hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
126c70cfd4f5dd58b70d6563429b9753c64c44e8
| 941
|
py
|
Python
|
merpy/__init__.py
|
AndreLamurias/merpy
|
eebfa602f07603178d8ae8706e2e7d18b068f7c8
|
[
"Apache-2.0"
] | 5
|
2019-06-25T11:00:14.000Z
|
2022-03-31T17:31:38.000Z
|
merpy/__init__.py
|
AndreLamurias/merpy
|
eebfa602f07603178d8ae8706e2e7d18b068f7c8
|
[
"Apache-2.0"
] | 4
|
2020-01-08T11:05:35.000Z
|
2020-11-12T12:32:38.000Z
|
merpy/__init__.py
|
AndreLamurias/merpy
|
eebfa602f07603178d8ae8706e2e7d18b068f7c8
|
[
"Apache-2.0"
] | 2
|
2021-03-14T21:24:59.000Z
|
2021-10-03T10:53:36.000Z
|
from .merpy import (
get_entities,
get_similarities,
generate_lexicon,
process_lexicon,
show_lexicons,
get_lexicons,
download_lexicon,
create_lexicon,
create_mappings,
download_mer,
download_lexicons,
mer_path,
get_entities_mp,
create_lexicon_from_file,
delete_lexicon,
merge_processed_lexicons,
delete_entity,
delete_entity_by_uri,
delete_obsolete,
rename_lexicon
)
name = "merpy"
__all__ = [
"get_entities",
"get_similarities",
"generate_lexicon",
"process_lexicon",
"show_lexicons",
"get_lexicons",
"download_lexicon",
"create_mappings",
"create_lexicon",
"download_mer",
"download_lexicons",
"mer_path",
"get_entities_mp",
"create_lexicon_from_file",
"delete_lexicon",
"merge_processed_lexicons",
"delete_entity",
"delete_entity_by_uri",
"delete_obsolete",
"rename_lexicon"
]
| 20.021277
| 31
| 0.68119
| 98
| 941
| 5.969388
| 0.285714
| 0.075214
| 0.047863
| 0.088889
| 0.882051
| 0.882051
| 0.882051
| 0.882051
| 0.882051
| 0.882051
| 0
| 0
| 0.226355
| 941
| 46
| 32
| 20.456522
| 0.803571
| 0
| 0
| 0
| 1
| 0
| 0.329437
| 0.05101
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.022222
| 0
| 0.022222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
89d3819062a399528d942dfc8efc7adbf2f43f4d
| 219
|
py
|
Python
|
lazymodel/models.py
|
apnarm/django-lazycache
|
a68f782ff989fadf30fd3ecf0dad0af799b0341b
|
[
"MIT"
] | null | null | null |
lazymodel/models.py
|
apnarm/django-lazycache
|
a68f782ff989fadf30fd3ecf0dad0af799b0341b
|
[
"MIT"
] | null | null | null |
lazymodel/models.py
|
apnarm/django-lazycache
|
a68f782ff989fadf30fd3ecf0dad0af799b0341b
|
[
"MIT"
] | null | null | null |
# TODO: define these models and get tests working again
from django.db import models
from lazymodel import ModelWithCaching
class Account(ModelWithCaching):
pass
class PhotoGallery(ModelWithCaching):
pass
| 15.642857
| 55
| 0.785388
| 26
| 219
| 6.615385
| 0.730769
| 0.232558
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173516
| 219
| 13
| 56
| 16.846154
| 0.950276
| 0.242009
| 0
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
d60206cec0a5f383271617545d203b99f0b179aa
| 14,440
|
py
|
Python
|
dialogflow_v2beta1/proto/intent_pb2_grpc.py
|
gertventer1970/dialogflow-python-client-v2
|
e78b0e6765f94fb9ecbbc72c9fc30ce7245703e2
|
[
"Apache-2.0"
] | 1
|
2020-10-14T07:43:19.000Z
|
2020-10-14T07:43:19.000Z
|
dialogflow_v2beta1/proto/intent_pb2_grpc.py
|
everydaycodings/dialogflow-python-client-v2
|
c1c925d94726f49e549870e087ad79ee491056e2
|
[
"Apache-2.0"
] | null | null | null |
dialogflow_v2beta1/proto/intent_pb2_grpc.py
|
everydaycodings/dialogflow-python-client-v2
|
c1c925d94726f49e549870e087ad79ee491056e2
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from dialogflow_v2beta1.proto import (
intent_pb2 as google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2,
)
from google.longrunning import (
operations_pb2 as google_dot_longrunning_dot_operations__pb2,
)
from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
class IntentsStub(object):
"""Service for managing [Intents][google.cloud.dialogflow.v2beta1.Intent].
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.ListIntents = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/ListIntents",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsResponse.FromString,
)
self.GetIntent = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/GetIntent",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.GetIntentRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
)
self.CreateIntent = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/CreateIntent",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.CreateIntentRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
)
self.UpdateIntent = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/UpdateIntent",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.UpdateIntentRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
)
self.DeleteIntent = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/DeleteIntent",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.DeleteIntentRequest.SerializeToString,
response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
)
self.BatchUpdateIntents = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/BatchUpdateIntents",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchUpdateIntentsRequest.SerializeToString,
response_deserializer=google_dot_longrunning_dot_operations__pb2.Operation.FromString,
)
self.BatchDeleteIntents = channel.unary_unary(
"/google.cloud.dialogflow.v2beta1.Intents/BatchDeleteIntents",
request_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchDeleteIntentsRequest.SerializeToString,
response_deserializer=google_dot_longrunning_dot_operations__pb2.Operation.FromString,
)
class IntentsServicer(object):
"""Service for managing [Intents][google.cloud.dialogflow.v2beta1.Intent].
"""
def ListIntents(self, request, context):
"""Returns the list of all intents in the specified agent.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def GetIntent(self, request, context):
"""Retrieves the specified intent.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def CreateIntent(self, request, context):
"""Creates an intent in the specified agent.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def UpdateIntent(self, request, context):
"""Updates the specified intent.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def DeleteIntent(self, request, context):
"""Deletes the specified intent and its direct or indirect followup intents.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def BatchUpdateIntents(self, request, context):
"""Updates/Creates multiple intents in the specified agent.
Operation <response: [BatchUpdateIntentsResponse][google.cloud.dialogflow.v2beta1.BatchUpdateIntentsResponse]>
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def BatchDeleteIntents(self, request, context):
"""Deletes intents in the specified agent.
Operation <response: [google.protobuf.Empty][google.protobuf.Empty]>
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details("Method not implemented!")
raise NotImplementedError("Method not implemented!")
def add_IntentsServicer_to_server(servicer, server):
rpc_method_handlers = {
"ListIntents": grpc.unary_unary_rpc_method_handler(
servicer.ListIntents,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsRequest.FromString,
response_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsResponse.SerializeToString,
),
"GetIntent": grpc.unary_unary_rpc_method_handler(
servicer.GetIntent,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.GetIntentRequest.FromString,
response_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.SerializeToString,
),
"CreateIntent": grpc.unary_unary_rpc_method_handler(
servicer.CreateIntent,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.CreateIntentRequest.FromString,
response_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.SerializeToString,
),
"UpdateIntent": grpc.unary_unary_rpc_method_handler(
servicer.UpdateIntent,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.UpdateIntentRequest.FromString,
response_serializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.SerializeToString,
),
"DeleteIntent": grpc.unary_unary_rpc_method_handler(
servicer.DeleteIntent,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.DeleteIntentRequest.FromString,
response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
),
"BatchUpdateIntents": grpc.unary_unary_rpc_method_handler(
servicer.BatchUpdateIntents,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchUpdateIntentsRequest.FromString,
response_serializer=google_dot_longrunning_dot_operations__pb2.Operation.SerializeToString,
),
"BatchDeleteIntents": grpc.unary_unary_rpc_method_handler(
servicer.BatchDeleteIntents,
request_deserializer=google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchDeleteIntentsRequest.FromString,
response_serializer=google_dot_longrunning_dot_operations__pb2.Operation.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
"google.cloud.dialogflow.v2beta1.Intents", rpc_method_handlers
)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class Intents(object):
"""Service for managing [Intents][google.cloud.dialogflow.v2beta1.Intent].
"""
@staticmethod
def ListIntents(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/ListIntents",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsRequest.SerializeToString,
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.ListIntentsResponse.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def GetIntent(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/GetIntent",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.GetIntentRequest.SerializeToString,
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def CreateIntent(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/CreateIntent",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.CreateIntentRequest.SerializeToString,
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def UpdateIntent(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/UpdateIntent",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.UpdateIntentRequest.SerializeToString,
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.Intent.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def DeleteIntent(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/DeleteIntent",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.DeleteIntentRequest.SerializeToString,
google_dot_protobuf_dot_empty__pb2.Empty.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def BatchUpdateIntents(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/BatchUpdateIntents",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchUpdateIntentsRequest.SerializeToString,
google_dot_longrunning_dot_operations__pb2.Operation.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
@staticmethod
def BatchDeleteIntents(
request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None,
):
return grpc.experimental.unary_unary(
request,
target,
"/google.cloud.dialogflow.v2beta1.Intents/BatchDeleteIntents",
google_dot_cloud_dot_dialogflow__v2beta1_dot_proto_dot_intent__pb2.BatchDeleteIntentsRequest.SerializeToString,
google_dot_longrunning_dot_operations__pb2.Operation.FromString,
options,
channel_credentials,
call_credentials,
compression,
wait_for_ready,
timeout,
metadata,
)
| 40.448179
| 142
| 0.696122
| 1,395
| 14,440
| 6.749104
| 0.091039
| 0.097504
| 0.050558
| 0.061391
| 0.845353
| 0.835794
| 0.809453
| 0.768561
| 0.721827
| 0.721827
| 0
| 0.014249
| 0.236981
| 14,440
| 356
| 143
| 40.561798
| 0.840261
| 0.072091
| 0
| 0.682274
| 1
| 0
| 0.09121
| 0.060029
| 0
| 0
| 0
| 0
| 0
| 1
| 0.053512
| false
| 0
| 0.013378
| 0.023411
| 0.100334
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d624afc005581dffbfc08e82838a1665027d366a
| 12,517
|
py
|
Python
|
1/feature.py
|
houzeyu2683/NCKUCSIEIIR
|
5289de95caed724b395136fcfc8e44fc522aad68
|
[
"MIT"
] | null | null | null |
1/feature.py
|
houzeyu2683/NCKUCSIEIIR
|
5289de95caed724b395136fcfc8e44fc522aad68
|
[
"MIT"
] | null | null | null |
1/feature.py
|
houzeyu2683/NCKUCSIEIIR
|
5289de95caed724b395136fcfc8e44fc522aad68
|
[
"MIT"
] | null | null | null |
import cv2
class color:
def __init__(self, table, use):
self.table = table
self.use = use
return
def process(self, item):
if(self.use=='rgb'):
size = (64,64)
image = cv2.imread(item['path'])
if(not isinstance(image, numpy.ndarray)):
capture = cv2.VideoCapture(item['path'])
_, image = capture.read()
pass
image = cv2.resize(image, size)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
channel = [0, 1, 2]
bin = [8, 8, 8]
range = [0, 256, 0, 256, 0, 256]
histogram = cv2.calcHist([image], channel, None, bin, range)
histogram = cv2.normalize(histogram, histogram).flatten()
output = item['path'], item['label'], histogram
pass
if(self.use=='gray'):
size = (64,64)
image = cv2.imread(item['path'])
if(not isinstance(image, numpy.ndarray)):
capture = cv2.VideoCapture(item['path'])
_, image = capture.read()
pass
image = cv2.resize(image, size)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
channel = [0]
bin = [20]
range = [0, 256]
histogram = cv2.calcHist([image], channel, None, bin, range)
histogram = cv2.normalize(histogram, histogram).flatten()
output = item['path'], item['label'], histogram
pass
return(output)
def compare(self, method):
score = []
for index, item in self.table.iterrows():
path, label, histogram = self.process(item=item)
if(index==0):
objective = {'path':path, 'label':label, "histogram":histogram}
pass
'''原始輸出結果介於 -1 ~ 1 之間，轉到 0 ~ 1 之間。'''
if(method=='correlation'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_CORREL)
value = (value + 1) / 2
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='l2'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.NORM_L2)
value = 1 / (value + 1 )
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='intersection'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_INTERSECT)
value = 1 - (1/(value+1))
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='chisquare'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_CHISQR)
value = 1/(value+1)
score += [value]
pass
pass # break
table = self.table.copy()
table[method] = score
table = table.sort_values([method], ascending=False).reset_index(drop=True)
table['score'] = table[method]
table['method'] = method
return(table)
# '''與自己越像分數越高，進行遞減排序。讓第一筆資料就是輸入的圖片。'''
# if(method in ['correlation', 'intersection']):
# table = table.sort_values([method], ascending=False).reset_index(drop=True)
# pass
# '''與自己越像分數越低，進行遞減排序。讓第一筆資料就是輸入的圖片。'''
# if(method in ['l2', 'chisquare']):
# table = table.sort_values([method], ascending=False).reset_index(drop=True)
# pass
#
# return(table)
pass
import cv2
import pandas
import numpy
# from skimage.metrics import structural_similarity
class texture:
def __init__(self, table, use):
self.table = table
self.use = use
return
def process(self, item):
size = (64,64)
image = cv2.imread(item['path'])
if(not isinstance(image, numpy.ndarray)):
capture = cv2.VideoCapture(item['path'])
_, image = capture.read()
pass
image = cv2.resize(image, size)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if(self.use=='sobel(xy)'):
texture = cv2.Sobel(src=image, ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5)
pass
if(self.use=='sobel(x)'):
texture = cv2.Sobel(src=image, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=5)
pass
if(self.use=='sobel(y)'):
texture = cv2.Sobel(src=image, ddepth=cv2.CV_64F, dx=0, dy=1, ksize=5)
pass
if(self.use=='canny'):
texture = cv2.Canny(numpy.uint8(image), 30, 150)
pass
if(self.use=='laplacian'):
texture = cv2.Laplacian(image, ksize=3, ddepth=cv2.CV_16S)
pass
channel = [0]
bin = [20]
range = [0, 256]
histogram = cv2.calcHist([numpy.uint8(texture)], channel, None, bin, range)
histogram = cv2.normalize(histogram, histogram).flatten()
output = item['path'], item['label'], histogram
return(output)
def compare(self, method):
score = []
for index, item in self.table.iterrows():
path, label, histogram = self.process(item=item)
if(index==0):
objective = {'path':path, 'label':label, "histogram":histogram}
pass
'''原始輸出結果介於 -1 ~ 1 之間，轉到 0 ~ 1 之間。'''
if(method=='correlation'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_CORREL)
value = (value + 1) / 2
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='l2'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.NORM_L2)
value = 1 / (value + 1 )
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='intersection'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_INTERSECT)
value = 1 - (1/(value+1))
score += [value]
pass
'''原始輸出結果大於 0 ，轉到 0 ~ 1 之間。'''
if(method=='chisquare'):
value = cv2.compareHist(objective['histogram'], histogram, cv2.HISTCMP_CHISQR)
value = 1/(value+1)
score += [value]
pass
pass # break
table = self.table.copy()
table[method] = score
table = table.sort_values([method], ascending=False).reset_index(drop=True)
table['score'] = table[method]
table['method'] = method
return(table)
# def compare(group, by):
# cache = {'by':by, "score":[]}
# for index, item in group.iterrows():
# if(index==0):
# objective = {}
# objective['image'] = cv2.imread(item['link'])
# objective['image'] = cv2.GaussianBlur(objective['image'], (5, 5), 0)
# objective['image'] = cv2.resize(objective['image'], (256, 256))
# objective['image'] = cv2.cvtColor(objective['image'], cv2.COLOR_BGR2GRAY)
# objective['image'] = numpy.float32(objective['image'])
# pass
# if(cache['by']=="DFT"):
# objective['texture'] = cv2.dft(src=objective['image'], dst=None, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows=None)
# objective['texture'] = numpy.fft.fftshift(objective['texture'])
# objective['texture'] = 10 * numpy.log(cv2.magnitude(objective['texture'][:, :, 0], objective['texture'][:, :, 1]))
# cache['score'] += [structural_similarity(objective['texture'], objective['texture'])]
# pass
# if(cache['by']=='Sobel'):
# objective['texture'] = cv2.Sobel(src=objective['image'], ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5)
# cache['score'] += [structural_similarity(objective['texture'], objective['texture'])]
# pass
# if(cache['by']=='Canny'):
# objective['texture'] = cv2.Canny(numpy.uint8(objective['image']), 30, 150)
# cache['score'] += [structural_similarity(objective['texture'], objective['texture'])]
# pass
# continue
# else:
# sample = {}
# sample['image'] = cv2.imread(item['link'])
# sample['image'] = cv2.GaussianBlur(sample['image'], (5, 5), 0)
# sample['image'] = cv2.resize(sample['image'], (256, 256))
# sample['image'] = cv2.cvtColor(sample['image'], cv2.COLOR_BGR2GRAY)
# sample['image'] = numpy.float32(sample['image'])
# pass
# if(cache['by']=="DFT"):
# sample['texture'] = cv2.dft(src=sample['image'], dst=None, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows=None)
# sample['texture'] = numpy.fft.fftshift(sample['texture'])
# sample['texture'] = 10 * numpy.log(cv2.magnitude(sample['texture'][:, :, 0], sample['texture'][:, :, 1]))
# cache['score'] += [structural_similarity(objective['texture'], sample['texture'])]
# pass
# if(cache['by']=='Sobel'):
# sample['texture'] = cv2.Sobel(src=sample['image'], ddepth=cv2.CV_64F, dx=1, dy=1, ksize=5)
# cache['score'] += [structural_similarity(objective['texture'], sample['texture'])]
# pass
# if(cache['by']=='Canny'):
# sample['texture'] = cv2.Canny(numpy.uint8(sample['image']), 30, 150)
# cache['score'] += [structural_similarity(objective['texture'], sample['texture'])]
# pass
# pass
# pass
# group['by'] = cache['by']
# group['score'] = cache['score']
# output = group.sort_values(["score"], ascending=False).copy()
# return(output)
# # import cv2
# # import pandas
# # import numpy
# # '''
# # '''
# def compare(group):
# result = {
# "Dft":[],
# "B":[]
# }
# for index, item in group.iterrows():
# if(index==0):
# objective = {}
# objective['image'] = cv2.imread(item['link'])
# objective['image'] = cv2.cvtColor(objective['image'], cv2.COLOR_BGR2GRAY)
# objective['image'] = numpy.float32(objective['image'])
# objective['texture'] = cv2.dft(src=objective['image'], dst=None, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows=None)
# objective['texture'] = numpy.fft.fftshift(objective['texture'])
# objective['texture'] = 10 * numpy.log(cv2.magnitude(objective['texture'][:, :, 0], objective['texture'][:, :, 1]))
# objective['bin'] = cv2.calcHist([objective['texture']], [0], None, [128], [0, 256])
# objective['bin'] = objective['bin'] / objective['bin'] .sum()
# result['Dft'] += [cv2.compareHist(objective['bin'], objective['bin'], cv2.NORM_L1)]
# continue
# else:
# sample = {}
# sample['image'] = cv2.imread(item['link'])
# sample['image'] = cv2.cvtColor(sample['image'], cv2.COLOR_BGR2GRAY)
# sample['image'] = numpy.float32(sample['image'])
# sample['texture'] = cv2.dft(src=sample['image'], dst=None, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows=None)
# sample['texture'] = numpy.fft.fftshift(sample['texture'])
# sample['texture'] = 10 * numpy.log(cv2.magnitude(sample['texture'][:, :, 0], sample['texture'][:, :, 1]))
# sample['bin'] = cv2.calcHist([sample['texture']], [0], None, [128], [0, 256])
# sample['bin'] = sample['bin'] / sample['bin'] .sum()
# result['Dft'] += [cv2.compareHist(objective['bin'], sample['bin'], cv2.NORM_L1)]
# pass
# pass
# # e ^ x / sum(e ^ x)
# result = pandas.DataFrame(result)
# output = pandas.concat([group, result], axis=1)
# # output['L1'] = numpy.exp(output['L1']) / sum(numpy.exp(output['L1']))
# return(output)
| 33.026385
| 132
| 0.502596
| 1,269
| 12,517
| 4.911742
| 0.109535
| 0.035938
| 0.0369
| 0.007701
| 0.841168
| 0.819028
| 0.794321
| 0.789668
| 0.770897
| 0.746992
| 0
| 0.036442
| 0.333546
| 12,517
| 379
| 133
| 33.026385
| 0.710741
| 0.450427
| 0
| 0.858108
| 0
| 0
| 0.045329
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.040541
| false
| 0.155405
| 0.027027
| 0
| 0.094595
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
612ecfda35cd8d93573ac60f077c553e163ffa6a
| 160
|
py
|
Python
|
accounts/admin.py
|
Dev-Mehta/AskaDev
|
4514383cb1f94178e8082f0b710c7efbdd3225a7
|
[
"MIT"
] | 7
|
2020-08-26T12:32:50.000Z
|
2020-09-20T09:17:12.000Z
|
accounts/admin.py
|
Dev-Mehta/AskaDev
|
4514383cb1f94178e8082f0b710c7efbdd3225a7
|
[
"MIT"
] | null | null | null |
accounts/admin.py
|
Dev-Mehta/AskaDev
|
4514383cb1f94178e8082f0b710c7efbdd3225a7
|
[
"MIT"
] | 3
|
2020-08-27T06:06:43.000Z
|
2020-10-10T15:53:26.000Z
|
from django.contrib import admin
from .models import ProgrammingLanguage, UserProfile
admin.site.register(UserProfile)
admin.site.register(ProgrammingLanguage)
| 32
| 52
| 0.8625
| 18
| 160
| 7.666667
| 0.555556
| 0.231884
| 0.289855
| 0.405797
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06875
| 160
| 5
| 53
| 32
| 0.926175
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
61bca4eaa32bb1d8f9fcdd23e9b7147440128669
| 108
|
py
|
Python
|
tests/test_artificial_objects_2d.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | 2
|
2020-07-01T06:20:44.000Z
|
2020-07-01T09:36:48.000Z
|
tests/test_artificial_objects_2d.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_artificial_objects_2d.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | 1
|
2020-06-29T18:40:54.000Z
|
2020-06-29T18:40:54.000Z
|
def test_artificial_objects_2d():
import pyclesperanto_prototype as cle
cle.artificial_objects_2d()
| 27
| 41
| 0.805556
| 14
| 108
| 5.785714
| 0.714286
| 0.419753
| 0.469136
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021505
| 0.138889
| 108
| 3
| 42
| 36
| 0.849462
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
61bce430d360996e97a91a55ee3188cadb7521ab
| 132,733
|
py
|
Python
|
polls/esp_xlsx.py
|
raqsilva/VCFDataExporter
|
b5eaabd82ade95a837fd98dff5e1600046eeb029
|
[
"Apache-2.0"
] | null | null | null |
polls/esp_xlsx.py
|
raqsilva/VCFDataExporter
|
b5eaabd82ade95a837fd98dff5e1600046eeb029
|
[
"Apache-2.0"
] | null | null | null |
polls/esp_xlsx.py
|
raqsilva/VCFDataExporter
|
b5eaabd82ade95a837fd98dff5e1600046eeb029
|
[
"Apache-2.0"
] | null | null | null |
import vcf
from django.http import HttpResponse
from django.core.files import File
import xlsxwriter
import os
from .vcf_functions import getBasePath, save_binary, getFilePath, parse_fasta, getEspPath
import subprocess
import collections
from .dictionaries import esp_col_dic
from pytera.settings import BASE_DIR
# PYTERA_PATH = str(os.getenv('PYTERA_PATH'))
PYTERA_PATH = BASE_DIR
def evs_xlsx_file(chromo, start, stop, user_profile, columns, ea, aa, total, ea_sign, aa_sign, total_sign):
basePath = getBasePath()
espPath = getEspPath(chromo)
subprocess.call(
PYTERA_PATH + "/static/tabix/tabix -f -p vcf -h " + espPath + " " + str(chromo) + ":" + str(start) + "-" + str(
stop) + " > " + PYTERA_PATH + "/static/downloads/subset.vcf", shell=True)
vcf_reader = vcf.Reader(filename=PYTERA_PATH + "/static/downloads/subset.vcf")
text = {}
for key in columns:
text[esp_col_dic[key][0]] = [esp_col_dic[key][1]]
text[0] = ['CHROM']
text[1] = ['POS']
text[2] = ['ID']
text[3] = ['Type'] # SNP/INDEL
text[4] = ['REF']
text[5] = ['ALT']
if ea_sign == '<':
if aa_sign == '<':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) < aa and float(
record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) < aa and float(
record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) < aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif aa_sign == '>':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) > aa and float(
record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) > aa and float(
record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][1]) > aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
breaks
elif aa_sign == '':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea and float(record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) < ea:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
##################################
elif ea_sign == '>':
if aa_sign == '<':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) < aa and float(
record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) < aa and float(
record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) < aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif aa_sign == '>':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) > aa and float(
record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) > aa and float(
record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][1]) > aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
breaks
elif aa_sign == '':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea and float(record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][0]) > ea:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
#########################################
elif ea_sign == '':
if aa_sign == '<':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) < aa and float(record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) < aa and float(record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) < aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif aa_sign == '>':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) > aa and float(record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) > aa and float(record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][1]) > aa:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
breaks
elif aa_sign == '':
if total_sign == '<':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][2]) < total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '>':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo and float(
record.INFO['MAF'][2]) > total:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
elif total_sign == '':
for record in vcf_reader:
maf = record.INFO['MAF']
if record.POS >= start and record.POS <= stop and record.CHROM == chromo:
text[0].append(str(record.CHROM))
text[1].append(str(record.POS))
ID = str(record.ID)
text[3].append(str(record.var_type))
text[4].append(str(record.REF))
text[5].append(str(record.ALT[0]))
try:
text[6].append('A=' + str(record.INFO['EA_AC'][0]) + ' / R=' + str(record.INFO['EA_AC'][1]))
except KeyError:
pass
try:
text[7].append('A=' + str(record.INFO['AA_AC'][0]) + ' / R=' + str(record.INFO['AA_AC'][1]))
except KeyError:
pass
try:
text[8].append('A=' + str(record.INFO['TAC'][0]) + ' / R=' + str(record.INFO['TAC'][1]))
except KeyError:
pass
try:
text[9].append(str('EA=' + str(record.INFO['MAF'][0]) + ' / ' + 'AA=' + str(
record.INFO['MAF'][1] + ' / ' + 'All=' + str(record.INFO['MAF'][2]))))
except KeyError:
pass
try:
text[10].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['EA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['EA_GTC'][1]))
except KeyError:
pass
try:
text[11].append(
str(record.INFO['GTS'][0]) + '=' + str(record.INFO['AA_GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['AA_GTC'][1]))
except KeyError:
pass
try:
text[12].append(str(record.INFO['GTS'][0]) + '=' + str(record.INFO['GTC'][0]) + ' / ' + str(
record.INFO['GTS'][1]) + '=' + str(record.INFO['GTC'][1]))
except KeyError:
pass
try:
text[13].append(str(record.INFO['DP']))
except KeyError:
pass
FG = str(record.INFO['FG'][0])
if FG.startswith('NM'):
GVS = FG.split(':')
try:
text[14].append(GVS[1])
except KeyError:
pass
else:
try:
text[14].append(FG)
except KeyError:
pass
try:
text[15].append(str(record.INFO['CDS_SIZES'][0]))
except KeyError:
pass
gene1 = record.INFO['GL'][0]
try:
gene2 = record.INFO['GL'][1]
try:
text[16].append(str(gene1) + '/' + str(gene2))
except KeyError:
pass
except IndexError:
try:
text[16].append(gene1)
except KeyError:
pass
try:
text[17].append(str(record.INFO['GRCh38_POSITION'][0]))
except KeyError:
pass
if ID != 'None':
if ID.startswith('rs'):
text[2].append(
('http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=' + ID[2:], ID))
else:
text[2].append(ID)
else:
text[2].append('None')
elif record.POS >= stop and record.CHROM == chromo:
break
text = collections.OrderedDict(sorted(text.items()))
workbook = xlsxwriter.Workbook(
basePath + '/excel_esp-' + str(chromo) + '-' + str(start) + '-' + str(stop) + '.xlsx')
link_format = workbook.add_format({'color': 'blue', 'underline': 1})
worksheet = workbook.add_worksheet()
worksheet.set_column(5, 20, 18)
worksheet.set_column(0, 0, 8)
worksheet.set_column(3, 5, 8)
worksheet.set_column(1, 2, 14)
col = 0
for key in text.keys():
row = 0
if key != 2:
for par in text[key]:
worksheet.write(row, col, str(par))
row = row + 1
else:
for par in text[key]:
if par[1][0:2] == 'rs':
worksheet.write_url(row, col, str(par[0]), link_format, str(par[1]))
row = row + 1
else:
worksheet.write(row, col, str(par))
row = row + 1
col = col + 1
workbook.close()
file = 'excel_esp-' + str(chromo) + '-' + str(start) + '-' + str(stop) + '.xlsx'
name = save_binary(file, user_profile)
os.remove(basePath + '/excel_esp-' + str(chromo) + '-' + str(start) + '-' + str(stop) + '.xlsx')
os.remove(basePath + "/subset.vcf")
path = basePath + '/' + name
with open(path, "rb") as excel:
data = excel.read()
response = HttpResponse(data, content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')
response['Content-Disposition'] = 'attachment; filename=' + name.split('/')[1]
return response
| 51.407049
| 120
| 0.317638
| 10,909
| 132,733
| 3.825465
| 0.016133
| 0.180509
| 0.21027
| 0.12228
| 0.969879
| 0.96916
| 0.966956
| 0.966956
| 0.966956
| 0.964512
| 0
| 0.032531
| 0.553721
| 132,733
| 2,581
| 121
| 51.426966
| 0.671973
| 0.000399
| 0
| 0.970855
| 0
| 0.010634
| 0.052609
| 0.000913
| 0.010634
| 0
| 0
| 0
| 0
| 1
| 0.000394
| false
| 0.148878
| 0.003939
| 0
| 0.004726
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 10
|
4edf4e5da1cf00b7d7db246168a8366bec39843f
| 3,568
|
py
|
Python
|
python/modeltools/imagetools/test_result.py
|
GT-AcerZhang/PaddleSelfTools
|
ad7e45ddc3166f6834e8798011a33488a9f7f70e
|
[
"Apache-2.0"
] | 1
|
2021-02-27T18:01:44.000Z
|
2021-02-27T18:01:44.000Z
|
python/modeltools/imagetools/test_result.py
|
GT-AcerZhang/PaddleSelfTools
|
ad7e45ddc3166f6834e8798011a33488a9f7f70e
|
[
"Apache-2.0"
] | 1
|
2020-03-18T15:55:50.000Z
|
2020-03-18T15:55:50.000Z
|
python/modeltools/imagetools/test_result.py
|
GT-AcerZhang/PaddleSelfTools
|
ad7e45ddc3166f6834e8798011a33488a9f7f70e
|
[
"Apache-2.0"
] | 1
|
2021-03-05T10:23:37.000Z
|
2021-03-05T10:23:37.000Z
|
# coding=utf-8
import numpy as np
# fromfile = np.fromfile('/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.npfile', 'f')
# fromfile = np.fromfile('/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs/0000_0.9946-35960_36550-0377100ea28c4800ca76c01539327637.jpg_p0_0.970266780694.jpg.npfile', 'f')
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.conv1BeforeRelu.npfile',
'f')
print '第1层 conv add :------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.conv2BeforeRelu.npfile',
'f')
print '第2层 conv add :------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.Beforepool.npfile',
'f')
print 'pool前 :------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.Afterpool.npfile',
'f')
print 'pool后 :------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs2/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.fc7.npfile',
'f')
print '第fc输出层:------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
fromfile = np.fromfile(
'/Users/xiebaiyuan/PaddleProject/paddle-mobile/tools/python/imagetools/datas/jpgs/0000_0.9834-148196_82452-0ad4b83ec6bc0f9c5f28101539267054.jpg_p0_0.126571263346.jpg.npfile',
'f')
print '最终 :------------------------------- '
stride = len(fromfile) / 20
if stride > 0:
stride = stride
else:
stride = 1
for i in range(0, len(fromfile), stride):
print fromfile[i]
print '前20个数: '
for i in range(0, 20):
print fromfile[i]
print len(fromfile)
print fromfile
| 29.00813
| 204
| 0.69815
| 470
| 3,568
| 5.231915
| 0.12766
| 0.080521
| 0.02928
| 0.05368
| 0.906466
| 0.906466
| 0.906466
| 0.906466
| 0.906466
| 0.906466
| 0
| 0.168659
| 0.132567
| 3,568
| 122
| 205
| 29.245902
| 0.625848
| 0.116872
| 0
| 0.865979
| 0
| 0.061856
| 0.440737
| 0.408643
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.010309
| null | null | 0.371134
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
f637e3392c8c6461ada801c17a43ac56bfdc0ba3
| 123
|
py
|
Python
|
python/pygimli/misc/__init__.py
|
mjziebarth/gimli
|
196ac4d6dd67e0326cccc44a87b367f64051e490
|
[
"Apache-2.0"
] | 3
|
2021-07-10T00:56:59.000Z
|
2022-02-17T12:43:38.000Z
|
python/pygimli/misc/__init__.py
|
ivek1312/gimli
|
5fafebb7c96dd0e04e2616df402fa27a01609d63
|
[
"Apache-2.0"
] | null | null | null |
python/pygimli/misc/__init__.py
|
ivek1312/gimli
|
5fafebb7c96dd0e04e2616df402fa27a01609d63
|
[
"Apache-2.0"
] | 1
|
2022-03-29T04:28:40.000Z
|
2022-03-29T04:28:40.000Z
|
# -*- coding: utf-8 -*-
"""
Unsorted miscellaneous stuff
"""
from .unsorted import *
from .unsorted import streamline
| 15.375
| 32
| 0.666667
| 13
| 123
| 6.307692
| 0.692308
| 0.292683
| 0.439024
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01
| 0.186992
| 123
| 7
| 33
| 17.571429
| 0.81
| 0.414634
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 0
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0
| 7
|
f641c36178a65bbfc610b9a5571bcb7865d2f5ec
| 16,135
|
py
|
Python
|
magenta/models/lookback_rnn/lookback_rnn_encoder_decoder_test.py
|
Sprog-gle/Magenta
|
55bfd53f8112cf34952e67efc646b98523837f8f
|
[
"Apache-2.0"
] | null | null | null |
magenta/models/lookback_rnn/lookback_rnn_encoder_decoder_test.py
|
Sprog-gle/Magenta
|
55bfd53f8112cf34952e67efc646b98523837f8f
|
[
"Apache-2.0"
] | null | null | null |
magenta/models/lookback_rnn/lookback_rnn_encoder_decoder_test.py
|
Sprog-gle/Magenta
|
55bfd53f8112cf34952e67efc646b98523837f8f
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for lookback_rnn_encoder_decoder."""
# internal imports
import tensorflow as tf
from magenta.models.lookback_rnn import lookback_rnn_encoder_decoder
from magenta.music import melodies_lib
NOTE_OFF = melodies_lib.MELODY_NOTE_OFF
NO_EVENT = melodies_lib.MELODY_NO_EVENT
class LookbackRnnEncoderDecoderTest(tf.test.TestCase):
def testDefaultRange(self):
lookback_rnn_encoder_decoder.MIN_NOTE = 48
lookback_rnn_encoder_decoder.MAX_NOTE = 84
self.assertEqual(lookback_rnn_encoder_decoder.TRANSPOSE_TO_KEY, 0)
melody_encoder_decoder = lookback_rnn_encoder_decoder.MelodyEncoderDecoder()
self.assertEqual(melody_encoder_decoder.input_size, 121)
self.assertEqual(melody_encoder_decoder.num_classes, 40)
melody_events = ([48, NO_EVENT, 49, 83, NOTE_OFF] + [NO_EVENT] * 11 +
[48, NOTE_OFF] + [NO_EVENT] * 14 +
[48, NOTE_OFF, 49, 82])
melody = melodies_lib.Melody(melody_events)
melody_indices = [0, 1, 2, 3, 4, 16, 17, 32, 33, 34, 35]
expected_inputs = [
[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -1.0, 1.0, -1.0, -1.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, 0.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, -1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0]
]
expected_labels = [2, 39, 3, 37, 1, 38, 1, 39, 38, 39, 36]
melodies = [melody, melody]
full_length_inputs_batch = melody_encoder_decoder.get_inputs_batch(
melodies, True)
for i, melody_index in enumerate(melody_indices):
self.assertListEqual(
melody_encoder_decoder.events_to_input(melody, melody_index),
expected_inputs[i])
self.assertEqual(
melody_encoder_decoder.events_to_label(melody, melody_index),
expected_labels[i])
partial_melody = melodies_lib.Melody(melody_events[:melody_index])
self.assertEqual(
melody_encoder_decoder.class_index_to_event(expected_labels[i],
partial_melody),
melody_events[melody_index])
self.assertListEqual(full_length_inputs_batch[0][melody_index],
expected_inputs[i])
self.assertListEqual(full_length_inputs_batch[1][melody_index],
expected_inputs[i])
partial_melody = melodies_lib.Melody(melody_events[:melody_index])
softmax = [[[0.0] * melody_encoder_decoder.num_classes]]
softmax[0][0][expected_labels[i]] = 1.0
melody_encoder_decoder.extend_event_sequences([partial_melody], softmax)
self.assertEqual(list(partial_melody)[-1], melody_events[melody_index])
self.assertListEqual(
[expected_inputs[-1:], expected_inputs[-1:]],
melody_encoder_decoder.get_inputs_batch(melodies))
def testCustomRange(self):
lookback_rnn_encoder_decoder.MIN_NOTE = 24
lookback_rnn_encoder_decoder.MAX_NOTE = 36
self.assertEqual(lookback_rnn_encoder_decoder.TRANSPOSE_TO_KEY, 0)
melody_encoder_decoder = lookback_rnn_encoder_decoder.MelodyEncoderDecoder()
self.assertEqual(melody_encoder_decoder.input_size, 49)
self.assertEqual(melody_encoder_decoder.num_classes, 16)
melody_events = ([24, NO_EVENT, 25, 35, NOTE_OFF] + [NO_EVENT] * 11 +
[24, NOTE_OFF] + [NO_EVENT] * 14 +
[24, NOTE_OFF, 25, 34])
melody = melodies_lib.Melody(melody_events)
melody_indices = [0, 1, 2, 3, 4, 16, 17, 32, 33, 34, 35]
expected_inputs = [
[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0],
[1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, 1.0, -1.0, -1.0, 1.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, 1.0, -1.0, -1.0, -1.0, 1.0, 0.0],
[1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 1.0, -1.0, -1.0, -1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0,
0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
-1.0, -1.0, 1.0, -1.0, -1.0, 0.0, 0.0]
]
expected_labels = [2, 15, 3, 13, 1, 14, 1, 15, 14, 15, 12]
melodies = [melody, melody]
full_length_inputs_batch = melody_encoder_decoder.get_inputs_batch(
melodies, True)
for i, melody_index in enumerate(melody_indices):
self.assertListEqual(
melody_encoder_decoder.events_to_input(melody, melody_index),
expected_inputs[i])
self.assertEqual(
melody_encoder_decoder.events_to_label(melody, melody_index),
expected_labels[i])
partial_melody = melodies_lib.Melody(melody_events[:melody_index])
self.assertEqual(
melody_encoder_decoder.class_index_to_event(expected_labels[i],
partial_melody),
melody_events[melody_index])
self.assertListEqual(full_length_inputs_batch[0][melody_index],
expected_inputs[i])
self.assertListEqual(full_length_inputs_batch[1][melody_index],
expected_inputs[i])
partial_melody = melodies_lib.Melody(melody_events[:melody_index])
softmax = [[[0.0] * melody_encoder_decoder.num_classes]]
softmax[0][0][expected_labels[i]] = 1.0
melody_encoder_decoder.extend_event_sequences([partial_melody], softmax)
self.assertEqual(list(partial_melody)[-1], melody_events[melody_index])
self.assertListEqual(
[expected_inputs[-1:], expected_inputs[-1:]],
melody_encoder_decoder.get_inputs_batch(melodies))
if __name__ == '__main__':
tf.test.main()
| 59.759259
| 80
| 0.464642
| 4,424
| 16,135
| 1.643083
| 0.035036
| 0.928876
| 1.360297
| 1.771908
| 0.874398
| 0.865594
| 0.856789
| 0.834503
| 0.834503
| 0.834503
| 0
| 0.328276
| 0.264642
| 16,135
| 269
| 81
| 59.981413
| 0.284366
| 0.038798
| 0
| 0.757447
| 0
| 0
| 0.000516
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 1
| 0.008511
| false
| 0
| 0.012766
| 0
| 0.025532
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 13
|
9c9944bcc47bf407be2e97f0b042b46e0079024f
| 36
|
py
|
Python
|
tests/test_multi/views.py
|
cacahootie/deckmaster
|
6f8585ee35b8cfc2fb0b68adefc1308322da618d
|
[
"MIT"
] | null | null | null |
tests/test_multi/views.py
|
cacahootie/deckmaster
|
6f8585ee35b8cfc2fb0b68adefc1308322da618d
|
[
"MIT"
] | null | null | null |
tests/test_multi/views.py
|
cacahootie/deckmaster
|
6f8585ee35b8cfc2fb0b68adefc1308322da618d
|
[
"MIT"
] | null | null | null |
def c():
return "HappyFilmore"
| 9
| 25
| 0.611111
| 4
| 36
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 36
| 3
| 26
| 12
| 0.814815
| 0
| 0
| 0
| 0
| 0
| 0.342857
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
9cc4a956152fdbca76b3675be69befe26d5b5929
| 41
|
py
|
Python
|
0x02-python-import_modules/101-easy_print.py
|
Nahi-Terefe/alx-higher_level_programming
|
c67a78a6f79e853918963971f8352979e7691541
|
[
"MIT"
] | null | null | null |
0x02-python-import_modules/101-easy_print.py
|
Nahi-Terefe/alx-higher_level_programming
|
c67a78a6f79e853918963971f8352979e7691541
|
[
"MIT"
] | null | null | null |
0x02-python-import_modules/101-easy_print.py
|
Nahi-Terefe/alx-higher_level_programming
|
c67a78a6f79e853918963971f8352979e7691541
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
import easy_print_101
| 13.666667
| 21
| 0.804878
| 7
| 41
| 4.428571
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 0.073171
| 41
| 2
| 22
| 20.5
| 0.710526
| 0.414634
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
|
0
| 7
|
9cc9655c102180c64f11f7ecabab9a8083456ce9
| 318
|
py
|
Python
|
PythonModulo1/ex006.py
|
BossNX/ExerciciosDePython
|
27c79d284794f65f94d3a07de11429d665ec92da
|
[
"MIT"
] | null | null | null |
PythonModulo1/ex006.py
|
BossNX/ExerciciosDePython
|
27c79d284794f65f94d3a07de11429d665ec92da
|
[
"MIT"
] | null | null | null |
PythonModulo1/ex006.py
|
BossNX/ExerciciosDePython
|
27c79d284794f65f94d3a07de11429d665ec92da
|
[
"MIT"
] | null | null | null |
n = int(input('Digite um número: '))
d = n * 2
t = n * 3
r = n ** (1/2)
print('O dobro de \033[4;31m{}\033[m vale \033[34m{}\033[m. '.format(n, d))
print('O triplo de \033[4;33m{}\033[m vale \033[36m{}\033[m. '.format(n, t))
print('A raíz quadrada de \033[4;35m{}\033[m é igual a \033[32m{:.2f}\033[m. '.format(n, r))
| 39.75
| 92
| 0.578616
| 71
| 318
| 2.591549
| 0.478873
| 0.130435
| 0.097826
| 0.179348
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.207407
| 0.150943
| 318
| 7
| 93
| 45.428571
| 0.474074
| 0
| 0
| 0
| 0
| 0.428571
| 0.613208
| 0.066038
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.428571
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
9cff114514a4e53b9d73b004333fbe341dfc861e
| 10,438
|
py
|
Python
|
tests/bugs/core_2251_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
tests/bugs/core_2251_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
tests/bugs/core_2251_test.py
|
reevespaul/firebird-qa
|
98f16f425aa9ab8ee63b86172f959d63a2d76f21
|
[
"MIT"
] | null | null | null |
#coding:utf-8
#
# id: bugs.core_2251
# title: gbak doesn't return error code
# decription:
# Inaccessible folder is defined here as $tmp + GUID (i.e. it 100% not yet exists).
# We have to check allof kind for inaccessible file:
# * .fbk when trying to make backup of existing database;
# * .fdb when trying to restore from existing .fbk;
# * .log - for any of these operation
# We are NOT interested when all of these files are in accessible folder(s).
#
# Query to obtain set of interested combinations (all of them should have retcode = 1):
# with
# a as (
# select 'backup' as action from rdb$database union all
# select 'restore' from rdb$database
# )
# ,s as (
# select 'inaccessible' as path_to_source_file from rdb$database union all
# select 'accessible' from rdb$database
# )
# ,t as (
# select 'inaccessible' as path_to_target_file from rdb$database union all
# select 'accessible' from rdb$database
# )
# ,g as (
# select 'inaccessible' as path_to_action_log from rdb$database union all
# select 'accessible' from rdb$database
# )
# select * from a,s,t,g
# where NOT (s.path_to_source_file = 'accessible' and t.path_to_target_file = 'accessible' and g.path_to_action_log = 'accessible')
