.00: print("Eixo Y") elif x > 0.00 and y == 0.00: print("Eixo X") elif x < 0.00 and y == 0.00: print("Eixo X")

18.88

28

0.502119

104

472

2.278846

0.182692

0.21519

0.151899

0.265823

0.818565

0.763713

0.696203

0.35865

0

0.168142

0.28178

472

24

29

19.666667

0.530973

0

0.190476

0

0.082803

0

1

0

false

0

0.428571

0

null

1

0

1

0

null

0

1

0

7

a20cee3b296a8b6dd8940a6a7c701d9584ddc2a8

34

py

Python

toolkit/src/plugin.py

Mariusz1970/enigma2-plugins-1

126d31d075c156f32b09d4321ebe1a17f93a5bd6

[ "OLDAP-2.3" ]

41

2016-01-21T17:54:44.000Z

2021-06-26T05:54:41.000Z

toolkit/src/plugin.py

Mariusz1970/enigma2-plugins-1

126d31d075c156f32b09d4321ebe1a17f93a5bd6

[ "OLDAP-2.3" ]

124

2015-04-27T21:30:48.000Z

2022-03-29T10:21:39.000Z

toolkit/src/plugin.py

Mariusz1970/enigma2-plugins-1

126d31d075c156f32b09d4321ebe1a17f93a5bd6

[ "OLDAP-2.3" ]

193

2015-01-10T09:21:26.000Z

2022-03-21T08:19:33.000Z

def Plugins(**kwargs): return []

11.333333

22

0.647059

4

34

5.5

1

0

0.147059

34

2

23

17

0.758621

0

1

0.5

true

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

bf5445dcf546e9349f616c8fb502c5b373550188

28,531

py

Python

v6.0.5/system/test_fortios_system_admin.py

fortinet-solutions-cse/ansible_fgt_modules

c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719

[ "Apache-2.0" ]

14

2018-09-25T20:35:25.000Z

2021-07-14T04:30:54.000Z

v6.0.6/system/test_fortios_system_admin.py

fortinet-solutions-cse/ansible_fgt_modules

c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719

[ "Apache-2.0" ]

32

2018-10-09T04:13:42.000Z

2020-05-11T07:20:28.000Z

v6.0.5/system/test_fortios_system_admin.py

fortinet-solutions-cse/ansible_fgt_modules

c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719

[ "Apache-2.0" ]

11

2018-10-09T00:14:53.000Z

2021-11-03T10:54:09.000Z

# Copyright 2019 Fortinet, Inc. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <https://www.gnu.org/licenses/>. # Make coding more python3-ish from __future__ import (absolute_import, division, print_function) __metaclass__ = type import os import json import pytest from mock import ANY from ansible.module_utils.network.fortios.fortios import FortiOSHandler try: from ansible.modules.network.fortios import fortios_system_admin except ImportError: pytest.skip("Could not load required modules for testing", allow_module_level=True) @pytest.fixture(autouse=True) def connection_mock(mocker): connection_class_mock = mocker.patch('ansible.modules.network.fortios.fortios_system_admin.Connection') return connection_class_mock fos_instance = FortiOSHandler(connection_mock) def test_system_admin_creation(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'system_admin': { 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) expected_data = { 'accprofile': 'test_value_3', 'accprofile-override': 'enable', 'allow-remove-admin-session': 'enable', 'comments': 'test_value_6', 'email-to': 'test_value_7', 'force-password-change': 'enable', 'fortitoken': 'test_value_9', 'guest-auth': 'disable', 'guest-lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6-trusthost1': 'test_value_15', 'ip6-trusthost10': 'test_value_16', 'ip6-trusthost2': 'test_value_17', 'ip6-trusthost3': 'test_value_18', 'ip6-trusthost4': 'test_value_19', 'ip6-trusthost5': 'test_value_20', 'ip6-trusthost6': 'test_value_21', 'ip6-trusthost7': 'test_value_22', 'ip6-trusthost8': 'test_value_23', 'ip6-trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password-expire': 'test_value_27', 'peer-auth': 'enable', 'peer-group': 'test_value_29', 'radius-vdom-override': 'enable', 'remote-auth': 'enable', 'remote-group': 'test_value_32', 'schedule': 'test_value_33', 'sms-custom-server': 'test_value_34', 'sms-phone': 'test_value_35', 'sms-server': 'fortiguard', 'ssh-certificate': 'test_value_37', 'ssh-public-key1': 'test_value_38', 'ssh-public-key2': 'test_value_39', 'ssh-public-key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two-factor': 'disable', 'wildcard': 'enable' } set_method_mock.assert_called_with('system', 'admin', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200 def test_system_admin_creation_fails(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'system_admin': { 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) expected_data = { 'accprofile': 'test_value_3', 'accprofile-override': 'enable', 'allow-remove-admin-session': 'enable', 'comments': 'test_value_6', 'email-to': 'test_value_7', 'force-password-change': 'enable', 'fortitoken': 'test_value_9', 'guest-auth': 'disable', 'guest-lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6-trusthost1': 'test_value_15', 'ip6-trusthost10': 'test_value_16', 'ip6-trusthost2': 'test_value_17', 'ip6-trusthost3': 'test_value_18', 'ip6-trusthost4': 'test_value_19', 'ip6-trusthost5': 'test_value_20', 'ip6-trusthost6': 'test_value_21', 'ip6-trusthost7': 'test_value_22', 'ip6-trusthost8': 'test_value_23', 'ip6-trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password-expire': 'test_value_27', 'peer-auth': 'enable', 'peer-group': 'test_value_29', 'radius-vdom-override': 'enable', 'remote-auth': 'enable', 'remote-group': 'test_value_32', 'schedule': 'test_value_33', 'sms-custom-server': 'test_value_34', 'sms-phone': 'test_value_35', 'sms-server': 'fortiguard', 'ssh-certificate': 'test_value_37', 'ssh-public-key1': 'test_value_38', 'ssh-public-key2': 'test_value_39', 'ssh-public-key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two-factor': 'disable', 'wildcard': 'enable' } set_method_mock.assert_called_with('system', 'admin', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 500 def test_system_admin_removal(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') delete_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result) input_data = { 'username': 'admin', 'state': 'absent', 'system_admin': { 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) delete_method_mock.assert_called_with('system', 'admin', mkey=ANY, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200 def test_system_admin_deletion_fails(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') delete_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500} delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result) input_data = { 'username': 'admin', 'state': 'absent', 'system_admin': { 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) delete_method_mock.assert_called_with('system', 'admin', mkey=ANY, vdom='root') schema_method_mock.assert_not_called() assert is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 500 def test_system_admin_idempotent(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'error', 'http_method': 'DELETE', 'http_status': 404} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'system_admin': { 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) expected_data = { 'accprofile': 'test_value_3', 'accprofile-override': 'enable', 'allow-remove-admin-session': 'enable', 'comments': 'test_value_6', 'email-to': 'test_value_7', 'force-password-change': 'enable', 'fortitoken': 'test_value_9', 'guest-auth': 'disable', 'guest-lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6-trusthost1': 'test_value_15', 'ip6-trusthost10': 'test_value_16', 'ip6-trusthost2': 'test_value_17', 'ip6-trusthost3': 'test_value_18', 'ip6-trusthost4': 'test_value_19', 'ip6-trusthost5': 'test_value_20', 'ip6-trusthost6': 'test_value_21', 'ip6-trusthost7': 'test_value_22', 'ip6-trusthost8': 'test_value_23', 'ip6-trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password-expire': 'test_value_27', 'peer-auth': 'enable', 'peer-group': 'test_value_29', 'radius-vdom-override': 'enable', 'remote-auth': 'enable', 'remote-group': 'test_value_32', 'schedule': 'test_value_33', 'sms-custom-server': 'test_value_34', 'sms-phone': 'test_value_35', 'sms-server': 'fortiguard', 'ssh-certificate': 'test_value_37', 'ssh-public-key1': 'test_value_38', 'ssh-public-key2': 'test_value_39', 'ssh-public-key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two-factor': 'disable', 'wildcard': 'enable' } set_method_mock.assert_called_with('system', 'admin', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert not changed assert response['status'] == 'error' assert response['http_status'] == 404 def test_system_admin_filter_foreign_attributes(mocker): schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema') set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200} set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result) input_data = { 'username': 'admin', 'state': 'present', 'system_admin': { 'random_attribute_not_valid': 'tag', 'accprofile': 'test_value_3', 'accprofile_override': 'enable', 'allow_remove_admin_session': 'enable', 'comments': 'test_value_6', 'email_to': 'test_value_7', 'force_password_change': 'enable', 'fortitoken': 'test_value_9', 'guest_auth': 'disable', 'guest_lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6_trusthost1': 'test_value_15', 'ip6_trusthost10': 'test_value_16', 'ip6_trusthost2': 'test_value_17', 'ip6_trusthost3': 'test_value_18', 'ip6_trusthost4': 'test_value_19', 'ip6_trusthost5': 'test_value_20', 'ip6_trusthost6': 'test_value_21', 'ip6_trusthost7': 'test_value_22', 'ip6_trusthost8': 'test_value_23', 'ip6_trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password_expire': 'test_value_27', 'peer_auth': 'enable', 'peer_group': 'test_value_29', 'radius_vdom_override': 'enable', 'remote_auth': 'enable', 'remote_group': 'test_value_32', 'schedule': 'test_value_33', 'sms_custom_server': 'test_value_34', 'sms_phone': 'test_value_35', 'sms_server': 'fortiguard', 'ssh_certificate': 'test_value_37', 'ssh_public_key1': 'test_value_38', 'ssh_public_key2': 'test_value_39', 'ssh_public_key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two_factor': 'disable', 'wildcard': 'enable' }, 'vdom': 'root'} is_error, changed, response = fortios_system_admin.fortios_system(input_data, fos_instance) expected_data = { 'accprofile': 'test_value_3', 'accprofile-override': 'enable', 'allow-remove-admin-session': 'enable', 'comments': 'test_value_6', 'email-to': 'test_value_7', 'force-password-change': 'enable', 'fortitoken': 'test_value_9', 'guest-auth': 'disable', 'guest-lang': 'test_value_11', 'hidden': '12', 'history0': 'test_value_13', 'history1': 'test_value_14', 'ip6-trusthost1': 'test_value_15', 'ip6-trusthost10': 'test_value_16', 'ip6-trusthost2': 'test_value_17', 'ip6-trusthost3': 'test_value_18', 'ip6-trusthost4': 'test_value_19', 'ip6-trusthost5': 'test_value_20', 'ip6-trusthost6': 'test_value_21', 'ip6-trusthost7': 'test_value_22', 'ip6-trusthost8': 'test_value_23', 'ip6-trusthost9': 'test_value_24', 'name': 'default_name_25', 'password': 'test_value_26', 'password-expire': 'test_value_27', 'peer-auth': 'enable', 'peer-group': 'test_value_29', 'radius-vdom-override': 'enable', 'remote-auth': 'enable', 'remote-group': 'test_value_32', 'schedule': 'test_value_33', 'sms-custom-server': 'test_value_34', 'sms-phone': 'test_value_35', 'sms-server': 'fortiguard', 'ssh-certificate': 'test_value_37', 'ssh-public-key1': 'test_value_38', 'ssh-public-key2': 'test_value_39', 'ssh-public-key3': 'test_value_40', 'trusthost1': 'test_value_41', 'trusthost10': 'test_value_42', 'trusthost2': 'test_value_43', 'trusthost3': 'test_value_44', 'trusthost4': 'test_value_45', 'trusthost5': 'test_value_46', 'trusthost6': 'test_value_47', 'trusthost7': 'test_value_48', 'trusthost8': 'test_value_49', 'trusthost9': 'test_value_50', 'two-factor': 'disable', 'wildcard': 'enable' } set_method_mock.assert_called_with('system', 'admin', data=expected_data, vdom='root') schema_method_mock.assert_not_called() assert not is_error assert changed assert response['status'] == 'success' assert response['http_status'] == 200

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bf55ec3569b277ee488fa6212b05bd68c75f05b1

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py

Python

insights/parsers/tests/test_grub_conf_efi.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

121

2017-05-30T20:23:25.000Z

2022-03-23T12:52:15.000Z

insights/parsers/tests/test_grub_conf_efi.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

1,977

2017-05-26T14:36:03.000Z

2022-03-31T10:38:53.000Z

insights/parsers/tests/test_grub_conf_efi.py

lhuett/insights-core

1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8

[ "Apache-2.0" ]

244

2017-05-30T20:22:57.000Z

2022-03-26T10:09:39.000Z

from insights.tests import context_wrap from insights.parsers.grub_conf import Grub2EFIConfig GRUB2_EFI_CFG = """ ### BEGIN /etc/grub.d/10_linux ### menuentry 'Red Hat Enterprise Linux Server (3.10.0-514.16.1.el7.x86_64) 7.3 (Maipo)' --class red --class gnu-linux --class gnu --class os --unrestricted $menuentry_id_option 'gnulinux-3.10.0-514.el7.x86_64-advanced-9727cab4-12c2-41a8-9527-9644df34e586' { load_video set gfxpayload=keep insmod gzio insmod part_gpt insmod xfs set root='hd0,gpt2' if [ x$feature_platform_search_hint = xy ]; then search --no-floppy --fs-uuid --set=root --hint-bios=hd0,gpt2 --hint-efi=hd0,gpt2 --hint-baremetal=ahci0,gpt2 d80fa96c-ffa1-4894-9282-aeda37f0befe else search --no-floppy --fs-uuid --set=root d80fa96c-ffa1-4894-9282-aeda37f0befe fi linuxefi /vmlinuz-3.10.0-514.16.1.el7.x86_64 root=/dev/mapper/rhel-root ro rd.luks.uuid=luks-a40b320e-0711-4cd6-8f9e-ce32810e2a79 rd.lvm.lv=rhel/root rd.lvm.lv=rhel/swap rhgb quiet LANG=en_US.UTF-8 initrdefi /initramfs-3.10.0-514.16.1.el7.x86_64.img } menuentry 'Red Hat Enterprise Linux Server (3.10.0-514.10.2.el7.x86_64) 7.3 (Maipo)' --class red --class gnu-linux --class gnu --class os --unrestricted $menuentry_id_option 'gnulinux-3.10.0-514.el7.x86_64-advanced-9727cab4-12c2-41a8-9527-9644df34e586' { load_video set gfxpayload=keep insmod gzio insmod part_gpt insmod xfs set root='hd0,gpt2' if [ x$feature_platform_search_hint = xy ]; then search --no-floppy --fs-uuid --set=root --hint-bios=hd0,gpt2 --hint-efi=hd0,gpt2 --hint-baremetal=ahci0,gpt2 d80fa96c-ffa1-4894-9282-aeda37f0befe else search --no-floppy --fs-uuid --set=root d80fa96c-ffa1-4894-9282-aeda37f0befe fi linuxefi /vmlinuz-3.10.0-514.10.2.el7.x86_64 root=/dev/mapper/rhel-root ro rd.luks.uuid=luks-a40b320e-0711-4cd6-8f9e-ce32810e2a79 rd.lvm.lv=rhel/root rd.lvm.lv=rhel/swap rhgb quiet LANG=en_US.UTF-8 initrdefi /initramfs-3.10.0-514.10.2.el7.x86_64.img } menuentry 'Red Hat Enterprise Linux Server (3.10.0-514.el7.x86_64) 7.3 (Maipo)' --class red --class gnu-linux --class gnu --class os --unrestricted $menuentry_id_option 'gnulinux-3.10.0-514.el7.x86_64-advanced-9727cab4-12c2-41a8-9527-9644df34e586' { load_video set gfxpayload=keep insmod gzio insmod part_gpt insmod xfs set root='hd0,gpt2' if [ x$feature_platform_search_hint = xy ]; then search --no-floppy --fs-uuid --set=root --hint-bios=hd0,gpt2 --hint-efi=hd0,gpt2 --hint-baremetal=ahci0,gpt2 d80fa96c-ffa1-4894-9282-aeda37f0befe else search --no-floppy --fs-uuid --set=root d80fa96c-ffa1-4894-9282-aeda37f0befe fi linuxefi /vmlinuz-3.10.0-514.el7.x86_64 root=/dev/mapper/rhel-root ro rd.luks.uuid=luks-a40b320e-0711-4cd6-8f9e-ce32810e2a79 rd.lvm.lv=rhel/root rd.lvm.lv=rhel/swap rhgb quiet LANG=en_US.UTF-8 initrdefi /initramfs-3.10.0-514.el7.x86_64.img } menuentry 'Red Hat Enterprise Linux Server (0-rescue-f1340b5dd6ee4c26b587621566111421) 7.3 (Maipo)' --class red --class gnu-linux --class gnu --class os --unrestricted $menuentry_id_option 'gnulinux-0-rescue-f1340b5dd6ee4c26b587621566111421-advanced-9727cab4-12c2-41a8-9527-9644df34e586' { load_video insmod gzio insmod part_gpt insmod xfs set root='hd0,gpt2' if [ x$feature_platform_search_hint = xy ]; then search --no-floppy --fs-uuid --set=root --hint-bios=hd0,gpt2 --hint-efi=hd0,gpt2 --hint-baremetal=ahci0,gpt2 d80fa96c-ffa1-4894-9282-aeda37f0befe else search --no-floppy --fs-uuid --set=root d80fa96c-ffa1-4894-9282-aeda37f0befe fi linuxefi /vmlinuz-0-rescue-f1340b5dd6ee4c26b587621566111421 root=/dev/mapper/rhel-root ro rd.luks.uuid=luks-a40b320e-0711-4cd6-8f9e-ce32810e2a79 rd.lvm.lv=rhel/root rd.lvm.lv=rhel/swap rhgb quiet initrdefi /initramfs-0-rescue-f1340b5dd6ee4c26b587621566111421.img } ### END /etc/grub.d/10_linux ### """.strip() # noqa class TestGrub2EFI(): def test_get_grub_kernel_initrd(self): expected = {'grub_kernels': ['vmlinuz-3.10.0-514.16.1.el7.x86_64', 'vmlinuz-3.10.0-514.10.2.el7.x86_64', 'vmlinuz-3.10.0-514.el7.x86_64', 'vmlinuz-0-rescue-f1340b5dd6ee4c26b587621566111421'], 'grub_initrds': ['initramfs-3.10.0-514.16.1.el7.x86_64.img', 'initramfs-3.10.0-514.10.2.el7.x86_64.img', 'initramfs-3.10.0-514.el7.x86_64.img', 'initramfs-0-rescue-f1340b5dd6ee4c26b587621566111421.img']} assert expected == Grub2EFIConfig((context_wrap(GRUB2_EFI_CFG))).kernel_initrds

57.939024

289

0.693959

734

4,751

4.40327

0.168937

0.016708

0.022277

0.038985

0.86974

0.828899

0.806931

0.79672

0

0.191296

0.168175

4,751

81

290

58.654321

0.626518

0.000842

0

0.565789

0

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0.875659

0.406744

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1

0.013158

false

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0.052632

0

null

0

1

0

1

null

0

9

bf7391b8dd5654538957312b821e00d43600eb68

53

py

Python

bandwitch/list_common_enzymes/__init__.py

Edinburgh-Genome-Foundry/BandWitch

23f7faee9a955313ade66c77ab474a12712e14fc

[ "MIT" ]

12

2018-02-12T13:12:00.000Z

2021-08-15T11:36:28.000Z

bandwitch/list_common_enzymes/__init__.py

Edinburgh-Genome-Foundry/BandWitch

23f7faee9a955313ade66c77ab474a12712e14fc

[ "MIT" ]

2

2020-09-07T21:53:27.000Z

2020-09-20T18:49:17.000Z

bandwitch/list_common_enzymes/__init__.py

Edinburgh-Genome-Foundry/BandWitch

23f7faee9a955313ade66c77ab474a12712e14fc

[ "MIT" ]

null

from .list_common_enzymes import list_common_enzymes

26.5

52

0.90566

8

53

5.5

0.625

0.454545

0.772727

0

0.075472

53

1

53

0.897959

0

1

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true

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1

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1

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null

0

1

0

1

0

1

0

7

bf94a41fcefdb781e83c1af1d2e7b72026656d60

24,871

py

Python

datasets.py

skywolf829/MRSR

4adc682d5aaa308db0a4dcd47a04d1588789f39e

[ "MIT" ]

null

datasets.py

skywolf829/MRSR

4adc682d5aaa308db0a4dcd47a04d1588789f39e

[ "MIT" ]

null

datasets.py

skywolf829/MRSR

4adc682d5aaa308db0a4dcd47a04d1588789f39e

[ "MIT" ]

null

import numpy as np from concurrent.futures import ThreadPoolExecutor, as_completed import os import numpy as np import zeep import base64 from concurrent.futures import ThreadPoolExecutor, as_completed import struct import torch import h5py from utility_functions import AvgPool3D, AvgPool2D import torch.nn.functional as F class NetworkDataset(torch.utils.data.Dataset): def __init__(self, opt): self.client = zeep.Client('http://turbulence.pha.jhu.edu/service/turbulence.asmx?WSDL') self.token="edu.osu.buckeyemail.wurster.18-92fb557b" self.opt = opt self.channel_mins = [] self.channel_maxs = [] self.max_mag = None self.num_items = 0 self.items = [] self.subsample_dist = 1 self.num_items = opt['num_dataset_timesteps'] def get_frame(self, x_start, x_end, x_step, y_start, y_end, y_step, z_start, z_end, z_step, sim_name, timestep, field, num_components): self.client = zeep.Client('http://turbulence.pha.jhu.edu/service/turbulence.asmx?WSDL') result=self.client.service.GetAnyCutoutWeb(self.token, sim_name, field, timestep, x_start+1, y_start+1, z_start+1, x_end, y_end, z_end, x_step, y_step, z_step, 0, "") # put empty string for the last parameter # transfer base64 format to numpy nx=int((x_end-x_start)/x_step) ny=int((y_end-y_start)/y_step) nz=int((z_end-z_start)/z_step) base64_len=int(nx*ny*nz*num_components) base64_format='<'+str(base64_len)+'f' result=struct.unpack(base64_format, result) result=np.array(result).reshape((nz, ny, nx, num_components)).swapaxes(0,2) return result, x_start, x_end, y_start, y_end, z_start, z_end def get_full_frame_parallel(self, x_start, x_end, x_step, y_start, y_end, y_step, z_start, z_end, z_step, sim_name, timestep, field, num_components, num_workers): threads= [] full = np.zeros((int((x_end-x_start)/x_step), int((y_end-y_start)/y_step), int((z_end-z_start)/z_step), num_components), dtype=np.float32) with ThreadPoolExecutor(max_workers=num_workers) as executor: done = 0 # "Try to limit the number of points in a single query to 2 million" # 128^3 is just over 2 million, so we choose that as the maximum x_len = 128 y_len = 128 z_len = 128 for k in range(x_start, x_end, x_len): for i in range(y_start, y_end, y_len): for j in range(z_start, z_end, z_len): x_stop = min(k+x_len, x_end) y_stop = min(i+y_len, y_end) z_stop = min(j+z_len, z_end) threads.append(executor.submit(self.get_frame, k, x_stop, x_step, i, y_stop, y_step, j, z_stop, z_step, sim_name, timestep, field, num_components)) for task in as_completed(threads): r, x1, x2, y1, y2, z1, z2 = task.result() x1 -= x_start x2 -= x_start y1 -= y_start y2 -= y_start z1 -= z_start z2 -= z_start x1 = int(x1 / x_step) x2 = int(x2 / x_step) y1 = int(y1 / y_step) y2 = int(y2 / y_step) z1 = int(z1 / z_step) z2 = int(z2 / z_step) full[x1:x2,y1:y2,z1:z2,:] = r.astype(np.float32) del r done += 1 #print("Done: %i/%i" % (done, len(threads))) return full def set_subsample_dist(self,dist): self.subsample_dist = dist def __len__(self): return self.num_items - 100 def resolution(self): return self.resolution def scale(self, data): return data def unscale(self, data): return data def __getitem__(self, index): x_start = 0 x_end = self.opt['x_resolution'] y_start = 0 y_end = self.opt['y_resolution'] z_start = 0 z_end = self.opt['z_resolution'] if((z_end-z_start) / self.subsample_dist > self.opt['cropping_resolution']): z_start = torch.randint(self.opt['z_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() z_end = z_start + self.opt['cropping_resolution']*self.subsample_dist if((y_end-y_start) / self.subsample_dist > self.opt['cropping_resolution']): y_start = torch.randint(self.opt['y_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() y_end = y_start + self.opt['cropping_resolution']*self.subsample_dist if((x_end-x_start) / self.subsample_dist > self.opt['cropping_resolution']): x_start = torch.randint(self.opt['x_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() x_end = x_start + self.opt['cropping_resolution']*self.subsample_dist f = self.get_full_frame_parallel(x_start, x_end, self.subsample_dist,#x y_start, y_end, self.subsample_dist, #y z_start, z_end, self.subsample_dist, #z self.opt['dataset_name'], index*self.opt["ts_skip"], # skip the first 100 timesteps, duplicates for temporal interpolation "u", 3, self.opt['num_networked_workers']) ''' f, _, _, _, _, _, _ = self.get_frame(x_start, x_end, self.subsample_dist, y_start, y_end, self.subsample_dist, z_start, z_end, self.subsample_dist, self.opt['dataset_name'], index+100, "u", 3) ''' f = f.astype(np.float32).swapaxes(0,3).swapaxes(3,2).swapaxes(2,1) data = torch.tensor(f) if(self.opt['random_flipping']): if(torch.rand(1).item() > 0.5): data = torch.flip(data,[1]) if(torch.rand(1).item() > 0.5): data = torch.flip(data,[2]) if(torch.rand(1).item() > 0.5): data = torch.flip(data,[3]) return data class LocalTemporalDataset(torch.utils.data.Dataset): def __init__(self, opt): self.opt = opt self.channel_mins = [] self.channel_maxs = [] self.max_mag = None self.num_items = 0 self.items = [] self.item_names = [] self.subsample_dist = 1 print("Initializing dataset") for filename in os.listdir(self.opt['data_folder']): self.item_names.append(filename) if(opt['load_data_at_start'] or (self.num_items > 0 and \ (opt['scaling_mode'] == "magnitude" or opt['scaling_mode'] == "channel"))): print("Loading " + filename) f = h5py.File(os.path.join(self.opt['data_folder'], filename), 'r') d = torch.tensor(f.get('data')) f.close() if(self.num_items == 0): f = h5py.File(os.path.join(self.opt['data_folder'], filename), 'r') d = torch.tensor(f.get('data')) f.close() self.num_channels = d.shape[0] self.resolution = d.shape[1:] if(self.opt['mode'] == "3Dto2D"): self.resolution = self.resolution[0:len(self.resolution)-1] if(opt['load_data_at_start']): self.items.append(d) if(opt['scaling_mode'] == "magnitude"): mags = torch.norm(d, dim=0) m_mag = mags.max() if(self.max_mag is None or self.max_mag < m_mag): self.max_mag = m_mag if(opt['scaling_mode'] == "channel"): for i in range(d.shape[0]): if(len(self.channel_mins) <= i): self.channel_mins.append(d[i].min()) self.channel_maxs.append(d[i].max()) else: if(d[i].max() > self.channel_maxs[i]): self.channel_maxs[i] = d[i].max() if(d[i].min() < self.channel_mins[i]): self.channel_mins[i] = d[i].min() self.num_items += 1 def __len__(self): return self.num_items - self.opt['training_seq_length'] + 1 def scale(self, data): d = data.clone() if(self.opt['scaling_mode'] == "magnitude"): d *= (1/self.max_mag) elif (self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): d[:,i] -= self.channel_mins[i] d[:,i] /= (self.channel_maxs[i] - self.channel_mins[i]) d[:,i] -= 0.5 d[:,i] *= 2 return d def unscale(self, data): d = data.clone() if(self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): d[:, i] *= 0.5 d[:, i] += 0.5 d[:, i] *= (self.channel_maxs[i] - self.channel_mins[i]) d[:, i] += self.channel_mins[i] elif(self.opt['scaling_mode'] == "magnitude"): d *= self.max_mag return d def get_patch_ranges(self, frame, patch_size, receptive_field, mode): starts = [] rf = receptive_field ends = [] if(mode == "3D"): for z in range(0,max(1,frame.shape[2]), patch_size-2*rf): z = min(z, max(0, frame.shape[2] - patch_size)) z_stop = min(frame.shape[2], z + patch_size) for y in range(0, max(1,frame.shape[3]), patch_size-2*rf): y = min(y, max(0, frame.shape[3] - patch_size)) y_stop = min(frame.shape[3], y + patch_size) for x in range(0, max(1,frame.shape[4]), patch_size-2*rf): x = min(x, max(0, frame.shape[4] - patch_size)) x_stop = min(frame.shape[4], x + patch_size) starts.append([z, y, x]) ends.append([z_stop, y_stop, x_stop]) elif(mode == "2D" or mode == "3Dto2D"): for y in range(0, max(1,frame.shape[2]-patch_size+1), patch_size-2*rf): y = min(y, max(0, frame.shape[2] - patch_size)) y_stop = min(frame.shape[2], y + patch_size) for x in range(0, max(1,frame.shape[3]-patch_size+1), patch_size-2*rf): x = min(x, max(0, frame.shape[3] - patch_size)) x_stop = min(frame.shape[3], x + patch_size) starts.append([y, x]) ends.append([y_stop, x_stop]) return starts, ends def set_subsample_dist(self,dist): self.subsample_dist = dist def __getitem__(self, index): if(self.opt['load_data_at_start']): data = self.items[index] else: #print("trying to load " + str(self.item_names[index]) + ".h5") x_start = 0 x_end = self.opt['x_resolution'] y_start = 0 y_end = self.opt['y_resolution'] z_start = 0 z_end = self.opt['z_resolution'] if((z_end-z_start) / self.subsample_dist > self.opt['cropping_resolution']): z_start = torch.randint(self.opt['z_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() z_end = z_start + self.opt['cropping_resolution']*self.subsample_dist if((y_end-y_start) / self.subsample_dist > self.opt['cropping_resolution']): y_start = torch.randint(self.opt['y_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() y_end = y_start + self.opt['cropping_resolution']*self.subsample_dist if((x_end-x_start) / self.subsample_dist > self.opt['cropping_resolution']): x_start = torch.randint(self.opt['x_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() x_end = x_start + self.opt['cropping_resolution']*self.subsample_dist #print("converting " + self.item_names[index] + " to tensor") all_frames = [] for i in range(self.opt['training_seq_length']): f = h5py.File(os.path.join(self.opt['data_folder'], self.item_names[index+i]), 'r') data = torch.tensor(f['data'][:,x_start:x_end, y_start:y_end, z_start:z_end]) f.close() if(self.subsample_dist > 1): data = AvgPool3D(data.unsqueeze(0), self.subsample_dist)[0] all_frames.append(data) data = torch.stack(all_frames, dim=0) #print("converted " + self.item_names[index] + ".h5 to tensor") ''' if(self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): data[i] -= self.channel_mins[i] data[i] *= (1 / (self.channel_maxs[i] - self.channel_mins[i])) data[i] -= 0.5 data[i] *= 2 elif(self.opt['scaling_mode'] == "magnitude"): data *= (1 / self.max_mag) ''' if(self.opt['mode'] == "3Dto2D"): data = data[:,:,:,int(data.shape[3]/2)] #data = np2torch(data, "cpu") #print("returning " + str(index) + " data") if(self.opt['random_flipping']): if(torch.rand(1).item() > 0.5): data = torch.flip(data,[1]) if(torch.rand(1).item() > 0.5): data = torch.flip(data,[2]) if(torch.rand(1).item() > 0.5): data = torch.flip(data,[3]) return (data[0:1], data[self.opt['training_seq_length']-1:self.opt['training_seq_length']], data[1:self.opt['training_seq_length']-1], (index, index+self.opt['training_seq_length']-1)) class TestingDataset(torch.utils.data.Dataset): def __init__(self, location): self.location = location print("Initializing dataset") self.ext = "" self.item_names = [] for filename in os.listdir(location): self.item_names.append(filename.split(".")[0]) self.ext = filename.split(".")[1] self.item_names.sort(key=int) print("Dataset has " + str(len(self.item_names)) + " items") def __len__(self): return len(self.item_names) def __getitem__(self, index): print("Loading " + str(index)) f = h5py.File(os.path.join(self.location, self.item_names[index]+"."+self.ext), 'r') a = torch.Tensor(f['data']) f.close() return a.unsqueeze(0) class LocalDataset(torch.utils.data.Dataset): def __init__(self, opt): self.opt = opt self.channel_mins = [] self.channel_maxs = [] self.max_mag = None self.num_items = 0 self.items = [] self.item_names = [] self.subsample_dist = 1 print("Initializing dataset") for filename in os.listdir(self.opt['data_folder']): self.item_names.append(filename) if(opt['load_data_at_start'] or (self.num_items > 0 and \ (opt['scaling_mode'] == "magnitude" or opt['scaling_mode'] == "channel"))): print("Loading " + filename) f = h5py.File(os.path.join(self.opt['data_folder'], filename), 'r') d = torch.tensor(f.get('data')) f.close() if(self.num_items == 0): f = h5py.File(os.path.join(self.opt['data_folder'], filename), 'r') d = torch.tensor(f.get('data')) f.close() self.num_channels = d.shape[0] self.resolution = d.shape[1:] if(self.opt['mode'] == "3Dto2D"): self.resolution = self.resolution[0:len(self.resolution)-1] if(opt['load_data_at_start']): self.items.append(d) if(opt['scaling_mode'] == "magnitude"): mags = torch.norm(d, dim=0) m_mag = mags.max() if(self.max_mag is None or self.max_mag < m_mag): self.max_mag = m_mag if(opt['scaling_mode'] == "channel"): for i in range(d.shape[0]): if(len(self.channel_mins) <= i): self.channel_mins.append(d[i].min()) self.channel_maxs.append(d[i].max()) else: if(d[i].max() > self.channel_maxs[i]): self.channel_maxs[i] = d[i].max() if(d[i].min() < self.channel_mins[i]): self.channel_mins[i] = d[i].min() self.num_items += 1 self.item_names.sort() if(opt['training_data_amount'] < 1.0): end = int(opt['training_data_amount'] * len(self.item_names)) import random random.seed(0) random.shuffle(self.item_names) while(len(self.item_names) > end): self.item_names.pop(len(self.item_names)-1) self.num_items -= 1 if(opt['coarse_training'] > 2): self.item_names = self.item_names[::opt['coarse_training']] self.num_items = len(self.item_names) def __len__(self): return self.num_items def resolution(self): return self.resolution def scale(self, data): d = data.clone() if(self.opt['scaling_mode'] == "magnitude"): d *= (1/self.max_mag) elif (self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): d[:,i] -= self.channel_mins[i] d[:,i] /= (self.channel_maxs[i] - self.channel_mins[i]) d[:,i] -= 0.5 d[:,i] *= 2 return d def unscale(self, data): d = data.clone() if(self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): d[:, i] *= 0.5 d[:, i] += 0.5 d[:, i] *= (self.channel_maxs[i] - self.channel_mins[i]) d[:, i] += self.channel_mins[i] elif(self.opt['scaling_mode'] == "magnitude"): d *= self.max_mag return d def get_patch_ranges(self, frame, patch_size, receptive_field, mode): starts = [] rf = receptive_field ends = [] if(mode == "3D"): for z in range(0,max(1,frame.shape[2]), patch_size-2*rf): z = min(z, max(0, frame.shape[2] - patch_size)) z_stop = min(frame.shape[2], z + patch_size) for y in range(0, max(1,frame.shape[3]), patch_size-2*rf): y = min(y, max(0, frame.shape[3] - patch_size)) y_stop = min(frame.shape[3], y + patch_size) for x in range(0, max(1,frame.shape[4]), patch_size-2*rf): x = min(x, max(0, frame.shape[4] - patch_size)) x_stop = min(frame.shape[4], x + patch_size) starts.append([z, y, x]) ends.append([z_stop, y_stop, x_stop]) elif(mode == "2D" or mode == "3Dto2D"): for y in range(0, max(1,frame.shape[2]-patch_size+1), patch_size-2*rf): y = min(y, max(0, frame.shape[2] - patch_size)) y_stop = min(frame.shape[2], y + patch_size) for x in range(0, max(1,frame.shape[3]-patch_size+1), patch_size-2*rf): x = min(x, max(0, frame.shape[3] - patch_size)) x_stop = min(frame.shape[3], x + patch_size) starts.append([y, x]) ends.append([y_stop, x_stop]) return starts, ends def set_subsample_dist(self,dist): self.subsample_dist = dist def __getitem__(self, index): if(self.opt['load_data_at_start']): data = self.items[index] else: #print("trying to load " + str(self.item_names[index]) + ".h5") f = h5py.File(os.path.join(self.opt['data_folder'], self.item_names[index]), 'r') data = f['data'] f.close() x_start = 0 x_end = self.opt['x_resolution'] y_start = 0 y_end = self.opt['y_resolution'] if(self.opt['mode'] == "3D"): z_start = 0 z_end = self.opt['z_resolution'] if((z_end-z_start) / self.subsample_dist > self.opt['cropping_resolution']): z_start = torch.randint(self.opt['z_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() z_end = z_start + self.opt['cropping_resolution']*self.subsample_dist if((y_end-y_start) / self.subsample_dist > self.opt['cropping_resolution']): y_start = torch.randint(self.opt['y_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() y_end = y_start + self.opt['cropping_resolution']*self.subsample_dist if((x_end-x_start) / self.subsample_dist > self.opt['cropping_resolution']): x_start = torch.randint(self.opt['x_resolution'] - self.opt['cropping_resolution']*self.subsample_dist, [1]).item() x_end = x_start + self.opt['cropping_resolution']*self.subsample_dist if(self.opt['downsample_mode'] == "average_pooling"): #print("converting " + self.item_names[index] + " to tensor") if(self.opt['mode'] == "3D"): data = torch.tensor(data[:,x_start:x_end, y_start:y_end, z_start:z_end]) elif(self.opt['mode'] == "2D"): data = torch.tensor(data[:,x_start:x_end, y_start:y_end]) if(self.subsample_dist > 1): if(self.opt["mode"] == "3D"): data = AvgPool3D(data.unsqueeze(0), self.subsample_dist)[0] elif(self.opt['mode'] == "2D"): data = AvgPool2D(data.unsqueeze(0), self.subsample_dist)[0] elif(self.opt['downsample_mode'] == "subsampling"): if(self.opt["mode"] == "3D"): data = torch.tensor(data[:,x_start:x_end:self.subsample_dist, y_start:y_end:self.subsample_dist, z_start:z_end:self.subsample_dist]) elif(self.opt['mode'] == "2D"): data = torch.tensor(data[:,x_start:x_end:self.subsample_dist, y_start:y_end:self.subsample_dist]) else: if(self.opt["mode"] == "3D"): data = torch.tensor(data[:,x_start:x_end, y_start:y_end, z_start:z_end]) elif(self.opt['mode'] == "2D"): data = torch.tensor(data[:,x_start:x_end:self.subsample_dist, y_start:y_end:self.subsample_dist]) data = F.interpolate(data.unsqueeze(0), scaling_factor=float(1/self.subsample_dist), mode = self.opt['downsample_mode'], align_corners=True)[0] #print("converted " + self.item_names[index] + ".h5 to tensor") ''' if(self.opt['scaling_mode'] == "channel"): for i in range(self.num_channels): data[i] -= self.channel_mins[i] data[i] *= (1 / (self.channel_maxs[i] - self.channel_mins[i])) data[i] -= 0.5 data[i] *= 2 elif(self.opt['scaling_mode'] == "magnitude"): data *= (1 / self.max_mag) ''' if(self.opt['mode'] == "3Dto2D"): data = data[:,:,:,int(data.shape[3]/2)] #data = np2torch(data, "cpu") #print("returning " + str(index) + " data") if(self.opt['random_flipping']): if(torch.rand(1).item() > 0.5): data = torch.flip(data,[1]) if(torch.rand(1).item() > 0.5): data = torch.flip(data,[2]) if(self.opt['mode'] == "3D"): if(torch.rand(1).item() > 0.5): data = torch.flip(data,[3]) return data

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Python

napari_plot/components/_viewer_utils.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

13

2021-08-27T23:01:09.000Z

2022-03-22T13:51:35.000Z

napari_plot/components/_viewer_utils.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

71

2021-08-28T13:29:17.000Z

2022-03-28T21:22:12.000Z

napari_plot/components/_viewer_utils.py

lukasz-migas/napari-1d

b0f081a8711ae941b3e4b5c58c3aea56bd0e3277

[ "BSD-3-Clause" ]

null

import typing as ty import numpy as np from napari.layers import Layer from .. import layers from ..utils.utilities import find_nearest_index, get_min_max def get_x_region_extent(x_min: float, x_max: float, layer: Layer) -> ty.Tuple[ty.Optional[float], ...]: """Get extent for specified range""" if not layer.visible: return None, None if layer.ndim != 2: return None, None if isinstance(layer, (layers.Line, layers.Centroids)): idx_min, idx_max = find_nearest_index(layer.data[:, 0], [x_min, x_max]) if idx_min == idx_max: idx_max += 1 if idx_max > len(layer.data): return None, None try: return get_min_max(layer.data[idx_min:idx_max, 1]) except ValueError: return None, None if isinstance(layer, layers.Scatter): idx_min, idx_max = find_nearest_index(layer.data[:, 1], [x_min, x_max]) if idx_min == idx_max: idx_max += 1 if idx_max > len(layer.data): return None, None try: return get_min_max(layer.data[idx_min:idx_max, 0]) except ValueError: return None, None return None, None def get_layers_x_region_extent(x_min: float, x_max: float, layer_list) -> ty.Tuple[ty.Optional[float], ...]: """Get layer extents""" extents = [] for layer in layer_list: y_min, y_max = get_x_region_extent(x_min, x_max, layer) if y_min is None: continue extents.extend([y_min, y_max]) if extents: extents = np.asarray(extents) return get_min_max(extents) return None, None def get_y_region_extent(x_min: float, x_max: float, layer: Layer) -> ty.Tuple[ty.Optional[float], ...]: """Get extent for specified range""" if not layer.visible: return None, None if layer.ndim != 2: return None, None if isinstance(layer, (layers.Line, layers.Centroids)): idx_min, idx_max = find_nearest_index(layer.data[:, 1], [x_min, x_max]) if idx_min == idx_max: idx_max += 1 if idx_max > len(layer.data): return None, None try: return get_min_max(layer.data[idx_min:idx_max, 1]) except ValueError: return None, None if isinstance(layer, layers.Scatter): idx_min, idx_max = find_nearest_index(layer.data[:, 0], [x_min, x_max]) if idx_min == idx_max: idx_max += 1 if idx_max > len(layer.data): return None, None try: return get_min_max(layer.data[idx_min:idx_max, 1]) except ValueError: return None, None return None, None def get_layers_y_region_extent(y_min: float, y_max: float, layer_list) -> ty.Tuple[ty.Optional[float], ...]: """Get layer extents""" extents = [] for layer in layer_list: x_min, x_max = get_y_region_extent(y_min, y_max, layer) if x_min is None: continue extents.extend([x_min, x_max]) if extents: extents = np.asarray(extents) return get_min_max(extents) return None, None def get_range_extent(full_min, full_max, range_min, range_max, min_val: float = None) -> ty.Tuple[float, float]: """Get tuple of specified range""" if range_min is None: range_min = full_min if range_max is None: range_max = full_max if min_val is None: min_val = range_min return get_min_max([range_min, range_max, min_val])

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44e58e27939c8302e09b2c298ac47a521c03cb06

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py

Python

tests/test_mod_detection.py

Systemcluster/w3modmanager

5ba91ce1b20bc97d75dd91c477764d13e1f95122

[ "BSD-2-Clause" ]

6

2019-10-06T03:52:08.000Z

2021-01-27T20:20:58.000Z

tests/test_mod_detection.py

Systemcluster/w3modmanager

5ba91ce1b20bc97d75dd91c477764d13e1f95122

[ "BSD-2-Clause" ]

1

2019-10-07T04:54:27.000Z

2019-10-14T15:26:14.000Z

tests/test_mod_detection.py

Systemcluster/w3modmanager

5ba91ce1b20bc97d75dd91c477764d13e1f95122

[ "BSD-2-Clause" ]

2

2020-09-05T19:51:14.000Z

2021-06-22T00:21:18.000Z

""" Test cases for mod detection and name formatting Bin file parsing not part of this test package. """ from .framework import * from w3modmanager.core.model import * from w3modmanager.domain.mod.mod import * @pytest.mark.asyncio async def test_mod_normal(mockdata: Path) -> None: source = mockdata.joinpath('mods/normal') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'normal' assert mod.filename == 'modNormal' assert mod.datatype == 'mod' assert mod.source == source.joinpath('modNormal') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_direct(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-direct') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'direct' assert mod.filename == 'modDirect' assert mod.datatype == 'mod' assert mod.source == source assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_contains_no_dlc(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-without-dlc') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'without dlc' assert mod.filename == 'modWithoutDlc' assert mod.datatype == 'mod' assert mod.source == source assert mod.contentFiles == [ 'content/blob0.bundle', 'content/metadata.store', 'content/dlc/EP1/content/de.w3strings', 'content/dlc/EP1/content/en.w3strings'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_dlc(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-with-dlc') mods = await Mod.fromDirectory(source) assert len(mods) == 2 mod = mods[0] assert mod.package == 'with dlc' assert mod.filename == 'mod-dlc' assert mod.datatype == 'dlc' assert mod.source == source.joinpath('dlc/mod-dlc') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[1] assert mod.package == 'with dlc' assert mod.filename == 'modDlc' assert mod.datatype == 'mod' assert mod.source == source.joinpath('mods/mod-dlc') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_valid(mockdata: Path) -> None: source = mockdata.joinpath('mods/valid') mods = await Mod.fromDirectory(source) assert len(mods) == 4 mod = mods[0] assert mod.package == 'valid' assert mod.filename == 'dlcTestmod' assert mod.datatype == 'dlc' assert mod.source == source.joinpath('dlcTestmod') assert mod.contentFiles == [ 'content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[1] assert mod.package == 'valid' assert mod.filename == 'modTestmod' assert mod.datatype == 'mod' assert mod.source == source.joinpath('MODTestmod') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == ['content/scripts/game/r4game.ws'] assert mod.binFiles == [] assert mod.menuFiles == [ 'modTest.xml (bin/config/r4game/user_config_matrix/pc/modTest.xml)', 'bin/config/r4game/user_config_matrix/pc/input.xml', 'bin/config/r4game/user_config_matrix/pc/modTestConfig.xml', 'bin/config/r4game/user_config_matrix/pc/rendering.xml'] assert mod.settings[0].source == Path('validuser.settings.part.txt') assert mod.inputs[0].source == Path('inputsettings.txt') mod = mods[2] assert mod.package == 'valid' assert mod.filename == 'modTestmodExtra' assert mod.datatype == 'mod' assert mod.source == source.joinpath('mods/modTestmodExtra') assert mod.contentFiles == [ 'content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == ['inputxml.txt (bin/config/r4game/user_config_matrix/pc/input.xml)'] assert mod.settings == [] assert mod.inputs == [] mod = mods[3] assert mod.package == 'valid' assert mod.filename == 'binValid' assert mod.datatype == 'bin' assert mod.source == source assert mod.contentFiles == [] assert mod.scriptFiles == [] assert mod.binFiles == ['bin/config/base/localization.ini'] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_weird(mockdata: Path) -> None: source = mockdata.joinpath('mods/weird') mods = await Mod.fromDirectory(source) assert len(mods) == 3 mod = mods[0] assert mod.package == 'weird' assert mod.filename == 'modFoo' assert mod.datatype == 'udf' assert mod.source == source.joinpath('foo 1.2') assert mod.contentFiles == ['content/placeholder.txt'] assert mod.scriptFiles == [] assert mod.binFiles == ['bin/config/performance.xml'] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[1] assert mod.package == 'weird' assert mod.filename == 'modFooExtras' assert mod.datatype == 'udf' assert mod.source == source.joinpath('foo extras') assert mod.contentFiles == ['content/placeholder.txt'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[2] assert mod.package == 'weird' assert mod.filename == 'binFoo12Menus' assert mod.datatype == 'bin' assert mod.source == source.joinpath('foo 1.2 menus') assert mod.contentFiles == [] assert mod.scriptFiles == [] assert mod.binFiles == ['bin/config/performance.xml'] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_patch(mockdata: Path) -> None: source = mockdata.joinpath('mods/patch') mods = await Mod.fromDirectory(source) assert len(mods) == 2 mod = mods[0] assert mod.package == 'patch' assert mod.filename == 'mod0000____CompilationTrigger' assert mod.datatype == 'mod' assert mod.contentFiles == [] assert mod.scriptFiles == ['content/scripts/compilationTrigger.ws'] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[1] assert mod.package == 'patch' assert mod.filename == 'patCh' assert mod.datatype == 'pat' assert mod.contentFiles == [] assert mod.scriptFiles == ['content/content0/scripts/game/r4game.ws'] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_only_dlc(mockdata: Path) -> None: source = mockdata.joinpath('mods/only-dlc') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'only dlc' assert mod.filename == 'dlc__only' assert mod.datatype == 'dlc' assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_with_split_bins(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-with-split-bins') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'with split bins' assert mod.filename == 'binWithSplitBins' assert mod.datatype == 'bin' assert mod.contentFiles == [] assert mod.scriptFiles == [] assert mod.binFiles == [ 'a/bin/config/graphics.xml (bin/config/graphics.xml)', 'b/bin/config/performance.xml (bin/config/performance.xml)'] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_dlc_same_name(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-with-dlc-same-name') mods = await Mod.fromDirectory(source) assert len(mods) == 2 mod = mods[0] assert mod.package == 'with dlc same name' assert mod.filename == 'modDlc' assert mod.datatype == 'dlc' assert mod.source == source.joinpath('dlc/modDlc') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] mod = mods[1] assert mod.package == 'with dlc same name' assert mod.filename == 'modDlc' assert mod.datatype == 'mod' assert mod.source == source.joinpath('mods/modDlc') assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [] @pytest.mark.asyncio async def test_mod_with_inputs(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-with-inputs') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'with inputs' assert mod.filename == 'modWithInputs' assert mod.datatype == 'mod' assert mod.source == source assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [InputSettings(Path('input.settings.part.txt'), ''' [Test] IK_0=(Action=TEST_0) IK_1=(Action=TEST_1) IK_Tab=(Action=TEST_2) ''')] assert len(mod.inputs[0]) == 3 @pytest.mark.asyncio async def test_mod_with_inputs_readme(mockdata: Path) -> None: source = mockdata.joinpath('mods/mod-with-inputs-readme') mods = await Mod.fromDirectory(source) assert len(mods) == 1 mod = mods[0] assert mod.package == 'with inputs readme' assert mod.filename == 'modWithInputsReadme' assert mod.datatype == 'mod' assert mod.source == source assert mod.contentFiles == ['content/blob0.bundle', 'content/metadata.store'] assert mod.scriptFiles == [] assert mod.binFiles == [] assert mod.menuFiles == [] assert mod.settings == [] assert mod.inputs == [InputSettings(Path('input.settings.readme.txt'), ''' [Test] IK_0=(Action=TEST_0) IK_1=(Action=TEST_1) IK_Tab=(Action=TEST_2) [Exploration] IK_Z=(Action=OpenTestMenu) ''')] assert len(mod.inputs[0]) == 4

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9

786a30ba953a4b5d34a47cb50cf9dfa1a912e909

205

py

Python

ctypes_generation/extended_structs/_EVENT_DESCRIPTOR.py

IMULMUL/PythonForWindows

61e027a678d5b87aa64fcf8a37a6661a86236589

[ "BSD-3-Clause" ]

479

2016-01-08T00:53:34.000Z

2022-03-22T10:28:19.000Z

ctypes_generation/extended_structs/_EVENT_DESCRIPTOR.py

IMULMUL/PythonForWindows

61e027a678d5b87aa64fcf8a37a6661a86236589

[ "BSD-3-Clause" ]

38

2017-12-29T17:09:04.000Z

2022-01-31T08:27:47.000Z

ctypes_generation/extended_structs/_EVENT_DESCRIPTOR.py

IMULMUL/PythonForWindows

61e027a678d5b87aa64fcf8a37a6661a86236589

[ "BSD-3-Clause" ]

103

2016-01-10T01:32:17.000Z

2021-12-24T17:21:06.000Z

class _EVENT_DESCRIPTOR(_EVENT_DESCRIPTOR): def __repr__(self): return "<{0} Id={self.Id} Opcode={self.Opcode} Version={self.Version} Level={self.Level}>".format(type(self).__name__, self=self)

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7

78b291b34b72b048027e3b3b7f2da96c27348593

120

py

Python

src/daos/division_item_dao/__init__.py

taonguyen740/flask_based_3tier_framework

f02e492eff0206e661925dddcf0ba978ead38b5e

[ "MIT" ]

null

src/daos/division_item_dao/__init__.py

taonguyen740/flask_based_3tier_framework

f02e492eff0206e661925dddcf0ba978ead38b5e

[ "MIT" ]

null

src/daos/division_item_dao/__init__.py

taonguyen740/flask_based_3tier_framework

f02e492eff0206e661925dddcf0ba978ead38b5e

[ "MIT" ]

null

from .division_item_dao_interface import DivisionItemDaoInterface from .rds_division_item_dao import RdsDivisionItemDao

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py

Python

src/backend/marsha/core/tests/test_api_playlist.py

marin-leonard/marsha

b5d6bf98fda27acd3a08577b82dd98bcd39bfd8d

[ "MIT" ]

null

src/backend/marsha/core/tests/test_api_playlist.py

marin-leonard/marsha

b5d6bf98fda27acd3a08577b82dd98bcd39bfd8d

[ "MIT" ]

null

src/backend/marsha/core/tests/test_api_playlist.py

marin-leonard/marsha

b5d6bf98fda27acd3a08577b82dd98bcd39bfd8d

[ "MIT" ]

null

"""Tests for the Playlist API of the Marsha project.""" import uuid from django.test import TestCase from rest_framework_simplejwt.tokens import AccessToken from .. import factories, models class PlaylistAPITest(TestCase): """Test the API for playlist objects.""" def test_create_playlist_by_anonymous_user(self): """Anonymous users cannot create playlists.""" org = factories.OrganizationFactory() consumer_site = factories.ConsumerSiteFactory() response = self.client.post( "/api/playlists/", { "consumer_site": str(consumer_site.id), "lti_id": "playlist_twenty", "organization": str(org.id), "title": "Some playlist", }, ) self.assertEqual(response.status_code, 401) def test_create_playlist_by_random_logged_in_user(self): """ Random logged-in users. Cannot create playlists for organizations they have no role in. """ user = factories.UserFactory() org = factories.OrganizationFactory() consumer_site = factories.ConsumerSiteFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.post( "/api/playlists/", { "consumer_site": str(consumer_site.id), "lti_id": "playlist_twenty", "organization": str(org.id), "title": "Some playlist", }, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_create_playlist_by_random_logged_in_user_for_nonexistent_organization( self, ): """ Fails to create a playlist. Attempts to create a playlist for an organization that does not exist result in an error response. """ user = factories.UserFactory() random_uuid = uuid.uuid4() consumer_site = factories.ConsumerSiteFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.post( "/api/playlists/", { "consumer_site": str(consumer_site.id), "lti_id": "playlist_twenty", "organization": random_uuid, "title": "Some playlist", }, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_create_playlist_by_organization_instructor(self): """Organization instructors cannot create playlists.""" user = factories.UserFactory() org = factories.OrganizationFactory() factories.OrganizationAccessFactory( role=models.INSTRUCTOR, organization=org, user=user ) consumer_site = factories.ConsumerSiteFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.post( "/api/playlists/", { "consumer_site": str(consumer_site.id), "lti_id": "playlist_twenty", "organization": str(org.id), "title": "Some playlist", }, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_create_playlist_by_organization_administrator(self): """Organization administrators can create playlists.""" user = factories.UserFactory() org = factories.OrganizationFactory() factories.OrganizationAccessFactory( role=models.ADMINISTRATOR, organization=org, user=user ) consumer_site = factories.ConsumerSiteFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) self.assertEqual(models.Playlist.objects.count(), 0) response = self.client.post( "/api/playlists/", { "consumer_site": str(consumer_site.id), "lti_id": "playlist_twenty", "organization": str(org.id), "title": "Some playlist", }, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(models.Playlist.objects.count(), 1) self.assertEqual(response.status_code, 201) self.assertEqual( response.json(), { "consumer_site": str(consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(models.Playlist.objects.first().id), "is_portable_to_playlist": False, "is_portable_to_consumer_site": False, "is_public": False, "lti_id": "playlist_twenty", "organization": str(org.id), "portable_to": [], "title": "Some playlist", "users": [], }, ) def test_retrieve_playlist_by_anonymous_user(self): """Anonymous users cannot retrieve playlists.""" playlist = factories.PlaylistFactory() response = self.client.get(f"/api/playlists/{playlist.id}/") self.assertEqual(response.status_code, 401) def test_retrieve_playlist_by_random_logged_in_user(self): """Random logged-in users cannot retrieve playlists unrelated to them.""" user = factories.UserFactory() playlist = factories.PlaylistFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_retrieve_playlist_by_playlist_instructor(self): """Playlist instructors cannot retrieve playlists.""" user = factories.UserFactory() playlist = factories.PlaylistFactory() factories.PlaylistAccessFactory( user=user, playlist=playlist, role=models.INSTRUCTOR ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) def test_retrieve_playlist_by_playlist_admin(self): """Playlist administrators can retrieve playlists.""" user = factories.UserFactory() playlist = factories.PlaylistFactory() factories.PlaylistAccessFactory( user=user, playlist=playlist, role=models.ADMINISTRATOR ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertEqual( response.json(), { "consumer_site": str(playlist.consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(playlist.id), "is_portable_to_consumer_site": False, "is_portable_to_playlist": True, "is_public": False, "lti_id": playlist.lti_id, "organization": None, "portable_to": [], "title": playlist.title, "users": [str(user.id)], }, ) def test_retrieve_playlist_by_organization_admin(self): """Organization administrators can retrieve organization-related playlists.""" user = factories.UserFactory() organization = factories.OrganizationFactory() playlist = factories.PlaylistFactory(organization=organization) factories.OrganizationAccessFactory( user=user, organization=organization, role=models.ADMINISTRATOR ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertEqual( response.json(), { "consumer_site": str(playlist.consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(playlist.id), "is_portable_to_consumer_site": False, "is_portable_to_playlist": True, "is_public": False, "lti_id": playlist.lti_id, "organization": str(organization.id), "portable_to": [], "title": playlist.title, "users": [], }, ) def test_list_playlists_by_anonymous_user(self): """Anonymous users cannot make list requests for playlists.""" factories.PlaylistFactory() response = self.client.get("/api/playlists/") self.assertEqual(response.status_code, 401) def test_list_playlists_by_random_logged_in_user(self): """ Random logged-in users can make list requests. Will not receive playlists for organizations they are not a member of. """ user = factories.UserFactory() factories.PlaylistFactory() jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( "/api/playlists/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertEqual(response.json()["count"], 0) self.assertEqual(response.json()["results"], []) def test_list_playlists_by_logged_in_user_with_organization_memberships(self): """Organization members get all playlists they have access to.""" user = factories.UserFactory() org_1 = factories.OrganizationFactory() org_1.users.add(user) playlist_1 = factories.PlaylistFactory( lti_id="playlist#one", organization=org_1, title="First playlist" ) org_2 = factories.OrganizationFactory() org_2.users.add(user) playlist_2 = factories.PlaylistFactory( lti_id="playlist#two", organization=org_2, title="Second playlist" ) # User is not a member of this organization org_3 = factories.OrganizationFactory() factories.PlaylistFactory(organization=org_3) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( "/api/playlists/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertEqual(response.json()["count"], 2) self.assertEqual( response.json()["results"], [ { "consumer_site": str(playlist_1.consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(playlist_1.id), "is_portable_to_consumer_site": False, "is_portable_to_playlist": True, "is_public": False, "lti_id": "playlist#one", "organization": str(org_1.id), "portable_to": [], "title": "First playlist", "users": [], }, { "consumer_site": str(playlist_2.consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(playlist_2.id), "is_portable_to_consumer_site": False, "is_portable_to_playlist": True, "is_public": False, "lti_id": "playlist#two", "organization": str(org_2.id), "portable_to": [], "title": "Second playlist", "users": [], }, ], ) def test_list_playlists_for_organization_by_logged_in_user_with_organization_memberships( self, ): """ Organization members. They can list all the playlists for a given organization of which they are a member. """ user = factories.UserFactory() org_1 = factories.OrganizationFactory() org_1.users.add(user) playlist_1 = factories.PlaylistFactory( lti_id="playlist#eleven", organization=org_1, title="First playlist" ) # User is a member of this organization, but it is not included in the request below org_2 = factories.OrganizationFactory() org_2.users.add(user) factories.PlaylistFactory(organization=org_2, title="Second playlist") # User is not a member of this organization org_3 = factories.OrganizationFactory() factories.PlaylistFactory(organization=org_3) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.get( f"/api/playlists/?organization={str(org_1.id)}", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 200) self.assertEqual(response.json()["count"], 1) self.assertEqual( response.json()["results"], [ { "consumer_site": str(playlist_1.consumer_site.id), "created_by": None, "duplicated_from": None, "id": str(playlist_1.id), "is_portable_to_consumer_site": False, "is_portable_to_playlist": True, "is_public": False, "lti_id": "playlist#eleven", "organization": str(org_1.id), "portable_to": [], "title": "First playlist", "users": [], }, ], ) def test_delete_playlist_by_anonymous_user(self): """Anonymous users cannot delete playlists.""" playlist = factories.PlaylistFactory() self.assertEqual(models.Playlist.objects.count(), 1) response = self.client.delete(f"/api/playlists/{playlist.id}/") self.assertEqual(response.status_code, 401) self.assertEqual(models.Playlist.objects.count(), 1) def test_delete_playlist_by_random_logged_in_user(self): """Random logged-in users cannot delete playlists unrelated to them.""" user = factories.UserFactory() playlist = factories.PlaylistFactory() self.assertEqual(models.Playlist.objects.count(), 1) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.delete( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) self.assertEqual(models.Playlist.objects.count(), 1) def test_delete_playlist_by_playlist_admin(self): """Playlist administrators cannot delete playlists.""" user = factories.UserFactory() playlist = factories.PlaylistFactory() factories.PlaylistAccessFactory( user=user, playlist=playlist, role=models.ADMINISTRATOR ) self.assertEqual(models.Playlist.objects.count(), 1) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.delete( f"/api/playlists/{playlist.id}/", HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) self.assertEqual(models.Playlist.objects.count(), 1) def test_update_playlist_by_anonymous_user(self): """Anonymous users cannot update playlists.""" playlist = factories.PlaylistFactory(title="existing title") response = self.client.put( f"/api/playlists/{playlist.id}/", {"title": "new playlist title"} ) self.assertEqual(response.status_code, 401) playlist.refresh_from_db() self.assertEqual(playlist.title, "existing title") def test_update_playlist_by_random_logged_in_user(self): """Random logged-in users cannot update playlists unrelated to them.""" user = factories.UserFactory() playlist = factories.PlaylistFactory(title="existing title") jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.delete( f"/api/playlists/{playlist.id}/", {"title": "new playlist title"}, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) playlist.refresh_from_db() self.assertEqual(playlist.title, "existing title") def test_update_playlist_by_playlist_admin(self): """Playlist administrators cannot update playlists.""" user = factories.UserFactory() playlist = factories.PlaylistFactory(title="existing title") factories.PlaylistAccessFactory( user=user, playlist=playlist, role=models.ADMINISTRATOR ) jwt_token = AccessToken() jwt_token.payload["resource_id"] = str(user.id) jwt_token.payload["user_id"] = str(user.id) response = self.client.delete( f"/api/playlists/{playlist.id}/", {"title": "new playlist title"}, HTTP_AUTHORIZATION=f"Bearer {jwt_token}", ) self.assertEqual(response.status_code, 403) playlist.refresh_from_db() self.assertEqual(playlist.title, "existing title")

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78d5c169e2d566951ed34b7bb00514204d0b76ee

792

py

Python

sales-forecaster/predict/models.py

sevmardi/ml-projects

0eb218c77cda61285cfcf599599ff28a8a8deba7

[ "MIT" ]

null

sales-forecaster/predict/models.py

sevmardi/ml-projects

0eb218c77cda61285cfcf599599ff28a8a8deba7

[ "MIT" ]

7

2020-06-06T01:26:08.000Z

2022-02-10T11:26:58.000Z

sales-forecaster/predict/models.py

sevmardi/ml-projects

0eb218c77cda61285cfcf599599ff28a8a8deba7

[ "MIT" ]

null

from django.db import models # Create your models here. class Product(models.Model): back_camera = models.DecimalField(decimal_places=2, max_digits=10) front_camera = models.DecimalField(decimal_places=2, max_digits=10) resolution_1 = models.DecimalField(decimal_places=2, max_digits=10) resolution_2 = models.DecimalField(decimal_places=2, max_digits=10) screen_size = models.DecimalField(decimal_places=2, max_digits=10) battery = models.DecimalField(decimal_places=2, max_digits=10) price = models.DecimalField(decimal_places=2, max_digits=10) pre_release_demand = models.DecimalField(decimal_places=2, max_digits=10) # sales = models.DecimalField(decimal_places=2, max_digits=10) # quarter = models.DecimalField(decimal_places=2, max_digits=10)

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7

15508da1fe0f73b360dc092f9d5466fb3bf73bc5

248

py

Python

application/controller/errorctl.py

haiyoumeiyou/cherrybrigde

f00a0592240b60cc42b895ad194b0273485956d0

[ "BSD-3-Clause" ]

null

application/controller/errorctl.py

haiyoumeiyou/cherrybrigde

f00a0592240b60cc42b895ad194b0273485956d0

[ "BSD-3-Clause" ]

null

application/controller/errorctl.py

haiyoumeiyou/cherrybrigde

f00a0592240b60cc42b895ad194b0273485956d0

[ "BSD-3-Clause" ]

null

import cherrypy class Error: @cherrypy.tools.template def broken(self): return {'authmenu': cherrypy.session['authmenu']} @cherrypy.tools.template def noauth(self): return {'authmenu': cherrypy.session['authmenu']}

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57

0.669355

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248

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0.5

0.289157

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0.289157

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0

0.197581

248

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58

24.8

0.834171

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null

1

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null

0

1

0

1

0

7

15bc259a9cbcd28dbbb94cfbe70c296026ee5edd

5,074

py

Python

fabric/switchdb.py

salran40/POAP

9ff2ab68b55aeffe104d127c4beb8b1372b2c8de

[ "Apache-2.0" ]

null

fabric/switchdb.py

salran40/POAP

9ff2ab68b55aeffe104d127c4beb8b1372b2c8de

[ "Apache-2.0" ]

null

fabric/switchdb.py

salran40/POAP

9ff2ab68b55aeffe104d127c4beb8b1372b2c8de

[ "Apache-2.0" ]

null

#Switch database SPINE_SWITCH_TYPE = ["NX3164Q","NX9332PQ", "NX3132Q", "NX9504", "NX9508", "NX9516"] LEAF_SWITCH_TYPE = ["NX9372PX", "NX9372TX", "NX9396PX", "NX9396TX", "NX93120TX", "NX93128TX", "NX3172Q"] #Leaf Switch Interfaces SWITCH_HOST_IF_MAP = {"NX9372PX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX9372TX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX9396PX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX9396TX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX93120PX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX93128TX":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX3172Q":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32", "e1/33", "e1/34", "e1/35","e1/36", "e1/37", "e1/38", "e1/39", "e1/40","e1/41", "e1/42", "e1/43", "e1/44", "e1/45","e1/46", "e1/47", "e1/48"],\ "NX3164Q":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"],\ "NX9332PQ":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"],\ "NX3132Q":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"],\ "NX9504":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"],\ "NX9508":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"],\ "NX9516":["e1/1", "e1/2", "e1/3", "e1/4", "e1/5","e1/6", "e1/7", "e1/8", "e1/9", "e1/10","e1/11", "e1/12", "e1/13", "e1/14", "e1/15","e1/16", "e1/17", "e1/18", "e1/19", "e1/20","e1/21", "e1/22", "e1/23", "e1/24", "e1/25","e1/26", "e1/27", "e1/28", "e1/29", "e1/30", "e1/31", "e1/32"]} #Link Types TOPOLOGY_LINK_TYPES = ['Linkset-[1-9]+Link','VPC-[1-9]+Link']

220.608696

451

0.458021

1,110

5,074

2.085586

0.069369

0.016847

0.028078

0.033693

0.861771

0

0.344835

0.097556

5,074

22

452

230.636364

0.160734

0.009263

0

0.54958

0

1

0

false

0

null

0

1

0

1

0

1

0

null

0

9

ec6f4b32273f6c8ebe8bbd04990fc3ab0b63d2e7

2,312

py

Python

TrekBot2_WS/build/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Rafcin/RescueRoboticsLHMV

d3dc63e6c16a040b16170f143556ef358018b7da

[ "Unlicense" ]

1

2018-10-04T14:37:00.000Z

TrekBot2_WS/build/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Rafcin/TrekBot

d3dc63e6c16a040b16170f143556ef358018b7da

[ "Unlicense" ]

null

TrekBot2_WS/build/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Rafcin/TrekBot

d3dc63e6c16a040b16170f143556ef358018b7da

[ "Unlicense" ]

null

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/BoundingBox.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeographicMapChanges.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeographicMap.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeoPath.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeoPoint.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeoPointStamped.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeoPose.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/GeoPoseStamped.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/KeyValue.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/MapFeature.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/RouteNetwork.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/RoutePath.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/RouteSegment.msg;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg/WayPoint.msg" services_str = "/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/srv/GetGeographicMap.srv;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/srv/GetGeoPath.srv;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/srv/GetRoutePlan.srv;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/srv/UpdateGeographicMap.srv" pkg_name = "geographic_msgs" dependencies_str = "geometry_msgs;std_msgs;uuid_msgs" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "geographic_msgs;/xavier_ssd/TrekBot/TrekBot2_WS/src/geographic_info/geographic_msgs/msg;geometry_msgs;/opt/ros/melodic/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/melodic/share/std_msgs/cmake/../msg;uuid_msgs;/xavier_ssd/TrekBot/TrekBot2_WS/src/unique_identifier/uuid_msgs/msg" PYTHON_EXECUTABLE = "/usr/bin/python2" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

192.666667

1,248

0.862457

340

2,312

5.552941

0.217647

0.15572

0.169492

0.254237

0.675318

0.652013

0.632415

0

0.009219

0.014706

2,312

11

1,249

210.181818

0.819579

0.021194

0

1

0.333333

0.910217

0.894737

0

1

0

false

0

null

0

1

0

1

0

1

0

1

null

0

8

01c720d317430dffe130c9086f0b06bd572f9f82

167

py

Python

lizardanalysis/start_new_analysis/__init__.py

JojoReikun/ClimbingLizardDLCAnalysis

6cc38090217a3ffd4860ef6d06ba7967d3c10b7c

[ "MIT" ]

1

2021-03-09T19:12:44.000Z

lizardanalysis/start_new_analysis/__init__.py

JojoReikun/ClimbingLizardDLCAnalysis

6cc38090217a3ffd4860ef6d06ba7967d3c10b7c

[ "MIT" ]

null

lizardanalysis/start_new_analysis/__init__.py

JojoReikun/ClimbingLizardDLCAnalysis

6cc38090217a3ffd4860ef6d06ba7967d3c10b7c

[ "MIT" ]

null

from lizardanalysis.start_new_analysis.new import create_new_project from lizardanalysis.start_new_analysis.gui_define_video_orientation import gui_choose_video_config

83.5

98

0.934132

24

167

6

0.583333

0.25

0.319444

0.361111

0.472222

0

0.041916

167

2

98

83.5

0.9

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

01dbc0373365123a77ad514bfe7caa6d707478f6

3,685

py

Python

src/unit_tests/test_updater.py

Lituta/sparql-jsonld

590bfa70d4d1e74ea4a60e43bce8819157a81f7d

[ "MIT" ]

null

src/unit_tests/test_updater.py

Lituta/sparql-jsonld

590bfa70d4d1e74ea4a60e43bce8819157a81f7d

[ "MIT" ]

null

src/unit_tests/test_updater.py

Lituta/sparql-jsonld

590bfa70d4d1e74ea4a60e43bce8819157a81f7d

[ "MIT" ]

null

import unittest from rdflib.plugins.sparql.parser import parseQuery from src.updater import Updater class TestUpdater(unittest.TestCase): def test_updater(self) -> None: ori = parseQuery("""PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX ns: <http://example.org/ns#> SELECT ?title (?p*(1-?discount) AS ?price) { ?x ns:price ?p . ?x dc:title ?title . ?x ns:discount ?discount }""") frame = { '@context': { 'price': { '@id': 'http://example.org/ns#price' }, 'title': { '@id': 'http://purl.org/dc/elements/1.1/title' } }, 'price': {}, 'title': {} } u = Updater() res = u.update(ori, frame).dump().split('\n')[0].replace('\n', '') expect = """[[PrefixDecl_{'prefix': 'dc', 'iri': rdflib.term.URIRef('http://purl.org/dc/elements/1.1/')}, \ PrefixDecl_{'prefix': 'ns', 'iri': rdflib.term.URIRef('http://example.org/ns#')}], ConstructQuery_{'template': \ [([rdflib.term.Variable('title'), rdflib.term.URIRef('http://example.org/ns#price'), rdflib.term.Variable\ ('var1')], {}), ([rdflib.term.Variable('title'), pname_{'prefix': 'dc', 'localname': 'title'}, rdflib.term.\ Variable('var2')], {})], 'where': GroupGraphPatternSub_{'part': [TriplesBlock_{'triples': [([rdflib.term.\ Variable('x'), PathAlternative_{'part': [PathSequence_{'part': [PathElt_{'part': pname_{'prefix': 'ns', \ 'localname': 'price'}}]}]}, rdflib.term.Variable('p')], {}), ([rdflib.term.Variable('x'), PathAlternative_\ {'part': [PathSequence_{'part': [PathElt_{'part': pname_{'prefix': 'dc', 'localname': 'title'}}]}]}, rdflib.\ term.Variable('title')], {}), ([rdflib.term.Variable('x'), PathAlternative_{'part': [PathSequence_{'part': \ [PathElt_{'part': pname_{'prefix': 'ns', 'localname': 'discount'}}]}]}, rdflib.term.Variable('discount')], \ {})]}, OptionalGraphPattern_{'graph': TriplesBlock_{'triples': [([rdflib.term.Variable('title'), rdflib.term.\ URIRef('http://example.org/ns#price'), rdflib.term.Variable('var1')], {})]}}, OptionalGraphPattern_{'graph': \ TriplesBlock_{'triples': [([rdflib.term.Variable('title'), pname_{'prefix': 'dc', 'localname': 'title'}, \ rdflib.term.Variable('var2')], {})]}}]}}]""" self.assertEqual(res.strip(), expect) def test_updater_no_frame(self) -> None: ori = parseQuery("""PREFIX dc: <http://purl.org/dc/elements/1.1/> PREFIX ns: <http://example.org/ns#> SELECT ?title (?p*(1-?discount) AS ?price) { ?x ns:price ?p . ?x dc:title ?title . ?x ns:discount ?discount }""") u = Updater() res = u.update(ori, {}).dump().split('\n')[0].replace('\n', '') expect = """[[PrefixDecl_{'prefix': 'dc', 'iri': rdflib.term.URIRef('http://purl.org/dc/elements/1.1/')}, \ PrefixDecl_{'prefix': 'ns', 'iri': rdflib.term.URIRef('http://example.org/ns#')}], ConstructQuery_{'template': \ [], 'where': GroupGraphPatternSub_{'part': [TriplesBlock_{'triples': [([rdflib.term.Variable('x'), \ PathAlternative_{'part': [PathSequence_{'part': [PathElt_{'part': pname_{'prefix': 'ns', 'localname': 'price'\ }}]}]}, rdflib.term.Variable('p')], {}), ([rdflib.term.Variable('x'), PathAlternative_{'part': [PathSequence_\ {'part': [PathElt_{'part': pname_{'prefix': 'dc', 'localname': 'title'}}]}]}, rdflib.term.Variable('title')], \ {}), ([rdflib.term.Variable('x'), PathAlternative_{'part': [PathSequence_{'part': [PathElt_{'part': pname_{\ 'prefix': 'ns', 'localname': 'discount'}}]}]}, rdflib.term.Variable('discount')], {})]}]}}]""" self.assertEqual(res, expect)

53.405797

115

0.591045

403

3,685

5.295285

0.173697

0.121837

0.168697

0.052484

0.881912

0.872071

0.85239

0.841612

0.800375

0

0.005793

0.156852

3,685

68

116

54.191176

0.681043

0

0.333333

0

0.35

0.771235

0.303392

0

0.033333

1

0.033333

false

0

0.05

0

0.1

0

null

0

1

0

1

0

null

0

8

01f23c726adf68c5ce9d037e3b4d0beefba6b6ce

216

py

Python

CleanMyData/manager/standardMethods/getMinMaxValue.py

asa19aau/CleanMyData

5c7f301382ff33b357b687e5c0296ede274ff011

[ "MIT" ]

2

2021-12-12T13:26:33.000Z

2021-12-16T08:27:51.000Z

CleanMyData/manager/standardMethods/getMinMaxValue.py

asa19aau/CleanMyData

5c7f301382ff33b357b687e5c0296ede274ff011

[ "MIT" ]

1

2021-11-24T15:35:06.000Z

CleanMyData/manager/standardMethods/getMinMaxValue.py

asa19aau/CleanMyData

5c7f301382ff33b357b687e5c0296ede274ff011

[ "MIT" ]

4

2021-11-24T13:11:37.000Z

2022-01-29T22:59:39.000Z

import pyspark.pandas as pan #Calculate median from column values def getMinimumValue(dataFrame: pan.DataFrame): return dataFrame.min() def getMaximumValue(dataFrame: pan.DataFrame): return dataFrame.max()

24

46

0.782407

26

216

6.5

0.653846

0.142012

0.248521

0.319527

0.426036

0

0.134259

216

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01ff41587fceac5f3e697624dbfb150e817fa466

8,729

py

Python

src/sardana/taurus/qt/qtgui/extra_pool/motor.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

43

2016-11-25T15:21:23.000Z

2021-08-20T06:09:40.000Z

src/sardana/taurus/qt/qtgui/extra_pool/motor.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

1,263

2016-11-25T15:58:37.000Z

2021-11-02T22:23:47.000Z

src/sardana/taurus/qt/qtgui/extra_pool/motor.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

58

2016-11-21T11:33:55.000Z

2021-09-01T06:21:21.000Z

#!/usr/bin/env python ############################################################################## ## # This file is part of Sardana ## # http://www.sardana-controls.org/ ## # Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## # Sardana is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. ## # Sardana is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. ## # You should have received a copy of the GNU Lesser General Public License # along with Sardana. If not, see <http://www.gnu.org/licenses/>. ## ############################################################################## """ motor.py: """ from taurus.external.qt import Qt from taurus.qt.qtgui.base import TaurusBaseWidget from taurus.qt.qtgui.util.ui import UILoadable def showDialogConfigureMotor(parent): Dialog = Qt.QDialog(parent) Dialog.resize((Qt.QSize(Qt.QRect(0, 0, 310, 309).size() ).expandedTo(Dialog.minimumSizeHint()))) motorV2 = TaurusMotorV2(Dialog) motorV2.setModel(parent.model) motorV2.setGeometry(Qt.QRect(10, 10, 291, 291)) Dialog.show() @UILoadable(with_ui='ui') class TaurusMotorH(Qt.QWidget, TaurusBaseWidget): __pyqtSignals__ = ("modelChanged(const QString &)",) def __init__(self, parent=None, designMode=False): self.call__init__wo_kw(Qt.QWidget, parent) self.call__init__(TaurusBaseWidget, str( self.objectName()), designMode=designMode) self.loadUi() self.ui.config.clicked.connect(self.configureMotor) def sizeHint(self): return Qt.QSize(330, 50) def configureMotor(self): showDialogConfigureMotor(self.ui.TaurusGroupBox) @classmethod def getQtDesignerPluginInfo(cls): return None # ret = TaurusBaseWidget.getQtDesignerPluginInfo() # ret['module'] = 'taurus.qt.qtgui.extra_pool' # ret['group'] = 'Taurus Sardana' # ret['icon'] = ':/designer/extra_pool.png' # return ret #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- # QT properties #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- @Qt.pyqtSlot() def getModel(self): return self.ui.TaurusGroupBox.getModel() @Qt.pyqtSlot("QString") def setModel(self, model): self.ui.TaurusGroupBox.setModel(model) @Qt.pyqtSlot() def resetModel(self): self.ui.TaurusGroupBox.resetModel() @Qt.pyqtSlot() def getShowText(self): return self.ui.TaurusGroupBox.getShowText() @Qt.pyqtSlot(bool) def setShowText(self, showText): self.ui.TaurusGroupBox.setShowText(showText) @Qt.pyqtSlot() def resetShowText(self): self.ui.TaurusGroupBox.resetShowText() model = Qt.pyqtProperty("QString", getModel, setModel, resetModel) showText = Qt.pyqtProperty("bool", getShowText, setShowText, resetShowText) @UILoadable(with_ui='ui') class TaurusMotorH2(Qt.QWidget, TaurusBaseWidget): __pyqtSignals__ = ("modelChanged(const QString &)",) def __init__(self, parent=None, designMode=False): self.call__init__wo_kw(Qt.QWidget, parent) self.call__init__(TaurusBaseWidget, str( self.objectName()), designMode=designMode) self.loadUi() self.ui.config.clicked.connect(self.configureMotor) def sizeHint(self): return Qt.QSize(215, 85) def configureMotor(self): showDialogConfigureMotor(self.ui.TaurusGroupBox) @classmethod def getQtDesignerPluginInfo(cls): return None # ret = TaurusBaseWidget.getQtDesignerPluginInfo() # ret['module'] = 'taurus.qt.qtgui.extra_pool' # ret['group'] = 'Taurus Sardana' # ret['icon'] = ':/designer/extra_pool.png' # return ret #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- # QT properties #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- @Qt.pyqtSlot() def getModel(self): return self.ui.TaurusGroupBox.getModel() @Qt.pyqtSlot("QString") def setModel(self, model): self.ui.TaurusGroupBox.setModel(model) @Qt.pyqtSlot() def resetModel(self): self.ui.TaurusGroupBox.resetModel() @Qt.pyqtSlot() def getShowText(self): return self.ui.TaurusGroupBox.getShowText() @Qt.pyqtSlot(bool) def setShowText(self, showText): self.ui.TaurusGroupBox.setShowText(showText) @Qt.pyqtSlot() def resetShowText(self): self.ui.TaurusGroupBox.resetShowText() model = Qt.pyqtProperty("QString", getModel, setModel, resetModel) showText = Qt.pyqtProperty("bool", getShowText, setShowText, resetShowText) @UILoadable(with_ui='ui') class TaurusMotorV(Qt.QWidget, TaurusBaseWidget): __pyqtSignals__ = ("modelChanged(const QString &)",) def __init__(self, parent=None, designMode=False): self.call__init__wo_kw(Qt.QWidget, parent) self.call__init__(TaurusBaseWidget, str( self.objectName()), designMode=designMode) self.loadUi() self.ui.config.clicked.connect(self.configureMotor) def sizeHint(self): return Qt.QSize(120, 145) def configureMotor(self): showDialogConfigureMotor(self.ui.TaurusGroupBox) @classmethod def getQtDesignerPluginInfo(cls): return None # ret = TaurusBaseWidget.getQtDesignerPluginInfo() # ret['module'] = 'taurus.qt.qtgui.extra_pool' # ret['group'] = 'Taurus Sardana' # ret['icon'] = ':/designer/extra_pool.png' # return ret #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- # QT properties #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- @Qt.pyqtSlot() def getModel(self): return self.ui.TaurusGroupBox.getModel() @Qt.pyqtSlot("QString") def setModel(self, model): self.ui.TaurusGroupBox.setModel(model) @Qt.pyqtSlot() def resetModel(self): self.ui.TaurusGroupBox.resetModel() @Qt.pyqtSlot() def getShowText(self): return self.ui.TaurusGroupBox.getShowText() @Qt.pyqtSlot(bool) def setShowText(self, showText): self.ui.TaurusGroupBox.setShowText(showText) @Qt.pyqtSlot() def resetShowText(self): self.ui.TaurusGroupBox.resetShowText() model = Qt.pyqtProperty("QString", getModel, setModel, resetModel) showText = Qt.pyqtProperty("bool", getShowText, setShowText, resetShowText) @UILoadable(with_ui='ui') class TaurusMotorV2(Qt.QWidget, TaurusBaseWidget): __pyqtSignals__ = ("modelChanged(const QString &)",) def __init__(self, parent=None, designMode=False): self.call__init__wo_kw(Qt.QWidget, parent) self.call__init__(TaurusBaseWidget, str( self.objectName()), designMode=designMode) self.loadUi() def sizeHint(self): return Qt.QSize(300, 275) @classmethod def getQtDesignerPluginInfo(cls): return None # ret = TaurusBaseWidget.getQtDesignerPluginInfo() # ret['module'] = 'taurus.qt.qtgui.extra_pool' # ret['group'] = 'Taurus Sardana' # ret['icon'] = ':/designer/extra_pool.png' # return ret #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- # QT properties #-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- @Qt.pyqtSlot() def getModel(self): return self.ui.TaurusGroupBox.getModel() @Qt.pyqtSlot("QString") def setModel(self, model): self.ui.TaurusGroupBox.setModel(model) @Qt.pyqtSlot() def resetModel(self): self.ui.TaurusGroupBox.resetModel() @Qt.pyqtSlot() def getShowText(self): return self.ui.TaurusGroupBox.getShowText() @Qt.pyqtSlot(bool) def setShowText(self, showText): self.ui.TaurusGroupBox.setShowText(showText) @Qt.pyqtSlot() def resetShowText(self): self.ui.TaurusGroupBox.resetShowText() model = Qt.pyqtProperty("QString", getModel, setModel, resetModel) showText = Qt.pyqtProperty("bool", getShowText, setShowText, resetShowText) if __name__ == "__main__": import sys app = Qt.QApplication(sys.argv) form = TaurusMotorH() form.setModel(sys.argv[1]) form.show() sys.exit(app.exec_())

30.735915

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0.209491

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0.102975

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0.806827

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0

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0.191889

8,729

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0.736036

0.261084

0

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false

0

0.025157

0.100629

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null

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0

1

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null

0

1

0

1

0

9

bf067908f78782e9017ea25ba545e57b19a0244d

103,215

py

Python

code/pair_dataset.py

Gorov/zucc

4d0057f08e879f441d68e1d01e54bf59f1d7f1d1

[ "MIT" ]

