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public_data/serializers.py | MTES-MCT/sparte | 0 | 0 | <reponame>MTES-MCT/sparte
from rest_framework_gis import serializers
from rest_framework import serializers as s
from .models import (
Artificialisee2015to2018,
Artificielle2018,
CommunesSybarval,
CouvertureSol,
EnveloppeUrbaine2018,
Ocsge,
Renaturee2018to2015,
Sybarval,
Voirie2018,
ZonesBaties2018,
UsageSol,
)
def get_label(code="", label=""):
if code is None:
code = "-"
if label is None:
label = "inconnu"
return f"{code} {label[:30]}"
class Artificialisee2015to2018Serializer(serializers.GeoFeatureModelSerializer):
usage_2015 = s.SerializerMethodField()
usage_2018 = s.SerializerMethodField()
couverture_2015 = s.SerializerMethodField()
couverture_2018 = s.SerializerMethodField()
def get_usage_2015(self, obj):
return get_label(code=obj.us_2015, label=obj.us_2015_label)
def get_usage_2018(self, obj):
return get_label(code=obj.us_2018, label=obj.us_2018_label)
def get_couverture_2015(self, obj):
return get_label(code=obj.cs_2015, label=obj.cs_2015_label)
def get_couverture_2018(self, obj):
return get_label(code=obj.cs_2018, label=obj.cs_2018_label)
class Meta:
fields = (
"id",
"surface",
"usage_2015",
"usage_2018",
"couverture_2015",
"couverture_2018",
)
geo_field = "mpoly"
model = Artificialisee2015to2018
class Artificielle2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
class Meta:
fields = (
"id",
"surface",
"couverture",
)
geo_field = "mpoly"
model = Artificielle2018
class CommunesSybarvalSerializer(serializers.GeoFeatureModelSerializer):
"""Marker GeoJSON serializer."""
class Meta:
"""Marker serializer meta class."""
fields = (
"nom",
"code_insee",
"surface",
)
geo_field = "mpoly"
model = CommunesSybarval
class EnveloppeUrbaine2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
class Meta:
fields = (
"id",
"couverture",
"surface",
)
geo_field = "mpoly"
model = EnveloppeUrbaine2018
class OcsgeSerializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"couverture",
"usage",
"millesime",
"map_color",
"year",
)
geo_field = "mpoly"
model = Ocsge
class Renaturee2018to2015Serializer(serializers.GeoFeatureModelSerializer):
usage_2015 = s.SerializerMethodField()
usage_2018 = s.SerializerMethodField()
couverture_2015 = s.SerializerMethodField()
couverture_2018 = s.SerializerMethodField()
def get_usage_2015(self, obj):
return get_label(code=obj.us_2015, label=obj.us_2015_label)
def get_usage_2018(self, obj):
return get_label(code=obj.us_2018, label=obj.us_2018_label)
def get_couverture_2015(self, obj):
return get_label(code=obj.cs_2015, label=obj.cs_2015_label)
def get_couverture_2018(self, obj):
return get_label(code=obj.cs_2018, label=obj.cs_2018_label)
class Meta:
fields = (
"id",
"surface",
"usage_2015",
"usage_2018",
"couverture_2015",
"couverture_2018",
)
geo_field = "mpoly"
model = Renaturee2018to2015
class SybarvalSerializer(serializers.GeoFeatureModelSerializer):
class Meta:
fields = (
"id",
"surface",
)
geo_field = "mpoly"
model = Sybarval
class Voirie2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"surface",
"couverture",
"usage",
)
geo_field = "mpoly"
model = Voirie2018
class ZonesBaties2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"couverture",
"usage",
"surface",
)
geo_field = "mpoly"
model = ZonesBaties2018
class CouvertureSolSerializer(serializers.ModelSerializer):
class Meta:
fields = (
"id",
"parent",
"code",
"label",
"is_artificial",
)
model = CouvertureSol
class UsageSolSerializer(serializers.ModelSerializer):
class Meta:
fields = (
"id",
"parent",
"code",
"label",
)
model = UsageSol
| from rest_framework_gis import serializers
from rest_framework import serializers as s
from .models import (
Artificialisee2015to2018,
Artificielle2018,
CommunesSybarval,
CouvertureSol,
EnveloppeUrbaine2018,
Ocsge,
Renaturee2018to2015,
Sybarval,
Voirie2018,
ZonesBaties2018,
UsageSol,
)
def get_label(code="", label=""):
if code is None:
code = "-"
if label is None:
label = "inconnu"
return f"{code} {label[:30]}"
class Artificialisee2015to2018Serializer(serializers.GeoFeatureModelSerializer):
usage_2015 = s.SerializerMethodField()
usage_2018 = s.SerializerMethodField()
couverture_2015 = s.SerializerMethodField()
couverture_2018 = s.SerializerMethodField()
def get_usage_2015(self, obj):
return get_label(code=obj.us_2015, label=obj.us_2015_label)
def get_usage_2018(self, obj):
return get_label(code=obj.us_2018, label=obj.us_2018_label)
def get_couverture_2015(self, obj):
return get_label(code=obj.cs_2015, label=obj.cs_2015_label)
def get_couverture_2018(self, obj):
return get_label(code=obj.cs_2018, label=obj.cs_2018_label)
class Meta:
fields = (
"id",
"surface",
"usage_2015",
"usage_2018",
"couverture_2015",
"couverture_2018",
)
geo_field = "mpoly"
model = Artificialisee2015to2018
class Artificielle2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
class Meta:
fields = (
"id",
"surface",
"couverture",
)
geo_field = "mpoly"
model = Artificielle2018
class CommunesSybarvalSerializer(serializers.GeoFeatureModelSerializer):
"""Marker GeoJSON serializer."""
class Meta:
"""Marker serializer meta class."""
fields = (
"nom",
"code_insee",
"surface",
)
geo_field = "mpoly"
model = CommunesSybarval
class EnveloppeUrbaine2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
class Meta:
fields = (
"id",
"couverture",
"surface",
)
geo_field = "mpoly"
model = EnveloppeUrbaine2018
class OcsgeSerializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"couverture",
"usage",
"millesime",
"map_color",
"year",
)
geo_field = "mpoly"
model = Ocsge
class Renaturee2018to2015Serializer(serializers.GeoFeatureModelSerializer):
usage_2015 = s.SerializerMethodField()
usage_2018 = s.SerializerMethodField()
couverture_2015 = s.SerializerMethodField()
couverture_2018 = s.SerializerMethodField()
def get_usage_2015(self, obj):
return get_label(code=obj.us_2015, label=obj.us_2015_label)
def get_usage_2018(self, obj):
return get_label(code=obj.us_2018, label=obj.us_2018_label)
def get_couverture_2015(self, obj):
return get_label(code=obj.cs_2015, label=obj.cs_2015_label)
def get_couverture_2018(self, obj):
return get_label(code=obj.cs_2018, label=obj.cs_2018_label)
class Meta:
fields = (
"id",
"surface",
"usage_2015",
"usage_2018",
"couverture_2015",
"couverture_2018",
)
geo_field = "mpoly"
model = Renaturee2018to2015
class SybarvalSerializer(serializers.GeoFeatureModelSerializer):
class Meta:
fields = (
"id",
"surface",
)
geo_field = "mpoly"
model = Sybarval
class Voirie2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"surface",
"couverture",
"usage",
)
geo_field = "mpoly"
model = Voirie2018
class ZonesBaties2018Serializer(serializers.GeoFeatureModelSerializer):
couverture = s.SerializerMethodField()
usage = s.SerializerMethodField()
def get_couverture(self, obj):
return get_label(code=obj.couverture, label=obj.couverture_label)
def get_usage(self, obj):
return get_label(code=obj.usage, label=obj.usage_label)
class Meta:
fields = (
"id",
"couverture",
"usage",
"surface",
)
geo_field = "mpoly"
model = ZonesBaties2018
class CouvertureSolSerializer(serializers.ModelSerializer):
class Meta:
fields = (
"id",
"parent",
"code",
"label",
"is_artificial",
)
model = CouvertureSol
class UsageSolSerializer(serializers.ModelSerializer):
class Meta:
fields = (
"id",
"parent",
"code",
"label",
)
model = UsageSol | en | 0.404844 | Marker GeoJSON serializer. Marker serializer meta class. | 2.186829 | 2 |
quick_search/admin.py | naman1901/django-quick-search | 0 | 1 | from django.contrib import admin
from .models import SearchResult
# Register your models here.
class SearchResultAdmin(admin.ModelAdmin):
fields = ["query", "heading", "url", "text"]
admin.site.register(SearchResult, SearchResultAdmin) | from django.contrib import admin
from .models import SearchResult
# Register your models here.
class SearchResultAdmin(admin.ModelAdmin):
fields = ["query", "heading", "url", "text"]
admin.site.register(SearchResult, SearchResultAdmin) | en | 0.968259 | # Register your models here. | 1.640673 | 2 |
rasa/train.py | Amirali-Shirkh/rasa-for-botfront | 0 | 2 | import asyncio
import os
import tempfile
from contextlib import ExitStack
from typing import Text, Optional, List, Union, Dict
from rasa.importers.importer import TrainingDataImporter
from rasa import model
from rasa.model import FingerprintComparisonResult
from rasa.core.domain import Domain
from rasa.utils.common import TempDirectoryPath
from rasa.cli.utils import (
print_success,
print_warning,
print_error,
bcolors,
print_color,
)
from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CORE_SUBDIRECTORY_NAME
def train(
domain: Text,
config: Text,
training_files: Union[Text, List[Text]],
output: Text = DEFAULT_MODELS_PATH,
force_training: bool = False,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
loop: Optional[asyncio.AbstractEventLoop] = None,
) -> Optional[Text]:
if loop is None:
try:
loop = asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(
train_async(
domain=domain,
config=config,
training_files=training_files,
output_path=output,
force_training=force_training,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
additional_arguments=additional_arguments,
)
)
async def train_async(
domain: Union[Domain, Text],
config: Dict[Text, Text],
training_files: Optional[Union[Text, List[Text]]],
output_path: Text = DEFAULT_MODELS_PATH,
force_training: bool = False,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Rasa model (Core and NLU).
Args:
domain: Path to the domain file.
config: Dict of paths to the config for Core and NLU. Keys are language codes
training_files: Paths to the training data for Core and NLU.
output_path: Output path.
force_training: If `True` retrain model even if data has not changed.
fixed_model_name: Name of model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
additional_arguments: Additional training parameters.
Returns:
Path of the trained model archive.
"""
# file_importer = TrainingDataImporter.load_from_config(
# config, domain, training_files
# )
with ExitStack() as stack:
train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# bf mod
from rasa_addons.importers import BotfrontFileImporter
file_importer = BotfrontFileImporter(config, domain, training_files)
# domain = await file_importer.get_domain()
# if domain.is_empty():
# return await handle_domain_if_not_exists(
# file_importer, output_path, fixed_model_name
# )
# /bf mod
return await _train_async_internal(
file_importer,
train_path,
output_path,
force_training,
fixed_model_name,
persist_nlu_training_data,
additional_arguments,
)
async def handle_domain_if_not_exists(
file_importer: TrainingDataImporter, output_path, fixed_model_name
):
nlu_model_only = await _train_nlu_with_validated_data(
file_importer, output=output_path, fixed_model_name=fixed_model_name
)
print_warning(
"Core training was skipped because no valid domain file was found. Only an nlu-model was created."
"Please specify a valid domain using '--domain' argument or check if the provided domain file exists."
)
return nlu_model_only
async def _train_async_internal(
file_importer: TrainingDataImporter,
train_path: Text,
output_path: Text,
force_training: bool,
fixed_model_name: Optional[Text],
persist_nlu_training_data: bool,
additional_arguments: Optional[Dict],
) -> Optional[Text]:
"""Trains a Rasa model (Core and NLU). Use only from `train_async`.
Args:
file_importer: `TrainingDataImporter` which supplies the training data.
train_path: Directory in which to train the model.
output_path: Output path.
force_training: If `True` retrain model even if data has not changed.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
fixed_model_name: Name of model to be stored.
additional_arguments: Additional training parameters.
Returns:
Path of the trained model archive.
"""
stories, nlu_data = await asyncio.gather(
file_importer.get_stories(), file_importer.get_nlu_data()
)
# if stories.is_empty() and nlu_data.is_empty():
# print_error(
# "No training data given. Please provide stories and NLU data in "
# "order to train a Rasa model using the '--data' argument."
# )
# return
# if nlu_data.is_empty():
# print_warning("No NLU data present. Just a Rasa Core model will be trained.")
# return await _train_core_with_validated_data(
# file_importer,
# output=output_path,
# fixed_model_name=fixed_model_name,
# additional_arguments=additional_arguments,
# )
new_fingerprint = await model.model_fingerprint(file_importer)
old_model = model.get_latest_model(output_path)
fingerprint_comparison = FingerprintComparisonResult(force_training=force_training)
if not force_training:
fingerprint_comparison = model.should_retrain(
new_fingerprint, old_model, train_path
)
# bf mod >
if fingerprint_comparison.nlu == True: # replace True with list of all langs
fingerprint_comparison.nlu = list(new_fingerprint.get("nlu-config", {}).keys())
domain = await file_importer.get_domain()
core_untrainable = domain.is_empty() or stories.is_empty()
nlu_untrainable = [l for l, d in nlu_data.items() if d.is_empty()]
fingerprint_comparison.core = fingerprint_comparison.core and not core_untrainable
fingerprint_comparison.nlu = [l for l in fingerprint_comparison.nlu if l not in nlu_untrainable]
if core_untrainable:
print_color("Skipping Core training since domain or stories are empty.", color=bcolors.OKBLUE)
for lang in nlu_untrainable:
print_color("No NLU data found for language <{}>, skipping training...".format(lang), color=bcolors.OKBLUE)
# </ bf mod
if fingerprint_comparison.is_training_required():
await _do_training(
file_importer,
output_path=output_path,
train_path=train_path,
fingerprint_comparison_result=fingerprint_comparison,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
additional_arguments=additional_arguments,
)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
)
print_success(
"Nothing changed. You can use the old model stored at '{}'."
"".format(os.path.abspath(old_model))
)
return old_model
async def _do_training(
file_importer: TrainingDataImporter,
output_path: Text,
train_path: Text,
fingerprint_comparison_result: Optional[FingerprintComparisonResult] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
):
if not fingerprint_comparison_result:
fingerprint_comparison_result = FingerprintComparisonResult()
if fingerprint_comparison_result.should_retrain_core():
await _train_core_with_validated_data(
file_importer,
output=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
elif fingerprint_comparison_result.should_retrain_nlg():
print_color(
"Core stories/configuration did not change. "
"Only the templates section has been changed. A new model with "
"the updated templates will be created.",
color=bcolors.OKBLUE,
)
await model.update_model_with_new_domain(file_importer, train_path)
else:
print_color(
"Core stories/configuration did not change. No need to retrain Core model.",
color=bcolors.OKBLUE,
)
if fingerprint_comparison_result.should_retrain_nlu():
await _train_nlu_with_validated_data(
file_importer,
output=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
retrain_nlu=fingerprint_comparison_result.nlu,
persist_nlu_training_data=persist_nlu_training_data,
)
else:
print_color(
"NLU data/configuration did not change. No need to retrain NLU model.",
color=bcolors.OKBLUE,
)
def train_core(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
loop = asyncio.get_event_loop()
return loop.run_until_complete(
train_core_async(
domain=domain,
config=config,
stories=stories,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
)
async def train_core_async(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Core model.
Args:
domain: Path to the domain file.
config: Path to the config file for Core.
stories: Path to the Core training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of model to be stored.
uncompress: If `True` the model will not be compressed.
additional_arguments: Additional training parameters.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
file_importer = TrainingDataImporter.load_core_importer_from_config(
config, domain, [stories]
)
domain = await file_importer.get_domain()
if domain.is_empty():
print_error(
"Core training was skipped because no valid domain file was found. "
"Please specify a valid domain using '--domain' argument or check if the provided domain file exists."
)
return None
if not await file_importer.get_stories():
print_error(
"No stories given. Please provide stories in order to "
"train a Rasa Core model using the '--stories' argument."