# order by 1,2,3,4;
#
# Confirmed wrong results on: 4.0.0.1714 SC; 4.0.0.1715 CS; 3.0.5.33221 SC; 3.0.5.33225 CS
# Checked on:
# 4.0.0.1726 SS: 3.759s.
# 3.0.5.33232 SS: 1.671s.
#
# tracker_id: CORE-2251
# min_versions: ['3.0.5']
# versions: 3.0.5
# qmid: None
import pytest
from firebird.qa import db_factory, isql_act, Action
# version: 3.0.5
# resources: None
substitutions_1 = [('\t+', ' ')]
init_script_1 = """"""
db_1 = db_factory(sql_dialect=3, init=init_script_1)
# test_script_1
#---
#
# import os
# import sys
# import uuid
# import subprocess
#
# os.environ["ISC_USER"] = user_name
# os.environ["ISC_PASSWORD"] = user_password
# correct_fdb=db_conn.database_name
# db_conn.close()
#
# #--------------------------------------------
#
# def cleanup( f_names_list ):
# global os
# for i in range(len( f_names_list )):
# if os.path.isfile( f_names_list[i]):
# os.remove( f_names_list[i] )
#
# #--------------------------------------------
#
# inaccessible_dir = os.path.join(context['temp_directory'], str(uuid.uuid4()) )
#
# invalid_fdb=os.path.join(inaccessible_dir,'tmp_2251.fdb')
#
# invalid_fbk=os.path.join(inaccessible_dir,'tmp_2251.fbk')
# correct_fbk=os.path.join(context['temp_directory'],'tmp_2251.fbk')
#
# invalid_res=os.path.join(inaccessible_dir,'tmp_2251.tmp')
# correct_res=os.path.join(context['temp_directory'],'tmp_2251.tmp')
#
# invalid_log=os.path.join(inaccessible_dir,'tmp_2251.log')
# correct_log=os.path.join(context['temp_directory'],'tmp_2251.log')
#
# ##########################################################################################
#
# f_null_log = open(os.devnull,"w")
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', correct_fdb, correct_fbk, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'backup source_fdb=accessible target_fbk=accessible log_file=inaccessible:'.ljust(100), gbak_retcode )
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', correct_fdb, invalid_fbk, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=accessible target_fbk=inaccessible log_file=accessible:'.ljust(100), gbak_retcode)
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', correct_fdb, invalid_fbk, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=accessible target_fbk=inaccessible log_file=inaccessible:'.ljust(100), gbak_retcode)
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', invalid_fdb, correct_fbk, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=inaccessible target_fbk=accessible log_file=accessible:'.ljust(100), gbak_retcode)
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', invalid_fdb, correct_fbk, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=inaccessible target_fbk=accessible log_file=inaccessible:'.ljust(100), gbak_retcode)
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', invalid_fdb, invalid_fbk, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=inaccessible target_fbk=inaccessible log_file=accessible:'.ljust(100), gbak_retcode)
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-b', '-se', 'localhost:service_mgr', invalid_fdb, invalid_fbk, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print('backup source_fdb=inaccessible target_fbk=inaccessible log_file=inaccessible:'.ljust(100), gbak_retcode)
#
# ######################################################################################
# runProgram('gbak', [ '-b', '-se', 'localhost:service_mgr', correct_fdb, correct_fbk] )
# ######################################################################################
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', correct_fbk, correct_fdb, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=accessible target_fdb=accessible log_file=inaccessible:'.ljust(100), gbak_retcode )
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', correct_fbk, invalid_fdb, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=accessible target_fdb=inaccessible log_file=accessible:'.ljust(100), gbak_retcode )
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', correct_fbk, invalid_fdb, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=accessible target_fdb=inaccessible log_file=inaccessible:'.ljust(100), gbak_retcode )
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', invalid_fbk, correct_fdb, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=inaccessible target_fdb=accessible log_file=accessible:'.ljust(100), gbak_retcode )
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', invalid_fbk, correct_fdb, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=inaccessible target_fdb=accessible log_file=inaccessible:'.ljust(100), gbak_retcode )
#
# cleanup( (correct_log,) )
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', invalid_fbk, invalid_fdb, '-y', correct_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=inaccessible target_fdb=inaccessible log_file=accessible:'.ljust(100), gbak_retcode )
#
# gbak_retcode = subprocess.call( [context['gbak_path'], '-rep', '-se', 'localhost:service_mgr', invalid_fbk, invalid_fdb, '-y', invalid_log ], stdout=f_null_log, stderr=subprocess.STDOUT)
# print( 'restore source_fbk=inaccessible target_fdb=inaccessible log_file=inaccessible:'.ljust(100), gbak_retcode )
#
# f_null_log.close()
#
# cleanup( (correct_log,correct_fbk,) )
#
#
#---
#act_1 = python_act('db_1', test_script_1, substitutions=substitutions_1)
expected_stdout_1 = """
backup source_fdb=accessible target_fbk=accessible log_file=inaccessible: 1
backup source_fdb=accessible target_fbk=inaccessible log_file=accessible: 1
backup source_fdb=accessible target_fbk=inaccessible log_file=inaccessible: 1
backup source_fdb=inaccessible target_fbk=accessible log_file=accessible: 1
backup source_fdb=inaccessible target_fbk=accessible log_file=inaccessible: 1
backup source_fdb=inaccessible target_fbk=inaccessible log_file=accessible: 1
backup source_fdb=inaccessible target_fbk=inaccessible log_file=inaccessible: 1
restore source_fbk=accessible target_fdb=accessible log_file=inaccessible: 1
restore source_fbk=accessible target_fdb=inaccessible log_file=accessible: 1
restore source_fbk=accessible target_fdb=inaccessible log_file=inaccessible: 1
restore source_fbk=inaccessible target_fdb=accessible log_file=accessible: 1
restore source_fbk=inaccessible target_fdb=accessible log_file=inaccessible: 1
restore source_fbk=inaccessible target_fdb=inaccessible log_file=accessible: 1
restore source_fbk=inaccessible target_fdb=inaccessible log_file=inaccessible: 1
"""
@pytest.mark.version('>=3.0.5')
@pytest.mark.xfail
def test_1(db_1):
pytest.fail("Test not IMPLEMENTED")
| 58.312849
| 189
| 0.617934
| 1,239
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| 4.958031
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| 10,438
| 178
| 190
| 58.640449
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0
| 7
|
1406cc74764fb413f8c4e2e6fa7df223ab8a97a3
| 4,191
|
py
|
Python
|
traffic_control/tests/test_owner_api.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 2
|
2020-11-23T22:08:58.000Z
|
2022-03-02T13:13:20.000Z
|
traffic_control/tests/test_owner_api.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 170
|
2019-12-31T13:37:04.000Z
|
2022-03-12T14:03:35.000Z
|
traffic_control/tests/test_owner_api.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 3
|
2020-05-08T05:58:02.000Z
|
2022-03-15T16:07:25.000Z
|
import pytest
from django.urls import reverse
from rest_framework import status
from traffic_control.models import Owner
from traffic_control.tests.factories import get_api_client, get_owner, get_user
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__list(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
get_owner(name_fi="owner 1")
get_owner(name_fi="owner 2")
response = client.get(reverse("v1:owner-list"))
assert response.status_code == status.HTTP_200_OK
assert response.data["count"] == 2
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__retrieve(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
owner_1 = get_owner(name_fi="owner 1")
get_owner(name_fi="owner 2")
response = client.get(reverse("v1:owner-detail", kwargs={"pk": owner_1.pk}))
assert response.status_code == status.HTTP_200_OK
assert response.data["id"] == str(owner_1.pk)
assert response.data["name_fi"] == owner_1.name_fi
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__create(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
data = {
"name_fi": "Omistajan nimi",
"name_en": "Owner name",
}
response = client.post(reverse("v1:owner-list"), data=data)
if is_admin:
owner = Owner.objects.first()
assert response.status_code == status.HTTP_201_CREATED
assert Owner.objects.count() == 1
assert owner.name_fi == data["name_fi"]
assert owner.name_en == data["name_en"]
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert Owner.objects.count() == 0
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__update(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
owner = get_owner(name_fi="foo", name_en="bar")
data = {
"name_fi": "Omistajan nimi",
"name_en": "Owner name",
}
response = client.put(
reverse("v1:owner-detail", kwargs={"pk": owner.pk}), data=data
)
owner.refresh_from_db()
assert Owner.objects.count() == 1
if is_admin:
assert response.status_code == status.HTTP_200_OK
assert owner.name_fi == data["name_fi"]
assert owner.name_en == data["name_en"]
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert owner.name_fi == "foo"
assert owner.name_en == "bar"
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__partial_update(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
owner = get_owner(name_fi="foo", name_en="bar")
data = {
"name_fi": "Omistajan nimi",
}
response = client.patch(
reverse("v1:owner-detail", kwargs={"pk": owner.pk}), data=data
)
owner.refresh_from_db()
assert Owner.objects.count() == 1
if is_admin:
assert response.status_code == status.HTTP_200_OK
assert owner.name_fi == data["name_fi"]
assert owner.name_en == "bar"
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert owner.name_fi == "foo"
assert owner.name_en == "bar"
@pytest.mark.django_db
@pytest.mark.parametrize("is_admin", (True, False))
def test__owner_api__destroy(is_admin):
Owner.objects.all().delete()
user = get_user(admin=is_admin)
client = get_api_client(user)
owner = get_owner(name_fi="foo", name_en="bar")
response = client.delete(reverse("v1:owner-detail", kwargs={"pk": owner.pk}))
if is_admin:
assert response.status_code == status.HTTP_204_NO_CONTENT
assert Owner.objects.count() == 0
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert Owner.objects.count() == 1
| 31.044444
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| 4,191
| 134
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0
| 7
|
141608780ace7d6b13085fdb6ef40b7ba4d59c94
| 65,519
|
py
|
Python
|
billforward/apis/amendments_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | 2
|
2016-11-23T17:32:37.000Z
|
2022-02-24T05:13:20.000Z
|
billforward/apis/amendments_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | null | null | null |
billforward/apis/amendments_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | 1
|
2016-12-30T20:02:48.000Z
|
2016-12-30T20:02:48.000Z
|
# coding: utf-8
"""
BillForward REST API
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class AmendmentsApi(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def create_amendment(self, amendment, **kwargs):
"""
Create an amendment.
{\"nickname\":\"Create a new amendment\",\"request\":\"createAmendmentRequest.html\",\"response\":\"createAmendmentResponse.html\" }
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_amendment(amendment, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Amendment amendment: The amendment object to be created. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.create_amendment_with_http_info(amendment, **kwargs)
else:
(data) = self.create_amendment_with_http_info(amendment, **kwargs)
return data
def create_amendment_with_http_info(self, amendment, **kwargs):
"""
Create an amendment.
{\"nickname\":\"Create a new amendment\",\"request\":\"createAmendmentRequest.html\",\"response\":\"createAmendmentResponse.html\" }
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_amendment_with_http_info(amendment, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Amendment amendment: The amendment object to be created. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['amendment']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_amendment" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'amendment' is set
if ('amendment' not in params) or (params['amendment'] is None):
raise ValueError("Missing the required parameter `amendment` when calling `create_amendment`")
resource_path = '/amendments'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'amendment' in params:
body_params = params['amendment']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['text/xml', 'application/xml', 'application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_all_amendments(self, **kwargs):
"""
Returns a collection of all amendments. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Get all amendments\",\"response\":\"getAmendmentAll.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_all_amendments(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param str invoice_id: Ihe invoice ID associated with the amendment.
:param str state: Ihe state of the amendment.
:param str type: Ihe type of amendment.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_all_amendments_with_http_info(**kwargs)
else:
(data) = self.get_all_amendments_with_http_info(**kwargs)
return data
def get_all_amendments_with_http_info(self, **kwargs):
"""
Returns a collection of all amendments. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Get all amendments\",\"response\":\"getAmendmentAll.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_all_amendments_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param str invoice_id: Ihe invoice ID associated with the amendment.
:param str state: Ihe state of the amendment.
:param str type: Ihe type of amendment.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['organizations', 'offset', 'records', 'order_by', 'order', 'invoice_id', 'state', 'type']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_all_amendments" % key
)
params[key] = val
del params['kwargs']
resource_path = '/amendments'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
if 'invoice_id' in params:
query_params['invoice_id'] = params['invoice_id']
if 'state' in params:
query_params['state'] = params['state']
if 'type' in params:
query_params['type'] = params['type']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendment_by_id(self, amendment_id, **kwargs):
"""
Returns a single amendment, specified by the amendment-ID parameter.
{\"nickname\":\"Retrieve an existing amendment\",\"response\":\"getAmendmentByID.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_id(amendment_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str amendment_id: The unique string-ID of the amendment. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendment_by_id_with_http_info(amendment_id, **kwargs)
else:
(data) = self.get_amendment_by_id_with_http_info(amendment_id, **kwargs)
return data
def get_amendment_by_id_with_http_info(self, amendment_id, **kwargs):
"""
Returns a single amendment, specified by the amendment-ID parameter.
{\"nickname\":\"Retrieve an existing amendment\",\"response\":\"getAmendmentByID.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_id_with_http_info(amendment_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str amendment_id: The unique string-ID of the amendment. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['amendment_id', 'organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendment_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'amendment_id' is set
if ('amendment_id' not in params) or (params['amendment_id'] is None):
raise ValueError("Missing the required parameter `amendment_id` when calling `get_amendment_by_id`")
resource_path = '/amendments/{amendment-ID}'.replace('{format}', 'json')
path_params = {}
if 'amendment_id' in params:
path_params['amendment-ID'] = params['amendment_id']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendment_by_state(self, state, **kwargs):
"""
Returns a collection of amendments, specified by the state parameter. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by state\",\"response\":\"getAmendmentsByState.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_state(state, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str state: The current state of the amendment, either pending, succeeded, failed or discarded (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendment_by_state_with_http_info(state, **kwargs)
else:
(data) = self.get_amendment_by_state_with_http_info(state, **kwargs)
return data
def get_amendment_by_state_with_http_info(self, state, **kwargs):
"""
Returns a collection of amendments, specified by the state parameter. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by state\",\"response\":\"getAmendmentsByState.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_state_with_http_info(state, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str state: The current state of the amendment, either pending, succeeded, failed or discarded (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['state', 'organizations', 'offset', 'records', 'order_by', 'order']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendment_by_state" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'state' is set
if ('state' not in params) or (params['state'] is None):
raise ValueError("Missing the required parameter `state` when calling `get_amendment_by_state`")
resource_path = '/amendments/state/{state}'.replace('{format}', 'json')
path_params = {}
if 'state' in params:
path_params['state'] = params['state']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendment_by_subscription_id(self, subscription_id, **kwargs):
"""
Returns a collection of amendments, specified by the subscription-ID parameter. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by subscription\",\"response\":\"getAmendmentsBySubscription.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_subscription_id(subscription_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str subscription_id: ID of the subscription (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: The direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendment_by_subscription_id_with_http_info(subscription_id, **kwargs)
else:
(data) = self.get_amendment_by_subscription_id_with_http_info(subscription_id, **kwargs)
return data
def get_amendment_by_subscription_id_with_http_info(self, subscription_id, **kwargs):
"""
Returns a collection of amendments, specified by the subscription-ID parameter. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by subscription\",\"response\":\"getAmendmentsBySubscription.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_by_subscription_id_with_http_info(subscription_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str subscription_id: ID of the subscription (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: The direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['subscription_id', 'organizations', 'offset', 'records', 'order_by', 'order']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendment_by_subscription_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'subscription_id' is set
if ('subscription_id' not in params) or (params['subscription_id'] is None):
raise ValueError("Missing the required parameter `subscription_id` when calling `get_amendment_by_subscription_id`")
resource_path = '/amendments/subscription/{subscription-ID}'.replace('{format}', 'json')
path_params = {}
if 'subscription_id' in params:
path_params['subscription-ID'] = params['subscription_id']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendment_swagger(self, query_string, **kwargs):
"""
{\"nickname\":\"\",\"response\":\"\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_swagger(query_string, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str query_string: The query string used to search. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The starting index of the search results.
:param int records: The number of search results to return.
:param bool wildcard: Toggle if we search for full words or whether a wildcard is used.
:param bool entity: Is an entity returned with the search results.
:return: SwaggerTypeList
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendment_swagger_with_http_info(query_string, **kwargs)
else:
(data) = self.get_amendment_swagger_with_http_info(query_string, **kwargs)
return data
def get_amendment_swagger_with_http_info(self, query_string, **kwargs):
"""
{\"nickname\":\"\",\"response\":\"\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendment_swagger_with_http_info(query_string, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str query_string: The query string used to search. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The starting index of the search results.
:param int records: The number of search results to return.
:param bool wildcard: Toggle if we search for full words or whether a wildcard is used.
:param bool entity: Is an entity returned with the search results.
:return: SwaggerTypeList
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['query_string', 'organizations', 'offset', 'records', 'wildcard', 'entity']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendment_swagger" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'query_string' is set
if ('query_string' not in params) or (params['query_string'] is None):
raise ValueError("Missing the required parameter `query_string` when calling `get_amendment_swagger`")
resource_path = '/amendments/swagger-end-point/{query-string}'.replace('{format}', 'json')
path_params = {}
if 'query_string' in params:
path_params['query-string'] = params['query_string']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'wildcard' in params:
query_params['wildcard'] = params['wildcard']
if 'entity' in params:
query_params['entity'] = params['entity']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SwaggerTypeList',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendments_by_actioning_time(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with an actioning-time within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by actioning\",\"response\":\"getAmendmentByActioningTime.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_actioning_time(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendments_by_actioning_time_with_http_info(lower_threshold, upper_threshold, **kwargs)
else:
(data) = self.get_amendments_by_actioning_time_with_http_info(lower_threshold, upper_threshold, **kwargs)
return data
def get_amendments_by_actioning_time_with_http_info(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with an actioning-time within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by actioning\",\"response\":\"getAmendmentByActioningTime.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_actioning_time_with_http_info(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['lower_threshold', 'upper_threshold', 'organizations', 'offset', 'records', 'order_by', 'order']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendments_by_actioning_time" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'lower_threshold' is set
if ('lower_threshold' not in params) or (params['lower_threshold'] is None):
raise ValueError("Missing the required parameter `lower_threshold` when calling `get_amendments_by_actioning_time`")
# verify the required parameter 'upper_threshold' is set
if ('upper_threshold' not in params) or (params['upper_threshold'] is None):
raise ValueError("Missing the required parameter `upper_threshold` when calling `get_amendments_by_actioning_time`")
resource_path = '/amendments/actioning-time/{lower-threshold}/{upper-threshold}'.replace('{format}', 'json')
path_params = {}
if 'lower_threshold' in params:
path_params['lower-threshold'] = params['lower_threshold']
if 'upper_threshold' in params:
path_params['upper-threshold'] = params['upper_threshold']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendments_by_created_date(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with created times within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by creation\",\"response\":\"getAmendmentByCreated.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_created_date(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendments_by_created_date_with_http_info(lower_threshold, upper_threshold, **kwargs)
else:
(data) = self.get_amendments_by_created_date_with_http_info(lower_threshold, upper_threshold, **kwargs)
return data
def get_amendments_by_created_date_with_http_info(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with created times within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by creation\",\"response\":\"getAmendmentByCreated.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_created_date_with_http_info(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['lower_threshold', 'upper_threshold', 'organizations', 'offset', 'records', 'order_by', 'order']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendments_by_created_date" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'lower_threshold' is set
if ('lower_threshold' not in params) or (params['lower_threshold'] is None):
raise ValueError("Missing the required parameter `lower_threshold` when calling `get_amendments_by_created_date`")
# verify the required parameter 'upper_threshold' is set
if ('upper_threshold' not in params) or (params['upper_threshold'] is None):
raise ValueError("Missing the required parameter `upper_threshold` when calling `get_amendments_by_created_date`")
resource_path = '/amendments/created/{lower-threshold}/{upper-threshold}'.replace('{format}', 'json')
path_params = {}
if 'lower_threshold' in params:
path_params['lower-threshold'] = params['lower_threshold']
if 'upper_threshold' in params:
path_params['upper-threshold'] = params['upper_threshold']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_amendments_by_updated_date(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with updated times within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by updated\",\"response\":\"getAmendmentByUpdated.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_updated_date(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_amendments_by_updated_date_with_http_info(lower_threshold, upper_threshold, **kwargs)
else:
(data) = self.get_amendments_by_updated_date_with_http_info(lower_threshold, upper_threshold, **kwargs)
return data
def get_amendments_by_updated_date_with_http_info(self, lower_threshold, upper_threshold, **kwargs):
"""
Returns a collection of amendment objects with updated times within the period specified by the lower-threshold and upper-threshold parameters. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Retrieve by updated\",\"response\":\"getAmendmentByUpdated.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_amendments_by_updated_date_with_http_info(lower_threshold, upper_threshold, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str lower_threshold: The UTC DateTime specifying the start of the result period. (required)
:param str upper_threshold: The UTC DateTime specifying the end of the result period. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first amendment to return.
:param int records: The maximum number of amendments to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['lower_threshold', 'upper_threshold', 'organizations', 'offset', 'records', 'order_by', 'order']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_amendments_by_updated_date" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'lower_threshold' is set
if ('lower_threshold' not in params) or (params['lower_threshold'] is None):
raise ValueError("Missing the required parameter `lower_threshold` when calling `get_amendments_by_updated_date`")
# verify the required parameter 'upper_threshold' is set
if ('upper_threshold' not in params) or (params['upper_threshold'] is None):
raise ValueError("Missing the required parameter `upper_threshold` when calling `get_amendments_by_updated_date`")
resource_path = '/amendments/updated/{lower-threshold}/{upper-threshold}'.replace('{format}', 'json')
path_params = {}
if 'lower_threshold' in params:
path_params['lower-threshold'] = params['lower_threshold']
if 'upper_threshold' in params:
path_params['upper-threshold'] = params['upper_threshold']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def retire_amendment(self, amendment_id, organizations, **kwargs):
"""
Retires the amendment specified by the amendment-ID parameter. Retiring a amendment causes it to cancel based on the specificed retirement settings for the product.
{\"nickname\":\"Delete an amendment\",\"response\":\"deleteAmendment.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.retire_amendment(amendment_id, organizations, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str amendment_id: ID of the amendment. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.retire_amendment_with_http_info(amendment_id, organizations, **kwargs)
else:
(data) = self.retire_amendment_with_http_info(amendment_id, organizations, **kwargs)
return data
def retire_amendment_with_http_info(self, amendment_id, organizations, **kwargs):
"""
Retires the amendment specified by the amendment-ID parameter. Retiring a amendment causes it to cancel based on the specificed retirement settings for the product.
{\"nickname\":\"Delete an amendment\",\"response\":\"deleteAmendment.html\"}
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.retire_amendment_with_http_info(amendment_id, organizations, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str amendment_id: ID of the amendment. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['amendment_id', 'organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method retire_amendment" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'amendment_id' is set
if ('amendment_id' not in params) or (params['amendment_id'] is None):
raise ValueError("Missing the required parameter `amendment_id` when calling `retire_amendment`")
# verify the required parameter 'organizations' is set
if ('organizations' not in params) or (params['organizations'] is None):
raise ValueError("Missing the required parameter `organizations` when calling `retire_amendment`")
resource_path = '/amendments/{amendment-ID}'.replace('{format}', 'json')
path_params = {}
if 'amendment_id' in params:
path_params['amendment-ID'] = params['amendment_id']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def update_amendment(self, amendment, **kwargs):
"""
Update an amendment.
{\"nickname\":\"Update an amendment\",\"request\":\"updateAmendmentRequest.html\",\"response\":\"updateAmendmentResponse.html\" }
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_amendment(amendment, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Amendment amendment: The amendment object to be updated. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.update_amendment_with_http_info(amendment, **kwargs)
else:
(data) = self.update_amendment_with_http_info(amendment, **kwargs)
return data
def update_amendment_with_http_info(self, amendment, **kwargs):
"""
Update an amendment.
{\"nickname\":\"Update an amendment\",\"request\":\"updateAmendmentRequest.html\",\"response\":\"updateAmendmentResponse.html\" }
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_amendment_with_http_info(amendment, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Amendment amendment: The amendment object to be updated. (required)
:return: AmendmentPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['amendment']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_amendment" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'amendment' is set
if ('amendment' not in params) or (params['amendment'] is None):
raise ValueError("Missing the required parameter `amendment` when calling `update_amendment`")
resource_path = '/amendments'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'amendment' in params:
body_params = params['amendment']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['text/xml', 'application/xml', 'application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = []
return self.api_client.call_api(resource_path, 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AmendmentPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
| 47.719592
| 229
| 0.606358
| 7,057
| 65,519
| 5.448774
| 0.042086
| 0.045771
| 0.023874
| 0.020597
| 0.949079
| 0.941355
| 0.932903
| 0.926532
| 0.914621
| 0.907963
| 0
| 0.000753
| 0.310521
| 65,519
| 1,372
| 230
| 47.754373
| 0.850445
| 0.410308
| 0
| 0.803101
| 1
| 0
| 0.195939
| 0.050362
| 0
| 0
| 0
| 0
| 0
| 1
| 0.035659
| false
| 0
| 0.010853
| 0
| 0.099225
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
1437eacaa0c14b24a0c529618cf50cc8b86516e3
| 101
|
py
|
Python
|
1-Iniciante-Beginner/1007 - Diferenca - Difference.py
|
Limarceu/Uri_Online_Judge
|
3328dacd5419b5a6bce4cb7dfc38e355e9fdab1f
|
[
"MIT"
] | null | null | null |
1-Iniciante-Beginner/1007 - Diferenca - Difference.py
|
Limarceu/Uri_Online_Judge
|
3328dacd5419b5a6bce4cb7dfc38e355e9fdab1f
|
[
"MIT"
] | null | null | null |
1-Iniciante-Beginner/1007 - Diferenca - Difference.py
|
Limarceu/Uri_Online_Judge
|
3328dacd5419b5a6bce4cb7dfc38e355e9fdab1f
|
[
"MIT"
] | null | null | null |
a,b,c,d = int(input()),int(input()),int(input()),int(input())
print('DIFERENCA = {}'.format(a*b-c*d))
| 50.5
| 61
| 0.594059
| 19
| 101
| 3.157895
| 0.473684
| 0.533333
| 0.55
| 0.8
| 0.533333
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0.049505
| 101
| 2
| 62
| 50.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0.137255
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
14605133b5a68f936b862bf051b6b8b818564c9d
| 21,324
|
py
|
Python
|
inn/inn_hotels/doctype/inn_room_charge_posting/inn_room_charge_posting.py
|
vinhnguyent090/front-desk
|
7384642e9206e30855986465a7ef63c8fd76ef2a
|
[
"MIT"
] | 4
|
2021-08-19T03:33:36.000Z
|
2021-08-28T16:37:52.000Z
|
inn/inn_hotels/doctype/inn_room_charge_posting/inn_room_charge_posting.py
|
vinhnguyent090/front-desk
|
7384642e9206e30855986465a7ef63c8fd76ef2a
|
[
"MIT"
] | 98
|
2020-02-24T08:12:47.000Z
|
2021-08-21T07:54:03.000Z
|
inn/inn_hotels/doctype/inn_room_charge_posting/inn_room_charge_posting.py
|
vinhnguyent090/front-desk
|
7384642e9206e30855986465a7ef63c8fd76ef2a
|
[
"MIT"
] | 13
|
2021-01-24T18:08:43.000Z
|
2022-03-29T09:23:25.000Z
|
# -*- coding: utf-8 -*-
# Copyright (c) 2020, Core Initiative and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import json
import frappe
import math
import datetime
from frappe.model.document import Document
from inn.inn_hotels.doctype.inn_folio_transaction_type.inn_folio_transaction_type import get_accounts_from_id
from inn.inn_hotels.doctype.inn_folio_transaction.inn_folio_transaction import get_idx
from inn.inn_hotels.doctype.inn_audit_log.inn_audit_log import get_last_audit_date
from inn.inn_hotels.doctype.inn_tax.inn_tax import calculate_inn_tax_and_charges
class InnRoomChargePosting(Document):
pass
@frappe.whitelist()
def is_there_open_room_charge_posting():
if frappe.get_all('Inn Room Charge Posting', {'status': 'Open'}):
return 1
else:
return 2
@frappe.whitelist()
def is_there_closed_room_charge_posting_at():
date = get_last_audit_date().strftime('%Y-%m-%d')
if frappe.db.exists('Inn Room Charge Posting', {'audit_date': date, 'status': 'Closed'}):
return 1
else:
return 2
@frappe.whitelist()
def populate_tobe_posted():
tobe_posted_list = []
folio_list = frappe.get_all('Inn Folio', filters={'status': 'Open', 'type': 'Guest'}, fields=['*'])
for item in folio_list:
reservation = frappe.get_doc('Inn Reservation', item.reservation_id)
if reservation.status == 'In House' or reservation.status == 'Finish':
room_charge_remark = 'Room Charge: Room Rate (Nett): ' + reservation.actual_room_id + " - " + \
get_last_audit_date().strftime("%d-%m-%Y")
if not frappe.db.exists('Inn Folio Transaction',
{'parent': item.name, 'transaction_type': 'Room Charge', 'remark': room_charge_remark, 'is_void': 0}):
tobe_posted = frappe.new_doc('Inn Room Charge To Be Posted')
tobe_posted.reservation_id = item.reservation_id
tobe_posted.folio_id = item.name
tobe_posted.room_id = reservation.actual_room_id
tobe_posted.customer_id = reservation.customer_id
tobe_posted.room_rate_id = reservation.room_rate
tobe_posted.actual_room_rate = reservation.actual_room_rate
tobe_posted_list.append(tobe_posted)
return tobe_posted_list
@frappe.whitelist()
def post_individual_room_charges(parent_id, tobe_posted_list):
return_value = ''
room_charge_posting_doc = frappe.get_doc('Inn Room Charge Posting', parent_id)
list_json = json.loads(tobe_posted_list)
# for difference calculations
fdc_reservation = ''
fdc_folio_trx_tax_name = ''
for item in list_json:
# Create Inn Folio Transaction Bundle
ftb_doc = frappe.new_doc('Inn Folio Transaction Bundle')
ftb_doc.transaction_type = 'Room Charge'
ftb_doc.insert()
# Posting Room Charge
item_doc = frappe.get_doc('Inn Room Charge To Be Posted', item)
accumulated_amount = 0.00
room_charge_debit_account, room_charge_credit_account = get_accounts_from_id('Room Charge')
reservation = frappe.get_doc('Inn Reservation', item_doc.reservation_id)
fdc_reservation = reservation
room_charge_folio_trx = frappe.new_doc('Inn Folio Transaction')
room_charge_folio_trx.flag = 'Debit'
room_charge_folio_trx.is_void = 0
room_charge_folio_trx.idx = get_idx(item_doc.folio_id)
room_charge_folio_trx.transaction_type = 'Room Charge'
room_charge_folio_trx.amount = float(int(reservation.nett_actual_room_rate))
accumulated_amount += float(int(reservation.nett_actual_room_rate))
room_charge_folio_trx.debit_account = room_charge_debit_account
room_charge_folio_trx.credit_account = room_charge_credit_account
room_charge_folio_trx.remark = 'Room Charge: Room Rate (Nett): ' + item_doc.room_id + " - " + get_last_audit_date().strftime("%d-%m-%Y")
room_charge_folio_trx.parent = item_doc.folio_id
room_charge_folio_trx.parenttype = 'Inn Folio'
room_charge_folio_trx.parentfield = 'folio_transaction'
room_charge_folio_trx.ftb_id = ftb_doc.name
room_charge_folio_trx.insert()
return_value = return_value + '<li>' + room_charge_folio_trx.remark + '</li>'
# Create Inn Folio Transaction Bundle Detail Item Room Charge
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = room_charge_folio_trx.transaction_type
ftbd_doc.transaction_id = room_charge_folio_trx.name
ftb_doc.append('transaction_detail', ftbd_doc)
fdc_room_rate = frappe.get_doc('Inn Room Rate', fdc_reservation.room_rate)
fdc_room_rate_tax = frappe.get_doc('Inn Tax', fdc_room_rate.room_rate_tax)
fdc_room_rate_tax_breakdown = fdc_room_rate_tax.inn_tax_breakdown
if fdc_room_rate_tax_breakdown[-1].breakdown_rate != 0.0:
fdc_room_rate_tax_account = fdc_room_rate_tax_breakdown[-1].breakdown_account
else:
fdc_room_rate_tax_account = fdc_room_rate_tax_breakdown[-2].breakdown_account
# Posting Room Charge Tax/Service
room_tb_id, room_tb_amount, _ = calculate_inn_tax_and_charges(reservation.nett_actual_room_rate,
reservation.actual_room_rate_tax)
for index, room_tax_item_name in enumerate(room_tb_id):
room_tax_doc = frappe.new_doc('Inn Folio Transaction')
room_tax_doc.flag = 'Debit'
room_tax_doc.is_void = 0
room_tax_doc.idx = get_idx(item_doc.folio_id)
room_tax_doc.transaction_type = 'Room Charge Tax/Service'
room_tax_doc.amount = room_tb_amount[index]
accumulated_amount += room_tb_amount[index]
room_tax_doc.credit_account = frappe.get_doc('Inn Tax Breakdown', room_tax_item_name).breakdown_account
room_tax_doc.debit_account = room_charge_debit_account
room_tax_doc.remark = 'Room Charge Tax Room Rate ' + room_tax_item_name + ' : ' + item_doc.room_id + " - " + get_last_audit_date().strftime("%d-%m-%Y")
room_tax_doc.parent = item_doc.folio_id
room_tax_doc.parenttype = 'Inn Folio'
room_tax_doc.parentfield = 'folio_transaction'
room_tax_doc.ftb_id = ftb_doc.name
room_tax_doc.insert()
if room_tax_doc.credit_account == fdc_room_rate_tax_account:
fdc_folio_trx_tax_name = room_tax_doc.name
# Create Inn Folio Transaction Bundle Detail Item Room Charge Tax/Service
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = room_tax_doc.transaction_type
ftbd_doc.transaction_id = room_tax_doc.name
ftb_doc.append('transaction_detail', ftbd_doc)
# Posting Breakfast Charge
breakfast_charge_debit_account, breakfast_charge_credit_account = get_accounts_from_id('Breakfast Charge')
breakfast_charge_folio_trx = frappe.new_doc('Inn Folio Transaction')
breakfast_charge_folio_trx.flag = 'Debit'
breakfast_charge_folio_trx.is_void = 0
breakfast_charge_folio_trx.idx = get_idx(item_doc.folio_id)
breakfast_charge_folio_trx.transaction_type = 'Breakfast Charge'
breakfast_charge_folio_trx.amount = float(int(reservation.nett_actual_breakfast_rate))
accumulated_amount += float(int(reservation.nett_actual_breakfast_rate))
breakfast_charge_folio_trx.debit_account = breakfast_charge_debit_account
breakfast_charge_folio_trx.credit_account = breakfast_charge_credit_account
breakfast_charge_folio_trx.remark = 'Room Charge: Breakfast (Nett): ' + item_doc.room_id + " - " + get_last_audit_date().strftime("%d-%m-%Y")
breakfast_charge_folio_trx.parent = item_doc.folio_id
breakfast_charge_folio_trx.parenttype = 'Inn Folio'
breakfast_charge_folio_trx.parentfield = 'folio_transaction'
breakfast_charge_folio_trx.ftb_id = ftb_doc.name
breakfast_charge_folio_trx.insert()
# Create Inn Folio Transaction Bundle Detail Item Breakfast Charge
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = breakfast_charge_folio_trx.transaction_type
ftbd_doc.transaction_id = breakfast_charge_folio_trx.name
ftb_doc.append('transaction_detail', ftbd_doc)
# Posting Breakfast Tax/Service
breakfast_tb_id, breakfast_tb_amount, _ = calculate_inn_tax_and_charges(reservation.nett_actual_breakfast_rate,
reservation.actual_breakfast_rate_tax)
for index, breakfast_tax_item_name in enumerate(breakfast_tb_id):
breakfast_tax_doc = frappe.new_doc('Inn Folio Transaction')
breakfast_tax_doc.flag = 'Debit'
breakfast_tax_doc.is_void = 0
breakfast_tax_doc.idx = get_idx(item_doc.folio_id)
breakfast_tax_doc.transaction_type = 'Breakfast Charge Tax/Service'
breakfast_tax_doc.amount = breakfast_tb_amount[index]
accumulated_amount += breakfast_tb_amount[index]
breakfast_tax_doc.credit_account = frappe.get_doc('Inn Tax Breakdown',
breakfast_tax_item_name).breakdown_account
breakfast_tax_doc.debit_account = breakfast_charge_debit_account
breakfast_tax_doc.remark = 'Breakfast Charge Tax Room Rate ' + breakfast_tax_item_name + ' : ' + item_doc.room_id + " - " + get_last_audit_date().strftime("%d-%m-%Y")
breakfast_tax_doc.parent = item_doc.folio_id
breakfast_tax_doc.parenttype = 'Inn Folio'
breakfast_tax_doc.parentfield = 'folio_transaction'
breakfast_tax_doc.ftb_id = ftb_doc.name
breakfast_tax_doc.insert()
# Create Inn Folio Transaction Bundle Detail Item Breakfast Charge Tax/Service
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = breakfast_tax_doc.transaction_type
ftbd_doc.transaction_id = breakfast_tax_doc.name
ftb_doc.append('transaction_detail', ftbd_doc)
print("accumulated amount = " + str(accumulated_amount))
print("math_ceil(accumulated amount) = " + str(math.ceil(accumulated_amount)))
print("actual room rate = " + str(reservation.actual_room_rate))
print ("abs = " + str(abs(math.ceil(accumulated_amount) - int(reservation.actual_room_rate))))
if abs(math.ceil(accumulated_amount) - int(reservation.actual_room_rate)) != 0:
difference = math.ceil(accumulated_amount) - int(reservation.actual_room_rate)
# hasil perhitungan lebih besar daripada room rate yang tersimpan di db
if difference > 0:
adjusted_room_charge_amount = room_charge_folio_trx.amount
adjusted_breakfast_charge_amount = breakfast_charge_folio_trx.amount
for i in range(0, abs(difference)):
adjusted_room_charge_amount = adjusted_room_charge_amount - 1.0
# hasil perhitungan lebih kecil daripada room rate yang tersimpan di db
elif difference < 0:
adjusted_room_charge_amount = room_charge_folio_trx.amount
adjusted_breakfast_charge_amount = breakfast_charge_folio_trx.amount
fdc_folio_trx_tax = frappe.get_doc('Inn Folio Transaction', fdc_folio_trx_tax_name)
adjusted_room_rate_tax_amount = fdc_folio_trx_tax.amount
for i in range(0, abs(difference)):
adjusted_room_rate_tax_amount = adjusted_room_rate_tax_amount + 1.0
room_charge_folio_trx.amount = adjusted_room_charge_amount
room_charge_folio_trx.save()
breakfast_charge_folio_trx.amount = adjusted_breakfast_charge_amount
breakfast_charge_folio_trx.save()
fdc_folio_trx_tax.amount = adjusted_room_rate_tax_amount
fdc_folio_trx_tax.save()
# Resave Bundle to save Detail
ftb_doc.save()
posted = frappe.new_doc('Inn Room Charge Posted')
posted.reservation_id = item_doc.reservation_id
posted.folio_id = item_doc.folio_id
posted.room_id = item_doc.room_id
posted.customer_id = item_doc.customer_id
posted.room_rate_id = item_doc.room_rate_id
posted.actual_room_rate = item_doc.actual_room_rate
posted.folio_transaction_id = room_charge_folio_trx.name
posted.parent = parent_id
posted.parentfield = 'already_posted'
posted.parenttype = 'Inn Room Charge Posting'
room_charge_posting_doc.append('already_posted', posted)
frappe.delete_doc('Inn Room Charge To Be Posted', item_doc.name)
room_charge_posting_doc.save()
calculate_already_posted_total(room_charge_posting_doc.name)
return return_value
@frappe.whitelist()
def post_room_charges(parent_id, tobe_posted_list):
return_value = ''
room_charge_posting_doc = frappe.get_doc('Inn Room Charge Posting', parent_id)
list_json = json.loads(tobe_posted_list)
# for difference calculations
fdc_reservation = ''
fdc_folio_trx_tax_name = ''
for item in list_json:
# Create Inn Folio Transaction Bundle
ftb_doc = frappe.new_doc('Inn Folio Transaction Bundle')
ftb_doc.transaction_type = 'Room Charge'
ftb_doc.insert()
# Posting Room Charge
accumulated_amount = 0.00
room_charge_debit_account, room_charge_credit_account = get_accounts_from_id('Room Charge')
reservation = frappe.get_doc('Inn Reservation', item['reservation_id'])
fdc_reservation = reservation
room_charge_folio_trx = frappe.new_doc('Inn Folio Transaction')
room_charge_folio_trx.flag = 'Debit'
room_charge_folio_trx.is_void = 0
room_charge_folio_trx.idx = get_idx(item['folio_id'])
room_charge_folio_trx.transaction_type = 'Room Charge'
room_charge_folio_trx.amount = float(int(reservation.nett_actual_room_rate))
accumulated_amount += float(int(reservation.nett_actual_room_rate))
room_charge_folio_trx.debit_account = room_charge_debit_account
room_charge_folio_trx.credit_account = room_charge_credit_account
room_charge_folio_trx.remark = 'Room Charge: Room Rate (Nett): ' + item[
'room_id'] + " - " + get_last_audit_date().strftime("%d-%m-%Y")
room_charge_folio_trx.parent = item['folio_id']
room_charge_folio_trx.parenttype = 'Inn Folio'
room_charge_folio_trx.parentfield = 'folio_transaction'
room_charge_folio_trx.ftb_id = ftb_doc.name
room_charge_folio_trx.insert()
return_value = return_value + '<li>' + room_charge_folio_trx.remark + '</li>'
# Create Inn Folio Transaction Bundle Detail Item Room Charge
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = room_charge_folio_trx.transaction_type
ftbd_doc.transaction_id = room_charge_folio_trx.name
ftb_doc.append('transaction_detail', ftbd_doc)
fdc_room_rate = frappe.get_doc('Inn Room Rate', fdc_reservation.room_rate)
fdc_room_rate_tax = frappe.get_doc('Inn Tax', fdc_room_rate.room_rate_tax)
fdc_room_rate_tax_breakdown = fdc_room_rate_tax.inn_tax_breakdown
if fdc_room_rate_tax_breakdown[-1].breakdown_rate != 0.0:
fdc_room_rate_tax_account = fdc_room_rate_tax_breakdown[-1].breakdown_account
else:
fdc_room_rate_tax_account = fdc_room_rate_tax_breakdown[-2].breakdown_account
# Posting Room Charge Tax/Service
room_tb_id, room_tb_amount, _ = calculate_inn_tax_and_charges(reservation.nett_actual_room_rate,
reservation.actual_room_rate_tax)
for index, room_tax_item_name in enumerate(room_tb_id):
room_tax_doc = frappe.new_doc('Inn Folio Transaction')
room_tax_doc.flag = 'Debit'
room_tax_doc.is_void = 0
room_tax_doc.idx = get_idx(item['folio_id'])
room_tax_doc.transaction_type = 'Room Charge Tax/Service'
room_tax_doc.amount = room_tb_amount[index]
accumulated_amount += room_tb_amount[index]
room_tax_doc.credit_account = frappe.get_doc('Inn Tax Breakdown', room_tax_item_name).breakdown_account
room_tax_doc.debit_account = room_charge_debit_account
room_tax_doc.remark = 'Room Charge Tax Room Rate ' + room_tax_item_name + ' : ' + item[
'room_id'] + " - " + get_last_audit_date().strftime("%d-%m-%Y")
room_tax_doc.parent = item['folio_id']
room_tax_doc.parenttype = 'Inn Folio'
room_tax_doc.parentfield = 'folio_transaction'
room_tax_doc.ftb_id = ftb_doc.name
room_tax_doc.insert()
if room_tax_doc.credit_account == fdc_room_rate_tax_account:
fdc_folio_trx_tax_name = room_tax_doc.name
# Create Inn Folio Transaction Bundle Detail Item Room Charge Tax/Service
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = room_tax_doc.transaction_type
ftbd_doc.transaction_id = room_tax_doc.name
ftb_doc.append('transaction_detail', ftbd_doc)
# Posting Breakfast Charge
breakfast_charge_debit_account, breakfast_charge_credit_account = get_accounts_from_id('Breakfast Charge')
breakfast_charge_folio_trx = frappe.new_doc('Inn Folio Transaction')
breakfast_charge_folio_trx.flag = 'Debit'
breakfast_charge_folio_trx.is_void = 0
breakfast_charge_folio_trx.idx = get_idx(item['folio_id'])
breakfast_charge_folio_trx.transaction_type = 'Breakfast Charge'
breakfast_charge_folio_trx.amount = float(int(reservation.nett_actual_breakfast_rate))
accumulated_amount += float(int(reservation.nett_actual_breakfast_rate))
breakfast_charge_folio_trx.debit_account = breakfast_charge_debit_account
breakfast_charge_folio_trx.credit_account = breakfast_charge_credit_account
breakfast_charge_folio_trx.remark = 'Room Charge: Breakfast (Nett): ' + item[
'room_id'] + " - " + get_last_audit_date().strftime("%d-%m-%Y")
breakfast_charge_folio_trx.parent = item['folio_id']
breakfast_charge_folio_trx.parenttype = 'Inn Folio'
breakfast_charge_folio_trx.parentfield = 'folio_transaction'
breakfast_charge_folio_trx.ftb_id = ftb_doc.name
breakfast_charge_folio_trx.insert()
# Create Inn Folio Transaction Bundle Detail Item Breakfast Charge
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = breakfast_charge_folio_trx.transaction_type
ftbd_doc.transaction_id = breakfast_charge_folio_trx.name
ftb_doc.append('transaction_detail', ftbd_doc)
# Posting Breakfast Tax/Service
breakfast_tb_id, breakfast_tb_amount, _ = calculate_inn_tax_and_charges(reservation.nett_actual_breakfast_rate,
reservation.actual_breakfast_rate_tax)
for index, breakfast_tax_item_name in enumerate(breakfast_tb_id):
breakfast_tax_doc = frappe.new_doc('Inn Folio Transaction')
breakfast_tax_doc.flag = 'Debit'
breakfast_tax_doc.is_void = 0
breakfast_tax_doc.idx = get_idx(item['folio_id'])
breakfast_tax_doc.transaction_type = 'Breakfast Charge Tax/Service'
breakfast_tax_doc.amount = breakfast_tb_amount[index]
accumulated_amount += breakfast_tb_amount[index]
breakfast_tax_doc.credit_account = frappe.get_doc('Inn Tax Breakdown',
breakfast_tax_item_name).breakdown_account
breakfast_tax_doc.debit_account = breakfast_charge_debit_account
breakfast_tax_doc.remark = 'Breakfast Charge Tax Room Rate ' + breakfast_tax_item_name + ' : ' + item[
'room_id'] + " - " + get_last_audit_date().strftime("%d-%m-%Y")
breakfast_tax_doc.parent = item['folio_id']
breakfast_tax_doc.parenttype = 'Inn Folio'
breakfast_tax_doc.parentfield = 'folio_transaction'
breakfast_tax_doc.ftb_id = ftb_doc.name
breakfast_tax_doc.insert()
# Create Inn Folio Transaction Bundle Detail Item Breakfast Charge Tax/Service
ftbd_doc = frappe.new_doc('Inn Folio Transaction Bundle Detail')
ftbd_doc.transaction_type = breakfast_tax_doc.transaction_type
ftbd_doc.transaction_id = breakfast_tax_doc.name
ftb_doc.append('transaction_detail', ftbd_doc)
print("accumulated amount = " + str(accumulated_amount))
print("math_ceil(accumulated amount) = " + str(math.ceil(accumulated_amount)))
print("actual room rate = " + str(reservation.actual_room_rate))
print("abs = " + str(abs(math.ceil(accumulated_amount) - int(reservation.actual_room_rate))))
if abs(math.ceil(accumulated_amount) - int(reservation.actual_room_rate)) != 0:
difference = math.ceil(accumulated_amount) - int(reservation.actual_room_rate)
if difference > 0:
adjusted_room_charge_amount = room_charge_folio_trx.amount
adjusted_breakfast_charge_amount = breakfast_charge_folio_trx.amount
for i in range(0, abs(difference)):
adjusted_room_charge_amount = adjusted_room_charge_amount - 1.0
elif difference < 0:
adjusted_room_charge_amount = room_charge_folio_trx.amount
adjusted_breakfast_charge_amount = breakfast_charge_folio_trx.amount
fdc_folio_trx_tax = frappe.get_doc('Inn Folio Transaction', fdc_folio_trx_tax_name)
adjusted_room_rate_tax_amount = fdc_folio_trx_tax.amount
# TODO: ganti tambah difference ke pajak, bukan ke room rate & breakfast
for i in range(0, abs(difference)):
adjusted_room_rate_tax_amount = adjusted_room_rate_tax_amount + 1.0
room_charge_folio_trx.amount = adjusted_room_charge_amount
room_charge_folio_trx.save()
breakfast_charge_folio_trx.amount = adjusted_breakfast_charge_amount
breakfast_charge_folio_trx.save()
fdc_folio_trx_tax.amount = adjusted_room_rate_tax_amount
fdc_folio_trx_tax.save()
# Resave Bundle to save Detail
ftb_doc.save()
posted = frappe.new_doc('Inn Room Charge Posted')
posted.reservation_id = item['reservation_id']
posted.folio_id = item['folio_id']
posted.room_id = item['room_id']
posted.customer_id = item['customer_id']
posted.room_rate_id = item['room_rate_id']
posted.actual_room_rate = item['actual_room_rate']
posted.folio_transaction_id = room_charge_folio_trx.name
posted.parent = parent_id
posted.parentfield = 'already_posted'
posted.parenttype = 'Inn Room Charge Posting'
room_charge_posting_doc.append('already_posted', posted)
frappe.delete_doc('Inn Room Charge To Be Posted', item['name'])
room_charge_posting_doc.save()
calculate_already_posted_total(room_charge_posting_doc.name)
return return_value
def calculate_already_posted_total(room_charge_posting_id):
total = 0.0
doc = frappe.get_doc('Inn Room Charge Posting', room_charge_posting_id)
posted = doc.get('already_posted')
if len(posted) > 0:
for item in posted:
total += item.actual_room_rate
frappe.db.set_value('Inn Room Charge Posting', doc.name, 'already_posted_total', total)
| 49.475638
| 169
| 0.790096
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0
| 7
|
14aaf108131e71fa427262f5ee129addc001ddae
| 33,527
|
py
|
Python
|
canopy/openapi/api/sim_version_api.py
|
CanopySimulations/canopy-python
|
9ec37e674e65d6fbef0402ac0c612c163d55631e
|
[
"MIT"
] | null | null | null |
canopy/openapi/api/sim_version_api.py
|
CanopySimulations/canopy-python
|
9ec37e674e65d6fbef0402ac0c612c163d55631e
|
[
"MIT"
] | 1
|
2022-01-31T10:18:08.000Z
|
2022-01-31T10:18:08.000Z
|
canopy/openapi/api/sim_version_api.py
|
CanopySimulations/canopy-python
|
9ec37e674e65d6fbef0402ac0c612c163d55631e
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
Canopy.Api
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: v1
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from canopy.openapi.api_client import ApiClient
from canopy.openapi.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class SimVersionApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def sim_version_get_document(self, sim_version, document_path, **kwargs): # noqa: E501
"""sim_version_get_document # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_document(sim_version, document_path, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str document_path: (required)
:param str tenant_id:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: GetSimVersionDocumentQueryResult
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_get_document_with_http_info(sim_version, document_path, **kwargs) # noqa: E501
def sim_version_get_document_with_http_info(self, sim_version, document_path, **kwargs): # noqa: E501
"""sim_version_get_document # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_document_with_http_info(sim_version, document_path, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str document_path: (required)
:param str tenant_id:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(GetSimVersionDocumentQueryResult, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'sim_version',
'document_path',
'tenant_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_get_document" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'sim_version' is set
if self.api_client.client_side_validation and ('sim_version' not in local_var_params or # noqa: E501
local_var_params['sim_version'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sim_version` when calling `sim_version_get_document`") # noqa: E501
# verify the required parameter 'document_path' is set
if self.api_client.client_side_validation and ('document_path' not in local_var_params or # noqa: E501
local_var_params['document_path'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `document_path` when calling `sim_version_get_document`") # noqa: E501
collection_formats = {}
path_params = {}
if 'sim_version' in local_var_params:
path_params['simVersion'] = local_var_params['sim_version'] # noqa: E501
if 'document_path' in local_var_params:
path_params['documentPath'] = local_var_params['document_path'] # noqa: E501
query_params = []
if 'tenant_id' in local_var_params and local_var_params['tenant_id'] is not None: # noqa: E501
query_params.append(('tenantId', local_var_params['tenant_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/{simVersion}/documents/{documentPath}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='GetSimVersionDocumentQueryResult', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def sim_version_get_documents(self, sim_version, **kwargs): # noqa: E501
"""sim_version_get_documents # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_documents(sim_version, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str tenant_id:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: GetSimVersionDocumentsQueryResult
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_get_documents_with_http_info(sim_version, **kwargs) # noqa: E501
def sim_version_get_documents_with_http_info(self, sim_version, **kwargs): # noqa: E501
"""sim_version_get_documents # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_documents_with_http_info(sim_version, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str tenant_id:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(GetSimVersionDocumentsQueryResult, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'sim_version',
'tenant_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_get_documents" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'sim_version' is set
if self.api_client.client_side_validation and ('sim_version' not in local_var_params or # noqa: E501
local_var_params['sim_version'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sim_version` when calling `sim_version_get_documents`") # noqa: E501
collection_formats = {}
path_params = {}
if 'sim_version' in local_var_params:
path_params['simVersion'] = local_var_params['sim_version'] # noqa: E501
query_params = []
if 'tenant_id' in local_var_params and local_var_params['tenant_id'] is not None: # noqa: E501
query_params.append(('tenantId', local_var_params['tenant_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/{simVersion}/documents', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='GetSimVersionDocumentsQueryResult', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def sim_version_get_downloads(self, sim_version, **kwargs): # noqa: E501
"""sim_version_get_downloads # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_downloads(sim_version, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str tenant_id:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: GetSimVersionDownloadsQueryResult
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_get_downloads_with_http_info(sim_version, **kwargs) # noqa: E501
def sim_version_get_downloads_with_http_info(self, sim_version, **kwargs): # noqa: E501
"""sim_version_get_downloads # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_downloads_with_http_info(sim_version, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str sim_version: (required)
:param str tenant_id:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(GetSimVersionDownloadsQueryResult, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'sim_version',
'tenant_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_get_downloads" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'sim_version' is set
if self.api_client.client_side_validation and ('sim_version' not in local_var_params or # noqa: E501
local_var_params['sim_version'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sim_version` when calling `sim_version_get_downloads`") # noqa: E501
collection_formats = {}
path_params = {}
if 'sim_version' in local_var_params:
path_params['simVersion'] = local_var_params['sim_version'] # noqa: E501
query_params = []
if 'tenant_id' in local_var_params and local_var_params['tenant_id'] is not None: # noqa: E501
query_params.append(('tenantId', local_var_params['tenant_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/{simVersion}/downloads', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='GetSimVersionDownloadsQueryResult', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def sim_version_get_sim_version(self, **kwargs): # noqa: E501
"""sim_version_get_sim_version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_sim_version(async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str tenant_id:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: str
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_get_sim_version_with_http_info(**kwargs) # noqa: E501
def sim_version_get_sim_version_with_http_info(self, **kwargs): # noqa: E501
"""sim_version_get_sim_version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_sim_version_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str tenant_id:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(str, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'tenant_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_get_sim_version" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'tenant_id' in local_var_params and local_var_params['tenant_id'] is not None: # noqa: E501
query_params.append(('tenantId', local_var_params['tenant_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/current', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='str', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def sim_version_get_wiki_document(self, wiki_version, document_path, **kwargs): # noqa: E501
"""sim_version_get_wiki_document # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_wiki_document(wiki_version, document_path, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str wiki_version: (required)
:param str document_path: (required)
:param str tenant_id:
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: GetWikiDocumentQueryResult
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_get_wiki_document_with_http_info(wiki_version, document_path, **kwargs) # noqa: E501
def sim_version_get_wiki_document_with_http_info(self, wiki_version, document_path, **kwargs): # noqa: E501
"""sim_version_get_wiki_document # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_get_wiki_document_with_http_info(wiki_version, document_path, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str wiki_version: (required)
:param str document_path: (required)
:param str tenant_id:
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(GetWikiDocumentQueryResult, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'wiki_version',
'document_path',
'tenant_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_get_wiki_document" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'wiki_version' is set
if self.api_client.client_side_validation and ('wiki_version' not in local_var_params or # noqa: E501
local_var_params['wiki_version'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `wiki_version` when calling `sim_version_get_wiki_document`") # noqa: E501
# verify the required parameter 'document_path' is set
if self.api_client.client_side_validation and ('document_path' not in local_var_params or # noqa: E501
local_var_params['document_path'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `document_path` when calling `sim_version_get_wiki_document`") # noqa: E501
collection_formats = {}
path_params = {}
if 'wiki_version' in local_var_params:
path_params['wikiVersion'] = local_var_params['wiki_version'] # noqa: E501
if 'document_path' in local_var_params:
path_params['documentPath'] = local_var_params['document_path'] # noqa: E501
query_params = []
if 'tenant_id' in local_var_params and local_var_params['tenant_id'] is not None: # noqa: E501
query_params.append(('tenantId', local_var_params['tenant_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/{wikiVersion}/wiki/{documentPath}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='GetWikiDocumentQueryResult', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def sim_version_post_sim_version(self, sim_version_data, **kwargs): # noqa: E501
"""sim_version_post_sim_version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_post_sim_version(sim_version_data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param NewSimVersionData sim_version_data: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.sim_version_post_sim_version_with_http_info(sim_version_data, **kwargs) # noqa: E501
def sim_version_post_sim_version_with_http_info(self, sim_version_data, **kwargs): # noqa: E501
"""sim_version_post_sim_version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.sim_version_post_sim_version_with_http_info(sim_version_data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param NewSimVersionData sim_version_data: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'sim_version_data'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method sim_version_post_sim_version" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'sim_version_data' is set
if self.api_client.client_side_validation and ('sim_version_data' not in local_var_params or # noqa: E501
local_var_params['sim_version_data'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `sim_version_data` when calling `sim_version_post_sim_version`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'sim_version_data' in local_var_params:
body_params = local_var_params['sim_version_data']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/xml', 'text/xml', 'application/x-www-form-urlencoded']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/sim-versions/current', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
| 45.063172
| 142
| 0.601068
| 3,704
| 33,527
| 5.142549
| 0.051836
| 0.067199
| 0.066884
| 0.028349
| 0.934954
| 0.929441
| 0.919834
| 0.913534
| 0.904819
| 0.901354
| 0
| 0.013593
| 0.326364
| 33,527
| 743
| 143
| 45.123822
| 0.829799
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| 0
| 0.726257
| 1
| 0
| 0.197947
| 0.06538
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036313
| false
| 0
| 0.013966
| 0
| 0.086592
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
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| 0
|
0
| 8
|
1ad3fcee088889246860a4fbec72d01b0688a10d
| 10,766
|
py
|
Python
|
tests/payoff_calculator_test.py
|
phillipgreenii/loan_payoff_tools
|
4ffb8a83f7fe6bf7eb37eb7165b3959422d3a515
|
[
"MIT"
] | null | null | null |
tests/payoff_calculator_test.py
|
phillipgreenii/loan_payoff_tools
|
4ffb8a83f7fe6bf7eb37eb7165b3959422d3a515
|
[
"MIT"
] | 3
|
2015-05-03T02:16:49.000Z
|
2015-05-08T21:25:01.000Z
|
tests/payoff_calculator_test.py
|
phillipgreenii/loan_payoff_tools
|
4ffb8a83f7fe6bf7eb37eb7165b3959422d3a515
|
[
"MIT"
] | null | null | null |
'''
loan_payoff_tools: Test module.