2

2021-07-07T01:33:03.000Z

2022-03-28T03:57:57.000Z

code/pair_dataset.py

Gorov/zucc

4d0057f08e879f441d68e1d01e54bf59f1d7f1d1

[ "MIT" ]

null

code/pair_dataset.py

Gorov/zucc

4d0057f08e879f441d68e1d01e54bf59f1d7f1d1

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[ ]: from dataset import TextDataset import numpy as np import sys, os, json import gzip from colored import fg, attr, bg from env import JerichoEnv from tqdm import tqdm from jericho import * import random # In[ ]: def my_lcs(string, sub): """ Calculates longest common subsequence for a pair of tokenized strings :param string : list of str : tokens from a string split using whitespace :param sub : list of str : shorter string, also split using whitespace :returns: length (list of int): length of the longest common subsequence between the two strings Note: my_lcs only gives length of the longest common subsequence, not the actual LCS """ if(len(string)< len(sub)): sub, string = string, sub lengths = [[0 for i in range(0,len(sub)+1)] for j in range(0,len(string)+1)] for j in range(1,len(sub)+1): for i in range(1,len(string)+1): if(string[i-1] == sub[j-1]): lengths[i][j] = lengths[i-1][j-1] + 1 else: lengths[i][j] = max(lengths[i-1][j] , lengths[i][j-1]) return lengths[len(string)][len(sub)] rouge_beta = 1.0 def calc_score(candidate, refs): """ Compute ROUGE-L score given one candidate and references for an image :param candidate: str : candidate sentence to be evaluated :param refs: list of str : COCO reference sentences for the particular image to be evaluated :returns score: int (ROUGE-L score for the candidate evaluated against references) """ # print(len(candidate)) # print(len(refs)) assert(len(candidate)==1) assert(len(refs)>0) # print(refs) prec = [] rec = [] # split into tokens token_c = candidate[0].split(" ") for reference in refs: # split into tokens token_r = reference.split(" ") # compute the longest common subsequence lcs = my_lcs(token_r, token_c) prec.append(lcs/float(len(token_c))) rec.append(lcs/float(len(token_r))) prec_max = max(prec) rec_max = max(rec) # print('n:', len(token_c)) # print('m:', len(token_r)) # print('lcs:', lcs) # print('p:', prec_max) # print('r:', rec_max) if(prec_max!=0 and rec_max !=0): score = ((1 + rouge_beta**2)*prec_max*rec_max)/float(rec_max + rouge_beta**2*prec_max) else: score = 0.0 return score # In[ ]: class Seq2SeqSet(object): ''' self.positive_pairs self.negative_pairs ''' def __init__(self): self.pairs = [] self.positive_dict = {} self.negative_dict = {} def add_one(self, observation, positives, candidates): self.pairs.append({'observation':observation, 'positives':positives, 'candidates':candidates}) if label == 0: self.positive_dict[len(self.pairs)] = 1 else: self.negative_dict[len(self.pairs)] = 1 def get_pairs(self): return self.pairs def size(self): return len(self.pairs) def get_samples_from_one_list(self, batch_idx, truncate_num=0): qs = [] ps = [] ys = [] max_q_len = -1 max_p_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] label = pair_dict_['label'] ys.append(label) question = pair_dict_['hypothesis'] if truncate_num > 0: question = question[:truncate_num] if len(question) > max_q_len: max_q_len = len(question) qs.append(question) passage = pair_dict_['premise'] if truncate_num > 0: passage = passage[:truncate_num] if len(passage) > max_p_len: max_p_len = len(passage) ps.append(passage) return qs, ps, ys, max_q_len, max_p_len def print_info(self): print('Number of positive pairs:', len(self.positive_dict)) print('Number of negative pairs:', len(self.negative_dict)) # In[ ]: class State2ActionDataset(TextDataset): def __init__(self, data_dir, truncate_num=300, freq_threshold=2): super(State2ActionDataset, self).__init__(data_dir) self.truncate_num = truncate_num self.freq_threshold = freq_threshold self.word_vocab = {'<PAD>':0, '<START>':1, '<END>':2, '<UNK>':3, '<ANSWER>':4, '<SPLIT>':5, ',':6} self.label_vocab = {'entails':0, 'neutral':1} self.load_dataset() print('Converting text to word indicies.') self.idx_2_word = self._index_to_word() def load_dataset(self): self.data_sets = {} # load train self.data_sets['train'] = self._load_pair_data(os.path.join(self.data_dir, 'train.tsv')) # self.data_sets['train'] = self._load_pair_data(os.path.join(self.data_dir, 'dev.tsv')) # self.data_sets['train'] = self._load_pair_data(os.path.join(self.data_dir, # 'scitail_1.0_structure_subset_triple.train.tsv')) # load dev self.data_sets['dev'] = self._load_pair_data(os.path.join(self.data_dir, 'dev.tsv')) self.data_sets['test'] = self._load_pair_data(os.path.join(self.data_dir, 'test.tsv')) # self.data_sets['dev'] = self._load_pair_data(os.path.join(self.data_dir, 'scitail_1.0_structure_subset_triple.dev.tsv')) # build vocab self._build_vocab() def _load_pair_data(self, fpath, neg_removal=True): """ Inputs: fpath -- the path of the file. Outputs: positive_pairs -- a list of positive question-passage pairs negative_pairs -- a list of negative question-passage pairs """ data_set = PairClassificationSet() f = open(fpath, "r") instances = f.readlines() for idx, instance in enumerate(instances): [premise, hypothesis, label] = instance.strip('\n').lower().split('\t') label = self.label_vocab[label] data_set.add_one(hypothesis, premise, label) data_set.print_info() return data_set def _numeralize_pairs(self, word_freq_dict, pairs): ''' numeralize passages in training pair lists ''' ret_pair_list = [] for pair_dict_ in pairs: new_pair_dict_ = {} for k, v in pair_dict_.items(): if k != 'label': new_pair_dict_[k] = self._add_vocab_from_sentence(word_freq_dict, v) else: new_pair_dict_[k] = pair_dict_[k] ret_pair_list.append(new_pair_dict_) return ret_pair_list def _add_vocab_from_sentence(self, word_freq_dict, sentence): tokens = sentence.split(' ') word_idx_list = [] for token in tokens: if word_freq_dict[token] < self.freq_threshold: word_idx_list.append(self.word_vocab['<UNK>']) else: if token not in self.word_vocab: self.word_vocab[token] = len(self.word_vocab) word_idx_list.append(self.word_vocab[token]) return word_idx_list def _build_vocab(self): """ Filter the vocabulary and numeralization """ word_freq_dict = self._get_word_freq(self.data_sets) for data_id, data_set in self.data_sets.items(): data_set.pairs = self._numeralize_pairs(word_freq_dict, data_set.get_pairs()) print('size of the final vocabulary:', len(self.word_vocab)) def _add_freq_from_sentence(self, word_freq_dict, sentence): tokens = sentence.split(' ') for token in tokens: if token not in word_freq_dict: word_freq_dict[token] = 1 else: word_freq_dict[token] += 1 def _get_word_freq(self, data_sets_): """ Building word frequency dictionary and filter the vocabulary """ word_freq_dict = {} for data_id, data_set in data_sets_.items(): for pair_dict in data_set.get_pairs(): for sentence in [pair_dict['hypothesis'], pair_dict['premise']]: self._add_freq_from_sentence(word_freq_dict, sentence) print('size of the raw vocabulary:', len(word_freq_dict)) return word_freq_dict def get_train_batch(self, batch_size, sort=False): """ randomly select a batch from a dataset Inputs: batch_size: Outputs: q_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) p_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) """ set_id = 'train' data_set = self.data_sets[set_id] batch_idx = np.random.randint(0, data_set.size(), size=batch_size) return self.get_batch(set_id, batch_idx, sort) def get_batch(self, set_id, batch_idx, sort=False): """ randomly select a batch from a dataset Inputs: batch_idx: Outputs (all numpy arrays are sorted according to q_length): q_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) p_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) q_mask -- numpy array of masks p_mask -- numpy array of masks p_sort_idx -- sorted idx according to p_length revert_p_idx -- revert idx from p_mat[p_sort_idx] to p_mat """ data_set = self.data_sets[set_id] qs_, ps_, ys_, max_q_len_, max_p_len_ = data_set.get_samples_from_one_list(batch_idx, self.truncate_num) q_masks_ = [] p_masks_ = [] for i, q in enumerate(qs_): qs_[i] = q + (max_q_len_ - len(q)) * [0] q_masks_.append([1] * len(q) + [0] * (max_q_len_ - len(q))) for i, p in enumerate(ps_): ps_[i] = p + (max_p_len_ - len(p)) * [0] p_masks_.append([1] * len(p) + [0] * (max_p_len_ - len(p))) q_mat = np.array(qs_, dtype=np.int64) p_mat = np.array(ps_, dtype=np.int64) q_mask = np.array(q_masks_, dtype=np.int64) p_mask = np.array(p_masks_, dtype=np.int64) y_vec = np.array(ys_, dtype=np.int64) if sort: # sort all according to q_length q_length = np.sum(q_mask, axis=1) q_sort_idx = np.argsort(-q_length) q_mat = q_mat[q_sort_idx, :] p_mat = p_mat[q_sort_idx, :] q_mask = q_mask[q_sort_idx, :] p_mask = p_mask[q_sort_idx, :] y_vec = y_vec[q_sort_idx] # get p_sorted_idx and the revert idx p_length = np.sum(p_mask, axis=1) p_sort_idx = np.argsort(-p_length) idx_dict = {p_sort_idx[i_]: i_ for i_ in range(p_length.shape[0])} revert_p_idx = np.array([ idx_dict[i_] for i_ in range(p_length.shape[0])]) return q_mat, p_mat, y_vec, q_mask, p_mask, p_sort_idx, revert_p_idx else: return q_mat, p_mat, y_vec, q_mask, p_mask def display_sentence(self, x): """ Display a suquence of word index Inputs: x -- input sequence of word indices, (sequence_length,) Outputs: None """ # apply threshold for word_index in x: word = self.idx_2_word[word_index] if word == '<PAD>': continue sys.stdout.write(" " + word) sys.stdout.write("\n") sys.stdout.flush() def initial_conceptnet_embedding(self, embedding_size, embedding_path=None): """ This function initialize embedding with glove embedding. If a word has embedding in glove, use the glove one. If not, initial with random. Inputs: embedding_size -- the dimension of the word embedding embedding_path -- the path to the glove embedding file Outputs: embeddings -- a numpy matrix in shape of (vocab_size, embedding_dim) the ith row indicates the word with index i from word_ind_dict """ vocab_size = len(self.word_vocab) # initialize a numpy embedding matrix embeddings = 0.1*np.random.randn(vocab_size, embedding_size).astype(np.float32) # replace <PAD> by all zero embeddings[0, :] = np.zeros(embedding_size, dtype=np.float32) if embedding_path and os.path.isfile(embedding_path): f = open(embedding_path, "r") counter = 0 f.readline() for line in f: data = line.strip().split(" ") word = data[0].strip() embedding = data[1::] embedding = map(np.float32, embedding) if word in self.word_vocab: embeddings[self.word_vocab[word], :] = embedding counter += 1 f.close() print("%d words has been switched." %counter) else: print("embedding is initialized fully randomly.") return embeddings # In[ ]: import re from jericho.util import verb_usage_count from jericho.template_action_generator import TemplateActionGenerator class TemplateActionParser(TemplateActionGenerator): def __init__(self, rom_bindings): self.templates_alias_dict = {} self.verb_to_templates = {} self.template2template = {} super(TemplateActionParser, self).__init__(rom_bindings) self.id2template = None self.template2id = None self.additional_templates = ['land'] self.templates = list(set(self.templates + self.additional_templates)) self.templates.sort() self._compute_template() BASIC_ACTIONS = 'north/south/west/east/northwest/southwest/northeast/southeast/up/down/enter/exit/take all'.split('/') self.BASIC_ACTIONS = {k:1 for k in BASIC_ACTIONS} self.add_template2template = {} for action in list(self.BASIC_ACTIONS.keys()) + self.additional_templates + ['examine OBJ']: self.add_template2template[action] = action def _preprocess_templates(self, templates, max_word_length): ''' Converts templates with multiple verbs and takes the first verb. ''' out = [] vb_usage_fn = lambda verb: verb_usage_count(verb, max_word_length) p = re.compile('\S+(/\S+)+') for template in templates: # print(template) if not template: continue has_alias = True while True: match = p.search(template) if not match: # print('{} not matched'.format(template)) has_alias = False break verb_alias = match.group().split('/') verb = max(match.group().split('/'), key=vb_usage_fn) verb_template = template[:match.start()] + verb + template[match.end():] for alias in verb_alias: alias_template = template[:match.start()] + alias + template[match.end():] self.template2template[alias_template] = verb_template if alias in self.verb_to_templates: self.verb_to_templates[alias].append(alias_template) else: self.verb_to_templates[alias] = [alias_template] # for alias in verb_alias: # if alias in self.verb_to_templates: # self.verb_to_templates[alias].append(template) # else: # self.verb_to_templates[alias] = [template] template = verb_template ts = template.split() if ts[0] in defines.ILLEGAL_ACTIONS: continue if ts[0] in defines.NO_EFFECT_ACTIONS and len(ts) == 1: continue if not has_alias: t_tokens = template.split() alias = t_tokens[0] verb_alias = [alias] if alias in self.verb_to_templates: self.verb_to_templates[alias].append(template) else: self.verb_to_templates[alias] = [template] self.template2template[template] = template self.templates_alias_dict[template] = verb_alias out.append(template) return out def _compute_template(self): self.id2template = {} self.template2id = {} for i, t in enumerate(self.templates): self.id2template[i] = t self.template2id[t] = i return def parse_action(self, action): tokens = action.split() verb = tokens[0] # if verb == 'down': # print(verb in self.BASIC_ACTIONS and len(tokens) == 1) if (verb in self.BASIC_ACTIONS or verb in self.additional_templates) and len(tokens) == 1: return [verb] if verb not in self.verb_to_templates: # if (verb in self.BASIC_ACTIONS or verb in self.additional_templates) and len(tokens) == 1: # # print(verb) # return [verb] if verb == 'examine': return ['examine OBJ', ' '.join(tokens[1:])] else: print('cannot recognize verb:', verb) return None else: templates = self.verb_to_templates[verb] for template in templates: # print(template.split()) t_tokens = template.split() # print(t_tokens) slot_num = 0 for t_token in t_tokens: # print(t_token, 'OBJ', t_token == 'OBJ') if t_token == 'OBJ': slot_num += 1 # ' \S+' re_str = template.replace('OBJ', '(\S+)') # print(re_str) # p = re.compile('\S+(/\S+)+') p = re.compile(re_str) match = p.search(action) if not match: continue elif match.group() == action: ret_tuple = [template] # print(slot_num) for i in range(slot_num): ret_tuple.append(match.group(i+1)) return ret_tuple else: continue templates = self.verb_to_templates[verb] for template in templates: t_tokens = template.split() slot_num = 0 for t_id, t_token in enumerate(t_tokens): if t_token == 'OBJ': slot_num += 1 t_tokens[t_id] = 'OBJ%d'%(slot_num - 1) # ' \S+' re_str = ' '.join(t_tokens) for i in range(slot_num): re_str = re_str.replace('OBJ%d'%(i), '(?P<obj%d>\S+( \S+)*)'%(i)) # print(re_str) # p = re.compile('\S+(/\S+)+') p = re.compile(re_str) match = p.search(action) if not match: continue elif match.group() == action: ret_tuple = [template] for i in range(slot_num): ret_tuple.append(match.group('obj%d'%(i))) return ret_tuple else: continue return None # act_par = TemplateActionParser(bindings) # print(act_par.templates_alias_dict) # print(act_par.verb_to_templates) # In[ ]: class Pair2SeqSet(object): ''' ''' def __init__(self): self.pairs = [] self.num_positive = 0 self.num_total = 0 def add_one(self, input1, input2, positives, candidates): self.pairs.append({'input1':input1, 'input2':input2, 'positives':positives, 'candidates':candidates}) self.num_positive += len(positives) self.num_total += len(candidates) def get_pairs(self): return self.pairs def size(self): return len(self.pairs) def get_samples_from_one_list(self, batch_idx, num_negative=10, truncate_num=0): x1 = [] x2 = [] positives = [] candidates = [] max_x1_len = -1 max_x2_len = -1 max_a_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] label = random.sample(pair_dict_['positives'], 1)[0] if len(label) > max_a_len: max_a_len = len(label) positives.append(label) cand_list = [] # num_neg_samples = min(len(pair_dict_["candidates"]), num_negative) neg_samples = random.choices(pair_dict_["candidates"], k=num_negative) for neg_sample in neg_samples: if len(neg_sample) > max_a_len: max_a_len = len(neg_sample) cand_list.append(neg_sample) candidates.append(cand_list) question = pair_dict_['input1'] if truncate_num > 0: question = question[:truncate_num] if len(question) > max_x1_len: max_x1_len = len(question) x1.append(question) passage = pair_dict_['input2'] if truncate_num > 0: passage = passage[:truncate_num] if len(passage) > max_x2_len: max_x2_len = len(passage) x2.append(passage) return x1, x2, positives, candidates, max_x1_len, max_x2_len, max_a_len def get_concat_samples_from_one_list(self, batch_idx, num_negative=10, truncate_num=0): concat_x = [] positives = [] candidates = [] max_x_len = -1 max_a_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] label = random.sample(pair_dict_['positives'], 1)[0] if len(label) > max_a_len: max_a_len = len(label) positives.append(label) cand_list = [] # num_neg_samples = min(len(pair_dict_["candidates"]), num_negative) # print('sampling {} actions'.format(num_negative)) neg_samples = random.choices(pair_dict_["candidates"], k=num_negative) # print('{} actions sampled'.format(len(neg_samples))) for neg_sample in neg_samples: if len(neg_sample) > max_a_len: max_a_len = len(neg_sample) cand_list.append(neg_sample) candidates.append(cand_list) # print(len(cand_list)) question = pair_dict_['input1'] + [5] + pair_dict_['input2'] if truncate_num > 0: question = question[:truncate_num] if len(question) > max_x_len: max_x_len = len(question) concat_x.append(question) return concat_x, positives, candidates, max_x_len, max_a_len def get_eval_samples_from_one_list(self, inst_idx, truncate_num=0): x1 = [] x2 = [] candidates = [] max_x1_len = -1 max_x2_len = -1 max_a_len = -1 pair_dict_ = self.pairs[inst_idx] cand_list = [] y_list = [] def _get_key_from_list(input_list): tmp_list = [str(x) for x in input_list] return ' '.join(tmp_list) positive_dict = {} for action in pair_dict_['positives']: if len(action) > max_a_len: max_a_len = len(action) cand_list.append(action) y_list.append(1) positive_dict[_get_key_from_list(action)] = 1 for action in pair_dict_["candidates"]: key = _get_key_from_list(action) if key in positive_dict: continue if len(action) > max_a_len: max_a_len = len(action) cand_list.append(action) y_list.append(0) zip_list = list(zip(cand_list, y_list)) random.shuffle(zip_list) cand_list = [x[0] for x in zip_list] y_list = [x[1] for x in zip_list] candidates.append(cand_list) question = pair_dict_['input1'] if truncate_num > 0: question = question[:truncate_num] if len(question) > max_x1_len: max_x1_len = len(question) x1.append(question) passage = pair_dict_['input2'] if truncate_num > 0: passage = passage[:truncate_num] if len(passage) > max_x2_len: max_x2_len = len(passage) x2.append(passage) return x1, x2, candidates, y_list, max_x1_len, max_x2_len, max_a_len def get_eval_concat_samples_from_one_list(self, inst_idx, truncate_num=0): concat_x = [] candidates = [] max_x_len = -1 max_a_len = -1 pair_dict_ = self.pairs[inst_idx] cand_list = [] y_list = [] def _get_key_from_list(input_list): tmp_list = [str(x) for x in input_list] return ' '.join(tmp_list) positive_dict = {} for action in pair_dict_['positives']: if len(action) > max_a_len: max_a_len = len(action) cand_list.append(action) y_list.append(1) positive_dict[_get_key_from_list(action)] = 1 for action in pair_dict_["candidates"]: key = _get_key_from_list(action) if key in positive_dict: continue if len(action) > max_a_len: max_a_len = len(action) cand_list.append(action) y_list.append(0) zip_list = list(zip(cand_list, y_list)) random.shuffle(zip_list) cand_list = [x[0] for x in zip_list] y_list = [x[1] for x in zip_list] candidates.append(cand_list) question = pair_dict_['input1'] + [5] + pair_dict_['input2'] if truncate_num > 0: question = question[:truncate_num] if len(question) > max_x_len: max_x_len = len(question) concat_x.append(question) return concat_x, candidates, y_list, max_x_len, max_a_len def print_info(self): print('Number of positive actions: {}'.format(self.num_positive)) print('Number of total action candidates: {}'.format(self.num_total)) # In[ ]: def _tokenize_original_data(games, data_dir): import spacy # nlp_pipe = spacy.blank("en") nlp_pipe = spacy.load('en') def _tokenize_observation(instance): # doc = nlp_pipe(line.strip()) new_inst = json.loads(instance) info = new_inst['observations'].split('|') for i in range(3): doc = nlp_pipe(info[i]) text_tokens = [token.text for token in doc if token.text != ' '] info[i] = ' '.join(text_tokens) new_inst['observations'] = '|'.join(info) return new_inst for game_name in games: print('# LOADING game data {} ...'.format(game_name)) f = open(os.path.join(data_dir, '{}.ssa.wt_traj.txt'.format(game_name)), "r") instances = f.readlines() fout = open(os.path.join(data_dir, '{}.ssa.wt_traj.tok'.format(game_name)), "w") for idx, instance in enumerate(instances): instance_tok = _tokenize_observation(instance) fout.write(json.dumps(instance_tok) + '\n') f.close() fout.close() # data_dir = "/dccstor/yum-worldmodel/shared_folder_2080/if_games/data/ssa_data/supervised/" # games = ['905', 'acorncourt', 'advent', 'adventureland', 'afflicted', 'anchor', 'awaken', # 'balances', 'deephome', 'detective', 'dragon', 'enchanter', 'gold', 'inhumane', # 'jewel', 'karn', 'library', 'ludicorp', 'moonlit', 'omniquest', 'pentari', 'reverb', # 'snacktime', 'sorcerer', 'spellbrkr', 'spirit', 'temple', 'tryst205', 'yomomma', # 'zenon', 'zork1', 'zork3', 'ztuu'] # _tokenize_original_data(games, data_dir) # In[ ]: def _tokenize_original_sas_data(games, data_dir): import spacy nlp_pipe = spacy.load('en') def _tokenize_observation(instance): # doc = nlp_pipe(line.strip()) new_inst = json.loads(instance) info = new_inst['observations'].split('|') for i in range(3): doc = nlp_pipe(info[i]) text_tokens = [token.text for token in doc if token.text != ' '] info[i] = ' '.join(text_tokens) new_inst['observations'] = '|'.join(info) for idx, action_group in enumerate(new_inst['valid_actions']): action_tuple = action_group[0] info = action_tuple['observations'].split('|') for i in range(3): doc = nlp_pipe(info[i]) text_tokens = [token.text for token in doc if token.text != ' '] info[i] = ' '.join(text_tokens) new_inst['valid_actions'][idx][0]['observations'] = '|'.join(info) return new_inst for game_name in games: print('# LOADING game data {} ...'.format(game_name)) f = open(os.path.join(data_dir, '{}.sas.wt_traj.txt'.format(game_name)), "r") instances = f.readlines() fout = open(os.path.join(data_dir, '{}.sas.wt_traj.tok'.format(game_name)), "w") for idx, instance in enumerate(instances): instance_tok = _tokenize_observation(instance) fout.write(json.dumps(instance_tok) + '\n') f.close() fout.close() # games = ['zork1', 'zork3', 'enchanter', 'sorcerer'] # data_dir = "/dccstor/yum-worldmodel/shared_folder_2080/if_games/data/ssa_data/zork_universe_sup/" # _tokenize_original_sas_data(games, data_dir) # In[ ]: def _preprocess_action(action): action = action.lower() if action == 'n': action = 'north' elif action == 's': action = 'south' elif action == 'e': action = 'east' elif action == 'w': action = 'west' elif action == 'se': action = 'southeast' elif action == 'sw': action = 'southwest' elif action == 'ne': action = 'northeast' elif action == 'nw': action = 'northwest' elif action == 'u': action = 'up' elif action == 'd': action = 'down' return action def _match_action(action_group, target): for action in action_group: action = action['a'] if target == action: return True return False def _process_instance(instance): new_inst = json.loads(instance) info = new_inst['observations'].split('|') new_inst['observations'] = {'obs':' | '.join(info[:3]), 'action':info[3]} return new_inst def _recover_root_template_action(template, root_template): t_tokens = root_template.split() count = 1 for tid, t_token in enumerate(t_tokens): if t_token == 'OBJ': t_tokens[tid] = template[count] count += 1 return ' '.join(t_tokens) class StateState2ActionDataset(TextDataset): def __init__(self, data_dir, rom_dir, game2rom, train_games=None, dev_games=None, setting='same_games', num_negative=20, truncate_num=300, freq_threshold=2): super(StateState2ActionDataset, self).__init__(data_dir) self.num_negative = num_negative self.truncate_num = truncate_num self.freq_threshold = freq_threshold self.word_vocab = {'<PAD>':0, '<START>':1, '<END>':2, '<UNK>':3, '<ANSWER>':4, '<SPLIT>':5, '|':6} self.rom_dir = rom_dir self.game2rom = game2rom self.setting = setting self.train_games = train_games self.dev_games = dev_games self.load_dataset() print('Converting text to word indicies.') self.idx_2_word = self._index_to_word() def load_dataset(self): self.data_sets = {} if self.setting == 'same_games': self.data_sets = self._load_pair_data_and_split(self.train_games) elif self.setting == 'transfer': # load train self.data_sets['train'] = self._load_pair_data(self.train_games) # load dev self.data_sets['dev'] = self._load_pair_data(self.dev_games) # self.data_sets['test'] = self._load_pair_data(os.path.join(self.data_dir, 'test.tsv')) # build vocab self._build_vocab() def _load_pair_data_and_split(self, games, neg_removal=True): """ Splitting trajectories with 8:1:1 """ datasets = {} datasets['train'] = Pair2SeqSet() datasets['dev'] = Pair2SeqSet() datasets['test'] = Pair2SeqSet() for game_name in games: # rom_path = "../roms/jericho-game-suite/{}.z5".format(game_name) print('# LOADING game data {} ...'.format(game_name)) rom_path = os.path.join(self.rom_dir, self.game2rom[game_name]) bindings = load_bindings(rom_path) act_par = TemplateActionParser(bindings) f = open(os.path.join(self.data_dir, '{}.ssa.wt_traj.tok'.format(game_name)), "r") instances = f.readlines() instances = [_process_instance(instance.lower()) for instance in instances] for idx, instance in enumerate(instances): if idx == len(instances) - 1: continue input1 = instance['observations']['obs'] input2 = instances[idx + 1]['observations']['obs'] action = _preprocess_action(instances[idx + 1]['observations']['action']) if action == '': continue template = act_par.parse_action(action) if template is None: print('unmatched action: \'{}\''.format(action)) action = action elif template[0] not in act_par.template2template: if template[0] not in act_par.add_template2template: print('cannot find root: {}'.format(action)) action = action else: action = _recover_root_template_action(template, act_par.add_template2template[template[0]]) else: action = _recover_root_template_action(template, act_par.template2template[template[0]]) positives = [] candidates = [] all_actions = instance['valid_actions'] # print(all_actions[0]) if isinstance(all_actions[0], dict): # print(all_actions) all_actions = [all_actions] for action_group in all_actions: if _match_action(action_group, action): for a in action_group: positives.append(a['a']) else: for a in action_group: candidates.append(a['a']) if len(candidates) == 0: continue if len(positives) == 0: positives.append(action) # print('adding an action \"{}\" not in valid list'.format(action)) # print(all_actions) # if action == 'east': # print(all_actions) if idx / len(instances) < 0.6: datasets['train'].add_one(input1, input2, positives, candidates) elif idx / len(instances) < 0.8: datasets['dev'].add_one(input1, input2, positives, candidates) else: datasets['test'].add_one(input1, input2, positives, candidates) for k, data_set in datasets.items(): print('# {} set'.format(k)) data_set.print_info() return datasets def _load_pair_data(self, games, neg_removal=True): """ Inputs: fpath -- the path of the file. Outputs: positive_pairs -- a list of positive question-passage pairs negative_pairs -- a list of negative question-passage pairs """ data_set = Pair2SeqSet() def _preprocess_action(action): action = action.lower() if action == 'n': action = 'north' elif action == 's': action = 'south' elif action == 'e': action = 'east' elif action == 'w': action = 'west' elif action == 'se': action = 'southeast' elif action == 'sw': action = 'southwest' elif action == 'ne': action = 'northeast' elif action == 'nw': action = 'northwest' elif action == 'u': action = 'up' elif action == 'd': action = 'down' return action def _match_action(action_group, target): for action in action_group: action = action['a'] if target == action: return True return False def _process_instance(instance): new_inst = json.loads(instance) info = new_inst['observations'].split('|') new_inst['observations'] = {'obs':' | '.join(info[:3]), 'action':info[3]} return new_inst def _recover_root_template_action(template, root_template): t_tokens = root_template.split() count = 1 for tid, t_token in enumerate(t_tokens): if t_token == 'OBJ': t_tokens[tid] = template[count] count += 1 return ' '.join(t_tokens) for game_name in games: rom_path = "../roms/jericho-game-suite/{}.z5".format(game_name) bindings = load_bindings(rom_path) act_par = TemplateActionParser(bindings) f = open(os.path.join(self.data_dir, '{}.ssa.wt_traj.tok'.format(game_name)), "r") instances = f.readlines() # instances = [json.loads(instance) for instance in instances] # for instance in instances: # info = instance['observations'].split('|') # instance['observation'] = {'obs':' | '.join(info[:3]), 'action':info[3]} instances = [_process_instance(instance.lower()) for instance in instances] for idx, instance in enumerate(instances): if idx == len(instances) - 1: continue input1 = instance['observations']['obs'] input2 = instances[idx + 1]['observations']['obs'] action = _preprocess_action(instances[idx + 1]['observations']['action']) template = act_par.parse_action(action) if template is None: print('unmatched action: {}'.format(action)) action = action elif template[0] not in act_par.template2template: if template[0] not in act_par.add_template2template: print('cannot find root: {}'.format(action)) action = action else: action = _recover_root_template_action(template, act_par.add_template2template[template[0]]) else: action = _recover_root_template_action(template, act_par.template2template[template[0]]) positives = [] candidates = [] all_actions = instance['valid_actions'] for action_group in all_actions: if _match_action(action_group, action): for a in action_group: positives.append(a['a']) else: for a in action_group: candidates.append(a['a']) if len(positives) == 0: positives.append(action) # print('adding an action \"{}\" not in valid list'.format(action)) # print(all_actions) # if action == 'east': # print(all_actions) data_set.add_one(input1, input2, positives, candidates) data_set.print_info() return data_set def _numeralize_pairs(self, word_freq_dict, pairs): ''' numeralize passages in training pair lists ''' ret_pair_list = [] for pair_dict_ in pairs: new_pair_dict_ = {} for k, v in pair_dict_.items(): if k == 'input1' or k == 'input2': new_pair_dict_[k] = self._add_vocab_from_sentence(word_freq_dict, v) elif k == 'positives' or k == 'candidates': new_pair_dict_[k] = [] for seq in v: new_pair_dict_[k].append(self._add_vocab_from_sentence(word_freq_dict, seq)) ret_pair_list.append(new_pair_dict_) return ret_pair_list def _add_vocab_from_sentence(self, word_freq_dict, sentence): tokens = sentence.split(' ') word_idx_list = [] for token in tokens: if word_freq_dict[token] < self.freq_threshold: word_idx_list.append(self.word_vocab['<UNK>']) else: if token not in self.word_vocab: self.word_vocab[token] = len(self.word_vocab) word_idx_list.append(self.word_vocab[token]) return word_idx_list def _build_vocab(self): """ Filter the vocabulary and numeralization """ word_freq_dict = self._get_word_freq(self.data_sets) for data_id, data_set in self.data_sets.items(): data_set.pairs = self._numeralize_pairs(word_freq_dict, data_set.get_pairs()) print('size of the final vocabulary:', len(self.word_vocab)) def _add_freq_from_sentence(self, word_freq_dict, sentence): tokens = sentence.split(' ') for token in tokens: if token not in word_freq_dict: word_freq_dict[token] = 1 else: word_freq_dict[token] += 1 def _get_word_freq(self, data_sets_): """ Building word frequency dictionary and filter the vocabulary """ word_freq_dict = {} for data_id, data_set in data_sets_.items(): for pair_dict in data_set.get_pairs(): for sentence in [pair_dict['input1'], pair_dict['input2']]: self._add_freq_from_sentence(word_freq_dict, sentence) for sentence in pair_dict['positives'] + pair_dict['candidates']: self._add_freq_from_sentence(word_freq_dict, sentence) print('size of the raw vocabulary:', len(word_freq_dict)) return word_freq_dict def get_train_batch(self, batch_size, num_negative=-1, inst_format='co_match'): """ randomly select a batch from a dataset Inputs: batch_size: Outputs: q_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) p_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) """ set_id = 'train' data_set = self.data_sets[set_id] # print(data_set.size()) # print(batch_size) batch_idx = np.random.randint(0, data_set.size(), size=batch_size) if num_negative < 0: num_negative = self.num_negative if inst_format == 'co_match': return self.get_batch(set_id, batch_idx, num_negative) elif inst_format == 'concat': return self.get_batch_concat(set_id, batch_idx, num_negative) def get_batch(self, set_id, batch_idx, num_negative=-1): """ randomly select a batch from a dataset Inputs: batch_idx: Outputs (all numpy arrays are sorted according to q_length): q_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) p_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) q_mask -- numpy array of masks p_mask -- numpy array of masks p_sort_idx -- sorted idx according to p_length revert_p_idx -- revert idx from p_mat[p_sort_idx] to p_mat """ if num_negative < 0: num_negative = self.num_negative data_set = self.data_sets[set_id] x1, x2, positives, candidates, max_x1_len, max_x2_len, max_a_len = data_set.get_samples_from_one_list(batch_idx, num_negative=num_negative, truncate_num=self.truncate_num) # qs_, ps_, ys_, max_q_len_, max_p_len_ = data_set.get_samples_from_one_list(batch_idx, self.truncate_num) x1_masks_ = [] x2_masks_ = [] a_masks_ = [[] for i in range(len(x1))] actions = [[] for i in range(len(x1))] # print(a_masks_) # print(actions) for i, q in enumerate(x1): x1[i] = q + (max_x1_len - len(q)) * [0] x1_masks_.append([1] * len(q) + [0] * (max_x1_len - len(q))) for i, p in enumerate(x2): x2[i] = p + (max_x2_len - len(p)) * [0] x2_masks_.append([1] * len(p) + [0] * (max_x2_len - len(p))) for i, a in enumerate(positives): actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) for i, a_list in enumerate(candidates): for a in a_list: actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) x1_mat = np.array(x1, dtype=np.int64) x2_mat = np.array(x2, dtype=np.int64) x1_mask = np.array(x1_masks_, dtype=np.int64) x2_mask = np.array(x2_masks_, dtype=np.int64) a_mat = np.array(actions, dtype=np.int64) a_mask = np.array(a_masks_, dtype=np.int64) y_vec = np.array([0] * len(x1), dtype=np.int64) return x1_mat, x2_mat, a_mat, y_vec, x1_mask, x2_mask, a_mask def get_batch_concat(self, set_id, batch_idx, num_negative=-1): """ randomly select a batch from a dataset Inputs: batch_idx: Outputs (all numpy arrays are sorted according to q_length): x_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) a_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) x_mask -- numpy array of masks """ if num_negative < 0: num_negative = self.num_negative data_set = self.data_sets[set_id] x, positives, candidates, max_x_len, max_a_len = data_set.get_concat_samples_from_one_list(batch_idx, num_negative=num_negative, truncate_num=self.truncate_num) x_masks_ = [] a_masks_ = [[] for i in range(len(x))] actions = [[] for i in range(len(x))] for i, q in enumerate(x): x[i] = q + (max_x_len - len(q)) * [0] x_masks_.append([1] * len(q) + [0] * (max_x_len - len(q))) for i, a in enumerate(positives): actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) for i, a_list in enumerate(candidates): # print(len(a_list)) for a in a_list: actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) x_mat = np.array(x, dtype=np.int64) x_mask = np.array(x_masks_, dtype=np.int64) a_mat = np.array(actions, dtype=np.int64) a_mask = np.array(a_masks_, dtype=np.int64) y_vec = np.array([0] * len(x), dtype=np.int64) return x_mat, a_mat, y_vec, x_mask, a_mask def get_eval_batch(self, set_id, inst_idx): data_set = self.data_sets[set_id] x1, x2, candidates, y_list, max_x1_len, max_x2_len, max_a_len = data_set.get_eval_samples_from_one_list(inst_idx, truncate_num=self.truncate_num) # qs_, ps_, ys_, max_q_len_, max_p_len_ = data_set.get_samples_from_one_list(batch_idx, self.truncate_num) x1_masks_ = [] x2_masks_ = [] a_masks_ = [[] for i in range(len(x1))] actions = [[] for i in range(len(x1))] for i, q in enumerate(x1): x1[i] = q + (max_x1_len - len(q)) * [0] x1_masks_.append([1] * len(q) + [0] * (max_x1_len - len(q))) for i, p in enumerate(x2): x2[i] = p + (max_x2_len - len(p)) * [0] x2_masks_.append([1] * len(p) + [0] * (max_x2_len - len(p))) for i, a_list in enumerate(candidates): for a in a_list: actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) x1_mat = np.array(x1, dtype=np.int64) x2_mat = np.array(x2, dtype=np.int64) x1_mask = np.array(x1_masks_, dtype=np.int64) x2_mask = np.array(x2_masks_, dtype=np.int64) a_mat = np.array(actions, dtype=np.int64) a_mask = np.array(a_masks_, dtype=np.int64) return x1_mat, x2_mat, a_mat, y_list, x1_mask, x2_mask, a_mask def get_eval_batch_concat(self, set_id, inst_idx): """ randomly select a batch from a dataset Inputs: batch_idx: Outputs (all numpy arrays are sorted according to q_length): x_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) a_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_list -- numpy array of binary labels, numpy array in shape of (batch_size,) x_mask -- numpy array of masks """ data_set = self.data_sets[set_id] x, candidates, y_list, max_x_len, max_a_len = data_set.get_eval_concat_samples_from_one_list(inst_idx, truncate_num=self.truncate_num) x_masks_ = [] a_masks_ = [[] for i in range(len(x))] actions = [[] for i in range(len(x))] for i, q in enumerate(x): x[i] = q + (max_x_len - len(q)) * [0] x_masks_.append([1] * len(q) + [0] * (max_x_len - len(q))) for i, a_list in enumerate(candidates): # print(len(a_list)) for a in a_list: actions[i].append(a + (max_a_len - len(a)) * [0]) a_masks_[i].append([1] * len(a) + [0] * (max_a_len - len(a))) x_mat = np.array(x, dtype=np.int64) x_mask = np.array(x_masks_, dtype=np.int64) a_mat = np.array(actions, dtype=np.int64) a_mask = np.array(a_masks_, dtype=np.int64) return x_mat, a_mat, y_list, x_mask, a_mask def display_sentence(self, x): """ Display a suquence of word index Inputs: x -- input sequence of word indices, (sequence_length,) Outputs: None """ # apply threshold for word_index in x: word = self.idx_2_word[word_index] if word == '<PAD>': continue sys.stdout.write(" " + word) sys.stdout.write("\n") sys.stdout.flush() # In[ ]: class ForwardPredictionSet(object): ''' ''' def __init__(self): self.pairs = [] self.num_positive = 0 self.num_tuples = 0 self.SEP_TOKEN = 5 self.action2tuples = {} def add_one(self, state, next_states, actions, wt_next_state, wt_action): pair_id = len(self.pairs) self.pairs.append({'state':state, 'next_states':next_states, 'actions':actions, 'wt_next_state':wt_next_state, 'wt_action':wt_action}) if wt_action not in self.action2tuples: self.action2tuples[wt_action] = [(pair_id, -1)] else: self.action2tuples[wt_action].append((pair_id, -1)) for idx, (next_state, action) in enumerate(zip(next_states, actions)): if action not in self.action2tuples: self.action2tuples[action] = [(pair_id, idx)] else: self.action2tuples[action].append((pair_id, idx)) self.num_positive += 1 self.num_tuples += len(actions) def get_pairs(self): return self.pairs def size(self): return len(self.pairs) def get_triple_samples(self, batch_idx, num_negative=10, truncate_num=0): states = [] actions = [] outputs = [] labels = [] positives = [] candidates = [] max_in_len = -1 max_a_len = -1 max_out_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] state = pair_dict_['state'] wt_action = pair_dict_['wt_action'] wt_action_str = pair_dict_['wt_action_str'] wt_next_state = pair_dict_['wt_next_state'] wt_question = state if truncate_num > 0: wt_question = wt_question[:truncate_num] if len(wt_question) > max_in_len: max_in_len = len(wt_question) states.append(wt_question) if len(wt_action) > max_a_len: max_a_len = len(wt_action) actions.append(wt_action) wt_next_state = wt_next_state if truncate_num > 0: wt_next_state = wt_next_state[:truncate_num] if len(wt_next_state) > max_out_len: max_out_len = len(wt_next_state) outputs.append(wt_next_state) # sample other actions under the state neg_sample_idxs = random.choices(list(range(len(pair_dict_["actions"]))), k=num_negative // 2) for idx in neg_sample_idxs: action = pair_dict_['actions'][idx] next_state = pair_dict_['next_states'][idx] question = state if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) states.append(question) if len(action) > max_a_len: max_a_len = len(action) actions.append(action) next_state = next_state if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) # sample other tuples with the same action neg_samples = random.choices(self.action2tuples[wt_action_str], k=num_negative // 2) for (pair_id, idx) in neg_samples: if idx >= 0: state = self.pairs[pair_id]['state'] next_state = self.pairs[pair_id]['next_states'][idx] else: state = self.pairs[pair_id]['state'] next_state = self.pairs[pair_id]['wt_next_state'] question = state if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) states.append(question) if len(wt_action) > max_a_len: max_a_len = len(wt_action) actions.append(wt_action) if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) return states, actions, outputs, max_in_len, max_a_len, max_out_len def get_concat_samples(self, batch_idx, num_negative=10, truncate_num=0): concat_inputs = [] outputs = [] labels = [] positives = [] candidates = [] max_in_len = -1 max_out_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] state = pair_dict_['state'] wt_action = pair_dict_['wt_action'] wt_action_str = pair_dict_['wt_action_str'] wt_next_state = pair_dict_['wt_next_state'] wt_question = state + [self.SEP_TOKEN] + wt_action if truncate_num > 0: wt_question = wt_question[:truncate_num] if len(wt_question) > max_in_len: max_in_len = len(wt_question) concat_inputs.append(wt_question) wt_next_state = wt_next_state if truncate_num > 0: wt_next_state = wt_next_state[:truncate_num] if len(wt_next_state) > max_out_len: max_out_len = len(wt_next_state) outputs.append(wt_next_state) # sample other actions under the state neg_sample_idxs = random.choices(list(range(len(pair_dict_["actions"]))), k=num_negative // 2) for idx in neg_sample_idxs: action = pair_dict_['actions'][idx] next_state = pair_dict_['next_states'][idx] question = state + [self.SEP_TOKEN] + action if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) concat_inputs.append(question) next_state = next_state if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) # sample other tuples with the same action neg_samples = random.choices(self.action2tuples[wt_action_str], k=num_negative // 2) for (pair_id, idx) in neg_samples: if idx >= 0: state = self.pairs[pair_id]['state'] next_state = self.pairs[pair_id]['next_states'][idx] else: state = self.pairs[pair_id]['state'] next_state = self.pairs[pair_id]['wt_next_state'] question = state + [self.SEP_TOKEN] + wt_action if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) concat_inputs.append(question) if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) return concat_inputs, outputs, max_in_len, max_out_len def get_eval_triple_samples(self, batch_idx, num_negative=9, truncate_num=0): states = [] actions = [] outputs = [] labels = [] max_in_len = -1 max_a_len = -1 max_out_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] state = pair_dict_['state'] wt_action = pair_dict_['wt_action'] wt_action_str = pair_dict_['wt_action_str'] wt_next_state = pair_dict_['wt_next_state'] wt_question = state if truncate_num > 0: wt_question = wt_question[:truncate_num] if len(wt_question) > max_in_len: max_in_len = len(wt_question) states.append(wt_question) if len(wt_action) > max_a_len: max_a_len = len(wt_action) actions.append(wt_action) wt_next_state = wt_next_state if truncate_num > 0: wt_next_state = wt_next_state[:truncate_num] if len(wt_next_state) > max_out_len: max_out_len = len(wt_next_state) outputs.append(wt_next_state) # sample other actions under the state for neg_idx in range(len(pair_dict_["actions"])): action = pair_dict_['actions'][neg_idx] next_state = pair_dict_['next_states'][neg_idx] question = state if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) states.append(question) if len(action) > max_a_len: max_a_len = len(action) actions.append(action) if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) return states, actions, outputs, max_in_len, max_a_len, max_out_len def get_eval_concat_samples(self, batch_idx, num_negative=9, truncate_num=0): concat_inputs = [] outputs = [] labels = [] positives = [] candidates = [] max_in_len = -1 max_out_len = -1 for i, idx in enumerate(batch_idx): pair_dict_ = self.pairs[idx] state = pair_dict_['state'] wt_action = pair_dict_['wt_action'] wt_action_str = pair_dict_['wt_action_str'] wt_next_state = pair_dict_['wt_next_state'] wt_question = state + [self.SEP_TOKEN] + wt_action if truncate_num > 0: wt_question = wt_question[:truncate_num] if len(wt_question) > max_in_len: max_in_len = len(wt_question) concat_inputs.append(wt_question) wt_next_state = wt_next_state if truncate_num > 0: wt_next_state = wt_next_state[:truncate_num] if len(wt_next_state) > max_out_len: max_out_len = len(wt_next_state) outputs.append(wt_next_state) # sample other actions under the state for neg_idx in range(len(pair_dict_["actions"])): action = pair_dict_['actions'][neg_idx] next_state = pair_dict_['next_states'][neg_idx] question = state + [self.SEP_TOKEN] + action if truncate_num > 0: question = question[:truncate_num] if len(question) > max_in_len: max_in_len = len(question) concat_inputs.append(question) if truncate_num > 0: next_state = next_state[:truncate_num] if len(next_state) > max_out_len: max_out_len = len(next_state) outputs.append(next_state) return concat_inputs, outputs, max_in_len, max_out_len def check_eval_triples(self, vocab): total_cand = 0 total_state = 0 max_cand = 0 for idx in range(len(self.pairs)): # max_len = -1 concat_triples = [] pair_dict_ = self.pairs[idx] state = pair_dict_['state'] wt_action = pair_dict_['wt_action'] wt_action_str = pair_dict_['wt_action_str'] wt_next_state = pair_dict_['wt_next_state'] num_cand = 0 obs_dict = {} for neg_idx in range(len(pair_dict_["actions"])): action = pair_dict_['actions'][neg_idx] next_state = pair_dict_['next_states'][neg_idx] next_state_words = [vocab[wid] for wid in next_state] next_state_str = ' '.join(next_state_words) if next_state_str not in obs_dict: obs_dict[next_state_str] = 1 num_cand += 1 if num_cand > max_cand: max_cand = num_cand total_cand += num_cand total_state += 1 print('max number of cand:', max_cand) print('avg number of cand: {}/{}={}'.format(total_cand, total_state, total_cand/total_state)) def print_info(self): print('Number of walkthrough tuples: {}'.format(self.num_positive)) print('Number of tuples: {}'.format(self.num_tuples)) print('Number of unique actions: {}'.format(len(self.action2tuples))) # In[ ]: class StateAction2StateDataset(StateState2ActionDataset): def __init__(self, data_dir, rom_dir, game2rom, train_games=None, dev_games=None, setting='same_games', num_negative=20, truncate_num=300, freq_threshold=2): super(StateAction2StateDataset, self).__init__(data_dir, rom_dir, game2rom, train_games, dev_games, setting, num_negative, truncate_num, freq_threshold) def load_dataset(self): self.data_sets = {} if self.setting == 'same_games': self.data_sets = self._load_pair_data_and_split(self.train_games) elif self.setting == 'transfer': # load train self.data_sets = self._load_pair_data_transfer(self.train_games, self.dev_games) # build vocab self._build_vocab() def _process_instance(self, instance): new_inst = json.loads(instance) info = new_inst['observations'].split('|') new_inst['observations'] = {'obs':' | '.join(info[:3]), 'action':info[3]} for idx, action_group in enumerate(new_inst['valid_actions']): action_tuple = action_group[0] info = action_tuple['observations'].split('|') # print(new_inst['valid_actions'][idx][0]['observations']) new_inst['valid_actions'][idx][0]['observations'] = ' | '.join(info) # print(new_inst['valid_actions'][idx][0]['observations']) # print(new_inst['observations']['obs']) # print(new_inst['observations']['action']) return new_inst def _load_pair_data_and_split(self, games, neg_removal=True): """ Splitting trajectories with 8:1:1 """ datasets = {} datasets['train'] = ForwardPredictionSet() datasets['dev'] = ForwardPredictionSet() datasets['test'] = ForwardPredictionSet() avg_cand_rouge_l = 0 total_cand = 0 avg_wt_rouge_l = 0 total_wt_cand = 0 for game_name in games: # rom_path = "../roms/jericho-game-suite/{}.z5".format(game_name) print('# LOADING game data {} ...'.format(game_name)) num_unmatched_wt_action = 0 f = open(os.path.join(self.data_dir, '{}.sas.wt_traj.tok'.format(game_name)), "r") instances = f.readlines() instances = [self._process_instance(instance.lower()) for instance in instances] for idx, instance in enumerate(instances): if idx == len(instances) - 1: continue state = instance['observations']['obs'] wt_next_state = instances[idx + 1]['observations']['obs'] wt_action_origin = instances[idx + 1]['observations']['action'] next_states = [] actions = [] # print(instance) # break wt_match_flag = False for valid_act_group in instance['valid_actions']: valid_act_tuple = valid_act_group[0] action = valid_act_tuple['a'] next_state = valid_act_tuple['observations'] next_states.append(next_state) actions.append(action) # print(next_state) # print(wt_next_state) if next_state == wt_next_state: wt_action = action wt_match_flag = True if not wt_match_flag: # print('unmatched action: \'{}\''.format(wt_action_origin)) wt_action = wt_action_origin num_unmatched_wt_action += 1 rouge_score = calc_score([wt_next_state], [state]) avg_wt_rouge_l += rouge_score total_wt_cand += 1 actions__ = [] next_states__ = [] rouge_scores = [] obs_dict = {wt_next_state:1} for neg_idx in range(len(actions)): action = actions[neg_idx] next_state = next_states[neg_idx] if action.startswith('drop') and len(action.split(' ')) == 2: continue if next_state not in obs_dict: rouge_score = calc_score([next_state], [state]) obs_dict[next_state] = 1 actions__.append(action) next_states__.append(next_state) rouge_scores.append((len(rouge_scores), rouge_score)) sorted_rouge_scores = sorted(rouge_scores, key = lambda x:x[1]) actions_ = [] next_states_ = [] for (neg_idx, _) in sorted_rouge_scores: # print(neg_idx) action = actions__[neg_idx] next_state = next_states__[neg_idx] actions_.append(action) next_states_.append(next_state) avg_cand_rouge_l += rouge_score total_cand += 1 if len(actions_) == 15: break if idx / len(instances) < 0.6: # datasets['train'].add_one(state, next_states, actions, wt_next_state, wt_action) datasets['train'].add_one(state, next_states_, actions_, wt_next_state, wt_action) else: if idx / len(instances) < 0.8: datasets['dev'].add_one(state, next_states_, actions_, wt_next_state, wt_action) else: datasets['test'].add_one(state, next_states_, actions_, wt_next_state, wt_action) print('# unmatched actions in the game: {}'.format(num_unmatched_wt_action)) for k, data_set in datasets.items(): print('# {} set'.format(k)) data_set.print_info() print('# averaged rouge-L between walkthrough dev/test (s, s\'): {}'.format(avg_wt_rouge_l/total_wt_cand)) print('# averaged rouge-L between dev/test (s, s\'): {}'.format(avg_cand_rouge_l/total_cand)) return datasets def _load_pair_data_transfer(self, games, dev_games, neg_removal=True): """ Splitting dev trajectories with 5:5 """ datasets = {} datasets['train'] = ForwardPredictionSet() datasets['dev'] = ForwardPredictionSet() datasets['test'] = ForwardPredictionSet() avg_cand_rouge_l = 0 total_cand = 0 avg_wt_rouge_l = 0 total_wt_cand = 0 for game_name in games + dev_games: # rom_path = "../roms/jericho-game-suite/{}.z5".format(game_name) print('# LOADING game data {} ...'.format(game_name)) num_unmatched_wt_action = 0 f = open(os.path.join(self.data_dir, '{}.sas.wt_traj.tok'.format(game_name)), "r") instances = f.readlines() instances = [self._process_instance(instance.lower()) for instance in instances] for idx, instance in enumerate(instances): if idx == len(instances) - 1: continue # info = instance['observations']['obs'].split(' | ') # state = ' | '.join([info[0], info[2]]) state = instance['observations']['obs'] wt_next_state = instances[idx + 1]['observations']['obs'] wt_action_origin = instances[idx + 1]['observations']['action'] next_states = [] actions = [] # print(instance) # break wt_match_flag = False for valid_act_group in instance['valid_actions']: valid_act_tuple = valid_act_group[0] action = valid_act_tuple['a'] next_state = valid_act_tuple['observations'] next_states.append(next_state) actions.append(action) # print(next_state) # print(wt_next_state) if next_state == wt_next_state: wt_action = action wt_match_flag = True if not wt_match_flag: # print('unmatched action: \'{}\''.format(wt_action_origin)) wt_action = wt_action_origin num_unmatched_wt_action += 1 rouge_score = calc_score([wt_next_state], [state]) avg_wt_rouge_l += rouge_score total_wt_cand += 1 actions__ = [] next_states__ = [] rouge_scores = [] obs_dict = {wt_next_state:1} for neg_idx in range(len(actions)): action = actions[neg_idx] next_state = next_states[neg_idx] if action.startswith('drop') and len(action.split(' ')) == 2: continue if game_name in dev_games: immed_obs = next_state.split(' | ')[2] if idx / len(instances) >= 0.5: if immed_obs == 'dropped .': if idx % 20 >= 1: continue # if immed_obs == 'taken .': # test_num_pick += 1 if action == 'burn repellent with torch': if idx % 10 == 1 or idx % 10 == 3 or idx % 10 == 5: continue if action == 'burn staff with torch': if idx % 10 == 2 or idx % 10 == 3 or idx % 10 == 6: continue if next_state not in obs_dict: rouge_score = calc_score([next_state], [state]) obs_dict[next_state] = 1 actions__.append(action) next_states__.append(next_state) rouge_scores.append((len(rouge_scores), rouge_score)) sorted_rouge_scores = sorted(rouge_scores, key = lambda x:x[1]) actions_ = [] next_states_ = [] for (neg_idx, _) in sorted_rouge_scores: action = actions__[neg_idx] next_state = next_states__[neg_idx] actions_.append(action) next_states_.append(next_state) avg_cand_rouge_l += rouge_score total_cand += 1 if len(actions_) == 15: break if game_name not in dev_games: # datasets['train'].add_one(state, next_states, actions, wt_next_state, wt_action) datasets['train'].add_one(state, next_states_, actions_, wt_next_state, wt_action) else: # rouge_score = calc_score([wt_next_state], [state]) # avg_wt_rouge_l += rouge_score # total_wt_cand += 1 # actions__ = [] # next_states__ = [] # rouge_scores = [] # obs_dict = {wt_next_state:1} # for neg_idx in range(len(actions)): # action = actions[neg_idx] # next_state = next_states[neg_idx] # if action.startswith('drop') and len(action.split(' ')) == 2: # continue # if next_state not in obs_dict: # rouge_score = calc_score([next_state], [state]) # obs_dict[next_state] = 1 # actions__.append(action) # next_states__.append(next_state) # rouge_scores.append((len(rouge_scores), rouge_score)) # sorted_rouge_scores = sorted(rouge_scores, key = lambda x:x[1]) # actions_ = [] # next_states_ = [] # for (neg_idx, _) in sorted_rouge_scores: # action = actions__[neg_idx] # next_state = next_states__[neg_idx] # actions_.append(action) # next_states_.append(next_state) # avg_cand_rouge_l += rouge_score # total_cand += 1 # if len(actions_) == 15: # break if idx / len(instances) < 0.5: datasets['dev'].add_one(state, next_states_, actions_, wt_next_state, wt_action) else: datasets['test'].add_one(state, next_states_, actions_, wt_next_state, wt_action) print('# unmatched actions in the game: {}'.format(num_unmatched_wt_action)) for k, data_set in datasets.items(): print('# {} set'.format(k)) data_set.print_info() print('# averaged rouge-L between walkthrough dev/test (s, s\'): {}'.format(avg_wt_rouge_l/total_wt_cand)) print('# averaged rouge-L between dev/test (s, s\'): {}'.format(avg_cand_rouge_l/total_cand)) return datasets def _numeralize_pairs(self, word_freq_dict, pairs): ''' numeralize passages in training pair lists ''' ret_pair_list = [] for pair_dict_ in pairs: new_pair_dict_ = {} for k, v in pair_dict_.items(): if k == 'state' or k == 'wt_next_state': new_pair_dict_[k] = self._add_vocab_from_sentence(word_freq_dict, v) elif k == 'wt_action': new_pair_dict_[k] = self._add_vocab_from_sentence(word_freq_dict, v) new_pair_dict_['wt_action_str'] = v elif k == 'next_states': new_pair_dict_[k] = [] for seq in v: new_pair_dict_[k].append(self._add_vocab_from_sentence(word_freq_dict, seq)) elif k == 'actions': new_pair_dict_[k] = [] for seq in v: new_pair_dict_[k].append(self._add_vocab_from_sentence(word_freq_dict, seq)) ret_pair_list.append(new_pair_dict_) return ret_pair_list def _get_word_freq(self, data_sets_): """ Building word frequency dictionary and filter the vocabulary """ word_freq_dict = {} for data_id, data_set in data_sets_.items(): for pair_dict in data_set.get_pairs(): for k, v in pair_dict.items(): if k == 'state' or k == 'wt_next_state': self._add_freq_from_sentence(word_freq_dict, v) elif k == 'wt_action': self._add_freq_from_sentence(word_freq_dict, v) elif k == 'next_states': for seq in v: self._add_freq_from_sentence(word_freq_dict, seq) elif k == 'actions': for seq in v: self._add_freq_from_sentence(word_freq_dict, seq) print('size of the raw vocabulary:', len(word_freq_dict)) return word_freq_dict def get_batch_concat(self, set_id, batch_idx, num_negative=-1): """ randomly select a batch from a dataset Inputs: batch_idx: Outputs (all numpy arrays are sorted according to q_length): x_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) a_mat -- numpy array in shape of (batch_size, max length of the sequence in the batch) y_vec -- numpy array of binary labels, numpy array in shape of (batch_size,) x_mask -- numpy array of masks """ if num_negative < 0: num_negative = self.num_negative data_set = self.data_sets[set_id] concat_inputs, outputs, max_in_len, max_out_len = data_set.get_concat_samples(batch_idx, num_negative=num_negative, truncate_num=self.truncate_num) i_masks_ = [] o_masks_ = [] for i, q in enumerate(concat_inputs): concat_inputs[i] = q + (max_in_len - len(q)) * [0] i_masks_.append([1] * len(q) + [0] * (max_in_len - len(q))) for i, a in enumerate(outputs): outputs[i] = a + (max_out_len - len(a)) * [0] o_masks_.append([1] * len(a) + [0] * (max_out_len - len(a))) i_mat = np.array(concat_inputs, dtype=np.int64) i_mask = np.array(i_masks_, dtype=np.int64) o_mat = np.array(outputs, dtype=np.int64) o_mask = np.array(o_masks_, dtype=np.int64) y_vec = np.array(range(len(concat_inputs)), dtype=np.int64) return i_mat, o_mat, y_vec, i_mask, o_mask def get_eval_batch_concat(self, set_id, batch_idx, num_negative=-1): if num_negative < 0: num_negative = self.num_negative data_set = self.data_sets[set_id] concat_inputs, outputs, max_in_len, max_out_len = data_set.get_eval_concat_samples(batch_idx, num_negative=num_negative, truncate_num=self.truncate_num) i_masks_ = [] o_masks_ = [] for i, q in enumerate(concat_inputs): concat_inputs[i] = q + (max_in_len - len(q)) * [0] i_masks_.append([1] * len(q) + [0] * (max_in_len - len(q))) for i, a in enumerate(outputs): outputs[i] = a + (max_out_len - len(a)) * [0] o_masks_.append([1] * len(a) + [0] * (max_out_len - len(a))) i_mat = np.array(concat_inputs, dtype=np.int64) i_mask = np.array(i_masks_, dtype=np.int64) o_mat = np.array(outputs, dtype=np.int64) o_mask = np.array(o_masks_, dtype=np.int64) y_vec = np.array(range(len(concat_inputs)), dtype=np.int64) return i_mat, o_mat, y_vec, i_mask, o_mask def get_eval_batch_triple(self, set_id, batch_idx, num_negative=-1): if num_negative < 0: num_negative = self.num_negative data_set = self.data_sets[set_id] states, actions, outputs, max_in_len, max_a_len, max_out_len = data_set.get_eval_triple_samples( batch_idx, num_negative=num_negative, truncate_num=self.truncate_num) i_masks_ = [] a_masks_ = [] o_masks_ = [] for i, q in enumerate(states): states[i] = q + (max_in_len - len(q)) * [0] i_masks_.append([1] * len(q) + [0] * (max_in_len - len(q))) for i, a in enumerate(actions): actions[i] = a + (max_a_len - len(a)) * [0] a_masks_.append([1] * len(a) + [0] * (max_a_len - len(a))) for i, a in enumerate(outputs): outputs[i] = a + (max_out_len - len(a)) * [0] o_masks_.append([1] * len(a) + [0] * (max_out_len - len(a))) i_mat = np.array(states, dtype=np.int64) i_mask = np.array(i_masks_, dtype=np.int64) a_mat = np.array(actions, dtype=np.int64) a_mask = np.array(a_masks_, dtype=np.int64) o_mat = np.array(outputs, dtype=np.int64) o_mask = np.array(o_masks_, dtype=np.int64) y_vec = np.array(range(len(states)), dtype=np.int64) return i_mat, a_mat, o_mat, y_vec, i_mask, a_mask, o_mask # In[ ]: def find_game_roms(games, rom_dir): print('#number of games: {}'.format(len(games))) roms = os.listdir(rom_dir) game2rom = {} logs = [] for game in games: for rom in roms: if rom.startswith(game + '.z'): game2rom[game] = rom # print('find {} for {}'.format(rom, game)) logs.append('find {} for {}'.format(rom, game)) if game not in game2rom: print('cannot find rom for {}'.format(game)) print('#number of roms founds: {}'.format(len(logs))) return game2rom if __name__=='__main__': # data_dir = "/dccstor/yum-worldmodel/shared_folder_2080/if_games/data/ssa_data/supervised/" # # data_dir = "./" # games = ['905', 'acorncourt', 'advent', 'adventureland', 'afflicted', 'anchor', 'awaken', # 'balances', 'deephome', 'detective', 'dragon', 'enchanter', 'gold', 'inhumane', # 'jewel', 'karn', 'library', 'ludicorp', 'moonlit', 'omniquest', 'pentari', 'reverb', # 'snacktime', 'sorcerer', 'spellbrkr', 'spirit', 'temple', 'tryst205', 'yomomma', # 'zenon', 'zork1', 'zork3', 'ztuu'] # # games = ['tryst205', 'yomomma', # # 'zenon', 'zork1', 'zork3', 'ztuu'] # games = ['zork1', 'zork3'] # dev_games = ['zork3'] # rom_dir = '../roms/jericho-game-suite/' # game2rom = find_game_roms(games, rom_dir) # print(game2rom) # game_task_data = StateState2ActionDataset(data_dir, rom_dir=rom_dir, game2rom=game2rom, # train_games=games, dev_games=games, # setting='same_games') # # game_task_data = StateState2ActionDataset(data_dir, train_games, dev_games) # x1, x2, positives, candidates, max_x1_len, max_x2_len, max_a_len = game_task_data.data_sets['train'].get_samples_from_one_list([0,1]) # print(x1) # print(x2) # print(positives) # print(candidates) # x1_mat, x2_mat, a_mat, y_vec, x1_mask, x2_mask, a_mask = game_task_data.get_batch('train', [0,1]) # print(x1_mat) # print(a_mat) # game_task_data.display_sentence(x1[0]) # game_task_data.display_sentence(x1_mat[0]) # game_task_data.display_sentence(x2[0]) # game_task_data.display_sentence(x2_mat[0]) # game_task_data.display_sentence(x2_mat[1]) # # print(positives) # # print(candidates) # # game_task_data.display_sentence(positives[0]) # # game_task_data.display_sentence(positives[1]) # # game_task_data.display_sentence(candidates[0][3]) # # game_task_data.display_sentence(a_mat[0][0]) # # game_task_data.display_sentence(a_mat[1][0]) # # game_task_data.display_sentence(a_mat[0][4]) # x_mat, a_mat, y_vec, x_mask, a_mask = game_task_data.get_batch_concat('train', [0,1]) # game_task_data.display_sentence(x_mat[0]) # print(x_mat.shape) # print(a_mat.shape) # print(x_mask.shape) # print(a_mask.shape) # print(y_vec.shape) data_dir = "/dccstor/yum-worldmodel/shared_folder_2080/if_games/data/ssa_data/zork_universe_sup/" games = ['zork1', 'zork3'] dev_games = ['zork3'] # games = ['zork1', 'zork3', 'enchanter', 'spellbrkr', 'sorcerer'] games = ['zork1', 'zork3', 'enchanter', 'sorcerer'] train_games = ['zork1', 'enchanter', 'sorcerer'] dev_games = ['zork3'] # games = ['spellbrkr'] # games = ['zork1'] rom_dir = '../roms/jericho-game-suite/' game2rom = find_game_roms(games, rom_dir) print(game2rom) pretrain_path = '/dccstor/gaot1/MultiHopReason/comprehension_tasks/narrativeqa/passage_ranker/bert-base-uncased/' # game_task_data = StateAction2StateDataset(data_dir, rom_dir=rom_dir, game2rom=game2rom, # train_games=games, dev_games=games, # setting='same_games') game_task_data = StateAction2StateDataset(data_dir, rom_dir=rom_dir, game2rom=game2rom, train_games=train_games, dev_games=dev_games, setting='transfer') game_task_data.data_sets['dev'].check_eval_triples(game_task_data.idx_2_word) game_task_data.data_sets['test'].check_eval_triples(game_task_data.idx_2_word) i_mat, o_mat, y_vec, i_mask, o_mask = game_task_data.get_eval_batch_concat('train', [2]) for i in range(i_mat.shape[0]): game_task_data.display_sentence(i_mat[i]) game_task_data.display_sentence(o_mat[i]) # print(concat_inputs[i]) # print(outputs[i]) print('') print(y_vec) print(i_mat.shape) concat_inputs, outputs, max_in_len, max_out_len = game_task_data.data_sets['train'].get_eval_concat_samples([2]) print(len(concat_inputs)) for i in range(len(concat_inputs)): game_task_data.display_sentence(concat_inputs[i]) game_task_data.display_sentence(outputs[i]) # print(concat_inputs[i]) # print(outputs[i]) print('') i_mat, a_mat, o_mat, y_vec, i_mask, a_mask, o_mask = game_task_data.get_eval_batch_triple('train', [2]) for i in range(i_mat.shape[0]): game_task_data.display_sentence(i_mat[i]) game_task_data.display_sentence(a_mat[i]) game_task_data.display_sentence(o_mat[i]) # print(concat_inputs[i]) # print(outputs[i]) print('') # In[ ]: # concat_inputs, outputs, max_in_len, max_out_len = game_task_data.data_sets['test'].get_eval_concat_samples([111]) # print(len(concat_inputs)) # for i in range(len(concat_inputs)): # game_task_data.display_sentence(concat_inputs[i]) # game_task_data.display_sentence(outputs[i]) # states, actions, outputs, max_in_len, max_a_len, max_out_len = game_task_data.data_sets['train'].get_eval_triple_samples([2]) # print(len(states)) # for i in range(len(states)): # game_task_data.display_sentence(states[i]) # game_task_data.display_sentence(actions[i]) # game_task_data.display_sentence(outputs[i]) # # print(concat_inputs[i]) # # print(outputs[i]) # print('') # In[ ]: # # set_id = 'train' # # data_set = game_task_data.data_sets[set_id] # # batch_idx = np.random.randint(0, data_set.size(), size=40) # # print(batch_idx) # # x_mat, a_mat, y_vec, x_mask, a_mask = game_task_data.get_train_batch(40, inst_format='concat') # # print(x_mat.shape) # # print(a_mat.shape) # # print(x_mask.shape) # # print(a_mask.shape) # # print(y_vec.shape) # x_mat, a_mat, y_list, x_mask, a_mask = game_task_data.get_eval_batch_concat('train', 1) # print(x_mat.shape) # print(a_mat.shape) # print(x_mask.shape) # print(a_mask.shape) # print(len(y_list)) # game_task_data.display_sentence(x_mat[0]) # for i in range(a_mat.shape[1]): # game_task_data.display_sentence(a_mat[0][i]) # print(y_list) # x1_mat, x2_mat, a_mat, y_list, x1_mask, x2_mask, a_mask = game_task_data.get_eval_batch('train', 1) # print(x1_mat.shape) # print(a_mat.shape) # print(x_mask.shape) # print(a_mask.shape) # print(len(y_list)) # game_task_data.display_sentence(x1_mat[0]) # game_task_data.display_sentence(x2_mat[0]) # for i in range(a_mat.shape[1]): # game_task_data.display_sentence(a_mat[0][i]) # print(y_list) # In[ ]: # rom_path = "../roms/jericho-game-suite/zork1.z5" # "../roms/jericho-game-suite/zork1.z5" # bindings = load_bindings(rom_path) # print(bindings['grammar']) # In[ ]: # act_par = TemplateActionParser(bindings) # # print(act_par.templates_alias_dict) # # print(act_par.verb_to_templates) # f = open(os.path.join('.', 'zork1.ssa.wt_traj.txt'), "r") # instances = f.readlines() # def _process_instance(instance): # new_inst = json.loads(instance) # info = new_inst['observations'].split('|') # new_inst['observations'] = {'obs':' | '.join(info[:3]), 'action':info[3]} # return new_inst # instances = [_process_instance(instance) for instance in instances] # In[ ]: # # print(act_par.templates[227]) # # print(act_par.templates[173]) # def _preprocess_action(action): # action = action.lower() # if action == 'n': # action = 'north' # elif action == 's': # action = 'south' # elif action == 'e': # action = 'east' # elif action == 'w': # action = 'west' # elif action == 'se': # action = 'southeast' # elif action == 'sw': # action = 'southwest' # elif action == 'ne': # action = 'northeast' # elif action == 'nw': # action = 'northwest' # elif action == 'u': # action = 'up' # elif action == 'd': # action = 'down' # return action # 'north/south/west/east/northwest/southwest/northeast/southeast/up/down/enter/exit/take all'.split('/') # for idx, instance in enumerate(instances): # # print(instance['valid_actions']) # action = _preprocess_action(instance['observations']['action']) # # print(action) # template = act_par.parse_action(action) # if template is None: # print('unmatched action: {}'.format(action)) # elif template[0] not in act_par.template2template: # if template[0] not in act_par.add_template2template: # print('cannot find root: {}'.format(action)) # else: # pass # else: # pass # # for a_list in instance['valid_actions']: # # for action in a_list: # # template_id = action['t'] # # # print(act_par.templates[template_id]) # # template = act_par.parse_action(action['a']) # # if template is None: # # print('unmatched action: {}'.format(action['a'])) # # print('{}: {}'.format(action['a'],template)) # unmatched action: turn page # unmatched action: drop all except torch and lamp # unmatched action: get knife and bag # cannot recognize verb: odysseus # unmatched action: odysseus # unmatched action: drop rusty knife # cannot recognize verb: inflate # unmatched action: inflate pile # unmatched action: launch # unmatched action: get out of boat # unmatched action: dig sand # unmatched action: dig sand # unmatched action: dig sand # unmatched action: dig sand # unmatched action: get rusty knife # unmatched action: get nasty knife # unmatched action: kill thief with nasty knife # unmatched action: kill thief with nasty knife # unmatched action: kill thief with nasty knife # unmatched action: attack thief with nasty knife # unmatched action: drop rusty knife # unmatched action: drop nasty knife # cannot recognize verb: examine # unmatched action: examine map # In[ ]: # act_str = 'apply elephant to fridge' # act_str = 'fix/glue/patch/plug/repair OBJ with OBJ' # act_str = 'glue elephant with fridge' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # act_str = 'get out of boat' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # act_str = 'kill thief with nasty knife' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # act_str = 'drop rusty knife' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # In[ ]: # def _recover_root_template_action(template): # root_template = act_par.template2template[template[0]] # t_tokens = root_template.split() # count = 1 # for tid, t_token in enumerate(t_tokens): # if t_token == 'OBJ': # t_tokens[tid] = template[count] # count += 1 # return ' '.join(t_tokens) # act_str = 'light candles with match' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # act_str = 'exting lamp' # template = act_par.parse_action(act_str) # print('{}: {}'.format(act_str, template)) # print('root template: {}'.format(act_par.template2template[template[0]])) # print('recovered action: {}'.format(_recover_root_template_action(template))) # In[ ]:

37.261733

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171429b9db5bfd73e12ee4c971222c93da0b8cd1

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Python

Lista6/Relembrando_primeira_tentativa.py

hugo-paiva/IntroducaoCienciasDaComputacao

a563f2fd5b773acbffaf4c858b86423b1130ae1f

[ "MIT" ]

null

Lista6/Relembrando_primeira_tentativa.py

hugo-paiva/IntroducaoCienciasDaComputacao

a563f2fd5b773acbffaf4c858b86423b1130ae1f

[ "MIT" ]

null

Lista6/Relembrando_primeira_tentativa.py

hugo-paiva/IntroducaoCienciasDaComputacao

a563f2fd5b773acbffaf4c858b86423b1130ae1f

[ "MIT" ]

null

def hanoi_iterativo(discos, A, B, C, passos): cont = 0 if len(A) % 2 == 0: while len(C) != discos: if len(B) == 0 or len(A) != 0 and A[-1] < B[-1]: B.append(A.pop()) cont += 1 if cont == passos: return 0 elif len(A) == 0 or len(B) != 0 and B[-1] < A[-1]: # Do contrário faço o movimento inverso A.append(B.pop()) cont += 1 if cont == passos: return 0 if len(C) == 0 or len(A) != 0 and A[-1] < C[-1]: C.append(A.pop()) cont += 1 if cont == passos: return 0 elif len(A) == 0 or len(C) != 0 and C[-1] < A[-1]: # Do contrário faço o movimento inverso A.append(C.pop()) cont += 1 if cont == passos: return 0 if len(C) == 0 or len(B) != 0 and B[-1] < C[-1]: C.append(B.pop()) cont += 1 if cont == passos: return 0 elif len(B) == 0 or len(C) != 0 and C[-1] < B[-1]: # Do contrário faço o movimento inverso B.append(C.pop()) cont += 1 if cont == passos: return 0 if len(A) % 2 != 0: while len(C) != discos: if len(C) == 0 or len(A) != 0 and A[-1] < C[-1]: C.append(A.pop()) cont += 1 if cont == passos: return 0 elif len(A) == 0 or len(C) != 0 and C[-1] < A[-1]: # Do contrário faço o movimento inverso A.append(C.pop()) cont += 1 if cont == passos: return 0 if len(B) == 0 or len(A) != 0 and A[-1] < B[-1]: B.append(A.pop()) cont += 1 if cont == passos: return 0 elif len(A) == 0 or len(B) != 0 and B[-1] < A[-1]: # Do contrário faço o movimento inverso A.append(B.pop()) cont += 1 if cont == passos: return 0 if len(C) == 0 or len(B) != 0 and B[-1] < C[-1]: C.append(B.pop()) cont += 1 if cont == passos: return 0 elif len(B) == 0 or len(C) != 0 and C[-1] < B[-1]: # Do contrário faço o movimento inverso B.append(C.pop()) cont += 1 if cont == passos: return 0 discos, passos = input().split() discos, passos = int(discos), int(passos) A = list(range(discos, 0, -1)) B = [] C = [] hanoi_iterativo(discos, A, B, C, passos) print(len(A), len(B), len(C), sep=' ')

35.6125

102

0.379782

385

2,849

2.805195

0.088312

0.033333

0.066667

0.111111

0.908333

0.85463

0

0.05302

0.477009

2,849

80

103

35.6125

0.671812

0.079677

0

0.849315

0

0.000382

0

1

0.013699

false

0.219178

0

0.178082

0.013699

0

null

0

1

0

1

0

null

0

1

0

8

171eb0916c8fb502522b15a3d8b1c590e4c0cab3

112

py

Python

tests/schemas/test_schemas.py

st3107/pdfstream

6e1829d889e5f5400386513efe993ad0596da8a5

[ "BSD-3-Clause" ]

null

tests/schemas/test_schemas.py

st3107/pdfstream

6e1829d889e5f5400386513efe993ad0596da8a5

[ "BSD-3-Clause" ]

34

2020-07-08T16:24:52.000Z

2020-11-21T17:55:13.000Z

tests/schemas/test_schemas.py

xpdAcq/PDFstream

dcd9a368ab80cfb61c4198b9f06d8c972b2e2538

[ "BSD-3-Clause" ]

5

2020-12-02T11:26:06.000Z

2022-03-30T00:25:30.000Z

import pdfstream.schemas as mod def test_print_data_keys(): mod.print_data_keys(mod.analysis_out_schemas)

18.666667

49

0.8125

18

112

4.666667

0.666667

0.214286

0.309524

0.380952

0

0.116071

112

5

50

22.4

0.848485

0

1

0.333333

true

0

0.333333

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

10

172bfd3eb531e391c4825f3dd16f7c96e606ca26

153

py

Python

Ekeopara_Praise/Phase 1/Python Basic 1/Day6 Tasks/Task10.py

obasi28/python-challenge-solutions

2e9564263e0b47e20672cb0bf44e3e287b12ce2b

[ "MIT" ]

null

Ekeopara_Praise/Phase 1/Python Basic 1/Day6 Tasks/Task10.py

obasi28/python-challenge-solutions

2e9564263e0b47e20672cb0bf44e3e287b12ce2b

[ "MIT" ]

null

Ekeopara_Praise/Phase 1/Python Basic 1/Day6 Tasks/Task10.py

obasi28/python-challenge-solutions

2e9564263e0b47e20672cb0bf44e3e287b12ce2b

[ "MIT" ]

null

'''10. Write a Python program to print without newline or space.''' print("This is a sentence", end='') print("This is a new line that will not show")

25.5

67

0.69281

27

153

3.925926

0.777778

0.169811

0.207547

0.226415

0

0.015873

0.176471

153

5

68

30.6

0.825397

0.398693

0

0.647059

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

17999adad0370d27ac5884d399429705559a11ee

19,077

py

Python

harness/determined/_swagger/client/api/tensorboards_api.py

wbwatkinson/determined

f9e099e06746a79a2eaf51a89acc264fc5c301e1

[ "Apache-2.0" ]

null

harness/determined/_swagger/client/api/tensorboards_api.py

wbwatkinson/determined

f9e099e06746a79a2eaf51a89acc264fc5c301e1

[ "Apache-2.0" ]

null

harness/determined/_swagger/client/api/tensorboards_api.py

wbwatkinson/determined

f9e099e06746a79a2eaf51a89acc264fc5c301e1

[ "Apache-2.0" ]

null

# coding: utf-8 """ Determined API (Beta) Determined helps deep learning teams train models more quickly, easily share GPU resources, and effectively collaborate. Determined allows deep learning engineers to focus on building and training models at scale, without needing to worry about DevOps or writing custom code for common tasks like fault tolerance or experiment tracking. You can think of Determined as a platform that bridges the gap between tools like TensorFlow and PyTorch --- which work great for a single researcher with a single GPU --- to the challenges that arise when doing deep learning at scale, as teams, clusters, and data sets all increase in size. # noqa: E501 OpenAPI spec version: 0.1 Contact: community@determined.ai Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from determined._swagger.client.api_client import ApiClient class TensorboardsApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def determined_get_tensorboard(self, tensorboard_id, **kwargs): # noqa: E501 """Get the requested tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_get_tensorboard(tensorboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str tensorboard_id: The id of the tensorboard. (required) :return: V1GetTensorboardResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.determined_get_tensorboard_with_http_info(tensorboard_id, **kwargs) # noqa: E501 else: (data) = self.determined_get_tensorboard_with_http_info(tensorboard_id, **kwargs) # noqa: E501 return data def determined_get_tensorboard_with_http_info(self, tensorboard_id, **kwargs): # noqa: E501 """Get the requested tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_get_tensorboard_with_http_info(tensorboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str tensorboard_id: The id of the tensorboard. (required) :return: V1GetTensorboardResponse If the method is called asynchronously, returns the request thread. """ all_params = ['tensorboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method determined_get_tensorboard" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tensorboard_id' is set if ('tensorboard_id' not in params or params['tensorboard_id'] is None): raise ValueError("Missing the required parameter `tensorboard_id` when calling `determined_get_tensorboard`") # noqa: E501 collection_formats = {} path_params = {} if 'tensorboard_id' in params: path_params['tensorboardId'] = params['tensorboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 return self.api_client.call_api( '/api/v1/tensorboards/{tensorboardId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1GetTensorboardResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def determined_get_tensorboards(self, **kwargs): # noqa: E501 """Get a list of tensorboards. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_get_tensorboards(async_req=True) >>> result = thread.get() :param async_req bool :param str sort_by: Sort tensorboards by the given field. - SORT_BY_UNSPECIFIED: Returns tensorboards in an unsorted list. - SORT_BY_ID: Returns tensorboards sorted by id. - SORT_BY_DESCRIPTION: Returns tensorboards sorted by description. - SORT_BY_START_TIME: Return tensorboards sorted by start time. :param str order_by: Order tensorboards in either ascending or descending order. - ORDER_BY_UNSPECIFIED: Returns records in no specific order. - ORDER_BY_ASC: Returns records in ascending order. - ORDER_BY_DESC: Returns records in descending order. :param int offset: Skip the number of tensorboards before returning results. Negative values denote number of tensorboards to skip from the end before returning results. :param int limit: Limit the number of tensorboards. A value of 0 denotes no limit. :param list[str] users: Limit tensorboards to those that are owned by the specified users. :return: V1GetTensorboardsResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.determined_get_tensorboards_with_http_info(**kwargs) # noqa: E501 else: (data) = self.determined_get_tensorboards_with_http_info(**kwargs) # noqa: E501 return data def determined_get_tensorboards_with_http_info(self, **kwargs): # noqa: E501 """Get a list of tensorboards. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_get_tensorboards_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param str sort_by: Sort tensorboards by the given field. - SORT_BY_UNSPECIFIED: Returns tensorboards in an unsorted list. - SORT_BY_ID: Returns tensorboards sorted by id. - SORT_BY_DESCRIPTION: Returns tensorboards sorted by description. - SORT_BY_START_TIME: Return tensorboards sorted by start time. :param str order_by: Order tensorboards in either ascending or descending order. - ORDER_BY_UNSPECIFIED: Returns records in no specific order. - ORDER_BY_ASC: Returns records in ascending order. - ORDER_BY_DESC: Returns records in descending order. :param int offset: Skip the number of tensorboards before returning results. Negative values denote number of tensorboards to skip from the end before returning results. :param int limit: Limit the number of tensorboards. A value of 0 denotes no limit. :param list[str] users: Limit tensorboards to those that are owned by the specified users. :return: V1GetTensorboardsResponse If the method is called asynchronously, returns the request thread. """ all_params = ['sort_by', 'order_by', 'offset', 'limit', 'users'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method determined_get_tensorboards" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'sort_by' in params: query_params.append(('sortBy', params['sort_by'])) # noqa: E501 if 'order_by' in params: query_params.append(('orderBy', params['order_by'])) # noqa: E501 if 'offset' in params: query_params.append(('offset', params['offset'])) # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # noqa: E501 if 'users' in params: query_params.append(('users', params['users'])) # noqa: E501 collection_formats['users'] = 'multi' # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 return self.api_client.call_api( '/api/v1/tensorboards', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1GetTensorboardsResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def determined_kill_tensorboard(self, tensorboard_id, **kwargs): # noqa: E501 """Kill the requested tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_kill_tensorboard(tensorboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str tensorboard_id: The id of the tensorboard. (required) :return: V1KillTensorboardResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.determined_kill_tensorboard_with_http_info(tensorboard_id, **kwargs) # noqa: E501 else: (data) = self.determined_kill_tensorboard_with_http_info(tensorboard_id, **kwargs) # noqa: E501 return data def determined_kill_tensorboard_with_http_info(self, tensorboard_id, **kwargs): # noqa: E501 """Kill the requested tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_kill_tensorboard_with_http_info(tensorboard_id, async_req=True) >>> result = thread.get() :param async_req bool :param str tensorboard_id: The id of the tensorboard. (required) :return: V1KillTensorboardResponse If the method is called asynchronously, returns the request thread. """ all_params = ['tensorboard_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method determined_kill_tensorboard" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tensorboard_id' is set if ('tensorboard_id' not in params or params['tensorboard_id'] is None): raise ValueError("Missing the required parameter `tensorboard_id` when calling `determined_kill_tensorboard`") # noqa: E501 collection_formats = {} path_params = {} if 'tensorboard_id' in params: path_params['tensorboardId'] = params['tensorboard_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 return self.api_client.call_api( '/api/v1/tensorboards/{tensorboardId}/kill', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1KillTensorboardResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def determined_launch_tensorboard(self, body, **kwargs): # noqa: E501 """Launch a tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_launch_tensorboard(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1LaunchTensorboardRequest body: (required) :return: V1LaunchTensorboardResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.determined_launch_tensorboard_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.determined_launch_tensorboard_with_http_info(body, **kwargs) # noqa: E501 return data def determined_launch_tensorboard_with_http_info(self, body, **kwargs): # noqa: E501 """Launch a tensorboard. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.determined_launch_tensorboard_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1LaunchTensorboardRequest body: (required) :return: V1LaunchTensorboardResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method determined_launch_tensorboard" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `determined_launch_tensorboard`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['BearerToken'] # noqa: E501 return self.api_client.call_api( '/api/v1/tensorboards', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1LaunchTensorboardResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

43.754587

647

0.64203

2,192

19,077

5.353558

0.118157

0.040903

0.019088

0.024542

0.876523

0.868513

0.854793

0.836302

0.829058

0

0.015018

0.273995

19,077

435

648

43.855172

0.832274

0.393091

0

0.735931

0

0.190441

0.059653

0

1

0.038961

false

0

0.017316

0

0.112554

0

null

0

1

0

1

0

null

0

7

bd811b6bafdf3725010f93ab6d37aac6d8de76b2

192,270

py

Python

isaacgym_utils/assets/franka_numerical_utils_raw.py

khush3/isaacgym-utils

cceb1752d32ab47e1aa05ed381b1e83ed280dafa

[ "Apache-2.0" ]

20

2021-09-27T15:17:26.000Z

2022-03-30T12:25:02.000Z

isaacgym_utils/assets/franka_numerical_utils_raw.py

khush3/isaacgym-utils

cceb1752d32ab47e1aa05ed381b1e83ed280dafa

[ "Apache-2.0" ]

6

2021-10-17T20:42:44.000Z

2022-03-20T12:41:09.000Z

isaacgym_utils/assets/franka_numerical_utils_raw.py

khush3/isaacgym-utils

cceb1752d32ab47e1aa05ed381b1e83ed280dafa

[ "Apache-2.0" ]