)
return
return await _train_core_with_validated_data(
file_importer,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
async def _train_core_with_validated_data(
file_importer: TrainingDataImporter,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Train Core with validated training and config data."""
import rasa.core.train
with ExitStack() as stack:
if train_path:
# If the train path was provided, do nothing on exit.
_train_path = train_path
else:
# Otherwise, create a temp train path and clean it up on exit.
_train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# normal (not compare) training
print_color("Training Core model...", color=bcolors.OKBLUE)
domain, config = await asyncio.gather(
file_importer.get_domain(), file_importer.get_config()
)
await rasa.core.train(
domain_file=domain,
training_resource=file_importer,
output_path=os.path.join(_train_path, DEFAULT_CORE_SUBDIRECTORY_NAME),
policy_config=config,
additional_arguments=additional_arguments,
)
print_color("Core model training completed.", color=bcolors.OKBLUE)
if train_path is None:
# Only Core was trained.
new_fingerprint = await model.model_fingerprint(file_importer)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output,
train_path=_train_path,
fixed_model_name=fixed_model_name,
model_prefix="core-",
)
return _train_path
def train_nlu(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
) -> Optional[Text]:
"""Trains an NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of the model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
loop = asyncio.get_event_loop()
return loop.run_until_complete(
_train_nlu_async(
config,
nlu_data,
output,
train_path,
fixed_model_name,
persist_nlu_training_data,
)
)
async def _train_nlu_async(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
):
if not nlu_data:
print_error(
"No NLU data given. Please provide NLU data in order to train "
"a Rasa NLU model using the '--nlu' argument."
)
return
# training NLU only hence the training files still have to be selected
file_importer = TrainingDataImporter.load_nlu_importer_from_config(
config, training_data_paths=[nlu_data]
)
training_datas = await file_importer.get_nlu_data()
if training_datas.is_empty():
print_error(
f"Path '{nlu_data}' doesn't contain valid NLU data in it. "
"Please verify the data format. "
"The NLU model training will be skipped now."
)
return
return await _train_nlu_with_validated_data(
file_importer,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
)
async def _train_nlu_with_validated_data(
file_importer: TrainingDataImporter,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
retrain_nlu: Union[bool, List[Text]] = True
) -> Optional[Text]:
"""Train NLU with validated training and config data."""
import rasa.nlu.train
with ExitStack() as stack:
models = {}
from rasa.nlu import config as cfg_loader
if train_path:
# If the train path was provided, do nothing on exit.
_train_path = train_path
else:
# Otherwise, create a temp train path and clean it up on exit.
_train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# bf mod
config = await file_importer.get_nlu_config(retrain_nlu)
for lang in config:
if config[lang]:
print_color("Start training {} NLU model ...".format(lang), color=bcolors.OKBLUE)
_, models[lang], _ = await rasa.nlu.train(
config[lang],
file_importer,
_train_path,
fixed_model_name="nlu-{}".format(lang),
persist_nlu_training_data=persist_nlu_training_data,
)
else:
print_color("NLU data for language <{}> didn't change, skipping training...".format(lang), color=bcolors.OKBLUE)
# /bf mod
print_color("NLU model training completed.", color=bcolors.OKBLUE)
if train_path is None:
# Only NLU was trained
new_fingerprint = await model.model_fingerprint(file_importer)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output,
train_path=_train_path,
fixed_model_name=fixed_model_name,
model_prefix="nlu-",
)
return _train_path
| import asyncio
import os
import tempfile
from contextlib import ExitStack
from typing import Text, Optional, List, Union, Dict
from rasa.importers.importer import TrainingDataImporter
from rasa import model
from rasa.model import FingerprintComparisonResult
from rasa.core.domain import Domain
from rasa.utils.common import TempDirectoryPath
from rasa.cli.utils import (
print_success,
print_warning,
print_error,
bcolors,
print_color,
)
from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CORE_SUBDIRECTORY_NAME
def train(
domain: Text,
config: Text,
training_files: Union[Text, List[Text]],
output: Text = DEFAULT_MODELS_PATH,
force_training: bool = False,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
loop: Optional[asyncio.AbstractEventLoop] = None,
) -> Optional[Text]:
if loop is None:
try:
loop = asyncio.get_event_loop()
except RuntimeError:
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(
train_async(
domain=domain,
config=config,
training_files=training_files,
output_path=output,
force_training=force_training,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
additional_arguments=additional_arguments,
)
)
async def train_async(
domain: Union[Domain, Text],
config: Dict[Text, Text],
training_files: Optional[Union[Text, List[Text]]],
output_path: Text = DEFAULT_MODELS_PATH,
force_training: bool = False,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Rasa model (Core and NLU).
Args:
domain: Path to the domain file.
config: Dict of paths to the config for Core and NLU. Keys are language codes
training_files: Paths to the training data for Core and NLU.
output_path: Output path.
force_training: If `True` retrain model even if data has not changed.
fixed_model_name: Name of model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
additional_arguments: Additional training parameters.
Returns:
Path of the trained model archive.
"""
# file_importer = TrainingDataImporter.load_from_config(
# config, domain, training_files
# )
with ExitStack() as stack:
train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# bf mod
from rasa_addons.importers import BotfrontFileImporter
file_importer = BotfrontFileImporter(config, domain, training_files)
# domain = await file_importer.get_domain()
# if domain.is_empty():
# return await handle_domain_if_not_exists(
# file_importer, output_path, fixed_model_name
# )
# /bf mod
return await _train_async_internal(
file_importer,
train_path,
output_path,
force_training,
fixed_model_name,
persist_nlu_training_data,
additional_arguments,
)
async def handle_domain_if_not_exists(
file_importer: TrainingDataImporter, output_path, fixed_model_name
):
nlu_model_only = await _train_nlu_with_validated_data(
file_importer, output=output_path, fixed_model_name=fixed_model_name
)
print_warning(
"Core training was skipped because no valid domain file was found. Only an nlu-model was created."
"Please specify a valid domain using '--domain' argument or check if the provided domain file exists."
)
return nlu_model_only
async def _train_async_internal(
file_importer: TrainingDataImporter,
train_path: Text,
output_path: Text,
force_training: bool,
fixed_model_name: Optional[Text],
persist_nlu_training_data: bool,
additional_arguments: Optional[Dict],
) -> Optional[Text]:
"""Trains a Rasa model (Core and NLU). Use only from `train_async`.
Args:
file_importer: `TrainingDataImporter` which supplies the training data.
train_path: Directory in which to train the model.
output_path: Output path.
force_training: If `True` retrain model even if data has not changed.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
fixed_model_name: Name of model to be stored.
additional_arguments: Additional training parameters.
Returns:
Path of the trained model archive.
"""
stories, nlu_data = await asyncio.gather(
file_importer.get_stories(), file_importer.get_nlu_data()
)
# if stories.is_empty() and nlu_data.is_empty():
# print_error(
# "No training data given. Please provide stories and NLU data in "
# "order to train a Rasa model using the '--data' argument."
# )
# return
# if nlu_data.is_empty():
# print_warning("No NLU data present. Just a Rasa Core model will be trained.")
# return await _train_core_with_validated_data(
# file_importer,
# output=output_path,
# fixed_model_name=fixed_model_name,
# additional_arguments=additional_arguments,
# )
new_fingerprint = await model.model_fingerprint(file_importer)
old_model = model.get_latest_model(output_path)
fingerprint_comparison = FingerprintComparisonResult(force_training=force_training)
if not force_training:
fingerprint_comparison = model.should_retrain(
new_fingerprint, old_model, train_path
)
# bf mod >
if fingerprint_comparison.nlu == True: # replace True with list of all langs
fingerprint_comparison.nlu = list(new_fingerprint.get("nlu-config", {}).keys())
domain = await file_importer.get_domain()
core_untrainable = domain.is_empty() or stories.is_empty()
nlu_untrainable = [l for l, d in nlu_data.items() if d.is_empty()]
fingerprint_comparison.core = fingerprint_comparison.core and not core_untrainable
fingerprint_comparison.nlu = [l for l in fingerprint_comparison.nlu if l not in nlu_untrainable]
if core_untrainable:
print_color("Skipping Core training since domain or stories are empty.", color=bcolors.OKBLUE)
for lang in nlu_untrainable:
print_color("No NLU data found for language <{}>, skipping training...".format(lang), color=bcolors.OKBLUE)
# </ bf mod
if fingerprint_comparison.is_training_required():
await _do_training(
file_importer,
output_path=output_path,
train_path=train_path,
fingerprint_comparison_result=fingerprint_comparison,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
additional_arguments=additional_arguments,
)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
)
print_success(
"Nothing changed. You can use the old model stored at '{}'."
"".format(os.path.abspath(old_model))
)
return old_model
async def _do_training(
file_importer: TrainingDataImporter,
output_path: Text,
train_path: Text,
fingerprint_comparison_result: Optional[FingerprintComparisonResult] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
additional_arguments: Optional[Dict] = None,
):
if not fingerprint_comparison_result:
fingerprint_comparison_result = FingerprintComparisonResult()
if fingerprint_comparison_result.should_retrain_core():
await _train_core_with_validated_data(
file_importer,
output=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
elif fingerprint_comparison_result.should_retrain_nlg():
print_color(
"Core stories/configuration did not change. "
"Only the templates section has been changed. A new model with "
"the updated templates will be created.",
color=bcolors.OKBLUE,
)
await model.update_model_with_new_domain(file_importer, train_path)
else:
print_color(
"Core stories/configuration did not change. No need to retrain Core model.",
color=bcolors.OKBLUE,
)
if fingerprint_comparison_result.should_retrain_nlu():
await _train_nlu_with_validated_data(
file_importer,
output=output_path,
train_path=train_path,
fixed_model_name=fixed_model_name,
retrain_nlu=fingerprint_comparison_result.nlu,
persist_nlu_training_data=persist_nlu_training_data,
)
else:
print_color(
"NLU data/configuration did not change. No need to retrain NLU model.",
color=bcolors.OKBLUE,
)
def train_core(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
loop = asyncio.get_event_loop()
return loop.run_until_complete(
train_core_async(
domain=domain,
config=config,
stories=stories,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
)
async def train_core_async(
domain: Union[Domain, Text],
config: Text,
stories: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Trains a Core model.
Args:
domain: Path to the domain file.
config: Path to the config file for Core.
stories: Path to the Core training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of model to be stored.
uncompress: If `True` the model will not be compressed.
additional_arguments: Additional training parameters.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
file_importer = TrainingDataImporter.load_core_importer_from_config(
config, domain, [stories]
)
domain = await file_importer.get_domain()
if domain.is_empty():
print_error(
"Core training was skipped because no valid domain file was found. "
"Please specify a valid domain using '--domain' argument or check if the provided domain file exists."
)
return None
if not await file_importer.get_stories():
print_error(
"No stories given. Please provide stories in order to "
"train a Rasa Core model using the '--stories' argument."
)
return
return await _train_core_with_validated_data(
file_importer,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
additional_arguments=additional_arguments,
)
async def _train_core_with_validated_data(
file_importer: TrainingDataImporter,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
additional_arguments: Optional[Dict] = None,
) -> Optional[Text]:
"""Train Core with validated training and config data."""
import rasa.core.train
with ExitStack() as stack:
if train_path:
# If the train path was provided, do nothing on exit.
_train_path = train_path
else:
# Otherwise, create a temp train path and clean it up on exit.
_train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# normal (not compare) training
print_color("Training Core model...", color=bcolors.OKBLUE)
domain, config = await asyncio.gather(
file_importer.get_domain(), file_importer.get_config()
)
await rasa.core.train(
domain_file=domain,
training_resource=file_importer,
output_path=os.path.join(_train_path, DEFAULT_CORE_SUBDIRECTORY_NAME),
policy_config=config,
additional_arguments=additional_arguments,
)
print_color("Core model training completed.", color=bcolors.OKBLUE)
if train_path is None:
# Only Core was trained.
new_fingerprint = await model.model_fingerprint(file_importer)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output,
train_path=_train_path,
fixed_model_name=fixed_model_name,
model_prefix="core-",
)
return _train_path
def train_nlu(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
) -> Optional[Text]:
"""Trains an NLU model.
Args:
config: Path to the config file for NLU.
nlu_data: Path to the NLU training data.
output: Output path.
train_path: If `None` the model will be trained in a temporary
directory, otherwise in the provided directory.
fixed_model_name: Name of the model to be stored.
persist_nlu_training_data: `True` if the NLU training data should be persisted
with the model.
Returns:
If `train_path` is given it returns the path to the model archive,
otherwise the path to the directory with the trained model files.
"""
loop = asyncio.get_event_loop()
return loop.run_until_complete(
_train_nlu_async(
config,
nlu_data,
output,
train_path,
fixed_model_name,
persist_nlu_training_data,
)
)
async def _train_nlu_async(
config: Text,
nlu_data: Text,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
):
if not nlu_data:
print_error(
"No NLU data given. Please provide NLU data in order to train "
"a Rasa NLU model using the '--nlu' argument."
)
return
# training NLU only hence the training files still have to be selected
file_importer = TrainingDataImporter.load_nlu_importer_from_config(
config, training_data_paths=[nlu_data]
)
training_datas = await file_importer.get_nlu_data()
if training_datas.is_empty():
print_error(
f"Path '{nlu_data}' doesn't contain valid NLU data in it. "
"Please verify the data format. "
"The NLU model training will be skipped now."
)
return
return await _train_nlu_with_validated_data(
file_importer,
output=output,
train_path=train_path,
fixed_model_name=fixed_model_name,
persist_nlu_training_data=persist_nlu_training_data,
)
async def _train_nlu_with_validated_data(
file_importer: TrainingDataImporter,
output: Text,
train_path: Optional[Text] = None,
fixed_model_name: Optional[Text] = None,
persist_nlu_training_data: bool = False,
retrain_nlu: Union[bool, List[Text]] = True
) -> Optional[Text]:
"""Train NLU with validated training and config data."""
