code stringlengths 2k 1.04M | repo_path stringlengths 5 517 | parsed_code stringlengths 0 1.04M | quality_prob float64 0.02 0.95 | learning_prob float64 0.02 0.93 |
|---|---|---|---|---|
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AccountEmailaddress',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('email', models.CharField(max_length=254, unique=True)),
('verified', models.IntegerField()),
('primary', models.IntegerField()),
],
options={
'db_table': 'account_emailaddress',
'managed': False,
},
),
migrations.CreateModel(
name='AccountEmailconfirmation',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('created', models.DateTimeField()),
('sent', models.DateTimeField(blank=True, null=True)),
('key', models.CharField(max_length=64, unique=True)),
],
options={
'db_table': 'account_emailconfirmation',
'managed': False,
},
),
migrations.CreateModel(
name='AuthGroup',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=150, unique=True)),
],
options={
'db_table': 'auth_group',
'managed': False,
},
),
migrations.CreateModel(
name='AuthGroupPermissions',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_group_permissions',
'managed': False,
},
),
migrations.CreateModel(
name='AuthPermission',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255)),
('codename', models.CharField(max_length=100)),
],
options={
'db_table': 'auth_permission',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUser',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('password', models.CharField(max_length=128)),
('last_login', models.DateTimeField(blank=True, null=True)),
('is_superuser', models.IntegerField()),
('username', models.CharField(max_length=150, unique=True)),
('first_name', models.CharField(max_length=150)),
('last_name', models.CharField(max_length=150)),
('email', models.CharField(max_length=254)),
('is_staff', models.IntegerField()),
('is_active', models.IntegerField()),
('date_joined', models.DateTimeField()),
],
options={
'db_table': 'auth_user',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUserGroups',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_user_groups',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUserUserPermissions',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_user_user_permissions',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoAdminLog',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('action_time', models.DateTimeField()),
('object_id', models.TextField(blank=True, null=True)),
('object_repr', models.CharField(max_length=200)),
('action_flag', models.PositiveSmallIntegerField()),
('change_message', models.TextField()),
],
options={
'db_table': 'django_admin_log',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoContentType',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('app_label', models.CharField(max_length=100)),
('model', models.CharField(max_length=100)),
],
options={
'db_table': 'django_content_type',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoMigrations',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('app', models.CharField(max_length=255)),
('name', models.CharField(max_length=255)),
('applied', models.DateTimeField()),
],
options={
'db_table': 'django_migrations',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoSession',
fields=[
('session_key', models.CharField(max_length=40, primary_key=True, serialize=False)),
('session_data', models.TextField()),
('expire_date', models.DateTimeField()),
],
options={
'db_table': 'django_session',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoSite',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('domain', models.CharField(max_length=100, unique=True)),
('name', models.CharField(max_length=50)),
],
options={
'db_table': 'django_site',
'managed': False,
},
),
migrations.CreateModel(
name='GroupArticleComments',
fields=[
('commentid', models.AutoField(primary_key=True, serialize=False)),
('comment', models.CharField(max_length=100)),
('writedate', models.DateTimeField()),
('is_talkback', models.IntegerField()),
],
options={
'db_table': 'grouparticlecomments',
'managed': False,
},
),
migrations.CreateModel(
name='GroupArticles',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('grouparticletitle', models.CharField(max_length=64)),
('grouparticlecontent', models.CharField(max_length=150)),
('grouparticlecategory', models.SmallIntegerField(db_column='groupArticleCategory')),
('uploaddate', models.DateTimeField()),
],
options={
'db_table': 'group_articles',
'managed': False,
},
),
migrations.CreateModel(
name='GroupAssignments',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('groupassignment', models.CharField(db_column='groupAssignment', max_length=32)),
('groupassignmentdetail', models.CharField(db_column='groupAssignmentdetail', max_length=500)),
('groupassignmentlimit', models.DateTimeField(db_column='groupAssignmentlimit')),
],
options={
'db_table': 'group_assignments',
'managed': False,
},
),
migrations.CreateModel(
name='GroupCalendar',
fields=[
('groupplanid', models.AutoField(db_column='groupPlanid', primary_key=True, serialize=False)),
('groupplanname', models.CharField(db_column='groupPlanname', max_length=64)),
('groupplaninfo', models.CharField(db_column='groupPlaninfo', max_length=128)),
('groupplanlink', models.CharField(blank=True, db_column='groupPlanlink', max_length=200, null=True)),
('groupplanstart', models.DateTimeField(db_column='groupPlanstart')),
('groupplanend', models.DateTimeField(db_column='groupPlanend')),
],
options={
'db_table': 'group_calendar',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialaccount',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('provider', models.CharField(max_length=30)),
('uid', models.CharField(max_length=191)),
('last_login', models.DateTimeField()),
('date_joined', models.DateTimeField()),
('extra_data', models.TextField()),
],
options={
'db_table': 'socialaccount_socialaccount',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialapp',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('provider', models.CharField(max_length=30)),
('name', models.CharField(max_length=40)),
('client_id', models.CharField(max_length=191)),
('secret', models.CharField(max_length=191)),
('key', models.CharField(max_length=191)),
],
options={
'db_table': 'socialaccount_socialapp',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialappSites',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('socialapp_id', models.BigIntegerField()),
],
options={
'db_table': 'socialaccount_socialapp_sites',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialtoken',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('token', models.TextField()),
('token_secret', models.TextField()),
('expires_at', models.DateTimeField(blank=True, null=True)),
],
options={
'db_table': 'socialaccount_socialtoken',
'managed': False,
},
),
migrations.CreateModel(
name='Studygroups',
fields=[
('groupid', models.AutoField(db_column='groupID', primary_key=True, serialize=False)),
('groupname', models.CharField(db_column='groupName', max_length=64)),
('grouppasscode', models.CharField(db_column='groupPasscode', max_length=64)),
],
options={
'db_table': 'studygroups',
'managed': False,
},
),
migrations.CreateModel(
name='UsersGroupsMapping',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'users_groups_mapping',
'managed': False,
},
),
] | whatshouldido/migrations/0001_initial.py |
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AccountEmailaddress',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('email', models.CharField(max_length=254, unique=True)),
('verified', models.IntegerField()),
('primary', models.IntegerField()),
],
options={
'db_table': 'account_emailaddress',
'managed': False,
},
),
migrations.CreateModel(
name='AccountEmailconfirmation',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('created', models.DateTimeField()),
('sent', models.DateTimeField(blank=True, null=True)),
('key', models.CharField(max_length=64, unique=True)),
],
options={
'db_table': 'account_emailconfirmation',
'managed': False,
},
),
migrations.CreateModel(
name='AuthGroup',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=150, unique=True)),
],
options={
'db_table': 'auth_group',
'managed': False,
},
),
migrations.CreateModel(
name='AuthGroupPermissions',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_group_permissions',
'managed': False,
},
),
migrations.CreateModel(
name='AuthPermission',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255)),
('codename', models.CharField(max_length=100)),
],
options={
'db_table': 'auth_permission',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUser',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('password', models.CharField(max_length=128)),
('last_login', models.DateTimeField(blank=True, null=True)),
('is_superuser', models.IntegerField()),
('username', models.CharField(max_length=150, unique=True)),
('first_name', models.CharField(max_length=150)),
('last_name', models.CharField(max_length=150)),
('email', models.CharField(max_length=254)),
('is_staff', models.IntegerField()),
('is_active', models.IntegerField()),
('date_joined', models.DateTimeField()),
],
options={
'db_table': 'auth_user',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUserGroups',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_user_groups',
'managed': False,
},
),
migrations.CreateModel(
name='AuthUserUserPermissions',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'auth_user_user_permissions',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoAdminLog',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('action_time', models.DateTimeField()),
('object_id', models.TextField(blank=True, null=True)),
('object_repr', models.CharField(max_length=200)),
('action_flag', models.PositiveSmallIntegerField()),
('change_message', models.TextField()),
],
options={
'db_table': 'django_admin_log',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoContentType',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('app_label', models.CharField(max_length=100)),
('model', models.CharField(max_length=100)),
],
options={
'db_table': 'django_content_type',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoMigrations',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('app', models.CharField(max_length=255)),
('name', models.CharField(max_length=255)),
('applied', models.DateTimeField()),
],
options={
'db_table': 'django_migrations',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoSession',
fields=[
('session_key', models.CharField(max_length=40, primary_key=True, serialize=False)),
('session_data', models.TextField()),
('expire_date', models.DateTimeField()),
],
options={
'db_table': 'django_session',
'managed': False,
},
),
migrations.CreateModel(
name='DjangoSite',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('domain', models.CharField(max_length=100, unique=True)),
('name', models.CharField(max_length=50)),
],
options={
'db_table': 'django_site',
'managed': False,
},
),
migrations.CreateModel(
name='GroupArticleComments',
fields=[
('commentid', models.AutoField(primary_key=True, serialize=False)),
('comment', models.CharField(max_length=100)),
('writedate', models.DateTimeField()),
('is_talkback', models.IntegerField()),
],
options={
'db_table': 'grouparticlecomments',
'managed': False,
},
),
migrations.CreateModel(
name='GroupArticles',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('grouparticletitle', models.CharField(max_length=64)),
('grouparticlecontent', models.CharField(max_length=150)),
('grouparticlecategory', models.SmallIntegerField(db_column='groupArticleCategory')),
('uploaddate', models.DateTimeField()),
],
options={
'db_table': 'group_articles',
'managed': False,
},
),
migrations.CreateModel(
name='GroupAssignments',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('groupassignment', models.CharField(db_column='groupAssignment', max_length=32)),
('groupassignmentdetail', models.CharField(db_column='groupAssignmentdetail', max_length=500)),
('groupassignmentlimit', models.DateTimeField(db_column='groupAssignmentlimit')),
],
options={
'db_table': 'group_assignments',
'managed': False,
},
),
migrations.CreateModel(
name='GroupCalendar',
fields=[
('groupplanid', models.AutoField(db_column='groupPlanid', primary_key=True, serialize=False)),
('groupplanname', models.CharField(db_column='groupPlanname', max_length=64)),
('groupplaninfo', models.CharField(db_column='groupPlaninfo', max_length=128)),
('groupplanlink', models.CharField(blank=True, db_column='groupPlanlink', max_length=200, null=True)),
('groupplanstart', models.DateTimeField(db_column='groupPlanstart')),
('groupplanend', models.DateTimeField(db_column='groupPlanend')),
],
options={
'db_table': 'group_calendar',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialaccount',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('provider', models.CharField(max_length=30)),
('uid', models.CharField(max_length=191)),
('last_login', models.DateTimeField()),
('date_joined', models.DateTimeField()),
('extra_data', models.TextField()),
],
options={
'db_table': 'socialaccount_socialaccount',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialapp',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('provider', models.CharField(max_length=30)),
('name', models.CharField(max_length=40)),
('client_id', models.CharField(max_length=191)),
('secret', models.CharField(max_length=191)),
('key', models.CharField(max_length=191)),
],
options={
'db_table': 'socialaccount_socialapp',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialappSites',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('socialapp_id', models.BigIntegerField()),
],
options={
'db_table': 'socialaccount_socialapp_sites',
'managed': False,
},
),
migrations.CreateModel(
name='SocialaccountSocialtoken',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
('token', models.TextField()),
('token_secret', models.TextField()),
('expires_at', models.DateTimeField(blank=True, null=True)),
],
options={
'db_table': 'socialaccount_socialtoken',
'managed': False,
},
),
migrations.CreateModel(
name='Studygroups',
fields=[
('groupid', models.AutoField(db_column='groupID', primary_key=True, serialize=False)),
('groupname', models.CharField(db_column='groupName', max_length=64)),
('grouppasscode', models.CharField(db_column='groupPasscode', max_length=64)),
],
options={
'db_table': 'studygroups',
'managed': False,
},
),
migrations.CreateModel(
name='UsersGroupsMapping',
fields=[
('id', models.BigAutoField(primary_key=True, serialize=False)),
],
options={
'db_table': 'users_groups_mapping',
'managed': False,
},
),
] | 0.503418 | 0.187486 |
import graphene
from graphene_django.types import DjangoObjectType
from rx import Observable
from graphene_subscriptions.events import CREATED, UPDATED, DELETED
from tests.models import SomeModel
CUSTOM_EVENT = "custom_event"
class SomeModelType(DjangoObjectType):
class Meta:
model = SomeModel
class SomeModelCreatedSubscription(graphene.ObjectType):
some_model_created = graphene.Field(SomeModelType)
def resolve_some_model_created(root, info):
return root.filter(
lambda event: event.operation == CREATED
and isinstance(event.instance, SomeModel)
).map(lambda event: event.instance)
class SomeModelUpdatedSubscription(graphene.ObjectType):
some_model_updated = graphene.Field(SomeModelType, id=graphene.ID())
def resolve_some_model_updated(root, info, id):
return root.filter(
lambda event: event.operation == UPDATED
and isinstance(event.instance, SomeModel)
and event.instance.pk == int(id)
).map(lambda event: event.instance)
class SomeModelDeletedSubscription(graphene.ObjectType):
some_model_deleted = graphene.Field(SomeModelType, id=graphene.ID())
def resolve_some_model_deleted(root, info, id):
return root.filter(
lambda event: event.operation == DELETED
and isinstance(event.instance, SomeModel)
and event.instance.pk == int(id)
).map(lambda event: event.instance)
class CustomEventSubscription(graphene.ObjectType):
custom_subscription = graphene.String()
def resolve_custom_subscription(root, info):
return root.filter(lambda event: event.operation == CUSTOM_EVENT).map(
lambda event: event.instance
)
class Subscription(
CustomEventSubscription,
SomeModelCreatedSubscription,
SomeModelUpdatedSubscription,
SomeModelDeletedSubscription,
):
hello = graphene.String()
def resolve_hello(root, info):
return Observable.of("hello world!")
class Query(graphene.ObjectType):
base = graphene.String()
schema = graphene.Schema(query=Query, subscription=Subscription) | tests/schema.py | import graphene
from graphene_django.types import DjangoObjectType
from rx import Observable
from graphene_subscriptions.events import CREATED, UPDATED, DELETED
from tests.models import SomeModel
CUSTOM_EVENT = "custom_event"
class SomeModelType(DjangoObjectType):
class Meta:
model = SomeModel
class SomeModelCreatedSubscription(graphene.ObjectType):
some_model_created = graphene.Field(SomeModelType)
def resolve_some_model_created(root, info):
return root.filter(
lambda event: event.operation == CREATED
and isinstance(event.instance, SomeModel)
).map(lambda event: event.instance)
class SomeModelUpdatedSubscription(graphene.ObjectType):
some_model_updated = graphene.Field(SomeModelType, id=graphene.ID())
def resolve_some_model_updated(root, info, id):
return root.filter(
lambda event: event.operation == UPDATED
and isinstance(event.instance, SomeModel)
and event.instance.pk == int(id)
).map(lambda event: event.instance)
class SomeModelDeletedSubscription(graphene.ObjectType):
some_model_deleted = graphene.Field(SomeModelType, id=graphene.ID())
def resolve_some_model_deleted(root, info, id):
return root.filter(
lambda event: event.operation == DELETED
and isinstance(event.instance, SomeModel)
and event.instance.pk == int(id)
).map(lambda event: event.instance)
class CustomEventSubscription(graphene.ObjectType):
custom_subscription = graphene.String()
def resolve_custom_subscription(root, info):
return root.filter(lambda event: event.operation == CUSTOM_EVENT).map(
lambda event: event.instance
)
class Subscription(
CustomEventSubscription,
SomeModelCreatedSubscription,
SomeModelUpdatedSubscription,
SomeModelDeletedSubscription,
):
hello = graphene.String()
def resolve_hello(root, info):
return Observable.of("hello world!")
class Query(graphene.ObjectType):
base = graphene.String()
schema = graphene.Schema(query=Query, subscription=Subscription) | 0.620047 | 0.203529 |
from polygon.rest.models import (
MarketHoliday,
MarketStatus,
MarketCurrencies,
MarketExchanges,
)
from base import BaseTest
class MarketsTest(BaseTest):
def test_get_market_holidays(self):
holidays = self.c.get_market_holidays()
expected = [
MarketHoliday(
close=None,
date="2022-05-30",
exchange="NYSE",
name="Memorial Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-05-30",
exchange="NASDAQ",
name="Memorial Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-06-20",
exchange="NASDAQ",
name="Juneteenth",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-06-20",
exchange="NYSE",
name="Juneteenth",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-07-04",
exchange="NYSE",
name="Independence Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-07-04",
exchange="NASDAQ",
name="Independence Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-09-05",
exchange="NYSE",
name="Labor Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-09-05",
exchange="NASDAQ",
name="Labor Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-11-24",
exchange="NYSE",
name="Thanksgiving",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-11-24",
exchange="NASDAQ",
name="Thanksgiving",
open=None,
status="closed",
),
MarketHoliday(
close="2022-11-25T18:00:00.000Z",
date="2022-11-25",
exchange="NYSE",
name="Thanksgiving",
open="2022-11-25T14:30:00.000Z",
status="early-close",
),
MarketHoliday(
close="2022-11-25T18:00:00.000Z",
date="2022-11-25",
exchange="NASDAQ",
name="Thanksgiving",
open="2022-11-25T14:30:00.000Z",
status="early-close",
),
MarketHoliday(
close=None,
date="2022-12-26",
exchange="NYSE",
name="Christmas",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-12-26",
exchange="NASDAQ",
name="Christmas",
open=None,
status="closed",
),
]
self.assertEqual(holidays, expected)
def test_get_market_status(self):
status = self.c.get_market_status()
expected = MarketStatus(
after_hours=True,
currencies=MarketCurrencies(crypto="open", fx="open"),
early_hours=False,
exchanges=MarketExchanges(
nasdaq="extended-hours", nyse="extended-hours", otc="extended-hours"
),
market="extended-hours",
server_time="2022-04-28T16:48:08-04:00",
)
self.assertEqual(status, expected) | test_rest/test_markets.py | from polygon.rest.models import (
MarketHoliday,
MarketStatus,
MarketCurrencies,
MarketExchanges,
)
from base import BaseTest
class MarketsTest(BaseTest):
def test_get_market_holidays(self):
holidays = self.c.get_market_holidays()
expected = [
MarketHoliday(
close=None,
date="2022-05-30",
exchange="NYSE",
name="Memorial Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-05-30",
exchange="NASDAQ",
name="Memorial Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-06-20",
exchange="NASDAQ",
name="Juneteenth",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-06-20",
exchange="NYSE",
name="Juneteenth",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-07-04",
exchange="NYSE",
name="Independence Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-07-04",
exchange="NASDAQ",
name="Independence Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-09-05",
exchange="NYSE",
name="Labor Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-09-05",
exchange="NASDAQ",
name="Labor Day",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-11-24",
exchange="NYSE",
name="Thanksgiving",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-11-24",
exchange="NASDAQ",
name="Thanksgiving",
open=None,
status="closed",
),
MarketHoliday(
close="2022-11-25T18:00:00.000Z",
date="2022-11-25",
exchange="NYSE",
name="Thanksgiving",
open="2022-11-25T14:30:00.000Z",
status="early-close",
),
MarketHoliday(
close="2022-11-25T18:00:00.000Z",
date="2022-11-25",
exchange="NASDAQ",
name="Thanksgiving",
open="2022-11-25T14:30:00.000Z",
status="early-close",
),
MarketHoliday(
close=None,
date="2022-12-26",
exchange="NYSE",
name="Christmas",
open=None,
status="closed",
),
MarketHoliday(
close=None,
date="2022-12-26",
exchange="NASDAQ",
name="Christmas",
open=None,
status="closed",
),
]
self.assertEqual(holidays, expected)
def test_get_market_status(self):
status = self.c.get_market_status()
expected = MarketStatus(
after_hours=True,
currencies=MarketCurrencies(crypto="open", fx="open"),
early_hours=False,
exchanges=MarketExchanges(
nasdaq="extended-hours", nyse="extended-hours", otc="extended-hours"
),
market="extended-hours",
server_time="2022-04-28T16:48:08-04:00",
)
self.assertEqual(status, expected) | 0.691706 | 0.305795 |
from unittest import TestCase, main
from project.student import Student
class TestStudent(TestCase):
def setUp(self):
self.student = Student("Ivan")
self.student_with_course = Student("Ivan", {"math": ["some notes"]})
def test_initializing(self):
self.assertEqual("Ivan", self.student.name)
self.assertEqual({}, self.student.courses)
self.assertEqual({"math": ["some notes"]}, self.student_with_course.courses)
def test_course_already_in(self):
result = self.student_with_course.enroll("math", ["more notes"])
self.assertEqual("Course already added. Notes have been updated.", result)
expected_notes = ["some notes", "more notes"]
actual_notes = self.student_with_course.courses['math']
self.assertEqual(expected_notes, actual_notes)
def test_add_course_notes(self):
result1 = self.student_with_course.enroll("physics", ["new notes"], "Y")
result2 = self.student_with_course.enroll("biology", ["new notes"])
self.assertEqual("Course and course notes have been added.", result1)
self.assertEqual("Course and course notes have been added.", result2)
self.assertEqual(["new notes"], self.student_with_course.courses["physics"])
self.assertEqual(["new notes"], self.student_with_course.courses["biology"])
def test_without_adding_notes(self):
result = self.student.enroll("math", "", "no notes")
self.assertEqual("Course has been added.", result)
self.assertEqual([], self.student.courses["math"])
def test_add_notes_on_existing_course(self):
result = self.student_with_course.add_notes("math", "a+b=c")
self.assertEqual("Notes have been updated", result)
self.assertEqual(["some notes", "a+b=c"], self.student_with_course.courses["math"])
def test_add_notes_to_non_existing_course(self):
with self.assertRaises(Exception) as ex:
self.student.add_notes("math", "a+b=c")
self.assertEqual("Cannot add notes. Course not found.", str(ex.exception))
def test_leaving_existing_course(self):
result = self.student_with_course.leave_course("math")
self.assertEqual("Course has been removed", result)
with self.assertRaises(KeyError):
result = self.student_with_course.courses["math"]
def test_leaving_non_existing_course(self):
with self.assertRaises(Exception) as ex:
self.student.leave_course("math")
self.assertEqual("Cannot remove course. Course not found.", str(ex.exception))
if __name__ == '__main__':
main() | Testing - Exercise/test/test_student.py | from unittest import TestCase, main
from project.student import Student
class TestStudent(TestCase):
def setUp(self):
self.student = Student("Ivan")
self.student_with_course = Student("Ivan", {"math": ["some notes"]})
def test_initializing(self):
self.assertEqual("Ivan", self.student.name)
self.assertEqual({}, self.student.courses)
self.assertEqual({"math": ["some notes"]}, self.student_with_course.courses)
def test_course_already_in(self):
result = self.student_with_course.enroll("math", ["more notes"])
self.assertEqual("Course already added. Notes have been updated.", result)
expected_notes = ["some notes", "more notes"]
actual_notes = self.student_with_course.courses['math']
self.assertEqual(expected_notes, actual_notes)
def test_add_course_notes(self):
result1 = self.student_with_course.enroll("physics", ["new notes"], "Y")
result2 = self.student_with_course.enroll("biology", ["new notes"])
self.assertEqual("Course and course notes have been added.", result1)
self.assertEqual("Course and course notes have been added.", result2)
self.assertEqual(["new notes"], self.student_with_course.courses["physics"])
self.assertEqual(["new notes"], self.student_with_course.courses["biology"])
def test_without_adding_notes(self):
result = self.student.enroll("math", "", "no notes")
self.assertEqual("Course has been added.", result)
self.assertEqual([], self.student.courses["math"])
def test_add_notes_on_existing_course(self):
result = self.student_with_course.add_notes("math", "a+b=c")
self.assertEqual("Notes have been updated", result)
self.assertEqual(["some notes", "a+b=c"], self.student_with_course.courses["math"])
def test_add_notes_to_non_existing_course(self):
with self.assertRaises(Exception) as ex:
self.student.add_notes("math", "a+b=c")
self.assertEqual("Cannot add notes. Course not found.", str(ex.exception))
def test_leaving_existing_course(self):
result = self.student_with_course.leave_course("math")
self.assertEqual("Course has been removed", result)
with self.assertRaises(KeyError):
result = self.student_with_course.courses["math"]
def test_leaving_non_existing_course(self):
with self.assertRaises(Exception) as ex:
self.student.leave_course("math")
self.assertEqual("Cannot remove course. Course not found.", str(ex.exception))
if __name__ == '__main__':
main() | 0.695028 | 0.627152 |
__author__ = "TetrisFinalBoss"
__version__ = "0.4.3"
import sys
import cv2
import numpy
import pafy
import re
import os.path
import getopt
AGS_DS2_PLAYLIST = 'PL_ftpUY_ldBTtHOUQLt5irghX1XfIzoy-'
def getFile(media):
for stream in media.streams:
if stream.dimensions[1] == 360 and stream.extension=='mp4':
m = re.search('\[Part\s?([\d]+)(\s-\sFinal)?\]',media.title)
fname = "%s - %s.%s"%(m.group(1),media.videoid,stream.extension)
if not os.path.isfile(fname):
print 'Downloading video %s from playlist'%(m.group(1))
stream.download(fname)
else:
print "File '%s' already exists, skipping download"%(fname)
return fname
def tsum(t1,t2):
return tuple(map(lambda x,y: x + y,t1,t2))
class DialogLocator:
D_MATCHMINIMUM = 0.5
D_WINDOW = {'left':396, 'top':61, 'right':418, 'bottom':84}
def __init__(self):
self.__dialogPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/dialog_pattern.png",cv2.IMREAD_GRAYSCALE)
def locate(self, img):
res = cv2.matchTemplate(img[self.D_WINDOW['top']:self.D_WINDOW['bottom'],self.D_WINDOW['left']:self.D_WINDOW['right']],
self.__dialogPattern,
cv2.TM_SQDIFF_NORMED)
min_val = cv2.minMaxLoc(res)[0]
if min_val>self.D_MATCHMINIMUM:
return False
return True
class SomethingExplainedDetector:
MATCHMINIMUM = 0.4
QUOT_WINDOW = {'left':20, 'top':15, 'right':50, 'bottom':42}
EXPL_WINDOW = {'left':20, 'top':15, 'right':400, 'bottom':42}
def __init__(self):
self.__count = 0
self.__ncount = 0
self.__quotPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/quot_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__explPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/expl_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__quotDim = self.__quotPattern.shape
self.__explDim = self.__explPattern.shape
def detect(self, img):
ret = []
# Search for patterns, first quotation mark
res = cv2.matchTemplate(img[self.QUOT_WINDOW['top']:self.QUOT_WINDOW['bottom'],
self.QUOT_WINDOW['left']:self.QUOT_WINDOW['right']],
self.__quotPattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.QUOT_WINDOW['left'],self.QUOT_WINDOW['top']))
bottom_right = tsum(top_left, (self.__quotDim[1],self.__quotDim[0]))
ret.append((top_left,bottom_right))
else:
# No new objects, but __count stays the same until dialog is over
self.__ncount = 0
return ret
# Second 'explained' word
res = cv2.matchTemplate(img[self.EXPL_WINDOW['top']:self.EXPL_WINDOW['bottom'],
self.EXPL_WINDOW['left']:self.EXPL_WINDOW['right']],
self.__explPattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.EXPL_WINDOW['left'],self.EXPL_WINDOW['top']))
bottom_right = tsum(top_left, (self.__explDim[1],self.__explDim[0]))
ret.append((top_left,bottom_right))
else:
# No new objects, but __count stays the same until dialog is over
self.__ncount = 0
return ret
# Both are found, mess with counters
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
return ret
def reset(self):
self.__count = 0
self.__ncount = 0
def dialogClosed(self):
# If no dialog arrow is found reset all values
self.__ncount = 0
self.__count = 0
def name(self):
return "'someone explained something'"
def uniqueObjects(self):
return False
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class CircumstancesExplainedDetector:
MATCHMINIMUM = 0.4
SEARCH_WINDOW = {'left':10, 'top':10, 'right':400, 'bottom':84}
def __init__(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
self.__ec1Pattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/etc_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__ec1Dim = self.__ec1Pattern.shape
def detect(self, img):
ret = self.__pobj
# Search for pattern
res = cv2.matchTemplate(img[self.SEARCH_WINDOW['top']:self.SEARCH_WINDOW['bottom'],
self.SEARCH_WINDOW['left']:self.SEARCH_WINDOW['right']],
self.__ec1Pattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.SEARCH_WINDOW['left'],self.SEARCH_WINDOW['top']))
bottom_right = tsum(top_left, (self.__ec1Dim[1],self.__ec1Dim[0]))
ret = [(top_left,bottom_right)]
self.__pobj = ret
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
else:
# Nothing is found, but if we've already found something in this dialog box
# let's assume that this object is still present, because this detector is blocking one
self.__count = len(self.__pobj)
self.__ncount = 0
return ret
def reset(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def dialogClosed(self):
# If no dialog arrow is found reset all values
self.__ncount = 0
self.__count = 0
self.__pobj = []
def name(self):
return "'explained the circumstances'"
def uniqueObjects(self):
return True
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class MeaningfulSilenceDetector:
MATCHMINIMUM = 0.4
PATTERN_SIZE = (16,60,1)
PATTERN_OFFSET = {'x':18,'y':10}
SEARCH_WINDOW = {'left':10, 'top':10, 'right':110, 'bottom':40}
PATTERN_COLOR = 127
def __init__(self):
self.__pattern = numpy.zeros(self.PATTERN_SIZE, numpy.uint8)
for i in xrange(6):
cv2.rectangle(self.__pattern,
(self.PATTERN_OFFSET['x']+7*i,self.PATTERN_OFFSET['y']),
(self.PATTERN_OFFSET['x']+1+7*i,self.PATTERN_OFFSET['y']+1),
self.PATTERN_COLOR,
-1)
self.__count = 0
self.__ncount = 0
self.__pobj = []
def detect(self, img):
# Set "default" return value to previously found object in this dialog entry
# which is reset to [] when dialog is closed
ret = self.__pobj
# Search for pattern
res = cv2.matchTemplate(img[self.SEARCH_WINDOW['top']:self.SEARCH_WINDOW['bottom'],
self.SEARCH_WINDOW['left']:self.SEARCH_WINDOW['right']],
self.__pattern,
cv2.TM_SQDIFF_NORMED)
# There can be only one "......" in dialog, so we totally fine with global minimum
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
if min_val < self.MATCHMINIMUM:
top_left = tsum(min_loc, (self.SEARCH_WINDOW['left'],self.SEARCH_WINDOW['top']))
bottom_right = tsum(top_left, (self.PATTERN_SIZE[1],self.PATTERN_SIZE[0]))
# Something is found, set return value and store this object for future use
ret = [(top_left,bottom_right)]
self.__pobj = ret
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
else:
# Nothing is found, but if we've already found something in this dialog box
# let's assume that this object is still present
self.__count = len(self.__pobj)
self.__ncount = 0
return ret
def dialogClosed(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def reset(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def name(self):
return "'......'"
def uniqueObjects(self):
return True
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class MidSentenceEllipsesDetector:
MATCHMINIMUM = 0.5
PATTERN_SIZE = (8,20,1)
PATTERN_OFFSET = {'x':1,'y':3}
PATTERN_COLOR = 127
def __init__(self):
self.__pattern = numpy.zeros(self.PATTERN_SIZE, numpy.uint8)
for i in xrange(3):
cv2.rectangle(self.__pattern,
(self.PATTERN_OFFSET['x']+7*i,self.PATTERN_OFFSET['y']),
(self.PATTERN_OFFSET['x']+1+7*i,self.PATTERN_OFFSET['y']+1),
self.PATTERN_COLOR,
-1)
self.__ncount = 0
self.__count = 0
def detect(self, img):
ret = []
res = cv2.matchTemplate(img,self.__pattern,cv2.TM_SQDIFF_NORMED)
# For each row in dialog do recursive search for global minimums
def localMinInRow(row,offset):
# Current dimensions
h,w = row.shape
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(row)
if min_val < self.MATCHMINIMUM:
x,y = min_loc
# Recalculate absolute position and append value
min_loc = tsum(min_loc,offset)
ret.append((min_loc,tsum(min_loc, (self.PATTERN_SIZE[1],self.PATTERN_SIZE[0]))))
# Add threshold around this point
mthresh = self.PATTERN_SIZE[1]
# Now search minimums in left region
if x-mthresh>self.PATTERN_SIZE[1]:
localMinInRow(row[0:h,0:x-mthresh],offset)
# And in right region
if w-x-mthresh > self.PATTERN_SIZE[1]:
localMinInRow(row[0:h,x+mthresh:w],tsum(offset,(x+mthresh,0)))
for i in xrange(3):
yoff = 20+i*20+4
localMinInRow(res[yoff:yoff+18,20:400],(20,yoff))
# Sometimes objects may be lost and caught again later
# Let's try to address this issue
l = len(ret)
# Get new objects count
self.__ncount = l - self.__count
if self.__ncount<0:
self.__ncount = 0
# Store object count, but assuming, that objects can't disappear
# during same dialog line, so it alway stays at maximum level
self.__count = max(l,self.__count)
return ret
def reset(self):
self.__ncount = 0
self.__count = 0
def dialogClosed(self):
self.__ncount = 0
self.__count = 0
def name(self):
return "'...'"
def uniqueObjects(self):
return False
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class EllipsesSearcher:
# Threshold values
THRESHOLD_VALUE = 90
THRESHOLD_COLOR = 127
# Dialog box window
DIALOG = {'left':104,'top':248,'right':538,'bottom':340}
# Dialog box highlight
DIALOG_HIGHLIGHT = {'lt': (1,1), 'br': (432, 90)}
def __init__(self):
# Init detectors
self.__detectors = []
self.__detectors.append(MeaningfulSilenceDetector())
self.__detectors.append(MidSentenceEllipsesDetector())
self.__detectors.append(CircumstancesExplainedDetector())
self.__detectors.append(SomethingExplainedDetector())
# Init dialog locator
self.__dialogLocator = DialogLocator()
# Reset other values
self.__total = len(self.__detectors)*[0]
self.__frames = len(self.__detectors)*[0]
self.snapshots = False
self.statFile = None
self.useStatFile = False
self.ignoreStat = False
self.preview = False
self.detectorMask = 0xff
def __thresh(self,img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, t = cv2.threshold(gray, self.THRESHOLD_VALUE, self.THRESHOLD_COLOR, cv2.THRESH_TOZERO)
return t
def __writeUserStatObject(self, det, m, s):
if self.useStatFile:
self.statFile.write("%s is found at %s:%s\n"%(det,m,s))
self.statFile.flush()
def __writeUserStatHeader(self, fname):
if self.useStatFile:
self.statFile.write("===\n%s\n===\n"%(fname))
self.statFile.flush()
def __writeUserStatTotal(self, lst):
if self.useStatFile:
self.statFile.write("===\n")
for e in lst:
self.statFile.write("%s is said %d times (%d frames)\n"%e)
self.statFile.write("\n")
self.statFile.flush()
def __readStat(self,fname):
if self.ignoreStat:
return False
try:
statfile = open('statistics/'+fname+'.stat','r')
count = len(self.__detectors)*[0]
frames = len(self.__detectors)*[0]
for ln in statfile.readlines():
m = re.search('OBJECT\s([\d]+)\s([\d]+):([\d]+)',ln)
if m:
# Last parameter is object type - i.e. detector number
det = int(m.group(1))
self.__writeUserStatObject(self.__detectors[det].name(),m.group(2),m.group(3))
# And increase counter
count[det]+=1
continue
m = re.search('FRAMES\s([\d]+)\s([\d]+)',ln)
if m:
frames[int(m.group(1))]+=int(m.group(2))
continue
statfile.close()
# Increase total value
self.__total = map(lambda x,y: x+y, count, self.__total)
self.__frames = map(lambda x,y: x+y, frames, self.__frames)
# Display progress
print "Reading statistics from file: Done - %d objects detected"%(sum(count))
# And also write to user specified file
self.__writeUserStatTotal(zip(map(lambda x: x.name(), self.__detectors), count, frames))
# And that's it, this file is done
return True
except (OSError, IOError):
return False
def count(self,fname):
self.__writeUserStatHeader(fname)
# First - try to get statistics from file,
# so we don't have to recalculate stats once again
if self.__readStat(fname):
return
count = len(self.__detectors)*[0]
frames = len(self.__detectors)*[0]
# Reset detectors before apply them to new file
for d in self.__detectors:
d.reset()
statfile = open('statistics/'+fname+'.stat','w')
v = cv2.VideoCapture(fname)
frame_count = int(v.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
frame_no = 0
previewRate = 1
while v.isOpened():
ret, frame = v.read()
frame_no+=1
if not ret:
break
# Use simple threshold for dialog box
box = frame[self.DIALOG['top']:self.DIALOG['bottom'],self.DIALOG['left']:self.DIALOG['right']]
t = self.__thresh(box)
objects = []
shouldSaveSnapshot = False
secs = int(v.get(cv2.cv.CV_CAP_PROP_POS_MSEC)/1000)
dialogClosed = not self.__dialogLocator.locate(t)
# Now apply all detectors for this frame
for i in xrange(len(self.__detectors)):
# Check if detector is enabled
if (self.detectorMask & (1 << i)) == 0:
continue
if dialogClosed:
self.__detectors[i].dialogClosed()
continue
# Apply detector to thresholded picture and store all found objects for this particular detector
items = self.__detectors[i].detect(t)
# If some of these objects are new
ncount = self.__detectors[i].newObjectsCount()
if ncount>0:
count[i] += ncount
self.__total[i] += ncount
shouldSaveSnapshot = self.snapshots
for j in xrange(ncount):
# Write to user specified file
self.__writeUserStatObject(self.__detectors[i].name(),secs/60,secs%60)
# And store stats for future use
statfile.write('OBJECT %d %d:%d\n'%(i,secs/60,secs%60))
if len(items):
objects += items
# We check stored objects count value for detector instead len(items)
# This way detectors can return objects just for preview without possible effect on statistics
if self.__detectors[i].objectsCount()>0:
# First of all - increase frame counter for that object
frames[i] += 1
self.__frames[i] += 1
# If we found unique object (i.e. there can't be any other objects in this picture) - stop applying detectors
if self.__detectors[i].uniqueObjects():
break
# Prepare images
if shouldSaveSnapshot or self.preview:
for item in objects:
if shouldSaveSnapshot:
cv2.rectangle(box,item[0],item[1],(0xff,0,0))
if self.preview:
cv2.rectangle(t,item[0],item[1],0xff)
# Save snapshot
if shouldSaveSnapshot:
cv2.imwrite("snapshots/%s.%d-%d.png"%(fname,secs/60,secs%60),box)
# Show preview window if enabled
if self.preview:
if not dialogClosed:
cv2.rectangle(t, self.DIALOG_HIGHLIGHT['lt'], self.DIALOG_HIGHLIGHT['br'], 0xff)
cv2.imshow("Picture",t)
k = cv2.waitKey(previewRate) & 0xff
if k==ord('q'):
sys.exit(0)
elif k==ord('s'):
cv2.imwrite('snapshots/snapshot_orig.png',box)
cv2.imwrite('snapshots/snapshot_modified.png',t)
elif k==ord('n'):
previewRate = 0
elif k==ord('p'):
previewRate = 1
# Display some progress
progress = frame_no*100/frame_count
sys.stdout.write("Processing video: %d%% - %d objects found\r"%(progress, sum(count)))
sys.stdout.flush()
# Display final state for this file
print "Processing video: Done - %d objects found"%(sum(count))
# And also write to user specified file
self.__writeUserStatTotal(zip(map(lambda x: x.name(), self.__detectors), count, frames))
# And save frame statistics
for e in enumerate(frames):
statfile.write('FRAMES %d %d\n'%e)
v.release()
statfile.close()
def total(self):
ret = ""
for e in zip(map(lambda x: x.name(), self.__detectors), self.__total, self.__frames):
ret = ret+"%s is said %d times (%d frames)\n"%e
return ret
if __name__=="__main__":
el = EllipsesSearcher()
downloadOnly = False
try:
opts, args = getopt.getopt(sys.argv[1:],"hirdvf:m:")
except getopt.GetoptError as err:
print str(err)
sys.exit(1)
for opt, arg in opts:
if opt == '-h':
print 'Usage: %s [-h] [-i] [-r] [-d] [-v] [-f filename] [-m mask]'%(sys.argv[0])
print '-h -- Show this help'
print '-i -- Save snapshots each time ellipses is found'
print '-r -- Ignore (reset) previously collected statistics'
print '-d -- Download only'
print '-v -- Display video preview (debug mode)'
print '-m <mask> -- Set detector mask to <mask>'
print '-f <file> -- Write statistics to <file>'
sys.exit()
elif opt == "-i":
print 'Snapshots is enabled'
if not os.path.isdir('snapshots'):
os.mkdir('snapshots')
el.snapshots = True
elif opt == "-r":
el.ignoreStat = True
elif opt == "-v":
el.preview = True
elif opt == "-d":
downloadOnly = True
elif opt == "-f":
el.useStatFile = True
el.statFile = open(arg,'w')
elif opt == "-m":
el.detectorMask = int(arg)
if not os.path.isdir('statistics'):
os.mkdir('statistics')
# get Devil Summoner 2 playlist
playList = pafy.get_playlist(AGS_DS2_PLAYLIST)
for media in playList['items']:
fname = getFile(media['pafy'])
if not downloadOnly:
el.count(fname)
print "We are done!"
if downloadOnly:
print "Playlist downloaded!"
else:
print el.total()
if el.useStatFile:
el.statFile.write("===\nTotal\n===\n%s"%(el.total()))
el.statFile.flush() | ags-ds2.py | __author__ = "TetrisFinalBoss"
__version__ = "0.4.3"
import sys
import cv2
import numpy
import pafy
import re
import os.path
import getopt
AGS_DS2_PLAYLIST = 'PL_ftpUY_ldBTtHOUQLt5irghX1XfIzoy-'
def getFile(media):
for stream in media.streams:
if stream.dimensions[1] == 360 and stream.extension=='mp4':
m = re.search('\[Part\s?([\d]+)(\s-\sFinal)?\]',media.title)
fname = "%s - %s.%s"%(m.group(1),media.videoid,stream.extension)
if not os.path.isfile(fname):
print 'Downloading video %s from playlist'%(m.group(1))
stream.download(fname)
else:
print "File '%s' already exists, skipping download"%(fname)
return fname
def tsum(t1,t2):
return tuple(map(lambda x,y: x + y,t1,t2))
class DialogLocator:
D_MATCHMINIMUM = 0.5
D_WINDOW = {'left':396, 'top':61, 'right':418, 'bottom':84}
def __init__(self):
self.__dialogPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/dialog_pattern.png",cv2.IMREAD_GRAYSCALE)
def locate(self, img):
res = cv2.matchTemplate(img[self.D_WINDOW['top']:self.D_WINDOW['bottom'],self.D_WINDOW['left']:self.D_WINDOW['right']],
self.__dialogPattern,
cv2.TM_SQDIFF_NORMED)
min_val = cv2.minMaxLoc(res)[0]
if min_val>self.D_MATCHMINIMUM:
return False
return True
class SomethingExplainedDetector:
MATCHMINIMUM = 0.4
QUOT_WINDOW = {'left':20, 'top':15, 'right':50, 'bottom':42}
EXPL_WINDOW = {'left':20, 'top':15, 'right':400, 'bottom':42}
def __init__(self):
self.__count = 0
self.__ncount = 0
self.__quotPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/quot_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__explPattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/expl_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__quotDim = self.__quotPattern.shape
self.__explDim = self.__explPattern.shape
def detect(self, img):
ret = []
# Search for patterns, first quotation mark
res = cv2.matchTemplate(img[self.QUOT_WINDOW['top']:self.QUOT_WINDOW['bottom'],
self.QUOT_WINDOW['left']:self.QUOT_WINDOW['right']],
self.__quotPattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.QUOT_WINDOW['left'],self.QUOT_WINDOW['top']))
bottom_right = tsum(top_left, (self.__quotDim[1],self.__quotDim[0]))
ret.append((top_left,bottom_right))
else:
# No new objects, but __count stays the same until dialog is over
self.__ncount = 0
return ret
# Second 'explained' word
res = cv2.matchTemplate(img[self.EXPL_WINDOW['top']:self.EXPL_WINDOW['bottom'],
self.EXPL_WINDOW['left']:self.EXPL_WINDOW['right']],
self.__explPattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.EXPL_WINDOW['left'],self.EXPL_WINDOW['top']))
bottom_right = tsum(top_left, (self.__explDim[1],self.__explDim[0]))
ret.append((top_left,bottom_right))
else:
# No new objects, but __count stays the same until dialog is over
self.__ncount = 0
return ret
# Both are found, mess with counters
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
return ret
def reset(self):
self.__count = 0
self.__ncount = 0
def dialogClosed(self):
# If no dialog arrow is found reset all values
self.__ncount = 0
self.__count = 0
def name(self):
return "'someone explained something'"
def uniqueObjects(self):
return False
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class CircumstancesExplainedDetector:
MATCHMINIMUM = 0.4
SEARCH_WINDOW = {'left':10, 'top':10, 'right':400, 'bottom':84}
def __init__(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
self.__ec1Pattern = cv2.imread(os.path.dirname(sys.argv[0]) + "/etc_pattern.png",cv2.IMREAD_GRAYSCALE)
self.__ec1Dim = self.__ec1Pattern.shape
def detect(self, img):
ret = self.__pobj
# Search for pattern
res = cv2.matchTemplate(img[self.SEARCH_WINDOW['top']:self.SEARCH_WINDOW['bottom'],
self.SEARCH_WINDOW['left']:self.SEARCH_WINDOW['right']],
self.__ec1Pattern,
cv2.TM_SQDIFF_NORMED)
minmax = cv2.minMaxLoc(res)
if minmax[0] < self.MATCHMINIMUM:
top_left = tsum(minmax[2], (self.SEARCH_WINDOW['left'],self.SEARCH_WINDOW['top']))
bottom_right = tsum(top_left, (self.__ec1Dim[1],self.__ec1Dim[0]))
ret = [(top_left,bottom_right)]
self.__pobj = ret
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
else:
# Nothing is found, but if we've already found something in this dialog box
# let's assume that this object is still present, because this detector is blocking one
self.__count = len(self.__pobj)
self.__ncount = 0
return ret
def reset(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def dialogClosed(self):
# If no dialog arrow is found reset all values
self.__ncount = 0
self.__count = 0
self.__pobj = []
def name(self):
return "'explained the circumstances'"
def uniqueObjects(self):
return True
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class MeaningfulSilenceDetector:
MATCHMINIMUM = 0.4
PATTERN_SIZE = (16,60,1)
PATTERN_OFFSET = {'x':18,'y':10}
SEARCH_WINDOW = {'left':10, 'top':10, 'right':110, 'bottom':40}
PATTERN_COLOR = 127
def __init__(self):
self.__pattern = numpy.zeros(self.PATTERN_SIZE, numpy.uint8)
for i in xrange(6):
cv2.rectangle(self.__pattern,
(self.PATTERN_OFFSET['x']+7*i,self.PATTERN_OFFSET['y']),
(self.PATTERN_OFFSET['x']+1+7*i,self.PATTERN_OFFSET['y']+1),
self.PATTERN_COLOR,
-1)
self.__count = 0
self.__ncount = 0
self.__pobj = []
def detect(self, img):
# Set "default" return value to previously found object in this dialog entry
# which is reset to [] when dialog is closed
ret = self.__pobj
# Search for pattern
res = cv2.matchTemplate(img[self.SEARCH_WINDOW['top']:self.SEARCH_WINDOW['bottom'],
self.SEARCH_WINDOW['left']:self.SEARCH_WINDOW['right']],
self.__pattern,
cv2.TM_SQDIFF_NORMED)
# There can be only one "......" in dialog, so we totally fine with global minimum
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
if min_val < self.MATCHMINIMUM:
top_left = tsum(min_loc, (self.SEARCH_WINDOW['left'],self.SEARCH_WINDOW['top']))
bottom_right = tsum(top_left, (self.PATTERN_SIZE[1],self.PATTERN_SIZE[0]))
# Something is found, set return value and store this object for future use
ret = [(top_left,bottom_right)]
self.__pobj = ret
self.__ncount = self.__count==0 and 1 or 0
self.__count = 1
else:
# Nothing is found, but if we've already found something in this dialog box
# let's assume that this object is still present
self.__count = len(self.__pobj)
self.__ncount = 0
return ret
def dialogClosed(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def reset(self):
self.__count = 0
self.__ncount = 0
self.__pobj = []
def name(self):
return "'......'"
def uniqueObjects(self):
return True
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class MidSentenceEllipsesDetector:
MATCHMINIMUM = 0.5
PATTERN_SIZE = (8,20,1)
PATTERN_OFFSET = {'x':1,'y':3}
PATTERN_COLOR = 127
def __init__(self):
self.__pattern = numpy.zeros(self.PATTERN_SIZE, numpy.uint8)
for i in xrange(3):
cv2.rectangle(self.__pattern,
(self.PATTERN_OFFSET['x']+7*i,self.PATTERN_OFFSET['y']),
(self.PATTERN_OFFSET['x']+1+7*i,self.PATTERN_OFFSET['y']+1),
self.PATTERN_COLOR,
-1)
self.__ncount = 0
self.__count = 0
def detect(self, img):
ret = []
res = cv2.matchTemplate(img,self.__pattern,cv2.TM_SQDIFF_NORMED)
# For each row in dialog do recursive search for global minimums
def localMinInRow(row,offset):
# Current dimensions
h,w = row.shape
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(row)
if min_val < self.MATCHMINIMUM:
x,y = min_loc
# Recalculate absolute position and append value
min_loc = tsum(min_loc,offset)
ret.append((min_loc,tsum(min_loc, (self.PATTERN_SIZE[1],self.PATTERN_SIZE[0]))))
# Add threshold around this point
mthresh = self.PATTERN_SIZE[1]
# Now search minimums in left region
if x-mthresh>self.PATTERN_SIZE[1]:
localMinInRow(row[0:h,0:x-mthresh],offset)
# And in right region
if w-x-mthresh > self.PATTERN_SIZE[1]:
localMinInRow(row[0:h,x+mthresh:w],tsum(offset,(x+mthresh,0)))
for i in xrange(3):
yoff = 20+i*20+4
localMinInRow(res[yoff:yoff+18,20:400],(20,yoff))
# Sometimes objects may be lost and caught again later
# Let's try to address this issue
l = len(ret)
# Get new objects count
self.__ncount = l - self.__count
if self.__ncount<0:
self.__ncount = 0
# Store object count, but assuming, that objects can't disappear
# during same dialog line, so it alway stays at maximum level
self.__count = max(l,self.__count)
return ret
def reset(self):
self.__ncount = 0
self.__count = 0
def dialogClosed(self):
self.__ncount = 0
self.__count = 0
def name(self):
return "'...'"
def uniqueObjects(self):
return False
def objectsCount(self):
return self.__count
def newObjectsCount(self):
return self.__ncount
class EllipsesSearcher:
# Threshold values
THRESHOLD_VALUE = 90
THRESHOLD_COLOR = 127
# Dialog box window
DIALOG = {'left':104,'top':248,'right':538,'bottom':340}
# Dialog box highlight
DIALOG_HIGHLIGHT = {'lt': (1,1), 'br': (432, 90)}
def __init__(self):
# Init detectors
self.__detectors = []
self.__detectors.append(MeaningfulSilenceDetector())
self.__detectors.append(MidSentenceEllipsesDetector())
self.__detectors.append(CircumstancesExplainedDetector())
self.__detectors.append(SomethingExplainedDetector())
# Init dialog locator
self.__dialogLocator = DialogLocator()
# Reset other values
self.__total = len(self.__detectors)*[0]
self.__frames = len(self.__detectors)*[0]
self.snapshots = False
self.statFile = None
self.useStatFile = False
self.ignoreStat = False
self.preview = False
self.detectorMask = 0xff
def __thresh(self,img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, t = cv2.threshold(gray, self.THRESHOLD_VALUE, self.THRESHOLD_COLOR, cv2.THRESH_TOZERO)
return t
def __writeUserStatObject(self, det, m, s):
if self.useStatFile:
self.statFile.write("%s is found at %s:%s\n"%(det,m,s))
self.statFile.flush()
def __writeUserStatHeader(self, fname):
if self.useStatFile:
self.statFile.write("===\n%s\n===\n"%(fname))
self.statFile.flush()
def __writeUserStatTotal(self, lst):
if self.useStatFile:
self.statFile.write("===\n")
for e in lst:
self.statFile.write("%s is said %d times (%d frames)\n"%e)
self.statFile.write("\n")
self.statFile.flush()
def __readStat(self,fname):
if self.ignoreStat:
return False
try:
statfile = open('statistics/'+fname+'.stat','r')
count = len(self.__detectors)*[0]
frames = len(self.__detectors)*[0]
for ln in statfile.readlines():
m = re.search('OBJECT\s([\d]+)\s([\d]+):([\d]+)',ln)
if m:
# Last parameter is object type - i.e. detector number
det = int(m.group(1))
self.__writeUserStatObject(self.__detectors[det].name(),m.group(2),m.group(3))
# And increase counter
count[det]+=1
continue
m = re.search('FRAMES\s([\d]+)\s([\d]+)',ln)
if m:
frames[int(m.group(1))]+=int(m.group(2))
continue
statfile.close()
# Increase total value
self.__total = map(lambda x,y: x+y, count, self.__total)
self.__frames = map(lambda x,y: x+y, frames, self.__frames)
# Display progress
print "Reading statistics from file: Done - %d objects detected"%(sum(count))
# And also write to user specified file
self.__writeUserStatTotal(zip(map(lambda x: x.name(), self.__detectors), count, frames))
# And that's it, this file is done
return True
except (OSError, IOError):
return False
def count(self,fname):
self.__writeUserStatHeader(fname)
# First - try to get statistics from file,
# so we don't have to recalculate stats once again
if self.__readStat(fname):
return
count = len(self.__detectors)*[0]
frames = len(self.__detectors)*[0]
# Reset detectors before apply them to new file
for d in self.__detectors:
d.reset()
statfile = open('statistics/'+fname+'.stat','w')
v = cv2.VideoCapture(fname)
frame_count = int(v.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))
frame_no = 0
previewRate = 1
while v.isOpened():
ret, frame = v.read()
frame_no+=1
if not ret:
break
# Use simple threshold for dialog box
box = frame[self.DIALOG['top']:self.DIALOG['bottom'],self.DIALOG['left']:self.DIALOG['right']]
t = self.__thresh(box)
objects = []
shouldSaveSnapshot = False
secs = int(v.get(cv2.cv.CV_CAP_PROP_POS_MSEC)/1000)
dialogClosed = not self.__dialogLocator.locate(t)
# Now apply all detectors for this frame
for i in xrange(len(self.__detectors)):
# Check if detector is enabled
if (self.detectorMask & (1 << i)) == 0:
continue
if dialogClosed:
self.__detectors[i].dialogClosed()
continue
# Apply detector to thresholded picture and store all found objects for this particular detector
items = self.__detectors[i].detect(t)
# If some of these objects are new
ncount = self.__detectors[i].newObjectsCount()
if ncount>0:
count[i] += ncount
self.__total[i] += ncount
shouldSaveSnapshot = self.snapshots
for j in xrange(ncount):
# Write to user specified file
self.__writeUserStatObject(self.__detectors[i].name(),secs/60,secs%60)
# And store stats for future use
statfile.write('OBJECT %d %d:%d\n'%(i,secs/60,secs%60))
if len(items):
objects += items
# We check stored objects count value for detector instead len(items)
# This way detectors can return objects just for preview without possible effect on statistics
if self.__detectors[i].objectsCount()>0:
# First of all - increase frame counter for that object
frames[i] += 1
self.__frames[i] += 1
# If we found unique object (i.e. there can't be any other objects in this picture) - stop applying detectors
if self.__detectors[i].uniqueObjects():
break
# Prepare images
if shouldSaveSnapshot or self.preview:
for item in objects:
if shouldSaveSnapshot:
cv2.rectangle(box,item[0],item[1],(0xff,0,0))
if self.preview:
cv2.rectangle(t,item[0],item[1],0xff)
# Save snapshot
if shouldSaveSnapshot:
cv2.imwrite("snapshots/%s.%d-%d.png"%(fname,secs/60,secs%60),box)
# Show preview window if enabled
if self.preview:
if not dialogClosed:
cv2.rectangle(t, self.DIALOG_HIGHLIGHT['lt'], self.DIALOG_HIGHLIGHT['br'], 0xff)
cv2.imshow("Picture",t)
k = cv2.waitKey(previewRate) & 0xff
if k==ord('q'):
sys.exit(0)
elif k==ord('s'):
cv2.imwrite('snapshots/snapshot_orig.png',box)
cv2.imwrite('snapshots/snapshot_modified.png',t)
elif k==ord('n'):
previewRate = 0
elif k==ord('p'):
previewRate = 1
# Display some progress
progress = frame_no*100/frame_count
sys.stdout.write("Processing video: %d%% - %d objects found\r"%(progress, sum(count)))
sys.stdout.flush()
# Display final state for this file
print "Processing video: Done - %d objects found"%(sum(count))
# And also write to user specified file
self.__writeUserStatTotal(zip(map(lambda x: x.name(), self.__detectors), count, frames))
# And save frame statistics
for e in enumerate(frames):
statfile.write('FRAMES %d %d\n'%e)
v.release()
statfile.close()
def total(self):
ret = ""
for e in zip(map(lambda x: x.name(), self.__detectors), self.__total, self.__frames):
ret = ret+"%s is said %d times (%d frames)\n"%e
return ret
if __name__=="__main__":
el = EllipsesSearcher()
downloadOnly = False
try:
opts, args = getopt.getopt(sys.argv[1:],"hirdvf:m:")
except getopt.GetoptError as err:
print str(err)
sys.exit(1)
for opt, arg in opts:
if opt == '-h':
print 'Usage: %s [-h] [-i] [-r] [-d] [-v] [-f filename] [-m mask]'%(sys.argv[0])
print '-h -- Show this help'
print '-i -- Save snapshots each time ellipses is found'
print '-r -- Ignore (reset) previously collected statistics'
print '-d -- Download only'
print '-v -- Display video preview (debug mode)'
print '-m <mask> -- Set detector mask to <mask>'
print '-f <file> -- Write statistics to <file>'
sys.exit()
elif opt == "-i":
print 'Snapshots is enabled'
if not os.path.isdir('snapshots'):
os.mkdir('snapshots')
el.snapshots = True
elif opt == "-r":
el.ignoreStat = True
elif opt == "-v":
el.preview = True
elif opt == "-d":
downloadOnly = True
elif opt == "-f":
el.useStatFile = True
el.statFile = open(arg,'w')
elif opt == "-m":
el.detectorMask = int(arg)
if not os.path.isdir('statistics'):
os.mkdir('statistics')
# get Devil Summoner 2 playlist
playList = pafy.get_playlist(AGS_DS2_PLAYLIST)
for media in playList['items']:
fname = getFile(media['pafy'])
if not downloadOnly:
el.count(fname)
print "We are done!"
if downloadOnly:
print "Playlist downloaded!"
else:
print el.total()
if el.useStatFile:
el.statFile.write("===\nTotal\n===\n%s"%(el.total()))
el.statFile.flush() | 0.278944 | 0.109372 |
import logging
import jwt
import requests
from jwt import PyJWKClient
from users_microservice.cfg import config
from users_microservice.constants import (
DEFAULT_AUDIENCE,
DEFAULT_GOOGLE_OPENID_CFG_JWKS_KEY,
DEFAULT_GOOGLE_OPENID_CFG_URI,
)
from users_microservice.exceptions import EmailAlreadyRegistered
from users_microservice.models import User, db
from users_microservice.utils import split_list
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def validated_token(token, verify=True):
"""Validate a token and return decoded token."""
url = (
requests.get(
config.oauth.google_openid_config_uri(default=DEFAULT_GOOGLE_OPENID_CFG_URI)
)
.json()
.get(
config.oauth.google_openid_jkws_key(
default=DEFAULT_GOOGLE_OPENID_CFG_JWKS_KEY
)
)
)
logger.info("JWK url is %s", url)
jwks_client = PyJWKClient(url)
signing_key = jwks_client.get_signing_key_from_jwt(token)
data = jwt.decode(
token,
signing_key.key,
algorithms=["RS256"],
audience=config.oauth.audience(default=DEFAULT_AUDIENCE, cast=split_list),
options={"verify_signature": verify},
)
return data
def oauth_user(token):
"""Get user from token."""
decoded_token = validated_token(token, False)
user = User.query.filter(User.email == decoded_token["email"]).first()
return user
def create_oauth_user(token, wallet_address, wallet_mnemonic):
"""Create a new user from OAuth token."""
if oauth_user(token) is not None:
raise EmailAlreadyRegistered
data = validated_token(token)
new_user_data = {
"first_name": data["given_name"],
"last_name": data["family_name"],
"password": data["sub"],
"profile_picture": data["picture"],
"wallet_address": wallet_address,
"wallet_mnemonic": wallet_mnemonic,
"email": data["email"],
}
new_user = User(**new_user_data)
db.session.add(new_user)
db.session.commit()
return new_user | users_microservice/controllers/oauth.py | import logging
import jwt
import requests
from jwt import PyJWKClient
from users_microservice.cfg import config
from users_microservice.constants import (
DEFAULT_AUDIENCE,
DEFAULT_GOOGLE_OPENID_CFG_JWKS_KEY,
DEFAULT_GOOGLE_OPENID_CFG_URI,
)
from users_microservice.exceptions import EmailAlreadyRegistered
from users_microservice.models import User, db
from users_microservice.utils import split_list
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def validated_token(token, verify=True):
"""Validate a token and return decoded token."""
url = (
requests.get(
config.oauth.google_openid_config_uri(default=DEFAULT_GOOGLE_OPENID_CFG_URI)
)
.json()
.get(
config.oauth.google_openid_jkws_key(
default=DEFAULT_GOOGLE_OPENID_CFG_JWKS_KEY
)
)
)
logger.info("JWK url is %s", url)
jwks_client = PyJWKClient(url)
signing_key = jwks_client.get_signing_key_from_jwt(token)
data = jwt.decode(
token,
signing_key.key,
algorithms=["RS256"],
audience=config.oauth.audience(default=DEFAULT_AUDIENCE, cast=split_list),
options={"verify_signature": verify},
)
return data
def oauth_user(token):
"""Get user from token."""
decoded_token = validated_token(token, False)
user = User.query.filter(User.email == decoded_token["email"]).first()
return user
def create_oauth_user(token, wallet_address, wallet_mnemonic):
"""Create a new user from OAuth token."""
if oauth_user(token) is not None:
raise EmailAlreadyRegistered
data = validated_token(token)
new_user_data = {
"first_name": data["given_name"],
"last_name": data["family_name"],
"password": data["sub"],
"profile_picture": data["picture"],
"wallet_address": wallet_address,
"wallet_mnemonic": wallet_mnemonic,
"email": data["email"],
}
new_user = User(**new_user_data)
db.session.add(new_user)
db.session.commit()
return new_user | 0.563858 | 0.152347 |
import base64
import traceback
from Crypto.Cipher import AES
from .type_tool import TypeTool
from .b64 import Base64
def pkcs7padding(data):
bs = AES.block_size
padding = bs - len(data) % bs
padding_text = chr(padding) * padding
return data + padding_text.encode()
def pkcs7unpadding(data):
lengt = len(data)
unpadding = data[lengt - 1] if type(data[lengt - 1]) is int else ord(data[lengt - 1])
return data[0:lengt - unpadding]
class Aes:
"""
AES加密
"""
def __init__(self, key: str = "_ZhangDapeng520%"):
self.key = key.encode()
def encrypt(self, data):
"""
AES 加密， 加密模式ECB，填充：pkcs7padding，密钥长度：256
:param data:
:return:
"""
data = pkcs7padding(data)
cipher = AES.new(self.key, AES.MODE_ECB)
encrypted = cipher.encrypt(data)
return base64.b64encode(encrypted)
def decrypt(self, data):
"""
AES解密
:param data: 要解密的数据
:return: 解密后的数据
"""
data = base64.b64decode(data)
cipher = AES.new(self.key, AES.MODE_ECB)
decrypted = cipher.decrypt(data)
decrypted = pkcs7unpadding(decrypted)
return decrypted.decode()
@classmethod
@TypeTool.type_assert
def encrypt_gcm(cls,
cdata: [str, bytes, bytearray],
key: [str, bytes, bytearray]) -> [tuple]:
"""
AES加密
:param cdata: 要加密的数据
:param key: 加密的key
:return:
"""
error_return = (bytes(), bytes(), bytes())
try:
# 将参数转换为字节数组
cdata = TypeTool.type_sbb_2_bytes(cdata)
key = TypeTool.type_sbb_2_bytes(key)
# 校验参数的长度
if len(key) != 16:
return error_return
# 创建cipher对象
aescipher = AES.new(key, AES.MODE_GCM)
# 加密
edata, tag = aescipher.encrypt_and_digest(cdata)
# 获取nonce
nonce = aescipher.nonce
# 返回加密结果
return edata, nonce, tag
except Exception as e:
print(e)
traceback.print_exc()
# 返回错误结果
return error_return
@classmethod
def encrypt_gcm_str(cls,
cdata: str,
key: str) -> (str, str, str):
"""
AES加密字符串
:param cdata: 要加密的数据
:param key: 加密的key
:return: 加密后的数据base6编码字符串
"""
# 加密
edata, nonce, tag = cls.encrypt_gcm(cdata.encode(), key.encode())
# 转换为base64编码
edata_b64 = Base64.encode_str(edata)
nonce_b64 = Base64.encode_str(nonce)
tag_b64 = Base64.encode_str(tag)
# 返回base64编码
return edata_b64, nonce_b64, tag_b64
@classmethod
@TypeTool.type_assert
def decrypt_gcm(cls,
edata: [str, bytes, bytearray],
key: [str, bytes, bytearray],
nonce: [str, bytes, bytearray],
tag: [str, bytes, bytearray]) -> [bytes]:
"""
AES解密
:param edata: 要解密的数据
:param key: 解密的key
:param nonce: 解密的nonce
:param tag: 解密的标签
:return: 解密后的数据
"""
error_return = bytes()
try:
# 将参数都转换为字节数组
edata = TypeTool.type_sbb_2_bytes(edata)
key = TypeTool.type_sbb_2_bytes(key)
nonce = TypeTool.type_sbb_2_bytes(nonce)
tag = TypeTool.type_sbb_2_bytes(tag)
# 判断参数的长度
if (len(key) != 16) or (len(nonce) != 16) or (len(tag) != 16):
return error_return
# 创建cipher
aescipher = AES.new(key, AES.MODE_GCM, nonce)
# 数据解密并校验
cdata = aescipher.decrypt_and_verify(edata, tag)
# 返回解密后的数据
return cdata
except Exception as e:
print(e)
traceback.print_exc()
return error_return
@classmethod
def decrypt_gcm_str(cls,
edata: str,
key: str,
nonce: str,
tag: str) -> str:
"""
解密字符串
:param edata: 要解密的数据
:param key: 解密的key
:param nonce: 解密的nonce
:param tag: 解密的tag
:return: 解密后的字符串
"""
# 将参数转换为字节数组
edata_bytes = Base64.decode(edata)
key_bytes = key.encode()
nonce_bytes = Base64.decode(nonce)
tag_bytes = Base64.decode(tag)
# 解密
result_bytes = cls.decrypt_gcm(edata_bytes, key_bytes, nonce_bytes, tag_bytes)
# 将解密结果解码
return result_bytes.decode('utf-8') | zdppy_password/aes.py | import base64
import traceback
from Crypto.Cipher import AES
from .type_tool import TypeTool
from .b64 import Base64
def pkcs7padding(data):
bs = AES.block_size
padding = bs - len(data) % bs
padding_text = chr(padding) * padding
return data + padding_text.encode()
def pkcs7unpadding(data):
lengt = len(data)
unpadding = data[lengt - 1] if type(data[lengt - 1]) is int else ord(data[lengt - 1])
return data[0:lengt - unpadding]
class Aes:
"""
AES加密
"""
def __init__(self, key: str = "_ZhangDapeng520%"):
self.key = key.encode()
def encrypt(self, data):
"""
AES 加密， 加密模式ECB，填充：pkcs7padding，密钥长度：256
:param data:
:return:
"""
data = pkcs7padding(data)
cipher = AES.new(self.key, AES.MODE_ECB)
encrypted = cipher.encrypt(data)
return base64.b64encode(encrypted)
def decrypt(self, data):
"""
AES解密
:param data: 要解密的数据
:return: 解密后的数据
"""
data = base64.b64decode(data)
cipher = AES.new(self.key, AES.MODE_ECB)
decrypted = cipher.decrypt(data)
decrypted = pkcs7unpadding(decrypted)
return decrypted.decode()
@classmethod
@TypeTool.type_assert
def encrypt_gcm(cls,
cdata: [str, bytes, bytearray],
key: [str, bytes, bytearray]) -> [tuple]:
"""
AES加密
:param cdata: 要加密的数据
:param key: 加密的key
:return:
"""
error_return = (bytes(), bytes(), bytes())
try:
# 将参数转换为字节数组
cdata = TypeTool.type_sbb_2_bytes(cdata)
key = TypeTool.type_sbb_2_bytes(key)
# 校验参数的长度
if len(key) != 16:
return error_return
# 创建cipher对象
aescipher = AES.new(key, AES.MODE_GCM)
# 加密
edata, tag = aescipher.encrypt_and_digest(cdata)
# 获取nonce
nonce = aescipher.nonce
# 返回加密结果
return edata, nonce, tag
except Exception as e:
print(e)
traceback.print_exc()
# 返回错误结果
return error_return
@classmethod
def encrypt_gcm_str(cls,
cdata: str,
key: str) -> (str, str, str):
"""
AES加密字符串
:param cdata: 要加密的数据
:param key: 加密的key
:return: 加密后的数据base6编码字符串
"""
# 加密
edata, nonce, tag = cls.encrypt_gcm(cdata.encode(), key.encode())
# 转换为base64编码
edata_b64 = Base64.encode_str(edata)
nonce_b64 = Base64.encode_str(nonce)
tag_b64 = Base64.encode_str(tag)
# 返回base64编码
return edata_b64, nonce_b64, tag_b64
@classmethod
@TypeTool.type_assert
def decrypt_gcm(cls,
edata: [str, bytes, bytearray],
key: [str, bytes, bytearray],
nonce: [str, bytes, bytearray],
tag: [str, bytes, bytearray]) -> [bytes]:
"""
AES解密
:param edata: 要解密的数据
:param key: 解密的key
:param nonce: 解密的nonce
:param tag: 解密的标签
:return: 解密后的数据
"""
error_return = bytes()
try:
# 将参数都转换为字节数组
edata = TypeTool.type_sbb_2_bytes(edata)
key = TypeTool.type_sbb_2_bytes(key)
nonce = TypeTool.type_sbb_2_bytes(nonce)
tag = TypeTool.type_sbb_2_bytes(tag)
# 判断参数的长度
if (len(key) != 16) or (len(nonce) != 16) or (len(tag) != 16):
return error_return
# 创建cipher
aescipher = AES.new(key, AES.MODE_GCM, nonce)
# 数据解密并校验
cdata = aescipher.decrypt_and_verify(edata, tag)
# 返回解密后的数据
return cdata
except Exception as e:
print(e)
traceback.print_exc()
return error_return
@classmethod
def decrypt_gcm_str(cls,
edata: str,
key: str,
nonce: str,
tag: str) -> str:
"""
解密字符串
:param edata: 要解密的数据
:param key: 解密的key
:param nonce: 解密的nonce
:param tag: 解密的tag
:return: 解密后的字符串
"""
# 将参数转换为字节数组
edata_bytes = Base64.decode(edata)
key_bytes = key.encode()
nonce_bytes = Base64.decode(nonce)
tag_bytes = Base64.decode(tag)
# 解密
result_bytes = cls.decrypt_gcm(edata_bytes, key_bytes, nonce_bytes, tag_bytes)
# 将解密结果解码
return result_bytes.decode('utf-8') | 0.491944 | 0.403684 |
import unittest
import numpy as np
from .. import qxrf
from ...utils import units
from ...patch import jsonpickle
class test_qxrf(unittest.TestCase):
def geometryinstance(self):
energy = 10
geometryinstance = qxrf.factory("sxm1", simplecalibration=False)
info = {
"I0_counts": 300,
"It_counts": 30,
"time": 1,
"dark": True,
"gaindiodeI0": 1e8,
"gaindiodeIt": 1e7,
}
geometryinstance.calibrate_diodes(**info)
info["I0_counts"] = 10000
info["It_counts"] = 100000
info["energy"] = energy - 2
info["dark"] = False
geometryinstance.calibrate_diodes(**info)
info["I0_counts"] = 5000
info["energy"] = energy + 2
geometryinstance.calibrate_diodes(**info)
geometryinstance.reference = units.Quantity(1e9, "Hz")
geometryinstance.defaultexpotime = units.Quantity(100, "ms")
return geometryinstance
@unittest.skipIf(
qxrf.xrfdetectors.compoundfromname.xraylib is None, "xraylib not installed"
)
def test_flux(self):
geometryinstance = self.geometryinstance()
energy = 10
time = 0.2
refflux = 1e9
flux = np.linspace(1e9, 1e8, 20) # ph/sec
iodet = geometryinstance.fluxtocps(energy, flux) * time
flux2 = geometryinstance.responsetoflux(energy, iodet / time)
np.testing.assert_allclose(flux, flux2)
# Normalize data to the real flux (use flux reference)
rates = np.random.poisson(np.full_like(flux, 100)) # 1/ph/sec
data = flux * time * rates # measured xrf
# measured when flux whould have been refflux at each poi1e9
dataref = refflux * time * rates
ref = units.Quantity(refflux, "hertz")
op, _, _, _ = geometryinstance.xrfnormop(energy, expotime=time, reference=ref)
np.testing.assert_allclose(dataref, data / op(iodet))
# Normalize data to the real flux (use iodet reference)
iodetref = geometryinstance.fluxtocps(energy, refflux) * time
ref = units.Quantity(iodetref, "dimensionless")
op, _, _, _ = geometryinstance.xrfnormop(energy, expotime=time, reference=ref)
np.testing.assert_allclose(dataref, data / op(iodet))
@unittest.skipIf(
qxrf.xrfgeometries.compoundfromname.xraylib is None, "xraylib not installed"
)
def test_serialize(self):
xrfgeometries = []
for detectorposition in [0, 1]:
geometry = {
"name": "sxm120",
"parameters": {"detectorposition": detectorposition},
}
detector = {"name": "leia", "parameters": {}}
xrfgeometries.append((geometry, detector))
g1 = qxrf.factory("sxm1", xrfgeometries=xrfgeometries)
g2 = jsonpickle.loads(jsonpickle.dumps(g1))
self.assertEqual(g1, g2)
exclude = ("QXRFGeometry",)
for name, cls in qxrf.QXRFGeometry.clsregistry.items():
if name not in exclude:
g1 = cls()
g2 = jsonpickle.loads(jsonpickle.dumps(g1))
self.assertEqual(g1, g2)
def test_suite():
"""Test suite including all test suites"""
testSuite = unittest.TestSuite()
testSuite.addTest(test_qxrf("test_flux"))
testSuite.addTest(test_qxrf("test_serialize"))
return testSuite
if __name__ == "__main__":
import sys
mysuite = test_suite()
runner = unittest.TextTestRunner()
if not runner.run(mysuite).wasSuccessful():
sys.exit(1) | spectrocrunch/geometries/tests/test_qxrf.py |
import unittest
import numpy as np
from .. import qxrf
from ...utils import units
from ...patch import jsonpickle
class test_qxrf(unittest.TestCase):
def geometryinstance(self):
energy = 10
geometryinstance = qxrf.factory("sxm1", simplecalibration=False)
info = {
"I0_counts": 300,
"It_counts": 30,
"time": 1,
"dark": True,
"gaindiodeI0": 1e8,
"gaindiodeIt": 1e7,
}
geometryinstance.calibrate_diodes(**info)
info["I0_counts"] = 10000
info["It_counts"] = 100000
info["energy"] = energy - 2
info["dark"] = False
geometryinstance.calibrate_diodes(**info)
info["I0_counts"] = 5000
info["energy"] = energy + 2
geometryinstance.calibrate_diodes(**info)
geometryinstance.reference = units.Quantity(1e9, "Hz")
geometryinstance.defaultexpotime = units.Quantity(100, "ms")
return geometryinstance
@unittest.skipIf(
qxrf.xrfdetectors.compoundfromname.xraylib is None, "xraylib not installed"
)
def test_flux(self):
geometryinstance = self.geometryinstance()
energy = 10
time = 0.2
refflux = 1e9
flux = np.linspace(1e9, 1e8, 20) # ph/sec
iodet = geometryinstance.fluxtocps(energy, flux) * time
flux2 = geometryinstance.responsetoflux(energy, iodet / time)
np.testing.assert_allclose(flux, flux2)
# Normalize data to the real flux (use flux reference)
rates = np.random.poisson(np.full_like(flux, 100)) # 1/ph/sec
data = flux * time * rates # measured xrf
# measured when flux whould have been refflux at each poi1e9
dataref = refflux * time * rates
ref = units.Quantity(refflux, "hertz")
op, _, _, _ = geometryinstance.xrfnormop(energy, expotime=time, reference=ref)
np.testing.assert_allclose(dataref, data / op(iodet))
# Normalize data to the real flux (use iodet reference)
iodetref = geometryinstance.fluxtocps(energy, refflux) * time
ref = units.Quantity(iodetref, "dimensionless")
op, _, _, _ = geometryinstance.xrfnormop(energy, expotime=time, reference=ref)
np.testing.assert_allclose(dataref, data / op(iodet))
@unittest.skipIf(
qxrf.xrfgeometries.compoundfromname.xraylib is None, "xraylib not installed"
)
def test_serialize(self):
xrfgeometries = []
for detectorposition in [0, 1]:
geometry = {
"name": "sxm120",
"parameters": {"detectorposition": detectorposition},
}
detector = {"name": "leia", "parameters": {}}
xrfgeometries.append((geometry, detector))
g1 = qxrf.factory("sxm1", xrfgeometries=xrfgeometries)
g2 = jsonpickle.loads(jsonpickle.dumps(g1))
self.assertEqual(g1, g2)
exclude = ("QXRFGeometry",)
for name, cls in qxrf.QXRFGeometry.clsregistry.items():
if name not in exclude:
g1 = cls()
g2 = jsonpickle.loads(jsonpickle.dumps(g1))
self.assertEqual(g1, g2)
def test_suite():
"""Test suite including all test suites"""
testSuite = unittest.TestSuite()
testSuite.addTest(test_qxrf("test_flux"))
testSuite.addTest(test_qxrf("test_serialize"))
return testSuite
if __name__ == "__main__":
import sys
mysuite = test_suite()
runner = unittest.TextTestRunner()
if not runner.run(mysuite).wasSuccessful():
sys.exit(1) | 0.674158 | 0.55266 |
import os
import cv2
import glob
import tqdm
import argparse
from lh_tool.Iterator import SingleProcess, MultiProcess
import lh_tool.imageio as iio
def images2video(image_path, video_file, postfix, fourcc, fps, frameSize=None):
image_file_list = glob.glob(os.path.join(image_path, f'*.{postfix}'))
if len(image_file_list) == 0:
return
video_file = os.path.abspath(image_path) + '.mp4' if video_file is None else video_file
if frameSize is None:
image = iio.imread(image_file_list[0])
frameSize = (image.shape[1], image.shape[0])
frameSize = tuple(frameSize)
videoWriter = cv2.VideoWriter(video_file, fourcc, fps, frameSize)
assert videoWriter.isOpened(), f'Failed to create file: {video_file}'
for image_file in tqdm.tqdm(image_file_list, desc=video_file):
image = iio.imread(image_file)
if frameSize != (image.shape[1], image.shape[0]):
image = cv2.resize(image, tuple(frameSize))
videoWriter.write(image)
videoWriter.release()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='.', help='path of image files')
parser.add_argument('-o', '--output', type=str, help='output video file')
parser.add_argument('-p', '--postfix', type=str, default='png', help='postfix of image filename')
parser.add_argument('-f', '--fps', type=float, default=29.97, help='desired fps for video')
parser.add_argument('-s', '--size', type=int, nargs=2, help='desired frame size for video')
parser.add_argument('-r', '--recursive', action='store_true', help='convert video to images recursively')
parser.add_argument('-n', '--nprocs', type=int, default=1, help='number of process')
opts = parser.parse_args()
print(opts)
try:
image_path = opts.input
video_file = opts.output
postfix = opts.postfix
fps = opts.fps
size = opts.size
recursive = opts.recursive
nprocs = opts.nprocs
fourcc = cv2.VideoWriter.fourcc('m', 'p', '4', 'v')
if recursive:
image_path_list = glob.glob(os.path.join(opts.input, '*/'))
if nprocs == 1:
iterator = SingleProcess(images2video)
else:
iterator = MultiProcess(images2video, nprocs=nprocs)
iterator.run(image_path_list, None, postfix, fourcc, fps, size)
else:
images2video(image_path, video_file, postfix, fourcc, fps, size)
except AssertionError as e:
print(e)
if __name__ == '__main__':
main() | src/lh_tool/image2video.py | import os
import cv2
import glob
import tqdm
import argparse
from lh_tool.Iterator import SingleProcess, MultiProcess
import lh_tool.imageio as iio
def images2video(image_path, video_file, postfix, fourcc, fps, frameSize=None):
image_file_list = glob.glob(os.path.join(image_path, f'*.{postfix}'))
if len(image_file_list) == 0:
return
video_file = os.path.abspath(image_path) + '.mp4' if video_file is None else video_file
if frameSize is None:
image = iio.imread(image_file_list[0])
frameSize = (image.shape[1], image.shape[0])
frameSize = tuple(frameSize)
videoWriter = cv2.VideoWriter(video_file, fourcc, fps, frameSize)
assert videoWriter.isOpened(), f'Failed to create file: {video_file}'
for image_file in tqdm.tqdm(image_file_list, desc=video_file):
image = iio.imread(image_file)
if frameSize != (image.shape[1], image.shape[0]):
image = cv2.resize(image, tuple(frameSize))
videoWriter.write(image)
videoWriter.release()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='.', help='path of image files')
parser.add_argument('-o', '--output', type=str, help='output video file')
parser.add_argument('-p', '--postfix', type=str, default='png', help='postfix of image filename')
parser.add_argument('-f', '--fps', type=float, default=29.97, help='desired fps for video')
parser.add_argument('-s', '--size', type=int, nargs=2, help='desired frame size for video')
parser.add_argument('-r', '--recursive', action='store_true', help='convert video to images recursively')
parser.add_argument('-n', '--nprocs', type=int, default=1, help='number of process')
opts = parser.parse_args()
print(opts)
try:
image_path = opts.input
video_file = opts.output
postfix = opts.postfix
fps = opts.fps
size = opts.size
recursive = opts.recursive
nprocs = opts.nprocs
fourcc = cv2.VideoWriter.fourcc('m', 'p', '4', 'v')
if recursive:
image_path_list = glob.glob(os.path.join(opts.input, '*/'))
if nprocs == 1:
iterator = SingleProcess(images2video)
else:
iterator = MultiProcess(images2video, nprocs=nprocs)
iterator.run(image_path_list, None, postfix, fourcc, fps, size)
else:
images2video(image_path, video_file, postfix, fourcc, fps, size)
except AssertionError as e:
print(e)
if __name__ == '__main__':
main() | 0.283285 | 0.136349 |
import numpy as np
import scipy.constants
def signal_delay(st1, st2, ecef):
'''Signal delay due to speed of light between station-1 to ecef position to station-2
'''
r1 = np.linalg.norm(ecef - st1.ecef[:,None], axis=0)
r2 = np.linalg.norm(ecef - st1.ecef[:,None], axis=0)
dt = (r1 + r2)/scipy.constants.c
return dt
def instantaneous_to_coherrent(gain, groups, N_IPP, IPP_scale=1.0, units = 'dB'):
'''Using pulse encoding schema, subgroup setup and coherent integration setup; convert from instantaneous gain to coherently integrated gain.
:param float gain: Instantaneous gain, linear units or in dB.
:param int groups: Number of subgroups from witch signals are coherently combined, assumes subgroups are identical.
:param int N_IPP: Number of pulses to coherently integrate.
:param float IPP_scale: Scale the IPP effective length in case e.g. the IPP is the same but the actual TX length is lowered.
:param str units: If string equals 'dB', assume input and output units should be dB, else use linear scale.
:return float: Gain after coherent integration, linear units or in dB.
'''
if units == 'dB':
return gain + 10.0*np.log10( groups*N_IPP*IPP_scale )
else:
return gain*(groups*N_IPP*IPP_scale)
def coherrent_to_instantaneous(gain,groups,N_IPP,IPP_scale=1.0,units = 'dB'):
'''Using pulse encoding schema, subgroup setup and coherent integration setup; convert from coherently integrated gain to instantaneous gain.
:param float gain: Coherently integrated gain, linear units or in dB.
:param int groups: Number of subgroups from witch signals are coherently combined, assumes subgroups are identical.
:param int N_IPP: Number of pulses to coherently integrate.
:param float IPP_scale: Scale the IPP effective length in case e.g. the IPP is the same but the actual TX length is lowered.
:param str units: If string equals 'dB', assume input and output units should be dB, else use linear scale.
:return float: Instantaneous gain, linear units or in dB.
'''
if units == 'dB':
return gain - 10.0*np.log10( groups*N_IPP*IPP_scale )
else:
return gain/(groups*N_IPP*IPP_scale) | sorts/functions.py | import numpy as np
import scipy.constants
def signal_delay(st1, st2, ecef):
'''Signal delay due to speed of light between station-1 to ecef position to station-2
'''
r1 = np.linalg.norm(ecef - st1.ecef[:,None], axis=0)
r2 = np.linalg.norm(ecef - st1.ecef[:,None], axis=0)
dt = (r1 + r2)/scipy.constants.c
return dt
def instantaneous_to_coherrent(gain, groups, N_IPP, IPP_scale=1.0, units = 'dB'):
'''Using pulse encoding schema, subgroup setup and coherent integration setup; convert from instantaneous gain to coherently integrated gain.
:param float gain: Instantaneous gain, linear units or in dB.
:param int groups: Number of subgroups from witch signals are coherently combined, assumes subgroups are identical.
:param int N_IPP: Number of pulses to coherently integrate.
:param float IPP_scale: Scale the IPP effective length in case e.g. the IPP is the same but the actual TX length is lowered.
:param str units: If string equals 'dB', assume input and output units should be dB, else use linear scale.
:return float: Gain after coherent integration, linear units or in dB.
'''
if units == 'dB':
return gain + 10.0*np.log10( groups*N_IPP*IPP_scale )
else:
return gain*(groups*N_IPP*IPP_scale)
def coherrent_to_instantaneous(gain,groups,N_IPP,IPP_scale=1.0,units = 'dB'):
'''Using pulse encoding schema, subgroup setup and coherent integration setup; convert from coherently integrated gain to instantaneous gain.
:param float gain: Coherently integrated gain, linear units or in dB.
:param int groups: Number of subgroups from witch signals are coherently combined, assumes subgroups are identical.
:param int N_IPP: Number of pulses to coherently integrate.
:param float IPP_scale: Scale the IPP effective length in case e.g. the IPP is the same but the actual TX length is lowered.
:param str units: If string equals 'dB', assume input and output units should be dB, else use linear scale.
:return float: Instantaneous gain, linear units or in dB.
'''
if units == 'dB':
return gain - 10.0*np.log10( groups*N_IPP*IPP_scale )
else:
return gain/(groups*N_IPP*IPP_scale) | 0.763572 | 0.550607 |
import os
import subprocess
from clams import arg, Command
from unb_cli.project import is_project_root
pip = Command(
name='pip',
title='pip interface and tools',
description='pip interface and tools',
)
@pip.register('install')
@arg('package', nargs='?', default='requirements.txt')
@arg('--nocache', action='store_true',
help="Don't use pip's cache (fetch all packages from server).")
@arg('-v', '--verbose', action='store_true', help="Enable verbose output.")
def install(package, nocache, verbose):
"""Install package or packages from a requirements file.
If `package` ends with `.txt` then `pip install -r package` is used. If
`package` is not supplied, it defaults to `requirements.txt`.
"""
if package.endswith('.txt'):
command = ['pip', 'install', '-r', package]
if not verbose:
command = command + ['-q']
# Find the file! It might not be in the current directory.
while True:
path = os.getcwd()
if os.path.exists(package):
print 'Installing packages from %s' % os.path.join(path, package)
subprocess.call(command)
break
if is_project_root(path) or path == os.path.abspath(os.sep):
print "%s not found in project." % package
break
os.chdir(os.pardir)
else:
subprocess.call(['pip', 'install', package])
@pip.register('install-local')
def install_local():
"""Install a Python egg locally (usually during development)."""
subprocess.call(['pip', 'install', '-e', '.'])
@pip.register('uninstall')
@arg('package')
def uninstall(package):
"""Uninstall a package using pip."""
subprocess.call(['pip', 'uninstall', package])
@pip.register('build')
def build():
"""Build a Python egg."""
subprocess.call(['python', 'setup.py', 'sdist', 'bdist_wheel'])
@pip.register('upload')
@arg('dist', help=("Package version (example: `0.0.3`). `*` will be appended "
"to upload all versions (source dist and a wheel, for "
"example)."))
@arg('repo', help=("Repository to upload to. Common ones include, `pypi` and "
"`testpypi` (they are defined in your `~/.pypirc`)."))
def upload(dist, repo):
"""Upload a pre-built Python package.
Requires [twine](https://pypi.python.org/pypi/twine).
"""
# TODO(nick): `cd $PROJECT_ROOT` first.
dist_version = 'dist/' + '*' + dist + '*'
twine_command = ['twine', 'upload', dist_version, '-r', repo]
subprocess.call(twine_command) | unb_cli/unb/pip.py | import os
import subprocess
from clams import arg, Command
from unb_cli.project import is_project_root
pip = Command(
name='pip',
title='pip interface and tools',
description='pip interface and tools',
)
@pip.register('install')
@arg('package', nargs='?', default='requirements.txt')
@arg('--nocache', action='store_true',
help="Don't use pip's cache (fetch all packages from server).")
@arg('-v', '--verbose', action='store_true', help="Enable verbose output.")
def install(package, nocache, verbose):
"""Install package or packages from a requirements file.
If `package` ends with `.txt` then `pip install -r package` is used. If
`package` is not supplied, it defaults to `requirements.txt`.
"""
if package.endswith('.txt'):
command = ['pip', 'install', '-r', package]
if not verbose:
command = command + ['-q']
# Find the file! It might not be in the current directory.
while True:
path = os.getcwd()
if os.path.exists(package):
print 'Installing packages from %s' % os.path.join(path, package)
subprocess.call(command)
break
if is_project_root(path) or path == os.path.abspath(os.sep):
print "%s not found in project." % package
break
os.chdir(os.pardir)
else:
subprocess.call(['pip', 'install', package])
@pip.register('install-local')
def install_local():
"""Install a Python egg locally (usually during development)."""
subprocess.call(['pip', 'install', '-e', '.'])
@pip.register('uninstall')
@arg('package')
def uninstall(package):
"""Uninstall a package using pip."""
subprocess.call(['pip', 'uninstall', package])
@pip.register('build')
def build():
"""Build a Python egg."""
subprocess.call(['python', 'setup.py', 'sdist', 'bdist_wheel'])
@pip.register('upload')
@arg('dist', help=("Package version (example: `0.0.3`). `*` will be appended "
"to upload all versions (source dist and a wheel, for "
"example)."))
@arg('repo', help=("Repository to upload to. Common ones include, `pypi` and "
"`testpypi` (they are defined in your `~/.pypirc`)."))
def upload(dist, repo):
"""Upload a pre-built Python package.
Requires [twine](https://pypi.python.org/pypi/twine).
"""
# TODO(nick): `cd $PROJECT_ROOT` first.
dist_version = 'dist/' + '*' + dist + '*'
twine_command = ['twine', 'upload', dist_version, '-r', repo]
subprocess.call(twine_command) | 0.474144 | 0.110807 |
import torch
from torch.utils.data import SubsetRandomSampler
import numpy as np
from Precipitation_Forecasting.precipitation_dataset import precipitation_maps_oversampled_h5
from Precipitation_Forecasting.precipitation_lightning import AA_TransUnet_base
class Precip_regression_base(TransUnet_base):
def __init__(self, hparams):
super(Precip_regression_base, self).__init__(hparams=hparams)
self.train_dataset = None
self.valid_dataset = None
self.train_sampler = None
self.valid_sampler = None
def prepare_data(self):
train_transform = None
valid_transform = None
if self.hparams['use_oversampled_dataset']:
self.train_dataset = precipitation_maps_oversampled_h5(
in_file=self.hparams['dataset_folder'], num_input_images=self.hparams['num_input_images'],
num_output_images=self.hparams['num_output_images'], train=True,
transform=train_transform
)
self.valid_dataset = precipitation_maps_oversampled_h5(
in_file=self.hparams['dataset_folder'], num_input_images=self.hparams['num_input_images'],
num_output_images=self.hparams['num_output_images'], train=True,
transform=valid_transform
)
num_train = len(self.train_dataset)
indices = list(range(num_train))
split = int(np.floor(self.hparams['valid_size'] * num_train))
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
self.train_sampler = SubsetRandomSampler(train_idx)
self.valid_sampler = SubsetRandomSampler(valid_idx)
def train_dataloader(self):
train_loader = torch.utils.data.DataLoader(
self.train_dataset, batch_size=self.hparams['batch_size'], sampler=self.train_sampler,
num_workers=2, pin_memory=True
)
return train_loader
def val_dataloader(self):
valid_loader = torch.utils.data.DataLoader(
self.valid_dataset, batch_size=self.hparams['batch_size'], sampler=self.valid_sampler,
num_workers=2, pin_memory=True
)
return valid_loader
def test_dataloader(self):
test_loader = torch.utils.data.DataLoader(
self.test_dataset, batch_size=self.hparams['batch_size'], sampler=self.test_sampler,
num_workers=2, pin_memory=True
)
return test_loader | Precipitation Forecasting/precipitation_lightning_base.py | import torch
from torch.utils.data import SubsetRandomSampler
import numpy as np
from Precipitation_Forecasting.precipitation_dataset import precipitation_maps_oversampled_h5
from Precipitation_Forecasting.precipitation_lightning import AA_TransUnet_base
class Precip_regression_base(TransUnet_base):
def __init__(self, hparams):
super(Precip_regression_base, self).__init__(hparams=hparams)
self.train_dataset = None
self.valid_dataset = None
self.train_sampler = None
self.valid_sampler = None
def prepare_data(self):
train_transform = None
valid_transform = None
if self.hparams['use_oversampled_dataset']:
self.train_dataset = precipitation_maps_oversampled_h5(
in_file=self.hparams['dataset_folder'], num_input_images=self.hparams['num_input_images'],
num_output_images=self.hparams['num_output_images'], train=True,
transform=train_transform
)
self.valid_dataset = precipitation_maps_oversampled_h5(
in_file=self.hparams['dataset_folder'], num_input_images=self.hparams['num_input_images'],
num_output_images=self.hparams['num_output_images'], train=True,
transform=valid_transform
)
num_train = len(self.train_dataset)
indices = list(range(num_train))
split = int(np.floor(self.hparams['valid_size'] * num_train))
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
self.train_sampler = SubsetRandomSampler(train_idx)
self.valid_sampler = SubsetRandomSampler(valid_idx)
def train_dataloader(self):
train_loader = torch.utils.data.DataLoader(
self.train_dataset, batch_size=self.hparams['batch_size'], sampler=self.train_sampler,
num_workers=2, pin_memory=True
)
return train_loader
def val_dataloader(self):
valid_loader = torch.utils.data.DataLoader(
self.valid_dataset, batch_size=self.hparams['batch_size'], sampler=self.valid_sampler,
num_workers=2, pin_memory=True
)
return valid_loader
def test_dataloader(self):
test_loader = torch.utils.data.DataLoader(
self.test_dataset, batch_size=self.hparams['batch_size'], sampler=self.test_sampler,
num_workers=2, pin_memory=True
)
return test_loader | 0.776284 | 0.394726 |
import serial, glob
import copy
import json
import time
from time import localtime, strftime
#initialization and open the port
#possible timeout values:
# 1. None: wait forever, block call
# 2. 0: non-blocking mode, return immediately
# 3. x, x is bigger than 0, float allowed, timeout block call
temp_list = glob.glob ('/dev/ttyACM*')
print temp_list
ser = serial.Serial()
ser.port = "/dev/ttyACM0"
# ser.port = "/dev/ttyUSB7"
#ser.port = "/dev/ttyS2"
ser.baudrate = 115200
ser.bytesize = serial.EIGHTBITS #number of bits per bytes
ser.parity = serial.PARITY_NONE #set parity check: no parity
ser.stopbits = serial.STOPBITS_ONE #number of stop bits
#ser.timeout = None #block read
ser.timeout = 1 #non-block read
#ser.timeout = 2 #timeout block read
ser.xonxoff = False #disable software flow control
ser.rtscts = False #disable hardware (RTS/CTS) flow control
ser.dsrdtr = False #disable hardware (DSR/DTR) flow control
ser.writeTimeout = 2 #timeout for write
arrayName = ["pH", "Soil Humidity", "Soil Temperature", "uV", " Air Humidity", "Air Temperature"]
addressNode = []
dataNode = []
jsonNode = {}
jsonSample = {}
jsonSensor = {}
def setVarBuff():
global jsonNode, jsonSample, jsonSensor
jsonNode = {
"name": "",
"payload":{}
}
jsonSample = {
"payload": {
}
}
jsonSensor = {
"name": "",
"value": 0
}
def resetVarBuff():
global jsonNode, jsonSample, jsonSensor
addressNode = []
dataNode = []
jsonNode = {}
jsonSample = {}
jsonSensor = {}
def operator(addressNode):
if len(addressNode)>0:
setVarBuff()
for i in range(0,len(addressNode)):
jsontemp = copy.deepcopy(jsonNode)
jsontemp['name'] = addressNode[i]
for x in range(0,6):
jsonTempSensor = copy.deepcopy(jsonSensor)
jsonTempSensor['name'] = arrayName[x]
jsonTempSensor['value'] = dataNode[i][x]
jsontemp['payload'].update({"sensor_0"+str(x+1):jsonTempSensor})
jsontemp.update({"rightNow": strftime("%Y-%m-%d %H:%M:%S", localtime())})
jsonSample['payload'].update({"Node_0"+str(i+1):jsontemp})
def check_frame(line):
if line.find("***|") != -1: # check Start
if line.find("|***") != -1: # check stop
return True
return False
return False
def getUart_CC1350():
count = 0
if ser.inWaiting()>0:
time.sleep(5) # Time to waiting buffer is done
setVarBuff() #set when get data, reset when send data to Cloud for the next time
while ser.inWaiting()>0:
line = ser.readline()
if check_frame(line):
start = time.time()
line = line.replace('\r\n','').replace("***|","").replace("|***","")
array = line.split('|')
# print array
nameNode = array.pop(0)
if nameNode in addressNode:
dataNode[addressNode.index(nameNode)] = array
# print dataNode
else:
addressNode.append(nameNode)
dataNode.append([])
dataNode[addressNode.index(nameNode)] = array
count+=1
print "Time to get Uart: " + str(time.time() - start)
else:
print "It's not my frame."
if count>0:
operator(addressNode)
return True
else:
print "Nothing"
return False
if __name__ == '__main__':
try:
ser.open()
except Exception as e:
ser.close()
ser.open()
if ser.isOpen():
ser.flushInput() #flush input buffer, discarding all its contents
ser.flushOutput() #flush output buffer, aborting current output
while ser.inWaiting()>0:
time.sleep(0.5) #give the serial port sometime to receive the data
temp = ser.read(ser.inWaiting())
del temp
while True:
time.sleep(3) # do another thing
if ser.isOpen():
if getUart_CC1350():
print json.dumps(jsonSample,sort_keys=True, indent=4, separators=(',',':'))
resetVarBuff()
else:
print "Nothing"
else:
print "cannot open serial port " | test_uart/uart1.py |
import serial, glob
import copy
import json
import time
from time import localtime, strftime
#initialization and open the port
#possible timeout values:
# 1. None: wait forever, block call
# 2. 0: non-blocking mode, return immediately
# 3. x, x is bigger than 0, float allowed, timeout block call
temp_list = glob.glob ('/dev/ttyACM*')
print temp_list
ser = serial.Serial()
ser.port = "/dev/ttyACM0"
# ser.port = "/dev/ttyUSB7"
#ser.port = "/dev/ttyS2"
ser.baudrate = 115200
ser.bytesize = serial.EIGHTBITS #number of bits per bytes
ser.parity = serial.PARITY_NONE #set parity check: no parity
ser.stopbits = serial.STOPBITS_ONE #number of stop bits
#ser.timeout = None #block read
ser.timeout = 1 #non-block read
#ser.timeout = 2 #timeout block read
ser.xonxoff = False #disable software flow control
ser.rtscts = False #disable hardware (RTS/CTS) flow control
ser.dsrdtr = False #disable hardware (DSR/DTR) flow control
ser.writeTimeout = 2 #timeout for write
arrayName = ["pH", "Soil Humidity", "Soil Temperature", "uV", " Air Humidity", "Air Temperature"]
addressNode = []
dataNode = []
jsonNode = {}
jsonSample = {}
jsonSensor = {}
def setVarBuff():
global jsonNode, jsonSample, jsonSensor
jsonNode = {
"name": "",
"payload":{}
}
jsonSample = {
"payload": {
}
}
jsonSensor = {
"name": "",
"value": 0
}
def resetVarBuff():
global jsonNode, jsonSample, jsonSensor
addressNode = []
dataNode = []
jsonNode = {}
jsonSample = {}
jsonSensor = {}
def operator(addressNode):
if len(addressNode)>0:
setVarBuff()
for i in range(0,len(addressNode)):
jsontemp = copy.deepcopy(jsonNode)
jsontemp['name'] = addressNode[i]
for x in range(0,6):
jsonTempSensor = copy.deepcopy(jsonSensor)
jsonTempSensor['name'] = arrayName[x]
jsonTempSensor['value'] = dataNode[i][x]
jsontemp['payload'].update({"sensor_0"+str(x+1):jsonTempSensor})
jsontemp.update({"rightNow": strftime("%Y-%m-%d %H:%M:%S", localtime())})
jsonSample['payload'].update({"Node_0"+str(i+1):jsontemp})
def check_frame(line):
if line.find("***|") != -1: # check Start
if line.find("|***") != -1: # check stop
return True
return False
return False
def getUart_CC1350():
count = 0
if ser.inWaiting()>0:
time.sleep(5) # Time to waiting buffer is done
setVarBuff() #set when get data, reset when send data to Cloud for the next time
while ser.inWaiting()>0:
line = ser.readline()
if check_frame(line):
start = time.time()
line = line.replace('\r\n','').replace("***|","").replace("|***","")
array = line.split('|')
# print array
nameNode = array.pop(0)
if nameNode in addressNode:
dataNode[addressNode.index(nameNode)] = array
# print dataNode
else:
addressNode.append(nameNode)
dataNode.append([])
dataNode[addressNode.index(nameNode)] = array
count+=1
print "Time to get Uart: " + str(time.time() - start)
else:
print "It's not my frame."
if count>0:
operator(addressNode)
return True
else:
print "Nothing"
return False
if __name__ == '__main__':
try:
ser.open()
except Exception as e:
ser.close()
ser.open()
if ser.isOpen():
ser.flushInput() #flush input buffer, discarding all its contents
ser.flushOutput() #flush output buffer, aborting current output
while ser.inWaiting()>0:
time.sleep(0.5) #give the serial port sometime to receive the data
temp = ser.read(ser.inWaiting())
del temp
while True:
time.sleep(3) # do another thing
if ser.isOpen():
if getUart_CC1350():
print json.dumps(jsonSample,sort_keys=True, indent=4, separators=(',',':'))
resetVarBuff()
else:
print "Nothing"
else:
print "cannot open serial port " | 0.156169 | 0.098209 |
import contribution.utils
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('accounts', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Contribution',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=30, verbose_name='title')),
('file', models.FileField(upload_to=contribution.utils.upload_contribution_path, verbose_name='file')),
('slug', models.SlugField(unique=True, verbose_name='slug')),
('is_commented', models.BooleanField(default=False, verbose_name='is commented')),
('is_selected', models.BooleanField(default=False, verbose_name='is selected')),
('created_at', models.DateTimeField(auto_now_add=True, verbose_name='created at')),
('updated_at', models.DateTimeField(auto_now=True, verbose_name='updated at')),
],
options={
'verbose_name': 'Contribution',
'verbose_name_plural': 'Contributions',
'ordering': ['-updated_at'],
},
),
migrations.CreateModel(
name='ContributionCategory',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('category_for', models.PositiveSmallIntegerField(choices=[(0, 'Documents'), (1, 'Images')], default=0, verbose_name='category for')),
('title', models.CharField(max_length=23, verbose_name='title')),
('slug', models.SlugField(unique=True, verbose_name='slug')),
('created_at', models.DateTimeField(auto_now_add=True, verbose_name='created at')),
('updated_at', models.DateTimeField(auto_now=True, verbose_name='updated at')),
],
options={
'verbose_name': 'Contribution Category',
'verbose_name_plural': 'Contribution Categories',
'ordering': ['-created_at'],
},
),
migrations.AddField(
model_name='contribution',
name='category',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='contribution_category', to='contribution.ContributionCategory', verbose_name='category'),
),
migrations.AddField(
model_name='contribution',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='user_contribution', to='accounts.UserProfile', verbose_name='user'),
),
] | contribution/migrations/0001_initial.py |
import contribution.utils
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('accounts', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Contribution',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=30, verbose_name='title')),
('file', models.FileField(upload_to=contribution.utils.upload_contribution_path, verbose_name='file')),
('slug', models.SlugField(unique=True, verbose_name='slug')),
('is_commented', models.BooleanField(default=False, verbose_name='is commented')),
('is_selected', models.BooleanField(default=False, verbose_name='is selected')),
('created_at', models.DateTimeField(auto_now_add=True, verbose_name='created at')),
('updated_at', models.DateTimeField(auto_now=True, verbose_name='updated at')),
],
options={
'verbose_name': 'Contribution',
'verbose_name_plural': 'Contributions',
'ordering': ['-updated_at'],
},
),
migrations.CreateModel(
name='ContributionCategory',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('category_for', models.PositiveSmallIntegerField(choices=[(0, 'Documents'), (1, 'Images')], default=0, verbose_name='category for')),
('title', models.CharField(max_length=23, verbose_name='title')),
('slug', models.SlugField(unique=True, verbose_name='slug')),
('created_at', models.DateTimeField(auto_now_add=True, verbose_name='created at')),
('updated_at', models.DateTimeField(auto_now=True, verbose_name='updated at')),
],
options={
'verbose_name': 'Contribution Category',
'verbose_name_plural': 'Contribution Categories',
'ordering': ['-created_at'],
},
),
migrations.AddField(
model_name='contribution',
name='category',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='contribution_category', to='contribution.ContributionCategory', verbose_name='category'),
),
migrations.AddField(
model_name='contribution',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='user_contribution', to='accounts.UserProfile', verbose_name='user'),
),
] | 0.521959 | 0.136522 |
import math
import operator
from functools import reduce
def cached_pure_function(fn):
cache = {}
def wrapper(*args):
if args in cache:
return cache[args]
x = fn(*args)
cache[args] = x
return x
return wrapper
def cached_pure_generator(fn):
caches = {}
iterator = fn()
def wrapper(*args):
index = 0
if args not in caches:
caches[args] = []
cache = caches[args]
while True:
if index < len(cache):
n = cache[index]
else:
n = next(iterator)
cache.append(n)
yield n
index += 1
return wrapper
def digit_product(n):
return reduce(operator.mul, [digit for digit in digits(n)])
def digit_sum(n):
return reduce(operator.add, [digit for digit in digits(n)])
def digits(n):
for digit in str(n):
yield int(digit)
def euler_phi(n):
if n < 1:
return 0
if n == 1:
return 1
x = 0
for i in range(1, n):
if is_coprime(n, i):
x += 1
return x
@cached_pure_function
def is_prime(n):
if n <= 1:
return False
if n == 2:
return True
if n % 2 == 0:
return False
# IDEA: Would checking only for prime factors be an optimization here?
# TODO: Leverage yield_prime() recursively or cache known primes
# SEE: https://en.wikipedia.org/wiki/Fundamental_theorem_of_arithmetic
for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):
if n % i == 0:
return False
return True
def is_coprime(a, b):
a = prime_factorization(a)
b = prime_factorization(b)
for n in a:
if n in b:
return False
return True
def is_square(n):
sqrt = math.sqrt(n)
return sqrt == math.floor(sqrt)
def prime_factorization(n, multiplicity = False):
if n <= 1:
return []
if is_prime(n):
return [n]
result = []
for i in yield_prime():
appended = False
while n % i == 0:
if multiplicity or not appended:
result.append(i)
appended = True
n /= i
if n == 1:
return result
def yield_prime():
n = 2
while True:
if is_prime(n):
yield n
n += 1 | intseq/utility.py | import math
import operator
from functools import reduce
def cached_pure_function(fn):
cache = {}
def wrapper(*args):
if args in cache:
return cache[args]
x = fn(*args)
cache[args] = x
return x
return wrapper
def cached_pure_generator(fn):
caches = {}
iterator = fn()
def wrapper(*args):
index = 0
if args not in caches:
caches[args] = []
cache = caches[args]
while True:
if index < len(cache):
n = cache[index]
else:
n = next(iterator)
cache.append(n)
yield n
index += 1
return wrapper
def digit_product(n):
return reduce(operator.mul, [digit for digit in digits(n)])
def digit_sum(n):
return reduce(operator.add, [digit for digit in digits(n)])
def digits(n):
for digit in str(n):
yield int(digit)
def euler_phi(n):
if n < 1:
return 0
if n == 1:
return 1
x = 0
for i in range(1, n):
if is_coprime(n, i):
x += 1
return x
@cached_pure_function
def is_prime(n):
if n <= 1:
return False
if n == 2:
return True
if n % 2 == 0:
return False
# IDEA: Would checking only for prime factors be an optimization here?
# TODO: Leverage yield_prime() recursively or cache known primes
# SEE: https://en.wikipedia.org/wiki/Fundamental_theorem_of_arithmetic
for i in range(3, math.ceil(math.sqrt(n)) + 1, 2):
if n % i == 0:
return False
return True
def is_coprime(a, b):
a = prime_factorization(a)
b = prime_factorization(b)
for n in a:
if n in b:
return False
return True
def is_square(n):
sqrt = math.sqrt(n)
return sqrt == math.floor(sqrt)
def prime_factorization(n, multiplicity = False):
if n <= 1:
return []
if is_prime(n):
return [n]
result = []
for i in yield_prime():
appended = False
while n % i == 0:
if multiplicity or not appended:
result.append(i)
appended = True
n /= i
if n == 1:
return result
def yield_prime():
n = 2
while True:
if is_prime(n):
yield n
n += 1 | 0.35768 | 0.306598 |
from __future__ import unicode_literals, print_function
import logging
from os import path
import os
import subprocess as sp
import sys
from chalmers import errors
import tempfile
python_exe = sys.executable
chalmers_script = sys.argv[0]
def read_data(filename):
filename = path.join(path.dirname(__file__), 'data', filename)
with open(filename) as fd:
return fd.read()
launchd_label = "org.continuum.chalmers"
log = logging.getLogger('chalmers.service')
class DarwinService(object):
def __init__(self, target_user):
self.target_user = target_user
log.info('Platform: Darwin')
log.info('Using Darwin launchd service')
if target_user:
log.info('Launching chalmers as target user %s' % target_user)
elif target_user is False:
log.info('Launching chalmers as current user (does not require root)')
else:
log.info('Launching chalmers as root user')
@property
def label(self):
if self.target_user:
return '%s.%s' % (launchd_label, self.target_user)
else:
return launchd_label
@property
def template(self):
return read_data('launchd.plist')
def check_output(self, command):
if self.target_user:
if os.getuid() != 0:
raise errors.ChalmersError("Can not perform system install without root")
log.info("Running command: %s" % ' '.join(command))
try:
output = sp.check_output(command, stderr=sp.STDOUT)
except OSError as err:
raise errors.ChalmersError("Could not access program 'launchctl' required for osx service install")
except sp.CalledProcessError as err:
if err.returncode == 1:
if 'Socket is not connected' in err.output:
log.error(err.output)
raise errors.ChalmersError("The user '%s' must be logged in via the osx gui to perform this operation" % self.target_user)
raise
return output
def get_launchd(self):
try:
command = ['launchctl', 'list', self.label]
return self.check_output(command)
except sp.CalledProcessError as err:
if err.returncode == 1:
return None
raise
def add_launchd(self):
if self.target_user:
username = '<key>UserName</key> <string>%s</string>' % self.target_user
else:
username = ''
plist = self.template.format(python_exe=python_exe,
chalmers=chalmers_script,
label=self.label,
username=username)
with tempfile.NamedTemporaryFile('w', suffix='.plist', prefix='chalmers') as fd:
fd.write(plist)
fd.flush()
try:
command = ['launchctl', 'load', fd.name]
self.check_output(command).strip()
except sp.CalledProcessError as err:
if err.returncode == 1:
raise errors.ChalmersError("Chalmers service is already installed")
raise
def install(self):
"""Create a launchd plist and load as a global daemon"""
log.info("Adding chalmers launchd plist")
self.add_launchd()
log.info("All chalmers programs will now run on boot")
return True
def uninstall(self):
"""Uninstall launchd plist for chalmers"""
log.info("Removing chalmers plist from launchd")
try:
command = ['launchctl', 'remove', self.label]
self.check_output(command).strip()
except sp.CalledProcessError as err:
if err.returncode == 1:
log.error("Chalmers service is not installed")
return False
raise
log.info("Chalmers service has been removed")
return True
def status(self):
"""Check if chalmers will be started at reboot"""
try:
launchd_lines = self.get_launchd()
except sp.CalledProcessError:
launchd_lines = None
if launchd_lines:
log.info("Chalmers is setup to start on boot")
return True
else:
log.info("Chalmers will not start on boot")
return False | chalmers/service/darwin_service.py | from __future__ import unicode_literals, print_function
import logging
from os import path
import os
import subprocess as sp
import sys
from chalmers import errors
import tempfile
python_exe = sys.executable
chalmers_script = sys.argv[0]
def read_data(filename):
filename = path.join(path.dirname(__file__), 'data', filename)
with open(filename) as fd:
return fd.read()
launchd_label = "org.continuum.chalmers"
log = logging.getLogger('chalmers.service')
class DarwinService(object):
def __init__(self, target_user):
self.target_user = target_user
log.info('Platform: Darwin')
log.info('Using Darwin launchd service')
if target_user:
log.info('Launching chalmers as target user %s' % target_user)
elif target_user is False:
log.info('Launching chalmers as current user (does not require root)')
else:
log.info('Launching chalmers as root user')
@property
def label(self):
if self.target_user:
return '%s.%s' % (launchd_label, self.target_user)
else:
return launchd_label
@property
def template(self):
return read_data('launchd.plist')
def check_output(self, command):
if self.target_user:
if os.getuid() != 0:
raise errors.ChalmersError("Can not perform system install without root")
log.info("Running command: %s" % ' '.join(command))
try:
output = sp.check_output(command, stderr=sp.STDOUT)
except OSError as err:
raise errors.ChalmersError("Could not access program 'launchctl' required for osx service install")
except sp.CalledProcessError as err:
if err.returncode == 1:
if 'Socket is not connected' in err.output:
log.error(err.output)
raise errors.ChalmersError("The user '%s' must be logged in via the osx gui to perform this operation" % self.target_user)
raise
return output
def get_launchd(self):
try:
command = ['launchctl', 'list', self.label]
return self.check_output(command)
except sp.CalledProcessError as err:
if err.returncode == 1:
return None
raise
def add_launchd(self):
if self.target_user:
username = '<key>UserName</key> <string>%s</string>' % self.target_user
else:
username = ''
plist = self.template.format(python_exe=python_exe,
chalmers=chalmers_script,
label=self.label,
username=username)
with tempfile.NamedTemporaryFile('w', suffix='.plist', prefix='chalmers') as fd:
fd.write(plist)
fd.flush()
try:
command = ['launchctl', 'load', fd.name]
self.check_output(command).strip()
except sp.CalledProcessError as err:
if err.returncode == 1:
raise errors.ChalmersError("Chalmers service is already installed")
raise
def install(self):
"""Create a launchd plist and load as a global daemon"""
log.info("Adding chalmers launchd plist")
self.add_launchd()
log.info("All chalmers programs will now run on boot")
return True
def uninstall(self):
"""Uninstall launchd plist for chalmers"""
log.info("Removing chalmers plist from launchd")
try:
command = ['launchctl', 'remove', self.label]
self.check_output(command).strip()
except sp.CalledProcessError as err:
if err.returncode == 1:
log.error("Chalmers service is not installed")
return False
raise
log.info("Chalmers service has been removed")
return True
def status(self):
"""Check if chalmers will be started at reboot"""
try:
launchd_lines = self.get_launchd()
except sp.CalledProcessError:
launchd_lines = None
if launchd_lines:
log.info("Chalmers is setup to start on boot")
return True
else:
log.info("Chalmers will not start on boot")
return False | 0.286968 | 0.085939 |
import time, json
from collections import OrderedDict as od
class kormerDict:
def __init__(self):
self._1mer_ = {}
self._2mer_ = {}
self._3mer_ = {}
def getData(self,fname):
self.fname = fname
with open(self.fname, "r") as rf :
self._udict_ = json.load(rf)
self.kl = list(self._udict_.keys())
def dict1Mer(self):
for word in self.kl :
for chr in word:
if chr not in self._1mer_ :
self._1mer_ [chr]=1
else :
self._1mer_ [chr]+=1
def dict2Mer(self):
for word in self.kl :
word = "_"+word+"_"
for i in range(len(word)):
e = i+1
if e > len(word)-1:
break
else :
if word[i:e+1] not in self._2mer_ :
self._2mer_ [word[i:e+1]]=1
else :
self._2mer_ [word[i:e+1]]+=1
def dict3Mer(self):
for word in self.kl :
word = "_"+word+"_"
for i in range(len(word)):
e = i+2
if e > len(word)-1:
break
else :
if word[i:e+1] not in self._3mer_ :
self._3mer_ [word[i:e+1]]=1
else :
self._3mer_ [word[i:e+1]]+=1
def colDict(self, dicName):
self._1mer_ = dict(od(sorted(self._1mer_.items(), key=lambda t: t[0])))
self._2mer_ = dict(od(sorted(self._2mer_.items(), key=lambda t: t[0])))
self._3mer_ = dict(od(sorted(self._3mer_.items(), key=lambda t: t[0])))
with open("Joongang"+ dicName + "Py.json", "a", encoding="utf-8") as jwf:
if dicName == "1merDic":
print("1merDic 제시어 수 :", len(self._1mer_))
jwf.write(json.dumps(self._1mer_))
elif dicName == "2merDic":
print("2merDic 제시어 수 :", len(self._2mer_))
jwf.write(json.dumps(self._2mer_))
elif dicName == "3merDic":
print("3merDic 제시어 수 :", len(self._3mer_))
jwf.write(json.dumps(self._3mer_))
with open("Joongang"+ dicName + ".txt", "a", encoding="utf-8") as twf:
if dicName == "1merDic":
for k, v in self._1mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
elif dicName == "2merDic":
for k, v in self._2mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
elif dicName == "3merDic":
for k,v in self._3mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
def main():
beg = time.time()
ex = kormerDict()
for i in range(1,29+1):
ex.getData("중앙기사%dResPy.json" % i)
ex.dict1Mer()
ex.dict2Mer()
ex.dict3Mer()
ex.colDict("1merDic")
ex.colDict("2merDic")
ex.colDict("3merDic")
end = time.time()
print("exec time : %.6g sec(s)" % (end-beg))
return
if __name__ == '__main__':
main() | kormerDict.py | import time, json
from collections import OrderedDict as od
class kormerDict:
def __init__(self):
self._1mer_ = {}
self._2mer_ = {}
self._3mer_ = {}
def getData(self,fname):
self.fname = fname
with open(self.fname, "r") as rf :
self._udict_ = json.load(rf)
self.kl = list(self._udict_.keys())
def dict1Mer(self):
for word in self.kl :
for chr in word:
if chr not in self._1mer_ :
self._1mer_ [chr]=1
else :
self._1mer_ [chr]+=1
def dict2Mer(self):
for word in self.kl :
word = "_"+word+"_"
for i in range(len(word)):
e = i+1
if e > len(word)-1:
break
else :
if word[i:e+1] not in self._2mer_ :
self._2mer_ [word[i:e+1]]=1
else :
self._2mer_ [word[i:e+1]]+=1
def dict3Mer(self):
for word in self.kl :
word = "_"+word+"_"
for i in range(len(word)):
e = i+2
if e > len(word)-1:
break
else :
if word[i:e+1] not in self._3mer_ :
self._3mer_ [word[i:e+1]]=1
else :
self._3mer_ [word[i:e+1]]+=1
def colDict(self, dicName):
self._1mer_ = dict(od(sorted(self._1mer_.items(), key=lambda t: t[0])))
self._2mer_ = dict(od(sorted(self._2mer_.items(), key=lambda t: t[0])))
self._3mer_ = dict(od(sorted(self._3mer_.items(), key=lambda t: t[0])))
with open("Joongang"+ dicName + "Py.json", "a", encoding="utf-8") as jwf:
if dicName == "1merDic":
print("1merDic 제시어 수 :", len(self._1mer_))
jwf.write(json.dumps(self._1mer_))
elif dicName == "2merDic":
print("2merDic 제시어 수 :", len(self._2mer_))
jwf.write(json.dumps(self._2mer_))
elif dicName == "3merDic":
print("3merDic 제시어 수 :", len(self._3mer_))
jwf.write(json.dumps(self._3mer_))
with open("Joongang"+ dicName + ".txt", "a", encoding="utf-8") as twf:
if dicName == "1merDic":
for k, v in self._1mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
elif dicName == "2merDic":
for k, v in self._2mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
elif dicName == "3merDic":
for k,v in self._3mer_.items():
twf.write(f"{k:>1s} {v:<6d}\n")
def main():
beg = time.time()
ex = kormerDict()
for i in range(1,29+1):
ex.getData("중앙기사%dResPy.json" % i)
ex.dict1Mer()
ex.dict2Mer()
ex.dict3Mer()
ex.colDict("1merDic")
ex.colDict("2merDic")
ex.colDict("3merDic")
end = time.time()
print("exec time : %.6g sec(s)" % (end-beg))
return
if __name__ == '__main__':
main() | 0.054588 | 0.154217 |
from profileapi.models import Profile
from profileapi.serializers import ProfileSerializer
from rest_framework import generics, status
from rest_framework.response import Response
from django.core.exceptions import ObjectDoesNotExist
from django.views.decorators.csrf import csrf_exempt
class Profiles(generics.ListCreateAPIView):
queryset = Profile.objects.all()
serializer_class = ProfileSerializer
search_fields = ('^username',)
def create(self, request, *args, **kwargs):
"""
Create a new user with given data. Returns all relevant data.