3

2022-03-19T01:31:40.000Z

2022-03-28T00:45:16.000Z

# From https://github.com/marcocognetti/FrankaEmikaPandaDynModel/ import numpy as np from numba.pycc import CC cc = CC('franka_numerical_utils') cc.verbose = True @cc.export('get_franka_mass_matrix', 'f4[:, :](f4[:])') def get_franka_mass_matrix(q): ''' Expect q to be a (7,) ndarray of type float32 ''' sq = np.sin(q) cq = np.cos(q) t2 = cq[1] t3 = sq[1] t4 = t2 * 2 t5 = cq[3] t6 = cq[2] t7 = t3 * 2 t8 = sq[3] t9 = sq[2] t10 = t6 * 2 t11 = t5 * 2 t12 = cq[4] t13 = t8 * 2 t14 = sq[5] t15 = t12 * 2 t16 = t9 * 2 t17 = sq[4] t18 = t17 * 2 t19 = cq[5] t20 = t19 * 2 t21 = cq[6] t22 = t14 * 2 t23 = sq[6] t24 = t23 * 2 t25 = t21 * 2 # real kin pars g0 = 9.80665 a4 = 0.0825 a5 = -0.0825 a7 = 0.088 d1 = 0.333 d3 = 0.316 d5 = 0.384 Bstack_hat = np.array([ t4 * 1.169633856586688e-1 + t7 * 9.819930066836413e-1-t2 * t3 * 1.038106728915494e-2 + t4 * t11 * 1.807861861545219e-2 + t4 * t13 * 5.324557667216729e-1 + t7 * t10 * 1.051885904832896e-2 + t7 * t16 * 6.369918696374153e-1-a4 * t4 * t5 * 9.772685114474167e-1-a4 * t4 * t8 * 3.435539633871837 + t2 * t3 * t6 * 4.556306244930731e-1-t2 * t3 * t9 * 2.446985713247007e-2 + t4 * t5 * t8 * 3.003448646049658e-1 + t6 * t7 * t9 * 4.439815888138549e-4 + t7 * t10 * t11 * 5.324557667216729e-1 + t7 * t10 * t13 * 1.807861861545219e-2 + t4 * t13 * t15 * 3.21778928866176e-2 + t4 * t11 * t20 * 1.272243011263065e-3 + t4 * t13 * t18 * 2.505201111020351e-2-t4 * t11 * t22 * 6.554082237488641e-3 + t7 * t15 * t16 * 2.505201111020351e-2 + t7 * t16 * t18 * 3.21778928866176e-2-a4 * t4 * t5 * t12 * 2.006356734599796e-2-a4 * t4 * t5 * t17 * 1.540556795982286e-1-a4 * t5 * t7 * t16 * 9.772685114474167e-1-a5 * t4 * t11 * t12 * 2.006356734599796e-2-a4 * t7 * t8 * t16 * 3.435539633871837-a5 * t4 * t11 * t17 * 1.540556795982286e-1-a5 * t7 * t12 * t16 * 2.006356734599796e-2-a5 * t7 * t16 * t17 * 1.540556795982286e-1 + d3 * t5 * t7 * t10 * 3.435539633871837-d3 * t7 * t8 * t10 * 9.772685114474167e-1 + d3 * t5 * t7 * t16 * 3.435539633871837-d3 * t7 * t8 * t16 * 9.772685114474167e-1 + t2 * t3 * t5 * t9 * 5.472851427793527e-3-t2 * t3 * t6 * t11 * 3.003448646049658e-1-t2 * t3 * t8 * t9 * 9.541478495916907e-3 + t2 * t3 * t6 * t13 * 3.003448646049658e-1 + t5 * t6 * t7 * t9 * 9.541478495916907e-3-t4 * t5 * t8 * t12 * 1.22815866196153e-2-t5 * t7 * t8 * t10 * 3.003448646049658e-1 + t6 * t7 * t8 * t9 * 5.472851427793527e-3-t4 * t5 * t8 * t17 * 1.54076417859336e-2 + t7 * t10 * t11 * t15 * 3.21778928866176e-2 + t4 * t12 * t13 * t17 * 7.462333571535709e-3 + t7 * t10 * t11 * t18 * 2.505201111020351e-2 + t4 * t11 * t14 * t19 * 1.073500939031497e-2 + t7 * t10 * t13 * t20 * 1.272243011263065e-3-t4 * t13 * t15 * t20 * 6.554082237488641e-3-t7 * t10 * t13 * t22 * 6.554082237488641e-3-t7 * t12 * t16 * t17 * 7.462333571535709e-3 + t4 * t13 * t15 * t22 * 1.272243011263065e-3-t4 * t13 * t18 * t24 * 1.765933827532306e-3-t7 * t16 * t18 * t20 * 6.554082237488641e-3-t4 * t11 * t22 * t25 * 1.765933827532306e-3-t7 * t15 * t16 * t24 * 1.765933827532306e-3 + t7 * t16 * t18 * t22 * 1.272243011263065e-3 + a4 * t2 * t3 * t5 * t6 * 3.435539633871837-a4 * t2 * t3 * t6 * t8 * 9.772685114474167e-1 + a4 * t4 * t5 * t12 * t14 * 1.354248200243321e-1 + a4 * t4 * t5 * t12 * t19 * 3.312522808627406e-1-a4 * t5 * t7 * t12 * t16 * 2.006356734599796e-2-a4 * t4 * t8 * t14 * t15 * 3.312522808627406e-1 + a5 * t4 * t11 * t12 * t14 * 1.354248200243321e-1-a5 * t7 * t10 * t12 * t13 * 2.006356734599796e-2-a4 * t4 * t8 * t14 * t18 * 3.312522808627406e-1-a4 * t5 * t7 * t16 * t17 * 1.540556795982286e-1 + a4 * t4 * t8 * t15 * t19 * 1.354248200243321e-1 + a5 * t4 * t11 * t12 * t19 * 3.312522808627406e-1-a5 * t7 * t10 * t13 * t17 * 1.540556795982286e-1 + a4 * t4 * t8 * t18 * t19 * 1.354248200243321e-1 + a5 * t7 * t12 * t14 * t16 * 1.354248200243321e-1 + a5 * t7 * t12 * t16 * t19 * 3.312522808627406e-1 + a7 * t4 * t11 * t20 * t21 * 1.249486888554935e-2 + a7 * t4 * t13 * t18 * t21 * 1.249486888554935e-2 + a7 * t4 * t11 * t20 * t23 * 9.42222077621846e-4 + a7 * t4 * t13 * t18 * t23 * 9.42222077621846e-4 + a7 * t7 * t15 * t16 * t21 * 1.249486888554935e-2 + a7 * t7 * t15 * t16 * t23 * 9.42222077621846e-4-d3 * t2 * t3 * t5 * t6 * 9.772685114474167e-1-d3 * t2 * t3 * t6 * t8 * 3.435539633871837 + d5 * t4 * t5 * t8 * t12 * 2.006356734599796e-2 + d5 * t4 * t5 * t8 * t17 * 1.540556795982286e-1-d3 * t7 * t8 * t10 * t12 * 2.006356734599796e-2-d3 * t7 * t8 * t10 * t17 * 1.540556795982286e-1-d3 * t7 * t8 * t12 * t16 * 2.006356734599796e-2-d3 * t7 * t8 * t16 * t17 * 1.540556795982286e-1 + d5 * t4 * t13 * t14 * t15 * 3.312522808627406e-1 + d5 * t4 * t13 * t14 * t18 * 3.312522808627406e-1-d5 * t4 * t13 * t15 * t19 * 1.354248200243321e-1 + d5 * t7 * t14 * t15 * t16 * 3.312522808627406e-1-d5 * t4 * t13 * t18 * t19 * 1.354248200243321e-1 + d5 * t7 * t14 * t16 * t18 * 3.312522808627406e-1-d5 * t7 * t15 * t16 * t19 * 1.354248200243321e-1-d5 * t7 * t16 * t18 * t19 * 1.354248200243321e-1-t2 * t3 * t5 * t6 * t8 * 1.028754296212441 + t2 * t3 * t5 * t9 * t12 * 1.54076417859336e-2 + t2 * t3 * t6 * t11 * t12 * 1.22815866196153e-2-t2 * t3 * t5 * t9 * t17 * 1.22815866196153e-2-t2 * t3 * t6 * t12 * t13 * 1.22815866196153e-2-t2 * t3 * t8 * t9 * t15 * 7.462333571535709e-3 + t2 * t3 * t6 * t11 * t17 * 1.54076417859336e-2 + t2 * t3 * t8 * t9 * t18 * 7.462333571535709e-3-t2 * t3 * t6 * t13 * t17 * 1.54076417859336e-2 + t5 * t6 * t7 * t9 * t15 * 7.462333571535709e-3 + t5 * t7 * t8 * t10 * t12 * 1.22815866196153e-2 + t6 * t7 * t8 * t9 * t12 * 1.54076417859336e-2-t5 * t6 * t7 * t9 * t18 * 7.462333571535709e-3 + t5 * t7 * t8 * t10 * t17 * 1.54076417859336e-2-t6 * t7 * t8 * t9 * t17 * 1.22815866196153e-2-t4 * t5 * t8 * t14 * t17 * 2.487313068943488e-5 + t4 * t5 * t8 * t12 * t20 * 1.073500939031497e-2-t4 * t5 * t8 * t12 * t22 * 1.073500939031497e-2 + t4 * t5 * t8 * t17 * t19 * 1.640860636489827e-3 + t7 * t10 * t11 * t12 * t17 * 7.462333571535709e-3-t4 * t12 * t13 * t14 * t17 * 1.640860636489827e-3-t7 * t10 * t11 * t15 * t20 * 6.554082237488641e-3 + t7 * t10 * t13 * t14 * t19 * 1.073500939031497e-2-t4 * t12 * t13 * t17 * t19 * 2.487313068943488e-5-t4 * t13 * t14 * t15 * t19 * 1.073500939031497e-2 + t7 * t10 * t11 * t15 * t22 * 1.272243011263065e-3 + t7 * t12 * t14 * t16 * t17 * 1.640860636489827e-3-t4 * t11 * t14 * t19 * t21 * 3.517483669838449e-3-t7 * t10 * t11 * t18 * t24 * 1.765933827532306e-3-t4 * t11 * t14 * t19 * t23 * 1.193879867928346e-3 + t7 * t12 * t16 * t17 * t19 * 2.487313068943488e-5-t7 * t14 * t16 * t18 * t19 * 1.073500939031497e-2-t4 * t13 * t15 * t20 * t25 * 1.765933827532306e-3-t7 * t10 * t13 * t22 * t25 * 1.765933827532306e-3 + t4 * t13 * t18 * t21 * t23 * 2.370893727874773e-3-t4 * t11 * t21 * t22 * t23 * 2.370893727874773e-3 + t7 * t15 * t16 * t21 * t23 * 2.370893727874773e-3-t7 * t16 * t18 * t20 * t25 * 1.765933827532306e-3-a4 * t2 * t3 * t6 * t8 * t12 * 2.006356734599796e-2-a5 * t2 * t3 * t8 * t9 * t12 * 1.540556795982286e-1-a4 * t2 * t3 * t6 * t8 * t17 * 1.540556795982286e-1 + a5 * t2 * t3 * t8 * t9 * t17 * 2.006356734599796e-2 + a5 * t5 * t6 * t7 * t9 * t12 * 1.540556795982286e-1 + a4 * t6 * t7 * t9 * t11 * t12 * 1.540556795982286e-1-a5 * t5 * t6 * t7 * t9 * t17 * 2.006356734599796e-2 + a4 * t6 * t7 * t9 * t12 * t13 * 1.540556795982286e-1-a5 * t4 * t5 * t8 * t14 * t15 * 3.312522808627406e-1-a4 * t6 * t7 * t9 * t11 * t17 * 2.006356734599796e-2-a5 * t4 * t5 * t8 * t14 * t18 * 3.312522808627406e-1-a4 * t6 * t7 * t9 * t13 * t17 * 2.006356734599796e-2 + a5 * t4 * t5 * t8 * t15 * t19 * 1.354248200243321e-1 + a4 * t5 * t7 * t12 * t14 * t16 * 1.354248200243321e-1 + a5 * t4 * t5 * t8 * t18 * t19 * 1.354248200243321e-1 + a5 * t7 * t10 * t12 * t13 * t14 * 1.354248200243321e-1 + a4 * t5 * t7 * t12 * t16 * t19 * 3.312522808627406e-1-a4 * t7 * t8 * t14 * t15 * t16 * 3.312522808627406e-1 + a4 * t4 * t5 * t12 * t19 * t21 * 1.249486888554935e-2-a4 * t4 * t8 * t14 * t15 * t21 * 1.249486888554935e-2 + a5 * t7 * t10 * t12 * t13 * t19 * 3.312522808627406e-1 + a4 * t4 * t5 * t12 * t19 * t23 * 9.42222077621846e-4-a4 * t7 * t8 * t14 * t16 * t18 * 3.312522808627406e-1-a4 * t4 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-a4 * t4 * t8 * t14 * t18 * t21 * 1.249486888554935e-2 + a4 * t7 * t8 * t15 * t16 * t19 * 1.354248200243321e-1-a4 * t4 * t5 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t4 * t8 * t14 * t18 * t23 * 9.42222077621846e-4 + a4 * t7 * t8 * t16 * t18 * t19 * 1.354248200243321e-1 + a5 * t4 * t11 * t12 * t19 * t21 * 1.249486888554935e-2 + a4 * t4 * t5 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t4 * t5 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t4 * t11 * t12 * t19 * t23 * 9.42222077621846e-4 + a7 * t7 * t10 * t11 * t18 * t21 * 1.249486888554935e-2 + a4 * t4 * t5 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t7 * t10 * t11 * t18 * t23 * 9.42222077621846e-4-a5 * t4 * t11 * t17 * t20 * t21 * 9.42222077621846e-4 + a7 * t7 * t10 * t13 * t20 * t21 * 1.249486888554935e-2 + a5 * t4 * t11 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t4 * t11 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t7 * t12 * t16 * t19 * t21 * 1.249486888554935e-2 + a7 * t7 * t10 * t13 * t20 * t23 * 9.42222077621846e-4 + a5 * t4 * t11 * t17 * t22 * t23 * 1.249486888554935e-2 + a5 * t7 * t12 * t16 * t19 * t23 * 9.42222077621846e-4 + a7 * t4 * t13 * t15 * t21 * t22 * 1.249486888554935e-2 + a7 * t4 * t13 * t15 * t22 * t23 * 9.42222077621846e-4-a5 * t7 * t16 * t17 * t20 * t21 * 9.42222077621846e-4 + a5 * t7 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t7 * t16 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t7 * t16 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t7 * t16 * t18 * t21 * t22 * 1.249486888554935e-2 + a7 * t7 * t16 * t18 * t22 * t23 * 9.42222077621846e-4-d3 * t2 * t3 * t5 * t6 * t12 * 2.006356734599796e-2-d3 * t2 * t3 * t5 * t6 * t17 * 1.540556795982286e-1-d5 * t2 * t3 * t5 * t9 * t12 * 1.540556795982286e-1-d5 * t2 * t3 * t6 * t11 * t12 * 2.006356734599796e-2-d3 * t2 * t3 * t9 * t11 * t12 * 1.540556795982286e-1 + d5 * t2 * t3 * t5 * t9 * t17 * 2.006356734599796e-2 + d5 * t2 * t3 * t6 * t12 * t13 * 2.006356734599796e-2-d3 * t2 * t3 * t9 * t12 * t13 * 1.540556795982286e-1-d5 * t2 * t3 * t6 * t11 * t17 * 1.540556795982286e-1 + d3 * t2 * t3 * t9 * t11 * t17 * 2.006356734599796e-2 + d5 * t2 * t3 * t6 * t13 * t17 * 1.540556795982286e-1 + d3 * t2 * t3 * t9 * t13 * t17 * 2.006356734599796e-2-d5 * t5 * t7 * t8 * t10 * t12 * 2.006356734599796e-2-d5 * t6 * t7 * t8 * t9 * t12 * 1.540556795982286e-1-d5 * t4 * t5 * t8 * t12 * t14 * 1.354248200243321e-1-d5 * t5 * t7 * t8 * t10 * t17 * 1.540556795982286e-1 + d5 * t6 * t7 * t8 * t9 * t17 * 2.006356734599796e-2-d5 * t4 * t5 * t8 * t12 * t19 * 3.312522808627406e-1 + d3 * t5 * t7 * t10 * t14 * t15 * 3.312522808627406e-1 + d3 * t7 * t8 * t10 * t12 * t14 * 1.354248200243321e-1 + d3 * t5 * t7 * t10 * t14 * t18 * 3.312522808627406e-1-d3 * t5 * t7 * t10 * t15 * t19 * 1.354248200243321e-1 + d3 * t7 * t8 * t10 * t12 * t19 * 3.312522808627406e-1 + d3 * t5 * t7 * t14 * t15 * t16 * 3.312522808627406e-1 + d3 * t7 * t8 * t12 * t14 * t16 * 1.354248200243321e-1-d3 * t5 * t7 * t10 * t18 * t19 * 1.354248200243321e-1 + d5 * t7 * t10 * t11 * t14 * t15 * 3.312522808627406e-1 + d3 * t5 * t7 * t14 * t16 * t18 * 3.312522808627406e-1-d3 * t5 * t7 * t15 * t16 * t19 * 1.354248200243321e-1 + d3 * t7 * t8 * t12 * t16 * t19 * 3.312522808627406e-1 + d5 * t7 * t10 * t11 * t14 * t18 * 3.312522808627406e-1-d5 * t7 * t10 * t11 * t15 * t19 * 1.354248200243321e-1-d3 * t5 * t7 * t16 * t18 * t19 * 1.354248200243321e-1-d5 * t7 * t10 * t11 * t18 * t19 * 1.354248200243321e-1 + d5 * t4 * t13 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t4 * t13 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t4 * t13 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t4 * t13 * t14 * t18 * t23 * 9.42222077621846e-4 + d5 * t7 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + d5 * t7 * t14 * t15 * t16 * t23 * 9.42222077621846e-4 + d5 * t7 * t14 * t16 * t18 * t21 * 1.249486888554935e-2 + d5 * t7 * t14 * t16 * t18 * t23 * 9.42222077621846e-4-t2 * t3 * t5 * t6 * t8 * t15 * 6.43557857732352e-2-t2 * t3 * t5 * t6 * t8 * t18 * 5.010402222040701e-2 + t2 * t3 * t5 * t6 * t8 * t20 * 2.54448602252613e-3 + t2 * t3 * t5 * t9 * t12 * t14 * 2.487313068943488e-5-t2 * t3 * t5 * t6 * t8 * t22 * 1.310816447497728e-2-t2 * t3 * t5 * t9 * t12 * t19 * 1.640860636489827e-3 + t2 * t3 * t8 * t9 * t12 * t17 * 1.425176355282819e-2 + t2 * t3 * t8 * t9 * t14 * t15 * 1.640860636489827e-3 + t2 * t3 * t6 * t11 * t14 * t17 * 2.487313068943488e-5-t2 * t3 * t6 * t11 * t12 * t20 * 1.073500939031497e-2-t2 * t3 * t8 * t9 * t14 * t18 * 1.640860636489827e-3-t2 * t3 * t6 * t13 * t14 * t17 * 2.487313068943488e-5 + t2 * t3 * t5 * t9 * t17 * t20 * 1.073500939031497e-2 + t2 * t3 * t6 * t11 * t12 * t22 * 1.073500939031497e-2 + t2 * t3 * t6 * t12 * t13 * t20 * 1.073500939031497e-2 + t2 * t3 * t8 * t9 * t15 * t19 * 2.487313068943488e-5-t5 * t6 * t7 * t9 * t12 * t17 * 1.425176355282819e-2-t5 * t6 * t7 * t9 * t14 * t15 * 1.640860636489827e-3 + t6 * t7 * t8 * t9 * t12 * t14 * 2.487313068943488e-5-t2 * t3 * t5 * t9 * t17 * t22 * 1.073500939031497e-2-t2 * t3 * t6 * t11 * t17 * t19 * 1.640860636489827e-3-t2 * t3 * t6 * t12 * t13 * t22 * 1.073500939031497e-2-t2 * t3 * t8 * t9 * t18 * t19 * 2.487313068943488e-5 + t5 * t6 * t7 * t9 * t14 * t18 * 1.640860636489827e-3 + t2 * t3 * t6 * t13 * t17 * t19 * 1.640860636489827e-3-t5 * t6 * t7 * t9 * t15 * t19 * 2.487313068943488e-5 + t5 * t7 * t8 * t10 * t14 * t17 * 2.487313068943488e-5-t6 * t7 * t8 * t9 * t12 * t19 * 1.640860636489827e-3-t4 * t5 * t8 * t12 * t14 * t19 * 1.565265049750341e-2-t5 * t7 * t8 * t10 * t12 * t20 * 1.073500939031497e-2 + t5 * t6 * t7 * t9 * t18 * t19 * 2.487313068943488e-5 + t5 * t7 * t8 * t10 * t12 * t22 * 1.073500939031497e-2-t5 * t7 * t8 * t10 * t17 * t19 * 1.640860636489827e-3 + t6 * t7 * t8 * t9 * t17 * t20 * 1.073500939031497e-2-t6 * t7 * t8 * t9 * t17 * t22 * 1.073500939031497e-2-t4 * t5 * t8 * t12 * t20 * t21 * 3.517483669838449e-3-t7 * t10 * t11 * t12 * t14 * t17 * 1.640860636489827e-3-t4 * t5 * t8 * t12 * t20 * t23 * 1.193879867928346e-3 + t4 * t5 * t8 * t12 * t21 * t22 * 3.517483669838449e-3-t4 * t5 * t8 * t14 * t17 * t24 * 2.370893727874773e-3 + t4 * t5 * t8 * t14 * t17 * t25 * 2.370893727874773e-3 + t4 * t5 * t8 * t12 * t22 * t23 * 1.193879867928346e-3 + t4 * t5 * t8 * t17 * t19 * t21 * 1.193879867928346e-3-t4 * t5 * t8 * t17 * t19 * t23 * 3.517483669838449e-3-t7 * t10 * t11 * t12 * t17 * t19 * 2.487313068943488e-5-t7 * t10 * t11 * t14 * t15 * t19 * 1.073500939031497e-2-t4 * t12 * t13 * t14 * t17 * t21 * 1.193879867928346e-3 + t4 * t12 * t13 * t14 * t17 * t23 * 3.517483669838449e-3-t7 * t10 * t13 * t14 * t19 * t21 * 3.517483669838449e-3 + t4 * t13 * t14 * t15 * t19 * t21 * 3.517483669838449e-3-t7 * t10 * t13 * t14 * t19 * t23 * 1.193879867928346e-3 + t7 * t12 * t14 * t16 * t17 * t21 * 1.193879867928346e-3 + t4 * t13 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t7 * t10 * t11 * t15 * t20 * t25 * 1.765933827532306e-3-t4 * t12 * t13 * t17 * t19 * t24 * 2.370893727874773e-3-t7 * t12 * t14 * t16 * t17 * t23 * 3.517483669838449e-3 + t4 * t12 * t13 * t17 * t19 * t25 * 2.370893727874773e-3 + t7 * t10 * t11 * t18 * t21 * t23 * 2.370893727874773e-3 + t7 * t12 * t16 * t17 * t19 * t24 * 2.370893727874773e-3 + t7 * t14 * t16 * t18 * t19 * t21 * 3.517483669838449e-3-t4 * t13 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-t7 * t10 * t13 * t21 * t22 * t23 * 2.370893727874773e-3-t7 * t12 * t16 * t17 * t19 * t25 * 2.370893727874773e-3 + t7 * t14 * t16 * t18 * t19 * t23 * 1.193879867928346e-3-t7 * t16 * t18 * t20 * t21 * t23 * 2.370893727874773e-3-a5 * t2 * t3 * t5 * t6 * t8 * t12 * 4.012713469199591e-2-a5 * t2 * t3 * t5 * t6 * t8 * t17 * 3.081113591964573e-1 + a4 * t2 * t3 * t5 * t6 * t14 * t15 * 3.312522808627406e-1 + a4 * t2 * t3 * t6 * t8 * t12 * t14 * 1.354248200243321e-1 + a4 * t2 * t3 * t5 * t6 * t14 * t18 * 3.312522808627406e-1-a4 * t2 * t3 * t5 * t6 * t15 * t19 * 1.354248200243321e-1 + a4 * t2 * t3 * t6 * t8 * t12 * t19 * 3.312522808627406e-1 + a5 * t2 * t3 * t6 * t11 * t14 * t15 * 3.312522808627406e-1-a4 * t2 * t3 * t5 * t6 * t18 * t19 * 1.354248200243321e-1-a5 * t2 * t3 * t6 * t13 * t14 * t15 * 3.312522808627406e-1-a5 * t2 * t3 * t8 * t9 * t14 * t17 * 1.354248200243321e-1 + a5 * t2 * t3 * t6 * t11 * t14 * t18 * 3.312522808627406e-1-a5 * t2 * t3 * t6 * t11 * t15 * t19 * 1.354248200243321e-1-a5 * t2 * t3 * t6 * t13 * t14 * t18 * 3.312522808627406e-1 + a5 * t2 * t3 * t6 * t13 * t15 * t19 * 1.354248200243321e-1-a5 * t2 * t3 * t8 * t9 * t17 * t19 * 3.312522808627406e-1 + a5 * t5 * t6 * t7 * t9 * t14 * t17 * 1.354248200243321e-1-a5 * t2 * t3 * t6 * t11 * t18 * t19 * 1.354248200243321e-1 + a5 * t5 * t7 * t8 * t10 * t14 * t15 * 3.312522808627406e-1 + a5 * t2 * t3 * t6 * t13 * t18 * t19 * 1.354248200243321e-1 + a5 * t5 * t7 * t8 * t10 * t14 * t18 * 3.312522808627406e-1 + a4 * t6 * t7 * t9 * t11 * t14 * t17 * 1.354248200243321e-1 + a5 * t5 * t6 * t7 * t9 * t17 * t19 * 3.312522808627406e-1-a5 * t5 * t7 * t8 * t10 * t15 * t19 * 1.354248200243321e-1 + a4 * t6 * t7 * t9 * t13 * t14 * t17 * 1.354248200243321e-1-a5 * t4 * t5 * t8 * t14 * t15 * t21 * 1.249486888554935e-2-a5 * t5 * t7 * t8 * t10 * t18 * t19 * 1.354248200243321e-1 + a4 * t6 * t7 * t9 * t11 * t17 * t19 * 3.312522808627406e-1-a5 * t4 * t5 * t8 * t14 * t15 * t23 * 9.42222077621846e-4 + a4 * t6 * t7 * t9 * t13 * t17 * t19 * 3.312522808627406e-1-a5 * t4 * t5 * t8 * t14 * t18 * t21 * 1.249486888554935e-2 + a7 * t4 * t5 * t8 * t14 * t17 * t21 * 9.42222077621846e-4-a5 * t4 * t5 * t8 * t14 * t18 * t23 * 9.42222077621846e-4-a7 * t4 * t5 * t8 * t14 * t17 * t23 * 1.249486888554935e-2 + a4 * t5 * t7 * t12 * t16 * t19 * t21 * 1.249486888554935e-2-a4 * t7 * t8 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + a4 * t5 * t7 * t12 * t16 * t19 * t23 * 9.42222077621846e-4-a4 * t7 * t8 * t14 * t15 * t16 * t23 * 9.42222077621846e-4 + a5 * t7 * t10 * t12 * t13 * t19 * t21 * 1.249486888554935e-2-a4 * t7 * t8 * t14 * t16 * t18 * t21 * 1.249486888554935e-2 + a5 * t7 * t10 * t12 * t13 * t19 * t23 * 9.42222077621846e-4-a4 * t5 * t7 * t16 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t7 * t8 * t14 * t16 * t18 * t23 * 9.42222077621846e-4 + a4 * t5 * t7 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t5 * t7 * t16 * t17 * t21 * t22 * 9.42222077621846e-4-a5 * t7 * t10 * t13 * t17 * t20 * t21 * 9.42222077621846e-4 + a7 * t4 * t12 * t13 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t7 * t10 * t11 * t15 * t21 * t22 * 1.249486888554935e-2 + a4 * t5 * t7 * t16 * t17 * t22 * t23 * 1.249486888554935e-2 + a5 * t7 * t10 * t13 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t7 * t10 * t13 * t17 * t21 * t22 * 9.42222077621846e-4-a7 * t4 * t12 * t13 * t17 * t19 * t23 * 1.249486888554935e-2 + a7 * t7 * t10 * t11 * t15 * t22 * t23 * 9.42222077621846e-4 + a5 * t7 * t10 * t13 * t17 * t22 * t23 * 1.249486888554935e-2-a7 * t7 * t12 * t16 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t7 * t12 * t16 * t17 * t19 * t23 * 1.249486888554935e-2 + d3 * t2 * t3 * t5 * t6 * t12 * t14 * 1.354248200243321e-1 + d3 * t2 * t3 * t5 * t6 * t12 * t19 * 3.312522808627406e-1-d3 * t2 * t3 * t6 * t8 * t14 * t15 * 3.312522808627406e-1 + d5 * t2 * t3 * t6 * t11 * t12 * t14 * 1.354248200243321e-1-d3 * t2 * t3 * t6 * t8 * t14 * t18 * 3.312522808627406e-1-d5 * t2 * t3 * t5 * t9 * t14 * t17 * 1.354248200243321e-1-d5 * t2 * t3 * t6 * t12 * t13 * t14 * 1.354248200243321e-1 + d3 * t2 * t3 * t6 * t8 * t15 * t19 * 1.354248200243321e-1 + d5 * t2 * t3 * t6 * t11 * t12 * t19 * 3.312522808627406e-1 + d3 * t2 * t3 * t6 * t8 * t18 * t19 * 1.354248200243321e-1-d3 * t2 * t3 * t9 * t11 * t14 * t17 * 1.354248200243321e-1-d5 * t2 * t3 * t5 * t9 * t17 * t19 * 3.312522808627406e-1-d5 * t2 * t3 * t6 * t12 * t13 * t19 * 3.312522808627406e-1-d3 * t2 * t3 * t9 * t13 * t14 * t17 * 1.354248200243321e-1 + d5 * t5 * t7 * t8 * t10 * t12 * t14 * 1.354248200243321e-1-d3 * t2 * t3 * t9 * t11 * t17 * t19 * 3.312522808627406e-1-d3 * t2 * t3 * t9 * t13 * t17 * t19 * 3.312522808627406e-1 + d5 * t5 * t7 * t8 * t10 * t12 * t19 * 3.312522808627406e-1-d5 * t6 * t7 * t8 * t9 * t14 * t17 * 1.354248200243321e-1-d5 * t6 * t7 * t8 * t9 * t17 * t19 * 3.312522808627406e-1-d5 * t4 * t5 * t8 * t12 * t19 * t21 * 1.249486888554935e-2 + d3 * t5 * t7 * t10 * t14 * t15 * t21 * 1.249486888554935e-2-d5 * t4 * t5 * t8 * t12 * t19 * t23 * 9.42222077621846e-4 + d3 * t5 * t7 * t10 * t14 * t15 * t23 * 9.42222077621846e-4 + d3 * t5 * t7 * t10 * t14 * t18 * t21 * 1.249486888554935e-2 + d3 * t5 * t7 * t10 * t14 * t18 * t23 * 9.42222077621846e-4 + d3 * t7 * t8 * t10 * t12 * t19 * t21 * 1.249486888554935e-2 + d5 * t4 * t5 * t8 * t17 * t20 * t21 * 9.42222077621846e-4 + d3 * t5 * t7 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + d3 * t7 * t8 * t10 * t12 * t19 * t23 * 9.42222077621846e-4-d5 * t4 * t5 * t8 * t17 * t20 * t23 * 1.249486888554935e-2 + d5 * t4 * t5 * t8 * t17 * t21 * t22 * 9.42222077621846e-4 + d3 * t5 * t7 * t14 * t15 * t16 * t23 * 9.42222077621846e-4 + d5 * t7 * t10 * t11 * t14 * t15 * t21 * 1.249486888554935e-2 + d3 * t5 * t7 * t14 * t16 * t18 * t21 * 1.249486888554935e-2-d5 * t4 * t5 * t8 * t17 * t22 * t23 * 1.249486888554935e-2 + d5 * t7 * t10 * t11 * t14 * t15 * t23 * 9.42222077621846e-4 + d3 * t5 * t7 * t14 * t16 * t18 * t23 * 9.42222077621846e-4-d3 * t7 * t8 * t10 * t17 * t20 * t21 * 9.42222077621846e-4 + d3 * t7 * t8 * t12 * t16 * t19 * t21 * 1.249486888554935e-2 + d5 * t7 * t10 * t11 * t14 * t18 * t21 * 1.249486888554935e-2 + d3 * t7 * t8 * t10 * t17 * t20 * t23 * 1.249486888554935e-2-d3 * t7 * t8 * t10 * t17 * t21 * t22 * 9.42222077621846e-4 + d3 * t7 * t8 * t12 * t16 * t19 * t23 * 9.42222077621846e-4 + d5 * t7 * t10 * t11 * t14 * t18 * t23 * 9.42222077621846e-4 + d3 * t7 * t8 * t10 * t17 * t22 * t23 * 1.249486888554935e-2-d3 * t7 * t8 * t16 * t17 * t20 * t21 * 9.42222077621846e-4 + d3 * t7 * t8 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-d3 * t7 * t8 * t16 * t17 * t21 * t22 * 9.42222077621846e-4 + d3 * t7 * t8 * t16 * t17 * t22 * t23 * 1.249486888554935e-2-t2 * t3 * t5 * t6 * t8 * t12 * t17 * 1.492466714307142e-2 + t2 * t3 * t5 * t6 * t8 * t14 * t19 * 2.147001878062994e-2 + t2 * t3 * t5 * t6 * t8 * t15 * t20 * 1.310816447497728e-2-t2 * t3 * t5 * t6 * t8 * t15 * t22 * 2.54448602252613e-3 + t2 * t3 * t5 * t6 * t8 * t18 * t24 * 3.531867655064613e-3 + t2 * t3 * t6 * t11 * t12 * t14 * t19 * 1.565265049750341e-2 + t2 * t3 * t5 * t9 * t12 * t14 * t24 * 2.370893727874773e-3-t2 * t3 * t5 * t9 * t14 * t17 * t19 * 1.565265049750341e-2-t2 * t3 * t6 * t12 * t13 * t14 * t19 * 1.565265049750341e-2-t2 * t3 * t5 * t9 * t12 * t14 * t25 * 2.370893727874773e-3-t2 * t3 * t5 * t6 * t8 * t22 * t25 * 3.531867655064613e-3-t2 * t3 * t5 * t9 * t12 * t19 * t21 * 1.193879867928346e-3-t2 * t3 * t8 * t9 * t12 * t17 * t20 * 1.310816447497728e-2 + t2 * t3 * t8 * t9 * t14 * t15 * t21 * 1.193879867928346e-3 + t2 * t3 * t5 * t9 * t12 * t19 * t23 * 3.517483669838449e-3 + t2 * t3 * t8 * t9 * t12 * t17 * t22 * 2.54448602252613e-3-t2 * t3 * t8 * t9 * t14 * t15 * t23 * 3.517483669838449e-3 + t2 * t3 * t6 * t11 * t12 * t20 * t21 * 3.517483669838449e-3 + t2 * t3 * t8 * t9 * t12 * t17 * t24 * 3.531867655064613e-3-t2 * t3 * t8 * t9 * t14 * t18 * t21 * 1.193879867928346e-3 + t5 * t7 * t8 * t10 * t12 * t14 * t19 * 1.565265049750341e-2 + t5 * t6 * t7 * t9 * t12 * t17 * t20 * 1.310816447497728e-2-t2 * t3 * t5 * t9 * t17 * t20 * t21 * 3.517483669838449e-3 + t2 * t3 * t6 * t11 * t12 * t20 * t23 * 1.193879867928346e-3-t2 * t3 * t6 * t11 * t12 * t21 * t22 * 3.517483669838449e-3 + t2 * t3 * t6 * t11 * t14 * t17 * t24 * 2.370893727874773e-3-t2 * t3 * t6 * t12 * t13 * t20 * t21 * 3.517483669838449e-3 + t2 * t3 * t8 * t9 * t14 * t18 * t23 * 3.517483669838449e-3-t5 * t6 * t7 * t9 * t14 * t15 * t21 * 1.193879867928346e-3-t2 * t3 * t6 * t11 * t14 * t17 * t25 * 2.370893727874773e-3-t5 * t6 * t7 * t9 * t12 * t17 * t22 * 2.54448602252613e-3-t2 * t3 * t5 * t9 * t17 * t20 * t23 * 1.193879867928346e-3 + t2 * t3 * t5 * t9 * t17 * t21 * t22 * 3.517483669838449e-3-t2 * t3 * t6 * t11 * t12 * t22 * t23 * 1.193879867928346e-3-t2 * t3 * t6 * t11 * t17 * t19 * t21 * 1.193879867928346e-3-t2 * t3 * t6 * t12 * t13 * t20 * t23 * 1.193879867928346e-3 + t2 * t3 * t6 * t12 * t13 * t21 * t22 * 3.517483669838449e-3-t2 * t3 * t6 * t13 * t14 * t17 * t24 * 2.370893727874773e-3 + t5 * t6 * t7 * t9 * t14 * t15 * t23 * 3.517483669838449e-3 + t2 * t3 * t6 * t13 * t14 * t17 * t25 * 2.370893727874773e-3 + t2 * t3 * t8 * t9 * t15 * t19 * t24 * 2.370893727874773e-3-t5 * t6 * t7 * t9 * t12 * t17 * t24 * 3.531867655064613e-3 + t5 * t6 * t7 * t9 * t14 * t18 * t21 * 1.193879867928346e-3 + t6 * t7 * t8 * t9 * t12 * t14 * t24 * 2.370893727874773e-3-t6 * t7 * t8 * t9 * t14 * t17 * t19 * 1.565265049750341e-2 + t2 * t3 * t5 * t9 * t17 * t22 * t23 * 1.193879867928346e-3 + t2 * t3 * t6 * t11 * t17 * t19 * t23 * 3.517483669838449e-3 + t2 * t3 * t6 * t12 * t13 * t22 * t23 * 1.193879867928346e-3 + t2 * t3 * t6 * t13 * t17 * t19 * t21 * 1.193879867928346e-3-t2 * t3 * t8 * t9 * t15 * t19 * t25 * 2.370893727874773e-3-t6 * t7 * t8 * t9 * t12 * t14 * t25 * 2.370893727874773e-3-t5 * t6 * t7 * t9 * t14 * t18 * t23 * 3.517483669838449e-3-t6 * t7 * t8 * t9 * t12 * t19 * t21 * 1.193879867928346e-3-t2 * t3 * t6 * t13 * t17 * t19 * t23 * 3.517483669838449e-3-t2 * t3 * t8 * t9 * t18 * t19 * t24 * 2.370893727874773e-3 + t5 * t7 * t8 * t10 * t12 * t20 * t21 * 3.517483669838449e-3 + t2 * t3 * t8 * t9 * t18 * t19 * t25 * 2.370893727874773e-3 + t6 * t7 * t8 * t9 * t12 * t19 * t23 * 3.517483669838449e-3-t5 * t6 * t7 * t9 * t15 * t19 * t24 * 2.370893727874773e-3 + t5 * t7 * t8 * t10 * t12 * t20 * t23 * 1.193879867928346e-3-t5 * t7 * t8 * t10 * t12 * t21 * t22 * 3.517483669838449e-3 + t5 * t7 * t8 * t10 * t14 * t17 * t24 * 2.370893727874773e-3 + t5 * t6 * t7 * t9 * t15 * t19 * t25 * 2.370893727874773e-3-t5 * t7 * t8 * t10 * t14 * t17 * t25 * 2.370893727874773e-3-t4 * t5 * t8 * t12 * t14 * t19 * t25 * 3.531867655064613e-3-t5 * t7 * t8 * t10 * t12 * t22 * t23 * 1.193879867928346e-3-t5 * t7 * t8 * t10 * t17 * t19 * t21 * 1.193879867928346e-3 + t5 * t6 * t7 * t9 * t18 * t19 * t24 * 2.370893727874773e-3-t6 * t7 * t8 * t9 * t17 * t20 * t21 * 3.517483669838449e-3-t5 * t6 * t7 * t9 * t18 * t19 * t25 * 2.370893727874773e-3 + t5 * t7 * t8 * t10 * t17 * t19 * t23 * 3.517483669838449e-3-t6 * t7 * t8 * t9 * t17 * t20 * t23 * 1.193879867928346e-3 + t6 * t7 * t8 * t9 * t17 * t21 * t22 * 3.517483669838449e-3-t4 * t5 * t8 * t14 * t17 * t21 * t23 * 3.531867655064613e-3 + t6 * t7 * t8 * t9 * t17 * t22 * t23 * 1.193879867928346e-3-t7 * t10 * t11 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t7 * t10 * t11 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t7 * t10 * t11 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t7 * t10 * t11 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t7 * t10 * t11 * t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t7 * t10 * t11 * t12 * t17 * t19 * t25 * 2.370893727874773e-3-t7 * t10 * t11 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-t4 * t12 * t13 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + t7 * t12 * t16 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + a5 * t2 * t3 * t5 * t6 * t8 * t12 * t14 * 2.708496400486642e-1 + a5 * t2 * t3 * t5 * t6 * t8 * t12 * t19 * 6.625045617254811e-1 + a4 * t2 * t3 * t5 * t6 * t14 * t15 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t5 * t6 * t8 * t18 * t21 * 2.49897377710987e-2 + a4 * t2 * t3 * t5 * t6 * t14 * t15 * t23 * 9.42222077621846e-4-a7 * t2 * t3 * t5 * t6 * t8 * t18 * t23 * 1.884444155243692e-3 + a7 * t2 * t3 * t5 * t6 * t8 * t20 * t21 * 2.49897377710987e-2 + a4 * t2 * t3 * t5 * t6 * t14 * t18 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t5 * t9 * t12 * t14 * t21 * 9.42222077621846e-4 + a7 * t2 * t3 * t5 * t6 * t8 * t20 * t23 * 1.884444155243692e-3 + a4 * t2 * t3 * t5 * t6 * t14 * t18 * t23 * 9.42222077621846e-4 + a4 * t2 * t3 * t6 * t8 * t12 * t19 * t21 * 1.249486888554935e-2 + a7 * t2 * t3 * t5 * t9 * t12 * t14 * t23 * 1.249486888554935e-2 + a4 * t2 * t3 * t6 * t8 * t12 * t19 * t23 * 9.42222077621846e-4 + a5 * t2 * t3 * t6 * t11 * t14 * t15 * t21 * 1.249486888554935e-2 + a5 * t2 * t3 * t6 * t11 * t14 * t15 * t23 * 9.42222077621846e-4-a5 * t2 * t3 * t6 * t13 * t14 * t15 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t8 * t9 * t12 * t17 * t21 * 2.49897377710987e-2 + a5 * t2 * t3 * t6 * t11 * t14 * t18 * t21 * 1.249486888554935e-2-a5 * t2 * t3 * t8 * t9 * t12 * t20 * t21 * 9.42222077621846e-4-a4 * t2 * t3 * t6 * t8 * t17 * t20 * t21 * 9.42222077621846e-4-a5 * t2 * t3 * t6 * t13 * t14 * t15 * t23 * 9.42222077621846e-4-a7 * t2 * t3 * t6 * t11 * t14 * t17 * t21 * 9.42222077621846e-4-a7 * t2 * t3 * t8 * t9 * t12 * t17 * t23 * 1.884444155243692e-3 + a5 * t2 * t3 * t6 * t11 * t14 * t18 * t23 * 9.42222077621846e-4-a5 * t2 * t3 * t6 * t13 * t14 * t18 * t21 * 1.249486888554935e-2 + a5 * t2 * t3 * t8 * t9 * t12 * t20 * t23 * 1.249486888554935e-2-a5 * t2 * t3 * t8 * t9 * t12 * t21 * t22 * 9.42222077621846e-4 + a4 * t2 * t3 * t6 * t8 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t2 * t3 * t6 * t8 * t17 * t21 * t22 * 9.42222077621846e-4 + a7 * t2 * t3 * t6 * t11 * t14 * t17 * t23 * 1.249486888554935e-2 + a7 * t2 * t3 * t6 * t13 * t14 * t17 * t21 * 9.42222077621846e-4-a5 * t2 * t3 * t6 * t13 * t14 * t18 * t23 * 9.42222077621846e-4 + a5 * t2 * t3 * t8 * t9 * t12 * t22 * t23 * 1.249486888554935e-2-a5 * t2 * t3 * t8 * t9 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t8 * t9 * t15 * t19 * t21 * 9.42222077621846e-4 + a7 * t5 * t6 * t7 * t9 * t12 * t17 * t21 * 2.49897377710987e-2-a7 * t6 * t7 * t8 * t9 * t12 * t14 * t21 * 9.42222077621846e-4 + a4 * t2 * t3 * t6 * t8 * t17 * t22 * t23 * 1.249486888554935e-2 + a5 * t5 * t6 * t7 * t9 * t12 * t20 * t21 * 9.42222077621846e-4 + a5 * t5 * t7 * t8 * t10 * t14 * t15 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t6 * t13 * t14 * t17 * t23 * 1.249486888554935e-2-a5 * t2 * t3 * t8 * t9 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t2 * t3 * t8 * t9 * t15 * t19 * t23 * 1.249486888554935e-2 + a7 * t5 * t6 * t7 * t9 * t12 * t17 * t23 * 1.884444155243692e-3 + a7 * t6 * t7 * t8 * t9 * t12 * t14 * t23 * 1.249486888554935e-2-a5 * t5 * t6 * t7 * t9 * t12 * t20 * t23 * 1.249486888554935e-2 + a5 * t5 * t6 * t7 * t9 * t12 * t21 * t22 * 9.42222077621846e-4 + a5 * t5 * t7 * t8 * t10 * t14 * t15 * t23 * 9.42222077621846e-4 + a7 * t2 * t3 * t8 * t9 * t18 * t19 * t21 * 9.42222077621846e-4 + a5 * t5 * t7 * t8 * t10 * t14 * t18 * t21 * 1.249486888554935e-2-a5 * t5 * t6 * t7 * t9 * t12 * t22 * t23 * 1.249486888554935e-2 + a5 * t5 * t6 * t7 * t9 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t2 * t3 * t8 * t9 * t18 * t19 * t23 * 1.249486888554935e-2 + a7 * t5 * t6 * t7 * t9 * t15 * t19 * t21 * 9.42222077621846e-4-a7 * t5 * t7 * t8 * t10 * t14 * t17 * t21 * 9.42222077621846e-4 + a4 * t6 * t7 * t9 * t11 * t12 * t20 * t21 * 9.42222077621846e-4 + a5 * t5 * t7 * t8 * t10 * t14 * t18 * t23 * 9.42222077621846e-4-a7 * t4 * t5 * t8 * t12 * t14 * t19 * t21 * 2.49897377710987e-2 + a5 * t5 * t6 * t7 * t9 * t17 * t19 * t23 * 9.42222077621846e-4-a7 * t5 * t6 * t7 * t9 * t15 * t19 * t23 * 1.249486888554935e-2 + a7 * t5 * t7 * t8 * t10 * t14 * t17 * t23 * 1.249486888554935e-2-a4 * t6 * t7 * t9 * t11 * t12 * t20 * t23 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t11 * t12 * t21 * t22 * 9.42222077621846e-4 + a4 * t6 * t7 * t9 * t12 * t13 * t20 * t21 * 9.42222077621846e-4-a7 * t4 * t5 * t8 * t12 * t14 * t19 * t23 * 1.884444155243692e-3-a7 * t5 * t6 * t7 * t9 * t18 * t19 * t21 * 9.42222077621846e-4-a4 * t6 * t7 * t9 * t11 * t12 * t22 * t23 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t11 * t17 * t19 * t21 * 1.249486888554935e-2-a4 * t6 * t7 * t9 * t12 * t13 * t20 * t23 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t12 * t13 * t21 * t22 * 9.42222077621846e-4 + a7 * t5 * t6 * t7 * t9 * t18 * t19 * t23 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t11 * t17 * t19 * t23 * 9.42222077621846e-4-a4 * t6 * t7 * t9 * t12 * t13 * t22 * t23 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t13 * t17 * t19 * t21 * 1.249486888554935e-2 + a4 * t6 * t7 * t9 * t13 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t7 * t10 * t11 * t12 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t7 * t10 * t11 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t15 * 6.625045617254811e-1-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t18 * 6.625045617254811e-1 + d5 * t2 * t3 * t5 * t6 * t8 * t15 * t19 * 2.708496400486642e-1 + d5 * t2 * t3 * t5 * t6 * t8 * t18 * t19 * 2.708496400486642e-1 + d3 * t2 * t3 * t5 * t6 * t12 * t19 * t21 * 1.249486888554935e-2-d3 * t2 * t3 * t6 * t8 * t14 * t15 * t21 * 1.249486888554935e-2 + d3 * t2 * t3 * t5 * t6 * t12 * t19 * t23 * 9.42222077621846e-4-d3 * t2 * t3 * t6 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-d3 * t2 * t3 * t6 * t8 * t14 * t18 * t21 * 1.249486888554935e-2-d3 * t2 * t3 * t5 * t6 * t17 * t20 * t21 * 9.42222077621846e-4-d3 * t2 * t3 * t6 * t8 * t14 * t18 * t23 * 9.42222077621846e-4-d5 * t2 * t3 * t5 * t9 * t12 * t20 * t21 * 9.42222077621846e-4 + d3 * t2 * t3 * t5 * t6 * t17 * t20 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t5 * t6 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t2 * t3 * t5 * t9 * t12 * t20 * t23 * 1.249486888554935e-2-d5 * t2 * t3 * t5 * t9 * t12 * t21 * t22 * 9.42222077621846e-4 + d5 * t2 * t3 * t6 * t11 * t12 * t19 * t21 * 1.249486888554935e-2 + d3 * t2 * t3 * t5 * t6 * t17 * t22 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t9 * t11 * t12 * t20 * t21 * 9.42222077621846e-4 + d5 * t2 * t3 * t5 * t9 * t12 * t22 * t23 * 1.249486888554935e-2-d5 * t2 * t3 * t5 * t9 * t17 * t19 * t21 * 1.249486888554935e-2 + d5 * t2 * t3 * t6 * t11 * t12 * t19 * t23 * 9.42222077621846e-4-d5 * t2 * t3 * t6 * t12 * t13 * t19 * t21 * 1.249486888554935e-2 + d3 * t2 * t3 * t9 * t11 * t12 * t20 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t9 * t11 * t12 * t21 * t22 * 9.42222077621846e-4-d3 * t2 * t3 * t9 * t12 * t13 * t20 * t21 * 9.42222077621846e-4-d5 * t2 * t3 * t5 * t9 * t17 * t19 * t23 * 9.42222077621846e-4-d5 * t2 * t3 * t6 * t12 * t13 * t19 * t23 * 9.42222077621846e-4 + d3 * t2 * t3 * t9 * t11 * t12 * t22 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t9 * t11 * t17 * t19 * t21 * 1.249486888554935e-2 + d3 * t2 * t3 * t9 * t12 * t13 * t20 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t9 * t12 * t13 * t21 * t22 * 9.42222077621846e-4-d5 * t2 * t3 * t6 * t11 * t17 * t20 * t21 * 9.42222077621846e-4-d3 * t2 * t3 * t9 * t11 * t17 * t19 * t23 * 9.42222077621846e-4 + d3 * t2 * t3 * t9 * t12 * t13 * t22 * t23 * 1.249486888554935e-2-d3 * t2 * t3 * t9 * t13 * t17 * t19 * t21 * 1.249486888554935e-2 + d5 * t2 * t3 * t6 * t11 * t17 * t20 * t23 * 1.249486888554935e-2-d5 * t2 * t3 * t6 * t11 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t2 * t3 * t6 * t13 * t17 * t20 * t21 * 9.42222077621846e-4 + d5 * t5 * t7 * t8 * t10 * t12 * t19 * t21 * 1.249486888554935e-2-d5 * t6 * t7 * t8 * t9 * t12 * t20 * t21 * 9.42222077621846e-4-d3 * t2 * t3 * t9 * t13 * t17 * t19 * t23 * 9.42222077621846e-4 + d5 * t2 * t3 * t6 * t11 * t17 * t22 * t23 * 1.249486888554935e-2-d5 * t2 * t3 * t6 * t13 * t17 * t20 * t23 * 1.249486888554935e-2 + d5 * t2 * t3 * t6 * t13 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t5 * t7 * t8 * t10 * t12 * t19 * t23 * 9.42222077621846e-4 + d5 * t6 * t7 * t8 * t9 * t12 * t20 * t23 * 1.249486888554935e-2-d5 * t6 * t7 * t8 * t9 * t12 * t21 * t22 * 9.42222077621846e-4-d5 * t2 * t3 * t6 * t13 * t17 * t22 * t23 * 1.249486888554935e-2 + d5 * t6 * t7 * t8 * t9 * t12 * t22 * t23 * 1.249486888554935e-2-d5 * t6 * t7 * t8 * t9 * t17 * t19 * t21 * 1.249486888554935e-2-d5 * t5 * t7 * t8 * t10 * t17 * t20 * t21 * 9.42222077621846e-4-d5 * t6 * t7 * t8 * t9 * t17 * t19 * t23 * 9.42222077621846e-4 + d5 * t5 * t7 * t8 * t10 * t17 * t20 * t23 * 1.249486888554935e-2-d5 * t5 * t7 * t8 * t10 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t5 * t7 * t8 * t10 * t17 * t22 * t23 * 1.249486888554935e-2 + t2 * t3 * t5 * t6 * t8 * t12 * t14 * t17 * 3.281721272979654e-3 + t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * 4.974626137886975e-5 + t2 * t3 * t5 * t6 * t8 * t14 * t15 * t19 * 2.147001878062994e-2-t2 * t3 * t5 * t6 * t8 * t14 * t19 * t21 * 7.034967339676897e-3-t2 * t3 * t5 * t6 * t8 * t14 * t19 * t23 * 2.387759735856691e-3 + t2 * t3 * t5 * t6 * t8 * t15 * t20 * t25 * 3.531867655064613e-3-t2 * t3 * t8 * t9 * t12 * t14 * t17 * t19 * 2.147001878062994e-2-t2 * t3 * t5 * t6 * t8 * t18 * t21 * t23 * 4.741787455749547e-3 + t2 * t3 * t5 * t9 * t12 * t14 * t21 * t23 * 3.531867655064613e-3 + t5 * t6 * t7 * t9 * t12 * t14 * t17 * t19 * 2.147001878062994e-2-t2 * t3 * t5 * t6 * t8 * t21 * t22 * t23 * 4.741787455749547e-3 + t2 * t3 * t6 * t11 * t12 * t14 * t19 * t25 * 3.531867655064613e-3-t2 * t3 * t5 * t9 * t14 * t17 * t19 * t25 * 3.531867655064613e-3-t2 * t3 * t6 * t12 * t13 * t14 * t19 * t25 * 3.531867655064613e-3-t2 * t3 * t8 * t9 * t12 * t17 * t21 * t23 * 4.741787455749547e-3-t2 * t3 * t8 * t9 * t12 * t17 * t20 * t25 * 3.531867655064613e-3 + t2 * t3 * t6 * t11 * t14 * t17 * t21 * t23 * 3.531867655064613e-3-t2 * t3 * t6 * t13 * t14 * t17 * t21 * t23 * 3.531867655064613e-3 + t2 * t3 * t8 * t9 * t15 * t19 * t21 * t23 * 3.531867655064613e-3 + t5 * t6 * t7 * t9 * t12 * t17 * t21 * t23 * 4.741787455749547e-3 + t5 * t7 * t8 * t10 * t12 * t14 * t19 * t25 * 3.531867655064613e-3 + t6 * t7 * t8 * t9 * t12 * t14 * t21 * t23 * 3.531867655064613e-3 + t5 * t6 * t7 * t9 * t12 * t17 * t20 * t25 * 3.531867655064613e-3-t2 * t3 * t8 * t9 * t18 * t19 * t21 * t23 * 3.531867655064613e-3-t5 * t6 * t7 * t9 * t15 * t19 * t21 * t23 * 3.531867655064613e-3 + t5 * t7 * t8 * t10 * t14 * t17 * t21 * t23 * 3.531867655064613e-3-t6 * t7 * t8 * t9 * t14 * t17 * t19 * t25 * 3.531867655064613e-3-t4 * t5 * t8 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3 + t5 * t6 * t7 * t9 * t18 * t19 * t21 * t23 * 3.531867655064613e-3-t7 * t10 * t11 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + a5 * t2 * t3 * t5 * t6 * t8 * t12 * t19 * t21 * 2.49897377710987e-2 + a5 * t2 * t3 * t5 * t6 * t8 * t12 * t19 * t23 * 1.884444155243692e-3-a5 * t2 * t3 * t5 * t6 * t8 * t17 * t20 * t21 * 1.884444155243692e-3 + a5 * t2 * t3 * t5 * t6 * t8 * t17 * t20 * t23 * 2.49897377710987e-2-a5 * t2 * t3 * t5 * t6 * t8 * t17 * t21 * t22 * 1.884444155243692e-3-a7 * t2 * t3 * t5 * t6 * t8 * t15 * t21 * t22 * 2.49897377710987e-2 + a5 * t2 * t3 * t5 * t6 * t8 * t17 * t22 * t23 * 2.49897377710987e-2-a7 * t2 * t3 * t5 * t6 * t8 * t15 * t22 * t23 * 1.884444155243692e-3 + a7 * t2 * t3 * t6 * t11 * t12 * t14 * t19 * t21 * 2.49897377710987e-2-a7 * t2 * t3 * t5 * t9 * t14 * t17 * t19 * t21 * 2.49897377710987e-2 + a7 * t2 * t3 * t6 * t11 * t12 * t14 * t19 * t23 * 1.884444155243692e-3-a7 * t2 * t3 * t6 * t12 * t13 * t14 * t19 * t21 * 2.49897377710987e-2-a7 * t2 * t3 * t5 * t9 * t14 * t17 * t19 * t23 * 1.884444155243692e-3-a7 * t2 * t3 * t6 * t12 * t13 * t14 * t19 * t23 * 1.884444155243692e-3 + a7 * t2 * t3 * t8 * t9 * t12 * t17 * t21 * t22 * 2.49897377710987e-2 + a7 * t2 * t3 * t8 * t9 * t12 * t17 * t22 * t23 * 1.884444155243692e-3 + a7 * t5 * t7 * t8 * t10 * t12 * t14 * t19 * t21 * 2.49897377710987e-2 + a7 * t5 * t7 * t8 * t10 * t12 * t14 * t19 * t23 * 1.884444155243692e-3-a7 * t5 * t6 * t7 * t9 * t12 * t17 * t21 * t22 * 2.49897377710987e-2-a7 * t5 * t6 * t7 * t9 * t12 * t17 * t22 * t23 * 1.884444155243692e-3-a7 * t6 * t7 * t8 * t9 * t14 * t17 * t19 * t21 * 2.49897377710987e-2-a7 * t6 * t7 * t8 * t9 * t14 * t17 * t19 * t23 * 1.884444155243692e-3-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t15 * t21 * 2.49897377710987e-2-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t15 * t23 * 1.884444155243692e-3-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t18 * t21 * 2.49897377710987e-2-d5 * t2 * t3 * t5 * t6 * t8 * t14 * t18 * t23 * 1.884444155243692e-3 + t2 * t3 * t5 * t6 * t8 * t12 * t14 * t17 * t21 * 2.387759735856691e-3-t2 * t3 * t5 * t6 * t8 * t12 * t14 * t17 * t23 * 7.034967339676897e-3-t2 * t3 * t5 * t6 * t8 * t14 * t15 * t19 * t21 * 7.034967339676897e-3-t2 * t3 * t5 * t6 * t8 * t14 * t15 * t19 * t23 * 2.387759735856691e-3 + t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t24 * 4.741787455749547e-3-t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t25 * 4.741787455749547e-3 + t2 * t3 * t5 * t6 * t8 * t15 * t20 * t21 * t23 * 4.741787455749547e-3 + t2 * t3 * t8 * t9 * t12 * t14 * t17 * t19 * t21 * 7.034967339676897e-3 + t2 * t3 * t8 * t9 * t12 * t14 * t17 * t19 * t23 * 2.387759735856691e-3-t5 * t6 * t7 * t9 * t12 * t14 * t17 * t19 * t21 * 7.034967339676897e-3 + t2 * t3 * t6 * t11 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3-t5 * t6 * t7 * t9 * t12 * t14 * t17 * t19 * t23 * 2.387759735856691e-3-t2 * t3 * t5 * t9 * t14 * t17 * t19 * t21 * t23 * 4.741787455749547e-3-t2 * t3 * t6 * t12 * t13 * t14 * t19 * t21 * t23 * 4.741787455749547e-3-t2 * t3 * t8 * t9 * t12 * t17 * t20 * t21 * t23 * 4.741787455749547e-3 + t5 * t7 * t8 * t10 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3 + t5 * t6 * t7 * t9 * t12 * t17 * t20 * t21 * t23 * 4.741787455749547e-3-t6 * t7 * t8 * t9 * t14 * t17 * t19 * t21 * t23 * 4.741787455749547e-3-a7 * t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t21 * 1.884444155243692e-3 + a7 * t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t23 * 2.49897377710987e-2 + t2 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t21 * t23 * 7.063735310129226e-3 + 2.921567431682902e-2, t2 * 3.492843154774605e-3-t3 * 2.841866661568585e-2-t2 * t6 * 1.223492856623504e-2-t2 * t9 * 2.278153122465366e-1 + t3 * t10 * 2.219907944069274e-4-t3 * t16 * 2.219907944069274e-4 + t2 * t5 * t6 * 2.736425713896764e-3-t2 * t6 * t8 * 4.770739247958454e-3 + t3 * t5 * t10 * 4.770739247958454e-3 + t3 * t6 * t9 * 6.264730105890863e-1 + t3 * t8 * t10 * 2.736425713896764e-3 + t2 * t9 * t11 * 1.501724323024829e-1-t2 * t9 * t13 * 1.501724323024829e-1-t3 * t5 * t16 * 4.770739247958454e-3-t3 * t8 * t16 * 2.736425713896764e-3-a4 * t2 * t5 * t9 * 1.717769816935919 + a4 * t2 * t8 * t9 * 4.886342557237083e-1 + d3 * t2 * t5 * t9 * 4.886342557237083e-1 + d3 * t2 * t8 * t9 * 1.717769816935919 + t2 * t5 * t8 * t9 * 5.143771481062207e-1 + t2 * t5 * t6 * t12 * 7.703820892966798e-3-t3 * t6 * t9 * t11 * 5.324557667216729e-1-t2 * t5 * t6 * t17 * 6.14079330980765e-3-t2 * t6 * t8 * t15 * 3.731166785767855e-3-t3 * t6 * t9 * t13 * 1.807861861545219e-2 + t3 * t5 * t10 * t15 * 3.731166785767855e-3 + t3 * t6 * t9 * t15 * 2.505201111020351e-2 + t3 * t8 * t10 * t12 * 7.703820892966798e-3 + t2 * t6 * t8 * t18 * 3.731166785767855e-3-t2 * t9 * t11 * t12 * 6.14079330980765e-3 + t2 * t9 * t12 * t13 * 6.14079330980765e-3-t3 * t5 * t10 * t18 * 3.731166785767855e-3 + t3 * t6 * t9 * t18 * 3.21778928866176e-2-t3 * t8 * t10 * t17 * 6.14079330980765e-3-t2 * t9 * t11 * t17 * 7.703820892966798e-3-t3 * t5 * t15 * t16 * 3.731166785767855e-3-t3 * t8 * t12 * t16 * 7.703820892966798e-3 + t2 * t9 * t13 * t17 * 7.703820892966798e-3 + t3 * t5 * t16 * t18 * 3.731166785767855e-3 + t3 * t8 * t16 * t17 * 6.14079330980765e-3-a4 * t3 * t5 * t6 * t9 * 9.772685114474167e-1-a4 * t3 * t6 * t8 * t9 * 3.435539633871837-a5 * t2 * t6 * t8 * t12 * 7.702783979911432e-2 + a4 * t2 * t8 * t9 * t12 * 1.003178367299898e-2 + a5 * t3 * t5 * t10 * t12 * 7.702783979911432e-2-a5 * t3 * t6 * t9 * t12 * 2.006356734599796e-2 + a5 * t2 * t6 * t8 * t17 * 1.003178367299898e-2 + a4 * t2 * t8 * t9 * t17 * 7.702783979911432e-2 + a4 * t3 * t10 * t11 * t12 * 7.702783979911432e-2-a5 * t3 * t5 * t10 * t17 * 1.003178367299898e-2-a5 * t3 * t6 * t9 * t17 * 1.540556795982286e-1-a5 * t3 * t5 * t12 * t16 * 7.702783979911432e-2 + a4 * t3 * t10 * t12 * t13 * 7.702783979911432e-2-a4 * t3 * t10 * t11 * t17 * 1.003178367299898e-2-a4 * t3 * t11 * t12 * t16 * 7.702783979911432e-2 + a5 * t3 * t5 * t16 * t17 * 1.003178367299898e-2-a4 * t3 * t10 * t13 * t17 * 1.003178367299898e-2-a4 * t3 * t12 * t13 * t16 * 7.702783979911432e-2 + a4 * t3 * t11 * t16 * t17 * 1.003178367299898e-2 + a4 * t3 * t13 * t16 * t17 * 1.003178367299898e-2-d5 * t2 * t5 * t6 * t12 * 7.702783979911432e-2 + d3 * t2 * t5 * t9 * t12 * 1.003178367299898e-2-d3 * t2 * t6 * t11 * t12 * 7.702783979911432e-2 + d5 * t2 * t5 * t6 * t17 * 1.003178367299898e-2 + d3 * t2 * t5 * t9 * t17 * 7.702783979911432e-2-d3 * t2 * t6 * t12 * t13 * 7.702783979911432e-2-d5 * t3 * t8 * t10 * t12 * 7.702783979911432e-2 + d3 * t2 * t6 * t11 * t17 * 1.003178367299898e-2 + d5 * t2 * t9 * t11 * t12 * 1.003178367299898e-2 + d3 * t2 * t6 * t13 * t17 * 1.003178367299898e-2-d5 * t2 * t9 * t12 * t13 * 1.003178367299898e-2 + d5 * t3 * t8 * t10 * t17 * 1.003178367299898e-2 + d5 * t2 * t9 * t11 * t17 * 7.702783979911432e-2 + d5 * t3 * t8 * t12 * t16 * 7.702783979911432e-2-d5 * t2 * t9 * t13 * t17 * 7.702783979911432e-2-d5 * t3 * t8 * t16 * t17 * 1.003178367299898e-2 + t3 * t5 * t6 * t8 * t9 * 3.003448646049658e-1 + t2 * t5 * t6 * t12 * t14 * 1.243656534471744e-5 + t2 * t5 * t8 * t9 * t15 * 3.21778928866176e-2 + t2 * t5 * t8 * t9 * t18 * 2.505201111020351e-2-t2 * t5 * t6 * t12 * t19 * 8.204303182449135e-4-t2 * t5 * t8 * t9 * t20 * 1.272243011263065e-3-t3 * t6 * t9 * t11 * t15 * 3.21778928866176e-2 + t2 * t6 * t8 * t12 * t17 * 7.125881776414093e-3 + t2 * t6 * t8 * t14 * t15 * 8.204303182449135e-4 + t2 * t5 * t8 * t9 * t22 * 6.554082237488641e-3-t3 * t5 * t10 * t12 * t17 * 7.125881776414093e-3-t3 * t5 * t10 * t14 * t15 * 8.204303182449135e-4-t3 * t6 * t9 * t11 * t18 * 2.505201111020351e-2-t3 * t6 * t9 * t12 * t17 * 7.462333571535709e-3 + t3 * t8 * t10 * t12 * t14 * 1.243656534471744e-5-t2 * t6 * t8 * t14 * t18 * 8.204303182449135e-4 + t2 * t5 * t6 * t17 * t20 * 5.367504695157484e-3 + t2 * t6 * t8 * t15 * t19 * 1.243656534471744e-5 + t3 * t5 * t10 * t14 * t18 * 8.204303182449135e-4-t3 * t6 * t9 * t13 * t20 * 1.272243011263065e-3-t2 * t5 * t6 * t17 * t22 * 5.367504695157484e-3-t3 * t5 * t10 * t15 * t19 * 1.243656534471744e-5-t3 * t8 * t10 * t12 * t19 * 8.204303182449135e-4-t2 * t6 * t8 * t18 * t19 * 1.243656534471744e-5-t2 * t9 * t11 * t14 * t17 * 1.243656534471744e-5 + t3 * t5 * t12 * t16 * t17 * 7.125881776414093e-3 + t3 * t5 * t14 * t15 * t16 * 8.204303182449135e-4 + t3 * t6 * t9 * t13 * t22 * 6.554082237488641e-3-t3 * t8 * t12 * t14 * t16 * 1.243656534471744e-5 + t2 * t9 * t11 * t12 * t20 * 5.367504695157484e-3 + t2 * t9 * t13 * t14 * t17 * 1.243656534471744e-5 + t3 * t5 * t10 * t18 * t19 * 1.243656534471744e-5-t2 * t9 * t11 * t12 * t22 * 5.367504695157484e-3-t2 * t9 * t12 * t13 * t20 * 5.367504695157484e-3-t3 * t5 * t14 * t16 * t18 * 8.204303182449135e-4-t3 * t6 * t9 * t18 * t20 * 6.554082237488641e-3-t3 * t6 * t9 * t15 * t24 * 1.765933827532306e-3 + t2 * t9 * t11 * t17 * t19 * 8.204303182449135e-4 + t2 * t9 * t12 * t13 * t22 * 5.367504695157484e-3 + t3 * t5 * t15 * t16 * t19 * 1.243656534471744e-5 + t3 * t6 * t9 * t18 * t22 * 1.272243011263065e-3 + t3 * t8 * t10 * t17 * t20 * 5.367504695157484e-3 + t3 * t8 * t12 * t16 * t19 * 8.204303182449135e-4-t2 * t9 * t13 * t17 * t19 * 8.204303182449135e-4-t3 * t8 * t10 * t17 * t22 * 5.367504695157484e-3-t3 * t5 * t16 * t18 * t19 * 1.243656534471744e-5-t3 * t8 * t16 * t17 * t20 * 5.367504695157484e-3 + t3 * t8 * t16 * t17 * t22 * 5.367504695157484e-3-a4 * t3 * t5 * t6 * t9 * t12 * 2.006356734599796e-2 + a5 * t2 * t5 * t8 * t9 * t12 * 2.006356734599796e-2-a4 * t3 * t5 * t6 * t9 * t17 * 1.540556795982286e-1 + a5 * t2 * t5 * t8 * t9 * t17 * 1.540556795982286e-1 + a5 * t3 * t6 * t9 * t12 * t13 * 2.006356734599796e-2-a4 * t2 * t5 * t9 * t14 * t15 * 1.656261404313703e-1-a4 * t2 * t8 * t9 * t12 * t14 * 6.771241001216606e-2 + a5 * t3 * t6 * t9 * t12 * t14 * 1.354248200243321e-1-a4 * t2 * t5 * t9 * t14 * t18 * 1.656261404313703e-1-a5 * t2 * t6 * t8 * t14 * t17 * 6.771241001216606e-2 + a5 * t3 * t6 * t9 * t13 * t17 * 1.540556795982286e-1 + a4 * t2 * t5 * t9 * t15 * t19 * 6.771241001216606e-2-a4 * t2 * t8 * t9 * t12 * t19 * 1.656261404313703e-1 + a5 * t3 * t5 * t10 * t14 * t17 * 6.771241001216606e-2 + a5 * t3 * t6 * t9 * t12 * t19 * 3.312522808627406e-1-a5 * t2 * t9 * t11 * t14 * t15 * 1.656261404313703e-1 + a4 * t2 * t5 * t9 * t18 * t19 * 6.771241001216606e-2-a5 * t2 * t6 * t8 * t17 * t19 * 1.656261404313703e-1 + a5 * t2 * t9 * t13 * t14 * t15 * 1.656261404313703e-1 + a4 * t3 * t10 * t11 * t14 * t17 * 6.771241001216606e-2-a5 * t2 * t9 * t11 * t14 * t18 * 1.656261404313703e-1 + a5 * t3 * t5 * t10 * t17 * t19 * 1.656261404313703e-1-a5 * t3 * t5 * t14 * t16 * t17 * 6.771241001216606e-2 + a4 * t3 * t10 * t13 * t14 * t17 * 6.771241001216606e-2 + a5 * t2 * t9 * t11 * t15 * t19 * 6.771241001216606e-2 + a5 * t2 * t9 * t13 * t14 * t18 * 1.656261404313703e-1 + a7 * t3 * t6 * t9 * t15 * t21 * 1.249486888554935e-2-a5 * t2 * t9 * t13 * t15 * t19 * 6.771241001216606e-2 + a7 * t3 * t6 * t9 * t15 * t23 * 9.42222077621846e-4 + a4 * t3 * t10 * t11 * t17 * t19 * 1.656261404313703e-1 + a5 * t2 * t9 * t11 * t18 * t19 * 6.771241001216606e-2-a4 * t3 * t11 * t14 * t16 * t17 * 6.771241001216606e-2-a5 * t3 * t5 * t16 * t17 * t19 * 1.656261404313703e-1 + a4 * t3 * t10 * t13 * t17 * t19 * 1.656261404313703e-1-a5 * t2 * t9 * t13 * t18 * t19 * 6.771241001216606e-2-a4 * t3 * t13 * t14 * t16 * t17 * 6.771241001216606e-2-a4 * t3 * t11 * t16 * t17 * t19 * 1.656261404313703e-1-a4 * t3 * t13 * t16 * t17 * t19 * 1.656261404313703e-1-d3 * t2 * t5 * t9 * t12 * t14 * 6.771241001216606e-2-d5 * t2 * t5 * t6 * t14 * t17 * 6.771241001216606e-2-d3 * t2 * t5 * t9 * t12 * t19 * 1.656261404313703e-1 + d3 * t2 * t8 * t9 * t14 * t15 * 1.656261404313703e-1 + d5 * t3 * t6 * t9 * t14 * t15 * 3.312522808627406e-1-d3 * t2 * t6 * t11 * t14 * t17 * 6.771241001216606e-2-d5 * t2 * t9 * t11 * t12 * t14 * 6.771241001216606e-2 + d3 * t2 * t8 * t9 * t14 * t18 * 1.656261404313703e-1-d5 * t2 * t5 * t6 * t17 * t19 * 1.656261404313703e-1-d3 * t2 * t6 * t13 * t14 * t17 * 6.771241001216606e-2 + d5 * t2 * t9 * t12 * t13 * t14 * 6.771241001216606e-2 + d5 * t3 * t6 * t9 * t14 * t18 * 3.312522808627406e-1-d3 * t2 * t8 * t9 * t15 * t19 * 6.771241001216606e-2-d5 * t3 * t6 * t9 * t15 * t19 * 1.354248200243321e-1-d5 * t3 * t8 * t10 * t14 * t17 * 6.771241001216606e-2-d3 * t2 * t6 * t11 * t17 * t19 * 1.656261404313703e-1-d5 * t2 * t9 * t11 * t12 * t19 * 1.656261404313703e-1-d3 * t2 * t8 * t9 * t18 * t19 * 6.771241001216606e-2-d3 * t2 * t6 * t13 * t17 * t19 * 1.656261404313703e-1 + d5 * t2 * t9 * t12 * t13 * t19 * 1.656261404313703e-1-d5 * t3 * t6 * t9 * t18 * t19 * 1.354248200243321e-1-d5 * t3 * t8 * t10 * t17 * t19 * 1.656261404313703e-1 + d5 * t3 * t8 * t14 * t16 * t17 * 6.771241001216606e-2 + d5 * t3 * t8 * t16 * t17 * t19 * 1.656261404313703e-1-t3 * t5 * t6 * t8 * t9 * t12 * 1.22815866196153e-2-t3 * t5 * t6 * t8 * t9 * t17 * 1.54076417859336e-2 + t2 * t5 * t8 * t9 * t12 * t17 * 7.462333571535709e-3-t2 * t5 * t8 * t9 * t14 * t19 * 1.073500939031497e-2-t3 * t6 * t9 * t11 * t12 * t17 * 7.462333571535709e-3-t2 * t5 * t8 * t9 * t15 * t20 * 6.554082237488641e-3 + t2 * t5 * t8 * t9 * t15 * t22 * 1.272243011263065e-3 + t3 * t6 * t9 * t12 * t14 * t17 * 1.640860636489827e-3 + t2 * t5 * t6 * t12 * t14 * t24 * 1.185446863937387e-3-t2 * t5 * t6 * t14 * t17 * t19 * 7.826325248751706e-3-t2 * t5 * t6 * t12 * t14 * t25 * 1.185446863937387e-3 + t3 * t6 * t9 * t11 * t15 * t20 * 6.554082237488641e-3-t3 * t6 * t9 * t13 * t14 * t19 * 1.073500939031497e-2-t2 * t5 * t6 * t12 * t19 * t21 * 5.969399339641728e-4-t2 * t6 * t8 * t12 * t17 * t20 * 6.554082237488641e-3-t2 * t5 * t8 * t9 * t18 * t24 * 1.765933827532306e-3 + t2 * t6 * t8 * t14 * t15 * t21 * 5.969399339641728e-4-t3 * t6 * t9 * t11 * t15 * t22 * 1.272243011263065e-3 + t3 * t6 * t9 * t12 * t17 * t19 * 2.487313068943488e-5 + t2 * t5 * t6 * t12 * t19 * t23 * 1.758741834919224e-3 + t2 * t6 * t8 * t12 * t17 * t22 * 1.272243011263065e-3-t2 * t9 * t11 * t12 * t14 * t19 * 7.826325248751706e-3 + t3 * t5 * t10 * t12 * t17 * t20 * 6.554082237488641e-3-t2 * t6 * t8 * t14 * t15 * t23 * 1.758741834919224e-3-t3 * t5 * t10 * t14 * t15 * t21 * 5.969399339641728e-4 + t2 * t6 * t8 * t12 * t17 * t24 * 1.765933827532306e-3-t2 * t6 * t8 * t14 * t18 * t21 * 5.969399339641728e-4 + t2 * t9 * t12 * t13 * t14 * t19 * 7.826325248751706e-3-t3 * t5 * t10 * t12 * t17 * t22 * 1.272243011263065e-3-t3 * t6 * t9 * t14 * t18 * t19 * 1.073500939031497e-2 + t3 * t5 * t10 * t14 * t15 * t23 * 1.758741834919224e-3-t2 * t5 * t6 * t17 * t20 * t21 * 1.758741834919224e-3 + t2 * t5 * t8 * t9 * t22 * t25 * 1.765933827532306e-3 + t2 * t6 * t8 * t14 * t18 * t23 * 1.758741834919224e-3-t3 * t5 * t10 * t12 * t17 * t24 * 1.765933827532306e-3 + t3 * t5 * t10 * t14 * t18 * t21 * 5.969399339641728e-4 + t3 * t6 * t9 * t11 * t18 * t24 * 1.765933827532306e-3 + t3 * t8 * t10 * t12 * t14 * t24 * 1.185446863937387e-3-t3 * t8 * t10 * t14 * t17 * t19 * 7.826325248751706e-3-t3 * t8 * t10 * t12 * t14 * t25 * 1.185446863937387e-3-t2 * t5 * t6 * t17 * t20 * t23 * 5.969399339641728e-4 + t2 * t5 * t6 * t17 * t21 * t22 * 1.758741834919224e-3-t3 * t5 * t10 * t14 * t18 * t23 * 1.758741834919224e-3-t3 * t5 * t12 * t16 * t17 * t20 * 6.554082237488641e-3-t3 * t8 * t10 * t12 * t19 * t21 * 5.969399339641728e-4 + t2 * t6 * t8 * t15 * t19 * t24 * 1.185446863937387e-3 + t3 * t5 * t14 * t15 * t16 * t21 * 5.969399339641728e-4 + t2 * t5 * t6 * t17 * t22 * t23 * 5.969399339641728e-4-t2 * t6 * t8 * t15 * t19 * t25 * 1.185446863937387e-3-t2 * t9 * t11 * t12 * t20 * t21 * 1.758741834919224e-3 + t3 * t5 * t12 * t16 * t17 * t22 * 1.272243011263065e-3 + t3 * t8 * t10 * t12 * t19 * t23 * 1.758741834919224e-3-t3 * t5 * t10 * t15 * t19 * t24 * 1.185446863937387e-3-t3 * t5 * t14 * t15 * t16 * t23 * 1.758741834919224e-3-t2 * t6 * t8 * t18 * t19 * t24 * 1.185446863937387e-3-t2 * t9 * t11 * t12 * t20 * t23 * 5.969399339641728e-4 + t2 * t9 * t11 * t12 * t21 * t22 * 1.758741834919224e-3-t2 * t9 * t11 * t14 * t17 * t24 * 1.185446863937387e-3 + t2 * t9 * t12 * t13 * t20 * t21 * 1.758741834919224e-3 + t3 * t5 * t10 * t15 * t19 * t25 * 1.185446863937387e-3 + t3 * t5 * t12 * t16 * t17 * t24 * 1.765933827532306e-3-t3 * t5 * t14 * t16 * t18 * t21 * 5.969399339641728e-4 + t3 * t6 * t9 * t15 * t21 * t23 * 2.370893727874773e-3-t3 * t8 * t12 * t14 * t16 * t24 * 1.185446863937387e-3 + t3 * t8 * t14 * t16 * t17 * t19 * 7.826325248751706e-3 + t2 * t6 * t8 * t18 * t19 * t25 * 1.185446863937387e-3 + t2 * t9 * t11 * t14 * t17 * t25 * 1.185446863937387e-3 + t3 * t6 * t9 * t13 * t22 * t25 * 1.765933827532306e-3 + t3 * t8 * t12 * t14 * t16 * t25 * 1.185446863937387e-3 + t2 * t9 * t11 * t12 * t22 * t23 * 5.969399339641728e-4 + t2 * t9 * t11 * t17 * t19 * t21 * 5.969399339641728e-4 + t2 * t9 * t12 * t13 * t20 * t23 * 5.969399339641728e-4-t2 * t9 * t12 * t13 * t21 * t22 * 1.758741834919224e-3 + t2 * t9 * t13 * t14 * t17 * t24 * 1.185446863937387e-3 + t3 * t5 * t10 * t18 * t19 * t24 * 1.185446863937387e-3 + t3 * t5 * t14 * t16 * t18 * t23 * 1.758741834919224e-3-t3 * t8 * t10 * t17 * t20 * t21 * 1.758741834919224e-3 + t3 * t8 * t12 * t16 * t19 * t21 * 5.969399339641728e-4-t2 * t9 * t13 * t14 * t17 * t25 * 1.185446863937387e-3-t3 * t5 * t10 * t18 * t19 * t25 * 1.185446863937387e-3-t2 * t9 * t11 * t17 * t19 * t23 * 1.758741834919224e-3-t2 * t9 * t12 * t13 * t22 * t23 * 5.969399339641728e-4-t2 * t9 * t13 * t17 * t19 * t21 * 5.969399339641728e-4-t3 * t6 * t9 * t18 * t20 * t25 * 1.765933827532306e-3-t3 * t8 * t10 * t17 * t20 * t23 * 5.969399339641728e-4 + t3 * t8 * t10 * t17 * t21 * t22 * 1.758741834919224e-3-t3 * t8 * t12 * t16 * t19 * t23 * 1.758741834919224e-3 + t3 * t5 * t15 * t16 * t19 * t24 * 1.185446863937387e-3 + t2 * t9 * t13 * t17 * t19 * t23 * 1.758741834919224e-3-t3 * t5 * t15 * t16 * t19 * t25 * 1.185446863937387e-3 + t3 * t8 * t10 * t17 * t22 * t23 * 5.969399339641728e-4-t3 * t5 * t16 * t18 * t19 * t24 * 1.185446863937387e-3 + t3 * t8 * t16 * t17 * t20 * t21 * 1.758741834919224e-3 + t3 * t5 * t16 * t18 * t19 * t25 * 1.185446863937387e-3 + t3 * t8 * t16 * t17 * t20 * t23 * 5.969399339641728e-4-t3 * t8 * t16 * t17 * t21 * t22 * 1.758741834919224e-3-t3 * t8 * t16 * t17 * t22 * t23 * 5.969399339641728e-4 + a4 * t3 * t5 * t6 * t9 * t12 * t14 * 1.354248200243321e-1-a5 * t2 * t5 * t8 * t9 * t12 * t14 * 1.354248200243321e-1 + a4 * t3 * t5 * t6 * t9 * t12 * t19 * 3.312522808627406e-1-a4 * t3 * t6 * t8 * t9 * t14 * t15 * 3.312522808627406e-1-a5 * t2 * t5 * t8 * t9 * t12 * t19 * 3.312522808627406e-1-a4 * t3 * t6 * t8 * t9 * t14 * t18 * 3.312522808627406e-1-a5 * t3 * t6 * t9 * t12 * t13 * t14 * 1.354248200243321e-1 + a4 * t3 * t6 * t8 * t9 * t15 * t19 * 1.354248200243321e-1 + a4 * t3 * t6 * t8 * t9 * t18 * t19 * 1.354248200243321e-1-a5 * t3 * t6 * t9 * t12 * t13 * t19 * 3.312522808627406e-1-a7 * t2 * t5 * t6 * t12 * t14 * t21 * 4.71111038810923e-4 + a7 * t2 * t5 * t6 * t12 * t14 * t23 * 6.247434442774674e-3-a4 * t2 * t5 * t9 * t14 * t15 * t21 * 6.247434442774674e-3 + a7 * t2 * t5 * t8 * t9 * t18 * t21 * 1.249486888554935e-2-a4 * t2 * t5 * t9 * t14 * t15 * t23 * 4.71111038810923e-4 + a7 * t2 * t5 * t8 * t9 * t18 * t23 * 9.42222077621846e-4-a7 * t2 * t5 * t8 * t9 * t20 * t21 * 1.249486888554935e-2-a4 * t2 * t5 * t9 * t14 * t18 * t21 * 6.247434442774674e-3-a7 * t2 * t6 * t8 * t12 * t17 * t21 * 1.249486888554935e-2-a5 * t2 * t6 * t8 * t12 * t20 * t21 * 4.71111038810923e-4-a7 * t2 * t5 * t8 * t9 * t20 * t23 * 9.42222077621846e-4-a4 * t2 * t5 * t9 * t14 * t18 * t23 * 4.71111038810923e-4-a4 * t2 * t8 * t9 * t12 * t19 * t21 * 6.247434442774674e-3 + a5 * t3 * t6 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-a7 * t2 * t6 * t8 * t12 * t17 * t23 * 9.42222077621846e-4 + a7 * t3 * t5 * t10 * t12 * t17 * t21 * 1.249486888554935e-2-a7 * t3 * t6 * t9 * t11 * t18 * t21 * 1.249486888554935e-2-a7 * t3 * t8 * t10 * t12 * t14 * t21 * 4.71111038810923e-4 + a5 * t2 * t6 * t8 * t12 * t20 * t23 * 6.247434442774674e-3-a5 * t2 * t6 * t8 * t12 * t21 * t22 * 4.71111038810923e-4 + a5 * t3 * t5 * t10 * t12 * t20 * t21 * 4.71111038810923e-4-a4 * t2 * t8 * t9 * t12 * t19 * t23 * 4.71111038810923e-4-a5 * t2 * t9 * t11 * t14 * t15 * t21 * 6.247434442774674e-3 + a5 * t3 * t6 * t9 * t12 * t19 * t23 * 9.42222077621846e-4 + a7 * t3 * t5 * t10 * t12 * t17 * t23 * 9.42222077621846e-4-a7 * t3 * t6 * t9 * t11 * t18 * t23 * 9.42222077621846e-4 + a7 * t3 * t8 * t10 * t12 * t14 * t23 * 6.247434442774674e-3 + a5 * t2 * t6 * t8 * t12 * t22 * t23 * 6.247434442774674e-3-a5 * t2 * t6 * t8 * t17 * t19 * t21 * 6.247434442774674e-3-a5 * t3 * t5 * t10 * t12 * t20 * t23 * 6.247434442774674e-3 + a5 * t3 * t5 * t10 * t12 * t21 * t22 * 4.71111038810923e-4-a7 * t2 * t6 * t8 * t15 * t19 * t21 * 4.71111038810923e-4-a5 * t2 * t9 * t11 * t14 * t15 * t23 * 4.71111038810923e-4 + a5 * t2 * t9 * t13 * t14 * t15 * t21 * 6.247434442774674e-3-a7 * t3 * t6 * t9 * t13 * t20 * t21 * 1.249486888554935e-2-a5 * t2 * t6 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-a5 * t2 * t9 * t11 * t14 * t18 * t21 * 6.247434442774674e-3-a5 * t3 * t5 * t10 * t12 * t22 * t23 * 6.247434442774674e-3 + a5 * t3 * t5 * t10 * t17 * t19 * t21 * 6.247434442774674e-3 + a7 * t2 * t6 * t8 * t15 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t5 * t10 * t15 * t19 * t21 * 4.71111038810923e-4 + a4 * t2 * t8 * t9 * t17 * t20 * t21 * 4.71111038810923e-4 + a4 * t3 * t10 * t11 * t12 * t20 * t21 * 4.71111038810923e-4 + a5 * t2 * t9 * t13 * t14 * t15 * t23 * 4.71111038810923e-4-a5 * t3 * t6 * t9 * t17 * t20 * t21 * 9.42222077621846e-4 + a7 * t2 * t6 * t8 * t18 * t19 * t21 * 4.71111038810923e-4 + a7 * t2 * t9 * t11 * t14 * t17 * t21 * 4.71111038810923e-4-a7 * t3 * t5 * t12 * t16 * t17 * t21 * 1.249486888554935e-2-a7 * t3 * t6 * t9 * t13 * t20 * t23 * 9.42222077621846e-4 + a7 * t3 * t8 * t12 * t14 * t16 * t21 * 4.71111038810923e-4-a5 * t2 * t9 * t11 * t14 * t18 * t23 * 4.71111038810923e-4 + a5 * t2 * t9 * t13 * t14 * t18 * t21 * 6.247434442774674e-3 + a5 * t3 * t5 * t10 * t17 * t19 * t23 * 4.71111038810923e-4-a5 * t3 * t5 * t12 * t16 * t20 * t21 * 4.71111038810923e-4-a7 * t3 * t5 * t10 * t15 * t19 * t23 * 6.247434442774674e-3-a4 * t2 * t8 * t9 * t17 * t20 * t23 * 6.247434442774674e-3 + a4 * t2 * t8 * t9 * t17 * t21 * t22 * 4.71111038810923e-4-a4 * t3 * t10 * t11 * t12 * t20 * t23 * 6.247434442774674e-3 + a4 * t3 * t10 * t11 * t12 * t21 * t22 * 4.71111038810923e-4 + a4 * t3 * t10 * t12 * t13 * t20 * t21 * 4.71111038810923e-4 + a5 * t3 * t6 * t9 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t3 * t6 * t9 * t17 * t21 * t22 * 9.42222077621846e-4-a7 * t2 * t6 * t8 * t18 * t19 * t23 * 6.247434442774674e-3-a7 * t2 * t9 * t11 * t14 * t17 * t23 * 6.247434442774674e-3-a7 * t2 * t9 * t13 * t14 * t17 * t21 * 4.71111038810923e-4-a7 * t3 * t5 * t10 * t18 * t19 * t21 * 4.71111038810923e-4-a7 * t3 * t5 * t12 * t16 * t17 * t23 * 9.42222077621846e-4-a7 * t3 * t8 * t12 * t14 * t16 * t23 * 6.247434442774674e-3 + a5 * t2 * t9 * t13 * t14 * t18 * t23 * 4.71111038810923e-4 + a5 * t3 * t5 * t12 * t16 * t20 * t23 * 6.247434442774674e-3-a5 * t3 * t5 * t12 * t16 * t21 * t22 * 4.71111038810923e-4-a4 * t2 * t8 * t9 * t17 * t22 * t23 * 6.247434442774674e-3-a4 * t3 * t10 * t11 * t12 * t22 * t23 * 6.247434442774674e-3 + a4 * t3 * t10 * t11 * t17 * t19 * t21 * 6.247434442774674e-3-a4 * t3 * t10 * t12 * t13 * t20 * t23 * 6.247434442774674e-3 + a4 * t3 * t10 * t12 * t13 * t21 * t22 * 4.71111038810923e-4 + a5 * t3 * t6 * t9 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t2 * t9 * t13 * t14 * t17 * t23 * 6.247434442774674e-3 + a7 * t3 * t5 * t10 * t18 * t19 * t23 * 6.247434442774674e-3 + a5 * t3 * t5 * t12 * t16 * t22 * t23 * 6.247434442774674e-3-a5 * t3 * t5 * t16 * t17 * t19 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t15 * t16 * t19 * t21 * 4.71111038810923e-4 + a7 * t3 * t6 * t9 * t18 * t21 * t22 * 1.249486888554935e-2 + a4 * t3 * t10 * t11 * t17 * t19 * t23 * 4.71111038810923e-4-a4 * t3 * t10 * t12 * t13 * t22 * t23 * 6.247434442774674e-3 + a4 * t3 * t10 * t13 * t17 * t19 * t21 * 6.247434442774674e-3-a4 * t3 * t11 * t12 * t16 * t20 * t21 * 4.71111038810923e-4-a5 * t3 * t5 * t16 * t17 * t19 * t23 * 4.71111038810923e-4 + a7 * t3 * t5 * t15 * t16 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t6 * t9 * t18 * t22 * t23 * 9.42222077621846e-4 + a4 * t3 * t10 * t13 * t17 * t19 * t23 * 4.71111038810923e-4 + a4 * t3 * t11 * t12 * t16 * t20 * t23 * 6.247434442774674e-3-a4 * t3 * t11 * t12 * t16 * t21 * t22 * 4.71111038810923e-4-a4 * t3 * t12 * t13 * t16 * t20 * t21 * 4.71111038810923e-4 + a7 * t3 * t5 * t16 * t18 * t19 * t21 * 4.71111038810923e-4 + a4 * t3 * t11 * t12 * t16 * t22 * t23 * 6.247434442774674e-3-a4 * t3 * t11 * t16 * t17 * t19 * t21 * 6.247434442774674e-3 + a4 * t3 * t12 * t13 * t16 * t20 * t23 * 6.247434442774674e-3-a4 * t3 * t12 * t13 * t16 * t21 * t22 * 4.71111038810923e-4-a7 * t3 * t5 * t16 * t18 * t19 * t23 * 6.247434442774674e-3-a4 * t3 * t11 * t16 * t17 * t19 * t23 * 4.71111038810923e-4 + a4 * t3 * t12 * t13 * t16 * t22 * t23 * 6.247434442774674e-3-a4 * t3 * t13 * t16 * t17 * t19 * t21 * 6.247434442774674e-3-a4 * t3 * t13 * t16 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t5 * t6 * t8 * t9 * t12 * 2.006356734599796e-2 + d5 * t3 * t5 * t6 * t8 * t9 * t17 * 1.540556795982286e-1 + d5 * t2 * t5 * t8 * t9 * t14 * t15 * 3.312522808627406e-1 + d5 * t2 * t5 * t8 * t9 * t14 * t18 * 3.312522808627406e-1-d5 * t2 * t5 * t8 * t9 * t15 * t19 * 1.354248200243321e-1-d5 * t3 * t6 * t9 * t11 * t14 * t15 * 3.312522808627406e-1-d5 * t2 * t5 * t8 * t9 * t18 * t19 * 1.354248200243321e-1-d5 * t3 * t6 * t9 * t11 * t14 * t18 * 3.312522808627406e-1 + d5 * t3 * t6 * t9 * t11 * t15 * t19 * 1.354248200243321e-1-d3 * t2 * t5 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-d5 * t2 * t5 * t6 * t12 * t20 * t21 * 4.71111038810923e-4 + d5 * t3 * t6 * t9 * t11 * t18 * t19 * 1.354248200243321e-1 + d3 * t2 * t8 * t9 * t14 * t15 * t21 * 6.247434442774674e-3-d3 * t2 * t5 * t9 * t12 * t19 * t23 * 4.71111038810923e-4 + d5 * t2 * t5 * t6 * t12 * t20 * t23 * 6.247434442774674e-3-d5 * t2 * t5 * t6 * t12 * t21 * t22 * 4.71111038810923e-4 + d5 * t3 * t6 * t9 * t14 * t15 * t21 * 1.249486888554935e-2 + d3 * t2 * t8 * t9 * t14 * t15 * t23 * 4.71111038810923e-4-d3 * t2 * t6 * t11 * t12 * t20 * t21 * 4.71111038810923e-4 + d3 * t2 * t8 * t9 * t14 * t18 * t21 * 6.247434442774674e-3 + d5 * t2 * t5 * t6 * t12 * t22 * t23 * 6.247434442774674e-3-d5 * t2 * t5 * t6 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t6 * t9 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t3 * t6 * t9 * t14 * t18 * t21 * 1.249486888554935e-2 + d3 * t2 * t5 * t9 * t17 * t20 * t21 * 4.71111038810923e-4 + d3 * t2 * t6 * t11 * t12 * t20 * t23 * 6.247434442774674e-3-d3 * t2 * t6 * t11 * t12 * t21 * t22 * 4.71111038810923e-4-d3 * t2 * t6 * t12 * t13 * t20 * t21 * 4.71111038810923e-4 + d3 * t2 * t8 * t9 * t14 * t18 * t23 * 4.71111038810923e-4-d5 * t2 * t5 * t6 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t6 * t9 * t14 * t18 * t23 * 9.42222077621846e-4-d3 * t2 * t5 * t9 * t17 * t20 * t23 * 6.247434442774674e-3 + d3 * t2 * t5 * t9 * t17 * t21 * t22 * 4.71111038810923e-4 + d3 * t2 * t6 * t11 * t12 * t22 * t23 * 6.247434442774674e-3-d3 * t2 * t6 * t11 * t17 * t19 * t21 * 6.247434442774674e-3 + d3 * t2 * t6 * t12 * t13 * t20 * t23 * 6.247434442774674e-3-d3 * t2 * t6 * t12 * t13 * t21 * t22 * 4.71111038810923e-4-d5 * t2 * t9 * t11 * t12 * t19 * t21 * 6.247434442774674e-3-d5 * t3 * t8 * t10 * t12 * t20 * t21 * 4.71111038810923e-4-d3 * t2 * t5 * t9 * t17 * t22 * t23 * 6.247434442774674e-3-d3 * t2 * t6 * t11 * t17 * t19 * t23 * 4.71111038810923e-4 + d3 * t2 * t6 * t12 * t13 * t22 * t23 * 6.247434442774674e-3-d3 * t2 * t6 * t13 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t2 * t9 * t11 * t12 * t19 * t23 * 4.71111038810923e-4 + d5 * t2 * t9 * t12 * t13 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t8 * t10 * t12 * t20 * t23 * 6.247434442774674e-3-d5 * t3 * t8 * t10 * t12 * t21 * t22 * 4.71111038810923e-4-d3 * t2 * t6 * t13 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t2 * t9 * t12 * t13 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t8 * t10 * t12 * t22 * t23 * 6.247434442774674e-3-d5 * t3 * t8 * t10 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t2 * t9 * t11 * t17 * t20 * t21 * 4.71111038810923e-4-d5 * t3 * t8 * t10 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t8 * t12 * t16 * t20 * t21 * 4.71111038810923e-4-d5 * t2 * t9 * t11 * t17 * t20 * t23 * 6.247434442774674e-3 + d5 * t2 * t9 * t11 * t17 * t21 * t22 * 4.71111038810923e-4-d5 * t2 * t9 * t13 * t17 * t20 * t21 * 4.71111038810923e-4-d5 * t3 * t8 * t12 * t16 * t20 * t23 * 6.247434442774674e-3 + d5 * t3 * t8 * t12 * t16 * t21 * t22 * 4.71111038810923e-4-d5 * t2 * t9 * t11 * t17 * t22 * t23 * 6.247434442774674e-3 + d5 * t2 * t9 * t13 * t17 * t20 * t23 * 6.247434442774674e-3-d5 * t2 * t9 * t13 * t17 * t21 * t22 * 4.71111038810923e-4-d5 * t3 * t8 * t12 * t16 * t22 * t23 * 6.247434442774674e-3 + d5 * t3 * t8 * t16 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t2 * t9 * t13 * t17 * t22 * t23 * 6.247434442774674e-3 + d5 * t3 * t8 * t16 * t17 * t19 * t23 * 4.71111038810923e-4-t3 * t5 * t6 * t8 * t9 * t14 * t17 * 2.487313068943488e-5 + t3 * t5 * t6 * t8 * t9 * t12 * t20 * 1.073500939031497e-2-t3 * t5 * t6 * t8 * t9 * t12 * t22 * 1.073500939031497e-2-t2 * t5 * t8 * t9 * t12 * t14 * t17 * 1.640860636489827e-3 + t3 * t5 * t6 * t8 * t9 * t17 * t19 * 1.640860636489827e-3-t2 * t5 * t8 * t9 * t12 * t17 * t19 * 2.487313068943488e-5-t2 * t5 * t8 * t9 * t14 * t15 * t19 * 1.073500939031497e-2 + t3 * t6 * t9 * t11 * t12 * t14 * t17 * 1.640860636489827e-3 + t3 * t6 * t9 * t11 * t12 * t17 * t19 * 2.487313068943488e-5 + t3 * t6 * t9 * t11 * t14 * t15 * t19 * 1.073500939031497e-2 + t2 * t5 * t8 * t9 * t14 * t19 * t21 * 3.517483669838449e-3-t2 * t6 * t8 * t12 * t14 * t17 * t19 * 1.073500939031497e-2 + t2 * t5 * t8 * t9 * t14 * t19 * t23 * 1.193879867928346e-3 + t3 * t5 * t10 * t12 * t14 * t17 * t19 * 1.073500939031497e-2 + t3 * t6 * t9 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t2 * t5 * t6 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t2 * t5 * t8 * t9 * t15 * t20 * t25 * 1.765933827532306e-3-t3 * t6 * t9 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t3 * t6 * t9 * t13 * t14 * t19 * t21 * 3.517483669838449e-3 + t2 * t5 * t8 * t9 * t18 * t21 * t23 * 2.370893727874773e-3-t3 * t5 * t12 * t14 * t16 * t17 * t19 * 1.073500939031497e-2 + t3 * t6 * t9 * t13 * t14 * t19 * t23 * 1.193879867928346e-3-t2 * t5 * t6 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t2 * t6 * t8 * t12 * t17 * t21 * t23 * 2.370893727874773e-3 + t3 * t6 * t9 * t11 * t15 * t20 * t25 * 1.765933827532306e-3 + t2 * t5 * t8 * t9 * t21 * t22 * t23 * 2.370893727874773e-3-t2 * t6 * t8 * t12 * t17 * t20 * t25 * 1.765933827532306e-3 + t3 * t6 * t9 * t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t3 * t6 * t9 * t14 * t18 * t19 * t21 * 3.517483669838449e-3 + t3 * t5 * t10 * t12 * t17 * t21 * t23 * 2.370893727874773e-3-t3 * t6 * t9 * t11 * t18 * t21 * t23 * 2.370893727874773e-3-t3 * t6 * t9 * t12 * t17 * t19 * t25 * 2.370893727874773e-3 + t3 * t8 * t10 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t2 * t9 * t11 * t12 * t14 * t19 * t25 * 1.765933827532306e-3 + t3 * t5 * t10 * t12 * t17 * t20 * t25 * 1.765933827532306e-3 + t3 * t6 * t9 * t14 * t18 * t19 * t23 * 1.193879867928346e-3 + t2 * t6 * t8 * t15 * t19 * t21 * t23 * 1.765933827532306e-3 + t2 * t9 * t12 * t13 * t14 * t19 * t25 * 1.765933827532306e-3-t3 * t5 * t10 * t15 * t19 * t21 * t23 * 1.765933827532306e-3-t3 * t8 * t10 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t2 * t6 * t8 * t18 * t19 * t21 * t23 * 1.765933827532306e-3-t2 * t9 * t11 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-t3 * t5 * t12 * t16 * t17 * t21 * t23 * 2.370893727874773e-3 + t3 * t6 * t9 * t13 * t21 * t22 * t23 * 2.370893727874773e-3-t3 * t8 * t12 * t14 * t16 * t21 * t23 * 1.765933827532306e-3-t3 * t5 * t12 * t16 * t17 * t20 * t25 * 1.765933827532306e-3 + t2 * t9 * t13 * t14 * t17 * t21 * t23 * 1.765933827532306e-3 + t3 * t5 * t10 * t18 * t19 * t21 * t23 * 1.765933827532306e-3-t3 * t6 * t9 * t18 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t5 * t15 * t16 * t19 * t21 * t23 * 1.765933827532306e-3 + t3 * t8 * t14 * t16 * t17 * t19 * t25 * 1.765933827532306e-3-t3 * t5 * t16 * t18 * t19 * t21 * t23 * 1.765933827532306e-3-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t15 * 3.312522808627406e-1-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t18 * 3.312522808627406e-1 + a5 * t3 * t5 * t6 * t8 * t9 * t15 * t19 * 1.354248200243321e-1 + a5 * t3 * t5 * t6 * t8 * t9 * t18 * t19 * 1.354248200243321e-1 + a4 * t3 * t5 * t6 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-a4 * t3 * t6 * t8 * t9 * t14 * t15 * t21 * 1.249486888554935e-2 + a4 * t3 * t5 * t6 * t9 * t12 * t19 * t23 * 9.42222077621846e-4-a5 * t2 * t5 * t8 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-a4 * t3 * t6 * t8 * t9 * t14 * t15 * t23 * 9.42222077621846e-4-a4 * t3 * t6 * t8 * t9 * t14 * t18 * t21 * 1.249486888554935e-2-a5 * t2 * t5 * t8 * t9 * t12 * t19 * t23 * 9.42222077621846e-4-a4 * t3 * t5 * t6 * t9 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t3 * t6 * t8 * t9 * t14 * t18 * t23 * 9.42222077621846e-4 + a4 * t3 * t5 * t6 * t9 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t3 * t5 * t6 * t9 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t2 * t5 * t8 * t9 * t17 * t20 * t21 * 9.42222077621846e-4-a5 * t3 * t6 * t9 * t12 * t13 * t19 * t21 * 1.249486888554935e-2 + a4 * t3 * t5 * t6 * t9 * t17 * t22 * t23 * 1.249486888554935e-2-a5 * t2 * t5 * t8 * t9 * t17 * t20 * t23 * 1.249486888554935e-2 + a5 * t2 * t5 * t8 * t9 * t17 * t21 * t22 * 9.42222077621846e-4 + a7 * t2 * t5 * t8 * t9 * t15 * t21 * t22 * 1.249486888554935e-2-a5 * t3 * t6 * t9 * t12 * t13 * t19 * t23 * 9.42222077621846e-4-a5 * t2 * t5 * t8 * t9 * t17 * t22 * t23 * 1.249486888554935e-2-a7 * t2 * t5 * t6 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t2 * t5 * t8 * t9 * t15 * t22 * t23 * 9.42222077621846e-4-a7 * t2 * t5 * t6 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + a5 * t3 * t6 * t9 * t13 * t17 * t20 * t21 * 9.42222077621846e-4-a7 * t3 * t6 * t9 * t11 * t15 * t21 * t22 * 1.249486888554935e-2-a7 * t3 * t6 * t9 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t2 * t6 * t8 * t12 * t17 * t21 * t22 * 1.249486888554935e-2-a7 * t2 * t9 * t11 * t12 * t14 * t19 * t21 * 1.249486888554935e-2-a5 * t3 * t6 * t9 * t13 * t17 * t20 * t23 * 1.249486888554935e-2 + a5 * t3 * t6 * t9 * t13 * t17 * t21 * t22 * 9.42222077621846e-4-a7 * t3 * t6 * t9 * t11 * t15 * t22 * t23 * 9.42222077621846e-4 + a7 * t3 * t6 * t9 * t12 * t17 * t19 * t23 * 1.249486888554935e-2 + a7 * t2 * t6 * t8 * t12 * t17 * t22 * t23 * 9.42222077621846e-4-a7 * t2 * t9 * t11 * t12 * t14 * t19 * t23 * 9.42222077621846e-4 + a7 * t2 * t9 * t12 * t13 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t3 * t5 * t10 * t12 * t17 * t21 * t22 * 1.249486888554935e-2-a5 * t3 * t6 * t9 * t13 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t2 * t9 * t12 * t13 * t14 * t19 * t23 * 9.42222077621846e-4-a7 * t3 * t5 * t10 * t12 * t17 * t22 * t23 * 9.42222077621846e-4-a7 * t3 * t8 * t10 * t14 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t3 * t8 * t10 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t3 * t5 * t12 * t16 * t17 * t21 * t22 * 1.249486888554935e-2 + a7 * t3 * t5 * t12 * t16 * t17 * t22 * t23 * 9.42222077621846e-4 + a7 * t3 * t8 * t14 * t16 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t3 * t8 * t14 * t16 * t17 * t19 * t23 * 9.42222077621846e-4-d5 * t3 * t5 * t6 * t8 * t9 * t12 * t14 * 1.354248200243321e-1-d5 * t3 * t5 * t6 * t8 * t9 * t12 * t19 * 3.312522808627406e-1 + d5 * t2 * t5 * t8 * t9 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t2 * t5 * t8 * t9 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t2 * t5 * t8 * t9 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t2 * t5 * t8 * t9 * t14 * t18 * t23 * 9.42222077621846e-4-d5 * t3 * t6 * t9 * t11 * t14 * t15 * t21 * 1.249486888554935e-2-d5 * t3 * t6 * t9 * t11 * t14 * t15 * t23 * 9.42222077621846e-4-d5 * t3 * t6 * t9 * t11 * t14 * t18 * t21 * 1.249486888554935e-2-d5 * t3 * t6 * t9 * t11 * t14 * t18 * t23 * 9.42222077621846e-4-t3 * t5 * t6 * t8 * t9 * t12 * t14 * t19 * 1.565265049750341e-2-t3 * t5 * t6 * t8 * t9 * t12 * t20 * t21 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t9 * t12 * t20 * t23 * 1.193879867928346e-3 + t3 * t5 * t6 * t8 * t9 * t12 * t21 * t22 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t9 * t14 * t17 * t24 * 2.370893727874773e-3 + t3 * t5 * t6 * t8 * t9 * t14 * t17 * t25 * 2.370893727874773e-3-t2 * t5 * t8 * t9 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t3 * t5 * t6 * t8 * t9 * t12 * t22 * t23 * 1.193879867928346e-3 + t3 * t5 * t6 * t8 * t9 * t17 * t19 * t21 * 1.193879867928346e-3 + t2 * t5 * t8 * t9 * t12 * t14 * t17 * t23 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t9 * t17 * t19 * t23 * 3.517483669838449e-3 + t2 * t5 * t8 * t9 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t3 * t6 * t9 * t11 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t2 * t5 * t8 * t9 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t3 * t6 * t9 * t11 * t12 * t14 * t17 * t23 * 3.517483669838449e-3-t2 * t5 * t8 * t9 * t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t2 * t5 * t8 * t9 * t12 * t17 * t19 * t25 * 2.370893727874773e-3-t3 * t6 * t9 * t11 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t2 * t6 * t8 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3-t3 * t6 * t9 * t11 * t14 * t15 * t19 * t23 * 1.193879867928346e-3 + t2 * t6 * t8 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3-t3 * t5 * t10 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3 + t3 * t6 * t9 * t11 * t12 * t17 * t19 * t24 * 2.370893727874773e-3-t3 * t6 * t9 * t11 * t12 * t17 * t19 * t25 * 2.370893727874773e-3-t2 * t5 * t8 * t9 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-t3 * t5 * t10 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3-t2 * t5 * t6 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3 + t3 * t5 * t12 * t14 * t16 * t17 * t19 * t21 * 3.517483669838449e-3 + t3 * t6 * t9 * t11 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-t2 * t6 * t8 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t5 * t12 * t14 * t16 * t17 * t19 * t23 * 1.193879867928346e-3 + t3 * t6 * t9 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3-t2 * t9 * t11 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3 + t3 * t5 * t10 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3 + t2 * t9 * t12 * t13 * t14 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t8 * t10 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t5 * t12 * t16 * t17 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t8 * t14 * t16 * t17 * t19 * t21 * t23 * 2.370893727874773e-3-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t15 * t21 * 1.249486888554935e-2-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t15 * t23 * 9.42222077621846e-4-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t18 * t21 * 1.249486888554935e-2 + a7 * t3 * t5 * t6 * t8 * t9 * t14 * t17 * t21 * 9.42222077621846e-4-a5 * t3 * t5 * t6 * t8 * t9 * t14 * t18 * t23 * 9.42222077621846e-4-a7 * t3 * t5 * t6 * t8 * t9 * t14 * t17 * t23 * 1.249486888554935e-2 + a7 * t2 * t5 * t8 * t9 * t12 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t2 * t5 * t8 * t9 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-a7 * t3 * t6 * t9 * t11 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t3 * t6 * t9 * t11 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-d5 * t3 * t5 * t6 * t8 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-d5 * t3 * t5 * t6 * t8 * t9 * t12 * t19 * t23 * 9.42222077621846e-4 + d5 * t3 * t5 * t6 * t8 * t9 * t17 * t20 * t21 * 9.42222077621846e-4-d5 * t3 * t5 * t6 * t8 * t9 * t17 * t20 * t23 * 1.249486888554935e-2 + d5 * t3 * t5 * t6 * t8 * t9 * t17 * t21 * t22 * 9.42222077621846e-4-d5 * t3 * t5 * t6 * t8 * t9 * t17 * t22 * t23 * 1.249486888554935e-2-t3 * t5 * t6 * t8 * t9 * t12 * t14 * t19 * t25 * 3.531867655064613e-3-t3 * t5 * t6 * t8 * t9 * t14 * t17 * t21 * t23 * 3.531867655064613e-3-t2 * t5 * t8 * t9 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + t3 * t6 * t9 * t11 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3-a7 * t3 * t5 * t6 * t8 * t9 * t12 * t14 * t19 * t21 * 2.49897377710987e-2-a7 * t3 * t5 * t6 * t8 * t9 * t12 * t14 * t19 * t23 * 1.884444155243692e-3-t3 * t5 * t6 * t8 * t9 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3, t10 * 6.369918696374153e-1 + t16 * 1.051885904832896e-2 + d3 * t5 * 3.435539633871837-d3 * t8 * 9.772685114474167e-1-t6 * t9 * 4.439815888138549e-4 + t10 * t15 * 2.505201111020351e-2 + t11 * t16 * 5.324557667216729e-1 + t10 * t18 * 3.21778928866176e-2 + t13 * t16 * 1.807861861545219e-2-a4 * t5 * t10 * 9.772685114474167e-1-a4 * t8 * t10 * 3.435539633871837-a5 * t10 * t12 * 2.006356734599796e-2-a5 * t10 * t17 * 1.540556795982286e-1-t5 * t6 * t9 * 9.541478495916907e-3-t6 * t8 * t9 * 5.472851427793527e-3-t5 * t8 * t16 * 3.003448646049658e-1-t10 * t12 * t17 * 7.462333571535709e-3 + t11 * t15 * t16 * 3.21778928866176e-2 + t11 * t16 * t18 * 2.505201111020351e-2-t10 * t18 * t20 * 6.554082237488641e-3-t10 * t15 * t24 * 1.765933827532306e-3 + t13 * t16 * t20 * 1.272243011263065e-3 + t10 * t18 * t22 * 1.272243011263065e-3-t13 * t16 * t22 * 6.554082237488641e-3-a4 * t5 * t10 * t12 * 2.006356734599796e-2-a4 * t5 * t10 * t17 * 1.540556795982286e-1 + a5 * t10 * t12 * t14 * 1.354248200243321e-1 + a5 * t10 * t12 * t19 * 3.312522808627406e-1-a5 * t12 * t13 * t16 * 2.006356734599796e-2-a5 * t13 * t16 * t17 * 1.540556795982286e-1 + a7 * t10 * t15 * t21 * 1.249486888554935e-2 + a7 * t10 * t15 * t23 * 9.42222077621846e-4-d3 * t8 * t10 * t12 * 2.006356734599796e-2-d3 * t8 * t10 * t17 * 1.540556795982286e-1-d3 * t8 * t12 * t16 * 2.006356734599796e-2-d3 * t8 * t16 * t17 * 1.540556795982286e-1 + d5 * t10 * t14 * t15 * 3.312522808627406e-1 + d5 * t10 * t14 * t18 * 3.312522808627406e-1-d5 * t10 * t15 * t19 * 1.354248200243321e-1-d5 * t10 * t18 * t19 * 1.354248200243321e-1-t5 * t6 * t9 * t15 * 7.462333571535709e-3-t6 * t8 * t9 * t12 * 1.54076417859336e-2 + t5 * t6 * t9 * t18 * 7.462333571535709e-3 + t6 * t8 * t9 * t17 * 1.22815866196153e-2 + t5 * t8 * t12 * t16 * 1.22815866196153e-2 + t5 * t8 * t16 * t17 * 1.54076417859336e-2 + t10 * t12 * t14 * t17 * 1.640860636489827e-3 + t11 * t12 * t16 * t17 * 7.462333571535709e-3 + t10 * t12 * t17 * t19 * 2.487313068943488e-5-t10 * t14 * t18 * t19 * 1.073500939031497e-2-t11 * t15 * t16 * t20 * 6.554082237488641e-3 + t13 * t14 * t16 * t19 * 1.073500939031497e-2 + t11 * t15 * t16 * t22 * 1.272243011263065e-3 + t10 * t15 * t21 * t23 * 2.370893727874773e-3-t11 * t16 * t18 * t24 * 1.765933827532306e-3-t10 * t18 * t20 * t25 * 1.765933827532306e-3-t13 * t16 * t22 * t25 * 1.765933827532306e-3-a5 * t5 * t6 * t9 * t12 * 1.540556795982286e-1-a4 * t6 * t9 * t11 * t12 * 1.540556795982286e-1 + a5 * t5 * t6 * t9 * t17 * 2.006356734599796e-2-a4 * t6 * t9 * t12 * t13 * 1.540556795982286e-1 + a4 * t5 * t10 * t12 * t14 * 1.354248200243321e-1 + a4 * t6 * t9 * t11 * t17 * 2.006356734599796e-2 + a4 * t6 * t9 * t13 * t17 * 2.006356734599796e-2 + a4 * t5 * t10 * t12 * t19 * 3.312522808627406e-1-a4 * t8 * t10 * t14 * t15 * 3.312522808627406e-1-a4 * t8 * t10 * t14 * t18 * 3.312522808627406e-1 + a4 * t8 * t10 * t15 * t19 * 1.354248200243321e-1 + a4 * t8 * t10 * t18 * t19 * 1.354248200243321e-1 + a5 * t12 * t13 * t14 * t16 * 1.354248200243321e-1 + a5 * t12 * t13 * t16 * t19 * 3.312522808627406e-1 + a5 * t10 * t12 * t19 * t21 * 1.249486888554935e-2 + a5 * t10 * t12 * t19 * t23 * 9.42222077621846e-4-a5 * t10 * t17 * t20 * t21 * 9.42222077621846e-4 + a7 * t11 * t16 * t18 * t21 * 1.249486888554935e-2 + a5 * t10 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t10 * t17 * t21 * t22 * 9.42222077621846e-4 + a7 * t11 * t16 * t18 * t23 * 9.42222077621846e-4 + a5 * t10 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t13 * t16 * t20 * t21 * 1.249486888554935e-2 + a7 * t10 * t18 * t21 * t22 * 1.249486888554935e-2 + a7 * t13 * t16 * t20 * t23 * 9.42222077621846e-4 + a7 * t10 * t18 * t22 * t23 * 9.42222077621846e-4 + d5 * t6 * t8 * t9 * t12 * 1.540556795982286e-1-d5 * t6 * t8 * t9 * t17 * 2.006356734599796e-2-d5 * t5 * t8 * t12 * t16 * 2.006356734599796e-2 + d3 * t5 * t10 * t14 * t15 * 3.312522808627406e-1 + d3 * t8 * t10 * t12 * t14 * 1.354248200243321e-1 + d3 * t5 * t10 * t14 * t18 * 3.312522808627406e-1-d5 * t5 * t8 * t16 * t17 * 1.540556795982286e-1-d3 * t5 * t10 * t15 * t19 * 1.354248200243321e-1 + d3 * t8 * t10 * t12 * t19 * 3.312522808627406e-1 + d3 * t5 * t14 * t15 * t16 * 3.312522808627406e-1 + d3 * t8 * t12 * t14 * t16 * 1.354248200243321e-1-d3 * t5 * t10 * t18 * t19 * 1.354248200243321e-1 + d3 * t5 * t14 * t16 * t18 * 3.312522808627406e-1-d3 * t5 * t15 * t16 * t19 * 1.354248200243321e-1 + d3 * t8 * t12 * t16 * t19 * 3.312522808627406e-1-d3 * t5 * t16 * t18 * t19 * 1.354248200243321e-1 + d5 * t11 * t14 * t15 * t16 * 3.312522808627406e-1 + d5 * t11 * t14 * t16 * t18 * 3.312522808627406e-1 + d5 * t10 * t14 * t15 * t21 * 1.249486888554935e-2-d5 * t11 * t15 * t16 * t19 * 1.354248200243321e-1 + d5 * t10 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t10 * t14 * t18 * t21 * 1.249486888554935e-2-d5 * t11 * t16 * t18 * t19 * 1.354248200243321e-1 + d5 * t10 * t14 * t18 * t23 * 9.42222077621846e-4 + t5 * t6 * t9 * t12 * t17 * 1.425176355282819e-2 + t5 * t6 * t9 * t14 * t15 * 1.640860636489827e-3-t6 * t8 * t9 * t12 * t14 * 2.487313068943488e-5-t5 * t6 * t9 * t14 * t18 * 1.640860636489827e-3 + t5 * t6 * t9 * t15 * t19 * 2.487313068943488e-5 + t6 * t8 * t9 * t12 * t19 * 1.640860636489827e-3-t5 * t6 * t9 * t18 * t19 * 2.487313068943488e-5 + t5 * t8 * t14 * t16 * t17 * 2.487313068943488e-5-t6 * t8 * t9 * t17 * t20 * 1.073500939031497e-2-t5 * t8 * t12 * t16 * t20 * 1.073500939031497e-2 + t6 * t8 * t9 * t17 * t22 * 1.073500939031497e-2 + t5 * t8 * t12 * t16 * t22 * 1.073500939031497e-2-t5 * t8 * t16 * t17 * t19 * 1.640860636489827e-3-t11 * t12 * t14 * t16 * t17 * 1.640860636489827e-3 + t10 * t12 * t14 * t17 * t21 * 1.193879867928346e-3-t11 * t12 * t16 * t17 * t19 * 2.487313068943488e-5-t11 * t14 * t15 * t16 * t19 * 1.073500939031497e-2-t10 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t10 * t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t10 * t14 * t18 * t19 * t21 * 3.517483669838449e-3-t10 * t12 * t17 * t19 * t25 * 2.370893727874773e-3-t13 * t14 * t16 * t19 * t21 * 3.517483669838449e-3 + t10 * t14 * t18 * t19 * t23 * 1.193879867928346e-3-t13 * t14 * t16 * t19 * t23 * 1.193879867928346e-3-t11 * t15 * t16 * t20 * t25 * 1.765933827532306e-3 + t11 * t16 * t18 * t21 * t23 * 2.370893727874773e-3-t10 * t18 * t20 * t21 * t23 * 2.370893727874773e-3-t13 * t16 * t21 * t22 * t23 * 2.370893727874773e-3-a5 * t5 * t6 * t9 * t14 * t17 * 1.354248200243321e-1-a4 * t6 * t9 * t11 * t14 * t17 * 1.354248200243321e-1-a5 * t5 * t6 * t9 * t17 * t19 * 3.312522808627406e-1-a4 * t6 * t9 * t13 * t14 * t17 * 1.354248200243321e-1 + a5 * t5 * t8 * t14 * t15 * t16 * 3.312522808627406e-1-a4 * t6 * t9 * t11 * t17 * t19 * 3.312522808627406e-1 + a5 * t5 * t8 * t14 * t16 * t18 * 3.312522808627406e-1-a4 * t6 * t9 * t13 * t17 * t19 * 3.312522808627406e-1-a5 * t5 * t8 * t15 * t16 * t19 * 1.354248200243321e-1 + a4 * t5 * t10 * t12 * t19 * t21 * 1.249486888554935e-2-a5 * t5 * t8 * t16 * t18 * t19 * 1.354248200243321e-1-a4 * t8 * t10 * t14 * t15 * t21 * 1.249486888554935e-2 + a4 * t5 * t10 * t12 * t19 * t23 * 9.42222077621846e-4-a4 * t8 * t10 * t14 * t15 * t23 * 9.42222077621846e-4-a4 * t8 * t10 * t14 * t18 * t21 * 1.249486888554935e-2-a4 * t5 * t10 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t8 * t10 * t14 * t18 * t23 * 9.42222077621846e-4 + a4 * t5 * t10 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t5 * t10 * t17 * t21 * t22 * 9.42222077621846e-4 + a4 * t5 * t10 * t17 * t22 * t23 * 1.249486888554935e-2 + a5 * t12 * t13 * t16 * t19 * t21 * 1.249486888554935e-2-a7 * t10 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a5 * t12 * t13 * t16 * t19 * t23 * 9.42222077621846e-4 + a7 * t10 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-a5 * t13 * t16 * t17 * t20 * t21 * 9.42222077621846e-4 + a7 * t11 * t15 * t16 * t21 * t22 * 1.249486888554935e-2 + a5 * t13 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t13 * t16 * t17 * t21 * t22 * 9.42222077621846e-4 + a7 * t11 * t15 * t16 * t22 * t23 * 9.42222077621846e-4 + a5 * t13 * t16 * t17 * t22 * t23 * 1.249486888554935e-2 + d5 * t6 * t8 * t9 * t14 * t17 * 1.354248200243321e-1 + d5 * t5 * t8 * t12 * t14 * t16 * 1.354248200243321e-1 + d5 * t6 * t8 * t9 * t17 * t19 * 3.312522808627406e-1 + d5 * t5 * t8 * t12 * t16 * t19 * 3.312522808627406e-1 + d3 * t5 * t10 * t14 * t15 * t21 * 1.249486888554935e-2 + d3 * t5 * t10 * t14 * t15 * t23 * 9.42222077621846e-4 + d3 * t5 * t10 * t14 * t18 * t21 * 1.249486888554935e-2 + d3 * t5 * t10 * t14 * t18 * t23 * 9.42222077621846e-4 + d3 * t8 * t10 * t12 * t19 * t21 * 1.249486888554935e-2 + d3 * t5 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + d3 * t8 * t10 * t12 * t19 * t23 * 9.42222077621846e-4 + d3 * t5 * t14 * t15 * t16 * t23 * 9.42222077621846e-4 + d3 * t5 * t14 * t16 * t18 * t21 * 1.249486888554935e-2 + d3 * t5 * t14 * t16 * t18 * t23 * 9.42222077621846e-4-d3 * t8 * t10 * t17 * t20 * t21 * 9.42222077621846e-4 + d3 * t8 * t12 * t16 * t19 * t21 * 1.249486888554935e-2 + d3 * t8 * t10 * t17 * t20 * t23 * 1.249486888554935e-2-d3 * t8 * t10 * t17 * t21 * t22 * 9.42222077621846e-4 + d3 * t8 * t12 * t16 * t19 * t23 * 9.42222077621846e-4 + d5 * t11 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + d3 * t8 * t10 * t17 * t22 * t23 * 1.249486888554935e-2 + d5 * t11 * t14 * t15 * t16 * t23 * 9.42222077621846e-4-d3 * t8 * t16 * t17 * t20 * t21 * 9.42222077621846e-4 + d5 * t11 * t14 * t16 * t18 * t21 * 1.249486888554935e-2 + d3 * t8 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-d3 * t8 * t16 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t11 * t14 * t16 * t18 * t23 * 9.42222077621846e-4 + d3 * t8 * t16 * t17 * t22 * t23 * 1.249486888554935e-2-t5 * t6 * t9 * t12 * t17 * t20 * 1.310816447497728e-2 + t5 * t6 * t9 * t14 * t15 * t21 * 1.193879867928346e-3 + t5 * t6 * t9 * t12 * t17 * t22 * 2.54448602252613e-3-t5 * t6 * t9 * t14 * t15 * t23 * 3.517483669838449e-3 + t5 * t6 * t9 * t12 * t17 * t24 * 3.531867655064613e-3-t5 * t6 * t9 * t14 * t18 * t21 * 1.193879867928346e-3-t6 * t8 * t9 * t12 * t14 * t24 * 2.370893727874773e-3 + t6 * t8 * t9 * t14 * t17 * t19 * 1.565265049750341e-2 + t5 * t8 * t12 * t14 * t16 * t19 * 1.565265049750341e-2 + t6 * t8 * t9 * t12 * t14 * t25 * 2.370893727874773e-3 + t5 * t6 * t9 * t14 * t18 * t23 * 3.517483669838449e-3 + t6 * t8 * t9 * t12 * t19 * t21 * 1.193879867928346e-3-t6 * t8 * t9 * t12 * t19 * t23 * 3.517483669838449e-3 + t5 * t6 * t9 * t15 * t19 * t24 * 2.370893727874773e-3-t5 * t6 * t9 * t15 * t19 * t25 * 2.370893727874773e-3-t5 * t6 * t9 * t18 * t19 * t24 * 2.370893727874773e-3 + t6 * t8 * t9 * t17 * t20 * t21 * 3.517483669838449e-3 + t5 * t6 * t9 * t18 * t19 * t25 * 2.370893727874773e-3 + t5 * t8 * t12 * t16 * t20 * t21 * 3.517483669838449e-3 + t6 * t8 * t9 * t17 * t20 * t23 * 1.193879867928346e-3-t6 * t8 * t9 * t17 * t21 * t22 * 3.517483669838449e-3 + t5 * t8 * t12 * t16 * t20 * t23 * 1.193879867928346e-3-t5 * t8 * t12 * t16 * t21 * t22 * 3.517483669838449e-3 + t5 * t8 * t14 * t16 * t17 * t24 * 2.370893727874773e-3-t5 * t8 * t14 * t16 * t17 * t25 * 2.370893727874773e-3-t6 * t8 * t9 * t17 * t22 * t23 * 1.193879867928346e-3-t5 * t8 * t12 * t16 * t22 * t23 * 1.193879867928346e-3-t5 * t8 * t16 * t17 * t19 * t21 * 1.193879867928346e-3 + t5 * t8 * t16 * t17 * t19 * t23 * 3.517483669838449e-3-t11 * t12 * t14 * t16 * t17 * t21 * 1.193879867928346e-3 + t11 * t12 * t14 * t16 * t17 * t23 * 3.517483669838449e-3 + t11 * t14 * t15 * t16 * t19 * t21 * 3.517483669838449e-3 + t11 * t14 * t15 * t16 * t19 * t23 * 1.193879867928346e-3-t11 * t12 * t16 * t17 * t19 * t24 * 2.370893727874773e-3 + t11 * t12 * t16 * t17 * t19 * t25 * 2.370893727874773e-3 + t10 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3-t11 * t15 * t16 * t20 * t21 * t23 * 2.370893727874773e-3-a7 * t5 * t6 * t9 * t12 * t17 * t21 * 2.49897377710987e-2 + a7 * t6 * t8 * t9 * t12 * t14 * t21 * 9.42222077621846e-4-a5 * t5 * t6 * t9 * t12 * t20 * t21 * 9.42222077621846e-4-a7 * t5 * t6 * t9 * t12 * t17 * t23 * 1.884444155243692e-3-a7 * t6 * t8 * t9 * t12 * t14 * t23 * 1.249486888554935e-2 + a5 * t5 * t6 * t9 * t12 * t20 * t23 * 1.249486888554935e-2-a5 * t5 * t6 * t9 * t12 * t21 * t22 * 9.42222077621846e-4 + a5 * t5 * t6 * t9 * t12 * t22 * t23 * 1.249486888554935e-2-a5 * t5 * t6 * t9 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t5 * t6 * t9 * t15 * t19 * t21 * 9.42222077621846e-4-a4 * t6 * t9 * t11 * t12 * t20 * t21 * 9.42222077621846e-4-a5 * t5 * t6 * t9 * t17 * t19 * t23 * 9.42222077621846e-4 + a5 * t5 * t8 * t14 * t15 * t16 * t21 * 1.249486888554935e-2 + a7 * t5 * t6 * t9 * t15 * t19 * t23 * 1.249486888554935e-2 + a4 * t6 * t9 * t11 * t12 * t20 * t23 * 1.249486888554935e-2-a4 * t6 * t9 * t11 * t12 * t21 * t22 * 9.42222077621846e-4-a4 * t6 * t9 * t12 * t13 * t20 * t21 * 9.42222077621846e-4 + a7 * t5 * t6 * t9 * t18 * t19 * t21 * 9.42222077621846e-4 + a5 * t5 * t8 * t14 * t15 * t16 * t23 * 9.42222077621846e-4 + a4 * t6 * t9 * t11 * t12 * t22 * t23 * 1.249486888554935e-2-a4 * t6 * t9 * t11 * t17 * t19 * t21 * 1.249486888554935e-2 + a4 * t6 * t9 * t12 * t13 * t20 * t23 * 1.249486888554935e-2-a4 * t6 * t9 * t12 * t13 * t21 * t22 * 9.42222077621846e-4 + a5 * t5 * t8 * t14 * t16 * t18 * t21 * 1.249486888554935e-2-a7 * t5 * t6 * t9 * t18 * t19 * t23 * 1.249486888554935e-2-a7 * t5 * t8 * t14 * t16 * t17 * t21 * 9.42222077621846e-4-a4 * t6 * t9 * t11 * t17 * t19 * t23 * 9.42222077621846e-4 + a4 * t6 * t9 * t12 * t13 * t22 * t23 * 1.249486888554935e-2-a4 * t6 * t9 * t13 * t17 * t19 * t21 * 1.249486888554935e-2 + a5 * t5 * t8 * t14 * t16 * t18 * t23 * 9.42222077621846e-4 + a7 * t5 * t8 * t14 * t16 * t17 * t23 * 1.249486888554935e-2-a4 * t6 * t9 * t13 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t11 * t12 * t16 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t11 * t12 * t16 * t17 * t19 * t23 * 1.249486888554935e-2 + d5 * t6 * t8 * t9 * t12 * t20 * t21 * 9.42222077621846e-4-d5 * t6 * t8 * t9 * t12 * t20 * t23 * 1.249486888554935e-2 + d5 * t6 * t8 * t9 * t12 * t21 * t22 * 9.42222077621846e-4-d5 * t6 * t8 * t9 * t12 * t22 * t23 * 1.249486888554935e-2 + d5 * t6 * t8 * t9 * t17 * t19 * t21 * 1.249486888554935e-2 + d5 * t5 * t8 * t12 * t16 * t19 * t21 * 1.249486888554935e-2 + d5 * t6 * t8 * t9 * t17 * t19 * t23 * 9.42222077621846e-4 + d5 * t5 * t8 * t12 * t16 * t19 * t23 * 9.42222077621846e-4-d5 * t5 * t8 * t16 * t17 * t20 * t21 * 9.42222077621846e-4 + d5 * t5 * t8 * t16 * t17 * t20 * t23 * 1.249486888554935e-2-d5 * t5 * t8 * t16 * t17 * t21 * t22 * 9.42222077621846e-4 + d5 * t5 * t8 * t16 * t17 * t22 * t23 * 1.249486888554935e-2-t5 * t6 * t9 * t12 * t14 * t17 * t19 * 2.147001878062994e-2-t5 * t6 * t9 * t12 * t17 * t21 * t23 * 4.741787455749547e-3-t6 * t8 * t9 * t12 * t14 * t21 * t23 * 3.531867655064613e-3-t5 * t6 * t9 * t12 * t17 * t20 * t25 * 3.531867655064613e-3 + t5 * t6 * t9 * t15 * t19 * t21 * t23 * 3.531867655064613e-3 + t6 * t8 * t9 * t14 * t17 * t19 * t25 * 3.531867655064613e-3 + t5 * t8 * t12 * t14 * t16 * t19 * t25 * 3.531867655064613e-3-t5 * t6 * t9 * t18 * t19 * t21 * t23 * 3.531867655064613e-3 + t5 * t8 * t14 * t16 * t17 * t21 * t23 * 3.531867655064613e-3-t11 * t12 * t16 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + a7 * t5 * t6 * t9 * t12 * t17 * t21 * t22 * 2.49897377710987e-2 + a7 * t5 * t6 * t9 * t12 * t17 * t22 * t23 * 1.884444155243692e-3 + a7 * t6 * t8 * t9 * t14 * t17 * t19 * t21 * 2.49897377710987e-2 + a7 * t5 * t8 * t12 * t14 * t16 * t19 * t21 * 2.49897377710987e-2 + a7 * t6 * t8 * t9 * t14 * t17 * t19 * t23 * 1.884444155243692e-3 + a7 * t5 * t8 * t12 * t14 * t16 * t19 * t23 * 1.884444155243692e-3 + t5 * t6 * t9 * t12 * t14 * t17 * t19 * t21 * 7.034967339676897e-3 + t5 * t6 * t9 * t12 * t14 * t17 * t19 * t23 * 2.387759735856691e-3-t5 * t6 * t9 * t12 * t17 * t20 * t21 * t23 * 4.741787455749547e-3 + t6 * t8 * t9 * t14 * t17 * t19 * t21 * t23 * 4.741787455749547e-3 + t5 * t8 * t12 * t14 * t16 * t19 * t21 * t23 * 4.741787455749547e-3 + 1.042949173505409, t2 * 1.169633856586688e-1 + t3 * t6 * 2.278153122465366e-1-t3 * t9 * 1.223492856623504e-2 + t2 * t11 * 1.807861861545219e-2 + t2 * t13 * 5.324557667216729e-1-a4 * t2 * t5 * 9.772685114474167e-1-a4 * t2 * t8 * 3.435539633871837 + t2 * t5 * t8 * 3.003448646049658e-1 + t3 * t5 * t9 * 2.736425713896764e-3-t3 * t6 * t11 * 1.501724323024829e-1-t3 * t8 * t9 * 4.770739247958454e-3 + t3 * t6 * t13 * 1.501724323024829e-1 + t2 * t13 * t15 * 3.21778928866176e-2 + t2 * t11 * t20 * 1.272243011263065e-3 + t2 * t13 * t18 * 2.505201111020351e-2-t2 * t11 * t22 * 6.554082237488641e-3 + a4 * t3 * t5 * t6 * 1.717769816935919-a4 * t3 * t6 * t8 * 4.886342557237083e-1-a4 * t2 * t5 * t12 * 2.006356734599796e-2-a4 * t2 * t5 * t17 * 1.540556795982286e-1-a5 * t2 * t11 * t12 * 2.006356734599796e-2-a5 * t2 * t11 * t17 * 1.540556795982286e-1-d3 * t3 * t5 * t6 * 4.886342557237083e-1-d3 * t3 * t6 * t8 * 1.717769816935919-t3 * t5 * t6 * t8 * 5.143771481062207e-1-t2 * t5 * t8 * t12 * 1.22815866196153e-2 + t3 * t5 * t9 * t12 * 7.703820892966798e-3-t2 * t5 * t8 * t17 * 1.54076417859336e-2 + t3 * t6 * t11 * t12 * 6.14079330980765e-3-t3 * t5 * t9 * t17 * 6.14079330980765e-3-t3 * t6 * t12 * t13 * 6.14079330980765e-3-t3 * t8 * t9 * t15 * 3.731166785767855e-3 + t3 * t6 * t11 * t17 * 7.703820892966798e-3 + t3 * t8 * t9 * t18 * 3.731166785767855e-3-t3 * t6 * t13 * t17 * 7.703820892966798e-3 + t2 * t12 * t13 * t17 * 7.462333571535709e-3 + t2 * t11 * t14 * t19 * 1.073500939031497e-2-t2 * t13 * t15 * t20 * 6.554082237488641e-3 + t2 * t13 * t15 * t22 * 1.272243011263065e-3-t2 * t13 * t18 * t24 * 1.765933827532306e-3-t2 * t11 * t22 * t25 * 1.765933827532306e-3-a4 * t3 * t6 * t8 * t12 * 1.003178367299898e-2 + a4 * t2 * t5 * t12 * t14 * 1.354248200243321e-1-a5 * t3 * t8 * t9 * t12 * 7.702783979911432e-2-a4 * t3 * t6 * t8 * t17 * 7.702783979911432e-2 + a4 * t2 * t5 * t12 * t19 * 3.312522808627406e-1 + a5 * t3 * t8 * t9 * t17 * 1.003178367299898e-2-a4 * t2 * t8 * t14 * t15 * 3.312522808627406e-1 + a5 * t2 * t11 * t12 * t14 * 1.354248200243321e-1-a4 * t2 * t8 * t14 * t18 * 3.312522808627406e-1 + a4 * t2 * t8 * t15 * t19 * 1.354248200243321e-1 + a5 * t2 * t11 * t12 * t19 * 3.312522808627406e-1 + a4 * t2 * t8 * t18 * t19 * 1.354248200243321e-1 + a7 * t2 * t11 * t20 * t21 * 1.249486888554935e-2 + a7 * t2 * t13 * t18 * t21 * 1.249486888554935e-2 + a7 * t2 * t11 * t20 * t23 * 9.42222077621846e-4 + a7 * t2 * t13 * t18 * t23 * 9.42222077621846e-4-d3 * t3 * t5 * t6 * t12 * 1.003178367299898e-2 + d5 * t2 * t5 * t8 * t12 * 2.006356734599796e-2-d3 * t3 * t5 * t6 * t17 * 7.702783979911432e-2-d5 * t3 * t5 * t9 * t12 * 7.702783979911432e-2 + d5 * t2 * t5 * t8 * t17 * 1.540556795982286e-1-d5 * t3 * t6 * t11 * t12 * 1.003178367299898e-2-d3 * t3 * t9 * t11 * t12 * 7.702783979911432e-2 + d5 * t3 * t5 * t9 * t17 * 1.003178367299898e-2 + d5 * t3 * t6 * t12 * t13 * 1.003178367299898e-2-d3 * t3 * t9 * t12 * t13 * 7.702783979911432e-2-d5 * t3 * t6 * t11 * t17 * 7.702783979911432e-2 + d3 * t3 * t9 * t11 * t17 * 1.003178367299898e-2 + d5 * t3 * t6 * t13 * t17 * 7.702783979911432e-2 + d3 * t3 * t9 * t13 * t17 * 1.003178367299898e-2 + d5 * t2 * t13 * t14 * t15 * 3.312522808627406e-1 + d5 * t2 * t13 * t14 * t18 * 3.312522808627406e-1-d5 * t2 * t13 * t15 * t19 * 1.354248200243321e-1-d5 * t2 * t13 * t18 * t19 * 1.354248200243321e-1-t3 * t5 * t6 * t8 * t15 * 3.21778928866176e-2-t3 * t5 * t6 * t8 * t18 * 2.505201111020351e-2 + t3 * t5 * t6 * t8 * t20 * 1.272243011263065e-3 + t3 * t5 * t9 * t12 * t14 * 1.243656534471744e-5-t3 * t5 * t6 * t8 * t22 * 6.554082237488641e-3-t2 * t5 * t8 * t14 * t17 * 2.487313068943488e-5 + t2 * t5 * t8 * t12 * t20 * 1.073500939031497e-2-t3 * t5 * t9 * t12 * t19 * 8.204303182449135e-4-t2 * t5 * t8 * t12 * t22 * 1.073500939031497e-2 + t3 * t8 * t9 * t12 * t17 * 7.125881776414093e-3 + t3 * t8 * t9 * t14 * t15 * 8.204303182449135e-4 + t2 * t5 * t8 * t17 * t19 * 1.640860636489827e-3 + t3 * t6 * t11 * t14 * t17 * 1.243656534471744e-5-t3 * t6 * t11 * t12 * t20 * 5.367504695157484e-3-t3 * t8 * t9 * t14 * t18 * 8.204303182449135e-4-t3 * t6 * t13 * t14 * t17 * 1.243656534471744e-5 + t3 * t5 * t9 * t17 * t20 * 5.367504695157484e-3 + t3 * t6 * t11 * t12 * t22 * 5.367504695157484e-3 + t3 * t6 * t12 * t13 * t20 * 5.367504695157484e-3 + t3 * t8 * t9 * t15 * t19 * 1.243656534471744e-5-t3 * t5 * t9 * t17 * t22 * 5.367504695157484e-3-t3 * t6 * t11 * t17 * t19 * 8.204303182449135e-4-t3 * t6 * t12 * t13 * t22 * 5.367504695157484e-3-t3 * t8 * t9 * t18 * t19 * 1.243656534471744e-5-t2 * t12 * t13 * t14 * t17 * 1.640860636489827e-3 + t3 * t6 * t13 * t17 * t19 * 8.204303182449135e-4-t2 * t12 * t13 * t17 * t19 * 2.487313068943488e-5-t2 * t13 * t14 * t15 * t19 * 1.073500939031497e-2-t2 * t11 * t14 * t19 * t21 * 3.517483669838449e-3-t2 * t11 * t14 * t19 * t23 * 1.193879867928346e-3-t2 * t13 * t15 * t20 * t25 * 1.765933827532306e-3 + t2 * t13 * t18 * t21 * t23 * 2.370893727874773e-3-t2 * t11 * t21 * t22 * t23 * 2.370893727874773e-3-a5 * t3 * t5 * t6 * t8 * t12 * 2.006356734599796e-2-a5 * t3 * t5 * t6 * t8 * t17 * 1.540556795982286e-1 + a4 * t3 * t5 * t6 * t14 * t15 * 1.656261404313703e-1 + a4 * t3 * t6 * t8 * t12 * t14 * 6.771241001216606e-2-a5 * t2 * t5 * t8 * t14 * t15 * 3.312522808627406e-1 + a4 * t3 * t5 * t6 * t14 * t18 * 1.656261404313703e-1-a4 * t3 * t5 * t6 * t15 * t19 * 6.771241001216606e-2 + a4 * t3 * t6 * t8 * t12 * t19 * 1.656261404313703e-1-a5 * t2 * t5 * t8 * t14 * t18 * 3.312522808627406e-1 + a5 * t2 * t5 * t8 * t15 * t19 * 1.354248200243321e-1 + a5 * t3 * t6 * t11 * t14 * t15 * 1.656261404313703e-1-a4 * t3 * t5 * t6 * t18 * t19 * 6.771241001216606e-2-a5 * t3 * t6 * t13 * t14 * t15 * 1.656261404313703e-1-a5 * t3 * t8 * t9 * t14 * t17 * 6.771241001216606e-2 + a5 * t2 * t5 * t8 * t18 * t19 * 1.354248200243321e-1 + a5 * t3 * t6 * t11 * t14 * t18 * 1.656261404313703e-1-a5 * t3 * t6 * t11 * t15 * t19 * 6.771241001216606e-2-a5 * t3 * t6 * t13 * t14 * t18 * 1.656261404313703e-1 + a5 * t3 * t6 * t13 * t15 * t19 * 6.771241001216606e-2-a5 * t3 * t8 * t9 * t17 * t19 * 1.656261404313703e-1-a5 * t3 * t6 * t11 * t18 * t19 * 6.771241001216606e-2 + a4 * t2 * t5 * t12 * t19 * t21 * 1.249486888554935e-2-a4 * t2 * t8 * t14 * t15 * t21 * 1.249486888554935e-2 + a5 * t3 * t6 * t13 * t18 * t19 * 6.771241001216606e-2 + a4 * t2 * t5 * t12 * t19 * t23 * 9.42222077621846e-4-a4 * t2 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-a4 * t2 * t8 * t14 * t18 * t21 * 1.249486888554935e-2-a4 * t2 * t5 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t2 * t8 * t14 * t18 * t23 * 9.42222077621846e-4 + a5 * t2 * t11 * t12 * t19 * t21 * 1.249486888554935e-2 + a4 * t2 * t5 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t2 * t5 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t2 * t11 * t12 * t19 * t23 * 9.42222077621846e-4 + a4 * t2 * t5 * t17 * t22 * t23 * 1.249486888554935e-2-a5 * t2 * t11 * t17 * t20 * t21 * 9.42222077621846e-4 + a5 * t2 * t11 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t2 * t11 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t2 * t11 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t2 * t13 * t15 * t21 * t22 * 1.249486888554935e-2 + a7 * t2 * t13 * t15 * t22 * t23 * 9.42222077621846e-4 + d3 * t3 * t5 * t6 * t12 * t14 * 6.771241001216606e-2-d5 * t2 * t5 * t8 * t12 * t14 * 1.354248200243321e-1 + d3 * t3 * t5 * t6 * t12 * t19 * 1.656261404313703e-1-d3 * t3 * t6 * t8 * t14 * t15 * 1.656261404313703e-1-d5 * t2 * t5 * t8 * t12 * t19 * 3.312522808627406e-1 + d5 * t3 * t6 * t11 * t12 * t14 * 6.771241001216606e-2-d3 * t3 * t6 * t8 * t14 * t18 * 1.656261404313703e-1-d5 * t3 * t5 * t9 * t14 * t17 * 6.771241001216606e-2-d5 * t3 * t6 * t12 * t13 * t14 * 6.771241001216606e-2 + d3 * t3 * t6 * t8 * t15 * t19 * 6.771241001216606e-2 + d5 * t3 * t6 * t11 * t12 * t19 * 1.656261404313703e-1 + d3 * t3 * t6 * t8 * t18 * t19 * 6.771241001216606e-2-d3 * t3 * t9 * t11 * t14 * t17 * 6.771241001216606e-2-d5 * t3 * t5 * t9 * t17 * t19 * 1.656261404313703e-1-d5 * t3 * t6 * t12 * t13 * t19 * 1.656261404313703e-1-d3 * t3 * t9 * t13 * t14 * t17 * 6.771241001216606e-2-d3 * t3 * t9 * t11 * t17 * t19 * 1.656261404313703e-1-d3 * t3 * t9 * t13 * t17 * t19 * 1.656261404313703e-1 + d5 * t2 * t13 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t2 * t13 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t2 * t13 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t2 * t13 * t14 * t18 * t23 * 9.42222077621846e-4-t3 * t5 * t6 * t8 * t12 * t17 * 7.462333571535709e-3 + t3 * t5 * t6 * t8 * t14 * t19 * 1.073500939031497e-2 + t3 * t5 * t6 * t8 * t15 * t20 * 6.554082237488641e-3-t3 * t5 * t6 * t8 * t15 * t22 * 1.272243011263065e-3-t2 * t5 * t8 * t12 * t14 * t19 * 1.565265049750341e-2 + t3 * t5 * t6 * t8 * t18 * t24 * 1.765933827532306e-3 + t3 * t6 * t11 * t12 * t14 * t19 * 7.826325248751706e-3 + t3 * t5 * t9 * t12 * t14 * t24 * 1.185446863937387e-3-t3 * t5 * t9 * t14 * t17 * t19 * 7.826325248751706e-3-t3 * t6 * t12 * t13 * t14 * t19 * 7.826325248751706e-3-t2 * t5 * t8 * t12 * t20 * t21 * 3.517483669838449e-3-t3 * t5 * t9 * t12 * t14 * t25 * 1.185446863937387e-3-t3 * t5 * t6 * t8 * t22 * t25 * 1.765933827532306e-3-t3 * t5 * t9 * t12 * t19 * t21 * 5.969399339641728e-4-t3 * t8 * t9 * t12 * t17 * t20 * 6.554082237488641e-3-t2 * t5 * t8 * t12 * t20 * t23 * 1.193879867928346e-3 + t2 * t5 * t8 * t12 * t21 * t22 * 3.517483669838449e-3-t2 * t5 * t8 * t14 * t17 * t24 * 2.370893727874773e-3 + t3 * t8 * t9 * t14 * t15 * t21 * 5.969399339641728e-4 + t2 * t5 * t8 * t14 * t17 * t25 * 2.370893727874773e-3 + t3 * t5 * t9 * t12 * t19 * t23 * 1.758741834919224e-3 + t3 * t8 * t9 * t12 * t17 * t22 * 1.272243011263065e-3 + t2 * t5 * t8 * t12 * t22 * t23 * 1.193879867928346e-3 + t2 * t5 * t8 * t17 * t19 * t21 * 1.193879867928346e-3-t3 * t8 * t9 * t14 * t15 * t23 * 1.758741834919224e-3 + t3 * t6 * t11 * t12 * t20 * t21 * 1.758741834919224e-3 + t3 * t8 * t9 * t12 * t17 * t24 * 1.765933827532306e-3-t3 * t8 * t9 * t14 * t18 * t21 * 5.969399339641728e-4-t2 * t5 * t8 * t17 * t19 * t23 * 3.517483669838449e-3-t3 * t5 * t9 * t17 * t20 * t21 * 1.758741834919224e-3 + t3 * t6 * t11 * t12 * t20 * t23 * 5.969399339641728e-4-t3 * t6 * t11 * t12 * t21 * t22 * 1.758741834919224e-3 + t3 * t6 * t11 * t14 * t17 * t24 * 1.185446863937387e-3-t3 * t6 * t12 * t13 * t20 * t21 * 1.758741834919224e-3 + t3 * t8 * t9 * t14 * t18 * t23 * 1.758741834919224e-3-t3 * t6 * t11 * t14 * t17 * t25 * 1.185446863937387e-3-t3 * t5 * t9 * t17 * t20 * t23 * 5.969399339641728e-4 + t3 * t5 * t9 * t17 * t21 * t22 * 1.758741834919224e-3-t3 * t6 * t11 * t12 * t22 * t23 * 5.969399339641728e-4-t3 * t6 * t11 * t17 * t19 * t21 * 5.969399339641728e-4-t3 * t6 * t12 * t13 * t20 * t23 * 5.969399339641728e-4 + t3 * t6 * t12 * t13 * t21 * t22 * 1.758741834919224e-3-t3 * t6 * t13 * t14 * t17 * t24 * 1.185446863937387e-3 + t3 * t6 * t13 * t14 * t17 * t25 * 1.185446863937387e-3 + t3 * t8 * t9 * t15 * t19 * t24 * 1.185446863937387e-3-t2 * t12 * t13 * t14 * t17 * t21 * 1.193879867928346e-3 + t3 * t5 * t9 * t17 * t22 * t23 * 5.969399339641728e-4 + t3 * t6 * t11 * t17 * t19 * t23 * 1.758741834919224e-3 + t3 * t6 * t12 * t13 * t22 * t23 * 5.969399339641728e-4 + t3 * t6 * t13 * t17 * t19 * t21 * 5.969399339641728e-4-t3 * t8 * t9 * t15 * t19 * t25 * 1.185446863937387e-3 + t2 * t12 * t13 * t14 * t17 * t23 * 3.517483669838449e-3-t3 * t6 * t13 * t17 * t19 * t23 * 1.758741834919224e-3-t3 * t8 * t9 * t18 * t19 * t24 * 1.185446863937387e-3 + t3 * t8 * t9 * t18 * t19 * t25 * 1.185446863937387e-3 + t2 * t13 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t2 * t13 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t2 * t12 * t13 * t17 * t19 * t24 * 2.370893727874773e-3 + t2 * t12 * t13 * t17 * t19 * t25 * 2.370893727874773e-3-t2 * t13 * t15 * t20 * t21 * t23 * 2.370893727874773e-3 + a5 * t3 * t5 * t6 * t8 * t12 * t14 * 1.354248200243321e-1 + a5 * t3 * t5 * t6 * t8 * t12 * t19 * 3.312522808627406e-1 + a4 * t3 * t5 * t6 * t14 * t15 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t6 * t8 * t18 * t21 * 1.249486888554935e-2 + a4 * t3 * t5 * t6 * t14 * t15 * t23 * 4.71111038810923e-4-a5 * t2 * t5 * t8 * t14 * t15 * t21 * 1.249486888554935e-2-a7 * t3 * t5 * t6 * t8 * t18 * t23 * 9.42222077621846e-4 + a7 * t3 * t5 * t6 * t8 * t20 * t21 * 1.249486888554935e-2 + a4 * t3 * t5 * t6 * t14 * t18 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t9 * t12 * t14 * t21 * 4.71111038810923e-4-a5 * t2 * t5 * t8 * t14 * t15 * t23 * 9.42222077621846e-4 + a7 * t3 * t5 * t6 * t8 * t20 * t23 * 9.42222077621846e-4 + a4 * t3 * t5 * t6 * t14 * t18 * t23 * 4.71111038810923e-4 + a4 * t3 * t6 * t8 * t12 * t19 * t21 * 6.247434442774674e-3-a5 * t2 * t5 * t8 * t14 * t18 * t21 * 1.249486888554935e-2 + a7 * t3 * t5 * t9 * t12 * t14 * t23 * 6.247434442774674e-3 + a7 * t2 * t5 * t8 * t14 * t17 * t21 * 9.42222077621846e-4 + a4 * t3 * t6 * t8 * t12 * t19 * t23 * 4.71111038810923e-4-a5 * t2 * t5 * t8 * t14 * t18 * t23 * 9.42222077621846e-4 + a5 * t3 * t6 * t11 * t14 * t15 * t21 * 6.247434442774674e-3-a7 * t2 * t5 * t8 * t14 * t17 * t23 * 1.249486888554935e-2 + a5 * t3 * t6 * t11 * t14 * t15 * t23 * 4.71111038810923e-4-a5 * t3 * t6 * t13 * t14 * t15 * t21 * 6.247434442774674e-3-a7 * t3 * t8 * t9 * t12 * t17 * t21 * 1.249486888554935e-2 + a5 * t3 * t6 * t11 * t14 * t18 * t21 * 6.247434442774674e-3-a5 * t3 * t8 * t9 * t12 * t20 * t21 * 4.71111038810923e-4-a4 * t3 * t6 * t8 * t17 * t20 * t21 * 4.71111038810923e-4-a5 * t3 * t6 * t13 * t14 * t15 * t23 * 4.71111038810923e-4-a7 * t3 * t6 * t11 * t14 * t17 * t21 * 4.71111038810923e-4-a7 * t3 * t8 * t9 * t12 * t17 * t23 * 9.42222077621846e-4 + a5 * t3 * t6 * t11 * t14 * t18 * t23 * 4.71111038810923e-4-a5 * t3 * t6 * t13 * t14 * t18 * t21 * 6.247434442774674e-3 + a5 * t3 * t8 * t9 * t12 * t20 * t23 * 6.247434442774674e-3-a5 * t3 * t8 * t9 * t12 * t21 * t22 * 4.71111038810923e-4 + a4 * t3 * t6 * t8 * t17 * t20 * t23 * 6.247434442774674e-3-a4 * t3 * t6 * t8 * t17 * t21 * t22 * 4.71111038810923e-4 + a7 * t3 * t6 * t11 * t14 * t17 * t23 * 6.247434442774674e-3 + a7 * t3 * t6 * t13 * t14 * t17 * t21 * 4.71111038810923e-4-a5 * t3 * t6 * t13 * t14 * t18 * t23 * 4.71111038810923e-4 + a5 * t3 * t8 * t9 * t12 * t22 * t23 * 6.247434442774674e-3-a5 * t3 * t8 * t9 * t17 * t19 * t21 * 6.247434442774674e-3-a7 * t3 * t8 * t9 * t15 * t19 * t21 * 4.71111038810923e-4 + a4 * t3 * t6 * t8 * t17 * t22 * t23 * 6.247434442774674e-3-a7 * t3 * t6 * t13 * t14 * t17 * t23 * 6.247434442774674e-3-a5 * t3 * t8 * t9 * t17 * t19 * t23 * 4.71111038810923e-4 + a7 * t3 * t8 * t9 * t15 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t8 * t9 * t18 * t19 * t21 * 4.71111038810923e-4-a7 * t3 * t8 * t9 * t18 * t19 * t23 * 6.247434442774674e-3 + a7 * t2 * t12 * t13 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t2 * t12 * t13 * t17 * t19 * t23 * 1.249486888554935e-2-d5 * t3 * t5 * t6 * t8 * t14 * t15 * 3.312522808627406e-1-d5 * t3 * t5 * t6 * t8 * t14 * t18 * 3.312522808627406e-1 + d5 * t3 * t5 * t6 * t8 * t15 * t19 * 1.354248200243321e-1 + d5 * t3 * t5 * t6 * t8 * t18 * t19 * 1.354248200243321e-1 + d3 * t3 * t5 * t6 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t3 * t6 * t8 * t14 * t15 * t21 * 6.247434442774674e-3 + d3 * t3 * t5 * t6 * t12 * t19 * t23 * 4.71111038810923e-4-d3 * t3 * t6 * t8 * t14 * t15 * t23 * 4.71111038810923e-4-d5 * t2 * t5 * t8 * t12 * t19 * t21 * 1.249486888554935e-2-d3 * t3 * t6 * t8 * t14 * t18 * t21 * 6.247434442774674e-3-d5 * t2 * t5 * t8 * t12 * t19 * t23 * 9.42222077621846e-4-d3 * t3 * t5 * t6 * t17 * t20 * t21 * 4.71111038810923e-4-d3 * t3 * t6 * t8 * t14 * t18 * t23 * 4.71111038810923e-4-d5 * t3 * t5 * t9 * t12 * t20 * t21 * 4.71111038810923e-4 + d3 * t3 * t5 * t6 * t17 * t20 * t23 * 6.247434442774674e-3-d3 * t3 * t5 * t6 * t17 * t21 * t22 * 4.71111038810923e-4 + d5 * t3 * t5 * t9 * t12 * t20 * t23 * 6.247434442774674e-3-d5 * t3 * t5 * t9 * t12 * t21 * t22 * 4.71111038810923e-4 + d5 * t3 * t6 * t11 * t12 * t19 * t21 * 6.247434442774674e-3 + d5 * t2 * t5 * t8 * t17 * t20 * t21 * 9.42222077621846e-4 + d3 * t3 * t5 * t6 * t17 * t22 * t23 * 6.247434442774674e-3-d3 * t3 * t9 * t11 * t12 * t20 * t21 * 4.71111038810923e-4 + d5 * t3 * t5 * t9 * t12 * t22 * t23 * 6.247434442774674e-3-d5 * t3 * t5 * t9 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t6 * t11 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t3 * t6 * t12 * t13 * t19 * t21 * 6.247434442774674e-3-d5 * t2 * t5 * t8 * t17 * t20 * t23 * 1.249486888554935e-2 + d5 * t2 * t5 * t8 * t17 * t21 * t22 * 9.42222077621846e-4 + d3 * t3 * t9 * t11 * t12 * t20 * t23 * 6.247434442774674e-3-d3 * t3 * t9 * t11 * t12 * t21 * t22 * 4.71111038810923e-4-d3 * t3 * t9 * t12 * t13 * t20 * t21 * 4.71111038810923e-4-d5 * t3 * t5 * t9 * t17 * t19 * t23 * 4.71111038810923e-4-d5 * t3 * t6 * t12 * t13 * t19 * t23 * 4.71111038810923e-4-d5 * t2 * t5 * t8 * t17 * t22 * t23 * 1.249486888554935e-2 + d3 * t3 * t9 * t11 * t12 * t22 * t23 * 6.247434442774674e-3-d3 * t3 * t9 * t11 * t17 * t19 * t21 * 6.247434442774674e-3 + d3 * t3 * t9 * t12 * t13 * t20 * t23 * 6.247434442774674e-3-d3 * t3 * t9 * t12 * t13 * t21 * t22 * 4.71111038810923e-4-d5 * t3 * t6 * t11 * t17 * t20 * t21 * 4.71111038810923e-4-d3 * t3 * t9 * t11 * t17 * t19 * t23 * 4.71111038810923e-4 + d3 * t3 * t9 * t12 * t13 * t22 * t23 * 6.247434442774674e-3-d3 * t3 * t9 * t13 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t6 * t11 * t17 * t20 * t23 * 6.247434442774674e-3-d5 * t3 * t6 * t11 * t17 * t21 * t22 * 4.71111038810923e-4 + d5 * t3 * t6 * t13 * t17 * t20 * t21 * 4.71111038810923e-4-d3 * t3 * t9 * t13 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t6 * t11 * t17 * t22 * t23 * 6.247434442774674e-3-d5 * t3 * t6 * t13 * t17 * t20 * t23 * 6.247434442774674e-3 + d5 * t3 * t6 * t13 * t17 * t21 * t22 * 4.71111038810923e-4-d5 * t3 * t6 * t13 * t17 * t22 * t23 * 6.247434442774674e-3 + t3 * t5 * t6 * t8 * t12 * t14 * t17 * 1.640860636489827e-3 + t3 * t5 * t6 * t8 * t12 * t17 * t19 * 2.487313068943488e-5 + t3 * t5 * t6 * t8 * t14 * t15 * t19 * 1.073500939031497e-2-t3 * t5 * t6 * t8 * t14 * t19 * t21 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t14 * t19 * t23 * 1.193879867928346e-3 + t3 * t5 * t6 * t8 * t15 * t20 * t25 * 1.765933827532306e-3-t3 * t8 * t9 * t12 * t14 * t17 * t19 * 1.073500939031497e-2-t3 * t5 * t6 * t8 * t18 * t21 * t23 * 2.370893727874773e-3-t2 * t5 * t8 * t12 * t14 * t19 * t25 * 3.531867655064613e-3 + t3 * t5 * t9 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t3 * t5 * t6 * t8 * t21 * t22 * t23 * 2.370893727874773e-3-t2 * t5 * t8 * t14 * t17 * t21 * t23 * 3.531867655064613e-3 + t3 * t6 * t11 * t12 * t14 * t19 * t25 * 1.765933827532306e-3-t3 * t5 * t9 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t3 * t6 * t12 * t13 * t14 * t19 * t25 * 1.765933827532306e-3-t3 * t8 * t9 * t12 * t17 * t21 * t23 * 2.370893727874773e-3-t3 * t8 * t9 * t12 * t17 * t20 * t25 * 1.765933827532306e-3 + t3 * t6 * t11 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-t3 * t6 * t13 * t14 * t17 * t21 * t23 * 1.765933827532306e-3 + t3 * t8 * t9 * t15 * t19 * t21 * t23 * 1.765933827532306e-3-t3 * t8 * t9 * t18 * t19 * t21 * t23 * 1.765933827532306e-3-t2 * t12 * t13 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + a5 * t3 * t5 * t6 * t8 * t12 * t19 * t21 * 1.249486888554935e-2 + a5 * t3 * t5 * t6 * t8 * t12 * t19 * t23 * 9.42222077621846e-4-a5 * t3 * t5 * t6 * t8 * t17 * t20 * t21 * 9.42222077621846e-4 + a5 * t3 * t5 * t6 * t8 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t3 * t5 * t6 * t8 * t17 * t21 * t22 * 9.42222077621846e-4-a7 * t3 * t5 * t6 * t8 * t15 * t21 * t22 * 1.249486888554935e-2-a7 * t2 * t5 * t8 * t12 * t14 * t19 * t21 * 2.49897377710987e-2 + a5 * t3 * t5 * t6 * t8 * t17 * t22 * t23 * 1.249486888554935e-2-a7 * t3 * t5 * t6 * t8 * t15 * t22 * t23 * 9.42222077621846e-4-a7 * t2 * t5 * t8 * t12 * t14 * t19 * t23 * 1.884444155243692e-3 + a7 * t3 * t6 * t11 * t12 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t3 * t5 * t9 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t3 * t6 * t11 * t12 * t14 * t19 * t23 * 9.42222077621846e-4-a7 * t3 * t6 * t12 * t13 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t3 * t5 * t9 * t14 * t17 * t19 * t23 * 9.42222077621846e-4-a7 * t3 * t6 * t12 * t13 * t14 * t19 * t23 * 9.42222077621846e-4 + a7 * t3 * t8 * t9 * t12 * t17 * t21 * t22 * 1.249486888554935e-2 + a7 * t3 * t8 * t9 * t12 * t17 * t22 * t23 * 9.42222077621846e-4-d5 * t3 * t5 * t6 * t8 * t14 * t15 * t21 * 1.249486888554935e-2-d5 * t3 * t5 * t6 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-d5 * t3 * t5 * t6 * t8 * t14 * t18 * t21 * 1.249486888554935e-2-d5 * t3 * t5 * t6 * t8 * t14 * t18 * t23 * 9.42222077621846e-4 + t3 * t5 * t6 * t8 * t12 * t14 * t17 * t21 * 1.193879867928346e-3-t3 * t5 * t6 * t8 * t12 * t14 * t17 * t23 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t14 * t15 * t19 * t21 * 3.517483669838449e-3-t3 * t5 * t6 * t8 * t14 * t15 * t19 * t23 * 1.193879867928346e-3 + t3 * t5 * t6 * t8 * t12 * t17 * t19 * t24 * 2.370893727874773e-3-t3 * t5 * t6 * t8 * t12 * t17 * t19 * t25 * 2.370893727874773e-3 + t3 * t5 * t6 * t8 * t15 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t8 * t9 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3-t2 * t5 * t8 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3 + t3 * t8 * t9 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3 + t3 * t6 * t11 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t5 * t9 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t6 * t12 * t13 * t14 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t8 * t9 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3-a7 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t3 * t5 * t6 * t8 * t12 * t17 * t19 * t23 * 1.249486888554935e-2 + t3 * t5 * t6 * t8 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3, t6 * (-1.223492856623504e-2)-t9 * 2.278153122465366e-1 + t5 * t6 * 2.736425713896764e-3-t6 * t8 * 4.770739247958454e-3 + t9 * t11 * 1.501724323024829e-1-t9 * t13 * 1.501724323024829e-1-a4 * t5 * t9 * 1.717769816935919 + a4 * t8 * t9 * 4.886342557237083e-1 + d3 * t5 * t9 * 4.886342557237083e-1 + d3 * t8 * t9 * 1.717769816935919 + d3 * t6 * t17 * 1.003178367299898e-2-d5 * t9 * t12 * 1.003178367299898e-2 + t5 * t8 * t9 * 5.143771481062207e-1 + t5 * t6 * t12 * 7.703820892966798e-3-t5 * t6 * t17 * 6.14079330980765e-3-t6 * t8 * t15 * 3.731166785767855e-3 + t6 * t8 * t18 * 3.731166785767855e-3-t9 * t11 * t12 * 6.14079330980765e-3 + t9 * t12 * t13 * 6.14079330980765e-3-t9 * t11 * t17 * 7.703820892966798e-3 + t9 * t13 * t17 * 7.703820892966798e-3-a5 * t6 * t8 * t12 * 7.702783979911432e-2 + a4 * t8 * t9 * t12 * 1.003178367299898e-2 + a5 * t6 * t8 * t17 * 1.003178367299898e-2 + a4 * t8 * t9 * t17 * 7.702783979911432e-2-d5 * t5 * t6 * t12 * 7.702783979911432e-2 + d3 * t5 * t9 * t12 * 1.003178367299898e-2-d3 * t6 * t11 * t12 * 7.702783979911432e-2 + d5 * t5 * t6 * t17 * 1.003178367299898e-2 + d3 * t5 * t9 * t17 * 7.702783979911432e-2-d3 * t6 * t12 * t13 * 7.702783979911432e-2 + d5 * t9 * t11 * t12 * 2.006356734599796e-2 + d5 * t9 * t11 * t17 * 7.702783979911432e-2-d5 * t9 * t13 * t17 * 7.702783979911432e-2 + t5 * t6 * t12 * t14 * 1.243656534471744e-5 + t5 * t8 * t9 * t15 * 3.21778928866176e-2 + t5 * t8 * t9 * t18 * 2.505201111020351e-2-t5 * t6 * t12 * t19 * 8.204303182449135e-4-t5 * t8 * t9 * t20 * 1.272243011263065e-3 + t6 * t8 * t12 * t17 * 7.125881776414093e-3 + t6 * t8 * t14 * t15 * 8.204303182449135e-4 + t5 * t8 * t9 * t22 * 6.554082237488641e-3-t6 * t8 * t14 * t18 * 8.204303182449135e-4 + t5 * t6 * t17 * t20 * 5.367504695157484e-3 + t6 * t8 * t15 * t19 * 1.243656534471744e-5-t5 * t6 * t17 * t22 * 5.367504695157484e-3-t6 * t8 * t18 * t19 * 1.243656534471744e-5-t9 * t11 * t14 * t17 * 1.243656534471744e-5 + t9 * t11 * t12 * t20 * 5.367504695157484e-3 + t9 * t13 * t14 * t17 * 1.243656534471744e-5-t9 * t11 * t12 * t22 * 5.367504695157484e-3-t9 * t12 * t13 * t20 * 5.367504695157484e-3 + t9 * t11 * t17 * t19 * 8.204303182449135e-4 + t9 * t12 * t13 * t22 * 5.367504695157484e-3-t9 * t13 * t17 * t19 * 8.204303182449135e-4 + a5 * t5 * t8 * t9 * t12 * 2.006356734599796e-2 + a5 * t5 * t8 * t9 * t17 * 1.540556795982286e-1-a4 * t5 * t9 * t14 * t15 * 1.656261404313703e-1-a4 * t8 * t9 * t12 * t14 * 6.771241001216606e-2-a4 * t5 * t9 * t14 * t18 * 1.656261404313703e-1-a5 * t6 * t8 * t14 * t17 * 6.771241001216606e-2 + a4 * t5 * t9 * t15 * t19 * 6.771241001216606e-2-a4 * t8 * t9 * t12 * t19 * 1.656261404313703e-1-a5 * t9 * t11 * t14 * t15 * 1.656261404313703e-1 + a4 * t5 * t9 * t18 * t19 * 6.771241001216606e-2-a5 * t6 * t8 * t17 * t19 * 1.656261404313703e-1 + a5 * t9 * t13 * t14 * t15 * 1.656261404313703e-1-a5 * t9 * t11 * t14 * t18 * 1.656261404313703e-1 + a5 * t9 * t11 * t15 * t19 * 6.771241001216606e-2 + a5 * t9 * t13 * t14 * t18 * 1.656261404313703e-1-a5 * t9 * t13 * t15 * t19 * 6.771241001216606e-2 + a5 * t9 * t11 * t18 * t19 * 6.771241001216606e-2-a5 * t9 * t13 * t18 * t19 * 6.771241001216606e-2-d3 * t5 * t9 * t12 * t14 * 6.771241001216606e-2-d5 * t5 * t6 * t14 * t17 * 6.771241001216606e-2-d3 * t5 * t9 * t12 * t19 * 1.656261404313703e-1 + d3 * t8 * t9 * t14 * t15 * 1.656261404313703e-1-d3 * t6 * t11 * t14 * t17 * 6.771241001216606e-2-d5 * t9 * t11 * t12 * t14 * 6.771241001216606e-2 + d3 * t8 * t9 * t14 * t18 * 1.656261404313703e-1-d5 * t5 * t6 * t17 * t19 * 1.656261404313703e-1-d3 * t6 * t13 * t14 * t17 * 6.771241001216606e-2 + d5 * t9 * t12 * t13 * t14 * 6.771241001216606e-2-d3 * t8 * t9 * t15 * t19 * 6.771241001216606e-2-d3 * t6 * t11 * t17 * t19 * 1.656261404313703e-1-d5 * t9 * t11 * t12 * t19 * 1.656261404313703e-1-d3 * t8 * t9 * t18 * t19 * 6.771241001216606e-2-d3 * t6 * t13 * t17 * t19 * 1.656261404313703e-1 + d5 * t9 * t12 * t13 * t19 * 1.656261404313703e-1 + t5 * t8 * t9 * t12 * t17 * 7.462333571535709e-3-t5 * t8 * t9 * t14 * t19 * 1.073500939031497e-2-t5 * t8 * t9 * t15 * t20 * 6.554082237488641e-3 + t5 * t8 * t9 * t15 * t22 * 1.272243011263065e-3 + t5 * t6 * t12 * t14 * t24 * 1.185446863937387e-3-t5 * t6 * t14 * t17 * t19 * 7.826325248751706e-3-t5 * t6 * t12 * t14 * t25 * 1.185446863937387e-3-t5 * t6 * t12 * t19 * t21 * 5.969399339641728e-4-t6 * t8 * t12 * t17 * t20 * 6.554082237488641e-3-t5 * t8 * t9 * t18 * t24 * 1.765933827532306e-3 + t6 * t8 * t14 * t15 * t21 * 5.969399339641728e-4 + t5 * t6 * t12 * t19 * t23 * 1.758741834919224e-3 + t6 * t8 * t12 * t17 * t22 * 1.272243011263065e-3-t9 * t11 * t12 * t14 * t19 * 7.826325248751706e-3-t6 * t8 * t14 * t15 * t23 * 1.758741834919224e-3 + t6 * t8 * t12 * t17 * t24 * 1.765933827532306e-3-t6 * t8 * t14 * t18 * t21 * 5.969399339641728e-4 + t9 * t12 * t13 * t14 * t19 * 7.826325248751706e-3-t5 * t6 * t17 * t20 * t21 * 1.758741834919224e-3 + t5 * t8 * t9 * t22 * t25 * 1.765933827532306e-3 + t6 * t8 * t14 * t18 * t23 * 1.758741834919224e-3-t5 * t6 * t17 * t20 * t23 * 5.969399339641728e-4 + t5 * t6 * t17 * t21 * t22 * 1.758741834919224e-3 + t6 * t8 * t15 * t19 * t24 * 1.185446863937387e-3 + t5 * t6 * t17 * t22 * t23 * 5.969399339641728e-4-t6 * t8 * t15 * t19 * t25 * 1.185446863937387e-3-t9 * t11 * t12 * t20 * t21 * 1.758741834919224e-3-t6 * t8 * t18 * t19 * t24 * 1.185446863937387e-3-t9 * t11 * t12 * t20 * t23 * 5.969399339641728e-4 + t9 * t11 * t12 * t21 * t22 * 1.758741834919224e-3-t9 * t11 * t14 * t17 * t24 * 1.185446863937387e-3 + t9 * t12 * t13 * t20 * t21 * 1.758741834919224e-3 + t6 * t8 * t18 * t19 * t25 * 1.185446863937387e-3 + t9 * t11 * t14 * t17 * t25 * 1.185446863937387e-3 + t9 * t11 * t12 * t22 * t23 * 5.969399339641728e-4 + t9 * t11 * t17 * t19 * t21 * 5.969399339641728e-4 + t9 * t12 * t13 * t20 * t23 * 5.969399339641728e-4-t9 * t12 * t13 * t21 * t22 * 1.758741834919224e-3 + t9 * t13 * t14 * t17 * t24 * 1.185446863937387e-3-t9 * t13 * t14 * t17 * t25 * 1.185446863937387e-3-t9 * t11 * t17 * t19 * t23 * 1.758741834919224e-3-t9 * t12 * t13 * t22 * t23 * 5.969399339641728e-4-t9 * t13 * t17 * t19 * t21 * 5.969399339641728e-4 + t9 * t13 * t17 * t19 * t23 * 1.758741834919224e-3-a5 * t5 * t8 * t9 * t12 * t14 * 1.354248200243321e-1-a5 * t5 * t8 * t9 * t12 * t19 * 3.312522808627406e-1-a7 * t5 * t6 * t12 * t14 * t21 * 4.71111038810923e-4 + a7 * t5 * t6 * t12 * t14 * t23 * 6.247434442774674e-3-a4 * t5 * t9 * t14 * t15 * t21 * 6.247434442774674e-3 + a7 * t5 * t8 * t9 * t18 * t21 * 1.249486888554935e-2-a4 * t5 * t9 * t14 * t15 * t23 * 4.71111038810923e-4 + a7 * t5 * t8 * t9 * t18 * t23 * 9.42222077621846e-4-a7 * t5 * t8 * t9 * t20 * t21 * 1.249486888554935e-2-a4 * t5 * t9 * t14 * t18 * t21 * 6.247434442774674e-3-a7 * t6 * t8 * t12 * t17 * t21 * 1.249486888554935e-2-a5 * t6 * t8 * t12 * t20 * t21 * 4.71111038810923e-4-a7 * t5 * t8 * t9 * t20 * t23 * 9.42222077621846e-4-a4 * t5 * t9 * t14 * t18 * t23 * 4.71111038810923e-4-a4 * t8 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-a7 * t6 * t8 * t12 * t17 * t23 * 9.42222077621846e-4 + a5 * t6 * t8 * t12 * t20 * t23 * 6.247434442774674e-3-a5 * t6 * t8 * t12 * t21 * t22 * 4.71111038810923e-4-a4 * t8 * t9 * t12 * t19 * t23 * 4.71111038810923e-4-a5 * t9 * t11 * t14 * t15 * t21 * 6.247434442774674e-3 + a5 * t6 * t8 * t12 * t22 * t23 * 6.247434442774674e-3-a5 * t6 * t8 * t17 * t19 * t21 * 6.247434442774674e-3-a7 * t6 * t8 * t15 * t19 * t21 * 4.71111038810923e-4-a5 * t9 * t11 * t14 * t15 * t23 * 4.71111038810923e-4 + a5 * t9 * t13 * t14 * t15 * t21 * 6.247434442774674e-3-a5 * t6 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-a5 * t9 * t11 * t14 * t18 * t21 * 6.247434442774674e-3 + a7 * t6 * t8 * t15 * t19 * t23 * 6.247434442774674e-3 + a4 * t8 * t9 * t17 * t20 * t21 * 4.71111038810923e-4 + a5 * t9 * t13 * t14 * t15 * t23 * 4.71111038810923e-4 + a7 * t6 * t8 * t18 * t19 * t21 * 4.71111038810923e-4 + a7 * t9 * t11 * t14 * t17 * t21 * 4.71111038810923e-4-a5 * t9 * t11 * t14 * t18 * t23 * 4.71111038810923e-4 + a5 * t9 * t13 * t14 * t18 * t21 * 6.247434442774674e-3-a4 * t8 * t9 * t17 * t20 * t23 * 6.247434442774674e-3 + a4 * t8 * t9 * t17 * t21 * t22 * 4.71111038810923e-4-a7 * t6 * t8 * t18 * t19 * t23 * 6.247434442774674e-3-a7 * t9 * t11 * t14 * t17 * t23 * 6.247434442774674e-3-a7 * t9 * t13 * t14 * t17 * t21 * 4.71111038810923e-4 + a5 * t9 * t13 * t14 * t18 * t23 * 4.71111038810923e-4-a4 * t8 * t9 * t17 * t22 * t23 * 6.247434442774674e-3 + a7 * t9 * t13 * t14 * t17 * t23 * 6.247434442774674e-3 + d5 * t5 * t8 * t9 * t14 * t15 * 3.312522808627406e-1 + d5 * t5 * t8 * t9 * t14 * t18 * 3.312522808627406e-1-d5 * t5 * t8 * t9 * t15 * t19 * 1.354248200243321e-1-d5 * t5 * t8 * t9 * t18 * t19 * 1.354248200243321e-1-d3 * t5 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-d5 * t5 * t6 * t12 * t20 * t21 * 4.71111038810923e-4 + d3 * t8 * t9 * t14 * t15 * t21 * 6.247434442774674e-3-d3 * t5 * t9 * t12 * t19 * t23 * 4.71111038810923e-4 + d5 * t5 * t6 * t12 * t20 * t23 * 6.247434442774674e-3-d5 * t5 * t6 * t12 * t21 * t22 * 4.71111038810923e-4 + d3 * t8 * t9 * t14 * t15 * t23 * 4.71111038810923e-4-d3 * t6 * t11 * t12 * t20 * t21 * 4.71111038810923e-4 + d3 * t8 * t9 * t14 * t18 * t21 * 6.247434442774674e-3 + d5 * t5 * t6 * t12 * t22 * t23 * 6.247434442774674e-3-d5 * t5 * t6 * t17 * t19 * t21 * 6.247434442774674e-3 + d3 * t5 * t9 * t17 * t20 * t21 * 4.71111038810923e-4 + d3 * t6 * t11 * t12 * t20 * t23 * 6.247434442774674e-3-d3 * t6 * t11 * t12 * t21 * t22 * 4.71111038810923e-4-d3 * t6 * t12 * t13 * t20 * t21 * 4.71111038810923e-4 + d3 * t8 * t9 * t14 * t18 * t23 * 4.71111038810923e-4-d5 * t5 * t6 * t17 * t19 * t23 * 4.71111038810923e-4-d3 * t5 * t9 * t17 * t20 * t23 * 6.247434442774674e-3 + d3 * t5 * t9 * t17 * t21 * t22 * 4.71111038810923e-4 + d3 * t6 * t11 * t12 * t22 * t23 * 6.247434442774674e-3-d3 * t6 * t11 * t17 * t19 * t21 * 6.247434442774674e-3 + d3 * t6 * t12 * t13 * t20 * t23 * 6.247434442774674e-3-d3 * t6 * t12 * t13 * t21 * t22 * 4.71111038810923e-4-d5 * t9 * t11 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t5 * t9 * t17 * t22 * t23 * 6.247434442774674e-3-d3 * t6 * t11 * t17 * t19 * t23 * 4.71111038810923e-4 + d3 * t6 * t12 * t13 * t22 * t23 * 6.247434442774674e-3-d3 * t6 * t13 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t9 * t11 * t12 * t19 * t23 * 4.71111038810923e-4 + d5 * t9 * t12 * t13 * t19 * t21 * 6.247434442774674e-3-d3 * t6 * t13 * t17 * t19 * t23 * 4.71111038810923e-4 + d5 * t9 * t12 * t13 * t19 * t23 * 4.71111038810923e-4 + d5 * t9 * t11 * t17 * t20 * t21 * 4.71111038810923e-4-d5 * t9 * t11 * t17 * t20 * t23 * 6.247434442774674e-3 + d5 * t9 * t11 * t17 * t21 * t22 * 4.71111038810923e-4-d5 * t9 * t13 * t17 * t20 * t21 * 4.71111038810923e-4-d5 * t9 * t11 * t17 * t22 * t23 * 6.247434442774674e-3 + d5 * t9 * t13 * t17 * t20 * t23 * 6.247434442774674e-3-d5 * t9 * t13 * t17 * t21 * t22 * 4.71111038810923e-4 + d5 * t9 * t13 * t17 * t22 * t23 * 6.247434442774674e-3-t5 * t8 * t9 * t12 * t14 * t17 * 1.640860636489827e-3-t5 * t8 * t9 * t12 * t17 * t19 * 2.487313068943488e-5-t5 * t8 * t9 * t14 * t15 * t19 * 1.073500939031497e-2 + t5 * t8 * t9 * t14 * t19 * t21 * 3.517483669838449e-3-t6 * t8 * t12 * t14 * t17 * t19 * 1.073500939031497e-2 + t5 * t8 * t9 * t14 * t19 * t23 * 1.193879867928346e-3 + t5 * t6 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t5 * t8 * t9 * t15 * t20 * t25 * 1.765933827532306e-3 + t5 * t8 * t9 * t18 * t21 * t23 * 2.370893727874773e-3-t5 * t6 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t6 * t8 * t12 * t17 * t21 * t23 * 2.370893727874773e-3 + t5 * t8 * t9 * t21 * t22 * t23 * 2.370893727874773e-3-t6 * t8 * t12 * t17 * t20 * t25 * 1.765933827532306e-3-t9 * t11 * t12 * t14 * t19 * t25 * 1.765933827532306e-3 + t6 * t8 * t15 * t19 * t21 * t23 * 1.765933827532306e-3 + t9 * t12 * t13 * t14 * t19 * t25 * 1.765933827532306e-3-t6 * t8 * t18 * t19 * t21 * t23 * 1.765933827532306e-3-t9 * t11 * t14 * t17 * t21 * t23 * 1.765933827532306e-3 + t9 * t13 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-a5 * t5 * t8 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-a5 * t5 * t8 * t9 * t12 * t19 * t23 * 9.42222077621846e-4 + a5 * t5 * t8 * t9 * t17 * t20 * t21 * 9.42222077621846e-4-a5 * t5 * t8 * t9 * t17 * t20 * t23 * 1.249486888554935e-2 + a5 * t5 * t8 * t9 * t17 * t21 * t22 * 9.42222077621846e-4 + a7 * t5 * t8 * t9 * t15 * t21 * t22 * 1.249486888554935e-2-a5 * t5 * t8 * t9 * t17 * t22 * t23 * 1.249486888554935e-2-a7 * t5 * t6 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t5 * t8 * t9 * t15 * t22 * t23 * 9.42222077621846e-4-a7 * t5 * t6 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t6 * t8 * t12 * t17 * t21 * t22 * 1.249486888554935e-2-a7 * t9 * t11 * t12 * t14 * t19 * t21 * 1.249486888554935e-2 + a7 * t6 * t8 * t12 * t17 * t22 * t23 * 9.42222077621846e-4-a7 * t9 * t11 * t12 * t14 * t19 * t23 * 9.42222077621846e-4 + a7 * t9 * t12 * t13 * t14 * t19 * t21 * 1.249486888554935e-2 + a7 * t9 * t12 * t13 * t14 * t19 * t23 * 9.42222077621846e-4 + d5 * t5 * t8 * t9 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t5 * t8 * t9 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t5 * t8 * t9 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t5 * t8 * t9 * t14 * t18 * t23 * 9.42222077621846e-4-t5 * t8 * t9 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t5 * t8 * t9 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t5 * t8 * t9 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t5 * t8 * t9 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t5 * t8 * t9 * t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t5 * t8 * t9 * t12 * t17 * t19 * t25 * 2.370893727874773e-3 + t6 * t8 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3 + t6 * t8 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3-t5 * t8 * t9 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-t5 * t6 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3-t6 * t8 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3-t9 * t11 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3 + t9 * t12 * t13 * t14 * t19 * t21 * t23 * 2.370893727874773e-3 + a7 * t5 * t8 * t9 * t12 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t5 * t8 * t9 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-t5 * t8 * t9 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3, t11 * 1.807861861545219e-2 + t13 * 5.324557667216729e-1-a4 * t5 * 9.772685114474167e-1-a4 * t8 * 3.435539633871837 + t5 * t8 * 3.003448646049658e-1 + t13 * t15 * 3.21778928866176e-2 + t11 * t20 * 1.272243011263065e-3 + t13 * t18 * 2.505201111020351e-2-t11 * t22 * 6.554082237488641e-3-a4 * t5 * t12 * 2.006356734599796e-2-a4 * t5 * t17 * 1.540556795982286e-1-a5 * t11 * t12 * 2.006356734599796e-2-a5 * t11 * t17 * 1.540556795982286e-1-t5 * t8 * t12 * 1.22815866196153e-2-t5 * t8 * t17 * 1.54076417859336e-2 + t12 * t13 * t17 * 7.462333571535709e-3 + t11 * t14 * t19 * 1.073500939031497e-2-t13 * t15 * t20 * 6.554082237488641e-3 + t13 * t15 * t22 * 1.272243011263065e-3-t13 * t18 * t24 * 1.765933827532306e-3-t11 * t22 * t25 * 1.765933827532306e-3 + a4 * t5 * t12 * t14 * 1.354248200243321e-1 + a4 * t5 * t12 * t19 * 3.312522808627406e-1-a4 * t8 * t14 * t15 * 3.312522808627406e-1 + a5 * t11 * t12 * t14 * 1.354248200243321e-1-a4 * t8 * t14 * t18 * 3.312522808627406e-1 + a4 * t8 * t15 * t19 * 1.354248200243321e-1 + a5 * t11 * t12 * t19 * 3.312522808627406e-1 + a4 * t8 * t18 * t19 * 1.354248200243321e-1 + a7 * t11 * t20 * t21 * 1.249486888554935e-2 + a7 * t13 * t18 * t21 * 1.249486888554935e-2 + a7 * t11 * t20 * t23 * 9.42222077621846e-4 + a7 * t13 * t18 * t23 * 9.42222077621846e-4 + d5 * t5 * t8 * t12 * 2.006356734599796e-2 + d5 * t5 * t8 * t17 * 1.540556795982286e-1 + d5 * t13 * t14 * t15 * 3.312522808627406e-1 + d5 * t13 * t14 * t18 * 3.312522808627406e-1-d5 * t13 * t15 * t19 * 1.354248200243321e-1-d5 * t13 * t18 * t19 * 1.354248200243321e-1-t5 * t8 * t14 * t17 * 2.487313068943488e-5 + t5 * t8 * t12 * t20 * 1.073500939031497e-2-t5 * t8 * t12 * t22 * 1.073500939031497e-2 + t5 * t8 * t17 * t19 * 1.640860636489827e-3-t12 * t13 * t14 * t17 * 1.640860636489827e-3-t12 * t13 * t17 * t19 * 2.487313068943488e-5-t13 * t14 * t15 * t19 * 1.073500939031497e-2-t11 * t14 * t19 * t21 * 3.517483669838449e-3-t11 * t14 * t19 * t23 * 1.193879867928346e-3-t13 * t15 * t20 * t25 * 1.765933827532306e-3 + t13 * t18 * t21 * t23 * 2.370893727874773e-3-t11 * t21 * t22 * t23 * 2.370893727874773e-3-a5 * t5 * t8 * t14 * t15 * 3.312522808627406e-1-a5 * t5 * t8 * t14 * t18 * 3.312522808627406e-1 + a5 * t5 * t8 * t15 * t19 * 1.354248200243321e-1 + a5 * t5 * t8 * t18 * t19 * 1.354248200243321e-1 + a4 * t5 * t12 * t19 * t21 * 1.249486888554935e-2-a4 * t8 * t14 * t15 * t21 * 1.249486888554935e-2 + a4 * t5 * t12 * t19 * t23 * 9.42222077621846e-4-a4 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-a4 * t8 * t14 * t18 * t21 * 1.249486888554935e-2-a4 * t5 * t17 * t20 * t21 * 9.42222077621846e-4-a4 * t8 * t14 * t18 * t23 * 9.42222077621846e-4 + a5 * t11 * t12 * t19 * t21 * 1.249486888554935e-2 + a4 * t5 * t17 * t20 * t23 * 1.249486888554935e-2-a4 * t5 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t11 * t12 * t19 * t23 * 9.42222077621846e-4 + a4 * t5 * t17 * t22 * t23 * 1.249486888554935e-2-a5 * t11 * t17 * t20 * t21 * 9.42222077621846e-4 + a5 * t11 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t11 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t11 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t13 * t15 * t21 * t22 * 1.249486888554935e-2 + a7 * t13 * t15 * t22 * t23 * 9.42222077621846e-4-d5 * t5 * t8 * t12 * t14 * 1.354248200243321e-1-d5 * t5 * t8 * t12 * t19 * 3.312522808627406e-1 + d5 * t13 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t13 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t13 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t13 * t14 * t18 * t23 * 9.42222077621846e-4-t5 * t8 * t12 * t14 * t19 * 1.565265049750341e-2-t5 * t8 * t12 * t20 * t21 * 3.517483669838449e-3-t5 * t8 * t12 * t20 * t23 * 1.193879867928346e-3 + t5 * t8 * t12 * t21 * t22 * 3.517483669838449e-3-t5 * t8 * t14 * t17 * t24 * 2.370893727874773e-3 + t5 * t8 * t14 * t17 * t25 * 2.370893727874773e-3 + t5 * t8 * t12 * t22 * t23 * 1.193879867928346e-3 + t5 * t8 * t17 * t19 * t21 * 1.193879867928346e-3-t5 * t8 * t17 * t19 * t23 * 3.517483669838449e-3-t12 * t13 * t14 * t17 * t21 * 1.193879867928346e-3 + t12 * t13 * t14 * t17 * t23 * 3.517483669838449e-3 + t13 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t13 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t12 * t13 * t17 * t19 * t24 * 2.370893727874773e-3 + t12 * t13 * t17 * t19 * t25 * 2.370893727874773e-3-t13 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-a5 * t5 * t8 * t14 * t15 * t21 * 1.249486888554935e-2-a5 * t5 * t8 * t14 * t15 * t23 * 9.42222077621846e-4-a5 * t5 * t8 * t14 * t18 * t21 * 1.249486888554935e-2 + a7 * t5 * t8 * t14 * t17 * t21 * 9.42222077621846e-4-a5 * t5 * t8 * t14 * t18 * t23 * 9.42222077621846e-4-a7 * t5 * t8 * t14 * t17 * t23 * 1.249486888554935e-2 + a7 * t12 * t13 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t12 * t13 * t17 * t19 * t23 * 1.249486888554935e-2-d5 * t5 * t8 * t12 * t19 * t21 * 1.249486888554935e-2-d5 * t5 * t8 * t12 * t19 * t23 * 9.42222077621846e-4 + d5 * t5 * t8 * t17 * t20 * t21 * 9.42222077621846e-4-d5 * t5 * t8 * t17 * t20 * t23 * 1.249486888554935e-2 + d5 * t5 * t8 * t17 * t21 * t22 * 9.42222077621846e-4-d5 * t5 * t8 * t17 * t22 * t23 * 1.249486888554935e-2-t5 * t8 * t12 * t14 * t19 * t25 * 3.531867655064613e-3-t5 * t8 * t14 * t17 * t21 * t23 * 3.531867655064613e-3-t12 * t13 * t17 * t19 * t21 * t23 * 3.531867655064613e-3-a7 * t5 * t8 * t12 * t14 * t19 * t21 * 2.49897377710987e-2-a7 * t5 * t8 * t12 * t14 * t19 * t23 * 1.884444155243692e-3-t5 * t8 * t12 * t14 * t19 * t21 * t23 * 4.741787455749547e-3 + 1.169633856586688e-1, t2 * t5 * (-2.736425713896764e-3) + t2 * t8 * 1.039572462190599e-3-t3 * t9 * 6.704420055352959e-1-t3 * t5 * t6 * 1.039572462190599e-3-t3 * t6 * t8 * 2.736425713896764e-3-t2 * t5 * t12 * 7.703820892966798e-3 + t2 * t5 * t17 * 1.150829800496513e-2 + t2 * t8 * t14 * 8.204303182449135e-4 + t2 * t8 * t15 * 7.462333571535709e-3 + t3 * t9 * t15 * 1.016405861520946e-2 + t2 * t8 * t19 * 1.197883429282104e-3 + t3 * t9 * t20 * 7.826325248751706e-3 + a4 * t3 * t5 * t9 * 4.886342557237083e-1 + a4 * t3 * t8 * t9 * 1.717769816935919 + a5 * t2 * t8 * t12 * 7.702783979911432e-2 + a5 * t3 * t9 * t12 * 2.006356734599796e-2-a5 * t2 * t8 * t17 * 1.003178367299898e-2 + a5 * t3 * t9 * t17 * 1.540556795982286e-1-a7 * t3 * t9 * t21 * 1.249486888554935e-2-a7 * t3 * t9 * t23 * 9.42222077621846e-4-d3 * t3 * t5 * t9 * 1.717769816935919 + d3 * t3 * t8 * t9 * 4.886342557237083e-1 + d5 * t2 * t5 * t12 * 7.702783979911432e-2-d5 * t2 * t5 * t17 * 1.003178367299898e-2-d5 * t3 * t9 * t14 * 3.312522808627406e-1 + d5 * t3 * t9 * t19 * 1.354248200243321e-1-t3 * t5 * t6 * t14 * 8.204303182449135e-4-t3 * t5 * t6 * t15 * 7.462333571535709e-3-t3 * t6 * t8 * t12 * 7.703820892966798e-3-t2 * t5 * t12 * t14 * 1.197883429282104e-3-t3 * t5 * t6 * t19 * 1.197883429282104e-3 + t3 * t6 * t8 * t17 * 1.150829800496513e-2 + t2 * t5 * t12 * t19 * 8.204303182449135e-4-t2 * t8 * t12 * t17 * 1.016405861520946e-2-t2 * t8 * t14 * t15 * 1.640860636489827e-3 + t3 * t9 * t12 * t17 * 7.462333571535709e-3-t2 * t5 * t17 * t20 * 1.073500939031497e-2-t2 * t8 * t15 * t19 * 2.395766858564208e-3-t2 * t5 * t17 * t21 * 1.758741834919224e-3 + t2 * t8 * t14 * t21 * 5.969399339641728e-4 + t3 * t9 * t14 * t19 * 1.073500939031497e-2-t2 * t5 * t17 * t23 * 5.969399339641728e-4-t2 * t8 * t14 * t23 * 1.758741834919224e-3-t3 * t9 * t15 * t20 * 7.826325248751706e-3-t3 * t9 * t15 * t25 * 1.765933827532306e-3-t2 * t8 * t19 * t25 * 2.370893727874773e-3 + t3 * t9 * t20 * t25 * 1.765933827532306e-3-a5 * t3 * t5 * t6 * t12 * 7.702783979911432e-2 + a4 * t3 * t5 * t9 * t12 * 1.003178367299898e-2 + a5 * t3 * t5 * t6 * t17 * 1.003178367299898e-2 + a4 * t3 * t5 * t9 * t17 * 7.702783979911432e-2 + a4 * t3 * t8 * t9 * t14 * 1.656261404313703e-1-a4 * t3 * t8 * t9 * t19 * 6.771241001216606e-2-a5 * t3 * t9 * t12 * t14 * 1.354248200243321e-1 + a5 * t2 * t8 * t14 * t17 * 6.771241001216606e-2 + a5 * t2 * t8 * t12 * t21 * 4.71111038810923e-4-a5 * t3 * t9 * t12 * t19 * 3.312522808627406e-1-a5 * t2 * t8 * t12 * t23 * 6.247434442774674e-3 + a5 * t2 * t8 * t17 * t19 * 1.656261404313703e-1 + a5 * t3 * t9 * t17 * t21 * 9.42222077621846e-4-a5 * t3 * t9 * t17 * t23 * 1.249486888554935e-2-a7 * t2 * t8 * t19 * t21 * 4.71111038810923e-4 + a7 * t2 * t8 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t9 * t20 * t21 * 1.249486888554935e-2 + a7 * t3 * t9 * t20 * t23 * 9.42222077621846e-4-d3 * t3 * t5 * t9 * t14 * 1.656261404313703e-1 + d5 * t3 * t6 * t8 * t12 * 7.702783979911432e-2 + d3 * t3 * t8 * t9 * t12 * 1.003178367299898e-2 + d3 * t3 * t5 * t9 * t19 * 6.771241001216606e-2-d5 * t3 * t6 * t8 * t17 * 1.003178367299898e-2 + d3 * t3 * t8 * t9 * t17 * 7.702783979911432e-2 + d5 * t2 * t5 * t14 * t17 * 6.771241001216606e-2 + d5 * t2 * t5 * t12 * t21 * 4.71111038810923e-4-d5 * t2 * t5 * t12 * t23 * 6.247434442774674e-3 + d5 * t2 * t5 * t17 * t19 * 1.656261404313703e-1-d5 * t3 * t9 * t14 * t21 * 1.249486888554935e-2-d5 * t3 * t9 * t14 * t23 * 9.42222077621846e-4 + t3 * t5 * t6 * t12 * t17 * 1.016405861520946e-2 + t3 * t5 * t6 * t14 * t15 * 1.640860636489827e-3-t3 * t6 * t8 * t12 * t14 * 1.197883429282104e-3 + t3 * t5 * t6 * t15 * t19 * 2.395766858564208e-3 + t3 * t6 * t8 * t12 * t19 * 8.204303182449135e-4-t3 * t5 * t6 * t14 * t21 * 5.969399339641728e-4 + t3 * t5 * t6 * t14 * t23 * 1.758741834919224e-3-t3 * t6 * t8 * t17 * t20 * 1.073500939031497e-2-t3 * t6 * t8 * t17 * t21 * 1.758741834919224e-3-t3 * t9 * t12 * t14 * t17 * 1.640860636489827e-3 + t2 * t5 * t14 * t17 * t19 * 7.826325248751706e-3-t3 * t6 * t8 * t17 * t23 * 5.969399339641728e-4 + t2 * t5 * t12 * t14 * t25 * 2.370893727874773e-3 + t3 * t5 * t6 * t19 * t25 * 2.370893727874773e-3 + t2 * t5 * t12 * t19 * t21 * 5.969399339641728e-4 + t2 * t8 * t12 * t17 * t20 * 7.826325248751706e-3-t2 * t8 * t14 * t15 * t21 * 1.193879867928346e-3-t3 * t9 * t12 * t17 * t19 * 2.395766858564208e-3-t3 * t9 * t14 * t15 * t19 * 1.073500939031497e-2-t2 * t5 * t12 * t19 * t23 * 1.758741834919224e-3 + t2 * t8 * t14 * t15 * t23 * 3.517483669838449e-3 + t2 * t8 * t12 * t17 * t25 * 1.765933827532306e-3 + t2 * t5 * t17 * t20 * t21 * 3.517483669838449e-3-t3 * t9 * t14 * t19 * t21 * 3.517483669838449e-3 + t2 * t5 * t17 * t20 * t23 * 1.193879867928346e-3-t3 * t9 * t14 * t19 * t23 * 1.193879867928346e-3 + t2 * t8 * t15 * t19 * t25 * 4.741787455749547e-3-t3 * t9 * t15 * t21 * t23 * 2.370893727874773e-3-t3 * t9 * t15 * t20 * t25 * 1.765933827532306e-3 + t2 * t8 * t19 * t21 * t23 * 1.765933827532306e-3 + t3 * t9 * t20 * t21 * t23 * 2.370893727874773e-3-a4 * t3 * t5 * t9 * t12 * t14 * 6.771241001216606e-2-a5 * t3 * t5 * t6 * t14 * t17 * 6.771241001216606e-2-a4 * t3 * t5 * t9 * t12 * t19 * 1.656261404313703e-1-a5 * t3 * t5 * t6 * t12 * t21 * 4.71111038810923e-4 + a5 * t3 * t5 * t6 * t12 * t23 * 6.247434442774674e-3-a5 * t3 * t5 * t6 * t17 * t19 * 1.656261404313703e-1 + a4 * t3 * t5 * t9 * t17 * t21 * 4.71111038810923e-4 + a4 * t3 * t8 * t9 * t14 * t21 * 6.247434442774674e-3-a4 * t3 * t5 * t9 * t17 * t23 * 6.247434442774674e-3 + a4 * t3 * t8 * t9 * t14 * t23 * 4.71111038810923e-4 + a7 * t2 * t5 * t12 * t14 * t21 * 4.71111038810923e-4 + a7 * t3 * t5 * t6 * t19 * t21 * 4.71111038810923e-4-a7 * t2 * t5 * t12 * t14 * t23 * 6.247434442774674e-3-a7 * t3 * t5 * t6 * t19 * t23 * 6.247434442774674e-3-a5 * t3 * t9 * t12 * t19 * t21 * 1.249486888554935e-2-a5 * t3 * t9 * t12 * t19 * t23 * 9.42222077621846e-4 + a5 * t2 * t8 * t17 * t19 * t21 * 6.247434442774674e-3 + a7 * t2 * t8 * t15 * t19 * t21 * 9.42222077621846e-4 + a5 * t2 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-a7 * t2 * t8 * t15 * t19 * t23 * 1.249486888554935e-2-a7 * t3 * t9 * t15 * t20 * t21 * 1.249486888554935e-2-a7 * t3 * t9 * t15 * t20 * t23 * 9.42222077621846e-4-d3 * t3 * t8 * t9 * t12 * t14 * 6.771241001216606e-2 + d5 * t3 * t6 * t8 * t14 * t17 * 6.771241001216606e-2-d3 * t3 * t8 * t9 * t12 * t19 * 1.656261404313703e-1-d3 * t3 * t5 * t9 * t14 * t21 * 6.247434442774674e-3 + d5 * t3 * t6 * t8 * t12 * t21 * 4.71111038810923e-4-d3 * t3 * t5 * t9 * t14 * t23 * 4.71111038810923e-4-d5 * t3 * t6 * t8 * t12 * t23 * 6.247434442774674e-3 + d5 * t3 * t6 * t8 * t17 * t19 * 1.656261404313703e-1 + d3 * t3 * t8 * t9 * t17 * t21 * 4.71111038810923e-4-d3 * t3 * t8 * t9 * t17 * t23 * 6.247434442774674e-3 + d5 * t2 * t5 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t2 * t5 * t17 * t19 * t23 * 4.71111038810923e-4-t3 * t5 * t6 * t12 * t17 * t20 * 7.826325248751706e-3 + t3 * t5 * t6 * t14 * t15 * t21 * 1.193879867928346e-3-t3 * t5 * t6 * t14 * t15 * t23 * 3.517483669838449e-3 + t3 * t6 * t8 * t14 * t17 * t19 * 7.826325248751706e-3-t3 * t5 * t6 * t12 * t17 * t25 * 1.765933827532306e-3 + t3 * t6 * t8 * t12 * t14 * t25 * 2.370893727874773e-3 + t3 * t6 * t8 * t12 * t19 * t21 * 5.969399339641728e-4-t3 * t6 * t8 * t12 * t19 * t23 * 1.758741834919224e-3 + t2 * t8 * t12 * t14 * t17 * t19 * 1.073500939031497e-2-t3 * t5 * t6 * t15 * t19 * t25 * 4.741787455749547e-3 + t3 * t6 * t8 * t17 * t20 * t21 * 3.517483669838449e-3-t3 * t9 * t12 * t14 * t17 * t21 * 1.193879867928346e-3-t2 * t5 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t3 * t5 * t6 * t19 * t21 * t23 * 1.765933827532306e-3 + t3 * t6 * t8 * t17 * t20 * t23 * 1.193879867928346e-3 + t3 * t9 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t3 * t9 * t14 * t15 * t19 * t21 * 3.517483669838449e-3 + t2 * t5 * t14 * t17 * t19 * t25 * 1.765933827532306e-3 + t2 * t8 * t12 * t17 * t21 * t23 * 2.370893727874773e-3 + t3 * t9 * t14 * t15 * t19 * t23 * 1.193879867928346e-3 + t2 * t8 * t12 * t17 * t20 * t25 * 1.765933827532306e-3 + t3 * t9 * t12 * t17 * t19 * t25 * 4.741787455749547e-3-t2 * t8 * t15 * t19 * t21 * t23 * 3.531867655064613e-3-t3 * t9 * t15 * t20 * t21 * t23 * 2.370893727874773e-3 + a7 * t3 * t6 * t8 * t12 * t14 * t21 * 4.71111038810923e-4-a4 * t3 * t5 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-a7 * t3 * t6 * t8 * t12 * t14 * t23 * 6.247434442774674e-3-a4 * t3 * t5 * t9 * t12 * t19 * t23 * 4.71111038810923e-4-a5 * t3 * t5 * t6 * t17 * t19 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t6 * t15 * t19 * t21 * 9.42222077621846e-4-a5 * t3 * t5 * t6 * t17 * t19 * t23 * 4.71111038810923e-4 + a7 * t3 * t5 * t6 * t15 * t19 * t23 * 1.249486888554935e-2 + a7 * t2 * t5 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t2 * t5 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t2 * t8 * t12 * t17 * t20 * t21 * 1.249486888554935e-2 + a7 * t3 * t9 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t2 * t8 * t12 * t17 * t20 * t23 * 9.42222077621846e-4-a7 * t3 * t9 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-d3 * t3 * t8 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t3 * t8 * t9 * t12 * t19 * t23 * 4.71111038810923e-4 + d5 * t3 * t6 * t8 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t6 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-t3 * t5 * t6 * t12 * t14 * t17 * t19 * 1.073500939031497e-2-t3 * t5 * t6 * t12 * t17 * t21 * t23 * 2.370893727874773e-3-t3 * t6 * t8 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t3 * t5 * t6 * t12 * t17 * t20 * t25 * 1.765933827532306e-3 + t3 * t5 * t6 * t15 * t19 * t21 * t23 * 3.531867655064613e-3 + t3 * t6 * t8 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t2 * t8 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3-t2 * t8 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3 + t2 * t5 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3 + t2 * t8 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3-t3 * t9 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3-a7 * t3 * t5 * t6 * t12 * t17 * t20 * t21 * 1.249486888554935e-2-a7 * t3 * t5 * t6 * t12 * t17 * t20 * t23 * 9.42222077621846e-4 + a7 * t3 * t6 * t8 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t3 * t6 * t8 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + t3 * t5 * t6 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3 + t3 * t5 * t6 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3-t3 * t5 * t6 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t6 * t8 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3, t6 * (-6.704420055352959e-1) + t5 * t9 * 1.039572462190599e-3 + t8 * t9 * 2.736425713896764e-3 + t6 * t15 * 1.016405861520946e-2 + t6 * t20 * 7.826325248751706e-3 + a4 * t5 * t6 * 4.886342557237083e-1 + a4 * t6 * t8 * 1.717769816935919 + a5 * t6 * t12 * 2.006356734599796e-2 + a5 * t6 * t17 * 1.540556795982286e-1-a7 * t6 * t21 * 1.249486888554935e-2-a7 * t6 * t23 * 9.42222077621846e-4-d3 * t5 * t6 * 1.717769816935919 + d3 * t6 * t8 * 4.886342557237083e-1-d5 * t6 * t14 * 3.312522808627406e-1 + d5 * t6 * t19 * 1.354248200243321e-1 + t5 * t9 * t14 * 8.204303182449135e-4 + t5 * t9 * t15 * 7.462333571535709e-3 + t8 * t9 * t12 * 7.703820892966798e-3 + t5 * t9 * t19 * 1.197883429282104e-3-t8 * t9 * t17 * 1.150829800496513e-2 + t6 * t12 * t17 * 7.462333571535709e-3 + t6 * t14 * t19 * 1.073500939031497e-2-t6 * t15 * t20 * 7.826325248751706e-3-t6 * t15 * t25 * 1.765933827532306e-3 + t6 * t20 * t25 * 1.765933827532306e-3 + a4 * t5 * t6 * t12 * 1.003178367299898e-2 + a5 * t5 * t9 * t12 * 7.702783979911432e-2 + a4 * t5 * t6 * t17 * 7.702783979911432e-2 + a4 * t6 * t8 * t14 * 1.656261404313703e-1-a5 * t5 * t9 * t17 * 1.003178367299898e-2-a4 * t6 * t8 * t19 * 6.771241001216606e-2-a5 * t6 * t12 * t14 * 1.354248200243321e-1-a5 * t6 * t12 * t19 * 3.312522808627406e-1 + a5 * t6 * t17 * t21 * 9.42222077621846e-4-a5 * t6 * t17 * t23 * 1.249486888554935e-2 + a7 * t6 * t20 * t21 * 1.249486888554935e-2 + a7 * t6 * t20 * t23 * 9.42222077621846e-4-d3 * t5 * t6 * t14 * 1.656261404313703e-1 + d3 * t6 * t8 * t12 * 1.003178367299898e-2 + d3 * t5 * t6 * t19 * 6.771241001216606e-2 + d3 * t6 * t8 * t17 * 7.702783979911432e-2-d5 * t8 * t9 * t12 * 7.702783979911432e-2 + d5 * t8 * t9 * t17 * 1.003178367299898e-2-d5 * t6 * t14 * t21 * 1.249486888554935e-2-d5 * t6 * t14 * t23 * 9.42222077621846e-4-t5 * t9 * t12 * t17 * 1.016405861520946e-2-t5 * t9 * t14 * t15 * 1.640860636489827e-3 + t8 * t9 * t12 * t14 * 1.197883429282104e-3-t5 * t9 * t15 * t19 * 2.395766858564208e-3-t8 * t9 * t12 * t19 * 8.204303182449135e-4 + t5 * t9 * t14 * t21 * 5.969399339641728e-4-t6 * t12 * t14 * t17 * 1.640860636489827e-3-t5 * t9 * t14 * t23 * 1.758741834919224e-3-t6 * t12 * t17 * t19 * 2.395766858564208e-3-t6 * t14 * t15 * t19 * 1.073500939031497e-2 + t8 * t9 * t17 * t20 * 1.073500939031497e-2 + t8 * t9 * t17 * t21 * 1.758741834919224e-3 + t8 * t9 * t17 * t23 * 5.969399339641728e-4-t5 * t9 * t19 * t25 * 2.370893727874773e-3-t6 * t14 * t19 * t21 * 3.517483669838449e-3-t6 * t14 * t19 * t23 * 1.193879867928346e-3-t6 * t15 * t21 * t23 * 2.370893727874773e-3-t6 * t15 * t20 * t25 * 1.765933827532306e-3 + t6 * t20 * t21 * t23 * 2.370893727874773e-3-a4 * t5 * t6 * t12 * t14 * 6.771241001216606e-2-a4 * t5 * t6 * t12 * t19 * 1.656261404313703e-1 + a5 * t5 * t9 * t14 * t17 * 6.771241001216606e-2 + a5 * t5 * t9 * t12 * t21 * 4.71111038810923e-4 + a4 * t5 * t6 * t17 * t21 * 4.71111038810923e-4 + a4 * t6 * t8 * t14 * t21 * 6.247434442774674e-3-a5 * t5 * t9 * t12 * t23 * 6.247434442774674e-3-a4 * t5 * t6 * t17 * t23 * 6.247434442774674e-3 + a4 * t6 * t8 * t14 * t23 * 4.71111038810923e-4 + a5 * t5 * t9 * t17 * t19 * 1.656261404313703e-1-a7 * t5 * t9 * t19 * t21 * 4.71111038810923e-4-a5 * t6 * t12 * t19 * t21 * 1.249486888554935e-2 + a7 * t5 * t9 * t19 * t23 * 6.247434442774674e-3-a5 * t6 * t12 * t19 * t23 * 9.42222077621846e-4-a7 * t6 * t15 * t20 * t21 * 1.249486888554935e-2-a7 * t6 * t15 * t20 * t23 * 9.42222077621846e-4-d3 * t6 * t8 * t12 * t14 * 6.771241001216606e-2-d3 * t6 * t8 * t12 * t19 * 1.656261404313703e-1-d3 * t5 * t6 * t14 * t21 * 6.247434442774674e-3-d3 * t5 * t6 * t14 * t23 * 4.71111038810923e-4-d5 * t8 * t9 * t14 * t17 * 6.771241001216606e-2 + d3 * t6 * t8 * t17 * t21 * 4.71111038810923e-4-d5 * t8 * t9 * t12 * t21 * 4.71111038810923e-4-d3 * t6 * t8 * t17 * t23 * 6.247434442774674e-3 + d5 * t8 * t9 * t12 * t23 * 6.247434442774674e-3-d5 * t8 * t9 * t17 * t19 * 1.656261404313703e-1 + t5 * t9 * t12 * t17 * t20 * 7.826325248751706e-3-t5 * t9 * t14 * t15 * t21 * 1.193879867928346e-3 + t5 * t9 * t14 * t15 * t23 * 3.517483669838449e-3-t8 * t9 * t14 * t17 * t19 * 7.826325248751706e-3 + t5 * t9 * t12 * t17 * t25 * 1.765933827532306e-3-t8 * t9 * t12 * t14 * t25 * 2.370893727874773e-3-t8 * t9 * t12 * t19 * t21 * 5.969399339641728e-4-t6 * t12 * t14 * t17 * t21 * 1.193879867928346e-3 + t8 * t9 * t12 * t19 * t23 * 1.758741834919224e-3 + t6 * t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t5 * t9 * t15 * t19 * t25 * 4.741787455749547e-3 + t6 * t14 * t15 * t19 * t21 * 3.517483669838449e-3-t8 * t9 * t17 * t20 * t21 * 3.517483669838449e-3 + t5 * t9 * t19 * t21 * t23 * 1.765933827532306e-3 + t6 * t14 * t15 * t19 * t23 * 1.193879867928346e-3-t8 * t9 * t17 * t20 * t23 * 1.193879867928346e-3 + t6 * t12 * t17 * t19 * t25 * 4.741787455749547e-3-t6 * t15 * t20 * t21 * t23 * 2.370893727874773e-3-a4 * t5 * t6 * t12 * t19 * t21 * 6.247434442774674e-3-a4 * t5 * t6 * t12 * t19 * t23 * 4.71111038810923e-4-a7 * t8 * t9 * t12 * t14 * t21 * 4.71111038810923e-4 + a7 * t8 * t9 * t12 * t14 * t23 * 6.247434442774674e-3 + a5 * t5 * t9 * t17 * t19 * t21 * 6.247434442774674e-3 + a7 * t5 * t9 * t15 * t19 * t21 * 9.42222077621846e-4 + a5 * t5 * t9 * t17 * t19 * t23 * 4.71111038810923e-4-a7 * t5 * t9 * t15 * t19 * t23 * 1.249486888554935e-2 + a7 * t6 * t12 * t17 * t19 * t21 * 9.42222077621846e-4-a7 * t6 * t12 * t17 * t19 * t23 * 1.249486888554935e-2-d3 * t6 * t8 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t6 * t8 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t8 * t9 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t8 * t9 * t17 * t19 * t23 * 4.71111038810923e-4 + t5 * t9 * t12 * t14 * t17 * t19 * 1.073500939031497e-2 + t5 * t9 * t12 * t17 * t21 * t23 * 2.370893727874773e-3 + t8 * t9 * t12 * t14 * t21 * t23 * 1.765933827532306e-3 + t5 * t9 * t12 * t17 * t20 * t25 * 1.765933827532306e-3-t5 * t9 * t15 * t19 * t21 * t23 * 3.531867655064613e-3-t8 * t9 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t6 * t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + a7 * t5 * t9 * t12 * t17 * t20 * t21 * 1.249486888554935e-2 + a7 * t5 * t9 * t12 * t17 * t20 * t23 * 9.42222077621846e-4-a7 * t8 * t9 * t14 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t8 * t9 * t14 * t17 * t19 * t23 * 9.42222077621846e-4-t5 * t9 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3-t5 * t9 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3 + t5 * t9 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3-t8 * t9 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3, t5 * (-2.736425713896764e-3) + t8 * 1.039572462190599e-3-t5 * t12 * 7.703820892966798e-3 + t5 * t17 * 1.150829800496513e-2 + t8 * t14 * 8.204303182449135e-4 + t8 * t15 * 7.462333571535709e-3 + t8 * t19 * 1.197883429282104e-3 + a5 * t8 * t12 * 7.702783979911432e-2-a5 * t8 * t17 * 1.003178367299898e-2 + d5 * t5 * t12 * 7.702783979911432e-2-d5 * t5 * t17 * 1.003178367299898e-2-t5 * t12 * t14 * 1.197883429282104e-3 + t5 * t12 * t19 * 8.204303182449135e-4-t8 * t12 * t17 * 1.016405861520946e-2-t8 * t14 * t15 * 1.640860636489827e-3-t5 * t17 * t20 * 1.073500939031497e-2-t8 * t15 * t19 * 2.395766858564208e-3-t5 * t17 * t21 * 1.758741834919224e-3 + t8 * t14 * t21 * 5.969399339641728e-4-t5 * t17 * t23 * 5.969399339641728e-4-t8 * t14 * t23 * 1.758741834919224e-3-t8 * t19 * t25 * 2.370893727874773e-3 + a5 * t8 * t14 * t17 * 6.771241001216606e-2 + a5 * t8 * t12 * t21 * 4.71111038810923e-4-a5 * t8 * t12 * t23 * 6.247434442774674e-3 + a5 * t8 * t17 * t19 * 1.656261404313703e-1-a7 * t8 * t19 * t21 * 4.71111038810923e-4 + a7 * t8 * t19 * t23 * 6.247434442774674e-3 + d5 * t5 * t14 * t17 * 6.771241001216606e-2 + d5 * t5 * t12 * t21 * 4.71111038810923e-4-d5 * t5 * t12 * t23 * 6.247434442774674e-3 + d5 * t5 * t17 * t19 * 1.656261404313703e-1 + t5 * t14 * t17 * t19 * 7.826325248751706e-3 + t5 * t12 * t14 * t25 * 2.370893727874773e-3 + t5 * t12 * t19 * t21 * 5.969399339641728e-4 + t8 * t12 * t17 * t20 * 7.826325248751706e-3-t8 * t14 * t15 * t21 * 1.193879867928346e-3-t5 * t12 * t19 * t23 * 1.758741834919224e-3 + t8 * t14 * t15 * t23 * 3.517483669838449e-3 + t8 * t12 * t17 * t25 * 1.765933827532306e-3 + t5 * t17 * t20 * t21 * 3.517483669838449e-3 + t5 * t17 * t20 * t23 * 1.193879867928346e-3 + t8 * t15 * t19 * t25 * 4.741787455749547e-3 + t8 * t19 * t21 * t23 * 1.765933827532306e-3 + a7 * t5 * t12 * t14 * t21 * 4.71111038810923e-4-a7 * t5 * t12 * t14 * t23 * 6.247434442774674e-3 + a5 * t8 * t17 * t19 * t21 * 6.247434442774674e-3 + a7 * t8 * t15 * t19 * t21 * 9.42222077621846e-4 + a5 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-a7 * t8 * t15 * t19 * t23 * 1.249486888554935e-2 + d5 * t5 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t5 * t17 * t19 * t23 * 4.71111038810923e-4 + t8 * t12 * t14 * t17 * t19 * 1.073500939031497e-2-t5 * t12 * t14 * t21 * t23 * 1.765933827532306e-3 + t5 * t14 * t17 * t19 * t25 * 1.765933827532306e-3 + t8 * t12 * t17 * t21 * t23 * 2.370893727874773e-3 + t8 * t12 * t17 * t20 * t25 * 1.765933827532306e-3-t8 * t15 * t19 * t21 * t23 * 3.531867655064613e-3 + a7 * t5 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t5 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + a7 * t8 * t12 * t17 * t20 * t21 * 1.249486888554935e-2 + a7 * t8 * t12 * t17 * t20 * t23 * 9.42222077621846e-4-t8 * t12 * t14 * t17 * t19 * t21 * 3.517483669838449e-3-t8 * t12 * t14 * t17 * t19 * t23 * 1.193879867928346e-3 + t5 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3 + t8 * t12 * t17 * t20 * t21 * t23 * 2.370893727874773e-3, t15 * 2.505201111020351e-2 + t18 * 3.21778928866176e-2-a5 * t12 * 2.006356734599796e-2-a5 * t17 * 1.540556795982286e-1 + d5 * t14 * 3.312522808627406e-1-d5 * t19 * 1.354248200243321e-1-t12 * t17 * 7.462333571535709e-3-t18 * t20 * 6.554082237488641e-3-t15 * t24 * 1.765933827532306e-3 + t18 * t22 * 1.272243011263065e-3 + a5 * t12 * t14 * 1.354248200243321e-1 + a5 * t12 * t19 * 3.312522808627406e-1 + a7 * t15 * t21 * 1.249486888554935e-2 + a7 * t15 * t23 * 9.42222077621846e-4 + t12 * t14 * t17 * 1.640860636489827e-3 + t12 * t17 * t19 * 2.487313068943488e-5-t14 * t18 * t19 * 1.073500939031497e-2 + t15 * t21 * t23 * 2.370893727874773e-3-t18 * t20 * t25 * 1.765933827532306e-3 + a5 * t12 * t19 * t21 * 1.249486888554935e-2 + a5 * t12 * t19 * t23 * 9.42222077621846e-4-a5 * t17 * t20 * t21 * 9.42222077621846e-4 + a5 * t17 * t20 * t23 * 1.249486888554935e-2-a5 * t17 * t21 * t22 * 9.42222077621846e-4 + a5 * t17 * t22 * t23 * 1.249486888554935e-2 + a7 * t18 * t21 * t22 * 1.249486888554935e-2 + a7 * t18 * t22 * t23 * 9.42222077621846e-4 + d5 * t14 * t15 * t21 * 1.249486888554935e-2 + d5 * t14 * t15 * t23 * 9.42222077621846e-4 + d5 * t14 * t18 * t21 * 1.249486888554935e-2 + d5 * t14 * t18 * t23 * 9.42222077621846e-4 + t12 * t14 * t17 * t21 * 1.193879867928346e-3-t12 * t14 * t17 * t23 * 3.517483669838449e-3 + t12 * t17 * t19 * t24 * 2.370893727874773e-3 + t14 * t18 * t19 * t21 * 3.517483669838449e-3-t12 * t17 * t19 * t25 * 2.370893727874773e-3 + t14 * t18 * t19 * t23 * 1.193879867928346e-3-t18 * t20 * t21 * t23 * 2.370893727874773e-3-a7 * t12 * t17 * t19 * t21 * 9.42222077621846e-4 + a7 * t12 * t17 * t19 * t23 * 1.249486888554935e-2 + t12 * t17 * t19 * t21 * t23 * 3.531867655064613e-3 + 6.369918696374153e-1, t2 * t5 * 1.152453637796355e-2-a4 * t2 * t12 * 1.003178367299898e-2-a4 * t2 * t17 * 7.702783979911432e-2 + t3 * t6 * t8 * 1.152453637796355e-2-t2 * t8 * t12 * 1.150829800496513e-2 + t3 * t9 * t12 * 7.703820892966798e-3 + t2 * t5 * t20 * 7.826325248751706e-3-t2 * t8 * t17 * 7.703820892966798e-3-t3 * t9 * t17 * 1.150829800496513e-2-t2 * t5 * t25 * 1.765933827532306e-3-a5 * t2 * t5 * t12 * 1.003178367299898e-2-a5 * t2 * t5 * t17 * 7.702783979911432e-2 + a4 * t2 * t12 * t14 * 6.771241001216606e-2 + a4 * t2 * t12 * t19 * 1.656261404313703e-1-a4 * t2 * t17 * t21 * 4.71111038810923e-4 + a4 * t2 * t17 * t23 * 6.247434442774674e-3-d3 * t3 * t6 * t12 * 1.003178367299898e-2 + d5 * t2 * t8 * t12 * 1.003178367299898e-2-d3 * t3 * t6 * t17 * 7.702783979911432e-2-d5 * t3 * t9 * t12 * 7.702783979911432e-2 + d5 * t2 * t8 * t17 * 7.702783979911432e-2 + d5 * t3 * t9 * t17 * 1.003178367299898e-2 + t3 * t5 * t6 * t12 * 1.150829800496513e-2 + t3 * t5 * t6 * t17 * 7.703820892966798e-3 + t3 * t6 * t8 * t20 * 7.826325248751706e-3 + t3 * t9 * t12 * t14 * 1.197883429282104e-3 + t2 * t5 * t14 * t19 * 1.073500939031497e-2-t2 * t8 * t14 * t17 * 1.197883429282104e-3 + t2 * t8 * t12 * t20 * 1.073500939031497e-2-t3 * t6 * t8 * t25 * 1.765933827532306e-3 + t2 * t8 * t12 * t21 * 1.758741834919224e-3-t3 * t9 * t12 * t19 * 8.204303182449135e-4 + t2 * t8 * t12 * t23 * 5.969399339641728e-4 + t2 * t8 * t17 * t19 * 8.204303182449135e-4 + t3 * t9 * t17 * t20 * 1.073500939031497e-2 + t3 * t9 * t17 * t21 * 1.758741834919224e-3-t2 * t5 * t21 * t23 * 2.370893727874773e-3 + t2 * t5 * t20 * t25 * 1.765933827532306e-3 + t3 * t9 * t17 * t23 * 5.969399339641728e-4-a5 * t3 * t6 * t8 * t12 * 1.003178367299898e-2 + a4 * t3 * t8 * t9 * t12 * 7.702783979911432e-2 + a5 * t2 * t5 * t12 * t14 * 6.771241001216606e-2-a5 * t3 * t6 * t8 * t17 * 7.702783979911432e-2-a4 * t3 * t8 * t9 * t17 * 1.003178367299898e-2 + a5 * t2 * t5 * t12 * t19 * 1.656261404313703e-1-a5 * t2 * t5 * t17 * t21 * 4.71111038810923e-4 + a5 * t2 * t5 * t17 * t23 * 6.247434442774674e-3 + a7 * t2 * t5 * t20 * t21 * 1.249486888554935e-2 + a7 * t2 * t5 * t20 * t23 * 9.42222077621846e-4 + a4 * t2 * t12 * t19 * t21 * 6.247434442774674e-3 + a4 * t2 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t3 * t5 * t6 * t12 * 1.003178367299898e-2-d3 * t3 * t5 * t9 * t12 * 7.702783979911432e-2-d5 * t3 * t5 * t6 * t17 * 7.702783979911432e-2 + d3 * t3 * t5 * t9 * t17 * 1.003178367299898e-2 + d3 * t3 * t6 * t12 * t14 * 6.771241001216606e-2-d5 * t2 * t8 * t12 * t14 * 6.771241001216606e-2 + d3 * t3 * t6 * t12 * t19 * 1.656261404313703e-1-d5 * t2 * t8 * t12 * t19 * 1.656261404313703e-1-d5 * t3 * t9 * t14 * t17 * 6.771241001216606e-2-d3 * t3 * t6 * t17 * t21 * 4.71111038810923e-4-d5 * t3 * t9 * t12 * t21 * 4.71111038810923e-4 + d3 * t3 * t6 * t17 * t23 * 6.247434442774674e-3 + d5 * t3 * t9 * t12 * t23 * 6.247434442774674e-3 + d5 * t2 * t8 * t17 * t21 * 4.71111038810923e-4-d5 * t3 * t9 * t17 * t19 * 1.656261404313703e-1-d5 * t2 * t8 * t17 * t23 * 6.247434442774674e-3 + t3 * t5 * t6 * t14 * t17 * 1.197883429282104e-3-t3 * t5 * t6 * t12 * t20 * 1.073500939031497e-2-t3 * t5 * t6 * t12 * t21 * 1.758741834919224e-3-t3 * t5 * t6 * t12 * t23 * 5.969399339641728e-4-t3 * t5 * t6 * t17 * t19 * 8.204303182449135e-4 + t3 * t6 * t8 * t14 * t19 * 1.073500939031497e-2-t2 * t8 * t12 * t14 * t19 * 7.826325248751706e-3-t2 * t5 * t14 * t19 * t21 * 3.517483669838449e-3-t3 * t6 * t8 * t21 * t23 * 2.370893727874773e-3 + t3 * t6 * t8 * t20 * t25 * 1.765933827532306e-3-t3 * t9 * t14 * t17 * t19 * 7.826325248751706e-3-t2 * t5 * t14 * t19 * t23 * 1.193879867928346e-3-t2 * t8 * t12 * t20 * t21 * 3.517483669838449e-3-t3 * t9 * t12 * t14 * t25 * 2.370893727874773e-3-t3 * t9 * t12 * t19 * t21 * 5.969399339641728e-4-t2 * t8 * t12 * t20 * t23 * 1.193879867928346e-3 + t2 * t8 * t14 * t17 * t25 * 2.370893727874773e-3 + t3 * t9 * t12 * t19 * t23 * 1.758741834919224e-3 + t2 * t8 * t17 * t19 * t21 * 5.969399339641728e-4-t2 * t8 * t17 * t19 * t23 * 1.758741834919224e-3-t3 * t9 * t17 * t20 * t21 * 3.517483669838449e-3 + t2 * t5 * t20 * t21 * t23 * 2.370893727874773e-3-t3 * t9 * t17 * t20 * t23 * 1.193879867928346e-3 + a5 * t3 * t6 * t8 * t12 * t14 * 6.771241001216606e-2 + a5 * t3 * t6 * t8 * t12 * t19 * 1.656261404313703e-1 + a4 * t3 * t8 * t9 * t14 * t17 * 6.771241001216606e-2 + a4 * t3 * t8 * t9 * t12 * t21 * 4.71111038810923e-4-a4 * t3 * t8 * t9 * t12 * t23 * 6.247434442774674e-3 + a4 * t3 * t8 * t9 * t17 * t19 * 1.656261404313703e-1-a5 * t3 * t6 * t8 * t17 * t21 * 4.71111038810923e-4 + a5 * t3 * t6 * t8 * t17 * t23 * 6.247434442774674e-3 + a5 * t2 * t5 * t12 * t19 * t21 * 6.247434442774674e-3 + a7 * t3 * t6 * t8 * t20 * t21 * 1.249486888554935e-2 + a5 * t2 * t5 * t12 * t19 * t23 * 4.71111038810923e-4-a7 * t3 * t9 * t12 * t14 * t21 * 4.71111038810923e-4 + a7 * t3 * t6 * t8 * t20 * t23 * 9.42222077621846e-4 + a7 * t3 * t9 * t12 * t14 * t23 * 6.247434442774674e-3 + a7 * t2 * t8 * t14 * t17 * t21 * 4.71111038810923e-4-a7 * t2 * t8 * t14 * t17 * t23 * 6.247434442774674e-3 + d5 * t3 * t5 * t6 * t12 * t14 * 6.771241001216606e-2 + d5 * t3 * t5 * t6 * t12 * t19 * 1.656261404313703e-1-d3 * t3 * t5 * t9 * t14 * t17 * 6.771241001216606e-2-d3 * t3 * t5 * t9 * t12 * t21 * 4.71111038810923e-4 + d3 * t3 * t5 * t9 * t12 * t23 * 6.247434442774674e-3-d3 * t3 * t5 * t9 * t17 * t19 * 1.656261404313703e-1-d5 * t3 * t5 * t6 * t17 * t21 * 4.71111038810923e-4 + d5 * t3 * t5 * t6 * t17 * t23 * 6.247434442774674e-3 + d3 * t3 * t6 * t12 * t19 * t21 * 6.247434442774674e-3 + d3 * t3 * t6 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t2 * t8 * t12 * t19 * t21 * 6.247434442774674e-3-d5 * t2 * t8 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t3 * t9 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t3 * t9 * t17 * t19 * t23 * 4.71111038810923e-4 + t3 * t5 * t6 * t12 * t14 * t19 * 7.826325248751706e-3 + t3 * t5 * t6 * t12 * t20 * t21 * 3.517483669838449e-3 + t3 * t5 * t6 * t12 * t20 * t23 * 1.193879867928346e-3-t3 * t5 * t6 * t14 * t17 * t25 * 2.370893727874773e-3-t3 * t5 * t6 * t17 * t19 * t21 * 5.969399339641728e-4-t3 * t6 * t8 * t14 * t19 * t21 * 3.517483669838449e-3 + t3 * t5 * t6 * t17 * t19 * t23 * 1.758741834919224e-3-t3 * t6 * t8 * t14 * t19 * t23 * 1.193879867928346e-3-t2 * t8 * t12 * t14 * t19 * t25 * 1.765933827532306e-3 + t3 * t6 * t8 * t20 * t21 * t23 * 2.370893727874773e-3 + t3 * t9 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t2 * t8 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-t3 * t9 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-a7 * t3 * t5 * t6 * t14 * t17 * t21 * 4.71111038810923e-4 + a5 * t3 * t6 * t8 * t12 * t19 * t21 * 6.247434442774674e-3 + a7 * t3 * t5 * t6 * t14 * t17 * t23 * 6.247434442774674e-3 + a5 * t3 * t6 * t8 * t12 * t19 * t23 * 4.71111038810923e-4 + a4 * t3 * t8 * t9 * t17 * t19 * t21 * 6.247434442774674e-3 + a4 * t3 * t8 * t9 * t17 * t19 * t23 * 4.71111038810923e-4-a7 * t2 * t8 * t12 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t2 * t8 * t12 * t14 * t19 * t23 * 9.42222077621846e-4-a7 * t3 * t9 * t14 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t3 * t9 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + d5 * t3 * t5 * t6 * t12 * t19 * t21 * 6.247434442774674e-3 + d5 * t3 * t5 * t6 * t12 * t19 * t23 * 4.71111038810923e-4-d3 * t3 * t5 * t9 * t17 * t19 * t21 * 6.247434442774674e-3-d3 * t3 * t5 * t9 * t17 * t19 * t23 * 4.71111038810923e-4 + t3 * t5 * t6 * t12 * t14 * t19 * t25 * 1.765933827532306e-3 + t3 * t5 * t6 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-t2 * t8 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3-t3 * t9 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3 + a7 * t3 * t5 * t6 * t12 * t14 * t19 * t21 * 1.249486888554935e-2 + a7 * t3 * t5 * t6 * t12 * t14 * t19 * t23 * 9.42222077621846e-4 + t3 * t5 * t6 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3, t8 * t9 * (-1.152453637796355e-2) + t6 * t12 * 7.703820892966798e-3-t6 * t17 * 1.150829800496513e-2-d5 * t6 * t12 * 7.702783979911432e-2 + d3 * t9 * t12 * 1.003178367299898e-2 + d5 * t6 * t17 * 1.003178367299898e-2 + d3 * t9 * t17 * 7.702783979911432e-2-t5 * t9 * t12 * 1.150829800496513e-2-t5 * t9 * t17 * 7.703820892966798e-3 + t6 * t12 * t14 * 1.197883429282104e-3-t6 * t12 * t19 * 8.204303182449135e-4-t8 * t9 * t20 * 7.826325248751706e-3 + t8 * t9 * t25 * 1.765933827532306e-3 + t6 * t17 * t20 * 1.073500939031497e-2 + t6 * t17 * t21 * 1.758741834919224e-3 + t6 * t17 * t23 * 5.969399339641728e-4 + a4 * t6 * t8 * t12 * 7.702783979911432e-2 + a5 * t8 * t9 * t12 * 1.003178367299898e-2-a4 * t6 * t8 * t17 * 1.003178367299898e-2 + a5 * t8 * t9 * t17 * 7.702783979911432e-2-d3 * t5 * t6 * t12 * 7.702783979911432e-2 + d3 * t5 * t6 * t17 * 1.003178367299898e-2 + d5 * t5 * t9 * t12 * 1.003178367299898e-2 + d5 * t5 * t9 * t17 * 7.702783979911432e-2-d3 * t9 * t12 * t14 * 6.771241001216606e-2-d5 * t6 * t14 * t17 * 6.771241001216606e-2-d3 * t9 * t12 * t19 * 1.656261404313703e-1-d5 * t6 * t12 * t21 * 4.71111038810923e-4 + d5 * t6 * t12 * t23 * 6.247434442774674e-3-d5 * t6 * t17 * t19 * 1.656261404313703e-1 + d3 * t9 * t17 * t21 * 4.71111038810923e-4-d3 * t9 * t17 * t23 * 6.247434442774674e-3-t5 * t9 * t14 * t17 * 1.197883429282104e-3 + t5 * t9 * t12 * t20 * 1.073500939031497e-2 + t5 * t9 * t12 * t21 * 1.758741834919224e-3 + t5 * t9 * t12 * t23 * 5.969399339641728e-4 + t5 * t9 * t17 * t19 * 8.204303182449135e-4-t8 * t9 * t14 * t19 * 1.073500939031497e-2-t6 * t14 * t17 * t19 * 7.826325248751706e-3-t6 * t12 * t14 * t25 * 2.370893727874773e-3-t6 * t12 * t19 * t21 * 5.969399339641728e-4 + t6 * t12 * t19 * t23 * 1.758741834919224e-3 + t8 * t9 * t21 * t23 * 2.370893727874773e-3-t8 * t9 * t20 * t25 * 1.765933827532306e-3-t6 * t17 * t20 * t21 * 3.517483669838449e-3-t6 * t17 * t20 * t23 * 1.193879867928346e-3-a5 * t8 * t9 * t12 * t14 * 6.771241001216606e-2 + a4 * t6 * t8 * t14 * t17 * 6.771241001216606e-2 + a4 * t6 * t8 * t12 * t21 * 4.71111038810923e-4-a4 * t6 * t8 * t12 * t23 * 6.247434442774674e-3-a5 * t8 * t9 * t12 * t19 * 1.656261404313703e-1 + a4 * t6 * t8 * t17 * t19 * 1.656261404313703e-1 + a5 * t8 * t9 * t17 * t21 * 4.71111038810923e-4-a7 * t6 * t12 * t14 * t21 * 4.71111038810923e-4-a5 * t8 * t9 * t17 * t23 * 6.247434442774674e-3 + a7 * t6 * t12 * t14 * t23 * 6.247434442774674e-3-a7 * t8 * t9 * t20 * t21 * 1.249486888554935e-2-a7 * t8 * t9 * t20 * t23 * 9.42222077621846e-4-d3 * t5 * t6 * t14 * t17 * 6.771241001216606e-2-d5 * t5 * t9 * t12 * t14 * 6.771241001216606e-2-d3 * t5 * t6 * t12 * t21 * 4.71111038810923e-4 + d3 * t5 * t6 * t12 * t23 * 6.247434442774674e-3-d3 * t5 * t6 * t17 * t19 * 1.656261404313703e-1-d5 * t5 * t9 * t12 * t19 * 1.656261404313703e-1 + d5 * t5 * t9 * t17 * t21 * 4.71111038810923e-4-d5 * t5 * t9 * t17 * t23 * 6.247434442774674e-3-d3 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t9 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t6 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t6 * t17 * t19 * t23 * 4.71111038810923e-4-t5 * t9 * t12 * t14 * t19 * 7.826325248751706e-3-t5 * t9 * t12 * t20 * t21 * 3.517483669838449e-3-t5 * t9 * t12 * t20 * t23 * 1.193879867928346e-3 + t5 * t9 * t14 * t17 * t25 * 2.370893727874773e-3 + t5 * t9 * t17 * t19 * t21 * 5.969399339641728e-4 + t8 * t9 * t14 * t19 * t21 * 3.517483669838449e-3-t5 * t9 * t17 * t19 * t23 * 1.758741834919224e-3 + t8 * t9 * t14 * t19 * t23 * 1.193879867928346e-3 + t6 * t12 * t14 * t21 * t23 * 1.765933827532306e-3-t6 * t14 * t17 * t19 * t25 * 1.765933827532306e-3-t8 * t9 * t20 * t21 * t23 * 2.370893727874773e-3 + a7 * t5 * t9 * t14 * t17 * t21 * 4.71111038810923e-4-a5 * t8 * t9 * t12 * t19 * t21 * 6.247434442774674e-3 + a4 * t6 * t8 * t17 * t19 * t21 * 6.247434442774674e-3-a7 * t5 * t9 * t14 * t17 * t23 * 6.247434442774674e-3-a5 * t8 * t9 * t12 * t19 * t23 * 4.71111038810923e-4 + a4 * t6 * t8 * t17 * t19 * t23 * 4.71111038810923e-4-a7 * t6 * t14 * t17 * t19 * t21 * 1.249486888554935e-2-a7 * t6 * t14 * t17 * t19 * t23 * 9.42222077621846e-4-d3 * t5 * t6 * t17 * t19 * t21 * 6.247434442774674e-3-d5 * t5 * t9 * t12 * t19 * t21 * 6.247434442774674e-3-d3 * t5 * t6 * t17 * t19 * t23 * 4.71111038810923e-4-d5 * t5 * t9 * t12 * t19 * t23 * 4.71111038810923e-4-t5 * t9 * t12 * t14 * t19 * t25 * 1.765933827532306e-3-t5 * t9 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-t6 * t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3-a7 * t5 * t9 * t12 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t5 * t9 * t12 * t14 * t19 * t23 * 9.42222077621846e-4-t5 * t9 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3, t5 * 1.152453637796355e-2-a4 * t12 * 1.003178367299898e-2-a4 * t17 * 7.702783979911432e-2-t8 * t12 * 1.150829800496513e-2 + t5 * t20 * 7.826325248751706e-3-t8 * t17 * 7.703820892966798e-3-t5 * t25 * 1.765933827532306e-3-a5 * t5 * t12 * 1.003178367299898e-2-a5 * t5 * t17 * 7.702783979911432e-2 + a4 * t12 * t14 * 6.771241001216606e-2 + a4 * t12 * t19 * 1.656261404313703e-1-a4 * t17 * t21 * 4.71111038810923e-4 + a4 * t17 * t23 * 6.247434442774674e-3 + d5 * t8 * t12 * 1.003178367299898e-2 + d5 * t8 * t17 * 7.702783979911432e-2 + t5 * t14 * t19 * 1.073500939031497e-2-t8 * t14 * t17 * 1.197883429282104e-3 + t8 * t12 * t20 * 1.073500939031497e-2 + t8 * t12 * t21 * 1.758741834919224e-3 + t8 * t12 * t23 * 5.969399339641728e-4 + t8 * t17 * t19 * 8.204303182449135e-4-t5 * t21 * t23 * 2.370893727874773e-3 + t5 * t20 * t25 * 1.765933827532306e-3 + a5 * t5 * t12 * t14 * 6.771241001216606e-2 + a5 * t5 * t12 * t19 * 1.656261404313703e-1-a5 * t5 * t17 * t21 * 4.71111038810923e-4 + a5 * t5 * t17 * t23 * 6.247434442774674e-3 + a7 * t5 * t20 * t21 * 1.249486888554935e-2 + a7 * t5 * t20 * t23 * 9.42222077621846e-4 + a4 * t12 * t19 * t21 * 6.247434442774674e-3 + a4 * t12 * t19 * t23 * 4.71111038810923e-4-d5 * t8 * t12 * t14 * 6.771241001216606e-2-d5 * t8 * t12 * t19 * 1.656261404313703e-1 + d5 * t8 * t17 * t21 * 4.71111038810923e-4-d5 * t8 * t17 * t23 * 6.247434442774674e-3-t8 * t12 * t14 * t19 * 7.826325248751706e-3-t5 * t14 * t19 * t21 * 3.517483669838449e-3-t5 * t14 * t19 * t23 * 1.193879867928346e-3-t8 * t12 * t20 * t21 * 3.517483669838449e-3-t8 * t12 * t20 * t23 * 1.193879867928346e-3 + t8 * t14 * t17 * t25 * 2.370893727874773e-3 + t8 * t17 * t19 * t21 * 5.969399339641728e-4-t8 * t17 * t19 * t23 * 1.758741834919224e-3 + t5 * t20 * t21 * t23 * 2.370893727874773e-3 + a5 * t5 * t12 * t19 * t21 * 6.247434442774674e-3 + a5 * t5 * t12 * t19 * t23 * 4.71111038810923e-4 + a7 * t8 * t14 * t17 * t21 * 4.71111038810923e-4-a7 * t8 * t14 * t17 * t23 * 6.247434442774674e-3-d5 * t8 * t12 * t19 * t21 * 6.247434442774674e-3-d5 * t8 * t12 * t19 * t23 * 4.71111038810923e-4-t8 * t12 * t14 * t19 * t25 * 1.765933827532306e-3-t8 * t14 * t17 * t21 * t23 * 1.765933827532306e-3-a7 * t8 * t12 * t14 * t19 * t21 * 1.249486888554935e-2-a7 * t8 * t12 * t14 * t19 * t23 * 9.42222077621846e-4-t8 * t12 * t14 * t19 * t21 * t23 * 2.370893727874773e-3, t12 * (-7.703820892966798e-3) + t17 * 1.150829800496513e-2 + d5 * t12 * 7.702783979911432e-2-d5 * t17 * 1.003178367299898e-2-t12 * t14 * 1.197883429282104e-3 + t12 * t19 * 8.204303182449135e-4-t17 * t20 * 1.073500939031497e-2-t17 * t21 * 1.758741834919224e-3-t17 * t23 * 5.969399339641728e-4 + d5 * t14 * t17 * 6.771241001216606e-2 + d5 * t12 * t21 * 4.71111038810923e-4-d5 * t12 * t23 * 6.247434442774674e-3 + d5 * t17 * t19 * 1.656261404313703e-1 + t14 * t17 * t19 * 7.826325248751706e-3 + t12 * t14 * t25 * 2.370893727874773e-3 + t12 * t19 * t21 * 5.969399339641728e-4-t12 * t19 * t23 * 1.758741834919224e-3 + t17 * t20 * t21 * 3.517483669838449e-3 + t17 * t20 * t23 * 1.193879867928346e-3 + a7 * t12 * t14 * t21 * 4.71111038810923e-4-a7 * t12 * t14 * t23 * 6.247434442774674e-3 + d5 * t17 * t19 * t21 * 6.247434442774674e-3 + d5 * t17 * t19 * t23 * 4.71111038810923e-4-t12 * t14 * t21 * t23 * 1.765933827532306e-3 + t14 * t17 * t19 * t25 * 1.765933827532306e-3 + a7 * t14 * t17 * t19 * t21 * 1.249486888554935e-2 + a7 * t14 * t17 * t19 * t23 * 9.42222077621846e-4 + t14 * t17 * t19 * t21 * t23 * 2.370893727874773e-3, t20 * 1.272243011263065e-3-t22 * 6.554082237488641e-3 + t14 * t19 * 1.073500939031497e-2-t22 * t25 * 1.765933827532306e-3 + a7 * t20 * t21 * 1.249486888554935e-2 + a7 * t20 * t23 * 9.42222077621846e-4-t14 * t19 * t21 * 3.517483669838449e-3-t14 * t19 * t23 * 1.193879867928346e-3-t21 * t22 * t23 * 2.370893727874773e-3 + 1.807861861545219e-2, t2 * t5 * t14 * (-1.197883429282104e-3)-t3 * t9 * t12 * 2.32860772826712e-2 + t2 * t5 * t19 * 8.204303182449135e-4 + t2 * t8 * t17 * 2.32860772826712e-2-a5 * t3 * t9 * t14 * 6.771241001216606e-2-a5 * t3 * t9 * t19 * 1.656261404313703e-1-a4 * t2 * t14 * t17 * 1.656261404313703e-1 + a4 * t2 * t17 * t19 * 6.771241001216606e-2-t3 * t5 * t6 * t17 * 2.32860772826712e-2-t3 * t6 * t8 * t14 * 1.197883429282104e-3-t2 * t8 * t12 * t14 * 8.204303182449135e-4 + t3 * t6 * t8 * t19 * 8.204303182449135e-4-t2 * t8 * t12 * t19 * 1.197883429282104e-3-t3 * t9 * t14 * t17 * 8.204303182449135e-4 + t2 * t5 * t14 * t25 * 2.370893727874773e-3 + t2 * t5 * t19 * t21 * 5.969399339641728e-4-t3 * t9 * t17 * t19 * 1.197883429282104e-3-t2 * t5 * t19 * t23 * 1.758741834919224e-3-t3 * t9 * t12 * t25 * 1.765933827532306e-3 + t2 * t8 * t17 * t25 * 1.765933827532306e-3-a4 * t3 * t5 * t9 * t14 * 6.771241001216606e-2-a4 * t3 * t5 * t9 * t19 * 1.656261404313703e-1-a5 * t2 * t5 * t14 * t17 * 1.656261404313703e-1 + a5 * t2 * t5 * t17 * t19 * 6.771241001216606e-2 + a7 * t2 * t5 * t14 * t21 * 4.71111038810923e-4-a7 * t2 * t5 * t14 * t23 * 6.247434442774674e-3-a7 * t3 * t9 * t12 * t21 * 1.249486888554935e-2-a7 * t3 * t9 * t12 * t23 * 9.42222077621846e-4 + a7 * t2 * t8 * t17 * t21 * 1.249486888554935e-2-a5 * t3 * t9 * t19 * t21 * 6.247434442774674e-3 + a7 * t2 * t8 * t17 * t23 * 9.42222077621846e-4-a4 * t2 * t14 * t17 * t21 * 6.247434442774674e-3-a5 * t3 * t9 * t19 * t23 * 4.71111038810923e-4-a4 * t2 * t14 * t17 * t23 * 4.71111038810923e-4-d3 * t3 * t8 * t9 * t14 * 6.771241001216606e-2-d3 * t3 * t8 * t9 * t19 * 1.656261404313703e-1-d3 * t3 * t6 * t14 * t17 * 1.656261404313703e-1-d5 * t3 * t9 * t12 * t14 * 1.656261404313703e-1 + d5 * t2 * t8 * t14 * t17 * 1.656261404313703e-1 + d3 * t3 * t6 * t17 * t19 * 6.771241001216606e-2 + d5 * t3 * t9 * t12 * t19 * 6.771241001216606e-2-d5 * t2 * t8 * t17 * t19 * 6.771241001216606e-2 + t3 * t5 * t6 * t12 * t14 * 8.204303182449135e-4 + t3 * t5 * t6 * t12 * t19 * 1.197883429282104e-3-t3 * t5 * t6 * t17 * t25 * 1.765933827532306e-3 + t3 * t6 * t8 * t14 * t25 * 2.370893727874773e-3-t2 * t8 * t12 * t14 * t21 * 5.969399339641728e-4 + t3 * t6 * t8 * t19 * t21 * 5.969399339641728e-4 + t2 * t8 * t12 * t14 * t23 * 1.758741834919224e-3-t3 * t6 * t8 * t19 * t23 * 1.758741834919224e-3-t3 * t9 * t14 * t17 * t21 * 5.969399339641728e-4-t2 * t5 * t14 * t21 * t23 * 1.765933827532306e-3 + t2 * t8 * t12 * t19 * t25 * 2.370893727874773e-3 + t3 * t9 * t14 * t17 * t23 * 1.758741834919224e-3-t3 * t9 * t12 * t21 * t23 * 2.370893727874773e-3 + t2 * t8 * t17 * t21 * t23 * 2.370893727874773e-3 + t3 * t9 * t17 * t19 * t25 * 2.370893727874773e-3 + a4 * t3 * t8 * t9 * t12 * t14 * 1.656261404313703e-1-a5 * t3 * t6 * t8 * t14 * t17 * 1.656261404313703e-1-a4 * t3 * t8 * t9 * t12 * t19 * 6.771241001216606e-2 + a5 * t3 * t6 * t8 * t17 * t19 * 6.771241001216606e-2-a7 * t3 * t5 * t6 * t17 * t21 * 1.249486888554935e-2 + a7 * t3 * t6 * t8 * t14 * t21 * 4.71111038810923e-4-a4 * t3 * t5 * t9 * t19 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t6 * t17 * t23 * 9.42222077621846e-4-a7 * t3 * t6 * t8 * t14 * t23 * 6.247434442774674e-3-a4 * t3 * t5 * t9 * t19 * t23 * 4.71111038810923e-4-a5 * t2 * t5 * t14 * t17 * t21 * 6.247434442774674e-3-a5 * t2 * t5 * t14 * t17 * t23 * 4.71111038810923e-4 + a7 * t2 * t8 * t12 * t19 * t21 * 4.71111038810923e-4-a7 * t2 * t8 * t12 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t9 * t17 * t19 * t21 * 4.71111038810923e-4-a7 * t3 * t9 * t17 * t19 * t23 * 6.247434442774674e-3-d3 * t3 * t5 * t9 * t12 * t14 * 1.656261404313703e-1-d5 * t3 * t5 * t6 * t14 * t17 * 1.656261404313703e-1 + d3 * t3 * t5 * t9 * t12 * t19 * 6.771241001216606e-2 + d5 * t3 * t5 * t6 * t17 * t19 * 6.771241001216606e-2-d3 * t3 * t8 * t9 * t19 * t21 * 6.247434442774674e-3-d3 * t3 * t6 * t14 * t17 * t21 * 6.247434442774674e-3-d5 * t3 * t9 * t12 * t14 * t21 * 6.247434442774674e-3-d3 * t3 * t8 * t9 * t19 * t23 * 4.71111038810923e-4-d3 * t3 * t6 * t14 * t17 * t23 * 4.71111038810923e-4-d5 * t3 * t9 * t12 * t14 * t23 * 4.71111038810923e-4 + d5 * t2 * t8 * t14 * t17 * t21 * 6.247434442774674e-3 + d5 * t2 * t8 * t14 * t17 * t23 * 4.71111038810923e-4 + t3 * t5 * t6 * t12 * t14 * t21 * 5.969399339641728e-4-t3 * t5 * t6 * t12 * t14 * t23 * 1.758741834919224e-3-t3 * t5 * t6 * t12 * t19 * t25 * 2.370893727874773e-3-t3 * t5 * t6 * t17 * t21 * t23 * 2.370893727874773e-3-t3 * t6 * t8 * t14 * t21 * t23 * 1.765933827532306e-3-t2 * t8 * t12 * t19 * t21 * t23 * 1.765933827532306e-3-t3 * t9 * t17 * t19 * t21 * t23 * 1.765933827532306e-3 + a4 * t3 * t8 * t9 * t12 * t14 * t21 * 6.247434442774674e-3 + a4 * t3 * t8 * t9 * t12 * t14 * t23 * 4.71111038810923e-4-a7 * t3 * t5 * t6 * t12 * t19 * t21 * 4.71111038810923e-4-a5 * t3 * t6 * t8 * t14 * t17 * t21 * 6.247434442774674e-3 + a7 * t3 * t5 * t6 * t12 * t19 * t23 * 6.247434442774674e-3-a5 * t3 * t6 * t8 * t14 * t17 * t23 * 4.71111038810923e-4-d3 * t3 * t5 * t9 * t12 * t14 * t21 * 6.247434442774674e-3-d3 * t3 * t5 * t9 * t12 * t14 * t23 * 4.71111038810923e-4-d5 * t3 * t5 * t6 * t14 * t17 * t21 * 6.247434442774674e-3-d5 * t3 * t5 * t6 * t14 * t17 * t23 * 4.71111038810923e-4 + t3 * t5 * t6 * t12 * t19 * t21 * t23 * 1.765933827532306e-3, t6 * t12 * (-2.32860772826712e-2)-a5 * t6 * t14 * 6.771241001216606e-2-a5 * t6 * t19 * 1.656261404313703e-1 + t5 * t9 * t17 * 2.32860772826712e-2 + t8 * t9 * t14 * 1.197883429282104e-3-t8 * t9 * t19 * 8.204303182449135e-4-t6 * t14 * t17 * 8.204303182449135e-4-t6 * t17 * t19 * 1.197883429282104e-3-t6 * t12 * t25 * 1.765933827532306e-3-a4 * t5 * t6 * t14 * 6.771241001216606e-2-a4 * t5 * t6 * t19 * 1.656261404313703e-1-a7 * t6 * t12 * t21 * 1.249486888554935e-2-a7 * t6 * t12 * t23 * 9.42222077621846e-4-a5 * t6 * t19 * t21 * 6.247434442774674e-3-a5 * t6 * t19 * t23 * 4.71111038810923e-4-d3 * t6 * t8 * t14 * 6.771241001216606e-2-d3 * t6 * t8 * t19 * 1.656261404313703e-1-d5 * t6 * t12 * t14 * 1.656261404313703e-1 + d5 * t6 * t12 * t19 * 6.771241001216606e-2 + d3 * t9 * t14 * t17 * 1.656261404313703e-1-d3 * t9 * t17 * t19 * 6.771241001216606e-2-t5 * t9 * t12 * t14 * 8.204303182449135e-4-t5 * t9 * t12 * t19 * 1.197883429282104e-3 + t5 * t9 * t17 * t25 * 1.765933827532306e-3-t8 * t9 * t14 * t25 * 2.370893727874773e-3-t8 * t9 * t19 * t21 * 5.969399339641728e-4-t6 * t14 * t17 * t21 * 5.969399339641728e-4 + t8 * t9 * t19 * t23 * 1.758741834919224e-3 + t6 * t14 * t17 * t23 * 1.758741834919224e-3-t6 * t12 * t21 * t23 * 2.370893727874773e-3 + t6 * t17 * t19 * t25 * 2.370893727874773e-3 + a4 * t6 * t8 * t12 * t14 * 1.656261404313703e-1-a4 * t6 * t8 * t12 * t19 * 6.771241001216606e-2 + a5 * t8 * t9 * t14 * t17 * 1.656261404313703e-1-a4 * t5 * t6 * t19 * t21 * 6.247434442774674e-3-a4 * t5 * t6 * t19 * t23 * 4.71111038810923e-4-a5 * t8 * t9 * t17 * t19 * 6.771241001216606e-2 + a7 * t5 * t9 * t17 * t21 * 1.249486888554935e-2-a7 * t8 * t9 * t14 * t21 * 4.71111038810923e-4 + a7 * t5 * t9 * t17 * t23 * 9.42222077621846e-4 + a7 * t8 * t9 * t14 * t23 * 6.247434442774674e-3 + a7 * t6 * t17 * t19 * t21 * 4.71111038810923e-4-a7 * t6 * t17 * t19 * t23 * 6.247434442774674e-3-d3 * t5 * t6 * t12 * t14 * 1.656261404313703e-1 + d3 * t5 * t6 * t12 * t19 * 6.771241001216606e-2 + d5 * t5 * t9 * t14 * t17 * 1.656261404313703e-1-d5 * t5 * t9 * t17 * t19 * 6.771241001216606e-2-d3 * t6 * t8 * t19 * t21 * 6.247434442774674e-3-d5 * t6 * t12 * t14 * t21 * 6.247434442774674e-3-d3 * t6 * t8 * t19 * t23 * 4.71111038810923e-4-d5 * t6 * t12 * t14 * t23 * 4.71111038810923e-4 + d3 * t9 * t14 * t17 * t21 * 6.247434442774674e-3 + d3 * t9 * t14 * t17 * t23 * 4.71111038810923e-4-t5 * t9 * t12 * t14 * t21 * 5.969399339641728e-4 + t5 * t9 * t12 * t14 * t23 * 1.758741834919224e-3 + t5 * t9 * t12 * t19 * t25 * 2.370893727874773e-3 + t5 * t9 * t17 * t21 * t23 * 2.370893727874773e-3 + t8 * t9 * t14 * t21 * t23 * 1.765933827532306e-3-t6 * t17 * t19 * t21 * t23 * 1.765933827532306e-3 + a4 * t6 * t8 * t12 * t14 * t21 * 6.247434442774674e-3 + a4 * t6 * t8 * t12 * t14 * t23 * 4.71111038810923e-4 + a7 * t5 * t9 * t12 * t19 * t21 * 4.71111038810923e-4 + a5 * t8 * t9 * t14 * t17 * t21 * 6.247434442774674e-3-a7 * t5 * t9 * t12 * t19 * t23 * 6.247434442774674e-3 + a5 * t8 * t9 * t14 * t17 * t23 * 4.71111038810923e-4-d3 * t5 * t6 * t12 * t14 * t21 * 6.247434442774674e-3-d3 * t5 * t6 * t12 * t14 * t23 * 4.71111038810923e-4 + d5 * t5 * t9 * t14 * t17 * t21 * 6.247434442774674e-3 + d5 * t5 * t9 * t14 * t17 * t23 * 4.71111038810923e-4-t5 * t9 * t12 * t19 * t21 * t23 * 1.765933827532306e-3, t5 * t14 * (-1.197883429282104e-3) + t5 * t19 * 8.204303182449135e-4 + t8 * t17 * 2.32860772826712e-2-a4 * t14 * t17 * 1.656261404313703e-1 + a4 * t17 * t19 * 6.771241001216606e-2-t8 * t12 * t14 * 8.204303182449135e-4-t8 * t12 * t19 * 1.197883429282104e-3 + t5 * t14 * t25 * 2.370893727874773e-3 + t5 * t19 * t21 * 5.969399339641728e-4-t5 * t19 * t23 * 1.758741834919224e-3 + t8 * t17 * t25 * 1.765933827532306e-3-a5 * t5 * t14 * t17 * 1.656261404313703e-1 + a5 * t5 * t17 * t19 * 6.771241001216606e-2 + a7 * t5 * t14 * t21 * 4.71111038810923e-4-a7 * t5 * t14 * t23 * 6.247434442774674e-3 + a7 * t8 * t17 * t21 * 1.249486888554935e-2 + a7 * t8 * t17 * t23 * 9.42222077621846e-4-a4 * t14 * t17 * t21 * 6.247434442774674e-3-a4 * t14 * t17 * t23 * 4.71111038810923e-4 + d5 * t8 * t14 * t17 * 1.656261404313703e-1-d5 * t8 * t17 * t19 * 6.771241001216606e-2-t8 * t12 * t14 * t21 * 5.969399339641728e-4 + t8 * t12 * t14 * t23 * 1.758741834919224e-3-t5 * t14 * t21 * t23 * 1.765933827532306e-3 + t8 * t12 * t19 * t25 * 2.370893727874773e-3 + t8 * t17 * t21 * t23 * 2.370893727874773e-3-a5 * t5 * t14 * t17 * t21 * 6.247434442774674e-3-a5 * t5 * t14 * t17 * t23 * 4.71111038810923e-4 + a7 * t8 * t12 * t19 * t21 * 4.71111038810923e-4-a7 * t8 * t12 * t19 * t23 * 6.247434442774674e-3 + d5 * t8 * t14 * t17 * t21 * 6.247434442774674e-3 + d5 * t8 * t14 * t17 * t23 * 4.71111038810923e-4-t8 * t12 * t19 * t21 * t23 * 1.765933827532306e-3, t12 * 2.32860772826712e-2 + a5 * t14 * 6.771241001216606e-2 + a5 * t19 * 1.656261404313703e-1 + t14 * t17 * 8.204303182449135e-4 + t17 * t19 * 1.197883429282104e-3 + t12 * t25 * 1.765933827532306e-3 + a7 * t12 * t21 * 1.249486888554935e-2 + a7 * t12 * t23 * 9.42222077621846e-4 + a5 * t19 * t21 * 6.247434442774674e-3 + a5 * t19 * t23 * 4.71111038810923e-4 + d5 * t12 * t14 * 1.656261404313703e-1-d5 * t12 * t19 * 6.771241001216606e-2 + t14 * t17 * t21 * 5.969399339641728e-4-t14 * t17 * t23 * 1.758741834919224e-3 + t12 * t21 * t23 * 2.370893727874773e-3-t17 * t19 * t25 * 2.370893727874773e-3-a7 * t17 * t19 * t21 * 4.71111038810923e-4 + a7 * t17 * t19 * t23 * 6.247434442774674e-3 + d5 * t12 * t14 * t21 * 6.247434442774674e-3 + d5 * t12 * t14 * t23 * 4.71111038810923e-4 + t17 * t19 * t21 * t23 * 1.765933827532306e-3, t14 * (-1.197883429282104e-3) + t19 * 8.204303182449135e-4 + t14 * t25 * 2.370893727874773e-3 + t19 * t21 * 5.969399339641728e-4-t19 * t23 * 1.758741834919224e-3 + a7 * t14 * t21 * 4.71111038810923e-4-a7 * t14 * t23 * 6.247434442774674e-3-t14 * t21 * t23 * 1.765933827532306e-3, t24 * (-1.765933827532306e-3) + a7 * t21 * 1.249486888554935e-2 + a7 * t23 * 9.42222077621846e-4 + t21 * t23 * 2.370893727874773e-3 + 2.505201111020351e-2, t2 * t5 * t19 * (-1.272243011263065e-3)-a4 * t2 * t12 * t21 * 6.247434442774674e-3-a4 * t2 * t12 * t23 * 4.71111038810923e-4 + t2 * t8 * t12 * t14 * 1.272243011263065e-3-t3 * t6 * t8 * t19 * 1.272243011263065e-3 + t2 * t5 * t14 * t21 * 1.758741834919224e-3 + t3 * t9 * t14 * t17 * 1.272243011263065e-3 + t2 * t5 * t14 * t23 * 5.969399339641728e-4 + t3 * t9 * t12 * t21 * 5.969399339641728e-4-t3 * t9 * t12 * t23 * 1.758741834919224e-3-t2 * t8 * t17 * t21 * 5.969399339641728e-4 + t2 * t8 * t17 * t23 * 1.758741834919224e-3-a5 * t2 * t5 * t12 * t21 * 6.247434442774674e-3-a5 * t2 * t5 * t12 * t23 * 4.71111038810923e-4-a5 * t3 * t9 * t14 * t21 * 4.71111038810923e-4 + a5 * t3 * t9 * t14 * t23 * 6.247434442774674e-3-a7 * t2 * t5 * t19 * t21 * 6.247434442774674e-3-a7 * t2 * t5 * t19 * t23 * 4.71111038810923e-4 + a4 * t2 * t17 * t19 * t21 * 4.71111038810923e-4-a4 * t2 * t17 * t19 * t23 * 6.247434442774674e-3-d3 * t3 * t6 * t12 * t21 * 6.247434442774674e-3-d3 * t3 * t6 * t12 * t23 * 4.71111038810923e-4 + d5 * t2 * t8 * t12 * t21 * 6.247434442774674e-3 + d5 * t2 * t8 * t12 * t23 * 4.71111038810923e-4 + d5 * t3 * t9 * t17 * t21 * 6.247434442774674e-3 + d5 * t3 * t9 * t17 * t23 * 4.71111038810923e-4-t3 * t5 * t6 * t12 * t14 * 1.272243011263065e-3 + t3 * t5 * t6 * t17 * t21 * 5.969399339641728e-4 + t3 * t6 * t8 * t14 * t21 * 1.758741834919224e-3-t3 * t5 * t6 * t17 * t23 * 1.758741834919224e-3 + t3 * t6 * t8 * t14 * t23 * 5.969399339641728e-4 + t2 * t8 * t12 * t19 * t21 * 1.758741834919224e-3 + t2 * t8 * t12 * t19 * t23 * 5.969399339641728e-4 + t3 * t9 * t17 * t19 * t21 * 1.758741834919224e-3 + t3 * t9 * t17 * t19 * t23 * 5.969399339641728e-4-a5 * t3 * t6 * t8 * t12 * t21 * 6.247434442774674e-3-a4 * t3 * t5 * t9 * t14 * t21 * 4.71111038810923e-4-a5 * t3 * t6 * t8 * t12 * t23 * 4.71111038810923e-4 + a4 * t3 * t5 * t9 * t14 * t23 * 6.247434442774674e-3-a4 * t3 * t8 * t9 * t17 * t21 * 6.247434442774674e-3-a4 * t3 * t8 * t9 * t17 * t23 * 4.71111038810923e-4 + a7 * t2 * t8 * t12 * t14 * t21 * 6.247434442774674e-3-a7 * t3 * t6 * t8 * t19 * t21 * 6.247434442774674e-3 + a7 * t2 * t8 * t12 * t14 * t23 * 4.71111038810923e-4-a7 * t3 * t6 * t8 * t19 * t23 * 4.71111038810923e-4 + a5 * t2 * t5 * t17 * t19 * t21 * 4.71111038810923e-4-a5 * t2 * t5 * t17 * t19 * t23 * 6.247434442774674e-3 + a7 * t3 * t9 * t14 * t17 * t21 * 6.247434442774674e-3 + a7 * t3 * t9 * t14 * t17 * t23 * 4.71111038810923e-4-d5 * t3 * t5 * t6 * t12 * t21 * 6.247434442774674e-3-d5 * t3 * t5 * t6 * t12 * t23 * 4.71111038810923e-4 + d3 * t3 * t5 * t9 * t17 * t21 * 6.247434442774674e-3-d3 * t3 * t8 * t9 * t14 * t21 * 4.71111038810923e-4 + d3 * t3 * t5 * t9 * t17 * t23 * 4.71111038810923e-4 + d3 * t3 * t8 * t9 * t14 * t23 * 6.247434442774674e-3 + d3 * t3 * t6 * t17 * t19 * t21 * 4.71111038810923e-4 + d5 * t3 * t9 * t12 * t19 * t21 * 4.71111038810923e-4-d3 * t3 * t6 * t17 * t19 * t23 * 6.247434442774674e-3-d5 * t3 * t9 * t12 * t19 * t23 * 6.247434442774674e-3-d5 * t2 * t8 * t17 * t19 * t21 * 4.71111038810923e-4 + d5 * t2 * t8 * t17 * t19 * t23 * 6.247434442774674e-3-t3 * t5 * t6 * t12 * t19 * t21 * 1.758741834919224e-3-t3 * t5 * t6 * t12 * t19 * t23 * 5.969399339641728e-4-a7 * t3 * t5 * t6 * t12 * t14 * t21 * 6.247434442774674e-3-a7 * t3 * t5 * t6 * t12 * t14 * t23 * 4.71111038810923e-4-a4 * t3 * t8 * t9 * t12 * t19 * t21 * 4.71111038810923e-4 + a4 * t3 * t8 * t9 * t12 * t19 * t23 * 6.247434442774674e-3 + a5 * t3 * t6 * t8 * t17 * t19 * t21 * 4.71111038810923e-4-a5 * t3 * t6 * t8 * t17 * t19 * t23 * 6.247434442774674e-3 + d3 * t3 * t5 * t9 * t12 * t19 * t21 * 4.71111038810923e-4-d3 * t3 * t5 * t9 * t12 * t19 * t23 * 6.247434442774674e-3 + d5 * t3 * t5 * t6 * t17 * t19 * t21 * 4.71111038810923e-4-d5 * t3 * t5 * t6 * t17 * t19 * t23 * 6.247434442774674e-3, t8 * t9 * t19 * 1.272243011263065e-3 + t6 * t14 * t17 * 1.272243011263065e-3 + t6 * t12 * t21 * 5.969399339641728e-4-t6 * t12 * t23 * 1.758741834919224e-3-a5 * t6 * t14 * t21 * 4.71111038810923e-4 + a5 * t6 * t14 * t23 * 6.247434442774674e-3 + d3 * t9 * t12 * t21 * 6.247434442774674e-3 + d3 * t9 * t12 * t23 * 4.71111038810923e-4 + d5 * t6 * t17 * t21 * 6.247434442774674e-3 + d5 * t6 * t17 * t23 * 4.71111038810923e-4 + t5 * t9 * t12 * t14 * 1.272243011263065e-3-t5 * t9 * t17 * t21 * 5.969399339641728e-4-t8 * t9 * t14 * t21 * 1.758741834919224e-3 + t5 * t9 * t17 * t23 * 1.758741834919224e-3-t8 * t9 * t14 * t23 * 5.969399339641728e-4 + t6 * t17 * t19 * t21 * 1.758741834919224e-3 + t6 * t17 * t19 * t23 * 5.969399339641728e-4-a4 * t5 * t6 * t14 * t21 * 4.71111038810923e-4 + a4 * t5 * t6 * t14 * t23 * 6.247434442774674e-3 + a5 * t8 * t9 * t12 * t21 * 6.247434442774674e-3-a4 * t6 * t8 * t17 * t21 * 6.247434442774674e-3 + a5 * t8 * t9 * t12 * t23 * 4.71111038810923e-4-a4 * t6 * t8 * t17 * t23 * 4.71111038810923e-4 + a7 * t8 * t9 * t19 * t21 * 6.247434442774674e-3 + a7 * t6 * t14 * t17 * t21 * 6.247434442774674e-3 + a7 * t8 * t9 * t19 * t23 * 4.71111038810923e-4 + a7 * t6 * t14 * t17 * t23 * 4.71111038810923e-4 + d3 * t5 * t6 * t17 * t21 * 6.247434442774674e-3-d3 * t6 * t8 * t14 * t21 * 4.71111038810923e-4 + d5 * t5 * t9 * t12 * t21 * 6.247434442774674e-3 + d3 * t5 * t6 * t17 * t23 * 4.71111038810923e-4 + d3 * t6 * t8 * t14 * t23 * 6.247434442774674e-3 + d5 * t5 * t9 * t12 * t23 * 4.71111038810923e-4 + d5 * t6 * t12 * t19 * t21 * 4.71111038810923e-4-d5 * t6 * t12 * t19 * t23 * 6.247434442774674e-3-d3 * t9 * t17 * t19 * t21 * 4.71111038810923e-4 + d3 * t9 * t17 * t19 * t23 * 6.247434442774674e-3 + t5 * t9 * t12 * t19 * t21 * 1.758741834919224e-3 + t5 * t9 * t12 * t19 * t23 * 5.969399339641728e-4 + a7 * t5 * t9 * t12 * t14 * t21 * 6.247434442774674e-3-a4 * t6 * t8 * t12 * t19 * t21 * 4.71111038810923e-4 + a7 * t5 * t9 * t12 * t14 * t23 * 4.71111038810923e-4 + a4 * t6 * t8 * t12 * t19 * t23 * 6.247434442774674e-3-a5 * t8 * t9 * t17 * t19 * t21 * 4.71111038810923e-4 + a5 * t8 * t9 * t17 * t19 * t23 * 6.247434442774674e-3 + d3 * t5 * t6 * t12 * t19 * t21 * 4.71111038810923e-4-d3 * t5 * t6 * t12 * t19 * t23 * 6.247434442774674e-3-d5 * t5 * t9 * t17 * t19 * t21 * 4.71111038810923e-4 + d5 * t5 * t9 * t17 * t19 * t23 * 6.247434442774674e-3, t5 * t19 * (-1.272243011263065e-3)-a4 * t12 * t21 * 6.247434442774674e-3-a4 * t12 * t23 * 4.71111038810923e-4 + t8 * t12 * t14 * 1.272243011263065e-3 + t5 * t14 * t21 * 1.758741834919224e-3 + t5 * t14 * t23 * 5.969399339641728e-4-t8 * t17 * t21 * 5.969399339641728e-4 + t8 * t17 * t23 * 1.758741834919224e-3-a5 * t5 * t12 * t21 * 6.247434442774674e-3-a5 * t5 * t12 * t23 * 4.71111038810923e-4-a7 * t5 * t19 * t21 * 6.247434442774674e-3-a7 * t5 * t19 * t23 * 4.71111038810923e-4 + a4 * t17 * t19 * t21 * 4.71111038810923e-4-a4 * t17 * t19 * t23 * 6.247434442774674e-3 + d5 * t8 * t12 * t21 * 6.247434442774674e-3 + d5 * t8 * t12 * t23 * 4.71111038810923e-4 + t8 * t12 * t19 * t21 * 1.758741834919224e-3 + t8 * t12 * t19 * t23 * 5.969399339641728e-4 + a7 * t8 * t12 * t14 * t21 * 6.247434442774674e-3 + a7 * t8 * t12 * t14 * t23 * 4.71111038810923e-4 + a5 * t5 * t17 * t19 * t21 * 4.71111038810923e-4-a5 * t5 * t17 * t19 * t23 * 6.247434442774674e-3-d5 * t8 * t17 * t19 * t21 * 4.71111038810923e-4 + d5 * t8 * t17 * t19 * t23 * 6.247434442774674e-3, t14 * t17 * (-1.272243011263065e-3)-t12 * t21 * 5.969399339641728e-4 + t12 * t23 * 1.758741834919224e-3 + a5 * t14 * t21 * 4.71111038810923e-4-a5 * t14 * t23 * 6.247434442774674e-3-d5 * t17 * t21 * 6.247434442774674e-3-d5 * t17 * t23 * 4.71111038810923e-4-t17 * t19 * t21 * 1.758741834919224e-3-t17 * t19 * t23 * 5.969399339641728e-4-a7 * t14 * t17 * t21 * 6.247434442774674e-3-a7 * t14 * t17 * t23 * 4.71111038810923e-4-d5 * t12 * t19 * t21 * 4.71111038810923e-4 + d5 * t12 * t19 * t23 * 6.247434442774674e-3, t19 * (-1.272243011263065e-3) + t14 * t21 * 1.758741834919224e-3 + t14 * t23 * 5.969399339641728e-4-a7 * t19 * t21 * 6.247434442774674e-3-a7 * t19 * t23 * 4.71111038810923e-4, t21 * (-5.969399339641728e-4) + t23 * 1.758741834919224e-3, 1.272243011263065e-3]) M = np.array([ [Bstack_hat[0] , Bstack_hat[1] , Bstack_hat[3] , Bstack_hat[6] , Bstack_hat[10] , Bstack_hat[15] , Bstack_hat[21]], [Bstack_hat[1] , Bstack_hat[2] , Bstack_hat[4] , Bstack_hat[7] , Bstack_hat[11] , Bstack_hat[16] , Bstack_hat[22]], [Bstack_hat[3] , Bstack_hat[4] , Bstack_hat[5] , Bstack_hat[8] , Bstack_hat[12] , Bstack_hat[17] , Bstack_hat[23]], [Bstack_hat[6] , Bstack_hat[7] , Bstack_hat[8] , Bstack_hat[9] , Bstack_hat[13] , Bstack_hat[18] , Bstack_hat[24]], [Bstack_hat[10] , Bstack_hat[11] , Bstack_hat[12] , Bstack_hat[13] , Bstack_hat[14] , Bstack_hat[19] , Bstack_hat[25]], [Bstack_hat[15] , Bstack_hat[16] , Bstack_hat[17] , Bstack_hat[18] , Bstack_hat[19] , Bstack_hat[20] , Bstack_hat[26]], [Bstack_hat[21] , Bstack_hat[22] , Bstack_hat[23] , Bstack_hat[24] , Bstack_hat[25] , Bstack_hat[26] , Bstack_hat[27]] ], dtype=np.float32) return M if __name__ == "__main__": cc.compile()