import rasa.nlu.train
with ExitStack() as stack:
models = {}
from rasa.nlu import config as cfg_loader
if train_path:
# If the train path was provided, do nothing on exit.
_train_path = train_path
else:
# Otherwise, create a temp train path and clean it up on exit.
_train_path = stack.enter_context(TempDirectoryPath(tempfile.mkdtemp()))
# bf mod
config = await file_importer.get_nlu_config(retrain_nlu)
for lang in config:
if config[lang]:
print_color("Start training {} NLU model ...".format(lang), color=bcolors.OKBLUE)
_, models[lang], _ = await rasa.nlu.train(
config[lang],
file_importer,
_train_path,
fixed_model_name="nlu-{}".format(lang),
persist_nlu_training_data=persist_nlu_training_data,
)
else:
print_color("NLU data for language <{}> didn't change, skipping training...".format(lang), color=bcolors.OKBLUE)
# /bf mod
print_color("NLU model training completed.", color=bcolors.OKBLUE)
if train_path is None:
# Only NLU was trained
new_fingerprint = await model.model_fingerprint(file_importer)
return model.package_model(
fingerprint=new_fingerprint,
output_directory=output,
train_path=_train_path,
fixed_model_name=fixed_model_name,
model_prefix="nlu-",
)
return _train_path
| en | 0.748063 | Trains a Rasa model (Core and NLU). Args: domain: Path to the domain file. config: Dict of paths to the config for Core and NLU. Keys are language codes training_files: Paths to the training data for Core and NLU. output_path: Output path. force_training: If `True` retrain model even if data has not changed. fixed_model_name: Name of model to be stored. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. additional_arguments: Additional training parameters. Returns: Path of the trained model archive. # file_importer = TrainingDataImporter.load_from_config( # config, domain, training_files # ) # bf mod # domain = await file_importer.get_domain() # if domain.is_empty(): # return await handle_domain_if_not_exists( # file_importer, output_path, fixed_model_name # ) # /bf mod Trains a Rasa model (Core and NLU). Use only from `train_async`. Args: file_importer: `TrainingDataImporter` which supplies the training data. train_path: Directory in which to train the model. output_path: Output path. force_training: If `True` retrain model even if data has not changed. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. fixed_model_name: Name of model to be stored. additional_arguments: Additional training parameters. Returns: Path of the trained model archive. # if stories.is_empty() and nlu_data.is_empty(): # print_error( # "No training data given. Please provide stories and NLU data in " # "order to train a Rasa model using the '--data' argument." # ) # return # if nlu_data.is_empty(): # print_warning("No NLU data present. Just a Rasa Core model will be trained.") # return await _train_core_with_validated_data( # file_importer, # output=output_path, # fixed_model_name=fixed_model_name, # additional_arguments=additional_arguments, # ) # bf mod > # replace True with list of all langs # </ bf mod Trains a Core model. Args: domain: Path to the domain file. config: Path to the config file for Core. stories: Path to the Core training data. output: Output path. train_path: If `None` the model will be trained in a temporary directory, otherwise in the provided directory. fixed_model_name: Name of model to be stored. uncompress: If `True` the model will not be compressed. additional_arguments: Additional training parameters. Returns: If `train_path` is given it returns the path to the model archive, otherwise the path to the directory with the trained model files. Train Core with validated training and config data. # If the train path was provided, do nothing on exit. # Otherwise, create a temp train path and clean it up on exit. # normal (not compare) training # Only Core was trained. Trains an NLU model. Args: config: Path to the config file for NLU. nlu_data: Path to the NLU training data. output: Output path. train_path: If `None` the model will be trained in a temporary directory, otherwise in the provided directory. fixed_model_name: Name of the model to be stored. persist_nlu_training_data: `True` if the NLU training data should be persisted with the model. Returns: If `train_path` is given it returns the path to the model archive, otherwise the path to the directory with the trained model files. # training NLU only hence the training files still have to be selected Train NLU with validated training and config data. # If the train path was provided, do nothing on exit. # Otherwise, create a temp train path and clean it up on exit. # bf mod # /bf mod # Only NLU was trained | 2.091617 | 2 |
coding_intereview/1475. Final Prices With a Special Discount in a Shop.py | Jahidul007/Python-Bootcamp | 2 | 3 | <gh_stars>1-10
class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(prices)-1:
res.append(prices[i])
res.append(prices[-1])
return res | class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
for i in range(len(prices)):
for j in range(i+1,len(prices)):
if prices[j]<=prices[i]:
res.append(prices[i]-prices[j])
break
if j==len(prices)-1:
res.append(prices[i])
res.append(prices[-1])
return res | none | 1 | 2.914667 | 3 |
|
rplugin/python3/denite/ui/default.py | timgates42/denite.nvim | 0 | 4 | <gh_stars>0
# ============================================================================
# FILE: default.py
# AUTHOR: <NAME> <<EMAIL> at g<EMAIL>>
# License: MIT license
# ============================================================================
import re
import typing
from denite.util import echo, error, clearmatch, regex_convert_py_vim
from denite.util import Nvim, UserContext, Candidates, Candidate
from denite.parent import SyncParent
class Default(object):
@property
def is_async(self) -> bool:
return self._is_async
def __init__(self, vim: Nvim) -> None:
self._vim = vim
self._denite: typing.Optional[SyncParent] = None
self._selected_candidates: typing.List[int] = []
self._candidates: Candidates = []
self._cursor = 0
self._entire_len = 0
self._result: typing.List[typing.Any] = []
self._context: UserContext = {}
self._bufnr = -1
self._winid = -1
self._winrestcmd = ''
self._initialized = False
self._winheight = 0
self._winwidth = 0
self._winminheight = -1
self._is_multi = False
self._is_async = False
self._matched_pattern = ''
self._displayed_texts: typing.List[str] = []
self._statusline_sources = ''
self._titlestring = ''
self._ruler = False
self._prev_action = ''
self._prev_status: typing.Dict[str, typing.Any] = {}
self._prev_curpos: typing.List[typing.Any] = []
self._save_window_options: typing.Dict[str, typing.Any] = {}
self._sources_history: typing.List[typing.Any] = []
self._previous_text = ''
self._floating = False
self._filter_floating = False
self._updated = False
self._timers: typing.Dict[str, int] = {}
self._matched_range_id = -1
self._matched_char_id = -1
self._check_matchdelete = bool(self._vim.call(
'denite#util#check_matchdelete'))
def start(self, sources: typing.List[typing.Any],
context: UserContext) -> typing.List[typing.Any]:
if not self._denite:
# if hasattr(self._vim, 'run_coroutine'):
# self._denite = ASyncParent(self._vim)
# else:
self._denite = SyncParent(self._vim)
self._result = []
context['sources_queue'] = [sources]
self._start_sources_queue(context)
return self._result
def do_action(self, action_name: str,
command: str = '', is_manual: bool = False) -> None:
if is_manual:
candidates = self._get_selected_candidates()
elif self._get_cursor_candidate():
candidates = [self._get_cursor_candidate()]
else:
candidates = []
if not self._denite or not candidates or not action_name:
return
self._prev_action = action_name
action = self._denite.get_action(
self._context, action_name, candidates)
if not action:
return
post_action = self._context['post_action']
is_quit = action['is_quit'] or post_action == 'quit'
if is_quit:
self.quit()
self._denite.do_action(self._context, action_name, candidates)
self._result = candidates
if command != '':
self._vim.command(command)
if is_quit and post_action == 'open':
# Re-open denite buffer
prev_cursor = self._cursor
cursor_candidate = self._get_cursor_candidate()
self._init_buffer()
self.redraw(False)
if cursor_candidate == self._get_candidate(prev_cursor):
# Restore the cursor
self._move_to_pos(prev_cursor)
# Disable quit flag
is_quit = False
if not is_quit and is_manual:
self._selected_candidates = []
self.redraw(action['is_redraw'])
if is_manual and self._context['sources_queue']:
self._context['input'] = ''
self._context['quick_move'] = ''
self._start_sources_queue(self._context)
return
def redraw(self, is_force: bool = True) -> None:
self._context['is_redraw'] = is_force
if is_force:
self._gather_candidates()
if self._update_candidates():
self._update_buffer()
else:
self._update_status()
self._context['is_redraw'] = False
def quit(self) -> None:
if self._denite:
self._denite.on_close(self._context)
self._quit_buffer()
self._result = []
return
def _restart(self) -> None:
self._context['input'] = ''
self._quit_buffer()
self._init_denite()
self._gather_candidates()
self._init_buffer()
self._update_candidates()
self._update_buffer()
def _start_sources_queue(self, context: UserContext) -> None:
if not context['sources_queue']:
return
self._sources_history.append({
'sources': context['sources_queue'][0],
'path': context['path'],
})
self._start(context['sources_queue'][0], context)
if context['sources_queue']:
context['sources_queue'].pop(0)
context['path'] = self._context['path']
def _start(self, sources: typing.List[typing.Any],
context: UserContext) -> None:
from denite.ui.map import do_map
self._vim.command('silent! autocmd! denite')
if re.search(r'\[Command Line\]$', self._vim.current.buffer.name):
# Ignore command line window.
return
resume = self._initialized and context['resume']
if resume:
# Skip the initialization
update = ('immediately', 'immediately_1',
'cursor_pos', 'prev_winid',
'start_filter', 'quick_move')
for key in update:
self._context[key] = context[key]
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
if context['refresh']:
self.redraw()
self._move_to_pos(self._cursor)
else:
if self._context != context:
self._context.clear()
self._context.update(context)
self._context['sources'] = sources
self._context['is_redraw'] = False
self._is_multi = len(sources) > 1
if not sources:
# Ignore empty sources.
error(self._vim, 'Empty sources')
return
self._init_denite()
self._gather_candidates()
self._update_candidates()
self._init_cursor()
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
self._update_displayed_texts()
self._update_buffer()
self._move_to_pos(self._cursor)
if self._context['quick_move'] and do_map(self, 'quick_move', []):
return
if self._context['start_filter']:
do_map(self, 'open_filter_buffer', [])
def _init_buffer(self) -> None:
self._prev_status = dict()
self._displayed_texts = []
self._prev_bufnr = self._vim.current.buffer.number
self._prev_curpos = self._vim.call('getcurpos')
self._prev_wininfo = self._get_wininfo()
self._prev_winid = self._context['prev_winid']
self._winrestcmd = self._vim.call('winrestcmd')
self._ruler = self._vim.options['ruler']
self._switch_buffer()
self._bufnr = self._vim.current.buffer.number
self._winid = self._vim.call('win_getid')
self._resize_buffer(True)
self._winheight = self._vim.current.window.height
self._winwidth = self._vim.current.window.width
self._bufvars = self._vim.current.buffer.vars
self._bufvars['denite'] = {
'buffer_name': self._context['buffer_name'],
}
self._bufvars['denite_statusline'] = {}
self._vim.vars['denite#_previewed_buffers'] = {}
self._save_window_options = {}
window_options = {
'colorcolumn',
'concealcursor',
'conceallevel',
'cursorcolumn',
'cursorline',
'foldcolumn',
'foldenable',
'list',
'number',
'relativenumber',
'signcolumn',
'spell',
'winfixheight',
'wrap',
}
for k in window_options:
self._save_window_options[k] = self._vim.current.window.options[k]
# Note: Have to use setlocal instead of "current.window.options"
# "current.window.options" changes global value instead of local in
# neovim.
self._vim.command('setlocal colorcolumn=')
self._vim.command('setlocal conceallevel=3')
self._vim.command('setlocal concealcursor=inv')
self._vim.command('setlocal nocursorcolumn')
self._vim.command('setlocal nofoldenable')
self._vim.command('setlocal foldcolumn=0')
self._vim.command('setlocal nolist')
self._vim.command('setlocal nonumber')
self._vim.command('setlocal norelativenumber')
self._vim.command('setlocal nospell')
self._vim.command('setlocal winfixheight')
self._vim.command('setlocal nowrap')
if self._context['prompt']:
self._vim.command('setlocal signcolumn=yes')
else:
self._vim.command('setlocal signcolumn=auto')
if self._context['cursorline']:
self._vim.command('setlocal cursorline')
options = self._vim.current.buffer.options
if self._floating:
# Disable ruler
self._vim.options['ruler'] = False
options['buftype'] = 'nofile'
options['bufhidden'] = 'delete'
options['swapfile'] = False
options['buflisted'] = False
options['modeline'] = False
options['modifiable'] = False
options['filetype'] = 'denite'
if self._vim.call('exists', '#WinEnter'):
self._vim.command('doautocmd WinEnter')
if self._vim.call('exists', '#BufWinEnter'):
self._vim.command('doautocmd BufWinEnter')
if not self._vim.call('has', 'nvim'):
# In Vim8, FileType autocmd is not fired after set filetype option.
self._vim.command('silent doautocmd FileType denite')
if self._context['auto_action']:
self._vim.command('autocmd denite '
'CursorMoved <buffer> '
'call denite#call_map("auto_action")')
self._init_syntax()
def _switch_buffer(self) -> None:
split = self._context['split']
if (split != 'no' and self._winid > 0 and
self._vim.call('win_gotoid', self._winid)):
if split != 'vertical' and not self._floating:
# Move the window to bottom
self._vim.command('wincmd J')
self._winrestcmd = ''
return
self._floating = split in [
'floating',
'floating_relative_cursor',
'floating_relative_window',
]
self._filter_floating = False
if self._vim.current.buffer.options['filetype'] != 'denite':
self._titlestring = self._vim.options['titlestring']
command = 'edit'
if split == 'tab':
self._vim.command('tabnew')
elif self._floating:
self._split_floating(split)
elif self._context['filter_split_direction'] == 'floating':
self._filter_floating = True
elif split != 'no':
command = self._get_direction()
command += ' vsplit' if split == 'vertical' else ' split'
bufname = '[denite]-' + self._context['buffer_name']
if self._vim.call('exists', '*bufadd'):
bufnr = self._vim.call('bufadd', bufname)
vertical = 'vertical' if split == 'vertical' else ''
command = (
'buffer' if split
in ['no', 'tab', 'floating',
'floating_relative_window',
'floating_relative_cursor'] else 'sbuffer')
self._vim.command(
'silent keepalt %s %s %s %s' % (
self._get_direction(),
vertical,
command,
bufnr,
)
)
else:
self._vim.call(
'denite#util#execute_path',
f'silent keepalt {command}', bufname)
def _get_direction(self) -> str:
direction = str(self._context['direction'])
if direction == 'dynamictop' or direction == 'dynamicbottom':
self._update_displayed_texts()
winwidth = self._vim.call('winwidth', 0)
is_fit = not [x for x in self._displayed_texts
if self._vim.call('strwidth', x) > winwidth]
if direction == 'dynamictop':
direction = 'aboveleft' if is_fit else 'topleft'
else:
direction = 'belowright' if is_fit else 'botright'
return direction
def _get_wininfo(self) -> typing.List[typing.Any]:
return [
self._vim.options['columns'], self._vim.options['lines'],
self._vim.call('win_getid'), self._vim.call('tabpagebuflist')
]
def _switch_prev_buffer(self) -> None:
if (self._prev_bufnr == self._bufnr or
self._vim.buffers[self._prev_bufnr].name == ''):
self._vim.command('enew')
else:
self._vim.command('buffer ' + str(self._prev_bufnr))
def _init_syntax(self) -> None:
self._vim.command('syntax case ignore')
self._vim.command('highlight default link deniteInput ModeMsg')
self._vim.command('highlight link deniteMatchedRange ' +
self._context['highlight_matched_range'])
self._vim.command('highlight link deniteMatchedChar ' +
self._context['highlight_matched_char'])
self._vim.command('highlight default link ' +
'deniteStatusLinePath Comment')
self._vim.command('highlight default link ' +
'deniteStatusLineNumber LineNR')
self._vim.command('highlight default link ' +
'deniteSelectedLine Statement')
if self._floating:
self._vim.current.window.options['winhighlight'] = (
'Normal:' + self._context['highlight_window_background']
)
self._vim.command(('syntax match deniteSelectedLine /^[%s].*/' +
' contains=deniteConcealedMark') % (
self._context['selected_icon']))
self._vim.command(('syntax match deniteConcealedMark /^[ %s]/' +
' conceal contained') % (
self._context['selected_icon']))
if self._denite:
self._denite.init_syntax(self._context, self._is_multi)
def _update_candidates(self) -> bool:
if not self._denite:
return False
[self._is_async, pattern, statuses, self._entire_len,
self._candidates] = self._denite.filter_candidates(self._context)
prev_displayed_texts = self._displayed_texts
self._update_displayed_texts()
prev_matched_pattern = self._matched_pattern
self._matched_pattern = pattern
prev_statusline_sources = self._statusline_sources
self._statusline_sources = ' '.join(statuses)
if self._is_async:
self._start_timer('update_candidates')
else:
self._stop_timer('update_candidates')
updated = (self._displayed_texts != prev_displayed_texts or
self._matched_pattern != prev_matched_pattern or
self._statusline_sources != prev_statusline_sources)
if updated:
self._updated = True
self._start_timer('update_buffer')
if self._context['search'] and self._context['input']:
self._vim.call('setreg', '/', self._context['input'])
return self._updated
def _update_displayed_texts(self) -> None:
candidates_len = len(self._candidates)
if not self._is_async and self._context['auto_resize']:
winminheight = self._context['winminheight']
max_height = min(self._context['winheight'],
self._get_max_height())
if (winminheight != -1 and candidates_len < winminheight):
self._winheight = winminheight
elif candidates_len > max_height:
self._winheight = max_height
elif candidates_len != self._winheight:
self._winheight = candidates_len
max_source_name_len = 0
if self._candidates:
max_source_name_len = max([
len(self._get_display_source_name(x['source_name']))
for x in self._candidates])
self._context['max_source_name_len'] = max_source_name_len
self._context['max_source_name_format'] = (
'{:<' + str(self._context['max_source_name_len']) + '}')
self._displayed_texts = [
self._get_candidate_display_text(i)
for i in range(0, candidates_len)
]
def _update_buffer(self) -> None:
is_current_buffer = self._bufnr == self._vim.current.buffer.number
self._update_status()
if self._check_matchdelete and self._context['match_highlight']:
matches = [x['id'] for x in
self._vim.call('getmatches', self._winid)]
if self._matched_range_id in matches:
self._vim.call('matchdelete',
self._matched_range_id, self._winid)
self._matched_range_id = -1
if self._matched_char_id in matches:
self._vim.call('matchdelete',
self._matched_char_id, self._winid)
self._matched_char_id = -1
if self._matched_pattern != '':
self._matched_range_id = self._vim.call(
'matchadd', 'deniteMatchedRange',
r'\c' + regex_convert_py_vim(self._matched_pattern),
10, -1, {'window': self._winid})
matched_char_pattern = '[{}]'.format(re.sub(
r'([\[\]\\^-])',
r'\\\1',
self._context['input'].replace(' ', '')
))
self._matched_char_id = self._vim.call(
'matchadd', 'deniteMatchedChar',
matched_char_pattern,
10, -1, {'window': self._winid})
prev_linenr = self._vim.call('line', '.')