"""
# check date is valid
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
try:
serializer.save()
except ValueError as e:
return Response({'success': 'False', 'message': str(e)},
status=status.HTTP_500_INTERNAL_SERVER_ERROR)
headers = self.get_success_headers(serializer.data)
response_data = {
"success": "True",
"message": "Profile Created",
"profile": serializer.data
}
return Response(response_data, status=status.HTTP_201_CREATED, headers=headers)
def get(self, request):
"""
Return profile information for given user
"""
serializer = ProfileSerializer(self.request.data['username'])
return Response(serializer.data)
class Login(generics.ListCreateAPIView):
@csrf_exempt
def create(self, request):
"""
Login view - verifies tha user is valid and returns information about user for profile page setup.
"""
try:
user = Profile.objects.get(username=self.request.data["username"])
except ObjectDoesNotExist:
return Response({'success': 'false','message':'User does not exist.'}, status=status.HTTP_400_BAD_REQUEST)
if user.password == self.request.data["password"]:
serializer = ProfileSerializer(user)
return Response(serializer.data)
else:
return Response({'success':'false', 'message':'Password is incorrect.'}) | taskit_backend/profileapi/views.py | from profileapi.models import Profile
from profileapi.serializers import ProfileSerializer
from rest_framework import generics, status
from rest_framework.response import Response
from django.core.exceptions import ObjectDoesNotExist
from django.views.decorators.csrf import csrf_exempt
class Profiles(generics.ListCreateAPIView):
queryset = Profile.objects.all()
serializer_class = ProfileSerializer
search_fields = ('^username',)
def create(self, request, *args, **kwargs):
"""
Create a new user with given data. Returns all relevant data.
"""
# check date is valid
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
try:
serializer.save()
except ValueError as e:
return Response({'success': 'False', 'message': str(e)},
status=status.HTTP_500_INTERNAL_SERVER_ERROR)
headers = self.get_success_headers(serializer.data)
response_data = {
"success": "True",
"message": "Profile Created",
"profile": serializer.data
}
return Response(response_data, status=status.HTTP_201_CREATED, headers=headers)
def get(self, request):
"""
Return profile information for given user
"""
serializer = ProfileSerializer(self.request.data['username'])
return Response(serializer.data)
class Login(generics.ListCreateAPIView):
@csrf_exempt
def create(self, request):
"""
Login view - verifies tha user is valid and returns information about user for profile page setup.
"""
try:
user = Profile.objects.get(username=self.request.data["username"])
except ObjectDoesNotExist:
return Response({'success': 'false','message':'User does not exist.'}, status=status.HTTP_400_BAD_REQUEST)
if user.password == self.request.data["password"]:
serializer = ProfileSerializer(user)
return Response(serializer.data)
else:
return Response({'success':'false', 'message':'Password is incorrect.'}) | 0.689096 | 0.117521 |
class FileLoader(object):
def __init__(self, fname, coltypes = {}, separator = None):
self.types = coltypes
if type(fname) == str:
ofile = open(fname)
else:
ofile = fname
self.rows = [x.split(separator) for x in ofile]
def __getitem__(self, *args):
index = args[0]
if type(index) != int:
raise TypeError("The index must be an integer, but I got '%s'" % index)
row = tuple(self.types.get(colno, str)(colval)
for (colno, colval)
in enumerate(self.rows[index]))
return row
def __iter__(self):
class IterObject(object):
def __init__(self, fl):
self.iterable = fl
self.pointer = 0
def next(self):
try:
val = self.iterable[self.pointer]
self.pointer += 1
return val
except IndexError:
raise StopIteration
return IterObject(self)
class WheelLoader(object):
def __init__(self, fname):
if type(fname) == str:
ofile = open(fname)
else:
ofile = fname
self.rows = [self.splitRow(x) for x in ofile]
def splitRow(self, string):
elements = []
partial = string.lstrip()
while partial:
if partial[0] == "'":
elem, partial = self.__processString(partial)
else:
elem, partial = [x.lstrip() for x in self.__processNonString(partial)]
elements.append(elem)
return elements
def __processNonString(self, string):
splitted = string.split(' ', 1)
if len(splitted) < 2:
rest = ""
else:
rest = splitted[1].lstrip()
return splitted[0].strip(), rest
def __processString(self, string):
retval = ""
done = False
partial = string[1:]
while not done:
end = partial.find("'")
if end == -1:
raise ValueError("Missing end quote in [%s]" % string)
retval += partial[:end]
partial = partial[end+1:]
if partial.startswith("'"):
retval += "'"
partial = partial[end+1:]
if not partial.startswith(" "):
retval += "'"
else:
partial = partial.lstrip()
done = True
return retval, partial
def __getitem__(self, *args):
index = args[0]
if type(index) != int:
raise TypeError("The index must be an integer, but I got '%s'" % index)
return tuple(self.rows[index])
def __iter__(self):
class IterObject(object):
def __init__(self, fl):
self.iterable = fl
self.pointer = 0
def next(self):
try:
val = self.iterable[self.pointer]
self.pointer += 1
return val
except IndexError:
raise StopIteration
return IterObject(self)
example_file = """0 1050.0 1013.92
1 1050.0 1025.65
2 1138.3 1010.90
3 1118.9 1050.0
4 1119.0 995.0
5 1050.0 1006.98
6 1050.0 1015.05
7 1050.0 1011.7
9 1021.0 880.0
10 1182.0 997.0
11 1116.0 999.9
12 1132.0 996.8
13 1220.0 992.0
14 750.0 1003.7
15 1107.0 902.1
16 999.9 999.8
17 1050.0 1015.0
33 1212. 1212.4
34 1086. 1080.
37 1152. 1370.
40 687. 1011.
55 1063.05 936.63
66 1181.69 1266.05
77 1175.0 1047.0
88 1103.9 1025.0
"""
example_wheel_file = """0 'Open' 0
1 'Hal_rs45 696_5' 58
2 'u'_Slo 350_65' 82
3 'u'_Slo 353_55' 109
4 'Halp 656_3' 21
5 'Cont 662_4' 77
6 '[SII] 672_5' 123
"""
if __name__ == '__main__':
from StringIO import StringIO
print "Sample: 6th row with no converters"
print FileLoader(StringIO(example_file))[5]
print
print "Sample: 6th row with converters = {0: int, 1:float}"
print FileLoader(StringIO(example_file),
coltypes = {0: int, 1:float})[5]
print
print "Sample: Iterate over the whole file; converters = {0: int, 1:float, 2:float}"
fl = FileLoader(StringIO(example_file),
coltypes = {0: int, 1:float, 2:float})
for tpl in fl:
print tpl
print "Sample: Iterate over a wheel file"
fl = WheelLoader(StringIO(example_wheel_file))
for tpl in fl:
print tpl | sandbox/src2/src/fileloader.py | class FileLoader(object):
def __init__(self, fname, coltypes = {}, separator = None):
self.types = coltypes
if type(fname) == str:
ofile = open(fname)
else:
ofile = fname
self.rows = [x.split(separator) for x in ofile]
def __getitem__(self, *args):
index = args[0]
if type(index) != int:
raise TypeError("The index must be an integer, but I got '%s'" % index)
row = tuple(self.types.get(colno, str)(colval)
for (colno, colval)
in enumerate(self.rows[index]))
return row
def __iter__(self):
class IterObject(object):
def __init__(self, fl):
self.iterable = fl
self.pointer = 0
def next(self):
try:
val = self.iterable[self.pointer]
self.pointer += 1
return val
except IndexError:
raise StopIteration
return IterObject(self)
class WheelLoader(object):
def __init__(self, fname):
if type(fname) == str:
ofile = open(fname)
else:
ofile = fname
self.rows = [self.splitRow(x) for x in ofile]
def splitRow(self, string):
elements = []
partial = string.lstrip()
while partial:
if partial[0] == "'":
elem, partial = self.__processString(partial)
else:
elem, partial = [x.lstrip() for x in self.__processNonString(partial)]
elements.append(elem)
return elements
def __processNonString(self, string):
splitted = string.split(' ', 1)
if len(splitted) < 2:
rest = ""
else:
rest = splitted[1].lstrip()
return splitted[0].strip(), rest
def __processString(self, string):
retval = ""
done = False
partial = string[1:]
while not done:
end = partial.find("'")
if end == -1:
raise ValueError("Missing end quote in [%s]" % string)
retval += partial[:end]
partial = partial[end+1:]
if partial.startswith("'"):
retval += "'"
partial = partial[end+1:]
if not partial.startswith(" "):
retval += "'"
else:
partial = partial.lstrip()
done = True
return retval, partial
def __getitem__(self, *args):
index = args[0]
if type(index) != int:
raise TypeError("The index must be an integer, but I got '%s'" % index)
return tuple(self.rows[index])
def __iter__(self):
class IterObject(object):
def __init__(self, fl):
self.iterable = fl
self.pointer = 0
def next(self):
try:
val = self.iterable[self.pointer]
self.pointer += 1
return val
except IndexError:
raise StopIteration
return IterObject(self)
example_file = """0 1050.0 1013.92
1 1050.0 1025.65
2 1138.3 1010.90
3 1118.9 1050.0
4 1119.0 995.0
5 1050.0 1006.98
6 1050.0 1015.05
7 1050.0 1011.7
9 1021.0 880.0
10 1182.0 997.0
11 1116.0 999.9
12 1132.0 996.8
13 1220.0 992.0
14 750.0 1003.7
15 1107.0 902.1
16 999.9 999.8
17 1050.0 1015.0
33 1212. 1212.4
34 1086. 1080.
37 1152. 1370.
40 687. 1011.
55 1063.05 936.63
66 1181.69 1266.05
77 1175.0 1047.0
88 1103.9 1025.0
"""
example_wheel_file = """0 'Open' 0
1 'Hal_rs45 696_5' 58
2 'u'_Slo 350_65' 82
3 'u'_Slo 353_55' 109
4 'Halp 656_3' 21
5 'Cont 662_4' 77
6 '[SII] 672_5' 123
"""
if __name__ == '__main__':
from StringIO import StringIO
print "Sample: 6th row with no converters"
print FileLoader(StringIO(example_file))[5]
print
print "Sample: 6th row with converters = {0: int, 1:float}"
print FileLoader(StringIO(example_file),
coltypes = {0: int, 1:float})[5]
print
print "Sample: Iterate over the whole file; converters = {0: int, 1:float, 2:float}"
fl = FileLoader(StringIO(example_file),
coltypes = {0: int, 1:float, 2:float})
for tpl in fl:
print tpl
print "Sample: Iterate over a wheel file"
fl = WheelLoader(StringIO(example_wheel_file))
for tpl in fl:
print tpl | 0.45423 | 0.167695 |
from typing import Type, Optional, Dict, Any, Iterator
from marshy.errors import MarshallError
from marshy.factory import marshaller_factory_abc
from marshy.factory.marshaller_factory_abc import MarshallerFactoryABC
from marshy.marshaller import marshaller_abc
from marshy.types import ExternalType
from marshy.utils import resolve_forward_refs
class MarshallerContext:
def __init__(self,
factories: Optional[marshaller_factory_abc.MarshallerFactoryABC] = None,
by_type: Optional[Dict[Type, marshaller_abc.MarshallerABC]] = None):
self._factories = sorted(factories or [], reverse=True)
self._by_type = dict(by_type or {})
def register_factory(self, marshaller_factory: marshaller_factory_abc.MarshallerFactoryABC):
self._factories.append(marshaller_factory)
self._factories = sorted(self._factories or [], reverse=True)
def register_marshaller(self, marshaller: marshaller_abc.MarshallerABC, type_: Type = None):
type_ = type_ or marshaller.marshalled_type
self._by_type[type_] = marshaller
def get_marshaller(self, type_: Type) -> marshaller_abc.MarshallerABC:
marshaller = self._by_type.get(type_)
if not marshaller:
resolved_type = resolve_forward_refs(type_)
for factory in self._factories:
marshaller = factory.create(self, resolved_type)
if marshaller:
break
if not marshaller:
raise MarshallError(f'NoMarshallerForType:{type_}')
self._by_type[type_] = marshaller
return marshaller
def load(self, type_: Type, to_load: ExternalType):
marshaller = self.get_marshaller(type_)
loaded = marshaller.load(to_load)
return loaded
def dump(self, obj: Any, type_: Optional[Type] = None) -> ExternalType:
if not type_:
type_ = type(obj)
marshaller = self.get_marshaller(type_)
dumped = marshaller.dump(obj)
return dumped
def get_factories(self) -> Iterator[MarshallerFactoryABC]:
return iter(self._factories) | marshy/marshaller_context.py | from typing import Type, Optional, Dict, Any, Iterator
from marshy.errors import MarshallError
from marshy.factory import marshaller_factory_abc
from marshy.factory.marshaller_factory_abc import MarshallerFactoryABC
from marshy.marshaller import marshaller_abc
from marshy.types import ExternalType
from marshy.utils import resolve_forward_refs
class MarshallerContext:
def __init__(self,
factories: Optional[marshaller_factory_abc.MarshallerFactoryABC] = None,
by_type: Optional[Dict[Type, marshaller_abc.MarshallerABC]] = None):
self._factories = sorted(factories or [], reverse=True)
self._by_type = dict(by_type or {})
def register_factory(self, marshaller_factory: marshaller_factory_abc.MarshallerFactoryABC):
self._factories.append(marshaller_factory)
self._factories = sorted(self._factories or [], reverse=True)
def register_marshaller(self, marshaller: marshaller_abc.MarshallerABC, type_: Type = None):
type_ = type_ or marshaller.marshalled_type
self._by_type[type_] = marshaller
def get_marshaller(self, type_: Type) -> marshaller_abc.MarshallerABC:
marshaller = self._by_type.get(type_)
if not marshaller:
resolved_type = resolve_forward_refs(type_)
for factory in self._factories:
marshaller = factory.create(self, resolved_type)
if marshaller:
break
if not marshaller:
raise MarshallError(f'NoMarshallerForType:{type_}')
self._by_type[type_] = marshaller
return marshaller
def load(self, type_: Type, to_load: ExternalType):
marshaller = self.get_marshaller(type_)
loaded = marshaller.load(to_load)
return loaded
def dump(self, obj: Any, type_: Optional[Type] = None) -> ExternalType:
if not type_:
type_ = type(obj)
marshaller = self.get_marshaller(type_)
dumped = marshaller.dump(obj)
return dumped
def get_factories(self) -> Iterator[MarshallerFactoryABC]:
return iter(self._factories) | 0.883022 | 0.07579 |
from timo.database_manager.databases.mysql import MySQL
import HtmlTestRunner
import xmlrunner
import unittest
import json
import os
import yaml
class TestMySQL(unittest.TestCase):
def setUp(self):
self.db = MySQL()
self.db.open_DB_session()
def tearDown(self):
self.db.close_DB_session()
def test_select_query_print(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save=None)
def test_select_query_save_json(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='json')
assert os.path.isfile(os.getcwd() + '/out.json')
with open(file=os.getcwd() + '/out.json', mode='r', encoding='utf-8') as f:
data = json.load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.json')
def test_select_query_save_yaml(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='yaml')
assert os.path.isfile(os.getcwd() + '/out.yaml')
with open(file=os.getcwd() + '/out.yaml', mode='r', encoding='utf-8') as f:
data = yaml.safe_load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.yaml')
def test_select_query_save_yml(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='yml')
assert os.path.isfile(os.getcwd() + '/out.yml')
with open(file=os.getcwd() + '/out.yml', mode='r', encoding='utf-8') as f:
data = yaml.safe_load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.yml')
if __name__ == "__main__":
unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(output='unittest-report'))
# with open(file='unittest-xml-report.xml', mode='wb') as output:
# unittest.main(
# testRunner=xmlrunner.XMLTestRunner(output=output),
# failfast=False, buffer=False, catchbreak=False
# ) | tests/test_mysql.py |
from timo.database_manager.databases.mysql import MySQL
import HtmlTestRunner
import xmlrunner
import unittest
import json
import os
import yaml
class TestMySQL(unittest.TestCase):
def setUp(self):
self.db = MySQL()
self.db.open_DB_session()
def tearDown(self):
self.db.close_DB_session()
def test_select_query_print(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save=None)
def test_select_query_save_json(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='json')
assert os.path.isfile(os.getcwd() + '/out.json')
with open(file=os.getcwd() + '/out.json', mode='r', encoding='utf-8') as f:
data = json.load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.json')
def test_select_query_save_yaml(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='yaml')
assert os.path.isfile(os.getcwd() + '/out.yaml')
with open(file=os.getcwd() + '/out.yaml', mode='r', encoding='utf-8') as f:
data = yaml.safe_load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.yaml')
def test_select_query_save_yml(self):
self.db.send_query(sql="SELECT MAX(BUILD_NO) FROM JENKINS_BUILD_RESULT WHERE PROJECT_NM = 'IRIS-E2E-SAAS'", type='select', save='yml')
assert os.path.isfile(os.getcwd() + '/out.yml')
with open(file=os.getcwd() + '/out.yml', mode='r', encoding='utf-8') as f:
data = yaml.safe_load(f)
assert len(data) == 1
assert type(data[0]['MAX(BUILD_NO)']) is int
os.remove(os.getcwd() + '/out.yml')
if __name__ == "__main__":
unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(output='unittest-report'))
# with open(file='unittest-xml-report.xml', mode='wb') as output:
# unittest.main(
# testRunner=xmlrunner.XMLTestRunner(output=output),
# failfast=False, buffer=False, catchbreak=False
# ) | 0.298185 | 0.17434 |
import os
import numpy as np
import pandas as pd
from datashader.utils import lnglat_to_meters as webm
from src.utils import write_log
def engineer_brazilian_zip_code() -> None:
"""
Engineers the brazilian zip code (CEP) in the geolocation dataset by Olist.
"""
write_log("Reading geolocation dataset from Brazilian E-Commerce Public Dataset by Olist...")
df_geolocation = read_geolocation_dataset()
write_log("Successful read geolocation dataset from Brazilian E-Commerce Public Dataset by Olist.")
write_log("Purging coordinates outside Brazil borders...")
df_geolocation = purge_outliers(df_geolocation)
write_log("Successful purged coordinates outside Brazil borders.")
write_log("Extracting the zip code prefixes...")
df_geolocation = get_zip_code_prefixes(df_geolocation)
write_log("Successfully extracted the zip code prefixes.")
write_log("Transforming coordinates to Mercator coordinates...")
df_geolocation = transform_coordinates_to_mercartor_coordinates(df_geolocation)
write_log("Successfully transformed coordinates to Mercator coordinates.")
write_log("Converting zip codes prefixes to type int...")
df_geolocation = convert_zip_code_prefix(df_geolocation)
write_log("Successfully coverted zip codes prefix to type int.")
write_log("Saving the engineered dataset of zip codes...")
df_geolocation.to_csv("data/interim/olist_engineered_geolocation_dataset.csv", index=False)
write_log("Successfully saved the engineered dataset of zip codes.")
def get_zip_code_prefixes(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Gets the first three and four first digits of zip codes.
"""
df = df_geolocation.copy()
df['geolocation_zip_code_prefix_1_digits'] = df['geolocation_zip_code_prefix'].str[0:1]
df['geolocation_zip_code_prefix_2_digits'] = df['geolocation_zip_code_prefix'].str[0:2]
df['geolocation_zip_code_prefix_3_digits'] = df['geolocation_zip_code_prefix'].str[0:3]
df['geolocation_zip_code_prefix_4_digits'] = df['geolocation_zip_code_prefix'].str[0:4]
return df
def purge_outliers(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Removes outliers.
"""
df = df_geolocation.copy()
# Brazils most Northern spot is at 5 deg 16′ 27.8″ N lat.
df = df[df["geolocation_lat"] <= 5.27438888]
# Brazils most Western spot is at 73 deg, 58′ 58.19″W lng.
df = df[df["geolocation_lng"] >= -73.98283055]
# Brazils most southern spot is at 33 deg, 45′ 04.21″ S lat.
df = df[df["geolocation_lat"] >= -33.75116944]
# Brazils most Eastern spot is 34 deg, 47′ 35.33″ W lng.
df = df[df["geolocation_lng"] <= -34.79314722]
return df
def transform_coordinates_to_mercartor_coordinates(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Transforms the latitute and longitude coordinates to Mercator x/y Coordinates.
"""
df = df_geolocation.copy()
x, y = webm(df.geolocation_lng, df.geolocation_lat)
df['x'] = pd.Series(x)
df['y'] = pd.Series(y)
return df
def convert_zip_code_prefix(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Converts the zip code prefix into int type for plotting purposes.
"""
df = df_geolocation.copy()
df['geolocation_zip_code_prefix'] = df['geolocation_zip_code_prefix'].astype(int)
df['geolocation_zip_code_prefix_1_digits'] = df['geolocation_zip_code_prefix_1_digits'].astype(int)
df['geolocation_zip_code_prefix_2_digits'] = df['geolocation_zip_code_prefix_2_digits'].astype(int)
df['geolocation_zip_code_prefix_3_digits'] = df['geolocation_zip_code_prefix_3_digits'].astype(int)
df['geolocation_zip_code_prefix_4_digits'] = df['geolocation_zip_code_prefix_4_digits'].astype(int)
return df
def read_geolocation_dataset() -> pd.DataFrame:
"""
Returns the geolocation dataset from Brazilian E-Commerce Public Dataset by Olist.
"""
return pd.read_csv("data/raw/Brazilian_E_Commerce_Public_Dataset_by_Olist/olist_geolocation_dataset.csv",\
dtype={'geolocation_zip_code_prefix': str})
if __name__ == '__main__':
engineer_brazilian_zip_code() | src/features/Engineer_Brazilian_ZIP_Code.py | import os
import numpy as np
import pandas as pd
from datashader.utils import lnglat_to_meters as webm
from src.utils import write_log
def engineer_brazilian_zip_code() -> None:
"""
Engineers the brazilian zip code (CEP) in the geolocation dataset by Olist.
"""
write_log("Reading geolocation dataset from Brazilian E-Commerce Public Dataset by Olist...")
df_geolocation = read_geolocation_dataset()
write_log("Successful read geolocation dataset from Brazilian E-Commerce Public Dataset by Olist.")
write_log("Purging coordinates outside Brazil borders...")
df_geolocation = purge_outliers(df_geolocation)
write_log("Successful purged coordinates outside Brazil borders.")
write_log("Extracting the zip code prefixes...")
df_geolocation = get_zip_code_prefixes(df_geolocation)
write_log("Successfully extracted the zip code prefixes.")
write_log("Transforming coordinates to Mercator coordinates...")
df_geolocation = transform_coordinates_to_mercartor_coordinates(df_geolocation)
write_log("Successfully transformed coordinates to Mercator coordinates.")
write_log("Converting zip codes prefixes to type int...")
df_geolocation = convert_zip_code_prefix(df_geolocation)
write_log("Successfully coverted zip codes prefix to type int.")
write_log("Saving the engineered dataset of zip codes...")
df_geolocation.to_csv("data/interim/olist_engineered_geolocation_dataset.csv", index=False)
write_log("Successfully saved the engineered dataset of zip codes.")
def get_zip_code_prefixes(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Gets the first three and four first digits of zip codes.
"""
df = df_geolocation.copy()
df['geolocation_zip_code_prefix_1_digits'] = df['geolocation_zip_code_prefix'].str[0:1]
df['geolocation_zip_code_prefix_2_digits'] = df['geolocation_zip_code_prefix'].str[0:2]
df['geolocation_zip_code_prefix_3_digits'] = df['geolocation_zip_code_prefix'].str[0:3]
df['geolocation_zip_code_prefix_4_digits'] = df['geolocation_zip_code_prefix'].str[0:4]
return df
def purge_outliers(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Removes outliers.
"""
df = df_geolocation.copy()
# Brazils most Northern spot is at 5 deg 16′ 27.8″ N lat.
df = df[df["geolocation_lat"] <= 5.27438888]
# Brazils most Western spot is at 73 deg, 58′ 58.19″W lng.
df = df[df["geolocation_lng"] >= -73.98283055]
# Brazils most southern spot is at 33 deg, 45′ 04.21″ S lat.
df = df[df["geolocation_lat"] >= -33.75116944]
# Brazils most Eastern spot is 34 deg, 47′ 35.33″ W lng.
df = df[df["geolocation_lng"] <= -34.79314722]
return df
def transform_coordinates_to_mercartor_coordinates(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Transforms the latitute and longitude coordinates to Mercator x/y Coordinates.
"""
df = df_geolocation.copy()
x, y = webm(df.geolocation_lng, df.geolocation_lat)
df['x'] = pd.Series(x)
df['y'] = pd.Series(y)
return df
def convert_zip_code_prefix(df_geolocation : pd.DataFrame) -> pd.DataFrame:
"""
Converts the zip code prefix into int type for plotting purposes.
"""
df = df_geolocation.copy()
df['geolocation_zip_code_prefix'] = df['geolocation_zip_code_prefix'].astype(int)
df['geolocation_zip_code_prefix_1_digits'] = df['geolocation_zip_code_prefix_1_digits'].astype(int)
df['geolocation_zip_code_prefix_2_digits'] = df['geolocation_zip_code_prefix_2_digits'].astype(int)
df['geolocation_zip_code_prefix_3_digits'] = df['geolocation_zip_code_prefix_3_digits'].astype(int)
df['geolocation_zip_code_prefix_4_digits'] = df['geolocation_zip_code_prefix_4_digits'].astype(int)
return df
def read_geolocation_dataset() -> pd.DataFrame:
"""
Returns the geolocation dataset from Brazilian E-Commerce Public Dataset by Olist.
"""
return pd.read_csv("data/raw/Brazilian_E_Commerce_Public_Dataset_by_Olist/olist_geolocation_dataset.csv",\
dtype={'geolocation_zip_code_prefix': str})
if __name__ == '__main__':
engineer_brazilian_zip_code() | 0.655115 | 0.534673 |
from .preprocessor import FortranPreprocessor
import re
from .smartopen import smart_open
UNIT_REGEX = re.compile(r"^\s*(?P<unit_type>module(?!\s+procedure)|program)\s*(?P<modname>\w*)",
re.IGNORECASE)
END_REGEX = re.compile(r"^\s*end\s*(?P<unit_type>module|program)\s*(?P<modname>\w*)?",
re.IGNORECASE)
USE_REGEX = re.compile(r"""^\s*use
(\s*,\s*intrinsic\s*)?(\s*::\s*|\s+) # Valid separators between "use" and module name
(?P<moduse>\w*) # The module name
\s*(, )?\s*(only)?\s*(:)?.*?$ # Stuff that might follow the name
""",
re.IGNORECASE | re.VERBOSE)
class FortranFile:
"""The modules and dependencies of a Fortran source file
Args:
filename (str): Source file
macros (iterable): Dict of preprocessor macros to be expanded
readfile (bool): Read and process the file [True]
cpp_includes (list of str): List of directories to add to preprocessor search path
use_preprocessor (bool): Preprocess the source file [True]
"""
def __init__(self, filename=None, macros=None, readfile=True, cpp_includes=None,
use_preprocessor=True):
self.filename = filename
self.uses = None
self.modules = None
self.depends_on = None
if readfile:
with smart_open(self.filename, 'r') as f:
contents = f.read()
preprocessor = FortranPreprocessor()
if macros:
if isinstance(macros, dict):
for k, v in macros.items():
preprocessor.define("{} {}".format(k, v))
else:
if not isinstance(macros, list):
macros = [macros]
for macro in macros:
if '=' in macro:
temp = macro.split('=')
preprocessor.define("{} {}".format(*temp))
else:
preprocessor.define(macro)
if cpp_includes:
if not isinstance(cpp_includes, list):
cpp_includes = [cpp_includes]
for include_dir in cpp_includes:
preprocessor.add_path(include_dir)
if use_preprocessor:
contents = preprocessor.parse_to_string(contents, source=self.filename)
self.modules = self.get_modules(contents.splitlines())
self.uses = self.get_uses()
def __str__(self):
return self.filename
def __repr__(self):
return "FortranFile('{}')".format(self.filename)
def get_modules(self, contents, macros=None):
"""Return all the modules or programs that are in the file
Args:
contents (str): Contents of the source file
"""
contains = {}
found_units = []
starts = []
ends = []
for num, line in enumerate(contents):
unit = re.match(UNIT_REGEX, line)
end = re.match(END_REGEX, line)
if unit:
found_units.append(unit)
starts.append(num)
if end:
ends.append(num)
if found_units:
if (len(found_units) != len(starts)) or (len(starts) != len(ends)):
error_string = ("Unmatched start/end of modules in {} ({} begins/{} ends)"
.format(self.filename, len(starts), len(ends)))
raise ValueError(error_string)
for unit, start, end in zip(found_units, starts, ends):
name = unit.group('modname')
contains[name] = FortranModule(unit_type=unit.group('unit_type'),
name=name,
source_file=self,
text=(contents, start, end),
macros=macros)
# Remove duplicates before returning
return contains
def get_uses(self):
"""Return a sorted list of the modules this file USEs
"""
if self.modules is None:
return []
# flatten list of lists
return sorted(set([mod for module in self.modules.values()
for mod in module.uses]))
class FortranModule:
"""A Fortran Module or Program
Args:
unit_type (str): 'module' or 'program'
name (str): Name of the module/program
source_file (str): Name of the file containing the module/program
text (tuple): Tuple containing source_file contents, and start and end lines of the module
macros (dict): Any defined macros
"""
def __init__(self, unit_type, name, source_file=None, text=None, macros=None):
self.unit_type = unit_type.strip().lower()
self.name = name.strip().lower()
if source_file is not None:
self.source_file = source_file
self.defined_at = text[1]
self.end = text[2]
self.uses = self.get_uses(text[0], macros)
else:
self.source_file = FortranFile(filename='empty',
readfile=False)
def __str__(self):
return self.name
def __repr__(self):
return "FortranModule({}, '{}', '{}')".format(self.unit_type, self.name,
self.source_file.filename)
def get_uses(self, contents, macros=None):
"""Return which modules are used in the file after expanding macros
Args:
contents (str): Contents of the source file
macros (dict): Dict of preprocessor macros to be expanded
"""
uses = []
for line in contents[self.defined_at:self.end]:
found = re.match(USE_REGEX, line)
if found:
uses.append(found.group('moduse').strip())
# Remove duplicates
uniq_mods = list(set(uses))
if macros is not None:
for i, mod in enumerate(uniq_mods):
for k, v in macros.items():
if re.match(k, mod, re.IGNORECASE):
uniq_mods[i] = mod.replace(k, v)
return uniq_mods | fortdepend/units.py | from .preprocessor import FortranPreprocessor
import re
from .smartopen import smart_open
UNIT_REGEX = re.compile(r"^\s*(?P<unit_type>module(?!\s+procedure)|program)\s*(?P<modname>\w*)",
re.IGNORECASE)
END_REGEX = re.compile(r"^\s*end\s*(?P<unit_type>module|program)\s*(?P<modname>\w*)?",
re.IGNORECASE)
USE_REGEX = re.compile(r"""^\s*use
(\s*,\s*intrinsic\s*)?(\s*::\s*|\s+) # Valid separators between "use" and module name
(?P<moduse>\w*) # The module name
\s*(, )?\s*(only)?\s*(:)?.*?$ # Stuff that might follow the name
""",
re.IGNORECASE | re.VERBOSE)
class FortranFile:
"""The modules and dependencies of a Fortran source file
Args:
filename (str): Source file
macros (iterable): Dict of preprocessor macros to be expanded
readfile (bool): Read and process the file [True]
cpp_includes (list of str): List of directories to add to preprocessor search path
use_preprocessor (bool): Preprocess the source file [True]
"""
def __init__(self, filename=None, macros=None, readfile=True, cpp_includes=None,
use_preprocessor=True):
self.filename = filename
self.uses = None
self.modules = None
self.depends_on = None
if readfile:
with smart_open(self.filename, 'r') as f:
contents = f.read()
preprocessor = FortranPreprocessor()
if macros:
if isinstance(macros, dict):
for k, v in macros.items():
preprocessor.define("{} {}".format(k, v))
else:
if not isinstance(macros, list):
macros = [macros]
for macro in macros:
if '=' in macro:
temp = macro.split('=')
preprocessor.define("{} {}".format(*temp))
else:
preprocessor.define(macro)
if cpp_includes:
if not isinstance(cpp_includes, list):
cpp_includes = [cpp_includes]
for include_dir in cpp_includes:
preprocessor.add_path(include_dir)
if use_preprocessor:
contents = preprocessor.parse_to_string(contents, source=self.filename)
self.modules = self.get_modules(contents.splitlines())
self.uses = self.get_uses()
def __str__(self):
return self.filename
def __repr__(self):
return "FortranFile('{}')".format(self.filename)
def get_modules(self, contents, macros=None):
"""Return all the modules or programs that are in the file
Args:
contents (str): Contents of the source file
"""
contains = {}
found_units = []
starts = []
ends = []
for num, line in enumerate(contents):
unit = re.match(UNIT_REGEX, line)
end = re.match(END_REGEX, line)
if unit:
found_units.append(unit)
starts.append(num)
if end:
ends.append(num)
if found_units:
if (len(found_units) != len(starts)) or (len(starts) != len(ends)):
error_string = ("Unmatched start/end of modules in {} ({} begins/{} ends)"
.format(self.filename, len(starts), len(ends)))
raise ValueError(error_string)
for unit, start, end in zip(found_units, starts, ends):
name = unit.group('modname')
contains[name] = FortranModule(unit_type=unit.group('unit_type'),
name=name,
source_file=self,
text=(contents, start, end),
macros=macros)
# Remove duplicates before returning
return contains
def get_uses(self):
"""Return a sorted list of the modules this file USEs
"""
if self.modules is None:
return []
# flatten list of lists
return sorted(set([mod for module in self.modules.values()
for mod in module.uses]))
class FortranModule:
"""A Fortran Module or Program
Args:
unit_type (str): 'module' or 'program'
name (str): Name of the module/program
source_file (str): Name of the file containing the module/program
text (tuple): Tuple containing source_file contents, and start and end lines of the module
macros (dict): Any defined macros
"""
def __init__(self, unit_type, name, source_file=None, text=None, macros=None):
self.unit_type = unit_type.strip().lower()
self.name = name.strip().lower()
if source_file is not None:
self.source_file = source_file
self.defined_at = text[1]
self.end = text[2]
self.uses = self.get_uses(text[0], macros)
else:
self.source_file = FortranFile(filename='empty',
readfile=False)
def __str__(self):
return self.name
def __repr__(self):
return "FortranModule({}, '{}', '{}')".format(self.unit_type, self.name,
self.source_file.filename)
def get_uses(self, contents, macros=None):
"""Return which modules are used in the file after expanding macros
Args:
contents (str): Contents of the source file
macros (dict): Dict of preprocessor macros to be expanded
"""
uses = []
for line in contents[self.defined_at:self.end]:
found = re.match(USE_REGEX, line)
if found:
uses.append(found.group('moduse').strip())
# Remove duplicates
uniq_mods = list(set(uses))
if macros is not None:
for i, mod in enumerate(uniq_mods):
for k, v in macros.items():
if re.match(k, mod, re.IGNORECASE):
uniq_mods[i] = mod.replace(k, v)
return uniq_mods | 0.539954 | 0.182316 |
import random
from django.conf import settings
from django.utils.text import slugify
import factory
from cms.api import add_plugin, create_page, create_title
def create_i18n_page(title=None, languages=None, is_homepage=False, **kwargs):
"""
Creating a multilingual page is not straightforward so we should have a helper
This content argument should be a dictionary with the title of the page in each language:
{
'en': 'About',
'fr': 'A propos',
'de': 'Impressum',
}
"""
template = kwargs.pop("template", None) or "richie/fullwidth.html"
if title is None:
# Create realistic titles in each language with faker
languages = languages or [settings.LANGUAGE_CODE]
i18n_titles = {
language: factory.Faker("catch_phrase", locale=language).generate({})
for language in languages
}
elif isinstance(title, dict):
# Check that the languages passed are coherent with the languages requested if any
if languages:
assert set(languages).issubset(title.keys())
else:
languages = title.keys()
i18n_titles = title
elif isinstance(title, str):
# Add a marker at the end of the string to differentiate each language
languages = languages or [settings.LANGUAGE_CODE]
i18n_titles = {
language: "{title:s} {language:s}".format(title=title, language=language)
for language in languages
}
else:
raise ValueError(
"Title should be a string or a dictionary of language/string pairs"
)
# Assert that the languages passed are declared in settings
assert set(languages).issubset({l[0] for l in settings.LANGUAGES})
# Make a copy of languages to avoid muting it in what follows
languages = list(languages)
# Create the page with a first language from what is given to us
first_language = languages.pop(0)
slug = slugify(i18n_titles[first_language])
page = create_page(
language=first_language,
menu_title=i18n_titles[first_language],
title=i18n_titles[first_language],
slug=slug,
template=template,
**kwargs
)
if is_homepage is True:
page.set_as_homepage()
# Add a title for each additional language
for language in languages:
create_title(
language=language,
menu_title=i18n_titles[language],
title=i18n_titles[language],
slug=slugify(i18n_titles[language]),
page=page,
)
# Publish page in each additional language
if kwargs.get("published") is True:
page.publish(language)
return page
# pylint: disable=too-many-arguments
def create_text_plugin(
page,
slot,
languages=None,
is_html=True,
max_nb_chars=None,
nb_paragraphs=None,
plugin_type="CKEditorPlugin",
):
"""
A common function to create and add a text plugin of any type instance to
a placeholder filled with some random text using Faker.
Arguments:
page (cms.models.pagemodel.Page): Instance of a Page used to search for
given slot (aka a placeholder name).
slot (string): A placeholder name available from page template.
Keyword Arguments:
languages (iterable): An iterable yielding language codes for which a text plugin should
be created. If ``None`` (default) it uses the default language from settings.
is_html (boolean): If True, every paragraph will be surrounded with an
HTML paragraph markup. Default is True.
max_nb_chars (integer): Number of characters limit to create each
paragraph. Default is None so a random number between 200 and 400
will be used at each paragraph.
nb_paragraphs (integer): Number of paragraphs to create in content.
Default is None so a random number between 2 and 4 will be used.
plugin_type (string or object): Type of plugin. Default use CKEditorPlugin
but you can use any other similar plugin that has a body attribute.
Returns:
object: Created plugin instance.
"""
languages = languages or [settings.LANGUAGE_CODE]
container = "<p>{:s}</p>" if is_html else "{:s}"
nb_paragraphs = nb_paragraphs or random.randint(2, 4)
placeholder = page.placeholders.get(slot=slot)
for language in languages:
paragraphs = []
for _ in range(nb_paragraphs):
max_nb_chars = max_nb_chars or random.randint(200, 400)
paragraphs.append(
factory.Faker(
"text", max_nb_chars=max_nb_chars, locale=language
).generate({})
)
body = [container.format(p) for p in paragraphs]
add_plugin(
language=language,
placeholder=placeholder,
plugin_type=plugin_type,
body="".join(body),
)
def recursive_page_creation(site, pages, parent=None):
"""
Recursively create page following tree structure with parent/children.
Arguments:
site (django.contrib.sites.models.Site): Site object which page will
be linked to.
pages (dict): Page items to create recursively such as 'children' key
value can be a dict to create child pages. The current page is
given to children for parent relation.
Keyword Arguments:
parent (cms.models.pagemodel.Page): Page used as a parent to create
page item from `pages` argument.
Returns:
dict: Created page items.
"""
pages_created = {}
for name, info in pages.items():
page = create_i18n_page(
info["content"],
is_homepage=(name == "home"),
in_navigation=info["in_navigation"],
published=True,
site=site,
parent=parent,
**info["kwargs"]
)
pages_created[name] = page
# Create children
if info.get("children", None):
pages_created[name].created_children = recursive_page_creation(
site, info["children"], parent=page
)
return pages_created | src/richie/apps/core/helpers.py | import random
from django.conf import settings
from django.utils.text import slugify
import factory
from cms.api import add_plugin, create_page, create_title
def create_i18n_page(title=None, languages=None, is_homepage=False, **kwargs):
"""
Creating a multilingual page is not straightforward so we should have a helper
This content argument should be a dictionary with the title of the page in each language:
{
'en': 'About',
'fr': 'A propos',
'de': 'Impressum',
}
"""
template = kwargs.pop("template", None) or "richie/fullwidth.html"
if title is None:
# Create realistic titles in each language with faker
languages = languages or [settings.LANGUAGE_CODE]
i18n_titles = {
language: factory.Faker("catch_phrase", locale=language).generate({})
for language in languages
}
elif isinstance(title, dict):
# Check that the languages passed are coherent with the languages requested if any
if languages:
assert set(languages).issubset(title.keys())
else:
languages = title.keys()
i18n_titles = title
elif isinstance(title, str):
# Add a marker at the end of the string to differentiate each language
languages = languages or [settings.LANGUAGE_CODE]
i18n_titles = {
language: "{title:s} {language:s}".format(title=title, language=language)
for language in languages
}
else:
raise ValueError(
"Title should be a string or a dictionary of language/string pairs"
)
# Assert that the languages passed are declared in settings
assert set(languages).issubset({l[0] for l in settings.LANGUAGES})
# Make a copy of languages to avoid muting it in what follows
languages = list(languages)
# Create the page with a first language from what is given to us
first_language = languages.pop(0)
slug = slugify(i18n_titles[first_language])
page = create_page(
language=first_language,
menu_title=i18n_titles[first_language],
title=i18n_titles[first_language],
slug=slug,
template=template,
**kwargs
)
if is_homepage is True:
page.set_as_homepage()
# Add a title for each additional language
for language in languages:
create_title(
language=language,
menu_title=i18n_titles[language],
title=i18n_titles[language],
slug=slugify(i18n_titles[language]),
page=page,
)
# Publish page in each additional language
if kwargs.get("published") is True:
page.publish(language)
return page
# pylint: disable=too-many-arguments
def create_text_plugin(
page,
slot,
languages=None,
is_html=True,
max_nb_chars=None,
nb_paragraphs=None,
plugin_type="CKEditorPlugin",
):
"""
A common function to create and add a text plugin of any type instance to
a placeholder filled with some random text using Faker.
Arguments:
page (cms.models.pagemodel.Page): Instance of a Page used to search for
given slot (aka a placeholder name).
slot (string): A placeholder name available from page template.
Keyword Arguments:
languages (iterable): An iterable yielding language codes for which a text plugin should
be created. If ``None`` (default) it uses the default language from settings.
is_html (boolean): If True, every paragraph will be surrounded with an
HTML paragraph markup. Default is True.
max_nb_chars (integer): Number of characters limit to create each
paragraph. Default is None so a random number between 200 and 400
will be used at each paragraph.
nb_paragraphs (integer): Number of paragraphs to create in content.
Default is None so a random number between 2 and 4 will be used.
plugin_type (string or object): Type of plugin. Default use CKEditorPlugin
but you can use any other similar plugin that has a body attribute.
Returns:
object: Created plugin instance.
"""
languages = languages or [settings.LANGUAGE_CODE]
container = "<p>{:s}</p>" if is_html else "{:s}"
nb_paragraphs = nb_paragraphs or random.randint(2, 4)
placeholder = page.placeholders.get(slot=slot)
for language in languages:
paragraphs = []
for _ in range(nb_paragraphs):
max_nb_chars = max_nb_chars or random.randint(200, 400)
paragraphs.append(
factory.Faker(
"text", max_nb_chars=max_nb_chars, locale=language
).generate({})
)
body = [container.format(p) for p in paragraphs]
add_plugin(
language=language,
placeholder=placeholder,
plugin_type=plugin_type,
body="".join(body),
)
def recursive_page_creation(site, pages, parent=None):
"""
Recursively create page following tree structure with parent/children.
Arguments:
site (django.contrib.sites.models.Site): Site object which page will
be linked to.
pages (dict): Page items to create recursively such as 'children' key
value can be a dict to create child pages. The current page is
given to children for parent relation.
Keyword Arguments:
parent (cms.models.pagemodel.Page): Page used as a parent to create
page item from `pages` argument.
Returns:
dict: Created page items.
"""
pages_created = {}
for name, info in pages.items():
page = create_i18n_page(
info["content"],
is_homepage=(name == "home"),
in_navigation=info["in_navigation"],
published=True,
site=site,
parent=parent,
**info["kwargs"]
)
pages_created[name] = page
# Create children
if info.get("children", None):
pages_created[name].created_children = recursive_page_creation(
site, info["children"], parent=page
)
return pages_created | 0.694303 | 0.33497 |
import MySQLdb
import time
import re
import os
import urllib
from datetime import datetime
from xml.sax.saxutils import unescape
f = open('workfile.tex', 'w')
def db(hst, usr, pw, dba):
db = MySQLdb.connect(host=hst, user=usr, passwd=pw, db=dba)
# you must create a Cursor object. It will let
# you execute all the queries you need
cur = db.cursor()
# Use all the SQL you like
cur.execute("SELECT `id`, `title`, `body`, `timestamp` as `date` FROM `entries` WHERE isdraft = 'false' ORDER BY timestamp ASC")
years = {}
# ([],[],[],[],[],[],[],[],[],[],[],[])
# print all the first cell of all the rows
for row in cur.fetchall():
if len(row) >= 4:
dt_obj = datetime.fromtimestamp(row[3])
if repr(dt_obj.year) not in years:
years[repr(dt_obj.year)] = ([],[],[],[],[],[],[],[],[],[],[],[])
title = row[1].decode('iso-8859-1')
body = row[2].decode('iso-8859-1')
# translation table for Umlaut
table = {
ord(u'$'): u'\$',
ord(u'"'): u'"{}',
ord(u'ä'): u'{\\\"a}',
ord(u'ö'): u'{\\\"o}',
ord(u'ü'): u'{\\\"u}',
ord(u'Ä'): u'{\\\"A}',
ord(u'Ö'): u'{\\\"O}',
ord(u'Ü'): u'{\\\"U}',
ord(u'ß'): u'{\\ss}',
ord(u'%'): u'\\%',
ord(u'†'): u'\\textdied',
ord(u'‡'): u'\\ddag',
ord(u'†'): u'\\textdagger',
ord(u'_'): u'\_',
}
title = title.translate(table)
body = body.translate(table)
title = re.sub(r'[^\x00-\x7F]','', title)
body = re.sub(r'[^\x00-\x7F]','', body)
title = title.encode('utf-8')
body = body.encode('utf-8')
years[repr(dt_obj.year)][dt_obj.month-1].append((row[0], title, body, row[3]))
db.close()
year(years)
f.close
def year(years):
for key in sorted(years.keys()):
(jan,feb,mar,apr,mai,jun,jul,aug,sep,okt,nov,dez) = years[key]
# print years
if len(jan)+len(feb)+len(mar)+len(apr)+len(mai)+len(jun)+len(jul)+len(aug)+len(sep)+len(okt)+len(nov)+len(dez) > 0:
f.write("\chapter{"+ key + "}\n")
# print months
if len(jan) > 0:
f.write("\section{Januar " + key + "}\n")
month(jan)
if len(feb) > 0:
f.write("\section{Februar " + key + "}\n")
month(feb)
if len(mar) > 0:
f.write("\section{M{\\\"a}rz " + key + "}\n")
month(mar)
if len(apr) > 0:
f.write("\section{April " + key + "}\n")
month(apr)
if len(mai) > 0:
f.write("\section{Mai " + key + "}\n")
month(mai)
if len(jun) > 0:
f.write("\section{Juni " + key + "}\n")
month(jun)
if len(jul) > 0:
f.write("\section{Juli " + key + "}\n")
month(jul)
if len(aug) > 0:
f.write("\section{August " + key + "}\n")
month(aug)
if len(sep) > 0:
f.write("\section{September " + key + "}\n")
month(sep)
if len(okt) > 0:
f.write("\section{Oktober " + key + "}\n")
month(okt)
if len(nov) > 0:
f.write("\section{November " + key + "}\n")
month(jan)
if len(nov) > 0:
f.write("\section{Dezember " + key + "}\n")
month(dez)
f.write("\n\n\n\\clearpage")
def month(month):
for (id, title, body, timestamp) in month:
f.write("\subsection{" + title + "}\n")
f.write(bodyCleaning(body, title)+"\n\n")
def bodyCleaning(body, title):
img = re.compile('<img src="{}([^"]+)"')
cleanedBody = body
for tag in img.finditer(cleanedBody):
cleanedURL = tag.group(1).replace('{}','').replace("\_","_").replace("\%","%")
# print title + ": " + cleanedURL
urllib.urlretrieve(cleanedURL, "images/" + os.path.basename(cleanedURL))
if os.path.splitext(cleanedURL)[1] != ".gif":
escapedURL = cleanedURL.replace("_","\_").replace("%","\%")
cleanedBody = re.sub('<img src="{}([^"]+)"[{}\s]* />', "\n\n \\begin{figure}[ht]\\centering\\href{" + escapedURL + "}{\\includegraphics[width=1.0 \\textwidth]{" + "images/" + os.path.basename(escapedURL.replace("%","\%")) + "}}\\caption{" + title + "}\\end{figure}\n\n", body, 1)
ahref = re.compile('<a href="([^"]+)"')
for tag in ahref.finditer(cleanedBody):
cleanedURL = tag.group(1).replace('{}','')
print title + ": " + cleanedURL
cleanedBody = cleanedBody + "\n \\\\ \\href{" + cleanedURL + "}{Link}"
cleanedBody = re.sub('<[^<]+?>', '', cleanedBody)
html_escape_table = {
"&":"\&",
""":'"{}',
"'":'"{}',
">":">",
"<":"<",
" ":" ",
"ʻ":"'",
"祝":"",#祝
"我":"",#我
"好":"",#好
"运":"",#运
}
cleanedBody = unescape(cleanedBody, html_escape_table)
return cleanedBody
db() | db.py | import MySQLdb
import time
import re
import os
import urllib
from datetime import datetime
from xml.sax.saxutils import unescape
f = open('workfile.tex', 'w')
def db(hst, usr, pw, dba):
db = MySQLdb.connect(host=hst, user=usr, passwd=pw, db=dba)
# you must create a Cursor object. It will let
# you execute all the queries you need
cur = db.cursor()
# Use all the SQL you like
cur.execute("SELECT `id`, `title`, `body`, `timestamp` as `date` FROM `entries` WHERE isdraft = 'false' ORDER BY timestamp ASC")
years = {}
# ([],[],[],[],[],[],[],[],[],[],[],[])
# print all the first cell of all the rows
for row in cur.fetchall():
if len(row) >= 4:
dt_obj = datetime.fromtimestamp(row[3])
if repr(dt_obj.year) not in years:
years[repr(dt_obj.year)] = ([],[],[],[],[],[],[],[],[],[],[],[])
title = row[1].decode('iso-8859-1')
body = row[2].decode('iso-8859-1')
# translation table for Umlaut
table = {
ord(u'$'): u'\$',
ord(u'"'): u'"{}',
ord(u'ä'): u'{\\\"a}',
ord(u'ö'): u'{\\\"o}',
ord(u'ü'): u'{\\\"u}',
ord(u'Ä'): u'{\\\"A}',
ord(u'Ö'): u'{\\\"O}',
ord(u'Ü'): u'{\\\"U}',
ord(u'ß'): u'{\\ss}',
ord(u'%'): u'\\%',
ord(u'†'): u'\\textdied',
ord(u'‡'): u'\\ddag',
ord(u'†'): u'\\textdagger',
ord(u'_'): u'\_',
}
title = title.translate(table)
body = body.translate(table)
title = re.sub(r'[^\x00-\x7F]','', title)
body = re.sub(r'[^\x00-\x7F]','', body)
title = title.encode('utf-8')
body = body.encode('utf-8')
years[repr(dt_obj.year)][dt_obj.month-1].append((row[0], title, body, row[3]))
db.close()
year(years)
f.close
def year(years):
for key in sorted(years.keys()):
(jan,feb,mar,apr,mai,jun,jul,aug,sep,okt,nov,dez) = years[key]
# print years
if len(jan)+len(feb)+len(mar)+len(apr)+len(mai)+len(jun)+len(jul)+len(aug)+len(sep)+len(okt)+len(nov)+len(dez) > 0:
f.write("\chapter{"+ key + "}\n")
# print months
if len(jan) > 0:
f.write("\section{Januar " + key + "}\n")
month(jan)
if len(feb) > 0:
f.write("\section{Februar " + key + "}\n")
month(feb)
if len(mar) > 0:
f.write("\section{M{\\\"a}rz " + key + "}\n")
month(mar)
if len(apr) > 0:
f.write("\section{April " + key + "}\n")
month(apr)
if len(mai) > 0:
f.write("\section{Mai " + key + "}\n")
month(mai)
if len(jun) > 0:
f.write("\section{Juni " + key + "}\n")
month(jun)
if len(jul) > 0:
f.write("\section{Juli " + key + "}\n")
month(jul)
if len(aug) > 0:
f.write("\section{August " + key + "}\n")
month(aug)
if len(sep) > 0:
f.write("\section{September " + key + "}\n")
month(sep)
if len(okt) > 0:
f.write("\section{Oktober " + key + "}\n")
month(okt)
if len(nov) > 0:
f.write("\section{November " + key + "}\n")
month(jan)
if len(nov) > 0:
f.write("\section{Dezember " + key + "}\n")
month(dez)
f.write("\n\n\n\\clearpage")
def month(month):
for (id, title, body, timestamp) in month:
f.write("\subsection{" + title + "}\n")
f.write(bodyCleaning(body, title)+"\n\n")
def bodyCleaning(body, title):
img = re.compile('<img src="{}([^"]+)"')
cleanedBody = body
for tag in img.finditer(cleanedBody):
cleanedURL = tag.group(1).replace('{}','').replace("\_","_").replace("\%","%")
# print title + ": " + cleanedURL
urllib.urlretrieve(cleanedURL, "images/" + os.path.basename(cleanedURL))
if os.path.splitext(cleanedURL)[1] != ".gif":
escapedURL = cleanedURL.replace("_","\_").replace("%","\%")
cleanedBody = re.sub('<img src="{}([^"]+)"[{}\s]* />', "\n\n \\begin{figure}[ht]\\centering\\href{" + escapedURL + "}{\\includegraphics[width=1.0 \\textwidth]{" + "images/" + os.path.basename(escapedURL.replace("%","\%")) + "}}\\caption{" + title + "}\\end{figure}\n\n", body, 1)
ahref = re.compile('<a href="([^"]+)"')
for tag in ahref.finditer(cleanedBody):
cleanedURL = tag.group(1).replace('{}','')
print title + ": " + cleanedURL
cleanedBody = cleanedBody + "\n \\\\ \\href{" + cleanedURL + "}{Link}"
cleanedBody = re.sub('<[^<]+?>', '', cleanedBody)
html_escape_table = {
"&":"\&",
""":'"{}',
"'":'"{}',
">":">",
"<":"<",
" ":" ",
"ʻ":"'",
"祝":"",#祝
"我":"",#我
"好":"",#好
"运":"",#运
}
cleanedBody = unescape(cleanedBody, html_escape_table)
return cleanedBody
db() | 0.164315 | 0.088939 |
import uuid
from dashboards.models import Dashboard, DashboardWidget, DashboardRow
from exceptions import InvalidDashboardParametersException
class DashboardFactory(object):
@staticmethod
def create_dashboard_from_query_params(query_params):
dashboard = Dashboard()
dashboard.name = query_params['name']
dashboard.description = query_params['description']
dashboard.template = query_params['template']
dashboard.monitored_object_id = query_params['monitored-object-uuid']
dashboard.transmitter_id = query_params['transmitter-uuid']
dashboard.uuid = uuid.uuid4()
for widget in DashboardFactory.__get_default_widgets_for(template=query_params['template']):
dashboard.widgets.append(widget)
return dashboard
@staticmethod
def __get_default_widgets_for(template):
widgets = []
if template == Dashboard.TEMPLATES['aircraft']:
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['altimeter']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['variometer']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['heading']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['airspeed']))
return widgets
class DashboardWidgetFactory(object):
DEFAULT_GAUGE_SIZE = 2
DEFAULT_CHART_SIZE = 5
DEFAULT_ALTITUDE_GAUGE_POSITION = 0
DEFAULT_VERTICAL_SPEED_GAUGE_POSITION = 1
DEFAULT_HEADING_INDICATOR_POSITION = 2
DEFAULT_AIR_SPEED_GAUGE_POSITION = 3
@staticmethod
def create_default_widget_from(widget_type):
widget = DashboardWidget()
if widget_type is DashboardWidget.TYPES['variometer']:
widget.name = "Vertical speed"
widget.description = "The vertical speed gauge(variometer) inform of the rate of descent or climb"
widget.measure_units = DashboardWidget.MEASURE_UNITS['ft/min']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_VERTICAL_SPEED_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['variometer']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['airspeed']:
widget.name = "Air speed"
widget.description = "The airspeed indicator or airspeed gauge is an instrument used in an aircraft to " \
"display the craft's airspeed, typically in knots"
widget.measure_units = DashboardWidget.MEASURE_UNITS['kn']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_AIR_SPEED_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['airspeed']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['altimeter']:
widget.name = "Altitude"
widget.description = "An altimeter or an altitude meter is an instrument used to measure the altitude" \
" of an object above a fixed level."
widget.measure_units = DashboardWidget.MEASURE_UNITS['ft']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_ALTITUDE_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['altimeter']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['heading']:
widget.name = "Heading"
widget.description = "The heading indicator (also called an HI) is a flight instrument used in an" \
" aircraft to inform the pilot of the aircraft's heading."
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_HEADING_INDICATOR_POSITION
widget.type = DashboardWidget.TYPES['heading']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['line-chart']:
widget.name = "Chart"
widget.description = "A line chart or line graph is a type of chart which displays information as a" \
" series of data points called 'markers' connected by straight line segments."
widget.width = DashboardWidgetFactory.DEFAULT_CHART_SIZE
widget.type = DashboardWidget.TYPES['line-chart']
widget.category = DashboardWidget.CATEGORIES['chart']
widget.uuid = uuid.uuid4()
else:
raise InvalidDashboardParametersException("The provided dashboard widget type is invalid")
return widget
@staticmethod
def create_widget_from_query_params(query_params):
widget = DashboardWidget()
widget.name = query_params['name']
widget.description = query_params['description']
widget.measure_units = DashboardWidget.MEASURE_UNITS[query_params['measure-units']]
widget.width = int(query_params['width'])
widget.grid_position = 0
widget.type = query_params['type']
widget.category = DashboardWidget.TYPES_TO_CATEGORY[query_params['type']]
widget.uuid = uuid.uuid4()
widget.sensor_id = query_params['sensor']
widget.sensor_measure = query_params['sensor-measure']
if widget.type == DashboardWidget.TYPES['gauge']:
widget.minValue = float(query_params['minimum-value'])
widget.maxValue = float(query_params['maximum-value'])
return widget
class DashboardRowsFactory(object):
@staticmethod
def create_dashboard_rows_from(widgets):
dashboard_rows = []
dashboard_row = DashboardRow()
for widget in widgets:
if dashboard_row.has_room_for(widget):
dashboard_row.add_widget(widget)
else:
dashboard_rows.append(dashboard_row)
dashboard_row = DashboardRow()
dashboard_row.add_widget(widget)
dashboard_rows.append(dashboard_row)
return dashboard_rows | dashboards/factories.py | import uuid
from dashboards.models import Dashboard, DashboardWidget, DashboardRow
from exceptions import InvalidDashboardParametersException
class DashboardFactory(object):
@staticmethod
def create_dashboard_from_query_params(query_params):
dashboard = Dashboard()
dashboard.name = query_params['name']
dashboard.description = query_params['description']
dashboard.template = query_params['template']
dashboard.monitored_object_id = query_params['monitored-object-uuid']
dashboard.transmitter_id = query_params['transmitter-uuid']
dashboard.uuid = uuid.uuid4()
for widget in DashboardFactory.__get_default_widgets_for(template=query_params['template']):
dashboard.widgets.append(widget)
return dashboard
@staticmethod
def __get_default_widgets_for(template):
widgets = []
if template == Dashboard.TEMPLATES['aircraft']:
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['altimeter']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['variometer']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['heading']))
widgets.append(DashboardWidgetFactory.create_default_widget_from(
widget_type=DashboardWidget.TYPES['airspeed']))
return widgets
class DashboardWidgetFactory(object):
DEFAULT_GAUGE_SIZE = 2
DEFAULT_CHART_SIZE = 5
DEFAULT_ALTITUDE_GAUGE_POSITION = 0
DEFAULT_VERTICAL_SPEED_GAUGE_POSITION = 1
DEFAULT_HEADING_INDICATOR_POSITION = 2
DEFAULT_AIR_SPEED_GAUGE_POSITION = 3
@staticmethod
def create_default_widget_from(widget_type):
widget = DashboardWidget()
if widget_type is DashboardWidget.TYPES['variometer']:
widget.name = "Vertical speed"
widget.description = "The vertical speed gauge(variometer) inform of the rate of descent or climb"
widget.measure_units = DashboardWidget.MEASURE_UNITS['ft/min']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_VERTICAL_SPEED_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['variometer']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['airspeed']:
widget.name = "Air speed"
widget.description = "The airspeed indicator or airspeed gauge is an instrument used in an aircraft to " \
"display the craft's airspeed, typically in knots"
widget.measure_units = DashboardWidget.MEASURE_UNITS['kn']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_AIR_SPEED_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['airspeed']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['altimeter']:
widget.name = "Altitude"
widget.description = "An altimeter or an altitude meter is an instrument used to measure the altitude" \
" of an object above a fixed level."
widget.measure_units = DashboardWidget.MEASURE_UNITS['ft']
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_ALTITUDE_GAUGE_POSITION
widget.type = DashboardWidget.TYPES['altimeter']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['heading']:
widget.name = "Heading"
widget.description = "The heading indicator (also called an HI) is a flight instrument used in an" \
" aircraft to inform the pilot of the aircraft's heading."
widget.width = DashboardWidgetFactory.DEFAULT_GAUGE_SIZE
widget.grid_position = DashboardWidgetFactory.DEFAULT_HEADING_INDICATOR_POSITION
widget.type = DashboardWidget.TYPES['heading']
widget.category = DashboardWidget.CATEGORIES['gauge']
widget.uuid = uuid.uuid4()
elif widget_type is DashboardWidget.TYPES['line-chart']:
widget.name = "Chart"
widget.description = "A line chart or line graph is a type of chart which displays information as a" \
" series of data points called 'markers' connected by straight line segments."
widget.width = DashboardWidgetFactory.DEFAULT_CHART_SIZE
widget.type = DashboardWidget.TYPES['line-chart']
widget.category = DashboardWidget.CATEGORIES['chart']
widget.uuid = uuid.uuid4()
else:
raise InvalidDashboardParametersException("The provided dashboard widget type is invalid")
return widget
@staticmethod
def create_widget_from_query_params(query_params):
widget = DashboardWidget()
widget.name = query_params['name']
widget.description = query_params['description']
widget.measure_units = DashboardWidget.MEASURE_UNITS[query_params['measure-units']]
widget.width = int(query_params['width'])
widget.grid_position = 0
widget.type = query_params['type']
widget.category = DashboardWidget.TYPES_TO_CATEGORY[query_params['type']]
widget.uuid = uuid.uuid4()
widget.sensor_id = query_params['sensor']
widget.sensor_measure = query_params['sensor-measure']
if widget.type == DashboardWidget.TYPES['gauge']:
widget.minValue = float(query_params['minimum-value'])
widget.maxValue = float(query_params['maximum-value'])
return widget
class DashboardRowsFactory(object):
@staticmethod
def create_dashboard_rows_from(widgets):
dashboard_rows = []
dashboard_row = DashboardRow()
for widget in widgets:
if dashboard_row.has_room_for(widget):
dashboard_row.add_widget(widget)
else:
dashboard_rows.append(dashboard_row)
dashboard_row = DashboardRow()
dashboard_row.add_widget(widget)
dashboard_rows.append(dashboard_row)
return dashboard_rows | 0.667364 | 0.110615 |
see: http://www.astroml.org/book_figures/chapter3/fig_bivariate_gaussian.html
'''
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
from astroML.stats.random import bivariate_normal
from astroML.plotting import setup_text_plots
from IPython.display import SVG,display
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
import math
import matplotlib.lines as lines
import matplotlib.patches as patches
from sklearn.mixture import GaussianMixture
from scipy import stats
import scipy
from scipy.optimize import curve_fit
from scipy.misc import factorial
import pandas as pd
from scipy.optimize import minimize
class ForestPreprocessing(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.filepath="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv"
self.filepath010="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.010.datefixed.csv"
self.filepath020="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.020.month.poissoncounters.csv"
self.filepath030="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.030.weekday.poissoncounters.csv"
self.df = pd.read_csv(self.filepath, delimiter=';')
self.months = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']
self.wdays = ['mon','tue','wed','thu','fri','sat','sun']
## test numerical approximation close to analytical one
def test01DummyRead(self):
pass
print (self.df)
def test02CheckMissingValues(self):
pass
print (self.df.isnull().values.sum())
# approximate to gaussian distribution for the generated data set.
def test03MakeDateNumeric(self):
#df2 = (self.df['month'] == 'oct')
for month_ in self.months:
self.df.month[self.df.month==month_] = self.months.index(month_)+1
for wday_ in self.wdays:
self.df.day[self.df.day==wday_] = self.wdays.index(wday_)+1
self.df.to_csv(self.filepath010, sep=';',index= False)
print (self.df)
pass
def test04AddMonthPoissonCounters(self):
self.df = pd.read_csv(self.filepath010, delimiter=';')
monthcounts = self.df['month'].value_counts()
self.df['month_firecount'] = 1
for idx,item in monthcounts.iteritems():
self.df.month_firecount[self.df.month==idx]=item
self.df.to_csv(self.filepath020, sep=';',index= False)
def test05AddWeeklyPoissonCounters(self):
self.df = pd.read_csv(self.filepath020, delimiter=';')
daycounts = self.df['day'].value_counts()
print (daycounts)
self.df['weekday_firecount'] = 1
for idx,item in daycounts.iteritems():
self.df.weekday_firecount[self.df.day==idx]=item
self.df.to_csv(self.filepath030, sep=';',index= False)
def poissonParam1(self, data_):
min_=np.min(data_)
if min_<0:
min_=0
RANGE = [min_, np.sum(data_)+1]
print ("range", RANGE)
# the bins should be of integer width, because poisson is an integer distribution
entries, bin_edges, patches = plt.hist(data_, bins=30, range=RANGE, normed=True)
# calculate binmiddles
bin_middles = 0.5*(bin_edges[1:] + bin_edges[:-1])
# poisson function, parameter lamb is the fit parameter
def poisson(k, lamb):
return (lamb**k/factorial(k)) * np.exp(-lamb)
# fit with curve_fit
parameters, cov_matrix = curve_fit(poisson, bin_middles, entries)
return parameters[0]
def poissonParam2(self, data_):
# poisson function, parameter lamb is the fit parameter
def poisson(k, lamb):
"""poisson pdf, parameter lamb is the fit parameter"""
return (lamb**k/factorial(k)) * np.exp(-lamb)
def negLogLikelihood(params, data):
""" the negative log-Likelohood-Function"""
lnl = - np.sum(np.log(poisson(data, params[0])))
return lnl
result = minimize(negLogLikelihood, # function to minimize
x0=np.ones(1), # start value
args=(data_,), # additional arguments for function
method='Powell', # minimization method, see docs
)
# fit with curve_fit, result.x = poisson_lambda
return result.x
def test06EstimateLambdaOfMonthlyPoisson(self):
self.df = pd.read_csv(self.filepath030, delimiter=';')
monthIndices = np.arange(1,len(self.months)+1)
DATA_SIZE = 12 #len(monthIndices)
month_firecounts = []
for i in monthIndices:
firecount = self.df.month_firecount[self.df.month==i].iloc[0]
month_firecounts.append(firecount) #zeroth month has this firecount
month_firecounts = np.sort(month_firecounts, axis=0)
print (month_firecounts)
#print (np.sort(np.unique(month_firecounts)))
synthetic_data= np.random.poisson(np.sum(month_firecounts)/DATA_SIZE, DATA_SIZE)
data = month_firecounts
#data = synthetic_data
lambda_ = self.poissonParam2(data )
print ("poisson.lambda",lambda_)
predicted = stats.poisson.rvs(lambda_, size=DATA_SIZE)
print (np.sum(data))
print (np.sum(predicted))
def start(self):
pass
#self.test01DummyRead()
#self.test02CheckMissingValues()
# self.test03MakeDateNumeric()
# self.test04AddMonthPoissonCounters()
# self.test05AddWeeklyPoissonCounters()
self.test06EstimateLambdaOfMonthlyPoisson()
np.random.seed(2018)
mmp = ForestPreprocessing()
mmp.start() | second-round-intreview/parcoord-brushing/backend/src/paper2declutter/preprocessing/ForestPreprocesing.py | see: http://www.astroml.org/book_figures/chapter3/fig_bivariate_gaussian.html
'''
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Ellipse
from astroML.stats.random import bivariate_normal
from astroML.plotting import setup_text_plots
from IPython.display import SVG,display
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
import math
import matplotlib.lines as lines
import matplotlib.patches as patches
from sklearn.mixture import GaussianMixture
from scipy import stats
import scipy
from scipy.optimize import curve_fit
from scipy.misc import factorial
import pandas as pd
from scipy.optimize import minimize
class ForestPreprocessing(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.filepath="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv"
self.filepath010="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.010.datefixed.csv"
self.filepath020="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.020.month.poissoncounters.csv"
self.filepath030="/Users/halil/Yandex.Disk.localized/root/academic/myphd/phd/0070-coding/declutter-pc-temporal/backend/src/data/forestfires.csv.030.weekday.poissoncounters.csv"
self.df = pd.read_csv(self.filepath, delimiter=';')
self.months = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']
self.wdays = ['mon','tue','wed','thu','fri','sat','sun']
## test numerical approximation close to analytical one
def test01DummyRead(self):
pass
print (self.df)
def test02CheckMissingValues(self):
pass
print (self.df.isnull().values.sum())
# approximate to gaussian distribution for the generated data set.
def test03MakeDateNumeric(self):
#df2 = (self.df['month'] == 'oct')
for month_ in self.months:
self.df.month[self.df.month==month_] = self.months.index(month_)+1
for wday_ in self.wdays:
self.df.day[self.df.day==wday_] = self.wdays.index(wday_)+1
self.df.to_csv(self.filepath010, sep=';',index= False)
print (self.df)
pass
def test04AddMonthPoissonCounters(self):
self.df = pd.read_csv(self.filepath010, delimiter=';')
monthcounts = self.df['month'].value_counts()
self.df['month_firecount'] = 1
for idx,item in monthcounts.iteritems():
self.df.month_firecount[self.df.month==idx]=item
self.df.to_csv(self.filepath020, sep=';',index= False)
def test05AddWeeklyPoissonCounters(self):
self.df = pd.read_csv(self.filepath020, delimiter=';')
daycounts = self.df['day'].value_counts()
print (daycounts)
self.df['weekday_firecount'] = 1
for idx,item in daycounts.iteritems():
self.df.weekday_firecount[self.df.day==idx]=item
self.df.to_csv(self.filepath030, sep=';',index= False)
def poissonParam1(self, data_):
min_=np.min(data_)
if min_<0:
min_=0
RANGE = [min_, np.sum(data_)+1]
print ("range", RANGE)
# the bins should be of integer width, because poisson is an integer distribution
entries, bin_edges, patches = plt.hist(data_, bins=30, range=RANGE, normed=True)
# calculate binmiddles
bin_middles = 0.5*(bin_edges[1:] + bin_edges[:-1])
# poisson function, parameter lamb is the fit parameter
def poisson(k, lamb):
return (lamb**k/factorial(k)) * np.exp(-lamb)
# fit with curve_fit
parameters, cov_matrix = curve_fit(poisson, bin_middles, entries)
return parameters[0]
def poissonParam2(self, data_):
# poisson function, parameter lamb is the fit parameter
def poisson(k, lamb):
"""poisson pdf, parameter lamb is the fit parameter"""
return (lamb**k/factorial(k)) * np.exp(-lamb)
def negLogLikelihood(params, data):
""" the negative log-Likelohood-Function"""
lnl = - np.sum(np.log(poisson(data, params[0])))
return lnl
result = minimize(negLogLikelihood, # function to minimize
x0=np.ones(1), # start value
args=(data_,), # additional arguments for function
method='Powell', # minimization method, see docs
)
# fit with curve_fit, result.x = poisson_lambda
return result.x
def test06EstimateLambdaOfMonthlyPoisson(self):
self.df = pd.read_csv(self.filepath030, delimiter=';')
monthIndices = np.arange(1,len(self.months)+1)
DATA_SIZE = 12 #len(monthIndices)
month_firecounts = []
for i in monthIndices:
firecount = self.df.month_firecount[self.df.month==i].iloc[0]
month_firecounts.append(firecount) #zeroth month has this firecount
month_firecounts = np.sort(month_firecounts, axis=0)
print (month_firecounts)
#print (np.sort(np.unique(month_firecounts)))
synthetic_data= np.random.poisson(np.sum(month_firecounts)/DATA_SIZE, DATA_SIZE)
data = month_firecounts
#data = synthetic_data
lambda_ = self.poissonParam2(data )
print ("poisson.lambda",lambda_)
predicted = stats.poisson.rvs(lambda_, size=DATA_SIZE)
print (np.sum(data))
print (np.sum(predicted))
def start(self):
pass
#self.test01DummyRead()
#self.test02CheckMissingValues()
# self.test03MakeDateNumeric()
# self.test04AddMonthPoissonCounters()
# self.test05AddWeeklyPoissonCounters()
self.test06EstimateLambdaOfMonthlyPoisson()
np.random.seed(2018)
mmp = ForestPreprocessing()
mmp.start() | 0.424173 | 0.45042 |
import re
from lib.nlp import helper
OFFSET_OF_NEXT_WORD = 2
BRACKET_MAP_OPEN = {'[': ']', '(': ')', '{': '}'}
BRACKET_MAP_CLOSE = {']': '[', ')': '(', '}': '{'}
TAILING_CHARS = [':', ';', '?', '!', ',']
def sent_tokenize(text):
'''
Sentence segmentation with user specific algorithm.
NOTE: It's might be better for bioinformatics publication only.
'''
text = helper.remove_newline(text)
text = helper.optimize_space(text)
sent_list = []
sent_buf = []
word_buf = []
bracket_stack = []
in_bracket = False
text_len = len(text)
max_index = text_len - 1
for i in range(text_len):
cur_char = text[i]
if i == max_index:
sent_buf.append(''.join(word_buf) + cur_char)
sent_list.append(helper.optimize_space(' '.join(sent_buf)))
word_buf = []
sent_buf = []
break
if cur_char == ' ':
sent_buf.append(''.join(word_buf))
word_buf = []
elif cur_char in TAILING_CHARS:
word_buf.append(cur_char)
sent_buf.append(''.join(word_buf))
word_buf = []
elif cur_char == '.':
word = ''.join(word_buf)
if (
i + OFFSET_OF_NEXT_WORD < max_index and
re.match(r'[A-Z]', text[i + OFFSET_OF_NEXT_WORD]) and
not in_bracket and
re.match(r'[a-z\)\]\}]', text[i - 1])
):
sent_buf.append(word + cur_char)
sent_list.append(helper.optimize_space(' '.join(sent_buf)))
sent_buf = []
word_buf = []
elif re.match(r'[A-Z]', word):
word_buf.append(cur_char)
else:
sent_buf.append(word + cur_char)
word_buf = []
elif cur_char in BRACKET_MAP_OPEN:
word_buf.append(cur_char)
bracket_stack.append(cur_char)
in_bracket = True
elif cur_char in BRACKET_MAP_CLOSE:
try:
open_bracket = bracket_stack.pop()
if BRACKET_MAP_CLOSE[cur_char] == open_bracket:
word_buf.append(cur_char)
else:
word_buf.append(BRACKET_MAP_OPEN[open_bracket])
except IndexError:
word_buf.append('')
in_bracket = False
else:
word_buf.append(cur_char)
return sent_list
def word_tokenize(text, clean=False):
'''
Tokenize sentence to words. In this version, we split by special characters.
If optional parameter 'clean' is set, special characters will cleaned.
'''
tokens = _clean_space(re.split(r'([\W\.]+)', text))
if clean:
tokens = _clean_special_chars(tokens)
return tokens
def _clean_space(tokens):
return filter(lambda token: token != ' ', tokens)
def _clean_special_chars(tokens):
return filter(lambda token: re.match(r'(\w+)', token), tokens) | lib/nlp/tokenizer.py | import re
from lib.nlp import helper
OFFSET_OF_NEXT_WORD = 2
BRACKET_MAP_OPEN = {'[': ']', '(': ')', '{': '}'}
BRACKET_MAP_CLOSE = {']': '[', ')': '(', '}': '{'}
TAILING_CHARS = [':', ';', '?', '!', ',']
def sent_tokenize(text):
'''
Sentence segmentation with user specific algorithm.
NOTE: It's might be better for bioinformatics publication only.
'''
text = helper.remove_newline(text)
text = helper.optimize_space(text)
sent_list = []
sent_buf = []
word_buf = []
bracket_stack = []
in_bracket = False
text_len = len(text)
max_index = text_len - 1
for i in range(text_len):
cur_char = text[i]
if i == max_index:
sent_buf.append(''.join(word_buf) + cur_char)
sent_list.append(helper.optimize_space(' '.join(sent_buf)))
word_buf = []
sent_buf = []
break
if cur_char == ' ':
sent_buf.append(''.join(word_buf))
word_buf = []
elif cur_char in TAILING_CHARS:
word_buf.append(cur_char)
sent_buf.append(''.join(word_buf))
word_buf = []
elif cur_char == '.':
word = ''.join(word_buf)
if (
i + OFFSET_OF_NEXT_WORD < max_index and
re.match(r'[A-Z]', text[i + OFFSET_OF_NEXT_WORD]) and
not in_bracket and
re.match(r'[a-z\)\]\}]', text[i - 1])
):
sent_buf.append(word + cur_char)
sent_list.append(helper.optimize_space(' '.join(sent_buf)))
sent_buf = []
word_buf = []
elif re.match(r'[A-Z]', word):
word_buf.append(cur_char)
else:
sent_buf.append(word + cur_char)
word_buf = []
elif cur_char in BRACKET_MAP_OPEN:
word_buf.append(cur_char)
bracket_stack.append(cur_char)
in_bracket = True
elif cur_char in BRACKET_MAP_CLOSE:
try:
open_bracket = bracket_stack.pop()
if BRACKET_MAP_CLOSE[cur_char] == open_bracket:
word_buf.append(cur_char)
else:
word_buf.append(BRACKET_MAP_OPEN[open_bracket])
except IndexError:
word_buf.append('')
in_bracket = False
else:
word_buf.append(cur_char)
return sent_list
def word_tokenize(text, clean=False):
'''
Tokenize sentence to words. In this version, we split by special characters.
If optional parameter 'clean' is set, special characters will cleaned.
'''
tokens = _clean_space(re.split(r'([\W\.]+)', text))
if clean:
tokens = _clean_special_chars(tokens)
return tokens
def _clean_space(tokens):
return filter(lambda token: token != ' ', tokens)
def _clean_special_chars(tokens):
return filter(lambda token: re.match(r'(\w+)', token), tokens) | 0.253491 | 0.155335 |
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA,TruncatedSVD,NMF
from sklearn.preprocessing import Normalizer
import argparse
import time
import numba
@numba.jit(nopython=True)
def year_binner(year,val=10):
return year - year%val
parser = argparse.ArgumentParser(description='Perform dimentionality reduction of the count-based vectors, using SVD')
parser.add_argument('--contextual', action='store_true',
help='Is the model contextual')
parser.add_argument('--temporal', action='store_true',
help='Is the model temporal')
parser.add_argument('--seed', type=int, default=1991,
help='random seed')
parser.add_argument('--dims', type=int, default=300,
help='Desired number of reduced dimensions')
parser.add_argument('--save_format', type=str,default='pkl',
help='In what format should the reduced datasets be saved : csv or pkl')
args = parser.parse_args()
modifier_list=pkl.load( open("modifier_list_reduced.pkl",'rb'))
head_list=pkl.load( open("head_list_reduced.pkl",'rb'))
t1=time.time()
# Dimentionality Reduction using SVD
def dim_reduction(df,rows):
df_svd = TruncatedSVD(n_components=args.dims, n_iter=10, random_state=args.seed)
print(f'Explained variance ratio {(df_svd.fit(df).explained_variance_ratio_.sum()):2.3f}')
#df_list=df_svd.fit(df).explained_variance_ratio_
df_reduced = df_svd.fit_transform(df)
df_reduced = Normalizer(copy=False).fit_transform(df_reduced)
df_reduced=pd.DataFrame(df_reduced,index=rows)
#df_reduced.reset_index(inplace=True)
if args.temporal:
df_reduced.index = pd.MultiIndex.from_tuples(df_reduced.index, names=['common', 'decade'])
return df_reduced
def common_reduction(df):
df.reset_index(inplace=True)
df.year=df.year.astype("int32")
df=df.query('1800 <= year <= 2010').copy()
df['time']=year_binner(df['year'].values,10)
df=df.groupby(['modifier','head','context','time'])['count'].sum().to_frame()
df.reset_index(inplace=True)
df=df.loc[df.groupby(['modifier','head','time'])['count'].transform('sum').gt(50)]
df=df.loc[df['modifier'].isin(modifier_list) & df['head'].isin(head_list)]
return df
if args.contextual:
print("CompoundCentric Model")
comp_str='CompoundCentric'
print("Loading the constituent and compound vector datasets")
heads=pd.read_csv("/data/dharp/compounding/datasets/heads_reduced.csv",sep="\t")
#heads=heads.query('decade != 2000')
heads.columns=['common','decade','context','count']
heads['common']=heads['common'].str.replace(r'_n$', r'_h', regex=True)
modifiers=pd.read_csv("/data/dharp/compounding/datasets/modifiers_reduced.csv",sep="\t")
#modifiers=modifiers.query('decade != 2000')
modifiers.columns=['common','decade','context','count']
modifiers['common']=modifiers['common'].str.replace(r'_n$', r'_m', regex=True)
compounds=pd.read_pickle("/data/dharp/compounding/datasets/compounds.pkl")
compounds=common_reduction(compounds)
compounds['common']=compounds['modifier']+" "+compounds['head']
if args.temporal:
print("DecadeCentric Model")
compounds=compounds.groupby(['common','decade','context'])['count'].sum()
modifiers=modifiers.groupby(['common','decade','context'])['count'].sum()
heads=heads.groupby(['common','decade','context'])['count'].sum()
else:
print("DecadeAgnostic Model")
compounds=compounds.groupby(['common','context'])['count'].sum()
modifiers=modifiers.groupby(['common','context'])['count'].sum()
heads=heads.groupby(['common','context'])['count'].sum()
print('Concatenating all the datasets together')
df=pd.concat([heads,modifiers,compounds])
else:
print("CompoundAgnostic Model")
comp_str='CompoundAgnostic'
print("Loading the word and phrase vector datasets")
constituents=pd.read_csv("/data/dharp/compounding/datasets/words.csv")
constituents.columns=['common','context','decade','count']
#constituents=constituents.query('decade != 2000')
compounds=pd.read_csv("/data/dharp/compounding/datasets/phrases.csv")
compounds.columns=['modifier','head','context','decade','count']
#compounds=compounds.query('decade != 2000')
compounds['common']=compounds['modifier']+" "+compounds['head']
if args.temporal:
print("DecadeCentric Model")
compounds=compounds.groupby(['common','decade','context'])['count'].sum()
constituents=constituents.groupby(['common','decade','context'])['count'].sum()
else:
print("DecadeAgnostic Model")
compounds=compounds.groupby(['common','context'])['count'].sum()
constituents=constituents.groupby(['common','context'])['count'].sum()
print('Concatenating all the datasets together')
df=pd.concat([constituents,compounds])
df=df.to_sparse()
if args.temporal:
df, rows, _ = df.to_coo(row_levels=['common','decade'],column_levels=['context'],sort_labels=False)
dec_str='DecadeCentric'
else:
df, rows, _ = df.to_coo(row_levels=['common'],column_levels=['context'],sort_labels=False)
dec_str='DecadeAgnostic'
print('Running SVD')
df_reduced=dim_reduction(df,rows)
#df_reduced.reset_index(inplace=True)
print('Splitting back into individual datasets are saving them')
if args.temporal:
df_reduced.index.names = ['common','decade']
else:
df_reduced.index.names = ['common']
compounds_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.contains(r'\w \w')]
compounds_reduced.reset_index(inplace=True)
#print(compounds_reduced.head())
compounds_reduced['modifier'],compounds_reduced['head']=compounds_reduced['common'].str.split(' ', 1).str
dim_str=str(args.dims)
if args.contextual:
heads_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.endswith(r'_h')]
heads_reduced.reset_index(inplace=True)
heads_reduced['head']=heads_reduced['common'].str.replace(r'_h$', r'_n', regex=True)
heads_reduced.drop(['common'],axis=1,inplace=True)
modifiers_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.endswith(r'_m')]
modifiers_reduced.reset_index(inplace=True)
modifiers_reduced['modifier']=modifiers_reduced['common'].str.replace(r'_m$', r'_n', regex=True)
modifiers_reduced.drop(['common'],axis=1,inplace=True)
if args.temporal:
compounds_reduced.set_index(['modifier','head','decade'],inplace=True)
heads_reduced.set_index(['head','decade'],inplace=True)
modifiers_reduced.set_index(['modifier','decade'],inplace=True)
else:
compounds_reduced.set_index(['modifier','head'],inplace=True)
heads_reduced.set_index(['head'],inplace=True)
modifiers_reduced.set_index(['modifier'],inplace=True)
print('Saving the files')
if args.save_format=='pkl':
compounds_reduced.to_pickle('/data/dharp/compounding/datasets/compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
heads_reduced.to_pickle('/data/dharp/compounding/datasets/heads_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
modifiers_reduced.to_pickle('/data/dharp/compounding/datasets/modifiers_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
elif args.save_format=='csv':
compounds_reduced.to_csv("/data/dharp/compounding/datasets/"+'compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
heads_reduced.to_csv('/data/dharp/compounding/datasets/heads_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
modifiers_reduced.to_pickle('/data/dharp/compounding/datasets/modifiers_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
else:
constituents_reduced=df_reduced.loc[~df_reduced.index.get_level_values(0).str.contains(r'\w \w')]
if args.temporal:
compounds_reduced.set_index(['modifier','head','decade'],inplace=True)
else:
compounds_reduced.set_index(['modifier','head'],inplace=True)
print('Saving the files')
if args.save_format=='pkl':
compounds_reduced.to_pickle('/data/dharp/compounding/datasets/compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
constituents_reduced.to_pickle('/data/dharp/compounding/datasets/constituents_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
elif args.save_format=='csv':
compounds_reduced.to_csv("/data/dharp/compounding/datasets/"+'compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
constituents_reduced.to_csv('/data/dharp/compounding/datasets/constituents_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
print(f'Time taken {time.time()-t1:10.3f}') | src/dimensionality_reduction.py | import pandas as pd
import numpy as np
from sklearn.decomposition import PCA,TruncatedSVD,NMF
from sklearn.preprocessing import Normalizer
import argparse
import time
import numba
@numba.jit(nopython=True)
def year_binner(year,val=10):
return year - year%val
parser = argparse.ArgumentParser(description='Perform dimentionality reduction of the count-based vectors, using SVD')
parser.add_argument('--contextual', action='store_true',
help='Is the model contextual')
parser.add_argument('--temporal', action='store_true',
help='Is the model temporal')
parser.add_argument('--seed', type=int, default=1991,
help='random seed')
parser.add_argument('--dims', type=int, default=300,
help='Desired number of reduced dimensions')
parser.add_argument('--save_format', type=str,default='pkl',
help='In what format should the reduced datasets be saved : csv or pkl')
args = parser.parse_args()
modifier_list=pkl.load( open("modifier_list_reduced.pkl",'rb'))
head_list=pkl.load( open("head_list_reduced.pkl",'rb'))
t1=time.time()
# Dimentionality Reduction using SVD
def dim_reduction(df,rows):
df_svd = TruncatedSVD(n_components=args.dims, n_iter=10, random_state=args.seed)
print(f'Explained variance ratio {(df_svd.fit(df).explained_variance_ratio_.sum()):2.3f}')
#df_list=df_svd.fit(df).explained_variance_ratio_
df_reduced = df_svd.fit_transform(df)
df_reduced = Normalizer(copy=False).fit_transform(df_reduced)
df_reduced=pd.DataFrame(df_reduced,index=rows)
#df_reduced.reset_index(inplace=True)
if args.temporal:
df_reduced.index = pd.MultiIndex.from_tuples(df_reduced.index, names=['common', 'decade'])
return df_reduced
def common_reduction(df):
df.reset_index(inplace=True)
df.year=df.year.astype("int32")
df=df.query('1800 <= year <= 2010').copy()
df['time']=year_binner(df['year'].values,10)
df=df.groupby(['modifier','head','context','time'])['count'].sum().to_frame()
df.reset_index(inplace=True)
df=df.loc[df.groupby(['modifier','head','time'])['count'].transform('sum').gt(50)]
df=df.loc[df['modifier'].isin(modifier_list) & df['head'].isin(head_list)]
return df
if args.contextual:
print("CompoundCentric Model")
comp_str='CompoundCentric'
print("Loading the constituent and compound vector datasets")
heads=pd.read_csv("/data/dharp/compounding/datasets/heads_reduced.csv",sep="\t")
#heads=heads.query('decade != 2000')
heads.columns=['common','decade','context','count']
heads['common']=heads['common'].str.replace(r'_n$', r'_h', regex=True)
modifiers=pd.read_csv("/data/dharp/compounding/datasets/modifiers_reduced.csv",sep="\t")
#modifiers=modifiers.query('decade != 2000')
modifiers.columns=['common','decade','context','count']
modifiers['common']=modifiers['common'].str.replace(r'_n$', r'_m', regex=True)
compounds=pd.read_pickle("/data/dharp/compounding/datasets/compounds.pkl")
compounds=common_reduction(compounds)
compounds['common']=compounds['modifier']+" "+compounds['head']
if args.temporal:
print("DecadeCentric Model")
compounds=compounds.groupby(['common','decade','context'])['count'].sum()
modifiers=modifiers.groupby(['common','decade','context'])['count'].sum()
heads=heads.groupby(['common','decade','context'])['count'].sum()
else:
print("DecadeAgnostic Model")
compounds=compounds.groupby(['common','context'])['count'].sum()
modifiers=modifiers.groupby(['common','context'])['count'].sum()
heads=heads.groupby(['common','context'])['count'].sum()
print('Concatenating all the datasets together')
df=pd.concat([heads,modifiers,compounds])
else:
print("CompoundAgnostic Model")
comp_str='CompoundAgnostic'
print("Loading the word and phrase vector datasets")
constituents=pd.read_csv("/data/dharp/compounding/datasets/words.csv")
constituents.columns=['common','context','decade','count']
#constituents=constituents.query('decade != 2000')
compounds=pd.read_csv("/data/dharp/compounding/datasets/phrases.csv")
compounds.columns=['modifier','head','context','decade','count']
#compounds=compounds.query('decade != 2000')
compounds['common']=compounds['modifier']+" "+compounds['head']
if args.temporal:
print("DecadeCentric Model")
compounds=compounds.groupby(['common','decade','context'])['count'].sum()
constituents=constituents.groupby(['common','decade','context'])['count'].sum()
else:
print("DecadeAgnostic Model")
compounds=compounds.groupby(['common','context'])['count'].sum()
constituents=constituents.groupby(['common','context'])['count'].sum()
print('Concatenating all the datasets together')
df=pd.concat([constituents,compounds])
df=df.to_sparse()
if args.temporal:
df, rows, _ = df.to_coo(row_levels=['common','decade'],column_levels=['context'],sort_labels=False)
dec_str='DecadeCentric'
else:
df, rows, _ = df.to_coo(row_levels=['common'],column_levels=['context'],sort_labels=False)
dec_str='DecadeAgnostic'
print('Running SVD')
df_reduced=dim_reduction(df,rows)
#df_reduced.reset_index(inplace=True)
print('Splitting back into individual datasets are saving them')
if args.temporal:
df_reduced.index.names = ['common','decade']
else:
df_reduced.index.names = ['common']
compounds_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.contains(r'\w \w')]
compounds_reduced.reset_index(inplace=True)
#print(compounds_reduced.head())
compounds_reduced['modifier'],compounds_reduced['head']=compounds_reduced['common'].str.split(' ', 1).str
dim_str=str(args.dims)
if args.contextual:
heads_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.endswith(r'_h')]
heads_reduced.reset_index(inplace=True)
heads_reduced['head']=heads_reduced['common'].str.replace(r'_h$', r'_n', regex=True)
heads_reduced.drop(['common'],axis=1,inplace=True)
modifiers_reduced=df_reduced.loc[df_reduced.index.get_level_values(0).str.endswith(r'_m')]
modifiers_reduced.reset_index(inplace=True)
modifiers_reduced['modifier']=modifiers_reduced['common'].str.replace(r'_m$', r'_n', regex=True)
modifiers_reduced.drop(['common'],axis=1,inplace=True)
if args.temporal:
compounds_reduced.set_index(['modifier','head','decade'],inplace=True)
heads_reduced.set_index(['head','decade'],inplace=True)
modifiers_reduced.set_index(['modifier','decade'],inplace=True)
else:
compounds_reduced.set_index(['modifier','head'],inplace=True)
heads_reduced.set_index(['head'],inplace=True)
modifiers_reduced.set_index(['modifier'],inplace=True)
print('Saving the files')
if args.save_format=='pkl':
compounds_reduced.to_pickle('/data/dharp/compounding/datasets/compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
heads_reduced.to_pickle('/data/dharp/compounding/datasets/heads_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
modifiers_reduced.to_pickle('/data/dharp/compounding/datasets/modifiers_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
elif args.save_format=='csv':
compounds_reduced.to_csv("/data/dharp/compounding/datasets/"+'compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
heads_reduced.to_csv('/data/dharp/compounding/datasets/heads_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
modifiers_reduced.to_pickle('/data/dharp/compounding/datasets/modifiers_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
else:
constituents_reduced=df_reduced.loc[~df_reduced.index.get_level_values(0).str.contains(r'\w \w')]
if args.temporal:
compounds_reduced.set_index(['modifier','head','decade'],inplace=True)
else:
compounds_reduced.set_index(['modifier','head'],inplace=True)
print('Saving the files')
if args.save_format=='pkl':
compounds_reduced.to_pickle('/data/dharp/compounding/datasets/compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
constituents_reduced.to_pickle('/data/dharp/compounding/datasets/constituents_'+comp_str+'_'+dec_str+'_'+dim_str+'.pkl')
elif args.save_format=='csv':
compounds_reduced.to_csv("/data/dharp/compounding/datasets/"+'compounds_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
constituents_reduced.to_csv('/data/dharp/compounding/datasets/constituents_'+comp_str+'_'+dec_str+'_'+dim_str+'.csv',header=False,sep='\t')
print(f'Time taken {time.time()-t1:10.3f}') | 0.479747 | 0.208209 |
from __future__ import division
import numpy as np
import random
import pygame
from shapely.geometry import LineString
# pyGame initialization
FPS = 60
QFPS = 240
SCREEN_WIDTH, SCREEN_HEIGHT = 640, 480
pygame.init()
FPS_CLOCK = pygame.time.Clock()
SCREEN = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption("King Pong")
pygame.font.init()
SCORE_FONT = pygame.font.Font(None, 64)
GAMES_FONT = pygame.font.Font(None, 16)
# Paddle dimensions
PADDLE_WIDTH, PADDLE_HEIGHT = 8, 64
PADDLE_UPPER_SECTION = 3*PADDLE_HEIGHT/8
PADDLE_BOTTOM_SECTION = 5*PADDLE_HEIGHT/8
TOP_SPEED = 5
PADDLE_SPEED = TOP_SPEED
PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE = 16, int(SCREEN_HEIGHT/2)
# Ball
BALL_SIZE = 8
class GameState:
"""
Game State Representation
Game state with function to act
based on user actions.
"""
def __init__(self, auto_draw = True):
self.auto_draw = auto_draw
self.print_scores = False
self.top_speed = TOP_SPEED
self.reset_positions()
self.first_to = [1000, 5]
self.games = [0, 0]
self.score = [0, 0]
self.score_changed = False
def score_last_changed(self):
"""
Checks if the scores has changed since
the last time this function was accessed
"""
current = self.score_changed
self.score_changed = False
return current
def game_over(self):
"""
The game is over when any player reaches
the number of games playing to
"""
return self.games[0] == self.first_to[0] or \
self.games[1] == self.first_to[0]
def reset_positions(self):
"""
Moves the players to a center position
and reset the direction and speed of
the ball randomly within acceptable range.
"""
self.playerx, self.playery = SCREEN_WIDTH-PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE
self.cpux, self.cpuy = PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE
self.ballx, self.bally = SCREEN_WIDTH/2, SCREEN_HEIGHT/2
self.ball_speed_x = random.choice(
range(-self.top_speed+1, -int(2*self.top_speed/3)) +
range(int(2*self.top_speed/3), self.top_speed))
self.ball_speed_y = random.choice(
range(-self.top_speed+1, -int(2*self.top_speed/3)) +
range(int(2*self.top_speed/3), self.top_speed))
def frame_step(self, input_actions):
"""
Moves the state of the game forward
one step with the given input actions
input_actions[0] == 1: do nothing
input_actions[1] == 1: move up
input_actions[2] == 1: move down
sum(input_actions) == 1
"""
pygame.event.pump()
if sum(input_actions) != 1:
raise ValueError('Multiple input actions!')
# move player
if input_actions[1] == 1:
# player moves up
self.playery = np.maximum(0,
self.playery - self.top_speed)
elif input_actions[2] == 1:
# player moves down
self.playery = np.minimum(self.playery + self.top_speed,
SCREEN_HEIGHT - PADDLE_HEIGHT)
# move cpu
if self.cpuy + (PADDLE_HEIGHT/2) > self.bally:
self.cpuy = np.maximum(0,
self.cpuy - self.top_speed)
elif self.cpuy + (PADDLE_HEIGHT/2) < self.bally:
self.cpuy = np.minimum(self.cpuy + self.top_speed,
SCREEN_HEIGHT - PADDLE_HEIGHT)
# move ball get reward the it produced
reward = self.move_ball()
# check for losing
terminal_good = self.ballx <= 0
terminal_bad = self.ballx + BALL_SIZE >= SCREEN_WIDTH
terminal = terminal_good or terminal_bad
if terminal: self.reset_positions()
self.score[0] += terminal_bad
self.score[1] += terminal_good
reward = -1.0 if terminal_bad else 1.0 if terminal_good else reward
# redraw game onto screen
SCREEN.fill((0, 0, 0)) # black screen
pygame.draw.rect(SCREEN, # left 'cpu' player
(255, 255, 255),
(self.cpux, self.cpuy, PADDLE_WIDTH, PADDLE_HEIGHT))
pygame.draw.rect(SCREEN, # right player
(255, 255, 255),
(self.playerx, self.playery, PADDLE_WIDTH, PADDLE_HEIGHT))
pygame.draw.rect(SCREEN, # ball
(255, 255, 255),
(self.ballx, self.bally, BALL_SIZE, BALL_SIZE))
# update pygame
image_data = pygame.surfarray.array3d(pygame.display.get_surface())
if self.auto_draw: self.complete_drawing()
if terminal: self.score_changed = True
# calculate who would be the winner
if self.score[0] == self.first_to[1]:
self.score = [0, 0]
self.games[0] += 1
elif self.score[1] == self.first_to[1]:
self.score = [0, 0]
self.games[1] += 1
return image_data, reward
def move_ball(self):
"""
Move the ball in game state
it calculates boundaries and it clips
the ball positioning when it is overlapping
with walls or paddles
return rewards when right player makes contact with the ball
and when ball leaves the game screen on the left side
"""
reward = 0.0
# get ball trajectory
prev_x, prev_y = self.ballx, self.bally
next_x, next_y = self.ballx + self.ball_speed_x, self.bally + self.ball_speed_y
ball_trajectory = LineString([(prev_x, prev_y), (next_x, next_y)])
# get possible collision lines
upper_wall = LineString([(0, 0),
(SCREEN_WIDTH, 0)])
bottom_wall = LineString([(0, SCREEN_HEIGHT - BALL_SIZE),
(SCREEN_WIDTH, SCREEN_HEIGHT - BALL_SIZE)])
left_paddle = LineString([(self.cpux + PADDLE_WIDTH, self.cpuy - BALL_SIZE),
(self.cpux + PADDLE_WIDTH, self.cpuy + PADDLE_HEIGHT)])
right_paddle = LineString([(self.playerx - BALL_SIZE, self.playery - BALL_SIZE),
(self.playerx - BALL_SIZE, self.playery + PADDLE_HEIGHT)])
# chop ball trajectory when colliding
if ball_trajectory.intersects(upper_wall):
self.ball_speed_y *= -1
upper = ball_trajectory.intersection(upper_wall)
self.ballx, self.bally = upper.x, upper.y + 1
elif ball_trajectory.intersects(bottom_wall):
self.ball_speed_y *= -1
bottom = ball_trajectory.intersection(bottom_wall)
self.ballx, self.bally = bottom.x, bottom.y - 1
elif ball_trajectory.intersects(left_paddle):
left = ball_trajectory.intersection(left_paddle)
contact_point = left.y - left_paddle.xy[1][0]
if contact_point < PADDLE_UPPER_SECTION or \
contact_point > PADDLE_BOTTOM_SECTION:
self.flip_and_spin_ball()
else:
self.flip_and_speed_ball()
self.ballx, self.bally = left.x + 1, left.y
elif ball_trajectory.intersects(right_paddle):
reward += 0.1
right = ball_trajectory.intersection(right_paddle)
contact_point = right.y - right_paddle.xy[1][0]
if contact_point < PADDLE_UPPER_SECTION or \
contact_point > PADDLE_BOTTOM_SECTION:
self.flip_and_spin_ball()
else:
self.flip_and_speed_ball()
self.ballx, self.bally = right.x - 1, right.y
else:
self.ballx += self.ball_speed_x
self.bally += self.ball_speed_y
return reward
def draw_scores(self):
"""
To be called when playing against
human only so that numbers pixels don't
interfere with learning
"""
cpu_score = SCORE_FONT.render(str(self.score[0]), 1, (255, 255, 255))
cpu_games = GAMES_FONT.render(str(self.games[0]), 1, (255, 255, 255))
my_score = SCORE_FONT.render(str(self.score[1]), 1, (255, 255, 255))
my_games = GAMES_FONT.render(str(self.games[1]), 1, (255, 255, 255))
SCREEN.blit(cpu_score, (32, 16))
SCREEN.blit(cpu_games, (32 - 4, 16))
SCREEN.blit(my_score, (SCREEN_HEIGHT+92, 16))
SCREEN.blit(my_games, (SCREEN_HEIGHT+92 - 4, 16))
def complete_drawing(self):
"""
Force the drawing of the screens
"""
if self.print_scores: self.draw_scores()
pygame.display.flip()
if self.auto_draw: FPS_CLOCK.tick(QFPS)
else: FPS_CLOCK.tick(FPS)
def flip_and_spin_ball(self):
"""
When ball makes contact with the upper
or lower ends of either paddle, the ball
will potentially randomly increase the y axis speed
and be return with the same speed
"""
self.ball_speed_x *= -1
self.ball_speed_y *= random.randint(1000, 1200)/1000.
def flip_and_speed_ball(self):
"""
When the ball makes contact with the center
of either paddle, it will return the ball with
potentially an increase in the x axis speed
y axis remains untouched
"""
self.ball_speed_x *= -1
self.ball_speed_x *= random.randint(1000, 1200)/1000.
def main(argv):
"""
When called `python king_pong.py`
a CPU is allocated to play against a human
"""
game_state = GameState(auto_draw = False)
# 2 game_states of 1 point
game_state.first_to = [3, 2]
game_state.top_speed = 5
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit()
keys = pygame.key.get_pressed()
a1 = keys[pygame.K_UP]
a2 = 0 if a1 else keys[pygame.K_DOWN]
a0 = 1 if not a1 and not a2 else 0
image_data, reward = game_state.frame_step([a0, a1, a2])
game_state.draw_scores()
game_state.complete_drawing()
if game_state.game_over():
exit(0)
if __name__ == "__main__":
from sys import argv
main(argv) | king_pong.py | from __future__ import division
import numpy as np
import random
import pygame
from shapely.geometry import LineString
# pyGame initialization
FPS = 60
QFPS = 240
SCREEN_WIDTH, SCREEN_HEIGHT = 640, 480
pygame.init()
FPS_CLOCK = pygame.time.Clock()
SCREEN = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption("King Pong")
pygame.font.init()
SCORE_FONT = pygame.font.Font(None, 64)
GAMES_FONT = pygame.font.Font(None, 16)
# Paddle dimensions
PADDLE_WIDTH, PADDLE_HEIGHT = 8, 64
PADDLE_UPPER_SECTION = 3*PADDLE_HEIGHT/8
PADDLE_BOTTOM_SECTION = 5*PADDLE_HEIGHT/8
TOP_SPEED = 5
PADDLE_SPEED = TOP_SPEED
PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE = 16, int(SCREEN_HEIGHT/2)
# Ball
BALL_SIZE = 8
class GameState:
"""
Game State Representation
Game state with function to act
based on user actions.