2,045.425532

40,276

0.604067

31,359

192,270

3.701585

0.006729

0.016644

0.051638

0.035321

0.968754

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0.810834

0.60418

0.354115

0.16109

0

0.619733

0.232595

192,270

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40,277

2,045.425532

0.166975

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false

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null

0

1

0

1

0

1

0

null

0

7

bda35b6cb7614ce07e8d5c1fdf5ebde30b890473

46

py

Python

peer_socket/__init__.py

Nnigmat/dlt-lab7

47235e6c37aeda62cc515de7b99a6ce7acf2849b

[ "MIT" ]

null

peer_socket/__init__.py

Nnigmat/dlt-lab7

47235e6c37aeda62cc515de7b99a6ce7acf2849b

[ "MIT" ]

null

peer_socket/__init__.py

Nnigmat/dlt-lab7

47235e6c37aeda62cc515de7b99a6ce7acf2849b

[ "MIT" ]

null

from peer_socket.peer_socket import PeerSocket

46

0.913043

7

46

5.714286

0.714286

0.5

0

0.065217

46

1

46

0.930233

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

bdb846187dc68319c0c313cefb361c236f485545

241

py

Python

once/once.py

matthewlucock/problems

a51b87decf9b95f2e22af8915e00e90749b610f0

[ "Unlicense" ]

null

once/once.py

matthewlucock/problems

a51b87decf9b95f2e22af8915e00e90749b610f0

[ "Unlicense" ]

null

once/once.py

matthewlucock/problems

a51b87decf9b95f2e22af8915e00e90749b610f0

[ "Unlicense" ]

null

def once(function): function_has_been_called = False def wrapper(*args, **kwargs): if not function_has_been_called: function_has_been_called = True return wrapper(*args, **kwargs) return wrapper

24.1

43

0.651452

29

241

5.103448

0.482759

0.222973

0.304054

0.425676

0

0.26971

241

9

44

26.777778

0.840909

0

1

0.285714

false

0

0.571429

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

bddbe5cd2c92bce3b955320bec208493e88e0380

1,241

py

Python

_test/test_none.py

grahamgower/ruyaml

9623d0a57e745999c0ec816909868e844b6ef030

[ "MIT" ]

null

_test/test_none.py

grahamgower/ruyaml

9623d0a57e745999c0ec816909868e844b6ef030

[ "MIT" ]

null

_test/test_none.py

grahamgower/ruyaml

9623d0a57e745999c0ec816909868e844b6ef030

[ "MIT" ]

null

# coding: utf-8 import pytest # NOQA class TestNone: def test_dump00(self): import ruyaml # NOQA data = None s = ruyaml.round_trip_dump(data) assert s == 'null\n...\n' d = ruyaml.round_trip_load(s) assert d == data def test_dump01(self): import ruyaml # NOQA data = None s = ruyaml.round_trip_dump(data, explicit_end=True) assert s == 'null\n...\n' d = ruyaml.round_trip_load(s) assert d == data def test_dump02(self): import ruyaml # NOQA data = None s = ruyaml.round_trip_dump(data, explicit_end=False) assert s == 'null\n...\n' d = ruyaml.round_trip_load(s) assert d == data def test_dump03(self): import ruyaml # NOQA data = None s = ruyaml.round_trip_dump(data, explicit_start=True) assert s == '---\n...\n' d = ruyaml.round_trip_load(s) assert d == data def test_dump04(self): import ruyaml # NOQA data = None s = ruyaml.round_trip_dump(data, explicit_start=True, explicit_end=False) assert s == '---\n...\n' d = ruyaml.round_trip_load(s) assert d == data

23.865385

81

0.557615

166

1,241

3.987952

0.198795

0.166163

0.226586

0.151057

0.879154

0.847432

0

0.013205

0.328767

1,241

51

82

24.333333

0.781513

0.034649

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0

0.044538

0

0.27027

1

0.135135

false

0

0.162162

0

0.324324

0

null

0

1

0

1

0

null

0

7

bde8ec6ac413ae679322ae5061e85f54b3da49a7

10,630

py

Python

esperclient/api/group_commands_api.py

pallavigopi/esper-client-py

f7e71d3f25a5d91f35628b414e8abe9e6849d316

[ "Apache-2.0" ]

null

esperclient/api/group_commands_api.py

pallavigopi/esper-client-py

f7e71d3f25a5d91f35628b414e8abe9e6849d316

[ "Apache-2.0" ]

null

esperclient/api/group_commands_api.py

pallavigopi/esper-client-py

f7e71d3f25a5d91f35628b414e8abe9e6849d316

[ "Apache-2.0" ]

null

# coding: utf-8 """ ESPER API REFERENCE OpenAPI spec version: 1.0.0 Contact: developer@esper.io --------------------------------------------------------- Copyright 2019 Shoonya Enterprises Inc. 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. """ from __future__ import absolute_import import re # python 2 and python 3 compatibility library import six from esperclient.api_client import ApiClient class GroupCommandsApi(object): """NOTE: This class is auto generated. Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def get_group_command(self, command_id, group_id, enterprise_id, **kwargs): """Get group command status Returns GroupCommand instance This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_group_command(command_id, group_id, enterprise_id, async_req=True) >>> result = thread.get() :param async_req bool :param str command_id: A UUID string identifying this device command. (required) :param str group_id: A UUID string identifying this group. (required) :param str enterprise_id: A UUID string identifying enterprise. (required) :return: GroupCommand If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_group_command_with_http_info(command_id, group_id, enterprise_id, **kwargs) else: (data) = self.get_group_command_with_http_info(command_id, group_id, enterprise_id, **kwargs) return data def get_group_command_with_http_info(self, command_id, group_id, enterprise_id, **kwargs): """Get group command status Returns GroupCommand instance This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_group_command_with_http_info(command_id, group_id, enterprise_id, async_req=True) >>> result = thread.get() :param async_req bool :param str command_id: A UUID string identifying this device command. (required) :param str group_id: A UUID string identifying this group. (required) :param str enterprise_id: A UUID string identifying enterprise. (required) :return: GroupCommand If the method is called asynchronously, returns the request thread. """ all_params = ['command_id', 'group_id', 'enterprise_id'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_group_command" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'command_id' is set if ('command_id' not in params or params['command_id'] is None): raise ValueError("Missing the required parameter `command_id` when calling `get_group_command`") # verify the required parameter 'group_id' is set if ('group_id' not in params or params['group_id'] is None): raise ValueError("Missing the required parameter `group_id` when calling `get_group_command`") # verify the required parameter 'enterprise_id' is set if ('enterprise_id' not in params or params['enterprise_id'] is None): raise ValueError("Missing the required parameter `enterprise_id` when calling `get_group_command`") collection_formats = {} path_params = {} if 'command_id' in params: path_params['command_id'] = params['command_id'] if 'group_id' in params: path_params['group_id'] = params['group_id'] if 'enterprise_id' in params: path_params['enterprise_id'] = params['enterprise_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # Authentication setting auth_settings = ['apiKey'] return self.api_client.call_api( '/enterprise/{enterprise_id}/devicegroup/{group_id}/command/{command_id}/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='GroupCommand', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def run_group_command(self, enterprise_id, group_id, data, **kwargs): """Run commands on group devices Fire commands on all the group devices This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.run_group_command(enterprise_id, group_id, data, async_req=True) >>> result = thread.get() :param async_req bool :param str enterprise_id: ID of the enterprise (required) :param str group_id: ID of the group (required) :param GroupCommandRequest data: Group command request (required) :return: GroupCommand If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.run_group_command_with_http_info(enterprise_id, group_id, data, **kwargs) else: (data) = self.run_group_command_with_http_info(enterprise_id, group_id, data, **kwargs) return data def run_group_command_with_http_info(self, enterprise_id, group_id, data, **kwargs): """Run commands on group devices Fire commands on all the group devices This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.run_group_command_with_http_info(enterprise_id, group_id, data, async_req=True) >>> result = thread.get() :param async_req bool :param str enterprise_id: ID of the enterprise (required) :param str group_id: ID of the group (required) :param GroupCommandRequest data: Group command request (required) :return: GroupCommand If the method is called asynchronously, returns the request thread. """ all_params = ['enterprise_id', 'group_id', 'data'] all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method run_group_command" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'enterprise_id' is set if ('enterprise_id' not in params or params['enterprise_id'] is None): raise ValueError("Missing the required parameter `enterprise_id` when calling `run_group_command`") # verify the required parameter 'group_id' is set if ('group_id' not in params or params['group_id'] is None): raise ValueError("Missing the required parameter `group_id` when calling `run_group_command`") # verify the required parameter 'data' is set if ('data' not in params or params['data'] is None): raise ValueError("Missing the required parameter `data` when calling `run_group_command`") collection_formats = {} path_params = {} if 'enterprise_id' in params: path_params['enterprise_id'] = params['enterprise_id'] if 'group_id' in params: path_params['group_id'] = params['group_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'data' in params: body_params = params['data'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # Authentication setting auth_settings = ['apiKey'] return self.api_client.call_api( '/enterprise/{enterprise_id}/devicegroup/{group_id}/command/', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='GroupCommand', auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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111

0.637159

1,278

10,630

5.053991

0.152582

0.036848

0.019508

0.022294

0.843319

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0.812665

0.800279

0.792847

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0

null

0

1

0

1

0

null

0

7

e51d0ba4f93fe30ccdc98294285bfaf86cacc04f

300

py

Python

uniswap/tokens.py

ErikBjare/uniswap-python

3f770cd68ada634b8b292ec8b46a62c5d74247ea

[ "MIT" ]

3

2020-08-06T23:18:49.000Z

2022-01-20T21:30:49.000Z

uniswap/tokens.py

ErikBjare/uniswap-python

3f770cd68ada634b8b292ec8b46a62c5d74247ea

[ "MIT" ]

1

2020-07-21T12:48:46.000Z

uniswap/tokens.py

ErikBjare/uniswap-python

3f770cd68ada634b8b292ec8b46a62c5d74247ea

[ "MIT" ]

1

2022-02-13T12:24:00.000Z

tokens = { "WETH": "0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2", "DAI": "0x6B175474E89094C44Da98b954EedeAC495271d0F", "BAT": "0x0D8775F648430679A709E98d2b0Cb6250d2887EF", "WBTC": "0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599", "UNI": "0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984", }

37.5

57

0.776667

11

300

21.181818

1

0

0.481203

0.113333

300

7

58

42.857143

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1

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1

0

1

null

0

1

0

8

e571617e2da3a12ab881428daec005484ae1482c

1,156

py

Python

interface/keyboard.py

Matvei-Fadeev/IKTON

c56c21b571edaad4f0551e3ad49c59af07b27ace

[ "MIT" ]

null

interface/keyboard.py

Matvei-Fadeev/IKTON

c56c21b571edaad4f0551e3ad49c59af07b27ace

[ "MIT" ]

null

interface/keyboard.py

Matvei-Fadeev/IKTON

c56c21b571edaad4f0551e3ad49c59af07b27ace

[ "MIT" ]

1

2020-11-24T17:42:46.000Z

import telebot from telebot import types # Основное меню, главное, при любом старте def main_display_keyboard(): markup = types.ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True) first_btn = types.KeyboardButton('Присоединиться') second_btn = types.KeyboardButton('Создать') markup.add(first_btn, second_btn) return markup def queue_display_keyboard(): markup = types.ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True) first_btn = types.KeyboardButton('Занять') second_btn = types.KeyboardButton('Список') third_btn = types.KeyboardButton('Меню') markup.add(first_btn, second_btn, third_btn) return markup def in_queue_display_keyboard(): markup = types.ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True) first_btn = types.KeyboardButton('Покинуть очередь') second_btn = types.KeyboardButton('Список') third_btn = types.KeyboardButton('Меню') markup.add(first_btn, second_btn, third_btn) return markup def default_keyboard(): markup = types.ReplyKeyboardMarkup(one_time_keyboard=True, resize_keyboard=True) btn = types.KeyboardButton('Меню') markup.add(btn) return markup

33.028571

84

0.796713

146

1,156

6.061644

0.246575

0.081356

0.223729

0.171751

0.79209

0.723164

0

0.104671

1,156

34

85

34

0.855072

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0

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0

0.060144

0

1

0.148148

false

0

0.074074

0

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0

null

0

1

0

1

0

null

0

7

e5847e85a5ac206d78d7f6c8562d646fd6437704

3,143

py

Python

biserici_inlemnite/biserici/migrations/0031_auto_20210803_1618.py

ck-tm/biserici-inlemnite

c9d12127b92f25d3ab2fcc7b4c386419fe308a4e

[ "MIT" ]

null

biserici_inlemnite/biserici/migrations/0031_auto_20210803_1618.py

ck-tm/biserici-inlemnite

c9d12127b92f25d3ab2fcc7b4c386419fe308a4e

[ "MIT" ]

null

biserici_inlemnite/biserici/migrations/0031_auto_20210803_1618.py

ck-tm/biserici-inlemnite

c9d12127b92f25d3ab2fcc7b4c386419fe308a4e

[ "MIT" ]

null

# Generated by Django 3.1.13 on 2021-08-03 13:18 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('biserici', '0030_auto_20210803_1617'), ] operations = [ migrations.AddField( model_name='conservare', name='stare_cosoroabe', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='conservare', name='stare_cosoroabe_detalii', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='conservare', name='stare_sarpanta_turn', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='conservare', name='stare_sarpanta_turn_detalii', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='conservare', name='stare_talpi', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='conservare', name='stare_talpi_detalii', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_cosoroabe', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_cosoroabe_detalii', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_sarpanta_turn', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_sarpanta_turn_detalii', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_talpi', field=models.IntegerField(blank=True, choices=[(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], null=True), ), migrations.AddField( model_name='historicalconservare', name='stare_talpi_detalii', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='descriere', name='toponim_sursa', field=models.TextField(blank=True, null=True, verbose_name='Sursă informații'), ), migrations.AlterField( model_name='historicaldescriere', name='toponim_sursa', field=models.TextField(blank=True, null=True, verbose_name='Sursă informații'), ), ]

37.416667

111

0.560293

323

3,143

5.318885

0.167183

0.073341

0.160652

0.188591

0.867288

0.845751

0

0.041573

0.295896

3,143

83

112

37.86747

0.734749

0.014636

0

0.883117

1

0

0.169628

0.039742

0

1

0

false

0

0.012987

0

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0

null

0

1

0

1

0

null

0

9

e5a205bd48178eca446478c45bb2f3472dfe99d4

30,992

py

Python

cloudroast/database/pos_regr/instances.py

lmaycotte/cloudroast

c1835aa45e0e86c755d4b24b33e12ba30eee1995

[ "Apache-2.0" ]

null

cloudroast/database/pos_regr/instances.py

lmaycotte/cloudroast

c1835aa45e0e86c755d4b24b33e12ba30eee1995

[ "Apache-2.0" ]

null

cloudroast/database/pos_regr/instances.py

lmaycotte/cloudroast

c1835aa45e0e86c755d4b24b33e12ba30eee1995

[ "Apache-2.0" ]

1

2020-11-17T19:04:33.000Z

""" Copyright 2013 Rackspace 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. """ from test_repo.database.fixtures import DBaaSFixture class TestCreateInstances(DBaaSFixture): instance_id = None tiny_instance_id = None sml_instance_id = None med_instance_id = None lrg_instance_id = None xlrg_instance_id = None xxlrg_instance_id = None multi_dbs_instance_id = None req_params_instance_id = None multi_users_instance_id = None all_instances = [instance_id, tiny_instance_id, sml_instance_id, med_instance_id, lrg_instance_id, xlrg_instance_id, xxlrg_instance_id, multi_dbs_instance_id, req_params_instance_id, multi_users_instance_id] dbaas = None stability_mode = False def _check_instance_attribs(self, instance, exp_flavor, exp_vol, exp_name): self.assertEqual(instance.flavor['id'], str(exp_flavor), "Expected %s | Actual %s" % (str(exp_flavor), instance.flavor['id'])) self.assertEqual(instance.volume['size'], exp_vol, "Expected %s | Actual %s" % (exp_vol, instance.volume['size'])) self.assertEqual(instance.name, exp_name, "Expected %s | Actual %s" % (exp_name, instance.name)) @classmethod def setUpClass(cls): """ Creating an instance for smoke testing """ super(TestCreateInstances, cls).setUpClass() cls.dbaas = cls.client.reddwarfclient @classmethod def tearDownClass(cls): """ Tearing down: Deleting the instance if in active state """ #Delete the instance ID created for test if active dbaas = cls.dbaas for instance_id in cls.all_instances: if instance_id is not None: status = cls.behavior.get_instance_status( dbaas, instanceId=instance_id) if cls.behavior.is_instance_active(dbaas, instanceStatus=status): dbaas.instances.get(instance_id).delete() def tearDown(self): """ Tearing down: Deleting the instance if in active state """ self.tearDownClass() def test_create_tiny_instance(self): """ Creating a tiny instance (512M) """ NAME = "qe-tiny-instance" FLAVOR = 1 VOLUME = 20 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: # such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_small_instance(self): """ Creating a small instance (1G) """ #print (self.instance_id) NAME = "qe-small-instance" FLAVOR = 2 VOLUME = 40 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.sml_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: # such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_medium_instance(self): """ Creating a medium instance (2G) """ NAME = "qe-medium-instance" FLAVOR = 3 VOLUME = 75 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.med_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: # such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_large_instance(self): """ Creating a 4G instance """ NAME = "qe-large-instance" FLAVOR = 4 VOLUME = 100 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.lrg_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_xlarge_instance(self): """ Creating an 8G instance """ NAME = "qe-xlarge-instance" FLAVOR = 5 VOLUME = 125 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.xlrg_instance_id_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource( self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_xxlarge_instance(self): """ Creating an 16G instance """ NAME = "qe-xxlarge-instance" FLAVOR = 6 VOLUME = 150 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = self.behavior.get_last_response_code(self.dbaas) self.xxlrg_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=instance.id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource( self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_2_dbs_instance(self): """ Creating 2 dbs instance """ NAME = "qe-2dbs-instance" FLAVOR = 1 VOLUME = 10 databases = [{"name": "firstdb", "character_set": "latin2", "collate": "latin2_general_ci"}, {"name": "db2"}] users = [{"name": "lite", "password": "litepass", "databases": [{"name": "firstdb"}, {"name": "db2"}]}] instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=databases, users=users) httpCode = self.behavior.get_last_response_code(self.dbaas) self.multi_dbs_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=instance.id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue(self.behavior.found_resource( self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_2_users_instance(self): """ Creating a 2 user instance """ NAME = "qe-2users-instance" FLAVOR = 1 VOLUME = 10 databases = [{"name": "firstdb", "character_set": "latin2", "collate": "latin2_general_ci"}] users = [{"name": "lite", "password": "litepass", "databases": [{"name": "firstdb"}]}, {"name": "lite1", "password": "litepass1", "databases": [{"name": "firstdb"}]}] instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=databases, users=users) httpCode = self.behavior.get_last_response_code(self.dbaas) self.multi_users_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=instance.id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue( self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) def test_create_required_params_instance(self): """ Creating an required param instance """ NAME = "qe-req-params-instance" FLAVOR = 1 VOLUME = 10 instance = self.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}) httpCode = self.behavior.get_last_response_code(self.dbaas) self.req_params_instance_id = instance.id self.assertTrue(httpCode == '200', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=instance.id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) #Get the instance and check instance attribs: #such as the flavor / volume size instance = self.dbaas.instances.get(instance) self._check_instance_attribs(instance, FLAVOR, VOLUME, NAME) #try to find our instance in the list self.assertTrue( self.behavior.found_resource(self.dbaas, instanceId=instance.id), "Did not find our instance id: %s in the list." % instance.id) class test_resize_instances(DBaaSFixture): instance_id = None dbaas = None class FlavorTypes(): tiny = 1 small = 2 med = 3 large = 4 xlarge = 5 xxlarge = 6 @classmethod def setUpClass(cls): """ Creating an instance for smoke testing """ tc_name = "Create Instance" super(test_resize_instances, cls).setUpClass() cls.dbaas = cls.client.reddwarfclient NAME = "qe-resize_instances" FLAVOR = 1 VOLUME = 10 instance = test_resize_instances.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = cls.behavior.get_last_response_code(test_resize_instances .dbaas) if httpCode != '200': raise Exception("Create instance failed with code %s" % httpCode) test_resize_instances.instance_id = instance.id status, elapsed_time = cls.behavior.wait_for_active( test_resize_instances.dbaas, instanceId=test_resize_instances.instance_id) assert (status == "ACTIVE") @classmethod def tearDownClass(cls): """ Tearing down: Deleting the instance if in active state """ instance_id = test_resize_instances.instance_id dbaas = test_resize_instances.dbaas #Delete the instance ID created for test if active if instance_id is not None: status = cls.behavior.get_instance_status(dbaas, instanceId=instance_id) if cls.behavior.is_instance_active(dbaas, instanceStatus=status): dbaas.instances.get(instance_id).delete() def test_resize_to_med_instance(self): """ Resize an instance to 2G """ if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.med) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newFlavorSize = self.dbaas.instances.get(self.instance_id).flavor['id'] self.assertTrue(newFlavorSize == str(self.FlavorTypes.med), "Unexpected flavor size for resize: %s" % newFlavorSize) #Resize back to tiny if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.tiny) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) def test_resize_to_large_instance(self): """ Resize the instance to 4G """ if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.large) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newFlavorSize = self.dbaas.instances.get(self.instance_id).flavor['id'] self.assertTrue(newFlavorSize == str(self.FlavorTypes.large), "Unexpected flavor size for resize: %s" % newFlavorSize) #Resize back to tiny if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.tiny) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) def test_resize_to_xlarge_instance(self): """ Resize the instance to 8G """ if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.xlarge) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newFlavorSize = self.dbaas.instances.get(self.instance_id).flavor['id'] self.assertTrue(newFlavorSize == str(self.FlavorTypes.xlarge), "Unexpected flavor size for resize: %s" % newFlavorSize) #Resize back to tiny if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.tiny) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) def test_resize_to_xxlarge_instance(self): """ Resize the instance to 16G """ if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.xxlarge) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newFlavorSize = self.dbaas.instances.get(self.instance_id).flavor['id'] self.assertTrue(newFlavorSize == str(self.FlavorTypes.xxlarge), "Unexpected flavor size for resize: %s" % newFlavorSize) #Resize back to tiny if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_instance(self.instance_id, self.FlavorTypes.tiny) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) class test_resize_volume_instances(DBaaSFixture): instance_id = None dbaas = None class ResizeUpSizes(): origLevel = 10 lev1 = 20 lev2 = 80 lev3 = 150 @classmethod def setUpClass(cls): """ Creating an instance for smoke testing """ super(test_resize_volume_instances, cls).setUpClass() cls.dbaas = cls.client.reddwarfclient NAME = "qe-resize_instances" FLAVOR = 1 VOLUME = test_resize_volume_instances.ResizeUpSizes.origLevel instance = test_resize_volume_instances.dbaas.instances.create( name=NAME, flavor_id=FLAVOR, volume={"size": VOLUME}, databases=[{"databases": [{"name": "databaseA"}], "name": "dbuser1", "password": "password"}]) httpCode = cls.behavior.get_last_response_code( test_resize_volume_instances.dbaas) if httpCode != '200': raise Exception("Create instance failed with code %s" % httpCode) test_resize_volume_instances.instance_id = instance.id #status = instance.status status, elapsed_time = cls.behavior.wait_for_active( test_resize_volume_instances.dbaas, instanceId=test_resize_volume_instances.instance_id) assert(status == "ACTIVE") @classmethod def tearDownClass(cls): """ Tearing down: Deleting the instance if in active state """ instance_id = test_resize_volume_instances.instance_id dbaas = test_resize_volume_instances.dbaas #Delete the instance ID created for test if active if instance_id is not None: status = cls.behavior.get_instance_status(dbaas, instanceId=instance_id) if cls.behavior.is_instance_active(dbaas, instanceStatus=status): dbaas.instances.get(instance_id).delete() def test_resize_volume_instance(self): """ Resize the volume of an instance """ if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_volume(self.instance_id, self.ResizeUpSizes.lev1) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newVolume = self.dbaas.instances.get(self.instance_id).volume['size'] self.assertTrue(newVolume == self.ResizeUpSizes.lev1, "Expected new volume size %s: Got %s " % (self.ResizeUpSizes.lev1, newVolume)) if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_volume(self.instance_id, self.ResizeUpSizes.lev2) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newVolume = self.dbaas.instances.get(self.instance_id).volume['size'] self.assertTrue(newVolume == self.ResizeUpSizes.lev2, "Expected new volume size %s: Got %s " % (self.ResizeUpSizes.lev2, newVolume)) if self.behavior.is_instance_active(self.dbaas, instanceId=self.instance_id): self.dbaas.instances.resize_volume(self.instance_id, self.ResizeUpSizes.lev3) httpCode = self.behavior.get_last_response_code(self.dbaas) self.assertTrue(httpCode == '202', "Create instance failed with code %s" % httpCode) status, elapsed_time = self.behavior.wait_for_active( self.dbaas, instanceId=self.instance_id) self.assertEqual(status, 'ACTIVE', "Instance fell into state: %s" % status) newVolume = self.dbaas.instances.get(self.instance_id).volume['size'] self.assertTrue(newVolume == self.ResizeUpSizes.lev3, "Expected new volume size %s: Got %s " % (self.ResizeUpSizes.lev3, newVolume))

39.280101

79

0.548561

3,089

30,992

5.349304

0.069602

0.08412

0.042363

0.041394

0.852457

0.842593

0.827282

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0.818688

0.799262

0

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0.362416

30,992

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39.329949

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0.082602

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0.038664

false

0.019332

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null

0

1

0

1

0

null

0

7

e5dfb4aac11ef0369923fe8f4ad193b0b82219fe

7,209

py

Python

src/genie/libs/parser/iosxe/tests/ShowCryptoIkev2Session/cli/equal/golden_output_csr8kv_detailed_expected.py

ykoehler/genieparser

b62cf622c3d8eab77c7b69e932c214ed04a2565a

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowCryptoIkev2Session/cli/equal/golden_output_csr8kv_detailed_expected.py

ykoehler/genieparser

b62cf622c3d8eab77c7b69e932c214ed04a2565a

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowCryptoIkev2Session/cli/equal/golden_output_csr8kv_detailed_expected.py

ykoehler/genieparser

b62cf622c3d8eab77c7b69e932c214ed04a2565a

[ "Apache-2.0" ]

null

expected_output = { 'ikev2_session': { 'IPv4':{ 1:{ 'session_id': 3, 'status': 'UP-ACTIVE', 'ike_count': 1, 'child_count': 1, 'tunnel_id': 1, 'local_ip': '1.1.1.1', 'local_port': 500, 'remote_ip': '1.1.1.2', 'remote_port': 500, 'fvrf': 'none', 'ivrf': 'none', 'session_status': 'READY', 'encryption': 'AES-CBC', 'key_length': 256, 'prf': 'SHA256', 'hash_algo': 'SHA256', 'dh_group': 14, 'auth_sign': 'PSK', 'auth_verify': 'PSK', 'lifetime': 86400, 'activetime': 38157, 'ce_id': 1008, 'id': 3, 'local_spi': '6F86196AB2C574E3', 'remote_spi': '74AD695CF23C4805', 'local_id': '1.1.1.1', 'remote_id': '1.1.1.2', 'local_mesg_id': 2, 'remote_mesg_id': 0, 'local_next_id': 2, 'remote_next_id': 0, 'local_queued': 2, 'remote_queued': 0, 'local_window': 5, 'remote_window': 5, 'dpd_time': 0, 'dpd_retry': 0, 'fragmentation': 'no', 'dynamic_route': 'enabled', 'nat_detected': 'no', 'cts_sgt': 'disabled', 'initiator_of_sa': 'Yes', 'child_sa':{ 1:{ 'local_selectors': ['30.10.10.0/0 - 50.10.10.255/65535','20.10.10.0/0 - 40.10.10.255/65535'], 'remote_selectors': ['172.17.2.0/0 - 172.17.2.255/65535','172.17.2.0/0 - 172.17.3.255/65535'], 'esp_spi_in': '0x232CB82D', 'esp_spi_out': '0x30767B6E', 'ah_spi_in': '0x0', 'ah_spi_out': '0x0', 'cpi_in': '0x0', 'cpi_out': '0x0', 'child_encr': 'AES-CBC', 'keysize': 256, 'esp_hmac': 'SHA256', 'ah_hmac': 'None', 'compression': 'IPCOMP_NONE', 'mode': 'tunnel', }, 2:{ 'local_selectors': ['20.10.10.0/0 - 40.10.10.255/65535'], 'remote_selectors': ['50.20.20.0/0 - 60.20.20.255/65535'], 'esp_spi_in': '0x232CB82D', 'esp_spi_out': '0x30767B6E', 'ah_spi_in': '0x0', 'ah_spi_out': '0x0', 'cpi_in': '0x0', 'cpi_out': '0x0', 'child_encr': 'AES-CBC', 'keysize': 256, 'esp_hmac': 'SHA256', 'ah_hmac': 'None', 'compression': 'IPCOMP_NONE', 'mode': 'tunnel', }, }, }, }, 'IPv6':{ 1:{ 'session_id': 5, 'status': 'UP-ACTIVE', 'ike_count': 1, 'child_count': 1, 'tunnel_id': 1, 'local_ip': '1.1.1::1', 'local_port': 500, 'remote_ip': '1.1.1::2', 'remote_port': 500, 'fvrf': 'none', 'ivrf': 'none', 'session_status': 'READY', 'encryption': 'AES-CBC', 'key_length': 256, 'prf': 'SHA256', 'hash_algo': 'SHA256', 'dh_group': 14, 'auth_sign': 'PSK', 'auth_verify': 'PSK', 'lifetime': 86400, 'activetime': 38157, 'ce_id': 1008, 'id': 3, 'local_spi': '6F86196AB2C574E5', 'remote_spi': '74AD695CF23C4806', 'local_id': '1.1.1::1', 'remote_id': '1.1.1::2', 'local_mesg_id': 2, 'remote_mesg_id': 0, 'local_next_id': 2, 'remote_next_id': 0, 'local_queued': 2, 'remote_queued': 0, 'local_window': 5, 'remote_window': 5, 'dpd_time': 0, 'dpd_retry': 0, 'fragmentation': 'no', 'dynamic_route': 'enabled', 'nat_detected': 'no', 'cts_sgt': 'disabled', 'initiator_of_sa': 'Yes', 'child_sa':{ 1:{ 'local_selectors': ['30.10.10::0/0 - 50.10.10::255/65535','20.10.10::0/0 - 40.10.10::255/65535'], 'remote_selectors': ['172.17.2::0/0 - 172.17.2::255/65535','172.17.2::0/0 - 172.17.3::255/65535'], 'esp_spi_in': '0x232CB82D', 'esp_spi_out': '0x30767B6E', 'ah_spi_in': '0x0', 'ah_spi_out': '0x0', 'cpi_in': '0x0', 'cpi_out': '0x0', 'child_encr': 'AES-CBC', 'keysize': 256, 'esp_hmac': 'SHA256', 'ah_hmac': 'None', 'compression': 'IPCOMP_NONE', 'mode': 'tunnel', }, 2:{ 'local_selectors': ['20.10.10::0/0 - 40.10.10::255/65535'], 'remote_selectors': ['50.20.20::0/0 - 60.20.20::255/65535'], 'esp_spi_in': '0x232CB82D', 'esp_spi_out': '0x30767B6E', 'ah_spi_in': '0x0', 'ah_spi_out': '0x0', 'cpi_in': '0x0', 'cpi_out': '0x0', 'child_encr': 'AES-CBC', 'keysize': 256, 'esp_hmac': 'SHA256', 'ah_hmac': 'None', 'compression': 'IPCOMP_NONE', 'mode': 'tunnel', }, }, }, }, }, }

43.957317

126

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0.175862

0.019066

0.017159

0.934223

0

0.170127

0.552365

7,209

164

127

43.957317

0.480012

0

0.768293

0

0.32025

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0.017753

0

1

0

false

0

null

0

1

0

1

0

1

0

1

0

null

0

9

00af60fcc85ca4b12e38c5350ed6ca84c08436ae

3,094

py

Python

AccountsApp/tests.py

Kolynes/uzu-accounts-app

21c182ec8497fe4fa1ca651fb6c622b59579aba2

[ "MIT" ]

null

AccountsApp/tests.py

Kolynes/uzu-accounts-app

21c182ec8497fe4fa1ca651fb6c622b59579aba2

[ "MIT" ]

null

AccountsApp/tests.py

Kolynes/uzu-accounts-app

21c182ec8497fe4fa1ca651fb6c622b59579aba2

[ "MIT" ]

1

2020-10-28T12:32:28.000Z

from django.test import TestCase from . import models import json from django.core import mail class SendVerificationCodeViewTestCase(TestCase): def test_send_mode_response(self): self.user = models.User.objects.create_user(username="Kolynes", password="password") self.user.save() response = self.client.post("/send_verification_code/", data={"username": "Kolynes", "mode": "send"}) self.assertEqual(response.status_code, 200) first = str(self.user.verification.code) response = self.client.post("/send_verification_code/", data={"username": "Kolynes", "mode": "send"}) self.assertEqual(response.status_code, 200) self.user.refresh_from_db() second = self.user.verification.code self.assertNotEqual(first, second) def test_resend_mode_response(self): self.user = models.User.objects.create_user(username="Kolynes", password="password") response = self.client.post("/send_verification_code/", data={"username": "Kolynes", "mode": "resend"}) self.assertEqual(response.status_code, 200) first = self.user.verification.code response = self.client.post("/send_verification_code/", data={"username": "Kolynes", "mode": "resend"}) self.assertEqual(response.status_code, 200) self.user.refresh_from_db() second = self.user.verification.code self.assertEqual(first, second) class VerifyViewTestCase(TestCase): def test_true_response(self): user = models.User.objects.create_user(username="Kolynes", password="password") verification = models.Verification.objects.create(user=user, code="1234") response = self.client.post("/verify_code/", data={"username": "Kolynes", "code": verification.code}) self.assertEqual(response.status_code, 200) def test_false_response(self): user = models.User.objects.create_user(username="Kolynes", password="password") verification = models.Verification.objects.create(user=user, code="1234") response = self.client.post("/verify_code/", data={"username": "Kolynes", "code": "123"}) self.assertEqual(response.status_code, 404) json_response = json.loads(response.content) class ResetPasswordViewTestCase(TestCase): def test_true_response(self): user = models.User.objects.create_user(username="Kolynes", password="password") verification = models.Verification.objects.create(user=user, code="1234") response = self.client.post("/reset_password/", data={"username": "Kolynes", "code": "1234", "new_password": "1234"}) json_response = json.loads(response.content) self.assertEqual(response.status_code, 200) class SignInViewTestCase(TestCase): def test_true_response(self): user = models.User.objects.create_user(username="Kolynes", password="password") response = self.client.post("/sign_in/", data={"username": "Kolynes", "password": "password"}) json_response = json.loads(response.content) self.assertEqual(response.status_code, 200)

49.903226

125

0.69554

352

3,094

5.977273

0.150568

0.079848

0.072719

0.08365

0.802757

0.787072

0.752852

0.748099

0

0.018161

0.163542

3,094

61

126

50.721311

0.794822

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0.588235

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0.176471

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0.27451

0

null

0

1

0

null

0

1

0

7

00b5ca61e8d39f49c57980fd6fac5119805e813c

111

py

Python

numerical_analysis/root_finding/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

numerical_analysis/root_finding/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

numerical_analysis/root_finding/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

from .root_finding import bisection, secant, newton_raphson, newton_raphson_2x2, newton_raphson_multiple_roots

55.5

110

0.882883

15

111

6.066667

0.733333

0.428571

0

0.019417

0.072072

111

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111

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0

1

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true

0

1

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null

0

1

0

1

0

1

0

7

00d3810658850f0a200bff5d22b4212f5119b4cf

284

py

Python

advpistepper/__init__.py

innot/AdvPiStepper

e98db5b19952b25cbcffdc118c599e546f026a9b

[ "MIT" ]

1

2022-01-22T09:18:56.000Z

advpistepper/__init__.py

innot/AdvPiStepper

e98db5b19952b25cbcffdc118c599e546f026a9b

[ "MIT" ]

2

2021-05-17T23:14:36.000Z

2022-01-22T09:23:38.000Z

advpistepper/__init__.py

innot/AdvPiStepper

e98db5b19952b25cbcffdc118c599e546f026a9b

[ "MIT" ]

1

2021-10-04T23:15:01.000Z

from advpistepper.stepper import AdvPiStepper from advpistepper.driver_unipolar_generic import DriverUnipolarGeneric from advpistepper.driver_unipolar_28byj48 import Driver28BYJ48 from advpistepper.driver_step_dir_generic import DriverStepDirGeneric from advpistepper.common import *

47.333333

70

0.908451

31

284

8.096774

0.451613

0.318725

0.262948

0.239044

0

0.030303

0.070423

284

5

71

56.8

0.920455

0

1

0

true

0

1

0

1

0

null

1

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1

0

null

0

1

0

1

0

1

0

7

dad58f87add262ccad314a0a6f25c698d209985e

6,641

py

Python

#765/#765_self.py

1uci3n/leetcode

1268c8ef6d9739e16035ab88c78751824886a92a

[ "MIT" ]

null

#765/#765_self.py

1uci3n/leetcode

1268c8ef6d9739e16035ab88c78751824886a92a

[ "MIT" ]

null

#765/#765_self.py

1uci3n/leetcode

1268c8ef6d9739e16035ab88c78751824886a92a

[ "MIT" ]

null

class Solution(object): def minSwapsCouples(self, row): """ :type row: List[int] :rtype: int """ couple_num = (len(row) + 1) / 2 error_index = [] error_num = 0 for i in range(couple_num): i = i * 2 if row[i] < row[i + 1]: if row[i] % 2 != 0: error_num += 1 error_index.append(i) continue elif row[i] + 1 != row[i + 1]: error_num += 1 error_index.append(i) continue else: if row[i + 1] % 2 != 0: error_num += 1 error_index.append(i) continue elif row[i + 1] + 1 != row[i]: error_num += 1 error_index.append(i) continue group_list = [] slod_index = [] for m in range(len(error_index)): group_size = 1 if m in slod_index: continue point = m while not(((row[error_index[point]] % 2 == 0) & ((row[error_index[point]] + 1) == row[error_index[point] + 1])) | (((row[error_index[point] + 1]) % 2 == 0) & (row[error_index[point]] == (row[error_index[point] + 1] + 1)))): if row[error_index[point]] < row[error_index[point] + 1]: if row[error_index[point]] % 2 == 0: target = row[error_index[point]] + 1 for i in range(0, len(error_index)): if i in slod_index: continue elif i == point: continue if target == row[error_index[i]]: temp = row[error_index[point] + 1] row[error_index[point] + 1] = row[error_index[i]] row[error_index[i]] = temp slod_index.append(point) point = i group_size += 1 break elif target == row[error_index[i] + 1]: temp = row[error_index[point] + 1] row[error_index[point] + 1] = row[error_index[i] + 1] row[error_index[i] + 1] = temp slod_index.append(point) point = i group_size += 1 break else: target = row[error_index[point]] - 1 for i in range(0, len(error_index)): if i in slod_index: continue elif i == point: continue if target == row[error_index[i]]: temp = row[error_index[point] + 1] row[error_index[point] + 1] = row[error_index[i]] row[error_index[i]] = temp slod_index.append(point) point = i group_size += 1 break elif target == row[error_index[i] + 1]: temp = row[error_index[point] + 1] row[error_index[point] + 1] = row[error_index[i] + 1] row[error_index[i] + 1] = temp slod_index.append(point) point = i group_size += 1 break else: if row[error_index[point] + 1] % 2 == 0: target = row[error_index[point] + 1] + 1 for i in range(1, len(error_index)): if i in slod_index: continue elif i == point: continue if target == row[error_index[i]]: temp = row[error_index[point]] row[error_index[point]] = row[error_index[i]] row[error_index[i]] = temp slod_index.append(point) point = i group_size += 1 break elif target == row[error_index[i] + 1]: temp = row[error_index[point]] row[error_index[point]] = row[error_index[i] + 1] row[error_index[i] + 1] = temp slod_index.append(point) point = i group_size += 1 break else: target = row[error_index[point] + 1] - 1 for i in range(1, len(error_index)): if i in slod_index: continue elif i == point: continue if target == row[error_index[i]]: temp = row[error_index[point]] row[error_index[point]] = row[error_index[i]] row[error_index[i]] = temp slod_index.append(point) point = i group_size += 1 break elif target == row[error_index[i] + 1]: temp = row[error_index[point]] row[error_index[point]] = row[error_index[i] + 1] row[error_index[i] + 1] = temp slod_index.append(point) point = i group_size += 1 break group_list.append(group_size) sum_change = 0 for i in group_list: sum_change += i - 1 return sum_change

48.474453

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0.341515

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6,641

3.660473

0.069257

0.295339

0.32395

0.249192

0.849562

0.848639

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0.819105

0.785879

0.751731

0

0.026241

0.575365

6,641

136

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null

1

0

1

0

1

0

null

0

11

daee4697a7fab3fa0387b95c534853fbf22b0c7f

14,001

py

Python

NetSurfP/plotcomp1kgnetsurfP.py

najmacherrad/master_thesis

4a5c68d6dddb98548ff93105a330e21148a1fa8d

[ "MIT" ]

1

2019-01-18T02:01:59.000Z

NetSurfP/plotcomp1kgnetsurfP.py

najmacherrad/master_thesis

4a5c68d6dddb98548ff93105a330e21148a1fa8d

[ "MIT" ]

null

NetSurfP/plotcomp1kgnetsurfP.py

najmacherrad/master_thesis

4a5c68d6dddb98548ff93105a330e21148a1fa8d

[ "MIT" ]

null

# NetSurfP #Compare results between wild type and mutant # coding=utf-8 import numpy as np import matplotlib.pyplot as plt import pandas as pd import csv from scipy import stats from pylab import plot, show, savefig, xlim, figure, \ hold, ylim, legend, boxplot, setp, axes def getColumn(filename, column,deli): results = csv.reader(open(filename), delimiter=deli) return [result[column] for result in results] #Importfiles file_wt = 'netsurfpresultsNEW2_wt.csv' file_mut = 'netsurfpresultsNEW2_1kg.csv' #----------------------------------------------------------------------------- # RSA #----------------------------------------------------------------------------- #---------------- # SCATTER PLOT RSA_wt = getColumn(file_wt,4,'\t') RSA_mut = getColumn(file_mut,4,'\t') RSA_wt.pop(0) RSA_mut.pop(0) x,y=[],[] for i in range(0,len(RSA_wt)): if RSA_wt[i]=='NA': x.append(np.nan) else: x.append(float(RSA_wt[i])) for i in range(0,len(RSA_mut)): if RSA_mut[i]=='NA': y.append(np.nan) else: y.append(float(RSA_mut[i])) fig = plt.figure() a=b=[0,0.2,0.3,0.4,0.5,0.6,0.9] plt.scatter(x, y,edgecolor = 'none', c= 'k') plt.plot(a,b,'r-') plt.grid('on') plt.xlim(0,0.9) plt.ylim(0,0.9) plt.xlabel('Wild types') plt.ylabel('Neutral 1KGP mutants') fig.savefig('RSA_wtVS1kg.jpg') #---------------- # PROBABILITY DENSITY CURVE fig = figure() mu1, std1 = stats.norm.fit(x) mu2, std2 = stats.norm.fit(y) xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'k',label='Wild types (fit results: mu=%.2f,std=%.2f)'%(mu1, std1)) plt.plot(x2, p2, 'b',label='Neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2)) plt.xlabel('Solvent accessibility predicted values') plt.ylabel('Frequency') plt.xlim(0,0.9) plt.ylim(0,4) plt.legend(loc='upper right') fig.savefig('histo_netsurfp_missense_wtVS1kg.png') #missense_wt - missense_mut miss=[] [miss.append(a_i - b_i) for a_i, b_i in zip(x, y)] #KOLMOGOROV-SMINORV: stats.kstest(miss,'norm') #(D,pvalue) = (0.42761364158461712, 0.0) #So we reject H0 -> not normal distribution #WILCOXON TEST: stats.wilcoxon(miss) #-> (T, pvalue) = (1403683.5, 0.020490035411006691) #So we reject H0 -> There is a significant difference between wt and mut #----------------------------------------------------------------------------- # RSA ENVIRONNEMENT #----------------------------------------------------------------------------- #----------------- # SCATTER PLOT #----------------------------------------------------------------------------- #RSA_envt RSA_wt = getColumn(file_wt,5,'\t') RSA_mut = getColumn(file_mut,5,'\t') RSA_wt.pop(0) RSA_mut.pop(0) x,y=[],[] for i in range(0,len(RSA_wt)): if RSA_wt[i]=='NA': x.append(np.nan) else: x.append(float(RSA_wt[i])) for i in range(0,len(RSA_mut)): if RSA_mut[i]=='NA': y.append(np.nan) else: y.append(float(RSA_mut[i])) fig = plt.figure() a=b=[0,0.2,0.3,0.4,0.5,0.6,0.9] plt.scatter(x, y,edgecolor = 'none', c= 'k') plt.plot(a,b,'r-') plt.grid('on') plt.xlim(0,0.9) plt.ylim(0,0.9) plt.xlabel('Wild types') plt.ylabel('Neutral 1KGP mutants') fig.savefig('RSA_envt_wtVS1kg.jpg') #---------------- # PROBABILITY DENSITY CURVE fig = figure() mu1, std1 = stats.norm.fit(x) mu2, std2 = stats.norm.fit(y) xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'k',label='Wild types (fit results: mu=%.2f,std=%.2f)'%(mu1, std1)) plt.plot(x2, p2, 'b',label='Neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2)) plt.xlabel('Solvent accessibility predicted values') plt.ylabel('Frequency') plt.xlim(0,0.9) plt.ylim(0,5) plt.legend(loc='upper right') fig.savefig('histo_netsurfp_missense_envt_wtVS1kg.png') # STATS miss=[] [miss.append(a_i - b_i) for a_i, b_i in zip(x, y)] #KOLMOGOROV-SMINORV: stats.kstest(miss,'norm') # (D,pvalue) = (0.45460452749063657, 0.0) #So we reject H0 -> not normal distribution #WILCOXON TEST: stats.wilcoxon(miss) #-> (T, pvalue) = (1548668.0, 0.43701657073338696) #So we do not reject H0 -> There is no significant difference between wt and mut #----------------------------------------------------------------------------- # OUTLIERS FOR RSA (270) #----------------------------------------------------------------------------- RSA_wt = getColumn(file_wt,4,'\t') RSA_mut = getColumn(file_mut,4,'\t') RSA_wt.pop(0) RSA_mut.pop(0) RSAe_wt = getColumn(file_wt,5,'\t') RSAe_mut = getColumn(file_mut,5,'\t') RSAe_wt.pop(0) RSAe_mut.pop(0) variant_liste = getColumn(file_wt,0,'\t') variant_liste.pop(0) output = open('netsurfp_outliers_1kg.csv','w') output.write('ID,RSA_wt,RSA_mut,difference,RSA_envt_wt,RSA_envt_mut,difference_envt\n') for i in range(0,len(RSA_wt)): for j in range(0,len(RSA_mut)): if i==j: if RSA_wt[i]!='NA'and RSA_mut[j]!='NA': if (abs(float(RSA_wt[i])-float(RSA_mut[j]))) > 0.1: output.write(variant_liste[i+1] + ',' + RSA_wt[i] + ',' + RSA_mut[j] + ',' + str(abs(float(RSA_wt[i])-float(RSA_mut[j]))) + ',' + RSAe_wt[i] + ',' + RSAe_mut[i] + ',' + str(abs(float(RSAe_wt[i])-float(RSAe_mut[j]))) + '\n') output.close() #----------------------------------------------------------------------------- # RSA depending on Z-score #----------------------------------------------------------------------------- #----------------- # SCATTER PLOT Zscore_wt = getColumn(file_wt,6,'\t') Zscore_mut = getColumn(file_mut,6,'\t') Zscore_wt.pop(0) Zscore_mut.pop(0) RSA_wt = getColumn(file_wt,4,'\t') RSA_mut = getColumn(file_mut,4,'\t') RSA_wt.pop(0) RSA_mut.pop(0) ID = getColumn(file_wt,0,'\t') ID.pop(0) x_pos,x_neg,y_pos,y_neg=[],[],[],[] IDwt_pos,IDwt_neg = [],[] for i in range(0,len(RSA_wt)): if float(Zscore_wt[i])>=0: x_pos.append(float(RSA_wt[i])) IDwt_pos.append(ID[i]) else: x_neg.append(float(RSA_wt[i])) IDwt_neg.append(ID[i]) IDmut_pos,IDmut_neg = [],[] for i in range(0,len(RSA_mut)): if ID[i] in IDwt_pos: y_pos.append(float(RSA_mut[i])) IDmut_pos.append(ID[i]) else: y_neg.append(float(RSA_mut[i])) IDmut_neg.append(ID[i]) # Z-score > 0 for wild types fig = plt.figure() a=b=[0,0,0.8] plt.scatter(x_pos, y_pos,edgecolor = 'none', c= 'k') plt.plot(a,b,'r-') plt.grid('on') plt.xlim(0,0.9) plt.ylim(0,0.9) plt.xlabel('Wild types') plt.ylabel('Neutral 1KGP mutants') fig.savefig('RSA_wtVS1kg_zscore_pos.jpg') #outliers (41) output = open('netsurfp1kg_outliers_zscore_pos.csv','w') output.write('ID,RSA_wt,RSA_mut,difference\n') for i in range(0,len(x_pos)): for j in range(0,len(y_pos)): if i==j: if (abs(float(x_pos[i])-float(y_pos[j]))) > 0.1: output.write(IDwt_pos[i] + ',' + str(x_pos[i]) + ',' + str(y_pos[j]) + ',' + str(abs(float(x_pos[i])-float(y_pos[j]))) + '\n') output.close() # Z-score < 0 fot wild types fig = plt.figure() a=b=[0,0,0.8] plt.scatter(x_neg, y_neg,edgecolor = 'none', c= 'k') plt.plot(a,b,'r-') plt.grid('on') plt.xlim(0,0.8) plt.ylim(0,0.8) plt.xlabel('Wild type residues') plt.ylabel('Mutant residues') fig.savefig('RSA_wtVS1kg_zscore_neg.jpg') #----------------------------------------------------------------------------- # RSA : COMPARISON deleterious DIDA mutants VS neutral 1KGP mutants #----------------------------------------------------------------------------- file_DIDA = 'netsurfpresults_mut_DIDA.csv' RSA_DIDA = getColumn(file_DIDA,4,'\t') RSA_1kg = getColumn(file_mut,4,'\t') RSA_DIDA.pop(0) RSA_1kg.pop(0) xRSA,yRSA=[],[] for i in range(0,len(RSA_DIDA)): #241 if RSA_DIDA[i]=='NA': xRSA.append(np.nan) else: xRSA.append(float(RSA_DIDA[i])) for i in range(0,len(RSA_1kg)): #2516 if RSA_1kg[i]=='NA': yRSA.append(np.nan) else: yRSA.append(float(RSA_1kg[i])) fig = figure() mu1, std1 = stats.norm.fit(xRSA) mu2, std2 = stats.norm.fit(yRSA) bins = np.linspace(-0.3, 1, 35) plt.hist(xRSA, bins, alpha=0.3, label='Deleterious DIDA mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu1, std1),normed=True,color='red') plt.hist(yRSA, bins, alpha=0.3, label='Neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2),normed=True,color='blue') xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'r', linewidth=2) plt.plot(x2, p2, 'b', linewidth=2) plt.xlabel('Solvent accessibility predicted values') plt.ylabel('Frequency') plt.ylim(0,5) plt.xlim(-0.3,1) plt.legend(loc='upper right') fig.savefig('histoRSA_DIDA1kg.png') #MANN-WHITNEY: stats.ranksums(xRSA,yRSA) # (U,p-value) = (-5.995280821744239, 2.0313410214210638e-09) # Reject H0 # The distributions of two sets of variables have a difference #----------------------------------------------------------------------------- # RSA ENVIRONMENT: COMPARISON deleterious DIDA mutants VS neutral 1KGP mutants #----------------------------------------------------------------------------- RSA_DIDA = getColumn(file_DIDA,5,'\t') RSA_1kg = getColumn(file_mut,5,'\t') RSA_DIDA.pop(0) RSA_1kg.pop(0) xRSA,yRSA=[],[] for i in range(0,len(RSA_DIDA)): #241 if RSA_DIDA[i]=='NA': xRSA.append(np.nan) else: xRSA.append(float(RSA_DIDA[i])) for i in range(0,len(RSA_1kg)): #2516 if RSA_1kg[i]=='NA': yRSA.append(np.nan) else: yRSA.append(float(RSA_1kg[i])) fig = figure() mu1, std1 = stats.norm.fit(xRSA) mu2, std2 = stats.norm.fit(yRSA) bins = np.linspace(-0.3, 1, 35) plt.hist(xRSA, bins, alpha=0.3, label='Deleterious DIDA mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu1, std1),normed=True,color='red') plt.hist(yRSA, bins, alpha=0.3, label='Neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2),normed=True,color='blue') xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'r', linewidth=2) plt.plot(x2, p2, 'b', linewidth=2) plt.xlabel('Solvent accessibility predicted values') plt.ylabel('Frequency') plt.ylim(0,5) plt.xlim(-0.3,1) plt.legend(loc='upper right') fig.savefig('histoRSAenvt_DIDA1kg.png') #MANN-WHITNEY: stats.ranksums(xRSA,yRSA) # (U,p-value) = (-7.4005610929180445, 1.356102615569394e-13) # Reject H0 # The distributions of two sets of variables have a difference #----------------------------------------------------------------------------- #Plot comparing solvent accessibility change : wt-DIDA VS wt-1kg #----------------------------------------------------------------------------- file_diff_DIDA = 'netsurfpresults_compare.csv' file_diff_1KGP = 'netsurfpresults_compare1kg.csv' RSA_DIDA = getColumn(file_diff_DIDA,3,',') RSA_1kg = getColumn(file_diff_1KGP,3,',') RSA_DIDA.pop(0) RSA_1kg.pop(0) xRSA,yRSA=[],[] for i in range(0,len(RSA_DIDA)): #241 if RSA_DIDA[i]=='NA': xRSA.append(np.nan) else: xRSA.append(float(RSA_DIDA[i])) for i in range(0,len(RSA_1kg)): #2516 if RSA_1kg[i]=='NA': yRSA.append(np.nan) else: yRSA.append(float(RSA_1kg[i])) fig = figure() mu1, std1 = stats.norm.fit(xRSA) mu2, std2 = stats.norm.fit(yRSA) bins = np.linspace(-0.4, 0.4, 35) plt.hist(xRSA, bins, alpha=0.3, label='wt - deleterious DIDA mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu1, std1),normed=True,color='red') plt.hist(yRSA, bins, alpha=0.3, label='wt - neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2),normed=True,color='blue') xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'r', linewidth=2) plt.plot(x2, p2, 'b', linewidth=2) plt.xlabel('delta(Solvent accessibility predicted values)') plt.ylabel('Frequency') plt.ylim(0,30) plt.xlim(-0.3,0.4) plt.legend(loc='upper right') fig.savefig('histoRSA_DIDA1kg_diff.png') #MANN-WHITNEY: stats.ranksums(xRSA,yRSA) # (U,p-value) = (1.3035870938300544, 0.19237440346309431) # Not reject H0 # The distributions of two sets of variables have no difference #Environnment RSA_DIDA = getColumn(file_diff_DIDA,4,',') RSA_1kg = getColumn(file_diff_1KGP,4,',') RSA_DIDA.pop(0) RSA_1kg.pop(0) xRSA,yRSA=[],[] for i in range(0,len(RSA_DIDA)): #241 if RSA_DIDA[i]=='NA': xRSA.append(np.nan) else: xRSA.append(float(RSA_DIDA[i])) for i in range(0,len(RSA_1kg)): #2516 if RSA_1kg[i]=='NA': yRSA.append(np.nan) else: yRSA.append(float(RSA_1kg[i])) fig = figure() mu1, std1 = stats.norm.fit(xRSA) mu2, std2 = stats.norm.fit(yRSA) bins = np.linspace(-0.4, 0.4, 35) plt.hist(xRSA, bins, alpha=0.3, label='wt - deleterious DIDA mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu1, std1),normed=True,color='red') plt.hist(yRSA, bins, alpha=0.3, label='wt - neutral 1KGP mutants \n(fit results: mu=%.2f,std=%.2f)'%(mu2, std2),normed=True,color='blue') xmin1, xmax1 = plt.xlim() xmin2, xmax2 = plt.xlim() x1 = np.linspace(xmin1, xmax1, 100) x2 = np.linspace(xmin2, xmax2, 100) p1 = stats.norm.pdf(x1, mu1, std1) p2 = stats.norm.pdf(x2, mu2, std2) plt.plot(x1, p1, 'r', linewidth=2) plt.plot(x2, p2, 'b', linewidth=2) plt.xlabel('delta(Solvent accessibility predicted values)') plt.ylabel('Frequency') plt.ylim(0,30) plt.xlim(-0.3,0.4) plt.legend(loc='upper right') fig.savefig('histoRSA_DIDA1kg_diff_envt.png') #MANN-WHITNEY: stats.ranksums(xRSA,yRSA) # (U,p-value) = (-0.40173252274280541, 0.68788088669316183) # Not reject H0 # The distributions of two sets of variables have no difference