prev_candidate = self._get_cursor_candidate()
buffer = self._vim.buffers[self._bufnr]
buffer.options['modifiable'] = True
self._vim.vars['denite#_candidates'] = [
x['word'] for x in self._candidates]
buffer[:] = self._displayed_texts
buffer.options['modifiable'] = False
self._previous_text = self._context['input']
self._resize_buffer(is_current_buffer)
is_changed = (self._context['reversed'] or
(is_current_buffer and
self._previous_text != self._context['input']))
if self._updated and is_changed:
if not is_current_buffer:
save_winid = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
self._init_cursor()
self._move_to_pos(self._cursor)
if not is_current_buffer:
self._vim.call('win_gotoid', save_winid)
elif is_current_buffer:
self._vim.call('cursor', [prev_linenr, 0])
if is_current_buffer:
if (self._context['auto_action'] and
prev_candidate != self._get_cursor_candidate()):
self.do_action(self._context['auto_action'])
self._updated = False
self._stop_timer('update_buffer')
def _update_status(self) -> None:
inpt = ''
if self._context['input']:
inpt = self._context['input'] + ' '
if self._context['error_messages']:
inpt = '[ERROR] ' + inpt
path = '[' + self._context['path'] + ']'
status = {
'input': inpt,
'sources': self._statusline_sources,
'path': path,
# Extra
'buffer_name': self._context['buffer_name'],
'line_total': len(self._candidates),
}
if status == self._prev_status:
return
self._bufvars['denite_statusline'] = status
self._prev_status = status
linenr = "printf('%'.(len(line('$'))+2).'d/%d',line('.'),line('$'))"
if self._context['statusline']:
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = (
"%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} " +
" %{denite#get_status('path')}%*" +
"%{" + linenr + "}%*")
else:
winnr = self._vim.call('win_id2win', self._winid)
self._vim.call('setwinvar', winnr, '&statusline', (
"%#deniteInput#%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} %=" +
"%#deniteStatusLinePath# %{denite#get_status('path')}%*" +
"%#deniteStatusLineNumber#%{" + linenr + "}%*"))
def _get_display_source_name(self, name: str) -> str:
source_names = self._context['source_names']
if not self._is_multi or source_names == 'hide':
source_name = ''
else:
short_name = (re.sub(r'([a-zA-Z])[a-zA-Z]+', r'\1', name)
if re.search(r'[^a-zA-Z]', name) else name[:2])
source_name = short_name if source_names == 'short' else name
return source_name
def _get_candidate_display_text(self, index: int) -> str:
source_names = self._context['source_names']
candidate = self._candidates[index]
terms = []
if self._is_multi and source_names != 'hide':
terms.append(self._context['max_source_name_format'].format(
self._get_display_source_name(candidate['source_name'])))
encoding = self._context['encoding']
abbr = candidate.get('abbr', candidate['word']).encode(
encoding, errors='replace').decode(encoding, errors='replace')
terms.append(abbr[:int(self._context['max_candidate_width'])])
return (str(self._context['selected_icon'])
if index in self._selected_candidates
else ' ') + ' '.join(terms).replace('\n', '')
def _get_max_height(self) -> int:
return int(self._vim.options['lines']) if not self._floating else (
int(self._vim.options['lines']) -
int(self._context['winrow']) -
int(self._vim.options['cmdheight']))
def _resize_buffer(self, is_current_buffer: bool) -> None:
split = self._context['split']
if (split == 'no' or split == 'tab' or
self._vim.call('winnr', '$') == 1):
return
winheight = max(self._winheight, 1)
winwidth = max(self._winwidth, 1)
is_vertical = split == 'vertical'
if not is_current_buffer:
restore = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
if not is_vertical and self._vim.current.window.height != winheight:
if self._floating:
wincol = self._context['winrow']
row = wincol
if split == 'floating':
if self._context['auto_resize'] and row > 1:
row += self._context['winheight']
row -= self._winheight
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'editor',
'row': row,
'col': self._context['wincol'],
'width': winwidth,
'height': winheight,
})
filter_row = 0 if wincol == 1 else row + winheight
filter_col = self._context['wincol']
else:
init_pos = self._vim.call('nvim_win_get_config',
self._winid)
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'win',
'win': init_pos['win'],
'row': init_pos['row'],
'col': init_pos['col'],
'width': winwidth,
'height': winheight,
})
filter_col = init_pos['col']
if init_pos['anchor'] == 'NW':
winpos = self._vim.call('nvim_win_get_position',
self._winid)
filter_row = winpos[0] + winheight
filter_winid = self._vim.vars['denite#_filter_winid']
self._context['filter_winrow'] = row
if self._vim.call('win_id2win', filter_winid) > 0:
self._vim.call('nvim_win_set_config', filter_winid, {
'relative': 'editor',
'row': filter_row,
'col': filter_col,
})
self._vim.command('resize ' + str(winheight))
if self._context['reversed']:
self._vim.command('normal! zb')
elif is_vertical and self._vim.current.window.width != winwidth:
self._vim.command('vertical resize ' + str(winwidth))
if not is_current_buffer:
self._vim.call('win_gotoid', restore)
def _check_do_option(self) -> bool:
if self._context['do'] != '':
self._do_command(self._context['do'])
return True
elif (self._candidates and self._context['immediately'] or
len(self._candidates) == 1 and self._context['immediately_1']):
self._do_immediately()
return True
return not (self._context['empty'] or
self._is_async or self._candidates)
def _check_move_option(self) -> None:
if self._context['cursor_pos'].isnumeric():
self._cursor = int(self._context['cursor_pos']) + 1
elif re.match(r'\+\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_next_line()
elif re.match(r'-\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_prev_line()
elif self._context['cursor_pos'] == '$':
self._move_to_last_line()
def _do_immediately(self) -> None:
goto = self._winid > 0 and self._vim.call(
'win_gotoid', self._winid)
if goto:
# Jump to denite window
self._init_buffer()
self.do_action('default')
candidate = self._get_cursor_candidate()
if not candidate:
return
echo(self._vim, 'Normal', '[{}/{}] {}'.format(
self._cursor, len(self._candidates),
candidate.get('abbr', candidate['word'])))
if goto:
# Move to the previous window
self._vim.command('wincmd p')
def _do_command(self, command: str) -> None:
self._init_cursor()
cursor = 1
while cursor < len(self._candidates):
self.do_action('default', command)
self._move_to_next_line()
self._quit_buffer()
def _cleanup(self) -> None:
self._stop_timer('update_candidates')
self._stop_timer('update_buffer')
if self._vim.current.buffer.number == self._bufnr:
self._cursor = self._vim.call('line', '.')
# Note: Close filter window before preview window
self._vim.call('denite#filter#_close_filter_window')
if not self._context['has_preview_window']:
self._vim.command('pclose!')
# Clear previewed buffers
for bufnr in self._vim.vars['denite#_previewed_buffers'].keys():
if not self._vim.call('win_findbuf', bufnr):
self._vim.command('silent bdelete ' + str(bufnr))
self._vim.vars['denite#_previewed_buffers'] = {}
self._vim.command('highlight! link CursorLine CursorLine')
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = self._titlestring
self._vim.options['ruler'] = self._ruler
def _close_current_window(self) -> None:
if self._vim.call('winnr', '$') == 1:
self._vim.command('buffer #')
else:
self._vim.command('close!')
def _quit_buffer(self) -> None:
self._cleanup()
if self._vim.call('bufwinnr', self._bufnr) < 0:
# Denite buffer is already closed
return
winids = self._vim.call('win_findbuf',
self._vim.vars['denite#_filter_bufnr'])
if winids:
# Quit filter buffer
self._vim.call('win_gotoid', winids[0])
self._close_current_window()
# Move to denite window
self._vim.call('win_gotoid', self._winid)
# Restore the window
if self._context['split'] == 'no':
self._switch_prev_buffer()
for k, v in self._save_window_options.items():
self._vim.current.window.options[k] = v
else:
if self._context['split'] == 'tab':
self._vim.command('tabclose!')
if self._context['split'] != 'tab':
self._close_current_window()
self._vim.call('win_gotoid', self._prev_winid)
# Restore the position
self._vim.call('setpos', '.', self._prev_curpos)
if self._get_wininfo() and self._get_wininfo() == self._prev_wininfo:
# Note: execute restcmd twice to restore layout properly
self._vim.command(self._winrestcmd)
self._vim.command(self._winrestcmd)
clearmatch(self._vim)
def _get_cursor_candidate(self) -> Candidate:
return self._get_candidate(self._cursor)
def _get_candidate(self, pos: int) -> Candidate:
if not self._candidates or pos > len(self._candidates):
return {}
return self._candidates[pos - 1]
def _get_selected_candidates(self) -> Candidates:
if not self._selected_candidates:
return [self._get_cursor_candidate()
] if self._get_cursor_candidate() else []
return [self._candidates[x] for x in self._selected_candidates]
def _init_denite(self) -> None:
if self._denite:
self._denite.start(self._context)
self._denite.on_init(self._context)
self._initialized = True
self._winheight = self._context['winheight']
self._winwidth = self._context['winwidth']
def _gather_candidates(self) -> None:
self._selected_candidates = []
if self._denite:
self._denite.gather_candidates(self._context)
def _init_cursor(self) -> None:
if self._context['reversed']:
self._move_to_last_line()
else:
self._move_to_first_line()
def _move_to_pos(self, pos: int) -> None:
self._vim.call('cursor', pos, 0)
self._cursor = pos
if self._context['reversed']:
self._vim.command('normal! zb')
def _move_to_next_line(self) -> None:
if self._cursor < len(self._candidates):
self._cursor += 1
def _move_to_prev_line(self) -> None:
if self._cursor >= 1:
self._cursor -= 1
def _move_to_first_line(self) -> None:
self._cursor = 1
def _move_to_last_line(self) -> None:
self._cursor = len(self._candidates)
def _start_timer(self, key: str) -> None:
if key in self._timers:
return
if key == 'update_candidates':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_candidates_timer', self._bufnr)
elif key == 'update_buffer':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_buffer_timer', self._bufnr)
def _stop_timer(self, key: str) -> None:
if key not in self._timers:
return
self._vim.call('timer_stop', self._timers[key])
# Note: After timer_stop is called, self._timers may be removed
if key in self._timers:
self._timers.pop(key)
def _split_floating(self, split: str) -> None:
# Use floating window
if split == 'floating':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'editor',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
})
elif split == 'floating_relative_cursor':
opened_pos = (self._vim.call('nvim_win_get_position', 0)[0] +
self._vim.call('winline') - 1)
if self._context['auto_resize']:
height = max(self._winheight, 1)
width = max(self._winwidth, 1)
else:
width = self._context['winwidth']
height = self._context['winheight']
if opened_pos + height + 3 > self._vim.options['lines']:
anchor = 'SW'
row = 0
self._context['filter_winrow'] = row + opened_pos
else:
anchor = 'NW'
row = 1
self._context['filter_winrow'] = row + height + opened_pos
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'cursor',
'row': row,
'col': 0,
'width': width,
'height': height,
'anchor': anchor,
})
elif split == 'floating_relative_window':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'win',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
})
| # ============================================================================
# FILE: default.py
# AUTHOR: <NAME> <<EMAIL> at g<EMAIL>>
# License: MIT license
# ============================================================================
import re
import typing
from denite.util import echo, error, clearmatch, regex_convert_py_vim
from denite.util import Nvim, UserContext, Candidates, Candidate
from denite.parent import SyncParent
class Default(object):
@property
def is_async(self) -> bool:
return self._is_async
def __init__(self, vim: Nvim) -> None:
self._vim = vim
self._denite: typing.Optional[SyncParent] = None
self._selected_candidates: typing.List[int] = []
self._candidates: Candidates = []
self._cursor = 0
self._entire_len = 0
self._result: typing.List[typing.Any] = []
self._context: UserContext = {}
self._bufnr = -1
self._winid = -1
self._winrestcmd = ''
self._initialized = False
self._winheight = 0
self._winwidth = 0
self._winminheight = -1
self._is_multi = False
self._is_async = False
self._matched_pattern = ''
self._displayed_texts: typing.List[str] = []
self._statusline_sources = ''
self._titlestring = ''
self._ruler = False
self._prev_action = ''
self._prev_status: typing.Dict[str, typing.Any] = {}
self._prev_curpos: typing.List[typing.Any] = []
self._save_window_options: typing.Dict[str, typing.Any] = {}
self._sources_history: typing.List[typing.Any] = []
self._previous_text = ''
self._floating = False
self._filter_floating = False
self._updated = False
self._timers: typing.Dict[str, int] = {}
self._matched_range_id = -1
self._matched_char_id = -1
self._check_matchdelete = bool(self._vim.call(
'denite#util#check_matchdelete'))
def start(self, sources: typing.List[typing.Any],
context: UserContext) -> typing.List[typing.Any]:
if not self._denite:
# if hasattr(self._vim, 'run_coroutine'):
# self._denite = ASyncParent(self._vim)
# else:
self._denite = SyncParent(self._vim)
self._result = []
context['sources_queue'] = [sources]
self._start_sources_queue(context)
return self._result
def do_action(self, action_name: str,
command: str = '', is_manual: bool = False) -> None:
if is_manual:
candidates = self._get_selected_candidates()
elif self._get_cursor_candidate():
candidates = [self._get_cursor_candidate()]
else:
candidates = []
if not self._denite or not candidates or not action_name:
return
self._prev_action = action_name
action = self._denite.get_action(
self._context, action_name, candidates)
if not action:
return
post_action = self._context['post_action']
is_quit = action['is_quit'] or post_action == 'quit'
if is_quit:
self.quit()
self._denite.do_action(self._context, action_name, candidates)
self._result = candidates
if command != '':
self._vim.command(command)
if is_quit and post_action == 'open':
# Re-open denite buffer
prev_cursor = self._cursor
cursor_candidate = self._get_cursor_candidate()
self._init_buffer()
self.redraw(False)
if cursor_candidate == self._get_candidate(prev_cursor):
# Restore the cursor
self._move_to_pos(prev_cursor)
# Disable quit flag
is_quit = False
if not is_quit and is_manual:
self._selected_candidates = []
self.redraw(action['is_redraw'])
if is_manual and self._context['sources_queue']:
self._context['input'] = ''
self._context['quick_move'] = ''
self._start_sources_queue(self._context)
return
def redraw(self, is_force: bool = True) -> None:
self._context['is_redraw'] = is_force
if is_force:
self._gather_candidates()
if self._update_candidates():
self._update_buffer()
else:
self._update_status()
self._context['is_redraw'] = False
def quit(self) -> None:
if self._denite:
self._denite.on_close(self._context)
self._quit_buffer()
self._result = []
return
def _restart(self) -> None:
self._context['input'] = ''
self._quit_buffer()
self._init_denite()
self._gather_candidates()
self._init_buffer()
self._update_candidates()
self._update_buffer()
def _start_sources_queue(self, context: UserContext) -> None:
if not context['sources_queue']:
return
self._sources_history.append({
'sources': context['sources_queue'][0],
'path': context['path'],
})
self._start(context['sources_queue'][0], context)
if context['sources_queue']:
context['sources_queue'].pop(0)
context['path'] = self._context['path']
def _start(self, sources: typing.List[typing.Any],
context: UserContext) -> None:
from denite.ui.map import do_map
self._vim.command('silent! autocmd! denite')
if re.search(r'\[Command Line\]$', self._vim.current.buffer.name):