"""
def __init__(self, auto_draw = True):
self.auto_draw = auto_draw
self.print_scores = False
self.top_speed = TOP_SPEED
self.reset_positions()
self.first_to = [1000, 5]
self.games = [0, 0]
self.score = [0, 0]
self.score_changed = False
def score_last_changed(self):
"""
Checks if the scores has changed since
the last time this function was accessed
"""
current = self.score_changed
self.score_changed = False
return current
def game_over(self):
"""
The game is over when any player reaches
the number of games playing to
"""
return self.games[0] == self.first_to[0] or \
self.games[1] == self.first_to[0]
def reset_positions(self):
"""
Moves the players to a center position
and reset the direction and speed of
the ball randomly within acceptable range.
"""
self.playerx, self.playery = SCREEN_WIDTH-PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE
self.cpux, self.cpuy = PADDLE_X_DISTANCE, PADDLE_Y_DISTANCE
self.ballx, self.bally = SCREEN_WIDTH/2, SCREEN_HEIGHT/2
self.ball_speed_x = random.choice(
range(-self.top_speed+1, -int(2*self.top_speed/3)) +
range(int(2*self.top_speed/3), self.top_speed))
self.ball_speed_y = random.choice(
range(-self.top_speed+1, -int(2*self.top_speed/3)) +
range(int(2*self.top_speed/3), self.top_speed))
def frame_step(self, input_actions):
"""
Moves the state of the game forward
one step with the given input actions
input_actions[0] == 1: do nothing
input_actions[1] == 1: move up
input_actions[2] == 1: move down
sum(input_actions) == 1
"""
pygame.event.pump()
if sum(input_actions) != 1:
raise ValueError('Multiple input actions!')
# move player
if input_actions[1] == 1:
# player moves up
self.playery = np.maximum(0,
self.playery - self.top_speed)
elif input_actions[2] == 1:
# player moves down
self.playery = np.minimum(self.playery + self.top_speed,
SCREEN_HEIGHT - PADDLE_HEIGHT)
# move cpu
if self.cpuy + (PADDLE_HEIGHT/2) > self.bally:
self.cpuy = np.maximum(0,
self.cpuy - self.top_speed)
elif self.cpuy + (PADDLE_HEIGHT/2) < self.bally:
self.cpuy = np.minimum(self.cpuy + self.top_speed,
SCREEN_HEIGHT - PADDLE_HEIGHT)
# move ball get reward the it produced
reward = self.move_ball()
# check for losing
terminal_good = self.ballx <= 0
terminal_bad = self.ballx + BALL_SIZE >= SCREEN_WIDTH
terminal = terminal_good or terminal_bad
if terminal: self.reset_positions()
self.score[0] += terminal_bad
self.score[1] += terminal_good
reward = -1.0 if terminal_bad else 1.0 if terminal_good else reward
# redraw game onto screen
SCREEN.fill((0, 0, 0)) # black screen
pygame.draw.rect(SCREEN, # left 'cpu' player
(255, 255, 255),
(self.cpux, self.cpuy, PADDLE_WIDTH, PADDLE_HEIGHT))
pygame.draw.rect(SCREEN, # right player
(255, 255, 255),
(self.playerx, self.playery, PADDLE_WIDTH, PADDLE_HEIGHT))
pygame.draw.rect(SCREEN, # ball
(255, 255, 255),
(self.ballx, self.bally, BALL_SIZE, BALL_SIZE))
# update pygame
image_data = pygame.surfarray.array3d(pygame.display.get_surface())
if self.auto_draw: self.complete_drawing()
if terminal: self.score_changed = True
# calculate who would be the winner
if self.score[0] == self.first_to[1]:
self.score = [0, 0]
self.games[0] += 1
elif self.score[1] == self.first_to[1]:
self.score = [0, 0]
self.games[1] += 1
return image_data, reward
def move_ball(self):
"""
Move the ball in game state
it calculates boundaries and it clips
the ball positioning when it is overlapping
with walls or paddles
return rewards when right player makes contact with the ball
and when ball leaves the game screen on the left side
"""
reward = 0.0
# get ball trajectory
prev_x, prev_y = self.ballx, self.bally
next_x, next_y = self.ballx + self.ball_speed_x, self.bally + self.ball_speed_y
ball_trajectory = LineString([(prev_x, prev_y), (next_x, next_y)])
# get possible collision lines
upper_wall = LineString([(0, 0),
(SCREEN_WIDTH, 0)])
bottom_wall = LineString([(0, SCREEN_HEIGHT - BALL_SIZE),
(SCREEN_WIDTH, SCREEN_HEIGHT - BALL_SIZE)])
left_paddle = LineString([(self.cpux + PADDLE_WIDTH, self.cpuy - BALL_SIZE),
(self.cpux + PADDLE_WIDTH, self.cpuy + PADDLE_HEIGHT)])
right_paddle = LineString([(self.playerx - BALL_SIZE, self.playery - BALL_SIZE),
(self.playerx - BALL_SIZE, self.playery + PADDLE_HEIGHT)])
# chop ball trajectory when colliding
if ball_trajectory.intersects(upper_wall):
self.ball_speed_y *= -1
upper = ball_trajectory.intersection(upper_wall)
self.ballx, self.bally = upper.x, upper.y + 1
elif ball_trajectory.intersects(bottom_wall):
self.ball_speed_y *= -1
bottom = ball_trajectory.intersection(bottom_wall)
self.ballx, self.bally = bottom.x, bottom.y - 1
elif ball_trajectory.intersects(left_paddle):
left = ball_trajectory.intersection(left_paddle)
contact_point = left.y - left_paddle.xy[1][0]
if contact_point < PADDLE_UPPER_SECTION or \
contact_point > PADDLE_BOTTOM_SECTION:
self.flip_and_spin_ball()
else:
self.flip_and_speed_ball()
self.ballx, self.bally = left.x + 1, left.y
elif ball_trajectory.intersects(right_paddle):
reward += 0.1
right = ball_trajectory.intersection(right_paddle)
contact_point = right.y - right_paddle.xy[1][0]
if contact_point < PADDLE_UPPER_SECTION or \
contact_point > PADDLE_BOTTOM_SECTION:
self.flip_and_spin_ball()
else:
self.flip_and_speed_ball()
self.ballx, self.bally = right.x - 1, right.y
else:
self.ballx += self.ball_speed_x
self.bally += self.ball_speed_y
return reward
def draw_scores(self):
"""
To be called when playing against
human only so that numbers pixels don't
interfere with learning
"""
cpu_score = SCORE_FONT.render(str(self.score[0]), 1, (255, 255, 255))
cpu_games = GAMES_FONT.render(str(self.games[0]), 1, (255, 255, 255))
my_score = SCORE_FONT.render(str(self.score[1]), 1, (255, 255, 255))
my_games = GAMES_FONT.render(str(self.games[1]), 1, (255, 255, 255))
SCREEN.blit(cpu_score, (32, 16))
SCREEN.blit(cpu_games, (32 - 4, 16))
SCREEN.blit(my_score, (SCREEN_HEIGHT+92, 16))
SCREEN.blit(my_games, (SCREEN_HEIGHT+92 - 4, 16))
def complete_drawing(self):
"""
Force the drawing of the screens
"""
if self.print_scores: self.draw_scores()
pygame.display.flip()
if self.auto_draw: FPS_CLOCK.tick(QFPS)
else: FPS_CLOCK.tick(FPS)
def flip_and_spin_ball(self):
"""
When ball makes contact with the upper
or lower ends of either paddle, the ball
will potentially randomly increase the y axis speed
and be return with the same speed
"""
self.ball_speed_x *= -1
self.ball_speed_y *= random.randint(1000, 1200)/1000.
def flip_and_speed_ball(self):
"""
When the ball makes contact with the center
of either paddle, it will return the ball with
potentially an increase in the x axis speed
y axis remains untouched
"""
self.ball_speed_x *= -1
self.ball_speed_x *= random.randint(1000, 1200)/1000.
def main(argv):
"""
When called `python king_pong.py`
a CPU is allocated to play against a human
"""
game_state = GameState(auto_draw = False)
# 2 game_states of 1 point
game_state.first_to = [3, 2]
game_state.top_speed = 5
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit()
keys = pygame.key.get_pressed()
a1 = keys[pygame.K_UP]
a2 = 0 if a1 else keys[pygame.K_DOWN]
a0 = 1 if not a1 and not a2 else 0
image_data, reward = game_state.frame_step([a0, a1, a2])
game_state.draw_scores()
game_state.complete_drawing()
if game_state.game_over():
exit(0)
if __name__ == "__main__":
from sys import argv
main(argv) | 0.744378 | 0.178526 |
import django.core.validators
import django.db.models.deletion
import django.utils.timezone
import django_fsm
import model_utils.fields
from django.db import migrations, models
import waldur_core.core.fields
import waldur_core.core.models
import waldur_core.logging.loggers
class Migration(migrations.Migration):
initial = True
dependencies = [
('structure', '0001_squashed_0054'),
]
operations = [
migrations.CreateModel(
name='Invoice',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
('uuid', waldur_core.core.fields.UUIDField()),
(
'state',
models.CharField(
choices=[
('DRAFT', 'Draft'),
('SENT', 'Sent'),
('PAID', 'Paid'),
('MARKED_AS_PAID', 'Marked as paid'),
('CANCELLED', 'Cancelled'),
('REFUNDED', 'Refunded'),
('PARTIALLY_REFUNDED', 'Partially refunded'),
('MARKED_AS_REFUNDED', 'Marked as refunded'),
('UNPAID', 'Unpaid'),
('PAYMENT_PENDING', 'Payment pending'),
],
default='DRAFT',
max_length=30,
),
),
('invoice_date', models.DateField()),
('end_date', models.DateField()),
(
'pdf',
models.FileField(
blank=True, null=True, upload_to='paypal-invoices'
),
),
('number', models.CharField(max_length=30)),
(
'tax_percent',
models.DecimalField(
decimal_places=2,
default=0,
max_digits=4,
validators=[
django.core.validators.MinValueValidator(0),
django.core.validators.MaxValueValidator(100),
],
),
),
('backend_id', models.CharField(blank=True, max_length=128)),
(
'issuer_details',
waldur_core.core.fields.JSONField(
blank=True,
default={},
help_text='Stores data about invoice issuer',
),
),
(
'payment_details',
waldur_core.core.fields.JSONField(
blank=True,
default={},
help_text='Stores data about customer payment details',
),
),
(
'month',
models.PositiveSmallIntegerField(
validators=[
django.core.validators.MinValueValidator(1),
django.core.validators.MaxValueValidator(12),
]
),
),
('year', models.PositiveSmallIntegerField()),
(
'customer',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name='paypal_invoices',
to='structure.Customer',
),
),
],
options={'ordering': ['-invoice_date'],},
bases=(
waldur_core.logging.loggers.LoggableMixin,
models.Model,
waldur_core.core.models.BackendModelMixin,
),
),
migrations.CreateModel(
name='InvoiceItem',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
('price', models.DecimalField(decimal_places=2, max_digits=9)),
('tax', models.DecimalField(decimal_places=2, default=0, max_digits=9)),
('unit_price', models.DecimalField(decimal_places=2, max_digits=9)),
('quantity', models.PositiveIntegerField(default=0)),
(
'unit_of_measure',
models.CharField(
choices=[
('QUANTITY', 'Quantity'),
('HOURS', 'Hours'),
('AMOUNT', 'Amount'),
],
default='HOURS',
max_length=30,
),
),
('name', models.CharField(max_length=255)),
('start', models.DateTimeField(null=True)),
('end', models.DateTimeField(null=True)),
(
'invoice',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name='items',
to='waldur_paypal.Invoice',
),
),
],
options={'ordering': ['invoice', '-start'],},
),
migrations.CreateModel(
name='Payment',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
(
'created',
model_utils.fields.AutoCreatedField(
default=django.utils.timezone.now,
editable=False,
verbose_name='created',
),
),
(
'modified',
model_utils.fields.AutoLastModifiedField(
default=django.utils.timezone.now,
editable=False,
verbose_name='modified',
),
),
('uuid', waldur_core.core.fields.UUIDField()),
('error_message', models.TextField(blank=True)),
(
'state',
django_fsm.FSMIntegerField(
choices=[
(0, 'Initial'),
(1, 'Created'),
(2, 'Approved'),
(4, 'Erred'),
],
default=0,
),
),
('amount', models.DecimalField(decimal_places=2, max_digits=9)),
('tax', models.DecimalField(decimal_places=2, default=0, max_digits=9)),
('backend_id', models.CharField(max_length=255, null=True)),
('token', models.CharField(max_length=255, null=True)),
('approval_url', models.URLField()),
(
'customer',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
to='structure.Customer',
),
),
],
options={'ordering': ['-modified'],},
bases=(waldur_core.logging.loggers.LoggableMixin, models.Model),
),
] | src/waldur_paypal/migrations/0001_initial.py | import django.core.validators
import django.db.models.deletion
import django.utils.timezone
import django_fsm
import model_utils.fields
from django.db import migrations, models
import waldur_core.core.fields
import waldur_core.core.models
import waldur_core.logging.loggers
class Migration(migrations.Migration):
initial = True
dependencies = [
('structure', '0001_squashed_0054'),
]
operations = [
migrations.CreateModel(
name='Invoice',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
('uuid', waldur_core.core.fields.UUIDField()),
(
'state',
models.CharField(
choices=[
('DRAFT', 'Draft'),
('SENT', 'Sent'),
('PAID', 'Paid'),
('MARKED_AS_PAID', 'Marked as paid'),
('CANCELLED', 'Cancelled'),
('REFUNDED', 'Refunded'),
('PARTIALLY_REFUNDED', 'Partially refunded'),
('MARKED_AS_REFUNDED', 'Marked as refunded'),
('UNPAID', 'Unpaid'),
('PAYMENT_PENDING', 'Payment pending'),
],
default='DRAFT',
max_length=30,
),
),
('invoice_date', models.DateField()),
('end_date', models.DateField()),
(
'pdf',
models.FileField(
blank=True, null=True, upload_to='paypal-invoices'
),
),
('number', models.CharField(max_length=30)),
(
'tax_percent',
models.DecimalField(
decimal_places=2,
default=0,
max_digits=4,
validators=[
django.core.validators.MinValueValidator(0),
django.core.validators.MaxValueValidator(100),
],
),
),
('backend_id', models.CharField(blank=True, max_length=128)),
(
'issuer_details',
waldur_core.core.fields.JSONField(
blank=True,
default={},
help_text='Stores data about invoice issuer',
),
),
(
'payment_details',
waldur_core.core.fields.JSONField(
blank=True,
default={},
help_text='Stores data about customer payment details',
),
),
(
'month',
models.PositiveSmallIntegerField(
validators=[
django.core.validators.MinValueValidator(1),
django.core.validators.MaxValueValidator(12),
]
),
),
('year', models.PositiveSmallIntegerField()),
(
'customer',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name='paypal_invoices',
to='structure.Customer',
),
),
],
options={'ordering': ['-invoice_date'],},
bases=(
waldur_core.logging.loggers.LoggableMixin,
models.Model,
waldur_core.core.models.BackendModelMixin,
),
),
migrations.CreateModel(
name='InvoiceItem',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
('price', models.DecimalField(decimal_places=2, max_digits=9)),
('tax', models.DecimalField(decimal_places=2, default=0, max_digits=9)),
('unit_price', models.DecimalField(decimal_places=2, max_digits=9)),
('quantity', models.PositiveIntegerField(default=0)),
(
'unit_of_measure',
models.CharField(
choices=[
('QUANTITY', 'Quantity'),
('HOURS', 'Hours'),
('AMOUNT', 'Amount'),
],
default='HOURS',
max_length=30,
),
),
('name', models.CharField(max_length=255)),
('start', models.DateTimeField(null=True)),
('end', models.DateTimeField(null=True)),
(
'invoice',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name='items',
to='waldur_paypal.Invoice',
),
),
],
options={'ordering': ['invoice', '-start'],},
),
migrations.CreateModel(
name='Payment',
fields=[
(
'id',
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name='ID',
),
),
(
'created',
model_utils.fields.AutoCreatedField(
default=django.utils.timezone.now,
editable=False,
verbose_name='created',
),
),
(
'modified',
model_utils.fields.AutoLastModifiedField(
default=django.utils.timezone.now,
editable=False,
verbose_name='modified',
),
),
('uuid', waldur_core.core.fields.UUIDField()),
('error_message', models.TextField(blank=True)),
(
'state',
django_fsm.FSMIntegerField(
choices=[
(0, 'Initial'),
(1, 'Created'),
(2, 'Approved'),
(4, 'Erred'),
],
default=0,
),
),
('amount', models.DecimalField(decimal_places=2, max_digits=9)),
('tax', models.DecimalField(decimal_places=2, default=0, max_digits=9)),
('backend_id', models.CharField(max_length=255, null=True)),
('token', models.CharField(max_length=255, null=True)),
('approval_url', models.URLField()),
(
'customer',
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
to='structure.Customer',
),
),
],
options={'ordering': ['-modified'],},
bases=(waldur_core.logging.loggers.LoggableMixin, models.Model),
),
] | 0.442396 | 0.146789 |
import abc
import torch
import torch.nn as nn
import torch.nn.functional as F
from .cfg import Config
from .SphereProjection import SphereProjection
class RowBilinear(nn.Module):
def __init__(self, n_in, kernel_shapes, pad=0):
super(RowBilinear, self).__init__()
n_transform = kernel_shapes.size(0)
weights = []
self.pad = pad
for i in range(n_transform):
kH = kernel_shapes[i,0].item()
kW = kernel_shapes[i,1].item()
n_out = (kH + 2 * pad) * (kW + 2 * pad)
weight = nn.Parameter(torch.Tensor(n_out, n_in))
weights.append(weight)
self.weights = nn.ParameterList(weights)
def forward(self, x, row):
weight = self.weights[row]
return F.linear(x, weight)
class KTN(nn.Module):
__metaclass__ = abc.ABCMeta
def __init__(self, kernel, bias, kernel_shapes, **kwargs):
super(KTN, self).__init__()
dtype = Config["FloatType"]
self.src_kernel = nn.Parameter(kernel)#.type(dtype)
self.src_bias = nn.Parameter(bias)#.type(dtype)
self.activation = torch.tanh
self.register_buffer("kernel_shapes", kernel_shapes)
n_out, n_in, kH, kW = kernel.size()
self.n_in = n_in
self.n_out = n_out
self.n_maps = n_out * n_in
kernel_size = kH * kW
self.initialize_ktn(kernel_size)
@abc.abstractmethod
def initialize_ktn(self, kernel_size):
pass
def forward(self, row):
x = self.src_kernel.view(self.n_maps, -1)
x = self.apply_ktn(x, row)
okH, okW = self.kernel_shapes[row]
kernel = x.view(self.n_out, self.n_in, okH, okW)
bias = self.src_bias
return kernel, bias
@abc.abstractmethod
def apply_ktn(self, x, row):
pass
def initialize_weight(self):
for name, param in self.named_parameters():
if ".bias" in name:
param.data.zero_()
elif ".weight" in name:
param.data.normal_(std=0.01)
def update_group(self, group):
for name, param in self.named_parameters():
param.requires_grad = False
if group == "kernel":
self.src_kernel.requires_grad = True
self.src_bias.requires_grad = True
elif group == "transform":
for name, param in self.named_parameters():
if ".weight" in name or ".bias" in name:
param.requires_grad = True
elif group == "all":
for name, param in self.named_parameters():
param.requires_grad = True
else:
raise ValueError("Unknown parameter group")
class BilinearKTN(KTN):
def initialize_ktn(self, kernel_size):
self.bilinear = RowBilinear(kernel_size, self.kernel_shapes)
def apply_ktn(self, x, row):
x = self.bilinear(x, row)
return x
def initialize_weight(self, **kwargs):
for name, param in self.named_parameters():
if name[-5:] == ".bias":
param.data.zero_()
elif name[-7:] == ".weight":
param.data.normal_(std=0.01)
self.initialize_bilinear(self.bilinear, **kwargs)
def initialize_bilinear(self,
bilinear,
sphereH=320,
fov=65.5,
imgW=640,
dilation=1,
tied_weights=5):
kH = self.src_kernel.size(2)
sphereW = sphereH * 2
projection = SphereProjection(kernel_size=kH,
sphereH=sphereH,
sphereW=sphereW,
view_angle=fov,
imgW=imgW)
center = sphereW / 2
for i, param in enumerate(bilinear.weights):
param.data.zero_()
tilt = i * tied_weights + tied_weights / 2
P = projection.buildP(tilt=tilt).transpose()
okH = self.kernel_shapes[i,0].item()
okW = self.kernel_shapes[i,1].item()
okH += bilinear.pad * 2
okW += bilinear.pad * 2
sH = int(tilt - okH / 2)
sW = int(center - okW / 2)
for y in range(okH):
row = y + sH
if row < 0 or row >= sphereH:
continue
for x in range(okW):
col = x + sW
if col < 0 or col >= sphereW:
continue
pixel = row * sphereW + col
p = P[pixel]
if p.nnz == 0:
continue
j = y * okW + x
for k in range(p.shape[1]):
param.data[j,k] = p[0,k]
class ResidualKTN(BilinearKTN):
def initialize_ktn(self, kernel_size):
self.bilinear = RowBilinear(kernel_size, self.kernel_shapes)
self.res1 = RowBilinear(kernel_size, self.kernel_shapes, pad=2)
self.res2 = nn.Conv2d(self.n_in, self.n_in, 1)
self.res3 = nn.Conv2d(1, 1, 3, padding=0)
self.res4 = nn.Conv2d(self.n_in, self.n_in, 1)
self.res5 = nn.Conv2d(1, 1, 3, padding=0)
def apply_ktn(self, x, row):
base = self.bilinear(x, row)
okH, okW = self.kernel_shapes[row]
x = self.res1(x, row)
x = x.view(-1, self.n_in, okH+4, okW+4)
x = self.res2(self.activation(x))
x = x.view(-1, 1, okH+4, okW+4)
x = self.res3(self.activation(x))
x = x.view(-1, self.n_in, okH+2, okW+2)
x = self.res4(self.activation(x))
x = x.view(-1, 1, okH+2, okW+2)
x = self.res5(self.activation(x))
x = x.view(base.size())
x = x + base
return x
def initialize_weight(self, **kwargs):
for name, param in self.named_parameters():
if name[-5:] == ".bias":
param.data.zero_()
elif name[-7:] == ".weight":
param.data.normal_(std=0.01)
self.initialize_bilinear(self.bilinear, **kwargs)
self.initialize_bilinear(self.res1, **kwargs)
KTN_ARCHS = {
"bilinear": BilinearKTN,
"residual": ResidualKTN,
} | model/KernelTransformer/KTN.py |
import abc
import torch
import torch.nn as nn
import torch.nn.functional as F
from .cfg import Config
from .SphereProjection import SphereProjection
class RowBilinear(nn.Module):
def __init__(self, n_in, kernel_shapes, pad=0):
super(RowBilinear, self).__init__()
n_transform = kernel_shapes.size(0)
weights = []
self.pad = pad
for i in range(n_transform):
kH = kernel_shapes[i,0].item()
kW = kernel_shapes[i,1].item()
n_out = (kH + 2 * pad) * (kW + 2 * pad)
weight = nn.Parameter(torch.Tensor(n_out, n_in))
weights.append(weight)
self.weights = nn.ParameterList(weights)
def forward(self, x, row):
weight = self.weights[row]
return F.linear(x, weight)
class KTN(nn.Module):
__metaclass__ = abc.ABCMeta
def __init__(self, kernel, bias, kernel_shapes, **kwargs):
super(KTN, self).__init__()
dtype = Config["FloatType"]
self.src_kernel = nn.Parameter(kernel)#.type(dtype)
self.src_bias = nn.Parameter(bias)#.type(dtype)
self.activation = torch.tanh
self.register_buffer("kernel_shapes", kernel_shapes)
n_out, n_in, kH, kW = kernel.size()
self.n_in = n_in
self.n_out = n_out
self.n_maps = n_out * n_in
kernel_size = kH * kW
self.initialize_ktn(kernel_size)
@abc.abstractmethod
def initialize_ktn(self, kernel_size):
pass
def forward(self, row):
x = self.src_kernel.view(self.n_maps, -1)
x = self.apply_ktn(x, row)
okH, okW = self.kernel_shapes[row]
kernel = x.view(self.n_out, self.n_in, okH, okW)
bias = self.src_bias
return kernel, bias
@abc.abstractmethod
def apply_ktn(self, x, row):
pass
def initialize_weight(self):
for name, param in self.named_parameters():
if ".bias" in name:
param.data.zero_()
elif ".weight" in name:
param.data.normal_(std=0.01)
def update_group(self, group):
for name, param in self.named_parameters():
param.requires_grad = False
if group == "kernel":
self.src_kernel.requires_grad = True
self.src_bias.requires_grad = True
elif group == "transform":
for name, param in self.named_parameters():
if ".weight" in name or ".bias" in name:
param.requires_grad = True
elif group == "all":
for name, param in self.named_parameters():
param.requires_grad = True
else:
raise ValueError("Unknown parameter group")
class BilinearKTN(KTN):
def initialize_ktn(self, kernel_size):
self.bilinear = RowBilinear(kernel_size, self.kernel_shapes)
def apply_ktn(self, x, row):
x = self.bilinear(x, row)
return x
def initialize_weight(self, **kwargs):
for name, param in self.named_parameters():
if name[-5:] == ".bias":
param.data.zero_()
elif name[-7:] == ".weight":
param.data.normal_(std=0.01)
self.initialize_bilinear(self.bilinear, **kwargs)
def initialize_bilinear(self,
bilinear,
sphereH=320,
fov=65.5,
imgW=640,
dilation=1,
tied_weights=5):
kH = self.src_kernel.size(2)
sphereW = sphereH * 2
projection = SphereProjection(kernel_size=kH,
sphereH=sphereH,
sphereW=sphereW,
view_angle=fov,
imgW=imgW)
center = sphereW / 2
for i, param in enumerate(bilinear.weights):
param.data.zero_()
tilt = i * tied_weights + tied_weights / 2
P = projection.buildP(tilt=tilt).transpose()
okH = self.kernel_shapes[i,0].item()
okW = self.kernel_shapes[i,1].item()
okH += bilinear.pad * 2
okW += bilinear.pad * 2
sH = int(tilt - okH / 2)
sW = int(center - okW / 2)
for y in range(okH):
row = y + sH
if row < 0 or row >= sphereH:
continue
for x in range(okW):
col = x + sW
if col < 0 or col >= sphereW:
continue
pixel = row * sphereW + col
p = P[pixel]
if p.nnz == 0:
continue
j = y * okW + x
for k in range(p.shape[1]):
param.data[j,k] = p[0,k]
class ResidualKTN(BilinearKTN):
def initialize_ktn(self, kernel_size):
self.bilinear = RowBilinear(kernel_size, self.kernel_shapes)
self.res1 = RowBilinear(kernel_size, self.kernel_shapes, pad=2)
self.res2 = nn.Conv2d(self.n_in, self.n_in, 1)
self.res3 = nn.Conv2d(1, 1, 3, padding=0)
self.res4 = nn.Conv2d(self.n_in, self.n_in, 1)
self.res5 = nn.Conv2d(1, 1, 3, padding=0)
def apply_ktn(self, x, row):
base = self.bilinear(x, row)
okH, okW = self.kernel_shapes[row]
x = self.res1(x, row)
x = x.view(-1, self.n_in, okH+4, okW+4)
x = self.res2(self.activation(x))
x = x.view(-1, 1, okH+4, okW+4)
x = self.res3(self.activation(x))
x = x.view(-1, self.n_in, okH+2, okW+2)
x = self.res4(self.activation(x))
x = x.view(-1, 1, okH+2, okW+2)
x = self.res5(self.activation(x))
x = x.view(base.size())
x = x + base
return x
def initialize_weight(self, **kwargs):
for name, param in self.named_parameters():
if name[-5:] == ".bias":
param.data.zero_()
elif name[-7:] == ".weight":
param.data.normal_(std=0.01)
self.initialize_bilinear(self.bilinear, **kwargs)
self.initialize_bilinear(self.res1, **kwargs)
KTN_ARCHS = {
"bilinear": BilinearKTN,
"residual": ResidualKTN,
} | 0.905259 | 0.304752 |
import typing
from pylark.lark_request import Response
from pylark.api_service_drive_file_search import (
SearchDriveFileReq,
SearchDriveFileResp,
_gen_search_drive_file_req,
)
from pylark.api_service_drive_file_meta_get import (
GetDriveFileMetaReq,
GetDriveFileMetaResp,
_gen_get_drive_file_meta_req,
)
from pylark.api_service_drive_file_create import (
CreateDriveFileReq,
CreateDriveFileResp,
_gen_create_drive_file_req,
)
from pylark.api_service_drive_file_copy import (
CopyDriveFileReq,
CopyDriveFileResp,
_gen_copy_drive_file_req,
)
from pylark.api_service_drive_file_delete import (
DeleteDriveFileReq,
DeleteDriveFileResp,
_gen_delete_drive_file_req,
)
from pylark.api_service_drive_file_sheet_delete import (
DeleteDriveSheetFileReq,
DeleteDriveSheetFileResp,
_gen_delete_drive_sheet_file_req,
)
from pylark.api_service_drive_folder_create import (
CreateDriveFolderReq,
CreateDriveFolderResp,
_gen_create_drive_folder_req,
)
from pylark.api_service_drive_folder_meta import (
GetDriveFolderMetaReq,
GetDriveFolderMetaResp,
_gen_get_drive_folder_meta_req,
)
from pylark.api_service_drive_folder_root_meta import (
GetDriveRootFolderMetaReq,
GetDriveRootFolderMetaResp,
_gen_get_drive_root_folder_meta_req,
)
from pylark.api_service_drive_folder_children_get import (
GetDriveFolderChildrenReq,
GetDriveFolderChildrenResp,
_gen_get_drive_folder_children_req,
)
from pylark.api_service_drive_file_statistics_get import (
GetDriveFileStatisticsReq,
GetDriveFileStatisticsResp,
_gen_get_drive_file_statistics_req,
)
from pylark.api_service_drive_file_download import (
DownloadDriveFileReq,
DownloadDriveFileResp,
_gen_download_drive_file_req,
)
from pylark.api_service_drive_file_upload_all import (
UploadDriveFileReq,
UploadDriveFileResp,
_gen_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_prepare import (
PrepareUploadDriveFileReq,
PrepareUploadDriveFileResp,
_gen_prepare_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_part import (
PartUploadDriveFileReq,
PartUploadDriveFileResp,
_gen_part_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_finish import (
FinishUploadDriveFileReq,
FinishUploadDriveFileResp,
_gen_finish_upload_drive_file_req,
)
from pylark.api_service_drive_media_download import (
DownloadDriveMediaReq,
DownloadDriveMediaResp,
_gen_download_drive_media_req,
)
from pylark.api_service_drive_media_upload_all import (
UploadDriveMediaReq,
UploadDriveMediaResp,
_gen_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_prepare import (
PrepareUploadDriveMediaReq,
PrepareUploadDriveMediaResp,
_gen_prepare_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_part import (
PartUploadDriveMediaReq,
PartUploadDriveMediaResp,
_gen_part_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_finish import (
FinishUploadDriveMediaReq,
FinishUploadDriveMediaResp,
_gen_finish_upload_drive_media_req,
)
from pylark.api_service_drive_permission_member_create_old import (
CreateDriveMemberPermissionOldReq,
CreateDriveMemberPermissionOldResp,
_gen_create_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_transfer import (
TransferDriveMemberPermissionReq,
TransferDriveMemberPermissionResp,
_gen_transfer_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_list import (
GetDriveMemberPermissionListReq,
GetDriveMemberPermissionListResp,
_gen_get_drive_member_permission_list_req,
)
from pylark.api_service_drive_permission_member_create import (
CreateDriveMemberPermissionReq,
CreateDriveMemberPermissionResp,
_gen_create_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_delete_old import (
DeleteDriveMemberPermissionOldReq,
DeleteDriveMemberPermissionOldResp,
_gen_delete_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_delete import (
DeleteDriveMemberPermissionReq,
DeleteDriveMemberPermissionResp,
_gen_delete_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_update_old import (
UpdateDriveMemberPermissionOldReq,
UpdateDriveMemberPermissionOldResp,
_gen_update_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_update import (
UpdateDriveMemberPermissionReq,
UpdateDriveMemberPermissionResp,
_gen_update_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_check import (
CheckDriveMemberPermissionReq,
CheckDriveMemberPermissionResp,
_gen_check_drive_member_permission_req,
)
from pylark.api_service_drive_permission_public_update_v1_old import (
UpdateDrivePublicPermissionV1OldReq,
UpdateDrivePublicPermissionV1OldResp,
_gen_update_drive_public_permission_v1_old_req,
)
from pylark.api_service_drive_permission_public_update_v2_old import (
UpdateDrivePublicPermissionV2OldReq,
UpdateDrivePublicPermissionV2OldResp,
_gen_update_drive_public_permission_v2_old_req,
)
from pylark.api_service_drive_permission_public_get_v2 import (
GetDrivePublicPermissionV2Req,
GetDrivePublicPermissionV2Resp,
_gen_get_drive_public_permission_v2_req,
)
from pylark.api_service_drive_permission_public_patch import (
UpdateDrivePublicPermissionReq,
UpdateDrivePublicPermissionResp,
_gen_update_drive_public_permission_req,
)
from pylark.api_service_drive_media_batch_get_tmp_download_url import (
BatchGetDriveMediaTmpDownloadURLReq,
BatchGetDriveMediaTmpDownloadURLResp,
_gen_batch_get_drive_media_tmp_download_url_req,
)
from pylark.api_service_drive_comment_list import (
GetDriveCommentListReq,
GetDriveCommentListResp,
_gen_get_drive_comment_list_req,
)
from pylark.api_service_drive_comment_get import (
GetDriveCommentReq,
GetDriveCommentResp,
_gen_get_drive_comment_req,
)
from pylark.api_service_drive_comment_create import (
CreateDriveCommentReq,
CreateDriveCommentResp,
_gen_create_drive_comment_req,
)
from pylark.api_service_drive_comment_update import (
UpdateDriveCommentReq,
UpdateDriveCommentResp,
_gen_update_drive_comment_req,
)
from pylark.api_service_drive_comment_delete import (
DeleteDriveCommentReq,
DeleteDriveCommentResp,
_gen_delete_drive_comment_req,
)
from pylark.api_service_drive_comment_patch import (
UpdateDriveCommentPatchReq,
UpdateDriveCommentPatchResp,
_gen_update_drive_comment_patch_req,
)
from pylark.api_service_drive_doc_create import (
CreateDriveDocReq,
CreateDriveDocResp,
_gen_create_drive_doc_req,
)
from pylark.api_service_drive_doc_content_get import (
GetDriveDocContentReq,
GetDriveDocContentResp,
_gen_get_drive_doc_content_req,
)
from pylark.api_service_drive_doc_raw_content_get import (
GetDriveDocRawContentReq,
GetDriveDocRawContentResp,
_gen_get_drive_doc_raw_content_req,
)
from pylark.api_service_drive_doc_meta_get import (
GetDriveDocMetaReq,
GetDriveDocMetaResp,
_gen_get_drive_doc_meta_req,
)
from pylark.api_service_drive_sheet_create import (
CreateSheetReq,
CreateSheetResp,
_gen_create_sheet_req,
)
from pylark.api_service_drive_sheet_meta_get import (
GetSheetMetaReq,
GetSheetMetaResp,
_gen_get_sheet_meta_req,
)
from pylark.api_service_drive_sheet_property_update import (
UpdateSheetPropertyReq,
UpdateSheetPropertyResp,
_gen_update_sheet_property_req,
)
from pylark.api_service_drive_sheet_batch_update import (
BatchUpdateSheetReq,
BatchUpdateSheetResp,
_gen_batch_update_sheet_req,
)
from pylark.api_service_drive_sheet_import import (
ImportSheetReq,
ImportSheetResp,
_gen_import_sheet_req,
)
from pylark.api_service_drive_import_task_create import (
CreateDriveImportTaskReq,
CreateDriveImportTaskResp,
_gen_create_drive_import_task_req,
)
from pylark.api_service_drive_import_task_get import (
GetDriveImportTaskReq,
GetDriveImportTaskResp,
_gen_get_drive_import_task_req,
)
from pylark.api_service_drive_sheet_dimension_move import (
MoveSheetDimensionReq,
MoveSheetDimensionResp,
_gen_move_sheet_dimension_req,
)
from pylark.api_service_drive_sheet_value_prepend import (
PrependSheetValueReq,
PrependSheetValueResp,
_gen_prepend_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_append import (
AppendSheetValueReq,
AppendSheetValueResp,
_gen_append_sheet_value_req,
)
from pylark.api_service_drive_sheet_dimension_range_insert import (
InsertSheetDimensionRangeReq,
InsertSheetDimensionRangeResp,
_gen_insert_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_add import (
AddSheetDimensionRangeReq,
AddSheetDimensionRangeResp,
_gen_add_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_update import (
UpdateSheetDimensionRangeReq,
UpdateSheetDimensionRangeResp,
_gen_update_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_delete import (
DeleteSheetDimensionRangeReq,
DeleteSheetDimensionRangeResp,
_gen_delete_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_value_get import (
GetSheetValueReq,
GetSheetValueResp,
_gen_get_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_batch_get import (
BatchGetSheetValueReq,
BatchGetSheetValueResp,
_gen_batch_get_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_set import (
SetSheetValueReq,
SetSheetValueResp,
_gen_set_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_batch_set import (
BatchSetSheetValueReq,
BatchSetSheetValueResp,
_gen_batch_set_sheet_value_req,
)
from pylark.api_service_drive_sheet_style_set import (
SetSheetStyleReq,
SetSheetStyleResp,
_gen_set_sheet_style_req,
)
from pylark.api_service_drive_sheet_style_batch_set import (
BatchSetSheetStyleReq,
BatchSetSheetStyleResp,
_gen_batch_set_sheet_style_req,
)
from pylark.api_service_drive_sheet_cell_merge import (
MergeSheetCellReq,
MergeSheetCellResp,
_gen_merge_sheet_cell_req,
)
from pylark.api_service_drive_sheet_cell_unmerge import (
UnmergeSheetCellReq,
UnmergeSheetCellResp,
_gen_unmerge_sheet_cell_req,
)
from pylark.api_service_drive_sheet_image_set import (
SetSheetValueImageReq,
SetSheetValueImageResp,
_gen_set_sheet_value_image_req,
)
from pylark.api_service_drive_sheet_find import (
FindSheetReq,
FindSheetResp,
_gen_find_sheet_req,
)
from pylark.api_service_drive_sheet_replace import (
ReplaceSheetReq,
ReplaceSheetResp,
_gen_replace_sheet_req,
)
from pylark.api_service_drive_sheet_condition_format_create import (
CreateSheetConditionFormatReq,
CreateSheetConditionFormatResp,
_gen_create_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_get import (
GetSheetConditionFormatReq,
GetSheetConditionFormatResp,
_gen_get_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_update import (
UpdateSheetConditionFormatReq,
UpdateSheetConditionFormatResp,
_gen_update_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_delete import (
DeleteSheetConditionFormatReq,
DeleteSheetConditionFormatResp,
_gen_delete_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_protected_dimension_create import (
CreateSheetProtectedDimensionReq,
CreateSheetProtectedDimensionResp,
_gen_create_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_get import (
GetSheetProtectedDimensionReq,
GetSheetProtectedDimensionResp,
_gen_get_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_update import (
UpdateSheetProtectedDimensionReq,
UpdateSheetProtectedDimensionResp,
_gen_update_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_delete import (
DeleteSheetProtectedDimensionReq,
DeleteSheetProtectedDimensionResp,
_gen_delete_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_create import (
CreateSheetDataValidationDropdownReq,
CreateSheetDataValidationDropdownResp,
_gen_create_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_delete import (
DeleteSheetDataValidationDropdownReq,
DeleteSheetDataValidationDropdownResp,
_gen_delete_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_update import (
UpdateSheetDataValidationDropdownReq,
UpdateSheetDataValidationDropdownResp,
_gen_update_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_get import (
GetSheetDataValidationDropdownReq,
GetSheetDataValidationDropdownResp,
_gen_get_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_filter_create import (
CreateSheetFilterReq,
CreateSheetFilterResp,
_gen_create_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_delete import (
DeleteSheetFilterReq,
DeleteSheetFilterResp,
_gen_delete_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_update import (
UpdateSheetFilterReq,
UpdateSheetFilterResp,
_gen_update_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_get import (
GetSheetFilterReq,
GetSheetFilterResp,
_gen_get_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_view_create import (
CreateSheetFilterViewReq,
CreateSheetFilterViewResp,
_gen_create_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_delete import (
DeleteSheetFilterViewReq,
DeleteSheetFilterViewResp,
_gen_delete_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_update import (
UpdateSheetFilterViewReq,
UpdateSheetFilterViewResp,
_gen_update_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_get import (
GetSheetFilterViewReq,
GetSheetFilterViewResp,
_gen_get_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_query import (
QuerySheetFilterViewReq,
QuerySheetFilterViewResp,
_gen_query_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_create import (
CreateSheetFilterViewConditionReq,
CreateSheetFilterViewConditionResp,
_gen_create_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_delete import (
DeleteSheetFilterViewConditionReq,
DeleteSheetFilterViewConditionResp,
_gen_delete_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_update import (
UpdateSheetFilterViewConditionReq,
UpdateSheetFilterViewConditionResp,
_gen_update_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_get import (
GetSheetFilterViewConditionReq,
GetSheetFilterViewConditionResp,
_gen_get_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_query import (
QuerySheetFilterViewConditionReq,
QuerySheetFilterViewConditionResp,
_gen_query_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_float_image_create import (
CreateSheetFloatImageReq,
CreateSheetFloatImageResp,
_gen_create_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_delete import (
DeleteSheetFloatImageReq,
DeleteSheetFloatImageResp,
_gen_delete_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_update import (
UpdateSheetFloatImageReq,
UpdateSheetFloatImageResp,
_gen_update_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_get import (
GetSheetFloatImageReq,
GetSheetFloatImageResp,
_gen_get_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_query import (
QuerySheetFloatImageReq,
QuerySheetFloatImageResp,
_gen_query_sheet_float_image_req,
)
from pylark.api_service_drive_wiki_space_create import (
CreateWikiSpaceReq,
CreateWikiSpaceResp,
_gen_create_wiki_space_req,
)
from pylark.api_service_drive_wiki_space_get_list import (
GetWikiSpaceListReq,
GetWikiSpaceListResp,
_gen_get_wiki_space_list_req,
)
from pylark.api_service_drive_wiki_space_get import (
GetWikiSpaceReq,
GetWikiSpaceResp,
_gen_get_wiki_space_req,
)
from pylark.api_service_drive_wiki_space_setting_update import (
UpdateWikiSpaceSettingReq,
UpdateWikiSpaceSettingResp,
_gen_update_wiki_space_setting_req,
)
from pylark.api_service_drive_wiki_space_member_add import (
AddWikiSpaceMemberReq,
AddWikiSpaceMemberResp,
_gen_add_wiki_space_member_req,
)
from pylark.api_service_drive_wiki_node_create import (
CreateWikiNodeReq,
CreateWikiNodeResp,
_gen_create_wiki_node_req,
)
from pylark.api_service_drive_wiki_node_list import (
GetWikiNodeListReq,
GetWikiNodeListResp,
_gen_get_wiki_node_list_req,
)
from pylark.api_service_drive_wiki_node_get import (
GetWikiNodeReq,
GetWikiNodeResp,
_gen_get_wiki_node_req,
)
from pylark.api_service_drive_wiki_move_docs_to_wiki import (
MoveDocsToWikiReq,
MoveDocsToWikiResp,
_gen_move_docs_to_wiki_req,
)
if typing.TYPE_CHECKING:
from lark import Lark
class LarkDriveService(object):
cli: "Lark"
def __init__(self, cli: "Lark"):
self.cli = cli
def search_drive_file(
self, request: SearchDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[SearchDriveFileResp, Response]:
return self.cli.raw_request(_gen_search_drive_file_req(request, options))
def get_drive_file_meta(
self, request: GetDriveFileMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFileMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_file_meta_req(request, options))
def create_drive_file(
self, request: CreateDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveFileResp, Response]:
return self.cli.raw_request(_gen_create_drive_file_req(request, options))
def copy_drive_file(
self, request: CopyDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[CopyDriveFileResp, Response]:
return self.cli.raw_request(_gen_copy_drive_file_req(request, options))
def delete_drive_file(
self, request: DeleteDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveFileResp, Response]:
return self.cli.raw_request(_gen_delete_drive_file_req(request, options))
def delete_drive_sheet_file(
self, request: DeleteDriveSheetFileReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveSheetFileResp, Response]:
return self.cli.raw_request(_gen_delete_drive_sheet_file_req(request, options))
def create_drive_folder(
self, request: CreateDriveFolderReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveFolderResp, Response]:
return self.cli.raw_request(_gen_create_drive_folder_req(request, options))
def get_drive_folder_meta(
self, request: GetDriveFolderMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFolderMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_folder_meta_req(request, options))
def get_drive_root_folder_meta(
self, request: GetDriveRootFolderMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveRootFolderMetaResp, Response]:
return self.cli.raw_request(
_gen_get_drive_root_folder_meta_req(request, options)
)
def get_drive_folder_children(
self, request: GetDriveFolderChildrenReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFolderChildrenResp, Response]:
return self.cli.raw_request(
_gen_get_drive_folder_children_req(request, options)
)
def get_drive_file_statistics(
self, request: GetDriveFileStatisticsReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFileStatisticsResp, Response]:
return self.cli.raw_request(
_gen_get_drive_file_statistics_req(request, options)
)
def download_drive_file(
self, request: DownloadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[DownloadDriveFileResp, Response]:
return self.cli.raw_request(_gen_download_drive_file_req(request, options))
def upload_drive_file(
self, request: UploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[UploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_upload_drive_file_req(request, options))
def prepare_upload_drive_file(
self, request: PrepareUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[PrepareUploadDriveFileResp, Response]:
return self.cli.raw_request(
_gen_prepare_upload_drive_file_req(request, options)
)
def part_upload_drive_file(
self, request: PartUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[PartUploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_part_upload_drive_file_req(request, options))
def finish_upload_drive_file(
self, request: FinishUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[FinishUploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_finish_upload_drive_file_req(request, options))
def download_drive_media(
self, request: DownloadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[DownloadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_download_drive_media_req(request, options))
def upload_drive_media(
self, request: UploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[UploadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_upload_drive_media_req(request, options))
def prepare_upload_drive_media(
self, request: PrepareUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[PrepareUploadDriveMediaResp, Response]:
return self.cli.raw_request(
_gen_prepare_upload_drive_media_req(request, options)
)
def part_upload_drive_media(
self, request: PartUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[PartUploadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_part_upload_drive_media_req(request, options))
def finish_upload_drive_media(
self, request: FinishUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[FinishUploadDriveMediaResp, Response]:
return self.cli.raw_request(
_gen_finish_upload_drive_media_req(request, options)
)
def create_drive_member_permission_old(
self,
request: CreateDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_create_drive_member_permission_old_req(request, options)
)
def transfer_drive_member_permission(
self,
request: TransferDriveMemberPermissionReq,
options: typing.List[str] = None,
) -> typing.Tuple[TransferDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_transfer_drive_member_permission_req(request, options)
)
def get_drive_member_permission_list(
self, request: GetDriveMemberPermissionListReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveMemberPermissionListResp, Response]:
return self.cli.raw_request(
_gen_get_drive_member_permission_list_req(request, options)
)
def create_drive_member_permission(
self, request: CreateDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_create_drive_member_permission_req(request, options)
)
def delete_drive_member_permission_old(
self,
request: DeleteDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_delete_drive_member_permission_old_req(request, options)
)
def delete_drive_member_permission(
self, request: DeleteDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_delete_drive_member_permission_req(request, options)
)
def update_drive_member_permission_old(
self,
request: UpdateDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_member_permission_old_req(request, options)
)
def update_drive_member_permission(
self, request: UpdateDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_update_drive_member_permission_req(request, options)
)
def check_drive_member_permission(
self, request: CheckDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[CheckDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_check_drive_member_permission_req(request, options)
)
def update_drive_public_permission_v1_old(
self,
request: UpdateDrivePublicPermissionV1OldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDrivePublicPermissionV1OldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_v1_old_req(request, options)
)
def update_drive_public_permission_v2_old(
self,
request: UpdateDrivePublicPermissionV2OldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDrivePublicPermissionV2OldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_v2_old_req(request, options)
)
def get_drive_public_permission_v2(
self, request: GetDrivePublicPermissionV2Req, options: typing.List[str] = None
) -> typing.Tuple[GetDrivePublicPermissionV2Resp, Response]:
return self.cli.raw_request(
_gen_get_drive_public_permission_v2_req(request, options)
)
def update_drive_public_permission(
self, request: UpdateDrivePublicPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDrivePublicPermissionResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_req(request, options)
)
def batch_get_drive_media_tmp_download_url(
self,
request: BatchGetDriveMediaTmpDownloadURLReq,
options: typing.List[str] = None,
) -> typing.Tuple[BatchGetDriveMediaTmpDownloadURLResp, Response]:
return self.cli.raw_request(
_gen_batch_get_drive_media_tmp_download_url_req(request, options)
)
def get_drive_comment_list(
self, request: GetDriveCommentListReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveCommentListResp, Response]:
return self.cli.raw_request(_gen_get_drive_comment_list_req(request, options))
def get_drive_comment(
self, request: GetDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveCommentResp, Response]:
return self.cli.raw_request(_gen_get_drive_comment_req(request, options))
def create_drive_comment(
self, request: CreateDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveCommentResp, Response]:
return self.cli.raw_request(_gen_create_drive_comment_req(request, options))
def update_drive_comment(
self, request: UpdateDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveCommentResp, Response]:
return self.cli.raw_request(_gen_update_drive_comment_req(request, options))
def delete_drive_comment(
self, request: DeleteDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveCommentResp, Response]:
return self.cli.raw_request(_gen_delete_drive_comment_req(request, options))
def update_drive_comment_patch(
self, request: UpdateDriveCommentPatchReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveCommentPatchResp, Response]:
return self.cli.raw_request(
_gen_update_drive_comment_patch_req(request, options)
)
def create_drive_doc(
self, request: CreateDriveDocReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveDocResp, Response]:
return self.cli.raw_request(_gen_create_drive_doc_req(request, options))
def get_drive_doc_content(
self, request: GetDriveDocContentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocContentResp, Response]:
return self.cli.raw_request(_gen_get_drive_doc_content_req(request, options))
def get_drive_doc_raw_content(
self, request: GetDriveDocRawContentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocRawContentResp, Response]:
return self.cli.raw_request(
_gen_get_drive_doc_raw_content_req(request, options)
)
def get_drive_doc_meta(
self, request: GetDriveDocMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_doc_meta_req(request, options))
def create_sheet(
self, request: CreateSheetReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetResp, Response]:
return self.cli.raw_request(_gen_create_sheet_req(request, options))
def get_sheet_meta(
self, request: GetSheetMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetMetaResp, Response]:
return self.cli.raw_request(_gen_get_sheet_meta_req(request, options))
def update_sheet_property(
self, request: UpdateSheetPropertyReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetPropertyResp, Response]:
return self.cli.raw_request(_gen_update_sheet_property_req(request, options))
def batch_update_sheet(
self, request: BatchUpdateSheetReq, options: typing.List[str] = None
) -> typing.Tuple[BatchUpdateSheetResp, Response]:
return self.cli.raw_request(_gen_batch_update_sheet_req(request, options))
def import_sheet(
self, request: ImportSheetReq, options: typing.List[str] = None
) -> typing.Tuple[ImportSheetResp, Response]:
return self.cli.raw_request(_gen_import_sheet_req(request, options))
def create_drive_import_task(
self, request: CreateDriveImportTaskReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveImportTaskResp, Response]:
return self.cli.raw_request(_gen_create_drive_import_task_req(request, options))
def get_drive_import_task(
self, request: GetDriveImportTaskReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveImportTaskResp, Response]:
return self.cli.raw_request(_gen_get_drive_import_task_req(request, options))
def move_sheet_dimension(
self, request: MoveSheetDimensionReq, options: typing.List[str] = None
) -> typing.Tuple[MoveSheetDimensionResp, Response]:
return self.cli.raw_request(_gen_move_sheet_dimension_req(request, options))
def prepend_sheet_value(
self, request: PrependSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[PrependSheetValueResp, Response]:
return self.cli.raw_request(_gen_prepend_sheet_value_req(request, options))
def append_sheet_value(
self, request: AppendSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[AppendSheetValueResp, Response]:
return self.cli.raw_request(_gen_append_sheet_value_req(request, options))
def insert_sheet_dimension_range(
self, request: InsertSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[InsertSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_insert_sheet_dimension_range_req(request, options)
)
def add_sheet_dimension_range(
self, request: AddSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[AddSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_add_sheet_dimension_range_req(request, options)
)
def update_sheet_dimension_range(
self, request: UpdateSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_dimension_range_req(request, options)
)
def delete_sheet_dimension_range(
self, request: DeleteSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_dimension_range_req(request, options)
)
def get_sheet_value(
self, request: GetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetValueResp, Response]:
return self.cli.raw_request(_gen_get_sheet_value_req(request, options))
def batch_get_sheet_value(
self, request: BatchGetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[BatchGetSheetValueResp, Response]:
return self.cli.raw_request(_gen_batch_get_sheet_value_req(request, options))
def set_sheet_value(
self, request: SetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetValueResp, Response]:
return self.cli.raw_request(_gen_set_sheet_value_req(request, options))
def batch_set_sheet_value(
self, request: BatchSetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[BatchSetSheetValueResp, Response]:
return self.cli.raw_request(_gen_batch_set_sheet_value_req(request, options))
def set_sheet_style(
self, request: SetSheetStyleReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetStyleResp, Response]:
return self.cli.raw_request(_gen_set_sheet_style_req(request, options))
def batch_set_sheet_style(
self, request: BatchSetSheetStyleReq, options: typing.List[str] = None
) -> typing.Tuple[BatchSetSheetStyleResp, Response]:
return self.cli.raw_request(_gen_batch_set_sheet_style_req(request, options))
def merge_sheet_cell(
self, request: MergeSheetCellReq, options: typing.List[str] = None
) -> typing.Tuple[MergeSheetCellResp, Response]:
return self.cli.raw_request(_gen_merge_sheet_cell_req(request, options))
def unmerge_sheet_cell(
self, request: UnmergeSheetCellReq, options: typing.List[str] = None
) -> typing.Tuple[UnmergeSheetCellResp, Response]:
return self.cli.raw_request(_gen_unmerge_sheet_cell_req(request, options))
def set_sheet_value_image(
self, request: SetSheetValueImageReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetValueImageResp, Response]:
return self.cli.raw_request(_gen_set_sheet_value_image_req(request, options))
def find_sheet(
self, request: FindSheetReq, options: typing.List[str] = None
) -> typing.Tuple[FindSheetResp, Response]:
return self.cli.raw_request(_gen_find_sheet_req(request, options))
def replace_sheet(
self, request: ReplaceSheetReq, options: typing.List[str] = None
) -> typing.Tuple[ReplaceSheetResp, Response]:
return self.cli.raw_request(_gen_replace_sheet_req(request, options))
def create_sheet_condition_format(
self, request: CreateSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_condition_format_req(request, options)
)
def get_sheet_condition_format(
self, request: GetSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_condition_format_req(request, options)
)
def update_sheet_condition_format(
self, request: UpdateSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_condition_format_req(request, options)
)
def delete_sheet_condition_format(
self, request: DeleteSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_condition_format_req(request, options)
)
def create_sheet_protected_dimension(
self,
request: CreateSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_protected_dimension_req(request, options)
)
def get_sheet_protected_dimension(
self, request: GetSheetProtectedDimensionReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_protected_dimension_req(request, options)
)
def update_sheet_protected_dimension(
self,
request: UpdateSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_protected_dimension_req(request, options)
)
def delete_sheet_protected_dimension(
self,
request: DeleteSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_protected_dimension_req(request, options)
)
def create_sheet_data_validation_dropdown(
self,
request: CreateSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_data_validation_dropdown_req(request, options)
)
def delete_sheet_data_validation_dropdown(
self,
request: DeleteSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_data_validation_dropdown_req(request, options)
)
def update_sheet_data_validation_dropdown(
self,
request: UpdateSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_data_validation_dropdown_req(request, options)
)
def get_sheet_data_validation_dropdown(
self,
request: GetSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[GetSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_data_validation_dropdown_req(request, options)
)
def create_sheet_filter(
self, request: CreateSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFilterResp, Response]:
return self.cli.raw_request(_gen_create_sheet_filter_req(request, options))
def delete_sheet_filter(
self, request: DeleteSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFilterResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_filter_req(request, options))
def update_sheet_filter(
self, request: UpdateSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFilterResp, Response]:
return self.cli.raw_request(_gen_update_sheet_filter_req(request, options))
def get_sheet_filter(
self, request: GetSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterResp, Response]:
return self.cli.raw_request(_gen_get_sheet_filter_req(request, options))
def create_sheet_filter_view(
self, request: CreateSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_create_sheet_filter_view_req(request, options))
def delete_sheet_filter_view(
self, request: DeleteSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_filter_view_req(request, options))
def update_sheet_filter_view(
self, request: UpdateSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_update_sheet_filter_view_req(request, options))
def get_sheet_filter_view(
self, request: GetSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_get_sheet_filter_view_req(request, options))
def query_sheet_filter_view(
self, request: QuerySheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[QuerySheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_query_sheet_filter_view_req(request, options))
def create_sheet_filter_view_condition(
self,
request: CreateSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_filter_view_condition_req(request, options)
)
def delete_sheet_filter_view_condition(
self,
request: DeleteSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_filter_view_condition_req(request, options)
)
def update_sheet_filter_view_condition(
self,
request: UpdateSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_filter_view_condition_req(request, options)
)
def get_sheet_filter_view_condition(
self, request: GetSheetFilterViewConditionReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_filter_view_condition_req(request, options)
)
def query_sheet_filter_view_condition(
self,
request: QuerySheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[QuerySheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_query_sheet_filter_view_condition_req(request, options)
)
def create_sheet_float_image(
self, request: CreateSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_create_sheet_float_image_req(request, options))
def delete_sheet_float_image(
self, request: DeleteSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_float_image_req(request, options))
def update_sheet_float_image(
self, request: UpdateSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_update_sheet_float_image_req(request, options))
def get_sheet_float_image(
self, request: GetSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_get_sheet_float_image_req(request, options))
def query_sheet_float_image(
self, request: QuerySheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[QuerySheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_query_sheet_float_image_req(request, options))
def create_wiki_space(
self, request: CreateWikiSpaceReq, options: typing.List[str] = None
) -> typing.Tuple[CreateWikiSpaceResp, Response]:
return self.cli.raw_request(_gen_create_wiki_space_req(request, options))
def get_wiki_space_list(
self, request: GetWikiSpaceListReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiSpaceListResp, Response]:
return self.cli.raw_request(_gen_get_wiki_space_list_req(request, options))
def get_wiki_space(
self, request: GetWikiSpaceReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiSpaceResp, Response]:
return self.cli.raw_request(_gen_get_wiki_space_req(request, options))
def update_wiki_space_setting(
self, request: UpdateWikiSpaceSettingReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateWikiSpaceSettingResp, Response]:
return self.cli.raw_request(
_gen_update_wiki_space_setting_req(request, options)
)
def add_wiki_space_member(
self, request: AddWikiSpaceMemberReq, options: typing.List[str] = None
) -> typing.Tuple[AddWikiSpaceMemberResp, Response]:
return self.cli.raw_request(_gen_add_wiki_space_member_req(request, options))
def create_wiki_node(
self, request: CreateWikiNodeReq, options: typing.List[str] = None
) -> typing.Tuple[CreateWikiNodeResp, Response]:
return self.cli.raw_request(_gen_create_wiki_node_req(request, options))
def get_wiki_node_list(
self, request: GetWikiNodeListReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiNodeListResp, Response]:
return self.cli.raw_request(_gen_get_wiki_node_list_req(request, options))
def get_wiki_node(
self, request: GetWikiNodeReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiNodeResp, Response]:
return self.cli.raw_request(_gen_get_wiki_node_req(request, options))
def move_docs_to_wiki(
self, request: MoveDocsToWikiReq, options: typing.List[str] = None
) -> typing.Tuple[MoveDocsToWikiResp, Response]:
return self.cli.raw_request(_gen_move_docs_to_wiki_req(request, options)) | pylark/api_service_drive.py |
import typing
from pylark.lark_request import Response
from pylark.api_service_drive_file_search import (
SearchDriveFileReq,
SearchDriveFileResp,
_gen_search_drive_file_req,
)
from pylark.api_service_drive_file_meta_get import (
GetDriveFileMetaReq,
GetDriveFileMetaResp,
_gen_get_drive_file_meta_req,
)
from pylark.api_service_drive_file_create import (
CreateDriveFileReq,
CreateDriveFileResp,
_gen_create_drive_file_req,
)
from pylark.api_service_drive_file_copy import (
CopyDriveFileReq,
CopyDriveFileResp,
_gen_copy_drive_file_req,
)
from pylark.api_service_drive_file_delete import (
DeleteDriveFileReq,
DeleteDriveFileResp,
_gen_delete_drive_file_req,
)
from pylark.api_service_drive_file_sheet_delete import (
DeleteDriveSheetFileReq,
DeleteDriveSheetFileResp,
_gen_delete_drive_sheet_file_req,
)
from pylark.api_service_drive_folder_create import (
CreateDriveFolderReq,
CreateDriveFolderResp,
_gen_create_drive_folder_req,
)
from pylark.api_service_drive_folder_meta import (
GetDriveFolderMetaReq,
GetDriveFolderMetaResp,
_gen_get_drive_folder_meta_req,
)
from pylark.api_service_drive_folder_root_meta import (
GetDriveRootFolderMetaReq,
GetDriveRootFolderMetaResp,
_gen_get_drive_root_folder_meta_req,
)
from pylark.api_service_drive_folder_children_get import (
GetDriveFolderChildrenReq,
GetDriveFolderChildrenResp,
_gen_get_drive_folder_children_req,
)
from pylark.api_service_drive_file_statistics_get import (
GetDriveFileStatisticsReq,
GetDriveFileStatisticsResp,
_gen_get_drive_file_statistics_req,
)
from pylark.api_service_drive_file_download import (
DownloadDriveFileReq,
DownloadDriveFileResp,
_gen_download_drive_file_req,
)
from pylark.api_service_drive_file_upload_all import (
UploadDriveFileReq,
UploadDriveFileResp,
_gen_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_prepare import (
PrepareUploadDriveFileReq,
PrepareUploadDriveFileResp,
_gen_prepare_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_part import (
PartUploadDriveFileReq,
PartUploadDriveFileResp,
_gen_part_upload_drive_file_req,
)
from pylark.api_service_drive_file_upload_finish import (
FinishUploadDriveFileReq,
FinishUploadDriveFileResp,
_gen_finish_upload_drive_file_req,
)
from pylark.api_service_drive_media_download import (
DownloadDriveMediaReq,
DownloadDriveMediaResp,
_gen_download_drive_media_req,
)
from pylark.api_service_drive_media_upload_all import (
UploadDriveMediaReq,
UploadDriveMediaResp,
_gen_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_prepare import (
PrepareUploadDriveMediaReq,
PrepareUploadDriveMediaResp,
_gen_prepare_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_part import (
PartUploadDriveMediaReq,
PartUploadDriveMediaResp,
_gen_part_upload_drive_media_req,
)
from pylark.api_service_drive_media_upload_finish import (
FinishUploadDriveMediaReq,
FinishUploadDriveMediaResp,
_gen_finish_upload_drive_media_req,
)
from pylark.api_service_drive_permission_member_create_old import (
CreateDriveMemberPermissionOldReq,
CreateDriveMemberPermissionOldResp,
_gen_create_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_transfer import (
TransferDriveMemberPermissionReq,
TransferDriveMemberPermissionResp,
_gen_transfer_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_list import (
GetDriveMemberPermissionListReq,
GetDriveMemberPermissionListResp,
_gen_get_drive_member_permission_list_req,
)
from pylark.api_service_drive_permission_member_create import (
CreateDriveMemberPermissionReq,
CreateDriveMemberPermissionResp,
_gen_create_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_delete_old import (
DeleteDriveMemberPermissionOldReq,
DeleteDriveMemberPermissionOldResp,
_gen_delete_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_delete import (
DeleteDriveMemberPermissionReq,
DeleteDriveMemberPermissionResp,
_gen_delete_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_update_old import (
UpdateDriveMemberPermissionOldReq,
UpdateDriveMemberPermissionOldResp,
_gen_update_drive_member_permission_old_req,
)
from pylark.api_service_drive_permission_member_update import (
UpdateDriveMemberPermissionReq,
UpdateDriveMemberPermissionResp,
_gen_update_drive_member_permission_req,
)
from pylark.api_service_drive_permission_member_check import (
CheckDriveMemberPermissionReq,
CheckDriveMemberPermissionResp,
_gen_check_drive_member_permission_req,
)
from pylark.api_service_drive_permission_public_update_v1_old import (
UpdateDrivePublicPermissionV1OldReq,
UpdateDrivePublicPermissionV1OldResp,
_gen_update_drive_public_permission_v1_old_req,
)
from pylark.api_service_drive_permission_public_update_v2_old import (
UpdateDrivePublicPermissionV2OldReq,
UpdateDrivePublicPermissionV2OldResp,
_gen_update_drive_public_permission_v2_old_req,
)
from pylark.api_service_drive_permission_public_get_v2 import (
GetDrivePublicPermissionV2Req,
GetDrivePublicPermissionV2Resp,
_gen_get_drive_public_permission_v2_req,
)
from pylark.api_service_drive_permission_public_patch import (
UpdateDrivePublicPermissionReq,
UpdateDrivePublicPermissionResp,
_gen_update_drive_public_permission_req,
)
from pylark.api_service_drive_media_batch_get_tmp_download_url import (
BatchGetDriveMediaTmpDownloadURLReq,
BatchGetDriveMediaTmpDownloadURLResp,
_gen_batch_get_drive_media_tmp_download_url_req,
)
from pylark.api_service_drive_comment_list import (
GetDriveCommentListReq,
GetDriveCommentListResp,
_gen_get_drive_comment_list_req,
)
from pylark.api_service_drive_comment_get import (
GetDriveCommentReq,
GetDriveCommentResp,
_gen_get_drive_comment_req,
)
from pylark.api_service_drive_comment_create import (
CreateDriveCommentReq,
CreateDriveCommentResp,
_gen_create_drive_comment_req,
)
from pylark.api_service_drive_comment_update import (
UpdateDriveCommentReq,
UpdateDriveCommentResp,
_gen_update_drive_comment_req,
)
from pylark.api_service_drive_comment_delete import (
DeleteDriveCommentReq,
DeleteDriveCommentResp,
_gen_delete_drive_comment_req,
)
from pylark.api_service_drive_comment_patch import (
UpdateDriveCommentPatchReq,
UpdateDriveCommentPatchResp,
_gen_update_drive_comment_patch_req,
)
from pylark.api_service_drive_doc_create import (
CreateDriveDocReq,
CreateDriveDocResp,
_gen_create_drive_doc_req,
)
from pylark.api_service_drive_doc_content_get import (
GetDriveDocContentReq,
GetDriveDocContentResp,
_gen_get_drive_doc_content_req,
)
from pylark.api_service_drive_doc_raw_content_get import (
GetDriveDocRawContentReq,
GetDriveDocRawContentResp,
_gen_get_drive_doc_raw_content_req,
)
from pylark.api_service_drive_doc_meta_get import (
GetDriveDocMetaReq,
GetDriveDocMetaResp,
_gen_get_drive_doc_meta_req,
)
from pylark.api_service_drive_sheet_create import (
CreateSheetReq,
CreateSheetResp,
_gen_create_sheet_req,
)
from pylark.api_service_drive_sheet_meta_get import (
GetSheetMetaReq,
GetSheetMetaResp,
_gen_get_sheet_meta_req,
)
from pylark.api_service_drive_sheet_property_update import (
UpdateSheetPropertyReq,
UpdateSheetPropertyResp,
_gen_update_sheet_property_req,
)
from pylark.api_service_drive_sheet_batch_update import (
BatchUpdateSheetReq,
BatchUpdateSheetResp,
_gen_batch_update_sheet_req,
)
from pylark.api_service_drive_sheet_import import (
ImportSheetReq,
ImportSheetResp,
_gen_import_sheet_req,
)
from pylark.api_service_drive_import_task_create import (
CreateDriveImportTaskReq,
CreateDriveImportTaskResp,
_gen_create_drive_import_task_req,
)
from pylark.api_service_drive_import_task_get import (
GetDriveImportTaskReq,
GetDriveImportTaskResp,
_gen_get_drive_import_task_req,
)
from pylark.api_service_drive_sheet_dimension_move import (
MoveSheetDimensionReq,
MoveSheetDimensionResp,
_gen_move_sheet_dimension_req,
)
from pylark.api_service_drive_sheet_value_prepend import (
PrependSheetValueReq,
PrependSheetValueResp,
_gen_prepend_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_append import (
AppendSheetValueReq,
AppendSheetValueResp,
_gen_append_sheet_value_req,
)
from pylark.api_service_drive_sheet_dimension_range_insert import (
InsertSheetDimensionRangeReq,
InsertSheetDimensionRangeResp,
_gen_insert_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_add import (
AddSheetDimensionRangeReq,
AddSheetDimensionRangeResp,
_gen_add_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_update import (
UpdateSheetDimensionRangeReq,
UpdateSheetDimensionRangeResp,
_gen_update_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_dimension_range_delete import (
DeleteSheetDimensionRangeReq,
DeleteSheetDimensionRangeResp,
_gen_delete_sheet_dimension_range_req,
)
from pylark.api_service_drive_sheet_value_get import (
GetSheetValueReq,
GetSheetValueResp,
_gen_get_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_batch_get import (
BatchGetSheetValueReq,
BatchGetSheetValueResp,
_gen_batch_get_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_set import (
SetSheetValueReq,
SetSheetValueResp,
_gen_set_sheet_value_req,
)
from pylark.api_service_drive_sheet_value_batch_set import (
BatchSetSheetValueReq,
BatchSetSheetValueResp,
_gen_batch_set_sheet_value_req,
)
from pylark.api_service_drive_sheet_style_set import (
SetSheetStyleReq,
SetSheetStyleResp,
_gen_set_sheet_style_req,
)
from pylark.api_service_drive_sheet_style_batch_set import (
BatchSetSheetStyleReq,
BatchSetSheetStyleResp,
_gen_batch_set_sheet_style_req,
)
from pylark.api_service_drive_sheet_cell_merge import (
MergeSheetCellReq,
MergeSheetCellResp,
_gen_merge_sheet_cell_req,
)
from pylark.api_service_drive_sheet_cell_unmerge import (
UnmergeSheetCellReq,
UnmergeSheetCellResp,
_gen_unmerge_sheet_cell_req,
)
from pylark.api_service_drive_sheet_image_set import (
SetSheetValueImageReq,
SetSheetValueImageResp,
_gen_set_sheet_value_image_req,
)
from pylark.api_service_drive_sheet_find import (
FindSheetReq,
FindSheetResp,
_gen_find_sheet_req,
)
from pylark.api_service_drive_sheet_replace import (
ReplaceSheetReq,
ReplaceSheetResp,
_gen_replace_sheet_req,
)
from pylark.api_service_drive_sheet_condition_format_create import (
CreateSheetConditionFormatReq,
CreateSheetConditionFormatResp,
_gen_create_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_get import (
GetSheetConditionFormatReq,
GetSheetConditionFormatResp,
_gen_get_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_update import (
UpdateSheetConditionFormatReq,
UpdateSheetConditionFormatResp,
_gen_update_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_condition_format_delete import (
DeleteSheetConditionFormatReq,
DeleteSheetConditionFormatResp,
_gen_delete_sheet_condition_format_req,
)
from pylark.api_service_drive_sheet_protected_dimension_create import (
CreateSheetProtectedDimensionReq,
CreateSheetProtectedDimensionResp,
_gen_create_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_get import (
GetSheetProtectedDimensionReq,
GetSheetProtectedDimensionResp,
_gen_get_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_update import (
UpdateSheetProtectedDimensionReq,
UpdateSheetProtectedDimensionResp,
_gen_update_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_protected_dimension_delete import (
DeleteSheetProtectedDimensionReq,
DeleteSheetProtectedDimensionResp,
_gen_delete_sheet_protected_dimension_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_create import (
CreateSheetDataValidationDropdownReq,
CreateSheetDataValidationDropdownResp,
_gen_create_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_delete import (
DeleteSheetDataValidationDropdownReq,
DeleteSheetDataValidationDropdownResp,
_gen_delete_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_update import (
UpdateSheetDataValidationDropdownReq,
UpdateSheetDataValidationDropdownResp,
_gen_update_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_data_validation_dropdown_get import (
GetSheetDataValidationDropdownReq,
GetSheetDataValidationDropdownResp,
_gen_get_sheet_data_validation_dropdown_req,
)
from pylark.api_service_drive_sheet_filter_create import (
CreateSheetFilterReq,
CreateSheetFilterResp,
_gen_create_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_delete import (
DeleteSheetFilterReq,
DeleteSheetFilterResp,
_gen_delete_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_update import (
UpdateSheetFilterReq,
UpdateSheetFilterResp,
_gen_update_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_get import (
GetSheetFilterReq,
GetSheetFilterResp,
_gen_get_sheet_filter_req,
)
from pylark.api_service_drive_sheet_filter_view_create import (
CreateSheetFilterViewReq,
CreateSheetFilterViewResp,
_gen_create_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_delete import (
DeleteSheetFilterViewReq,
DeleteSheetFilterViewResp,
_gen_delete_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_update import (
UpdateSheetFilterViewReq,
UpdateSheetFilterViewResp,
_gen_update_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_get import (
GetSheetFilterViewReq,
GetSheetFilterViewResp,
_gen_get_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_query import (
QuerySheetFilterViewReq,
QuerySheetFilterViewResp,
_gen_query_sheet_filter_view_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_create import (
CreateSheetFilterViewConditionReq,
CreateSheetFilterViewConditionResp,
_gen_create_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_delete import (
DeleteSheetFilterViewConditionReq,
DeleteSheetFilterViewConditionResp,
_gen_delete_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_update import (
UpdateSheetFilterViewConditionReq,
UpdateSheetFilterViewConditionResp,
_gen_update_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_get import (
GetSheetFilterViewConditionReq,
GetSheetFilterViewConditionResp,
_gen_get_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_filter_view_condition_query import (
QuerySheetFilterViewConditionReq,
QuerySheetFilterViewConditionResp,
_gen_query_sheet_filter_view_condition_req,
)
from pylark.api_service_drive_sheet_float_image_create import (
CreateSheetFloatImageReq,
CreateSheetFloatImageResp,
_gen_create_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_delete import (
DeleteSheetFloatImageReq,
DeleteSheetFloatImageResp,
_gen_delete_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_update import (
UpdateSheetFloatImageReq,
UpdateSheetFloatImageResp,
_gen_update_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_get import (
GetSheetFloatImageReq,
GetSheetFloatImageResp,
_gen_get_sheet_float_image_req,
)
from pylark.api_service_drive_sheet_float_image_query import (
QuerySheetFloatImageReq,
QuerySheetFloatImageResp,
_gen_query_sheet_float_image_req,
)
from pylark.api_service_drive_wiki_space_create import (
CreateWikiSpaceReq,
CreateWikiSpaceResp,
_gen_create_wiki_space_req,
)
from pylark.api_service_drive_wiki_space_get_list import (
GetWikiSpaceListReq,
GetWikiSpaceListResp,
_gen_get_wiki_space_list_req,
)
from pylark.api_service_drive_wiki_space_get import (
GetWikiSpaceReq,
GetWikiSpaceResp,
_gen_get_wiki_space_req,
)
from pylark.api_service_drive_wiki_space_setting_update import (
UpdateWikiSpaceSettingReq,
UpdateWikiSpaceSettingResp,
_gen_update_wiki_space_setting_req,
)
from pylark.api_service_drive_wiki_space_member_add import (
AddWikiSpaceMemberReq,
AddWikiSpaceMemberResp,
_gen_add_wiki_space_member_req,
)
from pylark.api_service_drive_wiki_node_create import (
CreateWikiNodeReq,
CreateWikiNodeResp,
_gen_create_wiki_node_req,
)
from pylark.api_service_drive_wiki_node_list import (
GetWikiNodeListReq,
GetWikiNodeListResp,
_gen_get_wiki_node_list_req,
)
from pylark.api_service_drive_wiki_node_get import (
GetWikiNodeReq,
GetWikiNodeResp,
_gen_get_wiki_node_req,
)
from pylark.api_service_drive_wiki_move_docs_to_wiki import (
MoveDocsToWikiReq,
MoveDocsToWikiResp,
_gen_move_docs_to_wiki_req,
)
if typing.TYPE_CHECKING:
from lark import Lark
class LarkDriveService(object):
cli: "Lark"
def __init__(self, cli: "Lark"):
self.cli = cli
def search_drive_file(
self, request: SearchDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[SearchDriveFileResp, Response]:
return self.cli.raw_request(_gen_search_drive_file_req(request, options))
def get_drive_file_meta(
self, request: GetDriveFileMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFileMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_file_meta_req(request, options))
def create_drive_file(
self, request: CreateDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveFileResp, Response]:
return self.cli.raw_request(_gen_create_drive_file_req(request, options))
def copy_drive_file(
self, request: CopyDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[CopyDriveFileResp, Response]:
return self.cli.raw_request(_gen_copy_drive_file_req(request, options))
def delete_drive_file(
self, request: DeleteDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveFileResp, Response]:
return self.cli.raw_request(_gen_delete_drive_file_req(request, options))
def delete_drive_sheet_file(
self, request: DeleteDriveSheetFileReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveSheetFileResp, Response]:
return self.cli.raw_request(_gen_delete_drive_sheet_file_req(request, options))
def create_drive_folder(
self, request: CreateDriveFolderReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveFolderResp, Response]:
return self.cli.raw_request(_gen_create_drive_folder_req(request, options))
def get_drive_folder_meta(
self, request: GetDriveFolderMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFolderMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_folder_meta_req(request, options))
def get_drive_root_folder_meta(
self, request: GetDriveRootFolderMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveRootFolderMetaResp, Response]:
return self.cli.raw_request(
_gen_get_drive_root_folder_meta_req(request, options)
)
def get_drive_folder_children(
self, request: GetDriveFolderChildrenReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFolderChildrenResp, Response]:
return self.cli.raw_request(
_gen_get_drive_folder_children_req(request, options)
)
def get_drive_file_statistics(
self, request: GetDriveFileStatisticsReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveFileStatisticsResp, Response]:
return self.cli.raw_request(
_gen_get_drive_file_statistics_req(request, options)
)
def download_drive_file(
self, request: DownloadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[DownloadDriveFileResp, Response]:
return self.cli.raw_request(_gen_download_drive_file_req(request, options))
def upload_drive_file(
self, request: UploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[UploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_upload_drive_file_req(request, options))
def prepare_upload_drive_file(
self, request: PrepareUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[PrepareUploadDriveFileResp, Response]:
return self.cli.raw_request(
_gen_prepare_upload_drive_file_req(request, options)
)
def part_upload_drive_file(
self, request: PartUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[PartUploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_part_upload_drive_file_req(request, options))
def finish_upload_drive_file(
self, request: FinishUploadDriveFileReq, options: typing.List[str] = None
) -> typing.Tuple[FinishUploadDriveFileResp, Response]:
return self.cli.raw_request(_gen_finish_upload_drive_file_req(request, options))
def download_drive_media(
self, request: DownloadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[DownloadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_download_drive_media_req(request, options))
def upload_drive_media(
self, request: UploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[UploadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_upload_drive_media_req(request, options))
def prepare_upload_drive_media(
self, request: PrepareUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[PrepareUploadDriveMediaResp, Response]:
return self.cli.raw_request(
_gen_prepare_upload_drive_media_req(request, options)
)
def part_upload_drive_media(
self, request: PartUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[PartUploadDriveMediaResp, Response]:
return self.cli.raw_request(_gen_part_upload_drive_media_req(request, options))
def finish_upload_drive_media(
self, request: FinishUploadDriveMediaReq, options: typing.List[str] = None
) -> typing.Tuple[FinishUploadDriveMediaResp, Response]:
return self.cli.raw_request(
_gen_finish_upload_drive_media_req(request, options)
)
def create_drive_member_permission_old(
self,
request: CreateDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_create_drive_member_permission_old_req(request, options)
)
def transfer_drive_member_permission(
self,
request: TransferDriveMemberPermissionReq,
options: typing.List[str] = None,
) -> typing.Tuple[TransferDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_transfer_drive_member_permission_req(request, options)
)
def get_drive_member_permission_list(
self, request: GetDriveMemberPermissionListReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveMemberPermissionListResp, Response]:
return self.cli.raw_request(
_gen_get_drive_member_permission_list_req(request, options)
)
def create_drive_member_permission(
self, request: CreateDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_create_drive_member_permission_req(request, options)
)
def delete_drive_member_permission_old(
self,
request: DeleteDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_delete_drive_member_permission_old_req(request, options)
)
def delete_drive_member_permission(
self, request: DeleteDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_delete_drive_member_permission_req(request, options)
)
def update_drive_member_permission_old(
self,
request: UpdateDriveMemberPermissionOldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDriveMemberPermissionOldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_member_permission_old_req(request, options)
)
def update_drive_member_permission(
self, request: UpdateDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_update_drive_member_permission_req(request, options)
)
def check_drive_member_permission(
self, request: CheckDriveMemberPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[CheckDriveMemberPermissionResp, Response]:
return self.cli.raw_request(
_gen_check_drive_member_permission_req(request, options)
)
def update_drive_public_permission_v1_old(
self,
request: UpdateDrivePublicPermissionV1OldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDrivePublicPermissionV1OldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_v1_old_req(request, options)
)
def update_drive_public_permission_v2_old(
self,
request: UpdateDrivePublicPermissionV2OldReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateDrivePublicPermissionV2OldResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_v2_old_req(request, options)
)
def get_drive_public_permission_v2(
self, request: GetDrivePublicPermissionV2Req, options: typing.List[str] = None
) -> typing.Tuple[GetDrivePublicPermissionV2Resp, Response]:
return self.cli.raw_request(
_gen_get_drive_public_permission_v2_req(request, options)
)
def update_drive_public_permission(
self, request: UpdateDrivePublicPermissionReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDrivePublicPermissionResp, Response]:
return self.cli.raw_request(
_gen_update_drive_public_permission_req(request, options)
)
def batch_get_drive_media_tmp_download_url(
self,
request: BatchGetDriveMediaTmpDownloadURLReq,
options: typing.List[str] = None,
) -> typing.Tuple[BatchGetDriveMediaTmpDownloadURLResp, Response]:
return self.cli.raw_request(
_gen_batch_get_drive_media_tmp_download_url_req(request, options)
)
def get_drive_comment_list(
self, request: GetDriveCommentListReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveCommentListResp, Response]:
return self.cli.raw_request(_gen_get_drive_comment_list_req(request, options))
def get_drive_comment(
self, request: GetDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveCommentResp, Response]:
return self.cli.raw_request(_gen_get_drive_comment_req(request, options))
def create_drive_comment(
self, request: CreateDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveCommentResp, Response]:
return self.cli.raw_request(_gen_create_drive_comment_req(request, options))
def update_drive_comment(
self, request: UpdateDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveCommentResp, Response]:
return self.cli.raw_request(_gen_update_drive_comment_req(request, options))
def delete_drive_comment(
self, request: DeleteDriveCommentReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteDriveCommentResp, Response]:
return self.cli.raw_request(_gen_delete_drive_comment_req(request, options))
def update_drive_comment_patch(
self, request: UpdateDriveCommentPatchReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateDriveCommentPatchResp, Response]:
return self.cli.raw_request(
_gen_update_drive_comment_patch_req(request, options)
)
def create_drive_doc(
self, request: CreateDriveDocReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveDocResp, Response]:
return self.cli.raw_request(_gen_create_drive_doc_req(request, options))
def get_drive_doc_content(
self, request: GetDriveDocContentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocContentResp, Response]:
return self.cli.raw_request(_gen_get_drive_doc_content_req(request, options))
def get_drive_doc_raw_content(
self, request: GetDriveDocRawContentReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocRawContentResp, Response]:
return self.cli.raw_request(
_gen_get_drive_doc_raw_content_req(request, options)
)
def get_drive_doc_meta(
self, request: GetDriveDocMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveDocMetaResp, Response]:
return self.cli.raw_request(_gen_get_drive_doc_meta_req(request, options))
def create_sheet(
self, request: CreateSheetReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetResp, Response]:
return self.cli.raw_request(_gen_create_sheet_req(request, options))
def get_sheet_meta(
self, request: GetSheetMetaReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetMetaResp, Response]:
return self.cli.raw_request(_gen_get_sheet_meta_req(request, options))
def update_sheet_property(
self, request: UpdateSheetPropertyReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetPropertyResp, Response]:
return self.cli.raw_request(_gen_update_sheet_property_req(request, options))
def batch_update_sheet(
self, request: BatchUpdateSheetReq, options: typing.List[str] = None
) -> typing.Tuple[BatchUpdateSheetResp, Response]:
return self.cli.raw_request(_gen_batch_update_sheet_req(request, options))
def import_sheet(
self, request: ImportSheetReq, options: typing.List[str] = None
) -> typing.Tuple[ImportSheetResp, Response]:
return self.cli.raw_request(_gen_import_sheet_req(request, options))
def create_drive_import_task(
self, request: CreateDriveImportTaskReq, options: typing.List[str] = None
) -> typing.Tuple[CreateDriveImportTaskResp, Response]:
return self.cli.raw_request(_gen_create_drive_import_task_req(request, options))
def get_drive_import_task(
self, request: GetDriveImportTaskReq, options: typing.List[str] = None
) -> typing.Tuple[GetDriveImportTaskResp, Response]:
return self.cli.raw_request(_gen_get_drive_import_task_req(request, options))
def move_sheet_dimension(
self, request: MoveSheetDimensionReq, options: typing.List[str] = None
) -> typing.Tuple[MoveSheetDimensionResp, Response]:
return self.cli.raw_request(_gen_move_sheet_dimension_req(request, options))
def prepend_sheet_value(
self, request: PrependSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[PrependSheetValueResp, Response]:
return self.cli.raw_request(_gen_prepend_sheet_value_req(request, options))
def append_sheet_value(
self, request: AppendSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[AppendSheetValueResp, Response]:
return self.cli.raw_request(_gen_append_sheet_value_req(request, options))
def insert_sheet_dimension_range(
self, request: InsertSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[InsertSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_insert_sheet_dimension_range_req(request, options)
)
def add_sheet_dimension_range(
self, request: AddSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[AddSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_add_sheet_dimension_range_req(request, options)
)
def update_sheet_dimension_range(
self, request: UpdateSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_dimension_range_req(request, options)
)
def delete_sheet_dimension_range(
self, request: DeleteSheetDimensionRangeReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetDimensionRangeResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_dimension_range_req(request, options)
)
def get_sheet_value(
self, request: GetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetValueResp, Response]:
return self.cli.raw_request(_gen_get_sheet_value_req(request, options))
def batch_get_sheet_value(
self, request: BatchGetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[BatchGetSheetValueResp, Response]:
return self.cli.raw_request(_gen_batch_get_sheet_value_req(request, options))
def set_sheet_value(
self, request: SetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetValueResp, Response]:
return self.cli.raw_request(_gen_set_sheet_value_req(request, options))
def batch_set_sheet_value(
self, request: BatchSetSheetValueReq, options: typing.List[str] = None
) -> typing.Tuple[BatchSetSheetValueResp, Response]:
return self.cli.raw_request(_gen_batch_set_sheet_value_req(request, options))
def set_sheet_style(
self, request: SetSheetStyleReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetStyleResp, Response]:
return self.cli.raw_request(_gen_set_sheet_style_req(request, options))
def batch_set_sheet_style(
self, request: BatchSetSheetStyleReq, options: typing.List[str] = None
) -> typing.Tuple[BatchSetSheetStyleResp, Response]:
return self.cli.raw_request(_gen_batch_set_sheet_style_req(request, options))
def merge_sheet_cell(
self, request: MergeSheetCellReq, options: typing.List[str] = None
) -> typing.Tuple[MergeSheetCellResp, Response]:
return self.cli.raw_request(_gen_merge_sheet_cell_req(request, options))
def unmerge_sheet_cell(
self, request: UnmergeSheetCellReq, options: typing.List[str] = None
) -> typing.Tuple[UnmergeSheetCellResp, Response]:
return self.cli.raw_request(_gen_unmerge_sheet_cell_req(request, options))
def set_sheet_value_image(
self, request: SetSheetValueImageReq, options: typing.List[str] = None
) -> typing.Tuple[SetSheetValueImageResp, Response]:
return self.cli.raw_request(_gen_set_sheet_value_image_req(request, options))
def find_sheet(
self, request: FindSheetReq, options: typing.List[str] = None
) -> typing.Tuple[FindSheetResp, Response]:
return self.cli.raw_request(_gen_find_sheet_req(request, options))
def replace_sheet(
self, request: ReplaceSheetReq, options: typing.List[str] = None
) -> typing.Tuple[ReplaceSheetResp, Response]:
return self.cli.raw_request(_gen_replace_sheet_req(request, options))
def create_sheet_condition_format(
self, request: CreateSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_condition_format_req(request, options)
)
def get_sheet_condition_format(
self, request: GetSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_condition_format_req(request, options)
)
def update_sheet_condition_format(
self, request: UpdateSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_condition_format_req(request, options)
)
def delete_sheet_condition_format(
self, request: DeleteSheetConditionFormatReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetConditionFormatResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_condition_format_req(request, options)
)
def create_sheet_protected_dimension(
self,
request: CreateSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_protected_dimension_req(request, options)
)
def get_sheet_protected_dimension(
self, request: GetSheetProtectedDimensionReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_protected_dimension_req(request, options)
)
def update_sheet_protected_dimension(
self,
request: UpdateSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_protected_dimension_req(request, options)
)
def delete_sheet_protected_dimension(
self,
request: DeleteSheetProtectedDimensionReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetProtectedDimensionResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_protected_dimension_req(request, options)
)
def create_sheet_data_validation_dropdown(
self,
request: CreateSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_data_validation_dropdown_req(request, options)
)
def delete_sheet_data_validation_dropdown(
self,
request: DeleteSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_data_validation_dropdown_req(request, options)
)
def update_sheet_data_validation_dropdown(
self,
request: UpdateSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_data_validation_dropdown_req(request, options)
)
def get_sheet_data_validation_dropdown(
self,
request: GetSheetDataValidationDropdownReq,
options: typing.List[str] = None,
) -> typing.Tuple[GetSheetDataValidationDropdownResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_data_validation_dropdown_req(request, options)
)
def create_sheet_filter(
self, request: CreateSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFilterResp, Response]:
return self.cli.raw_request(_gen_create_sheet_filter_req(request, options))
def delete_sheet_filter(
self, request: DeleteSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFilterResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_filter_req(request, options))
def update_sheet_filter(
self, request: UpdateSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFilterResp, Response]:
return self.cli.raw_request(_gen_update_sheet_filter_req(request, options))
def get_sheet_filter(
self, request: GetSheetFilterReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterResp, Response]:
return self.cli.raw_request(_gen_get_sheet_filter_req(request, options))
def create_sheet_filter_view(
self, request: CreateSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_create_sheet_filter_view_req(request, options))
def delete_sheet_filter_view(
self, request: DeleteSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_filter_view_req(request, options))
def update_sheet_filter_view(
self, request: UpdateSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_update_sheet_filter_view_req(request, options))
def get_sheet_filter_view(
self, request: GetSheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_get_sheet_filter_view_req(request, options))
def query_sheet_filter_view(
self, request: QuerySheetFilterViewReq, options: typing.List[str] = None
) -> typing.Tuple[QuerySheetFilterViewResp, Response]:
return self.cli.raw_request(_gen_query_sheet_filter_view_req(request, options))
def create_sheet_filter_view_condition(
self,
request: CreateSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[CreateSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_create_sheet_filter_view_condition_req(request, options)
)
def delete_sheet_filter_view_condition(
self,
request: DeleteSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[DeleteSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_delete_sheet_filter_view_condition_req(request, options)
)
def update_sheet_filter_view_condition(
self,
request: UpdateSheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[UpdateSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_update_sheet_filter_view_condition_req(request, options)
)
def get_sheet_filter_view_condition(
self, request: GetSheetFilterViewConditionReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_get_sheet_filter_view_condition_req(request, options)
)
def query_sheet_filter_view_condition(
self,
request: QuerySheetFilterViewConditionReq,
options: typing.List[str] = None,
) -> typing.Tuple[QuerySheetFilterViewConditionResp, Response]:
return self.cli.raw_request(
_gen_query_sheet_filter_view_condition_req(request, options)
)
def create_sheet_float_image(
self, request: CreateSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[CreateSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_create_sheet_float_image_req(request, options))
def delete_sheet_float_image(
self, request: DeleteSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[DeleteSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_delete_sheet_float_image_req(request, options))
def update_sheet_float_image(
self, request: UpdateSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_update_sheet_float_image_req(request, options))
def get_sheet_float_image(
self, request: GetSheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[GetSheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_get_sheet_float_image_req(request, options))
def query_sheet_float_image(
self, request: QuerySheetFloatImageReq, options: typing.List[str] = None
) -> typing.Tuple[QuerySheetFloatImageResp, Response]:
return self.cli.raw_request(_gen_query_sheet_float_image_req(request, options))
def create_wiki_space(
self, request: CreateWikiSpaceReq, options: typing.List[str] = None
) -> typing.Tuple[CreateWikiSpaceResp, Response]:
return self.cli.raw_request(_gen_create_wiki_space_req(request, options))
def get_wiki_space_list(
self, request: GetWikiSpaceListReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiSpaceListResp, Response]:
return self.cli.raw_request(_gen_get_wiki_space_list_req(request, options))
def get_wiki_space(
self, request: GetWikiSpaceReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiSpaceResp, Response]:
return self.cli.raw_request(_gen_get_wiki_space_req(request, options))
def update_wiki_space_setting(
self, request: UpdateWikiSpaceSettingReq, options: typing.List[str] = None
) -> typing.Tuple[UpdateWikiSpaceSettingResp, Response]:
return self.cli.raw_request(
_gen_update_wiki_space_setting_req(request, options)
)
def add_wiki_space_member(
self, request: AddWikiSpaceMemberReq, options: typing.List[str] = None
) -> typing.Tuple[AddWikiSpaceMemberResp, Response]:
return self.cli.raw_request(_gen_add_wiki_space_member_req(request, options))
def create_wiki_node(
self, request: CreateWikiNodeReq, options: typing.List[str] = None
) -> typing.Tuple[CreateWikiNodeResp, Response]:
return self.cli.raw_request(_gen_create_wiki_node_req(request, options))
def get_wiki_node_list(
self, request: GetWikiNodeListReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiNodeListResp, Response]:
return self.cli.raw_request(_gen_get_wiki_node_list_req(request, options))
def get_wiki_node(
self, request: GetWikiNodeReq, options: typing.List[str] = None
) -> typing.Tuple[GetWikiNodeResp, Response]:
return self.cli.raw_request(_gen_get_wiki_node_req(request, options))
def move_docs_to_wiki(
self, request: MoveDocsToWikiReq, options: typing.List[str] = None
) -> typing.Tuple[MoveDocsToWikiResp, Response]:
return self.cli.raw_request(_gen_move_docs_to_wiki_req(request, options)) | 0.227469 | 0.030416 |
from itertools import zip_longest
import json
from plmbr.pipe import pipe
from plmbr.pipes import *
class validate(Pipe):
def __init__(self, *vals):
self.vals = vals
def pipe(self, items: Iterator) -> Iterator:
for expected, actual in zip_longest(self.vals, items):
print(f'expecting {expected} got {actual}')
assert actual == expected
yield actual
def test_null(): (
range(3)
- null()
> validate(0, 1, 2))
def test_json_loads():
items = [{'a': 2}, {'b': 4}]
(
(json.dumps(i) for i in items)
- json_loads()
> validate(*items))
def test_json_dumps():
items = [{'a': 2}, {'b': 4}]
(
items
- json_dumps()
> validate(*[json.dumps(i) for i in items]))
def test_batch():
(
range(3)
- batch(batch_size=2)
> validate([0, 1], [2]))
(
[0, 1, 2]
- batch(batch_size=2)
> validate([0, 1], [2]))
def test_unbatch(): (
[range(2), range(3)]
- unbatch()
> validate(0, 1, 0, 1, 2))
def test_to(): (
range(3)
- to(lambda i: i + 1)
> validate(1, 2, 3))
def test_keep(): (
range(3)
- keep(lambda i: i > 0)
> validate(1, 2))
def test_drop_fields(): (
({'a': i, 'b': i, 'c': i} for i in range(3))
- drop_fields('b', 'c')
> validate({'a': 0}, {'a': 1}, {'a': 2}))
def test_uniq(): (
({'a': 0, 'b': i // 2, 'c': i} for i in range(3))
- uniq('a', 'b')
> validate(
{'a': 0, 'b': 0, 'c': 0},
{'a': 0, 'b': 1, 'c': 2}))
def test_sample(): (
range(10)
- sample(prob=.5)
> validate(1, 4, 8, 9))
def test_window():
(
range(4)
- window(size=2)
> validate((0, 1), (1, 2), (2, 3)))
(
[0, 1, 2, 3]
- window(size=2)
> validate((0, 1), (1, 2), (2, 3)))
def test_append():
res = [8]
(
range(4)
> append(res)
)
assert res == [8, 0, 1, 2, 3]
def test_tee(): (
[1, 2, 3]
- tee(
keep(lambda i: i < 3)
- to(lambda i: i * 2),
to(lambda i: i * 10))
> validate(2, 4, 10, 20, 30))
def test_catch():
class bad_pipe(Pipe):
def pipe(self, items):
for i in items:
if i % 2:
self.throw(i)
else:
yield i
err = []
(
range(5)
- bad_pipe()
- validate(0, 2, 4)
> catch(lambda i: err.append(i))
)
assert err == [1, 3]
def test_lambda():
res = []
(
range(3)
- to(lambda x: x + 1)
- (lambda es: (e + 1 for e in es))
> (lambda es: [res.append(e) for e in es])
)
assert res == [2, 3, 4] | test/test_pipes.py | from itertools import zip_longest
import json
from plmbr.pipe import pipe
from plmbr.pipes import *
class validate(Pipe):
def __init__(self, *vals):
self.vals = vals
def pipe(self, items: Iterator) -> Iterator:
for expected, actual in zip_longest(self.vals, items):
print(f'expecting {expected} got {actual}')
assert actual == expected
yield actual
def test_null(): (
range(3)
- null()
> validate(0, 1, 2))
def test_json_loads():
items = [{'a': 2}, {'b': 4}]
(
(json.dumps(i) for i in items)
- json_loads()
> validate(*items))
def test_json_dumps():
items = [{'a': 2}, {'b': 4}]
(
items
- json_dumps()
> validate(*[json.dumps(i) for i in items]))
def test_batch():
(
range(3)
- batch(batch_size=2)
> validate([0, 1], [2]))
(
[0, 1, 2]
- batch(batch_size=2)
> validate([0, 1], [2]))
def test_unbatch(): (
[range(2), range(3)]
- unbatch()
> validate(0, 1, 0, 1, 2))
def test_to(): (
range(3)
- to(lambda i: i + 1)
> validate(1, 2, 3))
def test_keep(): (
range(3)
- keep(lambda i: i > 0)
> validate(1, 2))
def test_drop_fields(): (
({'a': i, 'b': i, 'c': i} for i in range(3))
- drop_fields('b', 'c')
> validate({'a': 0}, {'a': 1}, {'a': 2}))
def test_uniq(): (
({'a': 0, 'b': i // 2, 'c': i} for i in range(3))
- uniq('a', 'b')
> validate(
{'a': 0, 'b': 0, 'c': 0},
{'a': 0, 'b': 1, 'c': 2}))
def test_sample(): (
range(10)
- sample(prob=.5)
> validate(1, 4, 8, 9))
def test_window():
(
range(4)
- window(size=2)
> validate((0, 1), (1, 2), (2, 3)))
(
[0, 1, 2, 3]
- window(size=2)
> validate((0, 1), (1, 2), (2, 3)))
def test_append():
res = [8]
(
range(4)
> append(res)
)
assert res == [8, 0, 1, 2, 3]
def test_tee(): (
[1, 2, 3]
- tee(
keep(lambda i: i < 3)
- to(lambda i: i * 2),
to(lambda i: i * 10))
> validate(2, 4, 10, 20, 30))
def test_catch():
class bad_pipe(Pipe):
def pipe(self, items):
for i in items:
if i % 2:
self.throw(i)
else:
yield i
err = []
(
range(5)
- bad_pipe()
- validate(0, 2, 4)
> catch(lambda i: err.append(i))
)
assert err == [1, 3]
def test_lambda():
res = []
(
range(3)
- to(lambda x: x + 1)
- (lambda es: (e + 1 for e in es))
> (lambda es: [res.append(e) for e in es])
)
assert res == [2, 3, 4] | 0.518059 | 0.557424 |
import os
import tkinter as tk
import webbrowser
from tkinter import messagebox
from tkinter import ttk
from thonnycontrib.codelive.views.session_status.user_list import UserList, UserListItem
SESSION_DIA_MIN_SIZE = {"width": 378, "height": 400}
BUG_ICON_PATH = os.path.join(os.path.dirname(__file__), "res", "bug-16.png")
BUG_REPORT_URL = "https://github.com/codelive-project/codelive/issues/new"
class SessionInfo(ttk.LabelFrame):
def __init__(self, parent, session):
ttk.LabelFrame.__init__(self, parent, width=100, text="Session Info")
# labels
frame = ttk.Frame(self)
name_label = ttk.Label(frame, text="Your name: ")
topic_label = ttk.Label(frame, text="Topic: ")
broker_label = ttk.Label(frame, text="Broker: ")
driver_label = ttk.Label(frame, text="Driver: ")
# feilds
connection_info = session.get_connection_info()
self.session = session
self.driver_name = tk.StringVar()
_id, name = session.get_driver()
self.driver_name.set(name + " (You)" if self.session.user_id == _id else name)
name = ttk.Label(frame, text=session.username)
topic = ttk.Label(frame, text=connection_info["topic"])
broker = ttk.Label(frame, text=connection_info["broker"])
driver = ttk.Label(frame, textvariable=self.driver_name)
# position
name_label.grid(row=0, column=0, sticky=tk.E)
topic_label.grid(row=1, column=0, sticky=tk.E)
broker_label.grid(row=2, column=0, sticky=tk.E)
driver_label.grid(row=3, column=0, sticky=tk.E)
name.grid(row=0, column=1, sticky=tk.W)
topic.grid(row=1, column=1, sticky=tk.W)
broker.grid(row=2, column=1, sticky=tk.W)
driver.grid(row=3, column=1, sticky=tk.W)
frame.pack(side=tk.TOP, fill=tk.X, expand=True, anchor=tk.CENTER)
def update_driver(self, s=None):
if s != None:
self.driver_name.set(s)
else:
_id, name = self.session.get_driver()
self.driver_name.set(
name + " (You)" if self.session.user_id == _id else name
)
def update_driver_id(self, _id):
name = (
self.session.get_name(_id) + " (You)"
if self.session.user_id == _id
else self.session.get_name(_id)
)
self.driver_name.set(name)
class ActionList(ttk.Frame):
def __init__(self, parent, session, dia):
ttk.Frame.__init__(self, parent)
self.dia = dia
self.session = session
self.request_control = ttk.Button(
self, text="Request Control", command=self._request_callback
)
leave = ttk.Button(self, text="Leave Session", command=self._leave_callback)
self.end = ttk.Button(self, text="End Session", command=self._end_callback)
self.request_control.pack(
side=tk.LEFT, padx=(5, 0)
) # grid(row = 0, column = 0, columnspan = 2, pady = (5, 2), padx = 10, sticky = tk.N + tk.E + tk.S + tk.W)
self.end.pack(
side=tk.RIGHT, padx=(0, 0)
) # grid(row = 1, column = 1, pady = (2, 10), padx = (2, 10), sticky = tk.N + tk.E + tk.S + tk.W)
leave.pack(
side=tk.RIGHT, padx=(0, 5)
) # .grid(row = 1, column = 0, pady = (2, 10), padx = (10, 2), sticky = tk.N + tk.E + tk.S + tk.W)
self.request_control["state"] = tk.DISABLED if session.is_host else tk.NORMAL
self.end["state"] = tk.NORMAL if session.is_host else tk.DISABLED
# configure for resize
# self.columnconfigure(0, weight = 1, minsize = 50)
# self.columnconfigure(1, weight = 1, minsize = 50)
# self.rowconfigure(0, weight = 1, minsize = 10)
# self.rowconfigure(1, weight = 1, minsize = 10)
self.retry_attempt = 0
def driver(self, val=None):
if val == None:
return self.end["state"] == tk.NORMAL
self.request_control["state"] = tk.DISABLED if val else tk.NORMAL
self.end["state"] = tk.NORMAL if val else tk.DISABLED
def toggle_driver(self):
self.end["state"] = tk.DISABLED if self.end["state"] == tk.NORMAL else tk.NORMAL
self.request_control["state"] = (
tk.DISABLED if self.request_control["state"] == tk.NORMAL else tk.NORMAL
)
def _request_callback(self):
status = self.session.request_control()
if status == 0:
# Success
pass
elif status == 1:
# Rejected
self.retry_attempt += 1
ret = messagebox.askretrycancel(
"Request rejected",
"Your request was rejected. Do you want to request control again?",
)
if ret:
if self.retry_attempt >= 5:
messagebox.showerror(
"Unable to Join",
"You cannot request control at the moment. Please try again later.",
)
else:
self._request_callback()
elif status == 2:
# out
messagebox.showerror(
"Request timed-out",
"Your request has timed out. Please try again later.",
)
else:
# general error
messagebox.showerror("Error", "Unable to join. Please try again later.")