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Python

src/text_processor/__init__.py

CasperAntonPoulsen/ConceptExtraction

41c1ae65dfab7e774a43a8f6f45f403f56cf1a0b

[ "Apache-2.0" ]

6

2020-08-28T17:08:42.000Z

2022-02-14T22:26:01.000Z

src/text_processor/__init__.py

CasperAntonPoulsen/ConceptExtraction

41c1ae65dfab7e774a43a8f6f45f403f56cf1a0b

[ "Apache-2.0" ]

1

2021-01-26T02:20:30.000Z

src/text_processor/__init__.py

CasperAntonPoulsen/ConceptExtraction

41c1ae65dfab7e774a43a8f6f45f403f56cf1a0b

[ "Apache-2.0" ]

3

2020-09-15T16:59:58.000Z

2022-03-16T10:05:10.000Z

from src.text_processor.token import Token from src.text_processor.tokenized_text import TokenizedText from src.text_processor.text_tokenizer import TextTokenizer

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Python

Agents/Networks/DQN.py

BY571/BY571

9ddff988ef2516e4752c0296cc88b06bb0221400

[ "MIT" ]

65

2020-04-26T13:15:34.000Z

2022-03-27T05:29:14.000Z

Agents/Networks/DQN.py

BY571/BY571

9ddff988ef2516e4752c0296cc88b06bb0221400

[ "MIT" ]

6

2020-08-14T06:14:38.000Z

2021-03-31T17:29:38.000Z

Agents/Networks/DQN.py

BY571/BY571

9ddff988ef2516e4752c0296cc88b06bb0221400

[ "MIT" ]

15

2020-07-23T17:32:59.000Z

2022-01-31T05:47:31.000Z

import torch import torch.nn as nn import torch.nn.functional as F import math def weight_init(layers): for layer in layers: torch.nn.init.kaiming_normal_(layer.weight, nonlinearity='relu') class NoisyLinear(nn.Linear): # Noisy Linear Layer for independent Gaussian Noise def __init__(self, in_features, out_features, sigma_init=0.017, bias=True): super(NoisyLinear, self).__init__(in_features, out_features, bias=bias) # make the sigmas trainable: self.sigma_weight = nn.Parameter(torch.full((out_features, in_features), sigma_init)) # not trainable tensor for the nn.Module self.register_buffer("epsilon_weight", torch.zeros(out_features, in_features)) # extra parameter for the bias and register buffer for the bias parameter if bias: self.sigma_bias = nn.Parameter(torch.full((out_features,), sigma_init)) self.register_buffer("epsilon_bias", torch.zeros(out_features)) # reset parameter as initialization of the layer self.reset_parameter() def reset_parameter(self): """ initialize the parameter of the layer and bias """ std = math.sqrt(3/self.in_features) self.weight.data.uniform_(-std, std) self.bias.data.uniform_(-std, std) def forward(self, input): # sample random noise in sigma weight buffer and bias buffer self.epsilon_weight.normal_() bias = self.bias if bias is not None: self.epsilon_bias.normal_() bias = bias + self.sigma_bias * self.epsilon_bias return F.linear(input, self.weight + self.sigma_weight * self.epsilon_weight, bias) class DDQN(nn.Module): def __init__(self, state_size, action_size,layer_size, n_step, seed, layer_type="ff"): super(DDQN, self).__init__() self.seed = torch.manual_seed(seed) self.input_shape = state_size self.action_size = action_size self.state_dim = len(state_size) if self.state_dim == 3: self.cnn_1 = nn.Conv2d(4, out_channels=32, kernel_size=8, stride=4) self.cnn_2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2) self.cnn_3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) weight_init([self.cnn_1, self.cnn_2, self.cnn_3]) if layer_type == "noisy": self.ff_1 = NoisyLinear(self.calc_input_layer(), layer_size) self.ff_2 = NoisyLinear(layer_size, action_size) else: self.ff_1 = nn.Linear(self.calc_input_layer(), layer_size) self.ff_2 = nn.Linear(layer_size, action_size) weight_init([self.ff_1]) elif self.state_dim == 1: if layer_type == "noisy": self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1 = NoisyLinear(layer_size, layer_size) self.ff_2 = NoisyLinear(layer_size, action_size) else: self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1 = nn.Linear(layer_size, layer_size) self.ff_2 = nn.Linear(layer_size, action_size) weight_init([self.head_1, self.ff_1]) else: print("Unknown input dimension!") def calc_input_layer(self): x = torch.zeros(self.input_shape).unsqueeze(0) x = self.cnn_1(x) x = self.cnn_2(x) x = self.cnn_3(x) return x.flatten().shape[0] def forward(self, input): """ """ if self.state_dim == 3: x = torch.relu(self.cnn_1(input)) x = torch.relu(self.cnn_2(x)) x = torch.relu(self.cnn_3(x)) x = x.view(input.size(0), -1) else: x = torch.relu(self.head_1(input)) x = torch.relu(self.ff_1(x)) out = self.ff_2(x) return out class Dueling_QNetwork(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size,layer_size, n_step, seed, layer_type="ff"): """Initialize parameters and build model. Params ====== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer """ super(Dueling_QNetwork, self).__init__() self.seed = torch.manual_seed(seed) self.input_shape = state_size self.state_dim = len(self.input_shape) self.action_size = action_size if self.state_dim == 3: self.cnn_1 = nn.Conv2d(4, out_channels=32, kernel_size=8, stride=4) self.cnn_2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2) self.cnn_3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) weight_init([self.cnn_1, self.cnn_2, self.cnn_3]) if layer_type == "noisy": self.ff_1_A = NoisyLinear(self.calc_input_layer(), layer_size) self.ff_1_V = NoisyLinear(self.calc_input_layer(), layer_size) self.advantage = NoisyLinear(layer_size,action_size) self.value = NoisyLinear(layer_size,1) weight_init([self.ff_1_A, self.ff_1_V]) else: self.ff_1_A = nn.Linear(self.calc_input_layer(), layer_size) self.ff_1_V = nn.Linear(self.calc_input_layer(), layer_size) self.advantage = nn.Linear(layer_size,action_size) self.value = nn.Linear(layer_size,1) weight_init([self.ff_1_A, self.ff_1_V]) elif self.state_dim == 1: if layer_type == "noisy": self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1_A = NoisyLinear(layer_size, layer_size) self.ff_1_V = NoisyLinear(layer_size, layer_size) self.advantage = NoisyLinear(layer_size,action_size) self.value = NoisyLinear(layer_size,1) weight_init([self.head_1,self.ff_1_A, self.ff_1_V]) else: self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1_A = nn.Linear(layer_size, layer_size) self.ff_1_V = nn.Linear(layer_size, layer_size) self.advantage = nn.Linear(layer_size,action_size) self.value = nn.Linear(layer_size,1) weight_init([self.head_1,self.ff_1_A, self.ff_1_V]) else: print("Unknown input dimension!") def calc_input_layer(self): x = torch.zeros(self.input_shape).unsqueeze(0) x = self.cnn_1(x) x = self.cnn_2(x) x = self.cnn_3(x) return x.flatten().shape[0] def forward(self, input): """ """ if self.state_dim == 3: x = torch.relu(self.cnn_1(input)) x = torch.relu(self.cnn_2(x)) x = torch.relu(self.cnn_3(x)) x = x.view(input.size(0), -1) x_A = torch.relu(self.ff_1_A(x)) x_V = torch.relu(self.ff_1_V(x)) else: x = torch.relu(self.head_1(input)) x_A = torch.relu(self.ff_1_A(x)) x_V = torch.relu(self.ff_1_V(x)) value = self.value(x_V) value = value.expand(input.size(0), self.action_size) advantage = self.advantage(x_A) Q = value + advantage - advantage.mean() return Q class Dueling_C51Network(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size,layer_size, n_step, seed, layer_type="ff", N_ATOMS=51, VMAX=10, VMIN=-10): """Initialize parameters and build model. Params ====== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed fc1_units (int): Number of nodes in first hidden layer fc2_units (int): Number of nodes in second hidden layer """ super(Dueling_C51Network, self).__init__() self.seed = torch.manual_seed(seed) self.input_shape = state_size self.state_dim = len(self.input_shape) self.action_size = action_size self.N_ATOMS = N_ATOMS self.VMAX = VMAX self.VMIN = VMIN self.DZ = (VMAX-VMIN) / (N_ATOMS - 1) if self.state_dim == 3: self.cnn_1 = nn.Conv2d(4, out_channels=32, kernel_size=8, stride=4) self.cnn_2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2) self.cnn_3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) weight_init([self.cnn_1, self.cnn_2, self.cnn_3]) if layer_type == "noisy": self.ff_1_A = NoisyLinear(self.calc_input_layer(), layer_size) self.ff_1_V = NoisyLinear(self.calc_input_layer(), layer_size) self.advantage = NoisyLinear(layer_size,action_size*N_ATOMS) self.value = NoisyLinear(layer_size,N_ATOMS) weight_init([self.ff_1_A, self.ff_1_V]) else: self.ff_1_A = nn.Linear(self.calc_input_layer(), layer_size) self.ff_1_V = nn.Linear(self.calc_input_layer(), layer_size) self.advantage = nn.Linear(layer_size,action_size*N_ATOMS) self.value = nn.Linear(layer_size,N_ATOMS) weight_init([self.ff_1_A, self.ff_1_V]) elif self.state_dim == 1: if layer_type == "noisy": self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1_A = NoisyLinear(layer_size, layer_size) self.ff_1_V = NoisyLinear(layer_size, layer_size) self.advantage = NoisyLinear(layer_size,action_size*N_ATOMS) self.value = NoisyLinear(layer_size,N_ATOMS) weight_init([self.head_1,self.ff_1_A, self.ff_1_V]) else: self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1_A = nn.Linear(layer_size, layer_size) self.ff_1_V = nn.Linear(layer_size, layer_size) self.advantage = nn.Linear(layer_size,action_size*N_ATOMS) self.value = nn.Linear(layer_size,N_ATOMS) weight_init([self.head_1,self.ff_1_A, self.ff_1_V]) else: print("Unknown input dimension!") self.register_buffer("supports", torch.arange(VMIN, VMAX+self.DZ, self.DZ)) # basic value vector - shape n_atoms stepsize dz self.softmax = nn.Softmax(dim = 1) def calc_input_layer(self): x = torch.zeros(self.input_shape).unsqueeze(0) x = self.cnn_1(x) x = self.cnn_2(x) x = self.cnn_3(x) return x.flatten().shape[0] def forward(self, input): batch_size = input.size()[0] if self.state_dim == 3: x = torch.relu(self.cnn_1(input)) x = torch.relu(self.cnn_2(x)) x = torch.relu(self.cnn_3(x)) x = x.view(input.size(0), -1) x_A = torch.relu(self.ff_1_A(x)) x_V = torch.relu(self.ff_1_V(x)) else: x = torch.relu(self.head_1(input)) x_A = torch.relu(self.ff_1_A(x)) x_V = torch.relu(self.ff_1_V(x)) value = self.value(x_V).view(batch_size,1,self.N_ATOMS) advantage = self.advantage(x_A).view(batch_size,-1, self.N_ATOMS) q_distr = value + advantage - advantage.mean(dim = 1, keepdim = True) prob = self.softmax(q_distr.view(-1, self.N_ATOMS)).view(-1, self.action_size, self.N_ATOMS) return prob def act(self,state): prob = self.forward(state).data.cpu() expected_value = prob.cpu() * self.supports.cpu() actions = expected_value.sum(2) return actions class DDQN_C51(nn.Module): def __init__(self, state_size, action_size,layer_size, n_step, seed, layer_type="ff", N_ATOMS=51, VMAX=10, VMIN=-10): super(DDQN_C51, self).__init__() self.seed = torch.manual_seed(seed) self.input_shape = state_size self.action_size = action_size self.state_dim = len(state_size) self.N_ATOMS = N_ATOMS self.VMAX = VMAX self.VMIN = VMIN self.DZ = (VMAX-VMIN) / (N_ATOMS - 1) if self.state_dim == 3: self.cnn_1 = nn.Conv2d(4, out_channels=32, kernel_size=8, stride=4) self.cnn_2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2) self.cnn_3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) weight_init([self.cnn_1, self.cnn_2, self.cnn_3]) if layer_type == "noisy": self.ff_1 = NoisyLinear(self.calc_input_layer(), layer_size) self.ff_2 = NoisyLinear(layer_size, action_size*N_ATOMS) else: self.ff_1 = nn.Linear(self.calc_input_layer(), layer_size) self.ff_2 = nn.Linear(layer_size, action_size*N_ATOMS) weight_init([self.ff_1]) elif self.state_dim == 1: if layer_type == "noisy": self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1 = NoisyLinear(layer_size, layer_size) self.ff_2 = NoisyLinear(layer_size, action_size*N_ATOMS) else: self.head_1 = nn.Linear(self.input_shape[0], layer_size) self.ff_1 = nn.Linear(layer_size, layer_size) self.ff_2 = nn.Linear(layer_size, action_size*N_ATOMS) weight_init([self.head_1, self.ff_1]) else: print("Unknown input dimension!") self.register_buffer("supports", torch.arange(VMIN, VMAX+self.DZ, self.DZ)) # basic value vector - shape n_atoms stepsize dz self.softmax = nn.Softmax(dim = 1) def calc_input_layer(self): x = torch.zeros(self.input_shape).unsqueeze(0) x = self.cnn_1(x) x = self.cnn_2(x) x = self.cnn_3(x) return x.flatten().shape[0] def forward(self, input): batch_size = input.size()[0] if self.state_dim == 3: x = torch.relu(self.cnn_1(input)) x = torch.relu(self.cnn_2(x)) x = torch.relu(self.cnn_3(x)) x = x.view(input.size(0), -1) x = torch.relu(self.ff_1(x)) else: x = torch.relu(self.head_1(input)) x = torch.relu(self.ff_1(x)) q_distr = self.ff_2(x) prob = self.softmax(q_distr.view(-1, self.N_ATOMS)).view(-1, self.action_size, self.N_ATOMS) return prob def act(self,state): prob = self.forward(state).data.cpu() # create value distribution for each action - shape: (batch_size, action_space, 51) expected_value = prob.cpu() * self.supports.cpu() # sum up the prob*values for the action dimension - shape: (batch_size, action_space) actions = expected_value.sum(2) return actions

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c11776facf8e213b8fb007642294fa5bae8d2b9a

76,257

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Python

gs_quant/target/instrument.py

dannyb2018/gs-quant

e963c4af1c7c65b2ee8f7995815542f6fb7b4957

[ "Apache-2.0" ]

null

gs_quant/target/instrument.py

dannyb2018/gs-quant

e963c4af1c7c65b2ee8f7995815542f6fb7b4957

[ "Apache-2.0" ]

null

gs_quant/target/instrument.py

dannyb2018/gs-quant

e963c4af1c7c65b2ee8f7995815542f6fb7b4957

[ "Apache-2.0" ]

null

""" Copyright 2019 Goldman Sachs. 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. """ from gs_quant.base import * from gs_quant.common import * import datetime from typing import Dict, Optional, Tuple, Union from dataclasses import dataclass, field from dataclasses_json import LetterCase, config, dataclass_json from gs_quant.instrument.core import Instrument, resolution_safe @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class AssetRef(Instrument): buy_sell: Optional[BuySell] = None product_code: Optional[ProductCode] = None size: Optional[float] = None asset_id: Optional[str] = None number_of_options: Optional[float] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset) type_: Optional[AssetType] = field(init=False, default=AssetType.Any, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class Bond(Instrument): buy_sell: Optional[BuySell] = None identifier: Optional[str] = None identifier_type: Optional[UnderlierType] = None size: Optional[float] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset) type_: Optional[AssetType] = field(init=False, default=AssetType.Bond, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class Cash(Instrument): currency: Optional[Currency] = None payment_date: Optional[datetime.date] = None notional_amount: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cash) type_: Optional[AssetType] = field(init=False, default=AssetType.Cash, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOTCOptionPeriod(Instrument): start: Optional[Union[datetime.date, str]] = None end: Optional[Union[datetime.date, str]] = None quantity: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionPeriod, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOTCSwapLeg(Instrument): fixing_currency: Optional[CurrencyName] = None leg_description: Optional[str] = None contract: Optional[str] = None fixing_currency_source: Optional[str] = None underlier: Optional[str] = None quantity_multiplier: Optional[int] = None fixed_price: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.SwapLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodSwap(Instrument): commodity: Optional[str] = None quantity: Optional[Union[float, str]] = None contract: Optional[str] = None fixing_currency_source: Optional[str] = None start: Optional[Union[datetime.date, str]] = None floating_type: Optional[str] = None number_of_periods: Optional[int] = None quantity_unit: Optional[str] = None fixed_price: Optional[Union[float, str]] = None settlement: Optional[str] = None fixing_currency: Optional[CurrencyName] = None fixed_price_unit: Optional[str] = None commodity_reference_price: Optional[str] = None end: Optional[Union[datetime.date, str]] = None quantity_period: Optional[Period] = None strategy: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.Swap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqAutoroll(Instrument): underlier: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None first_fixing_date: Optional[Union[datetime.date, str]] = None last_fixing_date: Optional[Union[datetime.date, str]] = None fixing_frequency: Optional[str] = None trigger_level: Optional[float] = None buffer_level: Optional[float] = None local_return_cap: Optional[float] = None upside_leverage: Optional[float] = None initial_fixing_override: Optional[float] = None notional: Optional[Union[float, str]] = None business_day_calendar: Optional[str] = None payment_currency: Optional[Currency] = None settlement_delay: Optional[str] = None underlier_type: Optional[UnderlierType] = None buy_sell: Optional[BuySell] = None premium: Optional[float] = None premium_payment_date: Optional[Union[datetime.date, str]] = None premium_currency: Optional[Currency] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Autoroll, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqBinary(Instrument): underlier: Optional[Union[float, str]] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None expiration_date: Optional[Union[datetime.date, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None strike_price: Optional[Union[float, str]] = None notional_amount: Optional[Union[float, str]] = None currency: Optional[str] = None premium: Optional[Union[float, str]] = None premium_settlement_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Binary, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqCliquet(Instrument): return_style: Optional[str] = 'Rate of Return' last_valuation_date: Optional[datetime.date] = None notional_amount: Optional[Union[float, str]] = None underlier_type: Optional[UnderlierType] = None underlier: Optional[Union[float, str]] = None payment_frequency: Optional[str] = 'Maturity' global_cap: Optional[float] = 1000000.0 first_valuation_date: Optional[datetime.date] = None currency: Optional[Currency] = None global_floor: Optional[float] = -1000000.0 strike_price: Optional[float] = None return_type: Optional[str] = 'Sum' valuation_period: Optional[str] = None expiration_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Cliquet, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqConvertibleBond(Instrument): underlier: Optional[Union[float, str]] = None underlier_type: Optional[UnderlierType] = None premium_settlement_date: Optional[Union[datetime.date, str]] = None ref_currency: Optional[Currency] = None buy_sell: Optional[BuySell] = None quantity: Optional[float] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Convertible, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqForward(Instrument): underlier: Optional[Union[float, str]] = None underlier_type: Optional[UnderlierType] = None expiration_date: Optional[Union[datetime.date, str]] = None forward_price: Optional[float] = None number_of_shares: Optional[int] = 1 asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqFuture(Instrument): total_quantity: float = None identifier: Optional[str] = None identifier_type: Optional[UnderlierType] = None underlier: Optional[str] = None multiplier: Optional[float] = None expiration_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None quantity: Optional[float] = None currency: Optional[Currency] = None traded_price: Optional[float] = 0.0 asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Future, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqOption(Instrument): underlier: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None strike_price: Optional[Union[float, str]] = None option_type: Optional[OptionType] = None option_style: Optional[OptionStyle] = None number_of_options: Optional[Union[float, str]] = None exchange: Optional[str] = None multiplier: Optional[float] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None premium: Optional[float] = 0.0 premium_payment_date: Optional[Union[datetime.date, str]] = None valuation_time: Optional[ValuationTime] = None method_of_settlement: Optional[OptionSettlementMethod] = None underlier_type: Optional[UnderlierType] = None buy_sell: Optional[BuySell] = None premium_currency: Optional[Currency] = None trade_as: Optional[TradeAs] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type')) def scale_in_place(self, scaling: Optional[float] = None): if self.unresolved is None: raise RuntimeError('Can only scale resolved instruments') if scaling is None or scaling == 1: return if scaling < 0: flip_dict = {BuySell.Buy: BuySell.Sell, BuySell.Sell: BuySell.Buy} self.buy_sell = flip_dict[self.buy_sell] self.number_of_options *= abs(scaling) return @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqOptionLeg(Instrument): method_of_settlement: Optional[OptionSettlementMethod] = None premium_payment_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None option_style: Optional[OptionStyle] = None multiplier: Optional[float] = None number_of_options: Optional[float] = None settlement_date: Optional[Union[datetime.date, str]] = None valuation_time: Optional[ValuationTime] = None option_type: Optional[OptionType] = None settlement_currency: Optional[Currency] = None premium: Optional[float] = None premium_currency: Optional[Currency] = None trade_as: Optional[TradeAs] = None exchange: Optional[str] = None strike_price: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqStock(Instrument): identifier: Optional[str] = None identifier_type: Optional[UnderlierType] = None buy_sell: Optional[BuySell] = None traded_price: Optional[float] = 0.0 currency: Optional[Currency] = None quantity: Optional[float] = None settlement_date: Optional[datetime.date] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Single_Stock, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqSynthetic(Instrument): underlier: Union[float, str] = None expiry: str = None currency: Optional[Currency] = None swap_type: Optional[str] = 'Eq Swap' buy_sell: Optional[BuySell] = None underlier_type: Optional[UnderlierType] = None effective_date: Optional[datetime.date] = None num_of_underlyers: Optional[float] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.Synthetic, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqVarianceSwap(Instrument): underlier: Optional[Union[float, str]] = None underlier_type: Optional[UnderlierType] = None expiration_date: Optional[Union[datetime.date, str]] = None strike_price: Optional[Union[float, str]] = None variance_cap: Optional[float] = None settlement_date: Optional[Union[datetime.date, str]] = None premium: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.VarianceSwap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXBinary(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None settlement_rate_option: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.Binary, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXDoubleKnockout(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None knock_in_or_out: Optional[InOut] = None lower_barrier_level: Optional[Union[float, str]] = None upper_barrier_level: Optional[Union[float, str]] = None knockout_convention: Optional[KnockoutConvention] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.DoubleKnockout, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXDoubleOneTouch(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None lower_barrier_level: Optional[Union[float, str]] = None upper_barrier_level: Optional[Union[float, str]] = None payout_type: Optional[PayoutType] = None knockout_convention: Optional[KnockoutConvention] = None touch_or_no_touch: Optional[TouchNoTouch] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.DoubleTouch, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXEuropeanKnockout(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None expiration_date: Optional[str] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None barrier_level: Optional[Union[float, str]] = None knock_up_or_down: Optional[UpDown] = None knock_in_or_out: Optional[InOut] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.EuropeanKnockout, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXForward(Instrument): pair: Optional[str] = None settlement_date: Optional[Union[datetime.date, str]] = None forward_rate: Optional[Union[float, str]] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount_in_other_currency: Optional[Union[float, str]] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXKnockout(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None knock_in_or_out: Optional[InOut] = None knock_up_or_down: Optional[UpDown] = None barrier_level: Optional[Union[float, str]] = None knockout_convention: Optional[KnockoutConvention] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.Knockout, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXMultiCrossBinaryLeg(Instrument): pair: Optional[str] = None option_type: Optional[OptionType] = None strike_price: Optional[Union[float, str]] = None fixing_source: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossBinaryLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXOneTouch(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None knock_up_or_down: Optional[UpDown] = None knockout_convention: Optional[KnockoutConvention] = None touch_or_no_touch: Optional[TouchNoTouch] = None payout_type: Optional[PayoutType] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.OneTouch, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXOption(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount_in_other_currency: Optional[Union[float, str]] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXOptionLeg(Instrument): buy_sell: Optional[BuySell] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount_in_other_currency: Optional[Union[float, str]] = None strike_price: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_time: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXShiftingBermForward(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount_in_other_currency: Optional[Union[float, str]] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None expiration_date: Optional[Union[datetime.date, str]] = None window_start_date: Optional[str] = None exercise_decision_freq: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.ShiftingBermForward, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXTarfScheduleLeg(Instrument): profit_strike: Optional[Union[float, str]] = None loss_strike: Optional[Union[float, str]] = None fixing_date: Optional[Union[datetime.date, str]] = None payment_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.TarfScheduleLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXVolatilitySwap(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None strike_vol: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None first_fixing_date: Optional[Union[datetime.date, str]] = None last_fixing_date: Optional[Union[datetime.date, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None fixing_source: Optional[str] = None fixing_frequency: Optional[str] = None annualization_factor: Optional[float] = None calculate_mean_return: Optional[float] = 0.0 asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.VolatilitySwap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class Forward(Instrument): currency: Optional[Currency] = None expiration_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cash) type_: Optional[AssetType] = field(init=False, default=AssetType.Forward, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRBondFuture(Instrument): buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None underlier: Optional[Union[float, str]] = None currency: Optional[Currency] = None expiration_date: Optional[Union[datetime.date, str]] = None exchange: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.BondFuture, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRCap(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None cap_rate: Optional[Union[float, str]] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.Cap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRFloor(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None floor_rate: Optional[Union[float, str]] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.Floor, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class InflationSwap(Instrument): pay_or_receive: Optional[PayReceive] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None effective_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None index: Optional[str] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate: Optional[Union[float, str]] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 base_cpi: Optional[float] = None clearing_house: Optional[SwapClearingHouse] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.InflationSwap, metadata=config(field_name='type')) def scale_in_place(self, scaling: Optional[float] = None): if self.unresolved is None: raise RuntimeError('Can only scale resolved instruments') if scaling is None or scaling == 1: return if scaling < 0: flip_dict = {PayReceive.Pay: PayReceive.Receive, PayReceive.Receive: PayReceive.Pay} self.pay_or_receive = flip_dict[self.pay_or_receive] self.fee *= -1 self.notional_amount *= abs(scaling) return @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class InstrumentsRepoIRDiscreteLock(Instrument): buy_sell: Optional[BuySell] = None underlier: Optional[Union[float, str]] = None underlier_type: Optional[UnderlierType] = None settlement_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None currency: Optional[Currency] = None spot_clean_price: Optional[float] = None settlement: Optional[str] = None repo_rate: Optional[float] = None forward_clean_price: Optional[float] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Repo) type_: Optional[AssetType] = field(init=False, default=AssetType.Bond_Forward, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CDIndex(Instrument): buy_sell: Optional[BuySell] = None clearinghouse: Optional[SwapClearingHouse] = None effective_date: Optional[Union[datetime.date, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None first_payment_date: Optional[Union[datetime.date, str]] = None first_roll_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None termination_date: Optional[Union[datetime.date, str]] = None index_family: Optional[str] = None index_for_basis: Optional[str] = None index_series: Optional[float] = None index_version: Optional[float] = None isda_docs: Optional[str] = field(default='2014', metadata=config(field_name='ISDADocs')) asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Credit) type_: Optional[AssetType] = field(init=False, default=AssetType.Index, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CDIndexOption(Instrument): automatic_exercise: Optional[float] = 0.0 buy_sell: Optional[BuySell] = None clearinghouse: Optional[SwapClearingHouse] = None notional_currency: Optional[Currency] = None earliest_exercise_time: Optional[str] = None earliest_exercise_time_centre: Optional[str] = None effective_date: Optional[Union[datetime.date, str]] = None exercise_date_business_day_convention: Optional[BusinessDayConvention] = 'Following' exercise_holidays: Optional[str] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None expiration_time_centre: Optional[str] = None premium: Optional[float] = 0.0 premium_currency: Optional[Currency] = None premium_payment_date: Optional[Union[datetime.date, str]] = None first_payment_date: Optional[Union[datetime.date, str]] = None first_roll_date: Optional[Union[datetime.date, str]] = None index_family: Optional[str] = None index_for_basis: Optional[str] = None index_series: Optional[float] = None index_version: Optional[float] = None isda_docs: Optional[str] = field(default='2014', metadata=config(field_name='ISDADocs')) termination_date: Optional[Union[datetime.date, str]] = None option_type: Optional[OptionType] = None method_of_settlement: Optional[OptionSettlementMethod] = None notional_amount: Optional[Union[float, str]] = None fixed_rate: Optional[float] = None strike: Optional[Union[float, str]] = None strike_type: Optional[str] = 'Spread' settlement_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Credit) type_: Optional[AssetType] = field(init=False, default=AssetType.IndexOption, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOTCOptionLeg(Instrument): option_type: Optional[OptionType] = None fixing_currency: Optional[CurrencyName] = None premium: Optional[CommodPrice] = None leg_description: Optional[str] = None contract: Optional[str] = None fixing_currency_source: Optional[str] = None strike: Optional[Union[float, str]] = None underlier: Optional[str] = None premium_settlement: Optional[str] = None quantity_multiplier: Optional[int] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOTCSwap(Instrument): quantity: Optional[Union[float, str]] = None legs: Optional[Tuple[CommodOTCSwapLeg, ...]] = None start: Optional[Union[datetime.date, str]] = None end: Optional[Union[datetime.date, str]] = None number_of_periods: Optional[int] = None quantity_unit: Optional[str] = None quantity_period: Optional[Period] = None strategy: Optional[str] = None settlement: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.SwapStrategy, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOption(Instrument): commodity: Optional[str] = None number_of_periods: Optional[int] = None quantity_unit: Optional[str] = None currency_summary: Optional[CurrencyName] = None option_types: Optional[Tuple[str, ...]] = None settlement: Optional[str] = None option_type: Optional[str] = None strike_unit: Optional[str] = None strikes: Optional[Tuple[str, ...]] = None end: Optional[Union[datetime.date, str]] = None buy_sells: Optional[Tuple[str, ...]] = None underlier_short_name: Optional[str] = None settlement_frequency: Optional[str] = None buy_sell: Optional[BuySell] = None strike_currency: Optional[CurrencyName] = None quantity: Optional[Union[float, str]] = None contract: Optional[str] = None fixing_currency_source: Optional[str] = None strike: Optional[str] = None start: Optional[Union[datetime.date, str]] = None floating_type: Optional[str] = None fixing_currency: Optional[CurrencyName] = None commodity_reference_price: Optional[str] = None quantity_period: Optional[str] = None strategy: Optional[str] = None premium: Optional[str] = None period_details: Optional[Tuple[CommodOTCOptionPeriod, ...]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.Option, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodVolVarSwap(Instrument): notional_currency: Optional[CurrencyName] = None notional: Optional[float] = 1.0 floating_rate_is_capped: Optional[str] = None end_date: Optional[Union[datetime.date, str]] = None margined: Optional[float] = None market_disruption_agreement: Optional[str] = None mean_rule: Optional[CommodMeanRule] = None divisor: Optional[str] = None fixed_mean: Optional[float] = None first_fixing: Optional[Union[float, str]] = None floating_rate_cap: Optional[float] = None fx_fixing_source: Optional[str] = None annualization_factor: Optional[float] = None buy_sell: Optional[BuySell] = None contract: Optional[str] = None strike: Optional[Union[float, str]] = None swap_type: Optional[str] = None settlement_date: Optional[Union[datetime.date, str]] = None fixing_currency: Optional[CurrencyName] = None asset_fixing_source: Optional[str] = None sampling_frequency: Optional[str] = None variance_convention: Optional[VarianceConvention] = None extra_sampling_calendars: Optional[str] = '--Blank--' asset: Optional[str] = None start_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.VolVarSwap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class EqOptionStrategy(Instrument): underlier: Union[float, str] = None strategy: str = None legs: Tuple[EqOptionLeg, ...] = None underlier_type: Optional[UnderlierType] = None expiration_date: Optional[Union[datetime.date, str]] = None strike_price: Optional[Union[float, str]] = None option_type: Optional[OptionType] = None option_style: Optional[OptionStyle] = None number_of_options: Optional[float] = None multiplier: Optional[float] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None premium: Optional[float] = None premium_payment_date: Optional[Union[datetime.date, str]] = None valuation_time: Optional[ValuationTime] = None method_of_settlement: Optional[OptionSettlementMethod] = None buy_sell: Optional[BuySell] = None premium_currency: Optional[Currency] = None exchange: Optional[str] = None trade_as: Optional[TradeAs] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Equity) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FRA(Instrument): buy_sell: Optional[BuySell] = None clearing_house: Optional[SwapClearingHouse] = None clearing_legally_binding: Optional[float] = None day_count_fraction: Optional[DayCountFraction] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None fixed_rate: Optional[Union[float, str]] = None frequency: Optional[str] = None calendar: Optional[str] = None rate_option: Optional[str] = None maturity: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None payment_delay: Optional[str] = None roll_convention: Optional[str] = None notional_amount: Optional[Union[float, str]] = None spread: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.FRA, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXMultiCrossBinary(Instrument): legs: Tuple[FXMultiCrossBinaryLeg, ...] = None buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None settlement_date: Optional[Union[datetime.date, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.MultiCrossBinary, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXOptionStrategy(Instrument): pair: Optional[str] = None buy_sell: Optional[BuySell] = None strategy_name: Optional[str] = None legs: Optional[Tuple[FXOptionLeg, ...]] = None option_type: Optional[OptionType] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None notional_amount_in_other_currency: Optional[Union[float, str]] = None strike_price: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None settlement_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None expiration_time: Optional[str] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class FXTarf(Instrument): pair: Optional[str] = None new_or_unwind: Optional[NewOrUnwind] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None profit_strike: Optional[Union[float, str]] = None loss_strike: Optional[Union[float, str]] = None settlement_date: Optional[Union[datetime.date, str]] = None settlement_currency: Optional[Currency] = None fixing_rate_option: Optional[str] = None method_of_settlement: Optional[OptionSettlementMethod] = None expiration_date: Optional[Union[datetime.date, str]] = None premium: Optional[Union[float, str]] = None premium_currency: Optional[Currency] = None premium_payment_date: Optional[str] = None long_or_short: Optional[LongShort] = None european_knock_in: Optional[Union[float, str]] = None number_of_expiry: Optional[Union[float, str]] = None coupon_frequency: Optional[str] = None first_fixing_date: Optional[Union[datetime.date, str]] = None leverage_ratio: Optional[Union[float, str]] = None target_type: Optional[TargetType] = None target: Optional[Union[float, str]] = None schedules: Optional[Tuple[FXTarfScheduleLeg, ...]] = None target_adj_notional_or_strike: Optional[NotionalOrStrike] = None payment_on_hitting_target: Optional[TargetPaymentType] = None settlement_rate_option: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.FX) type_: Optional[AssetType] = field(init=False, default=AssetType.Tarf, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRAssetSwapFxdFlt(Instrument): asw_type: Optional[AswType] = None clearing_house: Optional[SwapClearingHouse] = None fee: Optional[float] = None fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_first_stub: Optional[Union[datetime.date, str]] = None fixed_rate_frequency: Optional[str] = None fixed_holidays: Optional[str] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate: Optional[Union[float, str]] = None floating_rate_currency: Optional[Currency] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_first_stub: Optional[Union[datetime.date, str]] = None floating_rate_frequency: Optional[str] = None floating_rate_fx: Optional[float] = None floating_holidays: Optional[str] = None floating_maturity: Optional[Union[datetime.date, str]] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None identifier: Optional[str] = None identifier_type: Optional[str] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None pay_or_receive: Optional[PayReceive] = None roll_convention: Optional[str] = None notional_amount: Optional[Union[float, str]] = None floating_rate_spread: Optional[Union[float, str]] = None traded_clean_price: Optional[float] = 100.0 settlement_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.AssetSwapFxdFlt, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRAssetSwapFxdFxd(Instrument): asw_type: Optional[AswType] = None buy_sell: Optional[BuySell] = None fee: Optional[float] = None fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_first_stub: Optional[Union[datetime.date, str]] = None fixed_rate_frequency: Optional[str] = None fixed_holidays: Optional[str] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate: Optional[Union[float, str]] = None coupon: Optional[Union[float, str]] = None fixed_rate_currency: Optional[Currency] = None asset_day_count_fraction: Optional[DayCountFraction] = None asset_first_stub: Optional[Union[datetime.date, str]] = None asset_frequency: Optional[str] = None asset_holidays: Optional[str] = None asset_business_day_convention: Optional[BusinessDayConvention] = None identifier: Optional[str] = None identifier_type: Optional[str] = None asset_maturity: Optional[Union[datetime.date, str]] = None fixed_maturity: Optional[Union[datetime.date, str]] = None roll_convention: Optional[str] = None notional_amount: Optional[Union[float, str]] = None fixed_amount: Optional[Union[float, str]] = None clean_price: Optional[float] = 100.0 settlement_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.AssetSwapFxdFxd, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRBasisSwap(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None notional_currency: Optional[Currency] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None payer_spread: Optional[Union[float, str]] = None payer_rate_option: Optional[str] = None payer_designated_maturity: Optional[str] = None payer_frequency: Optional[str] = None payer_day_count_fraction: Optional[DayCountFraction] = None payer_business_day_convention: Optional[BusinessDayConvention] = None receiver_spread: Optional[Union[float, str]] = None receiver_rate_option: Optional[str] = None receiver_designated_maturity: Optional[str] = None receiver_frequency: Optional[str] = None receiver_day_count_fraction: Optional[DayCountFraction] = None receiver_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None clearing_house: Optional[SwapClearingHouse] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.BasisSwap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRBondOption(Instrument): underlier: Optional[Union[float, str]] = None notional_amount: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None option_type: Optional[OptionType] = None effective_date: Optional[Union[datetime.date, str]] = None strike: Optional[Union[float, str]] = None strike_type: Optional[BondStrikeType] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None settlement: Optional[SettlementType] = None underlier_type: Optional[UnderlierType] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.BondOption, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRCMSOption(Instrument): cap_floor: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None strike: Optional[Union[float, str]] = None index: Optional[str] = None multiplier: Optional[float] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.CMSOption, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRCMSOptionStrip(Instrument): cap_floor: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None strike: Optional[Union[float, str]] = None index: Optional[str] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None reset_delay: Optional[str] = None multiplier: Optional[float] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.CMSOptionStrip, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRCMSSpreadOption(Instrument): cap_floor: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None strike: Optional[Union[float, str]] = None index1_tenor: Optional[str] = None index2_tenor: Optional[str] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.CMSSpreadOption, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRCMSSpreadOptionStrip(Instrument): cap_floor: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None strike: Optional[Union[float, str]] = None index1: Optional[str] = None index2: Optional[str] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None reset_delay: Optional[str] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.CMSSpreadOptionStrip, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRFixedLeg(Instrument): buy_sell: Optional[BuySell] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_first_stub: Optional[Union[datetime.date, str]] = None fixed_rate_frequency: Optional[str] = None fixed_holidays: Optional[str] = None fixed_last_stub: Optional[Union[datetime.date, str]] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate: Optional[Union[float, str]] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None principal_exchange: Optional[PrincipalExchange] = None roll_convention: Optional[str] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.FixedLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRFloatLeg(Instrument): buy_sell: Optional[BuySell] = None floating_rate_for_the_initial_calculation_period: Optional[float] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_first_stub: Optional[Union[datetime.date, str]] = None floating_rate_frequency: Optional[str] = None floating_holidays: Optional[str] = None floating_last_stub: Optional[Union[datetime.date, str]] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None principal_exchange: Optional[PrincipalExchange] = None roll_convention: Optional[str] = None notional_amount: Optional[Union[float, str]] = None floating_rate_spread: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.FloatLeg, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRSwap(Instrument): pay_or_receive: Optional[PayReceive] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None floating_rate_for_the_initial_calculation_period: Optional[float] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None floating_rate_spread: Optional[Union[float, str]] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate: Optional[Union[float, str]] = None fixed_rate_frequency: Optional[str] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None clearing_house: Optional[SwapClearingHouse] = None fixed_first_stub: Optional[Union[datetime.date, str]] = None floating_first_stub: Optional[Union[datetime.date, str]] = None fixed_last_stub: Optional[Union[datetime.date, str]] = None floating_last_stub: Optional[Union[datetime.date, str]] = None fixed_holidays: Optional[str] = None floating_holidays: Optional[str] = None roll_convention: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.Swap, metadata=config(field_name='type')) def scale_in_place(self, scaling: Optional[float] = None): if self.unresolved is None: raise RuntimeError('Can only scale resolved instruments') if scaling is None or scaling == 1: return if scaling < 0: flip_dict = {PayReceive.Pay: PayReceive.Receive, PayReceive.Receive: PayReceive.Pay} self.pay_or_receive = flip_dict[self.pay_or_receive] self.fee *= -1 self.notional_amount *= abs(scaling) return @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRSwaption(Instrument): pay_or_receive: Optional[PayReceive] = None termination_date: Optional[Union[datetime.date, str]] = None notional_currency: Optional[Currency] = None effective_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None expiration_date: Optional[Union[datetime.date, str]] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None floating_rate_spread: Optional[float] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate_frequency: Optional[str] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None strike: Optional[Union[float, str]] = None premium: Optional[Union[float, str]] = None premium_payment_date: Optional[Union[datetime.date, str]] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None clearing_house: Optional[SwapClearingHouse] = None settlement: Optional[SwapSettlement] = None buy_sell: Optional[BuySell] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.Swaption, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRXccySwap(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[float] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None payer_currency: Optional[Currency] = None payer_spread: Optional[Union[float, str]] = None payer_rate_option: Optional[str] = None payer_designated_maturity: Optional[str] = None payer_frequency: Optional[str] = None payer_day_count_fraction: Optional[DayCountFraction] = None payer_business_day_convention: Optional[BusinessDayConvention] = None receiver_currency: Optional[Currency] = None receiver_spread: Optional[Union[float, str]] = None receiver_rate_option: Optional[str] = None receiver_designated_maturity: Optional[str] = None receiver_frequency: Optional[str] = None receiver_day_count_fraction: Optional[DayCountFraction] = None receiver_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None initial_fx_rate: Optional[float] = None payer_first_stub: Optional[Union[datetime.date, str]] = None receiver_first_stub: Optional[Union[datetime.date, str]] = None payer_last_stub: Optional[Union[datetime.date, str]] = None receiver_last_stub: Optional[Union[datetime.date, str]] = None payer_holidays: Optional[str] = None receiver_holidays: Optional[str] = None notional_reset_side: Optional[PayReceive] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapMTM, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRXccySwapFixFix(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[float] = None receiver_notional_amount: Optional[float] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None payer_currency: Optional[Currency] = None payer_rate: Optional[Union[float, str]] = None payer_frequency: Optional[str] = None payer_day_count_fraction: Optional[DayCountFraction] = None payer_business_day_convention: Optional[BusinessDayConvention] = None receiver_currency: Optional[Currency] = None receiver_rate: Optional[Union[float, str]] = None receiver_frequency: Optional[str] = None receiver_day_count_fraction: Optional[DayCountFraction] = None receiver_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapFixFix, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRXccySwapFixFlt(Instrument): pay_or_receive: Optional[PayReceive] = None termination_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None floating_rate_currency: Optional[Currency] = None floating_rate_for_the_initial_calculation_period: Optional[float] = None floating_rate_option: Optional[str] = None floating_rate_designated_maturity: Optional[str] = None floating_rate_spread: Optional[Union[float, str]] = None floating_rate_frequency: Optional[str] = None floating_rate_day_count_fraction: Optional[DayCountFraction] = None floating_rate_business_day_convention: Optional[BusinessDayConvention] = None fixed_rate_currency: Optional[Currency] = None fixed_rate: Optional[Union[float, str]] = None fixed_rate_frequency: Optional[str] = None fixed_rate_day_count_fraction: Optional[DayCountFraction] = None fixed_rate_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None fixed_first_stub: Optional[Union[datetime.date, str]] = None floating_first_stub: Optional[Union[datetime.date, str]] = None fixed_last_stub: Optional[Union[datetime.date, str]] = None floating_last_stub: Optional[Union[datetime.date, str]] = None fixed_holidays: Optional[str] = None floating_holidays: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwapFixFlt, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class IRXccySwapFltFlt(Instrument): termination_date: Optional[Union[datetime.date, str]] = None notional_amount: Optional[Union[float, str]] = None effective_date: Optional[Union[datetime.date, str]] = None principal_exchange: Optional[PrincipalExchange] = None payer_currency: Optional[Currency] = None payer_spread: Optional[Union[float, str]] = None payer_rate_option: Optional[str] = None payer_designated_maturity: Optional[str] = None payer_frequency: Optional[str] = None payer_day_count_fraction: Optional[DayCountFraction] = None payer_business_day_convention: Optional[BusinessDayConvention] = None receiver_currency: Optional[Currency] = None receiver_spread: Optional[Union[float, str]] = None receiver_rate_option: Optional[str] = None receiver_designated_maturity: Optional[str] = None receiver_frequency: Optional[str] = None receiver_day_count_fraction: Optional[DayCountFraction] = None receiver_business_day_convention: Optional[BusinessDayConvention] = None fee: Optional[float] = 0.0 fee_currency: Optional[Currency] = None fee_payment_date: Optional[Union[datetime.date, str]] = None payer_first_stub: Optional[Union[datetime.date, str]] = None receiver_first_stub: Optional[Union[datetime.date, str]] = None payer_last_stub: Optional[Union[datetime.date, str]] = None receiver_last_stub: Optional[Union[datetime.date, str]] = None payer_holidays: Optional[str] = None receiver_holidays: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.XccySwap, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CommodOTCOption(Instrument): buy_sell: Optional[BuySell] = None quantity: Optional[Union[float, str]] = None start: Optional[Union[datetime.date, str]] = None number_of_periods: Optional[int] = None quantity_unit: Optional[str] = None settlement: Optional[str] = None premium_summary: Optional[Union[float, str]] = None legs: Optional[Tuple[CommodOTCOptionLeg, ...]] = None end: Optional[Union[datetime.date, str]] = None quantity_period: Optional[Period] = None strategy: Optional[str] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Commod) type_: Optional[AssetType] = field(init=False, default=AssetType.OptionStrategy, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class InvoiceSpread(Instrument): buy_sell: Optional[BuySell] = None notional_amount: Optional[Union[float, str]] = None underlier: Optional[Union[float, str]] = None swap: Optional[IRSwap] = None future: Optional[IRBondFuture] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Rates) type_: Optional[AssetType] = field(init=False, default=AssetType.InvoiceSpread, metadata=config(field_name='type')) @resolution_safe @fix_args @dataclass_json(letter_case=LetterCase.CAMEL) @dataclass(unsafe_hash=True, repr=False) class CSLPython(Instrument): class_name: Optional[str] = None denominated: Optional[Currency] = None double_params: Optional[Tuple[CSLDouble, ...]] = None date_params: Optional[Tuple[CSLDate, ...]] = None string_params: Optional[Tuple[CSLString, ...]] = None simple_schedule_params: Optional[Tuple[CSLSimpleSchedule, ...]] = None schedule_params: Optional[Tuple[CSLSchedule, ...]] = None currency_params: Optional[Tuple[CSLCurrency, ...]] = None stock_params: Optional[Tuple[CSLStock, ...]] = None index_params: Optional[Tuple[CSLIndex, ...]] = None fx_cross_params: Optional[Tuple[CSLFXCross, ...]] = None double_array_params: Optional[Tuple[CSLDoubleArray, ...]] = None date_array_params: Optional[Tuple[CSLDateArray, ...]] = None string_array_params: Optional[Tuple[CSLStringArray, ...]] = None simple_schedule_array_params: Optional[Tuple[CSLSimpleScheduleArray, ...]] = None schedule_array_params: Optional[Tuple[CSLScheduleArray, ...]] = None currency_array_params: Optional[Tuple[CSLCurrencyArray, ...]] = None stock_array_params: Optional[Tuple[CSLStockArray, ...]] = None index_array_params: Optional[Tuple[CSLIndexArray, ...]] = None fx_cross_array_params: Optional[Tuple[CSLFXCrossArray, ...]] = None asset_class: Optional[AssetClass] = field(init=False, default=AssetClass.Cross_Asset) type_: Optional[AssetType] = field(init=False, default=AssetType.CSL, metadata=config(field_name='type'))

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Python

tests/_projects/a_references_b_b_references_a/b_module.py

marek-trmac/pycycle

f477e70b7a6875eada05475c27bc20d19587d585

[ "MIT" ]

319

2017-01-28T19:29:16.000Z

2022-03-18T08:45:42.000Z

tests/_projects/a_references_b_b_references_a/b_module.py

marek-trmac/pycycle

f477e70b7a6875eada05475c27bc20d19587d585

[ "MIT" ]

18

2017-01-31T14:12:38.000Z

2022-03-08T12:15:10.000Z

tests/_projects/a_references_b_b_references_a/b_module.py

marek-trmac/pycycle

f477e70b7a6875eada05475c27bc20d19587d585

[ "MIT" ]

31

2017-01-29T19:52:15.000Z

2022-03-09T13:32:33.000Z

from a_module import some_other_func def some_func(): some_other_func()

13

36

0.769231

13

78

4.153846

0.615385

0.333333

0.481481

0

0.166667

78

5

37

15.6

0.830769

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

c13b8a8577cf60a7a5d439571ea18d3956929094

70,045

py

Python

signing_today_client/api/backoffice_api.py

signingtoday/signingtoday-sdk-python

ed267279622fb59f2ad8fa289157fc9cdf9d8a5b

[ "MIT" ]

null

signing_today_client/api/backoffice_api.py

signingtoday/signingtoday-sdk-python

ed267279622fb59f2ad8fa289157fc9cdf9d8a5b

[ "MIT" ]

null

signing_today_client/api/backoffice_api.py

signingtoday/signingtoday-sdk-python

ed267279622fb59f2ad8fa289157fc9cdf9d8a5b

[ "MIT" ]

null

# coding: utf-8 """ Signing Today Web *Signing Today* is the perfect Digital Signature Gateway. Whenever in Your workflow You need to add one or more Digital Signatures to Your document, *Signing Today* is the right choice. You prepare Your documents, *Signing Today* takes care of all the rest: send invitations (`signature tickets`) to signers, collects their signatures, send You back the signed document. Integrating *Signing Today* in Your existing applications is very easy. Just follow these API specifications and get inspired by the many examples presented hereafter. # noqa: E501 The version of the OpenAPI document: 2.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from signing_today_client.api_client import ApiClient from signing_today_client.exceptions import ( ApiTypeError, ApiValueError ) class BackofficeApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def organization_id_alfresco_sync_get(self, id, **kwargs): # noqa: E501 """Sync all completed DSTs on Alfresco # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_alfresco_sync_get(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: AlfrescoSync If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_alfresco_sync_get_with_http_info(id, **kwargs) # noqa: E501 def organization_id_alfresco_sync_get_with_http_info(self, id, **kwargs): # noqa: E501 """Sync all completed DSTs on Alfresco # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_alfresco_sync_get_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(AlfrescoSync, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_alfresco_sync_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_alfresco_sync_get`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', '*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/alfrescoSync', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='AlfrescoSync', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_alfresco_sync_post(self, id, alfresco_sync, **kwargs): # noqa: E501 """Sync all completed DSTs on Alfresco # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_alfresco_sync_post(id, alfresco_sync, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param AlfrescoSync alfresco_sync: Domain associated to the account. (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_alfresco_sync_post_with_http_info(id, alfresco_sync, **kwargs) # noqa: E501 def organization_id_alfresco_sync_post_with_http_info(self, id, alfresco_sync, **kwargs): # noqa: E501 """Sync all completed DSTs on Alfresco # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_alfresco_sync_post_with_http_info(id, alfresco_sync, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param AlfrescoSync alfresco_sync: Domain associated to the account. (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'alfresco_sync'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_alfresco_sync_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_alfresco_sync_post`") # noqa: E501 # verify the required parameter 'alfresco_sync' is set if self.api_client.client_side_validation and ('alfresco_sync' not in local_var_params or # noqa: E501 local_var_params['alfresco_sync'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `alfresco_sync` when calling `organization_id_alfresco_sync_post`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'alfresco_sync' in local_var_params: body_params = local_var_params['alfresco_sync'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/alfrescoSync', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_delete(self, id, **kwargs): # noqa: E501 """Enable or disable an Organization account. # noqa: E501 Enable or disable an Organization. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_delete(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param bool enabled: New status to set :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_delete_with_http_info(id, **kwargs) # noqa: E501 def organization_id_delete_with_http_info(self, id, **kwargs): # noqa: E501 """Enable or disable an Organization account. # noqa: E501 Enable or disable an Organization. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_delete_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param bool enabled: New status to set :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'enabled'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_delete" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] if 'enabled' in local_var_params and local_var_params['enabled'] is not None: # noqa: E501 query_params.append(('enabled', local_var_params['enabled'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_get(self, id, **kwargs): # noqa: E501 """Retrieve info on one organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_get(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Organization If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_get_with_http_info(id, **kwargs) # noqa: E501 def organization_id_get_with_http_info(self, id, **kwargs): # noqa: E501 """Retrieve info on one organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_get_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(Organization, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_get`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', '*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Organization', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_public_get(self, res, **kwargs): # noqa: E501 """Retrieve public resources # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_public_get(res, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str res: resource id (required) :param str id: organization id :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: file If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_public_get_with_http_info(res, **kwargs) # noqa: E501 def organization_id_public_get_with_http_info(self, res, **kwargs): # noqa: E501 """Retrieve public resources # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_public_get_with_http_info(res, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str res: resource id (required) :param str id: organization id :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(file, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['res', 'id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_public_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'res' is set if self.api_client.client_side_validation and ('res' not in local_var_params or # noqa: E501 local_var_params['res'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `res` when calling `organization_id_public_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'res' in local_var_params and local_var_params['res'] is not None: # noqa: E501 query_params.append(('res', local_var_params['res'])) # noqa: E501 if 'id' in local_var_params and local_var_params['id'] is not None: # noqa: E501 query_params.append(('id', local_var_params['id'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/octet-stream', '*/*']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/organization/public', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='file', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_put(self, id, **kwargs): # noqa: E501 """Update info on one organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_put(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param Organization organization: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_put_with_http_info(id, **kwargs) # noqa: E501 def organization_id_put_with_http_info(self, id, **kwargs): # noqa: E501 """Update info on one organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_put_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param Organization organization: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'organization'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_put`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'organization' in local_var_params: body_params = local_var_params['organization'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_resource_get(self, id, res_path, **kwargs): # noqa: E501 """Get an organization resource # noqa: E501 Get an organization resource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_resource_get(id, res_path, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: file If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_resource_get_with_http_info(id, res_path, **kwargs) # noqa: E501 def organization_id_resource_get_with_http_info(self, id, res_path, **kwargs): # noqa: E501 """Get an organization resource # noqa: E501 Get an organization resource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_resource_get_with_http_info(id, res_path, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(file, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'res_path'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_resource_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_resource_get`") # noqa: E501 # verify the required parameter 'res_path' is set if self.api_client.client_side_validation and ('res_path' not in local_var_params or # noqa: E501 local_var_params['res_path'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `res_path` when calling `organization_id_resource_get`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] if 'res_path' in local_var_params and local_var_params['res_path'] is not None: # noqa: E501 query_params.append(('resPath', local_var_params['res_path'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/octet-stream', '*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/resource', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='file', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_id_resource_put(self, id, res_path, file, **kwargs): # noqa: E501 """Create or overwrite an organization resource # noqa: E501 Create or overwrite an organization resource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_resource_put(id, res_path, file, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param file file: The file to upload. (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_id_resource_put_with_http_info(id, res_path, file, **kwargs) # noqa: E501 def organization_id_resource_put_with_http_info(self, id, res_path, file, **kwargs): # noqa: E501 """Create or overwrite an organization resource # noqa: E501 Create or overwrite an organization resource # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_id_resource_put_with_http_info(id, res_path, file, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param file file: The file to upload. (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'res_path', 'file'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_id_resource_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_id_resource_put`") # noqa: E501 # verify the required parameter 'res_path' is set if self.api_client.client_side_validation and ('res_path' not in local_var_params or # noqa: E501 local_var_params['res_path'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `res_path` when calling `organization_id_resource_put`") # noqa: E501 # verify the required parameter 'file' is set if self.api_client.client_side_validation and ('file' not in local_var_params or # noqa: E501 local_var_params['file'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `file` when calling `organization_id_resource_put`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] if 'res_path' in local_var_params and local_var_params['res_path'] is not None: # noqa: E501 query_params.append(('resPath', local_var_params['res_path'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} if 'file' in local_var_params: local_var_files['file'] = local_var_params['file'] # noqa: E501 body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['multipart/form-data']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/resource', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_resource_id_delete(self, id, res_path, **kwargs): # noqa: E501 """Delete an organization resource # noqa: E501 Deletes a Resource. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_resource_id_delete(id, res_path, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_resource_id_delete_with_http_info(id, res_path, **kwargs) # noqa: E501 def organization_resource_id_delete_with_http_info(self, id, res_path, **kwargs): # noqa: E501 """Delete an organization resource # noqa: E501 Deletes a Resource. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_resource_id_delete_with_http_info(id, res_path, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param str res_path: (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id', 'res_path'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_resource_id_delete" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_resource_id_delete`") # noqa: E501 # verify the required parameter 'res_path' is set if self.api_client.client_side_validation and ('res_path' not in local_var_params or # noqa: E501 local_var_params['res_path'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `res_path` when calling `organization_resource_id_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] if 'res_path' in local_var_params and local_var_params['res_path'] is not None: # noqa: E501 query_params.append(('resPath', local_var_params['res_path'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/resource', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_resources_get(self, id, **kwargs): # noqa: E501 """List all the organization resources # noqa: E501 List all the organization resources. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_resources_get(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_resources_get_with_http_info(id, **kwargs) # noqa: E501 def organization_resources_get_with_http_info(self, id, **kwargs): # noqa: E501 """List all the organization resources # noqa: E501 List all the organization resources. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_resources_get_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str id: The value of the unique id (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[str], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_resources_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `organization_resources_get`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', '*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/{id}/resources', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organization_tags_get(self, **kwargs): # noqa: E501 """Retrieve organization tags # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_tags_get(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organization_tags_get_with_http_info(**kwargs) # noqa: E501 def organization_tags_get_with_http_info(self, **kwargs): # noqa: E501 """Retrieve organization tags # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organization_tags_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[str], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organization_tags_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json', '*/*']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organization/tags', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organizations_get(self, **kwargs): # noqa: E501 """Get the list of organizations # noqa: E501 Get the list of organizations # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organizations_get(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int top: A number of results to return. Applied after **$skip** :param int skip: An offset into the collection of results :param bool count: If true, the server includes the count of all the items in the response :param str filter: A filter definition (eg. $filter=name == \"Milk\" or surname == \"Bread\") :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: OrganizationsGetResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organizations_get_with_http_info(**kwargs) # noqa: E501 def organizations_get_with_http_info(self, **kwargs): # noqa: E501 """Get the list of organizations # noqa: E501 Get the list of organizations # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organizations_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param int top: A number of results to return. Applied after **$skip** :param int skip: An offset into the collection of results :param bool count: If true, the server includes the count of all the items in the response :param str filter: A filter definition (eg. $filter=name == \"Milk\" or surname == \"Bread\") :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(OrganizationsGetResponse, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['top', 'skip', 'count', 'filter'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organizations_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'top' in local_var_params and local_var_params['top'] is not None: # noqa: E501 query_params.append(('$top', local_var_params['top'])) # noqa: E501 if 'skip' in local_var_params and local_var_params['skip'] is not None: # noqa: E501 query_params.append(('$skip', local_var_params['skip'])) # noqa: E501 if 'count' in local_var_params and local_var_params['count'] is not None: # noqa: E501 query_params.append(('$count', local_var_params['count'])) # noqa: E501 if 'filter' in local_var_params and local_var_params['filter'] is not None: # noqa: E501 query_params.append(('$filter', local_var_params['filter'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organizations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='OrganizationsGetResponse', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def organizations_post(self, **kwargs): # noqa: E501 """Create a new organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organizations_post(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Organization organization: :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.organizations_post_with_http_info(**kwargs) # noqa: E501 def organizations_post_with_http_info(self, **kwargs): # noqa: E501 """Create a new organization # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.organizations_post_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param Organization organization: :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['organization'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method organizations_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'organization' in local_var_params: body_params = local_var_params['organization'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['*/*']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['OAuth2'] # noqa: E501 return self.api_client.call_api( '/organizations', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

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c1c9212841817d674049a2f2cc4c4cefe4a28da6

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Python

tests/common_setup.py

zffgithub/integration

396d9ef053b28814b0e3323a2ebaa5c0b5fc75d3

[ "Apache-2.0" ]

null

tests/common_setup.py

zffgithub/integration

396d9ef053b28814b0e3323a2ebaa5c0b5fc75d3

[ "Apache-2.0" ]

null

tests/common_setup.py

zffgithub/integration

396d9ef053b28814b0e3323a2ebaa5c0b5fc75d3

[ "Apache-2.0" ]

null

# Copyright 2022 Northern.tech AS # # 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 json import pytest import uuid from . import conftest from .MenderAPI import authentication, auth, devauth, reset_mender_api, DeviceAuthV2 from testutils.common import User, new_tenant_client from testutils.infra.cli import CliTenantadm from testutils.infra.device import MenderDevice, MenderDeviceGroup from testutils.infra.container_manager import factory container_factory = factory.get_factory() @pytest.fixture(scope="function") def standard_setup_one_client(request): env = container_factory.getStandardSetup(num_clients=1) request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) env.auth = auth return env @pytest.fixture(scope="function") def monitor_commercial_setup_no_client(request): env = container_factory.getMonitorCommercialSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) return env def standard_setup_one_client_bootstrapped_impl(request): env = container_factory.getStandardSetup(num_clients=1) request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_one_client_bootstrapped(request): return standard_setup_one_client_bootstrapped_impl(request) @pytest.fixture(scope="class") def class_persistent_standard_setup_one_client_bootstrapped(request): return standard_setup_one_client_bootstrapped_impl(request) @pytest.fixture(scope="function") def standard_setup_one_client_bootstrapped_with_gateway(request): env = container_factory.getStandardSetupWithGateway(num_clients=1) request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients(network="mender_local")[0]) env.device.ssh_is_opened() env.device_gateway = MenderDevice(env.get_mender_gateways(network="mender")[0]) env.device_gateway.ssh_is_opened() reset_mender_api(env) # Two devices: the client device and the gateway device (which also runs mender client) devauth.accept_devices(2) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_two_clients_bootstrapped_with_gateway(request): env = container_factory.getStandardSetupWithGateway(num_clients=2) request.addfinalizer(env.teardown) env.setup() env.device_group = MenderDeviceGroup(env.get_mender_clients(network="mender_local")) env.device_group.ssh_is_opened() env.device_gateway = MenderDevice(env.get_mender_gateways(network="mender")[0]) env.device_gateway.ssh_is_opened() reset_mender_api(env) # Three devices: two client devices and the gateway device (which also runs mender client) devauth.accept_devices(3) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_one_rofs_client_bootstrapped(request): env = container_factory.getRofsClientSetup() request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_one_docker_client_bootstrapped(request): env = container_factory.getDockerClientSetup() request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_two_clients_bootstrapped(request): env = container_factory.getStandardSetup(num_clients=2) request.addfinalizer(env.teardown) env.setup() env.device_group = MenderDeviceGroup(env.get_mender_clients()) env.device_group.ssh_is_opened() reset_mender_api(env) devauth.accept_devices(2) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_without_client(request): env = container_factory.getStandardSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) return env @pytest.fixture(scope="function") def setup_with_legacy_client(request): # The legacy 1.7.0 client was only built for qemux86-64, so skip tests using # it when running other platforms. if conftest.machine_name != "qemux86-64": pytest.skip( "Test only works with qemux86-64, and this is %s" % conftest.machine_name ) env = container_factory.getLegacyClientSetup() request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_with_signed_artifact_client(request): env = container_factory.getSignedArtifactClientSetup() request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) auth.reset_auth_token() devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def standard_setup_with_short_lived_token(request): env = container_factory.getShortLivedTokenSetup() request.addfinalizer(env.teardown) env.setup() env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() reset_mender_api(env) auth.reset_auth_token() devauth.accept_devices(1) env.auth = auth return env @pytest.fixture(scope="function") def setup_failover(request): env = container_factory.getFailoverServerSetup() request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) env.device = MenderDevice(env.get_mender_clients()[0]) env.device.ssh_is_opened() auth.reset_auth_token() devauth.accept_devices(1) return env @pytest.fixture(scope="function") def running_custom_production_setup(request): conftest.production_setup_lock.acquire() env = container_factory.getCustomSetup() def fin(): env.teardown() conftest.production_setup_lock.release() request.addfinalizer(fin) reset_mender_api(env) return env @pytest.fixture(scope="function") def enterprise_no_client(request): env = container_factory.getEnterpriseSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) return env @pytest.fixture(scope="function") def enterprise_one_client(request): env = container_factory.getEnterpriseSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() return env def enterprise_one_client_bootstrapped_impl(request): env = container_factory.getEnterpriseSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env @pytest.fixture(scope="function") def enterprise_one_client_bootstrapped(request): return enterprise_one_client_bootstrapped_impl(request) @pytest.fixture(scope="class") def class_persistent_enterprise_one_client_bootstrapped(request): return enterprise_one_client_bootstrapped_impl(request) @pytest.fixture(scope="class") def enterprise_one_client_bootstrapped_with_gateway(request): env = container_factory.getEnterpriseSetupWithGateway(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client( env, "mender-client", tenant["tenant_token"], network="mender_local" ) env.start_tenant_mender_gateway(tenant["tenant_token"]) env.device_gateway = MenderDevice(env.get_mender_gateways(network="mender").pop()) env.device_group.ssh_is_opened() env.device_gateway.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) # Two devices: the client device and the gateway device (which also runs mender client) devauth_tenant.accept_devices(2) devices = devauth_tenant.get_devices_status("accepted") assert 2 == len(devices) return env @pytest.fixture(scope="class") def enterprise_two_clients_bootstrapped_with_gateway(request): env = container_factory.getEnterpriseSetupWithGateway(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) tenant = create_tenant(env) new_tenant_client( env, "mender-client-1", tenant["tenant_token"], network="mender_local" ) new_tenant_client( env, "mender-client-2", tenant["tenant_token"], network="mender_local" ) env.device_group.ssh_is_opened() env.start_tenant_mender_gateway(tenant["tenant_token"]) env.device_gateway = MenderDevice(env.get_mender_gateways(network="mender").pop()) env.device_gateway.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) # Three devices: two client devices and the gateway device (which also runs mender client) devauth_tenant.accept_devices(3) devices = devauth_tenant.get_devices_status("accepted") assert 3 == len(devices) return env @pytest.fixture(scope="function") def enterprise_two_clients_bootstrapped(request): env = container_factory.getEnterpriseSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client-1", tenant["tenant_token"]) new_tenant_client(env, "mender-client-2", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(2) devices = devauth_tenant.get_devices_status("accepted", expected_devices=2) assert 2 == len(devices) return env @pytest.fixture(scope="function") def enterprise_one_docker_client_bootstrapped(request): env = container_factory.getEnterpriseDockerClientSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"], docker=True) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env @pytest.fixture(scope="function") def enterprise_one_rofs_client_bootstrapped(request): env = container_factory.getEnterpriseRofsClientSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env @pytest.fixture(scope="class") def enterprise_no_client_class(request): env = container_factory.getEnterpriseSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) return env def create_tenant(env): uuidv4 = str(uuid.uuid4()) tname = "test.mender.io-{}".format(uuidv4) email = "some.user+{}@example.com".format(uuidv4) u = User("", email, "whatsupdoc") cli = CliTenantadm(containers_namespace=env.name) tid = cli.create_org(tname, u.name, u.pwd, plan="os") tenant = cli.get_tenant(tid) tenant = json.loads(tenant) env.tenant = tenant auth = authentication.Authentication( name="os-tenant", username=u.name, password=u.pwd ) auth.create_org = False auth.reset_auth_token() env.auth = auth return tenant @pytest.fixture(scope="function") def enterprise_with_signed_artifact_client(request): env = container_factory.getEnterpriseSignedArtifactClientSetup() request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env @pytest.fixture(scope="function") def enterprise_with_short_lived_token(request): env = container_factory.getEnterpriseShortLivedTokenSetup() request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env @pytest.fixture(scope="function") def enterprise_with_legacy_client(request): env = container_factory.getEnterpriseLegacyClientSetup(num_clients=0) request.addfinalizer(env.teardown) env.setup() reset_mender_api(env) tenant = create_tenant(env) new_tenant_client(env, "mender-client", tenant["tenant_token"]) env.device_group.ssh_is_opened() devauth_tenant = DeviceAuthV2(env.auth) devauth_tenant.accept_devices(1) devices = devauth_tenant.get_devices_status("accepted") assert 1 == len(devices) return env

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c1f6a515f27555c406eaf8d6be4b1ede7191d581

2,937

py

Python

mistree/tests/test_levy_flight.py

knaidoo29/MiSTree

20ef822ca349d2cc8118bbeca277713f03e10cd2

[ "MIT" ]

27

2019-07-03T08:01:10.000Z

2022-03-29T09:28:58.000Z

mistree/tests/test_levy_flight.py

knaidoo29/MiSTree

20ef822ca349d2cc8118bbeca277713f03e10cd2

[ "MIT" ]

15

2019-09-12T03:56:20.000Z

2021-12-14T22:27:44.000Z

mistree/tests/test_levy_flight.py

knaidoo29/MiSTree

20ef822ca349d2cc8118bbeca277713f03e10cd2

[ "MIT" ]

15

2019-07-03T05:00:20.000Z

2022-03-03T19:37:32.000Z

import numpy as np import mistree as mist def test_get_random_flight(): steps = np.random.random_sample(100) p = mist.get_random_flight(steps) assert len(p) == 3 p = mist.get_random_flight(steps, mode='2D') assert len(p) == 2 box_size = 1. x, y, z = mist.get_random_flight(steps, box_size=box_size) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) == len(x) box_size = 1. steps = 10.*np.ones(10) x, y, z = mist.get_random_flight(steps, box_size=box_size, periodic=False) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) != len(x) def test_get_levy_flight(): p = mist.get_levy_flight(10) assert len(p) == 3 p = mist.get_levy_flight(10, mode='2D') assert len(p) == 2 x, y, z = mist.get_levy_flight(10) assert len(x) == 10 assert len(y) == len(x) assert len(z) == len(y) box_size = 1. x, y, z = mist.get_levy_flight(10, box_size=box_size) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) == len(x) box_size = 0.1 x, y = mist.get_levy_flight(10, mode='2D', box_size=box_size, periodic=False) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size))[0] assert len(condition) != len(x) box_size = 0.1 x, y, z = mist.get_levy_flight(10, box_size=box_size, periodic=False) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) != len(x) def test_get_adjusted_levy_flight(): p = mist.get_adjusted_levy_flight(10) assert len(p) == 3 p = mist.get_adjusted_levy_flight(10, mode='2D') assert len(p) == 2 x, y, z = mist.get_adjusted_levy_flight(10) assert len(x) == 10 assert len(y) == len(x) assert len(z) == len(y) box_size = 1. x, y, z = mist.get_adjusted_levy_flight(10, box_size=box_size) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) == len(x) box_size = 0.01 x, y, z = mist.get_adjusted_levy_flight(10, box_size=box_size, periodic=False) condition = np.where((x >= 0.) & (x <= box_size) & (y >= 0.) & (y <= box_size) & (z >= 0.) & (z <= box_size))[0] assert len(condition) != len(x) x, y, z = mist.get_adjusted_levy_flight(10, box_size=box_size, gamma=None)

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1

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0

7

de114ce661062d9d0dc7d09692349c6cac926122

677,149

py

Python

ipythonhistory2508.py

tmannen/fast-depth

8189b6c7832a6fd65977872d5bb7dd9437cbe098

[ "MIT" ]

null

ipythonhistory2508.py

tmannen/fast-depth

8189b6c7832a6fd65977872d5bb7dd9437cbe098

[ "MIT" ]

null

ipythonhistory2508.py

tmannen/fast-depth

8189b6c7832a6fd65977872d5bb7dd9437cbe098

[ "MIT" ]