# Ignore command line window.
return
resume = self._initialized and context['resume']
if resume:
# Skip the initialization
update = ('immediately', 'immediately_1',
'cursor_pos', 'prev_winid',
'start_filter', 'quick_move')
for key in update:
self._context[key] = context[key]
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
if context['refresh']:
self.redraw()
self._move_to_pos(self._cursor)
else:
if self._context != context:
self._context.clear()
self._context.update(context)
self._context['sources'] = sources
self._context['is_redraw'] = False
self._is_multi = len(sources) > 1
if not sources:
# Ignore empty sources.
error(self._vim, 'Empty sources')
return
self._init_denite()
self._gather_candidates()
self._update_candidates()
self._init_cursor()
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
self._update_displayed_texts()
self._update_buffer()
self._move_to_pos(self._cursor)
if self._context['quick_move'] and do_map(self, 'quick_move', []):
return
if self._context['start_filter']:
do_map(self, 'open_filter_buffer', [])
def _init_buffer(self) -> None:
self._prev_status = dict()
self._displayed_texts = []
self._prev_bufnr = self._vim.current.buffer.number
self._prev_curpos = self._vim.call('getcurpos')
self._prev_wininfo = self._get_wininfo()
self._prev_winid = self._context['prev_winid']
self._winrestcmd = self._vim.call('winrestcmd')
self._ruler = self._vim.options['ruler']
self._switch_buffer()
self._bufnr = self._vim.current.buffer.number
self._winid = self._vim.call('win_getid')
self._resize_buffer(True)
self._winheight = self._vim.current.window.height
self._winwidth = self._vim.current.window.width
self._bufvars = self._vim.current.buffer.vars
self._bufvars['denite'] = {
'buffer_name': self._context['buffer_name'],
}
self._bufvars['denite_statusline'] = {}
self._vim.vars['denite#_previewed_buffers'] = {}
self._save_window_options = {}
window_options = {
'colorcolumn',
'concealcursor',
'conceallevel',
'cursorcolumn',
'cursorline',
'foldcolumn',
'foldenable',
'list',
'number',
'relativenumber',
'signcolumn',
'spell',
'winfixheight',
'wrap',
}
for k in window_options:
self._save_window_options[k] = self._vim.current.window.options[k]
# Note: Have to use setlocal instead of "current.window.options"
# "current.window.options" changes global value instead of local in
# neovim.
self._vim.command('setlocal colorcolumn=')
self._vim.command('setlocal conceallevel=3')
self._vim.command('setlocal concealcursor=inv')
self._vim.command('setlocal nocursorcolumn')
self._vim.command('setlocal nofoldenable')
self._vim.command('setlocal foldcolumn=0')
self._vim.command('setlocal nolist')
self._vim.command('setlocal nonumber')
self._vim.command('setlocal norelativenumber')
self._vim.command('setlocal nospell')
self._vim.command('setlocal winfixheight')
self._vim.command('setlocal nowrap')
if self._context['prompt']:
self._vim.command('setlocal signcolumn=yes')
else:
self._vim.command('setlocal signcolumn=auto')
if self._context['cursorline']:
self._vim.command('setlocal cursorline')
options = self._vim.current.buffer.options
if self._floating:
# Disable ruler
self._vim.options['ruler'] = False
options['buftype'] = 'nofile'
options['bufhidden'] = 'delete'
options['swapfile'] = False
options['buflisted'] = False
options['modeline'] = False
options['modifiable'] = False
options['filetype'] = 'denite'
if self._vim.call('exists', '#WinEnter'):
self._vim.command('doautocmd WinEnter')
if self._vim.call('exists', '#BufWinEnter'):
self._vim.command('doautocmd BufWinEnter')
if not self._vim.call('has', 'nvim'):
# In Vim8, FileType autocmd is not fired after set filetype option.
self._vim.command('silent doautocmd FileType denite')
if self._context['auto_action']:
self._vim.command('autocmd denite '
'CursorMoved <buffer> '
'call denite#call_map("auto_action")')
self._init_syntax()
def _switch_buffer(self) -> None:
split = self._context['split']
if (split != 'no' and self._winid > 0 and
self._vim.call('win_gotoid', self._winid)):
if split != 'vertical' and not self._floating:
# Move the window to bottom
self._vim.command('wincmd J')
self._winrestcmd = ''
return
self._floating = split in [
'floating',
'floating_relative_cursor',
'floating_relative_window',
]
self._filter_floating = False
if self._vim.current.buffer.options['filetype'] != 'denite':
self._titlestring = self._vim.options['titlestring']
command = 'edit'
if split == 'tab':
self._vim.command('tabnew')
elif self._floating:
self._split_floating(split)
elif self._context['filter_split_direction'] == 'floating':
self._filter_floating = True
elif split != 'no':
command = self._get_direction()
command += ' vsplit' if split == 'vertical' else ' split'
bufname = '[denite]-' + self._context['buffer_name']
if self._vim.call('exists', '*bufadd'):
bufnr = self._vim.call('bufadd', bufname)
vertical = 'vertical' if split == 'vertical' else ''
command = (
'buffer' if split
in ['no', 'tab', 'floating',
'floating_relative_window',
'floating_relative_cursor'] else 'sbuffer')
self._vim.command(
'silent keepalt %s %s %s %s' % (
self._get_direction(),
vertical,
command,
bufnr,
)
)
else:
self._vim.call(
'denite#util#execute_path',
f'silent keepalt {command}', bufname)
def _get_direction(self) -> str:
direction = str(self._context['direction'])
if direction == 'dynamictop' or direction == 'dynamicbottom':
self._update_displayed_texts()
winwidth = self._vim.call('winwidth', 0)
is_fit = not [x for x in self._displayed_texts
if self._vim.call('strwidth', x) > winwidth]
if direction == 'dynamictop':
direction = 'aboveleft' if is_fit else 'topleft'
else:
direction = 'belowright' if is_fit else 'botright'
return direction
def _get_wininfo(self) -> typing.List[typing.Any]:
return [
self._vim.options['columns'], self._vim.options['lines'],
self._vim.call('win_getid'), self._vim.call('tabpagebuflist')
]
def _switch_prev_buffer(self) -> None:
if (self._prev_bufnr == self._bufnr or
self._vim.buffers[self._prev_bufnr].name == ''):
self._vim.command('enew')
else:
self._vim.command('buffer ' + str(self._prev_bufnr))
def _init_syntax(self) -> None:
self._vim.command('syntax case ignore')
self._vim.command('highlight default link deniteInput ModeMsg')
self._vim.command('highlight link deniteMatchedRange ' +
self._context['highlight_matched_range'])
self._vim.command('highlight link deniteMatchedChar ' +
self._context['highlight_matched_char'])
self._vim.command('highlight default link ' +
'deniteStatusLinePath Comment')
self._vim.command('highlight default link ' +
'deniteStatusLineNumber LineNR')
self._vim.command('highlight default link ' +
'deniteSelectedLine Statement')
if self._floating:
self._vim.current.window.options['winhighlight'] = (
'Normal:' + self._context['highlight_window_background']
)
self._vim.command(('syntax match deniteSelectedLine /^[%s].*/' +
' contains=deniteConcealedMark') % (
self._context['selected_icon']))
self._vim.command(('syntax match deniteConcealedMark /^[ %s]/' +
' conceal contained') % (
self._context['selected_icon']))
if self._denite:
self._denite.init_syntax(self._context, self._is_multi)
def _update_candidates(self) -> bool:
if not self._denite:
return False
[self._is_async, pattern, statuses, self._entire_len,
self._candidates] = self._denite.filter_candidates(self._context)
prev_displayed_texts = self._displayed_texts
self._update_displayed_texts()
prev_matched_pattern = self._matched_pattern
self._matched_pattern = pattern
prev_statusline_sources = self._statusline_sources
self._statusline_sources = ' '.join(statuses)
if self._is_async:
self._start_timer('update_candidates')
else:
self._stop_timer('update_candidates')
updated = (self._displayed_texts != prev_displayed_texts or
self._matched_pattern != prev_matched_pattern or
self._statusline_sources != prev_statusline_sources)
if updated:
self._updated = True
self._start_timer('update_buffer')
if self._context['search'] and self._context['input']:
self._vim.call('setreg', '/', self._context['input'])
return self._updated
def _update_displayed_texts(self) -> None:
candidates_len = len(self._candidates)
if not self._is_async and self._context['auto_resize']:
winminheight = self._context['winminheight']
max_height = min(self._context['winheight'],
self._get_max_height())
if (winminheight != -1 and candidates_len < winminheight):
self._winheight = winminheight
elif candidates_len > max_height:
self._winheight = max_height
elif candidates_len != self._winheight:
self._winheight = candidates_len
max_source_name_len = 0
if self._candidates:
max_source_name_len = max([
len(self._get_display_source_name(x['source_name']))
for x in self._candidates])
self._context['max_source_name_len'] = max_source_name_len
self._context['max_source_name_format'] = (
'{:<' + str(self._context['max_source_name_len']) + '}')
self._displayed_texts = [
self._get_candidate_display_text(i)
for i in range(0, candidates_len)
]
def _update_buffer(self) -> None:
is_current_buffer = self._bufnr == self._vim.current.buffer.number
self._update_status()
if self._check_matchdelete and self._context['match_highlight']:
matches = [x['id'] for x in
self._vim.call('getmatches', self._winid)]
if self._matched_range_id in matches:
self._vim.call('matchdelete',
self._matched_range_id, self._winid)
self._matched_range_id = -1
if self._matched_char_id in matches:
self._vim.call('matchdelete',
self._matched_char_id, self._winid)
self._matched_char_id = -1
if self._matched_pattern != '':
self._matched_range_id = self._vim.call(
'matchadd', 'deniteMatchedRange',
r'\c' + regex_convert_py_vim(self._matched_pattern),
10, -1, {'window': self._winid})
matched_char_pattern = '[{}]'.format(re.sub(
r'([\[\]\\^-])',
r'\\\1',
self._context['input'].replace(' ', '')
))
self._matched_char_id = self._vim.call(
'matchadd', 'deniteMatchedChar',
matched_char_pattern,
10, -1, {'window': self._winid})
prev_linenr = self._vim.call('line', '.')
prev_candidate = self._get_cursor_candidate()
buffer = self._vim.buffers[self._bufnr]
buffer.options['modifiable'] = True
self._vim.vars['denite#_candidates'] = [
x['word'] for x in self._candidates]
buffer[:] = self._displayed_texts
buffer.options['modifiable'] = False
self._previous_text = self._context['input']
self._resize_buffer(is_current_buffer)
is_changed = (self._context['reversed'] or
(is_current_buffer and
self._previous_text != self._context['input']))
if self._updated and is_changed:
if not is_current_buffer:
save_winid = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
self._init_cursor()
self._move_to_pos(self._cursor)
if not is_current_buffer:
self._vim.call('win_gotoid', save_winid)
elif is_current_buffer:
self._vim.call('cursor', [prev_linenr, 0])
if is_current_buffer:
if (self._context['auto_action'] and
prev_candidate != self._get_cursor_candidate()):
self.do_action(self._context['auto_action'])
self._updated = False
self._stop_timer('update_buffer')
def _update_status(self) -> None:
inpt = ''
if self._context['input']:
inpt = self._context['input'] + ' '
if self._context['error_messages']:
inpt = '[ERROR] ' + inpt
path = '[' + self._context['path'] + ']'
status = {
'input': inpt,
'sources': self._statusline_sources,
'path': path,
# Extra
'buffer_name': self._context['buffer_name'],
'line_total': len(self._candidates),
}
if status == self._prev_status:
return
self._bufvars['denite_statusline'] = status
self._prev_status = status
linenr = "printf('%'.(len(line('$'))+2).'d/%d',line('.'),line('$'))"
if self._context['statusline']:
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = (
"%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} " +
" %{denite#get_status('path')}%*" +
"%{" + linenr + "}%*")
else:
winnr = self._vim.call('win_id2win', self._winid)
self._vim.call('setwinvar', winnr, '&statusline', (
"%#deniteInput#%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} %=" +
"%#deniteStatusLinePath# %{denite#get_status('path')}%*" +
"%#deniteStatusLineNumber#%{" + linenr + "}%*"))
def _get_display_source_name(self, name: str) -> str:
source_names = self._context['source_names']
if not self._is_multi or source_names == 'hide':
source_name = ''
else:
short_name = (re.sub(r'([a-zA-Z])[a-zA-Z]+', r'\1', name)
if re.search(r'[^a-zA-Z]', name) else name[:2])
source_name = short_name if source_names == 'short' else name
return source_name
def _get_candidate_display_text(self, index: int) -> str:
source_names = self._context['source_names']
candidate = self._candidates[index]
terms = []
if self._is_multi and source_names != 'hide':
terms.append(self._context['max_source_name_format'].format(
self._get_display_source_name(candidate['source_name'])))
encoding = self._context['encoding']
abbr = candidate.get('abbr', candidate['word']).encode(
encoding, errors='replace').decode(encoding, errors='replace')
terms.append(abbr[:int(self._context['max_candidate_width'])])
return (str(self._context['selected_icon'])
if index in self._selected_candidates
else ' ') + ' '.join(terms).replace('\n', '')
def _get_max_height(self) -> int:
return int(self._vim.options['lines']) if not self._floating else (
int(self._vim.options['lines']) -
int(self._context['winrow']) -
int(self._vim.options['cmdheight']))
def _resize_buffer(self, is_current_buffer: bool) -> None:
split = self._context['split']
if (split == 'no' or split == 'tab' or
self._vim.call('winnr', '$') == 1):
return
winheight = max(self._winheight, 1)
winwidth = max(self._winwidth, 1)
is_vertical = split == 'vertical'
if not is_current_buffer:
restore = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
if not is_vertical and self._vim.current.window.height != winheight:
if self._floating:
wincol = self._context['winrow']
row = wincol
if split == 'floating':
if self._context['auto_resize'] and row > 1:
row += self._context['winheight']
row -= self._winheight
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'editor',
'row': row,
'col': self._context['wincol'],
'width': winwidth,
'height': winheight,
})
filter_row = 0 if wincol == 1 else row + winheight
filter_col = self._context['wincol']
else:
init_pos = self._vim.call('nvim_win_get_config',
self._winid)
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'win',
'win': init_pos['win'],
'row': init_pos['row'],
'col': init_pos['col'],
'width': winwidth,
'height': winheight,
})
filter_col = init_pos['col']
if init_pos['anchor'] == 'NW':
winpos = self._vim.call('nvim_win_get_position',
self._winid)
filter_row = winpos[0] + winheight
filter_winid = self._vim.vars['denite#_filter_winid']
self._context['filter_winrow'] = row
if self._vim.call('win_id2win', filter_winid) > 0:
self._vim.call('nvim_win_set_config', filter_winid, {
'relative': 'editor',
'row': filter_row,
'col': filter_col,
})
self._vim.command('resize ' + str(winheight))
if self._context['reversed']:
self._vim.command('normal! zb')
elif is_vertical and self._vim.current.window.width != winwidth:
self._vim.command('vertical resize ' + str(winwidth))
if not is_current_buffer:
self._vim.call('win_gotoid', restore)
def _check_do_option(self) -> bool:
if self._context['do'] != '':
self._do_command(self._context['do'])
return True
elif (self._candidates and self._context['immediately'] or
len(self._candidates) == 1 and self._context['immediately_1']):
self._do_immediately()
return True
return not (self._context['empty'] or
self._is_async or self._candidates)
def _check_move_option(self) -> None:
if self._context['cursor_pos'].isnumeric():
self._cursor = int(self._context['cursor_pos']) + 1
elif re.match(r'\+\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_next_line()
elif re.match(r'-\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_prev_line()
elif self._context['cursor_pos'] == '$':
self._move_to_last_line()
def _do_immediately(self) -> None:
goto = self._winid > 0 and self._vim.call(
'win_gotoid', self._winid)
if goto:
# Jump to denite window
self._init_buffer()
self.do_action('default')
candidate = self._get_cursor_candidate()
if not candidate:
return
echo(self._vim, 'Normal', '[{}/{}] {}'.format(
self._cursor, len(self._candidates),
candidate.get('abbr', candidate['word'])))
if goto:
# Move to the previous window
self._vim.command('wincmd p')
def _do_command(self, command: str) -> None:
self._init_cursor()
cursor = 1
while cursor < len(self._candidates):
self.do_action('default', command)
self._move_to_next_line()
self._quit_buffer()
def _cleanup(self) -> None:
self._stop_timer('update_candidates')
self._stop_timer('update_buffer')
if self._vim.current.buffer.number == self._bufnr:
self._cursor = self._vim.call('line', '.')