Meant for use with py.test.
Write each test as a function named test_<something>.
Read more here: http://pytest.org/
Copyright 2014, Phillip Green II
Licensed under MIT
'''
import unittest
from datetime import date
from loan_payoff_tools.payment_manager import Account
from loan_payoff_tools.payment_manager import MinimumPaymentManager
from loan_payoff_tools.payment_manager import EvenSplitPaymentManager
from loan_payoff_tools.max_payment_determiner import ConstantMaxPaymentDeterminer
from loan_payoff_tools.money import Money
import loan_payoff_tools.payoff_calculator as payoff_calculator
class PayoffCalculatorTestCase(unittest.TestCase):
def test_build_date_incrementer_12(self):
incrementer = payoff_calculator._build_date_incrementer(12)
self.assertEqual(incrementer(date(2014, 1, 2)), date(2014, 2, 2))
self.assertEqual(incrementer(date(2014, 1, 30)), date(2014, 2, 28))
self.assertEqual(incrementer(date(2014, 12, 2)), date(2015, 1, 2))
def test_combine_payments_single(self):
account0 = Account("Bank0", "00", "Joe", 1000, 0.03, 100.00, date(2014, 5, 1))
account1 = Account("Bank0", "01", "Joe", 7500, 0.05, 50.00, date(2014, 5, 1))
account2 = Account("Bank1", "00", "Sam", 9000, 0.06, 900.00, date(2014, 5, 1))
payment_group0 = {account0: Money(100.00), account1: Money(50.00), account2: Money(900.00)}
expected_payment_group = payment_group0
self.assertEqual(payoff_calculator._combine_payments(payment_group0), expected_payment_group)
def test_combine_payments_non_empty_multiple(self):
account0 = Account("Bank0", "00", "Joe", 1000, 0.03, 100.00, date(2014, 5, 1))
account1 = Account("Bank0", "01", "Joe", 7500, 0.05, 50.00, date(2014, 5, 1))
account2 = Account("Bank1", "00", "Sam", 9000, 0.06, 900.00, date(2014, 5, 1))
payment_group0 = {account0: Money(100.00), account1: Money(50.00), account2: Money(900.00)}
payment_group1 = {account0: Money(100.00), account1: Money(100.00), account2: Money(100.00)}
expected_payment_group = {account0: Money(200.00), account1: Money(150.00), account2: Money(1000.00)}
self.assertEqual(payoff_calculator._combine_payments(payment_group0, payment_group1), expected_payment_group)
def test_combine_payments_empty_multiple(self):
account0 = Account("Bank0", "00", "Joe", 1000, 0.03, 100.00, date(2014, 5, 1))
account1 = Account("Bank0", "01", "Joe", 7500, 0.05, 50.00, date(2014, 5, 1))
account2 = Account("Bank1", "00", "Sam", 9000, 0.06, 900.00, date(2014, 5, 1))
payment_group0 = {account0: Money(100.00), account1: Money(50.00), account2: Money(900.00)}
payment_group1 = {}
expected_payment_group = {account0: Money(100.00), account1: Money(50.00), account2: Money(900.00)}
self.assertEqual(payoff_calculator._combine_payments(payment_group0, payment_group1), expected_payment_group)
def test_calculate_payoff_with_single_account_and_no_interest(self):
max_payment_determiner = ConstantMaxPaymentDeterminer(1000)
payment_determiner = MinimumPaymentManager()
bonus_payment_determiner = payment_determiner
account0 = Account("Bank0", "00", "Joe", 1000, 0, 100.00, date(2014, 5, 1))
accounts = (account0,)
starting_date = date(2014, 6, 30)
expected_total_payments_amount = Money(1000)
expected_total_payments_count = 10
expected_total_payments = [(date(2014, 6, 30), {account0: (Money(100), Money(900.00))}),
(date(2014, 7, 30), {account0: (Money(100), Money(800.00))}),
(date(2014, 8, 30), {account0: (Money(100), Money(700.00))}),
(date(2014, 9, 30), {account0: (Money(100), Money(600.00))}),
(date(2014, 10, 30), {account0: (Money(100), Money(500.00))}),
(date(2014, 11, 30), {account0: (Money(100), Money(400.00))}),
(date(2014, 12, 30), {account0: (Money(100), Money(300.00))}),
(date(2015, 1, 30), {account0: (Money(100), Money(200.00))}),
(date(2015, 2, 28), {account0: (Money(100), Money(100.00))}),
(date(2015, 3, 28), {account0: (Money(100), Money(0))})]
(total_amount, payments_count, total_payoffs) = payoff_calculator.calculate_payoff(max_payment_determiner, payment_determiner, bonus_payment_determiner, accounts, starting_date)
self.assertEqual(total_amount, expected_total_payments_amount)
self.assertEqual(payments_count, expected_total_payments_count)
self.assertEqual(total_payoffs, expected_total_payments)
def test_calculate_payoff_with_single_account_and_interest(self):
max_payment_determiner = ConstantMaxPaymentDeterminer(1000)
payment_determiner = MinimumPaymentManager()
bonus_payment_determiner = payment_determiner
account0 = Account("Bank0", "00", "Joe", 1000, 0.05, 100.00, date(2014, 5, 1))
accounts = (account0,)
starting_date = date(2014, 6, 30)
expected_total_payments_amount = Money(1023.61)
expected_total_payments_count = 11
expected_total_payments = [(date(2014, 6, 30), {account0: (Money(100), Money(904.17))}),
(date(2014, 7, 30), {account0: (Money(100), Money(807.94))}),
(date(2014, 8, 30), {account0: (Money(100), Money(711.31))}),
(date(2014, 9, 30), {account0: (Money(100), Money(614.27))}),
(date(2014, 10, 30), {account0: (Money(100), Money(516.83))}),
(date(2014, 11, 30), {account0: (Money(100), Money(418.98))}),
(date(2014, 12, 30), {account0: (Money(100), Money(320.73))}),
(date(2015, 1, 30), {account0: (Money(100), Money(222.07))}),
(date(2015, 2, 28), {account0: (Money(100), Money(123.00))}),
(date(2015, 3, 28), {account0: (Money(100), Money(23.51))}),
(date(2015, 4, 28), {account0: (Money(23.61), Money(0))})]
(total_amount, payments_count, total_payoffs) = payoff_calculator.calculate_payoff(max_payment_determiner, payment_determiner, bonus_payment_determiner, accounts, starting_date)
self.assertEqual(total_amount, expected_total_payments_amount)
self.assertEqual(payments_count, expected_total_payments_count)
self.assertEqual(total_payoffs, expected_total_payments)
def test_calculate_payoff_with_single_account_and_no_interest_and_bonus(self):
max_payment_determiner = ConstantMaxPaymentDeterminer(50, 50)
payment_determiner = EvenSplitPaymentManager()
bonus_payment_determiner = EvenSplitPaymentManager()
account0 = Account("Bank0", "00", "Joe", 1000, 0, 50.00, date(2014, 5, 1))
accounts = (account0,)
starting_date = date(2014, 6, 30)
expected_total_payments_amount = Money(1000)
expected_total_payments_count = 10
expected_total_payments = [(date(2014, 6, 30), {account0: (Money(100), Money(900.00))}),
(date(2014, 7, 30), {account0: (Money(100), Money(800.00))}),
(date(2014, 8, 30), {account0: (Money(100), Money(700.00))}),
(date(2014, 9, 30), {account0: (Money(100), Money(600.00))}),
(date(2014, 10, 30), {account0: (Money(100), Money(500.00))}),
(date(2014, 11, 30), {account0: (Money(100), Money(400.00))}),
(date(2014, 12, 30), {account0: (Money(100), Money(300.00))}),
(date(2015, 1, 30), {account0: (Money(100), Money(200.00))}),
(date(2015, 2, 28), {account0: (Money(100), Money(100.00))}),
(date(2015, 3, 28), {account0: (Money(100), Money(0))})]
(total_amount, payments_count, total_payoffs) = payoff_calculator.calculate_payoff(max_payment_determiner, payment_determiner, bonus_payment_determiner, accounts, starting_date)
self.assertEqual(total_amount, expected_total_payments_amount)
self.assertEqual(payments_count, expected_total_payments_count)
self.assertEqual(total_payoffs, expected_total_payments)
def test_calculate_payoff_with_single_account_and_interest_and_bonus(self):
max_payment_determiner = ConstantMaxPaymentDeterminer(50, 50)
payment_determiner = EvenSplitPaymentManager()
bonus_payment_determiner = EvenSplitPaymentManager()
account0 = Account("Bank0", "00", "Joe", 1000, 0.05, 50.00, date(2014, 5, 1))
accounts = (account0,)
starting_date = date(2014, 6, 30)
expected_total_payments_amount = Money(1023.61)
expected_total_payments_count = 11
expected_total_payments = [(date(2014, 6, 30), {account0: (Money(100), Money(904.17))}),
(date(2014, 7, 30), {account0: (Money(100), Money(807.94))}),
(date(2014, 8, 30), {account0: (Money(100), Money(711.31))}),
(date(2014, 9, 30), {account0: (Money(100), Money(614.27))}),
(date(2014, 10, 30), {account0: (Money(100), Money(516.83))}),
(date(2014, 11, 30), {account0: (Money(100), Money(418.98))}),
(date(2014, 12, 30), {account0: (Money(100), Money(320.73))}),
(date(2015, 1, 30), {account0: (Money(100), Money(222.07))}),
(date(2015, 2, 28), {account0: (Money(100), Money(123.00))}),
(date(2015, 3, 28), {account0: (Money(100), Money(23.51))}),
(date(2015, 4, 28), {account0: (Money(23.61), Money(0))})]
(total_amount, payments_count, total_payoffs) = payoff_calculator.calculate_payoff(max_payment_determiner, payment_determiner, bonus_payment_determiner, accounts, starting_date)
self.assertEqual(total_amount, expected_total_payments_amount)
self.assertEqual(payments_count, expected_total_payments_count)
self.assertEqual(total_payoffs, expected_total_payments)
if __name__ == '__main__':
unittest.main()
| 59.480663
| 185
| 0.606911
| 1,258
| 10,766
| 4.985692
| 0.112083
| 0.063776
| 0.114796
| 0.133929
| 0.893017
| 0.871492
| 0.855389
| 0.827647
| 0.817602
| 0.817602
| 0
| 0.147447
| 0.254133
| 10,766
| 180
| 186
| 59.811111
| 0.633624
| 0.018763
| 0
| 0.75188
| 0
| 0
| 0.013073
| 0
| 0
| 0
| 0
| 0
| 0.135338
| 1
| 0.06015
| false
| 0
| 0.06015
| 0
| 0.12782
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b4a7d54ebf43275a91b1af9ae00ff9bd58753690
| 118
|
py
|
Python
|
dataservice/api/outcome/__init__.py
|
ConnorBarnhill/kf-api-dataservice
|
547df467a307788882469a25c947a14965a26336
|
[
"Apache-2.0"
] | 6
|
2018-01-25T13:49:24.000Z
|
2020-03-07T16:25:09.000Z
|
dataservice/api/outcome/__init__.py
|
ConnorBarnhill/kf-api-dataservice
|
547df467a307788882469a25c947a14965a26336
|
[
"Apache-2.0"
] | 369
|
2018-01-17T15:22:18.000Z
|
2022-03-10T19:14:56.000Z
|
dataservice/api/outcome/__init__.py
|
ConnorBarnhill/kf-api-dataservice
|
547df467a307788882469a25c947a14965a26336
|
[
"Apache-2.0"
] | 3
|
2018-04-11T14:18:37.000Z
|
2018-10-31T19:09:48.000Z
|
from dataservice.api.outcome.resources import OutcomeAPI
from dataservice.api.outcome.resources import OutcomeListAPI
| 39.333333
| 60
| 0.881356
| 14
| 118
| 7.428571
| 0.571429
| 0.288462
| 0.346154
| 0.480769
| 0.769231
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067797
| 118
| 2
| 61
| 59
| 0.945455
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 9
|
371f4ea7362504af95802b40a1ae8e406cb61f3a
| 10,522
|
py
|
Python
|
tests/test_directory_view_get_context_data.py
|
wildfish/django-directory
|
7a097e1eca66a2e5f5ae02905b92ecc37ae0db34
|
[
"MIT"
] | 5
|
2015-10-05T11:54:59.000Z
|
2021-07-07T05:08:56.000Z
|
tests/test_directory_view_get_context_data.py
|
wildfish/django-directory
|
7a097e1eca66a2e5f5ae02905b92ecc37ae0db34
|
[
"MIT"
] | null | null | null |
tests/test_directory_view_get_context_data.py
|
wildfish/django-directory
|
7a097e1eca66a2e5f5ae02905b92ecc37ae0db34
|
[
"MIT"
] | 1
|
2016-03-16T07:59:50.000Z
|
2016-03-16T07:59:50.000Z
|
from django.http import QueryDict
from django.test import TestCase, RequestFactory
from directory.views import DirectoryView
from .models import TestModelB, MultipleFieldModel
class DirectoryViewGetContextData(TestCase):
def test_filter_is_not_supplied_in_kwargs___filter_is_added(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(v.get_filter(), context_data['filter'])
def test_filter_is_supplied_in_kwargs___filter_is_not_changed(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data(filter='some filter')
self.assertEqual('some filter', context_data['filter'])
def test_field_names_are_not_supplied_in_kwargs___field_names_are_added(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(('field_b', 'id'), context_data['field_names'])
def test_field_names_are_supplied_in_kwargs___field_names_are_not_changed(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data(field_names=['some name'])
self.assertEqual(['some name'], context_data['field_names'])
def test_field_headings_are_not_supplied_in_kwargs___field_headings_are_added(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(('Field B', 'Id'), context_data['field_headings'])
def test_field_headings_are_supplied_in_kwargs___field_names_are_not_changed(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data(field_headings=['some heading'])
self.assertEqual(['some heading'], context_data['field_headings'])
def test_display_headings_is_not_supplied_in_kwargs_or_meta___display_headings_is_true(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertTrue(context_data['display_headings'])
def test_display_headings_is_not_supplied_in_kwargs_but_is_on_meta___display_headings_matches_meta(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
display_headings = False
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertFalse(context_data['display_headings'])
def test_display_headings_is_supplied_in_kwargs___display_headings_matches_the_kwargs(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = TestModelB
search_fields = ['field_b']
display_headings = False
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data(display_headings=True)
self.assertTrue(context_data['display_headings'])
def test_link_on_field_is_not_supplied_in_kwargs_or_meta___link_on_field_is_first_field(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(context_data['field_names'][0], context_data['link_on_field'])
def test_link_on_field_is_not_supplied_in_kwargs_but_is_on_meta___link_on_field_is_field_in_meta(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = 'second'
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual('second', context_data['link_on_field'])
def test_link_on_field_is_not_supplied_in_kwargs_but_is_on_meta_as_none___link_on_field_is_none(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertIsNone(context_data['link_on_field'])
def test_link_on_field_is_supplied_in_kwargs___link_on_field_matches_the_kwargs(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data(link_on_field='third')
self.assertEqual('third', context_data['link_on_field'])
def test_filter_query_string_is_not_set_in_kwargs_and_is_empty___filter_query_string_is_empty(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual('', context_data['filter_query_string'])
def test_filter_query_string_is_not_set_in_kwargs_and_is_not_empty_page_is_only_arg___filter_query_string_is_empty(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/?page=12345')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual('', context_data['filter_query_string'])
def test_filter_query_string_is_not_set_in_kwargs_and_is_not_empty_page_is_first_arg___filter_query_string_does_not_contain_the_page_arg(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/?page=12345&prop1=a&prop2=b')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(QueryDict('prop1=a&prop2=b'), QueryDict(context_data['filter_query_string']))
def test_filter_query_string_is_not_set_in_kwargs_and_is_not_empty_page_is_not_first_arg___filter_query_string_does_not_contain_the_page_arg(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/?prop1=a&page=12345&prop2=b')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(QueryDict('prop1=a&prop2=b'), QueryDict(context_data['filter_query_string']))
def test_filter_query_string_is_not_set_in_kwargs_and_is_not_empty_page_is_last_arg___filter_query_string_does_not_contain_the_page_arg(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/?prop1=a&prop2=b&page=12345')
v.object_list = []
context_data = v.get_context_data()
self.assertEqual(QueryDict('prop1=a&prop2=b'), QueryDict(context_data['filter_query_string']))
def test_filter_query_string_is_supplied_in_kwargs___filter_query_string_matches_the_kwargs(self):
class TestDirectoryView(DirectoryView):
class Meta:
model = MultipleFieldModel
search_fields = ['first']
display_headings = False
link_on_field = None
v = TestDirectoryView()
v.request = RequestFactory().get('/?prop1=a&prop2=b&page=12345')
v.object_list = []
context_data = v.get_context_data(filter_query_string='other_qs')
self.assertEqual('other_qs', context_data['filter_query_string'])
| 35.789116
| 151
| 0.642558
| 1,144
| 10,522
| 5.451049
| 0.072552
| 0.102309
| 0.038807
| 0.118826
| 0.897691
| 0.867383
| 0.843329
| 0.829859
| 0.820558
| 0.80789
| 0
| 0.005193
| 0.267915
| 10,522
| 293
| 152
| 35.911263
| 0.804362
| 0
| 0
| 0.787037
| 0
| 0
| 0.066812
| 0.010644
| 0
| 0
| 0
| 0
| 0.087963
| 1
| 0.087963
| false
| 0
| 0.018519
| 0
| 0.287037
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
372eaae9ab2c16dd902249d10915b4c6382a3850
| 3,143
|
py
|
Python
|
fake_modules.py
|
meissnert/StarCluster-Plugins
|
a84dc5f62b5a37e7843c0fb4ac69011ecd766e51
|
[
"MIT"
] | 1
|
2016-05-27T19:58:53.000Z
|
2016-05-27T19:58:53.000Z
|
fake_modules.py
|
meissnert/StarCluster-Plugins
|
a84dc5f62b5a37e7843c0fb4ac69011ecd766e51
|
[
"MIT"
] | null | null | null |
fake_modules.py
|
meissnert/StarCluster-Plugins
|
a84dc5f62b5a37e7843c0fb4ac69011ecd766e51
|
[
"MIT"
] | null | null | null |
from starcluster.clustersetup import ClusterSetup
from starcluster.logger import log
class FakeModInstaller(ClusterSetup):
def run(self, nodes, master, user, user_shell, volumes):
for node in nodes:
log.info("Creating python 2.6.5 module files on %s" % (node.alias))
node.ssh.execute('mkdir /usr/local/Modules/applications/python')
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/python/2.6.5')
node.ssh.execute('echo "set root /usr/bin/python" >> /usr/local/Modules/applications/python/2.6.5')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root/bin" >> /usr/local/Modules/applications/python/2.6.5')
log.info("Creating python 2.7.4 module files on %s" % (node.alias))
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/python/2.7.4')
node.ssh.execute('echo "set root /usr/bin/python" >> /usr/local/Modules/applications/python/2.7.4')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root/bin" >> /usr/local/Modules/applications/python/2.7.4')
log.info("Creating GATK 3.2-2 module files on %s" % (node.alias))
node.ssh.execute('mkdir /usr/local/Modules/applications/gatk')
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/gatk/3.2-2')
node.ssh.execute('echo "set root /opt/software/gatk/3.2-2" >> /usr/local/Modules/applications/gatk/3.2-2')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root" >> /usr/local/Modules/applications/gatk/3.2-2')
node.ssh.execute('mkdir -p /opt/software/gatk/3.2-2')
log.info("Creating GATK 3.3-0 module files on %s" % (node.alias))
node.ssh.execute('mkdir /usr/local/Modules/applications/gatk')
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/gatk/3.3-0')
node.ssh.execute('echo "set root /opt/software/gatk/3.3-0" >> /usr/local/Modules/applications/gatk/3.3-0')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root" >> /usr/local/Modules/applications/gatk/3.3-0')
node.ssh.execute('mkdir -p /opt/software/gatk/3.3-0')
log.info("Creating GATK 3.5-0 module files on %s" % (node.alias))
node.ssh.execute('mkdir /usr/local/Modules/applications/gatk')
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/gatk/3.5-0')
node.ssh.execute('echo "set root /opt/software/gatk/3.5-0" >> /usr/local/Modules/applications/gatk/3.5-0')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root" >> /usr/local/Modules/applications/gatk/3.5-0')
node.ssh.execute('mkdir -p /opt/software/gatk/3.5-0')
log.info("Creating MuTect 1.1.4 module files on %s" % (node.alias))
node.ssh.execute('mkdir /usr/local/Modules/applications/mutect')
node.ssh.execute('echo "#%Module" >> /usr/local/Modules/applications/mutect/1.1.4')
node.ssh.execute('echo "set root /opt/software/mutect/1.1.4" >> /usr/local/Modules/applications/mutect/1.1.4')
node.ssh.execute('echo -e "prepend-path\tPATH\t\$root" >> /usr/local/Modules/applications/mutect/1.1.4')
node.ssh.execute('mkdir -p /opt/software/mutect/1.1.4')
| 69.844444
| 130
| 0.673242
| 495
| 3,143
| 4.272727
| 0.113131
| 0.089362
| 0.178723
| 0.293617
| 0.908747
| 0.853901
| 0.840662
| 0.831678
| 0.821277
| 0.799527
| 0
| 0.035385
| 0.136812
| 3,143
| 44
| 131
| 71.431818
| 0.744195
| 0
| 0
| 0.078947
| 0
| 0.473684
| 0.625517
| 0.424435
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.052632
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
2eabbc492325693d84c09f8314053d43a9d2b56b
| 20,786
|
py
|
Python
|
pyne/tests/spatial_solvers/ahot_script.py
|
AllSafeCyberSecur1ty/Nuclear-Engineering
|
302d6dcc7c0a85a9191098366b076cf9cb5a9f6e
|
[
"MIT"
] | 1
|
2022-03-26T20:01:13.000Z
|
2022-03-26T20:01:13.000Z
|
pyne/tests/spatial_solvers/ahot_script.py
|
AllSafeCyberSecur1ty/Nuclear-Engineering
|
302d6dcc7c0a85a9191098366b076cf9cb5a9f6e
|
[
"MIT"
] | null | null | null |
pyne/tests/spatial_solvers/ahot_script.py
|
AllSafeCyberSecur1ty/Nuclear-Engineering
|
302d6dcc7c0a85a9191098366b076cf9cb5a9f6e
|
[
"MIT"
] | 1
|
2022-03-26T19:59:13.000Z
|
2022-03-26T19:59:13.000Z
|
"""AHOT Spatial Solver tests"""
# TODO:
# Add tests with supported warning configurations?
# a = populate_with_warnings("AHOTN")
# a = populate_with_warnings("DGFEM")
import numpy as np
from numpy.testing import assert_array_almost_equal
import pyne.spatialsolver
from .dictionary_populate_test import (
populate_simple,
populate_simple_with_warnings,
populate_intermediate_1,
)
def test_ahotn_ln():
a = populate_simple("AHOTN", "LN")
dict_results = pyne.spatialsolver.solve(a)
if dict_results["success"] == 0:
raise AssertionError("Error: " + dict_results["error_msg"])
exp = np.array(
[
[
[3.52650199, 3.09260257, 3.09260257, 3.52650199],
[3.09260257, 2.73209732, 2.73209732, 3.09260257],
[3.09260257, 2.73209732, 2.73209732, 3.09260257],
[3.52650199, 3.09260257, 3.09260257, 3.52650199],
],
[
[2.89021832, 2.61284811, 2.61284811, 2.89021832],
[2.61284811, 2.38571678, 2.38571678, 2.61284811],
[2.61284811, 2.38571678, 2.38571678, 2.61284811],
[2.89021832, 2.61284811, 2.61284811, 2.89021832],
],
[
[2.89021832, 2.61284811, 2.61284811, 2.89021832],
[2.61284811, 2.38571678, 2.38571678, 2.61284811],
[2.61284811, 2.38571678, 2.38571678, 2.61284811],
[2.89021832, 2.61284811, 2.61284811, 2.89021832],
],
[
[3.52650199, 3.09260257, 3.09260257, 3.52650199],
[3.09260257, 2.73209732, 2.73209732, 3.09260257],
[3.09260257, 2.73209732, 2.73209732, 3.09260257],
[3.52650199, 3.09260257, 3.09260257, 3.52650199],
],
]
)
obs = np.array(dict_results["flux"])
assert_array_almost_equal(exp, obs, 4)
def test_ahotn_ll():
a = populate_simple("AHOTN", "LL")
dict_results = pyne.spatialsolver.solve(a)
exp = np.array(
[
[
[3.52588507, 3.09173385, 3.09173385, 3.52588507],
[3.09173385, 2.73355777, 2.73355777, 3.09173385],
[3.09173385, 2.73355777, 2.73355777, 3.09173385],
[3.52588507, 3.09173385, 3.09173385, 3.52588507],
],
[
[2.88989501, 2.61223446, 2.61223446, 2.88989501],
[2.61223446, 2.38668358, 2.38668358, 2.61223446],
[2.61223446, 2.38668358, 2.38668358, 2.61223446],
[2.88989501, 2.61223446, 2.61223446, 2.88989501],
],
[
[2.88989501, 2.61223446, 2.61223446, 2.88989501],
[2.61223446, 2.38668358, 2.38668358, 2.61223446],
[2.61223446, 2.38668358, 2.38668358, 2.61223446],
[2.88989501, 2.61223446, 2.61223446, 2.88989501],
],
[
[3.52588507, 3.09173385, 3.09173385, 3.52588507],
[3.09173385, 2.73355777, 2.73355777, 3.09173385],
[3.09173385, 2.73355777, 2.73355777, 3.09173385],
[3.52588507, 3.09173385, 3.09173385, 3.52588507],
],
]
)
obs = np.array(dict_results["flux"])
assert_array_almost_equal(exp, obs, 4)
def test_ahotn_nefd():
a = populate_simple("AHOTN", "NEFD")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[3.524073, 3.091501, 3.091501, 3.524073],
[3.091501, 2.734906, 2.734906, 3.091501],
[3.091501, 2.734906, 2.734906, 3.091501],
[3.524073, 3.091501, 3.091501, 3.524073],
],
[
[2.888798, 2.612178, 2.612178, 2.888798],
[2.612178, 2.387341, 2.387341, 2.612178],
[2.612178, 2.387341, 2.387341, 2.612178],
[2.888798, 2.612178, 2.612178, 2.888798],
],
[
[2.888798, 2.612178, 2.612178, 2.888798],
[2.612178, 2.387341, 2.387341, 2.612178],
[2.612178, 2.387341, 2.387341, 2.612178],
[2.888798, 2.612178, 2.612178, 2.888798],
],
[
[3.524073, 3.091501, 3.091501, 3.524073],
[3.091501, 2.734906, 2.734906, 3.091501],
[3.091501, 2.734906, 2.734906, 3.091501],
[3.524073, 3.091501, 3.091501, 3.524073],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_ld():
a = populate_simple("DGFEM", "LD")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[3.540511, 3.104096, 3.104096, 3.540511],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.540511, 3.104096, 3.104096, 3.540511],
],
[
[2.899079, 2.620152, 2.620152, 2.899079],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.899079, 2.620152, 2.620152, 2.899079],
],
[
[2.899079, 2.620152, 2.620152, 2.899079],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.899079, 2.620152, 2.620152, 2.899079],
],
[
[3.540511, 3.104096, 3.104096, 3.540511],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.540511, 3.104096, 3.104096, 3.540511],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_dense():
a = populate_simple("DGFEM", "DENSE")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[3.540511, 3.104096, 3.104096, 3.540511],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.540511, 3.104096, 3.104096, 3.540511],
],
[
[2.899079, 2.620152, 2.620152, 2.899079],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.899079, 2.620152, 2.620152, 2.899079],
],
[
[2.899079, 2.620152, 2.620152, 2.899079],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.620152, 2.383940, 2.383940, 2.620152],
[2.899079, 2.620152, 2.620152, 2.899079],
],
[
[3.540511, 3.104096, 3.104096, 3.540511],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.104096, 2.730554, 2.730554, 3.104096],
[3.540511, 3.104096, 3.104096, 3.540511],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_lagrange():
a = populate_simple("DGFEM", "LAGRANGE")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[3.536038, 3.096808, 3.096808, 3.536038],
[3.096808, 2.732475, 2.732475, 3.096808],
[3.096808, 2.732475, 2.732475, 3.096808],
[3.536038, 3.096808, 3.096808, 3.536038],
],
[
[2.896267, 2.615275, 2.615275, 2.896267],
[2.615275, 2.385484, 2.385484, 2.615275],
[2.615275, 2.385484, 2.385484, 2.615275],
[2.896267, 2.615275, 2.615275, 2.896267],
],
[
[2.896267, 2.615275, 2.615275, 2.896267],
[2.615275, 2.385484, 2.385484, 2.615275],
[2.615275, 2.385484, 2.385484, 2.615275],
[2.896267, 2.615275, 2.615275, 2.896267],
],
[
[3.536038, 3.096808, 3.096808, 3.536038],
[3.096808, 2.732475, 2.732475, 3.096808],
[3.096808, 2.732475, 2.732475, 3.096808],
[3.536038, 3.096808, 3.096808, 3.536038],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_sct_step():
a = populate_simple("SCTSTEP", "anything")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[3.273572, 2.948301, 2.948502, 3.291909],
[2.811363, 2.464789, 2.468086, 2.813676],
[2.921249, 2.576771, 2.593078, 2.919847],
[3.138840, 2.784381, 2.785791, 3.139999],
],
[
[2.466767, 2.188464, 2.191274, 2.465690],
[2.168904, 1.883310, 1.884325, 2.169292],
[2.181507, 1.891052, 1.895120, 2.178766],
[2.438198, 2.161378, 2.161873, 2.438270],
],
[
[2.429940, 2.143983, 2.143274, 2.427243],
[2.144259, 1.849312, 1.848996, 2.143790],
[2.142347, 1.843699, 1.841852, 2.140937],
[2.425510, 2.142483, 2.142357, 2.425371],
],
[
[3.091479, 2.729188, 2.728940, 3.091578],
[2.727627, 2.366091, 2.365882, 2.727488],
[2.726782, 2.365203, 2.364727, 2.726503],
[3.087793, 2.725209, 2.725085, 3.087700],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_ahotn_ln_alternating():
a = populate_intermediate_1("AHOTN", "LN")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.302715, 2.230236, 1.817902, 2.952883],
[2.230236, 1.292285, 1.620001, 1.817902],
[1.817902, 1.620001, 1.292285, 2.230236],
[2.952883, 1.817902, 2.230236, 2.302715],
],
[
[2.289555, 1.443020, 1.762396, 1.811167],
[1.443020, 1.283541, 1.038793, 1.762396],
[1.762396, 1.038793, 1.283541, 1.443020],
[1.811167, 1.762396, 1.443020, 2.289555],
],
[
[1.811167, 1.762396, 1.443020, 2.289555],
[1.762396, 1.038793, 1.283541, 1.443020],
[1.443020, 1.283541, 1.038793, 1.762396],
[2.289555, 1.443020, 1.762396, 1.811167],
],
[
[2.952883, 1.817902, 2.230236, 2.302715],
[1.817902, 1.620001, 1.292285, 2.230236],
[2.230236, 1.292285, 1.620001, 1.817902],
[2.302715, 2.230236, 1.817902, 2.952883],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_ahotn_ll_alternating():
a = populate_intermediate_1("AHOTN", "LL")
dict_results = pyne.spatialsolver.solve(a)
exp = np.array(
[
[
[2.31140733, 2.20295478, 1.83219443, 2.93370678],
[2.20295478, 1.32420289, 1.56965005, 1.83219443],
[1.83219443, 1.56965005, 1.32420289, 2.20295478],
[2.93370678, 1.83219443, 2.20295478, 2.31140733],
],
[
[2.27440404, 1.45579431, 1.74010961, 1.81996174],
[1.45579431, 1.24553997, 1.0624916, 1.74010961],
[1.74010961, 1.0624916, 1.24553997, 1.45579431],
[1.81996174, 1.74010961, 1.45579431, 2.27440404],
],
[
[1.81996174, 1.74010961, 1.45579431, 2.27440404],
[1.74010961, 1.0624916, 1.24553997, 1.45579431],
[1.45579431, 1.24553997, 1.0624916, 1.74010961],
[2.27440404, 1.45579431, 1.74010961, 1.81996174],
],
[
[2.93370678, 1.83219443, 2.20295478, 2.31140733],
[1.83219443, 1.56965005, 1.32420289, 2.20295478],
[2.20295478, 1.32420289, 1.56965005, 1.83219443],
[2.31140733, 2.20295478, 1.83219443, 2.93370678],
],
]
)
obs = np.array(dict_results["flux"])
assert_array_almost_equal(exp, obs, 4)
def test_ahotn_nefd_alternating():
a = populate_intermediate_1("AHOTN", "NEFD")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.320847, 2.193170, 1.836823, 2.923995],
[2.193170, 1.310507, 1.568554, 1.836823],
[1.836823, 1.568554, 1.310507, 2.193170],
[2.923995, 1.836823, 2.193170, 2.320847],
],
[
[2.266863, 1.456056, 1.732060, 1.824538],
[1.456056, 1.241531, 1.049696, 1.732060],
[1.732060, 1.049696, 1.241531, 1.456056],
[1.824538, 1.732060, 1.456056, 2.266863],
],
[
[1.824538, 1.732060, 1.456056, 2.266863],
[1.732060, 1.049696, 1.241531, 1.456056],
[1.456056, 1.241531, 1.049696, 1.732060],
[2.266863, 1.456056, 1.732060, 1.824538],
],
[
[2.923995, 1.836823, 2.193170, 2.320847],
[1.836823, 1.568554, 1.310507, 2.193170],
[2.193170, 1.310507, 1.568554, 1.836823],
[2.320847, 2.193170, 1.836823, 2.923995],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_ld_alternating():
a = populate_intermediate_1("DGFEM", "LD")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.420725, 2.104426, 1.900442, 2.889886],
[2.104426, 1.299636, 1.433389, 1.900442],
[1.900442, 1.433389, 1.299636, 2.104426],
[2.889886, 1.900442, 2.104426, 2.420725],
],
[
[2.224013, 1.498666, 1.647904, 1.894524],
[1.498666, 1.119896, 1.039153, 1.647904],
[1.647904, 1.039153, 1.119896, 1.498666],
[1.894524, 1.647904, 1.498666, 2.224013],
],
[
[1.894524, 1.647904, 1.498666, 2.224013],
[1.647904, 1.039153, 1.119896, 1.498666],
[1.498666, 1.119896, 1.039153, 1.647904],
[2.224013, 1.498666, 1.647904, 1.894524],
],
[
[2.889886, 1.900442, 2.104426, 2.420725],
[1.900442, 1.433389, 1.299636, 2.104426],
[2.104426, 1.299636, 1.433389, 1.900442],
[2.420725, 2.104426, 1.900442, 2.889886],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_dense_alternating():
a = populate_intermediate_1("DGFEM", "DENSE")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.420725, 2.104426, 1.900442, 2.889886],
[2.104426, 1.299636, 1.433389, 1.900442],
[1.900442, 1.433389, 1.299636, 2.104426],
[2.889886, 1.900442, 2.104426, 2.420725],
],
[
[2.224013, 1.498666, 1.647904, 1.894524],
[1.498666, 1.119896, 1.039153, 1.647904],
[1.647904, 1.039153, 1.119896, 1.498666],
[1.894524, 1.647904, 1.498666, 2.224013],
],
[
[1.894524, 1.647904, 1.498666, 2.224013],
[1.647904, 1.039153, 1.119896, 1.498666],
[1.498666, 1.119896, 1.039153, 1.647904],
[2.224013, 1.498666, 1.647904, 1.894524],
],
[
[2.889886, 1.900442, 2.104426, 2.420725],
[1.900442, 1.433389, 1.299636, 2.104426],
[2.104426, 1.299636, 1.433389, 1.900442],
[2.420725, 2.104426, 1.900442, 2.889886],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_dgfem_lagrange_alternating():
a = populate_intermediate_1("DGFEM", "LAGRANGE")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.403548, 2.135009, 1.885348, 2.906123],
[2.135009, 1.300693, 1.469197, 1.885348],
[1.885348, 1.469197, 1.300693, 2.135009],
[2.906123, 1.885348, 2.135009, 2.403548],
],
[
[2.241881, 1.486578, 1.673153, 1.882209],
[1.486578, 1.145347, 1.036189, 1.673153],
[1.673153, 1.036189, 1.145347, 1.486578],
[1.882209, 1.673153, 1.486578, 2.241881],
],
[
[1.882209, 1.673153, 1.486578, 2.241881],
[1.673153, 1.036189, 1.145347, 1.486578],
[1.486578, 1.145347, 1.036189, 1.673153],
[2.241881, 1.486578, 1.673153, 1.882209],
],
[
[2.906123, 1.885348, 2.135009, 2.403548],
[1.885348, 1.469197, 1.300693, 2.135009],
[2.135009, 1.300693, 1.469197, 1.885348],
[2.403548, 2.135009, 1.885348, 2.906123],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
def test_sct_step_alternating():
a = populate_intermediate_1("SCTSTEP", "anything")
dict_results = pyne.spatialsolver.solve(a)
rounded_flux = np.around(dict_results["flux"], decimals=4)
correct_flux = [
[
[2.103727, 2.129333, 1.775806, 2.709218],
[1.984849, 1.172710, 1.337597, 1.664623],
[1.757312, 1.459605, 1.282230, 2.107971],
[2.551582, 1.644416, 1.966496, 1.996478],
],
[
[1.909362, 1.216011, 1.443766, 1.521228],
[1.198507, 0.8426090, 0.7858172, 1.423269],
[1.435932, 0.7960783, 0.8584189, 1.209827],
[1.500600, 1.417286, 1.194468, 1.887075],
],
[
[1.497664, 1.410221, 1.186999, 1.881503],
[1.408052, 0.7672912, 0.8230592, 1.185632],
[1.186346, 0.8224311, 0.7656347, 1.407697],
[1.878868, 1.184635, 1.406690, 1.494015],
],
[
[2.519203, 1.608783, 1.927761, 1.963608],
[1.608023, 1.265341, 1.108607, 1.927101],
[1.9271, 1.108730, 1.265047, 1.608085],
[1.962463, 1.926423, 1.607454, 2.518035],
],
]
correct_flux_rounded = np.around(correct_flux, decimals=4)
print(correct_flux_rounded)
print(rounded_flux)
if (rounded_flux == correct_flux_rounded).all():
print("flux's are equal!")
else:
raise AssertionError(
"Flux outputs are not equal for ahotn-nefd example. Check system setup."