null

1/1: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) 1/2: improt tensorflow as tf 1/3: import tensorflow as tf 2/1: import tensorflow as tf 3/1: import tensorflow as tf 3/2: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) 4/1: import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') sess = tf.Session() print(sess.run(hello)) 5/1: import pandas import numpy import xgboost as xgb 5/2: import pandas as pd import numpy as np import xgboost as xgb 5/3: data = pd.read_csv("data/cons_training.csv") 5/4: data.head() 5/5: data.describe() 5/6: data.dtypes() 5/7: data.dtype() 5/8: data.dtypes 5/9: data = pd.read_csv("data/cons_training.csv", parse_dates=[1,2]) 5/10: data.dtypes 5/11: data.head() 5/12: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) 5/13: data.head() 5/14: data.dtypes 5/15: data.head() 5/16: data.sort_values("start_time_utc").head() 5/17: data.sort_values("start_time_utc", ascending=False).head() 5/18: data.sort_values("start_time_utc", inplace=True).head() 5/19: data.sort_values("start_time_utc", inplace=True) 5/20: data.head() 5/21: data.isnull().sum() 5/22: data.isnull() 5/23: data[data.isnull()] 5/24: data.isnull().sum() 5/25: data[data['s101042'].isnull()] 5/26: data.isnull().sum() 5/27: data.columns() 5/28: data.columns 5/29: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] 5/30: feature_columns_to_use 5/31: data.dropna().describe() 5/32: data.describe() 5/33: data_na_dropped = data.dropna() X = data_na_dropped[feature_columns_to_use].drop(targets, axis=1) y = data_na_dropped[targets] 5/34: data_na_dropped.columns 5/35: data_na_dropped = data.dropna() X = data_na_dropped[feature_columns_to_use].drop(targets) y = data_na_dropped[targets] 5/36: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm 5/37: data_na_dropped.columns 5/38: data_na_dropped.drop(targets, axis=1) 5/39: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm 5/40: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] feature_columns_to_use 5/41: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm 5/42: X.columns 5/43: y.columns 5/44: y.head() 5/45: y.count 5/46: y.size() 5/47: y.length 5/48: import pandas as pd import numpy as np import xgboost as xgb from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 5/49: import pandas as pd import numpy as np import xgboost as xgb from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 5/50: data_na_dropped.dtypes 5/51: X.dtypes 5/52: train_X, test_X, train_y, test_y = train_test_split(X.as_matrix(), y.as_matrix(), test_size=0.2) 5/53: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) 5/54: train_X.head() 5/55: train_X[:5] 5/56: train_X.shape 5/57: my_model = XGBRegressor() # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, verbose=False) 5/58: predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/59: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] 5/60: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/61: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) 5/62: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/63: my_model = XGBRegressor(n_estimators=1000) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) 5/64: predictions = my_model.predict(test_X[feature_columns_to_use].values) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/65: predictions = my_model.predict(test_X.values) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/66: predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/67: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/68: my_model = XGBRegressor(n_estimators=1000, max_depth=6) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) 5/69: predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/70: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/71: my_model = XGBRegressor(n_estimators=1000, max_depth=6, n_jobs=4) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) 5/72: predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/73: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/74: my_model = XGBRegressor(n_estimators=1000, max_depth=6, n_jobs=4, learning_rate=0.05) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) 5/75: predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 5/76: predictions = my_model.predict(test_data[feature_columns_to_use].values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_data.cons_actual_excl_umm.values))) 5/77: data.sort_values("start_time_utc", inplace=True) data.dtypes 5/78: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) test_data.head() 5/79: #TODO: add previous days/hours values, impute missing values for temps with last value, keras lstm in other notebook #Add day of week as feature? Cross validation test_X.head() 5/80: #TODO: add previous days/hours values, impute missing values for temps with last value, keras lstm in other notebook #Add day of week as feature? Cross validation test_X[:5] 5/81: print("Mean Absolute Error : " + str(mean_absolute_error(X['cons_actual_24h_ago'].values, test_y))) 5/82: print("Mean Absolute Error : " + str(mean_absolute_error(test_X['cons_actual_24h_ago'].values, test_y))) 5/83: X.columns 5/84: X.columns[17] 5/85: X.columns[18] 5/86: X.columns[20] 5/87: X.columns[19] 5/88: test_X[:,19] 5/89: print("Mean Absolute Error : " + str(mean_absolute_error(test_X[:,19], test_y))) 6/1: import pandas as pd import numpy as np import xgboost as xgb from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 6/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 6/3: data.start_of_day_utc[0] 6/4: data.start_time_utc[0] 6/5: date.today(data.start_time_utc[0]).weekday() 6/6: import pandas as pd import numpy as np import xgboost as xgb import datetime.datetime as date from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 6/7: datetime.datetime.today(data.start_time_utc[0]).weekday() 6/8: import pandas as pd import numpy as np import xgboost as xgb import datetime from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 6/9: datetime.datetime.today(data.start_time_utc[0]).weekday() 6/10: datetime.datetime(data.start_time_utc[0]) 6/11: #datetime.datetime(data.start_time_utc[0]) datetime.datetime.today() 6/12: data.start_time_utc[0] 6/13: datetime.date.fromtimestamp(data.start_time_utc[0]) 6/14: timestamp() 6/15: import time time.time() 6/16: data.start_time_utc[0] 6/17: data.start_time_utc[0] datetime.datetime.strptime(data.start_time_utc[0], "%Y-%m-%d %H:%M:%S") 6/18: daate = data.start_time_utc[0] 6/19: daate.dayofweek 6/20: daate = data.start_time_utc[0] 6/21: data.start_time_utc[0] 6/22: def day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.dayofweek, axis=1) 6/23: def add_day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.dayofweek, axis=1) 6/24: def add_day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.dayofweek, axis=1) return df 6/25: new_df = add_day_of_week(data) 6/26: def add_day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.dayofweek(), axis=1) return df 6/27: add_day_of_week(data) 6/28: def add_day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek(), axis=1) return df 6/29: add_day_of_week(data) 6/30: def add_day_of_week(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df 6/31: add_day_of_week(data) 7/1: import pandas as pd import numpy as np import xgboost as xgb import datetime from xgboost.sklearn import XGBRegressor from sklearn import cross_validation, metrics #Additional scklearn functions from sklearn.grid_search import GridSearchCV #Perforing grid search from sklearn.model_selection import train_test_split from sklearn.preprocessing import Imputer 7/2: import pandas as pd import numpy as np import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 7/3: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 7/4: def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df 7/5: data = pre_process(data) 7/6: data.describe() 7/7: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] 7/8: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm #y2 = data_na_dropped.cons_actual_plus_umm 7/9: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='mae', n_jobs=4, cv=skf.split(X, y), verbose=3) 7/10: #Tuning from https://www.kaggle.com/tilii7/hyperparameter-grid-search-with-xgboost/notebook def timer(start_time=None): if not start_time: start_time = datetime.now() return start_time elif start_time: thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600) tmin, tsec = divmod(temp_sec, 60) print('\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2))) 7/11: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='mae', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, Y) timer(start_time) # timing ends here for "start_time" variable 7/12: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 7/13: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='mae', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, Y) timer(start_time) # timing ends here for "start_time" variable 7/14: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='mae', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/15: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold, KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 7/16: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = KFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='mae', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/17: tuning_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=1, learning_rate=0.05) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 4, 5, 7] } folds = 5 param_comb = 5 skf = KFold(n_splits=folds, shuffle = True, random_state = 1001) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/18: print('Best MAE:') print(random_search.best_score_) print('\n Best hyperparameters:') print(random_search.best_params_) 7/19: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=5, n_jobs=4, gamma=0.5, colsample_bytree=0.6, min_child_weight=5, subsample=0.6) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/20: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=3, n_jobs=4, gamma=2, colsample_bytree=0.8, min_child_weight=1, subsample=0.6) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/21: tuning_model = XGBRegressor(n_estimators=800, n_jobs=1, learning_rate=0.1) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9] } folds = 5 param_comb = 25 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/22: print('Best negative MAE:') print(random_search.best_score_) print('\n Best hyperparameters:') print(random_search.best_params_) 7/23: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=1, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/24: predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/25: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/26: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/27: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/28: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/29: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/30: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/31: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/32: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, max_depth=6, n_jobs=4, gamma=1, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/33: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/34: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=800, random_search.best_params_) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/35: tuning_model = XGBRegressor(n_estimators=800, n_jobs=1, learning_rate=0.1) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9] } folds = 8 param_comb = 25 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y, early_stopping_rounds=10) timer(start_time) # timing ends here for "start_time" variable 7/36: tuning_model = XGBRegressor(n_estimators=800, n_jobs=1, learning_rate=0.1, early_stopping_rounds=10) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9] } folds = 8 param_comb = 25 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/37: tuning_model = XGBRegressor(n_estimators=1000, n_jobs=1, learning_rate=0.07, early_stopping_rounds=10) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9, 13] } folds = 5 param_comb = 25 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/38: print('Best negative MAE:') print(random_search.best_score_) print('\n Best hyperparameters:') print(random_search.best_params_) 7/39: random_search.best_estimator_ 7/40: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = random_search.best_estimator_ # Add silent=True to avoid printing out updates with each cycle predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/41: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=13, n_jobs=4, gamma=0.5, colsample_bytree=0.8, min_child_weight=10, subsample=0.6, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/42: my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/43: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=13, n_jobs=4, gamma=0.5, colsample_bytree=0.8, min_child_weight=10, subsample=0.6, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/44: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/45: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=13, n_jobs=4, gamma=2, colsample_bytree=0.8, min_child_weight=10, subsample=0.6, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/46: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/47: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=13, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.6, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/48: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/49: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=11, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/50: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/51: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=11, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.15) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/52: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/53: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=11, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/54: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/55: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=15, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/56: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/57: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/58: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/59: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=3, n_jobs=4, gamma=1.5, colsample_bytree=1, min_child_weight=1, subsample=1, learning_rate=0.07) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/60: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/61: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/62: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/63: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae' verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/64: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/65: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=True) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/66: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/67: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.05) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=True) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 7/68: cons_test_data = pre_process(cons_test_data) predictions = my_model.predict(cons_test_data[feature_columns_to_use].values) cons_test_y = cons_test_data.cons_actual_excl_umm.values print("Mean Absolute Error : " + str(mean_absolute_error(predictions, cons_test_y))) 7/69: tuning_model = XGBRegressor(n_estimators=1000, n_jobs=1, learning_rate=0.07, early_stopping_rounds=10, verbose=True) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9, 13] } folds = 5 param_comb = 2 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 7/70: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=10000, max_depth=9, n_jobs=4, gamma=0, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.05) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=10, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=True) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 8/1: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost import XGBRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 8/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 8/3: def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df 8/4: data = pre_process(data) 8/5: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] 8/6: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm #y2 = data_na_dropped.cons_actual_plus_umm 8/7: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1500, max_depth=9, n_jobs=4, gamma=1, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=True) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 8/8: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1500, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=True) predictions = my_model.predict(test_X) from sklearn.metrics import mean_absolute_error print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 9/1: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold, KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 9/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 9/3: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold, KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer from sklearn.ensemble import RandomForestRegressor 9/4: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 9/5: def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df 9/6: data = pre_process(data) 9/7: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] feature_columns_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] feature_columns_to_use = [c for c in data.columns if c not in targets + feature_columns_not_used] 9/8: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[feature_columns_to_use] y = data_na_dropped.cons_actual_excl_umm #y2 = data_na_dropped.cons_actual_plus_umm 9/9: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) 9/10: regr = RandomForestRegressor(max_depth=5, random_state=0) rf_model = regr.fit(train_X, train_y) 9/11: regr = RandomForestRegressor(max_depth=5, random_state=0) rf_model = regr.fit(train_X, train_y) preds = rf_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(preds, test_y))) 9/12: regr = RandomForestRegressor() rf_model = regr.fit(train_X, train_y) preds = rf_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(preds, test_y))) 9/13: regr = RandomForestRegressor(n_estimators=40) rf_model = regr.fit(train_X, train_y) preds = rf_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(preds, test_y))) 9/14: rf_model.feature_importances_ 9/15: X.columns 10/1: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold, KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 10/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 10/3: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.dtypes 10/4: def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df 10/5: #Baseline: predict the current value from the last day's value: y[i] = y[i-24]? data = pre_process(data) data.describe() 10/6: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in data.columns if c not in targets + features_not_used] 10/7: data_na_dropped = data.dropna() X = data_na_dropped.drop(targets, axis=1)[features_to_use] y = data_na_dropped.cons_actual_excl_umm #y2 = data_na_dropped.cons_actual_plus_umm 10/8: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) 10/9: #Tuning from https://www.kaggle.com/tilii7/hyperparameter-grid-search-with-xgboost/notebook def timer(start_time=None): if not start_time: start_time = datetime.now() return start_time elif start_time: thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600) tmin, tsec = divmod(temp_sec, 60) print('\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2))) tuning_model = XGBRegressor(n_estimators=800, n_jobs=1, learning_rate=0.1) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9] } folds = 5 param_comb = 25 skf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=skf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 10/10: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/11: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(xgb_X, label='cons_actual_plus_umm') for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/12: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(xgb_X, label='cons_actual_plus_umm') for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/13: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/14: evals[0] 10/15: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, num_boost_round=1000, xgb_X, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/16: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, num_boost_round=1000, early_stopping_rounds=12, metrics=['mae'], nfold=5) evals.append(cv) 10/17: len(evals[0]) 10/18: evals[999] 10/19: evals[800] 10/20: evals[0][999} 10/21: evals[0][999] 10/22: evals[0] 10/23: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): np.random.seed() params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, num_boost_round=100, early_stopping_rounds=12, metrics=['mae'], nfold=4) evals.append(cv) 10/24: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): np.random.seed() params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, num_boost_round=2000, early_stopping_rounds=12, metrics=['mae'], nfold=4, n_jobs=4) evals.append(cv) 10/25: #TODO: Try doing own random search because the sklearn doesn't support early stopping? or does it? iterations = 2 evals = [] xgb_X = data_na_dropped.drop(['cons_actual_excl_umm'], axis=1)[features_to_use] xgb_X = xgb.DMatrix(X, label=y.values) for i in range(iterations): np.random.seed() params_chosen = {} params_chosen['objective'] = 'reg:linear' params_chosen['n_jobs'] = 4 params_chosen['eval_metric'] = 'mae' params_chosen['min_child_weight'] = np.random.choice(params['min_child_weight']) params_chosen['gamma'] = np.random.choice(params['gamma']) params_chosen['subsample'] = np.random.choice(params['subsample']) params_chosen['colsample_bytree'] = np.random.choice(params['colsample_bytree']) params_chosen['max_depth'] = np.random.choice(params['max_depth']) print("Parameters for this run: ", params_chosen) print("Starting CV: ") cv = xgb.cv(params_chosen, xgb_X, num_boost_round=2000, early_stopping_rounds=12, metrics=['mae'], nfold=4) evals.append(cv) 10/26: evals[0] 10/27: evals[1 10/28: evals[1] 11/1: import pandas as pd import numpy as np from datetime import datetime import matplotlib.pylab as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams['figure.figsize'] = 12, 4 import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import StratifiedKFold, KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.preprocessing import Imputer 11/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing.csv", parse_dates=[0,1]) data.dtypes 11/3: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.dtypes 11/4: def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df #Baseline: predict the current value from the last day's value: y[i] = y[i-24]? data = pre_process(data) data.describe() 11/5: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in data.columns if c not in targets + features_not_used] 11/6: data = data.dropna() X = data.drop(targets, axis=1)[features_to_use] y = data.cons_actual_excl_umm #y2 = data.cons_actual_plus_umm 11/7: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) 11/8: #Tuning from https://www.kaggle.com/tilii7/hyperparameter-grid-search-with-xgboost/notebook def timer(start_time=None): if not start_time: start_time = datetime.now() return start_time elif start_time: thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600) tmin, tsec = divmod(temp_sec, 60) print('\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2))) tuning_model = XGBRegressor(n_estimators=1500, n_jobs=1, learning_rate=0.1) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9], 'learning_rate' } folds = 4 param_comb = 25 kf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=kf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 11/9: #Tuning from https://www.kaggle.com/tilii7/hyperparameter-grid-search-with-xgboost/notebook def timer(start_time=None): if not start_time: start_time = datetime.now() return start_time elif start_time: thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600) tmin, tsec = divmod(temp_sec, 60) print('\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2))) tuning_model = XGBRegressor(n_estimators=1500, n_jobs=1, learning_rate=0.1) params = { 'min_child_weight': [1, 5, 10], 'gamma': [0.5, 1, 1.5, 2, 5], 'subsample': [0.6, 0.8, 1.0], 'colsample_bytree': [0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9] } folds = 4 param_comb = 25 kf = KFold(n_splits=folds, shuffle = True) random_search = RandomizedSearchCV(tuning_model, param_distributions=params, n_iter=param_comb, scoring='neg_mean_absolute_error', n_jobs=4, cv=kf.split(X, y), verbose=3) start_time = timer(None) # timing starts from this point for "start_time" variable random_search.fit(X, y) timer(start_time) # timing ends here for "start_time" variable 11/10: print('Best negative MAE:') print(random_search.best_score_) print('\n Best hyperparameters:') print(random_search.best_params_) 11/11: random_search.best_index_ 11/12: random_search.cv_results_ 11/13: len(random_search.cv_results_) 11/14: len(random_search.score) 11/15: len(random_search.score()) 11/16: random_search.cv_results_ 11/17: random_search.cv_results_['mean_test_score'] 11/18: np.argmax(random_search.cv_results_['mean_test_score']) 11/19: random_search.cv_results_['mean_test_score'] 11/20: np.argsort(random_search.cv_results_['mean_test_score']) 11/21: random_search.cv_results_['mean_test_score'][24] 11/22: random_search.cv_results_['mean_test_score'][11] 11/23: random_search.cv_results_['mean_test_score'][8] 11/24: random_search.cv_results_['mean_test_score'][16] 11/25: random_search.cv_results_['mean_test_score'][24] 11/26: random_search.cv_results_['params'][24] 11/27: my_model = XGBRegressor(n_estimators=1500, max_depth=9, n_jobs=4, gamma=1.5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/28: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=1.5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/29: my_model = XGBRegressor(n_estimators=1500, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/30: my_model = XGBRegressor(n_estimators=1500, max_depth=9, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/31: my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/32: my_model = XGBRegressor(n_estimators=200, max_depth=9, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/33: my_model = XGBRegressor(n_estimators=100, max_depth=9, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/34: my_model = XGBRegressor(n_estimators=10, max_depth=9, n_jobs=4, gamma=4, colsample_bytree=0.8, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/35: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/36: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(text_X, test_y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/37: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/38: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/39: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.2) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/40: my_model = XGBRegressor(n_estimators=2000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/41: random_search.cv_results_['params'][8] 11/42: random_search.cv_results_['params'][16] 11/43: my_model = XGBRegressor(n_estimators=2000, max_depth=5, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/44: my_model = XGBRegressor(n_estimators=2000, max_depth=3, n_jobs=4, gamma=5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/45: my_model = XGBRegressor(n_estimators=2000, max_depth=11, n_jobs=4, gamma=5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/46: my_model = XGBRegressor(n_estimators=2000, max_depth=11, n_jobs=4, gamma=7, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/47: my_model = XGBRegressor(n_estimators=3000, max_depth=11, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.06) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/48: my_model = XGBRegressor(n_estimators=1200, max_depth=7, n_jobs=4, gamma=0.5, colsample_bytree=1, min_child_weight=5, subsample=0.6, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/49: my_model = XGBRegressor(n_estimators=1200, max_depth=3, n_jobs=4, gamma=0.5, colsample_bytree=1, min_child_weight=5, subsample=0.6, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/50: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=0.5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/51: my_model = XGBRegressor(n_estimators=800, max_depth=9, n_jobs=4, gamma=0.5, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/52: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 11/53: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/54: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/55: my_model 11/56: my_model.evals_result 11/57: my_model.feature_importances_ 11/58: my_model.score 11/59: my_model.score() 11/60: my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8, learning_rate=0.1) # Add silent=True to avoid printing out updates with each cycle my_model.fit(X, y, verbose=False) 11/61: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/62: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/63: train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.2) my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) # Add silent=True to avoid printing out updates with each cycle my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 11/64: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 11/65: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm predictions = my_model.predict(act_X.values) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 12/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 12/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.dtypes 12/3: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 12/4: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.dtypes 12/5: def pre_process(df, y_used = 'cons_actual_excl_umm'): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] df = df.dropna() #not many NA, just drop them df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) X = df.drop(targets, axis=1)[features_to_use] return df, X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/6: def pre_process(df, y_used = 'cons_actual_excl_umm'): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] df = df.dropna() #not many NA, just drop them #df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) X = df.drop(targets, axis=1)[features_to_use] return df, X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/7: def pre_process(df, y_used = 'cons_actual_excl_umm'): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] df = df.dropna() #not many NA, just drop them df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) X = df.drop(targets, axis=1)[features_to_use] return df, X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/8: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X.values, y.values, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 12/9: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, y1, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 12/10: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] df = df.dropna() #not many NA, just drop them df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) X = df.drop(targets, axis=1)[features_to_use] return df, X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/11: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 12/12: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) #test_set, test_set_X = pre_process(test_set) 12/13: test_set 12/14: def pre_process(df, drop_na=False): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] if drop_na: df = df.dropna() #not many NA, just drop them df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) X = df.drop(targets, axis=1)[features_to_use] return df, X data, X = pre_process(data, drop_na=True) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/15: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 12/16: test_set 12/17: test_set_X 12/18: test_set 12/19: test_set_X 12/20: predictions = my_model.predict(test_set_X) 12/21: test_set_X 12/22: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 12/23: test_set_X 12/24: test_set 12/25: def pre_process(df, drop_na=False): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] if drop_na: df = df.dropna() #not many NA, just drop them #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data, drop_na=True) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/26: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 12/27: test_set 12/28: test_set_X 12/29: def pre_process(df, drop_na=False): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] if drop_na: df = df.dropna() #not many NA, just drop them #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data, drop_na=True) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/30: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 12/31: X 12/32: test_set.null().sum() 12/33: test_set.null().issum() 12/34: test_set.isnull().sum() 11/66: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.dtypes data.isnull.sum() 11/67: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.dtypes data.isnull().sum() 12/35: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() 12/36: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = ['cons_actual_excl_umm'] data.describe() data.isnull().sum() 13/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 13/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.dtypes 13/3: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 13/4: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 13/5: test_set 13/6: test_set_X 13/7: X 13/8: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 13/9: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, y1, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 13/10: X 13/11: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df.fillna(method='ffill', inplace=True) #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) df.drop(targets, axis=1, inplace=True) pre_X = df[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 14/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.dtypes 14/3: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/4: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] print(pre_X.columns) return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/5: test_set = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set) 14/6: test_set 14/7: data 14/8: df 14/9: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used + ['day_of_week']] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/10: X.columns 14/11: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/12: X.columns 14/13: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] print(features_to_use) return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/14: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] print(df.columns) features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] print(features_to_use) return df, pre_X data, X = pre_process(data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 14/15: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 14/16: train_data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.dtypes 14/17: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] print(df.columns) features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] print(features_to_use) return df, pre_X data, X = pre_process(train_data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 15/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 15/2: train_data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) train_data.dtypes 15/3: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] print(df.columns) features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) print(df.columns) pre_X = df.drop(targets, axis=1)[features_to_use] print(features_to_use) return df, pre_X data, X = pre_process(train_data) y1 = data['cons_actual_plus_umm'] y2 = data['cons_actual_excl_umm'] 15/4: test_set_csv = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set_csv) 15/5: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, y1, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? my_model = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) my_model.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = my_model.predict(test_X) print("Mean Absolute Error : " + str(mean_absolute_error(predictions, test_y))) 15/6: predictions = my_model.predict(test_set_X) 15/7: predictions 16/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 16/2: train_data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) train_data.dtypes 16/3: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(train_data) cons_actual_plus_umm = data['cons_actual_plus_umm'] cons_actual_excl_umm = data['cons_actual_excl_umm'] 16/4: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/5: train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_plus_umm, test_size=0.15) model_plus_umm = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_plus_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_plus_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/6: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1200, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/7: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1200, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/8: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1200, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/9: train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_plus_umm, test_size=0.15) model_plus_umm = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_plus_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_plus_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/10: test_set_csv = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set_csv) 16/11: predictions_excl_umm = model_cons_excl_umm.predict(test_set_X) predictions_plus_umm = model_cons_plus_umm.predict(test_set_X) 16/12: train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_plus_umm, test_size=0.15) model_cons_plus_umm = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_plus_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_plus_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 16/13: test_set_csv = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set_csv) 16/14: predictions_excl_umm = model_cons_excl_umm.predict(test_set_X) predictions_plus_umm = model_cons_plus_umm.predict(test_set_X) 16/15: predictions_excl_umm 16/16: test_set 16/17: test_set_X 16/18: preds = pd.DataFrame() 16/19: preds = pd.DataFrame() preds['start_time_utc'] = test_set['start_time_utc'] preds['predicted_cons_actual_excl_umm'] = predictions_excl_umm preds['predicted_cons_actual_plus_umm'] = predictions_plus_umm 16/20: preds 16/21: preds.to_csv("data/predictions.csv") 16/22: preds.to_csv("data/predictions.csv", index=False) 11/68: predictions = pd.read_csv("/home/tman/challenge/data/predictions.csv") 11/69: predictions 11/70: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_y))) 11/71: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_y))) 11/72: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_y))) 11/73: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_y))) 11/74: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_y))) 11/75: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_test_data.cons_actual_plus_umm))) 11/76: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_test_data.cons_actual_excl_umm))) 11/77: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_test_data.cons_actual_excl_umm))) 11/78: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_test_data.cons_actual_plus_umm))) 17/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 17/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.isnull().sum() 17/3: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm #predictions = my_model.predict(act_X.values) #print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 17/4: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.isnull().sum() 17/5: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm #predictions = my_model.predict(act_X.values) #print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 17/6: # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df #Baseline: predict the current value from the last day's value: y[i] = y[i-24]? data = pre_process(data) data.describe() 17/7: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm #predictions = my_model.predict(act_X.values) #print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 18/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 18/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.isnull().sum() 18/3: # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df #Baseline: predict the current value from the last day's value: y[i] = y[i-24]? data = pre_process(data) data.describe() 18/4: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in data.columns if c not in targets + features_not_used] 18/5: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_test_data.cons_actual_plus_umm))) 18/6: predictions = pd.read_csv("/home/tman/Downloads/challenge/data/predictions.csv") 18/7: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_test_data.cons_actual_plus_umm))) 18/8: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm #predictions = my_model.predict(act_X.values) #print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 18/9: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_plus_umm'], act_test_data.cons_actual_plus_umm))) 18/10: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_test_data.cons_actual_excl_umm))) 21/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 21/2: data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) data.sort_values("start_time_utc", inplace=True) cons_test_data = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) data.isnull().sum() 21/3: # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev vals def pre_process(df): df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) return df #Baseline: predict the current value from the last day's value: y[i] = y[i-24]? data = pre_process(data) data.describe() 21/4: targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in data.columns if c not in targets + features_not_used] 21/5: act_test_data = pd.read_csv("~/data/cons_testing.csv", parse_dates=[0,1]) act_test_data = pre_process(act_test_data) act_X = act_test_data.drop(targets, axis=1)[features_to_use] act_y = act_test_data.cons_actual_excl_umm #predictions = my_model.predict(act_X.values) #print("Mean Absolute Error : " + str(mean_absolute_error(predictions, act_y))) 21/6: predictions = pd.read_csv("/home/tman/Downloads/challenge/data/predictions.csv") 21/7: print("Mean Absolute Error : " + str(mean_absolute_error(predictions['predicted_cons_actual_excl_umm'], act_test_data.cons_actual_excl_umm))) 23/1: import pandas as pd import numpy as np from datetime import datetime import xgboost as xgb from xgboost.sklearn import XGBRegressor from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error 23/2: train_data = pd.read_csv("data/cons_training.csv", parse_dates=[0,1]) train_data.dtypes 23/3: def pre_process(df): targets = ['cons_actual_plus_umm', 'cons_actual_excl_umm'] features_not_used = ['start_time_utc', 'start_time_local', 'cons_fcast_fingrid_excl_umm'] features_to_use = [c for c in df.columns if c not in targets + features_not_used] + ['day_of_week'] # Only some temperatures and cons_fcast (which can't be used) are null. Fill them with prev row vals df = df.fillna(method='ffill') #Add day of week as feature df['day_of_week'] = df.apply(lambda row: row.start_time_local.dayofweek, axis=1) pre_X = df.drop(targets, axis=1)[features_to_use] return df, pre_X data, X = pre_process(train_data) cons_actual_plus_umm = data['cons_actual_plus_umm'] cons_actual_excl_umm = data['cons_actual_excl_umm'] 23/4: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1200, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 23/5: train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_plus_umm, test_size=0.15) model_cons_plus_umm = XGBRegressor(n_estimators=1000, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_plus_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_plus_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 23/6: #Use a small test set so early stopping works train_X, test_X, train_y, test_y = train_test_split(X, cons_actual_excl_umm, test_size=0.15) #After some manual CV these seem to perform well. Possible add more gamma for regularization? model_cons_excl_umm = XGBRegressor(n_estimators=1700, max_depth=9, n_jobs=4, gamma=3, colsample_bytree=0.6, min_child_weight=10, subsample=0.8) model_cons_excl_umm.fit(train_X, train_y, early_stopping_rounds=5, eval_set=[(test_X, test_y)], eval_metric='mae', verbose=False) predictions = model_cons_excl_umm.predict(test_X) print("Mean Absolute Error on held-out test set: " + str(mean_absolute_error(predictions, test_y))) 23/7: test_set_csv = pd.read_csv("data/cons_testing_without_labels.csv", parse_dates=[0,1]) test_set, test_set_X = pre_process(test_set_csv) 24/1: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool 25/1: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool 26/1: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool 26/2: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool 26/3: input_dir = r"/home/tman/Work/data/harvester_data" labels_source = "harvest" image_source = "copernicus" X, y, input_shape, output_dim = data_loading.import_data(input_dir) 26/4: len(X) 26/5: from PIL import Image 26/6: y[0] 26/7: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool from PIL import Image import pandas as pd import json from tqdm import tqdm import re from sklearn.model_selection import train_test_split 26/8: filename = "SE_harvest_566321,6766769,566421,6766919.geojson" with open(os.path.join(input_dir, filename)) as f: data = json.load(f) 26/9: data 26/10: coord = np.asarray(data['features'][0]["geometry"]["coordinates"])[0] 26/11: coord 26/12: coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max() 26/13: coord[:, 0].min(), coord[:, 1].min(), coord[:, 0].max(), coord[:, 1].max() 26/14: data['features'][0] 26/15: data['features'][0]["properties"]["fid"] 26/16: data['features'][0]["properties"] 26/17: def cut_into_cells(input_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] big_image_id = 0 n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] min_x = coord[:, 0].min() min_y = coord[:, 1].min() max_x = coord[:, 0].max() max_y = coord[:, 1].max() fid = feature["properties"]["fid"] if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(big_image_id) tmp_y.append(min_x) tmp_y.append(min_y) tmp_y.append(max_x) tmp_y.append(max_y) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) big_image_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 26/18: input_dir = r"/home/tman/Work/data/harvester_data" labels_source = "harvest" image_source = "copernicus" X, y, input_shape, output_dim = data_loading.import_data(input_dir) 26/19: y[0] 29/1: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool from PIL import Image import pandas as pd import json from tqdm import tqdm import re from sklearn.model_selection import train_test_split 29/2: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings import sys sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool from PIL import Image import pandas as pd import json from tqdm import tqdm import re from sklearn.model_selection import train_test_split 29/3: def cut_into_cells(input_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] big_image_id = 0 n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] min_x = coord[:, 0].min() min_y = coord[:, 1].min() max_x = coord[:, 0].max() max_y = coord[:, 1].max() fid = feature["properties"]["fid"] if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(big_image_id) tmp_y.append(min_x) tmp_y.append(min_y) tmp_y.append(max_x) tmp_y.append(max_y) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) big_image_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 29/4: input_dir = r"/home/tman/Work/data/harvester_data" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y, input_shape, output_dim = cut_into_cells(input_dir, labels_source, image_source, prediction_features, cell_shape) 29/5: input_dir = r"/home/tman/Work/data/harvester_data" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, labels_source, image_source, prediction_features, cell_shape) 29/6: y[0] 29/7: y[1] 29/8: y_df = pd.DataFrame(y) y_df.columns = ["fid", "big_image_id", "x_min", "y_min", "x_max", "y_max"] + prediction_features y_df 29/9: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 29/10: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/1: import cv2 import datetime import time import numpy as np import os from sklearn.model_selection import train_test_split import argparse import warnings import sys sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from models.nn_models import NeuralNetwork from data import data_loading from features.preprocessing import preprocessing_dict from metrics.model_metrics import compute_metrics import config from utils import str2bool from PIL import Image import pandas as pd import json from tqdm import tqdm import re from sklearn.model_selection import train_test_split 30/2: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/3: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/4: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] print(len(geo_jsons)) X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/5: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/6: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) print("gets here lol") cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/7: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/8: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/9: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) print(os.path.join(output_path, str(fid), ".png")) cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/10: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/11: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) print(os.path.join(output_path, str(fid) + ".png")) cv2.imwrite(os.path.join(output_path, str(fid), ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/12: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/13: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. :return X: the image data returned cut into cells as defined in the geojsons. :return y: the labels/targets from the geojsons """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) print(os.path.join(output_path, str(fid) + ".png")) cv2.imwrite(os.path.join(output_path, str(fid) + ".png"), img_tmp) return X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/14: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/15: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features=['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/16: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. Saves the images cell by cell and saves the csv that has labels with the fid as the key. """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) print(os.path.join(output_path, str(fid) + ".png")) cv2.imwrite(os.path.join(output_path, str(fid) + ".png"), img_tmp) X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/17: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features = ['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/18: y_df = pd.DataFrame(y) y_df.columns = ["fid", "virtual_cluster_id", "x_min", "y_min", "x_max", "y_max"] + prediction_features y_df.to_csv(os.path.join(output_dir, "groundtruth.csv")) 30/19: def cut_into_cells(input_path, output_path, labels_source, image_source, prediction_features, cell_shape, __test__=False, verbose=False): """ Divides a large image into cells fit for training using the data in the geojsons. Saves the images cell by cell and saves the csv that has labels with the fid as the key. """ geo_jsons = [x for x in os.listdir(input_path) if '.geojson' in x and labels_source in x] X = [] y = [] virtual_cluster_id = 0 # Works as kind of a stand, since the big images are images where harvest cells are clustered. n_faulty_cells = 0 for file in tqdm(sorted(geo_jsons)): with open(os.path.join(input_path, file)) as f: data = json.load(f) file_bbox = np.asarray([int(x) for x in re.findall(r"\d+", file)]) num_labels = len(data["features"]) img = cv2.imread(os.path.join(input_path, file.replace(labels_source, image_source).replace(".geojson", ".png"))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) input_shape = img.shape assert input_shape[1] == (file_bbox[2] - file_bbox[0]) assert input_shape[0] == (file_bbox[3] - file_bbox[1]) dx = cell_shape[1] dy = cell_shape[0] if __test__: out_pine = np.zeros(input_shape) for feature in data["features"]: coord = np.asarray(feature["geometry"]["coordinates"])[0] # it is important to remember about horizontal flip. # i.e. y coord is reversed in qgis compared to numpy conventional order i = int((coord[:, 0].min() - file_bbox[0]) // dx) j = int(np.ceil((file_bbox[3] - coord[:, 1].max()) / dy)) img_tmp = img[j*dy:(j+1)*dy, i*dx:(i+1)*dx, :] x_min = coord[:, 0].min() y_min = coord[:, 1].min() x_max = coord[:, 0].max() y_max = coord[:, 1].max() fid = feature["properties"]["fid"] # Id in DB. if verbose: print("Expected bbox: ", coord[:, 0].min(), coord[:, 0].max(), coord[:, 1].min(), coord[:, 1].max()) print("Retrieved bbox: ", i*dx + file_bbox[0], (i+1)*dx + file_bbox[0], file_bbox[3] - (j+1)*dy, file_bbox[3] - j*dy) try: assert img_tmp.shape == cell_shape, ("Wrong input shape.", i, j) except AssertionError: # Log when cell is irregularly shaped and can't be used, and pass this cell # Can happen that the cell coordinates in the geojson go over the satellite image coordinates, # in which case there wouldn't be enough pixels to cut an appropriate sized cell from the sat image. n_faulty_cells += 1 continue # print("Assertion error") # print("coords were: ", coord) cv2.imwrite(os.path.join(output_path, str(fid) + ".png"), img_tmp) X.append(img_tmp) tmp_y = [] tmp_y.append(fid) tmp_y.append(virtual_cluster_id) tmp_y.append(x_min) tmp_y.append(y_min) tmp_y.append(x_max) tmp_y.append(y_max) for item in prediction_features: tmp_y.append(feature["properties"][item]) y.append(tmp_y) virtual_cluster_id += 1 X = np.asarray(X) y = np.asarray(y) print("Faulty cells: ", n_faulty_cells) return X, y 30/20: input_dir = r"/home/tman/Work/data/harvester_data" output_dir = r"/home/tman/Work/data/harvester_data_processed" labels_source = "harvest" image_source = "copernicus" prediction_features = ['pine_volume', 'spruce_volume', 'birch_volume', 'other_bl_volume', 'contorta_volume'] cell_shape = (25, 25, 3) X, y = cut_into_cells(input_dir, output_dir, labels_source, image_source, prediction_features, cell_shape) 30/21: y_df = pd.DataFrame(y) y_df.columns = ["fid", "virtual_cluster_id", "x_min", "y_min", "x_max", "y_max"] + prediction_features y_df.to_csv(os.path.join(output_dir, "groundtruth.csv")) 31/1: import sys, os 31/2: os.path.dirname(sys.executable) 32/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 32/2: X[0] 33/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 33/2: X_scalar[0] 33/3: X_scalar.shape 33/4: X_features = preprocessing_dict['image_features'](X) 33/5: X_features[0] 33/6: X_features[0].shape 33/7: X_features = preprocessing_dict['image_to_features'](X) 33/8: X_features[0] 33/9: X_features.shape 33/10: np.concatenate(X_scalar, X_features) 33/11: np.hstack(X_scalar, X_features) 33/12: np.hstack([X_scalar, X_features]).shape 34/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 34/2: X[0] 34/3: X[0].shape 34/4: np.sum(X > 10000) 34/5: np.isnan(X).sum() 34/6: gg = X[np.isnan(X)] 34/7: gg[0] 34/8: gg 34/9: np.where(np.isnan(X)) 35/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 35/2: y.shape 36/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 36/2: y.shape 37/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 37/2: y[0] 37/3: np.sum(np.isnan(y[:,0:3])) 37/4: np.sum(np.isnan(y[:,0:4])) 37/5: np.sum(np.isnan(y[:,0:2])) 38/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 39/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 39/2: non_nan_indexes = np.any(np.array[non_nan_X, non_nan_y], axis=1) 39/3: non_nan_indexes = np.any(np.array([non_nan_X, non_nan_y]), axis=1) 39/4: non_nan_indexes.shape 39/5: non_nan_indexes = np.any(np.array([non_nan_X, non_nan_y]), axis=0) 39/6: non_nan_indexes.shape 39/7: np.sum(non_nan_indexes) 39/8: non_nan_indexes = np.all(np.array([non_nan_X, non_nan_y]), axis=0) 39/9: non_nan_indexes.shape 39/10: np.sum(non_nan_indexes) 39/11: X[non_nan_indexes].shape 40/1: tf.test.is_gpu_available() 40/2: import tensorflow as tf 40/3: tf.test.is_gpu_available() 44/1: import data_loading 44/2: import data 44/3: import pickle 44/4: datapath = r"/home/tman/Work/data/FIsampletiles" 44/5: datapath = r"/home/tman/Work/data/FIsampletiles/cache" 44/6: X, y, input_shape, output_dim = pickle.load(open(datapath, "rb")) 44/7: datapath = r"/home/tman/Work/data/FIsampletiles/cache/pickled_data.p" 44/8: X, y, input_shape, output_dim = pickle.load(open(datapath, "rb")) 44/9: X 44/10: X.shape 44/11: from sklearn.preprocessing import OneHotEncoder 44/12: X[-5: ] 44/13: X[:5:-5] 44/14: X[:5,-5] 44/15: X[:5,-3] 44/16: test = X 44/17: test = X[:5,-3] 44/18: OneHotEncoder(test) 44/19: OneHotEncoder().fit(test) 44/20: test.shape 44/21: test.reshape(-1, 1) 44/22: test.reshape(-1, 1).shape 44/23: test = X[:5,-3].reshape(-1, 1) 44/24: OneHotEncoder().fit(test) 44/25: test = X[:10,-3].reshape(-1, 1) 44/26: test 44/27: test = X[:30,-3].reshape(-1, 1) 44/28: test 44/29: encoder = OneHotEncoder() 44/30: encoder.fit_transform(test) 44/31: encoder = OneHotEncoder(sparse=False) 44/32: encoder.fit_transform(test) 44/33: encoder.fit_transform(test).shape 44/34: test = X[:5,[-3, -1]] 44/35: test 44/36: test = X[:30,[-3, -1]] 44/37: test 44/38: encoder.fit_transform(test) 44/39: X_copy = X.copy() 44/40: X_cope 44/41: X_copy 44/42: X_copy.shape 44/43: columns = [-3, -1] 44/44: tt = X_copy[:,columns] 44/45: np.delete(X_copy, columns, axis=1) 44/46: import numpy as np 44/47: np.delete(X_copy, columns, axis=1) 44/48: np.delete(X_copy, columns, axis=1).shape 44/49: X_cope.shape 44/50: X_copy.shape 44/51: np.delete(X_copy, columns, axis=1).shape 44/52: np.delete(X_copy, [57,60], axis=1).shape 44/53: np.append(X, X, axis=1).shape 45/1: from tqdm import tqdm import os from PIL import Image import numpy as np import json import psycopg2 import pandas.io.sql as sqlio import pandas as pd import argparse import matplotlib.pyplot as plt import sys import cv2 import pickle pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is sometrics can be imported sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from data import data_loading 45/2: from tqdm import tqdm import os from PIL import Image import numpy as np import json import psycopg2 import pandas.io.sql as sqlio import pandas as pd import argparse import matplotlib.pyplot as plt import sys import cv2 import pickle pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is sometrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from data import data_loading 45/3: output_dim = 4 model_function = models_definition.create_xgboost ## output dim etc? model = model_function(2, output_dim, random_state=50) load = "../regressors/models/xgboost_scalars_generic.2018-11-13.15-21-24" model.load(load) 45/4: output_dim = 4 model_function = models_definition.create_xgboost ## output dim etc? model = model_function(2, output_dim, random_state=50) load = "../regressors/models/xgboost_scalars_generic.2018-11-13.15-21-24" model.load(load) 46/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 46/2: data[:5] 46/3: data['gridcellid'] 46/4: data['gridcellid'].shape 46/5: data['gridcellid'].values 46/6: data['gridcellid'].values.shape 46/7: gridcellids = np.expand_dims(data['gridcellid'].values, axis=1) 46/8: gridcellids.shape 51/1: from data import data_loading 51/2: data_loading.create_test_set_from_ids("/home/tman/Work/data/FIsampletiles/groundtruth.csv", "/home/tman/Work/data/FIsampletiles/") 51/3: data_loading.create_test_set_from_ids("/home/tman/Work/data/FIsampletiles/groundtruth.csv", "/home/tman/Work/data/FIsampletiles/") 52/1: from data import data_loading 52/2: data_loading.create_test_set_from_ids("/home/tman/Work/data/FIsampletiles/groundtruth.csv", "/home/tman/Work/data/FIsampletiles/") 53/1: import data_loading 53/2: data_loading.create_test_set_from_ids("/home/tman/Work/data/FIsampletiles/groundtruth.csv", "/home/tman/Work/data/FIsampletiles/") 55/1: cd data 55/2: import data_loading 55/3: input_path = r"/home/tman/Work/data/FIsampletiles", image_dir="azure_tiles_cleaned", image_type="jpg" 55/4: input_path = r"/home/tman/Work/data/FIsampletiles" 55/5: images, data = data_loading.import_data(input_path, "groundtruth.csv", image_dir="azure_tiles_cleaned", image_type="jpg") 55/6: len(data) 55/7: data['plot_type'] 55/8: data.groupby('plot_type').count() 55/9: data.groupby('plot_type').value_counts() 55/10: data['plot_type'].value_counts() 55/11: images, data = data_loading.import_data(input_path, "test.csv", image_dir="azure_tiles_cleaned", image_type="jpg") 55/12: data['plot_type'].value_counts() 55/13: images, data = data_loading.import_data(input_path, "train.csv", image_dir="azure_tiles_cleaned", image_type="jpg") 55/14: data['plot_type'].value_counts() 55/15: gg = data[data['plot_type'] in [1,4]] 55/16: gg = data[data['plot_type'].isin([1,4])] 55/17: len(gg) 55/18: gg.columns 57/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') from data import data_loading 57/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 57/3: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 57/4: input_path = r"C:\Users\Teemu\Work\data\FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 57/5: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 57/6: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 57/7: ### Cell for running own models from keras.models import load_model from features import preprocessing model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/" model = load_model(model_path) ### Use same preprocessing that the model used 57/8: ### Cell for running own models from keras.models import load_model from features import preprocessing model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 57/9: ### Cell for running own models from keras.models import load_model from features import preprocessing model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 57/10: ### Cell for running own models from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 57/11: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 57/12: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 58/1: X_preprocessed = preprocessing.preprocessing_dict['crop_center'](X) X_preprocessed = preprocessing.preprocessing_dict['resize'](X_preprocessed, input_dims=[128, 128]) 59/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 59/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 59/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 59/4: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 61/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 61/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 61/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 61/4: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 63/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 63/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 63/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 63/4: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 63/5: metsakeskus_predictions = scalar_df[['volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']] groundtruth = scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']] 63/6: X_preprocessed = preprocessing.crop_center(X) # X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=[128, 128]) 65/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 65/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 65/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 65/4: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 66/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 66/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 66/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 66/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=[128, 128]) 67/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 67/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 67/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 67/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=[128, 128]) 69/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 69/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 69/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 69/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=[128, 128, 3]) 71/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 71/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 71/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 71/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = np.array([cv2.resize(image, [128, 128]) for image in X]) 71/5: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = np.array([cv2.resize(image, (128, 128)) for image in X]) 72/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 72/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 72/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 72/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X) X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128)) 72/5: X_preprocessed.shapoe 72/6: X_preprocessed.shape 73/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 73/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 73/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 73/4: from features import preprocessing X_preprocessed, _ = preprocessing.crop_center(X) X_preprocessed, _ = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128)) 74/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 74/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 74/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 74/4: from features import preprocessing X_preprocessed, X_scalar, y, ids, y_clf = preprocessing.crop_center(X) X_preprocessed, X_scalar, y, ids, y_clf = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128)) 75/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 75/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") 75/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 75/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] 75/5: X_preprocessed.shape 76/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 76/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir) 76/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 76/4: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir) 76/5: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 76/6: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 76/7: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_12-05.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 76/8: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] 76/9: X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] preds = mode.predict(X_preprocessed) 76/10: X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] preds = model.predict(X_preprocessed) 76/11: preds[:5] 76/12: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:5] 76/13: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][5:10] 76/14: preds[5:10] 76/15: preds[:50] 76/16: ### Cell for running own models import keras from keras.models import load_model from features import preprocessing from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_11-54.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 76/17: X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] preds = model.predict(X_preprocessed) 76/18: preds[:50] 77/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 77/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 77/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 77/4: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] 77/5: plt.imshow(X[0]) plt.imshow(X_preprocessed[0]) 77/6: plt.imshow(X[0]) 77/7: plt.imshow(X_preprocessed[0]) 78/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 78/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 78/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 78/4: ### Cell for running own models import keras from keras.models import load_model from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/lenet_mature19-01-22_13-23.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 78/5: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] 78/6: preds = model.predict(X_preprocessed) preds[:10] 78/7: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 79/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 79/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 79/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 79/4: ### Cell for running own models import keras from keras.models import load_model from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/lenet_mature19-01-22_13-23.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 79/5: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] 79/6: preds = model.predict(X_preprocessed) preds[:10] 79/7: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 80/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 80/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 80/3: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 80/4: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 80/5: ### Cell for running own models import keras from keras.models import load_model from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/lenet_mature19-01-22_13-43.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 80/6: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] 80/7: preds = model.predict(X_preprocessed) preds[:10] 80/8: preds = model.predict(X_preprocessed) from sklearn.metrics import mean_squared_error 80/9: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 80/10: preds = model.predict(X_preprocessed) from sklearn.metrics import mean_squared_error groundtruth = scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous']] mean_squared_error(preds, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0) 80/11: preds = model.predict(X_preprocessed) from sklearn.metrics import mean_squared_error groundtruth = scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous']] mean_squared_error(preds, groundtruth, multioutput='raw_values') 81/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 81/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "train.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 81/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] scalar_df = data_unique.merge(df, on='plot_id').drop_duplicates(subset='plot_id') 81/4: ### Cell for running own models import keras from keras.models import load_model from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/lenet_mature19-01-22_13-43.hdf5" model = load_model(model_path) ### Use same preprocessing that the model used 81/5: from features import preprocessing X_preprocessed = preprocessing.crop_center(X)[0] 81/6: preds = model.predict(X_preprocessed) from sklearn.metrics import mean_squared_error groundtruth = scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous']] mean_squared_error(preds, groundtruth, multioutput='raw_values') 81/7: df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 81/8: scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 82/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors/') from data import data_loading 82/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 82/3: ### Get the metsäkeskus hila predictions on the test set and join them on the sample plot ids ### Adapted from lidar-height-and-density-analysis.ipynb notebook api_url = 'http://51.144.230.13:10323/api/point_list' locations = df[['easting', 'northing']].values.tolist() plot_ids = df.plot_id.values.tolist() # The API currently gives an error for large number of locations, so we must get the data in batches. batch_size = 1000 data_batches = [] for batch_start in tqdm(range(0, len(locations), batch_size)): locations_batch = locations[batch_start:batch_start+batch_size] plot_id_batch = plot_ids[batch_start:batch_start+batch_size] post_json = json.dumps({ 'srid': 3067, 'coordinates': locations_batch, 'fids': plot_id_batch }) params = { 'schema': 'metsakeskus_hila', 'table': 'gridcell', 'columns': ['volumepine,volumespruce,volumedeciduous,volume'] } post_headers = {'Content-Type': 'application/json'} res = requests.post(api_url, data=post_json, headers=post_headers, params=params) data_batch = res.json() data_batch = [(feature['properties']['fid'], feature['properties']['volumepine'],feature['properties']['volumespruce'], feature['properties']['volumedeciduous'],feature['properties']['volume'] ) for feature in data_batch['features']] data_batch = pd.DataFrame(data_batch, columns=['plot_id','volumepine', 'volumespruce', 'volumedeciduous', 'volumetotal']) data_batches.append(data_batch) data = pd.concat(data_batches, axis=0, ignore_index=True) data_unique = data.loc[data.plot_id.drop_duplicates().index] ### TODO: are the images and this scalar df matched? ALSO NORMALIZATION SHEESH scalar_df = data_unique.merge(df, on='plot_id') 82/4: ### Cell for running own models import keras from keras.models import load_model from keras.utils.generic_utils import CustomObjectScope from keras.layers import DepthwiseConv2D from keras_applications.mobilenet import relu6 model_path = r"/home/tman/Work/linda-forestry-ml/species_prediction/regressors/weights/mobilenet_mature19-01-22_11-54.hdf5" with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D}): model = load_model(model_path) ### Use same preprocessing that the model used 82/5: from features import preprocessing from keras.applications.mobilenet import preprocess_input as preprocess_input_mobilenet X_preprocessed = preprocessing.crop_center(X)[0] X_preprocessed = preprocessing.resize_images(X_preprocessed, input_dims=(128, 128))[0] X_preprocessed = preprocess_input_mobilenet(X_preprocessed) 82/6: preds = model.predict(X_preprocessed) preds[:10] 82/7: df[['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total']][:10] 82/8: metsakeskus_predictions = scalar_df[['volumepine', 'volumespruce', 'volumedeciduous']] groundtruth = scalar_df[['vol_pine', 'vol_spruce', 'vol_deciduous']] 82/9: from sklearn.metrics import mean_squared_error print("NRMSE% of metsakeskus predictions on the test set:") (np.sqrt(mean_squared_error(metsakeskus_predictions, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100 print("NRMSE% of metsakeskus predictions on the test set:") (np.sqrt(mean_squared_error(preds, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100 82/10: from sklearn.metrics import mean_squared_error print("NRMSE% of metsakeskus predictions on the test set:") print((np.sqrt(mean_squared_error(metsakeskus_predictions, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100) print("NRMSE% of metsakeskus predictions on the test set:") print((np.sqrt(mean_squared_error(preds, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100) 82/11: from sklearn.metrics import mean_squared_error print("NRMSE% of metsakeskus predictions on the test set:") print((np.sqrt(mean_squared_error(metsakeskus_predictions, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100) print("NRMSE% of our predictions on the test set:") print((np.sqrt(mean_squared_error(preds, groundtruth, multioutput='raw_values')) / np.mean(groundtruth, axis=0))*100) 87/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') from models import models_definition from data import data_loading 87/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported # sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') 87/3: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "groundtruth.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] #X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 87/4: df 87/5: def get_metsakeskus_predictions(df) schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous', 'volume']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index() return hiladata hd = get_metsakeskus_predictions(df) 87/6: def get_metsakeskus_predictions(df): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous', 'volume']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index() return hiladata hd = get_metsakeskus_predictions(df) 87/7: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests sys.path.append('../regressors/') from data.data_loading import import_data, GeoAPI, split_from_ids pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported # sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') 87/8: def get_metsakeskus_predictions(df): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous', 'volume']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index() return hiladata hd = get_metsakeskus_predictions(df) 87/9: hd 88/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests sys.path.append('../regressors/') from data.data_loading import import_data, GeoAPI, split_from_ids pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported # sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') 88/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "groundtruth.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] #X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir="tiles") df = pd.read_csv(os.path.join(input_path, 'test.csv')) 88/3: def get_metsakeskus_predictions(df): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index() return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error metsakeskus_predictions = get_metsakeskus_predictions(df) prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] groundtruth = df[prediction_features] mse = mean_squared_error(metsakeskus_predictions, groundtruth, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 88/4: len(df) 88/5: metsakeskus_predictions = get_metsakeskus_predictions(df) 88/6: len(metsakeskus_predictions) 88/7: metsakeskus_predictions 88/8: metsakeskus_predictions.isna().sum() 88/9: metsakeskus_predictions.duplicated().sum() 88/10: len(metsakeskus_predictions) 88/11: df.merge(metsakeskus_predictions) 88/12: metsakeskues_predictions 88/13: metsakeskus_predictions 88/14: def get_metsakeskus_predictions(df): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error metsakeskus_predictions = get_metsakeskus_predictions(df) prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] groundtruth = df[prediction_features] mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 88/15: metsakeskus_predictions = get_metsakeskus_predictions(df) 88/16: metsakeskus_predictions 88/17: df.merge(metsakeskus_predictions, on='plot_id') 88/18: df.merge(metsakeskus_predictions, on='plot_id').len() 88/19: df.merge(metsakeskus_predictions, on='plot_id').shape 88/20: df.merge(metsakeskus_predictions, on='plot_id').drop_duplicates() 88/21: df.merge(metsakeskus_predictions, on='plot_id').drop_duplicates().shape 88/22: df.merge(metsakeskus_predictions, on='plot_id').drop_duplicates(subset='plot_id').shape 88/23: def get_metsakeskus_predictions(df, columns_list): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] metsakeskus_pred_columns = ['volumepine', 'volumespruce', 'volumedeciduous'] metsakeskus_data = get_metsakeskus_predictions(df, metsakeskus_pred_columns) # API returns duplicated somewhat often with gridcell data, remove duplicates merged = df.merge(metsakeskus_predictions, on='plot_id').drop_duplicates(subset='plot_id') groundtruth = merged[prediction_features] metsakeskus_predictions = merged[metsakeskus_pred_columns] mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 88/24: def get_metsakeskus_predictions(df, columns_list): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] metsakeskus_pred_columns = ['volumepine', 'volumespruce', 'volumedeciduous'] metsakeskus_data = get_metsakeskus_predictions(df, metsakeskus_pred_columns) # API returns duplicated somewhat often with gridcell data, remove duplicates merged = df.merge(metsakeskus_data, on='plot_id').drop_duplicates(subset='plot_id') groundtruth = merged[prediction_features] metsakeskus_predictions = merged[metsakeskus_pred_columns] mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 88/25: def get_metsakeskus_predictions(df, columns_list): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] metsakeskus_pred_columns = ['volumepine', 'volumespruce', 'volumedeciduous'] metsakeskus_data = get_metsakeskus_predictions(df, metsakeskus_pred_columns) # API returns duplicated somewhat often with gridcell data, remove duplicates merged = df.merge(metsakeskus_data, on='plot_id').drop_duplicates(subset='plot_id') groundtruth = merged[prediction_features] metsakeskus_predictions = merged[metsakeskus_pred_columns] print(metsakeskus_predictions) mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 88/26: def get_metsakeskus_predictions(df, columns_list): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] metsakeskus_pred_columns = ['volumepine', 'volumespruce', 'volumedeciduous'] metsakeskus_data = get_metsakeskus_predictions(df, metsakeskus_pred_columns) # API returns duplicated somewhat often with gridcell data, remove duplicates merged = df.merge(metsakeskus_data, on='plot_id').drop_duplicates(subset='plot_id') groundtruth = merged[prediction_features] metsakeskus_predictions = merged[metsakeskus_pred_columns] print(np.mean(metsakeskus_predictions, axis=0)) mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 89/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests sys.path.append('../regressors/') from data.data_loading import import_data, GeoAPI, split_from_ids pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported # sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') 89/2: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "groundtruth.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = import_data(input_path, labels_name=labels_name, image_dir="tiles") #df = pd.read_csv(os.path.join(input_path, 'test.csv')) 89/3: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = import_data(input_path, labels_name=labels_name, image_dir="tiles") #df = pd.read_csv(os.path.join(input_path, 'test.csv')) 89/4: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = import_data(input_path, labels_name=labels_name, image_dir="tiles", image_type="jpg") #df = pd.read_csv(os.path.join(input_path, 'test.csv')) 89/5: X 89/6: df 89/7: input_path = r"/home/tman/Work/data/FIsampletiles" labels_name = "test.csv" image_dir = "azure_tiles_cleaned" #scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', # 'elevation', 'slope', 'aspect', 'soil_type', # 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] X, df = import_data(input_path, labels_name=labels_name, image_dir="azure_tiles_cleaned", image_type="jpg") #df = pd.read_csv(os.path.join(input_path, 'test.csv')) 89/8: def get_metsakeskus_predictions(df, columns_list): schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [['volumepine', 'volumespruce', 'volumedeciduous']] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) return hiladata def metsakeskus_errors(df): from sklearn.metrics import mean_squared_error prediction_features=['vol_pine', 'vol_spruce', 'vol_deciduous'] metsakeskus_pred_columns = ['volumepine', 'volumespruce', 'volumedeciduous'] metsakeskus_data = get_metsakeskus_predictions(df, metsakeskus_pred_columns) # API returns duplicated somewhat often with gridcell data, remove duplicates merged = df.merge(metsakeskus_data, on='plot_id').drop_duplicates(subset='plot_id') groundtruth = merged[prediction_features] metsakeskus_predictions = merged[metsakeskus_pred_columns] mse = mean_squared_error(groundtruth, metsakeskus_predictions, multioutput='raw_values') rmse = np.sqrt(mse) gt_means = np.mean(groundtruth, axis=0) nrmse = (rmse / gt_means)*100 return gt_means, rmse, nrmse gt_means, rmse, nrmse = metsakeskus_errors(df) print(gt_means) print(rmse) print(nrmse) 90/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_predictions/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids 90/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids 90/3: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" scalar_feature_names = ['easting', 'northing', 'measure_year', 'measure_date', 'elevation', 'slope', 'aspect', 'soil_type', 'tree_cover', 'leaf_type', 'plot_id'] prediction_features=['vol_total', 'vol_pine', 'vol_spruce', 'vol_deciduous'] df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) #X, df = data_loading.import_data(input_path, labels_name=labels_name, image_dir=image_dir, image_type="jpg") 90/4: ### Get the metsäkeskus hila data columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,\ laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [[columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) 90/5: ### Get the metsäkeskus hila data columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,\ laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [[columns_string.split(",")]] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) 90/6: ### Get the metsäkeskus hila data columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,\ laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) 90/7: hiladata 90/8: hiladata.isna().sum() 95/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 95/2: input_path = r"/home/tman/Work/data/FIsampletiles" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 95/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df) columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,\ laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 95/4: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,\ laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 95/5: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup, laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 95/6: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 95/7: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 95/8: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 95/9: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 95/10: # Get only the features to be used feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] # Test just pine for now target_columns = ['vol_pine'] # Get training data - features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 95/11: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(features_train, targets_train) 95/12: # Ridge regression from models import models_definition ridge = models_definition.create_ridge(len(feature_columns), len(target_columns)) ridge.fit(features_train, targets_train) 95/13: # Get predictions xgb_preds = xgb.predict(features_test) ridge_preds = ridge.predict(features_test) # Metsäkeskus errors target_metsakeskus_columns = ['volume'] 95/14: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 95/15: # Get predictions xgb_preds = xgb.predict(features_test) ridge_preds = ridge.predict(features_test) # Metsäkeskus errors target_metsakeskus_columns = ['volumepine'] 95/16: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 95/17: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("\n") print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("\n") print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 96/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 96/2: input_path = r"/home/tman/Work/data/FIsampletiles" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 96/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 96/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 96/5: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() # Filter so that only mature plots are used full_data_nona = full_data_nona[full_data_nona['plot_type'].isin([1, 4])].reset_index(drop=True) 96/6: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 96/7: # Get only the features to be used feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] # Test just pine for now target_columns = ['vol_pine'] # Get training data - features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 96/8: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(features_train, targets_train) 96/9: # Ridge regression from models import models_definition ridge = models_definition.create_ridge(len(feature_columns), len(target_columns)) ridge.fit(features_train, targets_train) 96/10: # Get predictions xgb_preds = xgb.predict(features_test) ridge_preds = ridge.predict(features_test) # Metsäkeskus errors target_metsakeskus_columns = ['volumepine'] 96/11: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("\n") print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("\n") print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 96/12: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() # Filter so that only mature plots are used # full_data_nona = full_data_nona[full_data_nona['plot_type'].isin([1, 4])].reset_index(drop=True) full_data_nona = full_data_nona[full_data_nona['vol_pine'] > 100] 96/13: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 96/14: len(full_data_test) 96/15: len(full_data_train) 96/16: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() # Filter so that only mature plots are used # full_data_nona = full_data_nona[full_data_nona['plot_type'].isin([1, 4])].reset_index(drop=True) full_data_nona = full_data_nona[full_data_nona['vol_total'] > 100] 96/17: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 96/18: # Get only the features to be used feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] # Test just pine for now target_columns = ['vol_pine'] # Get training data - features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 96/19: len(full_data_train) 96/20: len(full_data_test) 96/21: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(features_train, targets_train) 96/22: # Ridge regression from models import models_definition ridge = models_definition.create_ridge(len(feature_columns), len(target_columns)) ridge.fit(features_train, targets_train) 96/23: # Get predictions xgb_preds = xgb.predict(features_test) ridge_preds = ridge.predict(features_test) # Metsäkeskus errors target_metsakeskus_columns = ['volumepine'] 96/24: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("\n") print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("\n") print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 97/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 97/2: input_path = r"/home/tman/Work/data/FIsampletiles" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 97/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,maingroup,subgroup,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 97/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 97/5: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() # Filter so that only mature plots are used # full_data_nona = full_data_nona[full_data_nona['plot_type'].isin([1, 4])].reset_index(drop=True) # full_data_nona = full_data_nona[full_data_nona['vol_total'] > 100] 97/6: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 97/7: # Get only the features to be used feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] # Test just pine for now target_columns = ['vol_pine'] # Get training data - features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 97/8: len(full_data_test) 97/9: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(features_train, targets_train) 97/10: # Ridge regression from models import models_definition ridge = models_definition.create_ridge(len(feature_columns), len(target_columns)) ridge.fit(features_train, targets_train) 97/11: # Get predictions xgb_preds = xgb.predict(features_test) ridge_preds = ridge.predict(features_test) # Metsäkeskus errors target_metsakeskus_columns = ['volumepine'] 97/12: from metrics import model_metrics print("Metsakeskus errors on the set:") # compute_metrics requires a list, which is why it's wrapped this way. Warnings are related to ci_95 calcs model_metrics.compute_metrics([targets_test.values], [full_data_test[target_metsakeskus_columns].values]) print("\n") print("XGBoost prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [np.expand_dims(xgb_preds,axis=1)]) print("\n") print("Ridge prediction errors on the set:") model_metrics.compute_metrics([targets_test.values], [ridge_preds]) 100/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'C:/Users/Teemu/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 100/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 100/3: input_path = r"/home/tmanTeemu/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 100/4: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 100/5: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 100/6: onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: hiladata[column] = pd.Categorical(hiladata[column]) hiladata = pd.get_dummies(hiladata) 100/7: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 100/8: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 100/9: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 100/10: # Get only the features to be used feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 101/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 101/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 101/4: onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: hiladata[column] = pd.Categorical(hiladata[column]) hiladata = pd.get_dummies(hiladata) 101/5: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 101/6: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 101/7: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 101/8: # Get training data - features_train = full_data_train.drop(target_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/9: target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train.drop(target_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/10: target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train.drop(target_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/11: full_data_train 101/12: full_data_train.columns 101/13: target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train.drop(target_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/14: target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train.drop(target_columns, axis=1) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] 101/15: target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns, axis=1) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_train.drop(target_columns + metsakeskus_pred_columns, axis=1) targets_test = full_data_test[target_columns] 101/16: features_train[:2] 102/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 102/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 102/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 102/4: hiladata.columns 102/5: onehot = False if onehot: for column in ['soiltype', 'fertilityclass']: hiladata[column] = pd.Categorical(hiladata[column]) hiladata = pd.get_dummies(hiladata) 102/6: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 102/7: full_data_train.columns 102/8: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 102/9: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() full_data_nona.columns 103/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 103/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 103/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 103/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[usable_columns] full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 103/5: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 103/6: full_data.columns 103/7: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 103/8: onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: hiladata[column] = pd.Categorical(hiladata[column]) hiladata = pd.get_dummies(hiladata) 103/9: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) full_data = pd.get_dummies(full_data) 104/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 104/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 104/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 104/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) full_data = pd.get_dummies(full_data) 104/5: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 104/6: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 105/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 105/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 105/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 105/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['plot_id', 'easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) full_data = pd.get_dummies(full_data) 105/5: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[['plot_id'] + usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) full_data = pd.get_dummies(full_data) 105/6: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 105/7: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 105/8: full_data_train.columns 105/9: # Get only the features to be used target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns + metsakeskus_pred_columns) targets_test = full_data_test[target_columns] 105/10: # Get only the features to be used # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns + metsakeskus_pred_columns) targets_test = full_data_test[target_columns] 105/11: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] usable_columns = feature_columns + target_columns + metsakeskus_pred_columns full_data = full_data[['plot_id'] + usable_columns] # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) full_data = pd.get_dummies(full_data) 105/12: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 105/13: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 105/14: full_data_train.columns 105/15: # Get only the features to be used # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns + metsakeskus_pred_columns) targets_test = full_data_test[target_columns] 105/16: # Get only the features to be used # Get training data - features_train = full_data_train.drop(target_columns) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns) targets_test = full_data_test[target_columns] 105/17: full_data_train.columns 105/18: # Get only the features to be used # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns, axis=1) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns + metsakeskus_pred_columns, axis=1) targets_test = full_data_test[target_columns] 105/19: features_train.columns 105/20: # Get only the features to be used # Get training data - features_train = full_data_train.drop(target_columns + metsakeskus_pred_columns + ['plot_id'], axis=1) targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test.drop(target_columns + metsakeskus_pred_columns + ['plot_id'], axis=1) targets_test = full_data_test[target_columns] 105/21: features_train.columns 105/22: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor from models import models_definition from sklearn.metrics import mean_squared_error target_to_metsakeskus = { 'vol_pine': 'volumepine', 'vol_spruce': 'volumespruce', 'vol_deciduous': 'volumedeciduous', 'vol_total': 'volume', } for col in targets_train.columns: y_train, y_test = targets_train[col].values, targets_test[col].values X_train, X_test = features_train.values, features_test.values xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(X_train, y_train) pred = xgb.predict(X_test) metsakeskus_pred = full_data_test[target_to_metsakeskus[col]].values rmse = np.sqrt(mean_squared_error(y_test, pred)) y_mean = y_test.mean() nrmse = rmse / y_mean * 100 nrmse_metsakeskus = np.sqrt(mean_squared_error(y_test, metsakeskus_pred)) / y_mean * 100 print('Mean for {}: {:.5f}'.format(col, y_mean)) print('NRMSE for {}: {:.5f}'.format(col, nrmse)) print('Metsäkeskus NRMSE for {}: {:.5f}'.format(col, nrmse_metsakeskus)) 106/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 106/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 106/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 106/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 106/5: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 106/6: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 106/7: # Get only the features to be used # Get training data - feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] onehot = True if onehot: for column in ['soiltype', 'fertilityclass']: features_train[column] = pd.Categorical(features_train[column]) features_test[column] = pd.Categorical(features_test[column]) features_train = pd.get_dummies(features_train) features_test = pd.get_dummies(features_test) 107/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'/home/tman/Work/linda-forestry-ml/species_prediction/regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 107/2: input_path = r"/home/tman/Work/data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) 107/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(df): # Should we use maingroup/subgroup? columns_string = """volumepine,volumespruce,volumedeciduous,volume,soiltype,fertilityclass,laserheight,laserdensity""" schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) hiladata = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. hiladata.reset_index(inplace=True) hiladata = hiladata.drop_duplicates() return hiladata hiladata = get_metsakeskus_data(df) 107/4: # Drop duplicate plot ids - any chance of some kind of difference in data here? Merge scalar and hila data hiladata = hiladata.drop_duplicates(subset="plot_id") full_data = pd.merge(df, hiladata, on='plot_id', how='inner') # use gridcell soiltype, not sample plots soil_type - # former is metsäkeskus and apparently more accurate? the latter is afaik from LUKE. full_data = full_data.drop('soil_type', axis=1) # Drop LUKE soil type # Set these columns as categorical in case we try onehot later for column in ['soiltype', 'fertilityclass']: full_data[column] = pd.Categorical(full_data[column]) # Save for use with train.py? full_data.to_csv(input_path + "scalar_and_gridcell.csv", index=False) 107/5: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 107/6: # Split to train and test. Uses metsäkeskus test set. # full_data_high_pine = full_data_nona[full_data_nona.vol_pine > 100] full_data_train, full_data_test = split_from_ids(full_data_nona) 107/7: # Get only the features to be used # Get training data - feature_columns = ['easting', 'northing', 'elevation', 'slope', 'aspect', 'tree_cover', 'leaf_type', 'soiltype', 'fertilityclass', 'laserheight', 'laserdensity'] target_columns = ['vol_pine', 'vol_spruce', 'vol_deciduous', 'vol_total'] metsakeskus_pred_columns = ['volumepine','volumespruce','volumedeciduous','volume'] features_train = full_data_train[feature_columns] targets_train = full_data_train[target_columns] # Get testing data features_test = full_data_test[feature_columns] targets_test = full_data_test[target_columns] onehot = True if onehot: features_train = pd.get_dummies(features_train) features_test = pd.get_dummies(features_test) 107/8: features_train 107/9: # cv search of best model - actually gives worse results than default? from sklearn.model_selection import RandomizedSearchCV from sklearn.metrics import mean_squared_error, make_scorer param_distributions = {'max_depth': [6,8,10], 'learning_rate': [0.1,0.01,0.001,0.0001], 'n_estimators': [100, 200, 300, 400], 'min_child_weight': [2, 8, 15, 25], 'colsample_bytree': [1, 0.8, 0.5], 'subsample': [0.6, 0.8], 'reg_alpha': [0.01, 0.08, 0.2], 'colsample_bylevel': [0.6, 0.8], 'reg_lambda': [0.7, 0.8, 0.95] } # model = XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=500, n_jobs=3) search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=50) search.fit(features_train, targets_train) best_params = search.best_params_ model = XGBRegressor(**best_params) model.fit(features_train, targets_train) 107/10: # XGBoost. Try with just pine at first? from xgboost import XGBRegressor from models import models_definition from sklearn.metrics import mean_squared_error target_to_metsakeskus = { 'vol_pine': 'volumepine', 'vol_spruce': 'volumespruce', 'vol_deciduous': 'volumedeciduous', 'vol_total': 'volume', } for col in targets_train.columns: y_train, y_test = targets_train[col].values, targets_test[col].values X_train, X_test = features_train.values, features_test.values xgb = XGBRegressor(objective='reg:linear', nthread=-1) xgb.fit(X_train, y_train) pred = xgb.predict(X_test) metsakeskus_pred = full_data_test[target_to_metsakeskus[col]].values rmse = np.sqrt(mean_squared_error(y_test, pred)) y_mean = y_test.mean() nrmse = rmse / y_mean * 100 nrmse_metsakeskus = np.sqrt(mean_squared_error(y_test, metsakeskus_pred)) / y_mean * 100 print('Mean for {}: {:.5f}'.format(col, y_mean)) print('NRMSE for {}: {:.5f}'.format(col, nrmse)) print('Metsäkeskus NRMSE for {}: {:.5f}'.format(col, nrmse_metsakeskus)) 112/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 112/2: input_path = r"../../../data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) 112/3: ### Get the metsäkeskus hila data def get_metsakeskus_data() columns_string = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return hiladata def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data ']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/4: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_string = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] columns_list = [columns_string.split(",")] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return hiladata def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data ']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/5: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_string = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return hiladata def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data ']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/6: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return hiladata def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data ']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/7: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data ']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/8: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data #metsakeskus_data = get_metsakeskus_data() #copernicus_data = get_copernicus_data() #soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() 112/9: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data #metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() #soilgrids_data = get_soilgrids_data() #climate_data = get_climate_data() 112/10: metsakeskus_data[:2] 112/11: copernicus_data[:2] 112/12: soilgrids_data[:2] 112/13: climate_data[:2] 112/14: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","maingroup", "subgroup","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids_all']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data #metsakeskus_data = get_metsakeskus_data() #copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() #climate_data = get_climate_data() 112/15: metsakeskus_data[:2] 112/16: copernicus_data[:2] 112/17: soilgrids_data[:2] 112/18: climate_data[:2] 112/19: df[:2] 112/20: df.drop(["geom"], axis=1) 112/21: unusable_features = ["geom", "soil_type", "plot_type", "cluster_id", "vol_pine", "vol_spruce", "vol_deciduous", "vol_total", "measure_date", "measure_year"] full_data = df.drop(unusable_features, axis=1) 112/22: # Remove rows with NAs - gridcell data is missing from thousands of rows, aspect from about hundred. full_data_nona = full_data.dropna() 115/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 115/2: input_path = r"../../../data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) df[['aspect']] = df[['aspect']].fillna(0) df = df.dropna(subset=["soil_type"]) # only about 200 NAs here, just drop, not much lost api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) 115/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() soilgrids_data = soilgrids_data.dropna() # only 47 rows missing, ok to drop nas climate_data = get_climate_data() 115/4: print("NAs in df data:\n", df.isna().sum()) print("NAs in metsakeskus data:\n", metsakeskus_data.isna().sum()) print("NAs in copernicus data:\n", copernicus_data.isna().sum()) print("NAs in soilgrids data:\n", soilgrids_data.isna().sum()) print("NAs in climate data:\n", climate_data.isna().sum()) 115/5: metsakeskus_columns = list(metsakeskus_data.columns) copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) climate_columns = list(climate_data.columns) df_columns = ["easting", "northing", "elevation", "aspect" "slope", "soil_type", "tree_cover", "leaf_type"] full_data = df.merge(metsakeskus_data, on='plot_id').\ merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(climate_data, on="plot_id") ### drop na before or after how to make sure they're the same and such? #unusable_features = ["geom", "soil_type", "plot_type", "cluster_id", "vol_pine", "vol_spruce", "vol_deciduous", # "vol_total", "measure_date", "measure_year"] #targets = ["vol_pine"] #df = df.dropna() #full_data_features = df.drop(unusable_features, axis=1) #full_data_targets = df[targets] 115/6: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV param_distributions = {'max_depth': [3,8,15], 'learning_rate': [0.1,0.01,0.001], 'n_estimators': [100, 300, 500], 'min_child_weight': [1, 2, 5], 'colsample_bytree': [0.5, 0.8, 1], 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, all_data) 115/7: metsakeskus_columns = list(metsakeskus_data.columns) copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) climate_columns = list(climate_data.columns) df_columns = ["easting", "northing", "elevation", "aspect", "slope", "soil_type", "tree_cover", "leaf_type"] full_data = df.merge(metsakeskus_data, on='plot_id').\ merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(climate_data, on="plot_id") ### drop na before or after how to make sure they're the same and such? #unusable_features = ["geom", "soil_type", "plot_type", "cluster_id", "vol_pine", "vol_spruce", "vol_deciduous", # "vol_total", "measure_date", "measure_year"] #targets = ["vol_pine"] #df = df.dropna() #full_data_features = df.drop(unusable_features, axis=1) #full_data_targets = df[targets] 115/8: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV param_distributions = {'max_depth': [3,8,15], 'learning_rate': [0.1,0.01,0.001], 'n_estimators': [100, 300, 500], 'min_child_weight': [1, 2, 5], 'colsample_bytree': [0.5, 0.8, 1], 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, all_data) 116/1: from scipy.stats import uniform uniform.rvs(10) 116/2: from scipy.stats import uniform uniform.rvs(100) 116/3: from scipy.stats import uniform uniform.rvs(size=10) 116/4: from scipy.stats import uniform uniform.rvs(scale=100) 116/5: from scipy.stats import uniform uniform.rvs(scale=100) 116/6: from scipy.stats import uniform uniform.rvs(scale=100) 116/7: from scipy.stats import uniform uniform.rvs(scale=100) 116/8: from scipy.stats import uniform uniform.rvs(scale=100) 116/9: from scipy.stats import uniform uniform.rvs(scale=100) 116/10: from scipy.stats import uniform uniform.rvs(scale=100) 116/11: from scipy.stats import uniform uniform.rvs(scale=100) 116/12: from scipy.stats import uniform uniform.rvs(scale=100) 116/13: from scipy.stats import uniform uniform.rvs(scale=100) 116/14: from scipy.stats import uniform uniform.rvs(scale=100) 116/15: from scipy.stats import uniform uniform.rvs(scale=100) 116/16: from scipy.stats import uniform uniform.rvs(scale=100) 116/17: from scipy.stats import uniform uniform.rvs(scale=100) 116/18: from scipy.stats import uniform uniform.rvs(scale=[100, 400]) 116/19: from scipy.stats import uniform uniform.rvs(scale=[100, 400]) 116/20: from scipy.stats import uniform uniform.rvs(scale=[100, 400]) 116/21: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/22: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/23: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/24: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/25: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/26: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/27: from scipy.stats import uniform uniform.rvs(loc=100, scale=400) 116/28: from scipy.stats import uniform uniform(loc=100, scale=400) 116/29: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/30: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/31: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/32: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/33: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/34: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/35: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/36: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/37: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 116/38: from scipy.stats import uniform uni = uniform(loc=100, scale=400) uni.rvs() 118/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 118/2: input_path = r"../../../data/FIsampletiles" image_dir = "azure_tiles_cleaned" df = pd.read_csv(os.path.join(input_path, 'groundtruth.csv')) df[['aspect']] = df[['aspect']].fillna(0) df = df.dropna(subset=["soil_type"]) # only about 200 NAs here, just drop, not much lost api = GeoAPI(default_locations=df[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=df.plot_id.values.tolist()) 118/3: ### Get the metsäkeskus hila data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() soilgrids_data = soilgrids_data.dropna() # only 47 rows missing, ok to drop nas climate_data = get_climate_data() 118/4: print("NAs in df data:\n", df.isna().sum()) print("NAs in metsakeskus data:\n", metsakeskus_data.isna().sum()) print("NAs in copernicus data:\n", copernicus_data.isna().sum()) print("NAs in soilgrids data:\n", soilgrids_data.isna().sum()) print("NAs in climate data:\n", climate_data.isna().sum()) 118/5: metsakeskus_columns = list(metsakeskus_data.columns) copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) climate_columns = list(climate_data.columns) df_columns = ["easting", "northing", "elevation", "aspect", "slope", "soil_type", "tree_cover", "leaf_type"] columns_dict = { 'base': df_columns, 'metsakeskus': metsakeskus_columns, 'copernicus': copernicus_columns, 'soilgrids': soilgrids_columns, 'climate': climate_columns } full_data = df.merge(metsakeskus_data, on='plot_id').\ merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(climate_data, on="plot_id") ### drop na before or after how to make sure they're the same and such? #unusable_features = ["geom", "soil_type", "plot_type", "cluster_id", "vol_pine", "vol_spruce", "vol_deciduous", # "vol_total", "measure_date", "measure_year"] #targets = ["vol_pine"] #df = df.dropna() #full_data_features = df.drop(unusable_features, axis=1) #full_data_targets = df[targets] 118/6: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/7: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/8: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/9: from scipy.stats import uniform uniform(loc=0.001, scale=0.1).rvs() 118/10: from scipy.stats import uniform uniform(loc=0.001, scale=0.1).rvs() 118/11: from scipy.stats import uniform uniform(loc=0.001, scale=0.1).rvs() 118/12: from scipy.stats import uniform uniform(loc=0.001, scale=0.1).rvs() 118/13: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/14: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {#'max_depth': uniform(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/15: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/16: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': int(uniform(loc=3, scale=15)), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/17: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': np.float64(3), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/18: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/19: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': uniform(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/20: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': maxdepth, #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/21: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(loc=3, scale=15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/22: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), #'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/23: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), #'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], #'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/24: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': uniform(loc=100, scale=600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/25: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=1), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/26: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 118/27: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV from scipy.stats import uniform, randint features = data[feature_columns].drop("plot_id", axis=1) print(features.columns) targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) maxdepth = uniform(loc=3, scale=15) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } search = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error), param_distributions=param_distributions, n_jobs=-1, cv=5, verbose=True, n_iter=35) search.fit(features, targets) best_params = search.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) # df contains some basic features such as easting and northing, which have repeatedly proven to be good features # so add them in all copernicus = df_columns + copernicus_columns print("CV 5-fold RMSE using just copernicus data: \n") test_different_models(full_data, copernicus) climate = df_columns + climate_columns print("CV 5-fold RMSE using just climate data: \n") test_different_models(full_data, climate) copernicus_and_climate = df_columns + copernicus_columns + climate_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, copernicus_and_climate) soilgrids = df_columns + soilgrids_columns print("CV 5-fold RMSE using copernicus and climate data: \n") test_different_models(full_data, soilgrids) # So many NAs in metsakeskus, not worth? #metsakeskus = #print("CV 5-fold RMSE using copernicus and climate data: \n") #test_different_models(full_data, metsakeskus) soilgrids_and_climate = df_columns + soilgrids_columns + climate_columns print("CV 5-fold RMSE using soilgrids and climate data: \n") test_different_models(full_data, soilgrids_and_climate) soilgrids_and_copernicus = df_columns + soilgrids_columns + copernicus_columns print("CV 5-fold RMSE using soilgrids and copernicus data: \n") test_different_models(full_data, soilgrids_and_copernicus) all_data = df_columns + soilgrids_columns + climate_columns + copernicus_columns print("CV 5-fold RMSE using all data: \n") test_different_models(full_data, all_data) 119/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 119/2: stand_volumes = pd.read_csv("../../../data/harvester_data/ccgeodb_harvest_v_cell_volumes_smoothed.csv") stand_polygons = pd.read_csv("../../../data/harvester_data/tblforestands_geom.csv") 119/3: stand_volumes[:2] 119/4: stand_volumes[:2] 119/5: stand_variances = stand_volumes.groupby("stand_id").var() 119/6: stand_variances = stand_volumes.groupby("stand_id").var() stand_data = stand_variances.merge(stand_polygons, left_on="stand_id", right_on="placeid") 119/7: stand_data[:2] 119/8: stand_variances = stand_volumes.groupby("stand_id").var() stand_data_temp = stand_variances.merge(stand_polygons, left_on="stand_id", right_on="placeid") 119/9: stand_data = stand_data_temp.drop(['fid', 'stand_group_id', 'placeid_parent']) 119/10: stand_data = stand_data_temp.drop(['fid', 'stand_group_id', 'placeid_parent'], axis=1) 119/11: stand_data[:2] 119/12: stand_variances_areas = stand_data_temp.drop(['fid', 'stand_group_id', 'placeid_parent'], axis=1) 119/13: stand_[:2] 119/14: stand_variances_areas[:2] 121/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 121/2: stand_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_stand_level_features.csv") gridcell_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_gridcell_volumes_with_coords.csv") 121/3: gridcell_data[:2] 121/4: stand_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_stand_level_features.csv") gridcell_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon') 121/5: stand_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_stand_level_features.csv") gridcell_data = pd.read_csv("../../../data/harvest_FI/ccgeodb_harvest_koski_v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 121/6: gridcell_data[:2] 121/7: stand_data 121/8: stand_data[:2] 121/9: stand_data.dtypes 121/10: stand_data[:2] 121/11: stand_data.prd_id.unique() 121/12: stand_data.prd_id.unique().len() 121/13: stand_data.prd_id.unique().len 121/14: len(stand_data.prd_id.unique()) 121/15: stand_data[:2] 121/16: len(stand_data.stand_polygon_id.unique()) 121/17: gridcell_data[:2] 121/18: api = GeoAPI(default_locations=stand_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=stand_data.prd_id.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() #soilgrids_data = get_soilgrids_data() #soilgrids_data = soilgrids_data.dropna() # only 47 rows missing, ok to drop nas climate_data = get_climate_data() 121/19: metsakeskus_data[:2] 121/20: metsakeskus_data.nan() 121/21: metsakeskus_data.isna().sum() 121/22: metsakeskus_data[:2] 121/23: api = GeoAPI(default_locations=stand_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=stand_data.prd_id.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() #soilgrids_data = soilgrids_data.dropna() # only 47 rows missing, ok to drop nas climate_data = get_climate_data() 121/24: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import seaborn as sns import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 121/25: sns.distplot(stand_data.total_m3_ha, label='Stand data Total Volume Distribution') #sns.distplot(testing.vol_total, label='Test Set Total Volume Distribution') plt.legend() 126/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 126/2: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 126/3: slu_plots_since_2015[:2] 126/4: api = GeoAPI(default_locations=slu_plots_since_2015[['longitude', 'latitude']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_since_2015.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/5: terramonitor_predictions[:2] 126/6: terramonitor_predictions 126/7: terramonitor_predictions.shape 126/8: terramonitor_predictions.isna().sum() 126/9: api = GeoAPI(default_locations=slu_plots_since_2015[['latitude', 'longitude']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_since_2015.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/10: terramonitor_predictions[:2] 126/11: api = GeoAPI(default_locations=slu_plots_since_2015[['longitude', 'latitude']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_since_2015.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/12: terramonitor_predictions[:2] 126/13: slu_plots_since_2015.shape 126/14: slu_plots_with_distance[:2] 126/15: slu_plots_with_distance[slu_plots_with_distance['distance_km_from_kastet'] < 100].shape 126/16: slu_plots_since_2015[:2] 126/17: slu_plots_veri = slu_plots_with_distance[slu_plots_with_distance['distance_km_from_kastet'] < 100].shape 126/18: api = GeoAPI(default_locations=slu_plots_veri[['longitude', 'latitude']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_veri.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/19: slu_plots_veri = slu_plots_with_distance[slu_plots_with_distance['distance_km_from_kastet'] < 100] 126/20: api = GeoAPI(default_locations=slu_plots_veri[['longitude', 'latitude']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_veri.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/21: slu_plots_with_distance[:2] 126/22: api = GeoAPI(default_locations=slu_plots_veri[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_veri.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/23: terramonitor_predictions[:2] 126/24: terramonitor_predictions.isna().sum() 126/25: slu_plots_veri[:2] 126/26: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] 126/27: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] * terramonitor_predictions[['se_volumes_m3_ha']] 126/28: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] * terramonitor_predictions['se_volumes_m3_ha'] 126/29: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] * terramonitor_predictions['se_volumes_m3_ha'].vlues 126/30: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] * terramonitor_predictions['se_volumes_m3_ha'].values 126/31: terramonitor_predictions['se_volumes_m3_ha'].shape 126/32: terramonitor_predictions['se_volumes_m3_ha'].values.shape 126/33: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees] * terramonitor_predictions[['se_volumes_m3_ha']].values 126/34: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terramonitor_predictions[trees].values * terramonitor_predictions[['se_volumes_m3_ha']].values 126/35: terramonitor_predictions[['se_volumes_m3_ha']].values.shape 126/36: terramonitor_predictions[trees].values.shape 126/37: terramonitor_predictions[['se_volumes_m3_ha']].values 126/38: terramonitor_predictions[trees].values 126/39: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] (terramonitor_predictions[trees].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values 126/40: slu_plots_veri[:2] 126/41: slu_plots_veri.isna().sum() 126/42: slu_plots_veri = slu_plots_with_distance[slu_plots_with_distance['distance_km_from_kastet'] < 100].dropna() 126/43: api = GeoAPI(default_locations=slu_plots_veri[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_veri.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data terramonitor_predictions = get_terramonitor_predictions() 126/44: trees = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] (terramonitor_predictions[trees].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values 126/45: slu_plots_veri[:2] 126/46: trees_slu = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] slu_plots_veri[trees_slu].values * slu_plots_veri[['volume']].values 126/47: slu_plots_veri[:2] 126/48: trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_volumes = (terramonitor_predictions[trees_terra].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values 126/49: trees_slu = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] slu_volumes = slu_plots_veri[trees_slu].values * slu_plots_veri[['volume']].values 126/50: from sklearn.metrics import mean_squared_error np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) 126/51: np.mean(terra_volumes, axis=1) 126/52: np.mean(terra_volumes, axis=0) 126/53: trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_volumes = (terramonitor_predictions[trees_terra].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values terra_volumes = np.hstack(terramonitor_predictions[['se_volumes_m3_ha']].values, terra_volumes) 126/54: trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_volumes = (terramonitor_predictions[trees_terra].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values terra_volumes = np.hstack([terramonitor_predictions[['se_volumes_m3_ha']].values, terra_volumes]) 126/55: trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_volumes = (terramonitor_predictions[trees_terra].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values terra_volumes = np.hstack([terramonitor_predictions[['se_volumes_m3_ha']].values, terra_volumes]) 126/56: trees_slu = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] slu_volumes = slu_plots_veri[trees_slu].values * slu_plots_veri[['volume']].values terra_volumes = np.hstack([slu_plots_veri[['volume']].values, slu_volumes]) 126/57: from sklearn.metrics import mean_squared_error terra_means = np.mean(terra_volumes, axis=0) slu_means = np.mean(slu_volumes, axis=0) rmse = np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) nrmse = rmse / slu_means 126/58: trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_volumes = (terramonitor_predictions[trees_terra].values/100) * terramonitor_predictions[['se_volumes_m3_ha']].values terra_volumes = np.hstack([terramonitor_predictions[['se_volumes_m3_ha']].values, terra_volumes]) 126/59: trees_slu = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] slu_volumes = slu_plots_veri[trees_slu].values * slu_plots_veri[['volume']].values slu_volumes = np.hstack([slu_plots_veri[['volume']].values, slu_volumes]) 126/60: from sklearn.metrics import mean_squared_error terra_means = np.mean(terra_volumes, axis=0) slu_means = np.mean(slu_volumes, axis=0) rmse = np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) nrmse = rmse / slu_means 126/61: nrmse 126/62: from sklearn.metrics import mean_squared_error terra_means = np.mean(terra_volumes, axis=0) slu_means = np.mean(slu_volumes, axis=0) rmse = np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) nrmse = rmse / slu_means * 100 126/63: nrmse 126/64: rmse 126/65: nrmse 128/1: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 128/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 128/3: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 128/4: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 130/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 130/2: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 130/3: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 130/4: #TODO: get all values for all rows, filter to test set later. api = GeoAPI(default_locations=slu_plots_with_distance[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_with_distance.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect", "copernicus_leaf_type", "copernicus_tree_cover"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_physical_data(): tables_list = ["elev_16m_hila_grid", "aspect_16m_hila_grid", "slope_16m_hila_grid"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() copernicus_data = get_copernicus_data() physical_data = get_physical_data() soilgrids_data = get_soilgrids_data() soilgrids_data = soilgrids_data.dropna() climate_data = get_climate_data() terramonitor_predictions = get_terramonitor_predictions() 131/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 131/2: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 131/3: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 131/4: #TODO: get all values for all rows, filter to test set later. api = GeoAPI(default_locations=slu_plots_with_distance[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_with_distance.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect", "copernicus_leaf_type", "copernicus_tree_cover"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_physical_data(): tables_list = ["elev_16m_hila_grid", "aspect_16m_hila_grid", "slope_16m_hila_grid"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data copernicus_data = get_copernicus_data() physical_data = get_physical_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() terramonitor_predictions = get_terramonitor_predictions() 131/5: soilgrids_data[:2] 131/6: soilgrids_data.shape 131/7: soilgrids_data.isna().sum() 131/8: slu_plots_with_distance.shape 131/9: slu_plots_with_distance[2] 131/10: slu_plots_with_distance[:2] 131/11: # Merge all data into one dataframe copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) physical_columns = list(physical_data.columns) climate_columns = list(climate_data.columns) full_data = stand_data.merge(sku_plots_with_distance, on='plot_id').\ merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(physical_data, on="plot_id").\ merge(climate_data, on="plot_id") 131/12: # Merge all data into one dataframe copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) physical_columns = list(physical_data.columns) climate_columns = list(climate_data.columns) full_data = slu_plots_with_distance.merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(physical_data, on="plot_id").\ merge(climate_data, on="plot_id") 131/13: full_data[:2] 131/14: full_data.isna().sum() 131/15: terramonitor_predictions.isna().sum() 131/16: # Merge all data into one dataframe copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) physical_columns = list(physical_data.columns) climate_columns = list(climate_data.columns) full_data = slu_plots_with_distance.merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(physical_data, on="plot_id").\ merge(climate_data, on="plot_id").\ merge(terramonitor_predictions, on="plot_id") 131/17: full_data[full_data['distance_km_from_kastet'] > 100 & full_data['distance_km_from_kastet'] < 300] 131/18: full_data[full_data['distance_km_from_kastet'] > 100 && full_data['distance_km_from_kastet'] < 300] 131/19: full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] 131/20: full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] 131/21: full_data[(full_data['distance_km_from_kastet'] > 100) && (full_data['distance_km_from_kastet'] < 300)] 131/22: full_data[(full_data['distance_km_from_kastet'] > 100) and (full_data['distance_km_from_kastet'] < 300)] 131/23: full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] 131/24: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % ) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % ) 131/25: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) 131/26: test_set.isna().sum() 131/27: #TODO: get all values for all rows, filter to test set later. api = GeoAPI(default_locations=slu_plots_with_distance[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_with_distance.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect", "copernicus_leaf_type", "copernicus_tree_cover"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data copernicus_data = get_copernicus_data() physical_data = get_physical_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() terramonitor_predictions = get_terramonitor_predictions() 132/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 132/2: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 132/3: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 132/4: #TODO: get all values for all rows, filter to test set later. api = GeoAPI(default_locations=slu_plots_with_distance[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_with_distance.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect", "copernicus_leaf_type", "copernicus_tree_cover"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() terramonitor_predictions = get_terramonitor_predictions() 132/5: # Merge all data into one dataframe copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) climate_columns = list(climate_data.columns) full_data = slu_plots_with_distance.merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(climate_data, on="plot_id").\ merge(terramonitor_predictions, on="plot_id") 132/6: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) 132/7: feature_columns = copernicus_columns + soilgrids_columns + climate_columns gt_target_columns = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] 132/8: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer_nrmse) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return randomsearch test_different_models(full_data, feature_columns, gt_target_total) 132/9: feature_columns = copernicus_columns + soilgrids_columns + climate_columns gt_target_trres = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] gt_target_total = ['volume'] # Rescale the target column with the total volume 132/10: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer_nrmse) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return randomsearch test_different_models(full_data, feature_columns, gt_target_total) 132/11: feature_columns = list(set(copernicus_columns + soilgrids_columns + climate_columns)) gt_target_trres = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] gt_target_total = ['volume'] # Rescale the target column with the total volume 132/12: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer_nrmse) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return randomsearch test_different_models(full_data, feature_columns, gt_target_total) 132/13: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer_nrmse) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return randomsearch test_different_models(train_set, feature_columns, gt_target_total) 132/14: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return randomsearch test_different_models(train_set, feature_columns, gt_target_total) 132/15: def test_different_models(data, feature_columns, target_columns = ["vol_pine"]): # Test different models with this data (mix different types, eg. soilgrids with metsakeskus and so on) from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error, make_scorer from sklearn.model_selection import RandomizedSearchCV, GridSearchCV from scipy.stats import uniform, randint features = data[feature_columns] targets = data[target_columns] # Default XGB default_xgb = XGBRegressor(objective='reg:linear', nthread=-1) # Search best parameters by CV. Note: if all are lists, sampling is done without replacement? which is bad? param_distributions_random = {'max_depth': randint(3, 15), 'learning_rate': uniform(loc=0.001, scale=0.1), 'n_estimators': randint(100, 600), 'min_child_weight': [1, 2, 5], 'colsample_bytree': uniform(loc=0.5, scale=0.5), 'reg_alpha': [0, 0.1, 0.2], 'reg_lambda': [0.7, 1], 'subsample': [0.8, 0.9], 'gamma': [0, 0.07] } randomsearch = RandomizedSearchCV(XGBRegressor(), scoring=make_scorer(mean_squared_error, greater_is_better=False), param_distributions=param_distributions_random, n_jobs=5, cv=5, verbose=True, n_iter=35) randomsearch.fit(features, targets) best_params = randomsearch.best_params_ cv_xgb = XGBRegressor(**best_params) def scorer(estimator, x, y): preds = estimator.predict(x) error = np.sqrt(mean_squared_error(preds, y)) return error def scorer_nrmse(estimator, x, y): preds = estimator.predict(x) error = (np.sqrt(mean_squared_error(preds, y)) / np.mean(y))*100 return error scores = cross_val_score(default_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with default XGB: ", np.mean(scores)) scores = cross_val_score(cv_xgb, features, targets, cv=5, scoring=scorer) print("RMSE mean of 5-fold CV with CV optimized XGB: ", np.mean(scores)) return default_xgb, cv_xgb default_xgb, optimized_xgb = test_different_models(train_set, feature_columns, gt_target_total) 132/16: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading from notebook.services.config import ConfigManager c = ConfigManager() c.update('notebook', {"CodeCell": {"cm_config": {"autoCloseBrackets": False}}}) 132/17: def get_metrics(preds, targets) from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") our_preds = cv_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/18: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(terra_volumes, slu_volumes, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") our_preds = cv_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/19: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") our_preds = cv_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/20: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") our_preds = optimized.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/21: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") our_preds = optimized_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/22: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") optimized_xgb.fit(train_set[feature_columns]) our_preds = optimized_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/23: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") optimized_xgb.fit(train_set[feature_columns], train_set["volume"]) our_preds = optimized_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 132/24: test_set["se_volumes_m3_ha"] 132/25: test_set["volume"] 132/26: test_set["se_volumes_m3_ha"] 132/27: test_set["se_volumes_m3_ha"].isna().sum() 132/28: test_set["volume"].isna().sum() 132/29: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) return rmse print("Terramonitor RMSE with total volume on test set: ") print(get_metrics(test_set["se_volumes_m3_ha"], test_set["volume"])) print("Our prediction RMSE with total volume on test set: ") optimized_xgb.fit(train_set[feature_columns], train_set["volume"]) our_preds = optimized_xgb.predict(test_set[feature_columns]) print(get_metrics(our_preds, test_set["volume"])) 167/1: from data import data_loading 167/2: data_loading.create_test_set("/home/tman/Work/data/harvest_FI/v_stand_level_features", "/home/tman/Work/data/harvest_FI/") 167/3: data_loading.create_test_set("/home/tman/Work/data/harvest_FI/v_stand_level_features.csv", "/home/tman/Work/data/harvest_FI/") 169/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 169/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 169/3: def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data 169/4: stand_data = pd.read_csv("../../../../data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("../../../../data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 169/5: stand_data = pd.read_csv("/home/tman/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("/home/tman/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 169/6: gridcell_data[:2 169/7: gridcell_data[:2] 169/8: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data 169/9: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() 169/10: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() 170/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 170/2: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import sys import requests import seaborn as sns pd.options.display.float_format = '{:,.2f}'.format # Add path to where some utilities are so they can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 170/3: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 170/4: # load SLU data slu_plots_since_2015 = pd.read_csv("../../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_terramonitor.csv") slu_plots_with_distance = pd.read_csv("../../../../data/terramonitor_verification/ccgeodb_se_slu_v_slu_plots_since_2015_with_distance.csv") 170/5: print(slu_plots_since_2015.shape) print(slu_plots_with_distance.shape) 170/6: #TODO: get all values for all rows, filter to test set later. api = GeoAPI(default_locations=slu_plots_with_distance[['lon', 'lat']].values.tolist(), default_srid=4326, default_plot_ids=slu_plots_with_distance.plot_id.values.tolist()) def get_terramonitor_predictions(): tables_list = ["se_volumes_m3_ha", "se_pine_percent", "se_spruce_percent", "se_deciduous_percent"] columns_list = [None]*len(tables_list) schema_list = ['terramonitor']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_copernicus_data(): tables_list = ["copernicus_dem", "copernicus_slope", "copernicus_aspect", "copernicus_leaf_type", "copernicus_tree_cover"] columns_list = [None]*len(tables_list) schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_soilgrids_data(): data = api.request_data(data_groups=['soilgrids']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_climate_data(): data = api.request_data(data_groups=['climate_data']) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_lidar_data(): tables_list = ["lidar_vol_m3_ha", "lidar_height_dm", "lidar_diameter_cm"] columns_list = [None]*len(tables_list) schema_list = ['sweden']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data def get_mineral_data(): tables_list = ['se_mineral_soil'] columns_list = [['be', 'cd', 'dy', 'er', 'eu', 'lu', 'mo', 'nb', 'sn', 'tb', 'te', 'tl', 'tm']] schema_list = ['physical']*len(tables_list) data = api.request_data(schema_list, tables_list, columns_list, batch_size=1000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') return data copernicus_data = get_copernicus_data() soilgrids_data = get_soilgrids_data() climate_data = get_climate_data() lidar_data = get_lidar_data() mineral_data = get_mineral_data() terramonitor_predictions = get_terramonitor_predictions() 169/11: metsakeskus_data[:2 169/12: metsakeskus_data[:2] 170/7: # Merge all data into one dataframe copernicus_columns = list(copernicus_data.columns) soilgrids_columns = list(soilgrids_data.columns) climate_columns = list(climate_data.columns) lidar_columns = list(lidar_data.columns) mineral_columns = list(mineral_data.columns) full_data = slu_plots_with_distance.merge(copernicus_data, on="plot_id").\ merge(soilgrids_data, on="plot_id").\ merge(climate_data, on="plot_id").\ merge(lidar_data, on="plot_id").\ merge(mineral_data, on="plot_id").\ merge(terramonitor_predictions, on="plot_id") # full_data.to_csv(r"C:\Users\Teemu\Work\data\harvester_SE\terramonitor_data_ting.csv") 170/8: # Set removes duplicate column names such as plot_id feature_columns = list(set(copernicus_columns + soilgrids_columns + climate_columns + lidar_columns + mineral_columns)) # Rescale the target column with the total volume gt_target_trees = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] scaled_volumes = ['pine_volume', 'spruce_volume', 'deciduous_volume'] gt_target_total = ['volume'] trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_total = ['se_volumes_m3_ha'] terra_scaled = ['terra_pine', 'terra_spruce', 'terra_deciduous'] # volumes are NaN when the total volume is 0 (eg. other volumes are also 0), so it's ok to fill with na full_data[terra_scaled] = (full_data[terra_total].values * (full_data[trees_terra] / 100)).fillna(0) full_data[scaled_volumes] = (full_data[gt_target_total].values * full_data[gt_target_trees]).fillna(0) 170/9: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) train_set.to_csv("/home/tman/data/SEsampletiles/terramonitor_train.csv", index=False) test_set.to_csv("/home/tman/data/SEsampletiles/terramonitor_test.csv", index=False) 170/10: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) train_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_train.csv", index=False) test_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_test.csv", index=False) 170/11: dummy_means = np.mean(train_set[targets], axis=0) dummy_means.shape 170/12: # Set removes duplicate column names such as plot_id feature_columns = list(set(copernicus_columns + soilgrids_columns + climate_columns + lidar_columns + mineral_columns)) # Rescale the target column with the total volume gt_target_trees = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] scaled_volumes = ['pine_volume', 'spruce_volume', 'deciduous_volume'] gt_target_total = ['volume'] trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_total = ['se_volumes_m3_ha'] terra_scaled = ['terra_pine', 'terra_spruce', 'terra_deciduous'] all_columns = gt_target_total + scaled_volumes # volumes are NaN when the total volume is 0 (eg. other volumes are also 0), so it's ok to fill with na full_data[terra_scaled] = (full_data[terra_total].values * (full_data[trees_terra] / 100)).fillna(0) full_data[scaled_volumes] = (full_data[gt_target_total].values * full_data[gt_target_trees]).fillna(0) 170/13: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) train_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_train.csv", index=False) test_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_test.csv", index=False) 170/14: dummy_means = np.mean(train_set[targets], axis=0) dummy_means.shape 170/15: # Set removes duplicate column names such as plot_id feature_columns = list(set(copernicus_columns + soilgrids_columns + climate_columns + lidar_columns + mineral_columns)) # Rescale the target column with the total volume gt_target_trees = ['ratio_pine', 'ratio_spruce', 'ratio_deciduous'] scaled_volumes = ['pine_volume', 'spruce_volume', 'deciduous_volume'] gt_target_total = ['volume'] trees_terra = ['se_pine_percent', 'se_spruce_percent', 'se_deciduous_percent'] terra_total = ['se_volumes_m3_ha'] terra_scaled = ['terra_pine', 'terra_spruce', 'terra_deciduous'] all_columns = gt_target_total + scaled_volumes targets = all_columns # volumes are NaN when the total volume is 0 (eg. other volumes are also 0), so it's ok to fill with na full_data[terra_scaled] = (full_data[terra_total].values * (full_data[trees_terra] / 100)).fillna(0) full_data[scaled_volumes] = (full_data[gt_target_total].values * full_data[gt_target_trees]).fillna(0) 170/16: # Filter data to train and test: train_set = full_data[(full_data['distance_km_from_kastet'] > 100) & (full_data['distance_km_from_kastet'] < 300)] test_set = full_data[full_data['distance_km_from_kastet'] < 100] print("Training set: plots within 300km but outside 100km of Kastet. Number of plots in training: %d" % len(train_set)) print("Testing set: plots within 100km of Kastet. Number of plots in test: %d" % len(test_set)) train_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_train.csv", index=False) test_set.to_csv("/home/tman/Work/data/SEsampletiles/terramonitor_test.csv", index=False) 170/17: dummy_means = np.mean(train_set[targets], axis=0) dummy_means.shape 170/18: dummy_means = np.mean(train_set[targets], axis=0) dummy_means 170/19: dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[targets]) 170/20: dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[targets])) 170/21: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) return rmse terra_targets = terra_total + terra_scaled targets = all_columns print("Terramonitor RMSE with volumes on test set (total, pine, spruce, deciduous): ") print(get_metrics(test_set[terra_targets], test_set[targets])) print("Our prediction RMSE with volumes on test set (total, pine, spruce, deciduous): ") our_preds = [opt_model.predict(test_set[feature_columns]) for opt_model in opt_models] print(get_metrics(np.array(our_preds).T, test_set[targets])) dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[ 170/22: dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[targets])) 170/23: def get_metrics(preds, targets): from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(preds, targets, multioutput='raw_values')) return rmse terra_targets = terra_total + terra_scaled targets = all_columns print("Terramonitor RMSE with volumes on test set (total, pine, spruce, deciduous): ") print(get_metrics(test_set[terra_targets], test_set[targets])) print("Our prediction RMSE with volumes on test set (total, pine, spruce, deciduous): ") our_preds = [opt_model.predict(test_set[feature_columns]) for opt_model in opt_models] print(get_metrics(np.array(our_preds).T, test_set[targets])) dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[ 170/24: dummy_means = np.mean(train_set[targets], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[targets])) 170/25: test_set[targets] 170/26: dummy_means = np.mean(train_set[targets], axis=0) np.replicate(dummy_means, test_set[targets].shape[0], axis=0) #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/27: dummy_means = np.mean(train_set[targets], axis=0) np.repeat(dummy_means, test_set[targets].shape[0], axis=0) #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/28: dummy_means = np.mean(train_set[targets], axis=0) np.repeat(dummy_means, test_set[targets].shape[0]) #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/29: dummy_means = np.mean(train_set[targets], axis=0) np.repeat(dummy_means, test_set[targets].shape[0]).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/30: dummy_means = np.mean(train_set[targets], axis=0).values np.repeat(dummy_means, test_set[targets].shape[0]), axis=0).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/31: dummy_means = np.mean(train_set[targets], axis=0).values np.repeat(dummy_means, test_set[targets].shape[0], axis=0).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/32: dummy_means = np.mean(train_set[targets], axis=0).values np.repeat(dummy_means, test_set[targets].shape[0], axis=1).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/33: dummy_means = np.mean(train_set[targets], axis=0).values np.tile(dummy_means, test_set[targets].shape[0]) #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/34: dummy_means = np.mean(train_set[targets], axis=0).values np.tile(dummy_means, test_set[targets].shape[0]).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/35: dummy_means = np.mean(train_set[targets], axis=0).values np.tile(dummy_means, test_set[targets].shape[0], axis=0).shape #print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/36: dummy_means 170/37: dummy_means.shape 170/38: np.expand_dims(dummy_means, 1) 170/39: np.expand_dims(dummy_means, 1).shape 170/40: np.expand_dims(dummy_means, 0).shape 170/41: np.tile(np.expand_dims(dummy_means, 0), test_set[targets].shape[0]) 170/42: np.tile(np.expand_dims(dummy_means, 0), test_set[targets].shape[0]).shape 170/43: np.repeat(np.expand_dims(dummy_means, 0), test_set[targets].shape[0], axis=0).shape 170/44: dummy_means_temp = np.mean(train_set[targets], axis=0).values dummy_means = np.repeat(np.expand_dims(dummy_means_temp, 0), test_set[targets].shape[0], axis=0) print("Dummy mean of training set used as prediction: ") #print(get_metrics(dummy_means, test_set[targets])) 170/45: dummy_means_temp = np.mean(train_set[targets], axis=0).values dummy_means = np.repeat(np.expand_dims(dummy_means_temp, 0), test_set[targets].shape[0], axis=0) print("Dummy mean of training set used as prediction: ") print(get_metrics(dummy_means, test_set[targets])) 173/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 173/2: stand_data = pd.read_csv("~/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 173/3: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 173/4: stand_data = pd.read_csv("~/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 173/5: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 173/6: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() 173/7: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() 173/8: metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) 173/9: # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") 173/10: full_data.to_csv("~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv") 173/11: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] rmse = mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values') print(np.sqrt(rmse)) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/12: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']] 173/13: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].koski_prd_id 173/14: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].index 173/15: volume_means_times.shape 173/16: stand_data.shape 173/17: stand_data 173/18: stand_data.prd_id.unique() 173/19: len(stand_data.prd_id.unique()) 173/20: gridcell_data 173/21: len(gridcell_data.koski_prd_id.unique()) 173/22: preds_before = volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']] preds_before.index.to_csv("~/Work/data/koskisen/testids.csv") 173/23: preds_before.index.to_df() 173/24: preds_before.index.to_frame() 173/25: preds_before = volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']] preds_before.index.to_frame().rename("koski_prd_id", "prd_id").to_csv("~/Work/data/koskisen/testids.csv", index=False) 173/26: preds_before.index.to_frame().rename(columns={'koski_prd_id':'prd_id'} 173/27: preds_before.index.to_frame().rename(columns={'koski_prd_id':'prd_id'}) 173/28: preds_before.index.to_frame().rename(columns={'koski_prd_id':'prd_id'}).to_csv("~/Work/data/koskisen/testids.csv", index=False) 173/29: gridcell_data.shape 173/30: len(gridcell_data.hila_gridcellid.unique()) 173/31: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0) rmse = mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values') print(np.sqrt(rmse)) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/32: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0) print(koskisen_means) rmse = mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values') print(np.sqrt(rmse)) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/33: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0) print(koskisen_means) rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = rmse / koskisen_means print(rmse) print(nrmse) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/34: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0) print(koskisen_means) rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print(rmse) print(nrmse) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/35: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0) rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print(rmse) print(nrmse) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/36: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print(rmse) print(nrmse) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/37: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus RMSE on all stands (total, pine, spruce, deciduous):") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus RMSE on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 173/38: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 176/1: import data_loading 176/2: data_loading.create_test_set_from_ids("/home/tman/Work/data/koskisen/v_stand_level_features.csv", "/home/tman/Work/data/koskisen/", split_name="koskisen", id_column="prd_id") 175/1: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 175/2: koskisen_folder = '../../../../data/koskisen/' train = pd.read_csv(os.path.join(koskisen_folder, 'train.csv')) test = pd.read_csv(os.path.join(koskisen_folder, 'test.csv')) 175/3: train.shape 175/4: test.shape 181/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 181/2: stand_data = pd.read_csv("~/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 181/3: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 181/4: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): if not os.path.exists(cache): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" if not os.path.exists(cachefile) metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/5: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" if not os.path.exists(cachefile) metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/6: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/7: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" print(os.path.exists(cachefile)) if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/8: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "~/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" print(os.path.exists(cachefile)) full_data = pd.read_csv(cachefile) if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/9: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "/home/tman/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" print(os.path.exists(cachefile)) full_data = pd.read_csv(cachefile) if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/10: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "/home/tman/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile) else: full_data = pd.read_csv(cachefile) 181/11: full_data[:2] 181/12: full_data.columns 181/13: full_data.drop('Unnamed: 0') 181/14: full_data.drop('Unnamed: 0', axis=1) 181/15: full_data = full_data.drop('Unnamed: 0', axis=1) 182/1: from functools import reduce import os import sys sys.path.append('../../regressors/') import pandas as pd import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 182/2: def fetch_if_not_cached(data_group, api): cache = os.path.join(koskisen_folder, data_group + '.csv') if not os.path.exists(cache): data = api.request_data(data_groups=[data_group]) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data 182/3: koskisen_folder = '../../../../data/koskisen/' 182/4: stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) 182/5: api = data_loading.GeoAPI(default_locations=stand_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=stand_data.prd_id.values.tolist()) data_groups = ['soilgrids', 'climate_data'] data_frames = [fetch_if_not_cached(data_group, api) for data_group in data_groups] scalar_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) 182/6: api = data_loading.GeoAPI(default_locations=stand_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=stand_data.prd_id.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api) for data_group in data_groups] scalar_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) 182/7: features = scalar_df.copy() 182/8: features.isna().mean(axis=0) 182/9: features = scalar_df.dropna() assert features.isna().sum().sum() == 0 182/10: features.dtypes 182/11: categorical_columns = ['texture_class_usda_30cm', 'texture_class_usda_200cm', 'usda_2014_suborder_class', 'wrb_2006_subgroup_class'] features.loc[:, categorical_columns] = features[categorical_columns].astype('category') print(features[categorical_columns].describe()) features = pd.get_dummies(features) 182/12: features.describe().T 182/13: target_columns = ['total_m3_ha', 'pine_m3_ha', 'spruce_m3_ha', 'deciduous_m3_ha'] X = features.copy() y = stand_data[target_columns].loc[X.index, :] 182/14: X_train, X_test = data_loading.split_from_ids(features, split_name='koskisen', id_column='plot_id') y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] X_train = X_train.drop('plot_id', axis=1) X_test = X_test.drop('plot_id', axis=1) 182/15: assert X_train.shape[0] == y_train.shape[0] assert (X_train.index == y_train.index).all() 182/16: target_column = 'total_m3_ha' y_train_col, y_test_col = y_train[target_column], y_test[target_column] 182/17: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(42) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) 183/1: from functools import reduce import os import sys sys.path.append('../../regressors/') import pandas as pd import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 183/2: def fetch_if_not_cached(data_group, api): cache = os.path.join(koskisen_folder, data_group + '.csv') if not os.path.exists(cache): data = api.request_data(data_groups=[data_group]) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data 183/3: koskisen_folder = '../../../../data/koskisen/' 183/4: stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) 183/5: api = data_loading.GeoAPI(default_locations=stand_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=stand_data.prd_id.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api) for data_group in data_groups] scalar_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) 183/6: features = scalar_df.copy() 183/7: features.isna().mean(axis=0) 183/8: features = scalar_df.dropna() assert features.isna().sum().sum() == 0 183/9: features.dtypes 183/10: categorical_columns = ['texture_class_usda_30cm', 'texture_class_usda_200cm', 'usda_2014_suborder_class', 'wrb_2006_subgroup_class'] features.loc[:, categorical_columns] = features[categorical_columns].astype('category') print(features[categorical_columns].describe()) features = pd.get_dummies(features) 183/11: features.describe().T 183/12: target_columns = ['total_m3_ha', 'pine_m3_ha', 'spruce_m3_ha', 'deciduous_m3_ha'] X = features.copy() y = stand_data[target_columns].loc[X.index, :] 183/13: X_train, X_test = data_loading.split_from_ids(features, split_name='koskisen', id_column='plot_id') y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] X_train = X_train.drop('plot_id', axis=1) X_test = X_test.drop('plot_id', axis=1) 183/14: print(X_train.shape) 183/15: print(X_train.shape) print(X_test.shape) 183/16: assert X_train.shape[0] == y_train.shape[0] assert (X_train.index == y_train.index).all() 183/17: target_column = 'total_m3_ha' y_train_col, y_test_col = y_train[target_column], y_test[target_column] 183/18: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(42) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) 183/19: max_iter = 70 optimizer.run_optimization(max_iter=max_iter, verbosity=True) 183/20: optimizer.plot_convergence() 183/21: np.sqrt(optimizer.Y.min()) 183/22: parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) 183/23: best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) 183/24: model = XGBRegressor(**best_parameters) model.fit(X_train, y_train_col) 183/25: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) nrmse = rmse / np.mean(y_test_col) * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}'.format(mse, rmse, nrmse, mae)) 183/26: X_test.shape 184/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 184/2: stand_data = pd.read_csv("~/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 184/3: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 184/4: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data cachefile = "/home/tman/Work/data/koskisen/fulldata_metsakeskus_koskisen.csv" if not os.path.exists(cachefile): metsakeskus_data = get_metsakeskus_data() metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") full_data.to_csv(cachefile, index=False) else: full_data = pd.read_csv(cachefile) 184/5: full_data = full_data.drop('Unnamed: 0', axis=1) 184/6: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") print(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape) calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 183/27: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) colmean = np.mean(y_test_col) nrmse = rmse / colmean * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}\GT Mean: {:.2f}'.format(mse, rmse, nrmse, mae, colmean)) 183/28: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) colmean = np.mean(y_test_col) nrmse = rmse / colmean * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}\nGT Mean: {:.2f}'.format(mse, rmse, nrmse, mae, colmean)) 184/7: volume_means_times.shape 183/29: target_column = 'pine_m3_ha' y_train_col, y_test_col = y_train[target_column], y_test[target_column] 183/30: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(42) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) 183/31: max_iter = 70 optimizer.run_optimization(max_iter=max_iter, verbosity=True) 183/32: optimizer.plot_convergence() 183/33: np.sqrt(optimizer.Y.min()) 183/34: parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) 183/35: best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) 183/36: model = XGBRegressor(**best_parameters) model.fit(X_train, y_train_col) 183/37: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) colmean = np.mean(y_test_col) nrmse = rmse / colmean * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}\nGT Mean: {:.2f}'.format(mse, rmse, nrmse, mae, colmean)) 183/38: target_column = 'spruce_m3_ha' y_train_col, y_test_col = y_train[target_column], y_test[target_column] 183/39: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(42) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) 183/40: max_iter = 70 optimizer.run_optimization(max_iter=max_iter, verbosity=True) 183/41: optimizer.plot_convergence() 183/42: np.sqrt(optimizer.Y.min()) 183/43: parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) 183/44: best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) 183/45: model = XGBRegressor(**best_parameters) model.fit(X_train, y_train_col) 183/46: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) colmean = np.mean(y_test_col) nrmse = rmse / colmean * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}\nGT Mean: {:.2f}'.format(mse, rmse, nrmse, mae, colmean)) 183/47: target_column = 'deciduous_m3_ha' y_train_col, y_test_col = y_train[target_column], y_test[target_column] 183/48: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(42) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) 183/49: max_iter = 70 optimizer.run_optimization(max_iter=max_iter, verbosity=True) 183/50: optimizer.plot_convergence() 183/51: np.sqrt(optimizer.Y.min()) 183/52: parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) 183/53: best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) 183/54: model = XGBRegressor(**best_parameters) model.fit(X_train, y_train_col) 183/55: from sklearn.metrics import mean_squared_error, mean_absolute_error pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) colmean = np.mean(y_test_col) nrmse = rmse / colmean * 100 mae = mean_absolute_error(y_test_col, pred) print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}\nGT Mean: {:.2f}'.format(mse, rmse, nrmse, mae, colmean)) 186/1: from functools import reduce import os from tqdm import tqdm_notebook import sys sys.path.append('../../regressors/') import pandas as pd import seaborn as sns import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 186/2: koskisen_folder = "/home/tman/Work/data/koskisen" stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'v_gridcell_volumes_with_coords_unique.csv')) 186/3: def fetch_if_not_cached(data_group, api, output_folder): cache = os.path.join(output_folder, data_group + '.csv') if not os.path.exists(cache): data = api.request_data(data_groups=[data_group]) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data def fetch_specific_data(api, columns_list, schema_list, tables_list, output_folder, csv_name): # Fetch data that is not in a data group #columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", # "soiltype","fertilityclass","laserheight","laserdensity"]] #schema_list = ['metsakeskus_hila'] #tables_list = ['gridcell'] cache = os.path.join(output_folder, data_group + '.csv') if not os.path.exists(cache): data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data 186/4: # Get grid data grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) 186/5: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p10', 'lidar_p75', 'lidar_p80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = [None] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 186/6: def fetch_if_not_cached(data_group, api, output_folder): cache = os.path.join(output_folder, data_group + '.csv') if not os.path.exists(cache): data = api.request_data(data_groups=[data_group]) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data def fetch_specific_data(api, columns_list, schema_list, tables_list, output_folder, csv_name): # Fetch data that is not in a data group #columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", # "soiltype","fertilityclass","laserheight","laserdensity"]] #schema_list = ['metsakeskus_hila'] #tables_list = ['gridcell'] cache = os.path.join(output_folder, csv_name) if not os.path.exists(cache): data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) data = data.reset_index() data = data.drop_duplicates(subset='plot_id') data.to_csv(cache, index=False) else: data = pd.read_csv(cache) return data 186/7: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p10', 'lidar_p75', 'lidar_p80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = [None] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 186/8: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p10', 'lidar_p75', 'lidar_p80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = [[None]] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 186/9: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p10', 'lidar_p75', 'lidar_p80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = ['null'] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 186/10: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p10', 'lidar_p75', 'lidar_p80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = [None] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 186/11: # Get grid data with data groups grid_data_folder = os.path.join(koskisen_folder, 'grid') os.makedirs(grid_data_folder, exist_ok=True) api = data_loading.GeoAPI(default_locations=grid_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=grid_data.hila_gridcellid.values.tolist()) data_groups = ['soilgrids', 'climate_data', 'copernicus', 'physical'] data_frames = [fetch_if_not_cached(data_group, api, grid_data_folder) for data_group in data_groups] #scalar_grid_df = reduce(lambda x,y: pd.merge(x,y,on='plot_id', how='outer'), data_frames) tables_list = ['lidar_p_10', 'lidar_p_75', 'lidar_p_80', 'lidar_vol_cov', 'lidar_pct_r1_above_mean', 'lidar_z_mean_sq'] schema_list = ['finland'] * len(tables_list) columns_list = [None] * len(tables_list) lidar_data = fetch_specific_data(api, columns_list, schema_list, tables_list, grid_data_folder, "lidar_data.csv") 187/1: %run koskisen_grid_data_creation.py 187/2: %run koskisen_grid_data_creation.py --dir c 187/3: %run koskisen_grid_data_creation.py --dir /home/tman/Work/data/koskisen --output koskisen_grid_with_lidar.csv 187/4: %run koskisen_grid_data_creation.py --dir /home/tman/Work/data/koskisen --output koskisen_grid_with_lidar.csv 187/5: scalar_grid_df.head() 187/6: scalar_grid_df.agg("mean") 187/7: scalar_grid_df.groupby("stand_id")agg("mean") 187/8: scalar_grid_df.groupby("stand_id").agg("mean") 187/9: scalar_grid_df.groupby("stand_id").agg("mean").reset_index() 187/10: scalar_grid_df.columns 187/11: [col for col in scalar_grid_df.columns if "Unnamed" in col] 187/12: grid_and_stand_ids 187/13: grid_and_stand_ids.columns 188/1: %run koskisen_grid_data_creation.py 188/2: %run koskisen_grid_data_creation.py --dir /home/tman/Work/data/koskisen --output koskisen_grid_with_lidar.csv 188/3: scalar_grid_df['stand_id'] 188/4: grid_data['stand_id 188/5: grid_data['stand_id'} 188/6: grid_data['stand_id'] 188/7: grid_and_stand_ids['stand_id'] 188/8: data_frames[0] 188/9: data_frames[1] 188/10: data_frames[0] 188/11: data_frames[2] 188/12: data_frames[3] 188/13: grid_and_stand_ids.shape 189/1: # Adapted from 'Koskisen Modelling with Bayesian Hyperparameter Optimization.ipynb' from functools import reduce from tqdm import tqdm_notebook import os import sys sys.path.append('../../regressors/') import pandas as pd import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 189/2: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score def optimize_xgboost(X_train, y_train_col, max_iter=30, random_state=42): domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, cv=5, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(random_state) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) optimizer.run_optimization(max_iter=max_iter, verbosity=True) optimizer.plot_convergence() print("Best RMSE on CV: {:.2f}".format(np.sqrt(optimizer.Y.min()))) print("Best NRMSE on CV: {:.2f} %".format(np.sqrt(optimizer.Y.min()) / y_train_col.mean() * 100)) parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) return optimizer, best_parameters from sklearn.model_selection import KFold def get_95_ci(X_train, y_train_col, best_parameters, normalization_mean=None, random_state=42): cv_scores = np.concatenate( [-cross_val_score(XGBRegressor(**best_parameters), X_train, y_train_col, cv=KFold(n_splits=5, shuffle=True, random_state=random_state), n_jobs=1, scoring='neg_mean_squared_error', verbose=1) for i in tqdm_notebook(range(10))] ) cv_rmse = np.sqrt(cv_scores) mu = cv_rmse.mean() normalization_mean = y_train_col.mean() if normalization_mean is None else normalization_mean mu_nrmse = mu / normalization_mean * 100 se = cv_rmse.std() me = 1.96*se me_nrmse = 1.96*se / normalization_mean * 100 rmse_ci = '{:.2f} +/- {:.2f}'.format(mu, me) nrmse_ci = '{:.2f} +/- {:.2f}'.format(mu_nrmse, me_nrmse) print('CV RMSE 95% confidence interval: {}'.format(rmse_ci)) print('CV NRMSE 95% confidence interval: {}'.format(nrmse_ci)) return {'cv_rmse_ci': rmse_ci, 'cv_nrmse_ci': nrmse_ci} from sklearn.metrics import mean_squared_error, mean_absolute_error def get_test_metrics(model, X_test, y_test_col, normalization_mean=None): pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) normalization_mean = np.mean(y_test_col) if normalization_mean is None else normalization_mean nrmse = rmse / normalization_mean * 100 mae = mean_absolute_error(y_test_col, pred) print('Test Results: \n') print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}'.format(mse, rmse, nrmse, mae)) return {'test_mse': mse, 'test_rmse': rmse, 'test_nrmse': nrmse, 'test_mae': mae} def model_target(X_train, y_train, X_test, y_test, target_column, random_state=42, default=False): y_train_col, y_test_col = y_train[target_column], y_test[target_column] print('Optimizing model for {}...'.format(target_column)) optimizer, best_parameters = optimize_xgboost(X_train, y_train_col, random_state=random_state) print('Training with best hyperparameters found for {}...'.format(target_column)) if default == False: model = XGBRegressor(**best_parameters) else: model = XGBRegressor() model.fit(X_train, y_train_col) print('Evaluating the model for {}...'.format(target_column)) cv_metrics = get_95_ci(X_train, y_train_col, best_parameters, random_state=random_state) test_metrics = get_test_metrics(model, X_test, y_test_col) all_results = {**cv_metrics, **test_metrics} return all_results, best_parameters def run_experiment(X_train, y_train, X_test, y_test, target_columns, default): all_results = [] best_parameters = [] for target_column in target_columns: target_results, target_best_parameters = model_target(X_train, y_train, X_test, y_test, target_column, default) all_results.append(target_results) best_parameters.append(target_best_parameters) all_results = pd.DataFrame(all_results, index=target_columns) best_parameters = dict(zip(target_columns, best_parameters)) return all_results, best_parameters 189/3: koskisen_folder = '../../../../data/koskisen/' 189/4: stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stands_aggregated.csv')) #stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 189/5: stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', 'check_volume_diff'], axis=1) stand_data.isna().mean(axis=0) 189/6: #stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', # 'check_volume_diff'], axis=1) stand_data.isna().mean(axis=0) 189/7: #stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', # 'check_volume_diff'], axis=1) stand_data.shape 189/8: #stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', # 'check_volume_diff'], axis=1) stand_data.head() 189/9: koskisen_folder = '../../../../data/koskisen/' 189/10: #stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stands_aggregated.csv')) stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 189/11: stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', 'check_volume_diff'], axis=1) #stand_data.head() 189/12: stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', 'check_volume_diff'], axis=1) stand_data.head() 189/13: # Drop unneeded columns stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', 'check_volume_diff'], axis=1) 189/14: stand_data.head() 190/1: # Adapted from 'Koskisen Modelling with Bayesian Hyperparameter Optimization.ipynb' from functools import reduce from tqdm import tqdm_notebook import os import sys sys.path.append('../../regressors/') import pandas as pd import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 190/2: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score def optimize_xgboost(X_train, y_train_col, max_iter=30, random_state=42): domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, cv=5, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(random_state) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) optimizer.run_optimization(max_iter=max_iter, verbosity=True) optimizer.plot_convergence() print("Best RMSE on CV: {:.2f}".format(np.sqrt(optimizer.Y.min()))) print("Best NRMSE on CV: {:.2f} %".format(np.sqrt(optimizer.Y.min()) / y_train_col.mean() * 100)) parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) return optimizer, best_parameters from sklearn.model_selection import KFold def get_95_ci(X_train, y_train_col, best_parameters, normalization_mean=None, random_state=42): cv_scores = np.concatenate( [-cross_val_score(XGBRegressor(**best_parameters), X_train, y_train_col, cv=KFold(n_splits=5, shuffle=True, random_state=random_state), n_jobs=1, scoring='neg_mean_squared_error', verbose=1) for i in tqdm_notebook(range(10))] ) cv_rmse = np.sqrt(cv_scores) mu = cv_rmse.mean() normalization_mean = y_train_col.mean() if normalization_mean is None else normalization_mean mu_nrmse = mu / normalization_mean * 100 se = cv_rmse.std() me = 1.96*se me_nrmse = 1.96*se / normalization_mean * 100 rmse_ci = '{:.2f} +/- {:.2f}'.format(mu, me) nrmse_ci = '{:.2f} +/- {:.2f}'.format(mu_nrmse, me_nrmse) print('CV RMSE 95% confidence interval: {}'.format(rmse_ci)) print('CV NRMSE 95% confidence interval: {}'.format(nrmse_ci)) return {'cv_rmse_ci': rmse_ci, 'cv_nrmse_ci': nrmse_ci} from sklearn.metrics import mean_squared_error, mean_absolute_error def get_test_metrics(model, X_test, y_test_col, normalization_mean=None): pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) normalization_mean = np.mean(y_test_col) if normalization_mean is None else normalization_mean nrmse = rmse / normalization_mean * 100 mae = mean_absolute_error(y_test_col, pred) print('Test Results: \n') print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}'.format(mse, rmse, nrmse, mae)) return {'test_mse': mse, 'test_rmse': rmse, 'test_nrmse': nrmse, 'test_mae': mae} def model_target(X_train, y_train, X_test, y_test, target_column, random_state=42, default=False): y_train_col, y_test_col = y_train[target_column], y_test[target_column] print('Optimizing model for {}...'.format(target_column)) optimizer, best_parameters = optimize_xgboost(X_train, y_train_col, random_state=random_state) print('Training with best hyperparameters found for {}...'.format(target_column)) if default == False: model = XGBRegressor(**best_parameters) else: model = XGBRegressor() model.fit(X_train, y_train_col) print('Evaluating the model for {}...'.format(target_column)) cv_metrics = get_95_ci(X_train, y_train_col, best_parameters, random_state=random_state) test_metrics = get_test_metrics(model, X_test, y_test_col) all_results = {**cv_metrics, **test_metrics} return all_results, best_parameters def run_experiment(X_train, y_train, X_test, y_test, target_columns, default): all_results = [] best_parameters = [] for target_column in target_columns: target_results, target_best_parameters = model_target(X_train, y_train, X_test, y_test, target_column, default) all_results.append(target_results) best_parameters.append(target_best_parameters) all_results = pd.DataFrame(all_results, index=target_columns) best_parameters = dict(zip(target_columns, best_parameters)) return all_results, best_parameters 190/3: koskisen_folder = '../../../../data/koskisen/' 190/4: #stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stands_aggregated.csv')) stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 190/5: # Drop unneeded columns stand_data = stand_data.drop(['harvest_year', 'harvest_start', 'easting', 'northing', 'area_ha', 'unknown_m3_ha', 'check_volume_diff'], axis=1) 190/6: stand_data.head() 190/7: grid_data.head() 190/8: grid_data = pd.read_csv(os.path.join(koskisen_folder, "grid", 'koskisen_stands_aggregated.csv')) stand_data_aggregated = stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 190/9: grid_data = pd.read_csv(os.path.join(koskisen_folder, "grid", 'koskisen_grid_data.csv')) #stand_data_aggregated = stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 190/10: grid_data.head() 190/11: grid_data.columns 190/12: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean') stand_data_aggregated.head() 190/13: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() 190/14: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated..shape 190/15: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.shape 190/16: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated['lidared_before_harvest'] 190/17: stand_data_aggregated.columns 190/18: col_aggregations = {k: 'mode' if k in ['copernicus_leaf_type'] else k: 'mean' for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/19: col_aggregations = {k: 'mode' if k in ['copernicus_leaf_type'] else k:'mean' for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/20: col_aggregations = {k: 'mode' if k in {'copernicus_leaf_type'} else k: 'mean' for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/21: col_aggregations = {k:'mode' if k in {'copernicus_leaf_type'} else k:'mean' for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/22: col_aggregations = {k:str('mode' if k in {'copernicus_leaf_type'} else k:str('mean') for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/23: col_aggregations = {k:"mode" if k in {'copernicus_leaf_type'} else k:"mean" for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/24: col_aggregations = {k:"mode" if k in {'copernicus_leaf_type'} else: k:"mean" for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/25: col_aggregations = {k:("mode") if k in {'copernicus_leaf_type'} else k:("mean") for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/26: col_aggregations = {k:'mode' if k in {'copernicus_leaf_type'} for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/27: col_aggregations = {k:'mode' if k in ['copernicus_leaf_type'] for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/28: col_aggregations = {k:'mode' if k in ['copernicus_leaf_type'] for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/29: col_aggregations = {k:'mode' if k in ['copernicus_leaf_type'] else k for k in stand_data_aggregated.columns} stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/30: col_aggregations = {k:'mode' if k in ['copernicus_leaf_type'] else k for k in stand_data_aggregated.columns} print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/31: col_aggregations = {col:'mode' if col in ['copernicus_leaf_type'] else col for col in stand_data_aggregated.columns} print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat'] 190/32: col_aggregations = {col:'mode' if col in ['copernicus_leaf_type'] else col for col in stand_data_aggregated.columns} print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/33: col_aggregations = {col:'mode' if col in ['copernicus_leaf_type'] else col:'mean' for col in stand_data_aggregated.columns} print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/34: col_aggregations = {'copernicus_leaf_type':'mode'} [col_aggregations[col] = 'mean' for col in grid_data.columns] print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/35: col_aggregations = {'copernicus_leaf_type':'mode'} for col in grid_data.columns: col_aggregations[col] = 'mean' print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/36: col_aggregations = {} for col in grid_data.columns: col_aggregations[col] = 'mean' col_aggregations['copernicus_leaf_type'] = 'mode' print(col_aggregations) stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/37: col_aggregations = {} for col in grid_data.columns: col_aggregations[col] = 'mean' col_aggregations['copernicus_leaf_type'] = 'mode' stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/38: grid_data.columns 190/39: grid_data.columns 190/40: col_aggregations = {} for col in grid_data.columns: col_aggregations[col] = 'mean' col_aggregations['copernicus_leaf_type'] = 'mode' stand_data_aggregated = grid_data.groupby('prd_id').agg(col_aggregations).reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/41: col_aggregations['copernicus_leaf_type'] = 'mode' stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated = stand_data_aggregated.drop(['plot_id', 'lon', 'lat']) 190/42: col_aggregations['copernicus_leaf_type'] = 'mode' stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() 190/43: stand_data_aggregated.head() 190/44: stand_data_aggregated.isna().mean(axis=0) 190/45: features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 190/46: # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] 192/1: # Adapted from 'Koskisen Modelling with Bayesian Hyperparameter Optimization.ipynb' from functools import reduce from tqdm import tqdm_notebook import os import sys sys.path.append('../../regressors/') import pandas as pd import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 192/2: import GPyOpt from xgboost import XGBRegressor from sklearn.model_selection import cross_val_score def optimize_xgboost(X_train, y_train_col, max_iter=30, random_state=42): domain = [ {'name': 'learning_rate', 'type': 'continuous', 'domain': (0, 1)}, {'name': 'gamma', 'type': 'continuous', 'domain': (0, 5)}, {'name': 'max_depth', 'type': 'discrete', 'domain': (1, 50)}, {'name': 'n_estimators', 'type': 'discrete', 'domain': (1, 300)}, {'name': 'min_child_weight', 'type': 'discrete', 'domain': (1, 10)} ] def f(params): params = params[0] estimator = XGBRegressor(learning_rate=params[0], gamma=params[1], max_depth=int(params[2]), n_estimators=int(params[3]), min_child_weight=int(params[4]) ) score = -cross_val_score(estimator, X_train, y_train_col, cv=5, scoring='neg_mean_squared_error').mean() return np.array(score) np.random.seed(random_state) optimizer = GPyOpt.methods.BayesianOptimization(f=f, domain=domain, acquisition_type='MPI', num_cores=4, exact_feval=True) optimizer.run_optimization(max_iter=max_iter, verbosity=True) optimizer.plot_convergence() print("Best RMSE on CV: {:.2f}".format(np.sqrt(optimizer.Y.min()))) print("Best NRMSE on CV: {:.2f} %".format(np.sqrt(optimizer.Y.min()) / y_train_col.mean() * 100)) parameter_names = ['learning_rate', 'gamma', 'max_depth', 'n_estimators', 'min_child_weight'] best_parameters = dict(zip(parameter_names, optimizer.X[optimizer.Y.argmin()])) best_parameters['max_depth'] = int(best_parameters['max_depth']) best_parameters['n_estimators'] = int(best_parameters['n_estimators']) best_parameters['min_child_weight'] = int(best_parameters['min_child_weight']) return optimizer, best_parameters from sklearn.model_selection import KFold def get_95_ci(X_train, y_train_col, best_parameters, normalization_mean=None, random_state=42): cv_scores = np.concatenate( [-cross_val_score(XGBRegressor(**best_parameters), X_train, y_train_col, cv=KFold(n_splits=5, shuffle=True, random_state=random_state), n_jobs=1, scoring='neg_mean_squared_error', verbose=1) for i in tqdm_notebook(range(10))] ) cv_rmse = np.sqrt(cv_scores) mu = cv_rmse.mean() normalization_mean = y_train_col.mean() if normalization_mean is None else normalization_mean mu_nrmse = mu / normalization_mean * 100 se = cv_rmse.std() me = 1.96*se me_nrmse = 1.96*se / normalization_mean * 100 rmse_ci = '{:.2f} +/- {:.2f}'.format(mu, me) nrmse_ci = '{:.2f} +/- {:.2f}'.format(mu_nrmse, me_nrmse) print('CV RMSE 95% confidence interval: {}'.format(rmse_ci)) print('CV NRMSE 95% confidence interval: {}'.format(nrmse_ci)) return {'cv_rmse_ci': rmse_ci, 'cv_nrmse_ci': nrmse_ci} from sklearn.metrics import mean_squared_error, mean_absolute_error def get_test_metrics(model, X_test, y_test_col, normalization_mean=None): pred = model.predict(X_test) mse = mean_squared_error(y_test_col, pred) rmse = np.sqrt(mse) normalization_mean = np.mean(y_test_col) if normalization_mean is None else normalization_mean nrmse = rmse / normalization_mean * 100 mae = mean_absolute_error(y_test_col, pred) print('Test Results: \n') print('MSE: {:.2f}\nRMSE: {:.2f}\nNRMSE: {:.2f} %\nMAE: {:.2f}'.format(mse, rmse, nrmse, mae)) return {'test_mse': mse, 'test_rmse': rmse, 'test_nrmse': nrmse, 'test_mae': mae} def model_target(X_train, y_train, X_test, y_test, target_column, random_state=42, default=False): y_train_col, y_test_col = y_train[target_column], y_test[target_column] print('Optimizing model for {}...'.format(target_column)) optimizer, best_parameters = optimize_xgboost(X_train, y_train_col, random_state=random_state) print('Training with best hyperparameters found for {}...'.format(target_column)) if default == False: model = XGBRegressor(**best_parameters) else: model = XGBRegressor() model.fit(X_train, y_train_col) print('Evaluating the model for {}...'.format(target_column)) cv_metrics = get_95_ci(X_train, y_train_col, best_parameters, random_state=random_state) test_metrics = get_test_metrics(model, X_test, y_test_col) all_results = {**cv_metrics, **test_metrics} return all_results, best_parameters def run_experiment(X_train, y_train, X_test, y_test, target_columns, default): all_results = [] best_parameters = [] for target_column in target_columns: target_results, target_best_parameters = model_target(X_train, y_train, X_test, y_test, target_column, default) all_results.append(target_results) best_parameters.append(target_best_parameters) all_results = pd.DataFrame(all_results, index=target_columns) best_parameters = dict(zip(target_columns, best_parameters)) return all_results, best_parameters 192/3: koskisen_folder = '../../../../data/koskisen/' 192/4: grid_data = pd.read_csv(os.path.join(koskisen_folder, "grid", 'koskisen_grid_data.csv')) #stand_data_aggregated = stand_data = pd.read_csv(os.path.join(koskisen_folder, "stand", 'koskisen_stand_data.csv')) 192/5: grid_data.columns 192/6: col_aggregations['copernicus_leaf_type'] = 'mode' stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() 192/7: stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() 192/8: stand_data_aggregated.isna().mean(axis=0) 192/9: features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 192/10: # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] 192/11: X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop("prd_id", axis=1), X_test.drop("prd_id", axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 192/12: X_train.head() 192/13: drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 192/14: X_train.head() 192/15: assert X_train.shape[0] == y_train.shape[0] assert (X_train.index == y_train.index).all() 192/16: all_results, all_best_parameters = run_experiment(X_train, y_train, X_test, y_test, target_columns, default=False) all_results 194/1: %load_ext autoreload %autoreload 2 import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import json import os from tqdm import tqdm import re import sys from sklearn.model_selection import train_test_split import requests pd.options.display.float_format = '{:,.2f}'.format # Add path to where utils.py is so metrics can be imported sys.path.insert(0, r'../../regressors') from data.data_loading import import_data, GeoAPI, split_from_ids from data import data_loading 194/2: stand_data = pd.read_csv("~/Work/data/koskisen/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 194/3: stand_data = pd.read_csv("~/Work/data/koskisen/stand/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/grid/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 194/4: stand_data = pd.read_csv("~/Work/data/koskisen/rawdata/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/rawdata/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 194/5: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 194/6: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 194/7: stand_data = pd.read_csv("~/Work/data/koskisen/rawdata/v_stand_level_features.csv") gridcell_data = pd.read_csv("~/Work/data/koskisen/rawdata/v_gridcell_volumes_with_coords.csv") gridcell_data = gridcell_data.drop('hila_polygon', axis=1) 194/8: columns_from_stand = ['prd_id', 'harvest_year', 'harvest_start'] koskisen_grids = gridcell_data.merge(stand_data[columns_from_stand], left_on="koski_prd_id", right_on="prd_id") koskisen_grids['harvest_start'] = pd.to_datetime(koskisen_grids['harvest_start']) 194/9: api = GeoAPI(default_locations=gridcell_data[['easting', 'northing']].values.tolist(), default_srid=3067, default_plot_ids=gridcell_data.hila_gridcellid.values.tolist()) def get_metsakeskus_data(): columns_list = [["volumepine","volumespruce","volumedeciduous","volume","creationtime", "updatetime", "soiltype","fertilityclass","laserheight","laserdensity"]] schema_list = ['metsakeskus_hila'] tables_list = ['gridcell'] data = api.request_data(schema_list, tables_list, columns_list, batch_size=2000) # Return plot_ids from index to a column. data.reset_index(inplace=True) data = data.drop_duplicates(subset='plot_id') return data metsakeskus_data = get_metsakeskus_data() 194/10: metsakeskus_data['creationtime'] = pd.to_datetime(metsakeskus_data['creationtime']) metsakeskus_data['updatetime'] = pd.to_datetime(metsakeskus_data['updatetime']) 194/11: # GeoAPI adds plot_id to corresponding rows when fetching data. We used hila_gridcellid when fetching data full_data = koskisen_grids.merge(metsakeskus_data, left_on="hila_gridcellid", right_on="plot_id") 194/12: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'min' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") #calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") #calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 194/13: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape 194/14: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") #calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") #calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 194/15: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape 194/16: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} for col in time_columns: stat_dict[col] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 194/17: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} stat_dict['harvest_start'] = 'min' stat_dict['updatetime'] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 194/18: volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape 194/19: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} stat_dict['harvest_start'] = 'min' stat_dict['updatetime'] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") test_set = volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape calculate_metsakeskus_error(volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']]) 194/20: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} stat_dict['harvest_start'] = 'min' stat_dict['updatetime'] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") test_set = volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']].shape calculate_metsakeskus_error(test_set) 194/21: # Remember same volume order in both metsakeskus_pred_columns = ['volume', 'volumepine', 'volumespruce', 'volumedeciduous'] koskisen_vol_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] time_columns = ['updatetime', 'harvest_start'] # aggregate volume means and times. Take minimum of both times so we can compare and get just the stands where preds were made # before harvest stat_dict = {col: "mean" for col in (metsakeskus_pred_columns + koskisen_vol_columns)} stat_dict['harvest_start'] = 'min' stat_dict['updatetime'] = 'max' # get the means of the volumes per stand and minimum of each harvest_start and updatetime per stand. # OK to take the mean of koskisen gridcells as ground truth as they're all the same anyway volume_means_times = full_data.groupby("koski_prd_id")[metsakeskus_pred_columns + koskisen_vol_columns + time_columns].agg(stat_dict) def calculate_metsakeskus_error(df): from sklearn.metrics import mean_squared_error, mean_absolute_error metsakeskus_preds = df[metsakeskus_pred_columns] koskisen_vols = df[koskisen_vol_columns] koskisen_means = np.mean(koskisen_vols, axis=0).values rmse = np.sqrt(mean_squared_error(metsakeskus_preds, koskisen_vols, multioutput='raw_values')) nrmse = (rmse / koskisen_means)*100 print("Order: total, pine, spruce, deciduous") print("Groundtruth means:") print(koskisen_means) print("RMSE:") print(rmse) print("NRMSE (RMSE divided by the mean of respective species):") print(nrmse) print("Metsakeskus, all stands:") calculate_metsakeskus_error(volume_means_times) print("\nMetsakeskus, on stands where all gridcell preds were made before harvest:") test_set = volume_means_times[volume_means_times['updatetime'] < volume_means_times['harvest_start']] calculate_metsakeskus_error(test_set) 194/22: test_set.head() 194/23: test_set.shape 193/1: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading %load_ext autoreload %autoreload 2 %aimport data 193/2: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading from models import models_definition %load_ext autoreload %autoreload 2 %aimport data 193/3: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading from models import models_definition %load_ext autoreload %autoreload 2 %aimport data 193/4: pip install dill 197/1: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading from models import models_definition %load_ext autoreload %autoreload 2 %aimport data 198/1: import sys import os sys.path.append('../../regressors') import pandas as pd import seaborn as sns import matplotlib import matplotlib.pyplot as plt import numpy as np from data import data_loading from models import models_definition %load_ext autoreload %autoreload 2 %aimport data 194/24: test_set['prd_id'] 194/25: test_set.columns 194/26: test_set.reset_index() 194/27: prd_ids = test_set.reset_index()['koski_prd_id'] 194/28: prd_ids 194/29: prd_ids.rename('prd_id') 194/30: prd_ids = test_set.reset_index()['koski_prd_id'].rename('prd_id') 194/31: prd_ids.to_csv("/Home/tman/koskisen_testids.csv", index=False) 194/32: prd_ids.to_csv("/home/tman/koskisen_testids.csv", index=False) 194/33: prd_ids = test_set.reset_index()[['koski_prd_id']].rename('prd_id') 194/34: prd_ids = test_set.reset_index()[['koski_prd_id']] 194/35: prd_ids 194/36: prd_ids.rename({'koski_prd_id':'prd_id'}) 194/37: prd_ids.rename({'koski_prd_id':'prd_id'}, axis=1) 194/38: prd_ids = test_set.reset_index()[['koski_prd_id']] prd_ids.rename({'koski_prd_id':'prd_id'}, axis=1) 194/39: prd_ids = test_set.reset_index()[['koski_prd_id']] prd_ids = prd_ids.rename({'koski_prd_id':'prd_id'}, axis=1) 194/40: prd_ids.to_csv("/home/tman/koskisen_testids.csv", index=False) 194/41: prd_ids.shape 194/42: prd_ids.to_csv("/home/tman/Work/linda-forestry-ml/species_prediction/regressors/data/koskisen_testids.csv", index=False) 198/2: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) 198/3: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] 198/4: features.shape 198/5: stand_data_aggregated.shape 198/6: features.columns 198/7: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['total_volume_ha', 'pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 198/8: y.shape 198/9: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 198/10: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 198/11: y.shape 198/12: np.mean(y, axis=0).shape 198/13: np.mean(y, axis=1).shape 198/14: np.mean(y, axis=1)[:5] 198/15: np.sum(y, axis=1)[:5] 198/16: y / np.sum(y, axis=1) 198/17: y / np.sum(y, axis=1).values 198/18: y.values / np.sum(y, axis=1).values 198/19: y.values / np.sum(y, axis=1).values[:,np.newaxis] 198/20: (y.values / np.sum(y, axis=1).values[:,np.newaxis]).sum(axis=1) 198/21: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 198/22: def transform_targets(targets): # Transform from regression targets to relative targets for softmax return (targets.values / np.sum(targets, axis=1).values[:,np.newaxis]) 198/23: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] 198/24: dense = models_definition.create_dense(X_train.shape[1], y_train.shape[1], n_units=128, n_layers=4, final_activation='softmax') 198/25: dense = models_definition.create_dense((X_train.shape[1],), y_train.shape[1], n_units=128, n_layers=4, final_activation='softmax') 198/26: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = transformed_y.loc[X_train.index, :], transformed_y.loc[X_test.index, :] 198/27: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] y_train, y_test = transform_targets(y_train), transform_targets(y_test) 198/28: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] y_train_transformed, y_test_transformed = transform_targets(y_train), transform_targets(y_test) 198/29: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=4, final_activation='softmax') 198/30: dense.fit(X_train, y_train_transformed) 198/31: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=4, final_activation='softmax') dense.compile(loss='categorical_crossentropy', optimizer='adam') 198/32: dense.fit(X_train, y_train_transformed) 198/33: dense.fit(X_train, y_train_transformed, max_epochs=50) 198/34: dense.fit(X_train, y_train_transformed, epochs=50) 198/35: X_train[:5] 198/36: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=4, final_activation='softmax') dense.compile(loss='mean_squred_error', optimizer='adam') 198/37: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=4, final_activation='softmax') dense.compile(loss='mean_squared_error', optimizer='adam') 198/38: X_train[:5] 198/39: dense.fit(X_train, y_train_transformed, epochs=50) 198/40: y_train_transformed[:4) 198/41: y_train_transformed[:4] 198/42: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=4, final_activation='softmax') dense.compile(loss='mean_squared_error', optimizer='adam') 198/43: dense.fit(X_train, y_train_transformed, epochs=50) 198/44: dense.summary() 198/45: dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=4, final_activation='softmhjvjh,ax') dense.compile(loss='mean_squared_error', optimizer='adam') 198/46: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/47: dense.fit(X_train, y_train_transformed, epochs=50) 198/48: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/49: dense.fit(X_train, y_train_transformed, epochs=50) 198/50: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/51: dense.fit(X_train, y_train_transformed, epochs=50) 198/52: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/53: dense.summary() 198/54: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/55: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/56: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/57: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/58: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/59: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/60: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/61: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/62: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/63: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/64: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/65: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/66: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/67: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/68: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/69: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/70: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/71: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/72: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/73: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/74: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/75: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/76: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/77: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/78: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/79: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/80: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/81: dense.predict(X_test) 198/82: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/83: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/84: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/85: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/86: dense.predict(X_test) 198/87: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/88: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/89: dense.predict(X_test) 198/90: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=4, final_activation='softmax') opt = Adam(lr=0.000001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/91: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/92: dense.predict(X_test) 198/93: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=2, final_activation='softmax') opt = Adam(lr=0.000001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/94: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/95: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/96: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/97: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=1, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/98: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/99: dense.predict(X_test) 198/100: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=1, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/101: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/102: dense.predict(X_test) 198/103: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=512, n_layers=1, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/104: dense.predict(X_test) 198/105: np.sum(dense.predict(X_test) != 1) 198/106: np.sum(dense.predict(X_test) == 1) 198/107: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], dropout_probability=0.4, n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/108: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=64) 198/109: np.sum(dense.predict(X_test) != 1) 198/110: np.sum(dense.predict(X_test) == 1) 198/111: X_train[:5] 198/112: dense.fit(X_train.values, y_train_transformed, epochs=50, batch_size=64) 198/113: np.sum(dense.predict(X_test) == 1) 198/114: dense.predict(X_test) 198/115: print(dense.predict(X_test)) 198/116: from keras.optimizers import Adam dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/117: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=128) 198/118: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/119: dense.summary() 198/120: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/121: dense.fit(X_train, y_train_transformed, epochs=50, batch_size=128) 198/122: dense.fit(X_train.values, y_train_transformed, epochs=50, batch_size=128) 198/123: print(dense.predict(X_test)) 198/124: X_test[:5] 198/125: y_test[:5] 198/126: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/127: dense.fit(X_train.values, y_train_transformed, epochs=50, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/128: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/129: print(dense.predict(X_test)) 198/130: y_test[:5] 198/131: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/132: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/133: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/134: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/135: print(dense.predict(X_test)) 198/136: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/137: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/138: print(dense.predict(X_test)) 198/139: transformed_back = preds * X_train[['total_volume_ha']] 198/140: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']] 198/141: preds.shape 198/142: X_test[['total_volume_ha']] 198/143: X_test[['total_volume_ha']].shape 198/144: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values 198/145: X_test[['total_volume_ha']].values.shape 198/146: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(preds, y_test) 198/147: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/148: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/149: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(preds, y_test) 198/150: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/151: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/152: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=128, n_layers=2, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/153: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/154: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(preds, y_test) 198/155: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/156: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/157: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(preds, y_test) 198/158: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(transformed_back, y_test) 198/159: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat", "total_volume_ha"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] y_train_transformed, y_test_transformed = transform_targets(y_train), transform_targets(y_test) 198/160: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='mean_squared_error', optimizer=opt) 198/161: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/162: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * X_test[['total_volume_ha']].values metrics(transformed_back, y_test) 198/163: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat", "total_volume_ha"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') test_total_vols = X_test[['total_volume_ha']].values X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] y_train_transformed, y_test_transformed = transform_targets(y_train), transform_targets(y_test) 198/164: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * test_total_vols metrics(transformed_back, y_test) 198/165: from keras.optimizers import Adam from models import models_definition dense = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense.compile(loss='categorical_crossentropy', optimizer=opt) 198/166: dense.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) 198/167: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense.predict(X_test) transformed_back = preds * test_total_vols metrics(transformed_back, y_test) 198/168: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt) dense_total.compile(loss='mean_squared_error', optimizer=opt) 198/169: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt) dense_regression.compile(loss='mean_squared_error', optimizer=opt) 198/170: koskisen_folder = "/home/tman/Work/data/koskisen" #stand_data = pd.read_csv(os.path.join(koskisen_folder, 'v_stand_level_features.csv')) grid_data = pd.read_csv(os.path.join(koskisen_folder, 'grid', 'koskisen_grid_data.csv')) stand_data_aggregated = grid_data.groupby('prd_id').agg('mean').reset_index() stand_data_aggregated.head() stand_data_aggregated.isna().mean(axis=0) features = stand_data_aggregated.dropna() assert features.isna().sum().sum() == 0 # Drop rows where lidar was done after harvesting and the column after filtering features = features[features['lidared_before_harvest']].drop('lidared_before_harvest', axis=1) target_columns = ['pine_volume_ha', 'spruce_volume_ha', 'deciduous_volume_ha'] non_feature_columns = ['prd_id'] X = features.drop(target_columns, axis=1) y = features[target_columns] #transformed_y = transform_targets(y) drop_cols = ["prd_id", "plot_id", "lon", "lat", "total_volume_ha"] X_train, X_test = data_loading.split_from_ids(X, split_name='koskisen', id_column='prd_id') train_total_vols = X_train[['total_volume_ha']].values test_total_vols = X_test[['total_volume_ha']].values X_train, X_test = X_train.drop(drop_cols, axis=1), X_test.drop(drop_cols, axis=1) y_train, y_test = y.loc[X_train.index, :], y.loc[X_test.index, :] y_train_transformed, y_test_transformed = transform_targets(y_train), transform_targets(y_test) 198/171: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt) dense_regression.compile(loss='mean_squared_error', optimizer=opt) 198/172: #dense_distribution.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) dense_regression.fit(X_train.values, train_total_vols, epochs=200, batch_size=128, validation_data=(X_test, test_total_vols)) 198/173: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt_distribution) dense_regression.compile(loss='mean_squared_error', optimizer=opt_regression) 198/174: #dense_distribution.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) dense_regression.fit(X_train.values, train_total_vols, epochs=200, batch_size=128, validation_data=(X_test, test_total_vols)) 198/175: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt_distribution) dense_regression.compile(loss='mean_squared_error', optimizer=opt_regression) 198/176: #dense_distribution.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) dense_regression.fit(X_train.values, train_total_vols, epochs=200, batch_size=128, validation_data=(X_test, test_total_vols)) 198/177: from keras.optimizers import Adam from models import models_definition dense_distribution = models_definition.create_dense((X_train.shape[1],), y_train_transformed.shape[1], n_units=512, n_layers=3, final_activation='softmax') dense_regression = models_definition.create_dense((X_train.shape[1],), 1, n_units=512, n_layers=3, final_activation='linear') opt_distribution = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) opt_regression = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) dense_distribution.compile(loss='categorical_crossentropy', optimizer=opt_distribution) dense_regression.compile(loss='mean_squared_error', optimizer=opt_regression) 198/178: #dense_distribution.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) dense_regression.fit(X_train.values, train_total_vols, epochs=200, batch_size=128, validation_data=(X_test, test_total_vols)) 198/179: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test) total_vol_preds = dense_regression(X_test) transformed_back = preds * total_vol_preds metrics(transformed_back, y_test) 198/180: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test) total_vol_preds = dense_regression.predict(X_test) transformed_back = preds * total_vol_preds metrics(transformed_back, y_test) 198/181: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test.values) total_vol_preds = dense_regression.predict(X_test.values) transformed_back = preds * total_vol_preds metrics(transformed_back, y_test) 198/182: metrics(test_total_vols, total_vol_preds) 198/183: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test.values) total_vol_preds = dense_regression.predict(X_test.values) transformed_back = preds * test_total_vols metrics(transformed_back, y_test) 198/184: dense_distribution.fit(X_train.values, y_train_transformed, epochs=200, batch_size=128, validation_data=(X_test, y_test_transformed)) dense_regression.fit(X_train.values, train_total_vols, epochs=200, batch_size=128, validation_data=(X_test, test_total_vols)) 198/185: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test.values) total_vol_preds = dense_regression.predict(X_test.values) transformed_back = preds * test_total_vols metrics(transformed_back, y_test) 198/186: def metrics(y_pred, y_true): from sklearn.metrics import mean_squared_error, mean_absolute_error rmse = np.sqrt(mean_squared_error(y_pred, y_true, multioutput='raw_values')) print(rmse) preds = dense_distribution.predict(X_test.values) total_vol_preds = dense_regression.predict(X_test.values) transformed_back = preds * total_vol_preds metrics(transformed_back, y_test) 201/1: import pandas as pd 201/2: codes = pd.read_csv("Silvia_codes_translated.csv") 201/3: codes = pd.read_csv("Silvia_codes_translated.csv") 201/4: codes = pd.read_csv("Silvia_codes_translated.csv") 201/5: codes = pd.read_csv("Silvia_codes_translated.csv") 201/6: codes = pd.read_csv("Silvia_codes_translated.csv") 201/7: codes = pd.read_csv("Silvia_codes_translated.csv") 201/8: codes 202/1: import pandas as pd 202/2: codes = pd.read_csv("Silvia_codes_translated.csv") 202/3: codes['NAME'] 203/1: with open("ids.txt", "r") as f: for line in f: print(line) 203/2: ids = set() 203/3: with open("ids.txt", "r") as f: for line in f: set.add(line.trim()) 203/4: with open("ids.txt", "r") as f: for line in f: set.add(line.strip()) 203/5: with open("ids.txt", "r") as f: for line in f: ids.add(line.strip()) 203/6: ids 204/1: pytorch 205/1: from __future__ import print_function import torch x = torch.rand(5, 3) print(x) 205/2: torch.cuda.is_available() 206/1: import numpy as np 206/2: gg = numpy.load('0000.npy') 206/3: gg = no.load('0000.npy') 206/4: gg = np.load('0000.npy') 206/5: gg 206/6: gg.sum() 206/7: np.unique(gg) 206/8: gg.shape 207/1: import numpy as np 207/2: np.zeros((1,1,24)) 207/3: np.zeros((1,1,24)).shape 208/1: import numpy as np 208/2: from PIL import image 208/3: from PIL import Image 208/4: gg = Image.open("../data/bcs_floor6_play_only_formatted/images/val/official/0000.png") 208/5: gg.shape 208/6: np.array(gg).shape 208/7: gg.shape[:2] 208/8: npgg = np.array(gg) 208/9: npgg 208/10: npgg.shape 208/11: npgg.shape[:2] 208/12: np.zeros(npgg.shape[:2] + (,1)).shape 208/13: np.zeros(npgg.shape[:2] + (1)).shape 208/14: np.zeros(npgg.shape[:2] + (1,)).shape 209/1: import numpy as np 209/2: depth = np.load("../data/bcs_floor6_play_only_formatted/depth/0000.npy") 209/3: depth.shape 209/4: import os 210/1: import os 210/2: gg = os.path("../data/bcs_floor6_play_only_formatted/") 210/3: gg = os.path.join("../data/bcs_floor6_play_only_formatted/") 210/4: gg 210/5: gg.replace("data", "lol2) 210/6: gg.replace("data", "lol") 211/1: ls 211/2: cd .. 211/3: ls 211/4: cd data/ 211/5: ls 211/6: cd bcs_floor6_play_only_formatted/ 211/7: ls 211/8: poses = np.loadtxt("poses.txt") 211/9: import numpy as np 211/10: poses = np.loadtxt("poses.txt") 211/11: posts 211/12: poses 211/13: poses.shape 211/14: K = np.loadtxt("K.txt") 211/15: K 211/16: pose = poses[0] 211/17: pose 211/18: pose.reshape(4,4) 211/19: R = pose[:3,:3] 211/20: pose.shape 211/21: pose = pose.reshape(4,4) 211/22: R = pose[:3,:3] 211/23: R 211/24: t = pose[:,-1][:3] 211/25: t 211/26: t.dot(np.array([0,0,1/0.5])) 211/27: dep = np.array([0,0,1/0.5]) 211/28: dep 211/29: dep.shape 211/30: dep.transpose.shape 211/31: dep.transpose().shape 211/32: dep.shape = (1,3) 211/33: t.dot(dep) 211/34: t.shape 211/35: t.shape = (3,1) 211/36: t.dot(dep) 211/37: K 211/38: H = K.dot((R + t.dot(dep))).dot(K.inv()) 211/39: H = K.dot((R + t.dot(dep))).dot(np.linalg.inv(K)) 211/40: H 212/1: import numpy as np 212/2: K = np.loadtxt("K.txt") 212/3: poses = np.loadtxt("poses.txt") 212/4: K 212/5: poses 212/6: poses.shape 212/7: poss = poses.shape = (poses.shape[0], 4, 4) 212/8: poss.shape 212/9: poses.shape = (poses.shape[0], 4, 4) 212/10: poses.shape 212/11: poses[0] 212/12: t_j = poses[0, -1, :3] 212/13: t_j 212/14: t_j = poses[0, :3, -1] 212/15: t_j 212/16: poses[:, :3, -1] - t_j 212/17: ti_minus_tj = poses[:, :3, -1] - t_j 212/18: ti_minus_tj.shape 212/19: np.inner(ti_minus_tj, ti_minus_tj) 212/20: np.inner(ti_minus_tj, ti_minus_tj).shape 212/21: np.inner(ti_minus_tj, ti_minus_tj.T).shape 212/22: ti_minus_tj.T.shape 212/23: ti_minus_tj.dot(ti_minus_tj.T).shape 212/24: ti_minus_tj.dot(ti_minus_tj).shape 212/25: np.linalg.norm(ti_minus_tj, ord=2)**2 212/26: np.linalg.norm(ti_minus_tj, ord=2, axis=0)**2 212/27: np.linalg.norm(ti_minus_tj, ord=2, axis=1)**2 212/28: r_j = poses[0,:3,:3] 212/29: r_j 212/30: r_is = poses[:,:3,:3] 212/31: r_is.T 212/32: r_is.shape 212/33: r_is.T.shape 212/34: np.transmute(r_is, axes=(0, 2, 1)) 212/35: np.transpose(r_is, axes=(0, 2, 1)) 212/36: np.transpose(r_is, axes=(0, 2, 1)).shape 212/37: np.transpose(r_is, axes=(0, 2, 1)).dot(r_j) 212/38: np.transpose(r_is, axes=(0, 2, 1)).dot(r_j).shape 212/39: np.zeros(100, 100) 213/1: import numpy as np 213/2: from utils_mvs_temporal import * 213/3: poses = np.loadtxt("../data/bcs_floor6_play_only_formatted/poses.txt") 213/4: poses .shape = (poses.shape[0], 4, 4) 213/5: poses.shape 213/6: pose_distance_measure(poses) 213/7: from utils_mvs_temporal import * 213/8: pose_distance_measure(poses) 214/1: from utils_mvs_temporal import * 214/2: from utils_mvs_temporal import * 214/3: import numpy as np 214/4: poses = np.loadtxt("../data/bcs_floor6_play_only_formatted/poses.txt") 214/5: poses .shape = (poses.shape[0], 4, 4) 214/6: pose_distance_measure(poses) 214/7: import importlib 214/8: importlib.reload(from utils_mvs_temporal import *) 215/1: %load_ext autoreload 215/2: %autoreload 2 215/3: from utils_mvs_temporal import * 215/4: import numpy as np 215/5: poses = np.loadtxt("../data/bcs_floor6_play_only_formatted/poses.txt") 215/6: poses .shape = (poses.shape[0], 4, 4) 215/7: pose_distance_measure(poses) 215/8: idx = 0 215/9: t_j = poses[idx, :3, -1] ti_minus_tj_norm = np.linalg.norm(poses[:, :3, -1] - t_j, ord=2, axis=1)**2 r_j = poses[idx, :3, :3] r_is = poses[:, :3, :3] tr_in = np.transpose(r_is, axes=(0,2,1)).dot(r_j) 215/10: t_j = poses[idx, :3, -1] ti_minus_tj_norm = np.linalg.norm(poses[:, :3, -1] - t_j, ord=2, axis=1)**2 r_j = poses[idx, :3, :3] r_is = poses[:, :3, :3]tr_in = np.transpose(r_is, axes=(0,2,1)).dot(r_j) 215/11: t_j = poses[idx, :3, -1] ti_minus_tj_norm = np.linalg.norm(poses[:, :3, -1] - t_j, ord=2, axis=1)**2 r_j = poses[idx, :3, :3] r_is = poses[:, :3, :3]tr_in = np.transpose(r_is, axes=(0,2,1)).dot(r_j) 215/12: %paste 215/13: %paste 215/14: %paste 215/15: t_j 215/16: tr_in.shape 215/17: np.trace(np.eye(3) - tr_in) 215/18: np.trace(np.eye(3) - tr_in, axis1=1, axis2=2) 215/19: np.trace(np.eye(3) - tr_in, axis1=1, axis2=2).shape 215/20: pose_distance_measure(poses) 215/21: pose_distance_measure(poses) 215/22: distances = pose_distance_measure(poses) 215/23: wat = ti_minus_tj_norm + tr_calc 215/24: tr_calc = (2./3)*np.trace(np.eye(3) - tr_in, axis1=1, axis2=2) 215/25: wat = ti_minus_tj_norm + tr_calc 215/26: wat.shape 215/27: wat 215/28: np.sum(wat < 0) 215/29: wat[wat<0] = 0 215/30: wat 215/31: distances 215/32: distances == np.nan 215/33: np.isnan(distances) 215/34: distances = pose_distance_measure(poses) 215/35: distances 215/36: matern_kernel(distances) 215/37: matern_kernel(distances).shape 215/38: 18**2 215/39: 13.82**2 215/40: matern_kernel(distances)[0] 216/1: import torch 216/2: import torchvision.models 216/3: models. 216/4: torchvision.models.mobilenet() 217/1: import torch 217/2: checkpoint = torch.load("../models/mobilenet-nnconv5dw-skipadd-pruned.pth.tar") 217/3: checkpoint = torch.load("../models/mobilenet-nnconv5dw-skipadd-pruned.pth.tar") 217/4: checkpoint 218/1: import torch 218/2: state_dict = torch.hub.load_state_dict_from_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth') 218/3: state_dict = torch.utils.model_zoo.load_url('https://download.pytorch.org/models/mobilenet_v2-b0353104.pth') 219/1: import torch 219/2: torch.version 219/3: torch.version() 219/4: torch.version.debug() 219/5: torch.version.debug 219/6: torch.__version__ 220/1: import torch 220/2: torch.cuda_is_available() 220/3: torch.cuda.is_available() 221/1: import torch 221/2: impor torchvision.models 221/3: import torchvision.models 222/1: import torchvision.models 223/1: import torchvision.models 223/2: model = torchvision.models.mobilenet_v2(pretrained=True) 223/3: model 224/1: import torch 224/2: checkpoint = torch.load("../models/mobilenet_sgd_rmsprop_69.526.tar") 224/3: checkpoint 224/4: import imagenet 224/5: mobilenet = imagenet.mobilenet.MobileNet() 224/6: import imagenet.mobilenet 224/7: mobilenet = imagenet.mobilenet.MobileNet() 224/8: state_dict = checkpoint['state_dict'] 224/9: %paste 224/10: mobilenet.load_state_dict(new_state_dict) 225/1: import models 225/2: gg = models.MobileNetSkipAdd(10) 225/3: gg 226/1: import sys; print('Python %s on %s' % (sys.version, sys.platform)) 226/2: i 226/3: input.shape 226/4: target.shape 226/5: model 226/6: layer = getattr(model, 'conv13') 226/7: layer 226/8: model[:5] 226/9: model.children() 226/10: model.children()[:10] 226/11: *list(model.children())[:10] 226/12: list(model.children())[:10] 226/13: list(model.children())[:14] 226/14: list(model.children())[:15] 226/15: list(model.children())[:14] 1: %run ipython_start.py 2: %run ./ipython_start.py 3: %load ipython_start.py 4: # %load ipython_start.py %load_ext autoreload %autoreload 2 import numpy as np import torch import os 5: torch.load("../models/mobilenet-nnconv5dw-skipadd-pruned.pth.tar") 6: basemodel = torch.load("../models/mobilenet-nnconv5dw-skipadd-pruned.pth.tar") 7: from models_pose import * 8: augmented = AugmentedFastDepth("../models/mobilenet-nnconv5dw-skipadd-pruned.pth.tar") 9: augmented 10: base_model 11: basemodel 12: from models import MobileNetSkipAdd 13: gg = MobileNetSkipAdd(10) 14: gg 15: basemodel 16: augmented.load_state_dict(basemodel) 17: augmented.load_state_dict(basemodel['state_dict']) 18: history 19: basemodel.model 20: basemodel[0] 21: basemodel.layer 22: basemodel.layers 23: basemodel.keys() 24: basemodel.model 25: basemodel['model'] 26: basemodel['model'][0] 27: augmented['model'] 28: basemodel['model'].layers 29: basemodel['model'].layer 30: basemodel['model'].layer() 31: basemodel['model'].layers() 32: basemodel[:5] 33: basemodel['model'][:5] 34: augmented['model'] 35: basemodel['model'] 36: len(basemodel['model']) 37: getattr(basemodel, 'conv{}'.format(0)) 38: getattr(basemodel['model'], 'conv{}'.format(0)) 39: getattr(basemodel['model'], 'conv{}'.format(1)) 40: import models_pose.py 41: import models_pose 42: augmented = AugmentedFastDepth("asd") 43: import models_pose 44: augmented = AugmentedFastDepth("asd") 45: gg = MobileNetSkipAdd(10) 46: import models_pose 47: augmented = AugmentedFastDepth("asd") 48: augmented = AugmentedFastDepth("asd") 49: import models_pose 50: augmented = AugmentedFastDepth("asd") 51: import models_pose 52: augmented = AugmentedFastDepth("asd") 53: import models_pose 54: augmented = AugmentedFastDepth("asd") 55: augmented 56: import models_pose 57: augmented = AugmentedFastDepth("asd") 58: augmented 59: %paste 60: next(iter(val_loader)) 61: %paste 62: batch = next(iter(val_loader)) 63: batch 64: batch.shape 65: batch.shape[0] 66: batch[0] 67: batch[0].shape 68: len(batch) 69: history 70: basemodel(batch[0]) 71: basemodel.eval() 72: basemodel['model'](batch[0]) 73: torch.cuda.synchronize() 74: with torch.no_grad(): pred = basemodel['model'](batch[0]) 75: with torch.no_grad(): pred = basemodel['model'](batch[0].cuda()) 76: pred 77: with torch.no_grad(): pred2 = augmented(batch[0].cuda()) 78: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 79: import models_pose 80: augmented = AugmentedFastDepth("asd") 81: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 82: import models_pose 83: augmented = AugmentedFastDepth("asd") 84: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 85: pred2 86: pred 87: import models_pose 88: augmented = AugmentedFastDepth("asd") 89: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 90: import models_pose 91: augmented = AugmentedFastDepth("asd") 92: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 93: pred2 94: batch[1] 95: batch[2] 96: batch[2].shape 97: # set batch size to be 1 for validation val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=5, shuffle=False, num_workers=4, pin_memory=True) 98: batch = next(iter(val_loader)) 99: with torch.no_grad(): pred2 = augmented(batch[0].cuda(), batch[2].cuda()) 100: pred2.shape 101: pred2[0] 102: batch[0] 103: batch[0].shape 104: history 105: history > gg.txt 106: ls 107: %history 108: %history > gg.text 109: ls 110: %history -g -f ipythonhistory25088 111: ls 112: %history -g -f ipythonhistory2508.py