# Note: Close filter window before preview window
self._vim.call('denite#filter#_close_filter_window')
if not self._context['has_preview_window']:
self._vim.command('pclose!')
# Clear previewed buffers
for bufnr in self._vim.vars['denite#_previewed_buffers'].keys():
if not self._vim.call('win_findbuf', bufnr):
self._vim.command('silent bdelete ' + str(bufnr))
self._vim.vars['denite#_previewed_buffers'] = {}
self._vim.command('highlight! link CursorLine CursorLine')
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = self._titlestring
self._vim.options['ruler'] = self._ruler
def _close_current_window(self) -> None:
if self._vim.call('winnr', '$') == 1:
self._vim.command('buffer #')
else:
self._vim.command('close!')
def _quit_buffer(self) -> None:
self._cleanup()
if self._vim.call('bufwinnr', self._bufnr) < 0:
# Denite buffer is already closed
return
winids = self._vim.call('win_findbuf',
self._vim.vars['denite#_filter_bufnr'])
if winids:
# Quit filter buffer
self._vim.call('win_gotoid', winids[0])
self._close_current_window()
# Move to denite window
self._vim.call('win_gotoid', self._winid)
# Restore the window
if self._context['split'] == 'no':
self._switch_prev_buffer()
for k, v in self._save_window_options.items():
self._vim.current.window.options[k] = v
else:
if self._context['split'] == 'tab':
self._vim.command('tabclose!')
if self._context['split'] != 'tab':
self._close_current_window()
self._vim.call('win_gotoid', self._prev_winid)
# Restore the position
self._vim.call('setpos', '.', self._prev_curpos)
if self._get_wininfo() and self._get_wininfo() == self._prev_wininfo:
# Note: execute restcmd twice to restore layout properly
self._vim.command(self._winrestcmd)
self._vim.command(self._winrestcmd)
clearmatch(self._vim)
def _get_cursor_candidate(self) -> Candidate:
return self._get_candidate(self._cursor)
def _get_candidate(self, pos: int) -> Candidate:
if not self._candidates or pos > len(self._candidates):
return {}
return self._candidates[pos - 1]
def _get_selected_candidates(self) -> Candidates:
if not self._selected_candidates:
return [self._get_cursor_candidate()
] if self._get_cursor_candidate() else []
return [self._candidates[x] for x in self._selected_candidates]
def _init_denite(self) -> None:
if self._denite:
self._denite.start(self._context)
self._denite.on_init(self._context)
self._initialized = True
self._winheight = self._context['winheight']
self._winwidth = self._context['winwidth']
def _gather_candidates(self) -> None:
self._selected_candidates = []
if self._denite:
self._denite.gather_candidates(self._context)
def _init_cursor(self) -> None:
if self._context['reversed']:
self._move_to_last_line()
else:
self._move_to_first_line()
def _move_to_pos(self, pos: int) -> None:
self._vim.call('cursor', pos, 0)
self._cursor = pos
if self._context['reversed']:
self._vim.command('normal! zb')
def _move_to_next_line(self) -> None:
if self._cursor < len(self._candidates):
self._cursor += 1
def _move_to_prev_line(self) -> None:
if self._cursor >= 1:
self._cursor -= 1
def _move_to_first_line(self) -> None:
self._cursor = 1
def _move_to_last_line(self) -> None:
self._cursor = len(self._candidates)
def _start_timer(self, key: str) -> None:
if key in self._timers:
return
if key == 'update_candidates':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_candidates_timer', self._bufnr)
elif key == 'update_buffer':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_buffer_timer', self._bufnr)
def _stop_timer(self, key: str) -> None:
if key not in self._timers:
return
self._vim.call('timer_stop', self._timers[key])
# Note: After timer_stop is called, self._timers may be removed
if key in self._timers:
self._timers.pop(key)
def _split_floating(self, split: str) -> None:
# Use floating window
if split == 'floating':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'editor',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
})
elif split == 'floating_relative_cursor':
opened_pos = (self._vim.call('nvim_win_get_position', 0)[0] +
self._vim.call('winline') - 1)
if self._context['auto_resize']:
height = max(self._winheight, 1)
width = max(self._winwidth, 1)
else:
width = self._context['winwidth']
height = self._context['winheight']
if opened_pos + height + 3 > self._vim.options['lines']:
anchor = 'SW'
row = 0
self._context['filter_winrow'] = row + opened_pos
else:
anchor = 'NW'
row = 1
self._context['filter_winrow'] = row + height + opened_pos
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'cursor',
'row': row,
'col': 0,
'width': width,
'height': height,
'anchor': anchor,
})
elif split == 'floating_relative_window':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'win',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
}) | en | 0.465682 | # ============================================================================ # FILE: default.py # AUTHOR: <NAME> <<EMAIL> at g<EMAIL>> # License: MIT license # ============================================================================ #util#check_matchdelete')) # if hasattr(self._vim, 'run_coroutine'): # self._denite = ASyncParent(self._vim) # else: # Re-open denite buffer # Restore the cursor # Disable quit flag # Ignore command line window. # Skip the initialization # Ignore empty sources. #_previewed_buffers'] = {} # Note: Have to use setlocal instead of "current.window.options" # "current.window.options" changes global value instead of local in # neovim. # Disable ruler # In Vim8, FileType autocmd is not fired after set filetype option. #call_map("auto_action")') # Move the window to bottom #util#execute_path', #_candidates'] = [ # Extra #get_status('input')}%* " + #get_status('sources')} " + #get_status('path')}%*" + #deniteInput#%{denite#get_status('input')}%* " + #get_status('sources')} %=" + #deniteStatusLinePath# %{denite#get_status('path')}%*" + #deniteStatusLineNumber#%{" + linenr + "}%*")) #_filter_winid'] # Jump to denite window # Move to the previous window # Note: Close filter window before preview window #filter#_close_filter_window') # Clear previewed buffers #_previewed_buffers'].keys(): #_previewed_buffers'] = {} #') # Denite buffer is already closed #_filter_bufnr']) # Quit filter buffer # Move to denite window # Restore the window # Restore the position # Note: execute restcmd twice to restore layout properly #helper#_start_update_candidates_timer', self._bufnr) #helper#_start_update_buffer_timer', self._bufnr) # Note: After timer_stop is called, self._timers may be removed # Use floating window | 1.901279 | 2 |
PyDSTool/core/context_managers.py | yuanz271/PyDSTool | 0 | 5 | <filename>PyDSTool/core/context_managers.py
# -*- coding: utf-8 -*-
"""Context managers implemented for (mostly) internal use"""
import contextlib
import functools
from io import UnsupportedOperation
import os
import sys
__all__ = ["RedirectStdout", "RedirectStderr"]
@contextlib.contextmanager
def _stdchannel_redirected(stdchannel, dest_filename, mode="w"):
"""
A context manager to temporarily redirect stdout or stderr
Originally by <NAME>, 2013
(http://marc-abramowitz.com/archives/2013/07/19/python-context-manager-for-redirected-stdout-and-stderr/)
"""
oldstdchannel = None
dest_file = None
try:
if stdchannel is None:
yield iter([None])
else:
oldstdchannel = os.dup(stdchannel.fileno())
dest_file = open(dest_filename, mode)
os.dup2(dest_file.fileno(), stdchannel.fileno())
yield
except (UnsupportedOperation, AttributeError):
yield iter([None])
finally:
if oldstdchannel is not None:
os.dup2(oldstdchannel, stdchannel.fileno())
if dest_file is not None:
dest_file.close()
RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout)
RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr)
RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")
| <filename>PyDSTool/core/context_managers.py
# -*- coding: utf-8 -*-
"""Context managers implemented for (mostly) internal use"""
import contextlib
import functools
from io import UnsupportedOperation
import os
import sys
__all__ = ["RedirectStdout", "RedirectStderr"]
@contextlib.contextmanager
def _stdchannel_redirected(stdchannel, dest_filename, mode="w"):
"""
A context manager to temporarily redirect stdout or stderr
Originally by <NAME>, 2013
(http://marc-abramowitz.com/archives/2013/07/19/python-context-manager-for-redirected-stdout-and-stderr/)
"""
oldstdchannel = None
dest_file = None
try:
if stdchannel is None:
yield iter([None])
else:
oldstdchannel = os.dup(stdchannel.fileno())
dest_file = open(dest_filename, mode)
os.dup2(dest_file.fileno(), stdchannel.fileno())
yield
except (UnsupportedOperation, AttributeError):
yield iter([None])
finally:
if oldstdchannel is not None:
os.dup2(oldstdchannel, stdchannel.fileno())
if dest_file is not None:
dest_file.close()
RedirectStdout = functools.partial(_stdchannel_redirected, sys.stdout)
RedirectStderr = functools.partial(_stdchannel_redirected, sys.stderr)
RedirectNoOp = functools.partial(_stdchannel_redirected, None, "")
| en | 0.715551 | # -*- coding: utf-8 -*- Context managers implemented for (mostly) internal use A context manager to temporarily redirect stdout or stderr Originally by <NAME>, 2013 (http://marc-abramowitz.com/archives/2013/07/19/python-context-manager-for-redirected-stdout-and-stderr/) | 2.358697 | 2 |
pos_kiosk/hooks.py | Muzzy73/pos_kiosk | 1 | 6 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# ------------------
# include js, css files in header of desk.html
# app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js, css files in header of web template
# web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js in page
# page_js = {"page" : "public/js/file.js"}
# page_js = {
# "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"]
# }
# include js in doctype views
# doctype_js = {"doctype" : "public/js/doctype.js"}
# doctype_list_js = {"doctype" : "public/js/doctype_list.js"}
# doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"}
# doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"}
fixtures = [
{
"doctype": "Custom Field",
"filters": [
[
"name",
"in",
[
"Sales Invoice Item-pos_kiosk",
"Mode of Payment-logo"
]
]
]
}
]
# Home Pages
# ----------
# application home page (will override Website Settings)
# home_page = "login"
# website user home page (by Role)
# role_home_page = {
# "Role": "home_page"
# }
# Website user home page (by function)
# get_website_user_home_page = "pos_kiosk.utils.get_home_page"
# Generators
# ----------
# automatically create page for each record of this doctype
# website_generators = ["Web Page"]
# Installation
# ------------
# before_install = "pos_kiosk.install.before_install"
# after_install = "pos_kiosk.install.after_install"
# Desk Notifications
# ------------------
# See frappe.core.notifications.get_notification_config
# notification_config = "pos_kiosk.notifications.get_notification_config"
# Permissions
# -----------
# Permissions evaluated in scripted ways
# permission_query_conditions = {
# "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions",
# }
#
# has_permission = {
# "Event": "frappe.desk.doctype.event.event.has_permission",
# }
# Document Events
# ---------------
# Hook on document methods and events
# doc_events = {
# "*": {
# "on_update": "method",
# "on_cancel": "method",
# "on_trash": "method"
# }
# }
# Scheduled Tasks
# ---------------
# scheduler_events = {
# "all": [
# "pos_kiosk.tasks.all"
# ],
# "daily": [
# "pos_kiosk.tasks.daily"
# ],
# "hourly": [
# "pos_kiosk.tasks.hourly"
# ],
# "weekly": [
# "pos_kiosk.tasks.weekly"
# ]
# "monthly": [
# "pos_kiosk.tasks.monthly"
# ]
# }
# Testing
# -------
# before_tests = "pos_kiosk.install.before_tests"
# Overriding Whitelisted Methods
# ------------------------------
#
# override_whitelisted_methods = {
# "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa
# }
| # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from . import __version__ as app_version
app_name = "pos_kiosk"
app_title = "Pos Kiosk"
app_publisher = "9t9it"
app_description = "Kiosk App"
app_icon = "octicon octicon-file-directory"
app_color = "grey"
app_email = "<EMAIL>"
app_license = "MIT"
# Includes in <head>
# ------------------
# include js, css files in header of desk.html
# app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js, css files in header of web template
# web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css"
# web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js"
# include js in page
# page_js = {"page" : "public/js/file.js"}
# page_js = {
# "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"]
# }
# include js in doctype views
# doctype_js = {"doctype" : "public/js/doctype.js"}
# doctype_list_js = {"doctype" : "public/js/doctype_list.js"}
# doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"}
# doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"}
fixtures = [
{
"doctype": "Custom Field",
"filters": [
[
"name",
"in",
[
"Sales Invoice Item-pos_kiosk",
"Mode of Payment-logo"
]
]
]
}
]
# Home Pages
# ----------
# application home page (will override Website Settings)
# home_page = "login"
# website user home page (by Role)
# role_home_page = {
# "Role": "home_page"
# }
# Website user home page (by function)
# get_website_user_home_page = "pos_kiosk.utils.get_home_page"
# Generators
# ----------
# automatically create page for each record of this doctype
# website_generators = ["Web Page"]
# Installation
# ------------
# before_install = "pos_kiosk.install.before_install"
# after_install = "pos_kiosk.install.after_install"
# Desk Notifications
# ------------------
# See frappe.core.notifications.get_notification_config
# notification_config = "pos_kiosk.notifications.get_notification_config"
# Permissions
# -----------
# Permissions evaluated in scripted ways
# permission_query_conditions = {
# "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions",
# }
#
# has_permission = {
# "Event": "frappe.desk.doctype.event.event.has_permission",
# }
# Document Events
# ---------------
# Hook on document methods and events
# doc_events = {
# "*": {
# "on_update": "method",
# "on_cancel": "method",
# "on_trash": "method"
# }
# }
# Scheduled Tasks
# ---------------
# scheduler_events = {
# "all": [
# "pos_kiosk.tasks.all"
# ],
# "daily": [
# "pos_kiosk.tasks.daily"
# ],
# "hourly": [
# "pos_kiosk.tasks.hourly"
# ],
# "weekly": [
# "pos_kiosk.tasks.weekly"
# ]
# "monthly": [
# "pos_kiosk.tasks.monthly"
# ]
# }
# Testing
# -------
# before_tests = "pos_kiosk.install.before_tests"
# Overriding Whitelisted Methods
# ------------------------------
#
# override_whitelisted_methods = {
# "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa
# }
| en | 0.525243 | # -*- coding: utf-8 -*- # Includes in <head> # ------------------ # include js, css files in header of desk.html # app_include_css = "/assets/pos_kiosk/css/pos_kiosk.css" # app_include_js = "/assets/pos_kiosk/js/pos_kiosk.js" # include js, css files in header of web template # web_include_css = "/assets/pos_kiosk/css/pos_kiosk.css" # web_include_js = "/assets/pos_kiosk/js/pos_kiosk.js" # include js in page # page_js = {"page" : "public/js/file.js"} # page_js = { # "kiosk": ["public/js/pos_page_js.js", "public/js/includes/number_to_words.js"] # } # include js in doctype views # doctype_js = {"doctype" : "public/js/doctype.js"} # doctype_list_js = {"doctype" : "public/js/doctype_list.js"} # doctype_tree_js = {"doctype" : "public/js/doctype_tree.js"} # doctype_calendar_js = {"doctype" : "public/js/doctype_calendar.js"} # Home Pages # ---------- # application home page (will override Website Settings) # home_page = "login" # website user home page (by Role) # role_home_page = { # "Role": "home_page" # } # Website user home page (by function) # get_website_user_home_page = "pos_kiosk.utils.get_home_page" # Generators # ---------- # automatically create page for each record of this doctype # website_generators = ["Web Page"] # Installation # ------------ # before_install = "pos_kiosk.install.before_install" # after_install = "pos_kiosk.install.after_install" # Desk Notifications # ------------------ # See frappe.core.notifications.get_notification_config # notification_config = "pos_kiosk.notifications.get_notification_config" # Permissions # ----------- # Permissions evaluated in scripted ways # permission_query_conditions = { # "Event": "frappe.desk.doctype.event.event.get_permission_query_conditions", # } # # has_permission = { # "Event": "frappe.desk.doctype.event.event.has_permission", # } # Document Events # --------------- # Hook on document methods and events # doc_events = { # "*": { # "on_update": "method", # "on_cancel": "method", # "on_trash": "method" # } # } # Scheduled Tasks # --------------- # scheduler_events = { # "all": [ # "pos_kiosk.tasks.all" # ], # "daily": [ # "pos_kiosk.tasks.daily" # ], # "hourly": [ # "pos_kiosk.tasks.hourly" # ], # "weekly": [ # "pos_kiosk.tasks.weekly" # ] # "monthly": [ # "pos_kiosk.tasks.monthly" # ] # } # Testing # ------- # before_tests = "pos_kiosk.install.before_tests" # Overriding Whitelisted Methods # ------------------------------ # # override_whitelisted_methods = { # "pos_bahrain.api.get_item_details.get_item_details": "pos_kiosk.api.item.get_item_details" # noqa # } | 1.404778 | 1 |