)
| 36.024263
| 85
| 0.537285
| 2,650
| 20,786
| 4.138113
| 0.110943
| 0.04514
| 0.023345
| 0.035747
| 0.869323
| 0.866314
| 0.848258
| 0.848258
| 0.736002
| 0.665238
| 0
| 0.466147
| 0.309343
| 20,786
| 576
| 86
| 36.086806
| 0.297715
| 0.007361
| 0
| 0.69305
| 0
| 0
| 0.05736
| 0
| 0
| 0
| 0
| 0.001736
| 0.030888
| 1
| 0.027027
| false
| 0
| 0.007722
| 0
| 0.034749
| 0.025097
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
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|
0
| 8
|
2eccbc156b4505de0db85d95b51443c5f217b8c1
| 33,547
|
py
|
Python
|
pensa/comparison/statespecific.py
|
NeilJ-Thomson/pensa
|
f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb
|
[
"MIT"
] | 55
|
2020-11-18T07:03:46.000Z
|
2022-03-29T02:47:10.000Z
|
pensa/comparison/statespecific.py
|
NeilJ-Thomson/pensa
|
f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb
|
[
"MIT"
] | 11
|
2020-11-18T16:43:43.000Z
|
2022-02-22T20:02:22.000Z
|
pensa/comparison/statespecific.py
|
NeilJ-Thomson/pensa
|
f2cc586ad8c4b60177051fc9a5d2da087ac1b6fb
|
[
"MIT"
] | 11
|
2020-11-19T04:34:36.000Z
|
2022-03-01T23:48:57.000Z
|
import numpy as np
from tqdm import tqdm
from pensa.features import *
from pensa.statesinfo import *
# -- Functions to calculate SSI statistics across paired ensembles --
def ssi_ensemble_analysis(features_a, features_b, all_data_a, all_data_b, torsions=None, pocket_occupancy=None, pbc=True,
verbose=True, write_plots=None, override_name_check=False):
"""
Calculates State Specific Information statistic for a feature across two ensembles.
Parameters
----------
features_a : list of str
Feature names of the first ensemble.
features_b : list of str
Feature names of the first ensemble.
Must be the same as features_a. Provided as a sanity check.
all_data_a : float array
Trajectory data from the first ensemble. Format: [frames,frame_data].
all_data_b : float array
Trajectory data from the second ensemble. Format: [frames,frame_data].
torsions : str
Torsion angles to use for SSI, including backbone - 'bb', and sidechain - 'sc'.
Default is None.
pocket_occupancy : bool, optional
Set to 'True' if the data input is pocket occupancy distribution.
The default is None.
pbc : bool, optional
If true, the apply periodic bounary corrections on angular distribution inputs.
The input for periodic correction must be radians. The default is True.
verbose : bool, default=True
Print intermediate results.
write_plots : bool, optional
If true, visualise the states over the raw distribution. The default is None.
override_name_check : bool, default=False
Only check number of features, not their names.
Returns
-------
data_names : list of str
Feature names.
data_ssi : float array
State Specific Information statistics for each feature.
"""
# Get the multivariate timeseries data
if torsions is None:
mv_res_feat_a, mv_res_data_a = features_a,all_data_a
mv_res_feat_b, mv_res_data_b = features_b,all_data_b
else:
mv_res_feat_a, mv_res_data_a = get_multivar_res_timeseries(features_a,all_data_a,torsions+'-torsions',write=False,out_name='')
mv_res_feat_b, mv_res_data_b = get_multivar_res_timeseries(features_b,all_data_b,torsions+'-torsions',write=False,out_name='')
mv_res_feat_a, mv_res_data_a = mv_res_feat_a[torsions+'-torsions'], mv_res_data_a[torsions+'-torsions']
mv_res_feat_b, mv_res_data_b = mv_res_feat_b[torsions+'-torsions'], mv_res_data_b[torsions+'-torsions']
# Assert that the features are the same and data sets have same number of features
if override_name_check:
assert len(mv_res_feat_a) == len(mv_res_feat_b)
else:
assert mv_res_feat_a == mv_res_feat_b
assert mv_res_data_a.shape[0] == mv_res_data_b.shape[0]
# Extract the names of the features
data_names = mv_res_feat_a
# Initialize relative entropy and average value
data_ssi = np.zeros(len(data_names))
# Loop over all features
for residue in range(len(mv_res_data_a)):
data_a = mv_res_data_a[residue]
data_b = mv_res_data_b[residue]
combined_dist=[]
for dist_no in range(len(data_a)):
# # # combine the ensembles into one distribution (condition_a + condition_b)
data_both = list(data_a[dist_no]) + list(data_b[dist_no])
combined_dist.append(data_both)
## Saving distribution length
traj1_len = len(data_a[dist_no])
if pbc is True:
feat_distr = [correct_angle_periodicity(distr) for distr in combined_dist]
else:
feat_distr = combined_dist
if pocket_occupancy is True:
## Define states for water occupancy
feat_states = [[-0.5,0.5,1.5]]
else:
feat_states=[]
for dim_num in range(len(feat_distr)):
if write_plots is True:
plot_name = data_names[residue]
else:
plot_name = None
try:
feat_states.append(determine_state_limits(feat_distr[dim_num],
traj1_len,
write_plots=write_plots,
write_name=plot_name))
except:
print('Distribution A not clustering properly.\nTry altering Gaussian parameters or input custom states.')
H_feat=calculate_entropy(feat_states,feat_distr)
if H_feat != 0:
##calculating the entropy for set_distr_b
## if no dist (None) then apply the binary dist for two simulations
ens_distr=[[0.5]*traj1_len + [1.5]*int(len(feat_distr[0])-traj1_len)]
ens_states= [[0,1,2]]
traj_1_fraction = traj1_len/len(feat_distr[0])
traj_2_fraction = 1 - traj_1_fraction
norm_factor = -1*traj_1_fraction*math.log(traj_1_fraction,2) - 1*traj_2_fraction*math.log(traj_2_fraction,2)
H_ens = norm_factor
featens_joint_states= feat_states + ens_states
featens_joint_distr= feat_distr + ens_distr
H_featens=calculate_entropy(featens_joint_states,featens_joint_distr)
SSI = ((H_feat + H_ens) - H_featens)/norm_factor
else:
SSI = 0
data_ssi[residue] = SSI
if verbose is True:
print(data_names[residue],data_ssi[residue])
return data_names, data_ssi
def ssi_feature_analysis(features_a, features_b, all_data_a, all_data_b, torsions=None, verbose=True, override_name_check=False):
"""
Calculates State Specific Information statistic between two features across two ensembles.
Parameters
----------
features_a : list of str
Feature names of the first ensemble.
features_b : list of str
Feature names of the first ensemble.
Must be the same as features_a. Provided as a sanity check.
all_data_a : float array
Trajectory data from the first ensemble. Format: [frames,frame_data].
all_data_b : float array
Trajectory data from the second ensemble. Format: [frames,frame_data].
torsions : str
Torsion angles to use for SSI, including backbone - 'bb', and sidechain - 'sc'.
Default is None.
verbose : bool, default=True
Print intermediate results.
override_name_check : bool, default=False
Only check number of features, not their names.
Returns
-------
data_names : list of str
Feature names.
data_ssi : float array
State Specific Information statistics for each feature.
"""
# Get the multivariate timeseries data
if torsions is None:
mv_res_feat_a, mv_res_data_a = features_a,all_data_a
mv_res_feat_b, mv_res_data_b = features_b,all_data_b
else:
mv_res_feat_a, mv_res_data_a = get_multivar_res_timeseries(features_a,all_data_a,torsions+'-torsions',write=False,out_name='')
mv_res_feat_b, mv_res_data_b = get_multivar_res_timeseries(features_b,all_data_b,torsions+'-torsions',write=False,out_name='')
mv_res_feat_a, mv_res_data_a = mv_res_feat_a[torsions+'-torsions'], mv_res_data_a[torsions+'-torsions']
mv_res_feat_b, mv_res_data_b = mv_res_feat_b[torsions+'-torsions'], mv_res_data_b[torsions+'-torsions']
# Assert that the features are the same and data sets have same number of features
if override_name_check:
assert len(mv_res_feat_a) == len(mv_res_feat_b)
else:
assert mv_res_feat_a == mv_res_feat_b
assert mv_res_data_a.shape[0] == mv_res_data_b.shape[0]
# Extract the names of the features
data_names = []
for feat1 in range(len(mv_res_feat_a)):
for feat2 in range(feat1, len(mv_res_feat_a)):
data_names.append(torsions + ' ' + mv_res_feat_a[feat1] + ' & ' + torsions + ' ' + mv_res_feat_a[feat2])
# Initialize SSI
data_ssi = np.zeros(len(data_names))
# Loop over all features
count=0
for res1 in range(len(mv_res_data_a)):
# print(res1)
res1_data_ens1 = mv_res_data_a[res1]
res1_data_ens2 = mv_res_data_b[res1]
res1_combined_dist=[]
for dist_no_a in range(len(res1_data_ens1)):
# # # combine the ensembles into one distribution (condition_a + condition_b)
res1_data_both = list(res1_data_ens1[dist_no_a]) + list(res1_data_ens2[dist_no_a])
res1_combined_dist.append(res1_data_both)
## Saving distribution length
traj1_len = len(res1_data_ens1[dist_no_a])
# if calculate_ssi(res1_combined_dist, traj1_len)!=0:
set_distr_a=[correct_angle_periodicity(distr_a) for distr_a in res1_combined_dist]
set_a_states=[]
for dim_num_a in range(len(set_distr_a)):
set_a_states.append(determine_state_limits(set_distr_a[dim_num_a], traj1_len))
H_a=calculate_entropy(set_a_states,set_distr_a)
if H_a != 0:
for res2 in range(res1, len(mv_res_data_a)):
# Only run SSI if entropy is non-zero
res2_data_ens1 = mv_res_data_a[res2]
res2_data_ens2 = mv_res_data_b[res2]
res2_combined_dist=[]
for dist_no_b in range(len(res2_data_ens1)):
# # # combine the ensembles into one distribution (condition_a + condition_b)
res2_data_both = list(res2_data_ens1[dist_no_b]) + list(res2_data_ens2[dist_no_b])
res2_combined_dist.append(res2_data_both)
set_distr_b=[correct_angle_periodicity(distr_b) for distr_b in res2_combined_dist]
set_b_states=[]
for dim_num_b in range(len(set_distr_b)):
set_b_states.append(determine_state_limits(set_distr_b[dim_num_b], traj1_len))
H_b=calculate_entropy(set_b_states,set_distr_b)
if H_b!=0:
ab_joint_states= set_a_states + set_b_states
ab_joint_distributions= set_distr_a + set_distr_b
H_ab=calculate_entropy(ab_joint_states,ab_joint_distributions)
traj_1_fraction = traj1_len/len(set_distr_a[0])
traj_2_fraction = 1 - traj_1_fraction
norm_factor = -1*traj_1_fraction*math.log(traj_1_fraction,2) - 1*traj_2_fraction*math.log(traj_2_fraction,2)
SSI = ((H_a + H_b) - H_ab)/norm_factor
data_ssi[count] = SSI
if verbose is True:
print(data_names[count],'\nSSI[bits]: ',data_ssi[count])
count+=1
else:
if verbose is True:
print(data_names[count],'\nSSI[bits]: ',data_ssi[count])
count+=1
else:
for res2 in range(res1+1, len(mv_res_data_a)):
if verbose is True:
print(data_names[count],'\nSSI[bits]: ',data_ssi[count])
count+=1
return data_names, data_ssi
def cossi_featens_analysis(features_a, features_b, all_data_a, all_data_b, torsions=None, verbose=True, override_name_check=False):
"""
Calculates State Specific Information Co-SSI statistic between two features and the ensembles condition.
Parameters
----------
features_a : list of str
Feature names of the first ensemble.
features_b : list of str
Feature names of the first ensemble.
Must be the same as features_a. Provided as a sanity check.
all_data_a : float array
Trajectory data from the first ensemble. Format: [frames,frame_data].
all_data_b : float array
Trajectory data from the second ensemble. Format: [frames,frame_data].
torsions : str
Torsion angles to use for SSI, including backbone - 'bb', and sidechain - 'sc'.
Default is None.
verbose : bool, default=True
Print intermediate results.
override_name_check : bool, default=False
Only check number of features, not their names.
Returns
-------
data_names : list of str
Feature names.
data_ssi : float array
State Specific Information SSI statistics for each feature.
data_cossi : float array
State Specific Information Co-SSI statistics for each feature.
"""
# Get the multivariate timeseries data
if torsions is None:
mv_res_feat_a, mv_res_data_a = features_a,all_data_a
mv_res_feat_b, mv_res_data_b = features_b,all_data_b
else:
mv_res_feat_a, mv_res_data_a = get_multivar_res_timeseries(features_a,all_data_a,torsions+'-torsions',write=False,out_name='')
mv_res_feat_b, mv_res_data_b = get_multivar_res_timeseries(features_b,all_data_b,torsions+'-torsions',write=False,out_name='')
mv_res_feat_a, mv_res_data_a = mv_res_feat_a[torsions+'-torsions'], mv_res_data_a[torsions+'-torsions']
mv_res_feat_b, mv_res_data_b = mv_res_feat_b[torsions+'-torsions'], mv_res_data_b[torsions+'-torsions']
# Assert that the features are the same and data sets have same number of features
if override_name_check:
assert len(mv_res_feat_a) == len(mv_res_feat_b)
else:
assert mv_res_feat_a == mv_res_feat_b
assert mv_res_data_a.shape[0] == mv_res_data_b.shape[0]
# Extract the names of the features
data_names = []
for feat1 in range(len(mv_res_feat_a)):
for feat2 in range(feat1, len(mv_res_feat_a)):
data_names.append(torsions + ' ' + mv_res_feat_a[feat1] + ' & ' + torsions + ' ' + mv_res_feat_a[feat2])
# Initialize SSI and Co-SSI
data_ssi = np.zeros(len(data_names))
data_cossi = np.zeros(len(data_names))
# Loop over all features
count=0
for res1 in range(len(mv_res_data_a)):
# print(res1)
res1_data_ens1 = mv_res_data_a[res1]
res1_data_ens2 = mv_res_data_b[res1]
res1_combined_dist=[]
for dist_no_a in range(len(res1_data_ens1)):
# # # combine the ensembles into one distribution (condition_a + condition_b)
res1_data_both = list(res1_data_ens1[dist_no_a]) + list(res1_data_ens2[dist_no_a])
res1_combined_dist.append(res1_data_both)
## Saving distribution length
traj1_len = len(res1_data_ens1[dist_no_a])
# if calculate_ssi(res1_combined_dist, traj1_len)!=0:
set_distr_a=[correct_angle_periodicity(distr_a) for distr_a in res1_combined_dist]
set_a_states=[]
for dim_num_a in range(len(set_distr_a)):
set_a_states.append(determine_state_limits(set_distr_a[dim_num_a], traj1_len))
H_a=calculate_entropy(set_a_states,set_distr_a)
if H_a != 0:
for res2 in range(res1, len(mv_res_data_a)):
# Only run SSI if entropy is non-zero
res2_data_ens1 = mv_res_data_a[res2]
res2_data_ens2 = mv_res_data_b[res2]
res2_combined_dist=[]
for dist_no_b in range(len(res2_data_ens1)):
# # # combine the ensembles into one distribution (condition_a + condition_b)
res2_data_both = list(res2_data_ens1[dist_no_b]) + list(res2_data_ens2[dist_no_b])
res2_combined_dist.append(res2_data_both)
set_distr_b=[correct_angle_periodicity(distr_b) for distr_b in res2_combined_dist]
set_b_states=[]
for dim_num_b in range(len(set_distr_b)):
set_b_states.append(determine_state_limits(set_distr_b[dim_num_b], traj1_len))
H_b=calculate_entropy(set_b_states,set_distr_b)
if H_b!=0:
ab_joint_states= set_a_states + set_b_states
ab_joint_distributions= set_distr_a + set_distr_b
H_ab=calculate_entropy(ab_joint_states,ab_joint_distributions)
traj_1_fraction = traj1_len/len(set_distr_a[0])
traj_2_fraction = 1 - traj_1_fraction
norm_factor = -1*traj_1_fraction*math.log(traj_1_fraction,2) - 1*traj_2_fraction*math.log(traj_2_fraction,2)
set_distr_c=[[0.5]*traj1_len + [1.5]*int(len(set_distr_a[0])-traj1_len)]
set_c_states= [[0,1,2]]
H_c = norm_factor
##----------------
ab_joint_states = set_a_states + set_b_states
ab_joint_distributions = set_distr_a + set_distr_b
H_ab = calculate_entropy(ab_joint_states, ab_joint_distributions)
##----------------
ac_joint_states = set_a_states + set_c_states
ac_joint_distributions = set_distr_a + set_distr_c
H_ac = calculate_entropy(ac_joint_states, ac_joint_distributions)
##----------------
bc_joint_states = set_b_states + set_c_states
bc_joint_distributions = set_distr_b + set_distr_c
H_bc = calculate_entropy(bc_joint_states, bc_joint_distributions)
##----------------
abc_joint_states = set_a_states + set_b_states + set_c_states
abc_joint_distributions = set_distr_a + set_distr_b + set_distr_c
H_abc = calculate_entropy(abc_joint_states, abc_joint_distributions)
SSI = ((H_a + H_b) - H_ab)/norm_factor
coSSI = ((H_a + H_b + H_c) - (H_ab + H_ac + H_bc) + H_abc)/norm_factor
data_ssi[count] = SSI
data_cossi[count] = coSSI
if verbose is True:
print('\nFeature Pair: ', data_names[count],
'\nSSI[bits]: ', data_ssi[count],
'\nCo-SSI[bits]: ', data_cossi[count])
count+=1
else:
if verbose is True:
print('\nFeature Pair: ', data_names[count],
'\nSSI[bits]: ', data_ssi[count],
'\nCo-SSI[bits]: ', data_cossi[count])
count+=1
else:
for res2 in range(res1+1, len(mv_res_data_a)):
if verbose is True:
print('\nFeature Pair: ', data_names[count],
'\nSSI[bits]: ', data_ssi[count],
'\nCo-SSI[bits]: ', data_cossi[count])
count+=1
return data_names, data_ssi, data_cossi
# -- Functions with more customizable capabilities for users to adapt to their needs --
def _calculate_ssi(distr_a_input, traj1_len, distr_b_input=None, a_states=None, b_states=None,
gauss_bins=180, gauss_smooth=None, pbc=True, write_plots=None, write_name=None):
"""
Calculates the State Specific Information SSI [bits] between two features from two ensembles.
By default, the second feature is the binary switch between ensembles.
SSI(a,b) = H_a + H_b - H_ab
H = Conformational state entropy
Parameters
----------
distr_a_input : list of lists
A list containing multivariate distributions (lists) for a particular
residue or water
distr_b_input : list of lists, optional
A list containing multivariate distributions (lists) for a particular
residue or water. The default is None and a binary switch is assigned.
a_states : list of lists, optional
A list of values that represent the limits of each state for each
distribution. The default is None and state limits are calculated automatically.
b_states : list of lists, optional
A list of values that represent the limits of each state for each
distribution. The default is None and state limits are calculated automatically.
gauss_bins : int, optional
Number of histogram bins to assign for the clustering algorithm.
The default is 180.
gauss_smooth : int, optional
Number of bins to perform smoothing over. The default is ~10% of gauss_bins.
write_plots : bool, optional
If true, visualise the states over the raw distribution. The default is None.
write_name : str, optional
Filename for write_plots. The default is None.
Returns
-------
SSI : float
State Specific Information (SSI[bits]) shared between input a and input b (default is binary switch).
"""
try:
##calculating the entropy for set_distr_a
## if set_distr_a only contains one distributions
if pbc is True:
if type(distr_a_input[0]) is not list:
set_distr_a=[correct_angle_periodicity(distr_a_input)]
## else set_distr_a is a nested list of multiple distributions (bivariate)
else:
set_distr_a=[correct_angle_periodicity(distr_a) for distr_a in distr_a_input]
else:
set_distr_a=distr_a_input
if a_states is None:
set_a_states=[]
for dim_num in range(len(set_distr_a)):
if write_name is not None:
plot_name = write_name + '_dist' + str(dim_num)
else:
plot_name = None
try:
set_a_states.append(determine_state_limits(set_distr_a[dim_num], traj1_len, gauss_bins, gauss_smooth, write_plots, plot_name))
except:
print('Distribution A not clustering properly.\nTry altering Gaussian parameters or input custom states.')
else:
set_a_states = a_states
H_a=calculate_entropy(set_a_states,set_distr_a)
##calculating the entropy for set_distr_b
## if no dist (None) then apply the binary dist for two simulations
if distr_b_input is None:
set_distr_b=[[0.5]*traj1_len + [1.5]*int(len(set_distr_a[0])-traj1_len)]
set_b_states= [[0,1,2]]
else:
if pbc is True:
if type(distr_b_input[0]) is not list:
set_distr_b=[correct_angle_periodicity(distr_b_input)]
else:
set_distr_b=[correct_angle_periodicity(distr_b) for distr_b in distr_b_input]
else:
set_distr_b=distr_b_input
if b_states is None:
set_b_states=[]
for dim_num in range(len(set_distr_b)):
if write_name is not None:
plot_name = write_name + '_dist' + str(dim_num)
else:
plot_name = None
try:
set_b_states.append(determine_state_limits(set_distr_b[dim_num], traj1_len, gauss_bins, gauss_smooth, write_plots, plot_name))
except:
print('Distribution B not clustering properly.\nTry altering Gaussian parameters or input custom states.')
else:
set_b_states = b_states
H_b=calculate_entropy(set_b_states,set_distr_b)
ab_joint_states= set_a_states + set_b_states
ab_joint_distributions= set_distr_a + set_distr_b
H_ab=calculate_entropy(ab_joint_states,ab_joint_distributions)
traj_1_fraction = traj1_len/len(set_distr_a[0])
traj_2_fraction = 1 - traj_1_fraction
norm_factor = -1*traj_1_fraction*math.log(traj_1_fraction,2) - 1*traj_2_fraction*math.log(traj_2_fraction,2)
SSI = ((H_a + H_b) - H_ab)/norm_factor
except:
SSI = -1
if write_name is not None:
print('WARNING: Input error for ' + write_name)
else:
print('WARNING: Input error')
print('Default output of SSI= -1.')
return round(SSI,4)
def _calculate_cossi(distr_a_input, traj1_len, distr_b_input, distr_c_input=None, a_states=None, b_states=None,
c_states=None, gauss_bins=180, gauss_smooth=None, write_plots=None,write_name=None):
"""
Calculates the State Specific Information Co-SSI [bits] between three features from two ensembles.
By default, the third feature is the binary switch between ensembles.
CoSSI(a,b,c) = H_a + H_b + H_c - H_ab - H_bc - H_ac + H_abc
H = Conformational state entropy
Parameters
----------
distr_a_input : list of lists
A list containing multivariate distributions (lists) for a particular
residue or water
distr_b_input : list of lists
A list containing multivariate distributions (lists) for a particular
residue or water.
distr_c_input : list of lists, optional
A list containing multivariate distributions (lists) for a particular
residue or water. The default is None and a binary switch is assigned.
a_states : list of lists, optional
A list of values that represent the limits of each state for each
distribution. The default is None and state limits are calculated automatically.
b_states : list of lists, optional
A list of values that represent the limits of each state for each
distribution. The default is None and state limits are calculated automatically.
c_states : list of lists, optional
A list of values that represent the limits of each state for each
distribution. The default is None and state limits are calculated automatically.
gauss_bins : int, optional
Number of histogram bins to assign for the clustering algorithm.
The default is 180.
gauss_smooth : int, optional
Number of bins to perform smoothing over. The default is ~10% of gauss_bins.
write_plots : bool, optional
If true, visualise the states over the raw distribution. The default is None.
write_name : str, optional
Filename for write_plots. The default is None.
Returns
-------
SSI : float
SSI[bits] shared between input a and input b (default is binary switch).
coSSI : float
Co-SSI[bits] shared between input a, input b and input c (default is binary switch).
"""
try:
##calculating the entropy for set_distr_a
## if set_distr_a only contains one distributions
if type(distr_a_input[0]) is not list:
set_distr_a=[correct_angle_periodicity(distr_a_input)]
## else set_distr_a is a nested list of multiple distributions (bivariate)
else:
set_distr_a=[correct_angle_periodicity(distr_a) for distr_a in distr_a_input]
if a_states is None:
set_a_states=[]
for dim_num in range(len(set_distr_a)):
if write_name is not None:
plot_name = write_name + '_dist' + str(dim_num)
else:
plot_name = None
try:
set_a_states.append(determine_state_limits(set_distr_a[dim_num],
traj1_len,
gauss_bins,
gauss_smooth,
write_plots,
plot_name))
except:
print('Distribution A not clustering properly.\nTry altering Gaussian parameters or input custom states.')
else:
set_a_states = a_states
H_a=calculate_entropy(set_a_states,set_distr_a)
##----------------
##calculating the entropy for set_distr_b
if type(distr_b_input[0]) is not list:
set_distr_b=[correct_angle_periodicity(distr_b_input)]
## else set_distr_b is a nested list of multiple distributions (bivariate)
else:
set_distr_b=[correct_angle_periodicity(distr_b) for distr_b in distr_b_input]
if b_states is None:
set_b_states=[]
for dim_num in range(len(set_distr_b)):
if write_name is not None:
plot_name = write_name + '_dist' + str(dim_num)
else:
plot_name = None
try:
set_b_states.append(determine_state_limits(set_distr_b[dim_num],
traj1_len,
gauss_bins,
gauss_smooth,
write_plots,
plot_name))
except:
print('Distribution A not clustering properly.\nTry altering Gaussian parameters or input custom states.')
else:
set_b_states = b_states
H_b=calculate_entropy(set_b_states,set_distr_b)
##----------------
##calculating the entropy for set_distr_c
## if no dist (None) then apply the binary dist for two simulations
if distr_c_input is None:
set_distr_c=[[0.5]*traj1_len + [1.5]*int(len(set_distr_a[0])-traj1_len)]
set_c_states= [[0,1,2]]
else:
if type(distr_c_input[0]) is not list:
set_distr_c=[correct_angle_periodicity(distr_c_input)]
else:
set_distr_c=[correct_angle_periodicity(distr_c) for distr_c in distr_c_input]
if c_states is None:
set_c_states=[]
for dim_num in range(len(set_distr_c)):
if write_name is not None:
plot_name = write_name + '_dist' + str(dim_num)
else:
plot_name = None
try:
set_c_states.append(determine_state_limits(set_distr_c[dim_num], traj1_len, gauss_bins, gauss_smooth, write_plots, plot_name))
except:
print('Distribution C not clustering properly.\nTry altering Gaussian parameters or input custom states.')
else:
set_c_states = c_states
H_c=calculate_entropy(set_c_states,set_distr_c)
##----------------
ab_joint_states = set_a_states + set_b_states
ab_joint_distributions = set_distr_a + set_distr_b
H_ab = calculate_entropy(ab_joint_states, ab_joint_distributions)
##----------------
ac_joint_states = set_a_states + set_c_states
ac_joint_distributions = set_distr_a + set_distr_c
H_ac = calculate_entropy(ac_joint_states, ac_joint_distributions)
##----------------
bc_joint_states = set_b_states + set_c_states
bc_joint_distributions = set_distr_b + set_distr_c
H_bc = calculate_entropy(bc_joint_states, bc_joint_distributions)
##----------------
abc_joint_states = set_a_states + set_b_states + set_c_states
abc_joint_distributions = set_distr_a + set_distr_b + set_distr_c
H_abc = calculate_entropy(abc_joint_states, abc_joint_distributions)
traj_1_fraction = traj1_len/len(set_distr_a[0])
traj_2_fraction = 1 - traj_1_fraction
norm_factor = -1*traj_1_fraction*math.log(traj_1_fraction,2) - 1*traj_2_fraction*math.log(traj_2_fraction,2)
SSI = ((H_a + H_b) - H_ab)/norm_factor
coSSI = ((H_a + H_b + H_c) - (H_ab + H_ac + H_bc) + H_abc)/norm_factor
##conditional mutual info for sanity check
# con_mut_inf = H_ac + H_bc - H_c - H_abc
except:
SSI = -1
coSSI = -1
if write_name is not None:
print('WARNING: Error for ' + write_name)
else:
print('WARNING: Error')
print('Default output of -1.')
return round(SSI,4), round(coSSI,4)
| 44.551129
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0
| 7
|
2ee9442cf0b747887f56ef399cd05098b20a5c95
| 176,065
|
py
|
Python
|
intensio/test/python/advanced/output/basicRAT-example/core/TrNyQrwBvIPHpfSTViMBqueBppNSmHsjaYRzCIUnZGssHFZOnGvbeYBzohXKYFkpdJWLMHzjkYZVOwJOJLhASWYmJogCqrdkDFciQiRvNRYsljZPbmBTSKePnbUSviji.py
|
Chudry/Intensio-Obfuscator
|
62f6c8871704693ca79342efb03dcd2530d7614e
|
[
"MIT"
] | 1
|
2019-09-06T11:55:29.000Z
|
2019-09-06T11:55:29.000Z
|
intensio/test/python/advanced/output/basicRAT-example/core/TrNyQrwBvIPHpfSTViMBqueBppNSmHsjaYRzCIUnZGssHFZOnGvbeYBzohXKYFkpdJWLMHzjkYZVOwJOJLhASWYmJogCqrdkDFciQiRvNRYsljZPbmBTSKePnbUSviji.py
|
Chudry/Intensio-Obfuscator
|
62f6c8871704693ca79342efb03dcd2530d7614e
|
[
"MIT"
] | null | null | null |
intensio/test/python/advanced/output/basicRAT-example/core/TrNyQrwBvIPHpfSTViMBqueBppNSmHsjaYRzCIUnZGssHFZOnGvbeYBzohXKYFkpdJWLMHzjkYZVOwJOJLhASWYmJogCqrdkDFciQiRvNRYsljZPbmBTSKePnbUSviji.py
|
Chudry/Intensio-Obfuscator
|
62f6c8871704693ca79342efb03dcd2530d7614e
|
[
"MIT"
] | null | null | null |
MWZWSmMhtUsRrINuSTzFrxaxjBTBJqSWKuKnGWRXkqWQJqFZtHcGtyMgFWrExRGweSHLOCtzbXotCELvYZdsUDmsxKLYPEnCGzbpsNkLrKPigaoVayVePBGtbjfjhKyb="""
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"""
exec(MWZWSmMhtUsRrINuSTzFrxaxjBTBJqSWKuKnGWRXkqWQJqFZtHcGtyMgFWrExRGweSHLOCtzbXotCELvYZdsUDmsxKLYPEnCGzbpsNkLrKPigaoVayVePBGtbjfjhKyb)
| 912.253886
| 1,172
| 0.749513
| 43,904
| 176,065
| 3.005717
| 0.001686
| 0.075794
| 0.103256
| 0.123762
| 0.808136
| 0.802725
| 0.801611
| 0.80002
| 0.798678
| 0.797019
| 0
| 0.43499
| 0.001091
| 176,065
| 192
| 1,173
| 917.005208
| 0.315341
| 0
| 0
| 0.041667
| 0
| 0.963542
| 0.998461
| 0.997382
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 14
|
2c1930976901fb8aedd4548bfb8d6c15eb67bbc4
| 533
|
py
|
Python
|
catkin_ws/build/catkin_generated/order_packages.py
|
engboustani/3d-printer-arm
|
49173f30a19dcc5adb0e27d85b8b37fc5282e571
|
[
"Apache-2.0"
] | null | null | null |
catkin_ws/build/catkin_generated/order_packages.py
|
engboustani/3d-printer-arm
|
49173f30a19dcc5adb0e27d85b8b37fc5282e571
|
[
"Apache-2.0"
] | null | null | null |
catkin_ws/build/catkin_generated/order_packages.py
|
engboustani/3d-printer-arm
|
49173f30a19dcc5adb0e27d85b8b37fc5282e571
|
[
"Apache-2.0"
] | null | null | null |
# generated from catkin/cmake/template/order_packages.context.py.in
source_root_dir = "/home/engboustani/Documents/3d-printer-arm/catkin_ws/src"
whitelisted_packages = "".split(';') if "" != "" else []
blacklisted_packages = "".split(';') if "" != "" else []
underlay_workspaces = "/home/engboustani/Documents/3d-printer-arm/catkin_ws/devel;/home/engboustani/ws_moveit/devel;/opt/ros/melodic".split(';') if "/home/engboustani/Documents/3d-printer-arm/catkin_ws/devel;/home/engboustani/ws_moveit/devel;/opt/ros/melodic" != "" else []
| 88.833333
| 273
| 0.742964
| 71
| 533
| 5.422535
| 0.464789
| 0.194805
| 0.187013
| 0.202597
| 0.581818
| 0.581818
| 0.581818
| 0.581818
| 0.467532
| 0.467532
| 0
| 0.006012
| 0.06379
| 533
| 5
| 274
| 106.6
| 0.765531
| 0.121951
| 0
| 0
| 1
| 0.5
| 0.594421
| 0.587983
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
25b4f049fb62cc24f11e54dc8fc2ba379ccfdcb5
| 3,634
|
py
|
Python
|
test_bear_todo_counter.py
|
attomos/bear_todo_counter
|
4a42955f6a76f25cc4fdcdba3fe6dd79dcfeb217
|
[
"MIT"
] | 1
|
2019-07-27T14:51:45.000Z
|
2019-07-27T14:51:45.000Z
|
test_bear_todo_counter.py
|
attomos/bear_todo_counter
|
4a42955f6a76f25cc4fdcdba3fe6dd79dcfeb217
|
[
"MIT"
] | null | null | null |
test_bear_todo_counter.py
|
attomos/bear_todo_counter
|
4a42955f6a76f25cc4fdcdba3fe6dd79dcfeb217
|
[
"MIT"
] | null | null | null |
from bear_todo_counter import append_todo_count
def test_simple_todo():
txt = """\
Tasks
+ Task one
- Task two
- Task three"""
actual = append_todo_count(txt)
expected = """\
Tasks [1/3]
+ Task one
- Task two
- Task three"""
assert actual == expected
def test_todo_with_emoji():
txt = """\
Tasks 🐻
+ 🌈 Task
- Task two
- Task three"""
actual = append_todo_count(txt)
expected = """\
Tasks 🐻 [1/3]
+ 🌈 Task
- Task two
- Task three"""
assert actual == expected
def test_todo_with_existing_todo_counter():
txt = """\
Tasks 🐻 [1/3]
+ 🌈 Task
+ Task two
- Task three"""
actual = append_todo_count(txt)
expected = """\
Tasks 🐻 [2/3]
+ 🌈 Task
+ Task two
- Task three"""
assert actual == expected
def test_todo_with_subtasks():
txt = """\
Tasks 🐻
+ 🌈 Task
- Task two
- Task three
- subtask 1
- subtask 2"""
actual = append_todo_count(txt)
expected = """\
Tasks 🐻 [1/5]
+ 🌈 Task
- Task two
- Task three
- subtask 1
- subtask 2"""
assert actual == expected
def test_empty_todo():
txt = """\
Tasks 🙄
+
-
- """ # noqa: W291
actual = append_todo_count(txt)
expected = """\
"""
expected = """\
Tasks 🙄 [1/3]
+
-
- """ # noqa: W291
assert actual == expected
def test_todo_with_links():
txt = """\
Tasks 👍
+ https://good-article.com/1
- https://good-article.com/main
- https://good-article.com/sub1
+ https://good-article.com/sub2
+ [Working With XML in Scala - DZone Java](https://dzone.com/articles/working-with-xml-in-scala)
+ [Amundsen — Lyft’s data discovery & metadata engine – Lyft Engineering](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9)""" # noqa: E501
actual = append_todo_count(txt)
expected = """\
Tasks 👍 [4/6]
+ https://good-article.com/1
- https://good-article.com/main
- https://good-article.com/sub1
+ https://good-article.com/sub2
+ [Working With XML in Scala - DZone Java](https://dzone.com/articles/working-with-xml-in-scala)
+ [Amundsen — Lyft’s data discovery & metadata engine – Lyft Engineering](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9)""" # noqa: E501
assert actual == expected
def test_todo_with_dashes():
txt = """\
Tasks with dashes 😂
- - in the beginning of a task
- let's see - in the middle of a task
- what about at the end -
- -
+ - in the beginning of a task
+ let's see - in the middle of a task
+ what about at the end -
+ -"""
actual = append_todo_count(txt)
expected = """\
Tasks with dashes 😂 [4/8]
- - in the beginning of a task
- let's see - in the middle of a task
- what about at the end -
- -
+ - in the beginning of a task
+ let's see - in the middle of a task
+ what about at the end -
+ -"""
assert actual == expected
def test_todo_with_plus_signs():
txt = """\
Tasks with plus signs 😂
- + in the beginning of a task
- let's see + in the middle of a task
- what about at the end +
- +
+ + in the beginning of a task
+ let's see + in the middle of a task
+ what about at the end +
+ +"""
actual = append_todo_count(txt)
expected = """\
Tasks with plus signs 😂 [4/8]
- + in the beginning of a task
- let's see + in the middle of a task
- what about at the end +
- +
+ + in the beginning of a task
+ let's see + in the middle of a task
+ what about at the end +
+ +"""
assert actual == expected
def test_todo_with_line_separator():
txt = """\
# tryme 🐻
- read this
- read this too
---
+ done 1
+ done 2"""
actual = append_todo_count(txt)
expected = """\
# tryme 🐻 [2/4]
- read this
- read this too
---
+ done 1
+ done 2"""
assert actual == expected
| 21.00578
| 171
| 0.635938
| 568
| 3,634
| 4.024648
| 0.15669
| 0.034996
| 0.048994
| 0.082677
| 0.884952
| 0.86133
| 0.841207
| 0.780402
| 0.780402
| 0.730096
| 0
| 0.021777
| 0.216566
| 3,634
| 172
| 172
| 21.127907
| 0.772041
| 0.011833
| 0
| 0.811688
| 0
| 0.025974
| 0.63246
| 0
| 0
| 0
| 0
| 0
| 0.058442
| 1
| 0.058442
| false
| 0
| 0.006494
| 0
| 0.064935
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
25db060c714c394d50b7346b0ada7c29a4a01a98
| 5,182
|
py
|
Python
|
ratings/migrations/0010_auto_20211023_0238.py
|
CommanderStorm/rallyetool-v2
|
721413d6df8afc9347dac7ee83deb3a0ad4c01bc
|
[
"MIT"
] | 1
|
2021-10-03T17:49:53.000Z
|
2021-10-03T17:49:53.000Z
|
ratings/migrations/0010_auto_20211023_0238.py
|
FSTUM/rallyetool-v2
|
2f3e2b5cb8655abe023ed1215b7182430b75bb23
|
[
"MIT"
] | 9
|
2021-11-23T10:13:43.000Z
|
2022-03-01T15:04:15.000Z
|
ratings/migrations/0010_auto_20211023_0238.py
|
CommanderStorm/rallyetool-v2
|
721413d6df8afc9347dac7ee83deb3a0ad4c01bc
|
[
"MIT"
] | 1
|
2021-10-16T09:07:47.000Z
|
2021-10-16T09:07:47.000Z
|
# Generated by Django 3.2.7 on 2021-10-23 00:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("ratings", "0009_alter_group_name"),
]
operations = [
migrations.AddField(
model_name="station",
name="contact_person",
field=models.TextField(default="-", help_text="Displayed to the tutor.", verbose_name="Contact Person"),
),
migrations.AddField(
model_name="station",
name="scoring_instructions",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
verbose_name="Instructions, how to score a game.",
),
),
migrations.AddField(
model_name="station",
name="scoring_instructions_de",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to score a game.",
),
),
migrations.AddField(
model_name="station",
name="scoring_instructions_en",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to score a game.",
),
),
migrations.AddField(
model_name="station",
name="setup_instructions",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
verbose_name="Instructions, how to setup the station.",
),
),
migrations.AddField(
model_name="station",
name="setup_instructions_de",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to setup the station.",
),
),
migrations.AddField(
model_name="station",
name="setup_instructions_en",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to setup the station.",
),
),
migrations.AddField(
model_name="station",
name="setup_tools",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
verbose_name="Utensils/tools needed for this station",
),
),
migrations.AddField(
model_name="station",
name="station_game_instructions",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
verbose_name="Instructions, how to conduct a game.",
),
),
migrations.AddField(
model_name="station",
name="station_game_instructions_de",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to conduct a game.",
),
),
migrations.AddField(
model_name="station",
name="station_game_instructions_en",
field=models.TextField(
default="-",
help_text="Displayed to the tutor.",
null=True,
verbose_name="Instructions, how to conduct a game.",
),
),
migrations.AddField(
model_name="station",
name="tutor_amount",
field=models.PositiveSmallIntegerField(default=11.671, verbose_name="Longitude of the sation"),
),
migrations.AlterField(
model_name="station",
name="name",
field=models.CharField(
default="Station-name unknown",
help_text="Visible to logged in users on the map and to tutors",
max_length=150,
verbose_name="Name of the Station",
),
),
migrations.AlterField(
model_name="station",
name="name_de",
field=models.CharField(
default="Station-name unknown",
help_text="Visible to logged in users on the map and to tutors",
max_length=150,
null=True,
verbose_name="Name of the Station",
),
),
migrations.AlterField(
model_name="station",
name="name_en",
field=models.CharField(
default="Station-name unknown",
help_text="Visible to logged in users on the map and to tutors",
max_length=150,
null=True,
verbose_name="Name of the Station",
),
),
]
| 34.092105
| 116
| 0.502316
| 466
| 5,182
| 5.429185
| 0.167382
| 0.078261
| 0.094862
| 0.118577
| 0.881818
| 0.881818
| 0.866798
| 0.84664
| 0.826877
| 0.80751
| 0
| 0.010567
| 0.397337
| 5,182
| 151
| 117
| 34.317881
| 0.799552
| 0.008684
| 0
| 0.827586
| 1
| 0
| 0.259202
| 0.037001
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.006897
| 0
| 0.027586
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
d3065f109a6ce326cbde2152a2c5536f9adbb3e1
| 16,288
|
py
|
Python
|
tests/test_pyros_schemas/test_schema.py
|
pyros-dev/pyros-schemas
|
a460920260ee77a1b5b6d5c0b97df52f1572ff79
|
[
"MIT"
] | 3
|
2018-01-01T17:10:16.000Z
|
2018-11-15T15:41:46.000Z
|
tests/test_pyros_schemas/test_schema.py
|
pyros-dev/pyros-schemas
|
a460920260ee77a1b5b6d5c0b97df52f1572ff79
|
[
"MIT"
] | 7
|
2018-02-02T10:05:55.000Z
|
2018-02-17T15:15:46.000Z
|
tests/test_pyros_schemas/test_schema.py
|
pyros-dev/pyros-schemas
|
a460920260ee77a1b5b6d5c0b97df52f1572ff79
|
[
"MIT"
] | 2
|
2017-09-27T09:46:31.000Z
|
2018-02-02T09:37:13.000Z
|
from __future__ import absolute_import, print_function
import pytest
from pyros_schemas.ros.schemagic import create
from . import six_long
import hypothesis
import hypothesis.strategies as st
import std_msgs.msg as std_msgs
import genpy
from . import msg as pyros_schemas_test_msgs
from .strategies.ros import std_msgs_types_strat_ok, std_msgs_dicts_strat_ok
# Some tests are still failing on python3, related to strings. see https://github.com/ros/genpy/pull/85 and https://github.com/ros/genpy/pull/90 for related discussion
# also https://discourse.ros.org/t/python3-and-strings/2392
std_msgs_types = {
'std_msgs/Bool': std_msgs.Bool,
'std_msgs/Int8': std_msgs.Int8,
'std_msgs/Int16': std_msgs.Int16,
'std_msgs/Int32': std_msgs.Int32,
'std_msgs/Int64': std_msgs.Int64,
'std_msgs/UInt8': std_msgs.UInt8,
'std_msgs/UInt16': std_msgs.UInt16,
'std_msgs/UInt32': std_msgs.UInt32,
'std_msgs/UInt64': std_msgs.UInt64,
'std_msgs/Float32': std_msgs.Float32,
'std_msgs/Float64': std_msgs.Float64,
'std_msgs/String': std_msgs.String,
'std_msgs/Time': std_msgs.Time,
'std_msgs/Duration': std_msgs.Duration
}
pyros_schemas_opttypes = {
'pyros_schemas/test_opt_bool_as_array': pyros_schemas_test_msgs.test_opt_bool_as_array,
'pyros_schemas/test_opt_int8_as_array': pyros_schemas_test_msgs.test_opt_int8_as_array,
'pyros_schemas/test_opt_int16_as_array': pyros_schemas_test_msgs.test_opt_int16_as_array,
'pyros_schemas/test_opt_int32_as_array': pyros_schemas_test_msgs.test_opt_int32_as_array,
'pyros_schemas/test_opt_int64_as_array': pyros_schemas_test_msgs.test_opt_int64_as_array,
'pyros_schemas/test_opt_uint8_as_array': pyros_schemas_test_msgs.test_opt_uint8_as_array,
'pyros_schemas/test_opt_uint16_as_array': pyros_schemas_test_msgs.test_opt_uint16_as_array,
'pyros_schemas/test_opt_uint32_as_array': pyros_schemas_test_msgs.test_opt_uint32_as_array,
'pyros_schemas/test_opt_uint64_as_array': pyros_schemas_test_msgs.test_opt_uint64_as_array,
'pyros_schemas/test_opt_float32_as_array': pyros_schemas_test_msgs.test_opt_float32_as_array,
'pyros_schemas/test_opt_float64_as_array': pyros_schemas_test_msgs.test_opt_float64_as_array,
'pyros_schemas/test_opt_string_as_array': pyros_schemas_test_msgs.test_opt_string_as_array,
'pyros_schemas/test_opt_time_as_array': pyros_schemas_test_msgs.test_opt_time_as_array,
'pyros_schemas/test_opt_duration_as_array': pyros_schemas_test_msgs.test_opt_duration_as_array,
}
# simple way to define mapping between ros types and deserialized dictionary for testing
def std_msgs_dicts_from_rostype_map(msg_type, rostype_value):
if msg_type in (
'std_msgs/Bool',
'std_msgs/Int8', 'std_msgs/Int16', 'std_msgs/Int32', 'std_msgs/Int64',
'std_msgs/UInt8', 'std_msgs/UInt16', 'std_msgs/UInt32', 'std_msgs/UInt64',
):
return {'data': rostype_value.data}
elif msg_type in (
'std_msgs/Float32', 'std_msgs/Float64',
):
return {'data': rostype_value.data}
elif msg_type in (
'std_msgs/String',
):