40.927712

181

0.724448

100,268

677,149

4.641192

0.013504

0.010572

0.013188

0.0101

0.963742

0.956481

0.95057

0.94641

0.941762

0.937421

0

0.031494

0.156158

677,149

16,544

182

40.930186

0.782922

0

0.796346

0

0.013703

0.169077

0.040579

0

0.000121

0.005198

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null

0

0.145141

null

0.061742

0

null

0

1

0

1

0

null

0

1

0

8

de144697cb286b91939ce9f61b0f7cbfd2ec82c1

1,881

py

Python

gerencia/models.py

bcunhasa/nutriodonto

3ff20377bc85bc4960619f63688b7732e6eebba9

[ "CC0-1.0" ]

null

gerencia/models.py

bcunhasa/nutriodonto

3ff20377bc85bc4960619f63688b7732e6eebba9

[ "CC0-1.0" ]

null

gerencia/models.py

bcunhasa/nutriodonto

3ff20377bc85bc4960619f63688b7732e6eebba9

[ "CC0-1.0" ]

null

from django.db import models from .const import * class Noticia(models.Model): """Modelo que representa uma notícia do portal""" titulo = models.CharField(max_length=TAMANHO_TITULO, verbose_name='Título') data = models.CharField(max_length=TAMANHO_TITULO, verbose_name='Data de criação') texto = models.TextField(verbose_name='Texto') imagem = models.CharField(max_length=TAMANHO_TITULO, verbose_name='URL da imagem') def __str__(self): """Devolve a representação do modelo em string""" return self.titulo class Documento(models.Model): """Modelo que representa um documento público""" titulo = models.CharField(max_length=TAMANHO_TITULO, verbose_name='Título') data = models.DateTimeField(auto_now_add=True, verbose_name='Data de criação') url = models.CharField(max_length=TAMANHO_TITULO, verbose_name='URL do documento') def __str__(self): """Devolve a representação do modelo em string""" return self.titulo class Foto(models.Model): """Modelo que representa uma foto da página de mídia""" titulo = models.CharField(max_length=TAMANHO_TITULO, verbose_name='Título') data = models.DateTimeField(auto_now_add=True, verbose_name='Data de criação') url = models.CharField(max_length=TAMANHO_TITULO, verbose_name='URL da foto') def __str__(self): """Devolve a representação do modelo em string""" return self.titulo class Video(models.Model): """Modelo que representa um vídeo da página de mídia""" titulo = models.CharField(max_length=TAMANHO_TITULO, verbose_name='Título') data = models.DateTimeField(auto_now_add=True, verbose_name='Data de criação') url = models.CharField(max_length=TAMANHO_TITULO, verbose_name='URL do vídeo') def __str__(self): """Devolve a representação do modelo em string""" return self.titulo

38.387755

86

0.725146

249

1,881

5.26506

0.216867

0.109077

0.12357

0.16476

0.866514

0.856598

0.757437

0.7254

0

0.169591

1,881

48

87

39.1875

0.839309

0.192451

0

0.555556

0

0.095399

0

1

0.148148

false

0

0.074074

0

1

0

null

0

1

0

null

0

1

0

8

a9ec132b5e32375409ac9495d7187cf2fb638048

194

py

Python

server/apps/streamer/worker/v2_bin/tests/__init__.py

iotile/iotile_cloud

9dc65ac86d3a730bba42108ed7d9bbb963d22ba6

[ "MIT" ]

null

server/apps/streamer/worker/v2_bin/tests/__init__.py

iotile/iotile_cloud

9dc65ac86d3a730bba42108ed7d9bbb963d22ba6

[ "MIT" ]

null

server/apps/streamer/worker/v2_bin/tests/__init__.py

iotile/iotile_cloud

9dc65ac86d3a730bba42108ed7d9bbb963d22ba6

[ "MIT" ]

null

from .test_encoded_events import * from .test_handle_chopped import * from .test_one_reboot import * from .test_process_report import * from .test_reboots import * from .test_reprocess import *

27.714286

34

0.814433

28

194

5.285714

0.464286

0.324324

0.472973

0

0.123711

194

6

35

32.333333

0.870588

0

1

0

true

0

1

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1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

a9f6d98cb1e9ab659dc1637b0172d31fa56b3e9f

115

py

Python

shake-shake_pytorch/models/__init__.py

ychnlgy/Chebyshev-Lagrange

74292e72b83f992d6c42a2f2db04dfdce5a52aea

[ "MIT" ]

1

2021-08-19T14:28:45.000Z

shake-shake_pytorch/models/__init__.py

ychnlgy/Chebyshev-Lagrange

74292e72b83f992d6c42a2f2db04dfdce5a52aea

[ "MIT" ]

null

shake-shake_pytorch/models/__init__.py

ychnlgy/Chebyshev-Lagrange

74292e72b83f992d6c42a2f2db04dfdce5a52aea

[ "MIT" ]

1

2022-03-11T07:20:06.000Z

from . import polynomial from models.shake_resnet import ShakeResNet from models.shake_resnext import ShakeResNeXt

28.75

45

0.869565

15

115

6.533333

0.6

0.204082

0.306122

0

0.104348

115

3

46

38.333333

0.951456

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

e71c481582fc13eb92247dc075b3e2c260b9520d

91

py

Python

Wellington_python/exemplo_tipos_de_dados.py

jwellington58/Wellingtonlp220172vacation

c246c6d9604f93a6d846eeb4af34a4065b3f3c4d

[ "MIT" ]

null

Wellington_python/exemplo_tipos_de_dados.py

jwellington58/Wellingtonlp220172vacation

c246c6d9604f93a6d846eeb4af34a4065b3f3c4d

[ "MIT" ]

null

Wellington_python/exemplo_tipos_de_dados.py

jwellington58/Wellingtonlp220172vacation

c246c6d9604f93a6d846eeb4af34a4065b3f3c4d

[ "MIT" ]

null

a = int(input("Digite um numero: \n")) b = int(input("Digite um numero: \n")) print(a+b)

30.333333

40

0.604396

17

91

3.235294

0.529412

0.290909

0.509091

0.581818

0.836364

0

0.164835

91

3

41

30.333333

0.723684

0

0.445652

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

7

e72d9e76db4d215db999a4baccd75944781bfe4b

109

py

Python

test/tests/__init__.py

surfac3/specter-diy

ed27d93dc1411aebadf38d419337683fa4551a4e

[ "MIT" ]

null

test/tests/__init__.py

surfac3/specter-diy

ed27d93dc1411aebadf38d419337683fa4551a4e

[ "MIT" ]

null

test/tests/__init__.py

surfac3/specter-diy

ed27d93dc1411aebadf38d419337683fa4551a4e

[ "MIT" ]

null

from .test_keystore import * from .test_wallets import * from .test_sign import * from .test_revault import *

27.25

28

0.788991

16

109

5.125

0.4375

0.390244

0.512195

0

0.137615

109

4

29

27.25

0.87234

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

e7df6581e8224ab377a89720ce7993f5f7b70483

6,821

py

Python

loldib/getratings/models/NA/na_nautilus/na_nautilus_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_nautilus/na_nautilus_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_nautilus/na_nautilus_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Nautilus_Mid_Aatrox(Ratings): pass class NA_Nautilus_Mid_Ahri(Ratings): pass class NA_Nautilus_Mid_Akali(Ratings): pass class NA_Nautilus_Mid_Alistar(Ratings): pass class NA_Nautilus_Mid_Amumu(Ratings): pass class NA_Nautilus_Mid_Anivia(Ratings): pass class NA_Nautilus_Mid_Annie(Ratings): pass class NA_Nautilus_Mid_Ashe(Ratings): pass class NA_Nautilus_Mid_AurelionSol(Ratings): pass class NA_Nautilus_Mid_Azir(Ratings): pass class NA_Nautilus_Mid_Bard(Ratings): pass class NA_Nautilus_Mid_Blitzcrank(Ratings): pass class NA_Nautilus_Mid_Brand(Ratings): pass class NA_Nautilus_Mid_Braum(Ratings): pass class NA_Nautilus_Mid_Caitlyn(Ratings): pass class NA_Nautilus_Mid_Camille(Ratings): pass class NA_Nautilus_Mid_Cassiopeia(Ratings): pass class NA_Nautilus_Mid_Chogath(Ratings): pass class NA_Nautilus_Mid_Corki(Ratings): pass class NA_Nautilus_Mid_Darius(Ratings): pass class NA_Nautilus_Mid_Diana(Ratings): pass class NA_Nautilus_Mid_Draven(Ratings): pass class NA_Nautilus_Mid_DrMundo(Ratings): pass class NA_Nautilus_Mid_Ekko(Ratings): pass class NA_Nautilus_Mid_Elise(Ratings): pass class NA_Nautilus_Mid_Evelynn(Ratings): pass class NA_Nautilus_Mid_Ezreal(Ratings): pass class NA_Nautilus_Mid_Fiddlesticks(Ratings): pass class NA_Nautilus_Mid_Fiora(Ratings): pass class NA_Nautilus_Mid_Fizz(Ratings): pass class NA_Nautilus_Mid_Galio(Ratings): pass class NA_Nautilus_Mid_Gangplank(Ratings): pass class NA_Nautilus_Mid_Garen(Ratings): pass class NA_Nautilus_Mid_Gnar(Ratings): pass class NA_Nautilus_Mid_Gragas(Ratings): pass class NA_Nautilus_Mid_Graves(Ratings): pass class NA_Nautilus_Mid_Hecarim(Ratings): pass class NA_Nautilus_Mid_Heimerdinger(Ratings): pass class NA_Nautilus_Mid_Illaoi(Ratings): pass class NA_Nautilus_Mid_Irelia(Ratings): pass class NA_Nautilus_Mid_Ivern(Ratings): pass class NA_Nautilus_Mid_Janna(Ratings): pass class NA_Nautilus_Mid_JarvanIV(Ratings): pass class NA_Nautilus_Mid_Jax(Ratings): pass class NA_Nautilus_Mid_Jayce(Ratings): pass class NA_Nautilus_Mid_Jhin(Ratings): pass class NA_Nautilus_Mid_Jinx(Ratings): pass class NA_Nautilus_Mid_Kalista(Ratings): pass class NA_Nautilus_Mid_Karma(Ratings): pass class NA_Nautilus_Mid_Karthus(Ratings): pass class NA_Nautilus_Mid_Kassadin(Ratings): pass class NA_Nautilus_Mid_Katarina(Ratings): pass class NA_Nautilus_Mid_Kayle(Ratings): pass class NA_Nautilus_Mid_Kayn(Ratings): pass class NA_Nautilus_Mid_Kennen(Ratings): pass class NA_Nautilus_Mid_Khazix(Ratings): pass class NA_Nautilus_Mid_Kindred(Ratings): pass class NA_Nautilus_Mid_Kled(Ratings): pass class NA_Nautilus_Mid_KogMaw(Ratings): pass class NA_Nautilus_Mid_Leblanc(Ratings): pass class NA_Nautilus_Mid_LeeSin(Ratings): pass class NA_Nautilus_Mid_Leona(Ratings): pass class NA_Nautilus_Mid_Lissandra(Ratings): pass class NA_Nautilus_Mid_Lucian(Ratings): pass class NA_Nautilus_Mid_Lulu(Ratings): pass class NA_Nautilus_Mid_Lux(Ratings): pass class NA_Nautilus_Mid_Malphite(Ratings): pass class NA_Nautilus_Mid_Malzahar(Ratings): pass class NA_Nautilus_Mid_Maokai(Ratings): pass class NA_Nautilus_Mid_MasterYi(Ratings): pass class NA_Nautilus_Mid_MissFortune(Ratings): pass class NA_Nautilus_Mid_MonkeyKing(Ratings): pass class NA_Nautilus_Mid_Mordekaiser(Ratings): pass class NA_Nautilus_Mid_Morgana(Ratings): pass class NA_Nautilus_Mid_Nami(Ratings): pass class NA_Nautilus_Mid_Nasus(Ratings): pass class NA_Nautilus_Mid_Nautilus(Ratings): pass class NA_Nautilus_Mid_Nidalee(Ratings): pass class NA_Nautilus_Mid_Nocturne(Ratings): pass class NA_Nautilus_Mid_Nunu(Ratings): pass class NA_Nautilus_Mid_Olaf(Ratings): pass class NA_Nautilus_Mid_Orianna(Ratings): pass class NA_Nautilus_Mid_Ornn(Ratings): pass class NA_Nautilus_Mid_Pantheon(Ratings): pass class NA_Nautilus_Mid_Poppy(Ratings): pass class NA_Nautilus_Mid_Quinn(Ratings): pass class NA_Nautilus_Mid_Rakan(Ratings): pass class NA_Nautilus_Mid_Rammus(Ratings): pass class NA_Nautilus_Mid_RekSai(Ratings): pass class NA_Nautilus_Mid_Renekton(Ratings): pass class NA_Nautilus_Mid_Rengar(Ratings): pass class NA_Nautilus_Mid_Riven(Ratings): pass class NA_Nautilus_Mid_Rumble(Ratings): pass class NA_Nautilus_Mid_Ryze(Ratings): pass class NA_Nautilus_Mid_Sejuani(Ratings): pass class NA_Nautilus_Mid_Shaco(Ratings): pass class NA_Nautilus_Mid_Shen(Ratings): pass class NA_Nautilus_Mid_Shyvana(Ratings): pass class NA_Nautilus_Mid_Singed(Ratings): pass class NA_Nautilus_Mid_Sion(Ratings): pass class NA_Nautilus_Mid_Sivir(Ratings): pass class NA_Nautilus_Mid_Skarner(Ratings): pass class NA_Nautilus_Mid_Sona(Ratings): pass class NA_Nautilus_Mid_Soraka(Ratings): pass class NA_Nautilus_Mid_Swain(Ratings): pass class NA_Nautilus_Mid_Syndra(Ratings): pass class NA_Nautilus_Mid_TahmKench(Ratings): pass class NA_Nautilus_Mid_Taliyah(Ratings): pass class NA_Nautilus_Mid_Talon(Ratings): pass class NA_Nautilus_Mid_Taric(Ratings): pass class NA_Nautilus_Mid_Teemo(Ratings): pass class NA_Nautilus_Mid_Thresh(Ratings): pass class NA_Nautilus_Mid_Tristana(Ratings): pass class NA_Nautilus_Mid_Trundle(Ratings): pass class NA_Nautilus_Mid_Tryndamere(Ratings): pass class NA_Nautilus_Mid_TwistedFate(Ratings): pass class NA_Nautilus_Mid_Twitch(Ratings): pass class NA_Nautilus_Mid_Udyr(Ratings): pass class NA_Nautilus_Mid_Urgot(Ratings): pass class NA_Nautilus_Mid_Varus(Ratings): pass class NA_Nautilus_Mid_Vayne(Ratings): pass class NA_Nautilus_Mid_Veigar(Ratings): pass class NA_Nautilus_Mid_Velkoz(Ratings): pass class NA_Nautilus_Mid_Vi(Ratings): pass class NA_Nautilus_Mid_Viktor(Ratings): pass class NA_Nautilus_Mid_Vladimir(Ratings): pass class NA_Nautilus_Mid_Volibear(Ratings): pass class NA_Nautilus_Mid_Warwick(Ratings): pass class NA_Nautilus_Mid_Xayah(Ratings): pass class NA_Nautilus_Mid_Xerath(Ratings): pass class NA_Nautilus_Mid_XinZhao(Ratings): pass class NA_Nautilus_Mid_Yasuo(Ratings): pass class NA_Nautilus_Mid_Yorick(Ratings): pass class NA_Nautilus_Mid_Zac(Ratings): pass class NA_Nautilus_Mid_Zed(Ratings): pass class NA_Nautilus_Mid_Ziggs(Ratings): pass class NA_Nautilus_Mid_Zilean(Ratings): pass class NA_Nautilus_Mid_Zyra(Ratings): pass

16.357314

46

0.776133

972

6,821

5.020576

0.151235

0.197951

0.42418

0.509016

0.814139

0

0.162879

6,821

416

47

16.396635

0.854641

0

0.498195

0

1

0

true

0.498195

0.00361

0

0.501805

0

null

0

1

0

null

0

1

0

1

0

7

82223a01fa684b92ad9706c066254098b175f866

47

py

Python

easilyb/file_ops/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

easilyb/file_ops/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

easilyb/file_ops/__init__.py

xaled/easilyb

cdb5f738205f700b37e03c50d04061a2d1e730cc

[ "MIT" ]

null

from easilyb.file_ops._file_ops import read_all

47

0.893617

9

47

4.222222

0.777778

0.368421

0

0.06383

47

1

47

0.863636

0

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0

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0

1

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1

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1

0

null

0

1

0

1

0

1

0

7

823c04a14fe5623df3f1de894ec587adfc8e538d

162

py

Python

src/notebook_adventure/controllers/fight_tutorial_controller.py

kjamaal/notebook_adventure

703d6663212efcfd2ffe8c54e788e7dfd7feb221

[ "MIT" ]

null

src/notebook_adventure/controllers/fight_tutorial_controller.py

kjamaal/notebook_adventure

703d6663212efcfd2ffe8c54e788e7dfd7feb221

[ "MIT" ]

1

2021-06-01T23:59:47.000Z

src/notebook_adventure/controllers/fight_tutorial_controller.py

kjamaal/notebook_adventure_client

703d6663212efcfd2ffe8c54e788e7dfd7feb221

[ "MIT" ]

null

from ..services import api_caller def put_context(ctx): api_caller.put_context(ctx) def get_dialog(): return api_caller.get_dialog('fight_tutorial')

20.25

49

0.753086

24

162

4.75

0.583333

0.236842

0.22807

0

0.148148

162

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23.142857

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0.2

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1

0

null

0

1

0

1

0

7

82429f9db98f6c0c8d09dcb581552c7f03aae88d

9,376

py

Python

tests/integration/test_sign_out_help.py

ONSdigital/ras-frontstage

e8ff1931b49cb3ab47b421aed6780e9e944dceea

[ "MIT" ]

8

2017-06-30T12:32:02.000Z

2022-02-25T09:07:28.000Z

tests/integration/test_sign_out_help.py

ONSdigital/ras-frontstage

e8ff1931b49cb3ab47b421aed6780e9e944dceea

[ "MIT" ]

256

2017-05-16T09:38:09.000Z

2022-03-28T13:38:42.000Z

tests/integration/test_sign_out_help.py

ONSdigital/ras-frontstage

e8ff1931b49cb3ab47b421aed6780e9e944dceea

[ "MIT" ]

4

2017-09-29T08:58:36.000Z

2021-04-11T07:44:27.000Z

import unittest import requests_mock from frontstage import app from tests.integration.mocked_services import url_banner_api class TestSignOutHelp(unittest.TestCase): def setUp(self): self.app = app.test_client() self.app.set_cookie("localhost", "authorization", "session_key") @requests_mock.mock() def test_sign_out_help_get(self, mock_request): mock_request.get(url_banner_api, status_code=404) response = self.app.get("/help") self.assertEqual(response.status_code, 200) self.assertIn("Help".encode(), response.data) self.assertIn("Choose an option".encode(), response.data) self.assertIn("Information about the Office for National Statistics (ONS)".encode(), response.data) self.assertIn("Continue".encode(), response.data) self.assertIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "info-ons"} response = self.app.post("/help", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Information about the Office for National Statistics (ONS)".encode(), response.data) self.assertIn("Choose an option".encode(), response.data) self.assertIn("Who is the Office for National Statistics (ONS)?".encode(), response.data) self.assertIn("How do you keep my data safe?".encode(), response.data) self.assertIn("Something else".encode(), response.data) self.assertIn("Continue".encode(), response.data) self.assertIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post_select_option(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {} response = self.app.post("/help", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("At least one option should be selected.".encode(), response.data) self.assertIn("You need to choose an option".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post_ons_info_option_needs_to_be_selected(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {} response = self.app.post("/help/info-ons", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("At least one option should be selected.".encode(), response.data) self.assertIn("You need to choose an option".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post_who_is_ons(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "ons"} response = self.app.post("/help/info-ons", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Who is the Office for National Statistics (ONS)?".encode(), response.data) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post_ons_info_data_safe(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "data"} response = self.app.post("/help/info-ons", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("How do you keep my data safe?".encode(), response.data) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_post_ons_info_something_else(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "info-something-else"} response = self.app.post("/help/info-ons", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Information about the Office for National Statistics (ONS)".encode(), response.data) self.assertIn("Need more information?".encode(), response.data) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_my_password(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "password"} response = self.app.post("/help", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Help with my password".encode(), response.data) self.assertIn("Choose an option".encode(), response.data) self.assertIn("I have not received the password reset email".encode(), response.data) self.assertIn("I cannot reset my password using the link".encode(), response.data) self.assertIn("My new password is not being accepted".encode(), response.data) self.assertIn("Something else".encode(), response.data) self.assertIn("Continue".encode(), response.data) self.assertIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_password_post_ons_info_option_needs_to_be_selected(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {} response = self.app.post("/help/help-with-my-password", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("At least one option should be selected.".encode(), response.data) self.assertIn("You need to choose an option".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_password_post_not_received_password_reset_email(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "reset-email"} response = self.app.post("/help/help-with-my-password", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("I have not received the password reset email".encode(), response.data) self.assertIn( "that the email address ons.surveys@notifications.service.gov.uk is added to your list of " "approved senders".encode(), response.data, ) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_password_is_not_being_accepted(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "password-not-accept"} response = self.app.post("/help/help-with-my-password", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("My new password isn't being accepted".encode(), response.data) self.assertIn( "We recommend that you enter your password directly into the password box rather than copy and " "paste it in. This will prevent you pasting any hidden or special characters.".encode(), response.data, ) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_password_reset(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "reset-password"} response = self.app.post("/help/help-with-my-password", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("I cannot reset my password using the link".encode(), response.data) self.assertIn( "If 72 hours have passed since you reset, you should reset your password again.".encode(), response.data ) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_with_password_something_else(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "password-something-else"} response = self.app.post("/help/help-with-my-password", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Help with my password".encode(), response.data) self.assertIn("If you are having problems signing in, please".encode(), response.data) self.assertNotIn("Continue".encode(), response.data) self.assertNotIn("Cancel".encode(), response.data) @requests_mock.mock() def test_sign_out_help_something_else(self, mock_request): mock_request.get(url_banner_api, status_code=404) form = {"option": "something-else"} response = self.app.post("/help", data=form, follow_redirects=True) self.assertEqual(response.status_code, 200) self.assertIn("Further help".encode(), response.data) self.assertIn("You can find help for common issues if you".encode(), response.data) self.assertIn("If you are having problems signing in, please".encode(), response.data)

53.577143

116

0.693579

1,218

9,376

5.155993

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0.157643

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9,376

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117

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0.442308

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0.160256

0.025641

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null

0

1

0

null

0

1

0

1

0

8

413b3d310c3e1b03b45a669aaec09db8b79e260d

150

py

Python

src/api/api_v1/endpoints/__init__.py

shthiago/minimo-char-generator

289b9698e58deabf4b6376b2ac55dd2740b7f30c

[ "MIT" ]

null

src/api/api_v1/endpoints/__init__.py

shthiago/minimo-char-generator

289b9698e58deabf4b6376b2ac55dd2740b7f30c

[ "MIT" ]

null

src/api/api_v1/endpoints/__init__.py

shthiago/minimo-char-generator

289b9698e58deabf4b6376b2ac55dd2740b7f30c

[ "MIT" ]

null

'''Make all endpoints importable from package root''' from .listing import router as listing_router from .generate import router as generation_router

37.5

53

0.82

21

150

5.761905

0.619048

0.198347

0.231405

0

0.126667

150

3

54

50

0.923664

0.313333

0

1

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1

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true

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null

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1

0

null

0

1

0

1

0

1

0

7

4166e2797d556ec6f9565bc9eb1aa477dcaab7a1

121

py

Python

django_classy_settings/__init__.py

dipasqualew/django-classy-settings

9db69420e49962cd6c03f74e5d67fac74ee2f2eb

[ "MIT" ]

null

django_classy_settings/__init__.py

dipasqualew/django-classy-settings

9db69420e49962cd6c03f74e5d67fac74ee2f2eb

[ "MIT" ]

null

django_classy_settings/__init__.py

dipasqualew/django-classy-settings

9db69420e49962cd6c03f74e5d67fac74ee2f2eb

[ "MIT" ]

null

from django_classy_settings.version import VERSION from django_classy_settings.class_settings import DjangoClassSettings

40.333333

69

0.917355

15

121

7.066667

0.533333

0.188679

0.301887

0.45283

0

0.066116

121

2

70

60.5

0.938053

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

4185a9118e358164db7d6d42edc037172f068eb0

175

py

Python

qleet/interface/__init__.py

AnimeshSinha1309/qaoa-optimizer

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

9

2021-09-26T18:43:43.000Z

2022-03-30T12:34:01.000Z

qleet/interface/__init__.py

QLemma/qLEET

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

12

2021-09-19T13:29:33.000Z

2022-01-09T15:22:49.000Z

qleet/interface/__init__.py

QLemma/qLEET

2a93a46bacc99f22f49e7b5121eb3aa9f12c0163

[ "Apache-2.0" ]

1

2022-03-14T03:02:24.000Z

import qleet.interface.circuit import qleet.interface.metas import qleet.interface.metric_spec # import qleet.interface.dashboard FIXME: Try to get a default import on this

29.166667

78

0.828571

26

175

5.538462

0.615385

0.305556

0.555556

0

0.114286

175

5

79

35

0.929032

0.434286

0

0.2

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true

0

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1

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1

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null

1

0

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null

0

1

0

1

0

1

0

1

0

8

41b36ef905386adcd7b7fac20fa00f3d8cc95ec4

6,227

py

Python

networks-builder/builders/DiscriminatorSNGAN.py

febrianrachmadi/probunet-gan-vie

f50af34120ba106de3a1594fc37e3b31a9d1b922

[ "MIT" ]

null

networks-builder/builders/DiscriminatorSNGAN.py

febrianrachmadi/probunet-gan-vie

f50af34120ba106de3a1594fc37e3b31a9d1b922

[ "MIT" ]

null

networks-builder/builders/DiscriminatorSNGAN.py

febrianrachmadi/probunet-gan-vie

f50af34120ba106de3a1594fc37e3b31a9d1b922

[ "MIT" ]

null

# https://github.com/MrGiovanni/UNetPlusPlus/blob/master/keras/segmentation_models/unet/builder.py from tensorflow import image from tensorflow.keras import Model, Input from tensorflow.keras.layers import Conv2D, Activation, MaxPool2D, Flatten, Dense from tensorflow.keras.layers import Conv3D, MaxPool3D, Concatenate from tensorflow_addons.layers import SpectralNormalization from utils import get_layer_number, to_tuple, to_3Dtuple from blocks import Transpose2D_block, Upsample2D_block, Conv2DBlock, SpectralNormalizationConv2D from blocks import Transpose3D_block, Upsample3D_block, Conv3DBlock, SpectralNormalizationConv3D from blocks import down_block, attention_block, handle_block_names, handle_activation_names def build_discriminator_sngan( encoder_filters=(32,64,128,256,512), downsample_rates=(2,2,2,2,2), filter_sizes=(5,5,3,3,3), n_downsamples=5, n_convs_per_block=2, conv_activation='relu', encoder_block_type='downsampling', input_shape=(None,None,3)): # Using Conv+relu for the encoder input = Input(shape=input_shape) for i in range(n_downsamples): if i == 0: x = SpectralNormalizationConv2D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (input) else: down_rate = to_tuple(downsample_rates[i]) if encoder_block_type == 'downsampling': x = MaxPool2D(pool_size=down_rate) (x) elif encoder_block_type == 'stride': x = SpectralNormalization(Conv2D( encoder_filters[i], kernel_size=down_rate, strides=down_rate, padding="same", activation=conv_activation )) (x) x = SpectralNormalizationConv2D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (x) x = SpectralNormalization(Conv2D(1, kernel_size=1, padding='same', name='final_conv', activation=conv_activation))(x) x = Flatten()(x) x = Dense(1, kernel_initializer='he_normal')(x) model = Model(input, x) return model def build_discriminator_sngan3D( encoder_filters=(32,64,128,256,512), downsample_rates=(2,2,2,2,2), filter_sizes=(5,5,3,3,3), n_downsamples=5, n_convs_per_block=2, conv_activation='relu', encoder_block_type='downsampling', input_shape=(None,None,3)): # Using Conv+relu for the encoder input = Input(shape=input_shape) for i in range(n_downsamples): if i == 0: x = SpectralNormalizationConv3D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (input) else: down_rate = to_3Dtuple(downsample_rates[i]) if encoder_block_type == 'downsampling': x = MaxPool3D(pool_size=down_rate) (x) elif encoder_block_type == 'stride': x = SpectralNormalization(Conv3D( encoder_filters[i], kernel_size=down_rate, strides=down_rate, padding="same", activation=conv_activation )) (x) x = SpectralNormalizationConv3D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (x) x = SpectralNormalization(Conv3D(1, kernel_size=1, padding='same', name='final_conv', activation=conv_activation))(x) x = Flatten()(x) x = Dense(1, kernel_initializer='he_normal')(x) model = Model(input, x) return model def build_discriminator_sngan_3B( encoder_filters=(32,64,128,256,512), downsample_rates=(2,2,2,2,2), filter_sizes=(5,5,3,3,3), n_downsamples=5, n_convs_per_block=2, conv_activation='relu', encoder_block_type='downsampling', input_shape=(None,None,1)): # # Using Conv+relu for the encoder # input_1 = Input(shape=input_shape) # input_2 = Input(shape=input_shape) # input_3 = Input(shape=input_shape) # input_concatenated = Concatenate(axis=-1)([input_1, input_2]) # input_concatenated = Concatenate(axis=-1)([input_concatenated, input_3]) # Using Conv+relu for the encoder input_concatenated = Input(shape=input_shape) for i in range(n_downsamples): if i == 0: x = SpectralNormalizationConv2D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (input_concatenated) else: down_rate = to_tuple(downsample_rates[i]) if encoder_block_type == 'downsampling': x = MaxPool2D(pool_size=down_rate) (x) elif encoder_block_type == 'stride': x = SpectralNormalization(Conv2D( encoder_filters[i], kernel_size=down_rate, strides=down_rate, padding="same", activation=conv_activation )) (x) x = SpectralNormalizationConv2D( encoder_filters[i], i, 0, kernel_size=filter_sizes[i], n_convs_per_block=n_convs_per_block, activation=conv_activation ) (x) x = SpectralNormalization(Conv2D(1, kernel_size=1, padding='same', name='final_conv', activation=conv_activation))(x) x = Flatten()(x) x = Dense(1, kernel_initializer='he_normal')(x) # model = Model([input_1, input_2, input_3], x) model = Model(input_concatenated, x) return model

37.969512

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0.058939

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0.301269

6,227

164

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37.969512

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0

null

0

1

0

1

0

null

0

7

41bd0999ae00f27f96fd1da99f5d28e1be996394

1,627

py

Python

importers/tests/snapshots/snap_test_notification_importer.py

City-of-Helsinki/kukkuu

61f26bc622928fd04f6a397f832aaffff789e806

[ "MIT" ]

null

importers/tests/snapshots/snap_test_notification_importer.py

City-of-Helsinki/kukkuu

61f26bc622928fd04f6a397f832aaffff789e806

[ "MIT" ]

157

2019-10-08T07:58:59.000Z

2022-03-20T23:00:17.000Z

importers/tests/snapshots/snap_test_notification_importer.py

City-of-Helsinki/kukkuu

61f26bc622928fd04f6a397f832aaffff789e806

[ "MIT" ]

3

2019-10-07T12:06:26.000Z

2022-01-25T14:03:14.000Z

# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots[ "test_create_non_existing_and_update_existing_notifications 1" ] = """event_published|event_published fi updated subject|event_published en updated subject|event_published sv updated subject|event_published fi updated body_text|event_published en updated body_text|event_published sv updated body_text||| occurrence_enrolment|occurrence_enrolment fi updated subject|occurrence_enrolment en updated subject|occurrence_enrolment sv updated subject|occurrence_enrolment fi updated body_text|occurrence_enrolment en updated body_text|occurrence_enrolment sv updated body_text|||""" snapshots[ "test_create_non_existing_notifications 1" ] = """event_published|event_published fi original subject|event_published en original subject|event_published sv original subject|event_published fi original body_text|event_published en original body_text|event_published sv original body_text||| occurrence_enrolment|occurrence_enrolment fi updated subject|occurrence_enrolment en updated subject|occurrence_enrolment sv updated subject|occurrence_enrolment fi updated body_text|occurrence_enrolment en updated body_text|occurrence_enrolment sv updated body_text|||""" snapshots[ "test_update_notifications 1" ] = "event_published|event_published fi updated subject|event_published en updated subject|event_published sv updated subject|event_published fi updated body_text|event_published en updated body_text|event_published sv updated body_text|||"

73.954545

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0

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0

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1

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68dcdb7c30e81b0f4cb8986bd7d76a4f4fa28931

6,825

py

Python

vmware-ose-common-test-suites/prj/test_data/test_bucket/test_bucket.py

csgtree/object-storage-extension-samples

397f3033ddd4aa1bb1e2079a9e77309a78cc0b0d

[ "Apache-2.0" ]

6

2020-09-11T02:52:42.000Z

2021-04-19T11:20:42.000Z

vmware-ose-common-test-suites/prj/test_data/test_bucket/test_bucket.py

csgtree/object-storage-extension-samples

397f3033ddd4aa1bb1e2079a9e77309a78cc0b0d

[ "Apache-2.0" ]

5

2020-12-21T20:14:59.000Z

2022-03-21T14:35:43.000Z

vmware-ose-common-test-suites/prj/test_data/test_bucket/test_bucket.py

csgtree/object-storage-extension-samples

397f3033ddd4aa1bb1e2079a9e77309a78cc0b0d

[ "Apache-2.0" ]

4

2021-07-20T09:07:52.000Z

2022-03-21T14:33:44.000Z

from framework.libs.common.utils import load_csv import os def test_create_bucket_provider(): f_name = "test_create_bucket_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_location_provider(): f_name = "test_get_bucket_location_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_head_bucket_provider(): f_name = "test_head_bucket_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_provider(): f_name = "test_delete_bucket_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # acl def test_put_bucket_acl_provider(): f_name = "test_put_bucket_acl_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_acl_provider(): f_name = "test_get_bucket_acl_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_tagging_provider(): f_name = "test_put_bucket_tagging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_list_buckets_provider(): f_name = "test_list_buckets_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_bucket_acl_provider(): f_name = "test_bucket_acl_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_bucket_logging_provider(): f_name = "test_bucket_logging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) #tagging def test_get_bucket_tagging_provider(): f_name = "test_get_bucket_tagging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_tagging_provider(): f_name = "test_put_bucket_tagging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_tagging_provider(): f_name = "test_delete_bucket_tagging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # enc def test_get_bucket_encryption_provider(): f_name = "test_get_bucket_encryption_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_encryption_provider(): f_name = "test_delete_bucket_encryption_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_encryption_provider(): f_name = "test_put_bucket_encryption_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # lifecycle def test_get_bucket_lifecycle_provider(): f_name = "test_get_bucket_lifecycle_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_lifecycle_provider(): f_name = "test_delete_bucket_lifecycle_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_lifecycle_provider(): f_name = "test_put_bucket_lifecycle_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_lifecycle_configuration_provider(): f_name = "test_get_bucket_lifecycle_configuration_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_lifecycle_configuration_provider(): f_name = "test_put_bucket_lifecycle_configuration_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # policy def test_get_bucket_policy_provider(): f_name = "test_get_bucket_policy_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_policy_status_provider(): f_name = "test_get_bucket_policy_status_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_policy_provider(): f_name = "test_delete_bucket_policy_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_policy_provider(): f_name = "test_put_bucket_policy_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # logging def test_put_bucket_logging_provider(): f_name = "test_put_bucket_logging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_logging_provider(): f_name = "test_get_bucket_logging_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # versioning def test_put_bucket_versioning_provider(): f_name = "test_put_bucket_versioning_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_get_bucket_versioning_provider(): f_name = "test_get_bucket_versioning_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # cors def test_get_bucket_cors_provider(): f_name = "test_get_bucket_cors_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_cors_provider(): f_name = "test_put_bucket_cors_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_cors_provider(): f_name = "test_delete_bucket_cors_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) # bucket_analytics_configuration def test_get_bucket_analytics_configuration_provider(): f_name = "test_get_bucket_analytics_configuration_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_put_bucket_analytics_configuration_provider(): f_name = "test_put_bucket_analytics_configuration_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_delete_bucket_analytics_configuration_provider(): f_name = "test_delete_bucket_analytics_configuration_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) def test_list_bucket_analytics_configurations_provider(): f_name = "test_list_bucket_analytics_configurations_prov.csv" folder = os.path.dirname(__file__) return load_csv(os.path.join(folder, f_name)) if __name__ == "__main__": pass

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null

0

1

0

null

0

1

0

7

68e719455510a2221b46e744b1233f3791492df4

2,605

py

Python

Medium Clone Wbsite Using Flask/app.py

Shravyya/Python_Roboslog_Intern

fd3be88c67d5f018c5d641468ed6063a7a5f891a

[ "Apache-2.0" ]

null

Medium Clone Wbsite Using Flask/app.py

Shravyya/Python_Roboslog_Intern

fd3be88c67d5f018c5d641468ed6063a7a5f891a

[ "Apache-2.0" ]

null

Medium Clone Wbsite Using Flask/app.py

Shravyya/Python_Roboslog_Intern

fd3be88c67d5f018c5d641468ed6063a7a5f891a

[ "Apache-2.0" ]

null

from flask import Flask,render_template from article import Article #Router app = Flask("hello_world") posts = [ Article('title', 'subtitle' , 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.', 'shashank'), Article('title', 'subtitle' , 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.', 'shashank'), Article('title', 'subtitle' , 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.', 'shashank'), Article('title', 'subtitle' , 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.', 'shashank') ] @app.route('/') def index(): return render_template('index.html') @app.route('/articles') def articles(): return render_template('articles.html', articles=posts) @app.route('/articles/<int:id>') def article(id): try: post = posts[id-1] return render_template('article.html',article=post) except IndexError: return render_template('404.html') if __name__ == "__main__": app.run(port=8000, debug = True)

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1

0

null

0

7

6bc117bc617abacfe768a077964d9938c896c741

12,553

py

Python

test/test_q3.py

alvintangz/cscc43-a2-sql-queries-test

0531258375a7eee8bd1e9400b72943033290ad66

[ "PostgreSQL" ]

3

2021-07-12T21:52:32.000Z

2021-07-16T19:30:37.000Z

test/test_q3.py

alvintangz/cscc43-a2-sql-queries-test

0531258375a7eee8bd1e9400b72943033290ad66

[ "PostgreSQL" ]

null

test/test_q3.py

alvintangz/cscc43-a2-sql-queries-test

0531258375a7eee8bd1e9400b72943033290ad66

[ "PostgreSQL" ]

null

import unittest from test.utils.mixins import * from test.utils.generic_data import DEPARTMENTS, STUDENTS, INSTRUCTORS, COURSES from test.utils.enums import Semester COURSE_SECTIONS = [ { "csid": 1, "cid": 1, "dcode": "CSC", "year": 2015, "semester": Semester.FALL.value, "section": "LEC01", "iid": 2 }, { "csid": 2, "cid": 2, "dcode": "CSC", "year": 2016, "semester": Semester.FALL.value, "section": "LEC01", "iid": 2 }, { "csid": 3, "cid": 1, "dcode": "CSC", "year": 2017, "semester": Semester.WINTER.value, "section": "LEC01", "iid": 1 }, { "csid": 4, "cid": 2, "dcode": "CSC", "year": 2020, "semester": Semester.FALL.value, "section": "LEC01", "iid": 2 }, { "csid": 5, "cid": 1, "dcode": "CSC", "year": 2020, "semester": Semester.FALL.value, "section": "LEC02", "iid": 1 }, { "csid": 6, "cid": 2, "dcode": "CSC", "year": 2019, "semester": Semester.FALL.value, "section": "LEC01", "iid": 1 }, { "csid": 7, "cid": 2, "dcode": "CSC", "year": 2019, "semester": Semester.FALL.value, "section": "LEC02", "iid": 1 }, { "csid": 8, "cid": 3, "dcode": "AST", "year": 2019, "semester": Semester.FALL.value, "section": "LEC01", "iid": 3 } ] class QueryThreeTestCase(unittest.TestCase, SqlQueriesTestCaseMixin): table = "query3" query = """ --Query 3 """ def setUp(self): """Connect to the database.""" db.connect() self._create_instances(Department, DEPARTMENTS) self._create_instances(Course, COURSES) self._create_instances(Student, STUDENTS) self._create_instances(Instructor, INSTRUCTORS) self._create_instances(CourseSection, COURSE_SECTIONS) def tearDown(self): """Disconnect to the database.""" self._drop_generated_table() self._destroy_all_instances() db.close() def test_columns(self): self._execute_query() results = self._get_generated_table_columns() logging.info(results) self.assertEqual(len(results), 2) self.assertEqual(results[0], "year") self.assertEqual(results[1], "enrollment") def test_no_cs_enrollment_between_2016_and_2020(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 1, "grade": 92, }, { "sid": 2, "csid": 1, "grade": 80 }, { "sid": 4, "csid": 8, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 0) def test_one_cs_enrollment_in_2016(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 1, "grade": 92, }, { "sid": 2, "csid": 2, "grade": 80 }, { "sid": 4, "csid": 8, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2016) self.assertEqual(results[0]["enrollment"], 1) def test_multiple_cs_enrollments_in_2016(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 2, "grade": 92, }, { "sid": 2, "csid": 2, "grade": 80 }, { "sid": 3, "csid": 2, "grade": 55, }, { "sid": 4, "csid": 8, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2016) self.assertEqual(results[0]["enrollment"], 3) def test_multiple_cs_enrollments_in_2020(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 4, "grade": 92, }, { "sid": 2, "csid": 4, "grade": 80 }, { "sid": 3, "csid": 4, "grade": 55, }, { "sid": 4, "csid": 8, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2020) self.assertEqual(results[0]["enrollment"], 3) def test_multiple_cs_enrollments_in_2017(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 3, "grade": 92, }, { "sid": 2, "csid": 3, "grade": 80 }, { "sid": 3, "csid": 2, "grade": 55, }, { "sid": 4, "csid": 4, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2017) self.assertEqual(results[0]["enrollment"], 2) def test_multiple_cs_enrollments_in_2020_diff_sections(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 4, "grade": 92, }, { "sid": 2, "csid": 5, "grade": 80 }, { "sid": 3, "csid": 2, "grade": 55, }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2020) self.assertEqual(results[0]["enrollment"], 2) def test_multiple_cs_enrollments_in_2019_diff_sections(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 4, "grade": 92, }, { "sid": 2, "csid": 5, "grade": 80 }, { "sid": 3, "csid": 6, "grade": 55, }, { "sid": 4, "csid": 7, "grade": 55, }, { "sid": 1, "csid": 7, "grade": 55, } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2019) self.assertEqual(results[0]["enrollment"], 3) def test_two_cs_enrollments_in_2016_and_two_in_2020(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 2, "grade": 92, }, { "sid": 2, "csid": 4, "grade": 80 }, { "sid": 3, "csid": 2, "grade": 55, }, { "sid": 4, "csid": 5, "grade": 80 }, { "sid": 1, "csid": 8, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 2) self.assertEqual(results[0]["year"], 2016) self.assertEqual(results[0]["enrollment"], 2) self.assertEqual(results[1]["year"], 2020) self.assertEqual(results[1]["enrollment"], 2) def test_two_cs_enrollment_in_2016_and_three_in_2015(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 1, "grade": 92, }, { "sid": 2, "csid": 1, "grade": 80 }, { "sid": 3, "csid": 1, "grade": 55, }, { "sid": 1, "csid": 2, "grade": 80.3 }, { "sid": 2, "csid": 2, "grade": 52 } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2016) self.assertEqual(results[0]["enrollment"], 2) def test_multiple_cs_enrollments_in_2020_diff_courses(self): self._create_instances( StudentCourse, [ { "sid": 1, "csid": 4, "grade": 92, }, { "sid": 2, "csid": 5, "grade": 80 }, { "sid": 3, "csid": 3, "grade": 55, } ] ) self._execute_query() results = self._get_generated_table() logging.info(results) self.assertEqual(len(results), 1) self.assertEqual(results[0]["year"], 2020) self.assertEqual(results[0]["enrollment"], 2)

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null

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7

d42bce25f5e172ba77d7d210a945eabc2752a4ae

124

py

Python

releases/context_processors.py

sreekanth1990/djangoproject.com

69ab8573aadcc79e9da610ddad7b4e0c69a550d3

[ "BSD-3-Clause" ]

1,440

2015-01-05T13:06:12.000Z

2022-03-30T23:09:24.000Z

releases/context_processors.py

sreekanth1990/djangoproject.com

69ab8573aadcc79e9da610ddad7b4e0c69a550d3

[ "BSD-3-Clause" ]

711

2015-01-01T19:42:33.000Z

2022-03-29T08:36:29.000Z

releases/context_processors.py

sreekanth1990/djangoproject.com

69ab8573aadcc79e9da610ddad7b4e0c69a550d3

[ "BSD-3-Clause" ]

887

2015-01-01T03:17:20.000Z

2022-03-23T09:15:26.000Z

from .models import Release def django_version(request): return {'DJANGO_VERSION': Release.objects.current_version()}

20.666667

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1

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7

d45558bc67d347ecba76ad0e011e093f8f7063d5

101

py

Python

6_Builtin_Functions/_round.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

6_Builtin_Functions/_round.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

6_Builtin_Functions/_round.py

Oscar-Oliveira/Python3

fa791225a6810b75890d24407b73c5e1b514acbe

[ "MIT" ]

null

""" round """ print(round(233.33333)) print(round(233.33333, 2)) print(round(233.33333, -2))

12.625

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true

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null

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0

9

d4657dc3ba72c5bbbb757c303ab2288e40ebf7c3

2,000

py

Python

events/migrations/0050_auto_20210703_2308.py

horacexd/clist

9759dfea97b86514bec9825d2430abc36decacf0

[ "Apache-2.0" ]

166

2019-05-16T23:46:08.000Z

2022-03-31T05:20:23.000Z

events/migrations/0050_auto_20210703_2308.py

horacexd/clist

9759dfea97b86514bec9825d2430abc36decacf0

[ "Apache-2.0" ]

92

2020-01-18T22:51:53.000Z

2022-03-12T01:23:57.000Z

events/migrations/0050_auto_20210703_2308.py

horacexd/clist

9759dfea97b86514bec9825d2430abc36decacf0

[ "Apache-2.0" ]

23

2020-02-09T17:38:43.000Z

2021-12-09T14:39:07.000Z

# Generated by Django 3.1.12 on 2021-07-03 23:08 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('events', '0049_auto_20210308_1449'), ] operations = [ migrations.AlterField( model_name='event', name='created', field=models.DateTimeField(auto_now_add=True, db_index=True), ), migrations.AlterField( model_name='event', name='modified', field=models.DateTimeField(auto_now=True, db_index=True), ), migrations.AlterField( model_name='joinrequest', name='created', field=models.DateTimeField(auto_now_add=True, db_index=True), ), migrations.AlterField( model_name='joinrequest', name='modified', field=models.DateTimeField(auto_now=True, db_index=True), ), migrations.AlterField( model_name='login', name='created', field=models.DateTimeField(auto_now_add=True, db_index=True), ), migrations.AlterField( model_name='login', name='modified', field=models.DateTimeField(auto_now=True, db_index=True), ), migrations.AlterField( model_name='participant', name='created', field=models.DateTimeField(auto_now_add=True, db_index=True), ), migrations.AlterField( model_name='participant', name='modified', field=models.DateTimeField(auto_now=True, db_index=True), ), migrations.AlterField( model_name='team', name='created', field=models.DateTimeField(auto_now_add=True, db_index=True), ), migrations.AlterField( model_name='team', name='modified', field=models.DateTimeField(auto_now=True, db_index=True), ), ]

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d472aac69ba8f42a825c7a72d8d1ab7ef2809a8b

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py

Python

key_value_db_1/exception.py

varun-2108/KeyValueStore

4448053afb9b7bd39d926c941931a0421e262d19

[ "Unlicense" ]

null

key_value_db_1/exception.py

varun-2108/KeyValueStore

4448053afb9b7bd39d926c941931a0421e262d19

[ "Unlicense" ]

null

key_value_db_1/exception.py

varun-2108/KeyValueStore

4448053afb9b7bd39d926c941931a0421e262d19

[ "Unlicense" ]

null

class InvalidKeyType(Exception): def __init__(self, *args, **kwargs): Exception.__init__(self, *args, **kwargs) class KeyValueStoreDumpException(Exception): def __init__(self, *args, **kwargs): Exception.__init__(self, *args, **kwargs)

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