pypagai/models/model_lstm.py | gcouti/pypagAI | 1 | 7 | <gh_stars>1-10
from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, cfg):
super().__init__(cfg)
self._cfg_ = cfg
def _create_network_(self):
hidden = self._cfg_['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
conc = concatenate([story, question],)
conc = Reshape((1, int(conc.shape[1])))(conc)
conc = Permute((2, 1))(conc)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
class EmbedLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, cfg):
super().__init__(cfg)
self._cfg_ = cfg
def _create_network_(self):
hidden = self._cfg_['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
eb_story = Embedding(self._vocab_size, 64)(story)
eb_story = Dropout(0.3)(eb_story)
eb_question = Embedding(self._vocab_size, 64)(question)
eb_question = Dropout(0.3)(eb_question)
conc = concatenate([eb_story, eb_question], axis=1)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
class ConvLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, model_cfg):
super().__init__(model_cfg)
self._cfg = model_cfg
def _create_network_(self):
hidden = self._cfg['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
eb_story = Embedding(self._vocab_size, 64)(story)
eb_story = Convolution1D(64, 3, padding='same')(eb_story)
eb_story = Convolution1D(32, 3, padding='same')(eb_story)
eb_story = Convolution1D(16, 3, padding='same')(eb_story)
# eb_story = Flatten()(eb_story)
eb_question = Embedding(self._vocab_size, 64)(question)
eb_question = Convolution1D(64, 3, padding='same')(eb_question)
eb_question = Convolution1D(32, 3, padding='same')(eb_question)
eb_question = Convolution1D(16, 3, padding='same')(eb_question)
# eb_question = Flatten()(eb_question)
conc = concatenate([eb_story, eb_question], axis=1)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
| from keras import Model, Input
from keras.layers import Dense, concatenate, LSTM, Reshape, Permute, Embedding, Dropout, Convolution1D, Flatten
from keras.optimizers import Adam
from pypagai.models.base import KerasModel
class SimpleLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, cfg):
super().__init__(cfg)
self._cfg_ = cfg
def _create_network_(self):
hidden = self._cfg_['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
conc = concatenate([story, question],)
conc = Reshape((1, int(conc.shape[1])))(conc)
conc = Permute((2, 1))(conc)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
class EmbedLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, cfg):
super().__init__(cfg)
self._cfg_ = cfg
def _create_network_(self):
hidden = self._cfg_['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
eb_story = Embedding(self._vocab_size, 64)(story)
eb_story = Dropout(0.3)(eb_story)
eb_question = Embedding(self._vocab_size, 64)(question)
eb_question = Dropout(0.3)(eb_question)
conc = concatenate([eb_story, eb_question], axis=1)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
class ConvLSTM(KerasModel):
"""
Use a simple lstm neural network
"""
@staticmethod
def default_config():
config = KerasModel.default_config()
config['hidden'] = 32
return config
def __init__(self, model_cfg):
super().__init__(model_cfg)
self._cfg = model_cfg
def _create_network_(self):
hidden = self._cfg['hidden']
story = Input((self._story_maxlen, ), name='story')
question = Input((self._query_maxlen, ), name='question')
eb_story = Embedding(self._vocab_size, 64)(story)
eb_story = Convolution1D(64, 3, padding='same')(eb_story)
eb_story = Convolution1D(32, 3, padding='same')(eb_story)
eb_story = Convolution1D(16, 3, padding='same')(eb_story)
# eb_story = Flatten()(eb_story)
eb_question = Embedding(self._vocab_size, 64)(question)
eb_question = Convolution1D(64, 3, padding='same')(eb_question)
eb_question = Convolution1D(32, 3, padding='same')(eb_question)
eb_question = Convolution1D(16, 3, padding='same')(eb_question)
# eb_question = Flatten()(eb_question)
conc = concatenate([eb_story, eb_question], axis=1)
response = LSTM(hidden, dropout=0.2, recurrent_dropout=0.2)(conc)
response = Dense(self._vocab_size, activation='softmax')(response)
self._model = Model(inputs=[story, question], outputs=response)
self._model.compile(optimizer=Adam(lr=2e-4), loss='sparse_categorical_crossentropy', metrics=['accuracy']) | en | 0.751499 | Use a simple lstm neural network Use a simple lstm neural network Use a simple lstm neural network # eb_story = Flatten()(eb_story) # eb_question = Flatten()(eb_question) | 2.97849 | 3 |
lib/variables/latent_variables/__init__.py | joelouismarino/variational_rl | 15 | 8 | <filename>lib/variables/latent_variables/__init__.py
from .fully_connected import FullyConnectedLatentVariable
from .convolutional import ConvolutionalLatentVariable
| <filename>lib/variables/latent_variables/__init__.py
from .fully_connected import FullyConnectedLatentVariable
from .convolutional import ConvolutionalLatentVariable
| none | 1 | 1.085513 | 1 |
|
easyai/model/backbone/cls/pnasnet.py | lpj0822/image_point_cloud_det | 1 | 9 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_block import ConvBNActivationBlock
from easyai.model.base_block.cls.pnasnet_block import CellA, CellB
__all__ = ['pnasnet_A', 'pnasnet_B']
class PNASNet(BaseBackbone):
def __init__(self, data_channel=3, num_cells=6,
num_planes=44, block=CellA,
bnName=NormalizationType.BatchNormalize2d,
activationName=ActivationType.ReLU):
super().__init__()
self.set_name(BackboneName.PNASNetA)
self.data_channel = data_channel
self.num_cells = num_cells
self.block = block
self.activation_name = activationName
self.bn_name = bnName
self.first_output = num_planes
self.in_planes = self.first_output
self.create_block_list()
def create_block_list(self):
self.block_out_channels = []
self.index = 0
layer1 = ConvBNActivationBlock(in_channels=self.data_channel,
out_channels=self.first_output,
kernel_size=3,
stride=1,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(layer1.get_name(), layer1, self.first_output)
self.make_layer(self.first_output, self.num_cells)
self.downsample(self.first_output * 2)
self.make_layer(self.first_output * 2, self.num_cells)
self.downsample(self.first_output * 4)
self.make_layer(self.first_output * 4, self.num_cells)
def make_layer(self, planes, num_cells):
for _ in range(num_cells):
temp_block = self.block(self.in_planes, planes, stride=1,
bn_name=self.bn_name, activation_name=self.activation_name)
self.add_block_list(temp_block.get_name(), temp_block, planes)
self.in_planes = planes
def downsample(self, planes):
down_block = self.block(self.in_planes, planes, stride=2,
bn_name=self.bn_name, activation_name=self.activation_name)
self.add_block_list(down_block.get_name(), down_block, planes)
self.in_planes = planes
def forward(self, x):
output_list = []
for block in self._modules.values():
x = block(x)
output_list.append(x)
return output_list
def pnasnet_A(data_channel):
model = PNASNet(data_channel=data_channel,
num_cells=6,
num_planes=44,
block=CellA)
model.set_name(BackboneName.PNASNetA)
return model
def pnasnet_B(data_channel):
model = PNASNet(data_channel=data_channel,
num_cells=6, num_planes=32,
block=CellB)
model.set_name(BackboneName.PNASNetB)
return model
| #!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:
''' PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
from easyai.base_name.block_name import NormalizationType, ActivationType
from easyai.base_name.backbone_name import BackboneName
from easyai.model.backbone.utility.base_backbone import *
from easyai.model.base_block.utility.utility_block import ConvBNActivationBlock
from easyai.model.base_block.cls.pnasnet_block import CellA, CellB
__all__ = ['pnasnet_A', 'pnasnet_B']
class PNASNet(BaseBackbone):
def __init__(self, data_channel=3, num_cells=6,
num_planes=44, block=CellA,
bnName=NormalizationType.BatchNormalize2d,
activationName=ActivationType.ReLU):
super().__init__()
self.set_name(BackboneName.PNASNetA)
self.data_channel = data_channel
self.num_cells = num_cells
self.block = block
self.activation_name = activationName
self.bn_name = bnName
self.first_output = num_planes
self.in_planes = self.first_output
self.create_block_list()
def create_block_list(self):
self.block_out_channels = []
self.index = 0
layer1 = ConvBNActivationBlock(in_channels=self.data_channel,
out_channels=self.first_output,
kernel_size=3,
stride=1,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(layer1.get_name(), layer1, self.first_output)
self.make_layer(self.first_output, self.num_cells)
self.downsample(self.first_output * 2)
self.make_layer(self.first_output * 2, self.num_cells)
self.downsample(self.first_output * 4)
self.make_layer(self.first_output * 4, self.num_cells)
def make_layer(self, planes, num_cells):
for _ in range(num_cells):
temp_block = self.block(self.in_planes, planes, stride=1,
bn_name=self.bn_name, activation_name=self.activation_name)
self.add_block_list(temp_block.get_name(), temp_block, planes)
self.in_planes = planes
def downsample(self, planes):
down_block = self.block(self.in_planes, planes, stride=2,
bn_name=self.bn_name, activation_name=self.activation_name)
self.add_block_list(down_block.get_name(), down_block, planes)
self.in_planes = planes
def forward(self, x):
output_list = []
for block in self._modules.values():
x = block(x)
output_list.append(x)
return output_list
def pnasnet_A(data_channel):
model = PNASNet(data_channel=data_channel,
num_cells=6,
num_planes=44,
block=CellA)
model.set_name(BackboneName.PNASNetA)
return model
def pnasnet_B(data_channel):
model = PNASNet(data_channel=data_channel,
num_cells=6, num_planes=32,
block=CellB)
model.set_name(BackboneName.PNASNetB)
return model
| en | 0.536206 | #!/usr/bin/env python # -*- coding:utf-8 -*- # Author: PNASNet in PyTorch. Paper: Progressive Neural Architecture Search | 2.712146 | 3 |
map_download/cmd/TerrainDownloader.py | cugxy/map_download | 27 | 10 | # -*- coding: utf-8 -*-
# coding=utf-8
import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
def get_access_token(token):
resp = None
request_count = 0
url = "https://api.cesium.com/v1/assets/1/endpoint"
while True:
if request_count > 4:
break
try:
request_count += 1
param = {'access_token': token}
resp = requests.get(url, params=param, timeout=2)
if resp.status_code != 200:
continue
break
except Exception as e:
resp = None
time.sleep(3)
if resp is None:
return None
resp_json = resp.json()
return resp_json.get('accessToken')
class TerrainDownloaderThread(BaseDownloaderThread):
URL = "https://assets.cesium.com/1/{z}/{x}/{y}.terrain?extensions=octvertexnormals-watermask&v=1.1.0"
def __init__(self, root_dir, bbox, token, task_q, logger=None, write_db=False):
super(TerrainDownloaderThread, self).__init__(
root_dir, bbox, task_q, logger, write_db=write_db, db_file_name='Terrain.db')
self.token = token
self._init_metadata(
format='terrain',
bounds='%f,%f,%f,%f' % (self.bbox.min_lng, self.bbox.min_lat, self.bbox.max_lng, self.bbox.max_lat))
def get_url(self, x, y, z):
return self.URL.format(x=x, y=y, z=z)
def _download(self, x, y, z):
file_path = '%s/%s/%i/%i/%i.%s' % (self.root_dir, 'Terrain', z, x, y, 'terrain')
if os.path.exists(file_path):
self._data2DB(x, y, z, file_path)
return 0
os.makedirs(os.path.dirname(file_path), exist_ok=True)
resp = None
requre_count = 0
_url = ''
access_token = get_access_token(self.token)
if access_token is None:
return -1
param = {'extensions': 'octvertexnormals-watermask', 'v': '1.1.0', 'access_token': access_token}
while True:
if requre_count > 4: break
try:
_url = self.get_url(x, y, z)
resp = requests.get(_url, params=param, stream=True, timeout=2)
break
except Exception as e:
resp = None
time.sleep(3)
requre_count += 1
if resp is None:
return -1
if resp.status_code != 200:
return -1
try:
with open(file_path, 'wb') as f:
for chunk in resp.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
except Exception as e:
return -1
self._data2DB(x, y, z, file_path)
return 1
class TerrainDownloadEngine(DownloadEngine):
root_dir = ''
def __init__(self, root_dir, bbox, token, thread_num, logger=None, write_db=False):
super(TerrainDownloadEngine, self).__init__(bbox, thread_num, logger, write_db=write_db)
self.root_dir = root_dir
self.token = token
def bbox2xyz(self, bbox, z):
min_x, min_y = latlng2tile_terrain(bbox.min_lat, bbox.min_lng, z)
max_x, max_y = latlng2tile_terrain(bbox.max_lat, bbox.max_lng, z)
return math.floor(min_x), math.floor(min_y), math.ceil(max_x) + 1, math.ceil(max_y) + 1
def generate_metadata(self):
try:
metadatas = {
"attribution": "© Analytical Graphics Inc., © CGIAR-CSI, Produced using Copernicus data and "
"information funded by the European Union - EU-DEM layers",
"available": [
[
{
"endX": 1,
"endY": 0,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 3,
"endY": 1,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 7,
"endY": 3,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 15,
"endY": 7,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 31,
"endY": 15,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 63,
"endY": 31,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 127,
"endY": 63,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 255,
"endY": 127,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 511,
"endY": 255,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 1023,
"endY": 511,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 2047,
"endY": 1023,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 4095,
"endY": 2047,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 8191,
"endY": 4095,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 16383,
"endY": 8191,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 32767,
"endY": 16383,
"startX": 0,
"startY": 0
}
]
],
"bounds": [-180, -90, 180, 90, ],
"description": "STK World Terrain Premium Tileset, v1.3. 10m - 30m resolution CONUS, 30m resolution "
"SRTM between 60N and 60S, 30m Europe. Minimum global coverage of 1000m.",
"extensions": ["watermask", "vertexnormals", "octvertexnormals", ],
"format": "quantized-mesh-1.0",
"maxzoom": 13,
"minzoom": 0,
"name": "world",
"projection": "EPSG:4326",
"scheme": "tms",
"tilejson": "2.1.0",
"tiles": ["{z}/{x}/{y}.terrain?v={version}", ],
"version": "1.31376.0"
}
_dir = os.path.join(self.root_dir, 'Terrain')
os.makedirs(_dir, exist_ok=True)
metadatas_path = os.path.join(_dir, 'layer.json')
with open(metadatas_path, 'w') as f:
json.dump(metadatas, f)
except Exception as e:
if self.logger is not None:
self.logger.exception(e)
def run(self):
try:
self.generate_metadata()
count = 0
bboxs = self.cut_bbox()
for bbox in bboxs:
_count = self.get_task_count(bbox)
count += _count
self.division_done_signal.emit(count)
for bbox in bboxs:
while True:
if not self.running:
time.sleep(0.01)
else:
break
task_q = self.get_task_queue(bbox)
self.threads = []
for i in range(self.thread_num):
thread = TerrainDownloaderThread(self.root_dir, self.bbox, self.token, task_q, self.logger,
write_db=self.write_db)
thread.sub_progressBar_updated_signal.connect(self.sub_update_progressBar)
self.threads.append(thread)
for thread in self.threads:
thread.start()
for thread in self.threads:
thread.wait()
for t in self.threads:
t.stop()
t.quit()
self.threads = []
self.download_done_signal.emit()
except Exception as e:
if self.logger is not None:
self.logger.error(e)
if __name__ == '__main__':
if 1:
logger = logging.getLogger('down')
try:
root = r'/Users/cugxy/Documents/data/downloader'
formatter = logging.Formatter('%(levelname)s-%(message)s')
hdlr = logging.StreamHandler()
log_file = os.path.join(root, 'down.log')
file_hdlr = logging.FileHandler(log_file)
file_hdlr.setFormatter(formatter)
logger.addHandler(file_hdlr)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
min_lng = -180.0
max_lng = 180.0
min_lat = -90.0
max_lat = 90.0
start_zoom = 0
end_zoom = 5
bbox = BoundBox(max_lat, max_lng, min_lat, min_lng, start_zoom, end_zoom)
d = TerrainDownloadEngine(root, bbox, 8, logger)
d.start()
time.sleep(10000)
logger.error('main thread out')
except Exception as e:
logger.error(e)
if 0:
accessToken = get_access_token()
pass
| # -*- coding: utf-8 -*-
# coding=utf-8
import json
import os
import math