# no need to decode/encode here but be careful about non-printable control characters...
# Ref : http://www.madore.org/~david/computers/unicode/#faq_ascii
return {'data': rostype_value.data}
elif msg_type in (
'std_msgs/Time', 'std_msgs/Duration',
):
return {'data': rostype_value.data.to_nsec()}
def pyros_schemas_dicts_from_rostype_map(msg_type, rostype_value):
if msg_type in (
'pyros_schemas/test_opt_bool_as_array',
'pyros_schemas/test_opt_int8_as_array', 'pyros_schemas/test_opt_int16_as_array',
'pyros_schemas/test_opt_int32_as_array', 'pyros_schemas/test_opt_int64_as_array',
'pyros_schemas/test_opt_uint8_as_array', 'pyros_schemas/test_opt_uint16_as_array',
'pyros_schemas/test_opt_uint32_as_array', 'pyros_schemas/test_opt_uint64_as_array',
):
return {'data': rostype_value.data}
elif msg_type in (
'pyros_schemas/test_opt_float32_as_array', 'pyros_schemas/test_opt_float64_as_array',
):
return {'data': rostype_value.data}
elif msg_type in (
'pyros_schemas/test_opt_string_as_array',
):
# no need to decode/encode here but be careful about non-printable control characters...
# Ref : http://www.madore.org/~david/computers/unicode/#faq_ascii
return {'data': rostype_value.data}
elif msg_type in (
'pyros_schemas/test_opt_time_as_array', 'pyros_schemas/test_opt_duration_as_array'
):
return {'data': rostype_value.data.to_nsec()}
# simple way to define mapping between dictionary and serialized rostype for testing
def std_msgs_rostypes_from_dict_map(msg_type, dict_value):
if msg_type in (
'std_msgs/Bool',
'std_msgs/Int8', 'std_msgs/Int16', 'std_msgs/Int32', 'std_msgs/Int64',
'std_msgs/UInt8', 'std_msgs/UInt16', 'std_msgs/UInt32', 'std_msgs/UInt64',
):
rostype = std_msgs_types.get(msg_type)
return rostype(data=dict_value.get('data'))
elif msg_type in (
'std_msgs/Float32', 'std_msgs/Float64',
):
rostype = std_msgs_types.get(msg_type)
return rostype(data=dict_value.get('data'))
elif msg_type in (
'std_msgs/String',
):
rostype = std_msgs_types.get(msg_type)
return rostype(data=dict_value.get('data')) # careful about non-printable control characters
elif msg_type in (
'std_msgs/Time',
):
rostype = std_msgs_types.get(msg_type)
return rostype(data=genpy.Time(nsecs=dict_value.get('data')))
elif msg_type in (
'std_msgs/Duration',
):
rostype = std_msgs_types.get(msg_type)
return rostype(data=genpy.Duration(nsecs=dict_value.get('data')))
def pyros_schemas_rostypes_from_dict_map(msg_type, dict_value):
if msg_type in (
'pyros_schemas/test_opt_bool_as_array',
'pyros_schemas/test_opt_int8_as_array', 'pyros_schemas/test_opt_int16_as_array', 'pyros_schemas/test_opt_int32_as_array', 'pyros_schemas/test_opt_int64_as_array',
'pyros_schemas/test_opt_uint8_as_array', 'pyros_schemas/test_opt_uint16_as_array', 'pyros_schemas/test_opt_uint32_as_array', 'pyros_schemas/test_opt_uint64_as_array',
):
rostype = pyros_schemas_opttypes.get(msg_type)
return rostype(data=dict_value.get('data'))
elif msg_type in (
'pyros_schemas/test_opt_float32_as_array', 'pyros_schemas/test_opt_float64_as_array',
):
rostype = pyros_schemas_opttypes.get(msg_type)
return rostype(data=dict_value.get('data'))
elif msg_type in (
'pyros_schemas/test_opt_string_as_array',
):
rostype = pyros_schemas_opttypes.get(msg_type)
return rostype(data=dict_value.get('data')) # careful about non-printable control characters
elif msg_type in (
'pyros_schemas/test_opt_time_as_array',
):
rostype = pyros_schemas_opttypes.get(msg_type)
return rostype(data=genpy.Time(nsecs=dict_value.get('data')))
elif msg_type in (
'pyros_schemas/test_opt_duration_as_array',
):
rostype = pyros_schemas_opttypes.get(msg_type)
return rostype(data=genpy.Duration(nsecs=dict_value.get('data')))
# We need a composite strategy to link msg type and dict structure
@st.composite
def msg_rostype_and_dict(draw, msgs_type_strat_tuples):
msg_type_strat = draw(st.sampled_from(msgs_type_strat_tuples))
msg_value = draw(msg_type_strat[1])
msg_dict = std_msgs_dicts_from_rostype_map(msg_type_strat[0], msg_value)
return msg_type_strat[0], msg_value, msg_dict
def schema_load_dump_fromros_inverse(msg_rostype, ros_msg, py_inst_expected):
# msg_rostype is just for info/debug purposes
schema = create(type(ros_msg))
marshalled, errors = schema.load(ros_msg)
assert not errors and marshalled == py_inst_expected
value, errors = schema.dump(marshalled)
assert not errors and type(value) == type(ros_msg) and value == ros_msg
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Bool'))
def test_bool_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Bool', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Bool', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Int8'))
def test_int8_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Int8', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Int8', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Int16'))
def test_int16_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Int16', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Int16', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Int32'))
def test_int32_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Int32', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Int32', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Int64'))
def test_int64_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Int64', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Int64', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/UInt8'))
def test_uint8_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/UInt8', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/UInt8', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/UInt16'))
def test_uint16_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/UInt16', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/UInt16', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Int32'))
def test_uint32_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Int32', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/UInt32', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/UInt64'))
def test_uint64_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/UInt64', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/UInt64', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Float32'))
def test_float32_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Float32', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Float32', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Float64'))
def test_float64_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Float64', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Float64', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/String'))
def test_string_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/String', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/String', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Time'))
def test_time_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Time', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Time', msg_value))
@hypothesis.given(std_msgs_types_strat_ok.get('std_msgs/Duration'))
def test_duration_schema_load_dump_fromros_inverse(msg_value):
return schema_load_dump_fromros_inverse('std_msgs/Duration', msg_value, std_msgs_dicts_from_rostype_map('std_msgs/Duration', msg_value))
def schema_dump_load_frompy_inverse(msg_rostype, py_inst, ros_msg_expected):
# msg_rostype is just for info/debug purposes
schema = create(type(ros_msg_expected))
unmarshalled, errors = schema.dump(py_inst)
assert not errors and type(unmarshalled) == type(ros_msg_expected) and unmarshalled == ros_msg_expected
obj, errors = schema.load(unmarshalled)
assert not errors and type(obj) == type(py_inst) and obj == py_inst
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Bool'))
def test_bool_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Bool', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Bool', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Int8'))
def test_int8_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Int8', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Int8', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Int16'))
def test_int16_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Int16', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Int16', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Int32'))
def test_int32_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Int32', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Int32', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Int64'))
def test_int64_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Int64', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Int64', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/UInt8'))
def test_uint8_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/UInt8', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/UInt8', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/UInt16'))
def test_uint16_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/UInt16', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/UInt16', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Int32'))
def test_uint32_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Int32', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/UInt32', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/UInt64'))
def test_uint64_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/UInt64', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/UInt64', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Float32'))
def test_float32_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Float32', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Float32', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Float64'))
def test_float64_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Float64', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Float64', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/String'))
def test_string_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/String', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/String', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Time'))
def test_time_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Time', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Time', msg_value))
@hypothesis.given(std_msgs_dicts_strat_ok.get('std_msgs/Duration'))
def test_duration_schema_dump_load_frompy_inverse(msg_value):
return schema_dump_load_frompy_inverse('std_msgs/Duration', msg_value, std_msgs_rostypes_from_dict_map('std_msgs/Duration', msg_value))
# TODO :
# # MultiArrayDimension
# (std_msgs.msg.MultiArrayDimension(label=, size=, stride=), RosBool, bool, bool, bool),
# Just in case we run this directly
if __name__ == '__main__':
pytest.main([
'-s',
'test_schema.py::test_schema_load_dump_fromros_inverse',
'test_schema.py::test_schema_dump_load_frompy_inverse',
])
| 47.905882
| 174
| 0.786591
| 2,525
| 16,288
| 4.571485
| 0.063366
| 0.12735
| 0.079009
| 0.075717
| 0.900373
| 0.868752
| 0.839556
| 0.832019
| 0.781686
| 0.781686
| 0
| 0.020865
| 0.114317
| 16,288
| 339
| 175
| 48.047198
| 0.779287
| 0.06735
| 0
| 0.341463
| 0
| 0
| 0.237113
| 0.110943
| 0
| 0
| 0
| 0.00295
| 0.01626
| 1
| 0.142276
| false
| 0
| 0.04065
| 0.113821
| 0.373984
| 0.004065
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 7
|
d33489bffeda5130f23db3e3776ee3c503ebde42
| 2,920
|
py
|
Python
|
location_register/models/ratu_models.py
|
Iva-khar/Data_converter
|
9e991f479e2bf1c2ab430a9ad1be2da936ea139b
|
[
"MIT"
] | null | null | null |
location_register/models/ratu_models.py
|
Iva-khar/Data_converter
|
9e991f479e2bf1c2ab430a9ad1be2da936ea139b
|
[
"MIT"
] | null | null | null |
location_register/models/ratu_models.py
|
Iva-khar/Data_converter
|
9e991f479e2bf1c2ab430a9ad1be2da936ea139b
|
[
"MIT"
] | null | null | null |
from django.db import models
from data_ocean.models import DataOceanModel
from location_register.models.koatuu_models import KoatuuCategory
class RatuRegion(DataOceanModel):
name = models.CharField('назва', max_length=30, unique=True)
koatuu = models.CharField('код КОАТУУ', max_length=10, unique=True, null=True)
class Meta:
verbose_name = 'регіон'
class RatuDistrict(DataOceanModel):
region = models.ForeignKey(RatuRegion, on_delete=models.CASCADE, verbose_name='регіон')
name = models.CharField('назва', max_length=100)
koatuu = models.CharField('код КОАТУУ', max_length=10, unique=True, null=True)
code = models.CharField('код', max_length=200)
class Meta:
verbose_name = 'район'
class RatuCity(DataOceanModel):
region = models.ForeignKey(RatuRegion, on_delete=models.CASCADE, verbose_name='регіон')
district = models.ForeignKey(RatuDistrict, on_delete=models.CASCADE, verbose_name='район',
null=True)
category = models.ForeignKey(KoatuuCategory, on_delete=models.CASCADE, null=True,
verbose_name='категорія населеного пункта')
name = models.CharField('назва', max_length=100)
koatuu = models.CharField('код КОАТУУ', max_length=10, unique=True, null=True)
code = models.CharField('код', max_length=200)
class Meta:
verbose_name = 'населенний пункт'
class RatuCityDistrict(DataOceanModel):
region = models.ForeignKey(RatuRegion, on_delete=models.CASCADE, verbose_name='регіон')
district = models.ForeignKey(RatuDistrict, on_delete=models.CASCADE, verbose_name='район',
null=True)
city = models.ForeignKey(RatuCity, on_delete=models.CASCADE,
verbose_name='населений пункт')
category = models.ForeignKey(KoatuuCategory, on_delete=models.CASCADE, null=True,
verbose_name='категорія населеного пункта')
name = models.CharField('назва', max_length=100)
koatuu = models.CharField('код КОАТУУ', max_length=10, unique=True, null=True)
code = models.CharField('', max_length=200)
class Meta:
verbose_name = 'район у місті'
class RatuStreet(DataOceanModel):
region = models.ForeignKey(RatuRegion, on_delete=models.CASCADE, verbose_name='регіон')
district = models.ForeignKey(RatuDistrict, on_delete=models.CASCADE, verbose_name='район',
null=True)
city = models.ForeignKey(RatuCity, on_delete=models.CASCADE,
verbose_name='населений пункт')
citydistrict = models.ForeignKey(RatuCityDistrict, on_delete=models.CASCADE, null=True,
verbose_name='район у місті')
name = models.CharField('назва', max_length=100)
code = models.CharField('код', max_length=200)
class Meta:
verbose_name = 'вулиця'
| 42.941176
| 94
| 0.681507
| 326
| 2,920
| 5.966258
| 0.168712
| 0.096144
| 0.086375
| 0.129563
| 0.83599
| 0.827249
| 0.810283
| 0.791774
| 0.749614
| 0.749614
| 0
| 0.014815
| 0.214041
| 2,920
| 67
| 95
| 43.58209
| 0.83268
| 0
| 0
| 0.666667
| 0
| 0
| 0.087671
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.058824
| 0
| 0.745098
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
d33d11247d061e7ccfc35cffc890e5eb82902094
| 12,708
|
py
|
Python
|
tests/test_dataset_roll.py
|
Ouranosinc/clisops
|
d78c127e07503877ae87c40e3548146fb06258ff
|
[
"BSD-3-Clause"
] | 18
|
2020-05-19T21:22:37.000Z
|
2022-02-04T08:10:21.000Z
|
tests/test_dataset_roll.py
|
Ouranosinc/clisops
|
d78c127e07503877ae87c40e3548146fb06258ff
|
[
"BSD-3-Clause"
] | 166
|
2020-04-22T11:04:57.000Z
|
2022-03-31T11:14:21.000Z
|
tests/test_dataset_roll.py
|
Ouranosinc/clisops
|
d78c127e07503877ae87c40e3548146fb06258ff
|
[
"BSD-3-Clause"
] | 6
|
2020-04-02T14:30:21.000Z
|
2021-12-04T03:51:12.000Z
|
""" Test the different methods of transforming the longitude coordinates after rolling"""
import numpy as np
# calculate the offset in the same way as in dataset_utils
def calculate_offset(a, bounds):
low, high = bounds
# get resolution of data
res = a[1] - a[0]
# calculate how many degrees to move by to have lon[0] of rolled subset as lower bound of request
diff = a[0] - low
# work out how many elements to roll by to roll data by 1 degree
index = 1 / res
# calculate the corresponding offset needed to change data by diff
# round up to ensure rolling by enough
# offset = math.ceil(diff * index)
offset = int(round(diff * index))
return offset
def dataset_roll(a, offset, bounds):
# roll the dataset
low, high = bounds
a_roll = np.roll(a, offset)
if offset < 0:
a_new = np.where(
np.logical_and(low >= a_roll, a_roll <= -(360 + low)), a_roll, a_roll % 360
) # this doesn't work in all negative offset cases
else:
a_new = np.where(a_roll < (360 + low), a_roll, a_roll % -360)
return a_new
def dataset_roll_using_offset(a, offset):
# roll the dataset
a_roll = np.roll(a, offset)
if offset < 0:
a_roll[offset:] = a_roll[offset:] % 360
else:
a_roll[:offset] = a_roll[:offset] % -360
return a_roll
class TestLonRoll_0_360:
# offset = 359
def test_to_minus_359_0(self):
a = np.arange(start=0, stop=360, step=1)
bounds = (-359, 0)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-359, stop=1, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-359, stop=1, step=1))
# offset = 270
def test_to_minus_270_to_89(self):
a = np.arange(start=0, stop=360, step=1)
bounds = (-270, 89)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-270, stop=90, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-270, stop=90, step=1))
# offset = 180
def test_to_minus_180_179(self):
a = np.arange(start=0, stop=360, step=1)
bounds = (-180, 179)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-180, stop=180, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-180, stop=180, step=1))
# offset = 90
def test_to_minus_90_269(self):
a = np.arange(start=0, stop=360, step=1)
bounds = (-90, 269)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-90, stop=270, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-90, stop=270, step=1))
# offset = 0
def test_to_0_359(self):
a = np.arange(start=0, stop=360, step=1)
bounds = (0, 359)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=0, stop=360, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=0, stop=360, step=1))
class TestLonRoll_minus_90_270:
# offset = 269
def test_to_minus_359_0(self):
a = np.arange(start=-90, stop=270, step=1)
bounds = (-359, 0)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-359, stop=1, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-359, stop=1, step=1))
# offset = 180
def test_to_minus_270_to_89(self):
a = np.arange(start=-90, stop=270, step=1)
bounds = (-270, 89)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-270, stop=90, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-270, stop=90, step=1))
# offset = 90
def test_to_minus_180_179(self):
a = np.arange(start=-90, stop=270, step=1)
bounds = (-180, 179)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-180, stop=180, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-180, stop=180, step=1))
# offset = 0
def test_to_minus_90_269(self):
a = np.arange(start=-90, stop=270, step=1)
bounds = (-90, 269)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-90, stop=270, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-90, stop=270, step=1))
# offset = -90 - FAILS
def test_to_0_359(self):
a = np.arange(start=-90, stop=270, step=1)
bounds = (0, 359)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=0, stop=360, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=0, stop=360, step=1))
class TestLonRoll_minus_180_180:
# offset = 179
def test_to_minus_359_0(self):
a = np.arange(start=-180, stop=180, step=1)
bounds = (-359, 0)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-359, stop=1, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-359, stop=1, step=1))
# offset = 90
def test_to_minus_270_to_89(self):
a = np.arange(start=-180, stop=180, step=1)
bounds = (-270, 89)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-270, stop=90, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-270, stop=90, step=1))
# offset = 0
def test_to_minus_180_179(self):
a = np.arange(start=-180, stop=180, step=1)
bounds = (-180, 179)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-180, stop=180, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-180, stop=180, step=1))
# offset = -90 - FAILS
def test_to_minus_90_269(self):
a = np.arange(start=-180, stop=180, step=1)
bounds = (-90, 269)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-90, stop=270, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-90, stop=270, step=1))
# offset = -180 - FAILS
def test_to_0_359(self):
a = np.arange(start=-180, stop=180, step=1)
bounds = (0, 359)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=0, stop=360, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=0, stop=360, step=1))
class TestLonRoll_minus_270_90:
# offset = 89
def test_to_minus_359_0(self):
a = np.arange(start=-270, stop=90, step=1)
bounds = (-359, 0)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-359, stop=1, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-359, stop=1, step=1))
# offset = 0
def test_to_minus_270_to_89(self):
a = np.arange(start=-270, stop=90, step=1)
bounds = (-270, 89)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-270, stop=90, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-270, stop=90, step=1))
# offset = -90 - fAILS
def test_to_minus_180_179(self):
a = np.arange(start=-270, stop=90, step=1)
bounds = (-180, 179)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-180, stop=180, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-180, stop=180, step=1))
# offset = -180 - FAILS
def test_to_minus_90_269(self):
a = np.arange(start=-270, stop=90, step=1)
bounds = (-90, 269)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-90, stop=270, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-90, stop=270, step=1))
# offset = -270 - FAILS
def test_to_0_359(self):
a = np.arange(start=-270, stop=90, step=1)
bounds = (0, 359)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=0, stop=360, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=0, stop=360, step=1))
class TestLonRoll_minus_360_0:
# offset = 0
def test_to_minus_359_0(self):
a = np.arange(start=-359, stop=1, step=1)
bounds = (-359, 0)
offset = calculate_offset(a, bounds)
a_where = dataset_roll(a, offset, bounds)
assert np.array_equal(a_where, np.arange(start=-359, stop=1, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-359, stop=1, step=1))
# offset = -89
def test_to_minus_270_to_89(self):
a = np.arange(start=-359, stop=1, step=1)
bounds = (-270, 89)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-270, stop=90, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-270, stop=90, step=1))
# offset = -179
def test_to_minus_180_179(self):
a = np.arange(start=-359, stop=1, step=1)
bounds = (-180, 179)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-180, stop=180, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-180, stop=180, step=1))
# offset = -269
def test_to_minus_90_269(self):
a = np.arange(start=-359, stop=1, step=1)
bounds = (-90, 269)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=-90, stop=270, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=-90, stop=270, step=1))
# offset = -359
def test_to_0_359(self):
a = np.arange(start=-359, stop=1, step=1)
bounds = (0, 359)
offset = calculate_offset(a, bounds)
# a_where = dataset_roll(a, offset, bounds)
# assert np.array_equal(a_where, np.arange(start=0, stop=360, step=1))
a_offset = dataset_roll_using_offset(a, offset)
assert np.array_equal(a_offset, np.arange(start=0, stop=360, step=1))
| 34.721311
| 101
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0
| 7
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6cb558227ac1e84bef92016016290a758a6aca30
| 21,544
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py
|
Python
|
tests/primitives/test_repartition.py
|
philippwitte/distdl
|
e77e0c355d617def88b4acfcc12f0e92e9fb2fe5
|
[
"BSD-2-Clause"
] | null | null | null |
tests/primitives/test_repartition.py
|
philippwitte/distdl
|
e77e0c355d617def88b4acfcc12f0e92e9fb2fe5
|
[
"BSD-2-Clause"
] | null | null | null |
tests/primitives/test_repartition.py
|
philippwitte/distdl
|
e77e0c355d617def88b4acfcc12f0e92e9fb2fe5
|
[
"BSD-2-Clause"
] | null | null | null |
import os
import numpy as np
import pytest
import torch
from adjoint_test import check_adjoint_test_tight
use_cuda = 'USE_CUDA' in os.environ
adjoint_parametrizations = []
# Main functionality
adjoint_parametrizations.append(
pytest.param(
np.arange(4, 8), [4, 1], # P_x_ranks, P_x_shape
np.arange(0, 12), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
12, # passed to comm_split_fixture, required MPI ranks
id="distributed-overlap-3D",
marks=[pytest.mark.mpi(min_size=12)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 4), [4, 1], # P_x_ranks, P_x_shape
np.arange(4, 16), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
16, # passed to comm_split_fixture, required MPI ranks
id="distributed-disjoint-3D",
marks=[pytest.mark.mpi(min_size=16)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 4), [4, 1], # P_x_ranks, P_x_shape
np.arange(5, 17), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
17, # passed to comm_split_fixture, required MPI ranks
id="distributed-disjoint-inactive-3D",
marks=[pytest.mark.mpi(min_size=17)]
)
)
# Sequential functionality
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 1), [1, 1], # P_x_ranks, P_x_shape
np.arange(0, 1), [1, 1], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
1, # passed to comm_split_fixture, required MPI ranks
id="sequential-identity",
marks=[pytest.mark.mpi(min_size=1)]
)
)
# As a scatter
adjoint_parametrizations.append(
pytest.param(
np.arange(4, 5), [1, 1], # P_x_ranks, P_x_shape
np.arange(0, 12), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
12, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_scatter-overlap-3D",
marks=[pytest.mark.mpi(min_size=12)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 1), [1, 1], # P_x_ranks, P_x_shape
np.arange(1, 13), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
13, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_scatter-disjoint-3D",
marks=[pytest.mark.mpi(min_size=13)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 1), [1, 1], # P_x_ranks, P_x_shape
np.arange(2, 14), [3, 4], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
14, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_scatter-disjoint-inactive-3D",
marks=[pytest.mark.mpi(min_size=14)]
)
)
# As a gather
adjoint_parametrizations.append(
pytest.param(
np.arange(0, 12), [3, 4], # P_x_ranks, P_x_shape
np.arange(4, 5), [1, 1], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
12, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_gather-overlap-3D",
marks=[pytest.mark.mpi(min_size=12)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(1, 13), [3, 4], # P_x_ranks, P_x_shape
np.arange(0, 1), [1, 1], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
13, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_gather-disjoint-3D",
marks=[pytest.mark.mpi(min_size=13)]
)
)
adjoint_parametrizations.append(
pytest.param(
np.arange(2, 14), [3, 4], # P_x_ranks, P_x_shape
np.arange(0, 1), [1, 1], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
14, # passed to comm_split_fixture, required MPI ranks
id="distributed-as_gather-disjoint-inactive-3D",
marks=[pytest.mark.mpi(min_size=14)]
)
)
# For example of indirect, see https://stackoverflow.com/a/28570677
@pytest.mark.parametrize("P_x_ranks, P_x_shape,"
"P_y_ranks, P_y_shape,"
"x_global_shape,"
"comm_split_fixture",
adjoint_parametrizations,
indirect=["comm_split_fixture"])
@pytest.mark.parametrize("balanced", [True, False])
def test_repartition_adjoint(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
P_y_ranks, P_y_shape,
x_global_shape,
balanced):
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
# The global tensor size is the same for x and y
layer = Repartition(P_x, P_y, preserve_batch=False)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
if balanced:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
else:
quotient = np.atleast_1d(x_global_shape) // np.atleast_1d(P_x_shape)
remainder = np.atleast_1d(x_global_shape) % np.atleast_1d(P_x_shape)
loc = np.where(P_x.index == 0)
x_local_shape = quotient.copy()
x_local_shape[loc] += remainder[loc]
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
# Adjoint Input
dy = zero_volume_tensor(device=device)
if P_y.active:
y_local_shape = compute_subshape(P_y.shape,
P_y.index,
x_global_shape)
dy = torch.randn(*y_local_shape, device=device)
# y = F @ x
y = layer(x)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
@pytest.mark.parametrize("comm_split_fixture", [4], indirect=["comm_split_fixture"])
def test_excepts_mismatched_partitions(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
in_shape = (1, 4, 1, 1)
out_shape = (1, 2)
in_size = np.prod(in_shape)
out_size = np.prod(out_shape)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(np.arange(0, in_size))
P_x = P_x_base.create_cartesian_topology_partition(in_shape)
P_y_base = P_world.create_partition_inclusive(np.arange(P_world.size-out_size, P_world.size))
P_y = P_y_base.create_cartesian_topology_partition(out_shape)
with pytest.raises(ValueError) as e_info: # noqa: F841
Repartition(P_x, P_y)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
@pytest.mark.parametrize("comm_split_fixture", [4], indirect=["comm_split_fixture"])
def test_excepts_mismatched_input_partition_tensor(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Input partition rank must match tensor rank
in_shape = (1, 4, 1, 1)
out_shape = (1, 1, 1, 2)
x_global_shape = np.array([16, 5, 5])
in_size = np.prod(in_shape)
out_size = np.prod(out_shape)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(np.arange(0, in_size))
P_x = P_x_base.create_cartesian_topology_partition(in_shape)
P_y_base = P_world.create_partition_inclusive(np.arange(P_world.size-out_size, P_world.size))
P_y = P_y_base.create_cartesian_topology_partition(out_shape)
with pytest.raises(ValueError) as e_info: # noqa: F841
layer = Repartition(P_x, P_y)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
layer(x)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
@pytest.mark.parametrize("comm_split_fixture", [4], indirect=["comm_split_fixture"])
def test_excepts_mismatched_output_partition_tensor(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Output partition rank must match tensor rank
in_shape = (4, 1, 1)
out_shape = (1, 1, 1, 2)
x_global_shape = np.array([16, 5, 5])
in_size = np.prod(in_shape)
out_size = np.prod(out_shape)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(np.arange(0, in_size))
P_x = P_x_base.create_cartesian_topology_partition(in_shape)
P_y_base = P_world.create_partition_inclusive(np.arange(P_world.size-out_size, P_world.size))
P_y = P_y_base.create_cartesian_topology_partition(out_shape)
with pytest.raises(ValueError) as e_info: # noqa: F841
layer = Repartition(P_x, P_y)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
layer(x)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
@pytest.mark.parametrize("comm_split_fixture", [4], indirect=["comm_split_fixture"])
def test_excepts_mismatched_nondivisible_tensor(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# A tensor with size 1 in a dimension cannot be partitioned in that
# dimension. (See last dimension of output and tensor.)
in_shape = (1, 4, 1, 1)
out_shape = (1, 1, 1, 2)
x_global_shape = np.array([1, 16, 5, 1])
in_size = np.prod(in_shape)
out_size = np.prod(out_shape)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(np.arange(0, in_size))
P_x = P_x_base.create_cartesian_topology_partition(in_shape)
P_y_base = P_world.create_partition_inclusive(np.arange(P_world.size-out_size, P_world.size))
P_y = P_y_base.create_cartesian_topology_partition(out_shape)
with pytest.raises(ValueError) as e_info: # noqa: F841
layer = Repartition(P_x, P_y)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
layer(x)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
dtype_parametrizations = []
# Main functionality
dtype_parametrizations.append(
pytest.param(
torch.float32, True, # dtype, test_backward,
np.arange(0, 4), [4, 1], # P_x_ranks, P_x_shape
np.arange(0, 4), [2, 2], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
4, # passed to comm_split_fixture, required MPI ranks
id="distributed-dtype-float32",
marks=[pytest.mark.mpi(min_size=4)]
)
)
# Test that it works with ints as well, can't compute gradient here
dtype_parametrizations.append(
pytest.param(
torch.int32, False, # dtype, test_backward,
np.arange(0, 4), [4, 1], # P_x_ranks, P_x_shape
np.arange(0, 4), [2, 2], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
4, # passed to comm_split_fixture, required MPI ranks
id="distributed-dtype-int32",
marks=[pytest.mark.mpi(min_size=4)]
)
)
# Also test doubles
dtype_parametrizations.append(
pytest.param(
torch.float64, True, # dtype, test_backward,
np.arange(0, 4), [4, 1], # P_x_ranks, P_x_shape
np.arange(0, 4), [2, 2], # P_y_ranks, P_y_shape
[77, 55], # x_global_shape
4, # passed to comm_split_fixture, required MPI ranks
id="distributed-dtype-float64",
marks=[pytest.mark.mpi(min_size=4)]
)
)
# For example of indirect, see https://stackoverflow.com/a/28570677
@pytest.mark.parametrize("dtype, test_backward,"
"P_x_ranks, P_x_shape,"
"P_y_ranks, P_y_shape,"
"x_global_shape,"
"comm_split_fixture",
dtype_parametrizations,
indirect=["comm_split_fixture"])
def test_repartition_dtype(barrier_fence_fixture,
comm_split_fixture,
dtype, test_backward,
P_x_ranks, P_x_shape,
P_y_ranks, P_y_shape,
x_global_shape):
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
# The global tensor size is the same for x and y
layer = Repartition(P_x, P_y, preserve_batch=False)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(dtype=dtype, device=device)
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = 10*torch.randn(*x_local_shape, device=device).to(dtype)
x.requires_grad = test_backward
# y = F @ x
y = layer(x)
if P_y.active:
assert y.dtype == dtype
if test_backward:
# Adjoint Input
dy = zero_volume_tensor(dtype=dtype, device=device)
if P_y.active:
y_local_shape = compute_subshape(P_y.shape,
P_y.index,
x_global_shape)
dy = 10*torch.randn(*y_local_shape, device=device).to(dtype)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
if P_x.active:
assert dx.dtype == dtype
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
identity_parametrizations = []
# Main functionality
identity_parametrizations.append(
pytest.param(
np.arange(0, 12), [3, 4], # P_x_ranks, P_x_shape
[77, 55], # x_global_shape
12, # passed to comm_split_fixture, required MPI ranks
id="distributed-identity-2D",
marks=[pytest.mark.mpi(min_size=12)]
)
)
identity_parametrizations.append(
pytest.param(
np.arange(0, 4), [1, 4], # P_x_ranks, P_x_shape
[77, 55], # x_global_shape
4, # passed to comm_split_fixture, required MPI ranks
id="distributed-identity-1D",
marks=[pytest.mark.mpi(min_size=16)]
)
)
# For example of indirect, see https://stackoverflow.com/a/28570677
@pytest.mark.parametrize("P_x_ranks, P_x_shape,"
"x_global_shape,"
"comm_split_fixture",
identity_parametrizations,
indirect=["comm_split_fixture"])
@pytest.mark.parametrize("balanced", [True, False])
def test_repartition_identity(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
x_global_shape,
balanced):
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y = P_x
# The global tensor size is the same for x and y
layer = Repartition(P_x, P_y, preserve_batch=False)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
if balanced:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
else:
quotient = np.atleast_1d(x_global_shape) // np.atleast_1d(P_x_shape)
remainder = np.atleast_1d(x_global_shape) % np.atleast_1d(P_x_shape)
loc = np.where(P_x.index == 0)
x_local_shape = quotient.copy()
x_local_shape[loc] += remainder[loc]
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
# Adjoint Input
dy = zero_volume_tensor(device=device)
if P_y.active:
y_local_shape = compute_subshape(P_y.shape,
P_y.index,
x_global_shape)
dy = torch.randn(*y_local_shape, device=device)
# y = F @ x
y = layer(x)
# In the balanced case, this should be a true identity, so there should
# be no communication performed, just self-copies.
if balanced:
for sl, sz, p in layer.P_x_to_y_overlaps:
assert p == "self" or (sl, sz, p) == (None, None, None)
for sl, sz, p in layer.P_y_to_x_overlaps:
assert p == "self" or (sl, sz, p) == (None, None, None)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
| 32.741641
| 97
| 0.618084
| 2,942
| 21,544
| 4.230795
| 0.065602
| 0.0188
| 0.055274
| 0.015425
| 0.937093
| 0.932916
| 0.912188
| 0.891138
| 0.868
| 0.854342
| 0
| 0.023238
| 0.286901
| 21,544
| 657
| 98
| 32.791476
| 0.786956
| 0.143706
| 0
| 0.806653
| 0
| 0
| 0.050674
| 0.022528
| 0
| 0
| 0
| 0
| 0.008316
| 1
| 0.014553
| false
| 0
| 0.085239
| 0
| 0.114345
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9f1197dd664a9ad2b1d58438a66a2af11118261c
| 511,100
|
py
|
Python
|
pypsexec/paexec.py
|
0v3rride/pypsexec_mod
|
71f047744d3b416aa7ddb83cee06ef165984fb05
|
[
"MIT"
] | null | null | null |
pypsexec/paexec.py
|
0v3rride/pypsexec_mod
|
71f047744d3b416aa7ddb83cee06ef165984fb05
|
[
"MIT"
] | null | null | null |
pypsexec/paexec.py
|
0v3rride/pypsexec_mod
|
71f047744d3b416aa7ddb83cee06ef165984fb05
|
[
"MIT"
] | null | null | null |
import binascii
import os
import struct
from smbprotocol.structure import BoolField, BytesField, EnumField, \
IntField, ListField, Structure, StructureField, DateTimeField
from pypsexec.exceptions import PAExecException
try:
from collections import OrderedDict
except ImportError: # pragma: no cover
from ordereddict import OrderedDict
def paexec_out_stream(buffer_size=4096):
"""
Creates a generator to read the PAExec executable data as a bytes stream.
:param buffer_size: The size of the buffer yielded
:return: (bytes, offset) = the butes and the offset of the bytes string
"""
payload_bytes = binascii.unhexlify(PAEXEC_DATA)
byte_count = len(payload_bytes)
for i in range(0, byte_count, buffer_size):
yield payload_bytes[i:i + buffer_size], i
def get_unique_id(pid, computer_name):
"""
https://github.com/poweradminllc/PAExec/blob/master/Remote.cpp#L1045-L1065
DWORD RemMsg::GetUniqueID()
Creates a unique ID based on the PID of the local host and the name of the
local host. It is derived from the first 4 bytes of a UTF-16 Little Endian
encoded computer name and the local PID xor'd together.
This value is sent in the PAExecSettingsMsg to define the process details
and also the PAExecResponseMsg to control the execution and results of
the processed based on the settings.
:param pid: (int) the process id of the current host
:param computer_name: (str/unicode) of the current hostname
:return: int of the unique ID derived from the PID and Computer Name
"""
bcomp_name = computer_name.encode('utf-16-le')[:4]
bcomp_name = bcomp_name + (b"\x00" * (4 - len(bcomp_name)))
return pid ^ struct.unpack("<L", bcomp_name)[0]
class PAExecMsgId(object):
"""
https://github.com/poweradminllc/PAExec/blob/master/stdafx.h#L52-L57
The various ID's used by PAExec when sending messages to and from the
remote service.
"""
MSGID_SETTINGS = 1
MSGID_RESP_SEND_FILES = 2
MSGID_SENT_FILES = 3
MSGID_OK = 4
MSGID_START_APP = 5
MSGID_FAILED = 6
class ProcessPriority(object):
"""
https://msdn.microsoft.com/en-us/library/windows/desktop/ms683211(v=vs.85).aspx
Set's the priority of the thread in the current process
"""
ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
BELOW_NORMAL_PRIORITY_CLASS = 0x00004000
HIGH_PRIORITY_CLASS = 0x00000080
IDLE_PRIORITY_CLASS = 0x00000040
NORMAL_PRIORITY_CLASS = 0x00000020
REALTIME_PRIORITY_CLASS = 0x00000100
class PAExecMsg(Structure):
"""
Generic message from PAExec, the first 2 bytes denotes the Msg ID
that tells the host the type of message it is and the buffer contents
varies based on the type of message that is being sent of received.
This is slightly different to the PAExecSettingsMsg as the data in the
settings msg is xor'd to slightly obfuscate the data. The current buffer
structures that have been defined are PAStartBuffer, PAReturnBuffer
"""
def __init__(self):
self.fields = OrderedDict([
('msg_id', EnumField(
size=2,
enum_type=PAExecMsgId
)),
('unique_id', IntField(size=4)),
('buffer_length', IntField(
size=4,
default=lambda s: len(s['buffer'])
)),
('buffer', BytesField(
size=lambda s: s['buffer_length'].get_value()
))
])
super(PAExecMsg, self).__init__()
def check_resp(self):
msg_id = self['msg_id'].get_value()
if msg_id != PAExecMsgId.MSGID_OK:
raise PAExecException(msg_id, self['buffer'].get_value())
class PAExecSettingsMsg(Structure):
"""
Custom PAExecMsg structure that contains the settings used by PAExec to
configure the remote process. The structure is different from the standard
PAExecMsg as the values past the msg_id is xor'd and the initial XOR value
is generated randomly and stored after the msg_id.
This does not encrypt the data but rather scrambles it so that someone
snooping on the network traffic isn't easily able to see the settings as it
can contain the credentials of a user. SMB encryption should really be used
in most cases if it is available as that actually encrypts the data.
The buffer value contains the PAExecSettingsBuffer type that contains all
the settings used by PAExec.
"""
def __init__(self):
self.fields = OrderedDict([
('msg_id', EnumField(
size=2,
default=PAExecMsgId.MSGID_SETTINGS,
enum_type=PAExecMsgId
)),
('xor_val', IntField(
size=4,
default=os.urandom(4)
)),
('unique_id', IntField(size=4)),
('buffer_len', IntField(size=4)),
('buffer', StructureField(
structure_type=PAExecSettingsBuffer
))
])
super(PAExecSettingsMsg, self).__init__()
def pack(self):
# need to xor the buffer as expected by PAExec
xor_value = self['xor_val'].get_value()
# the id, length and buffer itself is xor'd
input_data = self['unique_id'].pack() + self['buffer_len'].pack() + \
self['buffer'].pack()
buffer = self._xor_data(xor_value, input_data)
# build the final data structure
data = self['msg_id'].pack()
data += self['xor_val'].pack()
data += buffer
return data
def unpack(self, data):
# need to de-xor the buffer to get human readable values
xor_value = struct.unpack("<L", data[2:6])[0]
buffer = data[6:]
buffer_data = self._xor_data(xor_value, buffer)
unique_id = buffer_data[:4]
buffer_len = buffer_data[4:8]
structure_a = PAExecSettingsBuffer()
structure_a.unpack(buffer_data[8:])
self['msg_id'] = data[:2]
self['xor_val'] = data[2:6]
self['unique_id'] = unique_id
self['buffer_len'] = buffer_len
self['buffer'] = structure_a
return b""
def _xor_data(self, xor_value, data):
buffer = b""
next_bytes = data[:4]
for i in range(0, len(data) - 4):
int_value = struct.unpack("<L", next_bytes)[0]
xored_value = int_value ^ xor_value
new_bytes = struct.pack("<L", xored_value)
buffer += new_bytes[:1]
next_bytes = new_bytes[1:] + data[i + 4:i + 5]
xor_value += 3
int_value = struct.unpack("<L", next_bytes)[0]
xored_value = int_value ^ xor_value
new_bytes = struct.pack("<L", xored_value)
buffer += new_bytes
return buffer
class PAExecSettingsBuffer(Structure):
"""
https://github.com/poweradminllc/PAExec/blob/master/stdafx.h#L132-L341
A PAExec buffer that contains the settings used by the remote PAExec
service to start a process. It contains a wide range of settings that can
be configured such as the remote user as well as the executable and
arguments used to start the process.