# reset retry attempts after last attempt
self.retry_attempt = 0
def _leave_callback(self):
ret = self.session.leave()
def _end_callback(self):
ret = self.session.end()
class SessionDialog(tk.Toplevel):
def __init__(self, parent, session):
tk.Toplevel.__init__(self)
self.title("Current Session - Beta")
frame = ttk.Frame(self)
self.session = session
self.session_info = SessionInfo(frame, session)
sep1 = ttk.Separator(frame, orient=tk.HORIZONTAL)
self.user_list = UserList(
frame, session, text="Active Users", borderwidth=1, width=1000
)
sep2 = ttk.Separator(frame, orient=tk.HORIZONTAL)
self.buttons = ActionList(frame, session, self)
self.session_info.grid(
row=0, column=0, sticky=tk.N + tk.E + tk.W, padx=10, pady=5
)
sep1.grid(row=1, column=0, sticky=tk.E + tk.W, padx=10)
self.user_list.grid(
row=2, column=0, sticky=tk.N + tk.E + tk.S + tk.W, padx=10, pady=(5, 5)
)
bug_frame = ttk.Frame(frame)
bug_icon = tk.PhotoImage(file=BUG_ICON_PATH)
bug = ttk.Button(
bug_frame,
text="Report Bug",
image=bug_icon,
compound=tk.LEFT,
command=lambda: webbrowser.open(BUG_REPORT_URL),
)
bug.image = bug_icon
bug.pack(side=tk.RIGHT)
bug_frame.grid(row=3, column=0, sticky=tk.E + tk.W, padx=10, pady=(0, 5))
sep2.grid(row=4, column=0, sticky=tk.E + tk.W, padx=10)
self.buttons.grid(
row=5, column=0, sticky=tk.S + tk.E + tk.W, padx=10, pady=(5, 10)
)
frame.pack(fill=tk.BOTH, expand=True)
self.protocol("WM_DELETE_WINDOW", self.on_closing)
self.minsize(SESSION_DIA_MIN_SIZE["width"], SESSION_DIA_MIN_SIZE["height"])
frame.columnconfigure(0, weight=1)
frame.rowconfigure(2, weight=1)
self._initial_place(parent)
def _initial_place(self, parent):
parent_dim, parent_x, parent_y = parent.geometry().split("+")
parent_w, parent_h = (int(l) for l in parent_dim.split("x"))
parent_x = int(parent_x)
parent_y = int(parent_y)
screen_width = parent.winfo_screenwidth()
screen_height = parent.winfo_screenheight()
w, h = SESSION_DIA_MIN_SIZE["width"], SESSION_DIA_MIN_SIZE["height"]
_x = _y = None
if screen_width < 10 + parent_x + parent_w + w:
_x = screen_width - (w + 10)
elif parent_x + parent_w < 0:
_x = 10
else:
_x = 10 + parent_x + parent_w
if screen_height < parent_y + h:
_y = screen_height - (h + 10)
elif parent_y < 0:
_y = 10
else:
_y = parent_y
self.geometry("%dx%d+%d+%d" % (w, h, _x, _y))
def update_host(self, _id=None):
self.session_info.update_driver_id(_id)
self.user_list.update_driver(_id)
self.buttons.driver(self.session.user_id == _id)
def add_user(self, user):
self.user_list.add_user(user)
def remove_id(self, rm_id, new_host=None):
self.user_list.remove_id(rm_id)
if new_host != None:
self.update_host(new_host)
def on_closing(self):
pass
if __name__ == "__main__":
import sys
import random
import string
colors = ["#75DBFF", "#50FF56", "#FF8D75", "#FF50AD", "#FF9B47"]
class DummyUser:
def __init__(self, _id, name=None, is_host=False):
self.name = (
name
if name != None
else str(_id)
+ " - John "
+ "".join(random.choice(string.ascii_uppercase) for i in range(10))
)
self.id = _id
self.position = "1.1"
self.color = random.choice(colors)
self.last_alive = 0
self.is_host = is_host
self.is_idle = False
self.cursor_colored = True
class DummySession:
def __init__(self, is_host=False):
self.user_id = 0
self._users = {i: DummyUser(i) for i in range(1, 10)}
self._users[0] = DummyUser(0, "Me", is_host)
self.username = "John Doe"
self.is_host = is_host
if self.is_host == False:
self._users[random.randint(1, 9)].is_host = True
def get_connection_info(self):
return {
"name": self.username,
"broker": "test_broker",
"topic": "test_topic",
}
def get_driver(self):
if self.is_host:
return 0, "You"
else:
for i in self._users:
if self._users[i].is_host == True:
return i, self._users[i].name
return -1, "null"
def get_users(self):
return self._users
def get_name(self, _id):
return self._users[_id].name
root = tk.Tk()
dummyUser = DummyUser(
random.randint(0, 9), len(sys.argv) > 2 and sys.argv[2] == "host"
)
dummySession = DummySession(len(sys.argv) > 2 and sys.argv[2] == "host")
if sys.argv[1] == "dialog":
frame = ttk.Frame(root)
r = SessionDialog(root, dummySession)
text = tk.Text(frame, width=10, height=1)
def make_host():
_id = int(text.get("0.0", tk.END).strip())
if r == None:
print("Start dialog first")
else:
r.update_host(_id)
button_mh = ttk.Button(frame, text="Make", command=make_host)
button_dest = ttk.Button(frame, text="Destroy", command=lambda: r.destroy())
text.grid(row=0, column=0, padx=(10, 2.5), pady=10)
button_mh.grid(row=0, column=1, padx=(2.5, 10), pady=(10, 0))
button_dest.grid(row=1, column=1, padx=(2.5, 10), pady=(0, 10))
frame.pack()
elif sys.argv[1] == "info":
frame = SessionInfo(root, dummySession)
frame.pack(padx=50, pady=50)
elif sys.argv[1] == "item":
frame = UserListItem(root, dummyUser)
frame.pack(fill=tk.BOTH, expand=True)
def t():
frame.toggle_driver()
button = ttk.Button(root, text="Hey", command=t)
button.pack()
elif sys.argv[1] == "list":
frame = UserList(root, dummySession)
frame.pack(fill=tk.BOTH, expand=True)
t_box = tk.Text(root)
t_box.pack(fill=tk.X, expand=True)
def add():
global frame
name = t_box.get("0.0", tk.END).strip()
if len(name) > 0:
usr = DummyUser(random.randint(100, 10000000), name)
frame.add(usr)
def remove():
global frame
try:
index = int(t_box.get("0.0", tk.END).strip())
frame.remove_id(index)
except Exception as e:
print(e)
ttk.Button(root, text="Add", command=add).pack(fill=tk.X, expand=True)
ttk.Button(root, text="Remove", command=remove).pack(fill=tk.X, expand=True)
elif sys.argv[1] == "action":
frame = ActionList(root, dummySession)
frame.pack(fill=tk.X, expand=True)
root.mainloop() | thonnycontrib/codelive/views/session_status/dialog.py | import os
import tkinter as tk
import webbrowser
from tkinter import messagebox
from tkinter import ttk
from thonnycontrib.codelive.views.session_status.user_list import UserList, UserListItem
SESSION_DIA_MIN_SIZE = {"width": 378, "height": 400}
BUG_ICON_PATH = os.path.join(os.path.dirname(__file__), "res", "bug-16.png")
BUG_REPORT_URL = "https://github.com/codelive-project/codelive/issues/new"
class SessionInfo(ttk.LabelFrame):
def __init__(self, parent, session):
ttk.LabelFrame.__init__(self, parent, width=100, text="Session Info")
# labels
frame = ttk.Frame(self)
name_label = ttk.Label(frame, text="Your name: ")
topic_label = ttk.Label(frame, text="Topic: ")
broker_label = ttk.Label(frame, text="Broker: ")
driver_label = ttk.Label(frame, text="Driver: ")
# feilds
connection_info = session.get_connection_info()
self.session = session
self.driver_name = tk.StringVar()
_id, name = session.get_driver()
self.driver_name.set(name + " (You)" if self.session.user_id == _id else name)
name = ttk.Label(frame, text=session.username)
topic = ttk.Label(frame, text=connection_info["topic"])
broker = ttk.Label(frame, text=connection_info["broker"])
driver = ttk.Label(frame, textvariable=self.driver_name)
# position
name_label.grid(row=0, column=0, sticky=tk.E)
topic_label.grid(row=1, column=0, sticky=tk.E)
broker_label.grid(row=2, column=0, sticky=tk.E)
driver_label.grid(row=3, column=0, sticky=tk.E)
name.grid(row=0, column=1, sticky=tk.W)
topic.grid(row=1, column=1, sticky=tk.W)
broker.grid(row=2, column=1, sticky=tk.W)
driver.grid(row=3, column=1, sticky=tk.W)
frame.pack(side=tk.TOP, fill=tk.X, expand=True, anchor=tk.CENTER)
def update_driver(self, s=None):
if s != None:
self.driver_name.set(s)
else:
_id, name = self.session.get_driver()
self.driver_name.set(
name + " (You)" if self.session.user_id == _id else name
)
def update_driver_id(self, _id):
name = (
self.session.get_name(_id) + " (You)"
if self.session.user_id == _id
else self.session.get_name(_id)
)
self.driver_name.set(name)
class ActionList(ttk.Frame):
def __init__(self, parent, session, dia):
ttk.Frame.__init__(self, parent)
self.dia = dia
self.session = session
self.request_control = ttk.Button(
self, text="Request Control", command=self._request_callback
)
leave = ttk.Button(self, text="Leave Session", command=self._leave_callback)
self.end = ttk.Button(self, text="End Session", command=self._end_callback)
self.request_control.pack(
side=tk.LEFT, padx=(5, 0)
) # grid(row = 0, column = 0, columnspan = 2, pady = (5, 2), padx = 10, sticky = tk.N + tk.E + tk.S + tk.W)
self.end.pack(
side=tk.RIGHT, padx=(0, 0)
) # grid(row = 1, column = 1, pady = (2, 10), padx = (2, 10), sticky = tk.N + tk.E + tk.S + tk.W)
leave.pack(
side=tk.RIGHT, padx=(0, 5)
) # .grid(row = 1, column = 0, pady = (2, 10), padx = (10, 2), sticky = tk.N + tk.E + tk.S + tk.W)
self.request_control["state"] = tk.DISABLED if session.is_host else tk.NORMAL
self.end["state"] = tk.NORMAL if session.is_host else tk.DISABLED
# configure for resize
# self.columnconfigure(0, weight = 1, minsize = 50)
# self.columnconfigure(1, weight = 1, minsize = 50)
# self.rowconfigure(0, weight = 1, minsize = 10)
# self.rowconfigure(1, weight = 1, minsize = 10)
self.retry_attempt = 0
def driver(self, val=None):
if val == None:
return self.end["state"] == tk.NORMAL
self.request_control["state"] = tk.DISABLED if val else tk.NORMAL
self.end["state"] = tk.NORMAL if val else tk.DISABLED
def toggle_driver(self):
self.end["state"] = tk.DISABLED if self.end["state"] == tk.NORMAL else tk.NORMAL
self.request_control["state"] = (
tk.DISABLED if self.request_control["state"] == tk.NORMAL else tk.NORMAL
)
def _request_callback(self):
status = self.session.request_control()
if status == 0:
# Success
pass
elif status == 1:
# Rejected
self.retry_attempt += 1
ret = messagebox.askretrycancel(
"Request rejected",
"Your request was rejected. Do you want to request control again?",
)
if ret:
if self.retry_attempt >= 5:
messagebox.showerror(
"Unable to Join",
"You cannot request control at the moment. Please try again later.",
)
else:
self._request_callback()
elif status == 2:
# out
messagebox.showerror(
"Request timed-out",
"Your request has timed out. Please try again later.",
)
else:
# general error
messagebox.showerror("Error", "Unable to join. Please try again later.")
# reset retry attempts after last attempt
self.retry_attempt = 0
def _leave_callback(self):
ret = self.session.leave()
def _end_callback(self):
ret = self.session.end()
class SessionDialog(tk.Toplevel):
def __init__(self, parent, session):
tk.Toplevel.__init__(self)
self.title("Current Session - Beta")
frame = ttk.Frame(self)
self.session = session
self.session_info = SessionInfo(frame, session)
sep1 = ttk.Separator(frame, orient=tk.HORIZONTAL)
self.user_list = UserList(
frame, session, text="Active Users", borderwidth=1, width=1000
)
sep2 = ttk.Separator(frame, orient=tk.HORIZONTAL)
self.buttons = ActionList(frame, session, self)
self.session_info.grid(
row=0, column=0, sticky=tk.N + tk.E + tk.W, padx=10, pady=5
)
sep1.grid(row=1, column=0, sticky=tk.E + tk.W, padx=10)
self.user_list.grid(
row=2, column=0, sticky=tk.N + tk.E + tk.S + tk.W, padx=10, pady=(5, 5)
)
bug_frame = ttk.Frame(frame)
bug_icon = tk.PhotoImage(file=BUG_ICON_PATH)
bug = ttk.Button(
bug_frame,
text="Report Bug",
image=bug_icon,
compound=tk.LEFT,
command=lambda: webbrowser.open(BUG_REPORT_URL),
)
bug.image = bug_icon
bug.pack(side=tk.RIGHT)
bug_frame.grid(row=3, column=0, sticky=tk.E + tk.W, padx=10, pady=(0, 5))
sep2.grid(row=4, column=0, sticky=tk.E + tk.W, padx=10)
self.buttons.grid(
row=5, column=0, sticky=tk.S + tk.E + tk.W, padx=10, pady=(5, 10)
)
frame.pack(fill=tk.BOTH, expand=True)
self.protocol("WM_DELETE_WINDOW", self.on_closing)
self.minsize(SESSION_DIA_MIN_SIZE["width"], SESSION_DIA_MIN_SIZE["height"])
frame.columnconfigure(0, weight=1)
frame.rowconfigure(2, weight=1)
self._initial_place(parent)
def _initial_place(self, parent):
parent_dim, parent_x, parent_y = parent.geometry().split("+")
parent_w, parent_h = (int(l) for l in parent_dim.split("x"))
parent_x = int(parent_x)
parent_y = int(parent_y)
screen_width = parent.winfo_screenwidth()
screen_height = parent.winfo_screenheight()
w, h = SESSION_DIA_MIN_SIZE["width"], SESSION_DIA_MIN_SIZE["height"]
_x = _y = None
if screen_width < 10 + parent_x + parent_w + w:
_x = screen_width - (w + 10)
elif parent_x + parent_w < 0:
_x = 10
else:
_x = 10 + parent_x + parent_w
if screen_height < parent_y + h:
_y = screen_height - (h + 10)
elif parent_y < 0:
_y = 10
else:
_y = parent_y
self.geometry("%dx%d+%d+%d" % (w, h, _x, _y))
def update_host(self, _id=None):
self.session_info.update_driver_id(_id)
self.user_list.update_driver(_id)
self.buttons.driver(self.session.user_id == _id)
def add_user(self, user):
self.user_list.add_user(user)
def remove_id(self, rm_id, new_host=None):
self.user_list.remove_id(rm_id)
if new_host != None:
self.update_host(new_host)
def on_closing(self):
pass
if __name__ == "__main__":
import sys
import random
import string
colors = ["#75DBFF", "#50FF56", "#FF8D75", "#FF50AD", "#FF9B47"]
class DummyUser:
def __init__(self, _id, name=None, is_host=False):
self.name = (
name
if name != None
else str(_id)
+ " - John "
+ "".join(random.choice(string.ascii_uppercase) for i in range(10))
)
self.id = _id
self.position = "1.1"
self.color = random.choice(colors)
self.last_alive = 0
self.is_host = is_host
self.is_idle = False
self.cursor_colored = True
class DummySession:
def __init__(self, is_host=False):
self.user_id = 0
self._users = {i: DummyUser(i) for i in range(1, 10)}
self._users[0] = DummyUser(0, "Me", is_host)
self.username = "John Doe"
self.is_host = is_host
if self.is_host == False:
self._users[random.randint(1, 9)].is_host = True
def get_connection_info(self):
return {
"name": self.username,
"broker": "test_broker",
"topic": "test_topic",
}
def get_driver(self):
if self.is_host:
return 0, "You"
else:
for i in self._users:
if self._users[i].is_host == True:
return i, self._users[i].name
return -1, "null"
def get_users(self):
return self._users
def get_name(self, _id):
return self._users[_id].name
root = tk.Tk()
dummyUser = DummyUser(
random.randint(0, 9), len(sys.argv) > 2 and sys.argv[2] == "host"
)
dummySession = DummySession(len(sys.argv) > 2 and sys.argv[2] == "host")
if sys.argv[1] == "dialog":
frame = ttk.Frame(root)
r = SessionDialog(root, dummySession)
text = tk.Text(frame, width=10, height=1)
def make_host():
_id = int(text.get("0.0", tk.END).strip())
if r == None:
print("Start dialog first")
else:
r.update_host(_id)
button_mh = ttk.Button(frame, text="Make", command=make_host)
button_dest = ttk.Button(frame, text="Destroy", command=lambda: r.destroy())
text.grid(row=0, column=0, padx=(10, 2.5), pady=10)
button_mh.grid(row=0, column=1, padx=(2.5, 10), pady=(10, 0))
button_dest.grid(row=1, column=1, padx=(2.5, 10), pady=(0, 10))
frame.pack()
elif sys.argv[1] == "info":
frame = SessionInfo(root, dummySession)
frame.pack(padx=50, pady=50)
elif sys.argv[1] == "item":
frame = UserListItem(root, dummyUser)
frame.pack(fill=tk.BOTH, expand=True)
def t():
frame.toggle_driver()
button = ttk.Button(root, text="Hey", command=t)
button.pack()
elif sys.argv[1] == "list":
frame = UserList(root, dummySession)
frame.pack(fill=tk.BOTH, expand=True)
t_box = tk.Text(root)
t_box.pack(fill=tk.X, expand=True)
def add():
global frame
name = t_box.get("0.0", tk.END).strip()
if len(name) > 0:
usr = DummyUser(random.randint(100, 10000000), name)
frame.add(usr)
def remove():
global frame
try:
index = int(t_box.get("0.0", tk.END).strip())
frame.remove_id(index)
except Exception as e:
print(e)
ttk.Button(root, text="Add", command=add).pack(fill=tk.X, expand=True)
ttk.Button(root, text="Remove", command=remove).pack(fill=tk.X, expand=True)
elif sys.argv[1] == "action":
frame = ActionList(root, dummySession)
frame.pack(fill=tk.X, expand=True)
root.mainloop() | 0.405213 | 0.101411 |
import optparse
import re
from pyang import plugin
from pyang import util
from pyang import grammar
def pyang_plugin_init():
plugin.register_plugin(StripPlugin())
class StripPlugin(plugin.PyangPlugin):
def add_output_format(self, fmts):
fmts['strip'] = self
self.handle_comments = True
def add_opts(self, optparser):
optlist = [
optparse.make_option("--strip-module", type=str,
dest="strip_module",
help="Colon-separated list of module names to strip out"),
optparse.make_option("--strip-yang-canonical",
dest="strip_yang_canonical",
action="store_true",
help="Print in canonical order"),
optparse.make_option("--strip-yang-remove-unused-imports",
dest="strip_yang_remove_unused_imports",
action="store_true"),
]
g = optparser.add_option_group("Strip output specific options")
g.add_options(optlist)
def emit(self, ctx, modules, fd):
module = modules[0]
ctx.opts.strip_module = ctx.opts.strip_module.split(':')
emit_yang(ctx, module, fd)
def emit_yang(ctx, module, fd):
emit_stmt(ctx, module, fd, 0, None, '', ' ')
_force_newline_arg = ('description', 'contact', 'organization')
_non_quote_arg_type = ('identifier', 'identifier-ref', 'boolean', 'integer',
'non-negative-integer', 'date', 'ordered-by-arg',
'fraction-digits-arg', 'deviate-arg', 'version',
'status-arg')
_kwd_class = {
'yang-version': 'header',
'namespace': 'header',
'prefix': 'header',
'belongs-to': 'header',
'organization': 'meta',
'contact': 'meta',
'description': 'meta',
'reference': 'meta',
'import': 'linkage',
'include': 'linkage',
'revision': 'revision',
'typedef': 'defs',
'grouping': 'defs',
'identity': 'defs',
'feature': 'defs',
'extension': 'defs',
'_comment': 'comment',
'module': None,
'submodule': None,
}
def get_kwd_class(keyword):
if util.is_prefixed(keyword):
return 'extension'
else:
try:
return _kwd_class[keyword]
except KeyError:
return 'body'
_keyword_with_trailing_newline = (
'typedef',
'grouping',
'identity',
'feature',
'extension',
)
def emit_stmt(ctx, stmt, fd, level, prev_kwd_class, indent, indentstep):
if ctx.opts.strip_module and stmt.keyword == 'import' and stmt.arg in ctx.opts.strip_module:
return
if isinstance(stmt.keyword, tuple):
kw_module, _ = stmt.keyword
if kw_module in ctx.opts.strip_module:
return
if ctx.opts.strip_yang_remove_unused_imports and stmt.keyword == 'import':
for p in stmt.parent.i_unused_prefixes:
if stmt.parent.i_unused_prefixes[p] == stmt:
return
if util.is_prefixed(stmt.raw_keyword):
(prefix, identifier) = stmt.raw_keyword
keyword = prefix + ':' + identifier
else:
keyword = stmt.keyword
kwd_class = get_kwd_class(stmt.keyword)
if ((level == 1 and
kwd_class != prev_kwd_class and kwd_class != 'extension') or
stmt.keyword in _keyword_with_trailing_newline):
fd.write('\n')
if keyword == '_comment':
emit_comment(stmt.arg, fd, indent)
return
fd.write(indent + keyword)
if stmt.arg != None:
if keyword in grammar.stmt_map:
(arg_type, _subspec) = grammar.stmt_map[keyword]
if arg_type in _non_quote_arg_type:
fd.write(' ' + stmt.arg)
else:
emit_arg(stmt, fd, indent, indentstep)
else:
emit_arg(stmt, fd, indent, indentstep)
if len(stmt.substmts) == 0:
fd.write(';\n')
else:
fd.write(' {\n')
if ctx.opts.strip_yang_canonical:
substmts = grammar.sort_canonical(stmt.keyword, stmt.substmts)
else:
substmts = stmt.substmts
if level == 0:
kwd_class = 'header'
for s in substmts:
emit_stmt(ctx, s, fd, level + 1, kwd_class,
indent + indentstep, indentstep)
kwd_class = get_kwd_class(s.keyword)
fd.write(indent + '}\n')
def emit_arg(stmt, fd, indent, indentstep):
"""Heuristically pretty print the argument string"""
# current alg. always print a double quoted string
arg = stmt.arg
arg = arg.replace('\\', r'\\')
arg = arg.replace('"', r'\"')
arg = arg.replace('\t', r'\t')
lines = arg.splitlines(True)
if len(lines) <= 1:
if len(arg) > 0 and arg[-1] == '\n':
arg = arg[:-1] + r'\n'
if stmt.keyword in _force_newline_arg:
fd.write('\n' + indent + indentstep + '"' + arg + '"')
else:
fd.write(' "' + arg + '"')
else:
fd.write('\n')
fd.write(indent + indentstep + '"' + lines[0])
for line in lines[1:-1]:
fd.write(indent + indentstep + ' ' + line)
# write last line
fd.write(indent + indentstep + ' ' + lines[-1])
if lines[-1][-1] == '\n':
# last line ends with a newline, indent the ending quote
fd.write(indent + indentstep + '"')
else:
fd.write('"')
def emit_comment(comment, fd, indent):
lines = comment.splitlines(True)
for x in lines:
if x[0] == '*':
fd.write(indent + ' ' + x)
else:
fd.write(indent + x)
fd.write('\n') | plugins/strip.py |
import optparse
import re
from pyang import plugin
from pyang import util
from pyang import grammar
def pyang_plugin_init():
plugin.register_plugin(StripPlugin())
class StripPlugin(plugin.PyangPlugin):
def add_output_format(self, fmts):
fmts['strip'] = self
self.handle_comments = True
def add_opts(self, optparser):
optlist = [
optparse.make_option("--strip-module", type=str,
dest="strip_module",
help="Colon-separated list of module names to strip out"),
optparse.make_option("--strip-yang-canonical",
dest="strip_yang_canonical",
action="store_true",
help="Print in canonical order"),
optparse.make_option("--strip-yang-remove-unused-imports",
dest="strip_yang_remove_unused_imports",
action="store_true"),
]
g = optparser.add_option_group("Strip output specific options")
g.add_options(optlist)
def emit(self, ctx, modules, fd):
module = modules[0]
ctx.opts.strip_module = ctx.opts.strip_module.split(':')
emit_yang(ctx, module, fd)
def emit_yang(ctx, module, fd):
emit_stmt(ctx, module, fd, 0, None, '', ' ')
_force_newline_arg = ('description', 'contact', 'organization')
_non_quote_arg_type = ('identifier', 'identifier-ref', 'boolean', 'integer',
'non-negative-integer', 'date', 'ordered-by-arg',
'fraction-digits-arg', 'deviate-arg', 'version',
'status-arg')
_kwd_class = {
'yang-version': 'header',
'namespace': 'header',
'prefix': 'header',
'belongs-to': 'header',
'organization': 'meta',
'contact': 'meta',
'description': 'meta',
'reference': 'meta',
'import': 'linkage',
'include': 'linkage',
'revision': 'revision',
'typedef': 'defs',
'grouping': 'defs',
'identity': 'defs',
'feature': 'defs',
'extension': 'defs',
'_comment': 'comment',
'module': None,
'submodule': None,
}
def get_kwd_class(keyword):
if util.is_prefixed(keyword):
return 'extension'
else:
try:
return _kwd_class[keyword]
except KeyError:
return 'body'
_keyword_with_trailing_newline = (
'typedef',
'grouping',
'identity',
'feature',
'extension',
)
def emit_stmt(ctx, stmt, fd, level, prev_kwd_class, indent, indentstep):
if ctx.opts.strip_module and stmt.keyword == 'import' and stmt.arg in ctx.opts.strip_module:
return
if isinstance(stmt.keyword, tuple):
kw_module, _ = stmt.keyword
if kw_module in ctx.opts.strip_module:
return
if ctx.opts.strip_yang_remove_unused_imports and stmt.keyword == 'import':
for p in stmt.parent.i_unused_prefixes:
if stmt.parent.i_unused_prefixes[p] == stmt:
return
if util.is_prefixed(stmt.raw_keyword):
(prefix, identifier) = stmt.raw_keyword
keyword = prefix + ':' + identifier
else:
keyword = stmt.keyword
kwd_class = get_kwd_class(stmt.keyword)
if ((level == 1 and
kwd_class != prev_kwd_class and kwd_class != 'extension') or
stmt.keyword in _keyword_with_trailing_newline):
fd.write('\n')
if keyword == '_comment':
emit_comment(stmt.arg, fd, indent)
return
fd.write(indent + keyword)
if stmt.arg != None:
if keyword in grammar.stmt_map:
(arg_type, _subspec) = grammar.stmt_map[keyword]
if arg_type in _non_quote_arg_type:
fd.write(' ' + stmt.arg)
else:
emit_arg(stmt, fd, indent, indentstep)
else:
emit_arg(stmt, fd, indent, indentstep)
if len(stmt.substmts) == 0:
fd.write(';\n')
else:
fd.write(' {\n')
if ctx.opts.strip_yang_canonical:
substmts = grammar.sort_canonical(stmt.keyword, stmt.substmts)
else:
substmts = stmt.substmts
if level == 0:
kwd_class = 'header'
for s in substmts:
emit_stmt(ctx, s, fd, level + 1, kwd_class,
indent + indentstep, indentstep)
kwd_class = get_kwd_class(s.keyword)
fd.write(indent + '}\n')
def emit_arg(stmt, fd, indent, indentstep):
"""Heuristically pretty print the argument string"""
# current alg. always print a double quoted string
arg = stmt.arg
arg = arg.replace('\\', r'\\')
arg = arg.replace('"', r'\"')
arg = arg.replace('\t', r'\t')
lines = arg.splitlines(True)
if len(lines) <= 1:
if len(arg) > 0 and arg[-1] == '\n':
arg = arg[:-1] + r'\n'
if stmt.keyword in _force_newline_arg:
fd.write('\n' + indent + indentstep + '"' + arg + '"')
else:
fd.write(' "' + arg + '"')
else:
fd.write('\n')
fd.write(indent + indentstep + '"' + lines[0])
for line in lines[1:-1]:
fd.write(indent + indentstep + ' ' + line)
# write last line
fd.write(indent + indentstep + ' ' + lines[-1])
if lines[-1][-1] == '\n':
# last line ends with a newline, indent the ending quote
fd.write(indent + indentstep + '"')
else:
fd.write('"')
def emit_comment(comment, fd, indent):
lines = comment.splitlines(True)
for x in lines:
if x[0] == '*':
fd.write(indent + ' ' + x)
else:
fd.write(indent + x)
fd.write('\n') | 0.284477 | 0.090695 |
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.MerchantBaseEnterOpenModel import MerchantBaseEnterOpenModel
from alipay.aop.api.domain.SubMerchantCommonEnterOpenModel import SubMerchantCommonEnterOpenModel
from alipay.aop.api.domain.SubMerchantEnterOpenModel import SubMerchantEnterOpenModel
class AlipayEbppInvoiceMerchantlistEnterApplyModel(object):
def __init__(self):
self._merchant_base = None
self._sub_merchant_common_info = None
self._sub_merchant_list = None
@property
def merchant_base(self):
return self._merchant_base
@merchant_base.setter
def merchant_base(self, value):
if isinstance(value, MerchantBaseEnterOpenModel):
self._merchant_base = value
else:
self._merchant_base = MerchantBaseEnterOpenModel.from_alipay_dict(value)
@property
def sub_merchant_common_info(self):
return self._sub_merchant_common_info
@sub_merchant_common_info.setter
def sub_merchant_common_info(self, value):
if isinstance(value, SubMerchantCommonEnterOpenModel):
self._sub_merchant_common_info = value
else:
self._sub_merchant_common_info = SubMerchantCommonEnterOpenModel.from_alipay_dict(value)
@property
def sub_merchant_list(self):
return self._sub_merchant_list
@sub_merchant_list.setter
def sub_merchant_list(self, value):
if isinstance(value, list):
self._sub_merchant_list = list()
for i in value:
if isinstance(i, SubMerchantEnterOpenModel):
self._sub_merchant_list.append(i)
else:
self._sub_merchant_list.append(SubMerchantEnterOpenModel.from_alipay_dict(i))
def to_alipay_dict(self):
params = dict()
if self.merchant_base:
if hasattr(self.merchant_base, 'to_alipay_dict'):
params['merchant_base'] = self.merchant_base.to_alipay_dict()
else:
params['merchant_base'] = self.merchant_base
if self.sub_merchant_common_info:
if hasattr(self.sub_merchant_common_info, 'to_alipay_dict'):
params['sub_merchant_common_info'] = self.sub_merchant_common_info.to_alipay_dict()
else:
params['sub_merchant_common_info'] = self.sub_merchant_common_info
if self.sub_merchant_list:
if isinstance(self.sub_merchant_list, list):
for i in range(0, len(self.sub_merchant_list)):
element = self.sub_merchant_list[i]
if hasattr(element, 'to_alipay_dict'):
self.sub_merchant_list[i] = element.to_alipay_dict()
if hasattr(self.sub_merchant_list, 'to_alipay_dict'):
params['sub_merchant_list'] = self.sub_merchant_list.to_alipay_dict()
else:
params['sub_merchant_list'] = self.sub_merchant_list
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayEbppInvoiceMerchantlistEnterApplyModel()
if 'merchant_base' in d:
o.merchant_base = d['merchant_base']
if 'sub_merchant_common_info' in d:
o.sub_merchant_common_info = d['sub_merchant_common_info']
if 'sub_merchant_list' in d:
o.sub_merchant_list = d['sub_merchant_list']
return o | alipay/aop/api/domain/AlipayEbppInvoiceMerchantlistEnterApplyModel.py | import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.MerchantBaseEnterOpenModel import MerchantBaseEnterOpenModel
from alipay.aop.api.domain.SubMerchantCommonEnterOpenModel import SubMerchantCommonEnterOpenModel
from alipay.aop.api.domain.SubMerchantEnterOpenModel import SubMerchantEnterOpenModel
class AlipayEbppInvoiceMerchantlistEnterApplyModel(object):
def __init__(self):
self._merchant_base = None
self._sub_merchant_common_info = None
self._sub_merchant_list = None
@property
def merchant_base(self):
return self._merchant_base
@merchant_base.setter
def merchant_base(self, value):
if isinstance(value, MerchantBaseEnterOpenModel):
self._merchant_base = value
else:
self._merchant_base = MerchantBaseEnterOpenModel.from_alipay_dict(value)
@property
def sub_merchant_common_info(self):
return self._sub_merchant_common_info
@sub_merchant_common_info.setter
def sub_merchant_common_info(self, value):
if isinstance(value, SubMerchantCommonEnterOpenModel):
self._sub_merchant_common_info = value
else:
self._sub_merchant_common_info = SubMerchantCommonEnterOpenModel.from_alipay_dict(value)
@property
def sub_merchant_list(self):
return self._sub_merchant_list
@sub_merchant_list.setter
def sub_merchant_list(self, value):
if isinstance(value, list):
self._sub_merchant_list = list()
for i in value:
if isinstance(i, SubMerchantEnterOpenModel):
self._sub_merchant_list.append(i)
else:
self._sub_merchant_list.append(SubMerchantEnterOpenModel.from_alipay_dict(i))
def to_alipay_dict(self):
params = dict()
if self.merchant_base:
if hasattr(self.merchant_base, 'to_alipay_dict'):
params['merchant_base'] = self.merchant_base.to_alipay_dict()
else:
params['merchant_base'] = self.merchant_base
if self.sub_merchant_common_info:
if hasattr(self.sub_merchant_common_info, 'to_alipay_dict'):
params['sub_merchant_common_info'] = self.sub_merchant_common_info.to_alipay_dict()
else:
params['sub_merchant_common_info'] = self.sub_merchant_common_info
if self.sub_merchant_list:
if isinstance(self.sub_merchant_list, list):
for i in range(0, len(self.sub_merchant_list)):
element = self.sub_merchant_list[i]
if hasattr(element, 'to_alipay_dict'):
self.sub_merchant_list[i] = element.to_alipay_dict()
if hasattr(self.sub_merchant_list, 'to_alipay_dict'):
params['sub_merchant_list'] = self.sub_merchant_list.to_alipay_dict()
else:
params['sub_merchant_list'] = self.sub_merchant_list
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayEbppInvoiceMerchantlistEnterApplyModel()
if 'merchant_base' in d:
o.merchant_base = d['merchant_base']
if 'sub_merchant_common_info' in d:
o.sub_merchant_common_info = d['sub_merchant_common_info']
if 'sub_merchant_list' in d:
o.sub_merchant_list = d['sub_merchant_list']
return o | 0.378115 | 0.040922 |
import os
import sys
import time
from argparse import ArgumentParser
import math
import numpy as np
import time
import torch
from torch.optim.lr_scheduler import MultiStepLR
import torch.utils.data.distributed
from src.model import model, Loss
from src.utils import dboxes300_coco, Encoder
from src.evaluate import evaluate
from src.train import train_loop, tencent_trick
from src.data import *
# Apex imports
try:
from apex.parallel.LARC import LARC
from apex import amp
from apex.fp16_utils import *
except ImportError:
raise ImportError("Please install APEX from https://github.com/nvidia/apex")
class Logger:
def __init__(self, batch_size, local_rank, n_gpu, print_freq=20):
self.batch_size = batch_size
self.local_rank = local_rank
self.n_gpu = n_gpu
self.print_freq = print_freq
self.processed_samples = 0
self.epochs_times = []
self.epochs_speeds = []
def update_iter(self, epoch, iteration, loss):
if self.local_rank != 0:
return
if iteration % self.print_freq == 0:
print('Epoch: {:2d}, Iteration: {}, Loss: {}'.format(epoch, iteration, loss))
self.processed_samples = self.processed_samples + self.batch_size
def start_epoch(self):
self.epoch_start = time.time()
def end_epoch(self):
epoch_time = time.time() - self.epoch_start
epoch_speed = self.processed_samples / epoch_time
self.epochs_times.append(epoch_time)
self.epochs_speeds.append(epoch_speed)
self.processed_samples = 0
if self.local_rank == 0:
print('Epoch {:2d} finished. Time: {:4f} s, Speed: {:4f} img/sec, Average speed: {:4f}'
.format(len(self.epochs_times)-1, epoch_time, epoch_speed * self.n_gpu, self.average_speed() * self.n_gpu))
def average_speed(self):
return sum(self.epochs_speeds) / len(self.epochs_speeds)
def make_parser():
parser = ArgumentParser(
description="Train Single Shot MultiBox Detector on COCO")
parser.add_argument(
'--data', '-d', type=str, default='/coco', required=True,
help='path to test and training data files')
parser.add_argument(
'--epochs', '-e', type=int, default=65,
help='number of epochs for training')
parser.add_argument(
'--batch-size', '--bs', type=int, default=32,
help='number of examples for each iteration')
parser.add_argument(
'--eval-batch-size', '--ebs', type=int, default=32,
help='number of examples for each evaluation iteration')
parser.add_argument(
'--seed', '-s', type=int, default=0,
help='manually set random seed for torch')
parser.add_argument(
'--evaluation', nargs='*', type=int,
default=[3, 21, 31, 37, 42, 48, 53, 59, 64],
help='epochs at which to evaluate')
parser.add_argument(
'--multistep', nargs='*', type=int, default=[43, 54],
help='epochs at which to decay learning rate')
parser.add_argument(
'--target', type=float, default=None,
help='target mAP to assert against at the end')
# Hyperparameters
parser.add_argument(
'--learning-rate', '--lr', type=float, default=2.6e-3, help='learning rate')
parser.add_argument(
'--momentum', '-m', type=float, default=0.9,
help='momentum argument for SGD optimizer')
parser.add_argument(
'--weight-decay', '--wd', type=float, default=0.0005,
help='momentum argument for SGD optimizer')
parser.add_argument('--warmup', type=int, default=None)
parser.add_argument(
'--backbone', type=str, default='resnet50',
choices=['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'])
parser.add_argument('--num-workers', type=int, default=4)
parser.add_argument('--fp16-mode', type=str, default='static', choices=['off', 'static', 'amp'],
help='Half precission mode to use')
# Distributed
parser.add_argument('--local_rank', default=0, type=int,
help='Used for multi-process training. Can either be manually set ' +
'or automatically set by using \'python -m multiproc\'.')
# Pipeline control
parser.add_argument(
'--data_pipeline', type=str, default='dali', choices=['dali', 'no_dali'],
help='data preprocessing pipline to use')
return parser
def train(args):
if args.amp:
amp_handle = amp.init(enabled=args.fp16)
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
args.N_gpu = torch.distributed.get_world_size()
else:
args.N_gpu = 1
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
cocoGt = get_coco_ground_truth(args)
ssd300 = model(args)
args.learning_rate = args.learning_rate * args.N_gpu * (args.batch_size / 32)
iteration = 0
loss_func = Loss(dboxes)
loss_func.cuda()
optimizer = torch.optim.SGD(
tencent_trick(ssd300),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = MultiStepLR(
optimizer=optimizer,
milestones=args.multistep,
gamma=0.1)
if args.fp16:
if args.amp:
optimizer = amp_handle.wrap_optimizer(optimizer)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=128.)
val_dataloader, inv_map = get_val_dataloader(args)
train_loader = get_train_loader(args, dboxes)
acc = 0
logger = Logger(args.batch_size, args.local_rank, args.N_gpu)
for epoch in range(0, args.epochs):
logger.start_epoch()
scheduler.step()
iteration = train_loop(
ssd300, loss_func, epoch, optimizer,
train_loader, iteration, logger, args)
logger.end_epoch()
if epoch in args.evaluation:
acc = evaluate(ssd300, val_dataloader, cocoGt, encoder, inv_map, args)
if args.local_rank == 0:
print('Epoch {:2d}, Accuracy: {:4f} mAP'.format(epoch, acc))
if args.data_pipeline == 'dali':
train_loader.reset()
return acc, logger.average_speed()
if __name__ == "__main__":
parser = make_parser()
args = parser.parse_args()
if args.local_rank == 0:
os.makedirs('./models', exist_ok=True)
torch.backends.cudnn.benchmark = True
if args.fp16_mode != 'off':
args.fp16 = True
args.amp = (args.fp16_mode == 'amp')
else:
args.fp16 = False
args.amp = False
start_time = time.time()
acc, avg_speed = train(args)
# avg_speed is reported per node, adjust for the global speed
try:
num_shards = torch.distributed.get_world_size()
except RuntimeError:
num_shards = 1
avg_speed = num_shards * avg_speed
training_time = time.time() - start_time
if args.local_rank == 0:
print("Training end: Average speed: {:3f} img/sec, Total time: {:3f} sec, Final accuracy: {:3f} mAP"
.format(avg_speed, training_time, acc))
if args.target is not None:
if args.target > acc:
print('Target mAP of {} not met. Possible regression'.format(args.target))
sys.exit(1) | docs/examples/use_cases/pytorch/single_stage_detector/main.py | import os
import sys
import time
from argparse import ArgumentParser
import math
import numpy as np
import time
import torch
from torch.optim.lr_scheduler import MultiStepLR
import torch.utils.data.distributed
from src.model import model, Loss
from src.utils import dboxes300_coco, Encoder
from src.evaluate import evaluate
from src.train import train_loop, tencent_trick
from src.data import *
# Apex imports
try:
from apex.parallel.LARC import LARC
from apex import amp
from apex.fp16_utils import *
except ImportError:
raise ImportError("Please install APEX from https://github.com/nvidia/apex")
class Logger:
def __init__(self, batch_size, local_rank, n_gpu, print_freq=20):
self.batch_size = batch_size
self.local_rank = local_rank
self.n_gpu = n_gpu
self.print_freq = print_freq
self.processed_samples = 0
self.epochs_times = []
self.epochs_speeds = []
def update_iter(self, epoch, iteration, loss):
if self.local_rank != 0:
return
if iteration % self.print_freq == 0:
print('Epoch: {:2d}, Iteration: {}, Loss: {}'.format(epoch, iteration, loss))
self.processed_samples = self.processed_samples + self.batch_size
def start_epoch(self):
self.epoch_start = time.time()
def end_epoch(self):
epoch_time = time.time() - self.epoch_start
epoch_speed = self.processed_samples / epoch_time
self.epochs_times.append(epoch_time)
self.epochs_speeds.append(epoch_speed)
self.processed_samples = 0
if self.local_rank == 0:
print('Epoch {:2d} finished. Time: {:4f} s, Speed: {:4f} img/sec, Average speed: {:4f}'
.format(len(self.epochs_times)-1, epoch_time, epoch_speed * self.n_gpu, self.average_speed() * self.n_gpu))
def average_speed(self):
return sum(self.epochs_speeds) / len(self.epochs_speeds)
def make_parser():
parser = ArgumentParser(
description="Train Single Shot MultiBox Detector on COCO")
parser.add_argument(
'--data', '-d', type=str, default='/coco', required=True,
help='path to test and training data files')
parser.add_argument(
'--epochs', '-e', type=int, default=65,
help='number of epochs for training')
parser.add_argument(
'--batch-size', '--bs', type=int, default=32,
help='number of examples for each iteration')
parser.add_argument(
'--eval-batch-size', '--ebs', type=int, default=32,
help='number of examples for each evaluation iteration')
parser.add_argument(
'--seed', '-s', type=int, default=0,
help='manually set random seed for torch')
parser.add_argument(
'--evaluation', nargs='*', type=int,
default=[3, 21, 31, 37, 42, 48, 53, 59, 64],
help='epochs at which to evaluate')
parser.add_argument(
'--multistep', nargs='*', type=int, default=[43, 54],
help='epochs at which to decay learning rate')
parser.add_argument(
'--target', type=float, default=None,
help='target mAP to assert against at the end')
# Hyperparameters
parser.add_argument(
'--learning-rate', '--lr', type=float, default=2.6e-3, help='learning rate')
parser.add_argument(
'--momentum', '-m', type=float, default=0.9,
help='momentum argument for SGD optimizer')
parser.add_argument(
'--weight-decay', '--wd', type=float, default=0.0005,
help='momentum argument for SGD optimizer')
parser.add_argument('--warmup', type=int, default=None)
parser.add_argument(
'--backbone', type=str, default='resnet50',
choices=['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'])
parser.add_argument('--num-workers', type=int, default=4)
parser.add_argument('--fp16-mode', type=str, default='static', choices=['off', 'static', 'amp'],
help='Half precission mode to use')
# Distributed
parser.add_argument('--local_rank', default=0, type=int,
help='Used for multi-process training. Can either be manually set ' +
'or automatically set by using \'python -m multiproc\'.')
# Pipeline control
parser.add_argument(
'--data_pipeline', type=str, default='dali', choices=['dali', 'no_dali'],
help='data preprocessing pipline to use')
return parser
def train(args):
if args.amp:
amp_handle = amp.init(enabled=args.fp16)
args.distributed = False
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
args.N_gpu = torch.distributed.get_world_size()
else:
args.N_gpu = 1
dboxes = dboxes300_coco()
encoder = Encoder(dboxes)
cocoGt = get_coco_ground_truth(args)
ssd300 = model(args)
args.learning_rate = args.learning_rate * args.N_gpu * (args.batch_size / 32)
iteration = 0
loss_func = Loss(dboxes)
loss_func.cuda()
optimizer = torch.optim.SGD(
tencent_trick(ssd300),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = MultiStepLR(
optimizer=optimizer,
milestones=args.multistep,
gamma=0.1)
if args.fp16:
if args.amp:
optimizer = amp_handle.wrap_optimizer(optimizer)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=128.)
val_dataloader, inv_map = get_val_dataloader(args)
train_loader = get_train_loader(args, dboxes)
acc = 0
logger = Logger(args.batch_size, args.local_rank, args.N_gpu)
for epoch in range(0, args.epochs):
logger.start_epoch()
scheduler.step()
iteration = train_loop(
ssd300, loss_func, epoch, optimizer,
train_loader, iteration, logger, args)
logger.end_epoch()
if epoch in args.evaluation:
acc = evaluate(ssd300, val_dataloader, cocoGt, encoder, inv_map, args)
if args.local_rank == 0:
print('Epoch {:2d}, Accuracy: {:4f} mAP'.format(epoch, acc))
if args.data_pipeline == 'dali':
train_loader.reset()
return acc, logger.average_speed()
if __name__ == "__main__":
parser = make_parser()
args = parser.parse_args()
if args.local_rank == 0:
os.makedirs('./models', exist_ok=True)
torch.backends.cudnn.benchmark = True
if args.fp16_mode != 'off':
args.fp16 = True
args.amp = (args.fp16_mode == 'amp')
else:
args.fp16 = False
args.amp = False
start_time = time.time()
acc, avg_speed = train(args)
# avg_speed is reported per node, adjust for the global speed
try:
num_shards = torch.distributed.get_world_size()
except RuntimeError:
num_shards = 1
avg_speed = num_shards * avg_speed
training_time = time.time() - start_time
if args.local_rank == 0:
print("Training end: Average speed: {:3f} img/sec, Total time: {:3f} sec, Final accuracy: {:3f} mAP"
.format(avg_speed, training_time, acc))
if args.target is not None:
if args.target > acc:
print('Target mAP of {} not met. Possible regression'.format(args.target))
sys.exit(1) | 0.542621 | 0.164282 |
from db import db
class PortfolioModel(db.Model):
"""SQLAlchemy Portfolio Model"""
# We assign the correct table
__tablename__ = 'portfolios'
# Table columns
portfolioId = db.Column(db.Integer, primary_key=True, nullable=False)
name = db.Column(db.String(300), nullable=False)
# Foreign key a portfolio belongs to a user
userId = db.Column(db.Integer, db.ForeignKey('users.userId'), nullable=False)
# Cascade SQL ALCHEMY
# In order to retrieve all portfolio books relations belonging to one portfolio
portfolio_books = db.relationship("PortfolioBookModel", cascade="save-update, merge, delete", lazy="dynamic")
# We reference the Parent
user = db.relationship('UserModel', cascade="save-update")
def __init__(self, name, userId):
"""Constructor of the Portfolio model
Arguments:
name {string} -- name of the portfolio
userId {string} -- id of the parent user
"""
self.name = name
self.userId = userId
def json(self):
"""Return a JSON data of the instance variables"""
return {'portfolioId': self.portfolioId ,'portfolioId' : self.portfolioId ,'name': self.name, 'userId': self.userId, 'Portfolio_Book' : [portfolio_book.json() for portfolio_book in self.portfolio_books.all()]}
# Important methods used to retrieve data through SQL Alchemy
@classmethod
def find_by_name(cls, name):
"""Retrieve the portfolio provided its name"""
return cls.query.filter_by(name=name).first()
@classmethod
def find_by_id(cls, portfolioId):
"""Retrieve the portfolio provided its id"""
return cls.query.filter_by(portfolioId=portfolioId).first()
@classmethod
def find_portfolios_by_user(cls, userId):
"""Retrieve the portfolio provided its userId"""
return cls.query.filter_by(userId=userId).all()
def save_to_db(self):
"""Methods used to push and commit to the database"""
db.session.add(self)
db.session.commit()
def delete_from_db(self):
"""Methods used to delete and commit to the database"""
db.session.delete(self)
db.session.commit() | models/portfolio.py |
from db import db
class PortfolioModel(db.Model):
"""SQLAlchemy Portfolio Model"""
# We assign the correct table
__tablename__ = 'portfolios'
# Table columns
portfolioId = db.Column(db.Integer, primary_key=True, nullable=False)
name = db.Column(db.String(300), nullable=False)
# Foreign key a portfolio belongs to a user
userId = db.Column(db.Integer, db.ForeignKey('users.userId'), nullable=False)
# Cascade SQL ALCHEMY
# In order to retrieve all portfolio books relations belonging to one portfolio
portfolio_books = db.relationship("PortfolioBookModel", cascade="save-update, merge, delete", lazy="dynamic")
# We reference the Parent
user = db.relationship('UserModel', cascade="save-update")
def __init__(self, name, userId):
"""Constructor of the Portfolio model
Arguments:
name {string} -- name of the portfolio
userId {string} -- id of the parent user
"""
self.name = name
self.userId = userId
def json(self):
"""Return a JSON data of the instance variables"""
return {'portfolioId': self.portfolioId ,'portfolioId' : self.portfolioId ,'name': self.name, 'userId': self.userId, 'Portfolio_Book' : [portfolio_book.json() for portfolio_book in self.portfolio_books.all()]}
# Important methods used to retrieve data through SQL Alchemy
@classmethod
def find_by_name(cls, name):
"""Retrieve the portfolio provided its name"""
return cls.query.filter_by(name=name).first()
@classmethod
def find_by_id(cls, portfolioId):
"""Retrieve the portfolio provided its id"""
return cls.query.filter_by(portfolioId=portfolioId).first()
@classmethod
def find_portfolios_by_user(cls, userId):
"""Retrieve the portfolio provided its userId"""
return cls.query.filter_by(userId=userId).all()
def save_to_db(self):
"""Methods used to push and commit to the database"""
db.session.add(self)
db.session.commit()
def delete_from_db(self):
"""Methods used to delete and commit to the database"""
db.session.delete(self)
db.session.commit() | 0.688364 | 0.176264 |
from face_lib import my_api, inference
import csv
import multiprocessing
import tensorflow as tf
class LFW_TEST:
def __init__(self,sia,se):
self.sia = sia
self.se = se
def csv_write(self,path, data):
out_test = open(path, 'w', newline='')
csv_test_writer = csv.writer(out_test, dialect='excel')
for n in data:
x1, x2 = n.get()
# 返回图片的128维编码信息
res = self.se.run(self.sia.look_like, feed_dict={self.sia.x1: [x1],self.sia.x2: [x2],self.sia.keep_f: 1.0})
csv_test_writer.writerow([res])
out_test.close()
def write(self, Flag,n=3000):
lfw_test = my_api.LfwTest() # 获取对象
neg_array = lfw_test.neg_array[:n]
pos_array = lfw_test.pos_array[:n]
# path_array = lfw_test.path_array
neg_data = []
pos_data = []
if Flag == 1 or Flag == 0:
# 开启多进程
pool = multiprocessing.Pool(processes=6)
for index in neg_array:
result = pool.apply_async(lfw_test.get_pair_image, (index,))
neg_data.append(result)
pool.close() # 调用join之前，先调用close函数，否则会出错。
pool.join() # 执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("Negative Down!")
self.csv_write('./temp/lfw/result/neg.csv', neg_data)
print('./temp/lfw/result/neg.csv 写入成功!')
if Flag == 2 or Flag == 0:
# 开启多进程
pool = multiprocessing.Pool(processes=6)
for index in pos_array:
result = pool.apply_async(lfw_test.get_pair_image, (index,))
pos_data.append(result)
pool.close() # 调用join之前，先调用close函数，否则会出错。
pool.join() # 执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("Positive Down!")
self.csv_write('./temp/lfw/result/pos.csv', pos_data)
print('./temp/lfw/result/pos.csv 写入成功!')
@staticmethod
def plot():
# 画图
print('正在绘制图形')
lfw_plot = my_api.LfwPlot()
lfw_plot.plot()
@staticmethod
def calculate():
lfw_plot = my_api.LfwPlot()
return lfw_plot.calulate()
if __name__ == '__main__':
size = my_api.size # 图片大小
model_file = 'model/train_faces.model' # 模型存放目录
# # setup siamese network
siamese = inference.Siamese(size)
sess = tf.Session()
saver = tf.train.Saver()
# 全局参数初始化
sess.run(tf.global_variables_initializer())
saver.restore(sess, model_file)
print('模型重载成功')
test = LFW_TEST(siamese,sess)
test.write(Flag=0)
test.plot()
print(test.calculate())
sess.close() | lfw_test.py | from face_lib import my_api, inference
import csv
import multiprocessing
import tensorflow as tf
class LFW_TEST:
def __init__(self,sia,se):
self.sia = sia
self.se = se
def csv_write(self,path, data):
out_test = open(path, 'w', newline='')
csv_test_writer = csv.writer(out_test, dialect='excel')
for n in data:
x1, x2 = n.get()
# 返回图片的128维编码信息
res = self.se.run(self.sia.look_like, feed_dict={self.sia.x1: [x1],self.sia.x2: [x2],self.sia.keep_f: 1.0})
csv_test_writer.writerow([res])
out_test.close()
def write(self, Flag,n=3000):
lfw_test = my_api.LfwTest() # 获取对象
neg_array = lfw_test.neg_array[:n]
pos_array = lfw_test.pos_array[:n]
# path_array = lfw_test.path_array
neg_data = []
pos_data = []
if Flag == 1 or Flag == 0:
# 开启多进程
pool = multiprocessing.Pool(processes=6)
for index in neg_array:
result = pool.apply_async(lfw_test.get_pair_image, (index,))
neg_data.append(result)
pool.close() # 调用join之前，先调用close函数，否则会出错。
pool.join() # 执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("Negative Down!")
self.csv_write('./temp/lfw/result/neg.csv', neg_data)
print('./temp/lfw/result/neg.csv 写入成功!')
if Flag == 2 or Flag == 0:
# 开启多进程
pool = multiprocessing.Pool(processes=6)
for index in pos_array:
result = pool.apply_async(lfw_test.get_pair_image, (index,))
pos_data.append(result)
pool.close() # 调用join之前，先调用close函数，否则会出错。
pool.join() # 执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束
print("Positive Down!")
self.csv_write('./temp/lfw/result/pos.csv', pos_data)
print('./temp/lfw/result/pos.csv 写入成功!')
@staticmethod
def plot():
# 画图
print('正在绘制图形')
lfw_plot = my_api.LfwPlot()
lfw_plot.plot()
@staticmethod
def calculate():
lfw_plot = my_api.LfwPlot()
return lfw_plot.calulate()
if __name__ == '__main__':
size = my_api.size # 图片大小
model_file = 'model/train_faces.model' # 模型存放目录
# # setup siamese network
siamese = inference.Siamese(size)
sess = tf.Session()
saver = tf.train.Saver()
# 全局参数初始化
sess.run(tf.global_variables_initializer())
saver.restore(sess, model_file)
print('模型重载成功')
test = LFW_TEST(siamese,sess)
test.write(Flag=0)
test.plot()
print(test.calculate())
sess.close() | 0.406273 | 0.333449 |
import json
from sqlalchemy import text
from profiler.domain.aggregation import Aggregation, AggregationBatch
from profiler.domain.errors import EntityWasNotStoredError
from profiler.ports.aggregations_repository import AggregationsRepository
from profiler.db.pg_engine import engine
from profiler.utils.json_dumper import dumper
class PgAggregationsRepository(AggregationsRepository):
def get_aggregation(self, model_name: str, model_version: int) -> Aggregation:
with engine.connect() as conn:
query = text(
"""SELECT model_name, batch_name, file_timestamp, aggregation
FROM aggregations
WHERE model_name=:model_name AND model_version=:model_version"""
).bindparams(model_name=model_name, model_version=model_version)
db_rows = conn.execute(query).fetchall()
if len(db_rows) == 0:
return Aggregation(
model_name=model_name,
model_version=model_version,
features=[],
batches=[],
)
batches = []
for row in db_rows:
(
model_name,
batch_name,
file_timestamp,
raw_aggregation,
) = row
batch = AggregationBatch(
model_name=model_name,
model_version=model_version,
batch_name=batch_name,
file_timestamp=file_timestamp,
feature_statistics=json.loads(raw_aggregation),
)
batches.append(batch)
return Aggregation(
model_name=model_name,
model_version=model_version,
features=[],
batches=batches,
)
def save(
self,
batch: AggregationBatch,
):
with engine.connect() as conn:
try:
conn.execute(
text(
"INSERT INTO aggregations VALUES (:model_name, :model_version, :batch_name, :file_timestamp, :data, :batch_rows_count)"
).bindparams(
model_name=batch.model_name,
model_version=batch.model_version,
batch_name=batch.batch_name,
file_timestamp=batch.file_timestamp,
data=json.dumps(batch.feature_statistics, default=dumper),
batch_rows_count=0,
),
)
except Exception as e:
raise EntityWasNotStoredError(
f"Aggregation for {batch.model_name}:{batch.model_version}/{batch.batch_name} was not stored",
e,
) | profiler/profiler/adapters/aggregations_repository/pg_aggregations_repository.py | import json
from sqlalchemy import text
from profiler.domain.aggregation import Aggregation, AggregationBatch
from profiler.domain.errors import EntityWasNotStoredError
from profiler.ports.aggregations_repository import AggregationsRepository
from profiler.db.pg_engine import engine
from profiler.utils.json_dumper import dumper
class PgAggregationsRepository(AggregationsRepository):
def get_aggregation(self, model_name: str, model_version: int) -> Aggregation:
with engine.connect() as conn:
query = text(
"""SELECT model_name, batch_name, file_timestamp, aggregation
FROM aggregations
WHERE model_name=:model_name AND model_version=:model_version"""
).bindparams(model_name=model_name, model_version=model_version)
db_rows = conn.execute(query).fetchall()
if len(db_rows) == 0:
return Aggregation(
model_name=model_name,
model_version=model_version,
features=[],
batches=[],
)
batches = []
for row in db_rows:
(
model_name,
batch_name,
file_timestamp,
raw_aggregation,
) = row
batch = AggregationBatch(
model_name=model_name,
model_version=model_version,
batch_name=batch_name,
file_timestamp=file_timestamp,
feature_statistics=json.loads(raw_aggregation),
)
batches.append(batch)
return Aggregation(
model_name=model_name,
model_version=model_version,
features=[],
batches=batches,
)
def save(
self,
batch: AggregationBatch,
):
with engine.connect() as conn:
try:
conn.execute(
text(
"INSERT INTO aggregations VALUES (:model_name, :model_version, :batch_name, :file_timestamp, :data, :batch_rows_count)"
).bindparams(
model_name=batch.model_name,
model_version=batch.model_version,
batch_name=batch.batch_name,
file_timestamp=batch.file_timestamp,
data=json.dumps(batch.feature_statistics, default=dumper),
batch_rows_count=0,
),
)
except Exception as e:
raise EntityWasNotStoredError(
f"Aggregation for {batch.model_name}:{batch.model_version}/{batch.batch_name} was not stored",
e,
) | 0.432183 | 0.090735 |
from doit.action import CmdAction
from doit.task import clean_targets
import sys
def gui_open_action(pth):
action = None
if sys.platform.startswith('linux'):
action = ["xdg-open", str(pth)]
elif sys.platform.startswith('darwin'):
action = ["open", str(pth)]
elif sys.platform.startswith('win'):
action = ["start", str(pth)]
return action
def _task_html(pth):
"""
see http://nbconvert.readthedocs.io/en/latest/usage.html
"""
return dict(
file_dep=[
'docs/{pth}.ipynb'.format(pth=pth),
'docs/html.tpl'.format(pth=pth),
],
# + [ str(p) for p in pathlib.Path('docs/ext_media').glob('*')],
targets=[
'docs/{pth}.html'.format(pth=pth),
# 'docs/refs.bib'.format(pth=pth)
],
actions=[
# 'mkdir -p docs',
# 'ln -f docs/refs.bib docs/'.format(pth=pth),
'jupyter nbconvert --to html '
# '--template=docs/{pth}_html.tpl '
'--TemplateExporter.exclude_output_prompt=True '
'--FilesWriter.build_directory=docs/ '
'docs/{pth}.ipynb'.format(pth=pth),
],
clean=[
# 'rm -rf docs/{pth}_files',
clean_targets,
],
)
def _task_latex(pth):
"""
see http://nbconvert.readthedocs.io/en/latest/usage.html
"""
return dict(
file_dep=[
'docs/{pth}.ipynb'.format(pth=pth),
'docs/{pth}_print.tplx'.format(pth=pth),
'docs/refs.bib'.format(pth=pth),
# 'docs/ext_media/',
],
targets=[
'_paper_output/{pth}.tex'.format(pth=pth),
'_paper_output/refs.bib'.format(pth=pth)
],
actions=[
'mkdir -p _paper_output',
'rm -rf _paper_output/{pth}_files',
'ln -f docs/refs.bib _paper_output'.format(pth=pth),
'jupyter nbconvert --to latex --template=docs/{pth}_print.tplx '
'--FilesWriter.build_directory=_paper_output/ '
'--TemplateExporter.exclude_output_prompt=True '
'docs/{pth}.ipynb'.format(pth=pth),
],
clean=[
'rm -rf _paper_output/{pth}_files',
clean_targets,
],
)
def _task_pdf(pth):
"""
"""
return dict(
file_dep=[
'_paper_output/refs.bib'.format(pth=pth),
'_paper_output/{pth}.tex'.format(pth=pth)
],
targets=[
'_paper_output/{pth}.pdf'.format(pth=pth),
'_paper_output/{pth}.aux'.format(pth=pth),
'_paper_output/{pth}.dvi'.format(pth=pth),
'_paper_output/{pth}.bcf'.format(pth=pth),
'_paper_output/{pth}.blg'.format(pth=pth),
'_paper_output/{pth}.bbl'.format(pth=pth),
'_paper_output/{pth}.run.xml'.format(pth=pth),
'_paper_output/texput.log',
'_paper_output/q.log',
],
actions=[
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'bibtex '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
],
verbosity=1,
clean=True,
)
def _task_view_pdf(pth):
"""
"""
return dict(
file_dep=['_paper_output/{pth}.pdf'.format(pth=pth)],
targets=[],
actions=[
gui_open_action('_paper_output/{pth}.pdf'.format(pth=pth)),
],
)
def _task_zdravko(srcpth, destpth):
"""
"""
return dict(
file_dep=[
'_paper_output/{srcpth}.pdf'.format(srcpth=srcpth),
'_paper_output/{srcpth}.tex'.format(srcpth=srcpth),
'_paper_output/refs.bib'
],
actions=[
'mkdir -p ~/Dropbox/dan-zdravko-stuff/tex/{destpth}/'.format(
destpth=destpth
),
CmdAction(
'rsync -av {srcpth}_files '
'refs.bib {srcpth}.tex '
'{srcpth}.pdf'
' ~/Dropbox/dan-zdravko-stuff/tex/{destpth}/'.format(
srcpth=srcpth,
destpth=destpth
),
cwd='_paper_output'
),
],
verbosity=2
)
def task_latex_chapter_sparse_hawkes():
return _task_latex('chapter_sparse_hawkes')
def task_pdf_chapter_sparse_hawkes():
return _task_pdf('chapter_sparse_hawkes')
def task_view_pdf_chapter_sparse_hawkes():
return _task_view_pdf('chapter_sparse_hawkes')
def task_zdravko_chapter_sparse_hawkes():
return _task_zdravko('chapter_sparse_hawkes', 'sparse_hawkes')
def task_html_chapter_sparse_hawkes():
return _task_html('chapter_sparse_hawkes')
def task_html_intro_to_cts_hawkes():
return _task_html('intro_to_cts_hawkes') | dodo.py | from doit.action import CmdAction
from doit.task import clean_targets
import sys
def gui_open_action(pth):
action = None
if sys.platform.startswith('linux'):
action = ["xdg-open", str(pth)]
elif sys.platform.startswith('darwin'):
action = ["open", str(pth)]
elif sys.platform.startswith('win'):
action = ["start", str(pth)]
return action
def _task_html(pth):
"""
see http://nbconvert.readthedocs.io/en/latest/usage.html
"""
return dict(
file_dep=[
'docs/{pth}.ipynb'.format(pth=pth),
'docs/html.tpl'.format(pth=pth),
],
# + [ str(p) for p in pathlib.Path('docs/ext_media').glob('*')],
targets=[
'docs/{pth}.html'.format(pth=pth),
# 'docs/refs.bib'.format(pth=pth)
],
actions=[
# 'mkdir -p docs',
# 'ln -f docs/refs.bib docs/'.format(pth=pth),
'jupyter nbconvert --to html '
# '--template=docs/{pth}_html.tpl '
'--TemplateExporter.exclude_output_prompt=True '
'--FilesWriter.build_directory=docs/ '
'docs/{pth}.ipynb'.format(pth=pth),
],
clean=[
# 'rm -rf docs/{pth}_files',
clean_targets,
],
)
def _task_latex(pth):
"""
see http://nbconvert.readthedocs.io/en/latest/usage.html
"""
return dict(
file_dep=[
'docs/{pth}.ipynb'.format(pth=pth),
'docs/{pth}_print.tplx'.format(pth=pth),
'docs/refs.bib'.format(pth=pth),
# 'docs/ext_media/',
],
targets=[
'_paper_output/{pth}.tex'.format(pth=pth),
'_paper_output/refs.bib'.format(pth=pth)
],
actions=[
'mkdir -p _paper_output',
'rm -rf _paper_output/{pth}_files',
'ln -f docs/refs.bib _paper_output'.format(pth=pth),
'jupyter nbconvert --to latex --template=docs/{pth}_print.tplx '
'--FilesWriter.build_directory=_paper_output/ '
'--TemplateExporter.exclude_output_prompt=True '
'docs/{pth}.ipynb'.format(pth=pth),
],
clean=[
'rm -rf _paper_output/{pth}_files',
clean_targets,
],
)
def _task_pdf(pth):
"""
"""
return dict(
file_dep=[
'_paper_output/refs.bib'.format(pth=pth),
'_paper_output/{pth}.tex'.format(pth=pth)
],
targets=[
'_paper_output/{pth}.pdf'.format(pth=pth),
'_paper_output/{pth}.aux'.format(pth=pth),
'_paper_output/{pth}.dvi'.format(pth=pth),
'_paper_output/{pth}.bcf'.format(pth=pth),
'_paper_output/{pth}.blg'.format(pth=pth),
'_paper_output/{pth}.bbl'.format(pth=pth),
'_paper_output/{pth}.run.xml'.format(pth=pth),
'_paper_output/texput.log',
'_paper_output/q.log',
],
actions=[
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'bibtex '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
CmdAction(
'pdflatex -halt-on-error -interaction=batchmode '
'{pth}'.format(pth=pth),
cwd='_paper_output'),
],
verbosity=1,
clean=True,
)
def _task_view_pdf(pth):
"""
"""
return dict(
file_dep=['_paper_output/{pth}.pdf'.format(pth=pth)],
targets=[],
actions=[
gui_open_action('_paper_output/{pth}.pdf'.format(pth=pth)),
],
)
def _task_zdravko(srcpth, destpth):
"""
"""
return dict(
file_dep=[
'_paper_output/{srcpth}.pdf'.format(srcpth=srcpth),
'_paper_output/{srcpth}.tex'.format(srcpth=srcpth),
'_paper_output/refs.bib'
],
actions=[
'mkdir -p ~/Dropbox/dan-zdravko-stuff/tex/{destpth}/'.format(
destpth=destpth
),
CmdAction(
'rsync -av {srcpth}_files '
'refs.bib {srcpth}.tex '
'{srcpth}.pdf'
' ~/Dropbox/dan-zdravko-stuff/tex/{destpth}/'.format(
srcpth=srcpth,
destpth=destpth
),
cwd='_paper_output'
),
],
verbosity=2
)
def task_latex_chapter_sparse_hawkes():
return _task_latex('chapter_sparse_hawkes')
def task_pdf_chapter_sparse_hawkes():
return _task_pdf('chapter_sparse_hawkes')
def task_view_pdf_chapter_sparse_hawkes():
return _task_view_pdf('chapter_sparse_hawkes')
def task_zdravko_chapter_sparse_hawkes():
return _task_zdravko('chapter_sparse_hawkes', 'sparse_hawkes')
def task_html_chapter_sparse_hawkes():
return _task_html('chapter_sparse_hawkes')
def task_html_intro_to_cts_hawkes():
return _task_html('intro_to_cts_hawkes') | 0.333829 | 0.079246 |
# Standard imports
from dataclasses import dataclass
from typing import List
# Third party imports
from github.GithubException import UnknownObjectException
from requests.exceptions import ReadTimeout
# Application imports
from app.logger import console
from app.techniques.abstract_handler import AbstractHandler
from app.entities.repository import Repository
class FakeCommits(AbstractHandler):
"""This technique creates a temporary private Git repository, add fake commits
with arbitrary email addresses and it push them on a remote repository.
On Github, emails are automatically resolved to Github accounts associated with them.
"""
repository: Repository = None
async def resolve(self, emails: List) -> List:
self.repository = Repository()
try:
console.print("Spoofing email addresses…")
await self.repository.create()
console.print("{:<40s} {:<10s}".format("Initializing a fake Git repository:", "done"))
with console.status("Spoofing email addresses…") as status:
for email in emails:
self.repository.config(name=email.address, email=email.address)
self.repository.add(filename="{}.txt".format(email.address), content=email.address)
self.repository.commit(email.address)
console.print("{:<40s} {:<10s}".format("Creating fake commits:", "done"))
await self.repository.push()
console.print("{:<40s} {:<10s}".format("Pushing commits to Github:", "done"))
with console.status("Spoofing email addresses…") as status:
for commit in self.repository.commits():
if not commit.author:
continue
for email in emails:
if commit.commit.message == email.address:
email.user = commit.author
break
console.print("{:<40s} {:<10s}".format("Resolving email addresses:", "done"))
except UnknownObjectException:
console.print("{:<40s} [red]{:<10s}[/red]".format(
"Spoofing email addresses:",
"fail (reason: Github API sends a 404 HTTP code)"))
except ReadTimeout:
console.print("{:<40s} [red]{:<10s}[/red]".format(
"Spoofing email addresses:",
"fail (reason: timeout)"))
finally:
await self.clean()
if need_to_be_resolved := list(filter(lambda email: not email.resolved(), emails)):
return await super().resolve(need_to_be_resolved)
async def clean(self):
if self.repository:
await self.repository.delete()
console.print("{:<40s} {:<10s}".format("Cleaning up the fake repository:", "done")) | email2github/app/techniques/fake_commits.py |
# Standard imports
from dataclasses import dataclass
from typing import List
# Third party imports
from github.GithubException import UnknownObjectException
from requests.exceptions import ReadTimeout
# Application imports
from app.logger import console
from app.techniques.abstract_handler import AbstractHandler
from app.entities.repository import Repository
class FakeCommits(AbstractHandler):
"""This technique creates a temporary private Git repository, add fake commits
with arbitrary email addresses and it push them on a remote repository.
On Github, emails are automatically resolved to Github accounts associated with them.
"""
repository: Repository = None
async def resolve(self, emails: List) -> List:
self.repository = Repository()
try:
console.print("Spoofing email addresses…")
await self.repository.create()
console.print("{:<40s} {:<10s}".format("Initializing a fake Git repository:", "done"))
with console.status("Spoofing email addresses…") as status:
for email in emails:
self.repository.config(name=email.address, email=email.address)
self.repository.add(filename="{}.txt".format(email.address), content=email.address)
self.repository.commit(email.address)
console.print("{:<40s} {:<10s}".format("Creating fake commits:", "done"))
await self.repository.push()
console.print("{:<40s} {:<10s}".format("Pushing commits to Github:", "done"))
with console.status("Spoofing email addresses…") as status:
for commit in self.repository.commits():
if not commit.author:
continue
for email in emails:
if commit.commit.message == email.address:
email.user = commit.author
break
console.print("{:<40s} {:<10s}".format("Resolving email addresses:", "done"))
except UnknownObjectException:
console.print("{:<40s} [red]{:<10s}[/red]".format(
"Spoofing email addresses:",
"fail (reason: Github API sends a 404 HTTP code)"))
except ReadTimeout:
console.print("{:<40s} [red]{:<10s}[/red]".format(
"Spoofing email addresses:",
"fail (reason: timeout)"))
finally:
await self.clean()
if need_to_be_resolved := list(filter(lambda email: not email.resolved(), emails)):
return await super().resolve(need_to_be_resolved)
async def clean(self):
if self.repository:
await self.repository.delete()
console.print("{:<40s} {:<10s}".format("Cleaning up the fake repository:", "done")) | 0.420005 | 0.108048 |
from __future__ import unicode_literals
import logging
from peewee import fn
import time
import datetime
from fetch.api import make_request, default_requests_session
from lock import lock_method
from models import SearchResult, WebPageVersion
logger = logging.getLogger('data')
ARCHIVE_URL = 'http://web.archive.org/cdx/search/cdx'
DEFAULT_PARAMS = {
'limit': 50, # default page size for CDX pagination
'output': 'json',
'showResumeKey': 'true', # lightweight pagination of results
}
REQUEST_DELAY = 1.5
LOCK_FILENAME = '/tmp/histories-fetcher.lock'
def get_history(url, fetch_index):
params = DEFAULT_PARAMS.copy()
params['url'] = url
# Flags for controlling paging and scanning results
more_results = True
watch_for_resume_key = False
while more_results:
more_results = False
response = make_request(default_requests_session.get, ARCHIVE_URL, params=params)
time.sleep(REQUEST_DELAY) # Pause so that we don't bombard the server with requests
if response is None:
break
results = response.json()
for result_index, result in enumerate(results):
# Read the field names from the first result
if result_index == 0:
field_names = result
continue
# Resumption key appears after one blank record after the rest of the records
# These two lines keep watch for the resumption key and exit the loop once
# it has been found.
if result == []:
watch_for_resume_key = True
continue
elif watch_for_resume_key:
# Setting this parameter advances the page of results for the next query
params['resumeKey'] = result[0]
more_results = True
watch_for_resume_key = False
break
# If the code has made it this far, this record is a web
# page version, and we want to save it.
data = dict(zip(field_names, result))
_save_record(url, data, fetch_index)
def _save_record(url, record, fetch_index):
# Convert string for the timestamp into a proper datetime object
try:
timestamp_datetime = datetime.datetime.strptime(
record['timestamp'],
'%Y%m%d%H%M%S',
)
except ValueError:
logger.warn("Invalid timestamp '%s' for URL %s. Skipping record", record['timestamp'], url)
return
# We'll create a new record for the version only if it doesn't yet exist.
try:
WebPageVersion.get(
url=url,
timestamp=timestamp_datetime,
)
except WebPageVersion.DoesNotExist:
# In a few exceptional cases, I've found that the length has
# the value '-'. We store a null length when we encounter '-'.
try:
length = int(record['length'])
except ValueError:
logger.warn("Length '%s' is not an integer for URL %s", record['length'], url)
length = None
WebPageVersion.create(
fetch_index=fetch_index,
url=url,
url_key=record['urlkey'],
timestamp=timestamp_datetime,
original=record['original'],
mime_type=record['mimetype'],
status_code=record['statuscode'],
digest=record['digest'],
length=length,
)
@lock_method(LOCK_FILENAME)
def main(*args, **kwargs):
# Create a new fetch index.
last_fetch_index = WebPageVersion.select(fn.Max(WebPageVersion.fetch_index)).scalar() or 0
fetch_index = last_fetch_index + 1
search_results = SearchResult.select(SearchResult.url).distinct()
for search_result in search_results:
get_history(search_result.url, fetch_index)
def configure_parser(parser):
parser.description = "Get Internet Archive histories for all stored search results." | fetch/histories.py |
from __future__ import unicode_literals
import logging
from peewee import fn
import time
import datetime
from fetch.api import make_request, default_requests_session
from lock import lock_method
from models import SearchResult, WebPageVersion
logger = logging.getLogger('data')
ARCHIVE_URL = 'http://web.archive.org/cdx/search/cdx'
DEFAULT_PARAMS = {
'limit': 50, # default page size for CDX pagination
'output': 'json',
'showResumeKey': 'true', # lightweight pagination of results
}
REQUEST_DELAY = 1.5
LOCK_FILENAME = '/tmp/histories-fetcher.lock'
def get_history(url, fetch_index):
params = DEFAULT_PARAMS.copy()
params['url'] = url
# Flags for controlling paging and scanning results
more_results = True
watch_for_resume_key = False
while more_results:
more_results = False
response = make_request(default_requests_session.get, ARCHIVE_URL, params=params)
time.sleep(REQUEST_DELAY) # Pause so that we don't bombard the server with requests
if response is None:
break
results = response.json()
for result_index, result in enumerate(results):
# Read the field names from the first result
if result_index == 0:
field_names = result
continue
# Resumption key appears after one blank record after the rest of the records
# These two lines keep watch for the resumption key and exit the loop once
# it has been found.
if result == []:
watch_for_resume_key = True
continue
elif watch_for_resume_key:
# Setting this parameter advances the page of results for the next query
params['resumeKey'] = result[0]
more_results = True
watch_for_resume_key = False
break
# If the code has made it this far, this record is a web
# page version, and we want to save it.
data = dict(zip(field_names, result))
_save_record(url, data, fetch_index)
def _save_record(url, record, fetch_index):
# Convert string for the timestamp into a proper datetime object
try:
timestamp_datetime = datetime.datetime.strptime(
record['timestamp'],
'%Y%m%d%H%M%S',
)
except ValueError:
logger.warn("Invalid timestamp '%s' for URL %s. Skipping record", record['timestamp'], url)
return
# We'll create a new record for the version only if it doesn't yet exist.
try:
WebPageVersion.get(
url=url,
timestamp=timestamp_datetime,
)
except WebPageVersion.DoesNotExist:
# In a few exceptional cases, I've found that the length has
# the value '-'. We store a null length when we encounter '-'.
try:
length = int(record['length'])
except ValueError:
logger.warn("Length '%s' is not an integer for URL %s", record['length'], url)
length = None
WebPageVersion.create(
fetch_index=fetch_index,
url=url,
url_key=record['urlkey'],
timestamp=timestamp_datetime,
original=record['original'],
mime_type=record['mimetype'],
status_code=record['statuscode'],
digest=record['digest'],
length=length,
)
@lock_method(LOCK_FILENAME)
def main(*args, **kwargs):
# Create a new fetch index.
last_fetch_index = WebPageVersion.select(fn.Max(WebPageVersion.fetch_index)).scalar() or 0
fetch_index = last_fetch_index + 1
search_results = SearchResult.select(SearchResult.url).distinct()
for search_result in search_results:
get_history(search_result.url, fetch_index)
def configure_parser(parser):
parser.description = "Get Internet Archive histories for all stored search results." | 0.483892 | 0.10217 |
from unittest.mock import MagicMock, call, mock_open, patch
from pytest import raises
from vang.pio.rsr import _in
from vang.pio.rsr import _replace_in_file
from vang.pio.rsr import _replace_file
from vang.pio.rsr import _rsr
from vang.pio.rsr import get_replace_function
from vang.pio.rsr import rsr
from vang.pio.rsr import main
from vang.pio.rsr import parse_args
import pytest
def test_get_replace_function():
assert 'Hello#World' == get_replace_function(False)('Hello.World', '.', '#')
assert '###########' == get_replace_function(True)('Hello.World', '.', '#')
@patch('vang.pio.rsr.remove')
@patch('vang.pio.rsr.replace')
def test__replace_in_file(mock_replace, mock_remove):
with patch('vang.pio.rsr.open', mock_open(), create=True) as m:
old_file = MagicMock()
old_file.__enter__.return_value.__iter__.return_value = [
'\n'.join(['foo.bar'] * 10)
]
old_file.__exit__.return_value = False
new_file = MagicMock()
new_file.__exit__.return_value = False
m.side_effect = (old_file, new_file)
_replace_in_file('.', '#', 'path', get_replace_function(True))
assert [call('path.tmp', 'path')] == mock_replace.mock_calls
assert [] == mock_remove.mock_calls
assert [
call('path', 'tr', encoding='UTF-8', errors='ignore'),
call('path.tmp', 'tw', encoding='UTF-8')
] == m.mock_calls
assert [
call.__enter__(),
call.__enter__().write(
'#######\n#######\n#######\n#######\n#######\n'
'#######\n#######\n#######\n#######\n#######'),
call.__exit__(None, None, None)
] == new_file.mock_calls
@pytest.mark.parametrize("file, expected", [
('foox', [call('path/foox', 'path/barx')]),
('baz', []),
])
@patch('vang.pio.rsr.rename')
def test__replace_file(mock_rename, file, expected):
_replace_file('foo', 'bar', 'path', file, get_replace_function(False))
assert expected == mock_rename.mock_calls
@pytest.mark.parametrize("name, expected", [
('foo', True),
('bar', True),
('.o.', True),
('baz', False),
('.o', False),
])
def test__in(name, expected):
assert expected == _in(name, ['foo', 'bar'])
@patch('vang.pio.rsr.walk', autospec=True)
@patch('vang.pio.rsr._replace_in_file', autospec=True)
@patch('vang.pio.rsr._replace_file', autospec=True)
def test__rsr(mock__replace_file, mock__replace_in_file, mock_walk):
mock_walk.return_value = [
('/old', ('older', '.git'), ('baz', '.gitignore')),
('/old/older', (), ('oldest', 'eggs')),
('/old/.git', (), ('oldest', 'eggs')),
]
def replace_function(x, y, z):
pass
_rsr(
'root',
['.git', '.gitignore', 'target'],
'old',
'new',
replace_function,
)
assert [call('root', False)] == mock_walk.mock_calls
assert [
call('old', 'new', '/old', 'baz', replace_function),
call('old', 'new', '/old', 'older', replace_function),
call('old', 'new', '/old/older', 'oldest', replace_function),
call('old', 'new', '/old/older', 'eggs', replace_function)
] == mock__replace_file.mock_calls
assert [
call('old', 'new', '/old/baz', replace_function),
call('old', 'new', '/old/older/oldest', replace_function),
call('old', 'new', '/old/older/eggs', replace_function)
] == mock__replace_in_file.mock_calls
@patch('vang.pio.rsr._rsr', autospec=True)
def test_rsr(mock__rsr):
rsr('old', 'new', ['d1', 'd2'], 'rf')
assert [
call('d1', ['.git', '.gitignore', 'target'], 'old', 'new', 'rf'),
call('d2', ['.git', '.gitignore', 'target'], 'old', 'new', 'rf')
] == mock__rsr.mock_calls
@patch('vang.pio.rsr.get_replace_function', autospec=True)
@patch('vang.pio.rsr.rsr', autospec=True)
def test_main(mock_rsr, mock_get_replace_function):
mock_get_replace_function.return_value = 'rf'
main('old', 'new', ['d1', 'd2'], True)
assert [call(True)] == mock_get_replace_function.mock_calls
assert [call('old', 'new', ['d1', 'd2'], 'rf')] == mock_rsr.mock_calls
@pytest.mark.parametrize("args", [
'',
'1 2 3',
])
def test_parse_args_raises(args):
with raises(SystemExit):
parse_args(args.split(' ') if args else args)
@pytest.mark.parametrize("args, expected", [
['old new', {
'old': 'old',
'new': 'new',
'dirs': ['.'],
'regexp': False
}],
[
'old new -d d1 d2 -r',
{
'old': 'old',
'new': 'new',
'dirs': ['d1', 'd2'],
'regexp': True
}
],
])
def test_parse_args_valid(args, expected):
assert expected == parse_args(args.split(' ')).__dict__ | vang/pio/tests/test_rsr.py | from unittest.mock import MagicMock, call, mock_open, patch
from pytest import raises
from vang.pio.rsr import _in
from vang.pio.rsr import _replace_in_file
from vang.pio.rsr import _replace_file
from vang.pio.rsr import _rsr
from vang.pio.rsr import get_replace_function
from vang.pio.rsr import rsr
from vang.pio.rsr import main
from vang.pio.rsr import parse_args
import pytest
def test_get_replace_function():
assert 'Hello#World' == get_replace_function(False)('Hello.World', '.', '#')
assert '###########' == get_replace_function(True)('Hello.World', '.', '#')
@patch('vang.pio.rsr.remove')
@patch('vang.pio.rsr.replace')
def test__replace_in_file(mock_replace, mock_remove):
with patch('vang.pio.rsr.open', mock_open(), create=True) as m:
old_file = MagicMock()
old_file.__enter__.return_value.__iter__.return_value = [
'\n'.join(['foo.bar'] * 10)
]
old_file.__exit__.return_value = False
new_file = MagicMock()
new_file.__exit__.return_value = False
m.side_effect = (old_file, new_file)
_replace_in_file('.', '#', 'path', get_replace_function(True))
assert [call('path.tmp', 'path')] == mock_replace.mock_calls
assert [] == mock_remove.mock_calls
assert [
call('path', 'tr', encoding='UTF-8', errors='ignore'),
call('path.tmp', 'tw', encoding='UTF-8')
] == m.mock_calls
assert [
call.__enter__(),
call.__enter__().write(
'#######\n#######\n#######\n#######\n#######\n'
'#######\n#######\n#######\n#######\n#######'),
call.__exit__(None, None, None)
] == new_file.mock_calls
@pytest.mark.parametrize("file, expected", [
('foox', [call('path/foox', 'path/barx')]),
('baz', []),
])
@patch('vang.pio.rsr.rename')
def test__replace_file(mock_rename, file, expected):
_replace_file('foo', 'bar', 'path', file, get_replace_function(False))
assert expected == mock_rename.mock_calls
@pytest.mark.parametrize("name, expected", [
('foo', True),
('bar', True),
('.o.', True),
('baz', False),
('.o', False),
])
def test__in(name, expected):
assert expected == _in(name, ['foo', 'bar'])
@patch('vang.pio.rsr.walk', autospec=True)
@patch('vang.pio.rsr._replace_in_file', autospec=True)
@patch('vang.pio.rsr._replace_file', autospec=True)
def test__rsr(mock__replace_file, mock__replace_in_file, mock_walk):
mock_walk.return_value = [
('/old', ('older', '.git'), ('baz', '.gitignore')),
('/old/older', (), ('oldest', 'eggs')),
('/old/.git', (), ('oldest', 'eggs')),
]
def replace_function(x, y, z):
pass
_rsr(
'root',
['.git', '.gitignore', 'target'],
'old',
'new',
replace_function,
)
assert [call('root', False)] == mock_walk.mock_calls
assert [
call('old', 'new', '/old', 'baz', replace_function),
call('old', 'new', '/old', 'older', replace_function),
call('old', 'new', '/old/older', 'oldest', replace_function),
call('old', 'new', '/old/older', 'eggs', replace_function)
] == mock__replace_file.mock_calls
assert [
call('old', 'new', '/old/baz', replace_function),
call('old', 'new', '/old/older/oldest', replace_function),
call('old', 'new', '/old/older/eggs', replace_function)
] == mock__replace_in_file.mock_calls
@patch('vang.pio.rsr._rsr', autospec=True)
def test_rsr(mock__rsr):
rsr('old', 'new', ['d1', 'd2'], 'rf')
assert [
call('d1', ['.git', '.gitignore', 'target'], 'old', 'new', 'rf'),
call('d2', ['.git', '.gitignore', 'target'], 'old', 'new', 'rf')
] == mock__rsr.mock_calls
@patch('vang.pio.rsr.get_replace_function', autospec=True)
@patch('vang.pio.rsr.rsr', autospec=True)
def test_main(mock_rsr, mock_get_replace_function):
mock_get_replace_function.return_value = 'rf'
main('old', 'new', ['d1', 'd2'], True)
assert [call(True)] == mock_get_replace_function.mock_calls
assert [call('old', 'new', ['d1', 'd2'], 'rf')] == mock_rsr.mock_calls
@pytest.mark.parametrize("args", [
'',
'1 2 3',
])
def test_parse_args_raises(args):
with raises(SystemExit):
parse_args(args.split(' ') if args else args)
@pytest.mark.parametrize("args, expected", [
['old new', {
'old': 'old',
'new': 'new',
'dirs': ['.'],
'regexp': False
}],
[
'old new -d d1 d2 -r',
{
'old': 'old',
'new': 'new',
'dirs': ['d1', 'd2'],
'regexp': True
}
],
])
def test_parse_args_valid(args, expected):
assert expected == parse_args(args.split(' ')).__dict__ | 0.452052 | 0.366533 |
import logging
import os
import re
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import pandas as pd
from .util import print_log, read_fasta_and_generate_seq
def create_bed_from_fa(fa_path, dest_dir_path, bgzip='bgzip',
human_autosome=False, target_letters='ACGT', n_cpu=1):
logger = logging.getLogger(__name__)
target_letter_set = set(target_letters)
print_log('Set target letters:\t{}'.format(target_letter_set))
fa = Path(fa_path).resolve()
assert fa.is_file(), f'file not found: {fa}'
bed = Path(dest_dir_path).resolve().joinpath(
re.sub(r'\.(gz|bz2|bgz)', '', Path(fa_path).name)
+ ('.autosome.' if human_autosome else '.')
+ ''.join(sorted(list(target_letter_set))) + '.bed'
)
autosomes = {f'chr{i}' for i in range(1, 23)}
fs = list()
with ProcessPoolExecutor(max_workers=n_cpu) as x:
for chrom, seq in read_fasta_and_generate_seq(path=str(fa)):
seq_len = len(seq)
if human_autosome and chrom in autosomes:
print_log(
f'Detect the target letters:\t{chrom}\t({seq_len} bp)'
)
fs.append(
x.submit(
_identify_target_region, chrom, seq, target_letter_set
)
)
else:
logger.info(f'Skip detection: {chrom} ({seq_len} bp)')
f_results = [f.result() for f in as_completed(fs)]
df_bed = pd.concat(
f_results, ignore_index=True, sort=False
).sort_values(['chrom', 'chromStart', 'chromEnd'])
logger.debug(f'df_bed:{os.linesep}{df_bed}')
print_log(f'Write a BED file:\t{bed}')
df_bed.to_csv(bed, sep='\t', header=False, index=False)
def _identify_target_region(chrom, sequence, target_letter_set):
bseq = pd.Series(list(sequence)).isin(target_letter_set).astype(int)
if bseq.sum() > 0:
return bseq.pipe(
lambda s: pd.DataFrame({
'chrom': chrom,
'chromStart': [
*([0] if s.iloc[0] == 1 else list()),
*s[s.diff() == 1].index
],
'chromEnd': [
*s[s.diff() == -1].index,
*([len(s)] if s.iloc[-1] == 1 else list())
]
})
)
else:
logger = logging.getLogger(__name__)
logger.info(f'Target letters not detected: {chrom}') | tmber/bed.py |
import logging
import os
import re
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
import pandas as pd
from .util import print_log, read_fasta_and_generate_seq
def create_bed_from_fa(fa_path, dest_dir_path, bgzip='bgzip',
human_autosome=False, target_letters='ACGT', n_cpu=1):
logger = logging.getLogger(__name__)
target_letter_set = set(target_letters)
print_log('Set target letters:\t{}'.format(target_letter_set))
fa = Path(fa_path).resolve()
assert fa.is_file(), f'file not found: {fa}'
bed = Path(dest_dir_path).resolve().joinpath(
re.sub(r'\.(gz|bz2|bgz)', '', Path(fa_path).name)
+ ('.autosome.' if human_autosome else '.')
+ ''.join(sorted(list(target_letter_set))) + '.bed'
)
autosomes = {f'chr{i}' for i in range(1, 23)}
fs = list()
with ProcessPoolExecutor(max_workers=n_cpu) as x:
for chrom, seq in read_fasta_and_generate_seq(path=str(fa)):
seq_len = len(seq)
if human_autosome and chrom in autosomes:
print_log(
f'Detect the target letters:\t{chrom}\t({seq_len} bp)'
)
fs.append(
x.submit(
_identify_target_region, chrom, seq, target_letter_set
)
)
else:
logger.info(f'Skip detection: {chrom} ({seq_len} bp)')
f_results = [f.result() for f in as_completed(fs)]
df_bed = pd.concat(
f_results, ignore_index=True, sort=False
).sort_values(['chrom', 'chromStart', 'chromEnd'])
logger.debug(f'df_bed:{os.linesep}{df_bed}')
print_log(f'Write a BED file:\t{bed}')
df_bed.to_csv(bed, sep='\t', header=False, index=False)
def _identify_target_region(chrom, sequence, target_letter_set):
bseq = pd.Series(list(sequence)).isin(target_letter_set).astype(int)
if bseq.sum() > 0:
return bseq.pipe(
lambda s: pd.DataFrame({
'chrom': chrom,
'chromStart': [
*([0] if s.iloc[0] == 1 else list()),
*s[s.diff() == 1].index
],
'chromEnd': [
*s[s.diff() == -1].index,
*([len(s)] if s.iloc[-1] == 1 else list())
]
})
)
else:
logger = logging.getLogger(__name__)
logger.info(f'Target letters not detected: {chrom}') | 0.284377 | 0.263368 |
import logging
import os
import re
import subprocess
import sys
import time
from webkitpy.benchmark_runner.http_server_driver.http_server_driver import HTTPServerDriver
_log = logging.getLogger(__name__)
class SimpleHTTPServerDriver(HTTPServerDriver):
"""This class depends on unix environment, need to be modified to achieve crossplatform compability
"""
platforms = ['osx', 'linux']
def __init__(self):
self._server_process = None
self._server_port = 0
self._ip = '127.0.0.1'
self._ensure_http_server_dependencies()
def serve(self, web_root):
_log.info('Launching an http server')
http_server_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "http_server/twisted_http_server.py")
interface_args = []
if self._ip:
interface_args.extend(['--interface', self._ip])
self._server_process = subprocess.Popen(["python", http_server_path, web_root] + interface_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
max_attempt = 5
interval = 0.5
_log.info('Start to fetching the port number of the http server')
try:
import psutil
for attempt in range(max_attempt):
connections = psutil.Process(self._server_process.pid).connections()
if connections and connections[0].laddr and connections[0].laddr[1] and connections[0].status == 'LISTEN':
self._server_port = connections[0].laddr[1]
_log.info('HTTP Server is serving at port: %d', self._server_port)
break
_log.info('Server port is not found this time, retry after %f seconds' % interval)
time.sleep(interval)
interval *= 2
else:
raise Exception("Server is not listening on port, max tries exceeded. HTTP server may be installing dependent modules.")
except ImportError:
for attempt in range(max_attempt):
try:
output = subprocess.check_output(['/usr/sbin/lsof', '-a', '-P', '-iTCP', '-sTCP:LISTEN', '-p', str(self._server_process.pid)])
self._server_port = int(re.search('TCP .*:(\d+) \(LISTEN\)', output).group(1))
if self._server_port:
_log.info('HTTP Server is serving at port: %d', self._server_port)
break
except Exception as error:
_log.info('Error: %s' % error)
_log.info('Server port is not found this time, retry after %f seconds' % interval)
time.sleep(interval)
interval *= 2
else:
raise Exception("Cannot listen to server, max tries exceeded")
self._wait_for_http_server()
def _wait_for_http_server(self):
max_attempt = 5
# Wait for server to be up completely before exiting
for attempt in range(max_attempt):
try:
subprocess.check_call(["curl", "--silent", "--head", "--fail", "--output", "/dev/null", self.base_url()])
return
except Exception as error:
_log.info('Server not running yet: %s' % error)
time.sleep(1)
raise Exception('Server not running, max tries exceeded: %s' % error)
def base_url(self):
return "http://%s:%d" % (self._ip, self._server_port)
def fetch_result(self):
(stdout, stderr) = self._server_process.communicate()
print(stderr)
return stdout
def kill_server(self):
try:
if not self._server_process:
return
if self._server_process.poll() is None:
self._server_process.terminate()
except OSError as error:
_log.info('Error terminating server process: %s' % (error))
def get_return_code(self):
return self._server_process.returncode
def set_device_id(self, device_id):
pass
def _ensure_http_server_dependencies(self):
_log.info('Ensure dependencies of http server is satisfied')
from pkg_resources import require, VersionConflict, DistributionNotFound
try:
require("Twisted>=15.5.0")
import twisted
except (ImportError, VersionConflict, DistributionNotFound):
_log.info("Will install twisted in webkitpy, and twisted will be used by webkitpy only")
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../..')))
from webkitpy.thirdparty.autoinstalled.twisted_15_5_0 import twisted | Tools/Scripts/webkitpy/benchmark_runner/http_server_driver/simple_http_server_driver.py |
import logging
import os
import re
import subprocess
import sys
import time
from webkitpy.benchmark_runner.http_server_driver.http_server_driver import HTTPServerDriver
_log = logging.getLogger(__name__)
class SimpleHTTPServerDriver(HTTPServerDriver):
"""This class depends on unix environment, need to be modified to achieve crossplatform compability
"""
platforms = ['osx', 'linux']
def __init__(self):
self._server_process = None
self._server_port = 0
self._ip = '127.0.0.1'
self._ensure_http_server_dependencies()
def serve(self, web_root):
_log.info('Launching an http server')
http_server_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "http_server/twisted_http_server.py")
interface_args = []
if self._ip:
interface_args.extend(['--interface', self._ip])
self._server_process = subprocess.Popen(["python", http_server_path, web_root] + interface_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
max_attempt = 5
interval = 0.5
_log.info('Start to fetching the port number of the http server')
try:
import psutil
for attempt in range(max_attempt):
connections = psutil.Process(self._server_process.pid).connections()
if connections and connections[0].laddr and connections[0].laddr[1] and connections[0].status == 'LISTEN':
self._server_port = connections[0].laddr[1]
_log.info('HTTP Server is serving at port: %d', self._server_port)
break
_log.info('Server port is not found this time, retry after %f seconds' % interval)
time.sleep(interval)
interval *= 2
else:
raise Exception("Server is not listening on port, max tries exceeded. HTTP server may be installing dependent modules.")
except ImportError:
for attempt in range(max_attempt):
try:
output = subprocess.check_output(['/usr/sbin/lsof', '-a', '-P', '-iTCP', '-sTCP:LISTEN', '-p', str(self._server_process.pid)])
self._server_port = int(re.search('TCP .*:(\d+) \(LISTEN\)', output).group(1))
if self._server_port:
_log.info('HTTP Server is serving at port: %d', self._server_port)
break
except Exception as error:
_log.info('Error: %s' % error)
_log.info('Server port is not found this time, retry after %f seconds' % interval)
time.sleep(interval)
interval *= 2
else:
raise Exception("Cannot listen to server, max tries exceeded")
self._wait_for_http_server()
def _wait_for_http_server(self):
max_attempt = 5
# Wait for server to be up completely before exiting
for attempt in range(max_attempt):
try:
subprocess.check_call(["curl", "--silent", "--head", "--fail", "--output", "/dev/null", self.base_url()])
return
except Exception as error:
_log.info('Server not running yet: %s' % error)
time.sleep(1)
raise Exception('Server not running, max tries exceeded: %s' % error)
def base_url(self):
return "http://%s:%d" % (self._ip, self._server_port)
def fetch_result(self):
(stdout, stderr) = self._server_process.communicate()
print(stderr)
return stdout
def kill_server(self):
try:
if not self._server_process:
return
if self._server_process.poll() is None:
self._server_process.terminate()
except OSError as error:
_log.info('Error terminating server process: %s' % (error))
def get_return_code(self):
return self._server_process.returncode
def set_device_id(self, device_id):
pass
def _ensure_http_server_dependencies(self):
_log.info('Ensure dependencies of http server is satisfied')
from pkg_resources import require, VersionConflict, DistributionNotFound
try:
require("Twisted>=15.5.0")
import twisted
except (ImportError, VersionConflict, DistributionNotFound):
_log.info("Will install twisted in webkitpy, and twisted will be used by webkitpy only")
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../..')))
from webkitpy.thirdparty.autoinstalled.twisted_15_5_0 import twisted | 0.257578 | 0.04904 |
import pickle
import sqlite3
import logging
from collections import namedtuple
from sanskrit_parser.base.sanskrit_base import SanskritImmutableString, SCHEMES
from sanskrit_parser.util.lexical_lookup import LexicalLookup
from sanskrit_parser.util.inriatagmapper import inriaTagMapper
from sanskrit_parser.util.data_manager import data_file_path
_db = namedtuple('_db', ['db_file', 'tags', 'stems', 'buf'])
class InriaXMLWrapper(LexicalLookup):
"""
Class to interface with the INRIA XML database released
by Prof. <NAME>
https://gitlab.inria.fr/huet/Heritage_Resources
"""
'''
The custom database format has two parts:
1. A pickle file that contains a list of stems,
a list of tags, and a serialized buffer of the
indices of stems and tags for each form. The indices
are used as it is more efficient to store such integers
instead of the string for each tag.
2. An sqlite file that maps each form to the position
within the buffer that contains the serialized tuple
of stems and tags for that form. An sqlite database
is used to avoid having to build a huge dict in
memory for the 600K forms that are present in this db,
which consumes a lot of memory. (See
https://github.com/kmadathil/sanskrit_parser/issues/151)
To lookup the tag for a form, we use the sqlite db to find the
position in the buffer, deserialize the data at that position,
which gives us a list of the tag set for that form. For each
item in that list, we then lookup the right stem and tag in
the list of stems and tags loaded from the pickle file
'''
def __init__(self, logger=None):
self.pickle_file = "inria_forms.pickle"
self.logger = logger or logging.getLogger(__name__)
db_file = data_file_path("inria_forms_pos.db")
pkl_path = data_file_path("inria_stems_tags_buf.pkl")
self.db = self._load_db(db_file, pkl_path)
@staticmethod
def _load_db(db_file, pkl_path):
with open(pkl_path, 'rb') as f:
stems = pickle.load(f)
tags = pickle.load(f)
buf = f.read()
db = _db(db_file, tags, stems, buf)
return db
def _get_tags(self, word):
db = self.db
conn = sqlite3.connect(db.db_file)
cursor = conn.cursor()
res = cursor.execute('SELECT * FROM forms WHERE form=?', (word,)).fetchone()
if res is None:
return None
pos = res[1]
tag_index_list = pickle.loads(db.buf[pos:])
tags = []
for tag_index in tag_index_list:
tags.append(self._decode_tags(tag_index, db.tags, db.stems))
return tags
@staticmethod
def _decode_tags(tag_index, tags, stems):
t = [tags[x] for x in tag_index[1]]
stem = stems[tag_index[0]]
return (stem, set(t))
def valid(self, word):
conn = sqlite3.connect(self.db.db_file)
cursor = conn.cursor()
res = cursor.execute('SELECT COUNT(1) FROM forms WHERE form = ?', (word,)).fetchone()
return res[0] > 0
def get_tags(self, word, tmap=True):
tags = self._get_tags(word)
if tmap and (tags is not None):
tags = inriaTagMapper(tags)
return tags
if __name__ == "__main__":
from argparse import ArgumentParser
def getArgs():
"""
Argparse routine.
Returns args variable
"""
# Parser Setup
parser = ArgumentParser(description='Interface to INRIA XML database')
# Input Encoding (autodetect by default)
parser.add_argument('--input-encoding', type=str, default=None)
parser.add_argument('--loglevel', type=str, default="info",
help="logging level. Can be any level supported by logging module")
parser.add_argument('word', nargs='?', type=str,
default=None,
help="Word to look up")
parser.add_argument('--no-map-tags', dest='map_tags',
action='store_false')
return parser.parse_args()
def main():
args = getArgs()
if args.input_encoding is None:
ie = None
else:
ie = SCHEMES[args.input_encoding]
if args.loglevel:
numeric_level = getattr(logging, args.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % args.loglevel)
logging.basicConfig(level=numeric_level)
word_in = SanskritImmutableString(args.word, encoding=ie).canonical()
xmlDB = InriaXMLWrapper()
print("Getting tags for", word_in)
tags = xmlDB.get_tags(word_in, tmap=args.map_tags)
if tags is not None:
for t in tags:
print(t)
main() | sanskrit_parser/util/inriaxmlwrapper.py | import pickle
import sqlite3
import logging
from collections import namedtuple
from sanskrit_parser.base.sanskrit_base import SanskritImmutableString, SCHEMES
from sanskrit_parser.util.lexical_lookup import LexicalLookup
from sanskrit_parser.util.inriatagmapper import inriaTagMapper
from sanskrit_parser.util.data_manager import data_file_path
_db = namedtuple('_db', ['db_file', 'tags', 'stems', 'buf'])
class InriaXMLWrapper(LexicalLookup):
"""
Class to interface with the INRIA XML database released
by Prof. <NAME>
https://gitlab.inria.fr/huet/Heritage_Resources
"""
'''
The custom database format has two parts:
1. A pickle file that contains a list of stems,
a list of tags, and a serialized buffer of the
indices of stems and tags for each form. The indices
are used as it is more efficient to store such integers
instead of the string for each tag.
2. An sqlite file that maps each form to the position
within the buffer that contains the serialized tuple
of stems and tags for that form. An sqlite database
is used to avoid having to build a huge dict in
memory for the 600K forms that are present in this db,
which consumes a lot of memory. (See
https://github.com/kmadathil/sanskrit_parser/issues/151)
To lookup the tag for a form, we use the sqlite db to find the
position in the buffer, deserialize the data at that position,
which gives us a list of the tag set for that form. For each
item in that list, we then lookup the right stem and tag in
the list of stems and tags loaded from the pickle file
'''
def __init__(self, logger=None):
self.pickle_file = "inria_forms.pickle"
self.logger = logger or logging.getLogger(__name__)
db_file = data_file_path("inria_forms_pos.db")
pkl_path = data_file_path("inria_stems_tags_buf.pkl")
self.db = self._load_db(db_file, pkl_path)
@staticmethod
def _load_db(db_file, pkl_path):
with open(pkl_path, 'rb') as f:
stems = pickle.load(f)
tags = pickle.load(f)
buf = f.read()
db = _db(db_file, tags, stems, buf)
return db
def _get_tags(self, word):
db = self.db
conn = sqlite3.connect(db.db_file)
cursor = conn.cursor()
res = cursor.execute('SELECT * FROM forms WHERE form=?', (word,)).fetchone()
if res is None:
return None
pos = res[1]
tag_index_list = pickle.loads(db.buf[pos:])
tags = []
for tag_index in tag_index_list:
tags.append(self._decode_tags(tag_index, db.tags, db.stems))
return tags
@staticmethod
def _decode_tags(tag_index, tags, stems):
t = [tags[x] for x in tag_index[1]]
stem = stems[tag_index[0]]
return (stem, set(t))
def valid(self, word):
conn = sqlite3.connect(self.db.db_file)
cursor = conn.cursor()
res = cursor.execute('SELECT COUNT(1) FROM forms WHERE form = ?', (word,)).fetchone()
return res[0] > 0
def get_tags(self, word, tmap=True):
tags = self._get_tags(word)
if tmap and (tags is not None):
tags = inriaTagMapper(tags)
return tags
if __name__ == "__main__":
from argparse import ArgumentParser
def getArgs():
"""
Argparse routine.
Returns args variable
"""
# Parser Setup
parser = ArgumentParser(description='Interface to INRIA XML database')
# Input Encoding (autodetect by default)
parser.add_argument('--input-encoding', type=str, default=None)
parser.add_argument('--loglevel', type=str, default="info",
help="logging level. Can be any level supported by logging module")
parser.add_argument('word', nargs='?', type=str,
default=None,
help="Word to look up")
parser.add_argument('--no-map-tags', dest='map_tags',
action='store_false')
return parser.parse_args()
def main():
args = getArgs()
if args.input_encoding is None:
ie = None
else:
ie = SCHEMES[args.input_encoding]
if args.loglevel:
numeric_level = getattr(logging, args.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % args.loglevel)
logging.basicConfig(level=numeric_level)
word_in = SanskritImmutableString(args.word, encoding=ie).canonical()
xmlDB = InriaXMLWrapper()
print("Getting tags for", word_in)
tags = xmlDB.get_tags(word_in, tmap=args.map_tags)
if tags is not None:
for t in tags:
print(t)
main() | 0.552781 | 0.339171 |
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.button import Button
from kivy.uix.textinput import TextInput
from kivy.uix.label import Label
class MainApp(App):
def build(self):
self.operators = ["/", "*", "+","%","^","Mod"]
self.last_was_operator = None
self.last_button = None
main_layout = BoxLayout(orientation="vertical")
#label
main_layout.add_widget(Label(text="Calvy - Kivy Calculator",
size_hint = (.4, .4),
pos_hint={"center_x": 0.5, "center_y": 0.5}))
#display
self.calculation = TextInput(
multiline=True, readonly=True, halign="right", font_size=250)
self.output = TextInput(
multiline=False, readonly=True, halign="right", font_size=250)
main_layout.add_widget(self.calculation)
main_layout.add_widget(self.output)
buttons = [
["Mod","(",")","Ans"],
["^", "%","Del","+"],
["7", "8", "9", "-"],
["4", "5", "6", "*"],
["1", "2", "3", "/"],
[".", "0", "C", "="],
]
for row in buttons:
h_layout = BoxLayout()
for label in row:
if label == "=":
button = Button(
text=label,
background_color = (0, 1, 1, 1),
pos_hint={"center_x": 0.5, "center_y": 0.5})
button.bind(on_press=self.calculate)
h_layout.add_widget(button)
continue
button = Button(
text=label,
pos_hint={"center_x": 0.4, "center_y": 0.4})
button.bind(on_press=self.on_button_press)
h_layout.add_widget(button)
main_layout.add_widget(h_layout)
return main_layout
def on_button_press(self, instance):
current = self.calculation.text
button_text = instance.text
if button_text == "C":
# Clear the calculation widget
self.calculation.text = ""
elif button_text == "Del":
self.calculation.text = self.calculation.text[:-1]
elif button_text == "Ans":
self.calculation.text += self.output.text
else:
if current and (
self.last_was_operator and button_text in self.operators):
# Don't add two operators right after each other
return
elif current == "" and ( button_text in self.operators or button_text == "0"):
# First character cannot be an operator or zero
return
else:
new_text = current + button_text
self.calculation.text = new_text
self.last_button = button_text
self.last_was_operator = self.last_button in self.operators
def calculate(self, instance):
text = self.calculation.text
if text:
try:
#replacing operators to python operators
y = text.replace("^", "**")
x = y.replace("%","*(0.01)*")
self.output.text = x.replace("Mod","%")
calculation = str(eval(self.output.text))
self.output.text = calculation
self.calculation.text = ""
except Exception:
self.output.text = "Error"
self.calculation.text = ""
if __name__ == "__main__":
app = MainApp()
app.run() | Calvy/main.py | from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.button import Button
from kivy.uix.textinput import TextInput
from kivy.uix.label import Label
class MainApp(App):
def build(self):
self.operators = ["/", "*", "+","%","^","Mod"]
self.last_was_operator = None
self.last_button = None
main_layout = BoxLayout(orientation="vertical")
#label
main_layout.add_widget(Label(text="Calvy - Kivy Calculator",
size_hint = (.4, .4),
pos_hint={"center_x": 0.5, "center_y": 0.5}))
#display
self.calculation = TextInput(
multiline=True, readonly=True, halign="right", font_size=250)
self.output = TextInput(
multiline=False, readonly=True, halign="right", font_size=250)
main_layout.add_widget(self.calculation)
main_layout.add_widget(self.output)
buttons = [
["Mod","(",")","Ans"],
["^", "%","Del","+"],
["7", "8", "9", "-"],
["4", "5", "6", "*"],
["1", "2", "3", "/"],
[".", "0", "C", "="],
]
for row in buttons:
h_layout = BoxLayout()
for label in row:
if label == "=":
button = Button(
text=label,
background_color = (0, 1, 1, 1),
pos_hint={"center_x": 0.5, "center_y": 0.5})
button.bind(on_press=self.calculate)
h_layout.add_widget(button)
continue
button = Button(
text=label,
pos_hint={"center_x": 0.4, "center_y": 0.4})
button.bind(on_press=self.on_button_press)
h_layout.add_widget(button)
main_layout.add_widget(h_layout)
return main_layout
def on_button_press(self, instance):
current = self.calculation.text
button_text = instance.text
if button_text == "C":
# Clear the calculation widget
self.calculation.text = ""
elif button_text == "Del":
self.calculation.text = self.calculation.text[:-1]
elif button_text == "Ans":
self.calculation.text += self.output.text
else:
if current and (
self.last_was_operator and button_text in self.operators):
# Don't add two operators right after each other
return
elif current == "" and ( button_text in self.operators or button_text == "0"):
# First character cannot be an operator or zero
return
else:
new_text = current + button_text
self.calculation.text = new_text
self.last_button = button_text
self.last_was_operator = self.last_button in self.operators
def calculate(self, instance):
text = self.calculation.text
if text:
try:
#replacing operators to python operators
y = text.replace("^", "**")
x = y.replace("%","*(0.01)*")
self.output.text = x.replace("Mod","%")
calculation = str(eval(self.output.text))
self.output.text = calculation
self.calculation.text = ""
except Exception:
self.output.text = "Error"
self.calculation.text = ""
if __name__ == "__main__":
app = MainApp()
app.run() | 0.421552 | 0.204878 |
import os
import sys
import requests
import time
from datetime import date, datetime, timedelta
import json
import pickle
import logging
from collections import deque
from configparser import ConfigParser
from optparse import OptionParser
from ifobfuscate import decode
import warnings
warnings.filterwarnings('ignore')
class GeneratePredictedIncidentServiceNowTicket:
'''
Get predicted incident data from InsightFinder and send to ServiceNow
'''
def __init__(self):
if os.path.exists(config_path):
self.get_config_vars()
else:
message = "No config file found. Exiting."
print(message)
logger.error(message)
sys.exit(1)
self.incident_record = incident_record
def get_config_vars(self):
'''
Get config variables from the config file
'''
config = ConfigParser()
config.read(config_path)
self.insightFinder_vars = {}
self.insightFinder_vars['host_url'] = config.get('insightFinder_vars', 'host_url')
self.insightFinder_vars['http_proxy'] = config.get('insightFinder_vars', 'http_proxy')
self.insightFinder_vars['https_proxy'] = config.get('insightFinder_vars', 'https_proxy')
self.insightFinder_vars['licenseKey'] = config.get('insightFinder_vars', 'licenseKey')
self.insightFinder_vars['retries'] = config.getint('insightFinder_vars', 'retries')
self.insightFinder_vars['sleep_seconds'] = config.getint('insightFinder_vars', 'sleep_seconds')
self.serviceNow_vars = {}
self.serviceNow_vars['host_url'] = config.get('serviceNow_vars', 'host_url')
self.serviceNow_vars['http_proxy'] = config.get('serviceNow_vars', 'http_proxy')
self.serviceNow_vars['https_proxy'] = config.get('serviceNow_vars', 'https_proxy')
self.serviceNow_vars['api'] = config.get('serviceNow_vars', 'api')
self.serviceNow_vars['username'] = config.get('serviceNow_vars', 'username')
self.serviceNow_vars['password'] = decode(config.get('serviceNow_vars', 'password'))
self.serviceNow_vars['target_table'] = config.get('serviceNow_vars', 'target_table')
self.serviceNow_vars['retries'] = config.getint('serviceNow_vars', 'retries')
self.serviceNow_vars['sleep_seconds'] = config.getint('serviceNow_vars', 'sleep_seconds')
self.serviceNow_vars['dampening_minutes'] = config.getint('serviceNow_vars', 'dampening_minutes')
self.payload_vars = {}
self.payload_vars['environment_name'] = config.get('payload_vars', 'environment_name')
self.payload_vars['system_id_list'] = config.get('payload_vars', 'system_id_list').split(',')
self.payload_vars['system_id_list'] = str([{'id': id.strip()} for id in self.payload_vars['system_id_list']])
self.payload_vars['customer_name'] = config.get('payload_vars', 'customer_name')
self.payload_vars['start_date'] = config.get('payload_vars', 'start_date')
self.payload_vars['end_date'] = config.get('payload_vars', 'end_date')
parser = OptionParser()
parser.add_option('-t', '--testing', action='store_true', dest='testing', default=False,
help='Set to testing mode (do not send data).')