import logging
import requests
import time
from map_download.cmd.BaseDownloader import DownloadEngine, BaseDownloaderThread, latlng2tile_terrain, BoundBox
def get_access_token(token):
resp = None
request_count = 0
url = "https://api.cesium.com/v1/assets/1/endpoint"
while True:
if request_count > 4:
break
try:
request_count += 1
param = {'access_token': token}
resp = requests.get(url, params=param, timeout=2)
if resp.status_code != 200:
continue
break
except Exception as e:
resp = None
time.sleep(3)
if resp is None:
return None
resp_json = resp.json()
return resp_json.get('accessToken')
class TerrainDownloaderThread(BaseDownloaderThread):
URL = "https://assets.cesium.com/1/{z}/{x}/{y}.terrain?extensions=octvertexnormals-watermask&v=1.1.0"
def __init__(self, root_dir, bbox, token, task_q, logger=None, write_db=False):
super(TerrainDownloaderThread, self).__init__(
root_dir, bbox, task_q, logger, write_db=write_db, db_file_name='Terrain.db')
self.token = token
self._init_metadata(
format='terrain',
bounds='%f,%f,%f,%f' % (self.bbox.min_lng, self.bbox.min_lat, self.bbox.max_lng, self.bbox.max_lat))
def get_url(self, x, y, z):
return self.URL.format(x=x, y=y, z=z)
def _download(self, x, y, z):
file_path = '%s/%s/%i/%i/%i.%s' % (self.root_dir, 'Terrain', z, x, y, 'terrain')
if os.path.exists(file_path):
self._data2DB(x, y, z, file_path)
return 0
os.makedirs(os.path.dirname(file_path), exist_ok=True)
resp = None
requre_count = 0
_url = ''
access_token = get_access_token(self.token)
if access_token is None:
return -1
param = {'extensions': 'octvertexnormals-watermask', 'v': '1.1.0', 'access_token': access_token}
while True:
if requre_count > 4: break
try:
_url = self.get_url(x, y, z)
resp = requests.get(_url, params=param, stream=True, timeout=2)
break
except Exception as e:
resp = None
time.sleep(3)
requre_count += 1
if resp is None:
return -1
if resp.status_code != 200:
return -1
try:
with open(file_path, 'wb') as f:
for chunk in resp.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
except Exception as e:
return -1
self._data2DB(x, y, z, file_path)
return 1
class TerrainDownloadEngine(DownloadEngine):
root_dir = ''
def __init__(self, root_dir, bbox, token, thread_num, logger=None, write_db=False):
super(TerrainDownloadEngine, self).__init__(bbox, thread_num, logger, write_db=write_db)
self.root_dir = root_dir
self.token = token
def bbox2xyz(self, bbox, z):
min_x, min_y = latlng2tile_terrain(bbox.min_lat, bbox.min_lng, z)
max_x, max_y = latlng2tile_terrain(bbox.max_lat, bbox.max_lng, z)
return math.floor(min_x), math.floor(min_y), math.ceil(max_x) + 1, math.ceil(max_y) + 1
def generate_metadata(self):
try:
metadatas = {
"attribution": "© Analytical Graphics Inc., © CGIAR-CSI, Produced using Copernicus data and "
"information funded by the European Union - EU-DEM layers",
"available": [
[
{
"endX": 1,
"endY": 0,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 3,
"endY": 1,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 7,
"endY": 3,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 15,
"endY": 7,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 31,
"endY": 15,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 63,
"endY": 31,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 127,
"endY": 63,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 255,
"endY": 127,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 511,
"endY": 255,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 1023,
"endY": 511,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 2047,
"endY": 1023,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 4095,
"endY": 2047,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 8191,
"endY": 4095,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 16383,
"endY": 8191,
"startX": 0,
"startY": 0
}
],
[
{
"endX": 32767,
"endY": 16383,
"startX": 0,
"startY": 0
}
]
],
"bounds": [-180, -90, 180, 90, ],
"description": "STK World Terrain Premium Tileset, v1.3. 10m - 30m resolution CONUS, 30m resolution "
"SRTM between 60N and 60S, 30m Europe. Minimum global coverage of 1000m.",
"extensions": ["watermask", "vertexnormals", "octvertexnormals", ],
"format": "quantized-mesh-1.0",
"maxzoom": 13,
"minzoom": 0,
"name": "world",
"projection": "EPSG:4326",
"scheme": "tms",
"tilejson": "2.1.0",
"tiles": ["{z}/{x}/{y}.terrain?v={version}", ],
"version": "1.31376.0"
}
_dir = os.path.join(self.root_dir, 'Terrain')
os.makedirs(_dir, exist_ok=True)
metadatas_path = os.path.join(_dir, 'layer.json')
with open(metadatas_path, 'w') as f:
json.dump(metadatas, f)
except Exception as e:
if self.logger is not None:
self.logger.exception(e)
def run(self):
try:
self.generate_metadata()
count = 0
bboxs = self.cut_bbox()
for bbox in bboxs:
_count = self.get_task_count(bbox)
count += _count
self.division_done_signal.emit(count)
for bbox in bboxs:
while True:
if not self.running:
time.sleep(0.01)
else:
break
task_q = self.get_task_queue(bbox)
self.threads = []
for i in range(self.thread_num):
thread = TerrainDownloaderThread(self.root_dir, self.bbox, self.token, task_q, self.logger,
write_db=self.write_db)
thread.sub_progressBar_updated_signal.connect(self.sub_update_progressBar)
self.threads.append(thread)
for thread in self.threads:
thread.start()
for thread in self.threads:
thread.wait()
for t in self.threads:
t.stop()
t.quit()
self.threads = []
self.download_done_signal.emit()
except Exception as e:
if self.logger is not None:
self.logger.error(e)
if __name__ == '__main__':
if 1:
logger = logging.getLogger('down')
try:
root = r'/Users/cugxy/Documents/data/downloader'
formatter = logging.Formatter('%(levelname)s-%(message)s')
hdlr = logging.StreamHandler()
log_file = os.path.join(root, 'down.log')
file_hdlr = logging.FileHandler(log_file)
file_hdlr.setFormatter(formatter)
logger.addHandler(file_hdlr)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
min_lng = -180.0
max_lng = 180.0
min_lat = -90.0
max_lat = 90.0
start_zoom = 0
end_zoom = 5
bbox = BoundBox(max_lat, max_lng, min_lat, min_lng, start_zoom, end_zoom)
d = TerrainDownloadEngine(root, bbox, 8, logger)
d.start()
time.sleep(10000)
logger.error('main thread out')
except Exception as e:
logger.error(e)
if 0:
accessToken = get_access_token()
pass
| en | 0.730894 | # -*- coding: utf-8 -*- # coding=utf-8 | 2.447342 | 2 |
tools/utils.py | vahini01/electoral_rolls | 16 | 11 | <reponame>vahini01/electoral_rolls
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 23:28:58 2017
@author: dhingratul
"""
import urllib.request
import os
from selenium import webdriver
from selenium.webdriver.support.ui import Select
from bs4 import BeautifulSoup
import ssl
import requests
import wget
from PyPDF2 import PdfFileReader
def download_file(pdf_url, mdir, filename, flag=False):
if flag is True:
context = ssl._create_unverified_context()
response = urllib.request.urlopen(pdf_url, context=context)
else:
response = urllib.request.urlopen(pdf_url)
filename = mdir + filename
file = open(filename, 'wb')
file.write(response.read())
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
file.close()
return flag
def download_file_R(pdf_url, mdir, filename, file_out):
requests.packages.urllib3.disable_warnings()
while True: # Keep trying until the webpage successfully downloads
try:
r = requests.get(pdf_url, verify=False, timeout=10)
break # If it downloads, get out and get on with life
# If it doesn't download after the timeout period, an exceptions is thrown, and we try again
except requests.exceptions.RequestException as e:
with open(file_out, "a") as myfile:
myfile.write(pdf_url + '\n')
filename = mdir + filename
with open(filename, 'wb') as f:
f.write(r.content)
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
return flag
def download_file_W(pdf_url, mdir, filename, flag=False):
filename = mdir + filename
ssl._create_default_https_context = ssl._create_unverified_context
wget.download(pdf_url, filename)
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
return flag
def getDriver(url):
driver = webdriver.Chrome()
driver.get(url)
return driver
def is_valid_pdf(fn):
"""Check is the PDF valid """
try:
with open(fn, 'rb') as f:
pdf = PdfFileReader(f)
numpages = pdf.numPages
return (numpages > 0)
except Exception as e:
return False
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 10 23:28:58 2017
@author: dhingratul
"""
import urllib.request
import os
from selenium import webdriver
from selenium.webdriver.support.ui import Select
from bs4 import BeautifulSoup
import ssl
import requests
import wget
from PyPDF2 import PdfFileReader
def download_file(pdf_url, mdir, filename, flag=False):
if flag is True:
context = ssl._create_unverified_context()
response = urllib.request.urlopen(pdf_url, context=context)
else:
response = urllib.request.urlopen(pdf_url)
filename = mdir + filename
file = open(filename, 'wb')
file.write(response.read())
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
file.close()
return flag
def download_file_R(pdf_url, mdir, filename, file_out):
requests.packages.urllib3.disable_warnings()
while True: # Keep trying until the webpage successfully downloads
try:
r = requests.get(pdf_url, verify=False, timeout=10)
break # If it downloads, get out and get on with life
# If it doesn't download after the timeout period, an exceptions is thrown, and we try again
except requests.exceptions.RequestException as e:
with open(file_out, "a") as myfile:
myfile.write(pdf_url + '\n')
filename = mdir + filename
with open(filename, 'wb') as f:
f.write(r.content)
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
return flag
def download_file_W(pdf_url, mdir, filename, flag=False):
filename = mdir + filename
ssl._create_default_https_context = ssl._create_unverified_context
wget.download(pdf_url, filename)
if os.stat(filename).st_size == 0:
flag = 0
else:
flag = 1
return flag
def getDriver(url):
driver = webdriver.Chrome()
driver.get(url)
return driver
def is_valid_pdf(fn):
"""Check is the PDF valid """
try:
with open(fn, 'rb') as f:
pdf = PdfFileReader(f)
numpages = pdf.numPages
return (numpages > 0)
except Exception as e:
return False | en | 0.882415 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- Created on Fri Nov 10 23:28:58 2017 @author: dhingratul # Keep trying until the webpage successfully downloads # If it downloads, get out and get on with life # If it doesn't download after the timeout period, an exceptions is thrown, and we try again Check is the PDF valid | 3.105863 | 3 |
exp/viz_raw_manhattan.py | ellencwade/coronavirus-2020 | 0 | 12 | <gh_stars>0
"""
Experiment summary
------------------
Treat each province/state in a country cases over time
as a vector, do a simple K-Nearest Neighbor between
countries. What country has the most similar trajectory
to a given country?
Plots similar countries
"""
import sys
sys.path.insert(0, '..')
from utils import data
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
# ------------------------------------------
confirmed = os.path.join(
BASE_PATH,
'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
dist_diff = os.path.join('../exp/results/', 'knn_raw.json')
f = open(dist_diff,)
dist_diff = json.load(f)
for region, dist in dist_diff.items():
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
other_region = dist['manhattan'][0]
regions = [region, other_region]
for val in regions:
df = data.filter_by_attribute(
confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
lines = ax.plot(cases, label=val)
ax.set_ylabel('# of confirmed cases')
ax.set_xlabel("Time (days since Jan 22, 2020)")
ax.set_yscale('log')
ax.legend()
plt.tight_layout()
region = region.replace('*', '')
other_region = other_region.replace('*', '')
plt.title(f'Comparing confirmed cases in {region} and {other_region}')
plt.savefig(f'results/raw_manhattan/{region}.png')
plt.close()
print(region) | """
Experiment summary
------------------
Treat each province/state in a country cases over time
as a vector, do a simple K-Nearest Neighbor between
countries. What country has the most similar trajectory
to a given country?
Plots similar countries
"""
import sys
sys.path.insert(0, '..')
from utils import data
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
# ------------------------------------------
confirmed = os.path.join(
BASE_PATH,
'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
dist_diff = os.path.join('../exp/results/', 'knn_raw.json')
f = open(dist_diff,)
dist_diff = json.load(f)
for region, dist in dist_diff.items():
plt.style.use('fivethirtyeight')
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
other_region = dist['manhattan'][0]
regions = [region, other_region]
for val in regions:
df = data.filter_by_attribute(
confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
lines = ax.plot(cases, label=val)
ax.set_ylabel('# of confirmed cases')
ax.set_xlabel("Time (days since Jan 22, 2020)")
ax.set_yscale('log')
ax.legend()
plt.tight_layout()
region = region.replace('*', '')
other_region = other_region.replace('*', '')
plt.title(f'Comparing confirmed cases in {region} and {other_region}')
plt.savefig(f'results/raw_manhattan/{region}.png')
plt.close()
print(region) | en | 0.734333 | Experiment summary ------------------ Treat each province/state in a country cases over time as a vector, do a simple K-Nearest Neighbor between countries. What country has the most similar trajectory to a given country? Plots similar countries # ------------ HYPERPARAMETERS ------------- # ------------------------------------------ | 3.331203 | 3 |
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Dataset Card for Starcoder Data with Python Education and Language Scores
Dataset Summary
The starcoderdata-python-edu-lang-score dataset contains the Python subset of the starcoderdata dataset. It augments the existing Python subset with features that assess the educational quality of code and classify the language of code comments. This dataset was created for high-quality Python education and language-based training, with a primary focus on facilitating models that can leverage educational scores and focus on specific languages for code comments (e.g., English or Portuguese). The dataset is suitable for various applications, including educational content evaluation and multilingual code understanding.
Uses
Direct Use
This dataset can be directly used to:
- Train models on code that has high educational value.
- Train language models to focus on specific languages in code comments.
Out-of-Scope Use
The dataset is not intended for tasks unrelated to code content analysis, such as general NLP classification or non-educational content filtering.
Dataset Structure
Each record in the dataset includes:
max_stars_repo_path: repo pathmax_stars_repo_name: repo namecontent: The original code content.content_cleaned: The content with specific metadata (e.g., reponame) removed for cleaner processing.language: The detected language of code comments.language_score: The confidence score for the language classification.comments: Extracted comments from the code content.edu_score: The educational score representing the quality of content (ranging from 0 to 5).edu_int_score: The integer representation of edu_score, rounded for simplified use cases.
Dataset Creation
Curation Rationale
The creation of the starcoderdata-python-edu-lang-score dataset had two purposes.
- Identification of high-quality code via an educational quality classification
- Filtering for natural languages that are used to write code comments
Data Collection and Processing
Data was collected from the Python subset of the starcoderdata dataset and received the following processing steps:
Content Cleaning: The preprocessing step removes metadata tags like or to ensure the content is ready for processing. This step is useful in creating a standardized input for further classification and scoring.
Language Classification:
- Model Used: The FastText language identification model (lid.176.bin) was employed to detect the language of comments within the code. This model supports a wide range of languages, ensuring robust language detection.
Educational Scoring:
- Model Used: The educational scoring model was pre-trained on sequence classification to evaluate the quality and educational value of Python code content. This model was sourced from Hugging Face’s HuggingFaceTB/python-edu-scorer.
Glossary
- Educational Score: A measure of the quality of code content based on its potential educational value, ranging from 0 (low quality) to 5 (high quality).
- Language Code: A code representing the detected language in code comments, based on FastText classification.
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