All BytesFields in this structure are utf-16-le encoded strings and should
be encoded before setting in the structure.
"""
def __init__(self):
self.fields = OrderedDict([
('version', IntField(
size=4,
default=1
)),
('num_processors', IntField(
size=4,
default=lambda s: len(s['processors'].get_value())
)),
('processors', ListField(
size=lambda s: s['num_processors'].get_value() * 4,
list_count=lambda s: s['num_processors'].get_value(),
list_type=IntField(size=4)
)),
('copy_files', BoolField(size=1)),
('force_copy', BoolField(size=1)),
('copy_if_newer_or_higher_ver', BoolField(size=1)),
('asynchronous', BoolField(size=1)),
('dont_load_profile', BoolField(size=1)),
('interactive_session', IntField(size=4)),
('interactive', BoolField(size=1)),
('run_elevated', BoolField(size=1)),
('run_limited', BoolField(size=1)),
('password_len', IntField(
size=4,
default=lambda s: int(len(s['password']) / 2)
)),
('password', BytesField(
size=lambda s: s['password_len'].get_value() * 2
)),
('username_len', IntField(
size=4,
default=lambda s: int(len(s['username']) / 2)
)),
('username', BytesField(
size=lambda s: s['username_len'].get_value() * 2
)),
('use_system_account', BoolField(size=1)),
('working_dir_len', IntField(
size=4,
default=lambda s: int(len(s['working_dir']) / 2)
)),
('working_dir', BytesField(
size=lambda s: s['working_dir_len'].get_value() * 2
)),
('show_ui_on_win_logon', BoolField(size=1)),
('priority', EnumField(
size=4,
default=ProcessPriority.NORMAL_PRIORITY_CLASS,
enum_type=ProcessPriority
)),
('executable_len', IntField(
size=4,
default=lambda s: int(len(s['executable']) / 2)
)),
('executable', BytesField(
size=lambda s: s['executable_len'].get_value() * 2
)),
('arguments_len', IntField(
size=4,
default=lambda s: int(len(s['arguments']) / 2)
)),
('arguments', BytesField(
size=lambda s: s['arguments_len'].get_value() * 2
)),
('disable_file_redirection', BoolField(size=1)),
('enable_debug', BoolField(size=1)),
('remote_log_path_len', IntField(
size=4,
default=lambda s: int(len(s['remote_log_path']) / 2)
)),
('remote_log_path', BytesField(
size=lambda s: s['remote_log_path_len'].get_value() * 2
)),
('no_delete', BoolField(size=1)),
('src_dir_len', IntField(
size=4,
default=lambda s: int(len(s['src_dir']) / 2)
)),
('src_dir', BytesField(
size=lambda s: s['src_dir_len'].get_value() * 2
)),
('dest_dir_len', IntField(
size=4,
default=lambda s: int(len(s['dest_dir']) / 2)
)),
('dest_dir', BytesField(
size=lambda s: s['dest_dir_len'].get_value() * 2
)),
('num_src_files', IntField(
size=4,
default=lambda s: len(s['src_files'].get_value())
)),
('src_files', ListField(
list_count=lambda s: s['num_src_files'].get_value(),
list_type=StructureField(structure_type=PAExecFileInfo),
unpack_func=lambda s, d:
self._unpack_file_list(s, d, 'num_src_files')
)),
('num_dest_files', IntField(
size=4,
default=lambda s: len(s['dest_files'].get_value())
)),
('dest_files', ListField(
list_count=lambda s: s['num_dest_files'].get_value(),
list_type=StructureField(structure_type=PAExecFileInfo),
unpack_func=lambda s, d:
self._unpack_file_list(s, d, 'num_dest_files')
)),
('timeout_seconds', IntField(size=4))
])
super(PAExecSettingsBuffer, self).__init__()
def _unpack_file_list(self, structure, data, len_field):
files = []
remaining_data = data
for i in range(0, structure[len_field].get_value()):
file_structure, remaining_data = self._get_file(remaining_data)
files.append(file_structure)
return files
def _get_file(self, data):
min_size = 21
filename_size = struct.unpack("<L", data[:4])[0]
structure_end_offset = min_size + (filename_size * 2)
file_structure_data = data[:structure_end_offset]
file_structure = PAExecFileInfo()
file_structure.unpack(file_structure_data)
return file_structure, data[structure_end_offset:]
class PAExecFileInfo(Structure):
"""
https://github.com/poweradminllc/PAExec/blob/master/stdafx.h#L59-L82
class FileInfo
Structure the contains information about a file to copy or move and is set
in PAExecSettingsBuffer. Like other PAExec messages, fields that take in a
string take in a utf-16-le encoded string as a bytes structure.
"""
def __init__(self):
self.fields = OrderedDict([
('filename_len', IntField(
size=4,
default=lambda s: int(len(s['filename']) / 2)
)),
('filename', BytesField(
size=lambda s: s['filename_len'].get_value() * 2
)),
('file_last_write', DateTimeField(size=8)),
('file_version_ls', IntField(size=4)),
('file_version_ms', IntField(size=4)),
('copy_file', BoolField(size=1))
])
super(PAExecFileInfo, self).__init__()
class PAExecStartBuffer(Structure):
"""
Can't find where this is explicitly defined but this is the buffer used in
the PAExecMsg to start a remote process. On receipt of this message, the
remote process will match the settings based on the unique_id passed in and
start the process based on those settings.
The comp_name is a utf-16-le encoded string of the local hostname and
should match the host used in the service name.
"""
def __init__(self):
self.fields = OrderedDict([
('process_id', IntField(size=4)),
('comp_name_length', IntField(
size=4,
default=lambda s: int(len(s['comp_name']) / 2)
)),
('comp_name', BytesField(
size=lambda s: s['comp_name_length'].get_value() * 2
))
])
super(PAExecStartBuffer, self).__init__()
class PAExecReturnBuffer(Structure):
"""
The buffer used in the PAExecMsg that is sent by the remote service on
completion of the remote process. It contains a single Int32 value that is
the return code of the process.
"""
def __init__(self):
self.fields = OrderedDict([
('return_code', IntField(size=4))
])
super(PAExecReturnBuffer, self).__init__()
# https://www.poweradmin.com/paexec/paexec.exe
# Hex string of the paexec executable as of v1.26, this is used when copying
# the executable from the current hos to the Windows host
PAEXEC_DATA = '4d5a90000300000004000000ffff0000b8000000000000004000000000000' \
'00000000000000000000000000000000000000000000000000000000000e8' \
'0000000e1fba0e00b409cd21b8014ccd21546869732070726f6772616d206' \
'3616e6e6f742062652072756e20696e20444f53206d6f64652e0d0d0a2400' \
'000000000000bc6f8748f80ee91bf80ee91bf80ee91b09c8241bf60ee91b0' \
'9c8271b8a0ee91b09c8261bd60ee91bf1767a1beb0ee91bf80ee81b400ee9' \
'1b9ee03a1bf20ee91b9ee0201bf90ee91bf80e7e1bf90ee91b9ee0251bf90' \
'ee91b52696368f80ee91b0000000000000000504500004c010500a9dded54' \
'0000000000000000e00002010b010b00008c0100005c010000000000bce90' \
'0000010000000a00100000040000010000000020000050001000000000005' \
'00010000000000002003000004000088eb020003004081000010000010000' \
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0
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py
|
Python
|
ocr-20191230/python/alibabacloud_ocr20191230/client.py
|
atptro/alibabacloud-sdk
|
65d4a000e4f4059b58ca1bc3d032853aedef4f3f
|
[
"Apache-2.0"
] | null | null | null |
ocr-20191230/python/alibabacloud_ocr20191230/client.py
|
atptro/alibabacloud-sdk
|
65d4a000e4f4059b58ca1bc3d032853aedef4f3f
|
[
"Apache-2.0"
] | null | null | null |
ocr-20191230/python/alibabacloud_ocr20191230/client.py
|
atptro/alibabacloud-sdk
|
65d4a000e4f4059b58ca1bc3d032853aedef4f3f
|
[
"Apache-2.0"
] | null | null | null |
# This file is auto-generated, don't edit it. Thanks.
from alibabacloud_tea_rpc.client import Client as RPCClient
from alibabacloud_ocr20191230 import models as ocr_20191230_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient
from alibabacloud_tea_rpc import models as _rpc_models
from alibabacloud_openplatform20191219.client import Client as OpenPlatformClient
from alibabacloud_openplatform20191219 import models as open_platform_models
from alibabacloud_oss_sdk import models as _oss_models
from alibabacloud_rpc_util.client import Client as RPCUtilClient
from alibabacloud_oss_sdk.client import Client as OSSClient
from alibabacloud_tea_fileform import models as file_form_models
from alibabacloud_oss_util import models as ossutil_models
from alibabacloud_endpoint_util.client import Client as EndpointUtilClient
class Client(RPCClient):
def __init__(self, config):
super().__init__(config)
self._endpoint_rule = "regional"
self.check_config(config)
self._endpoint = self.get_endpoint("ocr", self._region_id, self._endpoint_rule, self._network, self._suffix, self._endpoint_map, self._endpoint)
def get_async_job_result(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.GetAsyncJobResultResponse().from_map(self.do_request("GetAsyncJobResult", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def trim_document(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.TrimDocumentResponse().from_map(self.do_request("TrimDocument", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def trim_document_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.file_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
trim_documentreq = ocr_20191230_models.TrimDocumentRequest(
)
RPCUtilClient.convert(request, trim_documentreq)
trim_documentreq.file_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
trim_document_resp = self.trim_document(trim_documentreq, runtime)
return trim_document_resp
def recognize_chinapassport(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeChinapassportResponse().from_map(self.do_request("RecognizeChinapassport", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_chinapassport_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_chinapassportreq = ocr_20191230_models.RecognizeChinapassportRequest(
)
RPCUtilClient.convert(request, recognize_chinapassportreq)
recognize_chinapassportreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_chinapassport_resp = self.recognize_chinapassport(recognize_chinapassportreq, runtime)
return recognize_chinapassport_resp
def recognize_verificationcode(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeVerificationcodeResponse().from_map(self.do_request("RecognizeVerificationcode", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_verificationcode_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_verificationcodereq = ocr_20191230_models.RecognizeVerificationcodeRequest(
)
RPCUtilClient.convert(request, recognize_verificationcodereq)
recognize_verificationcodereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_verificationcode_resp = self.recognize_verificationcode(recognize_verificationcodereq, runtime)
return recognize_verificationcode_resp
def recognize_passport_mrz(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizePassportMRZResponse().from_map(self.do_request("RecognizePassportMRZ", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_passport_mrzadvance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_passport_mrzreq = ocr_20191230_models.RecognizePassportMRZRequest(
)
RPCUtilClient.convert(request, recognize_passport_mrzreq)
recognize_passport_mrzreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_passport_mrzresp = self.recognize_passport_mrz(recognize_passport_mrzreq, runtime)
return recognize_passport_mrzresp
def recognize_takeout_order(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeTakeoutOrderResponse().from_map(self.do_request("RecognizeTakeoutOrder", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_takeout_order_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_takeout_orderreq = ocr_20191230_models.RecognizeTakeoutOrderRequest(
)
RPCUtilClient.convert(request, recognize_takeout_orderreq)
recognize_takeout_orderreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_takeout_order_resp = self.recognize_takeout_order(recognize_takeout_orderreq, runtime)
return recognize_takeout_order_resp
def recognize_qr_code(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeQrCodeResponse().from_map(self.do_request("RecognizeQrCode", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_vatinvoice(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeVATInvoiceResponse().from_map(self.do_request("RecognizeVATInvoice", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_vatinvoice_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.file_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_vatinvoicereq = ocr_20191230_models.RecognizeVATInvoiceRequest(
)
RPCUtilClient.convert(request, recognize_vatinvoicereq)
recognize_vatinvoicereq.file_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_vatinvoice_resp = self.recognize_vatinvoice(recognize_vatinvoicereq, runtime)
return recognize_vatinvoice_resp
def recognize_character(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeCharacterResponse().from_map(self.do_request("RecognizeCharacter", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_character_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_characterreq = ocr_20191230_models.RecognizeCharacterRequest(
)
RPCUtilClient.convert(request, recognize_characterreq)
recognize_characterreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_character_resp = self.recognize_character(recognize_characterreq, runtime)
return recognize_character_resp
def recognize_taxi_invoice(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeTaxiInvoiceResponse().from_map(self.do_request("RecognizeTaxiInvoice", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_taxi_invoice_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_taxi_invoicereq = ocr_20191230_models.RecognizeTaxiInvoiceRequest(
)
RPCUtilClient.convert(request, recognize_taxi_invoicereq)
recognize_taxi_invoicereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_taxi_invoice_resp = self.recognize_taxi_invoice(recognize_taxi_invoicereq, runtime)
return recognize_taxi_invoice_resp
def recognize_identity_card(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeIdentityCardResponse().from_map(self.do_request("RecognizeIdentityCard", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_identity_card_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_identity_cardreq = ocr_20191230_models.RecognizeIdentityCardRequest(
)
RPCUtilClient.convert(request, recognize_identity_cardreq)
recognize_identity_cardreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_identity_card_resp = self.recognize_identity_card(recognize_identity_cardreq, runtime)
return recognize_identity_card_resp
def recognize_license_plate(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeLicensePlateResponse().from_map(self.do_request("RecognizeLicensePlate", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_license_plate_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_license_platereq = ocr_20191230_models.RecognizeLicensePlateRequest(
)
RPCUtilClient.convert(request, recognize_license_platereq)
recognize_license_platereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_license_plate_resp = self.recognize_license_plate(recognize_license_platereq, runtime)
return recognize_license_plate_resp
def recognize_table(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeTableResponse().from_map(self.do_request("RecognizeTable", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_table_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_tablereq = ocr_20191230_models.RecognizeTableRequest(
)
RPCUtilClient.convert(request, recognize_tablereq)
recognize_tablereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_table_resp = self.recognize_table(recognize_tablereq, runtime)
return recognize_table_resp
def recognize_driving_license(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeDrivingLicenseResponse().from_map(self.do_request("RecognizeDrivingLicense", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_driving_license_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_driving_licensereq = ocr_20191230_models.RecognizeDrivingLicenseRequest(
)
RPCUtilClient.convert(request, recognize_driving_licensereq)
recognize_driving_licensereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_driving_license_resp = self.recognize_driving_license(recognize_driving_licensereq, runtime)
return recognize_driving_license_resp
def recognize_bank_card(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeBankCardResponse().from_map(self.do_request("RecognizeBankCard", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_bank_card_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_bank_cardreq = ocr_20191230_models.RecognizeBankCardRequest(
)
RPCUtilClient.convert(request, recognize_bank_cardreq)
recognize_bank_cardreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_bank_card_resp = self.recognize_bank_card(recognize_bank_cardreq, runtime)
return recognize_bank_card_resp
def recognize_train_ticket(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeTrainTicketResponse().from_map(self.do_request("RecognizeTrainTicket", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_train_ticket_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_train_ticketreq = ocr_20191230_models.RecognizeTrainTicketRequest(
)
RPCUtilClient.convert(request, recognize_train_ticketreq)
recognize_train_ticketreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_train_ticket_resp = self.recognize_train_ticket(recognize_train_ticketreq, runtime)
return recognize_train_ticket_resp
def recognize_driver_license(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeDriverLicenseResponse().from_map(self.do_request("RecognizeDriverLicense", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_driver_license_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_driver_licensereq = ocr_20191230_models.RecognizeDriverLicenseRequest(
)
RPCUtilClient.convert(request, recognize_driver_licensereq)
recognize_driver_licensereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_driver_license_resp = self.recognize_driver_license(recognize_driver_licensereq, runtime)
return recognize_driver_license_resp
def recognize_account_page(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeAccountPageResponse().from_map(self.do_request("RecognizeAccountPage", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_account_page_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_account_pagereq = ocr_20191230_models.RecognizeAccountPageRequest(
)
RPCUtilClient.convert(request, recognize_account_pagereq)
recognize_account_pagereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_account_page_resp = self.recognize_account_page(recognize_account_pagereq, runtime)
return recognize_account_page_resp
def recognize_stamp(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeStampResponse().from_map(self.do_request("RecognizeStamp", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_stamp_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_stampreq = ocr_20191230_models.RecognizeStampRequest(
)
RPCUtilClient.convert(request, recognize_stampreq)
recognize_stampreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_stamp_resp = self.recognize_stamp(recognize_stampreq, runtime)
return recognize_stamp_resp
def recognize_business_card(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeBusinessCardResponse().from_map(self.do_request("RecognizeBusinessCard", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_business_card_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_business_cardreq = ocr_20191230_models.RecognizeBusinessCardRequest(
)
RPCUtilClient.convert(request, recognize_business_cardreq)
recognize_business_cardreq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_business_card_resp = self.recognize_business_card(recognize_business_cardreq, runtime)
return recognize_business_card_resp
def recognize_vincode(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeVINCodeResponse().from_map(self.do_request("RecognizeVINCode", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_vincode_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_vincodereq = ocr_20191230_models.RecognizeVINCodeRequest(
)
RPCUtilClient.convert(request, recognize_vincodereq)
recognize_vincodereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_vincode_resp = self.recognize_vincode(recognize_vincodereq, runtime)
return recognize_vincode_resp
def recognize_business_license(self, request, runtime):
UtilClient.validate_model(request)
return ocr_20191230_models.RecognizeBusinessLicenseResponse().from_map(self.do_request("RecognizeBusinessLicense", "HTTPS", "POST", "2019-12-30", "AK", None, request.to_map(), runtime))
def recognize_business_license_advance(self, request, runtime):
# Step 0: init client
access_key_id = self._credential.get_access_key_id()
access_key_secret = self._credential.get_access_key_secret()
auth_config = _rpc_models.Config(
access_key_id=access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint="openplatform.aliyuncs.com",
protocol=self._protocol,
region_id=self._region_id
)
auth_client = OpenPlatformClient(auth_config)
auth_request = open_platform_models.AuthorizeFileUploadRequest(
product="ocr",
region_id=self._region_id
)
auth_response = auth_client.authorize_file_upload_with_options(auth_request, runtime)
# Step 1: request OSS api to upload file
oss_config = _oss_models.Config(
access_key_id=auth_response.access_key_id,
access_key_secret=access_key_secret,
type="access_key",
endpoint=RPCUtilClient.get_endpoint(auth_response.endpoint, auth_response.use_accelerate, self._endpoint_type),
protocol=self._protocol,
region_id=self._region_id
)
oss_client = OSSClient(oss_config)
file_obj = file_form_models.FileField(
filename=auth_response.object_key,
content=request.image_urlobject,
content_type=""
)
oss_header = _oss_models.PostObjectRequestHeader(
access_key_id=auth_response.access_key_id,
policy=auth_response.encoded_policy,
signature=auth_response.signature,
key=auth_response.object_key,
file=file_obj,
success_action_status="201"
)
upload_request = _oss_models.PostObjectRequest(
bucket_name=auth_response.bucket,
header=oss_header
)
oss_runtime = ossutil_models.RuntimeOptions(
)
RPCUtilClient.convert(runtime, oss_runtime)
oss_client.post_object(upload_request, oss_runtime)
# Step 2: request final api
recognize_business_licensereq = ocr_20191230_models.RecognizeBusinessLicenseRequest(
)
RPCUtilClient.convert(request, recognize_business_licensereq)
recognize_business_licensereq.image_url = "http://" + str(auth_response.bucket) + "." + str(auth_response.endpoint) + "/" + str(auth_response.object_key) + ""
recognize_business_license_resp = self.recognize_business_license(recognize_business_licensereq, runtime)
return recognize_business_license_resp
def get_endpoint(self, product_id, region_id, endpoint_rule, network, suffix, endpoint_map, endpoint):
if not UtilClient.empty(endpoint):
return endpoint
if not UtilClient.is_unset(endpoint_map) and not UtilClient.empty(endpoint_map.get('regionId')):
return endpoint_map.get('regionId')
return EndpointUtilClient.get_endpoint_rules(product_id, region_id, endpoint_rule, network, suffix)
| 45.624242
| 195
| 0.680808
| 6,573
| 60,224
| 5.815914
| 0.036361
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| 0.035576
| 0.817882
| 0.799309
| 0.799309
| 0.799309
| 0.797112
| 0.797112
| 0
| 0.014515
| 0.240386
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| 1,319
| 196
| 45.658832
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| 7
|
9f2e1f309463ac4d74344bf610a616e724a8db09
| 13,793
|
py
|
Python
|
backmarker/migrations/0001_initial.py
|
jmp/backmarker
|
e12a094d92dec798ad10aa8890fabe84f946c303
|
[
"MIT"
] | null | null | null |
backmarker/migrations/0001_initial.py
|
jmp/backmarker
|
e12a094d92dec798ad10aa8890fabe84f946c303
|
[
"MIT"
] | null | null | null |
backmarker/migrations/0001_initial.py
|
jmp/backmarker
|
e12a094d92dec798ad10aa8890fabe84f946c303
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2.2 on 2021-05-15 15:45
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = []
operations = [
migrations.CreateModel(
name="Circuit",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("reference", models.CharField(max_length=255, unique=True)),
("name", models.CharField(max_length=255)),
("location", models.CharField(max_length=255)),
("country", models.CharField(max_length=255)),
("latitude", models.FloatField()),
("longitude", models.FloatField()),
("altitude", models.IntegerField()),
("wiki_url", models.URLField(db_column="url", unique=True)),
],
),
migrations.CreateModel(
name="Constructor",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("reference", models.CharField(max_length=255, unique=True)),
("name", models.CharField(max_length=255)),
("nationality", models.CharField(max_length=255)),
("wiki_url", models.URLField(db_column="url")),
],
),
migrations.CreateModel(
name="Driver",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("reference", models.CharField(max_length=255, unique=True)),
("number", models.IntegerField(null=True)),
("code", models.CharField(max_length=3, null=True)),
("first_name", models.CharField(max_length=255)),
("last_name", models.CharField(max_length=255)),
("date_of_birth", models.DateField()),
("nationality", models.CharField(max_length=255)),
("wiki_url", models.URLField(db_column="url", unique=True)),
],
),
migrations.CreateModel(
name="Race",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("year", models.IntegerField()),
("round", models.IntegerField()),
(
"circuit",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.circuit",
),
),
("name", models.CharField(max_length=255)),
("date", models.DateField()),
("time", models.TimeField(null=True)),
("wiki_url", models.URLField(db_column="url", unique=True)),
],
),
migrations.CreateModel(
name="Season",
fields=[
("year", models.IntegerField(primary_key=True, serialize=False)),
("wiki_url", models.URLField(db_column="url", unique=True)),
],
),
migrations.CreateModel(
name="Status",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("status", models.CharField(max_length=255)),
],
),
migrations.CreateModel(
name="Result",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"driver",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.driver",
),
),
(
"constructor",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.constructor",
),
),
("number", models.IntegerField(null=True)),
("grid", models.IntegerField()),
("position", models.IntegerField(null=True)),
("position_text", models.CharField(max_length=255)),
("position_order", models.IntegerField()),
("points", models.FloatField()),
("laps", models.IntegerField()),
("time", models.CharField(max_length=255, null=True)),
("milliseconds", models.FloatField(null=True)),
("fastest_lap", models.IntegerField(null=True)),
("rank", models.IntegerField(null=True)),
("fastest_lap_time", models.CharField(max_length=255, null=True)),
("fastest_lap_speed", models.CharField(max_length=255, null=True)),
(
"status",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.status",
),
),
],
),
migrations.CreateModel(
name="Qualifying",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"driver",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.driver",
),
),
(
"constructor",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.constructor",
),
),
("number", models.IntegerField()),
("position", models.IntegerField(null=True)),
("q1", models.CharField(max_length=255, null=True)),
("q2", models.CharField(max_length=255, null=True)),
("q3", models.CharField(max_length=255, null=True)),
],
),
migrations.CreateModel(
name="ConstructorResult",
fields=[
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"constructor",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.constructor",
),
),
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("points", models.FloatField(null=True)),
("status", models.CharField(max_length=255, null=True)),
],
),
migrations.CreateModel(
name="LapTime",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"driver",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.driver",
),
),
("lap", models.IntegerField()),
("position", models.IntegerField(null=True)),
("time", models.CharField(max_length=255, null=True)),
("milliseconds", models.IntegerField(null=True)),
],
),
migrations.CreateModel(
name="DriverStanding",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"driver",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.driver",
),
),
("points", models.FloatField()),
("position", models.IntegerField(null=True)),
("position_text", models.CharField(max_length=255, null=True)),
("wins", models.IntegerField()),
],
),
migrations.CreateModel(
name="PitStop",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"driver",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.driver",
),
),
("stop", models.IntegerField()),
("lap", models.IntegerField()),
("time", models.TimeField()),
("duration", models.CharField(max_length=255, null=True)),
("milliseconds", models.IntegerField(null=True)),
],
),
migrations.CreateModel(
name="ConstructorStanding",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"race",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.race",
),
),
(
"constructor",
models.ForeignKey(
on_delete=django.db.models.deletion.PROTECT,
to="backmarker.constructor",
),
),
("points", models.FloatField()),
("position", models.IntegerField(null=True)),
("position_text", models.CharField(max_length=255, null=True)),
("wins", models.IntegerField()),
],
),
]
| 36.297368
| 83
| 0.38418
| 878
| 13,793
| 5.916856
| 0.113895
| 0.075072
| 0.090087
| 0.120116
| 0.805775
| 0.782291
| 0.768816
| 0.715111
| 0.707603
| 0.707603
| 0
| 0.013854
| 0.508084
| 13,793
| 379
| 84
| 36.39314
| 0.751805
| 0.003263
| 0
| 0.763441
| 1
| 0
| 0.079441
| 0.006402
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.005376
| 0
| 0.016129
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
9f60bdd794cd662cbd883925c8025b2c7bf61851
| 16,921
|
py
|
Python
|
sdk/python/pulumi_keycloak/custom_identity_provider_mapping.py
|
davide-talesco/pulumi-keycloak
|
08d66be6f2bf578d4292e29eb6181794375bc4e5
|
[
"ECL-2.0",
"Apache-2.0"
] | 13
|
2020-04-28T15:20:56.000Z
|
2022-03-24T18:00:17.000Z
|
sdk/python/pulumi_keycloak/custom_identity_provider_mapping.py
|
davide-talesco/pulumi-keycloak
|
08d66be6f2bf578d4292e29eb6181794375bc4e5
|
[
"ECL-2.0",
"Apache-2.0"
] | 49
|
2020-02-06T17:53:35.000Z
|
2022-03-25T19:36:08.000Z
|
sdk/python/pulumi_keycloak/custom_identity_provider_mapping.py
|
davide-talesco/pulumi-keycloak
|
08d66be6f2bf578d4292e29eb6181794375bc4e5
|
[
"ECL-2.0",
"Apache-2.0"
] | 2
|
2020-06-09T01:08:56.000Z
|
2021-12-07T15:30:37.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['CustomIdentityProviderMappingArgs', 'CustomIdentityProviderMapping']
@pulumi.input_type
class CustomIdentityProviderMappingArgs:
def __init__(__self__, *,
identity_provider_alias: pulumi.Input[str],
identity_provider_mapper: pulumi.Input[str],
realm: pulumi.Input[str],
extra_config: Optional[pulumi.Input[Mapping[str, Any]]] = None,
name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a CustomIdentityProviderMapping resource.
:param pulumi.Input[str] identity_provider_alias: The alias of the associated identity provider.
:param pulumi.Input[str] identity_provider_mapper: The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
:param pulumi.Input[str] realm: The name of the realm.
:param pulumi.Input[Mapping[str, Any]] extra_config: Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
:param pulumi.Input[str] name: The name of the mapper.
"""
pulumi.set(__self__, "identity_provider_alias", identity_provider_alias)
pulumi.set(__self__, "identity_provider_mapper", identity_provider_mapper)
pulumi.set(__self__, "realm", realm)
if extra_config is not None:
pulumi.set(__self__, "extra_config", extra_config)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter(name="identityProviderAlias")
def identity_provider_alias(self) -> pulumi.Input[str]:
"""
The alias of the associated identity provider.
"""
return pulumi.get(self, "identity_provider_alias")
@identity_provider_alias.setter
def identity_provider_alias(self, value: pulumi.Input[str]):
pulumi.set(self, "identity_provider_alias", value)
@property
@pulumi.getter(name="identityProviderMapper")
def identity_provider_mapper(self) -> pulumi.Input[str]:
"""
The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
"""
return pulumi.get(self, "identity_provider_mapper")
@identity_provider_mapper.setter
def identity_provider_mapper(self, value: pulumi.Input[str]):
pulumi.set(self, "identity_provider_mapper", value)
@property
@pulumi.getter
def realm(self) -> pulumi.Input[str]:
"""
The name of the realm.
"""
return pulumi.get(self, "realm")
@realm.setter
def realm(self, value: pulumi.Input[str]):
pulumi.set(self, "realm", value)
@property
@pulumi.getter(name="extraConfig")
def extra_config(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
"""
return pulumi.get(self, "extra_config")
@extra_config.setter
def extra_config(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "extra_config", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the mapper.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class _CustomIdentityProviderMappingState:
def __init__(__self__, *,
extra_config: Optional[pulumi.Input[Mapping[str, Any]]] = None,
identity_provider_alias: Optional[pulumi.Input[str]] = None,
identity_provider_mapper: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
realm: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering CustomIdentityProviderMapping resources.
:param pulumi.Input[Mapping[str, Any]] extra_config: Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
:param pulumi.Input[str] identity_provider_alias: The alias of the associated identity provider.
:param pulumi.Input[str] identity_provider_mapper: The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
:param pulumi.Input[str] name: The name of the mapper.
:param pulumi.Input[str] realm: The name of the realm.
"""
if extra_config is not None:
pulumi.set(__self__, "extra_config", extra_config)
if identity_provider_alias is not None:
pulumi.set(__self__, "identity_provider_alias", identity_provider_alias)
if identity_provider_mapper is not None:
pulumi.set(__self__, "identity_provider_mapper", identity_provider_mapper)
if name is not None:
pulumi.set(__self__, "name", name)
if realm is not None:
pulumi.set(__self__, "realm", realm)
@property
@pulumi.getter(name="extraConfig")
def extra_config(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
"""
return pulumi.get(self, "extra_config")
@extra_config.setter
def extra_config(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "extra_config", value)
@property
@pulumi.getter(name="identityProviderAlias")
def identity_provider_alias(self) -> Optional[pulumi.Input[str]]:
"""
The alias of the associated identity provider.
"""
return pulumi.get(self, "identity_provider_alias")
@identity_provider_alias.setter
def identity_provider_alias(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "identity_provider_alias", value)
@property
@pulumi.getter(name="identityProviderMapper")
def identity_provider_mapper(self) -> Optional[pulumi.Input[str]]:
"""
The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
"""
return pulumi.get(self, "identity_provider_mapper")
@identity_provider_mapper.setter
def identity_provider_mapper(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "identity_provider_mapper", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the mapper.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def realm(self) -> Optional[pulumi.Input[str]]:
"""
The name of the realm.
"""
return pulumi.get(self, "realm")
@realm.setter
def realm(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "realm", value)
class CustomIdentityProviderMapping(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
extra_config: Optional[pulumi.Input[Mapping[str, Any]]] = None,
identity_provider_alias: Optional[pulumi.Input[str]] = None,
identity_provider_mapper: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
realm: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
## Import
Identity provider mappers can be imported using the format `{{realm_id}}/{{idp_alias}}/{{idp_mapper_id}}`, where `idp_alias` is the identity provider alias, and `idp_mapper_id` is the unique ID that Keycloak assigns to the mapper upon creation. This value can be found in the URI when editing this mapper in the GUI, and is typically a GUID. Examplebash
```sh
$ pulumi import keycloak:index/customIdentityProviderMapping:CustomIdentityProviderMapping test_mapper my-realm/my-mapper/f446db98-7133-4e30-b18a-3d28fde7ca1b
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Mapping[str, Any]] extra_config: Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
:param pulumi.Input[str] identity_provider_alias: The alias of the associated identity provider.
:param pulumi.Input[str] identity_provider_mapper: The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
:param pulumi.Input[str] name: The name of the mapper.
:param pulumi.Input[str] realm: The name of the realm.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: CustomIdentityProviderMappingArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
## Import
Identity provider mappers can be imported using the format `{{realm_id}}/{{idp_alias}}/{{idp_mapper_id}}`, where `idp_alias` is the identity provider alias, and `idp_mapper_id` is the unique ID that Keycloak assigns to the mapper upon creation. This value can be found in the URI when editing this mapper in the GUI, and is typically a GUID. Examplebash
```sh
$ pulumi import keycloak:index/customIdentityProviderMapping:CustomIdentityProviderMapping test_mapper my-realm/my-mapper/f446db98-7133-4e30-b18a-3d28fde7ca1b
```
:param str resource_name: The name of the resource.
:param CustomIdentityProviderMappingArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(CustomIdentityProviderMappingArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
extra_config: Optional[pulumi.Input[Mapping[str, Any]]] = None,
identity_provider_alias: Optional[pulumi.Input[str]] = None,
identity_provider_mapper: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
realm: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = CustomIdentityProviderMappingArgs.__new__(CustomIdentityProviderMappingArgs)
__props__.__dict__["extra_config"] = extra_config
if identity_provider_alias is None and not opts.urn:
raise TypeError("Missing required property 'identity_provider_alias'")
__props__.__dict__["identity_provider_alias"] = identity_provider_alias
if identity_provider_mapper is None and not opts.urn:
raise TypeError("Missing required property 'identity_provider_mapper'")
__props__.__dict__["identity_provider_mapper"] = identity_provider_mapper
__props__.__dict__["name"] = name
if realm is None and not opts.urn:
raise TypeError("Missing required property 'realm'")
__props__.__dict__["realm"] = realm
super(CustomIdentityProviderMapping, __self__).__init__(
'keycloak:index/customIdentityProviderMapping:CustomIdentityProviderMapping',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
extra_config: Optional[pulumi.Input[Mapping[str, Any]]] = None,
identity_provider_alias: Optional[pulumi.Input[str]] = None,
identity_provider_mapper: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
realm: Optional[pulumi.Input[str]] = None) -> 'CustomIdentityProviderMapping':
"""
Get an existing CustomIdentityProviderMapping resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Mapping[str, Any]] extra_config: Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
:param pulumi.Input[str] identity_provider_alias: The alias of the associated identity provider.
:param pulumi.Input[str] identity_provider_mapper: The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
:param pulumi.Input[str] name: The name of the mapper.
:param pulumi.Input[str] realm: The name of the realm.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _CustomIdentityProviderMappingState.__new__(_CustomIdentityProviderMappingState)
__props__.__dict__["extra_config"] = extra_config
__props__.__dict__["identity_provider_alias"] = identity_provider_alias
__props__.__dict__["identity_provider_mapper"] = identity_provider_mapper
__props__.__dict__["name"] = name
__props__.__dict__["realm"] = realm
return CustomIdentityProviderMapping(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="extraConfig")
def extra_config(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:
"""
Key/value attributes to add to the identity provider mapper model that is persisted to Keycloak. This can be used to extend the base model with new Keycloak features.
"""
return pulumi.get(self, "extra_config")
@property
@pulumi.getter(name="identityProviderAlias")
def identity_provider_alias(self) -> pulumi.Output[str]:
"""
The alias of the associated identity provider.
"""
return pulumi.get(self, "identity_provider_alias")
@property
@pulumi.getter(name="identityProviderMapper")
def identity_provider_mapper(self) -> pulumi.Output[str]:
"""
The type of the identity provider mapper. This can be a format string that includes a `%s` - this will be replaced by the provider id.
"""
return pulumi.get(self, "identity_provider_mapper")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The name of the mapper.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def realm(self) -> pulumi.Output[str]:
"""
The name of the realm.
"""
return pulumi.get(self, "realm")
| 48.207977
| 361
| 0.671651
| 2,032
| 16,921
| 5.376476
| 0.084154
| 0.130343
| 0.069199
| 0.054371
| 0.813272
| 0.794325
| 0.781419
| 0.765034
| 0.746362
| 0.710023
| 0
| 0.003018
| 0.236334
| 16,921
| 350
| 362
| 48.345714
| 0.842439
| 0.340228
| 0
| 0.653659
| 1
| 0
| 0.118416
| 0.073818
| 0
| 0
| 0
| 0
| 0
| 1
| 0.156098
| false
| 0.004878
| 0.02439
| 0
| 0.273171
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
4ce6d6a0bff2622c22fb1c1367e64eb0e4b661d8
| 2,440
|
py
|
Python
|
tests/test_models/test_common/test_ensemble.py
|
miaomiaojj/mmediting-master
|
4705db3000b3766af4932a7a8e090ed6e8198afe
|
[
"Apache-2.0"
] | 1
|
2022-03-22T03:00:20.000Z
|
2022-03-22T03:00:20.000Z
|
tests/test_models/test_common/test_ensemble.py
|
miaomiaojj/mmediting-master
|
4705db3000b3766af4932a7a8e090ed6e8198afe
|
[
"Apache-2.0"
] | null | null | null |
tests/test_models/test_common/test_ensemble.py
|
miaomiaojj/mmediting-master
|
4705db3000b3766af4932a7a8e090ed6e8198afe
|
[
"Apache-2.0"
] | 1
|
2022-03-10T01:00:24.000Z
|
2022-03-10T01:00:24.000Z
|
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmedit.models.common import SpatialTemporalEnsemble
def test_ensemble_cpu():
model = nn.Identity()
# spatial ensemble of an image
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)
inputs = torch.rand(1, 3, 4, 4)
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())
# spatial ensemble of a sequence
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)
inputs = torch.rand(1, 2, 3, 4, 4)
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())
# spatial and temporal ensemble of a sequence
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)
inputs = torch.rand(1, 2, 3, 4, 4)
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.numpy(), outputs.numpy())
# spatial and temporal ensemble of an image
with pytest.raises(ValueError):
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)
inputs = torch.rand(1, 3, 4, 4)
outputs = ensemble(inputs, model)
def test_ensemble_cuda():
if torch.cuda.is_available():
model = nn.Identity().cuda()
# spatial ensemble of an image
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)
inputs = torch.rand(1, 3, 4, 4).cuda()
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.cpu().numpy(), outputs.numpy())
# spatial ensemble of a sequence
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=False)
inputs = torch.rand(1, 2, 3, 4, 4).cuda()
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.cpu().numpy(), outputs.numpy())
# spatial and temporal ensemble of a sequence
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)
inputs = torch.rand(1, 2, 3, 4, 4).cuda()
outputs = ensemble(inputs, model)
np.testing.assert_almost_equal(inputs.cpu().numpy(), outputs.numpy())
# spatial and temporal ensemble of an image
with pytest.raises(ValueError):
ensemble = SpatialTemporalEnsemble(is_temporal_ensemble=True)
inputs = torch.rand(1, 3, 4, 4).cuda()
outputs = ensemble(inputs, model)
| 37.538462
| 77
| 0.686475
| 302
| 2,440
| 5.437086
| 0.172185
| 0.116931
| 0.16078
| 0.199756
| 0.856882
| 0.856882
| 0.856882
| 0.856882
| 0.856882
| 0.856882
| 0
| 0.018634
| 0.208197
| 2,440
| 64
| 78
| 38.125
| 0.831263
| 0.138115
| 0
| 0.761905
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| 0
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| 0
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| 0
| 0.142857
| 1
| 0.047619
| false
| 0
| 0.119048
| 0
| 0.166667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
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| 0
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| 0
| 0
| 0
| 1
| 0
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| null | 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
98053fe58d00f015f391cc682e1397842a6dae53
| 5,751
|
py
|
Python
|
Nombre/Nombre.py
|
willingtonino/Algoritmos_programacion_C4G2
|
2a2c94678ae981974539a8019f17108775521e23
|
[
"MIT"
] | null | null | null |
Nombre/Nombre.py
|
willingtonino/Algoritmos_programacion_C4G2
|
2a2c94678ae981974539a8019f17108775521e23
|
[
"MIT"
] | null | null | null |
Nombre/Nombre.py
|
willingtonino/Algoritmos_programacion_C4G2
|
2a2c94678ae981974539a8019f17108775521e23
|
[
"MIT"
] | 1
|
2021-10-31T22:54:45.000Z
|
2021-10-31T22:54:45.000Z
|
import turtle
nombre = turtle.Turtle()
colors=['red', 'purple', 'blue', 'green', 'yellow', 'orange', 'pink','light blue']
#Nombre:Willington
nombre.color("blue")
nombre.pu()
nombre.setposition(-500,300)
nombre.speed(10)
nombre.pensize(5)
nombre.pd()
nombre.left(270)
nombre.forward(350)
nombre.left(135)
nombre.forward(150)
nombre.right(90)
nombre.forward(150)
nombre.left(135)
nombre.forward(350)
nombre.right(180)
nombre.forward(350)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(100)
nombre.right(180)
nombre.forward(100)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(100)
nombre.right(180)
nombre.forward(100)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.left(180)
nombre.forward(10)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(40)
nombre.left(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(40)
nombre.left(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(40)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(100)
nombre.left(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(25)
nombre.right(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.left(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(10)
nombre.left(180)
nombre.forward(10)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
#Nombre:Andres
nombre.pu()
nombre.setposition(-500,-50)
nombre.color("orange")
nombre.pensize(5)
nombre.pd()
nombre.left(135)
nombre.forward(150)
nombre.right(90)
nombre.forward(150)
nombre.pu()
nombre.setposition(-450,0)
nombre.pd()
nombre.left(45)
nombre.forward(110)
nombre.pu()
nombre.setposition(-500,-50)
nombre.pd()
nombre.right(90)
nombre.color("black")
nombre.forward(50)
nombre.color("orange")
nombre.forward(50)
nombre.right(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.left(90)
nombre.forward(100)
nombre.left(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(60)
nombre.right(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(60)
nombre.right(180)
nombre.forward(10)
nombre.left(90)
nombre.forward(90)
nombre.right(90)
nombre.forward(10)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(10)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(100)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(25)
nombre.right(90)
nombre.forward(50)
#Nombre:Niño
nombre.pu()
nombre.setposition(-500,-50)
nombre.color("green")
nombre.pensize(5)
nombre.pd()
nombre.left(45)
nombre.forward(150)
nombre.right(90)
nombre.forward(150)
nombre.left(135)
nombre.forward(350)
nombre.pu()
nombre.setposition(-288,100)
nombre.pd()
nombre.left(270)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.left(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(25)
nombre.left(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(100)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.pu()
nombre.setposition(-238,160)
nombre.pd()
nombre.forward(50)
#Nombre:Perez
nombre.pu()
nombre.setposition(-288,200)
nombre.color("red")
nombre.pensize(5)
nombre.pd()
nombre.left(90)
nombre.forward(100)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(180)
nombre.forward(100)
nombre.right(90)
nombre.forward(10)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(10)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(50)
nombre.left(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(60)
nombre.left(90)
nombre.forward(10)
nombre.left(180)
nombre.forward(60)
nombre.left(180)
nombre.forward(50)
nombre.right(90)
nombre.forward(90)
nombre.right(90)
nombre.forward(10)
nombre.right(90)
nombre.forward(50)
nombre.right(90)
nombre.forward(10)
nombre.right(180)
nombre.forward(50)
nombre.left(90)
nombre.forward(100)
nombre.left(180)
nombre.forward(50)
nombre.right(135)
nombre.forward(71)
nombre.left(135)
nombre.forward(50)
nombre.right(180)
#Dos lineas de medio contorno
nombre.pu()
nombre.setposition(-520,300)
nombre.color("black")
nombre.pensize(5)
nombre.pd()
nombre.right(90)
nombre.forward(500)
nombre.left(90)
nombre.forward(1000)
#Primera figura:Triangulo en espiral
nombre.pu()
nombre.setposition(170,-10)
nombre.color("black")
nombre.speed(200)
nombre.pensize(1)
f=1
nombre.pd()
nombre.ht()
while True:
if f<=120:
nombre.forward(f)
nombre.left(120)
nombre.left(1)
f=f+1
else:
break
#Segunda figura: Poligono
nombre.pu()
nombre.setposition(-300,220)
nombre.pensize(1)
nombre.pd()
for i in range(200):
nombre.color(colors[i%8])
nombre.forward(80)
nombre.left(50)
nombre.speed(100)
| 19.298658
| 83
| 0.719701
| 883
| 5,751
| 4.687429
| 0.091733
| 0.354917
| 0.264557
| 0.279053
| 0.830635
| 0.787388
| 0.765402
| 0.707659
| 0.684706
| 0.677942
| 0
| 0.117195
| 0.117197
| 5,751
| 298
| 84
| 19.298658
| 0.69805
| 0.024344
| 0
| 0.875862
| 0
| 0
| 0.015637
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.003448
| 0
| 0.003448
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
e28c841373f2374d122cbff3f43b67a22b6729fe
| 21,378
|
py
|
Python
|
src/rnn_cells/skip_rnn_cells.py
|
imatge-upc/skiprnn-2017-tfm
|
63f93a539a3f2c7a713089fdd2c38bb7b0c581ca
|
[
"MIT"
] | 129
|
2017-08-24T00:27:47.000Z
|
2022-03-24T21:42:37.000Z
|
src/rnn_cells/skip_rnn_cells.py
|
imatge-upc/skiprnn-2018-iclr
|
63f93a539a3f2c7a713089fdd2c38bb7b0c581ca
|
[
"MIT"
] | 8
|
2018-02-28T15:05:48.000Z
|
2022-02-09T23:30:21.000Z
|
src/rnn_cells/skip_rnn_cells.py
|
imatge-upc/skiprnn-2018-iclr
|
63f93a539a3f2c7a713089fdd2c38bb7b0c581ca
|
[
"MIT"
] | 45
|
2017-08-24T13:16:11.000Z
|
2021-05-13T02:36:59.000Z
|
"""
Skip RNN cells that decide which timesteps should be attended.