(self.options, args) = parser.parse_args()
def post_all_incidents(self):
'''
Process all incidents between the start and end dates
If either date variable is a null value, it is set to today's datetime
'''
time_now = datetime.now()
start_date = datetime.fromisoformat(self.payload_vars['start_date']) if self.payload_vars['start_date'] else time_now
end_date = datetime.fromisoformat(self.payload_vars['end_date']) if self.payload_vars['end_date'] else time_now
if start_date > end_date:
message = "WARNING: Start Date ({}) > End Date ({}). No incidents would be transferred.".format(
start_date, end_date)
print(message)
logger.info(message)
day = start_date
while day <= end_date:
data = self.get_predicted_incident_json(day)
if not self.options.testing:
self.post_day_incidents(data)
day += timedelta(days=1)
def get_predicted_incident_json(self, day):
'''
Get predicted incident data for EXACTLY a day from InsightFinder in JSON format
RETURNS: dict/JSON
'''
url = self.insightFinder_vars['host_url'] + "/api/v2/servicenowagentpush"
proxies = {}
if len(self.insightFinder_vars['http_proxy']) > 0:
proxies['http'] = self.insightFinder_vars['http_proxy']
if len(self.insightFinder_vars['https_proxy']) > 0:
proxies['https'] = self.insightFinder_vars['https_proxy']
data = {
'environmentName': self.payload_vars['environment_name'],
'systemIds': self.payload_vars['system_id_list'],
'customerName': self.payload_vars['customer_name'],
'licenseKey': self.insightFinder_vars['licenseKey'],
'targetTable': self.serviceNow_vars['target_table'],
'startTime': int(time.mktime(day.astimezone().timetuple())) * 1000
}
attempts = 1
response = requests.get(url, params=data, proxies=proxies, verify=False)
while response.status_code != 200 and attempts < self.insightFinder_vars['retries']:
print("Failed to get data for {}. Retrying in {} seconds.".format(
day, self.insightFinder_vars['sleep_seconds']))
time.sleep(self.insightFinder_vars['sleep_seconds'])
response = requests.get(url, params=data, proxies=proxies, verify=False)
attempts += 1
if response.status_code == 200:
data = response.json()
message = "\nSuccessfully retrieved {} incidents for {} from InsightFinder.".format(len(data), day)
print(message)
logger.info(message)
return data
else:
message = "Failed to get data for {} in {} attempts. Check logs for details.".format(
day, self.insightFinder_vars['retries'])
print(message)
logger.warning("{} Status Code: {}. Response Text: {}. Response URL: {}.".format(
message, response.status_code, response.text, response.url))
return {}
def post_day_incidents(self, day_data):
'''
Process all incidents in a day's worth of incident data
EXCEPT those re-observed in 'dampening_minutes' interval
'''
total_incidents = len(day_data)
queued = 0
dampened = 0
for incident_data in day_data:
print("Total incidents: {} ; Queued for ServiceNow : {} ; Dampened : {}".format(
total_incidents, queued, dampened), end='\r')
incident_key = incident_data['incidentId']
incident_last_observed = self.incident_record.get(incident_key, None)
if incident_last_observed and ((incident_data['startTime'] - incident_last_observed) / (1000*60)
< self.serviceNow_vars['dampening_minutes']):
dampened += 1
continue
payload = self.create_serviceNow_payload(incident_data)
payload_queue.append(payload)
self.incident_record[incident_key] = incident_data['startTime']
queued += 1
message = "Total incidents: {} ; Queued for ServiceNow : {} ; Dampened : {}".format(
total_incidents, queued, dampened)
print(message)
logger.info(message)
queue_len = len(payload_queue)
while payload_queue:
if self.post_serviceNow_ticket(payload_queue[0]):
payload_queue.popleft()
print("Total incidents in the queue: {} ; Successfully sent: {}".format(
queue_len, queue_len - len(payload_queue)), end='\r')
else:
break
message = "Total incidents in the queue: {} ; Successfully sent: {}".format(
queue_len, queue_len - len(payload_queue))
print(message)
logger.info(message)
def create_serviceNow_payload(self, incident_data):
'''
Create single incident data paylaod to send to ServiceNow
RETURNS: str/JSON
'''
payload = incident_data.copy()
del payload['incidentId'], payload['startTime']
payload = json.dumps(payload)
return payload
def post_serviceNow_ticket(self, payload):
'''
Send a single incident ticket to ServiceNow
RETURNS: Boolean // whether the ticket was posted successfully
'''
url = self.serviceNow_vars['host_url'] + self.serviceNow_vars['api']
headers = {"Content-Type": "application/json", "Accept": "application/json"}
proxies = {}
if len(self.serviceNow_vars['http_proxy']) > 0:
proxies['http'] = self.serviceNow_vars['http_proxy']
if len(self.serviceNow_vars['https_proxy']) > 0:
proxies['https'] = self.serviceNow_vars['https_proxy']
attempts = 1
response = requests.post(url, auth=(self.serviceNow_vars['username'], self.serviceNow_vars['password']),
headers=headers, data=payload, proxies=proxies, verify=False)
while response.status_code != 201 and attempts < self.serviceNow_vars['retries']:
print("Failed to post data to ServiceNow. Retrying in {} seconds.".format(
self.serviceNow_vars['sleep_seconds']))
time.sleep(self.serviceNow_vars['sleep_seconds'])
response = requests.post(url, auth=(self.serviceNow_vars['username'], self.serviceNow_vars['password']),
headers=headers, data=payload, proxies=proxies, verify=False)
attempts += 1
if response.status_code != 201:
message = "Failed to post data to ServiceNow in {} attempts. Check logs for details.".format(
self.serviceNow_vars['retries'])
print(message)
logger.warning("Status Code: {}. Response Text: {}. Response URL: {}.".format(
response.status_code, response.text, response.url))
return response.status_code == 201
if __name__ == '__main__':
config_path = 'config.ini'
incident_record_path = 'incident_record.pkl'
payload_queue_path = 'payload_queue.pkl'
log_record_path = 'log_record.log'
'''
The incident_record pickle stores the past post requests made as a dictionary object
** ('projectName', 'componentName', 'patternId') -> latest_timeStamp_of_corresponding_post_request **
No same incident (defined by the key) will be posted before or within the 'dampening_minutes' interval of last such request
Delete the pickle file to reset the record (may especially be required for processing historical data)
'''
if os.path.exists(incident_record_path):
with open(incident_record_path, 'rb') as handle:
incident_record = pickle.load(handle)
else:
incident_record = {}
if os.path.exists(payload_queue_path):
with open(payload_queue_path, 'rb') as handle:
payload_queue = pickle.load(handle)
else:
payload_queue = deque()
logging.basicConfig(filename = log_record_path,
format = ('%(asctime)s %(filename)s: %(levelname)s: %(funcName)s(): %(lineno)d:\t %(message)s'))
logger = logging.getLogger()
logger.setLevel(logging.INFO)
try:
generator = GeneratePredictedIncidentServiceNowTicket()
generator.post_all_incidents()
except Exception as e:
print("ERROR. Check logs.")
logger.error(e, exc_info=True)
with open(incident_record_path, 'wb') as handle:
pickle.dump(incident_record, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(payload_queue_path, 'wb') as handle:
pickle.dump(payload_queue, handle, protocol=pickle.HIGHEST_PROTOCOL) | insightFinderToServiceNow/GeneratePredictedIncidentServiceNowTicket.py |
import os
import sys
import requests
import time
from datetime import date, datetime, timedelta
import json
import pickle
import logging
from collections import deque
from configparser import ConfigParser
from optparse import OptionParser
from ifobfuscate import decode
import warnings
warnings.filterwarnings('ignore')
class GeneratePredictedIncidentServiceNowTicket:
'''
Get predicted incident data from InsightFinder and send to ServiceNow
'''
def __init__(self):
if os.path.exists(config_path):
self.get_config_vars()
else:
message = "No config file found. Exiting."
print(message)
logger.error(message)
sys.exit(1)
self.incident_record = incident_record
def get_config_vars(self):
'''
Get config variables from the config file
'''
config = ConfigParser()
config.read(config_path)
self.insightFinder_vars = {}
self.insightFinder_vars['host_url'] = config.get('insightFinder_vars', 'host_url')
self.insightFinder_vars['http_proxy'] = config.get('insightFinder_vars', 'http_proxy')
self.insightFinder_vars['https_proxy'] = config.get('insightFinder_vars', 'https_proxy')
self.insightFinder_vars['licenseKey'] = config.get('insightFinder_vars', 'licenseKey')
self.insightFinder_vars['retries'] = config.getint('insightFinder_vars', 'retries')
self.insightFinder_vars['sleep_seconds'] = config.getint('insightFinder_vars', 'sleep_seconds')
self.serviceNow_vars = {}
self.serviceNow_vars['host_url'] = config.get('serviceNow_vars', 'host_url')
self.serviceNow_vars['http_proxy'] = config.get('serviceNow_vars', 'http_proxy')
self.serviceNow_vars['https_proxy'] = config.get('serviceNow_vars', 'https_proxy')
self.serviceNow_vars['api'] = config.get('serviceNow_vars', 'api')
self.serviceNow_vars['username'] = config.get('serviceNow_vars', 'username')
self.serviceNow_vars['password'] = decode(config.get('serviceNow_vars', 'password'))
self.serviceNow_vars['target_table'] = config.get('serviceNow_vars', 'target_table')
self.serviceNow_vars['retries'] = config.getint('serviceNow_vars', 'retries')
self.serviceNow_vars['sleep_seconds'] = config.getint('serviceNow_vars', 'sleep_seconds')
self.serviceNow_vars['dampening_minutes'] = config.getint('serviceNow_vars', 'dampening_minutes')
self.payload_vars = {}
self.payload_vars['environment_name'] = config.get('payload_vars', 'environment_name')
self.payload_vars['system_id_list'] = config.get('payload_vars', 'system_id_list').split(',')
self.payload_vars['system_id_list'] = str([{'id': id.strip()} for id in self.payload_vars['system_id_list']])
self.payload_vars['customer_name'] = config.get('payload_vars', 'customer_name')
self.payload_vars['start_date'] = config.get('payload_vars', 'start_date')
self.payload_vars['end_date'] = config.get('payload_vars', 'end_date')
parser = OptionParser()
parser.add_option('-t', '--testing', action='store_true', dest='testing', default=False,
help='Set to testing mode (do not send data).')
(self.options, args) = parser.parse_args()
def post_all_incidents(self):
'''
Process all incidents between the start and end dates
If either date variable is a null value, it is set to today's datetime
'''
time_now = datetime.now()
start_date = datetime.fromisoformat(self.payload_vars['start_date']) if self.payload_vars['start_date'] else time_now
end_date = datetime.fromisoformat(self.payload_vars['end_date']) if self.payload_vars['end_date'] else time_now
if start_date > end_date:
message = "WARNING: Start Date ({}) > End Date ({}). No incidents would be transferred.".format(
start_date, end_date)
print(message)
logger.info(message)
day = start_date
while day <= end_date:
data = self.get_predicted_incident_json(day)
if not self.options.testing:
self.post_day_incidents(data)
day += timedelta(days=1)
def get_predicted_incident_json(self, day):
'''
Get predicted incident data for EXACTLY a day from InsightFinder in JSON format
RETURNS: dict/JSON
'''
url = self.insightFinder_vars['host_url'] + "/api/v2/servicenowagentpush"
proxies = {}
if len(self.insightFinder_vars['http_proxy']) > 0:
proxies['http'] = self.insightFinder_vars['http_proxy']
if len(self.insightFinder_vars['https_proxy']) > 0:
proxies['https'] = self.insightFinder_vars['https_proxy']
data = {
'environmentName': self.payload_vars['environment_name'],
'systemIds': self.payload_vars['system_id_list'],
'customerName': self.payload_vars['customer_name'],
'licenseKey': self.insightFinder_vars['licenseKey'],
'targetTable': self.serviceNow_vars['target_table'],
'startTime': int(time.mktime(day.astimezone().timetuple())) * 1000
}
attempts = 1
response = requests.get(url, params=data, proxies=proxies, verify=False)
while response.status_code != 200 and attempts < self.insightFinder_vars['retries']:
print("Failed to get data for {}. Retrying in {} seconds.".format(
day, self.insightFinder_vars['sleep_seconds']))
time.sleep(self.insightFinder_vars['sleep_seconds'])
response = requests.get(url, params=data, proxies=proxies, verify=False)
attempts += 1
if response.status_code == 200:
data = response.json()
message = "\nSuccessfully retrieved {} incidents for {} from InsightFinder.".format(len(data), day)
print(message)
logger.info(message)
return data
else:
message = "Failed to get data for {} in {} attempts. Check logs for details.".format(
day, self.insightFinder_vars['retries'])
print(message)
logger.warning("{} Status Code: {}. Response Text: {}. Response URL: {}.".format(
message, response.status_code, response.text, response.url))
return {}
def post_day_incidents(self, day_data):
'''
Process all incidents in a day's worth of incident data
EXCEPT those re-observed in 'dampening_minutes' interval
'''
total_incidents = len(day_data)
queued = 0
dampened = 0
for incident_data in day_data:
print("Total incidents: {} ; Queued for ServiceNow : {} ; Dampened : {}".format(
total_incidents, queued, dampened), end='\r')
incident_key = incident_data['incidentId']
incident_last_observed = self.incident_record.get(incident_key, None)
if incident_last_observed and ((incident_data['startTime'] - incident_last_observed) / (1000*60)
< self.serviceNow_vars['dampening_minutes']):
dampened += 1
continue
payload = self.create_serviceNow_payload(incident_data)
payload_queue.append(payload)
self.incident_record[incident_key] = incident_data['startTime']
queued += 1
message = "Total incidents: {} ; Queued for ServiceNow : {} ; Dampened : {}".format(
total_incidents, queued, dampened)
print(message)
logger.info(message)
queue_len = len(payload_queue)
while payload_queue:
if self.post_serviceNow_ticket(payload_queue[0]):
payload_queue.popleft()
print("Total incidents in the queue: {} ; Successfully sent: {}".format(
queue_len, queue_len - len(payload_queue)), end='\r')
else:
break
message = "Total incidents in the queue: {} ; Successfully sent: {}".format(
queue_len, queue_len - len(payload_queue))
print(message)
logger.info(message)
def create_serviceNow_payload(self, incident_data):
'''
Create single incident data paylaod to send to ServiceNow
RETURNS: str/JSON
'''
payload = incident_data.copy()
del payload['incidentId'], payload['startTime']
payload = json.dumps(payload)
return payload
def post_serviceNow_ticket(self, payload):
'''
Send a single incident ticket to ServiceNow
RETURNS: Boolean // whether the ticket was posted successfully
'''
url = self.serviceNow_vars['host_url'] + self.serviceNow_vars['api']
headers = {"Content-Type": "application/json", "Accept": "application/json"}
proxies = {}
if len(self.serviceNow_vars['http_proxy']) > 0:
proxies['http'] = self.serviceNow_vars['http_proxy']
if len(self.serviceNow_vars['https_proxy']) > 0:
proxies['https'] = self.serviceNow_vars['https_proxy']
attempts = 1
response = requests.post(url, auth=(self.serviceNow_vars['username'], self.serviceNow_vars['password']),
headers=headers, data=payload, proxies=proxies, verify=False)
while response.status_code != 201 and attempts < self.serviceNow_vars['retries']:
print("Failed to post data to ServiceNow. Retrying in {} seconds.".format(
self.serviceNow_vars['sleep_seconds']))
time.sleep(self.serviceNow_vars['sleep_seconds'])
response = requests.post(url, auth=(self.serviceNow_vars['username'], self.serviceNow_vars['password']),
headers=headers, data=payload, proxies=proxies, verify=False)
attempts += 1
if response.status_code != 201:
message = "Failed to post data to ServiceNow in {} attempts. Check logs for details.".format(
self.serviceNow_vars['retries'])
print(message)
logger.warning("Status Code: {}. Response Text: {}. Response URL: {}.".format(
response.status_code, response.text, response.url))
return response.status_code == 201
if __name__ == '__main__':
config_path = 'config.ini'
incident_record_path = 'incident_record.pkl'
payload_queue_path = 'payload_queue.pkl'
log_record_path = 'log_record.log'
'''
The incident_record pickle stores the past post requests made as a dictionary object
** ('projectName', 'componentName', 'patternId') -> latest_timeStamp_of_corresponding_post_request **
No same incident (defined by the key) will be posted before or within the 'dampening_minutes' interval of last such request
Delete the pickle file to reset the record (may especially be required for processing historical data)
'''
if os.path.exists(incident_record_path):
with open(incident_record_path, 'rb') as handle:
incident_record = pickle.load(handle)
else:
incident_record = {}
if os.path.exists(payload_queue_path):
with open(payload_queue_path, 'rb') as handle:
payload_queue = pickle.load(handle)
else:
payload_queue = deque()
logging.basicConfig(filename = log_record_path,
format = ('%(asctime)s %(filename)s: %(levelname)s: %(funcName)s(): %(lineno)d:\t %(message)s'))
logger = logging.getLogger()
logger.setLevel(logging.INFO)
try:
generator = GeneratePredictedIncidentServiceNowTicket()
generator.post_all_incidents()
except Exception as e:
print("ERROR. Check logs.")
logger.error(e, exc_info=True)
with open(incident_record_path, 'wb') as handle:
pickle.dump(incident_record, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(payload_queue_path, 'wb') as handle:
pickle.dump(payload_queue, handle, protocol=pickle.HIGHEST_PROTOCOL) | 0.305594 | 0.070528 |
from app.database.models import SalesVolumes
from app.database import db
from app.log import logger
def create_new_record(record):
with db.auto_commit_db():
new_sales = SalesVolumes(pid=record['pid'], sid=record['sid'], pname=record['pname'], date=record['date'], sales=record['sales'])
db.session.add(new_sales)
db.session.flush()
rid = new_sales.id
return rid
def get_record_one_day(pid, date):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
return record
def get_shop_records_one_day(sid, date):
records = SalesVolumes.query.filter_by(sid=sid, date=date).all()
return records
def get_shop_records_by_period(sid, start, end):
records = SalesVolumes.query.filter_by(sid=sid) \
.filter((SalesVolumes.date <= end) & (SalesVolumes.date >= start)) \
.order_by(SalesVolumes.date).all()
return records
def get_records_by_period(pid, start, end):
records = SalesVolumes.query.filter_by(pid=pid) \
.filter((SalesVolumes.date <= end) & (SalesVolumes.date >= start)) \
.order_by(SalesVolumes.date).all()
return records
def update_record_sales(pid, date, sales):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
if record is not None:
record.sales = sales
db.session.commit()
return True
else:
return False
def delete_record(pid, date):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
if record is not None:
db.session.delete(record)
db.session.commit()
logger.info(f'delete record (pid:{pid}, date:{date}) succeed')
return True
else:
logger.info(f'delete record (pid:{pid}, date:{date}) failed, record not exists')
return False
def delete_records_by_date(date):
SalesVolumes.query.filter_by(date=date).delete()
db.session.commit()
logger.info(f'delete records (date:{date}) succeed')
return True
def delete_records_by_pid(pid):
SalesVolumes.query.filter_by(pid=pid).delete()
db.session.commit()
logger.info(f'delete records (pid:{pid}) succeed')
return True
def delete_records_by_sid(sid):
SalesVolumes.query.filter_by(sid=sid).delete()
db.session.commit()
logger.info(f'delete records (sid:{sid}) succeed')
return True | SA-be/app/database/salesVolumes.py | from app.database.models import SalesVolumes
from app.database import db
from app.log import logger
def create_new_record(record):
with db.auto_commit_db():
new_sales = SalesVolumes(pid=record['pid'], sid=record['sid'], pname=record['pname'], date=record['date'], sales=record['sales'])
db.session.add(new_sales)
db.session.flush()
rid = new_sales.id
return rid
def get_record_one_day(pid, date):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
return record
def get_shop_records_one_day(sid, date):
records = SalesVolumes.query.filter_by(sid=sid, date=date).all()
return records
def get_shop_records_by_period(sid, start, end):
records = SalesVolumes.query.filter_by(sid=sid) \
.filter((SalesVolumes.date <= end) & (SalesVolumes.date >= start)) \
.order_by(SalesVolumes.date).all()
return records
def get_records_by_period(pid, start, end):
records = SalesVolumes.query.filter_by(pid=pid) \
.filter((SalesVolumes.date <= end) & (SalesVolumes.date >= start)) \
.order_by(SalesVolumes.date).all()
return records
def update_record_sales(pid, date, sales):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
if record is not None:
record.sales = sales
db.session.commit()
return True
else:
return False
def delete_record(pid, date):
record = SalesVolumes.query.filter_by(pid=pid, date=date).first()
if record is not None:
db.session.delete(record)
db.session.commit()
logger.info(f'delete record (pid:{pid}, date:{date}) succeed')
return True
else:
logger.info(f'delete record (pid:{pid}, date:{date}) failed, record not exists')
return False
def delete_records_by_date(date):
SalesVolumes.query.filter_by(date=date).delete()
db.session.commit()
logger.info(f'delete records (date:{date}) succeed')
return True
def delete_records_by_pid(pid):
SalesVolumes.query.filter_by(pid=pid).delete()
db.session.commit()
logger.info(f'delete records (pid:{pid}) succeed')
return True
def delete_records_by_sid(sid):
SalesVolumes.query.filter_by(sid=sid).delete()
db.session.commit()
logger.info(f'delete records (sid:{sid}) succeed')
return True | 0.376165 | 0.125842 |
import logging
import logging.config
import os
import torch
import pickle
import numpy as np
logger=logging.getLogger(__name__)
def init_logging(exp_dir, config_path='config/logging_config.yaml'):
"""
initial logging module with config
:param config_path:
:return:
"""
import yaml, sys
try:
with open(config_path, 'r') as f:
config = yaml.load(f.read(), Loader=yaml.FullLoader)
config["handlers"]["info_file_handler"]["filename"] = os.path.join(exp_dir, "info.log")
config["handlers"]["time_file_handler"]["filename"] = os.path.join(exp_dir, "time.log")
config["handlers"]["error_file_handler"]["filename"] = os.path.join(exp_dir, "error.log")
logging.config.dictConfig(config)
except IOError:
sys.stderr.write('logging config file "%s" not found' % config_path)
logging.basicConfig(level=logging.DEBUG)
def get_hamming_dist(img_code, cap_code):
if torch.cuda.is_available():
device='cuda'
else:
device='cpu'
code_len = img_code.shape[1]
similarity_matrix = []
for i in range(0, img_code.shape[0], 10): # 分片计算防止爆内存
cur_query_code = img_code[i:i + 10].to(device) # size(10,code_len)
cur_matrix=[]
for j in range(0,cap_code.shape[0],1000):
cur_ref_code=cap_code[j:j+1000].to(device)
cur_part=(code_len - (cur_query_code.unsqueeze(1) * cur_ref_code.unsqueeze(0)).sum(dim=-1)) / 2 # size(10,1000)
cur_part=cur_part.cpu()
cur_matrix.append(cur_part)
cur_matrix = torch.cat(cur_matrix,dim=-1).cpu()
similarity_matrix.append(cur_matrix)
similarity_matrix = torch.cat(similarity_matrix, dim=0).cpu()
return similarity_matrix
def save_vector_to_file(data, file_name):
pickle.dump(data, open(file_name, 'wb'))
logger.info('save vector file to {}'.format(file_name))
def save_similarity_matrix(matrix_data,save_path):
np.save(save_path,matrix_data)
logger.info('save similarity matrix data into file: {}'.format(save_path)) | utils.py | import logging
import logging.config
import os
import torch
import pickle
import numpy as np
logger=logging.getLogger(__name__)
def init_logging(exp_dir, config_path='config/logging_config.yaml'):
"""
initial logging module with config
:param config_path:
:return:
"""
import yaml, sys
try:
with open(config_path, 'r') as f:
config = yaml.load(f.read(), Loader=yaml.FullLoader)
config["handlers"]["info_file_handler"]["filename"] = os.path.join(exp_dir, "info.log")
config["handlers"]["time_file_handler"]["filename"] = os.path.join(exp_dir, "time.log")
config["handlers"]["error_file_handler"]["filename"] = os.path.join(exp_dir, "error.log")
logging.config.dictConfig(config)
except IOError:
sys.stderr.write('logging config file "%s" not found' % config_path)
logging.basicConfig(level=logging.DEBUG)
def get_hamming_dist(img_code, cap_code):
if torch.cuda.is_available():
device='cuda'
else:
device='cpu'
code_len = img_code.shape[1]
similarity_matrix = []
for i in range(0, img_code.shape[0], 10): # 分片计算防止爆内存
cur_query_code = img_code[i:i + 10].to(device) # size(10,code_len)
cur_matrix=[]
for j in range(0,cap_code.shape[0],1000):
cur_ref_code=cap_code[j:j+1000].to(device)
cur_part=(code_len - (cur_query_code.unsqueeze(1) * cur_ref_code.unsqueeze(0)).sum(dim=-1)) / 2 # size(10,1000)
cur_part=cur_part.cpu()
cur_matrix.append(cur_part)
cur_matrix = torch.cat(cur_matrix,dim=-1).cpu()
similarity_matrix.append(cur_matrix)
similarity_matrix = torch.cat(similarity_matrix, dim=0).cpu()
return similarity_matrix
def save_vector_to_file(data, file_name):
pickle.dump(data, open(file_name, 'wb'))
logger.info('save vector file to {}'.format(file_name))
def save_similarity_matrix(matrix_data,save_path):
np.save(save_path,matrix_data)
logger.info('save similarity matrix data into file: {}'.format(save_path)) | 0.252937 | 0.059674 |
import os
import random
import traceback
import discord
from discord.ext import commands, tasks
GUILD = 384811165949231104
IMG_DIR = './data/server-icons'
PLAN_Z = 507429352720433152
def find_file(i):
images = os.listdir(IMG_DIR)
for img_name in images:
if img_name.startswith(str(i)):
return f'{IMG_DIR}/{img_name}'
return
def shuffle_server_icons():
names = list(range(len(os.listdir(IMG_DIR))))
random.shuffle(names)
for img_name in os.listdir(IMG_DIR):
ext = img_name.split('.')[-1]
os.rename(f'{IMG_DIR}/{img_name}', f'{IMG_DIR}/{names.pop()}.{ext}')
class ServerIcon(commands.Cog):
"""Automatic server icon rotation."""
def __init__(self, bot):
self.bot = bot
# self.check_if_new_week.start()
async def cog_command_error(self, ctx, error):
if not isinstance(error, commands.CheckFailure):
await ctx.send(f"```py\n{error.__class__.__name__}: {error}\n```")
async def rotate_server_icon(self):
try:
guild = self.bot.get_guild(GUILD)
img = random.choice(os.listdir(IMG_DIR))
img_path = f"{IMG_DIR}/{img}"
with open(img_path, 'rb') as fp:
icon = fp.read()
await guild.edit(icon=icon)
await self.log(f"Set server icon to `{img_path}`.")
except Exception as e:
error = ''.join(traceback.format_exception(e.__class__, e, e.__traceback__))
await self.log(f"Error rotating server icon:```\n{error}\n```")
async def log(self, msg):
await self.bot.get_channel(PLAN_Z).send(msg)
@tasks.loop(hours=1)
async def check_if_new_week(self):
# TODO
pass
@commands.group(invoke_without_command=True)
async def icons(self, ctx):
"""Base command for controlling server icon."""
images = os.listdir(IMG_DIR)
count = len(images)
await ctx.send(f"Found `{count}` total images: ```py\n{images}\n```")
@icons.command()
async def rotate(self, ctx):
"""Rotate to the next server icon."""
await self.rotate_server_icon()
@icons.command()
async def upload(self, ctx):
"""Add a new image to the icon folder."""
attachment = ctx.message.attachments[0]
filename = f"{IMG_DIR}/{attachment.filename}"
await attachment.save(filename)
await ctx.send(f"Saved as `{filename}`.")
def setup(bot):
bot.add_cog(ServerIcon(bot)) | archive/server_icon.py | import os
import random
import traceback
import discord
from discord.ext import commands, tasks
GUILD = 384811165949231104
IMG_DIR = './data/server-icons'
PLAN_Z = 507429352720433152
def find_file(i):
images = os.listdir(IMG_DIR)
for img_name in images:
if img_name.startswith(str(i)):
return f'{IMG_DIR}/{img_name}'
return
def shuffle_server_icons():
names = list(range(len(os.listdir(IMG_DIR))))
random.shuffle(names)
for img_name in os.listdir(IMG_DIR):
ext = img_name.split('.')[-1]
os.rename(f'{IMG_DIR}/{img_name}', f'{IMG_DIR}/{names.pop()}.{ext}')
class ServerIcon(commands.Cog):
"""Automatic server icon rotation."""
def __init__(self, bot):
self.bot = bot
# self.check_if_new_week.start()
async def cog_command_error(self, ctx, error):
if not isinstance(error, commands.CheckFailure):
await ctx.send(f"```py\n{error.__class__.__name__}: {error}\n```")
async def rotate_server_icon(self):
try:
guild = self.bot.get_guild(GUILD)
img = random.choice(os.listdir(IMG_DIR))
img_path = f"{IMG_DIR}/{img}"
with open(img_path, 'rb') as fp:
icon = fp.read()
await guild.edit(icon=icon)
await self.log(f"Set server icon to `{img_path}`.")
except Exception as e:
error = ''.join(traceback.format_exception(e.__class__, e, e.__traceback__))
await self.log(f"Error rotating server icon:```\n{error}\n```")
async def log(self, msg):
await self.bot.get_channel(PLAN_Z).send(msg)
@tasks.loop(hours=1)
async def check_if_new_week(self):
# TODO
pass
@commands.group(invoke_without_command=True)
async def icons(self, ctx):
"""Base command for controlling server icon."""
images = os.listdir(IMG_DIR)
count = len(images)
await ctx.send(f"Found `{count}` total images: ```py\n{images}\n```")
@icons.command()
async def rotate(self, ctx):
"""Rotate to the next server icon."""
await self.rotate_server_icon()
@icons.command()
async def upload(self, ctx):
"""Add a new image to the icon folder."""
attachment = ctx.message.attachments[0]
filename = f"{IMG_DIR}/{attachment.filename}"
await attachment.save(filename)
await ctx.send(f"Saved as `{filename}`.")
def setup(bot):
bot.add_cog(ServerIcon(bot)) | 0.386069 | 0.2296 |
import numpy as np
import histo_funcs as hf
from scipy.interpolate import interp1d
def get_shifts(ref, N, max_shift=150, global_shift_fun=None):
"""
This computes the optimal shift between a 1d reference array and the
columns of a 2d data array using an fft based cross correlation. The
optimal shift is assumed to be where the cross correlation is maximal.
Because no padding or normalization is applied, this works best for
relatively small shifts. This is explicitly enforced by the ``max_shift``
parameter.
Parameters
----------
ref : real numeric 1d array (shape is nx1)
The reference array. Should be in a nx1 column array.
N : real numeric 2d array (shape is nxm)
The data array. Each column of this array will be cross-correlated to
the reference array using an fft.
max_shift : int (default is 150)
The maximum number of bins that are searched for the cross-correlation.
The cross correlation is set to zero for lags less than -max_shift and
greater than +max_shift.
global_shift_fun : function handle (default is None)
This function applies an additional subtractive shift to the returned
shift array. I generally set this to numpy.mean or numpy.median, but
more complex shift functions could be used (including lambdas).
Returns
-------
shifts : numeric 1d array
The shift (in units of bin index) that was required to align each
column of data to the reference spectrum.
"""
# Note: Use real valued fft to improve speed/memory
# FFT the ref and data arrays
rfft_ref = np.fft.rfft(ref, axis=1)
rfft_N = np.fft.rfft(N, axis=1)
# Compute the cross-correlation and take the inverse fft
xc = np.fft.irfft(rfft_N*np.conj(rfft_ref), axis=1)
# Set the cross correlation to zero for lags greater than max_shift
xc[:, max_shift:xc.shape[1]-max_shift] = 0
# Find the lags corresponding to the maximum of the cross correlation and
# then shift them to correspond to the appropriate origin.
max_idxs = np.argmax(xc, axis=1)
max_idxs[max_idxs > xc.shape[1]//2] = \
max_idxs[max_idxs > xc.shape[1]//2] - xc.shape[1]
# Apply a global_shift_fun shift if specified
if global_shift_fun is not None:
shifts = max_idxs - global_shift_fun(max_idxs)
else:
shifts = max_idxs
return shifts
def get_all_scale_coeffs(event_dat,
max_scale=1.1,
roi=None,
cts_per_chunk=2**10,
delta_logdat=5e-4):
"""
This attempts to best align event data. The basic assumption is that if
the ``event_dat`` is binned into a 2d history (histogram) then there are
clearly defined features (i.e. peaks) that will shift around in a
systematic manner. When this data is projected onto a single dimension
then any features (peaks) will be broader than they really should be. This
algorithm discretizes the event_dat into `chunks' and attempts to align
each chunk to a reference dataset using a scalar multiplicative
coefficient. The reference dataset is the middle 50% of the ``event_dat``.
The alignment is performed using a logarithm based cross correlation
approach. Two iterations of this algorithm are performed before the result
is returned. In all tests performed thus far a single iteration was
sufficient however we used two iterations in an abundance of caution.
Parameters
----------
event_dat : real float 1d array
Event data. Typically the data is either the mass-to-charge or
time-of-flight of each event. The ordering of the data is assumed to
be chronological (i.e. the order in which they were detected).
max_scale : real float (default is 1.1)
The maximum possible scale factor allowed (relative to the reference
data)
roi : real numeric list or array (default is [0.5,200])
The domain that the data should be evaluated over.
Specified as [min, max] values.
cts_per_chunk : int (default is 1024)
The number of events to be collected into a single `chunk' of data.
delta_logdat : real float (default is 5e-4)
The discretization of the log(data) over the roi specified. Smaller
deltas are more time/memory intensive. For deltas much less than one,
this effectively gives a discretization/resolution of the
multiplicative factor of 1+delta_logdat. For the atom probe data I
have worked with, the noise on the shift is on the order of 1e-3 and
so setting the delta to be smaller than this, ensures that the
discretization error is not a significant problem.
Returns
-------
eventwise_scales : real float array
An array that contains the computed scale factor for each event that
best aligns the data. To correct the data, just divide the event_dat
array by the eventwise_scales array.
"""
if roi is None:
roi = [0.5, 200]
log_roi = np.log(roi)
# Take the log of data
logdat = np.log(event_dat)
# Create the histogram. Compute centers and delta y
N, seq_edges, logdat_edges = \
hf.create_histogram(logdat,
roi=log_roi,
cts_per_chunk=cts_per_chunk,
delta_dat=delta_logdat)
seq_centers, logdat_centers = hf.edges_to_centers(seq_edges, logdat_edges)
# print('specified delta_logdat = '+str(delta_logdat))
delta_logdat = logdat_edges[1]-logdat_edges[0]
# print('actual delta_logdat = '+str(delta_logdat))
# Initialize the total eventwise log(dat) shift
eventwise_logdat_shifts = np.zeros(event_dat.size)
# Do one iteration with the center 50% of the data as a reference
# Note: Make it is 2d (even though it is just a single column array)
ref = np.mean(N[N.shape[0]//4:3*N.shape[0]//4, :], axis=0)[None, :]
# Get the maximum possible shift in bins.
max_pixel_shift = int(np.ceil(np.log(max_scale)/delta_logdat))
# Determine the chunkwise shifts
chunkwise_shifts0 = delta_logdat*get_shifts(ref,
N,
max_shift=max_pixel_shift,
global_shift_fun=np.mean)
# Interpolate (linear) from chunkwise to eventwise shifts
f = interp1d(seq_centers, chunkwise_shifts0, fill_value='extrapolate')
# Accumulate the shift for the first iteration.
eventwise_logdat_shifts += f(np.arange(event_dat.size))
# Correct the log(data)
logdat_corr = logdat - eventwise_logdat_shifts
# Recompute the histogram with newly corrected log(data)
N, seq_edges, logdat_edges = \
hf.create_histogram(logdat_corr,
roi=log_roi,
cts_per_chunk=cts_per_chunk,
delta_dat=delta_logdat)
seq_centers, logdat_centers = hf.edges_to_centers(seq_edges, logdat_edges)
delta_logdat = logdat_edges[1]-logdat_edges[0]
# Use the center 50% of the data as a reference
# Note: Make it is 2d (even though it is just a single column array)
ref = np.mean(N[N.shape[0]//4:3*N.shape[0]//4, :], axis=0)[None, :]
# Get the maximum possible shift in bins.
max_pixel_shift = int(np.ceil(np.log(max_scale)/delta_logdat))
# Determine the chunkwise shifts
chunkwise_shifts1 = delta_logdat*get_shifts(ref,
N,
max_shift=max_pixel_shift,
global_shift_fun=np.mean)
# Interpolate to get eventwise shifts
f = interp1d(seq_centers, chunkwise_shifts1, fill_value='extrapolate')
# Accumulate the shift for the second iteration.
eventwise_logdat_shifts += f(np.arange(event_dat.size))
# Compute total eventwise shifts for output
eventwise_scales = np.exp(eventwise_logdat_shifts)
# # Uncomment this to see the relative importance of the two iterations
# import matplotlib.pyplot as plt
# plt.figure()
# plt.plot(np.exp(chunkwise_shifts0), label='iter 0')
# plt.plot(np.exp(chunkwise_shifts1), label='iter 1')
# plt.legend()
return eventwise_scales | SiO2/SEDcorr/sed_corr.py | import numpy as np
import histo_funcs as hf
from scipy.interpolate import interp1d
def get_shifts(ref, N, max_shift=150, global_shift_fun=None):
"""
This computes the optimal shift between a 1d reference array and the
columns of a 2d data array using an fft based cross correlation. The
optimal shift is assumed to be where the cross correlation is maximal.
Because no padding or normalization is applied, this works best for
relatively small shifts. This is explicitly enforced by the ``max_shift``
parameter.
Parameters
----------
ref : real numeric 1d array (shape is nx1)
The reference array. Should be in a nx1 column array.
N : real numeric 2d array (shape is nxm)
The data array. Each column of this array will be cross-correlated to
the reference array using an fft.
max_shift : int (default is 150)
The maximum number of bins that are searched for the cross-correlation.
The cross correlation is set to zero for lags less than -max_shift and
greater than +max_shift.
global_shift_fun : function handle (default is None)
This function applies an additional subtractive shift to the returned
shift array. I generally set this to numpy.mean or numpy.median, but
more complex shift functions could be used (including lambdas).
Returns
-------
shifts : numeric 1d array
The shift (in units of bin index) that was required to align each
column of data to the reference spectrum.
"""
# Note: Use real valued fft to improve speed/memory
# FFT the ref and data arrays
rfft_ref = np.fft.rfft(ref, axis=1)
rfft_N = np.fft.rfft(N, axis=1)
# Compute the cross-correlation and take the inverse fft
xc = np.fft.irfft(rfft_N*np.conj(rfft_ref), axis=1)
# Set the cross correlation to zero for lags greater than max_shift
xc[:, max_shift:xc.shape[1]-max_shift] = 0
# Find the lags corresponding to the maximum of the cross correlation and
# then shift them to correspond to the appropriate origin.
max_idxs = np.argmax(xc, axis=1)
max_idxs[max_idxs > xc.shape[1]//2] = \
max_idxs[max_idxs > xc.shape[1]//2] - xc.shape[1]
# Apply a global_shift_fun shift if specified
if global_shift_fun is not None:
shifts = max_idxs - global_shift_fun(max_idxs)
else:
shifts = max_idxs
return shifts
def get_all_scale_coeffs(event_dat,
max_scale=1.1,
roi=None,
cts_per_chunk=2**10,
delta_logdat=5e-4):
"""
This attempts to best align event data. The basic assumption is that if
the ``event_dat`` is binned into a 2d history (histogram) then there are
clearly defined features (i.e. peaks) that will shift around in a
systematic manner. When this data is projected onto a single dimension
then any features (peaks) will be broader than they really should be. This
algorithm discretizes the event_dat into `chunks' and attempts to align
each chunk to a reference dataset using a scalar multiplicative
coefficient. The reference dataset is the middle 50% of the ``event_dat``.
The alignment is performed using a logarithm based cross correlation
approach. Two iterations of this algorithm are performed before the result
is returned. In all tests performed thus far a single iteration was
sufficient however we used two iterations in an abundance of caution.
Parameters
----------
event_dat : real float 1d array
Event data. Typically the data is either the mass-to-charge or
time-of-flight of each event. The ordering of the data is assumed to
be chronological (i.e. the order in which they were detected).
max_scale : real float (default is 1.1)
The maximum possible scale factor allowed (relative to the reference
data)
roi : real numeric list or array (default is [0.5,200])
The domain that the data should be evaluated over.
Specified as [min, max] values.
cts_per_chunk : int (default is 1024)
The number of events to be collected into a single `chunk' of data.
delta_logdat : real float (default is 5e-4)
The discretization of the log(data) over the roi specified. Smaller
deltas are more time/memory intensive. For deltas much less than one,
this effectively gives a discretization/resolution of the
multiplicative factor of 1+delta_logdat. For the atom probe data I
have worked with, the noise on the shift is on the order of 1e-3 and
so setting the delta to be smaller than this, ensures that the
discretization error is not a significant problem.
Returns
-------
eventwise_scales : real float array
An array that contains the computed scale factor for each event that
best aligns the data. To correct the data, just divide the event_dat
array by the eventwise_scales array.
"""
if roi is None:
roi = [0.5, 200]
log_roi = np.log(roi)
# Take the log of data
logdat = np.log(event_dat)
# Create the histogram. Compute centers and delta y
N, seq_edges, logdat_edges = \
hf.create_histogram(logdat,
roi=log_roi,
cts_per_chunk=cts_per_chunk,
delta_dat=delta_logdat)
seq_centers, logdat_centers = hf.edges_to_centers(seq_edges, logdat_edges)
# print('specified delta_logdat = '+str(delta_logdat))
delta_logdat = logdat_edges[1]-logdat_edges[0]
# print('actual delta_logdat = '+str(delta_logdat))
# Initialize the total eventwise log(dat) shift
eventwise_logdat_shifts = np.zeros(event_dat.size)
# Do one iteration with the center 50% of the data as a reference
# Note: Make it is 2d (even though it is just a single column array)
ref = np.mean(N[N.shape[0]//4:3*N.shape[0]//4, :], axis=0)[None, :]
# Get the maximum possible shift in bins.
max_pixel_shift = int(np.ceil(np.log(max_scale)/delta_logdat))
# Determine the chunkwise shifts
chunkwise_shifts0 = delta_logdat*get_shifts(ref,
N,
max_shift=max_pixel_shift,
global_shift_fun=np.mean)
# Interpolate (linear) from chunkwise to eventwise shifts
f = interp1d(seq_centers, chunkwise_shifts0, fill_value='extrapolate')
# Accumulate the shift for the first iteration.
eventwise_logdat_shifts += f(np.arange(event_dat.size))
# Correct the log(data)
logdat_corr = logdat - eventwise_logdat_shifts
# Recompute the histogram with newly corrected log(data)
N, seq_edges, logdat_edges = \
hf.create_histogram(logdat_corr,
roi=log_roi,
cts_per_chunk=cts_per_chunk,
delta_dat=delta_logdat)
seq_centers, logdat_centers = hf.edges_to_centers(seq_edges, logdat_edges)
delta_logdat = logdat_edges[1]-logdat_edges[0]
# Use the center 50% of the data as a reference
# Note: Make it is 2d (even though it is just a single column array)
ref = np.mean(N[N.shape[0]//4:3*N.shape[0]//4, :], axis=0)[None, :]
# Get the maximum possible shift in bins.
max_pixel_shift = int(np.ceil(np.log(max_scale)/delta_logdat))
# Determine the chunkwise shifts
chunkwise_shifts1 = delta_logdat*get_shifts(ref,
N,
max_shift=max_pixel_shift,
global_shift_fun=np.mean)
# Interpolate to get eventwise shifts
f = interp1d(seq_centers, chunkwise_shifts1, fill_value='extrapolate')
# Accumulate the shift for the second iteration.
eventwise_logdat_shifts += f(np.arange(event_dat.size))
# Compute total eventwise shifts for output
eventwise_scales = np.exp(eventwise_logdat_shifts)
# # Uncomment this to see the relative importance of the two iterations
# import matplotlib.pyplot as plt
# plt.figure()
# plt.plot(np.exp(chunkwise_shifts0), label='iter 0')
# plt.plot(np.exp(chunkwise_shifts1), label='iter 1')
# plt.legend()
return eventwise_scales | 0.897852 | 0.75665 |
from textwrap import dedent
import unittest
from graphql import parse
from ...schema_transformation.utils import InvalidTypeNameError, SchemaStructureError, check_ast_schema_is_valid
class TestCheckSchemaValid(unittest.TestCase):
def test_missing_type_schema(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_schema_extension(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
extend type Human {
age: Int
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_input_type_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
id: String
}
input MessageInput {
content: String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_mutation_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
mutation: SchemaMutation
}
type SchemaQuery {
id: String
}
type SchemaMutation {
addId(id: String): String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_subscription_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
subscription: SchemaSubscription
}
type SchemaQuery {
id: String
}
type SchemaSubscription {
getId: String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_inconsistent_root_field_name(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type Human1 {
id: String
}
type Human2 {
id: String
}
type SchemaQuery {
human1: Human1
human2: Human2
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_double_underscore_name(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
__Human: __Human
}
type __Human {
id: String
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_type(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
type __Type {
id: String
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_enum(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
enum __Type {
ENUM1
ENUM2
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_scalar(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
scalar __Type
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string)) | graphql_compiler/tests/schema_transformation_tests/test_check_schema_valid.py | from textwrap import dedent
import unittest
from graphql import parse
from ...schema_transformation.utils import InvalidTypeNameError, SchemaStructureError, check_ast_schema_is_valid
class TestCheckSchemaValid(unittest.TestCase):
def test_missing_type_schema(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_schema_extension(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
extend type Human {
age: Int
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_input_type_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
id: String
}
input MessageInput {
content: String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_mutation_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
mutation: SchemaMutation
}
type SchemaQuery {
id: String
}
type SchemaMutation {
addId(id: String): String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_subscription_definition(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
subscription: SchemaSubscription
}
type SchemaQuery {
id: String
}
type SchemaSubscription {
getId: String
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_inconsistent_root_field_name(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type Human1 {
id: String
}
type Human2 {
id: String
}
type SchemaQuery {
human1: Human1
human2: Human2
}
"""
)
with self.assertRaises(SchemaStructureError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_double_underscore_name(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
__Human: __Human
}
type __Human {
id: String
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_type(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
type __Type {
id: String
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_enum(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
enum __Type {
ENUM1
ENUM2
}
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string))
def test_illegal_reserved_name_scalar(self):
schema_string = dedent(
"""\
schema {
query: SchemaQuery
}
type SchemaQuery {
Human: Human
}
type Human {
id: String
}
scalar __Type
"""
)
with self.assertRaises(InvalidTypeNameError):
check_ast_schema_is_valid(parse(schema_string)) | 0.525612 | 0.597579 |
import sys
from itertools import product
import iris
import iris.quickplot as qplt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from remake import Task, TaskControl, remake_task_control
from cosmic import util
from cosmic.config import CONSTRAINT_ASIA, PATHS
from orog_precip_paths import (land_sea_mask, extended_rclim_mask, precip_path_tpl,
diag_orog_precip_path_tpl, diag_orog_precip_frac_path_tpl,
diag_combine_frac_path, fmtp)
def calc_orog_precip(inputs, outputs, index_month):
extended_rclim_mask = iris.load_cube(str(inputs['extended_rclim_mask']), CONSTRAINT_ASIA)
lsm_asia = iris.load_cube(str(inputs['land_sea_mask']), CONSTRAINT_ASIA)
precip_asia = iris.load_cube(str(inputs['precip']))
precip_asia_mean = precip_asia.collapsed('time', iris.analysis.MEAN)
# Need to regrid to mask resolution.
lsm_asia_coarse = util.regrid(lsm_asia, extended_rclim_mask)
precip_asia_mean_coarse = util.regrid(precip_asia_mean, extended_rclim_mask)
orog_precip_asia = precip_asia_mean_coarse.copy()
orog_precip_asia.rename('orog_' + precip_asia_mean_coarse.name())
nonorog_precip_asia = precip_asia_mean_coarse.copy()
nonorog_precip_asia.rename('non_orog_' + precip_asia_mean_coarse.name())
ocean_precip_asia = precip_asia_mean_coarse.copy()
ocean_precip_asia.rename('ocean_' + precip_asia_mean_coarse.name())
orog_precip_asia.data = (precip_asia_mean_coarse.data *
lsm_asia_coarse.data *
extended_rclim_mask[index_month].data)
nonorog_precip_asia.data = (precip_asia_mean_coarse.data *
lsm_asia_coarse.data *
(1 - extended_rclim_mask[index_month].data))
ocean_precip_asia.data = (precip_asia_mean_coarse.data *
(1 - lsm_asia_coarse.data))
iris.save(iris.cube.CubeList([orog_precip_asia,
nonorog_precip_asia,
ocean_precip_asia]), str(outputs[0]))
def calc_orog_precip_fracs(inputs, outputs, index_month):
# TODO: area weighting.
orog_mask = iris.load_cube(str(inputs['extended_rclim_mask']))
lsm = iris.load_cube(str(inputs['land_sea_mask']))
orog_precip_cubes = iris.load(str(inputs['orog_precip']))
lsm_coarse = util.regrid(lsm, orog_mask)
orog_mask_asia = orog_mask.extract(CONSTRAINT_ASIA)
lsm_coarse_asia = lsm_coarse.extract(CONSTRAINT_ASIA)
orog_precip = orog_precip_cubes.extract_strict('orog_precipitation_flux')
non_orog_precip = orog_precip_cubes.extract_strict('non_orog_precipitation_flux')
land_precip = orog_precip + non_orog_precip
ocean_precip = orog_precip_cubes.extract_strict('ocean_precipitation_flux')
orog_frac = (orog_mask_asia[index_month].data * lsm_coarse_asia.data).sum() / lsm_coarse_asia.data.sum()
non_orog_frac = ((1 - orog_mask_asia[index_month].data) * lsm_coarse_asia.data).sum() / lsm_coarse_asia.data.sum()
land_precip_total = land_precip.data.sum()
ocean_precip_total = ocean_precip.data.sum()
orog_precip_total = orog_precip.data.sum()
non_orog_precip_total = non_orog_precip.data.sum()
land_precip_frac = land_precip_total / (ocean_precip_total + land_precip_total)
orog_precip_frac = orog_precip_total / land_precip_total
non_orog_precip_frac = non_orog_precip_total / land_precip_total
df = pd.DataFrame({
'orog_frac': [orog_frac],
'non_orog_frac': [non_orog_frac],
'land_total': [land_precip_total],
'ocean_total': [ocean_precip_total],
'land_frac': [land_precip_frac],
'orog_total': [orog_precip_total],
'non_orog_total': [non_orog_precip_total],
'orog_precip_frac': [orog_precip_frac],
'non_orog_precip_frac': [non_orog_precip_frac],
})
df.to_hdf(str(outputs[0]), 'orog_fracs')
def combine_orog_precip_fracs(inputs, outputs, variables, columns):
dfs = []
for input_path in inputs:
df = pd.read_hdf(str(input_path))
dfs.append(df)
df_combined = pd.concat(dfs, ignore_index=True)
df_combined['dataset'] = [str(p) for p in inputs]
df_combined = pd.concat([df_combined, pd.DataFrame(variables, columns=columns)], axis=1)
df_combined.to_hdf(str(outputs[0]), 'combined_orog_fracs')
@remake_task_control
def gen_task_ctrl():
tc = TaskControl(__file__)
# /gws/nopw/j04/cosmic/mmuetz/data/era_interim_orog_precip
years = [2006]
# years = [2005, 2006, 2007, 2008]
models = ['al508', 'ak543']
months = [6, 7, 8]
for model, year, month in product(models, years, months):
# al508a.p9200606.asia_precip.nc
precip_path = fmtp(precip_path_tpl, model=model, year=year, month=month)
orog_precip_inputs = {
'extended_rclim_mask': extended_rclim_mask,
'land_sea_mask': land_sea_mask,
'precip': precip_path
}
diag_orog_precip_path = fmtp(diag_orog_precip_path_tpl, model=model, year=year, month=month)
tc.add(Task(calc_orog_precip,
orog_precip_inputs,
[diag_orog_precip_path],
func_args=(month - 1, )))
orog_precip_fracs_inputs = {
'extended_rclim_mask': extended_rclim_mask,
'land_sea_mask': land_sea_mask,
'orog_precip': diag_orog_precip_path
}
diag_orog_precip_frac_path = fmtp(diag_orog_precip_frac_path_tpl, model=model, year=year, month=month)
tc.add(Task(calc_orog_precip_fracs,
orog_precip_fracs_inputs,
[diag_orog_precip_frac_path],
func_args=(month - 1, )))
variables = list(product(models, months))
columns = ['model', 'month']
combine_inputs = [fmtp(diag_orog_precip_frac_path_tpl, model=model, year=year, month=month)
for model, month in variables]
combine_fracs_output = [diag_combine_frac_path]
tc.add(Task(combine_orog_precip_fracs,
combine_inputs,
combine_fracs_output,
func_args=(variables, columns)
))
return tc | ctrl/WP2_analysis/orog_precip/diagnose_orog_mask.py | import sys
from itertools import product
import iris
import iris.quickplot as qplt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from remake import Task, TaskControl, remake_task_control
from cosmic import util
from cosmic.config import CONSTRAINT_ASIA, PATHS
from orog_precip_paths import (land_sea_mask, extended_rclim_mask, precip_path_tpl,
diag_orog_precip_path_tpl, diag_orog_precip_frac_path_tpl,
diag_combine_frac_path, fmtp)
def calc_orog_precip(inputs, outputs, index_month):
extended_rclim_mask = iris.load_cube(str(inputs['extended_rclim_mask']), CONSTRAINT_ASIA)
lsm_asia = iris.load_cube(str(inputs['land_sea_mask']), CONSTRAINT_ASIA)
precip_asia = iris.load_cube(str(inputs['precip']))
precip_asia_mean = precip_asia.collapsed('time', iris.analysis.MEAN)
# Need to regrid to mask resolution.
lsm_asia_coarse = util.regrid(lsm_asia, extended_rclim_mask)
precip_asia_mean_coarse = util.regrid(precip_asia_mean, extended_rclim_mask)
orog_precip_asia = precip_asia_mean_coarse.copy()
orog_precip_asia.rename('orog_' + precip_asia_mean_coarse.name())
nonorog_precip_asia = precip_asia_mean_coarse.copy()
nonorog_precip_asia.rename('non_orog_' + precip_asia_mean_coarse.name())
ocean_precip_asia = precip_asia_mean_coarse.copy()
ocean_precip_asia.rename('ocean_' + precip_asia_mean_coarse.name())
orog_precip_asia.data = (precip_asia_mean_coarse.data *
lsm_asia_coarse.data *
extended_rclim_mask[index_month].data)
nonorog_precip_asia.data = (precip_asia_mean_coarse.data *
lsm_asia_coarse.data *
(1 - extended_rclim_mask[index_month].data))
ocean_precip_asia.data = (precip_asia_mean_coarse.data *
(1 - lsm_asia_coarse.data))
iris.save(iris.cube.CubeList([orog_precip_asia,
nonorog_precip_asia,
ocean_precip_asia]), str(outputs[0]))
def calc_orog_precip_fracs(inputs, outputs, index_month):
# TODO: area weighting.
orog_mask = iris.load_cube(str(inputs['extended_rclim_mask']))
lsm = iris.load_cube(str(inputs['land_sea_mask']))
orog_precip_cubes = iris.load(str(inputs['orog_precip']))
lsm_coarse = util.regrid(lsm, orog_mask)
orog_mask_asia = orog_mask.extract(CONSTRAINT_ASIA)
lsm_coarse_asia = lsm_coarse.extract(CONSTRAINT_ASIA)
orog_precip = orog_precip_cubes.extract_strict('orog_precipitation_flux')
non_orog_precip = orog_precip_cubes.extract_strict('non_orog_precipitation_flux')
land_precip = orog_precip + non_orog_precip
ocean_precip = orog_precip_cubes.extract_strict('ocean_precipitation_flux')
orog_frac = (orog_mask_asia[index_month].data * lsm_coarse_asia.data).sum() / lsm_coarse_asia.data.sum()
non_orog_frac = ((1 - orog_mask_asia[index_month].data) * lsm_coarse_asia.data).sum() / lsm_coarse_asia.data.sum()
land_precip_total = land_precip.data.sum()
ocean_precip_total = ocean_precip.data.sum()
orog_precip_total = orog_precip.data.sum()
non_orog_precip_total = non_orog_precip.data.sum()
land_precip_frac = land_precip_total / (ocean_precip_total + land_precip_total)
orog_precip_frac = orog_precip_total / land_precip_total
non_orog_precip_frac = non_orog_precip_total / land_precip_total
df = pd.DataFrame({
'orog_frac': [orog_frac],
'non_orog_frac': [non_orog_frac],
'land_total': [land_precip_total],
'ocean_total': [ocean_precip_total],
'land_frac': [land_precip_frac],
'orog_total': [orog_precip_total],
'non_orog_total': [non_orog_precip_total],
'orog_precip_frac': [orog_precip_frac],
'non_orog_precip_frac': [non_orog_precip_frac],
})
df.to_hdf(str(outputs[0]), 'orog_fracs')
def combine_orog_precip_fracs(inputs, outputs, variables, columns):
dfs = []
for input_path in inputs:
df = pd.read_hdf(str(input_path))
dfs.append(df)
df_combined = pd.concat(dfs, ignore_index=True)
df_combined['dataset'] = [str(p) for p in inputs]
df_combined = pd.concat([df_combined, pd.DataFrame(variables, columns=columns)], axis=1)
df_combined.to_hdf(str(outputs[0]), 'combined_orog_fracs')
@remake_task_control
def gen_task_ctrl():
tc = TaskControl(__file__)
# /gws/nopw/j04/cosmic/mmuetz/data/era_interim_orog_precip
years = [2006]
# years = [2005, 2006, 2007, 2008]
models = ['al508', 'ak543']
months = [6, 7, 8]
for model, year, month in product(models, years, months):
# al508a.p9200606.asia_precip.nc
precip_path = fmtp(precip_path_tpl, model=model, year=year, month=month)
orog_precip_inputs = {
'extended_rclim_mask': extended_rclim_mask,
'land_sea_mask': land_sea_mask,
'precip': precip_path
}
diag_orog_precip_path = fmtp(diag_orog_precip_path_tpl, model=model, year=year, month=month)
tc.add(Task(calc_orog_precip,
orog_precip_inputs,
[diag_orog_precip_path],
func_args=(month - 1, )))
orog_precip_fracs_inputs = {
'extended_rclim_mask': extended_rclim_mask,
'land_sea_mask': land_sea_mask,
'orog_precip': diag_orog_precip_path
}
diag_orog_precip_frac_path = fmtp(diag_orog_precip_frac_path_tpl, model=model, year=year, month=month)
tc.add(Task(calc_orog_precip_fracs,
orog_precip_fracs_inputs,
[diag_orog_precip_frac_path],
func_args=(month - 1, )))
variables = list(product(models, months))
columns = ['model', 'month']
combine_inputs = [fmtp(diag_orog_precip_frac_path_tpl, model=model, year=year, month=month)
for model, month in variables]
combine_fracs_output = [diag_combine_frac_path]
tc.add(Task(combine_orog_precip_fracs,
combine_inputs,
combine_fracs_output,
func_args=(variables, columns)
))
return tc | 0.209712 | 0.340924 |
from __future__ import print_function
import os
import math
import tensorflow as tf
import horovod.tensorflow as hvd
from model import efficientnet_model
from utils import dataset_factory, hvd_utils, callbacks, preprocessing
__all__ = ['get_optimizer_params', 'get_metrics', 'get_learning_rate_params', 'build_model_params', 'get_models', 'build_augmenter_params', \
'get_image_size_from_model', 'get_dataset_builders', 'build_stats', 'parse_inference_input', 'preprocess_image_files']
def get_optimizer_params(name,
decay,
epsilon,
momentum,
moving_average_decay,
nesterov,
beta_1,
beta_2):
return {
'name': name,
'decay': decay,
'epsilon': epsilon,
'momentum': momentum,
'moving_average_decay': moving_average_decay,
'nesterov': nesterov,
'beta_1': beta_1,
'beta_2': beta_2
}
def get_metrics(one_hot: bool):
"""Get a dict of available metrics to track."""
if one_hot:
return {
# (name, metric_fn)
'acc': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'accuracy': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'top_1': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'top_5': tf.keras.metrics.TopKCategoricalAccuracy(
k=5,
name='top_5_accuracy'),
}
else:
return {
# (name, metric_fn)
'acc': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'accuracy': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'top_1': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'top_5': tf.keras.metrics.SparseTopKCategoricalAccuracy(
k=5,
name='top_5_accuracy'),
}
def get_learning_rate_params(name,
initial_lr,
decay_epochs,
decay_rate,
warmup_epochs):
return {
'name':name,
'initial_lr': initial_lr,
'decay_epochs': decay_epochs,
'decay_rate': decay_rate,
'warmup_epochs': warmup_epochs,
'examples_per_epoch': None,
'boundaries': None,
'multipliers': None,
'scale_by_batch_size': 1./128.,
'staircase': True
}
def build_model_params(model_name, is_training, batch_norm, num_classes, activation, dtype, weight_decay, weight_init):
return {
'model_name': model_name,
'model_weights_path': '',
'weights_format': 'saved_model',
'overrides': {
'is_training': is_training,
'batch_norm': batch_norm,
'rescale_input': True,
'num_classes': num_classes,
'weight_decay': weight_decay,
'activation': activation,
'dtype': dtype,
'weight_init': weight_init
}
}
def get_models():
"""Returns the mapping from model type name to Keras model."""
return {
'efficientnet': efficientnet_model.EfficientNet.from_name,
}
def build_augmenter_params(augmenter_name, cutout_const, translate_const, num_layers, magnitude, autoaugmentation_name):
if augmenter_name is None or augmenter_name not in ['randaugment', 'autoaugment']:
return {}
augmenter_params = {}
if cutout_const is not None:
augmenter_params['cutout_const'] = cutout_const
if translate_const is not None:
augmenter_params['translate_const'] = translate_const
if augmenter_name == 'randaugment':
if num_layers is not None:
augmenter_params['num_layers'] = num_layers
if magnitude is not None:
augmenter_params['magnitude'] = magnitude
if augmenter_name == 'autoaugment':
if autoaugmentation_name is not None:
augmenter_params['autoaugmentation_name'] = autoaugmentation_name
return augmenter_params
def get_image_size_from_model(arch):
"""If the given model has a preferred image size, return it."""
if 'efficientnet' in arch:
efficientnet_name = arch
if efficientnet_name in efficientnet_model.MODEL_CONFIGS:
return efficientnet_model.MODEL_CONFIGS[efficientnet_name]['resolution']
return None
def get_dataset_builders(params, one_hot):
"""Create and return train and validation dataset builders."""
if hvd.size() > 1:
num_gpus = hvd.size()
else:
num_devices = 1
image_size = get_image_size_from_model(params.arch)
print("Image size {}".format(image_size))
print("Train batch size {}".format(params.train_batch_size))
builders = []
validation_dataset_builder = None
train_dataset_builder = None
if "train" in params.mode:
train_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir,
index_file_dir=params.index_file,
split='train',
num_classes=params.num_classes,
image_size=image_size,
batch_size=params.train_batch_size,
one_hot=one_hot,
use_dali=params.use_dali,
augmenter=params.augmenter_name,
augmenter_params=build_augmenter_params(params.augmenter_name,
params.cutout_const,
params.translate_const,
params.num_layers,
params.magnitude,
params.autoaugmentation_name),
mixup_alpha=params.mixup_alpha
)
if "eval" in params.mode:
validation_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir,
index_file_dir=params.index_file,
split='validation',
num_classes=params.num_classes,
image_size=image_size,
batch_size=params.eval_batch_size,
one_hot=one_hot,
use_dali=params.use_dali_eval)
builders.append(train_dataset_builder)
builders.append(validation_dataset_builder)
return builders
def build_stats(history, validation_output, train_callbacks, eval_callback, logger):
stats = {}
if validation_output:
stats['eval_loss'] = float(validation_output[0])
stats['eval_accuracy_top_1'] = float(validation_output[1])
stats['eval_accuracy_top_5'] = float(validation_output[2])
#This part is train loss on GPU_0
if history and history.history:
train_hist = history.history
#Gets final loss from training.
stats['training_loss'] = float(hvd.allreduce(tf.constant(train_hist['loss'][-1], dtype=tf.float32), average=True))
# Gets top_1 training accuracy.
if 'categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['categorical_accuracy'][-1], dtype=tf.float32), average=True))
elif 'sparse_categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['sparse_categorical_accuracy'][-1], dtype=tf.float32), average=True))
elif 'accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['accuracy'][-1], dtype=tf.float32), average=True))
stats['training_accuracy_top_5'] = float(hvd.allreduce(tf.constant(train_hist['top_5_accuracy'][-1], dtype=tf.float32), average=True))
# Look for the time history callback which was used during keras.fit
if train_callbacks:
for callback in train_callbacks:
if isinstance(callback, callbacks.TimeHistory):
if callback.epoch_runtime_log:
stats['avg_exp_per_second_training'] = callback.average_examples_per_second
stats['avg_exp_per_second_training_per_GPU'] = callback.average_examples_per_second / hvd.size()
if eval_callback:
stats['avg_exp_per_second_eval'] = float(eval_callback.average_examples_per_second) * hvd.size()
stats['avg_exp_per_second_eval_per_GPU'] = float(eval_callback.average_examples_per_second)
stats['avg_time_per_exp_eval'] = 1000./stats['avg_exp_per_second_eval']
batch_time = eval_callback.batch_time
batch_time.sort()
latency_pct_per_batch = sum( batch_time[:-1] ) / int( len(batch_time) - 1 )
stats['latency_pct'] = 1000.0 * latency_pct_per_batch
latency_90pct_per_batch = sum( batch_time[:int( 0.9 * len(batch_time) )] ) / int( 0.9 * len(batch_time) )
stats['latency_90pct'] = 1000.0 * latency_90pct_per_batch
latency_95pct_per_batch = sum( batch_time[:int( 0.95 * len(batch_time) )] ) / int( 0.95 * len(batch_time) )
stats['latency_95pct'] = 1000.0 * latency_95pct_per_batch
latency_99pct_per_batch = sum( batch_time[:int( 0.99 * len(batch_time) )] ) / int( 0.99 * len(batch_time) )
stats['latency_99pct'] = 1000.0 * latency_99pct_per_batch
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
logger.log(step=(), data=stats)
def preprocess_image_files(directory_name, arch, batch_size, num_channels=3, dtype=tf.float32):
image_size = get_image_size_from_model(arch)
datagen = tf.keras.preprocessing.image.ImageDataGenerator(data_format="channels_last")
images = datagen.flow_from_directory(directory_name, class_mode=None, batch_size=batch_size, target_size=(image_size, image_size), shuffle=False)
return images
def parse_inference_input(to_predict):
filenames = []
image_formats = ['.jpg', '.jpeg', '.JPEG', '.JPG', '.png', '.PNG']
if os.path.isdir(to_predict):
filenames = [f for f in os.listdir(to_predict)
if os.path.isfile(os.path.join(to_predict, f))
and os.path.splitext(f)[1] in image_formats]
elif os.path.isfile(to_predict):
filenames.append(to_predict)
return filenames | DeepLearningExamples/TensorFlow2/Classification/ConvNets/efficientnet/runtime/runner_utils.py |
from __future__ import print_function
import os
import math
import tensorflow as tf
import horovod.tensorflow as hvd
from model import efficientnet_model
from utils import dataset_factory, hvd_utils, callbacks, preprocessing
__all__ = ['get_optimizer_params', 'get_metrics', 'get_learning_rate_params', 'build_model_params', 'get_models', 'build_augmenter_params', \
'get_image_size_from_model', 'get_dataset_builders', 'build_stats', 'parse_inference_input', 'preprocess_image_files']
def get_optimizer_params(name,
decay,
epsilon,
momentum,
moving_average_decay,
nesterov,
beta_1,
beta_2):
return {
'name': name,
'decay': decay,
'epsilon': epsilon,
'momentum': momentum,
'moving_average_decay': moving_average_decay,
'nesterov': nesterov,
'beta_1': beta_1,
'beta_2': beta_2
}
def get_metrics(one_hot: bool):
"""Get a dict of available metrics to track."""
if one_hot:
return {
# (name, metric_fn)
'acc': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'accuracy': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'top_1': tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
'top_5': tf.keras.metrics.TopKCategoricalAccuracy(
k=5,
name='top_5_accuracy'),
}
else:
return {
# (name, metric_fn)
'acc': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'accuracy': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'top_1': tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
'top_5': tf.keras.metrics.SparseTopKCategoricalAccuracy(
k=5,
name='top_5_accuracy'),
}
def get_learning_rate_params(name,
initial_lr,
decay_epochs,
decay_rate,
warmup_epochs):
return {
'name':name,
'initial_lr': initial_lr,
'decay_epochs': decay_epochs,
'decay_rate': decay_rate,
'warmup_epochs': warmup_epochs,
'examples_per_epoch': None,
'boundaries': None,
'multipliers': None,
'scale_by_batch_size': 1./128.,
'staircase': True
}
def build_model_params(model_name, is_training, batch_norm, num_classes, activation, dtype, weight_decay, weight_init):
return {
'model_name': model_name,
'model_weights_path': '',
'weights_format': 'saved_model',
'overrides': {
'is_training': is_training,
'batch_norm': batch_norm,
'rescale_input': True,
'num_classes': num_classes,
'weight_decay': weight_decay,
'activation': activation,
'dtype': dtype,
'weight_init': weight_init
}
}
def get_models():
"""Returns the mapping from model type name to Keras model."""
return {
'efficientnet': efficientnet_model.EfficientNet.from_name,
}
def build_augmenter_params(augmenter_name, cutout_const, translate_const, num_layers, magnitude, autoaugmentation_name):
if augmenter_name is None or augmenter_name not in ['randaugment', 'autoaugment']:
return {}
augmenter_params = {}
if cutout_const is not None:
augmenter_params['cutout_const'] = cutout_const
if translate_const is not None:
augmenter_params['translate_const'] = translate_const
if augmenter_name == 'randaugment':
if num_layers is not None:
augmenter_params['num_layers'] = num_layers
if magnitude is not None:
augmenter_params['magnitude'] = magnitude
if augmenter_name == 'autoaugment':
if autoaugmentation_name is not None:
augmenter_params['autoaugmentation_name'] = autoaugmentation_name
return augmenter_params
def get_image_size_from_model(arch):
"""If the given model has a preferred image size, return it."""
if 'efficientnet' in arch:
efficientnet_name = arch
if efficientnet_name in efficientnet_model.MODEL_CONFIGS:
return efficientnet_model.MODEL_CONFIGS[efficientnet_name]['resolution']
return None
def get_dataset_builders(params, one_hot):
"""Create and return train and validation dataset builders."""
if hvd.size() > 1:
num_gpus = hvd.size()
else:
num_devices = 1
image_size = get_image_size_from_model(params.arch)
print("Image size {}".format(image_size))
print("Train batch size {}".format(params.train_batch_size))
builders = []
validation_dataset_builder = None
train_dataset_builder = None
if "train" in params.mode:
train_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir,
index_file_dir=params.index_file,
split='train',
num_classes=params.num_classes,
image_size=image_size,
batch_size=params.train_batch_size,
one_hot=one_hot,
use_dali=params.use_dali,
augmenter=params.augmenter_name,
augmenter_params=build_augmenter_params(params.augmenter_name,
params.cutout_const,
params.translate_const,
params.num_layers,
params.magnitude,
params.autoaugmentation_name),
mixup_alpha=params.mixup_alpha
)
if "eval" in params.mode:
validation_dataset_builder = dataset_factory.Dataset(data_dir=params.data_dir,
index_file_dir=params.index_file,
split='validation',
num_classes=params.num_classes,
image_size=image_size,
batch_size=params.eval_batch_size,
one_hot=one_hot,
use_dali=params.use_dali_eval)
builders.append(train_dataset_builder)
builders.append(validation_dataset_builder)
return builders
def build_stats(history, validation_output, train_callbacks, eval_callback, logger):
stats = {}
if validation_output:
stats['eval_loss'] = float(validation_output[0])
stats['eval_accuracy_top_1'] = float(validation_output[1])
stats['eval_accuracy_top_5'] = float(validation_output[2])
#This part is train loss on GPU_0
if history and history.history:
train_hist = history.history
#Gets final loss from training.
stats['training_loss'] = float(hvd.allreduce(tf.constant(train_hist['loss'][-1], dtype=tf.float32), average=True))
# Gets top_1 training accuracy.
if 'categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['categorical_accuracy'][-1], dtype=tf.float32), average=True))
elif 'sparse_categorical_accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['sparse_categorical_accuracy'][-1], dtype=tf.float32), average=True))
elif 'accuracy' in train_hist:
stats['training_accuracy_top_1'] = float(hvd.allreduce(tf.constant(train_hist['accuracy'][-1], dtype=tf.float32), average=True))
stats['training_accuracy_top_5'] = float(hvd.allreduce(tf.constant(train_hist['top_5_accuracy'][-1], dtype=tf.float32), average=True))
# Look for the time history callback which was used during keras.fit
if train_callbacks:
for callback in train_callbacks:
if isinstance(callback, callbacks.TimeHistory):
if callback.epoch_runtime_log:
stats['avg_exp_per_second_training'] = callback.average_examples_per_second
stats['avg_exp_per_second_training_per_GPU'] = callback.average_examples_per_second / hvd.size()
if eval_callback:
stats['avg_exp_per_second_eval'] = float(eval_callback.average_examples_per_second) * hvd.size()
stats['avg_exp_per_second_eval_per_GPU'] = float(eval_callback.average_examples_per_second)
stats['avg_time_per_exp_eval'] = 1000./stats['avg_exp_per_second_eval']
batch_time = eval_callback.batch_time
batch_time.sort()
latency_pct_per_batch = sum( batch_time[:-1] ) / int( len(batch_time) - 1 )
stats['latency_pct'] = 1000.0 * latency_pct_per_batch
latency_90pct_per_batch = sum( batch_time[:int( 0.9 * len(batch_time) )] ) / int( 0.9 * len(batch_time) )
stats['latency_90pct'] = 1000.0 * latency_90pct_per_batch
latency_95pct_per_batch = sum( batch_time[:int( 0.95 * len(batch_time) )] ) / int( 0.95 * len(batch_time) )
stats['latency_95pct'] = 1000.0 * latency_95pct_per_batch
latency_99pct_per_batch = sum( batch_time[:int( 0.99 * len(batch_time) )] ) / int( 0.99 * len(batch_time) )
stats['latency_99pct'] = 1000.0 * latency_99pct_per_batch
if not hvd_utils.is_using_hvd() or hvd.rank() == 0:
logger.log(step=(), data=stats)
def preprocess_image_files(directory_name, arch, batch_size, num_channels=3, dtype=tf.float32):
image_size = get_image_size_from_model(arch)
datagen = tf.keras.preprocessing.image.ImageDataGenerator(data_format="channels_last")
images = datagen.flow_from_directory(directory_name, class_mode=None, batch_size=batch_size, target_size=(image_size, image_size), shuffle=False)
return images
def parse_inference_input(to_predict):
filenames = []
image_formats = ['.jpg', '.jpeg', '.JPEG', '.JPG', '.png', '.PNG']
if os.path.isdir(to_predict):
filenames = [f for f in os.listdir(to_predict)
if os.path.isfile(os.path.join(to_predict, f))
and os.path.splitext(f)[1] in image_formats]
elif os.path.isfile(to_predict):
filenames.append(to_predict)
return filenames | 0.874507 | 0.171824 |
from flask import Flask, render_template, request
import RPi.GPIO as GPIO
app = Flask(__name__)
GPIO.setmode(GPIO.BOARD)
pin_list = [12, 16]
for pin in pin_list:
GPIO.setup(pin, GPIO.OUT)
GPIO.output(pin, GPIO.LOW)
pin_dict = {
12: {
'led_colour': 'Green LED',
'led_state': GPIO.LOW
},
16: {
'led_colour': 'Red LED',
'led_state': GPIO.LOW
}
}
@app.route('/')
def main():
for pin in pin_dict:
pin_dict[pin]['led_state'] = GPIO.input(pin)
template_data = {
'title': 'LEDs current state',
'pin_dict': pin_dict,
}
return render_template('main.html', **template_data)
@app.route('/<led_colour>', methods=['GET', 'POST'])
def led_change(led_colour):
# soon will be DRY :)
if request.method == 'GET':
if led_colour == 'red':
state_read = GPIO.input(16)
if state_read == True:
msg = 'Red LED is currently ON.'
else:
msg = 'Red LED is currently OFF.'
template_data = {
'title': 'Red LED',
'message': msg,
}
return render_template('change_red.html', **template_data)
if led_colour == 'green':
state_read = GPIO.input(12)
if state_read == True:
msg = 'Green LED is currently ON.'
else:
msg = 'Green LED is currently OFF.'
template_data = {
'title': 'Green LED',
'message': msg,
}
return render_template('change_green.html', **template_data)
elif request.method =='POST':
if led_colour == 'red':
if request.form['red_led_change'] == 'red_led':
GPIO.output(16, not GPIO.input(16))
state_read = GPIO.input(16)
if state_read == True:
msg = 'Red LED is currently ON.'
else:
msg = 'Red LED is currently OFF.'
template_data = {
'title': 'Red LED',
'message': msg,
}
return render_template('change_red.html', **template_data)
if led_colour == 'green':
if request.form['green_led_change'] == 'green_led':
GPIO.output(12, not GPIO.input(12))
state_read = GPIO.input(12)
if state_read == True:
msg = 'Green LED is currently ON.'
else:
msg = 'Green LED is currently OFF.'
template_data = {
'title': 'Green LED',
'message': msg,
}
return render_template('change_green.html', **template_data)
if __name__ == '__main__':
app.run(host='192.168.0.20', port=80, debug=True)
GPIO.cleanup() | LED/FLASK/flask_red_green_LED.py | from flask import Flask, render_template, request
import RPi.GPIO as GPIO
app = Flask(__name__)
GPIO.setmode(GPIO.BOARD)
pin_list = [12, 16]
for pin in pin_list:
GPIO.setup(pin, GPIO.OUT)
GPIO.output(pin, GPIO.LOW)
pin_dict = {
12: {
'led_colour': 'Green LED',
'led_state': GPIO.LOW
},
16: {
'led_colour': 'Red LED',
'led_state': GPIO.LOW
}
}
@app.route('/')
def main():
for pin in pin_dict:
pin_dict[pin]['led_state'] = GPIO.input(pin)
template_data = {
'title': 'LEDs current state',
'pin_dict': pin_dict,
}
return render_template('main.html', **template_data)
@app.route('/<led_colour>', methods=['GET', 'POST'])
def led_change(led_colour):
# soon will be DRY :)
if request.method == 'GET':
if led_colour == 'red':
state_read = GPIO.input(16)
if state_read == True:
msg = 'Red LED is currently ON.'
else:
msg = 'Red LED is currently OFF.'
template_data = {
'title': 'Red LED',
'message': msg,
}
return render_template('change_red.html', **template_data)
if led_colour == 'green':
state_read = GPIO.input(12)
if state_read == True:
msg = 'Green LED is currently ON.'
else:
msg = 'Green LED is currently OFF.'
template_data = {
'title': 'Green LED',
'message': msg,
}
return render_template('change_green.html', **template_data)
elif request.method =='POST':
if led_colour == 'red':
if request.form['red_led_change'] == 'red_led':
GPIO.output(16, not GPIO.input(16))
state_read = GPIO.input(16)
if state_read == True:
msg = 'Red LED is currently ON.'
else:
msg = 'Red LED is currently OFF.'
template_data = {
'title': 'Red LED',
'message': msg,
}
return render_template('change_red.html', **template_data)
if led_colour == 'green':
if request.form['green_led_change'] == 'green_led':
GPIO.output(12, not GPIO.input(12))
state_read = GPIO.input(12)
if state_read == True:
msg = 'Green LED is currently ON.'
else:
msg = 'Green LED is currently OFF.'
template_data = {
'title': 'Green LED',
'message': msg,
}
return render_template('change_green.html', **template_data)
if __name__ == '__main__':
app.run(host='192.168.0.20', port=80, debug=True)
GPIO.cleanup() | 0.433262 | 0.088702 |
from collections import namedtuple
from datetime import date, datetime, time
from decimal import Context, Decimal, ROUND_HALF_UP
from html.parser import HTMLParser
from itertools import zip_longest
from re import compile, finditer, sub
from secrets import choice
from string import ascii_letters, digits
LOAD_VARIABLE_RE = compile(r"\[[\w()]+\]")
LOAD_OPERATOR_RE = compile(r"(?<![<\|>]{1})=|<>")
DUMP_VARIABLE_RE = compile(r"record\['\w+'\]")
DUMP_OPERATOR_RE = compile(r"==|!=")
def dump_field_name(value):
"""dump field_name"""
return value
def load_field_name(value):
"""load field_name"""
return value
def dump_form_name(value):
"""dump form_name"""
return value
def load_form_name(value):
"""load form_name"""
return value
def dump_section_header(value):
"""dump section_header"""
return value
def load_section_header(value):
"""load section_header"""
return value
def dump_field_type(value):
"""dump field_type"""
return value
def load_field_type(value):
"""load field_type"""
return value
def dump_field_label(value):
"""dump field_label"""
return value
def load_field_label(value):
"""load field_label"""
return value
def dump_select_choices_or_calculations(value):
"""dump select_choices_or_calculations"""
return value
def load_select_choices_or_calculations(value):
"""load select_choices_or_calculations"""
return value
def dump_field_note(value):
"""dump field_note"""
return value
def load_field_note(value):
"""load field_note"""
return value
def dump_text_validation_type_or_show_slider_number(value):
"""dump text_validation_type_or_show_slider_number"""
return value
def load_text_validation_type_or_show_slider_number(value):
"""load text_validation_type_or_show_slider_number"""
return value
def dump_text_validation_min(value):
"""dump text_validation_min"""
return value
def load_text_validation_min(value):
"""load text_validation_min"""
return value
def dump_text_validation_max(value):
"""dump text_validation_max"""
return value
def load_text_validation_max(value):
"""load text_validation_max"""
return value
def dump_identifier(value):
"""dump identifier"""
if value is True:
return "y"
return "n"
def load_identifier(value):
"""load identifier"""
if value == "y":
return True
return False
def dump_branching_logic(value):
"""dump branching_logic"""
if not value:
return ""
logic_fragments = zip_longest(
DUMP_VARIABLE_RE.split(value),
[m.group(0) for m in DUMP_VARIABLE_RE.finditer(value)],
fillvalue=""
)
value = ""
for oper_frag, vari_frag in logic_fragments:
for match in DUMP_OPERATOR_RE.finditer(oper_frag):
ope_str = match.group(0)
if ope_str == "==": ope_str = "="
elif ope_str == "!=": ope_str = "<>"
oper_frag = (
oper_frag[:match.start()]
+ ope_str
+ oper_frag[match.end():]
)
if vari_frag:
vari_frag = vari_frag.lstrip("record['").rstrip("']")
if "___" in vari_frag:
vari_frag = "(".join(
s for s in vari_frag.split("___")
) + ")"
vari_frag = "[" + vari_frag + "]"
value += oper_frag + vari_frag
return value
def load_branching_logic(value):
"""load branching_logic"""
if not value:
return ""
logic_fragments = zip_longest(
LOAD_VARIABLE_RE.split(value),
[m.group(0) for m in LOAD_VARIABLE_RE.finditer(value)],
fillvalue=""
)
value = ""
for oper_frag, vari_frag in logic_fragments:
for match in LOAD_OPERATOR_RE.finditer(oper_frag):
ope_str = match.group(0)
if ope_str == "=":
ope_str = "=="
elif ope_str == "<>":
ope_str = "!="
oper_frag = (
oper_frag[:match.start()]
+ ope_str
+ oper_frag[match.end():]
)
if vari_frag:
vari_frag = vari_frag.strip("[]")
if "(" in vari_frag and ")" in vari_frag:
vari_frag = "___".join(
s.strip(")") for s in vari_frag.split("(")
)
vari_frag = "record['" + vari_frag + "']"
value += oper_frag + vari_frag
return value
def dump_required_field(value):
"""dump required_field"""
if value is True:
return "y"
return "n"
def load_required_field(value):
"""load required_field"""
if value == "y":
return True
return False
def dump_custom_alignment(value):
"""dump custom_alignment"""
return value
def load_custom_alignment(value):
"""load custom_alignment"""
return value
def dump_question_number(value):
"""dump question_number"""
return value
def load_question_number(value):
"""load question_number"""
return value
def dump_matrix_group_name(value):
"""dump matrix_group_name"""
return value
def load_matrix_group_name(value):
"""load matrix_group_name"""
return value
def dump_matrix_ranking(value):
"""dump matrix_ranking"""
return value
def load_matrix_ranking(value):
"""load matrix_ranking"""
return value
def dump_field_annotation(value):
"""dump field_annotation"""
return value
def load_field_annotation(value):
"""load field_annotation"""
return value
dump_map = dict(
field_name=dump_field_name,
form_name=dump_form_name,
section_header=dump_section_header,
field_type=dump_field_type,
field_label=dump_field_label,
select_choices_or_calculations=dump_select_choices_or_calculations,
field_note=dump_field_note,
text_validation_type_or_show_slider_number=dump_text_validation_type_or_show_slider_number,
text_validation_min=dump_text_validation_min,
text_validation_max=dump_text_validation_max,
identifier=dump_identifier,
branching_logic=dump_branching_logic,
required_field=dump_required_field,
custom_alignment=dump_custom_alignment,
question_number=dump_question_number,
matrix_group_name=dump_matrix_group_name,
matrix_ranking=dump_matrix_ranking,
field_annotation=dump_field_annotation
)
load_map = dict(
field_name=load_field_name,
form_name=load_form_name,
section_header=load_section_header,
field_type=load_field_type,
field_label=load_field_label,
select_choices_or_calculations=load_select_choices_or_calculations,
field_note=load_field_note,
text_validation_type_or_show_slider_number=load_text_validation_type_or_show_slider_number,
text_validation_min=load_text_validation_min,
text_validation_max=load_text_validation_max,
identifier=load_identifier,
branching_logic=load_branching_logic,
required_field=load_required_field,
custom_alignment=load_custom_alignment,
question_number=load_question_number,
matrix_group_name=load_matrix_group_name,
matrix_ranking=load_matrix_ranking,
field_annotation=load_field_annotation
)
column_names = [
"field_name", "form_name", "section_header", "field_type",
"field_label", "select_choices_or_calculations", "field_note",
"text_validation_type_or_show_slider_number",
"text_validation_min", "text_validation_max", "identifier",
"branching_logic", "required_field", "custom_alignment",
"question_number", "matrix_group_name", "matrix_ranking",
"field_annotation",
]
TemplateHTML = """
<!DOCTYPE html>
<html>
<head>
</head>
<body>
<table id="metadata">{}</table>
</body>
</html>
""".strip()
class HTMLParser(HTMLParser):
"""extract metadata from HTML string"""
pass
class TemplateSQL:
"""statements for rendering SQL migration"""
create_schema = "CREATE SCHEMA IF NOT EXISTS {};\n"
create_table = "CREATE TABLE IF NOT EXISTS {}();\n"
add_column = "ALTER TABLE {} ADD COLUMN IF NOT EXISTS {} {};\n"
def nonce(length):
"""return pseudorandom string"""
return "".join(
choice(ascii_letters + digits) for _ in range(length)
)
DCM = { # decimal context map
"number": Context(prec=None, rounding=ROUND_HALF_UP),
"number_1dp_comma_decimal": Context(
prec=1, rounding=ROUND_HALF_UP
),
"number_1dp": Context(prec=1, rounding=ROUND_HALF_UP),
"number_2dp_comma_decimal": Context(
prec=2, rounding=ROUND_HALF_UP
),
"number_2dp": Context(prec=2, rounding=ROUND_HALF_UP),
"number_3dp_comma_decimal": Context(
prec=3, rounding=ROUND_HALF_UP
),
"number_3dp": Context(prec=3, rounding=ROUND_HALF_UP),
"number_4dp_comma_decimal": Context(
prec=4, rounding=ROUND_HALF_UP
),
"number_4dp": Context(prec=4, rounding=ROUND_HALF_UP),
"number_comma_decimal": Context(
prec=None, rounding=ROUND_HALF_UP
),
}
data_type_map = {
"date_dmy": (
lambda d: date.strptime(d, "%d-%m-%Y"),
lambda d: d.strftime("%d-%m-%Y"),
"DATE",
),
"date_mdy": (
lambda d: date.strptime(d, "%m-%d-%Y"),
lambda d: d.strftime("%m-%d-%Y"),
"DATE",
),
"date_ymd": (
lambda d: date.strptime(d, "%Y-%m-%d"),
lambda d: d.strftime("%Y-%m-%d"),
"DATE",
),
"datetime_dmy": (
lambda d: datetime.strptime(d, "%d-%m-%Y %H:%M"),
lambda d: d.strftime("%d-%m-%Y %H:%M"),
"DATETIME",
),
"datetime_mdy": (
lambda d: datetime.strptime(d, "%m-%d-%Y %H:%M"),
lambda d: d.strftime("%m-%d-%Y %H:%M"),
"DATETIME",
),
"datetime_ymd": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M"),
lambda d: d.strftime("%Y-%m-%d %H:%M"),
"DATETIME",
),
"datetime_seconds_dmy": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M:%S"),
lambda d: d.strftime("%Y-%m-%d %H:%M:%S"),
"DATETIME",
),
"datetime_seconds_mdy": (
lambda d: datetime.strptime(d, "%m-%d-%Y %H:%M:%S"),
lambda d: d.strftime("%m-%d-%Y %H:%M:%S"),
"DATETIME",
),
"datetime_seconds_ymd": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M:%S"),
lambda d: d.strftime("%Y-%m-%d %H:%M:%S"),
"DATETIME",
),
"email": (lambda s: s, lambda s: s, "TEXT",),
"integer": (int, str, "INT",),
"alpha_only": (lambda s: s, lambda s: s, "TEXT",),
"number": (
lambda n: Decimal(sub(r",", ".", n), context=DCM["number"]),
lambda n: str(n),
"FLOAT",
),
"number_1dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_1dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_1dp": (
lambda n: Decimal(n, context=DCM["number_1dp"]),
lambda n: str(n),
"FLOAT",
),
"number_2dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_2dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_2dp": (
lambda n: Decimal(n, context=DCM["number_2dp"]),
lambda n: str(n),
"FLOAT",
),
"number_3dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_3dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_3dp": (
lambda n: Decimal(n, context=DCM["number_3dp"]),
lambda n: str(n),
"FLOAT",
),
"number_4dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_4dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_4dp": (
lambda n: Decimal(n, context=DCM["number_4dp"]),
lambda n: str(n),
"FLOAT",
),
"number_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM["number_comma_decimal"]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"phone_australia": (lambda s: s, lambda s: s, "TEXT",),
"phone": (lambda s: s, lambda s: s, "TEXT",),
"postalcode_australia": (lambda s: s, lambda s: s, "TEXT",),
"postalcode_canada": (lambda s: s, lambda s: s, "TEXT",),
"ssn": (lambda s: s, lambda s: s, "TEXT",),
"time": (
lambda t: time.strptime(t, "%H:%M"),
lambda t: t.strftime("%H:%M"),
"TIME",
),
"time_mm_ss": (
lambda t: time.strptime(t, "%M:%S"),
lambda t: t.strftime("%M:%S"),
"TIME",
),
"vmrn": (lambda s: s, lambda s: s, "TEXT",),
"Zipcode": (lambda s: s, lambda s: s, "TEXT",),
"": (lambda s: s, lambda s: s, "TEXT",),
} | metadata/util.py | from collections import namedtuple
from datetime import date, datetime, time
from decimal import Context, Decimal, ROUND_HALF_UP
from html.parser import HTMLParser
from itertools import zip_longest
from re import compile, finditer, sub
from secrets import choice
from string import ascii_letters, digits
LOAD_VARIABLE_RE = compile(r"\[[\w()]+\]")
LOAD_OPERATOR_RE = compile(r"(?<![<\|>]{1})=|<>")
DUMP_VARIABLE_RE = compile(r"record\['\w+'\]")
DUMP_OPERATOR_RE = compile(r"==|!=")
def dump_field_name(value):
"""dump field_name"""
return value
def load_field_name(value):
"""load field_name"""
return value
def dump_form_name(value):
"""dump form_name"""
return value
def load_form_name(value):
"""load form_name"""
return value
def dump_section_header(value):
"""dump section_header"""
return value
def load_section_header(value):
"""load section_header"""
return value
def dump_field_type(value):
"""dump field_type"""
return value
def load_field_type(value):
"""load field_type"""
return value
def dump_field_label(value):
"""dump field_label"""
return value
def load_field_label(value):
"""load field_label"""
return value
def dump_select_choices_or_calculations(value):
"""dump select_choices_or_calculations"""
return value
def load_select_choices_or_calculations(value):
"""load select_choices_or_calculations"""
return value
def dump_field_note(value):
"""dump field_note"""
return value
def load_field_note(value):
"""load field_note"""
return value
def dump_text_validation_type_or_show_slider_number(value):
"""dump text_validation_type_or_show_slider_number"""
return value
def load_text_validation_type_or_show_slider_number(value):
"""load text_validation_type_or_show_slider_number"""
return value
def dump_text_validation_min(value):
"""dump text_validation_min"""
return value
def load_text_validation_min(value):
"""load text_validation_min"""
return value
def dump_text_validation_max(value):
"""dump text_validation_max"""
return value
def load_text_validation_max(value):
"""load text_validation_max"""
return value
def dump_identifier(value):
"""dump identifier"""
if value is True:
return "y"
return "n"
def load_identifier(value):
"""load identifier"""
if value == "y":
return True
return False
def dump_branching_logic(value):
"""dump branching_logic"""
if not value:
return ""
logic_fragments = zip_longest(
DUMP_VARIABLE_RE.split(value),
[m.group(0) for m in DUMP_VARIABLE_RE.finditer(value)],
fillvalue=""
)
value = ""
for oper_frag, vari_frag in logic_fragments:
for match in DUMP_OPERATOR_RE.finditer(oper_frag):
ope_str = match.group(0)
if ope_str == "==": ope_str = "="
elif ope_str == "!=": ope_str = "<>"
oper_frag = (
oper_frag[:match.start()]
+ ope_str
+ oper_frag[match.end():]
)
if vari_frag:
vari_frag = vari_frag.lstrip("record['").rstrip("']")
if "___" in vari_frag:
vari_frag = "(".join(
s for s in vari_frag.split("___")
) + ")"
vari_frag = "[" + vari_frag + "]"
value += oper_frag + vari_frag
return value
def load_branching_logic(value):
"""load branching_logic"""
if not value:
return ""
logic_fragments = zip_longest(
LOAD_VARIABLE_RE.split(value),
[m.group(0) for m in LOAD_VARIABLE_RE.finditer(value)],
fillvalue=""
)
value = ""
for oper_frag, vari_frag in logic_fragments:
for match in LOAD_OPERATOR_RE.finditer(oper_frag):
ope_str = match.group(0)
if ope_str == "=":
ope_str = "=="
elif ope_str == "<>":
ope_str = "!="
oper_frag = (
oper_frag[:match.start()]
+ ope_str
+ oper_frag[match.end():]
)
if vari_frag:
vari_frag = vari_frag.strip("[]")
if "(" in vari_frag and ")" in vari_frag:
vari_frag = "___".join(
s.strip(")") for s in vari_frag.split("(")
)
vari_frag = "record['" + vari_frag + "']"
value += oper_frag + vari_frag
return value
def dump_required_field(value):
"""dump required_field"""
if value is True:
return "y"
return "n"
def load_required_field(value):
"""load required_field"""
if value == "y":
return True
return False
def dump_custom_alignment(value):
"""dump custom_alignment"""
return value
def load_custom_alignment(value):
"""load custom_alignment"""
return value
def dump_question_number(value):
"""dump question_number"""
return value
def load_question_number(value):
"""load question_number"""
return value
def dump_matrix_group_name(value):
"""dump matrix_group_name"""
return value
def load_matrix_group_name(value):
"""load matrix_group_name"""
return value
def dump_matrix_ranking(value):
"""dump matrix_ranking"""
return value
def load_matrix_ranking(value):
"""load matrix_ranking"""
return value
def dump_field_annotation(value):
"""dump field_annotation"""
return value
def load_field_annotation(value):
"""load field_annotation"""
return value
dump_map = dict(
field_name=dump_field_name,
form_name=dump_form_name,
section_header=dump_section_header,
field_type=dump_field_type,
field_label=dump_field_label,
select_choices_or_calculations=dump_select_choices_or_calculations,
field_note=dump_field_note,
text_validation_type_or_show_slider_number=dump_text_validation_type_or_show_slider_number,
text_validation_min=dump_text_validation_min,
text_validation_max=dump_text_validation_max,
identifier=dump_identifier,
branching_logic=dump_branching_logic,
required_field=dump_required_field,
custom_alignment=dump_custom_alignment,
question_number=dump_question_number,
matrix_group_name=dump_matrix_group_name,
matrix_ranking=dump_matrix_ranking,
field_annotation=dump_field_annotation
)
load_map = dict(
field_name=load_field_name,
form_name=load_form_name,
section_header=load_section_header,
field_type=load_field_type,
field_label=load_field_label,
select_choices_or_calculations=load_select_choices_or_calculations,
field_note=load_field_note,
text_validation_type_or_show_slider_number=load_text_validation_type_or_show_slider_number,
text_validation_min=load_text_validation_min,
text_validation_max=load_text_validation_max,
identifier=load_identifier,
branching_logic=load_branching_logic,
required_field=load_required_field,
custom_alignment=load_custom_alignment,
question_number=load_question_number,
matrix_group_name=load_matrix_group_name,
matrix_ranking=load_matrix_ranking,
field_annotation=load_field_annotation
)
column_names = [
"field_name", "form_name", "section_header", "field_type",
"field_label", "select_choices_or_calculations", "field_note",
"text_validation_type_or_show_slider_number",
"text_validation_min", "text_validation_max", "identifier",
"branching_logic", "required_field", "custom_alignment",
"question_number", "matrix_group_name", "matrix_ranking",
"field_annotation",
]
TemplateHTML = """
<!DOCTYPE html>
<html>
<head>
</head>
<body>
<table id="metadata">{}</table>
</body>
</html>
""".strip()
class HTMLParser(HTMLParser):
"""extract metadata from HTML string"""
pass
class TemplateSQL:
"""statements for rendering SQL migration"""
create_schema = "CREATE SCHEMA IF NOT EXISTS {};\n"
create_table = "CREATE TABLE IF NOT EXISTS {}();\n"
add_column = "ALTER TABLE {} ADD COLUMN IF NOT EXISTS {} {};\n"
def nonce(length):
"""return pseudorandom string"""
return "".join(
choice(ascii_letters + digits) for _ in range(length)
)
DCM = { # decimal context map
"number": Context(prec=None, rounding=ROUND_HALF_UP),
"number_1dp_comma_decimal": Context(
prec=1, rounding=ROUND_HALF_UP
),
"number_1dp": Context(prec=1, rounding=ROUND_HALF_UP),
"number_2dp_comma_decimal": Context(
prec=2, rounding=ROUND_HALF_UP
),
"number_2dp": Context(prec=2, rounding=ROUND_HALF_UP),
"number_3dp_comma_decimal": Context(
prec=3, rounding=ROUND_HALF_UP
),
"number_3dp": Context(prec=3, rounding=ROUND_HALF_UP),
"number_4dp_comma_decimal": Context(
prec=4, rounding=ROUND_HALF_UP
),
"number_4dp": Context(prec=4, rounding=ROUND_HALF_UP),
"number_comma_decimal": Context(
prec=None, rounding=ROUND_HALF_UP
),
}
data_type_map = {
"date_dmy": (
lambda d: date.strptime(d, "%d-%m-%Y"),
lambda d: d.strftime("%d-%m-%Y"),
"DATE",
),
"date_mdy": (
lambda d: date.strptime(d, "%m-%d-%Y"),
lambda d: d.strftime("%m-%d-%Y"),
"DATE",
),
"date_ymd": (
lambda d: date.strptime(d, "%Y-%m-%d"),
lambda d: d.strftime("%Y-%m-%d"),
"DATE",
),
"datetime_dmy": (
lambda d: datetime.strptime(d, "%d-%m-%Y %H:%M"),
lambda d: d.strftime("%d-%m-%Y %H:%M"),
"DATETIME",
),
"datetime_mdy": (
lambda d: datetime.strptime(d, "%m-%d-%Y %H:%M"),
lambda d: d.strftime("%m-%d-%Y %H:%M"),
"DATETIME",
),
"datetime_ymd": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M"),
lambda d: d.strftime("%Y-%m-%d %H:%M"),
"DATETIME",
),
"datetime_seconds_dmy": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M:%S"),
lambda d: d.strftime("%Y-%m-%d %H:%M:%S"),
"DATETIME",
),
"datetime_seconds_mdy": (
lambda d: datetime.strptime(d, "%m-%d-%Y %H:%M:%S"),
lambda d: d.strftime("%m-%d-%Y %H:%M:%S"),
"DATETIME",
),
"datetime_seconds_ymd": (
lambda d: datetime.strptime(d, "%Y-%m-%d %H:%M:%S"),
lambda d: d.strftime("%Y-%m-%d %H:%M:%S"),
"DATETIME",
),
"email": (lambda s: s, lambda s: s, "TEXT",),
"integer": (int, str, "INT",),
"alpha_only": (lambda s: s, lambda s: s, "TEXT",),
"number": (
lambda n: Decimal(sub(r",", ".", n), context=DCM["number"]),
lambda n: str(n),
"FLOAT",
),
"number_1dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_1dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_1dp": (
lambda n: Decimal(n, context=DCM["number_1dp"]),
lambda n: str(n),
"FLOAT",
),
"number_2dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_2dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_2dp": (
lambda n: Decimal(n, context=DCM["number_2dp"]),
lambda n: str(n),
"FLOAT",
),
"number_3dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_3dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_3dp": (
lambda n: Decimal(n, context=DCM["number_3dp"]),
lambda n: str(n),
"FLOAT",
),
"number_4dp_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM[
"number_4dp_comma_decimal"
]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"number_4dp": (
lambda n: Decimal(n, context=DCM["number_4dp"]),
lambda n: str(n),
"FLOAT",
),
"number_comma_decimal": (
lambda n: Decimal(
sub(r",", ".", n), context=DCM["number_comma_decimal"]
),
lambda n: sub(r"\.", ",", str(n)),
"FLOAT",
),
"phone_australia": (lambda s: s, lambda s: s, "TEXT",),
"phone": (lambda s: s, lambda s: s, "TEXT",),
"postalcode_australia": (lambda s: s, lambda s: s, "TEXT",),
"postalcode_canada": (lambda s: s, lambda s: s, "TEXT",),
"ssn": (lambda s: s, lambda s: s, "TEXT",),
"time": (
lambda t: time.strptime(t, "%H:%M"),
lambda t: t.strftime("%H:%M"),
"TIME",
),
"time_mm_ss": (
lambda t: time.strptime(t, "%M:%S"),
lambda t: t.strftime("%M:%S"),
"TIME",
),
"vmrn": (lambda s: s, lambda s: s, "TEXT",),
"Zipcode": (lambda s: s, lambda s: s, "TEXT",),
"": (lambda s: s, lambda s: s, "TEXT",),
} | 0.643329 | 0.190686 |
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds
dataset, info = tfds.load('oxford_iiit_pet:3.*.*', with_info=True)
def normalize(input_image, input_mask):
input_image = tf.cast(input_image, tf.float32) / 255.0
input_mask -= 1
return input_image, input_mask
@tf.function
def load_image_train(datapoint):
input_image = tf.image.resize(datapoint['image'], (128, 128))
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
if tf.random.uniform(()) > 0.5:
input_image = tf.image.flip_left_right(input_image)
input_mask = tf.image.flip_left_right(input_mask)
input_image, input_mask = normalize(input_image, input_mask)
return input_image, input_mask
def load_image_test(datapoint):
input_image = tf.image.resize(datapoint['image'], (128, 128))
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
input_image, input_mask = normalize(input_image, input_mask)
return input_image, input_mask
TRAIN_LENGTH = info.splits['train'].num_examples
BATCH_SIZE = 64
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE
train = dataset['train'].map(load_image_train, num_parallel_calls=tf.data.AUTOTUNE)
test = dataset['test'].map(load_image_test)
train_dataset = train.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()
train_dataset = train_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
test_dataset = test.batch(BATCH_SIZE)
def display(display_list):
plt.figure(figsize=(15, 15))
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i + 1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
plt.axis('off')
def upsample(filters, size):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.ReLU())
return result
for image, mask in train.take(1):
sample_image, sample_mask = image, mask
display([sample_image, sample_mask])
OUTPUT_CHANNELS = 3
base_model = tf.keras.applications.MobileNetV2(input_shape=[128, 128, 3], include_top=False)
# Use the activations of these layers
layer_names = [
'block_1_expand_relu', # 64x64
'block_3_expand_relu', # 32x32
'block_6_expand_relu', # 16x16
'block_13_expand_relu', # 8x8
'block_16_project', # 4x4
]
layers = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=layers)
down_stack.trainable = False
up_stack = [
upsample(512, 3), # 4x4 -> 8x8
upsample(256, 3), # 8x8 -> 16x16
upsample(128, 3), # 16x16 -> 32x32
upsample(64, 3), # 32x32 -> 64x64
]
def unet_model(output_channels):
inputs = tf.keras.layers.Input(shape=[128, 128, 3])
x = inputs
# Downsampling through the model
skips = down_stack(x)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(output_channels, 3, strides=2, padding='same') # 64x64 -> 128x128
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
model = unet_model(OUTPUT_CHANNELS)
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
def create_mask(pred_mask):
pred_mask = tf.argmax(pred_mask, axis=-1)
pred_mask = pred_mask[..., tf.newaxis]
return pred_mask[0]
def show_predictions(dataset=None, num=1):
if dataset:
for image, mask in dataset.take(num):
pred_mask = model.predict(image)
display([image[0], mask[0], create_mask(pred_mask)])
else:
display([sample_image, sample_mask,
create_mask(model.predict(sample_image[tf.newaxis, ...]))])
show_predictions()
EPOCHS = 20
VAL_SUB_SPLITS = 5
VALIDATION_STEPS = info.splits['test'].num_examples // BATCH_SIZE // VAL_SUB_SPLITS
model.fit(train_dataset, epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
validation_steps=VALIDATION_STEPS,
validation_data=test_dataset)
show_predictions(test_dataset, 3)
plt.show() | unet.py | import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds
dataset, info = tfds.load('oxford_iiit_pet:3.*.*', with_info=True)
def normalize(input_image, input_mask):
input_image = tf.cast(input_image, tf.float32) / 255.0
input_mask -= 1
return input_image, input_mask
@tf.function
def load_image_train(datapoint):
input_image = tf.image.resize(datapoint['image'], (128, 128))
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
if tf.random.uniform(()) > 0.5:
input_image = tf.image.flip_left_right(input_image)
input_mask = tf.image.flip_left_right(input_mask)
input_image, input_mask = normalize(input_image, input_mask)
return input_image, input_mask
def load_image_test(datapoint):
input_image = tf.image.resize(datapoint['image'], (128, 128))
input_mask = tf.image.resize(datapoint['segmentation_mask'], (128, 128))
input_image, input_mask = normalize(input_image, input_mask)
return input_image, input_mask
TRAIN_LENGTH = info.splits['train'].num_examples
BATCH_SIZE = 64
BUFFER_SIZE = 1000
STEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE
train = dataset['train'].map(load_image_train, num_parallel_calls=tf.data.AUTOTUNE)
test = dataset['test'].map(load_image_test)
train_dataset = train.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()
train_dataset = train_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
test_dataset = test.batch(BATCH_SIZE)
def display(display_list):
plt.figure(figsize=(15, 15))
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i + 1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
plt.axis('off')
def upsample(filters, size):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.ReLU())
return result
for image, mask in train.take(1):
sample_image, sample_mask = image, mask
display([sample_image, sample_mask])
OUTPUT_CHANNELS = 3
base_model = tf.keras.applications.MobileNetV2(input_shape=[128, 128, 3], include_top=False)
# Use the activations of these layers
layer_names = [
'block_1_expand_relu', # 64x64
'block_3_expand_relu', # 32x32
'block_6_expand_relu', # 16x16
'block_13_expand_relu', # 8x8
'block_16_project', # 4x4
]
layers = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=layers)
down_stack.trainable = False
up_stack = [
upsample(512, 3), # 4x4 -> 8x8
upsample(256, 3), # 8x8 -> 16x16
upsample(128, 3), # 16x16 -> 32x32
upsample(64, 3), # 32x32 -> 64x64
]
def unet_model(output_channels):
inputs = tf.keras.layers.Input(shape=[128, 128, 3])
x = inputs
# Downsampling through the model
skips = down_stack(x)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(output_channels, 3, strides=2, padding='same') # 64x64 -> 128x128
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
model = unet_model(OUTPUT_CHANNELS)
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
def create_mask(pred_mask):
pred_mask = tf.argmax(pred_mask, axis=-1)
pred_mask = pred_mask[..., tf.newaxis]
return pred_mask[0]
def show_predictions(dataset=None, num=1):
if dataset:
for image, mask in dataset.take(num):
pred_mask = model.predict(image)
display([image[0], mask[0], create_mask(pred_mask)])
else:
display([sample_image, sample_mask,
create_mask(model.predict(sample_image[tf.newaxis, ...]))])
show_predictions()
EPOCHS = 20
VAL_SUB_SPLITS = 5
VALIDATION_STEPS = info.splits['test'].num_examples // BATCH_SIZE // VAL_SUB_SPLITS
model.fit(train_dataset, epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH,
validation_steps=VALIDATION_STEPS,
validation_data=test_dataset)
show_predictions(test_dataset, 3)
plt.show() | 0.858526 | 0.693022 |
import requests
import os
from people_detection import PeopleDetection
from PIL import Image
from io import BytesIO
from time import time, sleep
class Scam(object):
base_url = "http://scam.42.fr"
cam_endpoints = {
# region: [camera, camera, camera]
"e0": [
"cam-e1-sm-rue",
"cam-e1-sm-resto",
"cam-e0-petit-couloir",
],
"e1": [
"cam-e1-hall-porte",
"cam-e1-sm-so",
"cam-e1-sm-se",
"cam-e1-sm-ne",
"cam-e1-sm-no",
],
"e2": [
"cam-e2-playstation",
"cam-e2-detente-sud",
"cam-e2-detente-ouest",
"cam-e2-detente-est",
"cam-e2-sm-porte",
"cam-e2-sm-so",
"cam-e2-sm-se",
"cam-e2-sm-ne",
"cam-e2-sm-no",
],
"e3": [
"cam-e3-sm-porte",
"cam-e3-sm-so",
"cam-e3-sm-se",
"cam-e3-sm-ne",
"cam-e3-sm-no",
],
"amphi": [
"cam-e0-amphi-rue",
"cam-e0-amphi-resto",
],
"bocal": [
"cam-e3-bocal-out",
],
"kfet": [
"cam-ext-rie-nord",
"cam-ext-rie-sud",
"cam-kfet-cuisine-no",
"cam-kfet-cuisine-se",
"cam-kfet-bar-no",
"cam-kfet-bar-se",
"cam-kfet-resto-ne",
"cam-kfet-resto-so",
],
"cloture": [
"cam-ext-moto",
"cam-ext-moto2",
"cam-ext-angle-r-sud",
"cam-ext-angle-b-est",
"cam-ext-portillon-nord",
"cam-ext-portillon-sud",
"cam-ext-sas-nord",
"cam-ext-sas-sud",
"cam-ext-rie-nord",
],
}
scam_endpoint = "/cams/%s.jpg"
class CamDoesNotExist(Exception):
"""Only purpose of this exception is to give a clear message error for debug."""
def __init__(self, region, camera):
super(CamDoesNotExist, self).__init__("Error camera: %s (%s) does not exist." % (camera, region))
def __init__(self, camera):
"""Return the Scam object for the camera."""
self.region = self._get_camera_region(camera)
self.camera = camera
self.dir_path = "img/%s/%s" % (self.region, self.camera)
self.pd = PeopleDetection(self._get_background_img_path())
def _get_camera_region(self, camera):
"""Get the region of the camera. Return the region if camera is found. Else raise CamDoesNotExist exception."""
for cam_region, cam_list in self.cam_endpoints.items():
if camera in cam_list:
return cam_region
raise self.CamDoesNotExist("???", camera)
def _get_cam_data(self, camera):
"""
Perform an GET request to scam and return the camera image as binary content if success.