"""
from __future__ import absolute_import
from __future__ import print_function
import collections
import tensorflow as tf
from rnn_cells import rnn_ops
from tensorflow.python.framework import ops
SkipLSTMStateTuple = collections.namedtuple("SkipLSTMStateTuple", ("c", "h", "update_prob", "cum_update_prob"))
SkipLSTMOutputTuple = collections.namedtuple("SkipLSTMOutputTuple", ("h", "state_gate"))
LSTMStateTuple = tf.nn.rnn_cell.LSTMStateTuple
SkipGRUStateTuple = collections.namedtuple("SkipGRUStateTuple", ("h", "update_prob", "cum_update_prob"))
SkipGRUOutputTuple = collections.namedtuple("SkipGRUOutputTuple", ("h", "state_gate"))
def _binary_round(x):
"""
Rounds a tensor whose values are in [0,1] to a tensor with values in {0, 1},
using the straight through estimator for the gradient.
Based on http://r2rt.com/binary-stochastic-neurons-in-tensorflow.html
:param x: input tensor
:return: y=round(x) with gradients defined by the identity mapping (y=x)
"""
g = tf.get_default_graph()
with ops.name_scope("BinaryRound") as name:
with g.gradient_override_map({"Round": "Identity"}):
return tf.round(x, name=name)
class SkipLSTMCell(tf.nn.rnn_cell.RNNCell):
"""
Single Skip LSTM cell. Augments the basic LSTM cell with a binary output that decides whether to
update or copy the cell state. The binary neuron is optimized using the Straight Through Estimator.
"""
def __init__(self, num_units, forget_bias=1.0, activation=tf.tanh, layer_norm=False, update_bias=1.0):
"""
Initialize the Skip LSTM cell
:param num_units: int, the number of units in the LSTM cell
:param forget_bias: float, the bias added to forget gates
:param activation: activation function of the inner states
:param layer_norm: bool, whether to use layer normalization
:param update_bias: float, initial value for the bias added to the update state gate
"""
self._num_units = num_units
self._forget_bias = forget_bias
self._activation = activation
self._layer_norm = layer_norm
self._update_bias = update_bias
@property
def state_size(self):
return SkipLSTMStateTuple(self._num_units, self._num_units, 1, 1)
@property
def output_size(self):
return SkipLSTMOutputTuple(self._num_units, 1)
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
c_prev, h_prev, update_prob_prev, cum_update_prob_prev = state
# Parameters of gates are concatenated into one multiply for efficiency.
concat = rnn_ops.linear([inputs, h_prev], 4 * self._num_units, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(value=concat, num_or_size_splits=4, axis=1)
if self._layer_norm:
i = rnn_ops.layer_norm(i, name="i")
j = rnn_ops.layer_norm(j, name="j")
f = rnn_ops.layer_norm(f, name="f")
o = rnn_ops.layer_norm(o, name="o")
new_c_tilde = (c_prev * tf.sigmoid(f + self._forget_bias) + tf.sigmoid(i) * self._activation(j))
new_h_tilde = self._activation(new_c_tilde) * tf.sigmoid(o)
# Compute value for the update prob
with tf.variable_scope('state_update_prob'):
new_update_prob_tilde = rnn_ops.linear(new_c_tilde, 1, True, bias_start=self._update_bias)
new_update_prob_tilde = tf.sigmoid(new_update_prob_tilde)
# Compute value for the update gate
cum_update_prob = cum_update_prob_prev + tf.minimum(update_prob_prev, 1. - cum_update_prob_prev)
update_gate = _binary_round(cum_update_prob)
# Apply update gate
new_c = update_gate * new_c_tilde + (1. - update_gate) * c_prev
new_h = update_gate * new_h_tilde + (1. - update_gate) * h_prev
new_update_prob = update_gate * new_update_prob_tilde + (1. - update_gate) * update_prob_prev
new_cum_update_prob = update_gate * 0. + (1. - update_gate) * cum_update_prob
new_state = SkipLSTMStateTuple(new_c, new_h, new_update_prob, new_cum_update_prob)
new_output = SkipLSTMOutputTuple(new_h, update_gate)
return new_output, new_state
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the SkipLSTMCell
:param batch_size: number of samples per batch
:return: SkipLSTMStateTuple
"""
with tf.variable_scope('initial_c'):
initial_c = rnn_ops.create_initial_state(batch_size, self._num_units)
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units)
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.zeros_initializer())
return SkipLSTMStateTuple(initial_c, initial_h, initial_update_prob, initial_cum_update_prob)
class MultiSkipLSTMCell(tf.nn.rnn_cell.RNNCell):
"""
Stack of Skip LSTM cells. The selection binary output is computed from the state of the cell on top of
the stack.
"""
def __init__(self, num_units, forget_bias=1.0, activation=tf.tanh, layer_norm=False, update_bias=1.0):
"""
Initialize the stack of Skip LSTM cells
:param num_units: list of int, the number of units in each LSTM cell
:param forget_bias: float, the bias added to forget gates
:param activation: activation function of the inner states
:param layer_norm: bool, whether to use layer normalization
:param update_bias: float, initial value for the bias added to the update state gate
"""
if not isinstance(num_units, list):
num_units = [num_units]
self._num_units = num_units
self._num_layers = len(self._num_units)
self._forget_bias = forget_bias
self._activation = activation
self._layer_norm = layer_norm
self._update_bias = update_bias
@property
def state_size(self):
return [LSTMStateTuple(num_units, num_units) for num_units in self._num_units[:-1]] + \
[SkipLSTMStateTuple(self._num_units[-1], self._num_units[:-1], 1, 1)]
@property
def output_size(self):
return SkipLSTMOutputTuple(self._num_units[-1], 1)
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
update_prob_prev, cum_update_prob_prev = state[-1].update_prob, state[-1].cum_update_prob
cell_input = inputs
state_candidates = []
# Compute update candidates for all layers
for idx in range(self._num_layers):
with tf.variable_scope('layer_%d' % (idx + 1)):
c_prev, h_prev = state[idx].c, state[idx].h
# Parameters of gates are concatenated into one multiply for efficiency.
concat = rnn_ops.linear([cell_input, h_prev], 4 * self._num_units[idx], True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(value=concat, num_or_size_splits=4, axis=1)
if self._layer_norm:
i = rnn_ops.layer_norm(i, name="i")
j = rnn_ops.layer_norm(j, name="j")
f = rnn_ops.layer_norm(f, name="f")
o = rnn_ops.layer_norm(o, name="o")
new_c_tilde = (c_prev * tf.sigmoid(f + self._forget_bias) + tf.sigmoid(i) * self._activation(j))
new_h_tilde = self._activation(new_c_tilde) * tf.sigmoid(o)
state_candidates.append(LSTMStateTuple(new_c_tilde, new_h_tilde))
cell_input = new_h_tilde
# Compute value for the update prob
with tf.variable_scope('state_update_prob'):
new_update_prob_tilde = rnn_ops.linear(state_candidates[-1].c, 1, True, bias_start=self._update_bias)
new_update_prob_tilde = tf.sigmoid(new_update_prob_tilde)
# Compute value for the update gate
cum_update_prob = cum_update_prob_prev + tf.minimum(update_prob_prev, 1. - cum_update_prob_prev)
update_gate = _binary_round(cum_update_prob)
# Apply update gate
new_states = []
for idx in range(self._num_layers - 1):
new_c = update_gate * state_candidates[idx].c + (1. - update_gate) * state[idx].c
new_h = update_gate * state_candidates[idx].h + (1. - update_gate) * state[idx].h
new_states.append(LSTMStateTuple(new_c, new_h))
new_c = update_gate * state_candidates[-1].c + (1. - update_gate) * state[-1].c
new_h = update_gate * state_candidates[-1].h + (1. - update_gate) * state[-1].h
new_update_prob = update_gate * new_update_prob_tilde + (1. - update_gate) * update_prob_prev
new_cum_update_prob = update_gate * 0. + (1. - update_gate) * cum_update_prob
new_states.append(SkipLSTMStateTuple(new_c, new_h, new_update_prob, new_cum_update_prob))
new_output = SkipLSTMOutputTuple(new_h, update_gate)
return new_output, new_states
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the MultiSkipLSTMCell
:param batch_size: number of samples per batch
:return: list of SkipLSTMStateTuple
"""
initial_states = []
for idx in range(self._num_layers - 1):
with tf.variable_scope('layer_%d' % (idx + 1)):
with tf.variable_scope('initial_c'):
initial_c = rnn_ops.create_initial_state(batch_size, self._num_units[idx])
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units[idx])
initial_states.append(LSTMStateTuple(initial_c, initial_h))
with tf.variable_scope('layer_%d' % self._num_layers):
with tf.variable_scope('initial_c'):
initial_c = rnn_ops.create_initial_state(batch_size, self._num_units[-1])
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units[-1])
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.zeros_initializer())
initial_states.append(SkipLSTMStateTuple(initial_c, initial_h,
initial_update_prob, initial_cum_update_prob))
return initial_states
class SkipGRUCell(tf.nn.rnn_cell.RNNCell):
"""
Single Skip GRU cell. Augments the basic GRU cell with a binary output that decides whether to
update or copy the cell state. The binary neuron is optimized using the Straight Through Estimator.
"""
def __init__(self, num_units, activation=tf.tanh, layer_norm=False, update_bias=1.0):
"""
Initialize the Skip GRU cell
:param num_units: int, the number of units in the GRU cell
:param activation: activation function of the inner states
:param layer_norm: bool, whether to use layer normalization
:param update_bias: float, initial value for the bias added to the update state gate
"""
self._num_units = num_units
self._activation = activation
self._layer_norm = layer_norm
self._update_bias = update_bias
@property
def state_size(self):
return SkipGRUStateTuple(self._num_units, 1, 1)
@property
def output_size(self):
return SkipGRUOutputTuple(self._num_units, 1)
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
h_prev, update_prob_prev, cum_update_prob_prev = state
# Parameters of gates are concatenated into one multiply for efficiency.
with tf.variable_scope("gates"):
concat = rnn_ops.linear([inputs, h_prev], 2 * self._num_units, bias=True, bias_start=1.0)
# r = reset_gate, u = update_gate
r, u = tf.split(value=concat, num_or_size_splits=2, axis=1)
if self._layer_norm:
r = rnn_ops.layer_norm(r, name="r")
u = rnn_ops.layer_norm(u, name="u")
# Apply non-linearity after layer normalization
r = tf.sigmoid(r)
u = tf.sigmoid(u)
with tf.variable_scope("candidate"):
new_c_tilde = self._activation(rnn_ops.linear([inputs, r * h_prev], self._num_units, True))
new_h_tilde = u * h_prev + (1 - u) * new_c_tilde
# Compute value for the update prob
with tf.variable_scope('state_update_prob'):
new_update_prob_tilde = rnn_ops.linear(new_h_tilde, 1, True, bias_start=self._update_bias)
new_update_prob_tilde = tf.sigmoid(new_update_prob_tilde)
# Compute value for the update gate
cum_update_prob = cum_update_prob_prev + tf.minimum(update_prob_prev, 1. - cum_update_prob_prev)
update_gate = _binary_round(cum_update_prob)
# Apply update gate
new_h = update_gate * new_h_tilde + (1. - update_gate) * h_prev
new_update_prob = update_gate * new_update_prob_tilde + (1. - update_gate) * update_prob_prev
new_cum_update_prob = update_gate * 0. + (1. - update_gate) * cum_update_prob
new_state = SkipGRUStateTuple(new_h, new_update_prob, new_cum_update_prob)
new_output = SkipGRUOutputTuple(new_h, update_gate)
return new_output, new_state
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the SkipGRUCell
:param batch_size: number of samples per batch
:return: SkipGRUStateTuple
"""
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units)
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.zeros_initializer())
return SkipGRUStateTuple(initial_h, initial_update_prob, initial_cum_update_prob)
class MultiSkipGRUCell(tf.nn.rnn_cell.RNNCell):
"""
Stack of Skip GRU cells. The selection binary output is computed from the state of the cell on top of
the stack.
"""
def __init__(self, num_units, activation=tf.tanh, layer_norm=False, update_bias=1.0):
"""
Initialize the stack of Skip GRU cells
:param num_units: list of int, the number of units in each GRU cell
:param activation: activation function of the inner states
:param layer_norm: bool, whether to use layer normalization
:param update_bias: float, initial value for the bias added to the update state gate
"""
if not isinstance(num_units, list):
num_units = [num_units]
self._num_units = num_units
self._num_layers = len(self._num_units)
self._activation = activation
self._layer_norm = layer_norm
self._update_bias = update_bias
@property
def state_size(self):
return [num_units for num_units in self._num_units[:-1]] + [SkipGRUStateTuple(self._num_units[-1], 1, 1)]
@property
def output_size(self):
return SkipGRUOutputTuple(self._num_units[-1], 1)
def __call__(self, inputs, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
update_prob_prev, cum_update_prob_prev = state[-1].update_prob, state[-1].cum_update_prob
cell_input = inputs
state_candidates = []
# Compute update candidates for all layers
for idx in range(self._num_layers):
with tf.variable_scope('layer_%d' % (idx + 1)):
if isinstance(state[idx], SkipGRUStateTuple):
h_prev = state[idx].h
else:
h_prev = state[idx]
# Parameters of gates are concatenated into one multiply for efficiency.
with tf.variable_scope("gates"):
concat = rnn_ops.linear([cell_input, h_prev], 2 * self._num_units[idx], bias=True, bias_start=1.0,)
# r = reset_gate, u = update_gate
r, u = tf.split(value=concat, num_or_size_splits=2, axis=1)
if self._layer_norm:
r = rnn_ops.layer_norm(r, name="r")
u = rnn_ops.layer_norm(u, name="u")
# Apply non-linearity after layer normalization
r = tf.sigmoid(r)
u = tf.sigmoid(u)
with tf.variable_scope("candidate"):
new_c_tilde = self._activation(rnn_ops.linear([inputs, r * h_prev], self._num_units[idx], True))
new_h_tilde = u * h_prev + (1 - u) * new_c_tilde
state_candidates.append(new_h_tilde)
cell_input = new_h_tilde
# Compute value for the update prob
with tf.variable_scope('state_update_prob'):
new_update_prob_tilde = rnn_ops.linear(state_candidates[-1], 1, True, bias_start=self._update_bias)
new_update_prob_tilde = tf.sigmoid(new_update_prob_tilde)
# Compute value for the update gate
cum_update_prob = cum_update_prob_prev + tf.minimum(update_prob_prev, 1. - cum_update_prob_prev)
update_gate = _binary_round(cum_update_prob)
# Apply update gate
new_states = []
for idx in range(self._num_layers - 1):
new_h = update_gate * state_candidates[idx] + (1. - update_gate) * state[idx]
new_states.append(new_h)
new_h = update_gate * state_candidates[-1] + (1. - update_gate) * state[-1].h
new_update_prob = update_gate * new_update_prob_tilde + (1. - update_gate) * update_prob_prev
new_cum_update_prob = update_gate * 0. + (1. - update_gate) * cum_update_prob
new_states.append(SkipGRUStateTuple(new_h, new_update_prob, new_cum_update_prob))
new_output = SkipGRUOutputTuple(new_h, update_gate)
return new_output, new_states
def trainable_initial_state(self, batch_size):
"""
Create a trainable initial state for the MultiSkipGRUCell
:param batch_size: number of samples per batch
:return: list of tensors and SkipGRUStateTuple
"""
initial_states = []
for idx in range(self._num_layers - 1):
with tf.variable_scope('layer_%d' % (idx + 1)):
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units[idx])
initial_states.append(initial_h)
with tf.variable_scope('layer_%d' % self._num_layers):
with tf.variable_scope('initial_h'):
initial_h = rnn_ops.create_initial_state(batch_size, self._num_units[-1])
with tf.variable_scope('initial_update_prob'):
initial_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.ones_initializer())
with tf.variable_scope('initial_cum_update_prob'):
initial_cum_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
initializer=tf.zeros_initializer())
initial_states.append(SkipGRUStateTuple(initial_h, initial_update_prob, initial_cum_update_prob))
return initial_states
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0
| 7
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2c33c3656f1470ff41575901a7b5533eecb2a153
| 20,247
|
py
|
Python
|
out.py
|
CYB3R73/hiro-bot
|
a1217d950b4bb228689b1a4b585cecbb064f3a2b
|
[
"Apache-2.0"
] | null | null | null |
out.py
|
CYB3R73/hiro-bot
|
a1217d950b4bb228689b1a4b585cecbb064f3a2b
|
[
"Apache-2.0"
] | null | null | null |
out.py
|
CYB3R73/hiro-bot
|
a1217d950b4bb228689b1a4b585cecbb064f3a2b
|
[
"Apache-2.0"
] | null | null | null |
[2021-06-17T19:47:05.267189+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":983,"reset_after":62092171,"max_concurrency":1}} []
[2021-06-17T19:47:05.270016+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-17T19:47:05.507557+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-17T19:47:05.510023+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-17T19:47:05.511211+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-17T19:47:05.511348+03:00] DiscordPHP.INFO: identifying {"payload":{"op":2,"d":{"token":"xxxxxx","properties":{"$os":"Linux","$browser":"DiscordBot (https://github.com/discord-php/DiscordPHP, v5.1.3)","$device":"DiscordBot (https://github.com/discord-php/DiscordPHP, v5.1.3)","$referrer":"https://github.com/discord-php/DiscordPHP","$referring_domain":"https://github.com/discord-php/DiscordPHP"},"compress":true}}} []
[2021-06-17T19:47:05.792646+03:00] DiscordPHP.INFO: did not parse private channels [] []
[2021-06-17T19:47:05.795465+03:00] DiscordPHP.INFO: stored guilds {"count":0,"unavailable":5} []
[2021-06-17T19:47:06.310600+03:00] DiscordPHP.INFO: all guilds are now available {"count":5} []
[2021-06-17T19:47:06.310836+03:00] DiscordPHP.INFO: loadAllMembers option is disabled, not setting chunking up [] []
[2021-06-17T19:47:06.310874+03:00] DiscordPHP.INFO: client is ready [] []
Bot is ready!
sh: 1: figlet: not found
Loading Commands
====================
Loaded : Avatar
====================
====================
Loaded : Ban
====================
====================
Loaded : Clear
====================
====================
Loaded : Exec
====================
====================
Loaded : Help
====================
====================
Loaded : Howgay
====================
====================
Loaded : Hug
====================
====================
Loaded : Kick
====================
====================
Loaded : Kiss
====================
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Loaded : Marry
====================
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Loaded : Money
====================
====================
Loaded : Nick
====================
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Loaded : Ping
====================
====================
Loaded : Slap
====================
All Commands Are Loaded.
money is empty
User added
money is empty
User added
[2021-06-17T23:21:55.298261+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-17T23:21:55.299582+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-17T23:21:55.299932+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-17T23:21:57.300363+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":1} []
[2021-06-17T23:21:57.509430+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":49199926,"max_concurrency":1}} []
[2021-06-17T23:21:57.509621+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-17T23:21:57.749504+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-17T23:21:57.749824+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-17T23:21:57.749946+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-17T23:21:57.750080+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":7381,"token":"xxxxxx"}}} []
[2021-06-17T23:21:57.907928+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T00:33:55.663750+03:00] DiscordPHP.WARNING: websocket closed {"op":1006,"reason":"Underlying connection closed"} []
[2021-06-18T00:33:55.664167+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T00:33:57.664415+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":2} []
[2021-06-18T00:33:57.891569+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":44879553,"max_concurrency":1}} []
[2021-06-18T00:33:57.891766+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T00:33:58.121553+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T00:33:58.121857+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T00:33:58.121979+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T00:33:58.122073+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":10209,"token":"xxxxxx"}}} []
[2021-06-18T00:33:58.274516+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T01:10:34.918037+03:00] DiscordPHP.WARNING: websocket closed {"op":1006,"reason":"Underlying connection closed"} []
[2021-06-18T01:10:34.919375+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T01:10:36.919815+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":3} []
[2021-06-18T01:10:37.127751+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":42680308,"max_concurrency":1}} []
[2021-06-18T01:10:37.127968+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T01:10:37.357228+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T01:10:37.357460+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T01:10:37.357588+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T01:10:37.357725+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":11822,"token":"xxxxxx"}}} []
[2021-06-18T01:10:37.522047+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
User added
[2021-06-18T02:58:23.191598+03:00] DiscordPHP.WARNING: websocket closed {"op":1006,"reason":"Underlying connection closed"} []
[2021-06-18T02:58:23.192139+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T02:58:25.195836+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":4} []
[2021-06-18T02:58:25.408501+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":36212030,"max_concurrency":1}} []
[2021-06-18T02:58:25.408666+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T02:58:25.658126+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T02:58:25.658409+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T02:58:25.658576+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T02:58:25.658713+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":13708,"token":"xxxxxx"}}} []
[2021-06-18T02:58:25.816942+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T03:14:39.481984+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T03:14:39.484003+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T03:14:39.484202+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T03:14:41.484474+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":5} []
[2021-06-18T03:14:41.693538+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":35235742,"max_concurrency":1}} []
[2021-06-18T03:14:41.693948+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T03:14:41.929679+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T03:14:41.929981+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T03:14:41.930131+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T03:14:41.930248+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":13874,"token":"xxxxxx"}}} []
[2021-06-18T03:14:42.089582+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T06:18:09.437932+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T06:18:09.438513+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T06:18:09.438662+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T06:18:11.439099+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":6} []
[2021-06-18T06:18:11.642483+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":24225792,"max_concurrency":1}} []
[2021-06-18T06:18:11.642682+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T06:18:11.885919+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T06:18:11.886227+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T06:18:11.886401+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T06:18:11.886542+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":15813,"token":"xxxxxx"}}} []
[2021-06-18T06:18:12.044062+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T09:35:53.369145+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T09:35:53.371521+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T09:35:53.371642+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T09:35:55.372113+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":7} []
[2021-06-18T09:35:55.601470+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":12361838,"max_concurrency":1}} []
[2021-06-18T09:35:55.601597+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T09:35:55.827944+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T09:35:55.828314+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T09:35:55.828465+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T09:35:55.828601+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":17351,"token":"xxxxxx"}}} []
[2021-06-18T09:35:55.981752+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T11:00:10.901438+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T11:00:10.901995+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T11:00:10.902093+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T11:00:12.902328+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":8} []
[2021-06-18T11:00:13.125871+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":7304312,"max_concurrency":1}} []
[2021-06-18T11:00:13.126022+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T11:00:13.371357+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T11:00:13.371575+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T11:00:13.371730+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T11:00:13.371823+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":18557,"token":"xxxxxx"}}} []
[2021-06-18T11:00:13.528552+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
[2021-06-18T12:32:23.313544+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T12:32:23.314321+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T12:32:23.314426+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T12:32:25.314733+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":9} []
[2021-06-18T12:32:25.535448+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":982,"reset_after":1771902,"max_concurrency":1}} []
[2021-06-18T12:32:25.535761+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T12:32:25.773625+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T12:32:25.773888+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T12:32:25.774011+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T12:32:25.774117+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":20174,"token":"xxxxxx"}}} []
[2021-06-18T12:32:25.936317+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
[2021-06-18T15:14:59.071630+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T15:14:59.072393+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T15:14:59.072497+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T15:15:01.073390+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":10} []
[2021-06-18T15:15:01.538830+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":1000,"reset_after":0,"max_concurrency":1}} []
[2021-06-18T15:15:01.539044+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T15:15:01.817796+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T15:15:01.818098+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T15:15:01.818253+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T15:15:01.818395+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":23399,"token":"xxxxxx"}}} []
[2021-06-18T15:15:01.974783+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
[2021-06-18T16:44:31.608008+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T16:44:31.608506+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T16:44:31.608586+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T16:44:33.608869+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":11} []
[2021-06-18T16:44:33.822553+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":1000,"reset_after":0,"max_concurrency":1}} []
[2021-06-18T16:44:33.822836+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T16:44:34.051009+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T16:44:34.051283+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T16:44:34.051410+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T16:44:34.051536+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":25438,"token":"xxxxxx"}}} []
[2021-06-18T16:44:34.208818+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
[2021-06-18T20:32:59.868689+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T20:32:59.869145+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T20:32:59.869254+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T20:33:01.869511+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":12} []
[2021-06-18T20:33:02.105887+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":1000,"reset_after":0,"max_concurrency":1}} []
[2021-06-18T20:33:02.106057+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T20:33:02.347455+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T20:33:02.347657+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T20:33:02.347764+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T20:33:02.347876+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":33574,"token":"xxxxxx"}}} []
[2021-06-18T20:33:02.502469+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
[2021-06-18T23:25:50.994908+03:00] DiscordPHP.WARNING: received opcode 7 for reconnect [] []
[2021-06-18T23:25:50.995428+03:00] DiscordPHP.WARNING: websocket closed {"op":4000,"reason":"gateway redirecting - opcode 7"} []
[2021-06-18T23:25:50.995534+03:00] DiscordPHP.WARNING: reconnecting in 2 seconds [] []
[2021-06-18T23:25:52.996713+03:00] DiscordPHP.INFO: starting reconnect {"reconnect_count":13} []
[2021-06-18T23:25:53.625114+03:00] DiscordPHP.INFO: gateway retrieved and set {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json","session":{"total":1000,"remaining":1000,"reset_after":0,"max_concurrency":1}} []
[2021-06-18T23:25:53.625279+03:00] DiscordPHP.INFO: starting connection to websocket {"gateway":"wss://gateway.discord.gg/?v=6&encoding=json"} []
[2021-06-18T23:25:53.870634+03:00] DiscordPHP.INFO: websocket connection has been created [] []
[2021-06-18T23:25:53.870931+03:00] DiscordPHP.INFO: received hello [] []
[2021-06-18T23:25:53.871113+03:00] DiscordPHP.INFO: heartbeat timer initilized {"interval":41250.0} []
[2021-06-18T23:25:53.871225+03:00] DiscordPHP.INFO: resuming connection {"payload":{"op":6,"d":{"session_id":"f9fab91a71f8a4f070ac7126efe9ee1a","seq":41025,"token":"xxxxxx"}}} []
[2021-06-18T23:25:54.031194+03:00] DiscordPHP.INFO: websocket reconnected to discord [] []
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
money is empty
Array
(
[0] => HY000
[1] => 2006
[2] => MySQL server has gone away
)
cant added
| 54.721622
| 426
| 0.703265
| 2,975
| 20,247
| 4.767731
| 0.123025
| 0.063875
| 0.149041
| 0.145939
| 0.868866
| 0.753173
| 0.743866
| 0.740905
| 0.659475
| 0.659475
| 0
| 0.23695
| 0.080358
| 20,247
| 369
| 427
| 54.869919
| 0.524812
| 0
| 0
| 0.307116
| 0
| 0
| 0.198844
| 0.080012
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2c7a4215e79452e900349586a25792c9f22bcf7f
| 114
|
py
|
Python
|
layoutlm/examples/__init__.py
|
Cedrusco/unilm
|
f5485696aad9a39b733bc16d40e5e5d2c37284eb
|
[
"MIT"
] | null | null | null |
layoutlm/examples/__init__.py
|
Cedrusco/unilm
|
f5485696aad9a39b733bc16d40e5e5d2c37284eb
|
[
"MIT"
] | null | null | null |
layoutlm/examples/__init__.py
|
Cedrusco/unilm
|
f5485696aad9a39b733bc16d40e5e5d2c37284eb
|
[
"MIT"
] | null | null | null |
# flake8: noqa
from .classification.predict import make_prediction
from .classification.predict_api import predict
| 38
| 51
| 0.859649
| 14
| 114
| 6.857143
| 0.642857
| 0.375
| 0.520833
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009615
| 0.087719
| 114
| 3
| 52
| 38
| 0.913462
| 0.105263
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2c925b2706415b690a9c401ac995120ba9c0bb9d
| 220
|
py
|
Python
|
rdfs/__init__.py
|
Caterpillar3211/respect_your_dfs
|
c7fa9fc187aa4bd7fdcd7b58b79981f351adbd71
|
[
"MIT"
] | 1
|
2019-06-19T20:11:12.000Z
|
2019-06-19T20:11:12.000Z
|
rdfs/__init__.py
|
Caterpillar3211/respect_your_dfs
|
c7fa9fc187aa4bd7fdcd7b58b79981f351adbd71
|
[
"MIT"
] | null | null | null |
rdfs/__init__.py
|
Caterpillar3211/respect_your_dfs
|
c7fa9fc187aa4bd7fdcd7b58b79981f351adbd71
|
[
"MIT"
] | null | null | null |
from .helpers import load_dataset
from .core import Merger
from .core import CategoryEncoder
from .core import Imputer
from .core import AttributeAdder
from .core import Pipesystem
from .core import OptimizedPipesystem
| 24.444444
| 37
| 0.836364
| 29
| 220
| 6.310345
| 0.413793
| 0.262295
| 0.459016
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131818
| 220
| 9
| 37
| 24.444444
| 0.958115
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2ca4ea63da0f23de067acef30ee7375c05e958de
| 783
|
py
|
Python
|
src/ctc/db/schemas/protocol_schemas/fourbyte_schema_defs.py
|
fei-protocol/checkthechain
|
ec838f3d0d44af228f45394d9ba8d8eb7f677520
|
[
"MIT"
] | 94
|
2022-02-15T19:34:49.000Z
|
2022-03-26T19:26:22.000Z
|
src/ctc/db/schemas/protocol_schemas/fourbyte_schema_defs.py
|
fei-protocol/checkthechain
|
ec838f3d0d44af228f45394d9ba8d8eb7f677520
|
[
"MIT"
] | 7
|
2022-03-03T02:58:47.000Z
|
2022-03-11T18:41:05.000Z
|
src/ctc/db/schemas/protocol_schemas/fourbyte_schema_defs.py
|
fei-protocol/checkthechain
|
ec838f3d0d44af228f45394d9ba8d8eb7f677520
|
[
"MIT"
] | 7
|
2022-02-15T17:53:07.000Z
|
2022-03-17T19:14:17.000Z
|
from __future__ import annotations
import toolsql
fourbyte_schema: toolsql.DBSchema = {
'tables': {
'function_signatures': {
'columns': [
{'name': 'id', 'primary': True},
{'name': 'created_at'},
{'name': 'text_signature', 'index': True},
{'name': 'hex_signature', 'index': True},
{'name': 'bytes_signature'},
],
},
'event_signatures': {
'columns': [
{'name': 'id', 'primary': True},
{'name': 'created_at'},
{'name': 'text_signature', 'index': True},
{'name': 'hex_signature', 'index': True},
{'name': 'bytes_signature'},
],
},
},
}
| 27.964286
| 58
| 0.427842
| 57
| 783
| 5.614035
| 0.421053
| 0.15
| 0.225
| 0.275
| 0.725
| 0.725
| 0.725
| 0.725
| 0.725
| 0.725
| 0
| 0
| 0.394636
| 783
| 27
| 59
| 29
| 0.675105
| 0
| 0
| 0.583333
| 0
| 0
| 0.302682
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.083333
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
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| 0
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|
0
| 8
|
2cc59f6d545ed4eb26a63f14b39ba4426d435d78
| 17,950
|
py
|
Python
|
krun/tests/test_manifest_manager.py
|
softdevteam/krun
|
a0c8e5bfb91d192695df63a500be96b7e6764491
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | 68
|
2015-09-28T18:29:45.000Z
|
2022-01-25T21:54:18.000Z
|
krun/tests/test_manifest_manager.py
|
softdevteam/krun
|
a0c8e5bfb91d192695df63a500be96b7e6764491
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | 369
|
2015-07-01T14:43:22.000Z
|
2021-07-28T09:34:25.000Z
|
krun/tests/test_manifest_manager.py
|
softdevteam/krun
|
a0c8e5bfb91d192695df63a500be96b7e6764491
|
[
"Apache-2.0",
"MIT-0",
"MIT"
] | 9
|
2016-04-29T18:28:28.000Z
|
2020-10-02T21:17:26.000Z
|
import os.path
import pytest
from krun.config import Config
from krun.scheduler import ManifestManager
from krun.util import FatalKrunError
from krun.tests.mocks import MockPlatform, mock_platform
DEFAULT_MANIFEST = "krun.manifest"
TEST_DIR = os.path.abspath(os.path.dirname(__file__))
BLANK_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
O dummy:Java:default-java
O nbody:Java:default-java
O dummy:CPython:default-python
O nbody:CPython:default-python
O dummy:Java:default-java
O nbody:Java:default-java
O dummy:CPython:default-python
O nbody:CPython:default-python
"""
SKIPS_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
S dummy:Java:default-java
S nbody:Java:default-java
O dummy:CPython:default-python
O nbody:CPython:default-python
O dummy:Java:default-java
O nbody:Java:default-java
O dummy:CPython:default-python
O nbody:CPython:default-python
"""
SKIPS_END_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
O dummy:Java:default-java
O nbody:Java:default-java
O dummy:CPython:default-python
O nbody:CPython:default-python
O dummy:Java:default-java
O nbody:Java:default-java
S dummy:CPython:default-python
S nbody:CPython:default-python
"""
SKIPS_ALL_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
S dummy:Java:default-java
S nbody:Java:default-java
S dummy:CPython:default-python
S nbody:CPython:default-python
S dummy:Java:default-java
S nbody:Java:default-java
S dummy:CPython:default-python
S nbody:CPython:default-python
"""
ERRORS_ALL_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
E dummy:Java:default-java
E nbody:Java:default-java
E dummy:CPython:default-python
E nbody:CPython:default-python
E dummy:Java:default-java
E nbody:Java:default-java
E dummy:CPython:default-python
E nbody:CPython:default-python
"""
IRREGULAR_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
E dummy:Java:default-java
C nbody:Java:default-java
C dummy:CPython:default-python
S nbody:CPython:default-python
C dummy:Java:default-java
S nbody:Java:default-java
O dummy:CPython:default-python
E nbody:CPython:default-python
"""
# num_mails_sent is missing
MISSING_HEADER_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_reboots=00000000
keys
E dummy:Java:default-java
C nbody:Java:default-java
C dummy:CPython:default-python
S nbody:CPython:default-python
C dummy:Java:default-java
S nbody:Java:default-java
O dummy:CPython:default-python
E nbody:CPython:default-python
"""
THIRD_KEY_REP_EXAMPLE_MANIFEST = """eta_avail_idx=4
num_mails_sent=0000
num_reboots=00000000
keys
E dummy:Java:default-java
C nbody:Java:default-java
C dummy:CPython:default-python
S nbody:CPython:default-python
C dummy:Java:default-java
S nbody:Java:default-java
C dummy:CPython:default-python
E nbody:CPython:default-python
C dummy:Java:default-java
S nbody:Java:default-java
O dummy:CPython:default-python
E nbody:CPython:default-python
"""
def _setup(contents):
class FakeConfig(object):
filename = os.path.join(TEST_DIR, "manifest_tests.krun")
config = FakeConfig()
with open(ManifestManager.get_filename(config), "w") as fh:
fh.write(contents)
return ManifestManager(config, MockPlatform(None, config))
def _tear_down(filename):
if os.path.exists(filename):
os.unlink(filename)
else:
assert(False)
def test_parse_manifest():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.eta_avail_idx == 4
assert manifest.num_execs_left == 8
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == "dummy:Java:default-java"
assert manifest.next_exec_idx == 0
assert manifest.next_exec_flag_offset == 62
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.skipped_keys == set()
assert manifest.non_skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java", "dummy:CPython:default-python",
"nbody:CPython:default-python",]
)
assert manifest.num_reboots == 0
assert manifest.num_reboots_offset == 48
assert manifest.num_mails_sent == 0
assert manifest.num_mails_sent_offset == 31
_tear_down(manifest.path)
def test_parse_empty_manifest():
with pytest.raises(AssertionError):
_setup("")
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_parse_erroneous_manifest_001():
with pytest.raises(AssertionError):
_setup("""eta_avail_idx=4
keys
X dummy:Java:default-java""")
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_parse_erroneous_manifest_002():
with pytest.raises(FatalKrunError):
_setup("""bob=4
keys
O dummy:Java:default-java""")
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_parse_erroneous_manifest_003():
with pytest.raises(ValueError):
_setup("""eta_avail_idx=4
num_mails_sent=0000
keyz
O dummy:Java:default-java""")
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_parse_erroneous_manifest_004():
with pytest.raises(ValueError):
manifest = _setup("""eta_avail_idx=4,
num_mails_sent=0000
keys
O dummy:Java:default-java""")
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_parse_with_skips():
manifest = _setup(SKIPS_EXAMPLE_MANIFEST)
assert manifest.eta_avail_idx == 4
assert manifest.num_execs_left == 6
assert manifest.total_num_execs == 6
assert manifest.next_exec_key == "dummy:CPython:default-python"
assert manifest.next_exec_idx == 2
assert manifest.next_exec_flag_offset == 114
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 1,
"nbody:Java:default-java": 1,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java"])
assert manifest.non_skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java", "dummy:CPython:default-python",
"nbody:CPython:default-python",]
)
_tear_down(manifest.path)
def test_parse_with_all_skips():
manifest = _setup(SKIPS_ALL_EXAMPLE_MANIFEST)
assert manifest.eta_avail_idx == 4
assert manifest.num_execs_left == 0
assert manifest.total_num_execs == 0
assert manifest.next_exec_key == None
assert manifest.next_exec_idx == -1
assert manifest.next_exec_flag_offset == None
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java", "dummy:CPython:default-python",
"nbody:CPython:default-python",])
assert manifest.non_skipped_keys == set()
_tear_down(manifest.path)
def test_parse_with_all_errors():
manifest = _setup(ERRORS_ALL_EXAMPLE_MANIFEST)
assert manifest.eta_avail_idx == 4
assert manifest.num_execs_left == 0
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == None
assert manifest.next_exec_idx == -1
assert manifest.next_exec_flag_offset == None
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.skipped_keys == set()
assert manifest.non_skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java", "dummy:CPython:default-python",
"nbody:CPython:default-python",])
_tear_down(manifest.path)
def test_parse_with_skips_at_end():
manifest = _setup(SKIPS_END_EXAMPLE_MANIFEST)
assert manifest.eta_avail_idx == 4
assert manifest.num_execs_left == 6
assert manifest.total_num_execs == 6
assert manifest.next_exec_key == "dummy:Java:default-java"
assert manifest.next_exec_idx == 0
assert manifest.next_exec_flag_offset == 62
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 1,
"nbody:CPython:default-python": 1,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.skipped_keys == set(["dummy:CPython:default-python",
"nbody:CPython:default-python",])
assert manifest.non_skipped_keys == set(["dummy:Java:default-java",
"nbody:Java:default-java", "dummy:CPython:default-python",
"nbody:CPython:default-python",]
)
_tear_down(manifest.path)
def test_get_total_in_proc_iters():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
config = Config(os.path.join(TEST_DIR, "example.krun"))
assert manifest.get_total_in_proc_iters(config) == 8 * 5 # Executions * iterations
_tear_down(manifest.path)
def test_write_new_manifest0001(mock_platform):
_setup(BLANK_EXAMPLE_MANIFEST)
config = Config(os.path.join(TEST_DIR, "example.krun"))
manifest1 = ManifestManager(config, mock_platform, new_file=True)
manifest2 = ManifestManager(config, mock_platform) # reads the file in from the last line
assert manifest1 == manifest2
_tear_down(manifest2.path)
def test_write_new_manifest0002(mock_platform):
manifest_path = "example_000.manifest"
config_path = os.path.join(TEST_DIR, "more_complicated.krun")
config = Config(config_path)
manifest = ManifestManager(config, mock_platform, new_file=True)
assert manifest.total_num_execs == 90 # taking into account skips
_tear_down(manifest.path)
def test_update_blank():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.num_execs_left == 8
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == "dummy:Java:default-java"
assert manifest.next_exec_idx == 0
assert manifest.next_exec_flag_offset == 62
assert manifest.num_mails_sent_offset == 31
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
# Benchmark completed.
manifest.update("C")
assert manifest.num_execs_left == 7
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == "nbody:Java:default-java"
assert manifest.next_exec_idx == 1
assert manifest.next_exec_flag_offset == 88
assert manifest.num_mails_sent_offset == 31
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 1,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 1,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
# Benchmark failed.
manifest.update("E")
assert manifest.num_execs_left == 6
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == "dummy:CPython:default-python"
assert manifest.next_exec_idx == 2
assert manifest.next_exec_flag_offset == 114
assert manifest.num_mails_sent_offset == 31
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 1,
"nbody:Java:default-java": 1,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 1,
"nbody:Java:default-java": 1,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
_tear_down(manifest.path)
def test_update_to_completion():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.num_execs_left == 8
assert manifest.total_num_execs == 8
assert manifest.next_exec_key == "dummy:Java:default-java"