If request fail, return None.
"""
nowstamp = int(time())
try:
rep = requests.get("%s%s?%s" % (self.base_url, (self.scam_endpoint % camera), nowstamp))
if rep.ok:
return rep.content
except requests.exceptions.RequestException as e:
print("Error with request: %s" % e)
return None
def _get_background_mask_path(self):
"""Try to get the background mask image. Return the path if exist, else return None"""
if os.path.isfile("%s/mask.jpg" % (self.dir_path)):
return "%s/mask.jpg" % self.dir_path
else:
return None
def _get_background_img_path(self):
"""Try to get the background image. Return the path if exist, else return None"""
if os.path.isfile("%s/background.jpg" % (self.dir_path)):
return "%s/background.jpg" % (self.dir_path)
else:
return None
@staticmethod
def _get_and_crop_data_to_image(data):
"""Take Cam binary data and crop it to remove Date/Hour. Then return an Pillow Image object."""
img = Image.open(BytesIO(data))
w, h = img.size
try:
img.crop((0, 35, w, (h - 35)))
except OSError:
# Every image from scam always raise an 'image file is truncated' error.
# This first try catch this weird error. TODO (do it properly !)
pass
return img.crop((0, 35, w, (h - 35)))
def get_cam_image(self):
"""Return Image object of the camera current image. Else return None"""
data = self._get_cam_data(self.camera)
if data:
return self._get_and_crop_data_to_image(data)
else:
return None
def save_cam_image(self, filename="1.jpg"):
"""
Save the desired self.camera into the img path: img/{self.region}/{self.camera}/{filename}.
Return Image object of the current image on camera.
"""
os.makedirs(self.dir_path, exist_ok=True)
img = self.get_cam_image()
img.save("%s/%s" % (self.dir_path, filename), "JPEG")
return img | scam.py | import requests
import os
from people_detection import PeopleDetection
from PIL import Image
from io import BytesIO
from time import time, sleep
class Scam(object):
base_url = "http://scam.42.fr"
cam_endpoints = {
# region: [camera, camera, camera]
"e0": [
"cam-e1-sm-rue",
"cam-e1-sm-resto",
"cam-e0-petit-couloir",
],
"e1": [
"cam-e1-hall-porte",
"cam-e1-sm-so",
"cam-e1-sm-se",
"cam-e1-sm-ne",
"cam-e1-sm-no",
],
"e2": [
"cam-e2-playstation",
"cam-e2-detente-sud",
"cam-e2-detente-ouest",
"cam-e2-detente-est",
"cam-e2-sm-porte",
"cam-e2-sm-so",
"cam-e2-sm-se",
"cam-e2-sm-ne",
"cam-e2-sm-no",
],
"e3": [
"cam-e3-sm-porte",
"cam-e3-sm-so",
"cam-e3-sm-se",
"cam-e3-sm-ne",
"cam-e3-sm-no",
],
"amphi": [
"cam-e0-amphi-rue",
"cam-e0-amphi-resto",
],
"bocal": [
"cam-e3-bocal-out",
],
"kfet": [
"cam-ext-rie-nord",
"cam-ext-rie-sud",
"cam-kfet-cuisine-no",
"cam-kfet-cuisine-se",
"cam-kfet-bar-no",
"cam-kfet-bar-se",
"cam-kfet-resto-ne",
"cam-kfet-resto-so",
],
"cloture": [
"cam-ext-moto",
"cam-ext-moto2",
"cam-ext-angle-r-sud",
"cam-ext-angle-b-est",
"cam-ext-portillon-nord",
"cam-ext-portillon-sud",
"cam-ext-sas-nord",
"cam-ext-sas-sud",
"cam-ext-rie-nord",
],
}
scam_endpoint = "/cams/%s.jpg"
class CamDoesNotExist(Exception):
"""Only purpose of this exception is to give a clear message error for debug."""
def __init__(self, region, camera):
super(CamDoesNotExist, self).__init__("Error camera: %s (%s) does not exist." % (camera, region))
def __init__(self, camera):
"""Return the Scam object for the camera."""
self.region = self._get_camera_region(camera)
self.camera = camera
self.dir_path = "img/%s/%s" % (self.region, self.camera)
self.pd = PeopleDetection(self._get_background_img_path())
def _get_camera_region(self, camera):
"""Get the region of the camera. Return the region if camera is found. Else raise CamDoesNotExist exception."""
for cam_region, cam_list in self.cam_endpoints.items():
if camera in cam_list:
return cam_region
raise self.CamDoesNotExist("???", camera)
def _get_cam_data(self, camera):
"""
Perform an GET request to scam and return the camera image as binary content if success.
If request fail, return None.
"""
nowstamp = int(time())
try:
rep = requests.get("%s%s?%s" % (self.base_url, (self.scam_endpoint % camera), nowstamp))
if rep.ok:
return rep.content
except requests.exceptions.RequestException as e:
print("Error with request: %s" % e)
return None
def _get_background_mask_path(self):
"""Try to get the background mask image. Return the path if exist, else return None"""
if os.path.isfile("%s/mask.jpg" % (self.dir_path)):
return "%s/mask.jpg" % self.dir_path
else:
return None
def _get_background_img_path(self):
"""Try to get the background image. Return the path if exist, else return None"""
if os.path.isfile("%s/background.jpg" % (self.dir_path)):
return "%s/background.jpg" % (self.dir_path)
else:
return None
@staticmethod
def _get_and_crop_data_to_image(data):
"""Take Cam binary data and crop it to remove Date/Hour. Then return an Pillow Image object."""
img = Image.open(BytesIO(data))
w, h = img.size
try:
img.crop((0, 35, w, (h - 35)))
except OSError:
# Every image from scam always raise an 'image file is truncated' error.
# This first try catch this weird error. TODO (do it properly !)
pass
return img.crop((0, 35, w, (h - 35)))
def get_cam_image(self):
"""Return Image object of the camera current image. Else return None"""
data = self._get_cam_data(self.camera)
if data:
return self._get_and_crop_data_to_image(data)
else:
return None
def save_cam_image(self, filename="1.jpg"):
"""
Save the desired self.camera into the img path: img/{self.region}/{self.camera}/{filename}.
Return Image object of the current image on camera.
"""
os.makedirs(self.dir_path, exist_ok=True)
img = self.get_cam_image()
img.save("%s/%s" % (self.dir_path, filename), "JPEG")
return img | 0.583915 | 0.230259 |
import unittest
from envdiff.diff import Diff
class TestDiff(unittest.TestCase):
def test_loads_files_on_construction(self):
expected_contents = ['URL=https://www.test.com/', 'FOO=BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
self.assertEqual(self.differ.left, expected_contents)
self.assertEqual(self.differ.right, expected_contents)
def test_convert_to_dict(self):
contents = ['FOO=BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.convert_to_dict(contents, '=')
self.assertEqual(result, { 'FOO': 'BAR' })
def test_convert_to_dict_with_alternate_separator(self):
contents = ['FOO: BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.convert_to_dict(contents, ': ')
self.assertEqual(result, { 'FOO': 'BAR' })
def test_find_unique_keys(self):
left = { 'FOO': 'BAR', 'LEFT': 'parsnip' }
right = { 'FOO': 'BAR', 'RIGHT': 'persimmon' }
expected = { 'left': ['LEFT'], 'right': ['RIGHT'] }
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.find_unique_keys(left, right)
self.assertEqual(result, expected)
def test_find_distinct_shared_keys(self):
left = { 'FOO': 'BAR', 'LEFT': 'parsnip' }
right = { 'FOO': 'BOO', 'RIGHT': 'persimmon' }
expected = [ 'FOO' ]
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.find_distinct_shared_keys(left, right)
self.assertEqual(result, expected)
def test_files_are_indentical(self):
left = { 'FOO': 'BAR' }
right = { 'FOO': 'BAR' }
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
self.assertEqual(self.differ.find_unique_keys(left, right), { 'left': [], 'right': [] })
self.assertEqual(self.differ.find_distinct_shared_keys(left, right), [])
def test_diff_with_indentical_files(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, {})
self.assertEqual(unique_list, { 'left': {}, 'right': {} })
def test_diff_with_files_with_shared_keys_that_differ(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-shared')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'left': 'BAR', 'right': 'FOO' } })
def test_diff_with_files_with_unique_keys(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-unique')
shared_list, unique_list = self.differ.diff()
self.assertEqual(unique_list, { 'left': { 'FOO': 'BAR' }, 'right': { 'BAR': 'FOO' } })
def test_diff_with_files_with_unique_and_shared_keys(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-shared-and-unique')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'left': 'BAR', 'right': 'FOO' } })
self.assertEqual(unique_list, { 'left': { 'URL': 'https://www.test.com/' }, 'right': { 'BAR': 'FOO' } })
def test_diff_loading_files_in_opposite_order(self):
self.differ = Diff('test/fixtures/.env-simple-shared-and-unique', 'test/fixtures/.env-simple')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'right': 'BAR', 'left': 'FOO' } })
self.assertEqual(unique_list, { 'right': { 'URL': 'https://www.test.com/' }, 'left': { 'BAR': 'FOO' } })
def tearDown(self):
self.differ = None
if __name__ == '__main__':
unittest.main() | test/test_diff.py | import unittest
from envdiff.diff import Diff
class TestDiff(unittest.TestCase):
def test_loads_files_on_construction(self):
expected_contents = ['URL=https://www.test.com/', 'FOO=BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
self.assertEqual(self.differ.left, expected_contents)
self.assertEqual(self.differ.right, expected_contents)
def test_convert_to_dict(self):
contents = ['FOO=BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.convert_to_dict(contents, '=')
self.assertEqual(result, { 'FOO': 'BAR' })
def test_convert_to_dict_with_alternate_separator(self):
contents = ['FOO: BAR']
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.convert_to_dict(contents, ': ')
self.assertEqual(result, { 'FOO': 'BAR' })
def test_find_unique_keys(self):
left = { 'FOO': 'BAR', 'LEFT': 'parsnip' }
right = { 'FOO': 'BAR', 'RIGHT': 'persimmon' }
expected = { 'left': ['LEFT'], 'right': ['RIGHT'] }
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.find_unique_keys(left, right)
self.assertEqual(result, expected)
def test_find_distinct_shared_keys(self):
left = { 'FOO': 'BAR', 'LEFT': 'parsnip' }
right = { 'FOO': 'BOO', 'RIGHT': 'persimmon' }
expected = [ 'FOO' ]
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
result = self.differ.find_distinct_shared_keys(left, right)
self.assertEqual(result, expected)
def test_files_are_indentical(self):
left = { 'FOO': 'BAR' }
right = { 'FOO': 'BAR' }
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
self.assertEqual(self.differ.find_unique_keys(left, right), { 'left': [], 'right': [] })
self.assertEqual(self.differ.find_distinct_shared_keys(left, right), [])
def test_diff_with_indentical_files(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, {})
self.assertEqual(unique_list, { 'left': {}, 'right': {} })
def test_diff_with_files_with_shared_keys_that_differ(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-shared')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'left': 'BAR', 'right': 'FOO' } })
def test_diff_with_files_with_unique_keys(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-unique')
shared_list, unique_list = self.differ.diff()
self.assertEqual(unique_list, { 'left': { 'FOO': 'BAR' }, 'right': { 'BAR': 'FOO' } })
def test_diff_with_files_with_unique_and_shared_keys(self):
self.differ = Diff('test/fixtures/.env-simple', 'test/fixtures/.env-simple-shared-and-unique')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'left': 'BAR', 'right': 'FOO' } })
self.assertEqual(unique_list, { 'left': { 'URL': 'https://www.test.com/' }, 'right': { 'BAR': 'FOO' } })
def test_diff_loading_files_in_opposite_order(self):
self.differ = Diff('test/fixtures/.env-simple-shared-and-unique', 'test/fixtures/.env-simple')
shared_list, unique_list = self.differ.diff()
self.assertEqual(shared_list, { 'FOO': { 'right': 'BAR', 'left': 'FOO' } })
self.assertEqual(unique_list, { 'right': { 'URL': 'https://www.test.com/' }, 'left': { 'BAR': 'FOO' } })
def tearDown(self):
self.differ = None
if __name__ == '__main__':
unittest.main() | 0.608478 | 0.661718 |
from datetime import (
date,
datetime,
time,
timedelta
)
from babel.dates import (
format_date,
format_datetime,
format_time,
format_timedelta,
get_timezone
)
from pyramid.compat import text_type
def date_formatter(request, value, format='medium', locale_name=None):
"""Date formatter
"""
if not isinstance(value, datetime) and not isinstance(value, date):
return value
if not locale_name:
locale_name = request.locale_name
return text_type(format_date(value, format, locale_name))
def time_formatter(request, value, format='medium',
tzname=None, locale_name=None):
"""Time formatters
"""
if not isinstance(value, datetime) and not isinstance(value, time):
return value
tzinfo = None
if tzname:
tzinfo = get_timezone(tzname)
if not tzinfo:
settings = request.registry.settings
tzinfo = get_timezone(settings['pyramid.default_timezone_name'])
if not locale_name:
locale_name = request.locale_name
return text_type(format_time(value, format, tzinfo, locale_name))
def datetime_formatter(request, value, format='medium',
tzname=None, locale_name=None):
"""DateTime formatter
Short::
>> dt = datetime(2011, 2, 6, 10, 35, 45, 80, pytz.UTC)
>> request.format.datetime(dt, 'short')
'02/06/11 04:35 AM'
Medium::
>> request.format.datetime(dt, 'medium')
'Feb 06, 2011 04:35 AM'
Long::
>> request.format.datetime(dt, 'long')
'February 06, 2011 04:35 AM -0600'
Full::
>> request.format.datetime(dt, 'full')
'Sunday, February 06, 2011 04:35:45 AM CST'
"""
if not isinstance(value, datetime):
return value
tzinfo = None
if tzname:
tzinfo = get_timezone(tzname)
if not tzinfo:
settings = request.registry.settings
tzinfo = get_timezone(settings['pyramid.default_timezone_name'])
if not locale_name:
locale_name = request.locale_name
return text_type(format_datetime(value, format, tzinfo, locale_name))
def timedelta_formatter(request, value, granularity='second', threshold=.85,
add_direction=False, format='medium',
locale_name=None):
"""Timedelta formatter
Format::
>> td = timedelta(hours=10, minutes=5, seconds=45)
>> request.format.timedelta(td, format='medium')
'10 hours'
>> request.format.timedelta(td, format='short')
'10 hrs'
Default::
>> request.format.timedelta(td)
'10 hours'
"""
if not isinstance(value, timedelta):
return value
if not locale_name:
locale_name = request.locale_name
return text_type(format_timedelta(
value, format=format, granularity=granularity, threshold=threshold,
add_direction=add_direction, locale=locale_name)) | djed/formatter/datetime.py | from datetime import (
date,
datetime,
time,
timedelta
)
from babel.dates import (
format_date,
format_datetime,
format_time,
format_timedelta,
get_timezone
)
from pyramid.compat import text_type
def date_formatter(request, value, format='medium', locale_name=None):
"""Date formatter
"""
if not isinstance(value, datetime) and not isinstance(value, date):
return value
if not locale_name:
locale_name = request.locale_name
return text_type(format_date(value, format, locale_name))
def time_formatter(request, value, format='medium',
tzname=None, locale_name=None):
"""Time formatters
"""
if not isinstance(value, datetime) and not isinstance(value, time):
return value
tzinfo = None
if tzname:
tzinfo = get_timezone(tzname)
if not tzinfo:
settings = request.registry.settings
tzinfo = get_timezone(settings['pyramid.default_timezone_name'])
if not locale_name:
locale_name = request.locale_name
return text_type(format_time(value, format, tzinfo, locale_name))
def datetime_formatter(request, value, format='medium',
tzname=None, locale_name=None):
"""DateTime formatter
Short::
>> dt = datetime(2011, 2, 6, 10, 35, 45, 80, pytz.UTC)
>> request.format.datetime(dt, 'short')
'02/06/11 04:35 AM'
Medium::
>> request.format.datetime(dt, 'medium')
'Feb 06, 2011 04:35 AM'
Long::
>> request.format.datetime(dt, 'long')
'February 06, 2011 04:35 AM -0600'
Full::
>> request.format.datetime(dt, 'full')
'Sunday, February 06, 2011 04:35:45 AM CST'
"""
if not isinstance(value, datetime):
return value
tzinfo = None
if tzname:
tzinfo = get_timezone(tzname)
if not tzinfo:
settings = request.registry.settings
tzinfo = get_timezone(settings['pyramid.default_timezone_name'])
if not locale_name:
locale_name = request.locale_name
return text_type(format_datetime(value, format, tzinfo, locale_name))
def timedelta_formatter(request, value, granularity='second', threshold=.85,
add_direction=False, format='medium',
locale_name=None):
"""Timedelta formatter
Format::
>> td = timedelta(hours=10, minutes=5, seconds=45)
>> request.format.timedelta(td, format='medium')
'10 hours'
>> request.format.timedelta(td, format='short')
'10 hrs'
Default::
>> request.format.timedelta(td)
'10 hours'
"""
if not isinstance(value, timedelta):
return value
if not locale_name:
locale_name = request.locale_name
return text_type(format_timedelta(
value, format=format, granularity=granularity, threshold=threshold,
add_direction=add_direction, locale=locale_name)) | 0.605449 | 0.127979 |
import socket
import sys
import signal
import time
BUFFER_SIZE = 1024
def recv(sock):
buffer = sock.recv(BUFFER_SIZE)
out = buffer.decode('utf-8')
if out != "ok":
raise "Failed to receive a message"
print("receiving: " + out)
def sendto(sock, remote, cmd):
print("cmd: " + cmd)
sock.sendto(cmd.encode('utf-8'), remote)
if __name__ == '__main__':
print("commanding...")
local = ('', 8889)
remote = ('192.168.10.1', 8889)
signal.signal(signal.SIGINT, signal.SIG_DFL)
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(local)
sock.setblocking(False)
try:
print("Attempting to connect with drone..")
attempts = 3
ack = False
for i in range(attempts):
try:
print("Attempt number is " + str(i))
sock.sendto('command'.encode('utf-8'), remote)
buffer = sock.recv(BUFFER_SIZE)
out = buffer.decode('utf-8')
if out == 'ok':
print('accepted')
ack = True
break
else:
print('rejected')
time.sleep(0.5)
except Exception as e:
print("Failed to connect. Retrying...")
time.sleep(0.5)
pass
if not ack:
raise Exception("Failed to connect. Stop trying.")
sock.setblocking(True)
# commands
sendto(sock, remote, "takeoff")
recv(sock)
time.sleep(3.5)
sendto(sock, remote, "rc 0 0 0 30")
recv(sock)
time.sleep(7.5)
sendto(sock, remote, "rc 0 0 0 -30")
recv(sock)
time.sleep(7.5)
# sendto(sock, remote, "rc 0 0 5 0")
# recv(sock)
# time.sleep(3.5)
# sendto(sock, remote, "rc 0 0 -15 0")
# recv(sock)
# time.sleep(3.5)
sendto(sock, remote, "land")
recv(sock)
time.sleep(3.5)
except Exception as e:
sendto(sock, remote, "land")
print("Exception in run():" + str(e))
finally:
sock.close()
print("closing the socket") | command_drone.py | import socket
import sys
import signal
import time
BUFFER_SIZE = 1024
def recv(sock):
buffer = sock.recv(BUFFER_SIZE)
out = buffer.decode('utf-8')
if out != "ok":
raise "Failed to receive a message"
print("receiving: " + out)
def sendto(sock, remote, cmd):
print("cmd: " + cmd)
sock.sendto(cmd.encode('utf-8'), remote)
if __name__ == '__main__':
print("commanding...")
local = ('', 8889)
remote = ('192.168.10.1', 8889)
signal.signal(signal.SIGINT, signal.SIG_DFL)
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind(local)
sock.setblocking(False)
try:
print("Attempting to connect with drone..")
attempts = 3
ack = False
for i in range(attempts):
try:
print("Attempt number is " + str(i))
sock.sendto('command'.encode('utf-8'), remote)
buffer = sock.recv(BUFFER_SIZE)
out = buffer.decode('utf-8')
if out == 'ok':
print('accepted')
ack = True
break
else:
print('rejected')
time.sleep(0.5)
except Exception as e:
print("Failed to connect. Retrying...")
time.sleep(0.5)
pass
if not ack:
raise Exception("Failed to connect. Stop trying.")
sock.setblocking(True)
# commands
sendto(sock, remote, "takeoff")
recv(sock)
time.sleep(3.5)
sendto(sock, remote, "rc 0 0 0 30")
recv(sock)
time.sleep(7.5)
sendto(sock, remote, "rc 0 0 0 -30")
recv(sock)
time.sleep(7.5)
# sendto(sock, remote, "rc 0 0 5 0")
# recv(sock)
# time.sleep(3.5)
# sendto(sock, remote, "rc 0 0 -15 0")
# recv(sock)
# time.sleep(3.5)
sendto(sock, remote, "land")
recv(sock)
time.sleep(3.5)
except Exception as e:
sendto(sock, remote, "land")
print("Exception in run():" + str(e))
finally:
sock.close()
print("closing the socket") | 0.123617 | 0.064506 |
import os
import time
import fnmatch
def match(paths, atimeout=None, ctimeout=None, mtimeout=None, seed=None, patterns=None, verbose=False):
'''
:param paths: path for clean
:param atimeout: file will be deleted after access timeout
:param ctimeout: file will be deleted after creation timeout
:param mtimeout: file will be deleted after modification timeout
:param seed: base line of current time
:param patterns: includes and excludes patterns with format [('i', pattern), ('e', pattern), ...]
:return: file list
'''
# args check
if isinstance(paths, str): paths = [paths]
assert isinstance(paths, (tuple, list))
if seed is None: seed = time.time()
if patterns is None: patterns = ['i', '*']
# match function
def check_include(f):
# check patterns
for t, p in patterns:
m = fnmatch.fnmatch(file_path, p)
if t == 'i':
if not m: continue
break
else:
if not m: continue
return False
# check
at, ct, mt = os.path.getatime(f), os.path.getctime(f), os.path.getmtime(f)
if atimeout is not None and seed - at < atimeout: return False
if ctimeout is not None and seed - ct < ctimeout: return False
if mtimeout is not None and seed - mt < mtimeout: return False
return True
# scan all paths
include_files = []
for path in paths:
for root, dirs, files in os.walk(path):
for f in files:
file_path = os.path.join(root, f)
if not check_include(file_path): continue
include_files.append(file_path)
if verbose: print(file_path)
return include_files
def remove_empty_dirs(paths):
def _do(path):
empty = True
for f in os.listdir(path):
f = os.path.join(path, f)
if os.path.isfile(f):
empty = False
break
if not _do(f):
empty = False
break
return empty
for p in paths:
_do(p)
def clean(paths, atimeout=None, ctimeout=None, mtimeout=None, seed=None, patterns=None, remove_empty_dir=True, verbose=False):
'''
:params: see test method
:return: None
'''
# find all deleted files
files = match(paths, atimeout, ctimeout, mtimeout, seed, patterns, False)
# remove files
for f in files:
try:
os.remove(f)
except: pass
if verbose: print(f)
# clear empty directories
if remove_empty_dir: remove_empty_dirs(paths)
def main():
import argparse
class PatternAction(argparse.Action):
def __init__(self, *args, **kwargs):
super(PatternAction, self).__init__(*args, **kwargs)
def __call__(self, parser, namespace, values, option_string=None):
if not 'patterns' in namespace:
setattr(namespace, 'patterns', [])
tag = 'i' if self.dest == 'include' else 'e'
namespace.patterns.append((tag, values))
parser = argparse.ArgumentParser(prog='fclean', description="A clean tool for remove timeout files and path")
parser.add_argument('-p', '--path', type=str, required=True, action='append', help='Path for clean')
parser.add_argument('-t', '--timeout', type=int, help='File will be deleted after timeout')
parser.add_argument('-at', '--access-timeout', type=int, help='File will be deleted after last access timeout')
parser.add_argument('-ct', '--creation-timeout', type=int, help='File will be deleted after creation timeout')
parser.add_argument('-mt', '--modification-timeout', type=int, help='File will be deleted after modification timeout')
parser.add_argument('-s', '--seed', type=float, default=None, help='Base line of current time')
parser.add_argument('-i', '--include', type=str, action=PatternAction, help='Include files matching PATTERN')
parser.add_argument('-e', '--exclude', type=str, action=PatternAction, help='Exclude files matching PATTERN')
parser.add_argument('-m', '--match', action='store_true', default=False, help='Only execute match instead of remove files')
parser.add_argument('-v', '--verbose', action='store_true', default=False, help='Increase verbosity')
parser.add_argument('-k', '--keep', action='store_true', default=False, help='Keep empty directories')
args = parser.parse_args()
# parse timeout
if args.timeout is not None and args.access_timeout is None:
args.access_timeout = args.timeout
if args.match:
match(args.path, args.access_timeout, args.creation_timeout, args.modification_timeout,
args.seed, args.patterns, args.verbose)
else:
clean(args.path, args.access_timeout, args.creation_timeout, args.modification_timeout,
args.seed, args.patterns, not args.keep, args.verbose)
if __name__ == '__main__':
main() | pyplus/tools/file_cleaner.py | import os
import time
import fnmatch
def match(paths, atimeout=None, ctimeout=None, mtimeout=None, seed=None, patterns=None, verbose=False):
'''
:param paths: path for clean
:param atimeout: file will be deleted after access timeout
:param ctimeout: file will be deleted after creation timeout
:param mtimeout: file will be deleted after modification timeout
:param seed: base line of current time
:param patterns: includes and excludes patterns with format [('i', pattern), ('e', pattern), ...]
:return: file list
'''
# args check
if isinstance(paths, str): paths = [paths]
assert isinstance(paths, (tuple, list))
if seed is None: seed = time.time()
if patterns is None: patterns = ['i', '*']
# match function
def check_include(f):
# check patterns
for t, p in patterns:
m = fnmatch.fnmatch(file_path, p)
if t == 'i':
if not m: continue
break
else:
if not m: continue
return False
# check
at, ct, mt = os.path.getatime(f), os.path.getctime(f), os.path.getmtime(f)
if atimeout is not None and seed - at < atimeout: return False
if ctimeout is not None and seed - ct < ctimeout: return False
if mtimeout is not None and seed - mt < mtimeout: return False
return True
# scan all paths
include_files = []
for path in paths:
for root, dirs, files in os.walk(path):
for f in files:
file_path = os.path.join(root, f)
if not check_include(file_path): continue
include_files.append(file_path)
if verbose: print(file_path)
return include_files
def remove_empty_dirs(paths):
def _do(path):
empty = True
for f in os.listdir(path):
f = os.path.join(path, f)
if os.path.isfile(f):
empty = False
break
if not _do(f):
empty = False
break
return empty
for p in paths:
_do(p)
def clean(paths, atimeout=None, ctimeout=None, mtimeout=None, seed=None, patterns=None, remove_empty_dir=True, verbose=False):
'''
:params: see test method
:return: None
'''
# find all deleted files
files = match(paths, atimeout, ctimeout, mtimeout, seed, patterns, False)
# remove files
for f in files:
try:
os.remove(f)
except: pass
if verbose: print(f)
# clear empty directories
if remove_empty_dir: remove_empty_dirs(paths)
def main():
import argparse
class PatternAction(argparse.Action):
def __init__(self, *args, **kwargs):
super(PatternAction, self).__init__(*args, **kwargs)
def __call__(self, parser, namespace, values, option_string=None):
if not 'patterns' in namespace:
setattr(namespace, 'patterns', [])
tag = 'i' if self.dest == 'include' else 'e'
namespace.patterns.append((tag, values))
parser = argparse.ArgumentParser(prog='fclean', description="A clean tool for remove timeout files and path")
parser.add_argument('-p', '--path', type=str, required=True, action='append', help='Path for clean')
parser.add_argument('-t', '--timeout', type=int, help='File will be deleted after timeout')
parser.add_argument('-at', '--access-timeout', type=int, help='File will be deleted after last access timeout')
parser.add_argument('-ct', '--creation-timeout', type=int, help='File will be deleted after creation timeout')
parser.add_argument('-mt', '--modification-timeout', type=int, help='File will be deleted after modification timeout')
parser.add_argument('-s', '--seed', type=float, default=None, help='Base line of current time')
parser.add_argument('-i', '--include', type=str, action=PatternAction, help='Include files matching PATTERN')
parser.add_argument('-e', '--exclude', type=str, action=PatternAction, help='Exclude files matching PATTERN')
parser.add_argument('-m', '--match', action='store_true', default=False, help='Only execute match instead of remove files')
parser.add_argument('-v', '--verbose', action='store_true', default=False, help='Increase verbosity')
parser.add_argument('-k', '--keep', action='store_true', default=False, help='Keep empty directories')
args = parser.parse_args()
# parse timeout
if args.timeout is not None and args.access_timeout is None:
args.access_timeout = args.timeout
if args.match:
match(args.path, args.access_timeout, args.creation_timeout, args.modification_timeout,
args.seed, args.patterns, args.verbose)
else:
clean(args.path, args.access_timeout, args.creation_timeout, args.modification_timeout,
args.seed, args.patterns, not args.keep, args.verbose)
if __name__ == '__main__':
main() | 0.205894 | 0.160496 |
from assets.variables import *
import assets.tools as tools
from scenes import editor, helper, options
class MainMenu(tools.SceneBase):
"""Class that creates the main menu screen
Attributes
----------
counter: int
keeps track of the user's selection
selection: dict
uses the counter to get the object of the user's current selection
Methods
----------
process_input(events, pressed_keys):
Handles input
update():
Updates scene
render(screen):
Renders the helper's UI
"""
def __init__(self) -> None:
"""Initialize class attributes
Returns
----------
None
"""
tools.SceneBase.__init__(self)
# Sets the counter
self.counter = 0
self.selection = {0: "start", 1: "help",
2: "options", 3: "quit"}
def process_input(self, events, pressed_keys) -> None:
"""Handles input
Parameters
----------
events: int
the different game events
pressed_keys: str
the keys pressed by the user
Returns
----------
None
"""
for event in events:
if event.type == pg.KEYDOWN:
# Checks if down arrow is pressed
if event.key == pg.K_DOWN:
if self.counter < 3:
self.counter += 1
# Checks if up arrow is pressed
elif event.key == pg.K_UP:
if self.counter > 0:
self.counter -= 1
# Checks if enter is pressed
if event.key == pg.K_RETURN:
if self.selection[self.counter] == "start":
self.switch_to_scene(editor.Editor())
elif self.selection[self.counter] == "help":
self.switch_to_scene(helper.Help())
elif self.selection[self.counter] == "options":
self.switch_to_scene(options.Options())
elif self.selection[self.counter] == "quit":
self.terminate()
def update(self) -> None:
"""Updates scene
Returns
----------
None
"""
pass
def render(self, screen) -> None:
"""Renders the menu's UI
Parameters
----------
screen
the screen pygame displays on
Returns
----------
None
"""
# Fills screen
screen.fill(BLACK)
# Sets titles and main menu options
title = tools.text_format("Universum - Sim", 90, RED)
if self.selection[self.counter] == "start":
text_start = tools.text_format("START", 75, GREEN)
else:
text_start = tools.text_format("START", 75, WHITE)
if self.selection[self.counter] == "help":
text_help = tools.text_format("HELP", 75, GREEN)
else:
text_help = tools.text_format("HELP", 75, WHITE)
if self.selection[self.counter] == "options":
text_options = tools.text_format("OPTIONS", 75, GREEN)
else:
text_options = tools.text_format("OPTIONS", 75, WHITE)
if self.selection[self.counter] == "quit":
text_quit = tools.text_format("QUIT", 75, GREEN)
else:
text_quit = tools.text_format("QUIT", 75, WHITE)
title_rect = title.get_rect()
start_rect = text_start.get_rect()
help_rect = text_help.get_rect()
options_rect = text_options.get_rect()
quit_rect = text_quit.get_rect()
# Main Menu Text
screen.blit(title, (SCREEN_WIDTH / 2 - (title_rect[2] / 2), 80))
screen.blit(text_start, (SCREEN_WIDTH / 2 - (start_rect[2] / 2), 300))
screen.blit(text_help, (SCREEN_WIDTH / 2 - (help_rect[2] / 2), 380))
screen.blit(text_options, (SCREEN_WIDTH / 2 - (options_rect[2] / 2), 460))
screen.blit(text_quit, (SCREEN_WIDTH / 2 - (quit_rect[2] / 2), 540)) | scripts/scenes/menu.py | from assets.variables import *
import assets.tools as tools
from scenes import editor, helper, options
class MainMenu(tools.SceneBase):
"""Class that creates the main menu screen
Attributes
----------
counter: int
keeps track of the user's selection
selection: dict
uses the counter to get the object of the user's current selection
Methods
----------
process_input(events, pressed_keys):
Handles input
update():
Updates scene
render(screen):
Renders the helper's UI
"""
def __init__(self) -> None:
"""Initialize class attributes
Returns
----------
None
"""
tools.SceneBase.__init__(self)
# Sets the counter
self.counter = 0
self.selection = {0: "start", 1: "help",
2: "options", 3: "quit"}
def process_input(self, events, pressed_keys) -> None:
"""Handles input
Parameters
----------
events: int
the different game events
pressed_keys: str
the keys pressed by the user
Returns
----------
None
"""
for event in events:
if event.type == pg.KEYDOWN:
# Checks if down arrow is pressed
if event.key == pg.K_DOWN:
if self.counter < 3:
self.counter += 1
# Checks if up arrow is pressed
elif event.key == pg.K_UP:
if self.counter > 0:
self.counter -= 1
# Checks if enter is pressed
if event.key == pg.K_RETURN:
if self.selection[self.counter] == "start":
self.switch_to_scene(editor.Editor())
elif self.selection[self.counter] == "help":
self.switch_to_scene(helper.Help())
elif self.selection[self.counter] == "options":
self.switch_to_scene(options.Options())
elif self.selection[self.counter] == "quit":
self.terminate()
def update(self) -> None:
"""Updates scene
Returns
----------
None
"""
pass
def render(self, screen) -> None:
"""Renders the menu's UI
Parameters
----------
screen
the screen pygame displays on
Returns
----------
None
"""
# Fills screen
screen.fill(BLACK)
# Sets titles and main menu options
title = tools.text_format("Universum - Sim", 90, RED)
if self.selection[self.counter] == "start":
text_start = tools.text_format("START", 75, GREEN)
else:
text_start = tools.text_format("START", 75, WHITE)
if self.selection[self.counter] == "help":
text_help = tools.text_format("HELP", 75, GREEN)
else:
text_help = tools.text_format("HELP", 75, WHITE)
if self.selection[self.counter] == "options":
text_options = tools.text_format("OPTIONS", 75, GREEN)
else:
text_options = tools.text_format("OPTIONS", 75, WHITE)
if self.selection[self.counter] == "quit":
text_quit = tools.text_format("QUIT", 75, GREEN)
else:
text_quit = tools.text_format("QUIT", 75, WHITE)
title_rect = title.get_rect()
start_rect = text_start.get_rect()
help_rect = text_help.get_rect()
options_rect = text_options.get_rect()
quit_rect = text_quit.get_rect()
# Main Menu Text
screen.blit(title, (SCREEN_WIDTH / 2 - (title_rect[2] / 2), 80))
screen.blit(text_start, (SCREEN_WIDTH / 2 - (start_rect[2] / 2), 300))
screen.blit(text_help, (SCREEN_WIDTH / 2 - (help_rect[2] / 2), 380))
screen.blit(text_options, (SCREEN_WIDTH / 2 - (options_rect[2] / 2), 460))
screen.blit(text_quit, (SCREEN_WIDTH / 2 - (quit_rect[2] / 2), 540)) | 0.758779 | 0.348146 |
r"""
Copyright (c) 2015 <NAME>
This software is released under the MIT License.
http://opensource.org/licenses/mit-license.php
"""
__author__ = 'mori.yuichiro'
import time
import pprint
import logging
import json
import argparse
import tempfile
import boto
from boto.s3.key import Key
from crypt import Encryptor
from filelister import FileLister
from flock import SimpleFileLock
from util import *
CONFIG_DIR = '.s4backup'
CONFIG_FILE_NAME = 'config.json'
IGNORE_FILE_NAME = 'file_ignore'
IGNORE_DIR_NAME = 'dir_ignore'
LOCK_FILE_NAME = 'lock'
IGNORE_FILE_RULES = [
'.DS_Store',
'*~',
]
IGNORE_DIRS = [
'.git',
'.idea',
CONFIG_DIR
]
class S4Backupper():
def __init__(self, target_path, aws_access_key_id, aws_secret_access_key, s3bucket_name, s3prefix,
use_hash_filename=False, use_encryption=False, key_str=None, iv_str=None, aws_region=None,
dry_run_flg=False):
abs_path = os.path.abspath(target_path)
if not os.path.isdir(abs_path):
raise Exception('Invalid target path!')
self.target_path = abs_path
self.snapshot_version = time.strftime('%Y-%m-%d_%H-%M-%S', time.gmtime(time.time()))
# *** directoy initialization ***
log_base_path = os.path.join(abs_path, CONFIG_DIR, 'logs')
mkdir_p(log_base_path)
log_path = os.path.join(log_base_path, self.snapshot_version)
mkdir_p(log_path)
self.log_path = log_path
self.stats = {
'bytes_uploaded': 0,
'bytes_scanned': 0,
'files_uploaded': 0,
'files_scanned': 0,
'bytes_total': 0,
'files_total': 0,
}
self.s3keys = {}
# *** AWS S3 Connection ***
self.s3conn = boto.s3.connect_to_region(
aws_region or 'us-east-1',
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
)
self.s3bucket = self.s3conn.get_bucket(s3bucket_name)
self.s3prefix = str(s3prefix) # get lid of unicode
self.dry_run_flg = dry_run_flg
# *** Logger ***
# Logger to show progress
logger = logging.getLogger('S3ArchiverStdout')
logger.setLevel(logging.DEBUG)
h = logging.StreamHandler()
h.setLevel(logging.INFO)
h.setFormatter(logging.Formatter("%(asctime)s %(levelname)s: %(message)s"))
logger.addHandler(h)
h2 = logging.FileHandler(os.path.join(self.log_path, 'detail.log'))
h2.setLevel(logging.DEBUG)
h2.setFormatter(logging.Formatter("%(asctime)s %(levelname)s: %(message)s"))
logger.addHandler(h2)
self.logger = logger
self.file_lister = FileLister(
self.target_path,
ignore_dirs=IGNORE_DIRS,
ignore_file_patterns=IGNORE_FILE_RULES,
)
self.update_count = 0
self.hash_filename_flg = use_hash_filename
self.encryption_flg = use_encryption
if self.encryption_flg:
self.encryptor = Encryptor.initialize_by_hex(key_str, iv_str)
else:
self.encryptor = None
def _backup_file(self, file_path, upload_path):
with tempfile.TemporaryFile() as out_file_p:
with open(file_path, 'rb') as in_file_p:
file_backp_start_time = time.time()
(mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(file_path)
encryption_seconds = 0
encrypted_size = 0
if self.encryption_flg:
encryption_start_time = time.time()
self.encryptor.encrypt_file(in_file_p, out_file_p)
encryption_seconds = time.time() - encryption_start_time
encrypted_size = out_file_p.tell()
in_file_p.seek(0, os.SEEK_SET)
out_file_p.seek(0, os.SEEK_SET)
md5_start_time = time.time()
md5sum = calc_md5_from_file(out_file_p)
md5_seconds = time.time() - md5_start_time
out_file_p.seek(0, os.SEEK_SET)
log_parts = [
'file=%s' % file_path,
'path=%s' % upload_path,
'md5=%s' % md5sum,
'size=%s' % size,
'enc_size=%s' % encrypted_size,
'enc_sec={:.3f}'.format(encryption_seconds),
'md5_sec={:.3f}'.format(md5_seconds),
]
self.logger.debug(' '.join(log_parts))
self.stats['files_scanned'] += 1
self.stats['bytes_scanned'] += size
s3path = '/'.join([self.s3prefix, upload_path])
if s3path in self.s3keys:
cached_key = self.s3keys[s3path]
if cached_key.etag == '"%s"' % md5sum:
self.logger.debug('%s/%s skipped file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
return
fkey = self.s3bucket.get_key(s3path)
if fkey and fkey.etag == '"%s"' % md5sum:
self.logger.debug('%s/%s checked and skipped file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
return
# file does not exist or modified
if self.dry_run_flg:
self.logger.warn('Upload skipped due to dry run flg file:%s' % upload_path)
return
obj_key = Key(self.s3bucket)
obj_key.key = s3path
obj_key.set_metadata('original_size', str(size))
obj_key.set_contents_from_file(out_file_p, encrypt_key=True)
self.stats['files_uploaded'] += 1
self.stats['bytes_uploaded'] += size
self.logger.debug('%s/%s uploaded file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
def _auto_log_update(self):
self.update_count += 1
if self.update_count % 20 != 0:
return
bytes_total = self.stats['bytes_total']
bytes_uploaded = self.stats['bytes_uploaded']
bytes_scanned = self.stats['bytes_scanned']
self.logger.info(
'Bytes uploaded:{}({:.2f}%) '.format(humanize_bytes(bytes_uploaded), percentize(bytes_uploaded, bytes_total)) +
'scanned:{}({:.2f}%) '.format(humanize_bytes(bytes_scanned), percentize(bytes_scanned, bytes_total)) +
'total:{} '.format(humanize_bytes(self.stats['bytes_total'])) +
''
)
files_total = self.stats['files_total']
if files_total == 0:
files_total = 1
files_uploaded = self.stats['files_uploaded']
files_scanned = self.stats['files_scanned']
self.logger.info(
'Files uploaded:{}({:.2f}%) '.format(files_uploaded, percentize(files_uploaded, files_total)) +
'scanned:{}({:.2f}%) '.format(files_scanned, percentize(files_scanned, files_total)) +
'total:{} '.format(files_total) +
''
)
def _save_directory_state(self, files):
state_file_path = os.path.join(self.log_path, 'state.txt')
with open(state_file_path, 'wt') as f:
bytes_total = 0
files_total = 0
for found_file in files:
relative_path = found_file.replace(self.target_path + '/', "", 1)
(mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(found_file)
parts = [
'path=%s' % relative_path,
'size=%s' % size,
'ctime=%s' % ctime,
'mtime=%s' % mtime,
]
line = '\t'.join(parts) + '\n'
f.write(line)
bytes_total += size
files_total += 1
self.stats['bytes_total'] = bytes_total
self.stats['files_total'] = files_total
upload_path = '/'.join(['logs', self.snapshot_version, 'state.txt'])
self._backup_file(state_file_path, upload_path)
def _execute_backup(self):
self.logger.info('Snapshot version:%s' % self.snapshot_version)
time_start = time.time()
s3path = '/'.join([self.s3prefix, 'data'])
key_num = 0
for fkey in self.s3bucket.list(s3path):
self.s3keys[fkey.key.encode('utf-8')] = fkey
key_num += 1
self.logger.info('Cached keys:%s' % key_num)
files = self.file_lister.get_file_list()
self._save_directory_state(files)
for found_file in files:
relative_path = found_file.replace(self.target_path + '/', "", 1)
if self.hash_filename_flg:
relative_path = calc_sha1_from_str(relative_path)
self._backup_file(found_file, '/'.join(['data', relative_path]))
self._auto_log_update()
self.logger.info('Bytes uploaded:%s scanned:%s total:%s' % (self.stats['bytes_uploaded'], self.stats['bytes_scanned'], self.stats['bytes_total']))
self.logger.info('Files uploaded:%s scanned:%s total:%s' % (self.stats['files_uploaded'], self.stats['files_scanned'], self.stats['files_total']))
time_end = time.time()
summary_file_path = os.path.join(self.log_path, 'summary.txt')
with open(summary_file_path, 'wt') as f:
f.write('time_start :%s (%s)\n' % (time.strftime('%Y-%m-%d %H-%M-%S', time.gmtime(time_start)), time_start))
f.write('time_end :%s (%s)\n' % (time.strftime('%Y-%m-%d %H-%M-%S', time.gmtime(time_end)), time_end))
seconds_spent = time_end - time_start
f.write('seconds_spent:%s\n' % (seconds_spent))
f.write('\n')
f.write('Bytes uploaded:%s scanned:%s total:%s\n' % (self.stats['bytes_uploaded'], self.stats['bytes_scanned'], self.stats['bytes_total']))
f.write('Files uploaded:%s scanned:%s total:%s\n' % (self.stats['files_uploaded'], self.stats['files_scanned'], self.stats['files_total']))
upload_path = '/'.join(['logs', self.snapshot_version, 'summary.txt'])
self._backup_file(summary_file_path, upload_path)
def execute_backup(self):
locker = SimpleFileLock(os.path.join(self.target_path, CONFIG_DIR, LOCK_FILE_NAME))
if not locker.aquire_lock():
self.logger.error('Cannot get lock!')
return
try:
self._execute_backup()
except Exception as e:
self.logger.exception(e)
raise e
finally:
locker.release()
def init():
config_path = os.path.join(os.getcwd(), CONFIG_DIR)
config_json_path = os.path.join(config_path, CONFIG_FILE_NAME)
file_ignore_path = os.path.join(config_path, IGNORE_FILE_NAME)
dir_ignore_path = os.path.join(config_path, IGNORE_DIR_NAME)
if not os.path.isdir(config_path):
os.mkdir(config_path)
if not os.path.isfile(config_json_path):
with open(config_json_path, 'wt') as f:
json.dump({}, f)
if not os.path.isfile(file_ignore_path):
with open(file_ignore_path, 'wt') as f:
f.write('')
if not os.path.isfile(dir_ignore_path):
with open(dir_ignore_path, 'wt') as f:
f.write('')
print('Initialization finished!')
def _assure_initialized():
config_path = os.path.join(os.getcwd(), CONFIG_DIR)
config_json_path = os.path.join(config_path, CONFIG_FILE_NAME)
if not os.path.isdir(config_path) or not os.path.isfile(config_json_path):
raise Exception('Current working directory is not initialized!')
def config(args_obj):
_assure_initialized()
config_json_path = os.path.join(os.getcwd(), CONFIG_DIR, CONFIG_FILE_NAME)
with open(config_json_path) as f:
config_dict = json.load(f)
if not args_obj.list and 'set' in args_obj and args_obj.set is not None:
set_values = args_obj.set
key = set_values[0]
value = set_values[1]
if value == '':
config_dict.pop(key, None)
else:
config_dict[key] = value
with open(config_json_path, 'wt') as f:
json.dump(config_dict, f)
print('%s is set to %s' % (key, value))
return
if not args_obj.list and args_obj.keyg:
if config_dict.get('encryption', False):
raise Exception('Encryption is already turned on!')
iv_str, key_str = Encryptor.generate_str_keyset(1)
config_dict['encryption'] = 'true'
config_dict['iv'] = iv_str
config_dict['key'] = key_str
with open(config_json_path, 'wt') as f:
json.dump(config_dict, f)
print('encryption is turned on')
print('key %s' % key_str)
print('iv %s' % iv_str)
return
for key in sorted(config_dict.keys()):
print('%s=%s' % (key, config_dict[key]))
def execute_backup(dry_run_flg):
_assure_initialized()
config_json_path = os.path.join(os.getcwd(), CONFIG_DIR, CONFIG_FILE_NAME)
with open(config_json_path) as f:
config_dict = json.load(f)
if 'aws_access_key_id' in config_dict:
aws_access_key_id = config_dict['aws_access_key_id']
else:
aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')
if 'aws_secret_access_key' in config_dict:
aws_secret_access_key = config_dict['aws_secret_access_key']
else:
aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')
encryption_value = config_dict.get('encryption', None)
if encryption_value and encryption_value.lower() == 'true':
encryption_flg = True
else:
encryption_flg = False
hash_filename = config_dict.get('hash_filename', None)
if hash_filename and hash_filename.lower() == 'true':
hash_filename_flg = True
else:
hash_filename_flg = False
backupper = S4Backupper(
target_path=os.getcwd(),
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_region=config_dict.get('aws_region', None),
s3bucket_name=config_dict['s3bucket'],
s3prefix=config_dict['s3prefix'],
use_hash_filename=hash_filename_flg,
use_encryption=encryption_flg,
key_str=config_dict.get('key', ''),
iv_str=config_dict.get('iv', ''),
dry_run_flg=dry_run_flg,
)
backupper.execute_backup()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest="subparser", help='sub-command help')
parser_init = subparsers.add_parser('init', help='initialize current working directory as backup target')
parser_config = subparsers.add_parser('config', help='list / set current working directory config')
parser_config.add_argument('-l', '--list', dest='list', action='store_true', help='List')
parser_config.add_argument('-s', '--set', nargs=2, dest='set', help='Set')
parser_config.add_argument('-k', '--key', dest='keyg', action='store_true', help='Generate encryption key')
parser_push = subparsers.add_parser('push', help='execute backup against current working directory')
parser_push.add_argument('-d', '--dry', dest='dry_run', action='store_true', help='Dry run')
parsed_args = parser.parse_args()
if parsed_args.subparser == 'init':
init()
elif parsed_args.subparser == 'config':
config(parsed_args)
else:
execute_backup(parsed_args.dry_run) | s4backup.py |
r"""
Copyright (c) 2015 <NAME>
This software is released under the MIT License.
http://opensource.org/licenses/mit-license.php
"""
__author__ = 'mori.yuichiro'
import time
import pprint
import logging
import json
import argparse
import tempfile
import boto
from boto.s3.key import Key
from crypt import Encryptor
from filelister import FileLister
from flock import SimpleFileLock
from util import *
CONFIG_DIR = '.s4backup'
CONFIG_FILE_NAME = 'config.json'
IGNORE_FILE_NAME = 'file_ignore'
IGNORE_DIR_NAME = 'dir_ignore'
LOCK_FILE_NAME = 'lock'
IGNORE_FILE_RULES = [
'.DS_Store',
'*~',
]
IGNORE_DIRS = [
'.git',
'.idea',
CONFIG_DIR
]
class S4Backupper():
def __init__(self, target_path, aws_access_key_id, aws_secret_access_key, s3bucket_name, s3prefix,
use_hash_filename=False, use_encryption=False, key_str=None, iv_str=None, aws_region=None,
dry_run_flg=False):
abs_path = os.path.abspath(target_path)
if not os.path.isdir(abs_path):
raise Exception('Invalid target path!')
self.target_path = abs_path
self.snapshot_version = time.strftime('%Y-%m-%d_%H-%M-%S', time.gmtime(time.time()))
# *** directoy initialization ***
log_base_path = os.path.join(abs_path, CONFIG_DIR, 'logs')
mkdir_p(log_base_path)
log_path = os.path.join(log_base_path, self.snapshot_version)
mkdir_p(log_path)
self.log_path = log_path
self.stats = {
'bytes_uploaded': 0,
'bytes_scanned': 0,
'files_uploaded': 0,
'files_scanned': 0,
'bytes_total': 0,
'files_total': 0,
}
self.s3keys = {}
# *** AWS S3 Connection ***
self.s3conn = boto.s3.connect_to_region(
aws_region or 'us-east-1',
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
)
self.s3bucket = self.s3conn.get_bucket(s3bucket_name)
self.s3prefix = str(s3prefix) # get lid of unicode
self.dry_run_flg = dry_run_flg
# *** Logger ***
# Logger to show progress
logger = logging.getLogger('S3ArchiverStdout')
logger.setLevel(logging.DEBUG)
h = logging.StreamHandler()
h.setLevel(logging.INFO)
h.setFormatter(logging.Formatter("%(asctime)s %(levelname)s: %(message)s"))
logger.addHandler(h)
h2 = logging.FileHandler(os.path.join(self.log_path, 'detail.log'))
h2.setLevel(logging.DEBUG)
h2.setFormatter(logging.Formatter("%(asctime)s %(levelname)s: %(message)s"))
logger.addHandler(h2)
self.logger = logger
self.file_lister = FileLister(
self.target_path,
ignore_dirs=IGNORE_DIRS,
ignore_file_patterns=IGNORE_FILE_RULES,
)
self.update_count = 0
self.hash_filename_flg = use_hash_filename
self.encryption_flg = use_encryption
if self.encryption_flg:
self.encryptor = Encryptor.initialize_by_hex(key_str, iv_str)
else:
self.encryptor = None
def _backup_file(self, file_path, upload_path):
with tempfile.TemporaryFile() as out_file_p:
with open(file_path, 'rb') as in_file_p:
file_backp_start_time = time.time()
(mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(file_path)
encryption_seconds = 0
encrypted_size = 0
if self.encryption_flg:
encryption_start_time = time.time()
self.encryptor.encrypt_file(in_file_p, out_file_p)
encryption_seconds = time.time() - encryption_start_time
encrypted_size = out_file_p.tell()
in_file_p.seek(0, os.SEEK_SET)
out_file_p.seek(0, os.SEEK_SET)
md5_start_time = time.time()
md5sum = calc_md5_from_file(out_file_p)
md5_seconds = time.time() - md5_start_time
out_file_p.seek(0, os.SEEK_SET)
log_parts = [
'file=%s' % file_path,
'path=%s' % upload_path,
'md5=%s' % md5sum,
'size=%s' % size,
'enc_size=%s' % encrypted_size,
'enc_sec={:.3f}'.format(encryption_seconds),
'md5_sec={:.3f}'.format(md5_seconds),
]
self.logger.debug(' '.join(log_parts))
self.stats['files_scanned'] += 1
self.stats['bytes_scanned'] += size
s3path = '/'.join([self.s3prefix, upload_path])
if s3path in self.s3keys:
cached_key = self.s3keys[s3path]
if cached_key.etag == '"%s"' % md5sum:
self.logger.debug('%s/%s skipped file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
return
fkey = self.s3bucket.get_key(s3path)
if fkey and fkey.etag == '"%s"' % md5sum:
self.logger.debug('%s/%s checked and skipped file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
return
# file does not exist or modified
if self.dry_run_flg:
self.logger.warn('Upload skipped due to dry run flg file:%s' % upload_path)
return
obj_key = Key(self.s3bucket)
obj_key.key = s3path
obj_key.set_metadata('original_size', str(size))
obj_key.set_contents_from_file(out_file_p, encrypt_key=True)
self.stats['files_uploaded'] += 1
self.stats['bytes_uploaded'] += size
self.logger.debug('%s/%s uploaded file=%s' % (self.stats['files_scanned'], self.stats['files_total'], upload_path))
def _auto_log_update(self):
self.update_count += 1
if self.update_count % 20 != 0:
return
bytes_total = self.stats['bytes_total']
bytes_uploaded = self.stats['bytes_uploaded']
bytes_scanned = self.stats['bytes_scanned']
self.logger.info(
'Bytes uploaded:{}({:.2f}%) '.format(humanize_bytes(bytes_uploaded), percentize(bytes_uploaded, bytes_total)) +
'scanned:{}({:.2f}%) '.format(humanize_bytes(bytes_scanned), percentize(bytes_scanned, bytes_total)) +
'total:{} '.format(humanize_bytes(self.stats['bytes_total'])) +
''
)
files_total = self.stats['files_total']
if files_total == 0:
files_total = 1
files_uploaded = self.stats['files_uploaded']
files_scanned = self.stats['files_scanned']
self.logger.info(
'Files uploaded:{}({:.2f}%) '.format(files_uploaded, percentize(files_uploaded, files_total)) +
'scanned:{}({:.2f}%) '.format(files_scanned, percentize(files_scanned, files_total)) +
'total:{} '.format(files_total) +
''
)
def _save_directory_state(self, files):
state_file_path = os.path.join(self.log_path, 'state.txt')
with open(state_file_path, 'wt') as f:
bytes_total = 0
files_total = 0
for found_file in files:
relative_path = found_file.replace(self.target_path + '/', "", 1)
(mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(found_file)
parts = [
'path=%s' % relative_path,
'size=%s' % size,
'ctime=%s' % ctime,
'mtime=%s' % mtime,
]
line = '\t'.join(parts) + '\n'
f.write(line)
bytes_total += size
files_total += 1
self.stats['bytes_total'] = bytes_total
self.stats['files_total'] = files_total
upload_path = '/'.join(['logs', self.snapshot_version, 'state.txt'])
self._backup_file(state_file_path, upload_path)
def _execute_backup(self):
self.logger.info('Snapshot version:%s' % self.snapshot_version)
time_start = time.time()
s3path = '/'.join([self.s3prefix, 'data'])
key_num = 0
for fkey in self.s3bucket.list(s3path):
self.s3keys[fkey.key.encode('utf-8')] = fkey
key_num += 1
self.logger.info('Cached keys:%s' % key_num)
files = self.file_lister.get_file_list()
self._save_directory_state(files)
for found_file in files:
relative_path = found_file.replace(self.target_path + '/', "", 1)
if self.hash_filename_flg:
relative_path = calc_sha1_from_str(relative_path)
self._backup_file(found_file, '/'.join(['data', relative_path]))
self._auto_log_update()
self.logger.info('Bytes uploaded:%s scanned:%s total:%s' % (self.stats['bytes_uploaded'], self.stats['bytes_scanned'], self.stats['bytes_total']))
self.logger.info('Files uploaded:%s scanned:%s total:%s' % (self.stats['files_uploaded'], self.stats['files_scanned'], self.stats['files_total']))
time_end = time.time()
summary_file_path = os.path.join(self.log_path, 'summary.txt')
with open(summary_file_path, 'wt') as f:
f.write('time_start :%s (%s)\n' % (time.strftime('%Y-%m-%d %H-%M-%S', time.gmtime(time_start)), time_start))
f.write('time_end :%s (%s)\n' % (time.strftime('%Y-%m-%d %H-%M-%S', time.gmtime(time_end)), time_end))
seconds_spent = time_end - time_start
f.write('seconds_spent:%s\n' % (seconds_spent))
f.write('\n')
f.write('Bytes uploaded:%s scanned:%s total:%s\n' % (self.stats['bytes_uploaded'], self.stats['bytes_scanned'], self.stats['bytes_total']))
f.write('Files uploaded:%s scanned:%s total:%s\n' % (self.stats['files_uploaded'], self.stats['files_scanned'], self.stats['files_total']))
upload_path = '/'.join(['logs', self.snapshot_version, 'summary.txt'])
self._backup_file(summary_file_path, upload_path)
def execute_backup(self):
locker = SimpleFileLock(os.path.join(self.target_path, CONFIG_DIR, LOCK_FILE_NAME))
if not locker.aquire_lock():
self.logger.error('Cannot get lock!')
return
try:
self._execute_backup()
except Exception as e:
self.logger.exception(e)
raise e
finally:
locker.release()
def init():
config_path = os.path.join(os.getcwd(), CONFIG_DIR)
config_json_path = os.path.join(config_path, CONFIG_FILE_NAME)
file_ignore_path = os.path.join(config_path, IGNORE_FILE_NAME)
dir_ignore_path = os.path.join(config_path, IGNORE_DIR_NAME)
if not os.path.isdir(config_path):
os.mkdir(config_path)
if not os.path.isfile(config_json_path):
with open(config_json_path, 'wt') as f:
json.dump({}, f)
if not os.path.isfile(file_ignore_path):
with open(file_ignore_path, 'wt') as f:
f.write('')
if not os.path.isfile(dir_ignore_path):
with open(dir_ignore_path, 'wt') as f:
f.write('')
print('Initialization finished!')
def _assure_initialized():
config_path = os.path.join(os.getcwd(), CONFIG_DIR)
config_json_path = os.path.join(config_path, CONFIG_FILE_NAME)
if not os.path.isdir(config_path) or not os.path.isfile(config_json_path):
raise Exception('Current working directory is not initialized!')
def config(args_obj):
_assure_initialized()
config_json_path = os.path.join(os.getcwd(), CONFIG_DIR, CONFIG_FILE_NAME)
with open(config_json_path) as f:
config_dict = json.load(f)
if not args_obj.list and 'set' in args_obj and args_obj.set is not None:
set_values = args_obj.set
key = set_values[0]
value = set_values[1]
if value == '':
config_dict.pop(key, None)
else:
config_dict[key] = value
with open(config_json_path, 'wt') as f:
json.dump(config_dict, f)
print('%s is set to %s' % (key, value))
return
if not args_obj.list and args_obj.keyg:
if config_dict.get('encryption', False):
raise Exception('Encryption is already turned on!')
iv_str, key_str = Encryptor.generate_str_keyset(1)
config_dict['encryption'] = 'true'
config_dict['iv'] = iv_str
config_dict['key'] = key_str
with open(config_json_path, 'wt') as f:
json.dump(config_dict, f)
print('encryption is turned on')
print('key %s' % key_str)
print('iv %s' % iv_str)
return
for key in sorted(config_dict.keys()):
print('%s=%s' % (key, config_dict[key]))
def execute_backup(dry_run_flg):
_assure_initialized()
config_json_path = os.path.join(os.getcwd(), CONFIG_DIR, CONFIG_FILE_NAME)
with open(config_json_path) as f:
config_dict = json.load(f)
if 'aws_access_key_id' in config_dict:
aws_access_key_id = config_dict['aws_access_key_id']
else:
aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')
if 'aws_secret_access_key' in config_dict:
aws_secret_access_key = config_dict['aws_secret_access_key']
else:
aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')
encryption_value = config_dict.get('encryption', None)
if encryption_value and encryption_value.lower() == 'true':
encryption_flg = True
else:
encryption_flg = False
hash_filename = config_dict.get('hash_filename', None)
if hash_filename and hash_filename.lower() == 'true':
hash_filename_flg = True
else:
hash_filename_flg = False
backupper = S4Backupper(
target_path=os.getcwd(),
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_region=config_dict.get('aws_region', None),
s3bucket_name=config_dict['s3bucket'],
s3prefix=config_dict['s3prefix'],
use_hash_filename=hash_filename_flg,
use_encryption=encryption_flg,
key_str=config_dict.get('key', ''),
iv_str=config_dict.get('iv', ''),
dry_run_flg=dry_run_flg,
)
backupper.execute_backup()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest="subparser", help='sub-command help')
parser_init = subparsers.add_parser('init', help='initialize current working directory as backup target')
parser_config = subparsers.add_parser('config', help='list / set current working directory config')
parser_config.add_argument('-l', '--list', dest='list', action='store_true', help='List')
parser_config.add_argument('-s', '--set', nargs=2, dest='set', help='Set')
parser_config.add_argument('-k', '--key', dest='keyg', action='store_true', help='Generate encryption key')
parser_push = subparsers.add_parser('push', help='execute backup against current working directory')
parser_push.add_argument('-d', '--dry', dest='dry_run', action='store_true', help='Dry run')
parsed_args = parser.parse_args()
if parsed_args.subparser == 'init':
init()
elif parsed_args.subparser == 'config':
config(parsed_args)
else:
execute_backup(parsed_args.dry_run) | 0.414306 | 0.070848 |
import geopandas as gpd
import scipy.optimize
import scipy.sparse
def match_footprints(grnd_df, prop_df,
threshold=0.25, base_reward=100.):
"""
Optimal matching of ground truth footprints with proposal footprints
(for a single timestep).
Input dataframes should have "id" & "geometry" columns.
"""
# Supplement IDs with indices (which run from zero
# to one less than the number of unique IDs)
grnd_id_set = set(grnd_df['id'])
prop_id_set = set(prop_df['id'])
grnd_id_to_index = {id: index for index, id in
enumerate(sorted(list(grnd_id_set)))}
prop_id_to_index = {id: index for index, id in
enumerate(sorted(list(prop_id_set)))}
grnd_index_to_id = {index: id for id, index in grnd_id_to_index.items()}
prop_index_to_id = {index: id for id, index in prop_id_to_index.items()}
grnd_df['index'] = grnd_df.id.apply(lambda id: grnd_id_to_index[id])
prop_df['index'] = prop_df.id.apply(lambda id: prop_id_to_index[id])
# Calculate IOU for all intersections, and the corresponding reward
grnd_df['grnd_area'] = grnd_df.area
prop_df['prop_area'] = prop_df.area
if not (grnd_df.empty or prop_df.empty):
intersect = gpd.overlay(grnd_df, prop_df)
else:
intersect = None
if intersect is None or len(intersect) == 0:
return [], [], len(grnd_df), len(prop_df), 0, len(prop_df), len(grnd_df), 0., grnd_id_set, prop_id_set
intersect['inter_area'] = intersect.area
intersect['iou'] = intersect['inter_area'] / (intersect['grnd_area']
+ intersect['prop_area'] - intersect['inter_area'])
intersect['reward'] = intersect.apply(lambda row: (row.iou > threshold)
* (base_reward + row.iou), axis=1)
# Convert IOUs and rewards to 2D arrays (by way of sparse matrices)
iou_matrix = scipy.sparse.coo_matrix((intersect.iou, (intersect.index_1, intersect.index_2)),
shape=(len(grnd_df), len(prop_df)))
iou_arr = iou_matrix.toarray()
reward_matrix = scipy.sparse.coo_matrix((intersect.reward, (intersect.index_1, intersect.index_2)),
shape=(len(grnd_df), len(prop_df)))
reward_arr = reward_matrix.toarray()
# Solve unbalanced linear assignment problem
grnd_match, prop_match = scipy.optimize.linear_sum_assignment(reward_arr, maximize=True)
iou_match = iou_arr[grnd_match, prop_match]
# Remove matches that don't actually contribute to the total score
grnd_match_pruned = grnd_match[iou_match>threshold]
prop_match_pruned = prop_match[iou_match>threshold]
iou_match_pruned = iou_match[iou_match>threshold]
# Look up IDs for each match, and calculate descriptive statistics
grnd_match_ids = [grnd_index_to_id[index] for index in grnd_match_pruned]
prop_match_ids = [prop_index_to_id[index] for index in prop_match_pruned]
num_grnd = len(grnd_df)
num_prop = len(prop_df)
tp = len(iou_match_pruned)
fp = num_prop - tp
fn = num_grnd - tp
if 2*tp + fp + fn > 0:
f1 = (2*tp) / (2*tp + fp + fn)
else:
f1 = 0
return grnd_match_ids, prop_match_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set
def scot_one_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2.,
stats=False, verbose=False):
"""
SpaceNet Change and Object Tracking (SCOT) metric, for one AOI.
Input dataframes should have "timestep", "id", & "geometry" columns.
"""
# Get list of timesteps from ground truth and proposal dataframes
grnd_timestep_set = set(grnd_df.timestep.drop_duplicates())
prop_timestep_set = set(grnd_df.timestep.drop_duplicates())
timesteps = sorted(list(grnd_timestep_set.union(prop_timestep_set)))
# Loop through timesteps
if verbose:
print('Matching footprints')
tp_net = 0
fp_net = 0
fn_net = 0
num_grnd_net = 0
num_prop_net = 0
all_grnd_ids = []
all_prop_ids = []
change_tp_net = 0
change_fp_net = 0
change_fn_net = 0
change_grnd_ids = set()
change_prop_ids = set()
for i, timestep in enumerate(timesteps):
# Get just the data for this timestep
grnd_df_one_timestep = grnd_df.loc[grnd_df.timestep == timestep].copy()
prop_df_one_timestep = prop_df.loc[prop_df.timestep == timestep].copy()
# Find footprint matches for this timestep
grnd_ids, prop_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set = match_footprints(
grnd_df_one_timestep, prop_df_one_timestep,
threshold=threshold, base_reward=base_reward)
# Collect aggregate statistics for tracking, and retain all match IDs
tp_net += tp
fp_net += fp
fn_net += fn
num_grnd_net += num_grnd
num_prop_net += num_prop
all_grnd_ids = grnd_ids + all_grnd_ids # newest first
all_prop_ids = prop_ids + all_prop_ids # newest first
if verbose:
print(' %2i: F1 = %.4f' % (i + 1, f1))
# Collect aggregate statistics for change detection
if i > 0:
# Find change detection TPs, FPs, and FNs among matched footprints
new_grnd = [grnd_id not in change_grnd_ids for grnd_id in grnd_ids]
new_prop = [prop_id not in change_prop_ids for prop_id in prop_ids]
change_tp_list = [g and p for g, p in zip(new_grnd, new_prop)]
change_fp_list = [p and not g for g, p in zip(new_grnd, new_prop)]
change_fn_list = [g and not p for g, p in zip(new_grnd, new_prop)]
change_tp_net += sum(change_tp_list)
change_fp_net += sum(change_fp_list)
change_fn_net += sum(change_fn_list)
# Find change detection FPs and FNs among unmatched footprints
unmatched_fp = prop_id_set.difference(prop_ids).difference(change_prop_ids)
unmatched_fn = grnd_id_set.difference(grnd_ids).difference(change_grnd_ids)
change_fp_net += len(unmatched_fp)
change_fn_net += len(unmatched_fn)
change_grnd_ids = change_grnd_ids.union(grnd_id_set)
change_prop_ids = change_prop_ids.union(prop_id_set)
# Identify which matches are mismatches
# (i.e., inconsistent with previous timesteps)
if verbose:
print('Identifying mismatches')
mm_net = 0
for i in range(len(all_grnd_ids)):
grnd_id = all_grnd_ids[i]
prop_id = all_prop_ids[i]
previous_grnd_ids = all_grnd_ids[i+1:]
previous_prop_ids = all_prop_ids[i+1:]
grnd_mismatch = grnd_id in previous_grnd_ids and previous_prop_ids[previous_grnd_ids.index(grnd_id)] != prop_id
prop_mismatch = prop_id in previous_prop_ids and previous_grnd_ids[previous_prop_ids.index(prop_id)] != grnd_id
mismatch = grnd_mismatch or prop_mismatch
if mismatch:
mm_net += 1
# Compute and return score according to the metric
track_tp_net = tp_net - mm_net
track_fp_net = fp_net + mm_net
track_fn_net = fn_net + mm_net
if track_tp_net + (track_fp_net + track_fn_net)/2. > 0:
track_score = (track_tp_net) / (track_tp_net
+ (track_fp_net + track_fn_net)/2.)
else:
track_score = 0
if change_tp_net + (change_fp_net + change_fn_net)/2. > 0:
change_score = (change_tp_net) / (change_tp_net
+ (change_fp_net + change_fn_net)/2.)
else:
change_score = 0
if beta * beta * change_score + track_score > 0:
combo_score = (1 + beta * beta) * (change_score * track_score) / (beta * beta * change_score + track_score)
else:
combo_score = 0
if verbose:
print('Tracking:')
print(' Mismatches: %i' % mm_net)
print(' True Pos: %i' % track_tp_net)
print(' False Pos: %i' % track_fp_net)
print(' False Neg: %i' % track_fn_net)
print(' Track Score: %.4f' % track_score)
print('Change Detection:')
print(' True Pos: %i' % change_tp_net)
print(' False Pos: %i' % change_fp_net)
print(' False Neg: %i' % change_fn_net)
print(' Change Score: %.4f' % change_score)
print('Combined Score: %.4f' % combo_score)
if stats:
return combo_score, [mm_net, track_tp_net, track_fp_net, track_fn_net,
track_score, change_tp_net, change_fp_net,
change_fn_net, change_score, combo_score]
else:
return combo_score
def scot_multi_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2.,
stats=True, verbose=False):
"""
SpaceNet Change and Object Tracking (SCOT) metric,
for a SpaceNet 7 submission with multiple AOIs.
Input dataframes should have "aoi", "timestep", "id", & "geometry" columns.
"""
# Get list of AOIs from ground truth dataframe
aois = sorted(list(grnd_df.aoi.drop_duplicates()))
# Evaluate SCOT metric for each AOI
cumulative_score = 0.
all_stats = {}
for i, aoi in enumerate(aois):
if verbose:
print()
print('%i / %i: AOI %s' % (i + 1, len(aois), aoi))
grnd_df_one_aoi = grnd_df.loc[grnd_df.aoi == aoi].copy()
prop_df_one_aoi = prop_df.loc[prop_df.aoi == aoi].copy()
score_one_aoi, stats_one_aoi = scot_one_aoi(
grnd_df_one_aoi, prop_df_one_aoi,
threshold=threshold,
base_reward=base_reward,
beta=beta, stats=True, verbose=verbose)
cumulative_score += score_one_aoi
all_stats[aoi] = stats_one_aoi
# Return combined SCOT metric score
score = cumulative_score / len(aois)
if verbose:
print('Overall score: %f' % score)
if stats:
return score, all_stats
else:
return score | solaris/eval/scot.py | import geopandas as gpd
import scipy.optimize
import scipy.sparse
def match_footprints(grnd_df, prop_df,
threshold=0.25, base_reward=100.):
"""
Optimal matching of ground truth footprints with proposal footprints
(for a single timestep).
Input dataframes should have "id" & "geometry" columns.
"""
# Supplement IDs with indices (which run from zero
# to one less than the number of unique IDs)
grnd_id_set = set(grnd_df['id'])
prop_id_set = set(prop_df['id'])
grnd_id_to_index = {id: index for index, id in
enumerate(sorted(list(grnd_id_set)))}
prop_id_to_index = {id: index for index, id in
enumerate(sorted(list(prop_id_set)))}
grnd_index_to_id = {index: id for id, index in grnd_id_to_index.items()}
prop_index_to_id = {index: id for id, index in prop_id_to_index.items()}
grnd_df['index'] = grnd_df.id.apply(lambda id: grnd_id_to_index[id])
prop_df['index'] = prop_df.id.apply(lambda id: prop_id_to_index[id])
# Calculate IOU for all intersections, and the corresponding reward
grnd_df['grnd_area'] = grnd_df.area
prop_df['prop_area'] = prop_df.area
if not (grnd_df.empty or prop_df.empty):
intersect = gpd.overlay(grnd_df, prop_df)
else:
intersect = None
if intersect is None or len(intersect) == 0:
return [], [], len(grnd_df), len(prop_df), 0, len(prop_df), len(grnd_df), 0., grnd_id_set, prop_id_set
intersect['inter_area'] = intersect.area
intersect['iou'] = intersect['inter_area'] / (intersect['grnd_area']
+ intersect['prop_area'] - intersect['inter_area'])
intersect['reward'] = intersect.apply(lambda row: (row.iou > threshold)
* (base_reward + row.iou), axis=1)
# Convert IOUs and rewards to 2D arrays (by way of sparse matrices)
iou_matrix = scipy.sparse.coo_matrix((intersect.iou, (intersect.index_1, intersect.index_2)),
shape=(len(grnd_df), len(prop_df)))
iou_arr = iou_matrix.toarray()
reward_matrix = scipy.sparse.coo_matrix((intersect.reward, (intersect.index_1, intersect.index_2)),
shape=(len(grnd_df), len(prop_df)))
reward_arr = reward_matrix.toarray()
# Solve unbalanced linear assignment problem
grnd_match, prop_match = scipy.optimize.linear_sum_assignment(reward_arr, maximize=True)
iou_match = iou_arr[grnd_match, prop_match]
# Remove matches that don't actually contribute to the total score
grnd_match_pruned = grnd_match[iou_match>threshold]
prop_match_pruned = prop_match[iou_match>threshold]
iou_match_pruned = iou_match[iou_match>threshold]
# Look up IDs for each match, and calculate descriptive statistics
grnd_match_ids = [grnd_index_to_id[index] for index in grnd_match_pruned]
prop_match_ids = [prop_index_to_id[index] for index in prop_match_pruned]
num_grnd = len(grnd_df)
num_prop = len(prop_df)
tp = len(iou_match_pruned)
fp = num_prop - tp
fn = num_grnd - tp
if 2*tp + fp + fn > 0:
f1 = (2*tp) / (2*tp + fp + fn)
else:
f1 = 0
return grnd_match_ids, prop_match_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set
def scot_one_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2.,
stats=False, verbose=False):
"""
SpaceNet Change and Object Tracking (SCOT) metric, for one AOI.
Input dataframes should have "timestep", "id", & "geometry" columns.
"""
# Get list of timesteps from ground truth and proposal dataframes
grnd_timestep_set = set(grnd_df.timestep.drop_duplicates())
prop_timestep_set = set(grnd_df.timestep.drop_duplicates())
timesteps = sorted(list(grnd_timestep_set.union(prop_timestep_set)))
# Loop through timesteps
if verbose:
print('Matching footprints')
tp_net = 0
fp_net = 0
fn_net = 0
num_grnd_net = 0
num_prop_net = 0
all_grnd_ids = []
all_prop_ids = []
change_tp_net = 0
change_fp_net = 0
change_fn_net = 0
change_grnd_ids = set()
change_prop_ids = set()
for i, timestep in enumerate(timesteps):
# Get just the data for this timestep
grnd_df_one_timestep = grnd_df.loc[grnd_df.timestep == timestep].copy()
prop_df_one_timestep = prop_df.loc[prop_df.timestep == timestep].copy()
# Find footprint matches for this timestep
grnd_ids, prop_ids, num_grnd, num_prop, tp, fp, fn, f1, grnd_id_set, prop_id_set = match_footprints(
grnd_df_one_timestep, prop_df_one_timestep,
threshold=threshold, base_reward=base_reward)
# Collect aggregate statistics for tracking, and retain all match IDs
tp_net += tp
fp_net += fp
fn_net += fn
num_grnd_net += num_grnd
num_prop_net += num_prop
all_grnd_ids = grnd_ids + all_grnd_ids # newest first
all_prop_ids = prop_ids + all_prop_ids # newest first
if verbose:
print(' %2i: F1 = %.4f' % (i + 1, f1))
# Collect aggregate statistics for change detection
if i > 0:
# Find change detection TPs, FPs, and FNs among matched footprints
new_grnd = [grnd_id not in change_grnd_ids for grnd_id in grnd_ids]
new_prop = [prop_id not in change_prop_ids for prop_id in prop_ids]
change_tp_list = [g and p for g, p in zip(new_grnd, new_prop)]
change_fp_list = [p and not g for g, p in zip(new_grnd, new_prop)]
change_fn_list = [g and not p for g, p in zip(new_grnd, new_prop)]
change_tp_net += sum(change_tp_list)
change_fp_net += sum(change_fp_list)
change_fn_net += sum(change_fn_list)
# Find change detection FPs and FNs among unmatched footprints
unmatched_fp = prop_id_set.difference(prop_ids).difference(change_prop_ids)
unmatched_fn = grnd_id_set.difference(grnd_ids).difference(change_grnd_ids)
change_fp_net += len(unmatched_fp)
change_fn_net += len(unmatched_fn)
change_grnd_ids = change_grnd_ids.union(grnd_id_set)
change_prop_ids = change_prop_ids.union(prop_id_set)
# Identify which matches are mismatches
# (i.e., inconsistent with previous timesteps)
if verbose:
print('Identifying mismatches')
mm_net = 0
for i in range(len(all_grnd_ids)):
grnd_id = all_grnd_ids[i]
prop_id = all_prop_ids[i]
previous_grnd_ids = all_grnd_ids[i+1:]
previous_prop_ids = all_prop_ids[i+1:]
grnd_mismatch = grnd_id in previous_grnd_ids and previous_prop_ids[previous_grnd_ids.index(grnd_id)] != prop_id
prop_mismatch = prop_id in previous_prop_ids and previous_grnd_ids[previous_prop_ids.index(prop_id)] != grnd_id
mismatch = grnd_mismatch or prop_mismatch
if mismatch:
mm_net += 1
# Compute and return score according to the metric
track_tp_net = tp_net - mm_net
track_fp_net = fp_net + mm_net
track_fn_net = fn_net + mm_net
if track_tp_net + (track_fp_net + track_fn_net)/2. > 0:
track_score = (track_tp_net) / (track_tp_net
+ (track_fp_net + track_fn_net)/2.)
else:
track_score = 0
if change_tp_net + (change_fp_net + change_fn_net)/2. > 0:
change_score = (change_tp_net) / (change_tp_net
+ (change_fp_net + change_fn_net)/2.)
else:
change_score = 0
if beta * beta * change_score + track_score > 0:
combo_score = (1 + beta * beta) * (change_score * track_score) / (beta * beta * change_score + track_score)
else:
combo_score = 0
if verbose:
print('Tracking:')
print(' Mismatches: %i' % mm_net)
print(' True Pos: %i' % track_tp_net)
print(' False Pos: %i' % track_fp_net)
print(' False Neg: %i' % track_fn_net)
print(' Track Score: %.4f' % track_score)
print('Change Detection:')
print(' True Pos: %i' % change_tp_net)
print(' False Pos: %i' % change_fp_net)
print(' False Neg: %i' % change_fn_net)
print(' Change Score: %.4f' % change_score)
print('Combined Score: %.4f' % combo_score)
if stats:
return combo_score, [mm_net, track_tp_net, track_fp_net, track_fn_net,
track_score, change_tp_net, change_fp_net,
change_fn_net, change_score, combo_score]
else:
return combo_score
def scot_multi_aoi(grnd_df, prop_df, threshold=0.25, base_reward=100., beta=2.,
stats=True, verbose=False):
"""
SpaceNet Change and Object Tracking (SCOT) metric,
for a SpaceNet 7 submission with multiple AOIs.
Input dataframes should have "aoi", "timestep", "id", & "geometry" columns.
"""
# Get list of AOIs from ground truth dataframe
aois = sorted(list(grnd_df.aoi.drop_duplicates()))
# Evaluate SCOT metric for each AOI
cumulative_score = 0.
all_stats = {}
for i, aoi in enumerate(aois):
if verbose:
print()
print('%i / %i: AOI %s' % (i + 1, len(aois), aoi))
grnd_df_one_aoi = grnd_df.loc[grnd_df.aoi == aoi].copy()
prop_df_one_aoi = prop_df.loc[prop_df.aoi == aoi].copy()
score_one_aoi, stats_one_aoi = scot_one_aoi(
grnd_df_one_aoi, prop_df_one_aoi,
threshold=threshold,
base_reward=base_reward,
beta=beta, stats=True, verbose=verbose)
cumulative_score += score_one_aoi
all_stats[aoi] = stats_one_aoi
# Return combined SCOT metric score
score = cumulative_score / len(aois)
if verbose:
print('Overall score: %f' % score)
if stats:
return score, all_stats
else:
return score | 0.559049 | 0.560794 |
from pathlib import Path
import re
import json
from collections import defaultdict
section_tex_dir = Path("tacling_climate_change_source/sections")
bbl_file = Path("tacling_climate_change_source/main.bbl")
sections_out = Path("section_information.json")
citations_out = Path("section_citations.json")
authors_out = Path("bib_authors.json")
titles_out = Path("bib_titles.json")
sections = {}
citations = defaultdict(list)
citations_lookup = set()
bib_titles = {}
bib_authors = {}
for bibitem in bbl_file.read_text().split("\n\n")[1:-1]:
bibitem = bibitem.strip().replace("\n", " ")
bib_id = re.match(r"\\bibitem\{(.*?)\}", bibitem).group(1)
bib_author = re.match(r"\\bibitem\{.*?\}(.*?)\\newblock", bibitem).group(1)
bib_title = re.match(r".*\\newblock(.*?)\\newblock", bibitem)
if bib_title:
bib_title = bib_title.group(1)
else:
bib_title = bib_author
bib_author = None
# TODO: normalize tex strings (e.g. remove curly brackets and \em)
bib_titles[bib_id] = bib_title
bib_authors[bib_id] = bib_author
authors_out.write_text(json.dumps(bib_authors, indent=4))
titles_out.write_text(json.dumps(bib_titles, indent=4))
for section_tex in section_tex_dir.glob("*.tex"):
s = None
ss = None
sss = None
p = None
section_raw = section_tex.read_text()
for i, line in enumerate(section_raw.split("\n")):
if line.startswith("\\section{"):
s = re.search(r"\\section{(.*?)\\texorpdfstring", line).group(1)
s = s.strip()
s = "s: " + s
sections[s] = {}
ss = None
sss = None
p = None
if line.startswith("\\subsection{"):
ss = re.search(r"\\subsection\*?\{(.*?)(\}|\\Gap)", line).group(1)
ss = ss.strip()
ss = "ss: " + ss
sections[s][ss] = {}
sss = None
p = None
if line.startswith("\\subsubsection{"):
sss = re.search(r"\\subsubsection\*?\{(.*?)(\}|\\Gap)", line).group(1)
sss = sss.strip()
sss = "sss: " + sss
sections[s][ss][sss] = {}
p = None
if line.startswith("\\paragraph"):
p = re.search(r"\\paragraph\*?\{(.*?)\}", line).group(1)
p = p.strip()
p = "p: " + p
if sss:
sections[s][ss][sss][p] = {}
elif ss:
sections[s][ss][p] = {}
elif s:
sections[s][p] = {}
# add references
refs = re.findall(r"\\cite\{(.*?)\}", line)
refs = ",".join(refs)
refs = [r.strip() for r in refs.split(",") if r]
citations_lookup.update(refs)
# create section path as key
if refs:
k = section_tex.stem
if s:
k += ' | '+s
if ss:
k += ' | '+ss
if sss:
k += ' | '+sss
if p:
k += ' | '+p
citations[k] += refs
sections_out.write_text(json.dumps(sections, indent=4))
citations_out.write_text(json.dumps(citations, indent=4))
print(f"Found {len(citations_lookup)} unique citations") | parse_tex.py | from pathlib import Path
import re
import json
from collections import defaultdict
section_tex_dir = Path("tacling_climate_change_source/sections")
bbl_file = Path("tacling_climate_change_source/main.bbl")
sections_out = Path("section_information.json")
citations_out = Path("section_citations.json")
authors_out = Path("bib_authors.json")
titles_out = Path("bib_titles.json")
sections = {}
citations = defaultdict(list)
citations_lookup = set()
bib_titles = {}
bib_authors = {}
for bibitem in bbl_file.read_text().split("\n\n")[1:-1]:
bibitem = bibitem.strip().replace("\n", " ")
bib_id = re.match(r"\\bibitem\{(.*?)\}", bibitem).group(1)
bib_author = re.match(r"\\bibitem\{.*?\}(.*?)\\newblock", bibitem).group(1)
bib_title = re.match(r".*\\newblock(.*?)\\newblock", bibitem)
if bib_title:
bib_title = bib_title.group(1)
else:
bib_title = bib_author
bib_author = None
# TODO: normalize tex strings (e.g. remove curly brackets and \em)
bib_titles[bib_id] = bib_title
bib_authors[bib_id] = bib_author
authors_out.write_text(json.dumps(bib_authors, indent=4))
titles_out.write_text(json.dumps(bib_titles, indent=4))
for section_tex in section_tex_dir.glob("*.tex"):
s = None
ss = None
sss = None
p = None
section_raw = section_tex.read_text()
for i, line in enumerate(section_raw.split("\n")):
if line.startswith("\\section{"):
s = re.search(r"\\section{(.*?)\\texorpdfstring", line).group(1)
s = s.strip()
s = "s: " + s
sections[s] = {}
ss = None
sss = None
p = None
if line.startswith("\\subsection{"):
ss = re.search(r"\\subsection\*?\{(.*?)(\}|\\Gap)", line).group(1)
ss = ss.strip()
ss = "ss: " + ss
sections[s][ss] = {}
sss = None
p = None
if line.startswith("\\subsubsection{"):
sss = re.search(r"\\subsubsection\*?\{(.*?)(\}|\\Gap)", line).group(1)
sss = sss.strip()
sss = "sss: " + sss
sections[s][ss][sss] = {}
p = None
if line.startswith("\\paragraph"):
p = re.search(r"\\paragraph\*?\{(.*?)\}", line).group(1)
p = p.strip()
p = "p: " + p
if sss:
sections[s][ss][sss][p] = {}
elif ss:
sections[s][ss][p] = {}
elif s:
sections[s][p] = {}
# add references
refs = re.findall(r"\\cite\{(.*?)\}", line)
refs = ",".join(refs)
refs = [r.strip() for r in refs.split(",") if r]
citations_lookup.update(refs)
# create section path as key
if refs:
k = section_tex.stem
if s:
k += ' | '+s
if ss:
k += ' | '+ss
if sss:
k += ' | '+sss
if p:
k += ' | '+p
citations[k] += refs
sections_out.write_text(json.dumps(sections, indent=4))
citations_out.write_text(json.dumps(citations, indent=4))
print(f"Found {len(citations_lookup)} unique citations") | 0.150778 | 0.124372 |
from __future__ import unicode_literals
from django.db import migrations
current_montage = [
"SC-2078",
"SC-2197",
"SC-2190",
"SC-2189",
"SC-2298",
"SC-2059",
"SC-2058",
"SC-2299",
"SC-2163",
"SC-2162",
"SC-2284",
"SC-2043",
"SC-1196",
"SC-2161",
"SC-2160",
"SC-2149",
"SC-2148",
"SC-1188",
"SC-2035",
"SC-2034",
"SC-2036",
"SC-2277",
"SC-1189",
"SC-2153",
"SC-2150",
"SC-2017",
"SC-2018",
"SC-2259",
"SC-2144",
"SC-2023",
"SC-2265",
"SC-2147",
"SC-2266",
"SC-2263",
"SC-2140",
"SC-2264",
"SC-2022",
"SC-2249",
"SC-1039",
"SC-2369",
"SC-2257",
"SC-2254",
"SC-2015",
"SC-2255",
"SC-1162",
"SC-2252",
"SC-1161",
"SC-2253",
"SC-2250",
"SC-2371",
"SC-2493",
"SC-1163",
"SC-2490",
"SC-2370",
"SC-2491",
"SC-1389",
"SC-1397",
"SC-2002",
"SC-2123",
"SC-2488",
"SC-2001",
"SC-2004",
"SC-1399",
"SC-2486",
"SC-2484",
"SC-1392",
"SC-1395",
"SC-2481",
"SC-2000",
"SC-1391",
"SC-1819",
"SC-1937",
"SC-1936",
"SC-1818",
"SC-1812",
"SC-1811",
"SC-1935",
"SC-1813",
"SC-1810",
"SC-1930",
"SC-1809",
"SC-1808",
"SC-1805",
"SC-1804",
"SC-1807",
"SC-1806",
"SC-1801",
"SC-1800",
"SC-1803",
"SC-1802",
"SC-1915",
"SC-1904",
"SC-1903",
"SC-9999",
"SCdna-2",
"SC-2099",
"SC-2506",
"SC-1658",
"SC-1537",
"SC-1415",
"SC-1418",
"SC-1539",
"SC-2504",
"SC-2505",
"SC-1417",
"SC-1533",
"SC-1654",
"SC-1535",
"SC-1898",
"SC-1413",
"SC-1660",
"SC-1784",
"SC-1300",
"SC-1421",
"SC-1409",
"SC-1529",
"SC-1405",
"SC-1889",
"SC-1888",
"SC-1646",
"SC-1525",
"SC-1527",
"SC-1648",
"SC-1401",
"SC-1521",
"SC-1642",
"SC-1884",
"SC-1403",
"SC-1644",
"SC-1523",
"SC-1650",
"SC-1892",
"SC-1891",
"SC-1531",
"SC-1894",
"SC-1652",
"SC-1893",
"SC-1890",
"SC-1519",
"SC-1999",
"SC-1636",
"SC-1877",
"SC-1514",
"SC-1998",
"SC-1517",
"SC-1638",
"SC-1879",
"SC-1516",
"SC-1511",
"SC-1995",
"SC-1994",
"SC-1997",
"SC-1996",
"SC-1512",
"SC-1881",
"SC-1880",
"SC-2573",
"SC-1883",
"SC-1640",
"SC-1882",
"SC-1507",
"SC-1509",
"SC-1625",
"SC-1988",
"SC-1867",
"SC-1866",
"SC-1503",
"SC-1627",
"SC-1989",
"SC-1505",
"SC-1868",
"SC-1621",
"SC-1863",
"SC-1984",
"SC-1623",
"SC-1501",
"SC-1991",
"SC-1990",
"SC-1993",
"SC-1992",
"SC-1617",
"SC-1619",
"SC-1977",
"SC-1976",
"SC-1978",
"SC-1973",
"SC-1974",
"SC-2531",
"SC-930 ",
"SC-1728",
"SC-1727",
"SC-1729",
"SC-1845",
"SC-1965",
"SC-1844",
"SC-1967",
"SC-1841",
"SC-1962",
"SC-1840",
"SC-1601",
"SC-1963",
"SC-1842",
"SC-1838",
"SC-1837",
"SC-1839",
"SC-1836",
"SC-1711",
"SC-1944",
"SC-1823",
"SC-1943",
"SC-1701",
"SC-1822",
"SC-1824",
"SC-1945",
"SC-1942",
"SC-1941",
"SC-2109",
"SC-2229",
"SC-2469",
"SC-1378",
"SC-2228",
"SC-2104",
"SC-1499",
"SC-2468",
"SC-1385",
"SC-2474",
"SC-1387",
"SC-1382",
"SC-2230",
"SC-1384",
"SC-1380",
"SC-2219",
"SC-1489",
"SC-2216",
"SC-1368",
"SC-2457",
"SC-1374",
"SC-1495",
"SC-2100",
"SC-1135",
"SC-1134",
"SC-1497",
"SC-1376",
"SC-1492",
"SC-1370",
"SC-1372",
"SC-1493",
"SC-1599",
"SC-1357",
"SC-1477",
"SC-1359",
"SC-2203",
"SC-1358",
"SC-2204",
"SC-1479",
"SC-1485",
"SC-1364",
"SC-2573",
"SC-2331",
"SC-1366",
"SC-1487",
"SC-1360",
"SC-1481",
"SC-2330",
"SC-1362",
"SC-1483",
"SC-1482",
"SC-2319",
"SC-1467",
"SC-2557",
"SC-1587",
"SC-1345",
"SC-1469",
"SC-1348",
"SC-1589",
"SC-1347",
"SC-2556",
"SC-2201",
"SC-1353",
"SC-1595",
"SC-2202",
"SC-1473",
"SC-1352",
"SC-1597",
"SC-2320",
"SC-1234",
"SC-1355",
"SC-1475",
"SC-2321",
"SC-2200",
"SC-1354",
"SC-2560",
"SC-1591",
"SC-2561",
"SC-1593",
"SC-1471",
"SC-1350",
"SC-1339",
"SC-2308",
"SC-2309",
"SC-1459",
"SC-1577",
"SC-1335",
"SC-1455",
"SC-1697",
"SC-1579",
"SC-1457",
"SC-2311",
"SC-1342",
"SC-2553",
"SC-1341",
"SC-2312",
"SC-1583",
"SC-1465",
"SC-2310",
"SC-2552",
"SC-1343",
"SC-1585",
"SC-1581",
"SC-1448",
"SC-1569",
"SC-1687",
"SC-2535",
"SC-1565",
"SC-1444",
"SC-2533",
"SC-1446",
"SC-1688",
"SC-1567",
"SC-2534",
"SC-1573",
"SC-1452",
"SC-1696",
"SC-1575",
"SC-1454",
"SC-1695",
"SC-1571",
"SC-1450",
"SC-1559",
"SC-1437",
"SC-1439",
"SC-1555",
"SC-1797",
"SC-1796",
"SC-1678",
"SC-1557",
"SC-1799",
"SC-1798",
"SC-1435",
"SC-2531",
"SC-1441",
"SC-1561",
"SC-2532",
"SC-1443",
"SC-1563",
"SC-2517",
"SC-1668",
"SC-1789",
"SC-2515",
"SC-1429",
"SC-2516",
"SC-1307",
"SC-2513",
"SC-1423",
"SC-2514",
"SC-1425",
"SC-2511",
"SC-2512",
"SC-2520",
"SC-1792",
"SC-1550",
"SC-1553",
"SC-1795",
"SC-1794"
]
def update_montage_status(apps, schema_editor):
Analysis = apps.get_model('sisyphus', 'dlpanalysisinformation')
for a in Analysis.objects.all():
if a.analysis_jira_ticket in current_montage:
print("UDPATING ", a, " TO SUCCESS")
a.montage_status = "Success"
else:
print("UDPATING ", a, " TO IGNORE")
a.montage_status = "Ignore"
a.save()
class Migration(migrations.Migration):
dependencies = [
('sisyphus', '0002_auto_20190809_1118'),
]
operations = [
migrations.RunPython(update_montage_status)
] | sisyphus/migrations/0003_auto_20190809_1123.py | from __future__ import unicode_literals
from django.db import migrations
current_montage = [
"SC-2078",
"SC-2197",
"SC-2190",
"SC-2189",
"SC-2298",
"SC-2059",
"SC-2058",
"SC-2299",
"SC-2163",
"SC-2162",
"SC-2284",
"SC-2043",
"SC-1196",
"SC-2161",
"SC-2160",
"SC-2149",
"SC-2148",
"SC-1188",
"SC-2035",
"SC-2034",
"SC-2036",
"SC-2277",
"SC-1189",
"SC-2153",
"SC-2150",
"SC-2017",
"SC-2018",
"SC-2259",
"SC-2144",
"SC-2023",
"SC-2265",
"SC-2147",
"SC-2266",
"SC-2263",
"SC-2140",
"SC-2264",
"SC-2022",
"SC-2249",
"SC-1039",
"SC-2369",
"SC-2257",
"SC-2254",
"SC-2015",
"SC-2255",
"SC-1162",
"SC-2252",
"SC-1161",
"SC-2253",
"SC-2250",
"SC-2371",
"SC-2493",
"SC-1163",
"SC-2490",
"SC-2370",
"SC-2491",
"SC-1389",
"SC-1397",
"SC-2002",
"SC-2123",
"SC-2488",
"SC-2001",
"SC-2004",
"SC-1399",
"SC-2486",
"SC-2484",
"SC-1392",
"SC-1395",
"SC-2481",
"SC-2000",
"SC-1391",
"SC-1819",
"SC-1937",
"SC-1936",
"SC-1818",
"SC-1812",
"SC-1811",
"SC-1935",
"SC-1813",
"SC-1810",
"SC-1930",
"SC-1809",
"SC-1808",
"SC-1805",
"SC-1804",
"SC-1807",
"SC-1806",
"SC-1801",
"SC-1800",
"SC-1803",
"SC-1802",
"SC-1915",
"SC-1904",
"SC-1903",
"SC-9999",
"SCdna-2",
"SC-2099",
"SC-2506",
"SC-1658",
"SC-1537",
"SC-1415",
"SC-1418",
"SC-1539",
"SC-2504",
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"SC-1654",
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"SC-1413",
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"SC-1421",
"SC-1409",
"SC-1529",
"SC-1405",
"SC-1889",
"SC-1888",
"SC-1646",
"SC-1525",
"SC-1527",
"SC-1648",
"SC-1401",
"SC-1521",
"SC-1642",
"SC-1884",
"SC-1403",
"SC-1644",
"SC-1523",
"SC-1650",
"SC-1892",
"SC-1891",
"SC-1531",
"SC-1894",
"SC-1652",
"SC-1893",
"SC-1890",
"SC-1519",
"SC-1999",
"SC-1636",
"SC-1877",
"SC-1514",
"SC-1998",
"SC-1517",
"SC-1638",
"SC-1879",
"SC-1516",
"SC-1511",
"SC-1995",
"SC-1994",
"SC-1997",
"SC-1996",
"SC-1512",
"SC-1881",
"SC-1880",
"SC-2573",
"SC-1883",
"SC-1640",
"SC-1882",
"SC-1507",
"SC-1509",
"SC-1625",
"SC-1988",
"SC-1867",
"SC-1866",
"SC-1503",
"SC-1627",
"SC-1989",
"SC-1505",
"SC-1868",
"SC-1621",
"SC-1863",
"SC-1984",
"SC-1623",
"SC-1501",
"SC-1991",
"SC-1990",
"SC-1993",
"SC-1992",
"SC-1617",
"SC-1619",
"SC-1977",
"SC-1976",
"SC-1978",
"SC-1973",
"SC-1974",
"SC-2531",
"SC-930 ",
"SC-1728",
"SC-1727",
"SC-1729",
"SC-1845",
"SC-1965",
"SC-1844",
"SC-1967",
"SC-1841",
"SC-1962",
"SC-1840",
"SC-1601",
"SC-1963",
"SC-1842",
"SC-1838",
"SC-1837",
"SC-1839",
"SC-1836",
"SC-1711",
"SC-1944",
"SC-1823",
"SC-1943",
"SC-1701",
"SC-1822",
"SC-1824",
"SC-1945",
"SC-1942",
"SC-1941",
"SC-2109",
"SC-2229",
"SC-2469",
"SC-1378",
"SC-2228",
"SC-2104",
"SC-1499",
"SC-2468",
"SC-1385",
"SC-2474",
"SC-1387",
"SC-1382",
"SC-2230",
"SC-1384",
"SC-1380",
"SC-2219",
"SC-1489",
"SC-2216",
"SC-1368",
"SC-2457",
"SC-1374",
"SC-1495",
"SC-2100",
"SC-1135",
"SC-1134",
"SC-1497",
"SC-1376",
"SC-1492",
"SC-1370",
"SC-1372",
"SC-1493",
"SC-1599",
"SC-1357",
"SC-1477",
"SC-1359",
"SC-2203",
"SC-1358",
"SC-2204",
"SC-1479",
"SC-1485",
"SC-1364",
"SC-2573",
"SC-2331",
"SC-1366",
"SC-1487",
"SC-1360",
"SC-1481",
"SC-2330",
"SC-1362",
"SC-1483",
"SC-1482",
"SC-2319",
"SC-1467",
"SC-2557",
"SC-1587",
"SC-1345",
"SC-1469",
"SC-1348",
"SC-1589",
"SC-1347",
"SC-2556",
"SC-2201",
"SC-1353",
"SC-1595",
"SC-2202",
"SC-1473",
"SC-1352",
"SC-1597",
"SC-2320",
"SC-1234",
"SC-1355",
"SC-1475",
"SC-2321",
"SC-2200",
"SC-1354",
"SC-2560",
"SC-1591",
"SC-2561",
"SC-1593",
"SC-1471",
"SC-1350",
"SC-1339",
"SC-2308",
"SC-2309",
"SC-1459",
"SC-1577",
"SC-1335",
"SC-1455",
"SC-1697",
"SC-1579",
"SC-1457",
"SC-2311",
"SC-1342",
"SC-2553",
"SC-1341",
"SC-2312",
"SC-1583",
"SC-1465",
"SC-2310",
"SC-2552",
"SC-1343",
"SC-1585",
"SC-1581",
"SC-1448",
"SC-1569",
"SC-1687",
"SC-2535",
"SC-1565",
"SC-1444",
"SC-2533",
"SC-1446",
"SC-1688",
"SC-1567",
"SC-2534",
"SC-1573",
"SC-1452",
"SC-1696",
"SC-1575",
"SC-1454",
"SC-1695",
"SC-1571",
"SC-1450",
"SC-1559",
"SC-1437",
"SC-1439",
"SC-1555",
"SC-1797",
"SC-1796",
"SC-1678",
"SC-1557",
"SC-1799",
"SC-1798",
"SC-1435",
"SC-2531",
"SC-1441",
"SC-1561",
"SC-2532",
"SC-1443",
"SC-1563",
"SC-2517",
"SC-1668",
"SC-1789",
"SC-2515",
"SC-1429",
"SC-2516",
"SC-1307",
"SC-2513",
"SC-1423",
"SC-2514",
"SC-1425",
"SC-2511",
"SC-2512",
"SC-2520",
"SC-1792",
"SC-1550",
"SC-1553",
"SC-1795",
"SC-1794"
]
def update_montage_status(apps, schema_editor):
Analysis = apps.get_model('sisyphus', 'dlpanalysisinformation')
for a in Analysis.objects.all():
if a.analysis_jira_ticket in current_montage:
print("UDPATING ", a, " TO SUCCESS")
a.montage_status = "Success"
else:
print("UDPATING ", a, " TO IGNORE")
a.montage_status = "Ignore"
a.save()
class Migration(migrations.Migration):
dependencies = [
('sisyphus', '0002_auto_20190809_1118'),
]
operations = [
migrations.RunPython(update_montage_status)
] | 0.400984 | 0.29015 |
# IMPORT
from abc import ABCMeta
from abc import abstractmethod
from concurrent.futures import ThreadPoolExecutor
from bluepy.btle import BTLEException
from blue_st_sdk.utils.ble_node_definitions import Debug
from blue_st_sdk.utils.python_utils import lock
# CLASSES
class DebugConsole():
"""Class used to read/write debug messages."""
_MAXIMUM_MESSAGE_SIZE_BYTES = 20
"""Maximum size of the messages to send."""
_NUMBER_OF_THREADS = 5
"""Number of threads to be used to notify the listeners."""
def __init__(self, node, stdinout_characteristic, stderr_characteristic):
"""Constructor.
Args:
node (:class:`blue_st_sdk.node.Node`): Node that will send the data.
stdinout_characteristic (Characteristic): The BLE characteristic
used to read/write data from/to stdin/stdout. Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
stderr_characteristic (Characteristic): The BLE characteristic used
to read data from stderr. Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
"""
self._node = node
"""Node that sends data to this class."""
self._stdinout_characteristic = stdinout_characteristic
"""Characteristic used to read/write data from/to stdin/stdout."""
self._stderr_characteristic = stderr_characteristic
"""Characteristic used to read data from stderr."""
self._thread_pool = ThreadPoolExecutor(DebugConsole._NUMBER_OF_THREADS)
"""Pool of thread used to notify the listeners."""
self._listeners = []
"""List of listeners to the events of new data received.
It is a thread safe list, so a listener can subscribe itself through a
callback."""
def _decode_data(self, data):
"""Convert data to standard ascii characters.
Args:
data (bytearray): Data to be encoded.
Returns:
str: A string representing the given data.
"""
return data.decode('ISO-8859-1')
def write(self, data):
"""Write an array of bytes to the stdin.
The message might be sent through more iterations on the Bluetooth
channel.
Args:
data (bytearray): Data to be sent.
Returns:
int: The number of bytes sent to the stdin/stdout standard
characteristic.
"""
try:
char_handle = self._stdinout_characteristic.getHandle()
bytes_sent = 0
while bytes_sent < len(data):
# Computing data to send.
bytes_to_send = min(
self._MAXIMUM_MESSAGE_SIZE_BYTES,
len(data) - bytes_sent
)
data_to_send = data[bytes_sent:bytes_sent + bytes_to_send]
# Writing data.
self._node.writeCharacteristic(
char_handle,
data_to_send,
True)
bytes_sent += bytes_to_send
# Calling on-write callback for a debug characteristic.
self.on_write_characteristic(
self._stdinout_characteristic, data_to_send, True)
return bytes_sent
except BTLEException as e:
self._node._unexpected_disconnect()
def add_listener(self, listener):
"""Adding a listener.
Args:
listener (:class:`blue_st_sdk.debug.DebugListener`): Listener to
be added.
"""
if listener is not None:
with lock(self):
if not listener in self._listeners:
self._listeners.append(listener)
if self._listeners:
self._node.set_notification_status(
self._stdinout_characteristic, True)
self._node.set_notification_status(
self._stderr_characteristic, True)
def remove_listener(self, listener):
"""Remove a listener.
Args:
listener (:class:`blue_st_sdk.debug.DebugListener`): Listener to
be removed.
"""
if listener is not None:
with lock(self):
if listener in self._listeners:
self._listeners.remove(listener)
if not self._listeners:
self._node.set_notification_status(
self._stdinout_characteristic, False)
self._node.set_notification_status(
self._stderr_characteristic, False)
def on_update_characteristic(self, characteristic, data):
"""The characteristic has been updated.
If it is a debug characteristic, data are sent to the registered
listeners.
Args:
characteristic (Characteristic): The BLE characteristic that has
been updated.
Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
data (str): The data notified from the given characteristic.
"""
try:
if len(self._listeners) == 0:
return
data_str = self._decode_data(data)
if characteristic.uuid == \
Debug.DEBUG_STDINOUT_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stdout_receive(
self, data_str))
elif characteristic.uuid == \
Debug.DEBUG_STDERR_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stderr_receive(
self, data_str))
except BTLEException as e:
self._node._unexpected_disconnect()
def on_write_characteristic(self, characteristic, data, status):
"""The characteristic has been written.
Args:
characteristic (Characteristic): The BLE characteristic that has
been written.
data (bytearray): Received data.
status (bool): True if the writing operation was successfully, False
otherwise.
"""
try:
if len(self._listeners) == 0:
return
data_str = self._decode_data(data)
if characteristic.uuid == \
Debug.DEBUG_STDINOUT_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stdin_send(
self,
data_str[0:self._MAXIMUM_MESSAGE_SIZE_BYTES],
status))
except BTLEException as e:
self._node._unexpected_disconnect()
def get_node(self):
"""Getting the node that listen to / write to this debug console.
Returns:
node (:class:`blue_st_sdk.node.Node`): the node that listen/write to
this debug console.
"""
return self._node
class DebugConsoleListener(object):
"""Interface used by the :class:`blue_st_sdk.debug.DebugConsole` class to
notify changes on a debug console.
Data received/sent from/to a node are encoded with ISO-8859-1 charset.
"""
__metaclass__ = ABCMeta
@abstractmethod
def on_stdout_receive(self, debug_console, message):
"""Called whenever a new message is received on the standard output.
Args:
debug_console (object): Console that sends the message.
message (str): The message received on the stdout console.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stdut_received()" to use the "DebugListener"'
'class.')
@abstractmethod
def on_stderr_receive(self, debug_console, message):
"""Called whenever a new message is received on the standard error.
Args:
debug_console (object): Console that sends the message.
message (str): The message received on the stderr console.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stderr_receive()" to use the "DebugListener"'
'class.')
@abstractmethod
def on_stdin_send(self, debug_console, message, status):
"""Called whenever a new message is sent to the standard input.
Args:
debug_console (object): Console that receives the message.
message (str): The message sent to the stdin console.
status (bool): True if the message is sent correctly, False
otherwise.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stdin_send()" to use the "DebugListener"'
'class.') | blue_st_sdk/debug_console.py | # IMPORT
from abc import ABCMeta
from abc import abstractmethod
from concurrent.futures import ThreadPoolExecutor
from bluepy.btle import BTLEException
from blue_st_sdk.utils.ble_node_definitions import Debug
from blue_st_sdk.utils.python_utils import lock
# CLASSES
class DebugConsole():
"""Class used to read/write debug messages."""
_MAXIMUM_MESSAGE_SIZE_BYTES = 20
"""Maximum size of the messages to send."""
_NUMBER_OF_THREADS = 5
"""Number of threads to be used to notify the listeners."""
def __init__(self, node, stdinout_characteristic, stderr_characteristic):
"""Constructor.
Args:
node (:class:`blue_st_sdk.node.Node`): Node that will send the data.
stdinout_characteristic (Characteristic): The BLE characteristic
used to read/write data from/to stdin/stdout. Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
stderr_characteristic (Characteristic): The BLE characteristic used
to read data from stderr. Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
"""
self._node = node
"""Node that sends data to this class."""
self._stdinout_characteristic = stdinout_characteristic
"""Characteristic used to read/write data from/to stdin/stdout."""
self._stderr_characteristic = stderr_characteristic
"""Characteristic used to read data from stderr."""
self._thread_pool = ThreadPoolExecutor(DebugConsole._NUMBER_OF_THREADS)
"""Pool of thread used to notify the listeners."""
self._listeners = []
"""List of listeners to the events of new data received.
It is a thread safe list, so a listener can subscribe itself through a
callback."""
def _decode_data(self, data):
"""Convert data to standard ascii characters.
Args:
data (bytearray): Data to be encoded.
Returns:
str: A string representing the given data.
"""
return data.decode('ISO-8859-1')
def write(self, data):
"""Write an array of bytes to the stdin.
The message might be sent through more iterations on the Bluetooth
channel.
Args:
data (bytearray): Data to be sent.
Returns:
int: The number of bytes sent to the stdin/stdout standard
characteristic.
"""
try:
char_handle = self._stdinout_characteristic.getHandle()
bytes_sent = 0
while bytes_sent < len(data):
# Computing data to send.
bytes_to_send = min(
self._MAXIMUM_MESSAGE_SIZE_BYTES,
len(data) - bytes_sent
)
data_to_send = data[bytes_sent:bytes_sent + bytes_to_send]
# Writing data.
self._node.writeCharacteristic(
char_handle,
data_to_send,
True)
bytes_sent += bytes_to_send
# Calling on-write callback for a debug characteristic.
self.on_write_characteristic(
self._stdinout_characteristic, data_to_send, True)
return bytes_sent
except BTLEException as e:
self._node._unexpected_disconnect()
def add_listener(self, listener):
"""Adding a listener.
Args:
listener (:class:`blue_st_sdk.debug.DebugListener`): Listener to
be added.
"""
if listener is not None:
with lock(self):
if not listener in self._listeners:
self._listeners.append(listener)
if self._listeners:
self._node.set_notification_status(
self._stdinout_characteristic, True)
self._node.set_notification_status(
self._stderr_characteristic, True)
def remove_listener(self, listener):
"""Remove a listener.
Args:
listener (:class:`blue_st_sdk.debug.DebugListener`): Listener to
be removed.
"""
if listener is not None:
with lock(self):
if listener in self._listeners:
self._listeners.remove(listener)
if not self._listeners:
self._node.set_notification_status(
self._stdinout_characteristic, False)
self._node.set_notification_status(
self._stderr_characteristic, False)
def on_update_characteristic(self, characteristic, data):
"""The characteristic has been updated.
If it is a debug characteristic, data are sent to the registered
listeners.
Args:
characteristic (Characteristic): The BLE characteristic that has
been updated.
Refer to
`Characteristic <https://ianharvey.github.io/bluepy-doc/characteristic.html>`_
for more information.
data (str): The data notified from the given characteristic.
"""
try:
if len(self._listeners) == 0:
return
data_str = self._decode_data(data)
if characteristic.uuid == \
Debug.DEBUG_STDINOUT_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stdout_receive(
self, data_str))
elif characteristic.uuid == \
Debug.DEBUG_STDERR_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stderr_receive(
self, data_str))
except BTLEException as e:
self._node._unexpected_disconnect()
def on_write_characteristic(self, characteristic, data, status):
"""The characteristic has been written.
Args:
characteristic (Characteristic): The BLE characteristic that has
been written.
data (bytearray): Received data.
status (bool): True if the writing operation was successfully, False
otherwise.
"""
try:
if len(self._listeners) == 0:
return
data_str = self._decode_data(data)
if characteristic.uuid == \
Debug.DEBUG_STDINOUT_BLUESTSDK_SERVICE_UUID:
for listener in self._listeners:
# Calling user-defined callback.
self._thread_pool.submit(listener.on_stdin_send(
self,
data_str[0:self._MAXIMUM_MESSAGE_SIZE_BYTES],
status))
except BTLEException as e:
self._node._unexpected_disconnect()
def get_node(self):
"""Getting the node that listen to / write to this debug console.
Returns:
node (:class:`blue_st_sdk.node.Node`): the node that listen/write to
this debug console.
"""
return self._node
class DebugConsoleListener(object):
"""Interface used by the :class:`blue_st_sdk.debug.DebugConsole` class to
notify changes on a debug console.
Data received/sent from/to a node are encoded with ISO-8859-1 charset.
"""
__metaclass__ = ABCMeta
@abstractmethod
def on_stdout_receive(self, debug_console, message):
"""Called whenever a new message is received on the standard output.
Args:
debug_console (object): Console that sends the message.
message (str): The message received on the stdout console.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stdut_received()" to use the "DebugListener"'
'class.')
@abstractmethod
def on_stderr_receive(self, debug_console, message):
"""Called whenever a new message is received on the standard error.
Args:
debug_console (object): Console that sends the message.
message (str): The message received on the stderr console.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stderr_receive()" to use the "DebugListener"'
'class.')
@abstractmethod
def on_stdin_send(self, debug_console, message, status):
"""Called whenever a new message is sent to the standard input.
Args:
debug_console (object): Console that receives the message.
message (str): The message sent to the stdin console.
status (bool): True if the message is sent correctly, False
otherwise.
Raises:
:exc:`NotImplementedError` if the method has not been implemented.
"""
raise NotImplementedError(
'You must implement "on_stdin_send()" to use the "DebugListener"'
'class.') | 0.769946 | 0.191725 |
__author__ = "<NAME>"
__copyright__ = "Copyright (c) 2021 <NAME> All Rights Reserved."
__email__ = "<EMAIL>"
__license__ = "Apache 2"
import copy
import json
from pprint import pprint
import requests
DATASETS_URL = "https://phenotypes.healthdatagateway.org/api/v1/data-sources/?format=json"
PHENOTYPES_URL = "https://phenotypes.healthdatagateway.org/api/v1/public/phenotypes/?format=json"
PHENOTYPE_LIB_URL = "https://phenotypes.healthdatagateway.org/phenotypes/{phenotype_id}/version/{version}/detail/#home"
DATA_SOURCE_FIXES = {
3: {
"id": 3,
"name": "Civil Registration - Deaths",
"pid": "050163dc-1728-4ac5-a7d9-4dd3ca0ca12a",
"url": "https://web.www.healthdatagateway.org/dataset/050163dc-1728-4ac5-a7d9-4dd3ca0ca12a"
},
17: {
"id": 17,
"name": "General Acute Inpatient and Day Case - Scottish Morbidity Record (SMR01)",
"pid": "98cda353-0011-45b2-80ca-4ed24cd084bf",
"url": "https://web.www.healthdatagateway.org/dataset/98cda353-0011-45b2-80ca-4ed24cd084bf"
},
19: {
"id": 19,
"name": "Death Registration Data - Provisional Monthly Extracts",
"pid": "487222b7-5c13-4a92-8b41-044796048720",
"url": "https://web.www.healthdatagateway.org/dataset/487222b7-5c13-4a92-8b41-044796048720"
},
21: {
"id": 21,
"name": "Hospitalised patients with diabetic emergencies & acute diabetic health concerns",
"pid": "0d556d7e-be27-4979-a09e-d419b2e838f3",
"url": "https://web.www.healthdatagateway.org/dataset/0d556d7e-be27-4979-a09e-d419b2e838f3"
},
22: {
"id": 22,
"name": "Hospitalised patients with diabetic emergencies & acute diabetic health concerns",
"pid": "0d556d7e-be27-4979-a09e-d419b2e838f3",
"url": "https://web.www.healthdatagateway.org/dataset/0d556d7e-be27-4979-a09e-d419b2e838f3"
}
}
def request_url(URL):
"""HTTP GET request and load into json"""
print(URL)
r = requests.get(URL)
if r.status_code != requests.codes.ok:
r.raise_for_status()
return json.loads(r.text)
def write_json(data, filename=None, indent=2):
with (open(filename, 'w') if filename else sys.stdout) as file:
json.dump(data, file, indent=indent)
def get_datasets():
DATASETS = {}
datasets = request_url(DATASETS_URL)
for d in datasets:
dataset = {
'id': d['id'],
'name': d['name'],
'pid': d['uid'],
'url': d['url'],
}
if d['uid'] != "":
DATASETS[d['id']] = dataset
DATASETS.update(DATA_SOURCE_FIXES)
return DATASETS
def get_phenotypes(datasets):
PHENOTYPES = []
phenotypes = request_url(PHENOTYPES_URL)
for p in phenotypes:
if len(p['data_sources']):
phenotype = {
"id": p['phenotype_id'],
"name": p['phenotype_name'],
"type": p['type'],
}
latest_version = [v['version_id'] for v in p['versions'] if v['is_latest'] == True]
if len(latest_version):
phenotype['url'] = PHENOTYPE_LIB_URL.format(phenotype_id=p['phenotype_id'], version=latest_version[0])
data_sources = [datasets[ds['id']] for ds in p['data_sources'] if ds['id'] in datasets.keys()]
phenotype['datasets'] = data_sources
PHENOTYPES.append(phenotype)
return PHENOTYPES
def datasets2phenotypes(phenotypes):
DATASETS = {}
for p in phenotypes:
temp = copy.deepcopy(p)
del temp['datasets']
for d in p['datasets']:
DATASETS.setdefault(d['pid'], [])
DATASETS[d['pid']].append(temp)
return DATASETS
def main():
datasets = get_datasets()
phenotypes = get_phenotypes(datasets)
d2p = datasets2phenotypes(phenotypes)
write_json(d2p, "_data/dataset2phenotypes.json")
if __name__ == '__main__':
main() | scripts/generate_crossrefs.py | __author__ = "<NAME>"
__copyright__ = "Copyright (c) 2021 <NAME> All Rights Reserved."
__email__ = "<EMAIL>"
__license__ = "Apache 2"
import copy
import json
from pprint import pprint
import requests
DATASETS_URL = "https://phenotypes.healthdatagateway.org/api/v1/data-sources/?format=json"
PHENOTYPES_URL = "https://phenotypes.healthdatagateway.org/api/v1/public/phenotypes/?format=json"
PHENOTYPE_LIB_URL = "https://phenotypes.healthdatagateway.org/phenotypes/{phenotype_id}/version/{version}/detail/#home"
DATA_SOURCE_FIXES = {
3: {
"id": 3,
"name": "Civil Registration - Deaths",
"pid": "050163dc-1728-4ac5-a7d9-4dd3ca0ca12a",
"url": "https://web.www.healthdatagateway.org/dataset/050163dc-1728-4ac5-a7d9-4dd3ca0ca12a"
},
17: {
"id": 17,
"name": "General Acute Inpatient and Day Case - Scottish Morbidity Record (SMR01)",
"pid": "98cda353-0011-45b2-80ca-4ed24cd084bf",
"url": "https://web.www.healthdatagateway.org/dataset/98cda353-0011-45b2-80ca-4ed24cd084bf"
},
19: {
"id": 19,
"name": "Death Registration Data - Provisional Monthly Extracts",
"pid": "487222b7-5c13-4a92-8b41-044796048720",
"url": "https://web.www.healthdatagateway.org/dataset/487222b7-5c13-4a92-8b41-044796048720"
},
21: {
"id": 21,
"name": "Hospitalised patients with diabetic emergencies & acute diabetic health concerns",
"pid": "0d556d7e-be27-4979-a09e-d419b2e838f3",
"url": "https://web.www.healthdatagateway.org/dataset/0d556d7e-be27-4979-a09e-d419b2e838f3"
},
22: {
"id": 22,
"name": "Hospitalised patients with diabetic emergencies & acute diabetic health concerns",
"pid": "0d556d7e-be27-4979-a09e-d419b2e838f3",
"url": "https://web.www.healthdatagateway.org/dataset/0d556d7e-be27-4979-a09e-d419b2e838f3"
}
}
def request_url(URL):
"""HTTP GET request and load into json"""
print(URL)
r = requests.get(URL)
if r.status_code != requests.codes.ok:
r.raise_for_status()
return json.loads(r.text)
def write_json(data, filename=None, indent=2):
with (open(filename, 'w') if filename else sys.stdout) as file:
json.dump(data, file, indent=indent)
def get_datasets():
DATASETS = {}
datasets = request_url(DATASETS_URL)
for d in datasets:
dataset = {
'id': d['id'],
'name': d['name'],
'pid': d['uid'],
'url': d['url'],
}
if d['uid'] != "":
DATASETS[d['id']] = dataset
DATASETS.update(DATA_SOURCE_FIXES)
return DATASETS
def get_phenotypes(datasets):
PHENOTYPES = []
phenotypes = request_url(PHENOTYPES_URL)
for p in phenotypes:
if len(p['data_sources']):
phenotype = {
"id": p['phenotype_id'],
"name": p['phenotype_name'],
"type": p['type'],
}
latest_version = [v['version_id'] for v in p['versions'] if v['is_latest'] == True]
if len(latest_version):
phenotype['url'] = PHENOTYPE_LIB_URL.format(phenotype_id=p['phenotype_id'], version=latest_version[0])
data_sources = [datasets[ds['id']] for ds in p['data_sources'] if ds['id'] in datasets.keys()]
phenotype['datasets'] = data_sources
PHENOTYPES.append(phenotype)
return PHENOTYPES
def datasets2phenotypes(phenotypes):
DATASETS = {}
for p in phenotypes:
temp = copy.deepcopy(p)
del temp['datasets']
for d in p['datasets']:
DATASETS.setdefault(d['pid'], [])
DATASETS[d['pid']].append(temp)
return DATASETS
def main():
datasets = get_datasets()
phenotypes = get_phenotypes(datasets)
d2p = datasets2phenotypes(phenotypes)
write_json(d2p, "_data/dataset2phenotypes.json")
if __name__ == '__main__':
main() | 0.311532 | 0.201322 |
import weakref
from nfv_common import debug
from nfv_common.strategy._strategy_result import STRATEGY_STEP_RESULT
DLOG = debug.debug_get_logger('nfv_common.strategy.step')
class StrategyStep(object):
"""
Strategy Step
"""
def __init__(self, name, force_pass=False, timeout_in_secs=0, max_retries=1):
self._id = 0
self._name = name
self._force_pass = force_pass
self._timeout_in_secs = timeout_in_secs
self._max_retries = max_retries
self._result = STRATEGY_STEP_RESULT.INITIAL
self._result_reason = ''
self._stage_reference = None
self._start_date_time = ''
self._end_date_time = ''
@property
def name(self):
"""
Returns the name of the step
"""
return self._name
@property
def id(self):
"""
Returns the id of the step
"""
return self._id
@id.setter
def id(self, value):
"""
Sets the id of the step
"""
self._id = value
@property
def force_pass(self):
"""
Returns the true if force_pass has been set, otherwise false
"""
return self._force_pass
@property
def max_retries(self):
"""
Returns the maximum retry attempts for step to be completed
"""
return self._max_retries
@property
def timeout_in_secs(self):
"""
Returns the maximum amount of time to wait for completion
"""
return self._timeout_in_secs
@property
def result(self):
"""
Returns the result of the step
"""
return self._result
@result.setter
def result(self, result):
"""
Updates the result of the step
"""
self._result = result
@property
def result_reason(self):
"""
Returns the reason for the result of the step
"""
return self._result_reason
@result_reason.setter
def result_reason(self, reason):
"""
Updates the reason for the result of the step
"""
self._result_reason = reason
@property
def start_date_time(self):
"""
Returns the start date-time of the step
"""
return self._start_date_time
@start_date_time.setter
def start_date_time(self, date_time_str):
"""
Updates the start date-time of the step
"""
self._start_date_time = date_time_str
@property
def end_date_time(self):
"""
Returns the end date-time of the step
"""
return self._end_date_time
@end_date_time.setter
def end_date_time(self, date_time_str):
"""
Updates the end date-time of the step
"""
self._end_date_time = date_time_str
@property
def strategy(self):
"""
Returns the strategy this step is a member of
"""
if self.phase is not None:
return self.phase.strategy
return None
@property
def phase(self):
"""
Returns the phase this step is a member of
"""
if self.stage is not None:
return self.stage.phase
return None
@property
def stage(self):
"""
Returns the stage this step is a member of
"""
if self._stage_reference is not None:
return self._stage_reference()
return None
@stage.setter
def stage(self, stage_value):
"""
Set the stage that this step is a member of
"""
self._stage_reference = weakref.ref(stage_value)
def extend_timeout(self, timeout_in_secs):
"""
Allow the step timeout to be extended
"""
DLOG.verbose("Extending strategy step timeout for %s to %s."
% (self._name, timeout_in_secs))
self._timeout_in_secs = timeout_in_secs
if self._stage_reference is not None:
self.stage.step_extend_timeout()
def abort(self):
"""
Strategy Step Abort (can be overridden by child class)
"""
DLOG.info("Default strategy step abort for %s." % self._name)
return []
def apply(self):
"""
Strategy Step Apply (expected to be overridden by child class)
"""
DLOG.verbose("Default strategy step apply for %s." % self._name)
return STRATEGY_STEP_RESULT.SUCCESS, ''
def complete(self, result, result_reason):
"""
Strategy Step Completed (can be overridden by child class)
"""
DLOG.verbose("Default strategy step complete for %s, result=%s, "
"reason=%s." % (self._name, result, result_reason))
return result, result_reason
def timeout(self):
"""
Strategy Step Timeout (can be overridden by child class)
"""
DLOG.verbose("Default strategy step timeout for %s, timeout=%s secs."
% (self._name, self._timeout_in_secs))
return STRATEGY_STEP_RESULT.TIMED_OUT, ''
def handle_event(self, event, event_data=None):
"""
Strategy Step Handle Event (expected to be overridden by child class)
"""
DLOG.verbose("Default strategy step handle event for %s."
% self._name)
return False
def from_dict(self, data):
"""
Returns a strategy step object initialized using the given dictionary
"""
StrategyStep.__init__(self, data['name'], data['force_pass'],
data['timeout'], data['max_retries'])
self._result = data['result']
self._result_reason = data['result_reason']
self._start_date_time = data['start_date_time']
self._end_date_time = data['end_date_time']
return self
def as_dict(self):
"""
Represent the strategy step as a dictionary
"""
data = dict()
data['id'] = self._id
data['name'] = self._name
data['force_pass'] = self._force_pass
data['timeout'] = self._timeout_in_secs
data['max_retries'] = self._max_retries
data['result'] = self._result
data['result_reason'] = self._result_reason
data['start_date_time'] = self._start_date_time
data['end_date_time'] = self._end_date_time
return data | nfv/nfv-common/nfv_common/strategy/_strategy_step.py | import weakref
from nfv_common import debug
from nfv_common.strategy._strategy_result import STRATEGY_STEP_RESULT
DLOG = debug.debug_get_logger('nfv_common.strategy.step')
class StrategyStep(object):
"""
Strategy Step
"""
def __init__(self, name, force_pass=False, timeout_in_secs=0, max_retries=1):
self._id = 0
self._name = name
self._force_pass = force_pass
self._timeout_in_secs = timeout_in_secs
self._max_retries = max_retries
self._result = STRATEGY_STEP_RESULT.INITIAL
self._result_reason = ''
self._stage_reference = None
self._start_date_time = ''
self._end_date_time = ''
@property
def name(self):
"""
Returns the name of the step
"""
return self._name
@property
def id(self):
"""
Returns the id of the step
"""
return self._id
@id.setter
def id(self, value):
"""
Sets the id of the step
"""
self._id = value
@property
def force_pass(self):
"""
Returns the true if force_pass has been set, otherwise false
"""
return self._force_pass
@property
def max_retries(self):
"""
Returns the maximum retry attempts for step to be completed
"""
return self._max_retries
@property
def timeout_in_secs(self):
"""
Returns the maximum amount of time to wait for completion
"""
return self._timeout_in_secs
@property
def result(self):
"""
Returns the result of the step
"""
return self._result
@result.setter
def result(self, result):
"""
Updates the result of the step
"""
self._result = result
@property
def result_reason(self):
"""
Returns the reason for the result of the step
"""
return self._result_reason
@result_reason.setter
def result_reason(self, reason):
"""
Updates the reason for the result of the step
"""
self._result_reason = reason
@property
def start_date_time(self):
"""
Returns the start date-time of the step
"""
return self._start_date_time
@start_date_time.setter
def start_date_time(self, date_time_str):
"""
Updates the start date-time of the step
"""
self._start_date_time = date_time_str
@property
def end_date_time(self):
"""
Returns the end date-time of the step
"""
return self._end_date_time
@end_date_time.setter
def end_date_time(self, date_time_str):
"""
Updates the end date-time of the step
"""
self._end_date_time = date_time_str
@property
def strategy(self):
"""
Returns the strategy this step is a member of
"""
if self.phase is not None:
return self.phase.strategy
return None
@property
def phase(self):
"""
Returns the phase this step is a member of
"""
if self.stage is not None:
return self.stage.phase
return None
@property
def stage(self):
"""
Returns the stage this step is a member of
"""
if self._stage_reference is not None:
return self._stage_reference()
return None
@stage.setter
def stage(self, stage_value):
"""
Set the stage that this step is a member of
"""
self._stage_reference = weakref.ref(stage_value)
def extend_timeout(self, timeout_in_secs):
"""
Allow the step timeout to be extended
"""
DLOG.verbose("Extending strategy step timeout for %s to %s."
% (self._name, timeout_in_secs))
self._timeout_in_secs = timeout_in_secs
if self._stage_reference is not None:
self.stage.step_extend_timeout()
def abort(self):
"""
Strategy Step Abort (can be overridden by child class)
"""
DLOG.info("Default strategy step abort for %s." % self._name)
return []
def apply(self):
"""
Strategy Step Apply (expected to be overridden by child class)
"""
DLOG.verbose("Default strategy step apply for %s." % self._name)
return STRATEGY_STEP_RESULT.SUCCESS, ''
def complete(self, result, result_reason):
"""
Strategy Step Completed (can be overridden by child class)
"""
DLOG.verbose("Default strategy step complete for %s, result=%s, "
"reason=%s." % (self._name, result, result_reason))
return result, result_reason
def timeout(self):
"""
Strategy Step Timeout (can be overridden by child class)
"""
DLOG.verbose("Default strategy step timeout for %s, timeout=%s secs."
% (self._name, self._timeout_in_secs))
return STRATEGY_STEP_RESULT.TIMED_OUT, ''
def handle_event(self, event, event_data=None):
"""
Strategy Step Handle Event (expected to be overridden by child class)
"""
DLOG.verbose("Default strategy step handle event for %s."
% self._name)
return False
def from_dict(self, data):
"""
Returns a strategy step object initialized using the given dictionary
"""
StrategyStep.__init__(self, data['name'], data['force_pass'],
data['timeout'], data['max_retries'])
self._result = data['result']
self._result_reason = data['result_reason']
self._start_date_time = data['start_date_time']
self._end_date_time = data['end_date_time']
return self
def as_dict(self):
"""
Represent the strategy step as a dictionary
"""
data = dict()
data['id'] = self._id
data['name'] = self._name
data['force_pass'] = self._force_pass
data['timeout'] = self._timeout_in_secs
data['max_retries'] = self._max_retries
data['result'] = self._result
data['result_reason'] = self._result_reason
data['start_date_time'] = self._start_date_time
data['end_date_time'] = self._end_date_time
return data | 0.79999 | 0.198646 |
import warnings
import pandas as pd
import numpy as np
def simFireplace(
temperature,
occ_act,
n_ovens=1,
T_oven_on=5,
t_cool=5.0,
fullloadSteps=450,
seed=None,
):
"""
Creates the profile of the heating of wood ovens based on the outside
temperature and the activity of the occupants. The profile is generated
based on stochastics.
Parameters
----------
temperature: pandas.Series(), required
Outside temperature profile of the location.
occ_act: pandas.Series(), required
Series of values between 0 and 1 desribing the share of overall active
occpants at every time step.
n_ovens: int, optional (default:1)
Number of ovens in the building.
T_oven_on: int or float, optional (default:5.)
Threeshold outside temperature [°C] when the oven is turned on.
t_cool: int, optional (default:5)
Number of timesteps till when the oven is cooled down again.
fullloadSteps: int or float, optional (default:450)
Resulting number of full load timesteps. Attention: This value is not
exact since it is a stochastic profile generation.
seed: int, optional (default:None)
Seed required for reproduceability.
If none, it is completely random.
Returns
----------
load_profile: pandas.Series()
Relative load profile of the ovens in kW/kWp.
"""
# Oven is only turned under a temperature threeshold
tempBool = temperature < T_oven_on
# Increase probability that the oven is turned on in case it is colder outside
relCold = (T_oven_on - temperature) / (T_oven_on - temperature.min())
# Caclulate fire activation probability
prob = occ_act.values * tempBool.values * relCold.values
# avoid rounding errors
prob[prob < 0] = 0
load_profile = pd.Series(0, index=temperature.index)
for n in range(int(n_ovens)):
# Modifier to reduce probability in order to fit the full load hours
p_mod = fullloadSteps / (prob.sum() * t_cool / 2)
overallProb = prob * p_mod
overallProb[overallProb > 1.0] = 1.0
# Binary decision if an oven can be activated
initLogArr = pd.Series(np.random.RandomState(seed).binomial(1, overallProb))
# create the profile
heatLoad = []
loadBefore = 0
for initLog in initLogArr:
if initLog:
load = 1.0
else:
if loadBefore > 0.001:
load = loadBefore - 1.0 / t_cool
else:
load = 0
heatLoad.append(load)
loadBefore = load
load_profile += pd.Series(heatLoad, index=temperature.index)
profile = load_profile / n_ovens
if abs(profile.sum() - fullloadSteps) > 100:
warnings.warn(
"Fullload hour deviation is higher than 100. "
+ "Input parameters make it difficult or impossible "
+ "to generate the expected profile"
)
return load_profile / n_ovens | tsib/renewables/fireplace.py | import warnings
import pandas as pd
import numpy as np
def simFireplace(
temperature,
occ_act,
n_ovens=1,
T_oven_on=5,
t_cool=5.0,
fullloadSteps=450,
seed=None,
):
"""
Creates the profile of the heating of wood ovens based on the outside
temperature and the activity of the occupants. The profile is generated
based on stochastics.
Parameters
----------
temperature: pandas.Series(), required
Outside temperature profile of the location.
occ_act: pandas.Series(), required
Series of values between 0 and 1 desribing the share of overall active
occpants at every time step.
n_ovens: int, optional (default:1)
Number of ovens in the building.
T_oven_on: int or float, optional (default:5.)
Threeshold outside temperature [°C] when the oven is turned on.
t_cool: int, optional (default:5)
Number of timesteps till when the oven is cooled down again.
fullloadSteps: int or float, optional (default:450)
Resulting number of full load timesteps. Attention: This value is not
exact since it is a stochastic profile generation.
seed: int, optional (default:None)
Seed required for reproduceability.
If none, it is completely random.
Returns
----------
load_profile: pandas.Series()
Relative load profile of the ovens in kW/kWp.
"""
# Oven is only turned under a temperature threeshold
tempBool = temperature < T_oven_on
# Increase probability that the oven is turned on in case it is colder outside
relCold = (T_oven_on - temperature) / (T_oven_on - temperature.min())
# Caclulate fire activation probability
prob = occ_act.values * tempBool.values * relCold.values
# avoid rounding errors
prob[prob < 0] = 0
load_profile = pd.Series(0, index=temperature.index)
for n in range(int(n_ovens)):
# Modifier to reduce probability in order to fit the full load hours
p_mod = fullloadSteps / (prob.sum() * t_cool / 2)
overallProb = prob * p_mod
overallProb[overallProb > 1.0] = 1.0
# Binary decision if an oven can be activated
initLogArr = pd.Series(np.random.RandomState(seed).binomial(1, overallProb))
# create the profile
heatLoad = []
loadBefore = 0
for initLog in initLogArr:
if initLog:
load = 1.0
else:
if loadBefore > 0.001:
load = loadBefore - 1.0 / t_cool
else:
load = 0
heatLoad.append(load)
loadBefore = load
load_profile += pd.Series(heatLoad, index=temperature.index)
profile = load_profile / n_ovens
if abs(profile.sum() - fullloadSteps) > 100:
warnings.warn(
"Fullload hour deviation is higher than 100. "
+ "Input parameters make it difficult or impossible "
+ "to generate the expected profile"
)
return load_profile / n_ovens | 0.789761 | 0.543348 |
import re
import simple_history
from django import forms
from django.contrib import admin, messages
from django.contrib.admin import TabularInline
from django.db.models import Count, Exists, OuterRef
from django.utils import timezone
from django.utils.safestring import mark_safe
from clubs.management.commands.merge_duplicates import merge_clubs, merge_tags
from clubs.management.commands.remind import send_reminder_to_club
from clubs.models import (
AdminNote,
Advisor,
ApplicationCommittee,
ApplicationMultipleChoice,
ApplicationQuestion,
ApplicationQuestionResponse,
ApplicationSubmission,
Asset,
Badge,
Club,
ClubApplication,
ClubFair,
ClubFairBooth,
ClubFairRegistration,
ClubVisit,
Event,
Favorite,
Major,
Membership,
MembershipInvite,
MembershipRequest,
Note,
NoteTag,
Profile,
QuestionAnswer,
RecurringEvent,
Report,
School,
SearchQuery,
StudentType,
Subscribe,
Tag,
TargetMajor,
TargetSchool,
TargetStudentType,
TargetYear,
Testimonial,
Year,
ZoomMeetingVisit,
)
class HasOwnerListFilter(admin.SimpleListFilter):
title = "has owner"
parameter_name = "has_owner"
def lookups(self, request, model_admin):
return [("true", "True"), ("false", "False")]
def queryset(self, request, queryset):
val = self.value()
if val:
return queryset.filter(has_owner=val == "true")
else:
return queryset
class HasInviteListFilter(admin.SimpleListFilter):
title = "has invite"
parameter_name = "has_invite"
def lookups(self, request, model_admin):
return [("true", "True"), ("false", "False")]
def queryset(self, request, queryset):
val = self.value()
if val:
return queryset.filter(has_invite=val == "true")
else:
return queryset
def do_merge_clubs(modeladmin, request, queryset):
if queryset.count() < 2:
modeladmin.message_user(
request,
"You must select at least two clubs to merge!",
level=messages.ERROR,
)
return
if queryset.count() > 5:
modeladmin.message_user(
request,
"You have selected more than 5 clubs, "
"you probably do not want to do this.",
level=messages.ERROR,
)
return
club_names = list(queryset.order_by("name").values_list("name", flat=True))
tags = list(queryset)
first, rest = tags[0], tags[1:]
for club in rest:
merge_clubs(first, club)
modeladmin.message_user(
request,
"Merged the following clubs: {} into {}".format(
", ".join(club_names), first.name
),
level=messages.SUCCESS,
)
do_merge_clubs.short_description = "Merge selected clubs"
def send_edit_reminder(modeladmin, request, queryset):
success_count = 0
total_count = 0
for club in queryset.order_by("code"):
if send_reminder_to_club(club):
success_count += 1
total_count += 1
modeladmin.message_user(
request,
"Sent edit page reminder emails to {}/{} club(s).".format(
success_count, total_count
),
level=messages.SUCCESS,
)
send_edit_reminder.short_description = "Send edit page reminder"
def mark_approved(modeladmin, request, queryset):
if not request.user.has_perm("clubs.approve_club"):
modeladmin.message_user(
request,
"You do not have permission to approve clubs!",
level=messages.ERROR,
)
return
num_updated = queryset.filter(approved=False).update(
approved=True, approved_by=request.user, approved_on=timezone.now()
)
modeladmin.message_user(
request,
"Marked {} club(s) as approved!".format(num_updated),
level=messages.SUCCESS,
)
mark_approved.short_description = "Approve clubs"
class ClubAdminForm(forms.ModelForm):
parent_orgs = forms.ModelMultipleChoiceField(
queryset=Club.objects.annotate(num_children=Count("children_orgs")).order_by(
"-num_children"
),
required=False,
)
class ClubChildrenInline(TabularInline):
model = Club.children_orgs.through
fk_name = "to_club"
extra = 0
verbose_name = "Children org"
verbose_name_plural = "Children orgs"
class ClubAdmin(simple_history.admin.SimpleHistoryAdmin):
search_fields = ("name", "subtitle", "email", "code")
list_display = ("name", "email", "has_owner", "has_invite", "active", "approved")
list_filter = (
"size",
"application_required",
"accepting_members",
"enables_subscription",
"recruiting_cycle",
"active",
"approved",
HasOwnerListFilter,
HasInviteListFilter,
)
inlines = [ClubChildrenInline]
actions = [do_merge_clubs, send_edit_reminder, mark_approved]
form = ClubAdminForm
def get_queryset(self, request):
return (
super()
.get_queryset(request)
.annotate(
has_owner=Exists(
Membership.objects.filter(club=OuterRef("pk"), role__lte=0)
),
has_invite=Exists(MembershipInvite.objects.filter(club=OuterRef("pk"))),
)
)
def has_invite(self, obj):
return obj.has_invite
has_invite.boolean = True
def has_owner(self, obj):
return obj.has_owner
has_owner.boolean = True
class ClubFairAdmin(admin.ModelAdmin):
list_display = ("name", "organization", "contact", "start_time")
search_fields = ("name", "organization", "contact")
list_filter = ("start_time", "end_time")
class EventAdmin(admin.ModelAdmin):
list_display = ("name", "club", "type", "start_time", "end_time")
search_fields = ("name", "club__name")
list_filter = ("start_time", "end_time")
def club(self, obj):
return obj.club.name
class FavoriteAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
class SubscribeAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club", "email")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
def email(self, obj):
return obj.person.email
class MembershipRequestAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club", "email", "withdrew", "is_member")
list_filter = ("withdrew",)
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
def email(self, obj):
return obj.person.email
def is_member(self, obj):
return obj.club.membership_set.filter(person__pk=obj.person.pk).exists()
is_member.boolean = True
class MembershipAdmin(admin.ModelAdmin):
search_fields = (
"person__username",
"person__email",
"club__name",
"club__pk",
"title",
)
list_display = ("person", "club", "role", "title")
list_filter = ("role", "active", "public")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
class ProfileAdmin(admin.ModelAdmin):
search_fields = ("user__username", "user__email")
list_display = ("user", "email", "graduation_year", "studies", "has_been_prompted")
list_filter = ("graduation_year", "school", "major", "has_been_prompted")
def email(self, obj):
return str(obj.user.email or None)
def studies(self, obj):
major = ", ".join(obj.major.values_list("name", flat=True))
school = ", ".join(obj.school.values_list("name", flat=True))
return "{} - {}".format(school or None, major or None)
class MembershipInviteAdmin(admin.ModelAdmin):
search_fields = ("email", "club__name", "club__pk")
list_display = ("email", "club", "role", "title", "active")
list_filter = ("role", "active")
def club(self, obj):
return obj.club.name
class AdvisorAdmin(admin.ModelAdmin):
search_fields = ("name", "title", "email", "phone", "club__name")
list_display = ("name", "title", "email", "phone", "club", "public")
def club(self, obj):
return obj.club.name
def do_merge_tags(modeladmin, request, queryset):
if queryset.count() < 2:
modeladmin.message_user(
request, "You must select at least two tags to merge!", level=messages.ERROR
)
return
tag_names = list(queryset.order_by("name").values_list("name", flat=True))
tags = list(queryset)
first, rest = tags[0], tags[1:]
for tag in rest:
merge_tags(first, tag)
modeladmin.message_user(
request,
"Merged the following tags: {} into {}".format(
", ".join(tag_names), first.name
),
level=messages.SUCCESS,
)
do_merge_tags.short_description = "Merge selected tags"
class TagAdmin(admin.ModelAdmin):
def club_count(self, obj):
return obj.club_set.count()
search_fields = ("name",)
list_display = ("name", "club_count")
actions = [do_merge_tags]
class BadgeAdmin(admin.ModelAdmin):
def club_count(self, obj):
return obj.club_set.count()
def badge_color(self, obj):
if not re.match(r"^([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$", obj.color):
return obj.color
return mark_safe(
f"<div style='background-color: #{obj.color}; \
width:1em; \
height:1em; \
border:1px solid black; \
border-radius:3px' />"
)
search_fields = ("label",)
list_display = ("label", "purpose", "org", "club_count", "badge_color", "visible")
list_filter = ("visible", "purpose")
actions = [do_merge_tags]
class MajorAdmin(admin.ModelAdmin):
search_fields = ("name",)
class ReportAdmin(admin.ModelAdmin):
search_fields = ("name", "description")
list_display = ("name", "creator", "public")
list_filter = ("created_at", "public")
class YearAdmin(admin.ModelAdmin):
search_fields = ("name",)
list_display = ("name", "year")
class QuestionAnswerAdmin(admin.ModelAdmin):
search_fields = ("question", "answer")
list_display = ("club", "question", "answer", "approved")
list_filter = ("approved", "updated_at")
class ZoomMeetingVisitAdmin(admin.ModelAdmin):
search_fields = ("person__username", "event__club__code")
list_display = ("person", "event", "join_time")
list_filter = (("leave_time", admin.EmptyFieldListFilter),)
admin.site.register(Asset)
admin.site.register(ApplicationCommittee)
admin.site.register(ApplicationMultipleChoice)
admin.site.register(ApplicationQuestion)
admin.site.register(ApplicationQuestionResponse)
admin.site.register(ApplicationSubmission)
admin.site.register(Advisor, AdvisorAdmin)
admin.site.register(Club, ClubAdmin)
admin.site.register(ClubFair, ClubFairAdmin)
admin.site.register(ClubApplication)
admin.site.register(ClubFairRegistration)
admin.site.register(ClubVisit)
admin.site.register(Badge, BadgeAdmin)
admin.site.register(Event, EventAdmin)
admin.site.register(Favorite, FavoriteAdmin)
admin.site.register(School)
admin.site.register(SearchQuery)
admin.site.register(Subscribe, SubscribeAdmin)
admin.site.register(MembershipRequest, MembershipRequestAdmin)
admin.site.register(Major, MajorAdmin)
admin.site.register(Membership, MembershipAdmin)
admin.site.register(MembershipInvite, MembershipInviteAdmin)
admin.site.register(Profile, ProfileAdmin)
admin.site.register(QuestionAnswer, QuestionAnswerAdmin)
admin.site.register(RecurringEvent)
admin.site.register(Report, ReportAdmin)
admin.site.register(Tag, TagAdmin)
admin.site.register(TargetMajor)
admin.site.register(TargetSchool)
admin.site.register(TargetYear)
admin.site.register(TargetStudentType)
admin.site.register(Testimonial)
admin.site.register(StudentType)
admin.site.register(Note)
admin.site.register(ClubFairBooth)
admin.site.register(NoteTag)
admin.site.register(Year, YearAdmin)
admin.site.register(ZoomMeetingVisit, ZoomMeetingVisitAdmin)
admin.site.register(AdminNote) | backend/clubs/admin.py | import re
import simple_history
from django import forms
from django.contrib import admin, messages
from django.contrib.admin import TabularInline
from django.db.models import Count, Exists, OuterRef
from django.utils import timezone
from django.utils.safestring import mark_safe
from clubs.management.commands.merge_duplicates import merge_clubs, merge_tags
from clubs.management.commands.remind import send_reminder_to_club
from clubs.models import (
AdminNote,
Advisor,
ApplicationCommittee,
ApplicationMultipleChoice,
ApplicationQuestion,
ApplicationQuestionResponse,
ApplicationSubmission,
Asset,
Badge,
Club,
ClubApplication,
ClubFair,
ClubFairBooth,
ClubFairRegistration,
ClubVisit,
Event,
Favorite,
Major,
Membership,
MembershipInvite,
MembershipRequest,
Note,
NoteTag,
Profile,
QuestionAnswer,
RecurringEvent,
Report,
School,
SearchQuery,
StudentType,
Subscribe,
Tag,
TargetMajor,
TargetSchool,
TargetStudentType,
TargetYear,
Testimonial,
Year,
ZoomMeetingVisit,
)
class HasOwnerListFilter(admin.SimpleListFilter):
title = "has owner"
parameter_name = "has_owner"
def lookups(self, request, model_admin):
return [("true", "True"), ("false", "False")]
def queryset(self, request, queryset):
val = self.value()
if val:
return queryset.filter(has_owner=val == "true")
else:
return queryset
class HasInviteListFilter(admin.SimpleListFilter):
title = "has invite"
parameter_name = "has_invite"
def lookups(self, request, model_admin):
return [("true", "True"), ("false", "False")]
def queryset(self, request, queryset):
val = self.value()
if val:
return queryset.filter(has_invite=val == "true")
else:
return queryset
def do_merge_clubs(modeladmin, request, queryset):
if queryset.count() < 2:
modeladmin.message_user(
request,
"You must select at least two clubs to merge!",
level=messages.ERROR,
)
return
if queryset.count() > 5:
modeladmin.message_user(
request,
"You have selected more than 5 clubs, "
"you probably do not want to do this.",
level=messages.ERROR,
)
return
club_names = list(queryset.order_by("name").values_list("name", flat=True))
tags = list(queryset)
first, rest = tags[0], tags[1:]
for club in rest:
merge_clubs(first, club)
modeladmin.message_user(
request,
"Merged the following clubs: {} into {}".format(
", ".join(club_names), first.name
),
level=messages.SUCCESS,
)
do_merge_clubs.short_description = "Merge selected clubs"
def send_edit_reminder(modeladmin, request, queryset):
success_count = 0
total_count = 0
for club in queryset.order_by("code"):
if send_reminder_to_club(club):
success_count += 1
total_count += 1
modeladmin.message_user(
request,
"Sent edit page reminder emails to {}/{} club(s).".format(
success_count, total_count
),
level=messages.SUCCESS,
)
send_edit_reminder.short_description = "Send edit page reminder"
def mark_approved(modeladmin, request, queryset):
if not request.user.has_perm("clubs.approve_club"):
modeladmin.message_user(
request,
"You do not have permission to approve clubs!",
level=messages.ERROR,
)
return
num_updated = queryset.filter(approved=False).update(
approved=True, approved_by=request.user, approved_on=timezone.now()
)
modeladmin.message_user(
request,
"Marked {} club(s) as approved!".format(num_updated),
level=messages.SUCCESS,
)
mark_approved.short_description = "Approve clubs"
class ClubAdminForm(forms.ModelForm):
parent_orgs = forms.ModelMultipleChoiceField(
queryset=Club.objects.annotate(num_children=Count("children_orgs")).order_by(
"-num_children"
),
required=False,
)
class ClubChildrenInline(TabularInline):
model = Club.children_orgs.through
fk_name = "to_club"
extra = 0
verbose_name = "Children org"
verbose_name_plural = "Children orgs"
class ClubAdmin(simple_history.admin.SimpleHistoryAdmin):
search_fields = ("name", "subtitle", "email", "code")
list_display = ("name", "email", "has_owner", "has_invite", "active", "approved")
list_filter = (
"size",
"application_required",
"accepting_members",
"enables_subscription",
"recruiting_cycle",
"active",
"approved",
HasOwnerListFilter,
HasInviteListFilter,
)
inlines = [ClubChildrenInline]
actions = [do_merge_clubs, send_edit_reminder, mark_approved]
form = ClubAdminForm
def get_queryset(self, request):
return (
super()
.get_queryset(request)
.annotate(
has_owner=Exists(
Membership.objects.filter(club=OuterRef("pk"), role__lte=0)
),
has_invite=Exists(MembershipInvite.objects.filter(club=OuterRef("pk"))),
)
)
def has_invite(self, obj):
return obj.has_invite
has_invite.boolean = True
def has_owner(self, obj):
return obj.has_owner
has_owner.boolean = True
class ClubFairAdmin(admin.ModelAdmin):
list_display = ("name", "organization", "contact", "start_time")
search_fields = ("name", "organization", "contact")
list_filter = ("start_time", "end_time")
class EventAdmin(admin.ModelAdmin):
list_display = ("name", "club", "type", "start_time", "end_time")
search_fields = ("name", "club__name")
list_filter = ("start_time", "end_time")
def club(self, obj):
return obj.club.name
class FavoriteAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
class SubscribeAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club", "email")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
def email(self, obj):
return obj.person.email
class MembershipRequestAdmin(admin.ModelAdmin):
search_fields = ("person__username", "person__email", "club__name", "club__pk")
list_display = ("person", "club", "email", "withdrew", "is_member")
list_filter = ("withdrew",)
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
def email(self, obj):
return obj.person.email
def is_member(self, obj):
return obj.club.membership_set.filter(person__pk=obj.person.pk).exists()
is_member.boolean = True
class MembershipAdmin(admin.ModelAdmin):
search_fields = (
"person__username",
"person__email",
"club__name",
"club__pk",
"title",
)
list_display = ("person", "club", "role", "title")
list_filter = ("role", "active", "public")
def person(self, obj):
return obj.person.username
def club(self, obj):
return obj.club.name
class ProfileAdmin(admin.ModelAdmin):
search_fields = ("user__username", "user__email")
list_display = ("user", "email", "graduation_year", "studies", "has_been_prompted")
list_filter = ("graduation_year", "school", "major", "has_been_prompted")
def email(self, obj):
return str(obj.user.email or None)
def studies(self, obj):
major = ", ".join(obj.major.values_list("name", flat=True))
school = ", ".join(obj.school.values_list("name", flat=True))
return "{} - {}".format(school or None, major or None)
class MembershipInviteAdmin(admin.ModelAdmin):
search_fields = ("email", "club__name", "club__pk")
list_display = ("email", "club", "role", "title", "active")
list_filter = ("role", "active")
def club(self, obj):
return obj.club.name
class AdvisorAdmin(admin.ModelAdmin):
search_fields = ("name", "title", "email", "phone", "club__name")
list_display = ("name", "title", "email", "phone", "club", "public")
def club(self, obj):
return obj.club.name
def do_merge_tags(modeladmin, request, queryset):
if queryset.count() < 2:
modeladmin.message_user(
request, "You must select at least two tags to merge!", level=messages.ERROR
)
return
tag_names = list(queryset.order_by("name").values_list("name", flat=True))
tags = list(queryset)
first, rest = tags[0], tags[1:]
for tag in rest:
merge_tags(first, tag)
modeladmin.message_user(
request,
"Merged the following tags: {} into {}".format(
", ".join(tag_names), first.name
),
level=messages.SUCCESS,
)
do_merge_tags.short_description = "Merge selected tags"
class TagAdmin(admin.ModelAdmin):
def club_count(self, obj):
return obj.club_set.count()
search_fields = ("name",)
list_display = ("name", "club_count")
actions = [do_merge_tags]
class BadgeAdmin(admin.ModelAdmin):
def club_count(self, obj):
return obj.club_set.count()
def badge_color(self, obj):
if not re.match(r"^([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$", obj.color):
return obj.color
return mark_safe(
f"<div style='background-color: #{obj.color}; \
width:1em; \
height:1em; \
border:1px solid black; \
border-radius:3px' />"
)
search_fields = ("label",)
list_display = ("label", "purpose", "org", "club_count", "badge_color", "visible")
list_filter = ("visible", "purpose")
actions = [do_merge_tags]
class MajorAdmin(admin.ModelAdmin):
search_fields = ("name",)
class ReportAdmin(admin.ModelAdmin):
search_fields = ("name", "description")
list_display = ("name", "creator", "public")
list_filter = ("created_at", "public")
class YearAdmin(admin.ModelAdmin):
search_fields = ("name",)
list_display = ("name", "year")
class QuestionAnswerAdmin(admin.ModelAdmin):
search_fields = ("question", "answer")
list_display = ("club", "question", "answer", "approved")
list_filter = ("approved", "updated_at")
class ZoomMeetingVisitAdmin(admin.ModelAdmin):
search_fields = ("person__username", "event__club__code")
list_display = ("person", "event", "join_time")
list_filter = (("leave_time", admin.EmptyFieldListFilter),)
admin.site.register(Asset)
admin.site.register(ApplicationCommittee)
admin.site.register(ApplicationMultipleChoice)
admin.site.register(ApplicationQuestion)
admin.site.register(ApplicationQuestionResponse)
admin.site.register(ApplicationSubmission)
admin.site.register(Advisor, AdvisorAdmin)
admin.site.register(Club, ClubAdmin)
admin.site.register(ClubFair, ClubFairAdmin)
admin.site.register(ClubApplication)
admin.site.register(ClubFairRegistration)
admin.site.register(ClubVisit)
admin.site.register(Badge, BadgeAdmin)
admin.site.register(Event, EventAdmin)
admin.site.register(Favorite, FavoriteAdmin)
admin.site.register(School)
admin.site.register(SearchQuery)
admin.site.register(Subscribe, SubscribeAdmin)
admin.site.register(MembershipRequest, MembershipRequestAdmin)
admin.site.register(Major, MajorAdmin)
admin.site.register(Membership, MembershipAdmin)
admin.site.register(MembershipInvite, MembershipInviteAdmin)
admin.site.register(Profile, ProfileAdmin)
admin.site.register(QuestionAnswer, QuestionAnswerAdmin)
admin.site.register(RecurringEvent)
admin.site.register(Report, ReportAdmin)
admin.site.register(Tag, TagAdmin)
admin.site.register(TargetMajor)
admin.site.register(TargetSchool)
admin.site.register(TargetYear)
admin.site.register(TargetStudentType)
admin.site.register(Testimonial)
admin.site.register(StudentType)
admin.site.register(Note)
admin.site.register(ClubFairBooth)
admin.site.register(NoteTag)
admin.site.register(Year, YearAdmin)
admin.site.register(ZoomMeetingVisit, ZoomMeetingVisitAdmin)
admin.site.register(AdminNote) | 0.380183 | 0.132824 |
from pytest import raises # type: ignore
from pygritia import * # pylint: disable=wildcard-import,unused-wildcard-import
def test_if():
"""Test for If"""
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = If(cond, sym[0])
assert str(expr) == 'If(cond, sym[0])'
assert evaluate(expr, {cond: True, sym: arr}) == arr[0]
assert evaluate(expr, {cond: False, sym: arr}) is None
update(expr, 9, {cond: True, sym: arr})
assert arr[0] == 9
with raises(TypeError):
update(expr, 12, {cond: False, sym: arr})
def test_if_then_else():
"""Test for IfThenElse"""
cond = symbol('cond')
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = IfThenElse(cond, sym[0], sym[1])
assert str(expr) == 'IfThenElse(cond, sym[0], sym[1])'
assert evaluate(expr, {cond: True, sym: arr}) == arr[0]
assert evaluate(expr, {cond: False, sym: arr}) == arr[1]
update(expr, 9, {cond: True, sym: arr})
assert arr[0] == 9 and arr[1] == 2
update(expr, 12, {cond: False, sym: arr})
assert arr[0] == 9 and arr[1] == 12
def test_case():
"""Test for Case"""
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = Case(cond, {'first': sym[0], 'second': sym[1]}, sym[2])
assert str(expr) == "Case(cond, {'first': sym[0], 'second': sym[1]}, sym[2])"
assert evaluate(expr, {cond: 'first', sym: arr}) == arr[0]
assert evaluate(expr, {cond: 'second', sym: arr}) == arr[1]
assert evaluate(expr, {cond: 'third', sym: arr}) == arr[2]
update(expr, 9, {cond: 'first', sym: arr})
assert arr == [9, 2, 3]
update(expr, 12, {cond: 'second', sym: arr})
assert arr == [9, 12, 3]
update(expr, 15, {cond: 'third', sym: arr})
assert arr == [9, 12, 15]
expr = Case(cond, {'first': sym[0]}, None)
assert str(expr) == "If(cond == 'first', sym[0])"
def test_ensure():
"""Test for Ensure"""
obj = symbol('obj')
expr = Ensure(obj, 'none')
assert str(expr) == 'Ensure(obj)'
assert evaluate(expr, {obj: None}) == 'none'
assert evaluate(expr, {obj: 'hello'}) == 'hello' | tests/test_cases.py | from pytest import raises # type: ignore
from pygritia import * # pylint: disable=wildcard-import,unused-wildcard-import
def test_if():
"""Test for If"""
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = If(cond, sym[0])
assert str(expr) == 'If(cond, sym[0])'
assert evaluate(expr, {cond: True, sym: arr}) == arr[0]
assert evaluate(expr, {cond: False, sym: arr}) is None
update(expr, 9, {cond: True, sym: arr})
assert arr[0] == 9
with raises(TypeError):
update(expr, 12, {cond: False, sym: arr})
def test_if_then_else():
"""Test for IfThenElse"""
cond = symbol('cond')
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = IfThenElse(cond, sym[0], sym[1])
assert str(expr) == 'IfThenElse(cond, sym[0], sym[1])'
assert evaluate(expr, {cond: True, sym: arr}) == arr[0]
assert evaluate(expr, {cond: False, sym: arr}) == arr[1]
update(expr, 9, {cond: True, sym: arr})
assert arr[0] == 9 and arr[1] == 2
update(expr, 12, {cond: False, sym: arr})
assert arr[0] == 9 and arr[1] == 12
def test_case():
"""Test for Case"""
cond = symbol('cond')
sym = symbol('sym')
arr = [1, 2, 3]
expr = Case(cond, {'first': sym[0], 'second': sym[1]}, sym[2])
assert str(expr) == "Case(cond, {'first': sym[0], 'second': sym[1]}, sym[2])"
assert evaluate(expr, {cond: 'first', sym: arr}) == arr[0]
assert evaluate(expr, {cond: 'second', sym: arr}) == arr[1]
assert evaluate(expr, {cond: 'third', sym: arr}) == arr[2]
update(expr, 9, {cond: 'first', sym: arr})
assert arr == [9, 2, 3]
update(expr, 12, {cond: 'second', sym: arr})
assert arr == [9, 12, 3]
update(expr, 15, {cond: 'third', sym: arr})
assert arr == [9, 12, 15]
expr = Case(cond, {'first': sym[0]}, None)
assert str(expr) == "If(cond == 'first', sym[0])"
def test_ensure():
"""Test for Ensure"""
obj = symbol('obj')
expr = Ensure(obj, 'none')
assert str(expr) == 'Ensure(obj)'
assert evaluate(expr, {obj: None}) == 'none'
assert evaluate(expr, {obj: 'hello'}) == 'hello' | 0.498291 | 0.757638 |
import ast
import os.path
from typing import List
from typing import Tuple
from all_repos_depends.errors import DependsError
from all_repos_depends.lang import python
from all_repos_depends.types import Depends
class FindsInstallRequires(ast.NodeVisitor):
def __init__(self) -> None:
self.requires: List[Depends] = []
def visit_Call(self, node: ast.Call) -> None:
if python.node_is_setup_call(node):
for kwd in node.keywords:
if (
kwd.arg == 'install_requires' and
isinstance(kwd.value, ast.List)
):
if all(isinstance(e, ast.Str) for e in kwd.value.elts):
for elt in kwd.value.elts:
assert isinstance(elt, ast.Str)
req = python.to_depends('DEPENDS', elt.s)
self.requires.append(req)
else:
raise DependsError(
'Had setup.py with install_requires but it was '
'not a list of strings',
)
self.generic_visit(node)
def setup_py() -> Tuple[Depends, ...]:
if not os.path.exists('setup.py'):
return ()
visitor = FindsInstallRequires()
visitor.visit(python.load_setup_py_ast())
return tuple(visitor.requires)
def requirements_tools() -> Tuple[Depends, ...]:
reqs_minimal = 'requirements-minimal.txt'
reqs = 'requirements.txt'
reqs_dev_minimal = 'requirements-dev-minimal.txt'
reqs_dev = 'requirements-dev.txt'
ret: List[Depends] = []
if os.path.exists(reqs_minimal) and os.path.exists(reqs):
ret.extend(python.from_reqs_file('DEPENDS', reqs_minimal))
ret.extend(python.from_reqs_file('REQUIRES', reqs))
elif os.path.exists(reqs):
ret.extend(python.from_reqs_file('REQUIRES', reqs))
if os.path.exists(reqs_dev_minimal) and os.path.exists(reqs_dev):
ret.extend(python.from_reqs_file('DEPENDS_DEV', reqs_dev_minimal))
ret.extend(python.from_reqs_file('REQUIRES_DEV', reqs_dev))
elif os.path.exists(reqs_dev):
ret.extend(python.from_reqs_file('DEPENDS_DEV', reqs_dev))
return tuple(ret) | all_repos_depends/depends.py | import ast
import os.path
from typing import List
from typing import Tuple
from all_repos_depends.errors import DependsError
from all_repos_depends.lang import python
from all_repos_depends.types import Depends
class FindsInstallRequires(ast.NodeVisitor):
def __init__(self) -> None:
self.requires: List[Depends] = []
def visit_Call(self, node: ast.Call) -> None:
if python.node_is_setup_call(node):
for kwd in node.keywords:
if (
kwd.arg == 'install_requires' and
isinstance(kwd.value, ast.List)
):
if all(isinstance(e, ast.Str) for e in kwd.value.elts):
for elt in kwd.value.elts:
assert isinstance(elt, ast.Str)
req = python.to_depends('DEPENDS', elt.s)
self.requires.append(req)
else:
raise DependsError(
'Had setup.py with install_requires but it was '
'not a list of strings',
)
self.generic_visit(node)
def setup_py() -> Tuple[Depends, ...]:
if not os.path.exists('setup.py'):
return ()
visitor = FindsInstallRequires()
visitor.visit(python.load_setup_py_ast())
return tuple(visitor.requires)
def requirements_tools() -> Tuple[Depends, ...]:
reqs_minimal = 'requirements-minimal.txt'
reqs = 'requirements.txt'
reqs_dev_minimal = 'requirements-dev-minimal.txt'
reqs_dev = 'requirements-dev.txt'
ret: List[Depends] = []
if os.path.exists(reqs_minimal) and os.path.exists(reqs):
ret.extend(python.from_reqs_file('DEPENDS', reqs_minimal))
ret.extend(python.from_reqs_file('REQUIRES', reqs))
elif os.path.exists(reqs):
ret.extend(python.from_reqs_file('REQUIRES', reqs))
if os.path.exists(reqs_dev_minimal) and os.path.exists(reqs_dev):
ret.extend(python.from_reqs_file('DEPENDS_DEV', reqs_dev_minimal))
ret.extend(python.from_reqs_file('REQUIRES_DEV', reqs_dev))
elif os.path.exists(reqs_dev):
ret.extend(python.from_reqs_file('DEPENDS_DEV', reqs_dev))
return tuple(ret) | 0.496582 | 0.150903 |
import os
import sys
import yaml
import desiutil
import fitsio
import desisim
import argparse
import os.path as path
import numpy as np
import astropy.io.fits as fits
import matplotlib.pyplot as plt
from desiutil import depend
from astropy.convolution import convolve, Box1DKernel
from pathlib import Path
from desiutil.dust import mwdust_transmission
from desiutil.log import get_logger
from pkg_resources import resource_filename
from scipy.interpolate import interp1d
from astropy.table import Table, join
from desispec.tsnr import template_ensemble, gfa_template_ensemble
np.random.seed(seed=314)
# AR/DK DESI spectra wavelengths
# TODO: where are brz extraction wavelengths defined? https://github.com/desihub/desispec/issues/1006.
wmin, wmax, wdelta = 3600, 9824, 0.8
wave = np.round(np.arange(wmin, wmax + wdelta, wdelta), 1)
cslice = {"b": slice(0, 2751), "r": slice(2700, 5026), "z": slice(4900, 7781)}
def parse(options=None):
parser = argparse.ArgumentParser(description="Generate a sim. template ensemble stack of given type and write it to disk at --outdir.")
parser.add_argument('--nmodel', type = int, default = 1000, required=False,
help='Number of galaxies in the ensemble.')
parser.add_argument('--tracer', type = str, default = 'bgs', required=True,
help='Tracer to generate of [bgs, lrg, elg, qso].')
parser.add_argument('--smooth', type=float, default=100., required=False,
help='Smoothing scale [A] for DFLUX calc.')
parser.add_argument('--config-filename', type = str, default = None, required=False,
help='path to config filename (default is from python package desispec/data/tsnr/tsnr-config-{tracer}.yaml)')
parser.add_argument('--nz-filename', type = str, default = None, required=False,
help='path to n(z) filename (default is from $DESIMODEL/data/targets/nz_{tracer}.dat)')
parser.add_argument('--outdir', type = str, default = 'bgs', required=True,
help='Directory to write to.')
parser.add_argument('--no-nz-convolution', action='store_true',
help='Dont convolve each template dF^2 with redshift distribution')
parser.add_argument('--mag-range', action='store_true',
help='Monte Carlo the full mag range (given in config file) instead of using the same effective mag for all templates')
args = None
if options is None:
args = parser.parse_args()
else:
args = parser.parse_args(options)
return args
def main(args):
if args.tracer == 'gpb':
templates = gfa_template_ensemble()
templates.compute()
templates.plot()
templates.write(dirname=args.outdir)
elif args.tracer in ['bgs', 'lrg', 'elg', 'lya', 'qso']:
templates = template_ensemble(tracer=args.tracer,config_filename=args.config_filename)
templates.compute(nmodel=args.nmodel, smooth=args.smooth, nz_table_filename=args.nz_filename,
convolve_to_nz=(not args.no_nz_convolution), single_mag=(not args.mag_range))
filename = "{}/tsnr-ensemble-{}.fits".format(args.outdir,args.tracer)
templates.write(filename)
else:
raise ValueError('Unknown tracer {} to compute.'.format(args.tracer))
if __name__ == '__main__':
print("please run desi_compute_tsnr_ensemble") | py/desispec/scripts/compute_tsnr_ensemble.py | import os
import sys
import yaml
import desiutil
import fitsio
import desisim
import argparse
import os.path as path
import numpy as np
import astropy.io.fits as fits
import matplotlib.pyplot as plt
from desiutil import depend
from astropy.convolution import convolve, Box1DKernel
from pathlib import Path
from desiutil.dust import mwdust_transmission
from desiutil.log import get_logger
from pkg_resources import resource_filename
from scipy.interpolate import interp1d
from astropy.table import Table, join
from desispec.tsnr import template_ensemble, gfa_template_ensemble
np.random.seed(seed=314)
# AR/DK DESI spectra wavelengths
# TODO: where are brz extraction wavelengths defined? https://github.com/desihub/desispec/issues/1006.
wmin, wmax, wdelta = 3600, 9824, 0.8
wave = np.round(np.arange(wmin, wmax + wdelta, wdelta), 1)
cslice = {"b": slice(0, 2751), "r": slice(2700, 5026), "z": slice(4900, 7781)}
def parse(options=None):
parser = argparse.ArgumentParser(description="Generate a sim. template ensemble stack of given type and write it to disk at --outdir.")
parser.add_argument('--nmodel', type = int, default = 1000, required=False,
help='Number of galaxies in the ensemble.')
parser.add_argument('--tracer', type = str, default = 'bgs', required=True,
help='Tracer to generate of [bgs, lrg, elg, qso].')
parser.add_argument('--smooth', type=float, default=100., required=False,
help='Smoothing scale [A] for DFLUX calc.')
parser.add_argument('--config-filename', type = str, default = None, required=False,
help='path to config filename (default is from python package desispec/data/tsnr/tsnr-config-{tracer}.yaml)')
parser.add_argument('--nz-filename', type = str, default = None, required=False,
help='path to n(z) filename (default is from $DESIMODEL/data/targets/nz_{tracer}.dat)')
parser.add_argument('--outdir', type = str, default = 'bgs', required=True,
help='Directory to write to.')
parser.add_argument('--no-nz-convolution', action='store_true',
help='Dont convolve each template dF^2 with redshift distribution')
parser.add_argument('--mag-range', action='store_true',
help='Monte Carlo the full mag range (given in config file) instead of using the same effective mag for all templates')
args = None
if options is None:
args = parser.parse_args()
else:
args = parser.parse_args(options)
return args
def main(args):
if args.tracer == 'gpb':
templates = gfa_template_ensemble()
templates.compute()
templates.plot()
templates.write(dirname=args.outdir)
elif args.tracer in ['bgs', 'lrg', 'elg', 'lya', 'qso']:
templates = template_ensemble(tracer=args.tracer,config_filename=args.config_filename)
templates.compute(nmodel=args.nmodel, smooth=args.smooth, nz_table_filename=args.nz_filename,
convolve_to_nz=(not args.no_nz_convolution), single_mag=(not args.mag_range))
filename = "{}/tsnr-ensemble-{}.fits".format(args.outdir,args.tracer)
templates.write(filename)
else:
raise ValueError('Unknown tracer {} to compute.'.format(args.tracer))
if __name__ == '__main__':
print("please run desi_compute_tsnr_ensemble") | 0.229449 | 0.168275 |
from Estoque import Estoque_Menu
list_estoque = list() # Contém todas as produtos registrados e seu disponibilidade
list_verifica_id = list() # Contém todos os ids já registrados para tratamento de erro
list_entrada_de_produto = list() # Registra toda a entrada de produto
list_saída_de_produto = list() # Registra toda a saída de produto
dict_produto = dict() # Armazena todas as informações do produto
dict_nRegistro = dict() # Reúne as informações do produto e seu ID de reconhecimento
def registrar_produto_novo(): # Registra produtos ainda não existentes no estoque
class Produto():
def __init__(self):
self.__produto_id:int = int(input('ID do Produto : '))
self.__nome:str = input('Nome do produto : ')
self.__quantidade:int = int(input('Quantidade : '))
self.tamanho = input('Tamanho do produto : ')
self.cor:str = input('Cor do Produto : ')
self.__data = input('Data de entrada do produto : ')
self.__nRegistro:int = len(list_estoque) + 1
dict_produto['nRegistro']:int = self.__nRegistro
dict_produto['Nome']:str = self.__nome.title()
dict_produto['Quantidade']:int = self.__quantidade
dict_produto['Tamanho'] = self.tamanho
dict_produto['Cor']:str = self.cor
dict_produto['Data'] = self.__data
dict_nRegistro['ID DO PRODUTO']:int = self.__produto_id
dict_nRegistro['INFO']:str = dict_produto.copy()
def __repr__(self):
Produto()
Produto()
if dict_nRegistro['ID DO PRODUTO'] not in list_verifica_id:
list_entrada_de_produto.append(dict_produto.copy())
list_estoque.append(dict_nRegistro.copy())
print('\n', dict_produto, '- REGISTRADO')
# Adiciona o produto ao estoque e controle de entrada
else:
print('TÁ ERRADO ISSO AI IRMÃO')
list_verifica_id.append(dict_nRegistro.copy()['ID DO PRODUTO'])
# list_verifica_registro.append(dict_produto.copy()['nRegistro'])
def controle(): # Registra toda a entrada e saída de produtos para controlar o estoque
def menu_registrar_entrada_saida():
def registra_entrada_de_produto(): # Registra toda a entrada de produtos
while True:
try:
id_quest: int = int(input('\nDigite o ID do produto ao qual deseja registrar entrada : '))
if id_quest not in list_verifica_id:
print(f'O ID digitado ({id_quest}) não existe no estoque')
else:
break
except:
print('\n!Erro')
print('Use apenas valores valídos')
continue
nome_produto: dict = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Nome'] # Acessa o nome do produto
print('\nProduto', f'"{nome_produto}"', ' - SELECIONADO')
quantidade:str = int(input(f'\nQuantidade a ser adicionada ao estoque do "{nome_produto}" : '))
list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade'] += quantidade
# Soma a quantia solicitada ao estoque
x: dict = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade']
print(f'\nQuantidade do produto {nome_produto} atualiza : "{x}"')
def registrar_saída_de_produto(): # Registra toda a saída de produtos
while True:
try:
id_quest: int = int(input('\nDigite o ID do produto ao qual deseja registrar saída : '))
if id_quest not in list_verifica_id:
print(f'O ID digitado ({id_quest}) não existe no estoque')
else:break
except:
print('\n!Erro')
print('Use apenas valores valídos')
continue
nome_produto:str = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Nome'] # Acessa o nome do produto
print('\nProduto', f'"{nome_produto}"', ' - SELECIONADO')
quantidade:int = int(input(f'\nQuantidade a ser removida do estoque do "{nome_produto}" : '))
quantidade_nova = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade'] - quantidade
# Subtrai a quantia solicitada do estoque
print(f'\n Quantidade do produto "{nome_produto}" atualiza : "Quantidade : {quantidade_nova}"')
def menu_entrada_saida():
def entrada_de_entrada():
for produto in list_entrada_de_produto:
print(produto)
def saida_de_saida():
for produto in list_saída_de_produto:
print(produto)
print('*' * 40)
print(' CONTROLE DE ESTOQUE')
print('*' * 40)
print('\n[1] REGISTRAR ENTRADA/SAÍDA DE PRODUTO'
'\n[2] VER ENTRADA/SAÍDA DE PRODUTOS'
'\n[3] VOLTAR')
while True:
try:
menu_quest: int = int(input('\nESCOLHA UMA DAS OPÇÕES ACIMA : \n'))
if menu_quest == 1:
menu_registrar_entrada_saida()
break
elif menu_quest == 2:
menu_entrada_saida()
break
elif menu_quest == 3:
Estoque_Menu.menu_estoque()
break
else:
print('\n!Erro')
print('!Digite uma opção valída')
except:
print('\n!Erro')
print('!Digite uma opção valída')
def atualização_cadastral() -> dict : # Atualiza o armazenamento de informações de um produto mal registrado
while True:
quest: int = int(input('\nDigite o número de id do produto : '))
if quest in list_verifica_id:
break
elif quest != int:
print('\n!Erro')
print('Use apenas valores valídos')
continue
else: print('\n!Erro'), print('!Não encotrado')
index:int = list_verifica_id.index(quest) # Acessa o index do produto selecionado
class AtualizaProduto(): # Coleta e altera as informações do produto selecionado
def __init__(self):
self.__nome:str = input('Nome do produto : ')
self.__quantidade:int = int(input('Quantidade : '))
self.tamanho = input('Tamanho do produto : ')
self.cor:str = input('Cor do Produto : ')
self.__data = input('Data de entrada do produto : ')
self.__nRegistro:int = list_estoque[index]['INFO']['nRegistro']
list_estoque[index]['INFO']['nRegistro']:int = self.__nRegistro
list_estoque[index]['INFO']['Nome']:str = self.__nome.title()
list_estoque[index]['INFO']['Quantidade']:int = self.__quantidade
list_estoque[index]['INFO']['Tamanho'] = self.tamanho
list_estoque[index]['INFO']['Cor']:str = self.cor
list_estoque[index]['INFO']['Data'] = self.__data
def __repr__(self):
AtualizaProduto()
AtualizaProduto()
print('\n', list_estoque[index]['INFO'], '- ATUALIZADO')
def estoque() -> list: # Imprime todo o estoque disponível
for c in list_estoque:
print(c) | !Loja (Canceled Version)!/Estoque/Backup.py | from Estoque import Estoque_Menu
list_estoque = list() # Contém todas as produtos registrados e seu disponibilidade
list_verifica_id = list() # Contém todos os ids já registrados para tratamento de erro
list_entrada_de_produto = list() # Registra toda a entrada de produto
list_saída_de_produto = list() # Registra toda a saída de produto
dict_produto = dict() # Armazena todas as informações do produto
dict_nRegistro = dict() # Reúne as informações do produto e seu ID de reconhecimento
def registrar_produto_novo(): # Registra produtos ainda não existentes no estoque
class Produto():
def __init__(self):
self.__produto_id:int = int(input('ID do Produto : '))
self.__nome:str = input('Nome do produto : ')
self.__quantidade:int = int(input('Quantidade : '))
self.tamanho = input('Tamanho do produto : ')
self.cor:str = input('Cor do Produto : ')
self.__data = input('Data de entrada do produto : ')
self.__nRegistro:int = len(list_estoque) + 1
dict_produto['nRegistro']:int = self.__nRegistro
dict_produto['Nome']:str = self.__nome.title()
dict_produto['Quantidade']:int = self.__quantidade
dict_produto['Tamanho'] = self.tamanho
dict_produto['Cor']:str = self.cor
dict_produto['Data'] = self.__data
dict_nRegistro['ID DO PRODUTO']:int = self.__produto_id
dict_nRegistro['INFO']:str = dict_produto.copy()
def __repr__(self):
Produto()
Produto()
if dict_nRegistro['ID DO PRODUTO'] not in list_verifica_id:
list_entrada_de_produto.append(dict_produto.copy())
list_estoque.append(dict_nRegistro.copy())
print('\n', dict_produto, '- REGISTRADO')
# Adiciona o produto ao estoque e controle de entrada
else:
print('TÁ ERRADO ISSO AI IRMÃO')
list_verifica_id.append(dict_nRegistro.copy()['ID DO PRODUTO'])
# list_verifica_registro.append(dict_produto.copy()['nRegistro'])
def controle(): # Registra toda a entrada e saída de produtos para controlar o estoque
def menu_registrar_entrada_saida():
def registra_entrada_de_produto(): # Registra toda a entrada de produtos
while True:
try:
id_quest: int = int(input('\nDigite o ID do produto ao qual deseja registrar entrada : '))
if id_quest not in list_verifica_id:
print(f'O ID digitado ({id_quest}) não existe no estoque')
else:
break
except:
print('\n!Erro')
print('Use apenas valores valídos')
continue
nome_produto: dict = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Nome'] # Acessa o nome do produto
print('\nProduto', f'"{nome_produto}"', ' - SELECIONADO')
quantidade:str = int(input(f'\nQuantidade a ser adicionada ao estoque do "{nome_produto}" : '))
list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade'] += quantidade
# Soma a quantia solicitada ao estoque
x: dict = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade']
print(f'\nQuantidade do produto {nome_produto} atualiza : "{x}"')
def registrar_saída_de_produto(): # Registra toda a saída de produtos
while True:
try:
id_quest: int = int(input('\nDigite o ID do produto ao qual deseja registrar saída : '))
if id_quest not in list_verifica_id:
print(f'O ID digitado ({id_quest}) não existe no estoque')
else:break
except:
print('\n!Erro')
print('Use apenas valores valídos')
continue
nome_produto:str = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Nome'] # Acessa o nome do produto
print('\nProduto', f'"{nome_produto}"', ' - SELECIONADO')
quantidade:int = int(input(f'\nQuantidade a ser removida do estoque do "{nome_produto}" : '))
quantidade_nova = list_estoque[list_verifica_id.index(id_quest)]['INFO']['Quantidade'] - quantidade
# Subtrai a quantia solicitada do estoque
print(f'\n Quantidade do produto "{nome_produto}" atualiza : "Quantidade : {quantidade_nova}"')
def menu_entrada_saida():
def entrada_de_entrada():
for produto in list_entrada_de_produto:
print(produto)
def saida_de_saida():
for produto in list_saída_de_produto:
print(produto)
print('*' * 40)
print(' CONTROLE DE ESTOQUE')
print('*' * 40)
print('\n[1] REGISTRAR ENTRADA/SAÍDA DE PRODUTO'
'\n[2] VER ENTRADA/SAÍDA DE PRODUTOS'
'\n[3] VOLTAR')
while True:
try:
menu_quest: int = int(input('\nESCOLHA UMA DAS OPÇÕES ACIMA : \n'))
if menu_quest == 1:
menu_registrar_entrada_saida()
break
elif menu_quest == 2:
menu_entrada_saida()
break
elif menu_quest == 3:
Estoque_Menu.menu_estoque()
break
else:
print('\n!Erro')
print('!Digite uma opção valída')
except:
print('\n!Erro')
print('!Digite uma opção valída')
def atualização_cadastral() -> dict : # Atualiza o armazenamento de informações de um produto mal registrado
while True:
quest: int = int(input('\nDigite o número de id do produto : '))
if quest in list_verifica_id:
break
elif quest != int:
print('\n!Erro')
print('Use apenas valores valídos')
continue
else: print('\n!Erro'), print('!Não encotrado')
index:int = list_verifica_id.index(quest) # Acessa o index do produto selecionado
class AtualizaProduto(): # Coleta e altera as informações do produto selecionado
def __init__(self):
self.__nome:str = input('Nome do produto : ')
self.__quantidade:int = int(input('Quantidade : '))
self.tamanho = input('Tamanho do produto : ')
self.cor:str = input('Cor do Produto : ')
self.__data = input('Data de entrada do produto : ')
self.__nRegistro:int = list_estoque[index]['INFO']['nRegistro']
list_estoque[index]['INFO']['nRegistro']:int = self.__nRegistro
list_estoque[index]['INFO']['Nome']:str = self.__nome.title()
list_estoque[index]['INFO']['Quantidade']:int = self.__quantidade
list_estoque[index]['INFO']['Tamanho'] = self.tamanho
list_estoque[index]['INFO']['Cor']:str = self.cor
list_estoque[index]['INFO']['Data'] = self.__data
def __repr__(self):
AtualizaProduto()
AtualizaProduto()
print('\n', list_estoque[index]['INFO'], '- ATUALIZADO')
def estoque() -> list: # Imprime todo o estoque disponível
for c in list_estoque:
print(c) | 0.169784 | 0.451085 |
try:
from pyspark import SparkContext, SparkConf,SQLContext
from pyspark.sql.functions import to_date,lit,desc,col
from pyspark.sql import Row
from operator import add
from server.main.utils import get_requireddataframe_fromcsv
import sys
except:
print('error')
def create_task(words):
conf = SparkConf().setAppName('letter count')
sc = SparkContext(conf=conf)
seq = words.split()
data = sc.parallelize(seq)
counts = data.map(lambda word: (word, 1)).reduceByKey(add).collect()
sc.stop()
return dict(counts)
def get_recent(spark_dataframe,given_date=None):
result_data_frame = spark_dataframe.filter(to_date(spark_dataframe.dateAdded) == lit(given_date)).orderBy(
spark_dataframe.dateAdded.desc()).limit(1)
return result_data_frame
def get_brand_count(spark_dataframe,given_date=None):
result_data_frame = spark_dataframe.filter(to_date(spark_dataframe.dateAdded) == lit(given_date)).groupBy(spark_dataframe.brand).count().orderBy(
col('count').desc())
return result_data_frame
def get_by_color(spark_dataframe,given_color=None):
result_data_frame = spark_dataframe.filter(spark_dataframe.colors.contains(given_color)).orderBy(
spark_dataframe.dateAdded.desc()).limit(10)
return result_data_frame
def get_result(function,param=None):
pandas_dataframe = get_requireddataframe_fromcsv('Latest_women_shoes.csv', ['id', 'brand', 'colors', 'dateAdded'])
conf = SparkConf().setAppName('Women Catalog')
sc = SparkContext(conf=conf)
# df2 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('sample.csv')
#used pandas dataframe as using the above the file could not be located.
sqlContext = SQLContext(sc)
spark_dataframe = sqlContext.createDataFrame(pandas_dataframe)
#data=spark_dataframe.select("*").toPandas()
result_spark_dataframe=getattr(sys.modules[__name__], function)(spark_dataframe,param)
result_python_dataframe = result_spark_dataframe.toPandas()
result_dict = result_python_dataframe.to_dict('records')
sc.stop()
return result_dict
"""
def get_brandcount(given_date='2017-03-28'):
pandas_dataframe = get_requireddataframe_fromcsv('Latest_women_shoes.csv', ['id', 'brand', 'colors', 'dateAdded'])
conf = SparkConf().setAppName('Women Catalog')
sc = SparkContext(conf=conf)
# df2 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('sample.csv')
# used pandas dataframe as using the above the file could not be located.
sqlContext = SQLContext(sc)
spark_dataframe = sqlContext.createDataFrame(pandas_dataframe)
# data=spark_dataframe.select("*").toPandas()
result_python_dataframe = result_spark_dataframe.toPandas()
result_dict = result_python_dataframe.to_dict()
return result_dict
""" | services/web/project/server/main/tasks.py |
try:
from pyspark import SparkContext, SparkConf,SQLContext
from pyspark.sql.functions import to_date,lit,desc,col
from pyspark.sql import Row
from operator import add
from server.main.utils import get_requireddataframe_fromcsv
import sys
except:
print('error')
def create_task(words):
conf = SparkConf().setAppName('letter count')
sc = SparkContext(conf=conf)
seq = words.split()
data = sc.parallelize(seq)
counts = data.map(lambda word: (word, 1)).reduceByKey(add).collect()
sc.stop()
return dict(counts)
def get_recent(spark_dataframe,given_date=None):
result_data_frame = spark_dataframe.filter(to_date(spark_dataframe.dateAdded) == lit(given_date)).orderBy(
spark_dataframe.dateAdded.desc()).limit(1)
return result_data_frame
def get_brand_count(spark_dataframe,given_date=None):
result_data_frame = spark_dataframe.filter(to_date(spark_dataframe.dateAdded) == lit(given_date)).groupBy(spark_dataframe.brand).count().orderBy(
col('count').desc())
return result_data_frame
def get_by_color(spark_dataframe,given_color=None):
result_data_frame = spark_dataframe.filter(spark_dataframe.colors.contains(given_color)).orderBy(
spark_dataframe.dateAdded.desc()).limit(10)
return result_data_frame
def get_result(function,param=None):
pandas_dataframe = get_requireddataframe_fromcsv('Latest_women_shoes.csv', ['id', 'brand', 'colors', 'dateAdded'])
conf = SparkConf().setAppName('Women Catalog')
sc = SparkContext(conf=conf)
# df2 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('sample.csv')
#used pandas dataframe as using the above the file could not be located.
sqlContext = SQLContext(sc)
spark_dataframe = sqlContext.createDataFrame(pandas_dataframe)
#data=spark_dataframe.select("*").toPandas()
result_spark_dataframe=getattr(sys.modules[__name__], function)(spark_dataframe,param)
result_python_dataframe = result_spark_dataframe.toPandas()
result_dict = result_python_dataframe.to_dict('records')
sc.stop()
return result_dict
"""
def get_brandcount(given_date='2017-03-28'):
pandas_dataframe = get_requireddataframe_fromcsv('Latest_women_shoes.csv', ['id', 'brand', 'colors', 'dateAdded'])
conf = SparkConf().setAppName('Women Catalog')
sc = SparkContext(conf=conf)
# df2 = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('sample.csv')
# used pandas dataframe as using the above the file could not be located.
sqlContext = SQLContext(sc)
spark_dataframe = sqlContext.createDataFrame(pandas_dataframe)
# data=spark_dataframe.select("*").toPandas()
result_python_dataframe = result_spark_dataframe.toPandas()
result_dict = result_python_dataframe.to_dict()
return result_dict
""" | 0.36557 | 0.282118 |
from entrenamiento.app.app import db
from entrenamiento.models.base import BaseModel
class Torneo(BaseModel):
''' Tiene toda la informacion sobre el tipo de resultado.
Es importante tener en cuenta que esto se lo usa tanto para
los torneos reales como para las practicas de torneos.
:param int id: un numero autoincrementado.
:param date cuando: la fecha de cuando fue el torneo en cuestion.
:param int id_usuario: el id del usuario que va a cargar la informacion
sobre como le fue en el torneo.
:param int id_lugar: el identificador del lugar en donde fue el
torneo.
:param str tipo_de_torneo: identifica que tipo de torneo se esta haciendo.
:param int puntaje_final_torneo: es la suma del puntaje de las 4 o 2
series del torneo.
:param boolean fue_practica: si es True, entonces esto no fue un torneo
en si, sino que fue una practica.
:param str comentario: el comentario que quiere poner el usuario en cuestion.
:param int posicion_classificacion: la posicion que termino el tirador
teniendo en cuenta las X rondas del torneo.
Esto no es para la posicion dentro si se gano
medalla
:param int posicion_eliminatorias: la posicion que se tiene en las eliminatorias.
Basicamente esto es para ver si se termino 1,
2 o 3°
'''
id = db.Column(db.Integer, primary_key=True)
cuando = db.Column(db.Date, nullable=False)
id_usuario = db.Column(db.Integer, db.ForeignKey('usuario.id', ondelete='CASCADE'), nullable=False)
id_tipo_de_torneo = db.Column(db.Integer, db.ForeignKey('tipo_torneo.id'), nullable=False)
id_lugar = db.Column(db.Integer, db.ForeignKey('lugar.id', ondelete='SET NULL'))
id_arco = db.Column(db.Integer, db.ForeignKey('arco.id', ondelete='SET NULL'))
comentario = db.Column(db.Text)
puntaje_final_torneo = db.Column(db.Integer)
fue_practica = db.Column(db.Boolean, nullable=False)
posicion_classificacion = db.Column(db.Integer)
posicion_eliminatoria = db.Column(db.Integer)
class Ronda(BaseModel):
''' Tiene toda la informacion de una ronda del torneo.
:param int id: un valor unico autoincrementado
:param int id_torneo: el identificador del torneo a donde pertence
la ronda en cuestion.
:param int puntaje: el puntaje que se hizo en esta ronda.
:param int distancia: la distancia a la que se tiro en este
torneo.
:param str foto: en caso de que no se quiera cargar toda la
inforamcion de las series, tiene la foto de
la planilla de resultado que se le entrego
a los jueces
'''
id = db.Column(db.Integer, primary_key=True)
id_torneo = db.Column(db.Integer, db.ForeignKey('torneo.id', ondelete='CASCADE'), nullable=False)
puntaje = db.Column(db.Integer)
distancia = db.Column(db.Integer)
foto_path = db.Column(db.Text)
class Serie(BaseModel):
''' Tiene toda la informacion para una de las series del
torneo.
:param int id: un valor unico autoincrementado.
:param boolean fue_de_practica: si es True, entonces esta serie
fue una de las series de pracitca
antes de empezar las series que se
puntean
:param int puntaje_flecha_X: el puntaje de la flecha X. El mismo tiene
que ir desde el puntaje mas alto al puntaje
mas bajo. Es decir, puntaje_flecha_1 tiene
que ser el mas alto, y puntaje_flecha_6 el
mas bajo. En caso de que una flecha haya sido
mala, entonces se la pone como 0.
:param int puntaje_final: el puntaje de las 6 flechas.
'''
id = db.Column(db.Integer, primary_key=True)
id_ronda = db.Column(db.Integer, db.ForeignKey('ronda.id', ondelete='CASCADE'), nullable=False)
fue_de_practica = db.Column(db.Boolean)
puntaje_flecha_1 = db.Column(db.Integer)
puntaje_flecha_2 = db.Column(db.Integer)
puntaje_flecha_3 = db.Column(db.Integer)
puntaje_flecha_4 = db.Column(db.Integer)
puntaje_flecha_5 = db.Column(db.Integer)
puntaje_flecha_6 = db.Column(db.Integer)
puntaje_total = db.Column(db.Integer) | entrenamiento/models/torneo.py |
from entrenamiento.app.app import db
from entrenamiento.models.base import BaseModel
class Torneo(BaseModel):
''' Tiene toda la informacion sobre el tipo de resultado.
Es importante tener en cuenta que esto se lo usa tanto para
los torneos reales como para las practicas de torneos.
:param int id: un numero autoincrementado.
:param date cuando: la fecha de cuando fue el torneo en cuestion.
:param int id_usuario: el id del usuario que va a cargar la informacion
sobre como le fue en el torneo.
:param int id_lugar: el identificador del lugar en donde fue el
torneo.
:param str tipo_de_torneo: identifica que tipo de torneo se esta haciendo.
:param int puntaje_final_torneo: es la suma del puntaje de las 4 o 2
series del torneo.
:param boolean fue_practica: si es True, entonces esto no fue un torneo
en si, sino que fue una practica.
:param str comentario: el comentario que quiere poner el usuario en cuestion.
:param int posicion_classificacion: la posicion que termino el tirador
teniendo en cuenta las X rondas del torneo.
Esto no es para la posicion dentro si se gano
medalla
:param int posicion_eliminatorias: la posicion que se tiene en las eliminatorias.
Basicamente esto es para ver si se termino 1,
2 o 3°
'''
id = db.Column(db.Integer, primary_key=True)
cuando = db.Column(db.Date, nullable=False)
id_usuario = db.Column(db.Integer, db.ForeignKey('usuario.id', ondelete='CASCADE'), nullable=False)
id_tipo_de_torneo = db.Column(db.Integer, db.ForeignKey('tipo_torneo.id'), nullable=False)
id_lugar = db.Column(db.Integer, db.ForeignKey('lugar.id', ondelete='SET NULL'))
id_arco = db.Column(db.Integer, db.ForeignKey('arco.id', ondelete='SET NULL'))
comentario = db.Column(db.Text)
puntaje_final_torneo = db.Column(db.Integer)
fue_practica = db.Column(db.Boolean, nullable=False)
posicion_classificacion = db.Column(db.Integer)
posicion_eliminatoria = db.Column(db.Integer)
class Ronda(BaseModel):
''' Tiene toda la informacion de una ronda del torneo.
:param int id: un valor unico autoincrementado
:param int id_torneo: el identificador del torneo a donde pertence
la ronda en cuestion.
:param int puntaje: el puntaje que se hizo en esta ronda.
:param int distancia: la distancia a la que se tiro en este
torneo.
:param str foto: en caso de que no se quiera cargar toda la
inforamcion de las series, tiene la foto de
la planilla de resultado que se le entrego
a los jueces
'''
id = db.Column(db.Integer, primary_key=True)
id_torneo = db.Column(db.Integer, db.ForeignKey('torneo.id', ondelete='CASCADE'), nullable=False)
puntaje = db.Column(db.Integer)
distancia = db.Column(db.Integer)
foto_path = db.Column(db.Text)
class Serie(BaseModel):
''' Tiene toda la informacion para una de las series del
torneo.
:param int id: un valor unico autoincrementado.
:param boolean fue_de_practica: si es True, entonces esta serie
fue una de las series de pracitca
antes de empezar las series que se
puntean
:param int puntaje_flecha_X: el puntaje de la flecha X. El mismo tiene
que ir desde el puntaje mas alto al puntaje
mas bajo. Es decir, puntaje_flecha_1 tiene
que ser el mas alto, y puntaje_flecha_6 el
mas bajo. En caso de que una flecha haya sido
mala, entonces se la pone como 0.
:param int puntaje_final: el puntaje de las 6 flechas.
'''
id = db.Column(db.Integer, primary_key=True)
id_ronda = db.Column(db.Integer, db.ForeignKey('ronda.id', ondelete='CASCADE'), nullable=False)
fue_de_practica = db.Column(db.Boolean)
puntaje_flecha_1 = db.Column(db.Integer)
puntaje_flecha_2 = db.Column(db.Integer)
puntaje_flecha_3 = db.Column(db.Integer)
puntaje_flecha_4 = db.Column(db.Integer)
puntaje_flecha_5 = db.Column(db.Integer)
puntaje_flecha_6 = db.Column(db.Integer)
puntaje_total = db.Column(db.Integer) | 0.520984 | 0.440409 |
from fastapi import APIRouter, HTTPException, Depends
from pymongo.client_session import ClientSession
from autologging import logged
from app.api.deps import get_session
from app.db.operations import insert_one, update_one
from app.schemas.response import InsertOneResponse, UpdateOneResponse
from app.schemas.usage import AttemptStart, CheckpointStart, CheckpointEnd, AttemptEnd
router = APIRouter()
@logged
class Usage:
# Post the start attempt
@router.post("/attempt/start", status_code=201, response_model=InsertOneResponse)
async def post_start_attempt(
attempt: AttemptStart, session: ClientSession = Depends(get_session)
):
Usage.__log.info(attempt.dict())
return await insert_one(
"usageStats", "journeyMap", attempt.dict(), session=session
)
# Update with checkpoint start
@router.put("/checkpoint/start", response_model=UpdateOneResponse)
async def update_checkpoint(
checkpoint: CheckpointStart, session: ClientSession = Depends(get_session)
):
update_dict = checkpoint.dict()
update_query = {"attemptId": update_dict.pop("attemptId")}
update = {"$push": {"checkpoints": update_dict}}
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching attempt"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
session=session,
custom_exception=custom_exception,
)
# Update with checkpoint end
@router.put("/checkpoint/end", response_model=UpdateOneResponse)
async def update_checkpoint(
checkpoint: CheckpointEnd, session: ClientSession = Depends(get_session)
):
update_query = {
"attemptId": checkpoint.attemptId,
}
update = {"$set": {"checkpoints.$[cp].end": checkpoint.end}}
array_filters = [{"cp.description": checkpoint.description}]
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching checkpoint"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
array_filters=array_filters,
session=session,
custom_exception=custom_exception,
)
# Update with attempt end
@router.put("/attempt/end", response_model=UpdateOneResponse)
async def update_checkpoint(
attempt: AttemptEnd, session: ClientSession = Depends(get_session)
):
update_dict = attempt.dict()
update_query = {"attemptId": update_dict.pop("attemptId")}
update = {"$set": update_dict}
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching attempt"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
session=session,
custom_exception=custom_exception,
) | backend/app/api/endpoints/usage.py | from fastapi import APIRouter, HTTPException, Depends
from pymongo.client_session import ClientSession
from autologging import logged
from app.api.deps import get_session
from app.db.operations import insert_one, update_one
from app.schemas.response import InsertOneResponse, UpdateOneResponse
from app.schemas.usage import AttemptStart, CheckpointStart, CheckpointEnd, AttemptEnd
router = APIRouter()
@logged
class Usage:
# Post the start attempt
@router.post("/attempt/start", status_code=201, response_model=InsertOneResponse)
async def post_start_attempt(
attempt: AttemptStart, session: ClientSession = Depends(get_session)
):
Usage.__log.info(attempt.dict())
return await insert_one(
"usageStats", "journeyMap", attempt.dict(), session=session
)
# Update with checkpoint start
@router.put("/checkpoint/start", response_model=UpdateOneResponse)
async def update_checkpoint(
checkpoint: CheckpointStart, session: ClientSession = Depends(get_session)
):
update_dict = checkpoint.dict()
update_query = {"attemptId": update_dict.pop("attemptId")}
update = {"$push": {"checkpoints": update_dict}}
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching attempt"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
session=session,
custom_exception=custom_exception,
)
# Update with checkpoint end
@router.put("/checkpoint/end", response_model=UpdateOneResponse)
async def update_checkpoint(
checkpoint: CheckpointEnd, session: ClientSession = Depends(get_session)
):
update_query = {
"attemptId": checkpoint.attemptId,
}
update = {"$set": {"checkpoints.$[cp].end": checkpoint.end}}
array_filters = [{"cp.description": checkpoint.description}]
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching checkpoint"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
array_filters=array_filters,
session=session,
custom_exception=custom_exception,
)
# Update with attempt end
@router.put("/attempt/end", response_model=UpdateOneResponse)
async def update_checkpoint(
attempt: AttemptEnd, session: ClientSession = Depends(get_session)
):
update_dict = attempt.dict()
update_query = {"attemptId": update_dict.pop("attemptId")}
update = {"$set": update_dict}
custom_exception = HTTPException(
status_code=404, detail="Unable to find matching attempt"
)
Usage.__log.info(update)
return await update_one(
"usageStats",
"journeyMap",
update_query,
update,
session=session,
custom_exception=custom_exception,
) | 0.731346 | 0.098555 |
import logging
import os
import shutil
import subprocess # nosec
import uuid
from tempfile import mkdtemp
from textwrap import fill
from typing import Iterable, Generator, Optional, Sequence
from pkg_resources import resource_string
import markdown
from jinja2 import Template
from publish import __version__ as package_version
from publish.book import Book, Chapter
from publish.substitution import Substitution, apply_substitutions
LOG = logging.getLogger(__name__)
LOG.addHandler(logging.NullHandler())
SUPPORTED_EBOOKCONVERT_ATTRIBUTES = (
'author_sort',
'authors',
'book_producer',
'comments',
'cover',
'isbn',
'language',
'pubdate',
'publisher',
'rating',
'series',
'series_index',
'tags',
'title'
)
class HtmlOutput:
"""Turns a Book object and its chapters into an html document.
Args:
path: The output path.
**kwargs: Any other attribute of this class. (see Attributes below)
Attributes:
path (str): The output path.
stylesheet (str): The path to the style sheet.
force_publish (bool): Determines wether to force publish all chapters.
If set to true, all chapters of the book will be published
no matter how the chapters are configured.
Defaults to False.
"""
def __init__(self,
path: str,
**kwargs):
"""Initializes a new instance of the :class:`HtmlOutput` class.
"""
self.path = path
self.stylesheet = kwargs.pop('stylesheet', None)
self.force_publish = kwargs.pop('force_publish', False)
def make(self,
book: Book,
substitutions: Optional[Iterable[Substitution]] = None):
"""Makes the Output for the provided book and substitutions.
Args:
book: The book.
substitutions: The substitutions.
"""
LOG.info('Making HtmlOutput ...')
if not substitutions:
substitutions = []
html_document = self._get_html_document(book, substitutions)
with open(self.path, 'w') as file:
file.write(html_document)
LOG.info('... HtmlOutput finished')
def get_chapters_to_be_published(self,
chapters: Iterable[Chapter]
) -> Iterable[Chapter]:
"""Gets the list of chapters to be published based on each chapters
`publish` attribute.
If the outputs `force_publish` override is set to true, all chapters
will be published regardless of their individual `publish` attributes.
Returns:
The list of chapters to be published.
"""
if self.force_publish:
return chapters
return list(filter(lambda c: c.publish is True, chapters))
def _get_css(self) -> str:
"""Gets the css from the css file specified in stylesheet as a string.
Returns:
The css from the css file specified in stylesheet as a string.
"""
if not self.stylesheet:
return ''
css_path = os.path.join(os.getcwd(), self.stylesheet)
LOG.info('Collecting stylesheet ...')
with open(css_path, 'r') as file:
css = file.read()
return css if css else ''
def _get_html_document(self,
book: Book,
substitutions: Iterable[Substitution]
) -> str:
"""Takes a book, renders it to html, applying the list of substitutions in the process
and returns the finished html document as a string.
Args:
book: The book.
substitutions: The list of substitutions.
Returns:
The html document as a string.
"""
html_content = self._get_html_content(book.chapters, substitutions)
html_document = _apply_template(html_content=html_content,
title=book.title,
css=self._get_css(),
language=book.language)
return html_document
def _get_html_content(self,
chapters: Iterable[Chapter],
substitutions: Iterable[Substitution]) -> str:
"""Gets the content of the provided list of chapters as as an html string.
The list of substitutions is applied to the markdown content before it is rendered to
html.
The order of the chapters is preserved.
The resulting html string does not include a head or body, only the chapters markdown
turned into html.
Args:
chapters: The list of chapters.
substitutions: The list of substitutions.
Returns:
The content of the provided list of chapters as an html string.
"""
markdown_ = self._get_markdown_content(chapters)
markdown_ = apply_substitutions(
markdown_,
substitutions)
LOG.info('Rendering markdown to html ...')
return markdown.markdown(markdown_)
def _get_markdown_content(self,
chapters: Iterable[Chapter]) -> str:
"""Gets the markdown content of the provided list of chapters concatenated into a single
string.
The order of the chapters is preserved.
Args:
chapters: The list of chapters.
Returns:
The markdown content of the list of chapters concatenated into a single string.
"""
markdown_ = []
md_paragraph_sep = '\n\n'
if not chapters:
raise NoChaptersFoundError('Your book contains no chapters.')
chapters_to_publish = self.get_chapters_to_be_published(chapters)
if not chapters_to_publish:
raise NoChaptersFoundError('None of your chapters are set to be'
'published.')
LOG.info('Collecting chapters ...')
for chapter in chapters_to_publish:
with open(chapter.src, 'r') as file:
markdown_.append(file.read())
return md_paragraph_sep.join(markdown_)
class EbookConvertOutput(HtmlOutput):
"""Turns Book objects and its chapters into an ebook using
Kavid Goyals ebookconvert command line tool.
.. todo:: document format of ebookconvert_params -> {'key':'value'} or object.key = value
Args:
path: The output path.
**kwargs: Any other attribute of this class. (see Attributes below)
Attributes:
ebookconvert_params (List[str]): An optional list of additional command
line arguments that will be passed to ebookconvert.
path (str): The output path.
stylesheet (str): The path to the style sheet.
force_publish (bool): Determines wether to force publish all chapters.
If set to true, all chapters of the book will be published
no matter how the chapters are configured.
Defaults to False.
"""
def __init__(self,
path: str,
**kwargs):
"""Initializes a new instance of the :class:`EbookConvertOutput` class.
"""
super().__init__(path, **kwargs)
self.ebookconvert_params = kwargs.pop('ebookconvert_params', [])
def make(self,
book: Book,
substitutions: Optional[Iterable[Substitution]] = None):
"""Makes an ebook from the provided book object and the markdown chapters
specified therein.
Substitutions are applied to the raw markdown before the markdown is
processed.
Args:
book: The book.
substitutions: The list of substitutions.
"""
LOG.info('Making EbookConvertOutput ...')
if not book:
raise AttributeError("book must not be None")
if not substitutions:
substitutions = []
temp_directory = mkdtemp()
# mkstmp and NamedTemporaryFile won't work, because the html file
# will be kept open by EbookConvertOutput with exclusive access,
# which means ebook-convert can't read the html to create the epub.
# -> ebook-convert fails with 'Permission denied'.
try:
temp_path = os.path.join(
temp_directory, str(uuid.uuid4()) + '.html')
html_document = self._get_html_document(book, substitutions)
with open(temp_path, 'w') as file:
file.write(html_document)
call_params = _get_ebook_convert_params(book,
input_path=temp_path,
output_path=self.path,
additional_params=self.ebookconvert_params)
LOG.info('Calling ebook-convert ...')
try:
subprocess.call(call_params, shell=False) # nosec
except FileNotFoundError:
LOG.error(
fill('Could not find ebook-convert. Please install calibre if you want to '
'use EbookconvertOutput and make sure ebook-convert is accessible '
'through the PATH variable.'))
return
LOG.info('... EbookConvertOutput finished')
finally:
shutil.rmtree(temp_directory)
def _get_ebook_convert_params(book: Book,
input_path: str,
output_path: str,
additional_params: Optional[Iterable[str]] = None
) -> Sequence[str]:
"""Gets the call params for the ebookconvert commandline.
The book's attributes are translated into ebookconvert metadata commandline options
while any additional options present in EbookConvertOutput.ebookconvert_params are
appended to the call params as is.
Args:
book: The book object.
input_path: The path the html file that will be passed to ebookconvert.
output_path: The output path.
"""
if not additional_params:
additional_params = []
call_params = [
'ebook-convert',
input_path,
output_path
]
call_params.extend(_yield_attributes_as_params(book))
call_params.extend(additional_params)
return call_params
def _apply_template(html_content: str,
title: str,
css: str,
language: str) -> str:
"""Renders the html content, title, css and document language into the jinja2 formatted
template and returns the resulting html document.
Args:
html_content: The html content gets inserted into the {{ content }} of the template.
title: The title gets inserted into the {{ title }} of the template.
css: The css gets inserted into the {{ css }} of the template.
language: The language gets inserted into the {{ language }} of the template.
Returns:
The html document.
"""
template = resource_string(__name__, 'template.jinja') \
.decode('utf-8') \
.replace('\r\n', '\n')
# resource_string opens the file as bytes, which means that we
# have to decode to utf-8. The replace is necessary because
# resource_string, instead of open, does not automatically
# strip \r\n down to \n on windows systems. Leaving \r\n as is
# would produce double line breaks when writing the resulting string
# back to disc, thus we have to do the replacement ourselves, too.
return Template(template).render(content=html_content,
title=title,
css=css,
language=language,
package_version=package_version)
def _yield_attributes_as_params(object_) -> Generator[str, None, None]:
"""Takes an object or dictionary and returns a generator yielding all
attributes that can be processed by the ebookconvert command line as a
parameter array.
Args:
object_: An object or dictionary.
Returns:
A generator yielding all attributes of the object supported
by ebookconvert.
"""
# This way the book can contain attributes not supported by ebookconvert
# (or any other specific output that follows this explicit pattern)
for attr_name in SUPPORTED_EBOOKCONVERT_ATTRIBUTES:
if hasattr(object_, attr_name):
attr = getattr(object_, attr_name)
else:
try:
attr = object_[attr_name]
except (TypeError, KeyError):
continue
if not attr:
continue
attr = str(attr)
if attr and not attr.isspace():
yield '--{0}={1}'.format(attr_name, attr)
class NoChaptersFoundError(Exception):
"""No chapters found.""" | publish/output.py | import logging
import os
import shutil
import subprocess # nosec
import uuid
from tempfile import mkdtemp
from textwrap import fill
from typing import Iterable, Generator, Optional, Sequence
from pkg_resources import resource_string
import markdown
from jinja2 import Template
from publish import __version__ as package_version
from publish.book import Book, Chapter
from publish.substitution import Substitution, apply_substitutions
LOG = logging.getLogger(__name__)
LOG.addHandler(logging.NullHandler())
SUPPORTED_EBOOKCONVERT_ATTRIBUTES = (
'author_sort',
'authors',
'book_producer',
'comments',
'cover',
'isbn',
'language',
'pubdate',
'publisher',
'rating',
'series',
'series_index',
'tags',
'title'
)
class HtmlOutput:
"""Turns a Book object and its chapters into an html document.
Args:
path: The output path.
**kwargs: Any other attribute of this class. (see Attributes below)
Attributes:
path (str): The output path.
stylesheet (str): The path to the style sheet.
force_publish (bool): Determines wether to force publish all chapters.
If set to true, all chapters of the book will be published
no matter how the chapters are configured.
Defaults to False.
"""
def __init__(self,
path: str,
**kwargs):
"""Initializes a new instance of the :class:`HtmlOutput` class.
"""
self.path = path
self.stylesheet = kwargs.pop('stylesheet', None)
self.force_publish = kwargs.pop('force_publish', False)
def make(self,
book: Book,
substitutions: Optional[Iterable[Substitution]] = None):
"""Makes the Output for the provided book and substitutions.
Args:
book: The book.
substitutions: The substitutions.
"""
LOG.info('Making HtmlOutput ...')
if not substitutions:
substitutions = []
html_document = self._get_html_document(book, substitutions)
with open(self.path, 'w') as file:
file.write(html_document)
LOG.info('... HtmlOutput finished')
def get_chapters_to_be_published(self,
chapters: Iterable[Chapter]
) -> Iterable[Chapter]:
"""Gets the list of chapters to be published based on each chapters
`publish` attribute.
If the outputs `force_publish` override is set to true, all chapters
will be published regardless of their individual `publish` attributes.
Returns:
The list of chapters to be published.
"""
if self.force_publish:
return chapters
return list(filter(lambda c: c.publish is True, chapters))
def _get_css(self) -> str:
"""Gets the css from the css file specified in stylesheet as a string.
Returns:
The css from the css file specified in stylesheet as a string.
"""
if not self.stylesheet:
return ''
css_path = os.path.join(os.getcwd(), self.stylesheet)
LOG.info('Collecting stylesheet ...')
with open(css_path, 'r') as file:
css = file.read()
return css if css else ''
def _get_html_document(self,
book: Book,
substitutions: Iterable[Substitution]
) -> str:
"""Takes a book, renders it to html, applying the list of substitutions in the process
and returns the finished html document as a string.
Args:
book: The book.
substitutions: The list of substitutions.
Returns:
The html document as a string.
"""
html_content = self._get_html_content(book.chapters, substitutions)
html_document = _apply_template(html_content=html_content,
title=book.title,
css=self._get_css(),
language=book.language)
return html_document
def _get_html_content(self,
chapters: Iterable[Chapter],
substitutions: Iterable[Substitution]) -> str:
"""Gets the content of the provided list of chapters as as an html string.
The list of substitutions is applied to the markdown content before it is rendered to
html.
The order of the chapters is preserved.
The resulting html string does not include a head or body, only the chapters markdown
turned into html.
Args:
chapters: The list of chapters.
substitutions: The list of substitutions.
Returns:
The content of the provided list of chapters as an html string.
"""
markdown_ = self._get_markdown_content(chapters)
markdown_ = apply_substitutions(
markdown_,
substitutions)
LOG.info('Rendering markdown to html ...')
return markdown.markdown(markdown_)
def _get_markdown_content(self,
chapters: Iterable[Chapter]) -> str:
"""Gets the markdown content of the provided list of chapters concatenated into a single
string.
The order of the chapters is preserved.
Args:
chapters: The list of chapters.
Returns:
The markdown content of the list of chapters concatenated into a single string.
"""
markdown_ = []
md_paragraph_sep = '\n\n'
if not chapters:
raise NoChaptersFoundError('Your book contains no chapters.')
chapters_to_publish = self.get_chapters_to_be_published(chapters)
if not chapters_to_publish:
raise NoChaptersFoundError('None of your chapters are set to be'
'published.')
LOG.info('Collecting chapters ...')
for chapter in chapters_to_publish:
with open(chapter.src, 'r') as file:
markdown_.append(file.read())
return md_paragraph_sep.join(markdown_)
class EbookConvertOutput(HtmlOutput):
"""Turns Book objects and its chapters into an ebook using
Kavid Goyals ebookconvert command line tool.
.. todo:: document format of ebookconvert_params -> {'key':'value'} or object.key = value
Args:
path: The output path.
**kwargs: Any other attribute of this class. (see Attributes below)
Attributes:
ebookconvert_params (List[str]): An optional list of additional command
line arguments that will be passed to ebookconvert.
path (str): The output path.
stylesheet (str): The path to the style sheet.
force_publish (bool): Determines wether to force publish all chapters.
If set to true, all chapters of the book will be published
no matter how the chapters are configured.
Defaults to False.
"""
def __init__(self,
path: str,
**kwargs):
"""Initializes a new instance of the :class:`EbookConvertOutput` class.
"""
super().__init__(path, **kwargs)
self.ebookconvert_params = kwargs.pop('ebookconvert_params', [])
def make(self,
book: Book,
substitutions: Optional[Iterable[Substitution]] = None):
"""Makes an ebook from the provided book object and the markdown chapters
specified therein.
Substitutions are applied to the raw markdown before the markdown is
processed.
Args:
book: The book.
substitutions: The list of substitutions.
"""
LOG.info('Making EbookConvertOutput ...')
if not book:
raise AttributeError("book must not be None")
if not substitutions:
substitutions = []
temp_directory = mkdtemp()
# mkstmp and NamedTemporaryFile won't work, because the html file
# will be kept open by EbookConvertOutput with exclusive access,
# which means ebook-convert can't read the html to create the epub.
# -> ebook-convert fails with 'Permission denied'.
try:
temp_path = os.path.join(
temp_directory, str(uuid.uuid4()) + '.html')
html_document = self._get_html_document(book, substitutions)
with open(temp_path, 'w') as file:
file.write(html_document)
call_params = _get_ebook_convert_params(book,
input_path=temp_path,
output_path=self.path,
additional_params=self.ebookconvert_params)
LOG.info('Calling ebook-convert ...')
try:
subprocess.call(call_params, shell=False) # nosec
except FileNotFoundError:
LOG.error(
fill('Could not find ebook-convert. Please install calibre if you want to '
'use EbookconvertOutput and make sure ebook-convert is accessible '
'through the PATH variable.'))
return
LOG.info('... EbookConvertOutput finished')
finally:
shutil.rmtree(temp_directory)
def _get_ebook_convert_params(book: Book,
input_path: str,
output_path: str,
additional_params: Optional[Iterable[str]] = None
) -> Sequence[str]:
"""Gets the call params for the ebookconvert commandline.
The book's attributes are translated into ebookconvert metadata commandline options
while any additional options present in EbookConvertOutput.ebookconvert_params are
appended to the call params as is.
Args:
book: The book object.
input_path: The path the html file that will be passed to ebookconvert.
output_path: The output path.
"""
if not additional_params:
additional_params = []
call_params = [
'ebook-convert',
input_path,
output_path
]
call_params.extend(_yield_attributes_as_params(book))
call_params.extend(additional_params)
return call_params
def _apply_template(html_content: str,
title: str,
css: str,
language: str) -> str:
"""Renders the html content, title, css and document language into the jinja2 formatted
template and returns the resulting html document.
Args:
html_content: The html content gets inserted into the {{ content }} of the template.
title: The title gets inserted into the {{ title }} of the template.
css: The css gets inserted into the {{ css }} of the template.
language: The language gets inserted into the {{ language }} of the template.
Returns:
The html document.
"""
template = resource_string(__name__, 'template.jinja') \
.decode('utf-8') \
.replace('\r\n', '\n')
# resource_string opens the file as bytes, which means that we
# have to decode to utf-8. The replace is necessary because
# resource_string, instead of open, does not automatically
# strip \r\n down to \n on windows systems. Leaving \r\n as is
# would produce double line breaks when writing the resulting string
# back to disc, thus we have to do the replacement ourselves, too.
return Template(template).render(content=html_content,
title=title,
css=css,
language=language,
package_version=package_version)
def _yield_attributes_as_params(object_) -> Generator[str, None, None]:
"""Takes an object or dictionary and returns a generator yielding all
attributes that can be processed by the ebookconvert command line as a
parameter array.
Args:
object_: An object or dictionary.
Returns:
A generator yielding all attributes of the object supported
by ebookconvert.
"""
# This way the book can contain attributes not supported by ebookconvert
# (or any other specific output that follows this explicit pattern)
for attr_name in SUPPORTED_EBOOKCONVERT_ATTRIBUTES:
if hasattr(object_, attr_name):
attr = getattr(object_, attr_name)
else:
try:
attr = object_[attr_name]
except (TypeError, KeyError):
continue
if not attr:
continue
attr = str(attr)
if attr and not attr.isspace():
yield '--{0}={1}'.format(attr_name, attr)
class NoChaptersFoundError(Exception):
"""No chapters found.""" | 0.718397 | 0.207496 |
import pytest
from clean.entities.token import UserToken
from clean.auth.abs import DecodeToken
from clean.auth.decorator import DecoratorBuilder
from clean.exceptions import AuthException
class VerifyTokenAuth(DecodeToken):
def get_token(self):
raw = self.raw_token.get('token', None)
if raw is None:
raise AuthException('null token')
return raw
def verify(self, token: str):
return UserToken('crl', '<EMAIL>',
photo_url='',
scopes={'bar': 'foo'},
app_meta={'bar': 'foo'}).to_dict()
def test_create_decorator():
def token_finder():
return {'token': 'bar'}
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
usr_token = result['user_token']
assert res is True
assert usr_token is not None
assert usr_token['username'] == 'crl'
def test_auth_exception():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect()
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
assert res == {'error': 'token not found'}
def test_verify_class_is_subclass_of_decode_token():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
assert callable(protect) is True
def test_verify_class_is_not_subclass_of_decode_token():
def token_finder():
return None
class Foo:
pass
with pytest.raises(AttributeError):
DecoratorBuilder(verify_class=Foo, token_finder_func=token_finder)
def test_token_finder_func_is_not_callable():
token = {}
with pytest.raises(AttributeError):
DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token)
def test_auth_exception_with_invalid_scopes():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'}, rule={'path': 'scopes.bar', 'op': 'eq1', 'value': 'foo'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
assert res == {'error': 'token not found'}
def test_auth_with_valid_scopes():
def token_finder():
return {'token': 'bar'}
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'}, rule={'path': 'scopes.bar', 'op': 'eq', 'value': 'foo'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
usr_token = result['user_token']
assert res is True
assert usr_token is not None
assert usr_token['username'] == 'crl' | tests/clean/auth/test_decorator_builder.py | import pytest
from clean.entities.token import UserToken
from clean.auth.abs import DecodeToken
from clean.auth.decorator import DecoratorBuilder
from clean.exceptions import AuthException
class VerifyTokenAuth(DecodeToken):
def get_token(self):
raw = self.raw_token.get('token', None)
if raw is None:
raise AuthException('null token')
return raw
def verify(self, token: str):
return UserToken('crl', '<EMAIL>',
photo_url='',
scopes={'bar': 'foo'},
app_meta={'bar': 'foo'}).to_dict()
def test_create_decorator():
def token_finder():
return {'token': 'bar'}
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
usr_token = result['user_token']
assert res is True
assert usr_token is not None
assert usr_token['username'] == 'crl'
def test_auth_exception():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect()
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
assert res == {'error': 'token not found'}
def test_verify_class_is_subclass_of_decode_token():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
assert callable(protect) is True
def test_verify_class_is_not_subclass_of_decode_token():
def token_finder():
return None
class Foo:
pass
with pytest.raises(AttributeError):
DecoratorBuilder(verify_class=Foo, token_finder_func=token_finder)
def test_token_finder_func_is_not_callable():
token = {}
with pytest.raises(AttributeError):
DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token)
def test_auth_exception_with_invalid_scopes():
def token_finder():
return None
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'}, rule={'path': 'scopes.bar', 'op': 'eq1', 'value': 'foo'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
assert res == {'error': 'token not found'}
def test_auth_with_valid_scopes():
def token_finder():
return {'token': 'bar'}
db = DecoratorBuilder(verify_class=VerifyTokenAuth, token_finder_func=token_finder)
protect = db.create()
result = dict()
@protect(user={'req': 'user'}, rule={'path': 'scopes.bar', 'op': 'eq', 'value': 'foo'})
def my_endpoint(user_token, *args, **kwargs):
result['user_token'] = user_token
return True
res = my_endpoint()
usr_token = result['user_token']
assert res is True
assert usr_token is not None
assert usr_token['username'] == 'crl' | 0.577853 | 0.224682 |
from flask import Flask, json, render_template
from threading import Thread
from flask_socketio import SocketIO
from graphqlclient import GraphQLClient
import serial, time, serial.tools.list_ports, datetime, socket
app = Flask(__name__)
app.config['SECRET_KEY'] = 'SECRET!'
socketio = SocketIO(app)
uuid_last = ''
data = ''
connexion_genius = GraphQLClient('https://##.###.##/')
connexion_genius.inject_token('Bearer ####','Authorization')
REMOTE_SERVER = "##.###.##"
@app.route('/')
def index():
return render_template('index.html')
def is_connected():
try:
host = socket.gethostbyname(REMOTE_SERVER)
socket.create_connection((host, 80), 2)
return True
except:
pass
return False
def getprofilewithbadge(badge):
tmp = connexion_genius.execute('''{
profiles(where:{badge:"''' + badge + '''"}){
firstName
lastName
}
}
''')
return tmp
def sethello(badge):
tmp = connexion_genius.execute('''mutation{terminalHello(data:{badge:"''' + badge + '''",timeOfArrival:"''' + str(datetime.datetime.now().isoformat()) + '''"}){status}}''')
return tmp
class SerialRead(Thread):
global j
def __init__(self):
Thread.__init__(self)
ports = list(serial.tools.list_ports.comports())
for p in ports:
if "Arduino" in p[1] or "ttyACM0" in p[1]:
print("Arduino detecte sur le port : ", p[0])
self.serial = serial.Serial(str(p[0]), 9600, timeout=1)
socketio.emit('Internet', {'internet': True})
def init_serial(self):
ports = list(serial.tools.list_ports.comports())
self.serial.close()
for p in ports:
if "Arduino" in p[1] or "ttyACM0" in p[1]:
print("Arduino detecte sur le port : ", p[0])
self.serial = serial.Serial(str(p[0]), 9600, timeout=1)
socketio.emit('Internet', {'internet': True})
self.run()
def run(self):
global uuid_last
while True:
try:
if self.serial is not None:
data = self.serial.readline().strip(b'\n\r')
try:
if is_connected():
j = json.loads(data.decode('UTF-8'))
socketio.emit('Internet', {'internet': True})
if "ESTIAM" in j['uuid']:
if uuid_last != j['uuid']:
uuid_last = j['uuid']
try:
reponse = json.loads(sethello(uuid_last))
try:
if len(reponse['errors']) > 0:
socketio.emit('CardFound', {'error':True,'user': None, 'late':False})
except:
if reponse['data']['terminalHello']['status'] == "OK":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "ALREADYBADGED":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "NO_DATE":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "UNKNOWN_CARD":
socketio.emit('CardFound', {'error':True,'user':False,'late':False})
if reponse['data']['terminalHello']['status'] == "FAILED_SYS_ERROR":
socketio.emit('CardFound', {'error': True, 'user': False, 'late': False})
except:
continue
else:
socketio.emit('Internet', {'internet': False})
except:
continue
except:
socketio.emit('Internet', {'internet': False})
print("La liaison serie ne peut etre etablie")
time.sleep(1)
self.init_serial()
def first(self):
self.run()
if __name__ == '__main__':
ThreadSerial = SerialRead()
ThreadSerial.start()
socketio.run(app,host='0.0.0.0',port=8000) | app.py | from flask import Flask, json, render_template
from threading import Thread
from flask_socketio import SocketIO
from graphqlclient import GraphQLClient
import serial, time, serial.tools.list_ports, datetime, socket
app = Flask(__name__)
app.config['SECRET_KEY'] = 'SECRET!'
socketio = SocketIO(app)
uuid_last = ''
data = ''
connexion_genius = GraphQLClient('https://##.###.##/')
connexion_genius.inject_token('Bearer ####','Authorization')
REMOTE_SERVER = "##.###.##"
@app.route('/')
def index():
return render_template('index.html')
def is_connected():
try:
host = socket.gethostbyname(REMOTE_SERVER)
socket.create_connection((host, 80), 2)
return True
except:
pass
return False
def getprofilewithbadge(badge):
tmp = connexion_genius.execute('''{
profiles(where:{badge:"''' + badge + '''"}){
firstName
lastName
}
}
''')
return tmp
def sethello(badge):
tmp = connexion_genius.execute('''mutation{terminalHello(data:{badge:"''' + badge + '''",timeOfArrival:"''' + str(datetime.datetime.now().isoformat()) + '''"}){status}}''')
return tmp
class SerialRead(Thread):
global j
def __init__(self):
Thread.__init__(self)
ports = list(serial.tools.list_ports.comports())
for p in ports:
if "Arduino" in p[1] or "ttyACM0" in p[1]:
print("Arduino detecte sur le port : ", p[0])
self.serial = serial.Serial(str(p[0]), 9600, timeout=1)
socketio.emit('Internet', {'internet': True})
def init_serial(self):
ports = list(serial.tools.list_ports.comports())
self.serial.close()
for p in ports:
if "Arduino" in p[1] or "ttyACM0" in p[1]:
print("Arduino detecte sur le port : ", p[0])
self.serial = serial.Serial(str(p[0]), 9600, timeout=1)
socketio.emit('Internet', {'internet': True})
self.run()
def run(self):
global uuid_last
while True:
try:
if self.serial is not None:
data = self.serial.readline().strip(b'\n\r')
try:
if is_connected():
j = json.loads(data.decode('UTF-8'))
socketio.emit('Internet', {'internet': True})
if "ESTIAM" in j['uuid']:
if uuid_last != j['uuid']:
uuid_last = j['uuid']
try:
reponse = json.loads(sethello(uuid_last))
try:
if len(reponse['errors']) > 0:
socketio.emit('CardFound', {'error':True,'user': None, 'late':False})
except:
if reponse['data']['terminalHello']['status'] == "OK":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "ALREADYBADGED":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "NO_DATE":
profile = json.loads(getprofilewithbadge(uuid_last))
socketio.emit('CardFound', {'error':False,'user': {'firstName': profile['data']['profiles'][0]['firstName'],'lastName': profile['data']['profiles'][0]['lastName'],'late': None}, 'late':False})
if reponse['data']['terminalHello']['status'] == "UNKNOWN_CARD":
socketio.emit('CardFound', {'error':True,'user':False,'late':False})
if reponse['data']['terminalHello']['status'] == "FAILED_SYS_ERROR":
socketio.emit('CardFound', {'error': True, 'user': False, 'late': False})
except:
continue
else:
socketio.emit('Internet', {'internet': False})
except:
continue
except:
socketio.emit('Internet', {'internet': False})
print("La liaison serie ne peut etre etablie")
time.sleep(1)
self.init_serial()
def first(self):
self.run()
if __name__ == '__main__':
ThreadSerial = SerialRead()
ThreadSerial.start()
socketio.run(app,host='0.0.0.0',port=8000) | 0.254602 | 0.086632 |
from pero.properties import *
from pero import Legend, LegendBox
from .. enums import *
from . graphics import InGraphics, OutGraphics
class OutLegend(OutGraphics):
"""
OutLegend provides a wrapper for the pero.LegendBox glyph to draw the chart
legend outside the main data frame.
Properties:
items: (pero.Legend,), None or UNDEF
Specifies a collection of legend items to draw.
static: bool
Specifies whether the legend items are given by user directly (True)
or whether they should be retrieved automatically from parent chart
(False).
position: pero.POSITION_LRTB
Specifies the legend position within a chart as any item from the
pero.POSITION_LRTB enum.
orientation: pero.ORIENTATION
Specifies the legend orientation as any item from the
pero.ORIENTATION enum.
margin: int, float, (int,), (float,) or UNDEF
Specifies the space around the legend box as a single value or
values for individual sides starting from top.
padding: int, float, (int,), (float,) or UNDEF
Specifies the inner space of the legend box as a single value or
values for individual sides starting from top.
spacing: int or float
Specifies the additional space between legend items.
radius: int, float, (int,), (float,) or UNDEF
Specifies the corner radius of the legend box as a single value or
values for individual corners starting from top-left.
line properties:
Includes pero.LineProperties to specify the legend box outline.
fill properties:
Includes pero.FillProperties to specify the legend box fill.
"""
items = TupleProperty(UNDEF, types=(Legend,), dynamic=False, nullable=True)
static = BoolProperty(False, dynamic=False)
position = EnumProperty(POS_RIGHT, enum=POSITION_LRTB, dynamic=False)
orientation = EnumProperty(ORI_VERTICAL, enum=ORIENTATION)
radius = QuadProperty(3, dynamic=False)
padding = QuadProperty(5, dynamic=False)
spacing = NumProperty(5, dynamic=False)
line = Include(LineProperties, line_color="#ddd")
fill = Include(FillProperties, fill_color="#fffc")
def __init__(self, **overrides):
"""Initializes a new instance of the OutLegend."""
# init legend glyph
self._glyph = LegendBox()
# init base
super().__init__(**overrides)
def get_extent(self, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to get amount of
logical space needed to draw the object.
"""
# check if visible
if not self.is_visible(source, overrides):
return 0
# update glyph properties
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# get bbox
bbox = self._glyph.get_bbox(canvas, source, **overrides)
if bbox is None:
return 0
# get extent
position = self.get_property('position', source, overrides)
return bbox.height if position in POSITION_TB else bbox.width
def prepare(self, chart, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to prepare the
object.
"""
# check if static
static = self.get_property('static', source, overrides)
if static:
return
# clean items
self.items = []
# check if visible
if not self.is_visible(source, overrides):
return
# get items from objects
items = []
for obj in chart.graphics:
if isinstance(obj, InGraphics) and obj.visible:
items += obj.get_legends(canvas)
# set new items
self.items = items
def draw(self, canvas, source=UNDEF, **overrides):
"""Uses given canvas to draw the legend."""
# check if visible
if not self.is_visible(source, overrides):
return
# update legend glyph
self._update_glyph(canvas, source, **overrides)
# draw legend
self._glyph.draw(canvas)
def _update_glyph(self, canvas=None, source=UNDEF, **overrides):
"""Updates legend glyph."""
# get properties
frame = self.get_property('frame', source, overrides)
position = self.get_property('position', source, overrides)
# check values
position = position or POS_RIGHT
# get anchor
if position == POS_TOP:
anchor = POS_N
x = frame.cx
y = frame.y1
elif position == POS_RIGHT:
anchor = POS_E
x = frame.x2
y = frame.cy
elif position == POS_BOTTOM:
anchor = POS_S
x = frame.cx
y = frame.y2
elif position == POS_LEFT:
anchor = POS_W
x = frame.x1
y = frame.cy
else:
anchor = POS_E
x = frame.x2
y = frame.cy
# update glyph shared
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# update glyph
self._glyph.anchor = anchor
self._glyph.x = x
self._glyph.y = y
class InLegend(InGraphics):
"""
InLegend provides a wrapper for the pero.LegendBox glyph to draw the
chart legend inside the main data frame.
Properties:
items: (pero.Legend,), None or UNDEF
Specifies a collection of legend items to draw.
static: bool
Specifies whether the legend items are given by user directly (True)
or whether they should be retrieved automatically from parent chart
(False).
position: pero.POSITION_COMPASS
Specifies the legend position within a chart as any item from the
pero.POSITION_COMPASS enum.
orientation: pero.ORIENTATION
Specifies the legend orientation as any item from the
pero.ORIENTATION enum.
margin: int, float, (int,), (float,) or UNDEF
Specifies the space around the legend box as a single value or
values for individual sides starting from top.
padding: int, float, (int,), (float,) or UNDEF
Specifies the inner space of the legend box as a single value or
values for individual sides starting from top.
spacing: int or float
Specifies the additional space between legend items.
radius: int, float, (int,), (float,) or UNDEF
Specifies the corner radius of the legend box as a single value or
values for individual corners starting from top-left.
line properties:
Includes pero.LineProperties to specify the legend box outline.
fill properties:
Includes pero.FillProperties to specify the legend box fill.
"""
items = TupleProperty(UNDEF, types=(Legend,), dynamic=False, nullable=True)
static = BoolProperty(False, dynamic=False)
position = EnumProperty(POS_NE, enum=POSITION_COMPASS, dynamic=False)
orientation = EnumProperty(ORI_VERTICAL, enum=ORIENTATION)
margin = QuadProperty(10, dynamic=False)
radius = QuadProperty(3, dynamic=False)
padding = QuadProperty(5, dynamic=False)
spacing = NumProperty(5, dynamic=False)
line = Include(LineProperties, line_color="#ddd")
fill = Include(FillProperties, fill_color="#fffc")
def __init__(self, **overrides):
"""Initializes a new instance of the InLegend."""
# init legend glyph
self._glyph = LegendBox()
# init base
super().__init__(**overrides)
def prepare(self, chart, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to prepare the
object.
"""
# check if static
static = self.get_property('static', source, overrides)
if static:
return
# clean items
self.items = []
# check if visible
if not self.is_visible(source, overrides):
return
# get items from objects
items = []
for obj in chart.graphics:
if isinstance(obj, InGraphics) and obj.visible:
items += obj.get_legends(canvas)
# set new items
self.items = items
def draw(self, canvas, source=UNDEF, **overrides):
"""Uses given canvas to draw the legend."""
# check if visible
if not self.is_visible(source, overrides):
return
# update legend glyph
self._update_glyph(canvas, source, **overrides)
# draw legend
self._glyph.draw(canvas)
def _update_glyph(self, canvas=None, source=UNDEF, **overrides):
"""Updates legend glyph."""
# get properties
frame = self.get_property('frame', source, overrides)
position = self.get_property('position', source, overrides)
margin = self.get_property('margin', source, overrides)
# check values
position = position or POS_RIGHT
margin = margin or (10, 10, 10, 10)
# set anchor
self._glyph.anchor = position
if position == POS_N:
x = frame.cx
y = frame.y1 + margin[0]
elif position == POS_NE:
x = frame.x2 - margin[1]
y = frame.y1 + margin[0]
elif position == POS_E:
x = frame.x2 - margin[1]
y = frame.cy
elif position == POS_SE:
x = frame.x2 - margin[1]
y = frame.y2 - margin[2]
elif position == POS_S:
x = frame.cx
y = frame.y2 - margin[2]
elif position == POS_SW:
x = frame.x1 + margin[3]
y = frame.y2 - margin[2]
elif position == POS_W:
x = frame.x1 + margin[3]
y = frame.cy
elif position == POS_NW:
x = frame.x1 + margin[3]
y = frame.y1 + margin[0]
elif position == POS_C:
x = frame.cx
y = frame.cy
else:
x = frame.x2 - margin[1]
y = frame.y1 + margin[0]
# update glyph shared
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# update glyph
self._glyph.anchor = position
self._glyph.x = x
self._glyph.y = y | perrot/chart/legends.py |
from pero.properties import *
from pero import Legend, LegendBox
from .. enums import *
from . graphics import InGraphics, OutGraphics
class OutLegend(OutGraphics):
"""
OutLegend provides a wrapper for the pero.LegendBox glyph to draw the chart
legend outside the main data frame.
Properties:
items: (pero.Legend,), None or UNDEF
Specifies a collection of legend items to draw.
static: bool
Specifies whether the legend items are given by user directly (True)
or whether they should be retrieved automatically from parent chart
(False).
position: pero.POSITION_LRTB
Specifies the legend position within a chart as any item from the
pero.POSITION_LRTB enum.
orientation: pero.ORIENTATION
Specifies the legend orientation as any item from the
pero.ORIENTATION enum.
margin: int, float, (int,), (float,) or UNDEF
Specifies the space around the legend box as a single value or
values for individual sides starting from top.
padding: int, float, (int,), (float,) or UNDEF
Specifies the inner space of the legend box as a single value or
values for individual sides starting from top.
spacing: int or float
Specifies the additional space between legend items.
radius: int, float, (int,), (float,) or UNDEF
Specifies the corner radius of the legend box as a single value or
values for individual corners starting from top-left.
line properties:
Includes pero.LineProperties to specify the legend box outline.
fill properties:
Includes pero.FillProperties to specify the legend box fill.
"""
items = TupleProperty(UNDEF, types=(Legend,), dynamic=False, nullable=True)
static = BoolProperty(False, dynamic=False)
position = EnumProperty(POS_RIGHT, enum=POSITION_LRTB, dynamic=False)
orientation = EnumProperty(ORI_VERTICAL, enum=ORIENTATION)
radius = QuadProperty(3, dynamic=False)
padding = QuadProperty(5, dynamic=False)
spacing = NumProperty(5, dynamic=False)
line = Include(LineProperties, line_color="#ddd")
fill = Include(FillProperties, fill_color="#fffc")
def __init__(self, **overrides):
"""Initializes a new instance of the OutLegend."""
# init legend glyph
self._glyph = LegendBox()
# init base
super().__init__(**overrides)
def get_extent(self, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to get amount of
logical space needed to draw the object.
"""
# check if visible
if not self.is_visible(source, overrides):
return 0
# update glyph properties
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# get bbox
bbox = self._glyph.get_bbox(canvas, source, **overrides)
if bbox is None:
return 0
# get extent
position = self.get_property('position', source, overrides)
return bbox.height if position in POSITION_TB else bbox.width
def prepare(self, chart, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to prepare the
object.
"""
# check if static
static = self.get_property('static', source, overrides)
if static:
return
# clean items
self.items = []
# check if visible
if not self.is_visible(source, overrides):
return
# get items from objects
items = []
for obj in chart.graphics:
if isinstance(obj, InGraphics) and obj.visible:
items += obj.get_legends(canvas)
# set new items
self.items = items
def draw(self, canvas, source=UNDEF, **overrides):
"""Uses given canvas to draw the legend."""
# check if visible
if not self.is_visible(source, overrides):
return
# update legend glyph
self._update_glyph(canvas, source, **overrides)
# draw legend
self._glyph.draw(canvas)
def _update_glyph(self, canvas=None, source=UNDEF, **overrides):
"""Updates legend glyph."""
# get properties
frame = self.get_property('frame', source, overrides)
position = self.get_property('position', source, overrides)
# check values
position = position or POS_RIGHT
# get anchor
if position == POS_TOP:
anchor = POS_N
x = frame.cx
y = frame.y1
elif position == POS_RIGHT:
anchor = POS_E
x = frame.x2
y = frame.cy
elif position == POS_BOTTOM:
anchor = POS_S
x = frame.cx
y = frame.y2
elif position == POS_LEFT:
anchor = POS_W
x = frame.x1
y = frame.cy
else:
anchor = POS_E
x = frame.x2
y = frame.cy
# update glyph shared
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# update glyph
self._glyph.anchor = anchor
self._glyph.x = x
self._glyph.y = y
class InLegend(InGraphics):
"""
InLegend provides a wrapper for the pero.LegendBox glyph to draw the
chart legend inside the main data frame.
Properties:
items: (pero.Legend,), None or UNDEF
Specifies a collection of legend items to draw.
static: bool
Specifies whether the legend items are given by user directly (True)
or whether they should be retrieved automatically from parent chart
(False).
position: pero.POSITION_COMPASS
Specifies the legend position within a chart as any item from the
pero.POSITION_COMPASS enum.
orientation: pero.ORIENTATION
Specifies the legend orientation as any item from the
pero.ORIENTATION enum.
margin: int, float, (int,), (float,) or UNDEF
Specifies the space around the legend box as a single value or
values for individual sides starting from top.
padding: int, float, (int,), (float,) or UNDEF
Specifies the inner space of the legend box as a single value or
values for individual sides starting from top.
spacing: int or float
Specifies the additional space between legend items.
radius: int, float, (int,), (float,) or UNDEF
Specifies the corner radius of the legend box as a single value or
values for individual corners starting from top-left.
line properties:
Includes pero.LineProperties to specify the legend box outline.
fill properties:
Includes pero.FillProperties to specify the legend box fill.
"""
items = TupleProperty(UNDEF, types=(Legend,), dynamic=False, nullable=True)
static = BoolProperty(False, dynamic=False)
position = EnumProperty(POS_NE, enum=POSITION_COMPASS, dynamic=False)
orientation = EnumProperty(ORI_VERTICAL, enum=ORIENTATION)
margin = QuadProperty(10, dynamic=False)
radius = QuadProperty(3, dynamic=False)
padding = QuadProperty(5, dynamic=False)
spacing = NumProperty(5, dynamic=False)
line = Include(LineProperties, line_color="#ddd")
fill = Include(FillProperties, fill_color="#fffc")
def __init__(self, **overrides):
"""Initializes a new instance of the InLegend."""
# init legend glyph
self._glyph = LegendBox()
# init base
super().__init__(**overrides)
def prepare(self, chart, canvas, source=UNDEF, **overrides):
"""
This method is automatically called by parent chart to prepare the
object.
"""
# check if static
static = self.get_property('static', source, overrides)
if static:
return
# clean items
self.items = []
# check if visible
if not self.is_visible(source, overrides):
return
# get items from objects
items = []
for obj in chart.graphics:
if isinstance(obj, InGraphics) and obj.visible:
items += obj.get_legends(canvas)
# set new items
self.items = items
def draw(self, canvas, source=UNDEF, **overrides):
"""Uses given canvas to draw the legend."""
# check if visible
if not self.is_visible(source, overrides):
return
# update legend glyph
self._update_glyph(canvas, source, **overrides)
# draw legend
self._glyph.draw(canvas)
def _update_glyph(self, canvas=None, source=UNDEF, **overrides):
"""Updates legend glyph."""
# get properties
frame = self.get_property('frame', source, overrides)
position = self.get_property('position', source, overrides)
margin = self.get_property('margin', source, overrides)
# check values
position = position or POS_RIGHT
margin = margin or (10, 10, 10, 10)
# set anchor
self._glyph.anchor = position
if position == POS_N:
x = frame.cx
y = frame.y1 + margin[0]
elif position == POS_NE:
x = frame.x2 - margin[1]
y = frame.y1 + margin[0]
elif position == POS_E:
x = frame.x2 - margin[1]
y = frame.cy
elif position == POS_SE:
x = frame.x2 - margin[1]
y = frame.y2 - margin[2]
elif position == POS_S:
x = frame.cx
y = frame.y2 - margin[2]
elif position == POS_SW:
x = frame.x1 + margin[3]
y = frame.y2 - margin[2]
elif position == POS_W:
x = frame.x1 + margin[3]
y = frame.cy
elif position == POS_NW:
x = frame.x1 + margin[3]
y = frame.y1 + margin[0]
elif position == POS_C:
x = frame.cx
y = frame.cy
else:
x = frame.x2 - margin[1]
y = frame.y1 + margin[0]
# update glyph shared
self._glyph.set_properties_from(self, source=source, overrides=overrides)
# update glyph
self._glyph.anchor = position
self._glyph.x = x
self._glyph.y = y | 0.910376 | 0.572006 |
from typing import Optional
from pydantic import BaseModel, EmailStr, Field
from enum import Enum
from typing import List
class Gender(str, Enum): # Gender에 들어갈수있는 종류 4개 미리 선언
male = 'male'
female = 'female'
other = 'other'
not_given = 'not_given'
class UserSchema(BaseModel):
# 앱으로부터 받은 유저데이터가 MongoDB에 저장할것인지 알려주는 스키마
# ...은 이 필드가 필수임을 나타낸다. None으로 바꿔도 무방
userid: str = Field(...)
fullname: str = Field(...)
email: EmailStr = Field(...)
gender: Gender = Field(...)
age: int = Field(...,gt=0,lt=100) # gt(greater than)와 lt(less than)는 값이 1~9까지 세팅. 즉, 0, 10, 11 값은 에러 송출
height: int = Field(...)
weight: int = Field(...)
class UserBCM(BaseModel):
# 앱으로부터 받은 MDCW의 측정데이터가 MongoDB에 저장할것인지 알려주는 스키마
userid: str = Field(...) # 매치시킬것은 ID로 하자
bcmdata: str = Field(...) # bcm은 T를 제외한 값들 64byte인가를 받자.
gainPD1: float = Field(...)
gainPD2: float = Field(...)
gainPD3: float = Field(...)
class Config: # 입력되는 데이터가 어떠한 형식으로 되야하는지에 대한 예시
schema_extra = {
"example": {
"userid": "gwangjin",
"bcmdata": "T00DF039600F4h399F087B0C6Bh2310058E0730h2530072909C9h083D01EE0005h1E6F0106000D\nT1DBE07380B09h274808060B9Bh2341056F060Fh2493071909B7h0894021B03B1h1E9700F10089\nT1DC207610A0Fh271407FC0C00h22BF059F0673h247D06F90A28h0842020A0386h1E8500E10186\nT1DC407980B47h265907DA0BFDh229A054D0741h2518073009E9h08AC02240142h1E7B00D800E4\nT1DC407B60BB6h25E208130AE0h21EB055C05B9h249B06B80AA1h098603200119h1F8300FA0021\nT1DCB07710B04h272B07F60C15h232405AC06DFh23EA06FB0B0Fh096502640433h1ED201090180\nT1DCC07500BA6h2789081A0BCBh22B305490779h250907650AAEh08BC02990322h1ECA00E400FF\nT1DC1076C0B72h272708120AA7h22AC05BC06B6h249407070A6Eh08FE02120084h1F25010F0195\nT1DC507150AA3h273D08480AD9h21CC057D0742h248706B50AA2h09C0035101B7h1F6800F0017B\n",
"gainPD1": 10.832,
"gainPD2": 94.15,
"gainPD3": 119.391
}
}
class UserPhysio(BaseModel):
# 서버내에서 인공지능으로 예측한 결과 분석데이터가 MongoDB에 저장할것인지 알려주는 스키마
time: List[float] = Field(...)
mua685: List[float] = Field(...)
mua785: List[float] = Field(...)
mua830: List[float] = Field(...)
mua850: List[float] = Field(...)
mua915: List[float] = Field(...)
mua975: List[float] = Field(...)
mus685: List[float] = Field(...)
mus785: List[float] = Field(...)
mus830: List[float] = Field(...)
mus850: List[float] = Field(...)
mus915: List[float] = Field(...)
mus975: List[float] = Field(...)
hbo2: List[float] = Field(...)
hhb: List[float] = Field(...)
fat: List[float] = Field(...)
water: List[float] = Field(...)
class Config: # 입력되는 데이터가 어떠한 형식으로 되야하는지에 대한 예시
schema_extra = {
"example": {
"userid": "gwangjin",
"time":[1,2],
"mua685": [8.42641683, 1.22293563],
"mua785": [7.08869255, 1.62395152],
"mua830": [7.43068556, 1.67743087],
"mua850": [6.29488418 ,0.93608081],
"mua915": [6.90653301, 1.6422445 ],
"mua975": [6.90653301, 1.6422445 ],
"mus685": [6.90653301, 1.6422445 ],
"mus785": [6.90653301, 1.6422445 ],
"mus830": [6.90653301, 1.6422445 ],
"mus850": [6.90653301, 1.6422445 ],
"mus915": [6.90653301, 1.6422445 ],
"mus975": [6.90653301, 1.6422445 ],
"hbo2": [6.90653301, 1.6422445 ],
"hhb": [6.90653301, 1.6422445 ],
"fat": [6.90653301 ,1.6422445 ],
"water": [6.90653301 ,1.6422445 ],
}
}
class UserIn(UserSchema):
# 앱으로부터 받은 유저데이터가 서버내에서 어떻게 처리될지 알려주는 스키마 여기서는 password 필드를 추가해서 보여준다.
password: str = Field(...)
class Config:
schema_extra = {
"example": {
"userid": "abc123",
"password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 24,
"height": 171,
"weight": 75,
}
}
class UserOut(UserSchema):
pass
class UserInDB(UserSchema):
# 앱으로부터 받은 유저데이터가 MongoDB에 저장될때는 hash함수로 암호화되어 hase_password로 저장시킨다.
hashed_password: str
class Config:
schema_extra = {
"example": {
"userid": "abc123",
"hashed_password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 24,
"height": 171,
"weight": 75,
}
}
class UpdateUserModel(BaseModel):
# 앱으로부터 유저데이터 수정을 요청했을때 받는 서버에서 받을 수 있는 형식
userid: Optional[str]
password: Optional[str]
fullname: Optional[str]
email: Optional[EmailStr]
gender: Optional[str]
birth: Optional[int]
height: Optional[int]
weitht: Optional[int]
class Config:
schema_extra = {
"example": {
"userid": "dcf123",
"password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 25,
"height": 172,
"weight": 75,
}
}
def ResponseModel(data, message):
# 앱에서 호출한 결과를 반환해주는 함수 성공적으로 했을때, 아래와 같다.
return {
"data": [data],
"code": 200,
"message": message,
}
def ResponseModelForBCM(data, message):
# 앱에서 호출한 결과를 반환해주는 함수 성공적으로 했을때, 아래와 같다.
return {
"data": data,
"code": 200,
"message": message,
}
def ErrorResponseModel(error, code, message):
# 앱에서 호출한 결과를 반환해주는 함수 실패로 되었을때, 아래와 같다.
return {"error": error, "code": code, "message": message} | app/server/models/user.py | from typing import Optional
from pydantic import BaseModel, EmailStr, Field
from enum import Enum
from typing import List
class Gender(str, Enum): # Gender에 들어갈수있는 종류 4개 미리 선언
male = 'male'
female = 'female'
other = 'other'
not_given = 'not_given'
class UserSchema(BaseModel):
# 앱으로부터 받은 유저데이터가 MongoDB에 저장할것인지 알려주는 스키마
# ...은 이 필드가 필수임을 나타낸다. None으로 바꿔도 무방
userid: str = Field(...)
fullname: str = Field(...)
email: EmailStr = Field(...)
gender: Gender = Field(...)
age: int = Field(...,gt=0,lt=100) # gt(greater than)와 lt(less than)는 값이 1~9까지 세팅. 즉, 0, 10, 11 값은 에러 송출
height: int = Field(...)
weight: int = Field(...)
class UserBCM(BaseModel):
# 앱으로부터 받은 MDCW의 측정데이터가 MongoDB에 저장할것인지 알려주는 스키마
userid: str = Field(...) # 매치시킬것은 ID로 하자
bcmdata: str = Field(...) # bcm은 T를 제외한 값들 64byte인가를 받자.
gainPD1: float = Field(...)
gainPD2: float = Field(...)
gainPD3: float = Field(...)
class Config: # 입력되는 데이터가 어떠한 형식으로 되야하는지에 대한 예시
schema_extra = {
"example": {
"userid": "gwangjin",
"bcmdata": "T00DF039600F4h399F087B0C6Bh2310058E0730h2530072909C9h083D01EE0005h1E6F0106000D\nT1DBE07380B09h274808060B9Bh2341056F060Fh2493071909B7h0894021B03B1h1E9700F10089\nT1DC207610A0Fh271407FC0C00h22BF059F0673h247D06F90A28h0842020A0386h1E8500E10186\nT1DC407980B47h265907DA0BFDh229A054D0741h2518073009E9h08AC02240142h1E7B00D800E4\nT1DC407B60BB6h25E208130AE0h21EB055C05B9h249B06B80AA1h098603200119h1F8300FA0021\nT1DCB07710B04h272B07F60C15h232405AC06DFh23EA06FB0B0Fh096502640433h1ED201090180\nT1DCC07500BA6h2789081A0BCBh22B305490779h250907650AAEh08BC02990322h1ECA00E400FF\nT1DC1076C0B72h272708120AA7h22AC05BC06B6h249407070A6Eh08FE02120084h1F25010F0195\nT1DC507150AA3h273D08480AD9h21CC057D0742h248706B50AA2h09C0035101B7h1F6800F0017B\n",
"gainPD1": 10.832,
"gainPD2": 94.15,
"gainPD3": 119.391
}
}
class UserPhysio(BaseModel):
# 서버내에서 인공지능으로 예측한 결과 분석데이터가 MongoDB에 저장할것인지 알려주는 스키마
time: List[float] = Field(...)
mua685: List[float] = Field(...)
mua785: List[float] = Field(...)
mua830: List[float] = Field(...)
mua850: List[float] = Field(...)
mua915: List[float] = Field(...)
mua975: List[float] = Field(...)
mus685: List[float] = Field(...)
mus785: List[float] = Field(...)
mus830: List[float] = Field(...)
mus850: List[float] = Field(...)
mus915: List[float] = Field(...)
mus975: List[float] = Field(...)
hbo2: List[float] = Field(...)
hhb: List[float] = Field(...)
fat: List[float] = Field(...)
water: List[float] = Field(...)
class Config: # 입력되는 데이터가 어떠한 형식으로 되야하는지에 대한 예시
schema_extra = {
"example": {
"userid": "gwangjin",
"time":[1,2],
"mua685": [8.42641683, 1.22293563],
"mua785": [7.08869255, 1.62395152],
"mua830": [7.43068556, 1.67743087],
"mua850": [6.29488418 ,0.93608081],
"mua915": [6.90653301, 1.6422445 ],
"mua975": [6.90653301, 1.6422445 ],
"mus685": [6.90653301, 1.6422445 ],
"mus785": [6.90653301, 1.6422445 ],
"mus830": [6.90653301, 1.6422445 ],
"mus850": [6.90653301, 1.6422445 ],
"mus915": [6.90653301, 1.6422445 ],
"mus975": [6.90653301, 1.6422445 ],
"hbo2": [6.90653301, 1.6422445 ],
"hhb": [6.90653301, 1.6422445 ],
"fat": [6.90653301 ,1.6422445 ],
"water": [6.90653301 ,1.6422445 ],
}
}
class UserIn(UserSchema):
# 앱으로부터 받은 유저데이터가 서버내에서 어떻게 처리될지 알려주는 스키마 여기서는 password 필드를 추가해서 보여준다.
password: str = Field(...)
class Config:
schema_extra = {
"example": {
"userid": "abc123",
"password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 24,
"height": 171,
"weight": 75,
}
}
class UserOut(UserSchema):
pass
class UserInDB(UserSchema):
# 앱으로부터 받은 유저데이터가 MongoDB에 저장될때는 hash함수로 암호화되어 hase_password로 저장시킨다.
hashed_password: str
class Config:
schema_extra = {
"example": {
"userid": "abc123",
"hashed_password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 24,
"height": 171,
"weight": 75,
}
}
class UpdateUserModel(BaseModel):
# 앱으로부터 유저데이터 수정을 요청했을때 받는 서버에서 받을 수 있는 형식
userid: Optional[str]
password: Optional[str]
fullname: Optional[str]
email: Optional[EmailStr]
gender: Optional[str]
birth: Optional[int]
height: Optional[int]
weitht: Optional[int]
class Config:
schema_extra = {
"example": {
"userid": "dcf123",
"password": "<PASSWORD>",
"fullname": "<NAME>",
"email": "<EMAIL>",
"gender": "male",
"age": 25,
"height": 172,
"weight": 75,
}
}
def ResponseModel(data, message):
# 앱에서 호출한 결과를 반환해주는 함수 성공적으로 했을때, 아래와 같다.
return {
"data": [data],
"code": 200,
"message": message,
}
def ResponseModelForBCM(data, message):
# 앱에서 호출한 결과를 반환해주는 함수 성공적으로 했을때, 아래와 같다.
return {
"data": data,
"code": 200,
"message": message,
}
def ErrorResponseModel(error, code, message):
# 앱에서 호출한 결과를 반환해주는 함수 실패로 되었을때, 아래와 같다.
return {"error": error, "code": code, "message": message} | 0.549882 | 0.517998 |
import boto3
import click
import json
from operator import itemgetter
from sys import exit
@click.command()
@click.option('-r', '--region', help='AWS region to use')
@click.option('-z', '--availability-zone', multiple=True,
help="Availability Zones to use, use 'all' for all zones, multiple invocations supported, default all",
default=["all"])
@click.option('-t', '--instance-type', multiple=True,
help="Instance types to use, multiple invocations supported, default t3.micro", default=["t3.micro"])
@click.option('--cheapest/--all', default=False)
@click.option('-p', '--profile', help="AWS profile to use")
@click.option('-f', '--format', type=click.Choice(['json', 'text']), help="output format", default='text')
def cli(region, availability_zone, instance_type, format, profile, cheapest):
if profile is not None:
session = boto3.Session(profile_name=profile)
else:
session = boto3.Session()
if region is not None:
ec2 = session.client("ec2", region_name=region)
else:
ec2 = session.client("ec2")
az_query = ec2.describe_availability_zones()
az_available = []
for zone in az_query['AvailabilityZones']:
az_available.append(zone['ZoneName'])
if "all" in availability_zone:
azs = az_available
else:
azs = []
for zone in availability_zone:
zone_name = zone
if len(zone) == 1:
zone_name = "{}{}".format(region, zone)
if zone_name not in az_available:
print("Zone not available: {}, available zones: ".format(zone_name, az_available))
exit(1)
else:
azs.append(zone_name)
results = []
instance_types = instance_type # less ambiguous
for zone in azs:
for instance_type in instance_types:
# get last price
last = ec2.describe_spot_price_history(InstanceTypes=[instance_type], MaxResults=1,
ProductDescriptions=['Linux/UNIX (Amazon VPC)'],
AvailabilityZone=zone)
if len(last['SpotPriceHistory']) == 0:
print("warning, no spot price history for instance type: {}, AZ: {}. Instance type may not"
"be availablein this region.".format(instance_type, zone))
else:
results.append({'az': zone,
'type': instance_type,
'price': float(last['SpotPriceHistory'][-1]['SpotPrice'])})
if len(results) == 0:
print("No results, invalid instance types?")
exit(1)
if cheapest:
output = [sorted(results, key=itemgetter('price'))[0]]
else:
output = sorted(results, key=itemgetter('price'))
if format == "json":
print(json.dumps(output))
elif format == "text":
print("AZ\t\tInstance Type\tSpot Price")
for line in output:
print("{}\t{}\t{}".format(line['az'], line['type'], line['price'])) | spottpreis.py | import boto3
import click
import json
from operator import itemgetter
from sys import exit
@click.command()
@click.option('-r', '--region', help='AWS region to use')
@click.option('-z', '--availability-zone', multiple=True,
help="Availability Zones to use, use 'all' for all zones, multiple invocations supported, default all",
default=["all"])
@click.option('-t', '--instance-type', multiple=True,
help="Instance types to use, multiple invocations supported, default t3.micro", default=["t3.micro"])
@click.option('--cheapest/--all', default=False)
@click.option('-p', '--profile', help="AWS profile to use")
@click.option('-f', '--format', type=click.Choice(['json', 'text']), help="output format", default='text')
def cli(region, availability_zone, instance_type, format, profile, cheapest):
if profile is not None:
session = boto3.Session(profile_name=profile)
else:
session = boto3.Session()
if region is not None:
ec2 = session.client("ec2", region_name=region)
else:
ec2 = session.client("ec2")
az_query = ec2.describe_availability_zones()
az_available = []
for zone in az_query['AvailabilityZones']:
az_available.append(zone['ZoneName'])
if "all" in availability_zone:
azs = az_available
else:
azs = []
for zone in availability_zone:
zone_name = zone
if len(zone) == 1:
zone_name = "{}{}".format(region, zone)
if zone_name not in az_available:
print("Zone not available: {}, available zones: ".format(zone_name, az_available))
exit(1)
else:
azs.append(zone_name)
results = []
instance_types = instance_type # less ambiguous
for zone in azs:
for instance_type in instance_types:
# get last price
last = ec2.describe_spot_price_history(InstanceTypes=[instance_type], MaxResults=1,
ProductDescriptions=['Linux/UNIX (Amazon VPC)'],
AvailabilityZone=zone)
if len(last['SpotPriceHistory']) == 0:
print("warning, no spot price history for instance type: {}, AZ: {}. Instance type may not"
"be availablein this region.".format(instance_type, zone))
else:
results.append({'az': zone,
'type': instance_type,
'price': float(last['SpotPriceHistory'][-1]['SpotPrice'])})
if len(results) == 0:
print("No results, invalid instance types?")
exit(1)
if cheapest:
output = [sorted(results, key=itemgetter('price'))[0]]
else:
output = sorted(results, key=itemgetter('price'))
if format == "json":
print(json.dumps(output))
elif format == "text":
print("AZ\t\tInstance Type\tSpot Price")
for line in output:
print("{}\t{}\t{}".format(line['az'], line['type'], line['price'])) | 0.131898 | 0.094845 |
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS
# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
# OF THE POSSIBILITY OF SUCH DAMAGE.
"""Parsers for the language implementation."""
ALTERNATE = CONCAT = EXP = PROCESS = None
class Parser:
"""Generic parser from which all parsers must inherit."""
def __repr__(self):
return self.repr()
def __add__(self, other):
global CONCAT
if CONCAT is None:
from scripting.parser.combinator import Concat as CONCAT
return CONCAT(self, other)
def __mul__(self, other):
global EXP
if EXP is None:
from scripting.parser.combinator import Exp as EXP
return EXP(self, other)
def __or__(self, other):
global ALTERNATE
if ALTERNATE is None:
from scripting.parser.combinator import Alternate as ALTERNATE
return ALTERNATE(self, other)
def __xor__(self, function):
global PROCESS
if PROCESS is None:
from scripting.parser.combinator import Process as PROCESS
return PROCESS(self, function)
# Display methods
def repr(self, seen=None):
"""Display the given parsers."""
return type(self).__name__
def repr_several(self, connector, *parsers, seen=None):
"""Represent several parsers."""
seen = seen or []
results = []
for parser in parsers:
if parser in seen:
results.append(f"{type(parser).__name__}(...)")
else:
results.append(f"{parser.repr(seen)}")
return connector.join(results) | src/scripting/parser/parser.py |
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS
# BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
# OF THE POSSIBILITY OF SUCH DAMAGE.
"""Parsers for the language implementation."""
ALTERNATE = CONCAT = EXP = PROCESS = None
class Parser:
"""Generic parser from which all parsers must inherit."""
def __repr__(self):
return self.repr()
def __add__(self, other):
global CONCAT
if CONCAT is None:
from scripting.parser.combinator import Concat as CONCAT
return CONCAT(self, other)
def __mul__(self, other):
global EXP
if EXP is None:
from scripting.parser.combinator import Exp as EXP
return EXP(self, other)
def __or__(self, other):
global ALTERNATE
if ALTERNATE is None:
from scripting.parser.combinator import Alternate as ALTERNATE
return ALTERNATE(self, other)
def __xor__(self, function):
global PROCESS
if PROCESS is None:
from scripting.parser.combinator import Process as PROCESS
return PROCESS(self, function)
# Display methods
def repr(self, seen=None):
"""Display the given parsers."""
return type(self).__name__
def repr_several(self, connector, *parsers, seen=None):
"""Represent several parsers."""
seen = seen or []
results = []
for parser in parsers:
if parser in seen:
results.append(f"{type(parser).__name__}(...)")
else:
results.append(f"{parser.repr(seen)}")
return connector.join(results) | 0.680666 | 0.055643 |
from ..DB.Repositorio_Grado_De_Ocupacion_Por_Plazas_INE import RepositoryGradoOcupacionPlazasINE as DBRepository
from ..Utilidades.Conversores import Conversores as Conversor
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_anio(Ciudad, Anio):
"""
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año
:param Ciudad: Ciudad
:type Ciudad: str
:param Anio: Anio
:type Anio: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnAnio(Ciudad, Anio)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_anio_mensualmente(Ciudad, Anio):
"""
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año dividido por meses
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año dividido por meses
:param Ciudad: Ciudad
:type Ciudad: str
:param Anio: Anio
:type Anio: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnAnioMensualmente(Ciudad, Anio)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios(Ciudad, AnioInicio, AnioFin):
"""
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAnios(Ciudad, AnioInicio, AnioFin)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios_mensualmente(Ciudad, AnioInicio, AnioFin):
"""
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años mensualmente
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años mensualmente
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAnios(Ciudad, AnioInicio, AnioFin)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios_en_mes(Ciudad, AnioInicio, AnioFin, Mes):
"""
Dado una ciudad, un mes y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años en ese mes
Dado una ciudad, un mes y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años en ese mes
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:param Mes: Mes
:type Mes: str
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAniosEnMes(Ciudad, AnioInicio, AnioFin, Mes)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval | controllers/grado de ocupacion por plazas ine_controller.py | from ..DB.Repositorio_Grado_De_Ocupacion_Por_Plazas_INE import RepositoryGradoOcupacionPlazasINE as DBRepository
from ..Utilidades.Conversores import Conversores as Conversor
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_anio(Ciudad, Anio):
"""
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año
:param Ciudad: Ciudad
:type Ciudad: str
:param Anio: Anio
:type Anio: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnAnio(Ciudad, Anio)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_anio_mensualmente(Ciudad, Anio):
"""
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año dividido por meses
Dado una ciudad y un año obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en ese año dividido por meses
:param Ciudad: Ciudad
:type Ciudad: str
:param Anio: Anio
:type Anio: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnAnioMensualmente(Ciudad, Anio)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios(Ciudad, AnioInicio, AnioFin):
"""
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAnios(Ciudad, AnioInicio, AnioFin)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios_mensualmente(Ciudad, AnioInicio, AnioFin):
"""
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años mensualmente
Dado una ciudad y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años mensualmente
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAnios(Ciudad, AnioInicio, AnioFin)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval
def obtener_grado_de_ocupacion_por_tanto_por_cien_por_plazas_en_ciudad_en_rango_anios_en_mes(Ciudad, AnioInicio, AnioFin, Mes):
"""
Dado una ciudad, un mes y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años en ese mes
Dado una ciudad, un mes y un rango de años obtiene el grado de ocupacion por tanto por cien por plazas en dicha ciudad en esos años en ese mes
:param Ciudad: Ciudad
:type Ciudad: str
:param AnioInicio: Anio Inicio
:type AnioInicio: int
:param AnioFin: Anio Fin
:type AnioFin: int
:param Mes: Mes
:type Mes: str
:rtype: None
"""
conversor = Conversor()
repository = DBRepository()
cursor, labels = repository.ObtenerPorcentajeDelGradoDeOcupacionPorPlazasEnCiudadEnRangoAniosEnMes(Ciudad, AnioInicio, AnioFin, Mes)
arrayTuplas = conversor.ConvertirCursorToTuplas(cursor)
##Mostrar JSON Extendido
matriz, lista = conversor.ConvertirTuplasToMatriz(arrayTuplas, labels)
retval = conversor.ObtenerDataJSONExtendido(matriz)
return retval | 0.398992 | 0.577495 |
from __future__ import unicode_literals
from django.http import Http404
from django.contrib.contenttypes.models import ContentType
from django.contrib.auth.models import Permission
from django.contrib.auth import get_user_model
from django.contrib.auth.models import Group
from rolepermissions.exceptions import RoleDoesNotExist
from rolepermissions.roles import RolesManager
def get_permission(permission_name):
user_ct = ContentType.objects.get_for_model(get_user_model())
permission, created = Permission.objects.get_or_create(content_type=user_ct,
codename=permission_name)
return permission
def get_user_role(user):
if user:
roles = user.groups.filter(name__in=RolesManager.get_roles_names())
if roles:
return RolesManager.retrieve_role(roles[0].name)
return None
def available_perm_status(user):
role = get_user_role(user)
permissions = UserPermission.objects.filter(user=user)
permissions = { p.permission_name: p for p in permissions }
user_permissions = []
if role:
for permission_name in role.permission_names_list():
if permission_name in permissions:
permission = permissions[permission_name]
else:
permission = UserPermission(user=user,
permission_name=permission_name,
is_granted=role.get_default(permission_name))
permission.save()
user_permissions.append(permission)
permission_hash = { p.permission_name: p.is_granted for p in user_permissions }
return permission_hash
def grant_permission(user, permission_name):
role = get_user_role(user)
if role and permission_name in role.permission_names_list():
permission = get_permission(permission_name)
user.user_permissions.add(permission)
return True
return False
def revoke_permission(user, permission_name):
role = get_user_role(user)
if role and permission_name in role.permission_names_list():
permission = get_permission(permission_name)
user.user_permissions.remove(permission)
return True
return False
def retrieve_role(role_name):
return RolesManager.retrieve_role(role_name) | rolepermissions/shortcuts.py | from __future__ import unicode_literals
from django.http import Http404
from django.contrib.contenttypes.models import ContentType
from django.contrib.auth.models import Permission
from django.contrib.auth import get_user_model
from django.contrib.auth.models import Group
from rolepermissions.exceptions import RoleDoesNotExist
from rolepermissions.roles import RolesManager
def get_permission(permission_name):
user_ct = ContentType.objects.get_for_model(get_user_model())
permission, created = Permission.objects.get_or_create(content_type=user_ct,
codename=permission_name)
return permission
def get_user_role(user):
if user:
roles = user.groups.filter(name__in=RolesManager.get_roles_names())
if roles:
return RolesManager.retrieve_role(roles[0].name)
return None
def available_perm_status(user):
role = get_user_role(user)
permissions = UserPermission.objects.filter(user=user)
permissions = { p.permission_name: p for p in permissions }
user_permissions = []
if role:
for permission_name in role.permission_names_list():
if permission_name in permissions:
permission = permissions[permission_name]
else:
permission = UserPermission(user=user,
permission_name=permission_name,
is_granted=role.get_default(permission_name))
permission.save()
user_permissions.append(permission)
permission_hash = { p.permission_name: p.is_granted for p in user_permissions }
return permission_hash
def grant_permission(user, permission_name):
role = get_user_role(user)
if role and permission_name in role.permission_names_list():
permission = get_permission(permission_name)
user.user_permissions.add(permission)
return True
return False
def revoke_permission(user, permission_name):
role = get_user_role(user)
if role and permission_name in role.permission_names_list():
permission = get_permission(permission_name)
user.user_permissions.remove(permission)
return True
return False
def retrieve_role(role_name):
return RolesManager.retrieve_role(role_name) | 0.444565 | 0.092237 |
from __future__ import absolute_import, division, print_function
import operator
import bisect
from . import DDesc, Capabilities
def cat_descriptor_iter(ddlist):
for i, dd in enumerate(ddlist):
for el in dd:
yield el
class Cat_DDesc(DDesc):
"""
A Blaze data descriptor which concatenates a list
of data descriptors, all of which have the same
dshape after the first dimension.
This presently doesn't support leading dimensions
whose size is unknown (i.e. streaming dimensions).
"""
def __init__(self, ddlist):
if len(ddlist) <= 1:
raise ValueError('Need at least 2 data descriptors to concatenate')
for dd in ddlist:
if not isinstance(dd, DDesc):
raise ValueError('Provided ddlist has an element '
'which is not a data descriptor')
self._ddlist = ddlist
self._dshape = ds.cat_dshapes([dd.dshape for dd in ddlist])
self._ndim = len(self._dshape[:]) - 1
# Create a list of boundary indices
boundary_index = [0]
for dd in ddlist:
dim_size = operator.index(dd.dshape[0])
boundary_index.append(dim_size + boundary_index[-1])
self._boundary_index = boundary_index
@property
def dshape(self):
return self._dshape
@property
def capabilities(self):
"""The capabilities for the cat data descriptor."""
return Capabilities(
immutable = True,
deferred = True,
# persistency is not supported yet
persistent = False,
appendable = False,
remote = False,
)
def __len__(self):
return self._boundary_index[-1]
def __getitem__(self, key):
if not isinstance(key, tuple):
key = (key,)
# Just integer indices (no slices) for now
boundary_index = self._boundary_index
dim_size = boundary_index[-1]
# TODO: Handle a slice in key[0] too!
idx0 = operator.index(key[0])
# Determine which data descriptor in the list to use
if idx0 >= 0:
if idx0 >= dim_size:
raise IndexError(('Index %d is out of range '
'in dimension sized %d') % (idx0, dim_size))
else:
if idx0 < -dim_size:
raise IndexError(('Index %d is out of range '
'in dimension sized %d') % (idx0, dim_size))
idx0 += dim_size
i = bisect.bisect_right(boundary_index, idx0) - 1
# Call the i-th data descriptor to get the result
return self._ddlist[i][(idx0 - boundary_index[i],) + key[1:]]
def __iter__(self):
return cat_descriptor_iter(self._ddlist) | blaze/datadescriptor/cat_data_descriptor.py | from __future__ import absolute_import, division, print_function
import operator
import bisect
from . import DDesc, Capabilities
def cat_descriptor_iter(ddlist):
for i, dd in enumerate(ddlist):
for el in dd:
yield el
class Cat_DDesc(DDesc):
"""
A Blaze data descriptor which concatenates a list
of data descriptors, all of which have the same
dshape after the first dimension.
This presently doesn't support leading dimensions
whose size is unknown (i.e. streaming dimensions).
"""
def __init__(self, ddlist):
if len(ddlist) <= 1:
raise ValueError('Need at least 2 data descriptors to concatenate')
for dd in ddlist:
if not isinstance(dd, DDesc):
raise ValueError('Provided ddlist has an element '
'which is not a data descriptor')
self._ddlist = ddlist
self._dshape = ds.cat_dshapes([dd.dshape for dd in ddlist])
self._ndim = len(self._dshape[:]) - 1
# Create a list of boundary indices
boundary_index = [0]
for dd in ddlist:
dim_size = operator.index(dd.dshape[0])
boundary_index.append(dim_size + boundary_index[-1])
self._boundary_index = boundary_index
@property
def dshape(self):
return self._dshape
@property
def capabilities(self):
"""The capabilities for the cat data descriptor."""
return Capabilities(
immutable = True,
deferred = True,
# persistency is not supported yet
persistent = False,
appendable = False,
remote = False,
)
def __len__(self):
return self._boundary_index[-1]
def __getitem__(self, key):
if not isinstance(key, tuple):
key = (key,)
# Just integer indices (no slices) for now
boundary_index = self._boundary_index
dim_size = boundary_index[-1]
# TODO: Handle a slice in key[0] too!
idx0 = operator.index(key[0])
# Determine which data descriptor in the list to use
if idx0 >= 0:
if idx0 >= dim_size:
raise IndexError(('Index %d is out of range '
'in dimension sized %d') % (idx0, dim_size))
else:
if idx0 < -dim_size:
raise IndexError(('Index %d is out of range '
'in dimension sized %d') % (idx0, dim_size))
idx0 += dim_size
i = bisect.bisect_right(boundary_index, idx0) - 1
# Call the i-th data descriptor to get the result
return self._ddlist[i][(idx0 - boundary_index[i],) + key[1:]]
def __iter__(self):
return cat_descriptor_iter(self._ddlist) | 0.637031 | 0.313433 |
from typing import List, Optional, Union
import numpy as np
import pandas as pd
import pytest
import scipy.sparse as sps
import tabmat as tm
from tabmat import from_pandas
from tabmat.constructor import _split_sparse_and_dense_parts
from tabmat.dense_matrix import DenseMatrix
from tabmat.ext.sparse import csr_dense_sandwich
from tabmat.split_matrix import SplitMatrix
N = 100
def make_X() -> np.ndarray:
X = np.zeros((N, 4))
X[:, 0] = 1.0
X[:10, 1] = 0.5
X[-20:, 2] = 0.25
X[:, 3] = 2.0
return X
@pytest.fixture
def X() -> np.ndarray:
return make_X()
def test_csc_to_split(X: np.ndarray):
for T, D, S in [(0.05, 4, 0), (0.1, 3, 1), (0.2, 2, 2), (0.3, 2, 2), (1.0, 0, 4)]:
dense, sparse, dense_ix, sparse_ix = _split_sparse_and_dense_parts(
sps.csc_matrix(X), T
)
fully_dense = SplitMatrix([dense, sparse], [dense_ix, sparse_ix])
if S == 0:
assert fully_dense.indices[0].shape[0] == D
assert len(fully_dense.indices) == 1
elif D == 0:
assert fully_dense.indices[0].shape[0] == S
assert len(fully_dense.indices) == 1
else:
assert fully_dense.indices[0].shape[0] == D
assert fully_dense.indices[1].shape[0] == S
def split_mat() -> SplitMatrix:
X = make_X()
threshold = 0.1
cat_mat = tm.CategoricalMatrix(np.random.choice(range(4), X.shape[0]))
dense, sparse, dense_ix, sparse_ix = _split_sparse_and_dense_parts(
sps.csc_matrix(X), threshold
)
cat_start = 1 + max(dense_ix.max(), sparse_ix.max())
mat = SplitMatrix(
[dense, sparse, cat_mat],
[dense_ix, sparse_ix, range(cat_start, cat_start + cat_mat.shape[1])],
)
return mat
def get_split_with_cat_components() -> List[
Union[tm.SparseMatrix, tm.DenseMatrix, tm.CategoricalMatrix]
]:
n_rows = 10
np.random.seed(0)
dense_1 = tm.DenseMatrix(np.random.random((n_rows, 3)))
sparse_1 = tm.SparseMatrix(sps.random(n_rows, 3).tocsc())
cat = tm.CategoricalMatrix(np.random.choice(range(3), n_rows))
dense_2 = tm.DenseMatrix(np.random.random((n_rows, 3)))
sparse_2 = tm.SparseMatrix(sps.random(n_rows, 3, density=0.5).tocsc())
cat_2 = tm.CategoricalMatrix(np.random.choice(range(3), n_rows), drop_first=True)
return [dense_1, sparse_1, cat, dense_2, sparse_2, cat_2]
def split_with_cat() -> SplitMatrix:
"""Initialized with multiple sparse and dense parts and no indices."""
return tm.SplitMatrix(get_split_with_cat_components())
def split_with_cat_64() -> SplitMatrix:
mat = tm.SplitMatrix(get_split_with_cat_components())
matrices = mat.matrices
for i, mat_ in enumerate(mat.matrices):
if isinstance(mat_, tm.SparseMatrix):
matrices[i] = tm.SparseMatrix(
(
mat_.data,
mat_.indices.astype(np.int64),
mat_.indptr.astype(np.int64),
),
shape=mat_.shape,
)
elif isinstance(mat_, tm.DenseMatrix):
matrices[i] = mat_.astype(np.float64)
return tm.SplitMatrix(matrices, mat.indices)
@pytest.mark.parametrize("mat", [split_with_cat(), split_with_cat_64()])
def test_init(mat: SplitMatrix):
assert len(mat.indices) == 4
assert len(mat.matrices) == 4
assert (mat.indices[0] == np.concatenate([np.arange(3), np.arange(9, 12)])).all()
assert mat.matrices[0].shape == (10, 6)
assert mat.matrices[1].shape == (10, 6)
assert mat.matrices[2].shape == (10, 3)
def test_init_unsorted_indices():
dense = tm.DenseMatrix(np.random.random((10, 3)))
with pytest.raises(ValueError):
tm.SplitMatrix([dense], [[1, 0, 2]])
@pytest.mark.parametrize(
"Acols", [np.arange(2, dtype=np.int32), np.array([1], dtype=np.int32)]
)
@pytest.mark.parametrize(
"Bcols",
[
np.arange(4, dtype=np.int32),
np.array([1], dtype=np.int32),
np.array([1, 3], dtype=np.int32),
],
)
def test_sandwich_sparse_dense(X: np.ndarray, Acols, Bcols):
np.random.seed(0)
n, k = X.shape
d = np.random.random((n,))
A = sps.random(n, 2).tocsr()
rows = np.arange(d.shape[0], dtype=np.int32)
result = csr_dense_sandwich(A, X, d, rows, Acols, Bcols)
expected = A.T.A[Acols, :] @ np.diag(d) @ X[:, Bcols]
np.testing.assert_allclose(result, expected)
# TODO: ensure cols are in order
@pytest.mark.parametrize("mat", [split_mat(), split_with_cat(), split_with_cat_64()])
@pytest.mark.parametrize(
"cols",
[None, [0], [1, 2, 3], [1, 5]],
)
def test_sandwich(mat: tm.SplitMatrix, cols):
for _ in range(10):
v = np.random.rand(mat.shape[0])
y1 = mat.sandwich(v, cols=cols)
mat_limited = mat.A if cols is None else mat.A[:, cols]
y2 = (mat_limited.T * v[None, :]) @ mat_limited
np.testing.assert_allclose(y1, y2, atol=1e-12)
@pytest.mark.parametrize("mat", [split_mat(), split_with_cat(), split_with_cat_64()])
@pytest.mark.parametrize("cols", [None, [0], [1, 2, 3], [1, 5]])
def test_split_col_subsets(mat: tm.SplitMatrix, cols):
subset_cols_indices, subset_cols, n_cols = mat._split_col_subsets(cols)
n_cols_correct = mat.shape[1] if cols is None else len(cols)
def _get_lengths(vec_list: List[Optional[np.ndarray]]):
return (
mat_.shape[1] if v is None else len(v)
for v, mat_ in zip(vec_list, mat.matrices)
)
assert n_cols == n_cols_correct
assert sum(_get_lengths(subset_cols_indices)) == n_cols
assert sum(_get_lengths(subset_cols)) == n_cols
if cols is not None:
cols = np.asarray(cols)
for i in range(len(mat.indices)):
if cols is not None:
assert (
mat.indices[i][subset_cols[i]] == cols[subset_cols_indices[i]]
).all()
else:
assert subset_cols[i] is None
assert (mat.indices[i] == subset_cols_indices[i]).all()
def random_split_matrix(seed=0, n_rows=10, n_cols_per=3):
if seed is not None:
np.random.seed(seed)
dense_1 = tm.DenseMatrix(np.random.random((n_rows, n_cols_per)))
sparse = tm.SparseMatrix(sps.random(n_rows, n_cols_per).tocsc())
cat = tm.CategoricalMatrix(np.random.choice(range(n_cols_per), n_rows))
dense_2 = tm.DenseMatrix(np.random.random((n_rows, n_cols_per)))
cat_2 = tm.CategoricalMatrix(np.random.choice(range(n_cols_per), n_rows))
mat = tm.SplitMatrix([dense_1, sparse, cat, dense_2, cat_2])
return mat
def many_random_tests(checker):
for i in range(10):
mat = random_split_matrix(
seed=(1 if i == 0 else None),
n_rows=np.random.randint(130),
n_cols_per=1 + np.random.randint(10),
)
checker(mat)
def test_sandwich_many_types():
def check(mat):
d = np.random.random(mat.shape[0])
res = mat.sandwich(d)
expected = (mat.A.T * d[None, :]) @ mat.A
np.testing.assert_allclose(res, expected)
many_random_tests(check)
def test_transpose_matvec_many_types():
def check(mat):
d = np.random.random(mat.shape[0])
res = mat.transpose_matvec(d)
expected = mat.A.T.dot(d)
np.testing.assert_almost_equal(res, expected)
many_random_tests(check)
def test_matvec_many_types():
def check(mat):
d = np.random.random(mat.shape[1])
res = mat.matvec(d)
expected = mat.A.dot(d)
np.testing.assert_almost_equal(res, expected)
many_random_tests(check)
def test_init_from_1d():
m1 = DenseMatrix(np.arange(10, dtype=float))
m2 = DenseMatrix(np.ones(shape=(10, 2), dtype=float))
res = SplitMatrix([m1, m2])
assert res.shape == (10, 3)
@pytest.mark.parametrize("n_rows", [5, 10, 25])
def test_matvec(n_rows):
np.random.seed(1234)
n_cols = 2
categories = [f"cat_{val}" for val in range(5)]
X = pd.DataFrame(np.random.choice(categories, size=(n_rows, n_cols))).astype(
"category"
)
mat = from_pandas(X, cat_threshold=0)
np.testing.assert_allclose(mat.matvec(np.array(mat.shape[1] * [1])), n_cols) | tests/test_split_matrix.py | from typing import List, Optional, Union
import numpy as np
import pandas as pd
import pytest
import scipy.sparse as sps
import tabmat as tm
from tabmat import from_pandas
from tabmat.constructor import _split_sparse_and_dense_parts
from tabmat.dense_matrix import DenseMatrix
from tabmat.ext.sparse import csr_dense_sandwich
from tabmat.split_matrix import SplitMatrix
N = 100
def make_X() -> np.ndarray:
X = np.zeros((N, 4))
X[:, 0] = 1.0
X[:10, 1] = 0.5
X[-20:, 2] = 0.25
X[:, 3] = 2.0
return X
@pytest.fixture
def X() -> np.ndarray:
return make_X()
def test_csc_to_split(X: np.ndarray):
for T, D, S in [(0.05, 4, 0), (0.1, 3, 1), (0.2, 2, 2), (0.3, 2, 2), (1.0, 0, 4)]:
dense, sparse, dense_ix, sparse_ix = _split_sparse_and_dense_parts(
sps.csc_matrix(X), T
)
fully_dense = SplitMatrix([dense, sparse], [dense_ix, sparse_ix])
if S == 0:
assert fully_dense.indices[0].shape[0] == D
assert len(fully_dense.indices) == 1
elif D == 0:
assert fully_dense.indices[0].shape[0] == S
assert len(fully_dense.indices) == 1
else:
assert fully_dense.indices[0].shape[0] == D
assert fully_dense.indices[1].shape[0] == S
def split_mat() -> SplitMatrix:
X = make_X()
threshold = 0.1
cat_mat = tm.CategoricalMatrix(np.random.choice(range(4), X.shape[0]))
dense, sparse, dense_ix, sparse_ix = _split_sparse_and_dense_parts(
sps.csc_matrix(X), threshold
)
cat_start = 1 + max(dense_ix.max(), sparse_ix.max())
mat = SplitMatrix(
[dense, sparse, cat_mat],
[dense_ix, sparse_ix, range(cat_start, cat_start + cat_mat.shape[1])],
)
return mat
def get_split_with_cat_components() -> List[
Union[tm.SparseMatrix, tm.DenseMatrix, tm.CategoricalMatrix]
]:
n_rows = 10
np.random.seed(0)
dense_1 = tm.DenseMatrix(np.random.random((n_rows, 3)))
sparse_1 = tm.SparseMatrix(sps.random(n_rows, 3).tocsc())
cat = tm.CategoricalMatrix(np.random.choice(range(3), n_rows))
dense_2 = tm.DenseMatrix(np.random.random((n_rows, 3)))
sparse_2 = tm.SparseMatrix(sps.random(n_rows, 3, density=0.5).tocsc())
cat_2 = tm.CategoricalMatrix(np.random.choice(range(3), n_rows), drop_first=True)
return [dense_1, sparse_1, cat, dense_2, sparse_2, cat_2]
def split_with_cat() -> SplitMatrix:
"""Initialized with multiple sparse and dense parts and no indices."""
return tm.SplitMatrix(get_split_with_cat_components())
def split_with_cat_64() -> SplitMatrix:
mat = tm.SplitMatrix(get_split_with_cat_components())
matrices = mat.matrices
for i, mat_ in enumerate(mat.matrices):
if isinstance(mat_, tm.SparseMatrix):
matrices[i] = tm.SparseMatrix(
(
mat_.data,
mat_.indices.astype(np.int64),
mat_.indptr.astype(np.int64),
),
shape=mat_.shape,
)
elif isinstance(mat_, tm.DenseMatrix):
matrices[i] = mat_.astype(np.float64)
return tm.SplitMatrix(matrices, mat.indices)
@pytest.mark.parametrize("mat", [split_with_cat(), split_with_cat_64()])
def test_init(mat: SplitMatrix):
assert len(mat.indices) == 4
assert len(mat.matrices) == 4
assert (mat.indices[0] == np.concatenate([np.arange(3), np.arange(9, 12)])).all()
assert mat.matrices[0].shape == (10, 6)
assert mat.matrices[1].shape == (10, 6)
assert mat.matrices[2].shape == (10, 3)
def test_init_unsorted_indices():
dense = tm.DenseMatrix(np.random.random((10, 3)))
with pytest.raises(ValueError):
tm.SplitMatrix([dense], [[1, 0, 2]])
@pytest.mark.parametrize(
"Acols", [np.arange(2, dtype=np.int32), np.array([1], dtype=np.int32)]
)
@pytest.mark.parametrize(
"Bcols",
[
np.arange(4, dtype=np.int32),
np.array([1], dtype=np.int32),
np.array([1, 3], dtype=np.int32),
],
)
def test_sandwich_sparse_dense(X: np.ndarray, Acols, Bcols):
np.random.seed(0)
n, k = X.shape
d = np.random.random((n,))
A = sps.random(n, 2).tocsr()
rows = np.arange(d.shape[0], dtype=np.int32)
result = csr_dense_sandwich(A, X, d, rows, Acols, Bcols)
expected = A.T.A[Acols, :] @ np.diag(d) @ X[:, Bcols]
np.testing.assert_allclose(result, expected)
# TODO: ensure cols are in order
@pytest.mark.parametrize("mat", [split_mat(), split_with_cat(), split_with_cat_64()])
@pytest.mark.parametrize(
"cols",
[None, [0], [1, 2, 3], [1, 5]],
)
def test_sandwich(mat: tm.SplitMatrix, cols):
for _ in range(10):
v = np.random.rand(mat.shape[0])
y1 = mat.sandwich(v, cols=cols)
mat_limited = mat.A if cols is None else mat.A[:, cols]
y2 = (mat_limited.T * v[None, :]) @ mat_limited
np.testing.assert_allclose(y1, y2, atol=1e-12)
@pytest.mark.parametrize("mat", [split_mat(), split_with_cat(), split_with_cat_64()])
@pytest.mark.parametrize("cols", [None, [0], [1, 2, 3], [1, 5]])
def test_split_col_subsets(mat: tm.SplitMatrix, cols):
subset_cols_indices, subset_cols, n_cols = mat._split_col_subsets(cols)
n_cols_correct = mat.shape[1] if cols is None else len(cols)
def _get_lengths(vec_list: List[Optional[np.ndarray]]):
return (
mat_.shape[1] if v is None else len(v)
for v, mat_ in zip(vec_list, mat.matrices)
)
assert n_cols == n_cols_correct
assert sum(_get_lengths(subset_cols_indices)) == n_cols
assert sum(_get_lengths(subset_cols)) == n_cols
if cols is not None:
cols = np.asarray(cols)
for i in range(len(mat.indices)):
if cols is not None:
assert (
mat.indices[i][subset_cols[i]] == cols[subset_cols_indices[i]]
).all()
else:
assert subset_cols[i] is None
assert (mat.indices[i] == subset_cols_indices[i]).all()
def random_split_matrix(seed=0, n_rows=10, n_cols_per=3):
if seed is not None:
np.random.seed(seed)
dense_1 = tm.DenseMatrix(np.random.random((n_rows, n_cols_per)))
sparse = tm.SparseMatrix(sps.random(n_rows, n_cols_per).tocsc())
cat = tm.CategoricalMatrix(np.random.choice(range(n_cols_per), n_rows))
dense_2 = tm.DenseMatrix(np.random.random((n_rows, n_cols_per)))
cat_2 = tm.CategoricalMatrix(np.random.choice(range(n_cols_per), n_rows))
mat = tm.SplitMatrix([dense_1, sparse, cat, dense_2, cat_2])
return mat
def many_random_tests(checker):
for i in range(10):
mat = random_split_matrix(
seed=(1 if i == 0 else None),
n_rows=np.random.randint(130),
n_cols_per=1 + np.random.randint(10),
)
checker(mat)
def test_sandwich_many_types():
def check(mat):
d = np.random.random(mat.shape[0])
res = mat.sandwich(d)
expected = (mat.A.T * d[None, :]) @ mat.A
np.testing.assert_allclose(res, expected)
many_random_tests(check)
def test_transpose_matvec_many_types():
def check(mat):
d = np.random.random(mat.shape[0])
res = mat.transpose_matvec(d)
expected = mat.A.T.dot(d)
np.testing.assert_almost_equal(res, expected)
many_random_tests(check)
def test_matvec_many_types():
def check(mat):
d = np.random.random(mat.shape[1])
res = mat.matvec(d)
expected = mat.A.dot(d)
np.testing.assert_almost_equal(res, expected)
many_random_tests(check)
def test_init_from_1d():
m1 = DenseMatrix(np.arange(10, dtype=float))
m2 = DenseMatrix(np.ones(shape=(10, 2), dtype=float))
res = SplitMatrix([m1, m2])
assert res.shape == (10, 3)
@pytest.mark.parametrize("n_rows", [5, 10, 25])
def test_matvec(n_rows):
np.random.seed(1234)
n_cols = 2
categories = [f"cat_{val}" for val in range(5)]
X = pd.DataFrame(np.random.choice(categories, size=(n_rows, n_cols))).astype(
"category"
)
mat = from_pandas(X, cat_threshold=0)
np.testing.assert_allclose(mat.matvec(np.array(mat.shape[1] * [1])), n_cols) | 0.777215 | 0.729086 |
from utils.env import EnvStore
import os
import json
import pymqi
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# function to establish connection to MQ Queue Manager
def connect():
logger.info('Establising Connection with MQ Server')
try:
cd = None
if not EnvStore.ccdtCheck():
logger.info('CCDT URL export is not set, will be using json envrionment client connections settings')
cd = pymqi.CD(Version=pymqi.CMQXC.MQCD_VERSION_11)
cd.ChannelName = MQDetails[EnvStore.CHANNEL]
cd.ConnectionName = conn_info
cd.ChannelType = pymqi.CMQC.MQCHT_CLNTCONN
cd.TransportType = pymqi.CMQC.MQXPT_TCP
logger.info('Checking Cypher details')
# If a cipher is set then set the TLS settings
if MQDetails[EnvStore.CIPHER]:
logger.info('Making use of Cypher details')
cd.SSLCipherSpec = MQDetails[EnvStore.CIPHER]
# Key repository is not specified in CCDT so look in envrionment settings
# Create an empty SCO object
sco = pymqi.SCO()
if MQDetails[EnvStore.KEY_REPOSITORY]:
logger.info('Setting Key repository')
sco.KeyRepository = MQDetails[EnvStore.KEY_REPOSITORY]
#options = pymqi.CMQC.MQPMO_NO_SYNCPOINT | pymqi.CMQC.MQPMO_NEW_MSG_ID | pymqi.CMQC.MQPMO_NEW_CORREL_ID
options = pymqi.CMQC.MQPMO_NEW_CORREL_ID
qmgr = pymqi.QueueManager(None)
qmgr.connect_with_options(MQDetails[EnvStore.QMGR],
user=credentials[EnvStore.USER],
password=credentials[EnvStore.PASSWORD],
opts=options, cd=cd, sco=sco)
return qmgr
except pymqi.MQMIError as e:
logger.error("Error connecting")
logger.error(e)
return None
# function to establish connection to Topic
def getSubscription():
logger.info('Connecting to Subscription')
try:
sub_desc = pymqi.SD()
sub_desc["Options"] = pymqi.CMQC.MQSO_CREATE + pymqi.CMQC.MQSO_RESUME + \
pymqi.CMQC.MQSO_DURABLE + pymqi.CMQC.MQSO_MANAGED
sub_desc.set_vs("SubName", "MySub")
sub_desc.set_vs("ObjectString", MQDetails[EnvStore.TOPIC_NAME])
sub = pymqi.Subscription(qmgr)
sub.sub(sub_desc=sub_desc)
return sub
except pymqi.MQMIError as e:
logger.error("Error getting queue")
logger.error(e)
return None
# function to get messages from subscription
def getMessages():
logger.info('Attempting gets from Subscription')
subOptions = pymqi.CMQC.MQGMO_NO_SYNCPOINT + \
pymqi.CMQC.MQGMO_FAIL_IF_QUIESCING + \
pymqi.CMQC.MQGMO_WAIT + \
pymqi.CMQC.MQGMO_NO_PROPERTIES
gmo = pymqi.GMO(Options=subOptions)
gmo["WaitInterval"] = 30 * 1000
# Message Descriptor
md = pymqi.MD()
keep_running = True
while keep_running:
try:
# Reset the MsgId, CorrelId & GroupId so that we can reuse
# the same 'md' object again.
md.MsgId = pymqi.CMQC.MQMI_NONE
md.CorrelId = pymqi.CMQC.MQCI_NONE
md.GroupId = pymqi.CMQC.MQGI_NONE
#message = subscription.get(None, pymqi.md(), gmo)
message = subscription.get(None, md, gmo)
# Process the message here..
msgObject = json.loads(message.decode())
logger.info('Have message from Queue')
logger.info(msgObject)
except pymqi.MQMIError as e:
if e.comp == pymqi.CMQC.MQCC_FAILED and e.reason == pymqi.CMQC.MQRC_NO_MSG_AVAILABLE:
# No messages, that's OK, we can ignore it.
pass
else:
# Some other error condition.
raise
except (UnicodeDecodeError, ValueError) as e:
logger.info('Message is not valid json')
logger.info(e)
logger.info(message)
continue
except KeyboardInterrupt:
logger.info('Have received a keyboard interrupt')
keep_running = False
def buildMQDetails():
for key in [EnvStore.QMGR, EnvStore.TOPIC_NAME, EnvStore.CHANNEL, EnvStore.HOST,
EnvStore.PORT, EnvStore.KEY_REPOSITORY, EnvStore.CIPHER]:
MQDetails[key] = EnvStore.getEnvValue(key)
# Application Logic starts here
logger.info("Application is Starting")
envStore = EnvStore()
envStore.setEnv()
MQDetails = {}
credentials = {
EnvStore.USER: EnvStore.getEnvValue(EnvStore.APP_USER),
EnvStore.PASSWORD: EnvStore.getEnvValue(EnvStore.APP_PASSWORD)
}
buildMQDetails()
logger.info('Credentials are set')
#logger.info(credentials)
#conn_info = "%s(%s)" % (MQDetails[EnvStore.HOST], MQDetails[EnvStore.PORT])
conn_info = EnvStore.getConnection(EnvStore.HOST, EnvStore.PORT)
qmgr = None
subscription = None
qmgr = connect()
if (qmgr):
subscription = getSubscription()
if (subscription):
getMessages()
subscription.close(
sub_close_options=pymqi.CMQC.MQCO_KEEP_SUB, close_sub_queue=True)
if (qmgr):
qmgr.disconnect()
logger.info("Application is closing") | Python/basicsubscribe.py | from utils.env import EnvStore
import os
import json
import pymqi
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# function to establish connection to MQ Queue Manager
def connect():
logger.info('Establising Connection with MQ Server')
try:
cd = None
if not EnvStore.ccdtCheck():
logger.info('CCDT URL export is not set, will be using json envrionment client connections settings')
cd = pymqi.CD(Version=pymqi.CMQXC.MQCD_VERSION_11)
cd.ChannelName = MQDetails[EnvStore.CHANNEL]
cd.ConnectionName = conn_info
cd.ChannelType = pymqi.CMQC.MQCHT_CLNTCONN
cd.TransportType = pymqi.CMQC.MQXPT_TCP
logger.info('Checking Cypher details')
# If a cipher is set then set the TLS settings
if MQDetails[EnvStore.CIPHER]:
logger.info('Making use of Cypher details')
cd.SSLCipherSpec = MQDetails[EnvStore.CIPHER]
# Key repository is not specified in CCDT so look in envrionment settings
# Create an empty SCO object
sco = pymqi.SCO()
if MQDetails[EnvStore.KEY_REPOSITORY]:
logger.info('Setting Key repository')
sco.KeyRepository = MQDetails[EnvStore.KEY_REPOSITORY]
#options = pymqi.CMQC.MQPMO_NO_SYNCPOINT | pymqi.CMQC.MQPMO_NEW_MSG_ID | pymqi.CMQC.MQPMO_NEW_CORREL_ID
options = pymqi.CMQC.MQPMO_NEW_CORREL_ID
qmgr = pymqi.QueueManager(None)
qmgr.connect_with_options(MQDetails[EnvStore.QMGR],
user=credentials[EnvStore.USER],
password=credentials[EnvStore.PASSWORD],
opts=options, cd=cd, sco=sco)
return qmgr
except pymqi.MQMIError as e:
logger.error("Error connecting")
logger.error(e)
return None
# function to establish connection to Topic
def getSubscription():
logger.info('Connecting to Subscription')
try:
sub_desc = pymqi.SD()
sub_desc["Options"] = pymqi.CMQC.MQSO_CREATE + pymqi.CMQC.MQSO_RESUME + \
pymqi.CMQC.MQSO_DURABLE + pymqi.CMQC.MQSO_MANAGED
sub_desc.set_vs("SubName", "MySub")
sub_desc.set_vs("ObjectString", MQDetails[EnvStore.TOPIC_NAME])
sub = pymqi.Subscription(qmgr)
sub.sub(sub_desc=sub_desc)
return sub
except pymqi.MQMIError as e:
logger.error("Error getting queue")
logger.error(e)
return None
# function to get messages from subscription
def getMessages():
logger.info('Attempting gets from Subscription')
subOptions = pymqi.CMQC.MQGMO_NO_SYNCPOINT + \
pymqi.CMQC.MQGMO_FAIL_IF_QUIESCING + \
pymqi.CMQC.MQGMO_WAIT + \
pymqi.CMQC.MQGMO_NO_PROPERTIES
gmo = pymqi.GMO(Options=subOptions)
gmo["WaitInterval"] = 30 * 1000
# Message Descriptor
md = pymqi.MD()
keep_running = True
while keep_running:
try:
# Reset the MsgId, CorrelId & GroupId so that we can reuse
# the same 'md' object again.
md.MsgId = pymqi.CMQC.MQMI_NONE
md.CorrelId = pymqi.CMQC.MQCI_NONE
md.GroupId = pymqi.CMQC.MQGI_NONE
#message = subscription.get(None, pymqi.md(), gmo)
message = subscription.get(None, md, gmo)
# Process the message here..
msgObject = json.loads(message.decode())
logger.info('Have message from Queue')
logger.info(msgObject)
except pymqi.MQMIError as e:
if e.comp == pymqi.CMQC.MQCC_FAILED and e.reason == pymqi.CMQC.MQRC_NO_MSG_AVAILABLE:
# No messages, that's OK, we can ignore it.
pass
else:
# Some other error condition.
raise
except (UnicodeDecodeError, ValueError) as e:
logger.info('Message is not valid json')
logger.info(e)
logger.info(message)
continue
except KeyboardInterrupt:
logger.info('Have received a keyboard interrupt')
keep_running = False
def buildMQDetails():
for key in [EnvStore.QMGR, EnvStore.TOPIC_NAME, EnvStore.CHANNEL, EnvStore.HOST,
EnvStore.PORT, EnvStore.KEY_REPOSITORY, EnvStore.CIPHER]:
MQDetails[key] = EnvStore.getEnvValue(key)
# Application Logic starts here
logger.info("Application is Starting")
envStore = EnvStore()
envStore.setEnv()
MQDetails = {}
credentials = {
EnvStore.USER: EnvStore.getEnvValue(EnvStore.APP_USER),
EnvStore.PASSWORD: EnvStore.getEnvValue(EnvStore.APP_PASSWORD)
}
buildMQDetails()
logger.info('Credentials are set')
#logger.info(credentials)
#conn_info = "%s(%s)" % (MQDetails[EnvStore.HOST], MQDetails[EnvStore.PORT])
conn_info = EnvStore.getConnection(EnvStore.HOST, EnvStore.PORT)
qmgr = None
subscription = None
qmgr = connect()
if (qmgr):
subscription = getSubscription()
if (subscription):
getMessages()
subscription.close(
sub_close_options=pymqi.CMQC.MQCO_KEEP_SUB, close_sub_queue=True)
if (qmgr):
qmgr.disconnect()
logger.info("Application is closing") | 0.373876 | 0.079353 |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Course',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course_name', models.CharField(max_length=200)),
('course_description', models.CharField(max_length=200)),
('course_created_datetime', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='User',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('instructor', models.BooleanField(default=False)),
('user_created_datetime', models.DateTimeField(auto_now_add=True)),
('user_updated_datetime', models.DateTimeField(auto_now=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Test',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('test_title', models.CharField(max_length=200)),
('test_description', models.CharField(max_length=200)),
('test_due_date', models.DateField()),
('content_created_datetime', models.DateTimeField(auto_now_add=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
],
),
migrations.CreateModel(
name='Content',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('content_title', models.CharField(max_length=200)),
('content_description', models.CharField(max_length=200)),
('content_created_datetime', models.DateTimeField(auto_now_add=True)),
('content_updated_datetime', models.DateTimeField(auto_now=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
],
),
] | courses/migrations/0001_initial.py |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Course',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course_name', models.CharField(max_length=200)),
('course_description', models.CharField(max_length=200)),
('course_created_datetime', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='User',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('instructor', models.BooleanField(default=False)),
('user_created_datetime', models.DateTimeField(auto_now_add=True)),
('user_updated_datetime', models.DateTimeField(auto_now=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Test',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('test_title', models.CharField(max_length=200)),
('test_description', models.CharField(max_length=200)),
('test_due_date', models.DateField()),
('content_created_datetime', models.DateTimeField(auto_now_add=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
],
),
migrations.CreateModel(
name='Content',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('content_title', models.CharField(max_length=200)),
('content_description', models.CharField(max_length=200)),
('content_created_datetime', models.DateTimeField(auto_now_add=True)),
('content_updated_datetime', models.DateTimeField(auto_now=True)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.course')),
],
),
] | 0.551574 | 0.150903 |
__author__ = "<NAME>"
__email__ = "schmidt89 at informatik.uni-marburg.de"
from androlyze.log.Log import log
from androlyze.storage.exception import StorageException
class ImportStorageInterface:
'''
Interface for the import storage
'''
def create_entry_for_apk(self, apk, update = False, tag = None):
''' Create an entry for the `apk`.
Will also update the path, if the file
is already present in the database and has the same hash
(at least if `update`).
Parameters
----------
apk : Apk
update : bool, optional (default is False)
Update an `apk` that has already been imported.
tag : str, optional (default is None)
Tag the apk with some text.
Raises
------
StorageException
'''
raise NotImplementedError
def create_entry_for_apks(self, apks, update, tag = None):
''' Create entry for the `apks`.
Parameters
----------
apk: iterable<Apk>
update : bool
Update apks that have already been imported.
tag : str, optional (default is None)
Tag the apk with some text.
'''
for apk in apks:
try:
self.create_entry_for_apk(apk, update, tag)
except StorageException as e:
log.warn(e)
def delete_entry_for_apk(self, apk, delete_apk = False):
''' Delete the entry for `apk`.
Parameters
----------
apk: Apk
delete_apk : boolean, optional (default is False)
If true, also delete the .apk file from the file system
(but only if it is in the storage directory!).
Raises
------
StorageException
'''
raise NotImplementedError
def contains(self, apk):
''' Check if the `apk` is present in the storage.
Parameters
----------
apk: Apk
Returns
-------
bool
'''
raise NotImplementedError | androlyze/storage/ImportStorageInterface.py |
__author__ = "<NAME>"
__email__ = "schmidt89 at informatik.uni-marburg.de"
from androlyze.log.Log import log
from androlyze.storage.exception import StorageException
class ImportStorageInterface:
'''
Interface for the import storage
'''
def create_entry_for_apk(self, apk, update = False, tag = None):
''' Create an entry for the `apk`.
Will also update the path, if the file
is already present in the database and has the same hash
(at least if `update`).
Parameters
----------
apk : Apk
update : bool, optional (default is False)
Update an `apk` that has already been imported.
tag : str, optional (default is None)
Tag the apk with some text.
Raises
------
StorageException
'''
raise NotImplementedError
def create_entry_for_apks(self, apks, update, tag = None):
''' Create entry for the `apks`.
Parameters
----------
apk: iterable<Apk>
update : bool
Update apks that have already been imported.
tag : str, optional (default is None)
Tag the apk with some text.
'''
for apk in apks:
try:
self.create_entry_for_apk(apk, update, tag)
except StorageException as e:
log.warn(e)
def delete_entry_for_apk(self, apk, delete_apk = False):
''' Delete the entry for `apk`.
Parameters
----------
apk: Apk
delete_apk : boolean, optional (default is False)
If true, also delete the .apk file from the file system
(but only if it is in the storage directory!).
Raises
------
StorageException
'''
raise NotImplementedError
def contains(self, apk):
''' Check if the `apk` is present in the storage.
Parameters
----------
apk: Apk
Returns
-------
bool
'''
raise NotImplementedError | 0.63409 | 0.253185 |
from git import Repo, db
import os.path
import re
import sys
import glob
from parser_java_kotlin import Parser
from pathlib import Path
from tqdm import tqdm
class ChangedMethodsFinder:
file_extension = {'java': '.*.java', 'kotlin':'.*.kt'}
def __init__(self, path='.'):
self.repo = None
self.path = path
self.code_a = ''
self.code_b = ''
def collect_code_from_commit(self, diff_file, commit_step):
try:
return self.repo.git.show('{}:{}'.format(commit_step, diff_file)).split('\n')
except Exception:
return['error']
def is_match_lang_ext(self, filename):
return (re.match(self.file_extension['java'], filename) or re.match(self.file_extension['kotlin'], filename))
def collect_modified_files_last_two_commits(self, commits = ["HEAD", "HEAD~1"]):
commit_dev = self.repo.commit(commits[0])
commit_origin_dev = self.repo.commit(commits[1])
diff_index = commit_origin_dev.diff(commit_dev)
diff_files = []
for diff_item in diff_index.iter_change_type('M'):
diff_files.append(diff_item.b_path)
if len(diff_files) > 20:
return []
diff_files = [f for f in diff_files if self.is_match_lang_ext(f) and not re.search('auto_generated', f)]
return diff_files
def remove_tabs(self, code):
code = list(filter(lambda x: not (x.strip()[:2] == '//'), code))
code = '\n'.join(code)
code = re.sub(' +', ' ', code)
return re.sub('\t+', '', code)
def open_repo(self, path='.'):
try:
self.repo = Repo(path, odbt=db.GitDB)
except Exception:
print("Check path to repository. Maybe, you should write path in double quotes\"\"")
def code_fragment(self, bounds, code):
if not bounds:
return ''
if bounds[1]<= bounds[0]:
return ''
return ''.join(code)[bounds[0]: bounds[1]]
def get_method_info(self, ast):
methods_info = ast.get_method_names_and_bounds()
methods_info = dict(methods_info)
return methods_info
def compare_ast(self, ast_a, ast_b, diff_file):
methods_info_a = self.get_method_info(ast_a)
methods_info_b = self.get_method_info(ast_b)
all_methods = list(methods_info_a.keys()) + list(methods_info_b.keys())
changed_methods = set()
for method in all_methods:
if method in methods_info_a and method in methods_info_b:
method_code_a = self.code_fragment(methods_info_a[method][0], self.codes_a[diff_file])
method_code_b = self.code_fragment(methods_info_b[method][0], self.codes_b[diff_file])
if method_code_a != method_code_b:
changed_methods.add((method, methods_info_a[method][1]))
if method in methods_info_a and not (method in methods_info_b):
changed_methods.add((method, methods_info_a[method][1]))
return changed_methods
def get_code(self, diff_file, commit):
code = self.collect_code_from_commit(diff_file, commit)
code = self.remove_tabs(code)
return code
def construct_ast(self, code, language='java', diff_file=''):
parser = Parser(language)
ast = parser.parse(code, diff_file)
return ast
def find_changed_methods_by_language(self, language='java', diff_files=[], commits=["HEAD", "HEAD~1"]):
self.trees_a, self.trees_b = dict(), dict()
self.codes_a, self.codes_b = dict(), dict()
all_changed_methods = set()
for diff_file in diff_files:
self.codes_a[diff_file] = self.get_code(diff_file, commits[0])
self.codes_b[diff_file] = self.get_code(diff_file, commits[1])
self.trees_a[diff_file] = self.construct_ast(self.codes_a[diff_file], language, diff_file)
self.trees_b[diff_file] = self.construct_ast(self.codes_b[diff_file], language, diff_file)
all_changed_methods = all_changed_methods.union(self.compare_ast(self.trees_a[diff_file],
self.trees_b[diff_file], diff_file))
return all_changed_methods
def find_changed_methods(self, path='.', commits = ["HEAD", "HEAD~1"]):
self.open_repo(path)
diff_files = self.collect_modified_files_last_two_commits(commits)
java_changed_methods = self.find_changed_methods_by_language('java', diff_files, commits)
kotlin_changed_methods = self.find_changed_methods_by_language('kotlin', diff_files, commits)
return java_changed_methods.union(kotlin_changed_methods)
if __name__ == "__main__":
path = '.'
if len(sys.argv) > 1:
path = sys.argv[1]
cmf = ChangedMethodsFinder()
commits = ['ecdd37cc44f9beb6870c78c3432b1fddcdab8292~1','ecdd37cc44f9beb6870c78c3432b1fddcdab8292']
print(cmf.find_changed_methods(path, commits)) | data_aggregation/get_java_methods.py |
from git import Repo, db
import os.path
import re
import sys
import glob
from parser_java_kotlin import Parser
from pathlib import Path
from tqdm import tqdm
class ChangedMethodsFinder:
file_extension = {'java': '.*.java', 'kotlin':'.*.kt'}
def __init__(self, path='.'):
self.repo = None
self.path = path
self.code_a = ''
self.code_b = ''
def collect_code_from_commit(self, diff_file, commit_step):
try:
return self.repo.git.show('{}:{}'.format(commit_step, diff_file)).split('\n')
except Exception:
return['error']
def is_match_lang_ext(self, filename):
return (re.match(self.file_extension['java'], filename) or re.match(self.file_extension['kotlin'], filename))
def collect_modified_files_last_two_commits(self, commits = ["HEAD", "HEAD~1"]):
commit_dev = self.repo.commit(commits[0])
commit_origin_dev = self.repo.commit(commits[1])
diff_index = commit_origin_dev.diff(commit_dev)
diff_files = []
for diff_item in diff_index.iter_change_type('M'):
diff_files.append(diff_item.b_path)
if len(diff_files) > 20:
return []
diff_files = [f for f in diff_files if self.is_match_lang_ext(f) and not re.search('auto_generated', f)]
return diff_files
def remove_tabs(self, code):
code = list(filter(lambda x: not (x.strip()[:2] == '//'), code))
code = '\n'.join(code)
code = re.sub(' +', ' ', code)
return re.sub('\t+', '', code)
def open_repo(self, path='.'):
try:
self.repo = Repo(path, odbt=db.GitDB)
except Exception:
print("Check path to repository. Maybe, you should write path in double quotes\"\"")
def code_fragment(self, bounds, code):
if not bounds:
return ''
if bounds[1]<= bounds[0]:
return ''
return ''.join(code)[bounds[0]: bounds[1]]
def get_method_info(self, ast):
methods_info = ast.get_method_names_and_bounds()
methods_info = dict(methods_info)
return methods_info
def compare_ast(self, ast_a, ast_b, diff_file):
methods_info_a = self.get_method_info(ast_a)
methods_info_b = self.get_method_info(ast_b)
all_methods = list(methods_info_a.keys()) + list(methods_info_b.keys())
changed_methods = set()
for method in all_methods:
if method in methods_info_a and method in methods_info_b:
method_code_a = self.code_fragment(methods_info_a[method][0], self.codes_a[diff_file])
method_code_b = self.code_fragment(methods_info_b[method][0], self.codes_b[diff_file])
if method_code_a != method_code_b:
changed_methods.add((method, methods_info_a[method][1]))
if method in methods_info_a and not (method in methods_info_b):
changed_methods.add((method, methods_info_a[method][1]))
return changed_methods
def get_code(self, diff_file, commit):
code = self.collect_code_from_commit(diff_file, commit)
code = self.remove_tabs(code)
return code
def construct_ast(self, code, language='java', diff_file=''):
parser = Parser(language)
ast = parser.parse(code, diff_file)
return ast
def find_changed_methods_by_language(self, language='java', diff_files=[], commits=["HEAD", "HEAD~1"]):
self.trees_a, self.trees_b = dict(), dict()
self.codes_a, self.codes_b = dict(), dict()
all_changed_methods = set()
for diff_file in diff_files:
self.codes_a[diff_file] = self.get_code(diff_file, commits[0])
self.codes_b[diff_file] = self.get_code(diff_file, commits[1])
self.trees_a[diff_file] = self.construct_ast(self.codes_a[diff_file], language, diff_file)
self.trees_b[diff_file] = self.construct_ast(self.codes_b[diff_file], language, diff_file)
all_changed_methods = all_changed_methods.union(self.compare_ast(self.trees_a[diff_file],
self.trees_b[diff_file], diff_file))
return all_changed_methods
def find_changed_methods(self, path='.', commits = ["HEAD", "HEAD~1"]):
self.open_repo(path)
diff_files = self.collect_modified_files_last_two_commits(commits)
java_changed_methods = self.find_changed_methods_by_language('java', diff_files, commits)
kotlin_changed_methods = self.find_changed_methods_by_language('kotlin', diff_files, commits)
return java_changed_methods.union(kotlin_changed_methods)
if __name__ == "__main__":
path = '.'
if len(sys.argv) > 1:
path = sys.argv[1]
cmf = ChangedMethodsFinder()
commits = ['ecdd37cc44f9beb6870c78c3432b1fddcdab8292~1','ecdd37cc44f9beb6870c78c3432b1fddcdab8292']
print(cmf.find_changed_methods(path, commits)) | 0.233794 | 0.137851 |
from webparser.youtube import api
from django.db import transaction
from django.utils import timezone
from . import models
from core import models as core_models
def video__get(youtube_id):
try:
return models.Video.objects.get(youtube_id = youtube_id)
except models.Video.DoesNotExist:
return None
def channel__get_or_create(youtube_id, title):
obj, create = models.Channel.objects.get_or_create(
youtube_id = youtube_id,
defaults = {
'title': title,
}
)
return obj
def search(query, limit):
timestamp = timezone.now()
videos_id = api.search(query, limit)
with transaction.atomic():
search = models.Search.objects.create(
query = query,
timestamp = timestamp,
)
for index, video_id in enumerate(videos_id):
try:
if not video__get(video_id):
rank = index + 1
d = api.get_video_info(video_id)
channel = channel__get_or_create(d['user_id'], d['user_username'])
video = models.Video.objects.create(
youtube_id = video_id,
title = d['title'],
publish_date = d['publish_date'],
channel = channel,
category = d['category'],
license = d['license'],
view_count = d['view_count'],
likes = d['likes'],
dislikes = d['dislikes'],
description_text = d['description_text'],
description_html = d['description_html'],
url = core_models.URL.objects.create(
url = d['_url'],
timestamp = timestamp,
content = d['_response_obj'].html.html
)
)
models.VideoSearch.objects.create(
video_id = video,
search_id = search,
rank = rank,
)
except Exception:
print('Video ID: {}'.format(video_id))
raise | httpserver/youtube/api.py | from webparser.youtube import api
from django.db import transaction
from django.utils import timezone
from . import models
from core import models as core_models
def video__get(youtube_id):
try:
return models.Video.objects.get(youtube_id = youtube_id)
except models.Video.DoesNotExist:
return None
def channel__get_or_create(youtube_id, title):
obj, create = models.Channel.objects.get_or_create(
youtube_id = youtube_id,
defaults = {
'title': title,
}
)
return obj
def search(query, limit):
timestamp = timezone.now()
videos_id = api.search(query, limit)
with transaction.atomic():
search = models.Search.objects.create(
query = query,
timestamp = timestamp,
)
for index, video_id in enumerate(videos_id):
try:
if not video__get(video_id):
rank = index + 1
d = api.get_video_info(video_id)
channel = channel__get_or_create(d['user_id'], d['user_username'])
video = models.Video.objects.create(
youtube_id = video_id,
title = d['title'],
publish_date = d['publish_date'],
channel = channel,
category = d['category'],
license = d['license'],
view_count = d['view_count'],
likes = d['likes'],
dislikes = d['dislikes'],
description_text = d['description_text'],
description_html = d['description_html'],
url = core_models.URL.objects.create(
url = d['_url'],
timestamp = timestamp,
content = d['_response_obj'].html.html
)
)
models.VideoSearch.objects.create(
video_id = video,
search_id = search,
rank = rank,
)
except Exception:
print('Video ID: {}'.format(video_id))
raise | 0.375477 | 0.089415 |
from __future__ import print_function
# ------------------------------------------------------------------------------------------------
# Copyright (c) 2016 Microsoft Corporation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish, distribute,
# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or
# substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ------------------------------------------------------------------------------------------------
# Tutorial sample #1: Run simple mission
from builtins import range
import MalmoPython
from past.utils import old_div
import os
import sys
import time
import json
import tkinter as tk
import math
CANVAS_WIDTH = 390
CANVAS_HEIGHT = 540
ZERO_X = 11
ZERO_Y = -2
visited_list = []
def blockX(x):
act_x = math.floor(ZERO_X - x)
return act_x * 30
def blockY(y):
act_y = math.floor(y) - ZERO_Y
return (CANVAS_HEIGHT - act_y * 30) - 30
root = tk.Tk()
root.wm_title("Agent Tracker")
canvas = tk.Canvas(root, width=CANVAS_WIDTH, height=CANVAS_HEIGHT, borderwidth=0, highlightthickness=0, bg="black")
canvas.pack()
root.update()
def updateBlocks(xpos, ypos, stone):
canvas.delete('all')
if stone:
current_block = (blockX(xpos), blockY(ypos), blockX(xpos)+30, blockY(ypos)+30)
if current_block not in visited_list:
visited_list.append(current_block)
for block in visited_list:
canvas.create_rectangle(block[0], block[1], block[2], block[3], fill="grey")
canvas.create_rectangle(180, 420, 210, 450, fill="yellow")
canvas.create_rectangle(180, 90, 210, 120, fill="blue")
real_x = (ZERO_X - x) * 30
real_y = (CANVAS_HEIGHT - (y - ZERO_Y) * 30)
canvas.create_oval(real_x - 10, real_y - 10, real_x + 10, real_y + 10, fill="red")
#print("Block at: ", blockX(xpos), blockY(ypos), blockX(xpos)+30, blockY(ypos)+30)
root.update()
if sys.version_info[0] == 2:
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0) # flush print output immediately
else:
import functools
print = functools.partial(print, flush=True)
# Create default Malmo objects:
agent_host = MalmoPython.AgentHost()
try:
agent_host.parse( sys.argv )
except RuntimeError as e:
print('ERROR:',e)
print(agent_host.getUsage())
exit(1)
if agent_host.receivedArgument("help"):
print(agent_host.getUsage())
exit(0)
mission_file = './bridging.xml'
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission_record = MalmoPython.MissionRecordSpec()
# Attempt to start a mission:
max_retries = 3
for retry in range(max_retries):
try:
agent_host.startMission( my_mission, my_mission_record )
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:",e)
exit(1)
else:
time.sleep(2)
# Loop until mission starts:
print("Waiting for the mission to start ", end=' ')
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:",error.text)
print()
print("Mission running ", end=' ')
# Loop until mission ends:
while world_state.is_mission_running:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
if world_state.number_of_observations_since_last_state > 0: # Have any observations come in?
msg = world_state.observations[-1].text # Yes, so get the text
observations = json.loads(msg) # and parse the JSON
grid = observations['floor3x3']
distance = observations['distanceFromend']
x = observations["XPos"]
y = observations["ZPos"]
updateBlocks(x, y, grid[4]=="stone")
#print(distance)
#print(grid)
for error in world_state.errors:
print("Error:",error.text)
print()
print("Mission ended")
# Mission has ended. | Python_Examples/bridging.py | from __future__ import print_function
# ------------------------------------------------------------------------------------------------
# Copyright (c) 2016 Microsoft Corporation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge, publish, distribute,
# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or
# substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# ------------------------------------------------------------------------------------------------
# Tutorial sample #1: Run simple mission
from builtins import range
import MalmoPython
from past.utils import old_div
import os
import sys
import time
import json
import tkinter as tk
import math
CANVAS_WIDTH = 390
CANVAS_HEIGHT = 540
ZERO_X = 11
ZERO_Y = -2
visited_list = []
def blockX(x):
act_x = math.floor(ZERO_X - x)
return act_x * 30
def blockY(y):
act_y = math.floor(y) - ZERO_Y
return (CANVAS_HEIGHT - act_y * 30) - 30
root = tk.Tk()
root.wm_title("Agent Tracker")
canvas = tk.Canvas(root, width=CANVAS_WIDTH, height=CANVAS_HEIGHT, borderwidth=0, highlightthickness=0, bg="black")
canvas.pack()
root.update()
def updateBlocks(xpos, ypos, stone):
canvas.delete('all')
if stone:
current_block = (blockX(xpos), blockY(ypos), blockX(xpos)+30, blockY(ypos)+30)
if current_block not in visited_list:
visited_list.append(current_block)
for block in visited_list:
canvas.create_rectangle(block[0], block[1], block[2], block[3], fill="grey")
canvas.create_rectangle(180, 420, 210, 450, fill="yellow")
canvas.create_rectangle(180, 90, 210, 120, fill="blue")
real_x = (ZERO_X - x) * 30
real_y = (CANVAS_HEIGHT - (y - ZERO_Y) * 30)
canvas.create_oval(real_x - 10, real_y - 10, real_x + 10, real_y + 10, fill="red")
#print("Block at: ", blockX(xpos), blockY(ypos), blockX(xpos)+30, blockY(ypos)+30)
root.update()
if sys.version_info[0] == 2:
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', 0) # flush print output immediately
else:
import functools
print = functools.partial(print, flush=True)
# Create default Malmo objects:
agent_host = MalmoPython.AgentHost()
try:
agent_host.parse( sys.argv )
except RuntimeError as e:
print('ERROR:',e)
print(agent_host.getUsage())
exit(1)
if agent_host.receivedArgument("help"):
print(agent_host.getUsage())
exit(0)
mission_file = './bridging.xml'
with open(mission_file, 'r') as f:
print("Loading mission from %s" % mission_file)
mission_xml = f.read()
my_mission = MalmoPython.MissionSpec(mission_xml, True)
my_mission_record = MalmoPython.MissionRecordSpec()
# Attempt to start a mission:
max_retries = 3
for retry in range(max_retries):
try:
agent_host.startMission( my_mission, my_mission_record )
break
except RuntimeError as e:
if retry == max_retries - 1:
print("Error starting mission:",e)
exit(1)
else:
time.sleep(2)
# Loop until mission starts:
print("Waiting for the mission to start ", end=' ')
world_state = agent_host.getWorldState()
while not world_state.has_mission_begun:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
for error in world_state.errors:
print("Error:",error.text)
print()
print("Mission running ", end=' ')
# Loop until mission ends:
while world_state.is_mission_running:
print(".", end="")
time.sleep(0.1)
world_state = agent_host.getWorldState()
if world_state.number_of_observations_since_last_state > 0: # Have any observations come in?
msg = world_state.observations[-1].text # Yes, so get the text
observations = json.loads(msg) # and parse the JSON
grid = observations['floor3x3']
distance = observations['distanceFromend']
x = observations["XPos"]
y = observations["ZPos"]
updateBlocks(x, y, grid[4]=="stone")
#print(distance)
#print(grid)
for error in world_state.errors:
print("Error:",error.text)
print()
print("Mission ended")
# Mission has ended. | 0.357007 | 0.162546 |
from __future__ import print_function
import sys
import time
import Pyro4
import bench
if sys.version_info < (3, 0):
input = raw_input
uri = input("Uri of benchmark server? ").strip()
object = Pyro4.core.Proxy(uri)
object._pyroBind()
assert "oneway" in object._pyroOneway # make sure this method is indeed marked as @oneway
def f1():
_ = object.length('<NAME> Jong')
def f2():
_ = object.timestwo(21)
def f3():
_ = object.bigreply()
def f4():
_ = object.manyargs(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
def f5():
_ = object.noreply(99993333)
def f6():
_ = object.varargs('een', 2, (3,), [4])
def f7():
_ = object.keywords(arg1='zork')
def f8():
_ = object.echo('een', 2, (3,), [4])
def f9():
_ = object.meth1('stringetje')
def fa():
_ = object.meth2('stringetje')
def fb():
_ = object.meth3('stringetje')
def fc():
_ = object.meth4('stringetje')
def fd():
_ = object.bigarg('Argument' * 50)
def fe():
object.oneway('stringetje', 432423434, 9.8765432)
def ff():
_ = object.mapping({"aap": 42, "noot": 99, "mies": 987654})
funcs = (f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff)
print('-------- BENCHMARK REMOTE OBJECT ---------')
print('Pay attention to the "fe" test -- this is a Oneway call and should be *fast*')
print('(if you are running the server and client on different machines)')
begin = time.time()
iters = 1000
for f in funcs:
sys.stdout.write("%d times %s " % (iters, f.__name__))
voor = time.time()
for i in range(iters):
f()
sys.stdout.write("%.3f\n" % (time.time() - voor))
sys.stdout.flush()
duration = time.time() - begin
print('total time %.3f seconds' % duration)
amount = len(funcs) * iters
print('total method calls: %d' % (amount))
avg_pyro_msec = 1000.0 * duration / amount
print('avg. time per method call: %.3f msec (%d/sec) (serializer: %s)' % (avg_pyro_msec, amount / duration, Pyro4.config.SERIALIZER))
print('-------- BENCHMARK LOCAL OBJECT ---------')
object = bench.bench()
begin = time.time()
iters = 200000
for f in funcs:
sys.stdout.write("%d times %s " % (iters, f.__name__))
voor = time.time()
for i in range(iters):
f()
sys.stdout.write("%.3f\n" % (time.time() - voor))
sys.stdout.flush()
duration = time.time() - begin
print('total time %.3f seconds' % duration)
amount = len(funcs) * iters
print('total method calls: %d' % (amount))
avg_normal_msec = 1000.0 * duration / amount
print('avg. time per method call: %.3f msec (%d/sec)' % (avg_normal_msec, amount / duration // 1000 * 1000))
print('Normal method call is %.0f times faster than Pyro method call.' % (avg_pyro_msec / avg_normal_msec)) | examples/benchmark/client.py | from __future__ import print_function
import sys
import time
import Pyro4
import bench
if sys.version_info < (3, 0):
input = raw_input
uri = input("Uri of benchmark server? ").strip()
object = Pyro4.core.Proxy(uri)
object._pyroBind()
assert "oneway" in object._pyroOneway # make sure this method is indeed marked as @oneway
def f1():
_ = object.length('<NAME> Jong')
def f2():
_ = object.timestwo(21)
def f3():
_ = object.bigreply()
def f4():
_ = object.manyargs(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
def f5():
_ = object.noreply(99993333)
def f6():
_ = object.varargs('een', 2, (3,), [4])
def f7():
_ = object.keywords(arg1='zork')
def f8():
_ = object.echo('een', 2, (3,), [4])
def f9():
_ = object.meth1('stringetje')
def fa():
_ = object.meth2('stringetje')
def fb():
_ = object.meth3('stringetje')
def fc():
_ = object.meth4('stringetje')
def fd():
_ = object.bigarg('Argument' * 50)
def fe():
object.oneway('stringetje', 432423434, 9.8765432)
def ff():
_ = object.mapping({"aap": 42, "noot": 99, "mies": 987654})
funcs = (f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff)
print('-------- BENCHMARK REMOTE OBJECT ---------')
print('Pay attention to the "fe" test -- this is a Oneway call and should be *fast*')
print('(if you are running the server and client on different machines)')
begin = time.time()
iters = 1000
for f in funcs:
sys.stdout.write("%d times %s " % (iters, f.__name__))
voor = time.time()
for i in range(iters):
f()
sys.stdout.write("%.3f\n" % (time.time() - voor))
sys.stdout.flush()
duration = time.time() - begin
print('total time %.3f seconds' % duration)
amount = len(funcs) * iters
print('total method calls: %d' % (amount))
avg_pyro_msec = 1000.0 * duration / amount
print('avg. time per method call: %.3f msec (%d/sec) (serializer: %s)' % (avg_pyro_msec, amount / duration, Pyro4.config.SERIALIZER))
print('-------- BENCHMARK LOCAL OBJECT ---------')
object = bench.bench()
begin = time.time()
iters = 200000
for f in funcs:
sys.stdout.write("%d times %s " % (iters, f.__name__))
voor = time.time()
for i in range(iters):
f()
sys.stdout.write("%.3f\n" % (time.time() - voor))
sys.stdout.flush()
duration = time.time() - begin
print('total time %.3f seconds' % duration)
amount = len(funcs) * iters
print('total method calls: %d' % (amount))
avg_normal_msec = 1000.0 * duration / amount
print('avg. time per method call: %.3f msec (%d/sec)' % (avg_normal_msec, amount / duration // 1000 * 1000))
print('Normal method call is %.0f times faster than Pyro method call.' % (avg_pyro_msec / avg_normal_msec)) | 0.286568 | 0.128197 |