assert manifest.next_exec_idx == 0
assert manifest.next_exec_flag_offset == 62
assert manifest.num_mails_sent_offset == 31
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
# Complete each benchmark.
for completed in xrange(1, 9):
manifest.update("C")
assert manifest.num_execs_left == 8 - completed
assert manifest.total_num_execs == 8
assert manifest.next_exec_idx == -1
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 0,
"nbody:CPython:default-python": 0,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 2,
"dummy:CPython:default-python": 2,
"nbody:CPython:default-python": 2,
}
_tear_down(manifest.path)
def test_irregular_manifest():
manifest = _setup(IRREGULAR_EXAMPLE_MANIFEST)
assert manifest.num_execs_left == 1
assert manifest.total_num_execs == 6
assert manifest.next_exec_key == "dummy:CPython:default-python"
assert manifest.next_exec_idx == 6
assert manifest.next_exec_flag_offset == 228
assert manifest.num_mails_sent_offset == 31
assert manifest.outstanding_exec_counts == {
"dummy:Java:default-java": 0,
"nbody:Java:default-java": 0,
"dummy:CPython:default-python": 1,
"nbody:CPython:default-python": 0,
}
assert manifest.completed_exec_counts == {
"dummy:Java:default-java": 2,
"nbody:Java:default-java": 1,
"dummy:CPython:default-python": 1,
"nbody:CPython:default-python": 1,
}
_tear_down(manifest.path)
def test_update_num_mails_sent0001():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.num_mails_sent == 0
manifest.update_num_mails_sent()
assert manifest.num_mails_sent == 1
manifest.update_num_mails_sent()
manifest.update_num_mails_sent()
assert manifest.num_mails_sent == 3
_tear_down(manifest.path)
def test_update_num_mails_sent0002():
"""Tests the overflow case"""
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
manifest.num_mails_sent = manifest.num_mails_maxout
with pytest.raises(AssertionError):
manifest.update_num_mails_sent()
_tear_down(manifest.path)
def test_update_num_reboots0001():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.num_reboots == 0
manifest.update_num_reboots()
assert manifest.num_reboots == 1
manifest.update_num_reboots()
manifest.update_num_reboots()
assert manifest.num_reboots == 3
_tear_down(manifest.path)
def test_update_num_reboots0002():
"""Tests the overflow case"""
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
manifest.num_reboots = manifest.num_reboots_maxout
with pytest.raises(AssertionError):
manifest.update_num_reboots()
_tear_down(manifest.path)
def test_missing_header_manifest0001():
with pytest.raises(AssertionError):
manifest = _setup(MISSING_HEADER_EXAMPLE_MANIFEST)
_tear_down(os.path.join("krun", "tests", "manifest_tests.manifest"))
def test_next_exec_key_index0001():
manifest = _setup(BLANK_EXAMPLE_MANIFEST)
assert manifest.next_exec_key_index() == 0
_tear_down(manifest.path)
def test_next_exec_key_index0002():
manifest = _setup(SKIPS_EXAMPLE_MANIFEST)
assert manifest.next_exec_key_index() == 0
_tear_down(manifest.path)
def test_next_exec_key_index0003():
manifest = _setup(ERRORS_ALL_EXAMPLE_MANIFEST)
with pytest.raises(FatalKrunError) as e:
manifest.next_exec_key_index()
assert "Manifest ended unexpectedly" in str(e)
_tear_down(manifest.path)
def test_next_exec_key_index0004():
manifest = _setup(THIRD_KEY_REP_EXAMPLE_MANIFEST)
assert manifest.next_exec_key_index() == 2
_tear_down(manifest.path)
| 32.695811
| 94
| 0.710306
| 2,408
| 17,950
| 5.056063
| 0.070183
| 0.127639
| 0.121971
| 0.087064
| 0.872279
| 0.83885
| 0.821437
| 0.791869
| 0.752115
| 0.727885
| 0
| 0.023739
| 0.173928
| 17,950
| 548
| 95
| 32.755474
| 0.797343
| 0.012535
| 0
| 0.739583
| 0
| 0
| 0.339056
| 0.284617
| 0
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| 0.245833
| 1
| 0.05625
| false
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| 0.0125
| 0
| 0.075
| 0
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| null | 0
| 0
| 0
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| 1
| 1
| 1
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| 1
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| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
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| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
390bc903243dbca726f91a88351ec6f06ced30c7
| 151
|
py
|
Python
|
tcplotter/__init__.py
|
HUGG/gchron-plotters
|
6f8115c62431030f59bbe6203b243f88d96527e0
|
[
"MIT"
] | null | null | null |
tcplotter/__init__.py
|
HUGG/gchron-plotters
|
6f8115c62431030f59bbe6203b243f88d96527e0
|
[
"MIT"
] | 5
|
2022-02-04T07:13:32.000Z
|
2022-03-15T14:15:04.000Z
|
tcplotter/__init__.py
|
HUGG/gchron-plotters
|
6f8115c62431030f59bbe6203b243f88d96527e0
|
[
"MIT"
] | null | null | null |
from .tcplotter import time_vs_temp
from .tcplotter import eu_vs_radius
from .tcplotter import rate_vs_radius_eu
from .tcplotter import rate_vs_age_tc
| 30.2
| 40
| 0.86755
| 26
| 151
| 4.653846
| 0.423077
| 0.429752
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| 0.10596
| 151
| 4
| 41
| 37.75
| 0.896296
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| null | 0
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| 1
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| 1
| 0
|
0
| 8
|
1ac9d75cb11357f8d4a47e717d459835c97dc46a
| 38
|
py
|
Python
|
src/lib/symbol.py
|
DTenore/skulpt
|
098d20acfb088d6db85535132c324b7ac2f2d212
|
[
"MIT"
] | 2,671
|
2015-01-03T08:23:25.000Z
|
2022-03-31T06:15:48.000Z
|
src/lib/symbol.py
|
wakeupmuyunhe/skulpt
|
a8fb11a80fb6d7c016bab5dfe3712517a350b347
|
[
"MIT"
] | 972
|
2015-01-05T08:11:00.000Z
|
2022-03-29T13:47:15.000Z
|
src/lib/symbol.py
|
wakeupmuyunhe/skulpt
|
a8fb11a80fb6d7c016bab5dfe3712517a350b347
|
[
"MIT"
] | 845
|
2015-01-03T19:53:36.000Z
|
2022-03-29T18:34:22.000Z
|
import _sk_fail; _sk_fail._("symbol")
| 19
| 37
| 0.763158
| 6
| 38
| 4
| 0.666667
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078947
| 38
| 1
| 38
| 38
| 0.685714
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
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| 0
| 0
| 0
| 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
46e978e4d8c72a9619d5082735eae010a3835dc0
| 4,172
|
py
|
Python
|
trabConfeitaria/pycake/migrations/0002_auto_20210908_2214.py
|
mestrecalendo/trabalho_web
|
4b6c8c6029e67270146396b8421f4c65a172a52d
|
[
"MIT"
] | null | null | null |
trabConfeitaria/pycake/migrations/0002_auto_20210908_2214.py
|
mestrecalendo/trabalho_web
|
4b6c8c6029e67270146396b8421f4c65a172a52d
|
[
"MIT"
] | null | null | null |
trabConfeitaria/pycake/migrations/0002_auto_20210908_2214.py
|
mestrecalendo/trabalho_web
|
4b6c8c6029e67270146396b8421f4c65a172a52d
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2.6 on 2021-09-09 02:14
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pycake', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='pedido',
name='cobertura',
field=models.CharField(choices=[('Chantilly', 'Chantilly'), ('Pasta Americana', 'Pasta Americana'), ('Merengue', 'Merengue'), ('Nutella', 'Nutella'), ('Brigadeiro', 'Brigadeiro'), ('Brigadeiro Branco', 'Brigadeiro Branco'), ('Morango', 'Morango'), ('Ameixa', 'Ameixa'), ('Cream Cheese', 'Cream Cheese'), ('Doce de Leite', 'Doce de Leite'), ('Abacaxi', 'Abacaxi'), ('Côco', 'Coco'), ('Nozes', 'Nozes'), ('Pistache', 'Pistache'), ('Leite Ninho', 'Leite Ninho'), ('Creme Inglês', 'Creme Inglês')], default='Brigadeiro', max_length=255),
),
migrations.AlterField(
model_name='pedido',
name='data_entrega',
field=models.DateField(),
),
migrations.AlterField(
model_name='pedido',
name='data_pedido',
field=models.DateField(),
),
migrations.AlterField(
model_name='pedido',
name='massa',
field=models.CharField(choices=[('Chocolate', 'Chocolate'), ('Cenoura', 'Cenoura'), ('Limão', 'Limão'), ('Milho', 'Milho'), ('Baunilha', 'Baunilha'), ('Red Velvet', 'Red Velvet'), ('Laranja', 'Laranja'), ('Côco', 'Coco')], default='Chocolate', max_length=255),
),
migrations.AlterField(
model_name='pedido',
name='recheio',
field=models.CharField(choices=[('Brigadeiro', 'Brigadeiro'), ('Brigadeiro Branco', 'Brigadeiro Branco'), ('Morango', 'Morango'), ('Ameixa', 'Ameixa'), ('Cream Cheese', 'Cream Cheese'), ('Doce de Leite', 'Doce de Leite'), ('Abacaxi', 'Abacaxi'), ('Côco', 'Coco'), ('Nozes', 'Nozes'), ('Pistache', 'Pistache'), ('Leite Ninho', 'Leite Ninho'), ('Creme Ingês', 'Creme Inglês')], default='Brigadeiro', max_length=255),
),
migrations.AlterField(
model_name='pedido',
name='tamanho',
field=models.CharField(choices=[('Pequeno', 'BOLO PEQUENO'), ('Médio', 'BOLO MEDIO'), ('Grande', 'BOLO GRANDE')], default='Pequeno', max_length=255),
),
migrations.AlterField(
model_name='tamanhobolo',
name='tamanho',
field=models.CharField(choices=[('Pequeno', 'BOLO PEQUENO'), ('Médio', 'BOLO MEDIO'), ('Grande', 'BOLO GRANDE')], default='Pequeno', max_length=255),
),
migrations.AlterField(
model_name='tipocobertura',
name='cobertura',
field=models.CharField(choices=[('Chantilly', 'Chantilly'), ('Pasta Americana', 'Pasta Americana'), ('Merengue', 'Merengue'), ('Nutella', 'Nutella'), ('Brigadeiro', 'Brigadeiro'), ('Brigadeiro Branco', 'Brigadeiro Branco'), ('Morango', 'Morango'), ('Ameixa', 'Ameixa'), ('Cream Cheese', 'Cream Cheese'), ('Doce de Leite', 'Doce de Leite'), ('Abacaxi', 'Abacaxi'), ('Côco', 'Coco'), ('Nozes', 'Nozes'), ('Pistache', 'Pistache'), ('Leite Ninho', 'Leite Ninho'), ('Creme Inglês', 'Creme Inglês')], default='Brigadeiro', max_length=255),
),
migrations.AlterField(
model_name='tipomassa',
name='massa',
field=models.CharField(choices=[('Chocolate', 'Chocolate'), ('Cenoura', 'Cenoura'), ('Limão', 'Limão'), ('Milho', 'Milho'), ('Baunilha', 'Baunilha'), ('Red Velvet', 'Red Velvet'), ('Laranja', 'Laranja'), ('Côco', 'Coco')], default='Chocolate', max_length=255),
),
migrations.AlterField(
model_name='tiporecheio',
name='recheio',
field=models.CharField(choices=[('Brigadeiro', 'Brigadeiro'), ('Brigadeiro Branco', 'Brigadeiro Branco'), ('Morango', 'Morango'), ('Ameixa', 'Ameixa'), ('Cream Cheese', 'Cream Cheese'), ('Doce de Leite', 'Doce de Leite'), ('Abacaxi', 'Abacaxi'), ('Côco', 'Coco'), ('Nozes', 'Nozes'), ('Pistache', 'Pistache'), ('Leite Ninho', 'Leite Ninho'), ('Creme Ingês', 'Creme Inglês')], default='Brigadeiro', max_length=255),
),
]
| 65.1875
| 545
| 0.584372
| 395
| 4,172
| 6.118987
| 0.205063
| 0.082747
| 0.103434
| 0.119983
| 0.918494
| 0.918494
| 0.904013
| 0.900703
| 0.900703
| 0.851055
| 0
| 0.012944
| 0.203739
| 4,172
| 63
| 546
| 66.222222
| 0.71463
| 0.010786
| 0
| 0.77193
| 1
| 0
| 0.39297
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.017544
| 0
| 0.070175
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
200de3e606ff6ab210f5f66800ffdc8b73b8e1ee
| 902,000
|
py
|
Python
|
data.py
|
dskart/perceptron_classifier
|
936f763d8dcdb11898628cce2be50e68d0259296
|
[
"MIT"
] | null | null | null |
data.py
|
dskart/perceptron_classifier
|
936f763d8dcdb11898628cce2be50e68d0259296
|
[
"MIT"
] | null | null | null |
data.py
|
dskart/perceptron_classifier
|
936f763d8dcdb11898628cce2be50e68d0259296
|
[
"MIT"
] | null | null | null |
iris = [
((6.0, 2.2, 4.0, 1.0), "iris-versicolor"),
((6.9, 3.1, 5.4, 2.1), "iris-virginica" ),
((5.5, 2.4, 3.7, 1.0), "iris-versicolor"),
((6.3, 2.8, 5.1, 1.5), "iris-virginica" ),
((6.8, 3.0, 5.5, 2.1), "iris-virginica" ),
((6.3, 2.7, 4.9, 1.8), "iris-virginica" ),
((6.3, 3.4, 5.6, 2.4), "iris-virginica" ),
((5.9, 3.0, 4.2, 1.5), "iris-versicolor"),
((6.4, 2.9, 4.3, 1.3), "iris-versicolor"),
((5.7, 4.4, 1.5, 0.4), "iris-setosa" ),
((6.4, 3.2, 4.5, 1.5), "iris-versicolor"),
((6.9, 3.2, 5.7, 2.3), "iris-virginica" ),
((6.1, 2.6, 5.6, 1.4), "iris-virginica" ),
((4.6, 3.4, 1.4, 0.3), "iris-setosa" ),
((6.5, 3.0, 5.5, 1.8), "iris-virginica" ),
((6.9, 3.1, 4.9, 1.5), "iris-versicolor"),
((6.7, 2.5, 5.8, 1.8), "iris-virginica" ),
((5.5, 2.3, 4.0, 1.3), "iris-versicolor"),
((7.7, 2.8, 6.7, 2.0), "iris-virginica" ),
((5.7, 2.6, 3.5, 1.0), "iris-versicolor"),
((5.8, 2.8, 5.1, 2.4), "iris-virginica" ),
((6.3, 2.3, 4.4, 1.3), "iris-versicolor"),
((7.7, 2.6, 6.9, 2.3), "iris-virginica" ),
((6.3, 2.5, 5.0, 1.9), "iris-virginica" ),
((6.4, 2.7, 5.3, 1.9), "iris-virginica" ),
((5.1, 3.8, 1.9, 0.4), "iris-setosa" ),
((6.7, 3.1, 4.7, 1.5), "iris-versicolor"),
((5.2, 2.7, 3.9, 1.4), "iris-versicolor"),
((5.6, 3.0, 4.5, 1.5), "iris-versicolor"),
((4.5, 2.3, 1.3, 0.3), "iris-setosa" ),
((5.3, 3.7, 1.5, 0.2), "iris-setosa" ),
((5.1, 3.4, 1.5, 0.2), "iris-setosa" ),
((6.1, 2.9, 4.7, 1.4), "iris-versicolor"),
((5.4, 3.9, 1.3, 0.4), "iris-setosa" ),
((5.4, 3.9, 1.7, 0.4), "iris-setosa" ),
((4.4, 3.0, 1.3, 0.2), "iris-setosa" ),
((5.6, 2.7, 4.2, 1.3), "iris-versicolor"),
((6.6, 2.9, 4.6, 1.3), "iris-versicolor"),
((4.8, 3.1, 1.6, 0.2), "iris-setosa" ),
((6.1, 2.8, 4.0, 1.3), "iris-versicolor"),
((5.9, 3.0, 5.1, 1.8), "iris-virginica" ),
((5.5, 2.5, 4.0, 1.3), "iris-versicolor"),
((6.7, 3.3, 5.7, 2.1), "iris-virginica" ),
((5.0, 3.4, 1.6, 0.4), "iris-setosa" ),
((5.1, 2.5, 3.0, 1.1), "iris-versicolor"),
((6.5, 3.0, 5.2, 2.0), "iris-virginica" ),
((5.5, 4.2, 1.4, 0.2), "iris-setosa" ),
((5.1, 3.8, 1.6, 0.2), "iris-setosa" ),
((5.7, 3.8, 1.7, 0.3), "iris-setosa" ),
((5.7, 3.0, 4.2, 1.2), "iris-versicolor"),
((6.2, 3.4, 5.4, 2.3), "iris-virginica" ),
((4.9, 3.1, 1.5, 0.1), "iris-setosa" ),
((5.4, 3.4, 1.5, 0.4), "iris-setosa" ),
((5.1, 3.5, 1.4, 0.3), "iris-setosa" ),
((4.8, 3.0, 1.4, 0.3), "iris-setosa" ),
((5.8, 2.7, 5.1, 1.9), "iris-virginica" ),
((6.9, 3.1, 5.1, 2.3), "iris-virginica" ),
((6.7, 3.3, 5.7, 2.5), "iris-virginica" ),
((6.2, 2.8, 4.8, 1.8), "iris-virginica" ),
((5.0, 3.6, 1.4, 0.2), "iris-setosa" ),
((7.6, 3.0, 6.6, 2.1), "iris-virginica" ),
((5.2, 3.5, 1.5, 0.2), "iris-setosa" ),
((6.1, 3.0, 4.6, 1.4), "iris-versicolor"),
((6.0, 2.7, 5.1, 1.6), "iris-versicolor"),
((4.9, 2.4, 3.3, 1.0), "iris-versicolor"),
((4.8, 3.0, 1.4, 0.1), "iris-setosa" ),
((7.3, 2.9, 6.3, 1.8), "iris-virginica" ),
((5.7, 2.8, 4.1, 1.3), "iris-versicolor"),
((5.1, 3.8, 1.5, 0.3), "iris-setosa" ),
((6.7, 3.0, 5.2, 2.3), "iris-virginica" ),
((5.4, 3.4, 1.7, 0.2), "iris-setosa" ),
((7.2, 3.0, 5.8, 1.6), "iris-virginica" ),
((6.3, 2.5, 4.9, 1.5), "iris-versicolor"),
((7.7, 3.0, 6.1, 2.3), "iris-virginica" ),
((5.0, 3.2, 1.2, 0.2), "iris-setosa" ),
((5.6, 2.8, 4.9, 2.0), "iris-virginica" ),
((5.0, 3.0, 1.6, 0.2), "iris-setosa" ),
((5.1, 3.7, 1.5, 0.4), "iris-setosa" ),
((5.1, 3.3, 1.7, 0.5), "iris-setosa" ),
((4.6, 3.1, 1.5, 0.2), "iris-setosa" ),
((6.7, 3.0, 5.0, 1.7), "iris-versicolor"),
((5.5, 3.5, 1.3, 0.2), "iris-setosa" ),
((4.9, 3.0, 1.4, 0.2), "iris-setosa" ),
((5.0, 2.0, 3.5, 1.0), "iris-versicolor"),
((4.4, 3.2, 1.3, 0.2), "iris-setosa" ),
((7.2, 3.2, 6.0, 1.8), "iris-virginica" ),
((5.8, 2.6, 4.0, 1.2), "iris-versicolor"),
((4.9, 3.1, 1.5, 0.1), "iris-setosa" ),
((6.1, 3.0, 4.9, 1.8), "iris-virginica" ),
((5.0, 3.5, 1.6, 0.6), "iris-setosa" ),
((6.6, 3.0, 4.4, 1.4), "iris-versicolor"),
((6.3, 2.9, 5.6, 1.8), "iris-virginica" ),
((5.9, 3.2, 4.8, 1.8), "iris-versicolor"),
((4.9, 3.1, 1.5, 0.1), "iris-setosa" ),
((6.4, 3.1, 5.5, 1.8), "iris-virginica" ),
((5.0, 3.3, 1.4, 0.2), "iris-setosa" ),
((7.7, 3.8, 6.7, 2.2), "iris-virginica" ),
((6.8, 3.2, 5.9, 2.3), "iris-virginica" ),
((6.3, 3.3, 6.0, 2.5), "iris-virginica" ),
((5.5, 2.6, 4.4, 1.2), "iris-versicolor"),
((4.9, 2.5, 4.5, 1.7), "iris-virginica" ),
((5.0, 3.5, 1.3, 0.3), "iris-setosa" ),
((7.9, 3.8, 6.4, 2.0), "iris-virginica" ),
((6.5, 3.0, 5.8, 2.2), "iris-virginica" ),
((6.5, 2.8, 4.6, 1.5), "iris-versicolor"),
((5.8, 2.7, 4.1, 1.0), "iris-versicolor"),
((7.4, 2.8, 6.1, 1.9), "iris-virginica" ),
((5.8, 2.7, 3.9, 1.2), "iris-versicolor"),
((6.0, 3.4, 4.5, 1.6), "iris-versicolor"),
((5.6, 2.9, 3.6, 1.3), "iris-versicolor"),
((5.1, 3.5, 1.4, 0.2), "iris-setosa" ),
((5.4, 3.0, 4.5, 1.5), "iris-versicolor"),
((5.8, 4.0, 1.2, 0.2), "iris-setosa" ),
((5.2, 3.4, 1.4, 0.2), "iris-setosa" ),
((6.2, 2.9, 4.3, 1.3), "iris-versicolor"),
((6.0, 3.0, 4.8, 1.8), "iris-virginica" ),
((4.4, 2.9, 1.4, 0.2), "iris-setosa" ),
((4.7, 3.2, 1.6, 0.2), "iris-setosa" ),
((4.7, 3.2, 1.3, 0.2), "iris-setosa" ),
((6.8, 2.8, 4.8, 1.4), "iris-versicolor"),
((5.7, 2.5, 5.0, 2.0), "iris-virginica" ),
((4.6, 3.2, 1.4, 0.2), "iris-setosa" ),
((6.5, 3.2, 5.1, 2.0), "iris-virginica" ),
((6.3, 3.3, 4.7, 1.6), "iris-versicolor"),
((4.8, 3.4, 1.6, 0.2), "iris-setosa" ),
((5.4, 3.7, 1.5, 0.2), "iris-setosa" ),
((6.4, 2.8, 5.6, 2.1), "iris-virginica" ),
((4.8, 3.4, 1.9, 0.2), "iris-setosa" ),
((5.2, 4.1, 1.5, 0.1), "iris-setosa" ),
((4.3, 3.0, 1.1, 0.1), "iris-setosa" ),
((5.6, 3.0, 4.1, 1.3), "iris-versicolor"),
((7.2, 3.6, 6.1, 2.5), "iris-virginica" ),
((5.7, 2.9, 4.2, 1.3), "iris-versicolor"),
((6.2, 2.2, 4.5, 1.5), "iris-versicolor"),
((6.7, 3.1, 4.4, 1.4), "iris-versicolor"),
((6.0, 2.9, 4.5, 1.5), "iris-versicolor"),
((5.8, 2.7, 5.1, 1.9), "iris-virginica" ),
((5.6, 2.5, 3.9, 1.1), "iris-versicolor"),
((7.1, 3.0, 5.9, 2.1), "iris-virginica" ),
((7.0, 3.2, 4.7, 1.4), "iris-versicolor"),
((5.0, 2.3, 3.3, 1.0), "iris-versicolor"),
((5.0, 3.4, 1.5, 0.2), "iris-setosa" ),
((6.0, 2.2, 5.0, 1.5), "iris-virginica" ),
((6.4, 3.2, 5.3, 2.3), "iris-virginica" ),
((5.7, 2.8, 4.5, 1.3), "iris-versicolor"),
((5.5, 2.4, 3.8, 1.1), "iris-versicolor"),
((6.4, 2.8, 5.6, 2.2), "iris-virginica" ),
((4.6, 3.6, 1.0, 0.2), "iris-setosa" ),
((6.1, 2.8, 4.7, 1.2), "iris-versicolor"),
((6.7, 3.1, 5.6, 2.4), "iris-virginica" ),
]
digits = [
(( 0, 0, 0, 1,11,10, 0, 0, 0, 0, 0, 6,16,15, 0, 0, 0, 4,13,16,16,11, 0, 0, 0, 3, 8,10,16,10, 0, 0, 0, 0, 0, 4,16,12, 0, 0, 0, 0, 0, 0,16,14, 0, 0, 0, 0, 0, 0,16,16, 5, 0, 0, 0, 0, 0,12,13, 6, 0), 1),
(( 0, 0, 5,14, 7, 0, 0, 0, 0, 2,16,16,16, 3, 0, 0, 0, 1,16, 8,13, 9, 0, 0, 0, 0, 6, 3,16, 7, 0, 0, 0, 0, 0, 0,16, 7, 0, 0, 0, 0, 0,11,15, 0, 0, 0, 0, 0, 9,16,14, 8,10, 0, 0, 0, 6,15,12,16,16, 7), 2),
(( 0, 0, 6,12,14,15, 2, 0, 0, 0,14, 9, 8, 5, 0, 0, 0, 3,14, 6, 0, 0, 0, 0, 0, 6,16,16,15, 9, 0, 0, 0, 0, 4, 4, 7,15, 7, 0, 0, 0, 0, 0, 0,11, 8, 0, 0, 0, 2, 9,13,16, 4, 0, 0, 0, 7,15,12, 6, 0, 0), 5),
(( 0, 0, 1, 6,12,14, 4, 0, 0, 0, 4,13, 8, 3, 0, 0, 0, 0,13, 4, 0, 0, 0, 0, 0, 3,15, 5, 2, 0, 0, 0, 0, 8,16,16,16,12, 0, 0, 0, 2, 4, 3, 7,15, 3, 0, 0, 0, 0, 2, 7,14, 1, 0, 0, 0, 0, 8,12, 4, 0, 0), 5),
(( 0, 0, 8,15,10, 1, 0, 0, 0, 0,15,13,15,10, 0, 0, 0, 0,16, 2, 0,14, 1, 0, 0, 0,14, 5, 7,16, 2, 0, 0, 0, 7,12,11,15, 3, 0, 0, 0, 0, 0, 0,13, 4, 0, 0, 0, 6, 6, 9,16, 2, 0, 0, 0, 7,13,14, 3, 0, 0), 9),
(( 0, 0, 7,15,16, 9, 0, 0, 0, 1,16,12, 8, 7, 0, 0, 0, 0,14, 3, 0, 0, 0, 0, 0, 0,14,16,13, 1, 0, 0, 0, 0,12,16,15, 7, 0, 0, 0, 0, 0, 0, 9, 9, 0, 0, 0, 0, 4, 5,14, 9, 0, 0, 0, 0, 6,16,15, 3, 0, 0), 5),
(( 0, 0, 3,10,13, 5, 0, 0, 0, 0,15,12, 5,14, 1, 0, 0, 4,12, 1, 0,10, 4, 0, 0, 5, 8, 0, 0, 8, 7, 0, 0, 5, 8, 0, 0, 8, 8, 0, 0, 4,11, 0, 0,11, 5, 0, 0, 1,14, 6, 7,12, 0, 0, 0, 0, 4,15,14, 4, 0, 0), 0),
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(( 0.936425, 1.346230), False),
(( 0.449095, 0.642063), False),
((-2.216748, 0.336208), True ),
((-2.494915, -1.866903), True ),
(( 3.822628, 0.362185), True ),
(( 3.680024, 0.403873), True ),
(( 0.734658, -0.407478), False),
(( 1.722772, 0.076886), False),
(( 0.021439, -0.921383), False),
((-3.667259, -0.185621), True ),
(( 0.448968, 1.975604), True ),
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(( 2.930386, 0.282717), True ),
(( 0.277568, 0.041585), False),
(( 0.913799, -0.487033), False),
(( 0.360868, -1.640286), False),
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((-2.150810, -3.310351), True ),
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(( 3.310395, -0.669700), True ),
((-2.915167, 0.250758), True ),
(( 1.771232, 0.857799), False),
(( 1.923485, -2.956114), True ),
((-0.194136, 3.108405), True ),
((-2.061886, 0.085965), True ),
(( 0.275593, -0.306661), False),
((-1.950631, -0.164820), False),
(( 1.465842, -0.313809), False),
((-0.748123, -3.604735), True ),
((-2.359587, 1.281329), True ),
(( 1.259977, 1.080447), False),
((-2.297435, 2.659088), True ),
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(( 1.164691, -0.567937), False),
(( 2.715850, 2.435390), True ),
(( 0.297782, 0.502393), False),
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(( 2.889256, 2.280629), True ),
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(( 0.536222, 0.256907), False),
(( 2.173985, -0.265782), True ),
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(( 1.087777, 1.386078), False),
(( 0.439112, -1.280108), False),
(( 2.495124, -0.181465), True ),
((-0.074600, 0.733324), False),
((-0.183573, 1.973032), False),
]
mystery2 = [
((-0.059638, -0.700796, -0.140232), False),
(( 0.940426, 0.452941, -0.677537), False),
(( 0.136406, -0.878656, -0.905850), True ),
((-0.888245, 0.712932, 0.232893), False),
((-0.242083, 0.635014, -0.868913), True ),
(( 0.623895, 0.159497, 0.175498), True ),
((-0.456866, 0.444503, 0.401812), False),
(( 0.671421, -0.198850, 0.655754), False),
((-0.931818, 0.729689, 0.941633), False),
((-0.793811, -0.223779, -0.301822), False),
(( 0.126401, 0.328550, 0.909123), True ),
((-0.833068, 0.798700, -0.689521), True ),
(( 0.772959, 0.068004, -0.014457), False),
(( 0.113925, 0.466704, 0.848974), True ),
((-0.661095, 0.375638, 0.324919), False),
(( 0.190481, -0.367256, -0.207035), True ),
((-0.967230, 0.016583, 0.201924), False),
((-0.521037, 0.100344, 0.470321), False),
((-0.482895, -0.999660, -0.668749), False),
(( 0.396210, 0.808057, -0.072274), False),
(( 0.282586, 0.254767, 0.675908), True ),
((-0.412295, -0.672794, 0.370440), True ),
(( 0.530754, 0.267495, -0.588767), False),
((-0.942911, -0.391584, 0.563402), True ),
(( 0.747467, -0.649036, -0.413859), True ),
((-0.447659, -0.033893, 0.234886), True ),
((-0.502405, 0.841748, 0.047985), False),
((-0.629066, 0.427426, -0.963505), True ),
((-0.911877, -0.507482, 0.884788), True ),
((-0.394274, 0.673380, -0.242914), True ),
(( 0.896659, -0.410860, -0.554216), True ),
(( 0.648102, -0.920125, -0.693162), True ),
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(( 0.918292, -0.971123, 0.624699), False),
((-0.313126, 0.619198, -0.910335), True ),
(( 0.153060, 0.042764, 0.361189), True ),
(( 0.676325, -0.570312, 0.440865), False),
(( 0.775720, 0.891789, 0.043177), True ),
(( 0.762715, 0.671484, -0.634934), False),
((-0.693992, 0.610043, -0.735065), True ),
(( 0.732171, 0.932975, -0.324857), False),
(( 0.805651, 0.668056, -0.905158), False),
(( 0.322953, 0.524078, 0.546627), True ),
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(( 0.272034, -0.627010, -0.925551), True ),
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(( 0.475940, 0.111645, 0.686477), True ),
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(( 0.080315, -0.640674, -0.063679), True ),
(( 0.931195, -0.272079, -0.907637), True ),
(( 0.944099, -0.654656, 0.047126), False),
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(( 0.770714, -0.661499, 0.270988), False),
((-0.172689, 0.318050, -0.380925), True ),
(( 0.627220, -0.842492, -0.526222), True ),
(( 0.561800, 0.185037, 0.167082), True ),
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(( 0.473253, 0.368215, -0.046202), False),
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(( 0.990195, 0.663356, -0.372178), False),
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(( 0.105319, 0.159528, 0.153434), True ),
(( 0.312948, -0.823125, -0.004193), True ),
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((-0.492184, 0.792831, 0.116393), False),
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(( 0.556816, 0.614227, -0.847631), False),
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(( 0.694703, 0.526301, 0.690630), True ),
(( 0.118972, -0.353979, 0.333467), False),
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(( 0.686845, 0.791115, 0.726652), True ),
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((-0.461996, -0.918435, -0.770919), False),
(( 0.963949, -0.602054, -0.360918), True ),
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(( 0.567628, -0.773112, -0.689710), True ),
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(( 0.100412, -0.189617, 0.228017), False),
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(( 0.455588, 0.969379, -0.431454), False),
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(( 0.752756, -0.544388, 0.852901), False),
((-0.949432, -0.999415, 0.272190), True ),
(( 0.347524, 0.422879, -0.758646), False),
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(( 0.028915, 0.294855, 0.715146), True ),
(( 0.449689, -0.426290, -0.163254), True ),
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(( 0.209609, -0.038357, -0.747723), True ),
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(( 0.131845, 0.353869, -0.607846), False),
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(( 0.254844, 0.904736, 0.650643), True ),
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(( 0.841359, 0.170090, 0.798407), True ),
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(( 0.305009, -0.501099, 0.544190), False),
(( 0.273378, 0.676669, -0.540564), False),
(( 0.488329, 0.781147, -0.214800), False),
(( 0.713675, 0.733778, -0.323887), False),
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(( 0.916839, -0.725751, -0.605460), True ),
(( 0.940467, 0.068238, -0.326419), False),
(( 0.487205, -0.026893, -0.739255), True ),
((-0.419264, 0.040011, -0.414925), True ),
(( 0.336323, -0.536193, 0.133238), False),
(( 0.602222, 0.049035, 0.068130), True ),
(( 0.730716, 0.420401, -0.904450), False),
(( 0.630748, -0.185429, 0.457635), False),
((-0.943726, 0.508083, -0.911196), True ),
(( 0.164596, -0.635114, 0.632403), False),
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(( 0.475555, -0.016723, 0.215371), False),
(( 0.725335, 0.981584, -0.655014), False),
((-0.874447, 0.726160, -0.278856), True ),
(( 0.054603, 0.808808, -0.291826), False),
(( 0.286007, 0.175766, 0.236781), True ),
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(( 0.155944, 0.698386, -0.816294), False),
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(( 0.872594, 0.973032, -0.025915), False),
(( 0.789693, 0.538563, 0.177799), True ),
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(( 0.134427, 0.864931, -0.096851), False),
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(( 0.381114, 0.568921, -0.268082), False),
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(( 0.736332, 0.643931, -0.622043), False),
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(( 0.310947, 0.182261, 0.327966), True ),
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(( 0.213623, 0.047969, -0.442915), False),
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(( 0.393652, 0.549795, 0.648993), True ),
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(( 0.907836, 0.112122, -0.798352), False),
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(( 0.974350, 0.641213, 0.928430), True ),
(( 0.873492, 0.001479, 0.716309), True ),
(( 0.519167, -0.804616, -0.306681), True ),
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(( 0.356718, -0.860124, 0.167316), False),
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(( 0.116501, -0.050493, -0.775356), True ),
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(( 0.211105, 0.231155, 0.994341), True ),
(( 0.808068, -0.324133, -0.295606), True ),
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(( 0.752378, 0.316052, -0.565221), False),
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(( 0.876512, -0.350654, -0.051012), True ),
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(( 0.680469, 0.461997, -0.796299), False),
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| 0
| 0.002235
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 10
|
647db96c9aaf141903504ac8bd588d3cfc6e9c13
| 112,783
|
py
|
Python
|
api/source/main/python/datacube/api/query.py
|
alex-ip/agdc
|
9e9eb556c33792440a3736f64cd5f628cf3a1385
|
[
"BSD-3-Clause"
] | null | null | null |
api/source/main/python/datacube/api/query.py
|
alex-ip/agdc
|
9e9eb556c33792440a3736f64cd5f628cf3a1385
|
[
"BSD-3-Clause"
] | null | null | null |
api/source/main/python/datacube/api/query.py
|
alex-ip/agdc
|
9e9eb556c33792440a3736f64cd5f628cf3a1385
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python
# ===============================================================================
# Copyright (c) 2014 Geoscience Australia
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither Geoscience Australia nor the names of its contributors may be
# used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ===============================================================================
__author__ = "Simon Oldfield"
import logging
import psycopg2
import psycopg2.extras
import sys
import os
from collections import namedtuple
from datacube.api.utils import extract_feature_geometry_wkb
from datacube.config import Config
from datacube.api.model import Tile, Cell, DatasetType, Satellite
from datetime import date
from enum import Enum
_log = logging.getLogger(__name__)
class Month(Enum):
__order__ = "JANUARY FEBRUARY MARCH APRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER"
JANUARY = 1
FEBRUARY = 2
MARCH = 3
APRIL = 4
MAY = 5
JUNE = 6
JULY = 7
AUGUST = 8
SEPTEMBER = 9
OCTOBER = 10
NOVEMBER = 11
DECEMBER = 12
class Season(Enum):
__order__ = "SPRING SUMMER AUTUMN WINTER"
SPRING = "SPRING"
SUMMER = "SUMMER"
AUTUMN = "AUTUMN"
WINTER = "WINTER"
# NOTE: This is specific to Australia (well potentially) and is as per the Bureau of Meteorology Climate Glossary
# http://www.bom.gov.au/climate/glossary/seasons.shtml
MONTHS_BY_SEASON = {
Season.SPRING: [Month.SEPTEMBER, Month.OCTOBER, Month.NOVEMBER],
Season.SUMMER: [Month.DECEMBER, Month.JANUARY, Month.FEBRUARY],
Season.AUTUMN: [Month.MARCH, Month.APRIL, Month.MAY],
Season.WINTER: [Month.JUNE, Month.JULY, Month.AUGUST]
}
class TileClass(Enum):
__order__ = "SINGLE MOSAIC"
SINGLE = 1
MOSAIC = 4
TILE_CLASSES = [TileClass.SINGLE, TileClass.MOSAIC]
class TileType(Enum):
__order__ = "ONE_DEGREE"
ONE_DEGREE = 1
TILE_TYPE = TileType.ONE_DEGREE
class ProcessingLevel(Enum):
__order__ = "ORTHO NBAR PQA FC L1T MAP DSM DEM DEM_S DEM_H"
ORTHO = 1
NBAR = 2
PQA = 3
FC = 4
L1T = 5
MAP = 10
DSM = 100
DEM = 110
DEM_S = 120
DEM_H = 130
class SortType(Enum):
__order__ = "ASC DESC"
ASC = "ASC"
DESC = "DESC"
def print_tile(tile):
_log.debug("id=%7d x=%3d y=%3d start=[%s] end=[%s] year=[%4d] month=[%2d] datasets=[%s]",
tile.acquisition_id, tile.x, tile.y,
tile.start_datetime, tile.end_datetime,
tile.end_datetime_year, tile.end_datetime_month,
",".join("|".join([ds.type_id, ds.path]) for ds in tile.datasets))
def connect_to_db(config=None):
"""
Connect to the AGDC DB
:param config: Configuration
:type config: datacube.config.Config
:return: DB connection and cursor
:rtype: (psycopg2.connection, psycopg2.cursor)
"""
connection = cursor = None
if not config:
config = Config(os.path.expandvars("$HOME/.datacube/config"))
_log.debug(config.to_str())
connection_string = ""
if config.get_db_host():
connection_string += "host={host}".format(host=config.get_db_host())
if config.get_db_port():
connection_string += " port={port}".format(port=config.get_db_port())
connection_string += " dbname={database} user={user} password={password}".format(database=config.get_db_database(),
user=config.get_db_username(),
password=config.get_db_password())
connection = psycopg2.connect(connection_string)
cursor = connection.cursor(cursor_factory=psycopg2.extras.DictCursor)
cursor.execute("set search_path to public, {schema}".format(schema="gis, topology, ztmp"))
return connection, cursor
def to_file_ify_sql(sql):
"""
:param sql: The SQL string
:type sql: str
:return: The to file ified SQL
:rtype: str
"""
return """
copy (
{sql}
) to STDOUT csv header delimiter ',' escape '"' null '' quote '"'
""".format(sql=sql)
SatelliteDateExclusion = namedtuple("SatelliteDateExclusion", "satellite acq_min acq_max")
LS7_SLC_OFF_ACQ_MIN = date(2005, 5, 31)
LS7_SLC_OFF_ACQ_MAX = None
LS7_SLC_OFF_EXCLUSION = SatelliteDateExclusion(satellite=Satellite.LS7,
acq_min=LS7_SLC_OFF_ACQ_MIN, acq_max=LS7_SLC_OFF_ACQ_MAX)
LS8_PRE_WRS_2_ACQ_MIN = None
LS8_PRE_WRS_2_ACQ_MAX = date(2013, 4, 10)
LS8_PRE_WRS_2_EXCLUSION = SatelliteDateExclusion(satellite=Satellite.LS8,
acq_min=LS8_PRE_WRS_2_ACQ_MIN, acq_max=LS8_PRE_WRS_2_ACQ_MAX)
###
# CELLS...
###
# Cells that we DO have
def list_cells(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None, sort=SortType.ASC,
config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
.. warning::
Deprecated: use either datacube.api.query.list_cells_as_list() or datacube.api.query.list_cells_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list_cells_as_generator(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude, sort=sort,
config=config)
def list_cells_as_list(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None, sort=SortType.ASC,
config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list(list_cells_as_generator(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude, sort=sort,
config=config))
def list_cells_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None,
sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_sql_and_params(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude,
sort=sort)
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Cell.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_cells_to_file(x, y, satellites, acq_min, acq_max, dataset_types, filename, months=None, exclude=None,
sort=SortType.ASC, config=None):
"""
Write the list of cells matching the criteria to the specified file
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param filename: The output file
:type filename: str
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
"""
conn = cursor = None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_sql_and_params(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude,
sort=sort)
sql = to_file_ify_sql(sql)
if filename:
with open(filename, "w") as f:
cursor.copy_expert(cursor.mogrify(sql, params), f)
else:
cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def build_list_cells_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None,
sort=SortType.ASC):
"""
Build the SQL query string and parameters required to return the cells matching the criteria
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:return: The SQL query and params
:rtype: (str, dict)
"""
sql = """
SELECT DISTINCT nbar.x_index, nbar.y_index
FROM acquisition
JOIN satellite ON satellite.satellite_id=acquisition.satellite_id
"""
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_nbar)s
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
"""
if DatasetType.PQ25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_pqa)s
) as pq on
pq.acquisition_id=acquisition.acquisition_id
and pq.x_index=nbar.x_index and pq.y_index=nbar.y_index
and pq.tile_type_id=nbar.tile_type_id and pq.tile_class_id=nbar.tile_class_id
"""
if DatasetType.FC25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_fc)s
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DSM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dsm)s
) as dsm on
dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem)s
) as dem on
dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_h)s
) as dem_h on
dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_s)s
) as dem_s on
dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
"""
sql += """
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and nbar.x_index = ANY(%(x)s) and nbar.y_index = ANY(%(y)s)
and end_datetime::date between %(acq_min)s and %(acq_max)s
"""
if exclude:
for index, exclusion in enumerate(exclude):
esql = ""
if type(exclusion) is SatelliteDateExclusion:
esql += " and (satellite.satellite_tag <> %(exclude_satellite_{0})s".format(index)
if exclusion.acq_min and exclusion.acq_max:
esql += " or end_datetime not between %(exclude_acq_min_{0})s and %(exclude_acq_min_{0})s".format(index)
elif exclusion.acq_min:
esql += " or end_datetime < %(exclude_acq_min_{0})s".format(index)
elif exclusion.acq_max:
esql += " or end_datetime > %(exclude_acq_max_{0})s".format(index)
esql += ")"
sql += esql
if months:
sql += " and extract(month from end_datetime) = ANY(%(month)s)"
sql += """
order by nbar.x_index {sort}, nbar.y_index {sort}
""".format(sort=sort.value)
params = {"tile_type": [TILE_TYPE.value],
"tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"x": x, "y": y,
"acq_min": acq_min, "acq_max": acq_max,
"level_nbar": ProcessingLevel.NBAR.value}
if DatasetType.PQ25 in dataset_types:
params["level_pqa"] = ProcessingLevel.PQA.value
if DatasetType.FC25 in dataset_types:
params["level_fc"] = ProcessingLevel.FC.value
if DatasetType.DSM in dataset_types:
params["level_dsm"] = ProcessingLevel.DSM.value
if DatasetType.DEM in dataset_types:
params["level_dem"] = ProcessingLevel.DEM.value
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
params["level_dem_h"] = ProcessingLevel.DEM_H.value
if DatasetType.DEM_SMOOTHED in dataset_types:
params["level_dem_s"] = ProcessingLevel.DEM_S.value
if exclude:
for index, exclusion in enumerate(exclude):
if type(exclusion) is SatelliteDateExclusion:
params["exclude_satellite_{0}".format(index)] = exclusion.satellite.value
if exclusion.acq_min:
params["exclude_acq_min_{0}".format(index)] = exclusion.acq_min
if exclusion.acq_max:
params["exclude_acq_max_{0}".format(index)] = exclusion.acq_max
if months:
params["month"] = [month.value for month in months]
return sql, params
# Cells that we DON'T have
def list_cells_missing(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
.. note::
The dataset types supplied are tested for NOT being present
.. warning::
Deprecated: use either datacube.api.query.list_cells_missing_as_list() or datacube.api.query.list_cells_missing_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list_cells_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort, config)
def list_cells_missing_as_list(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
.. note::
The dataset types supplied are tested for NOT being present
.. warning::
Deprecated: use either datacube.api.query.list_cells_missing_as_list() or datacube.api.query.list_cells_missing_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list(list_cells_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort, config))
def list_cells_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
.. note::
The dataset types supplied are tested for NOT being present
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_missing_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort)
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Cell.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_cells_missing_to_file(x, y, satellites, acq_min, acq_max, dataset_types, filename, sort=SortType.ASC, config=None):
"""
Write the list of cells matching the criteria to the specified file
.. note::
The dataset types supplied are tested for NOT being present
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param filename: The output file
:type filename: str
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
"""
conn = cursor = None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_missing_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort)
sql = to_file_ify_sql(sql)
if filename:
with open(filename, "w") as f:
cursor.copy_expert(cursor.mogrify(sql, params), f)
else:
cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def build_list_cells_missing_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC):
"""
Build the SQL query string and parameters required to return the cells matching the criteria
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:return: The SQL query and params
:rtype: (str, dict)
"""
sql = """
select distinct nbar.x_index, nbar.y_index
from acquisition
join satellite on satellite.satellite_id=acquisition.satellite_id
"""
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_nbar)s
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
"""
if DatasetType.PQ25 in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_pqa)s
) as pqa on
pqa.acquisition_id=acquisition.acquisition_id
and pqa.x_index=nbar.x_index and pqa.y_index=nbar.y_index
and pqa.tile_type_id=nbar.tile_type_id and pqa.tile_class_id=nbar.tile_class_id
"""
if DatasetType.FC25 in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_fc)s
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DSM in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dsm)s
) as dsm on
dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem)s
) as dem on
dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_h)s
) as dem_h on
dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_s)s
) as dem_s on
dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
"""
sql += """
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and nbar.x_index = ANY(%(x)s) and nbar.y_index = ANY(%(y)s)
and end_datetime::date between %(acq_min)s and %(acq_max)s
"""
if DatasetType.PQ25 in dataset_types:
sql += """
and pqa.x_index is null
"""
if DatasetType.FC25 in dataset_types:
sql += """
and fc.x_index is null
"""
if DatasetType.DSM in dataset_types:
sql += """
and dsm.x_index is null
"""
if DatasetType.DEM in dataset_types:
sql += """
and dem.x_index is null
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
and dem_h.x_index is null
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
and dem_s.x_index is null
"""
sql += """
order by nbar.x_index {sort}, nbar.y_index {sort}
""".format(sort=sort.value)
params = {"tile_type": [TILE_TYPE.value], "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"x": x, "y": y,
"acq_min": acq_min, "acq_max": acq_max,
"level_nbar": ProcessingLevel.NBAR.value}
if DatasetType.PQ25 in dataset_types:
params["level_pqa"] = ProcessingLevel.PQA.value
if DatasetType.FC25 in dataset_types:
params["level_fc"] = ProcessingLevel.FC.value
if DatasetType.DSM in dataset_types:
params["level_dsm"] = ProcessingLevel.DSM.value
if DatasetType.DEM in dataset_types:
params["level_dem"] = ProcessingLevel.DEM.value
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
params["level_dem_h"] = ProcessingLevel.DEM_H.value
if DatasetType.DEM_SMOOTHED in dataset_types:
params["level_dem_s"] = ProcessingLevel.DEM_S.value
return sql, params
###
# TILES...
###
# Tiles that we DO have
def list_tiles(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None, sort=SortType.ASC,
config=None):
"""
Return a list of tiles matching the criteria as a SINGLE-USE generator
.. warning::
Deprecated: use either datacube.api.query.list_tiles_as_list() or datacube.api.query.list_tiles_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
return list_tiles_as_generator(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude, sort=sort,
config=config)
def list_tiles_as_list(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None, sort=SortType.ASC,
config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
return list(list_tiles_as_generator(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude, sort=sort,
config=config))
def list_tiles_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None,
sort=SortType.ASC, config=None):
"""
Return a list of tiles matching the criteria as a SINGLE-USE generator
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_tiles_sql_and_params(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude,
sort=sort)
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Tile.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_tiles_to_file(x, y, satellites, acq_min, acq_max, dataset_types, filename, months=None, exclude=None,
sort=SortType.ASC, config=None):
"""
Write the list of tiles matching the criteria to the specified file
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param filename: The output file
:type filename: str
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
"""
conn = cursor = None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_tiles_sql_and_params(x=x, y=y, satellites=satellites, acq_min=acq_min, acq_max=acq_max,
dataset_types=dataset_types, months=months, exclude=exclude,
sort=sort)
sql = to_file_ify_sql(sql)
if filename:
with open(filename, "w") as f:
cursor.copy_expert(cursor.mogrify(sql, params), f)
else:
cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def build_list_tiles_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, months=None, exclude=None, sort=SortType.ASC):
"""
Build the SQL query string and parameters required to return the tiles matching the criteria
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param months: Month(s) of acquisition to include
:type months: list[datacube.api.query.Month]
:param exclude: Exclusions - currently supports satellite/date combinations for LS7 SLC OFF and LS8 PRE WRS 2
:type exclude: list[datacube.api.query.SatelliteDateExclusion]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:return: The SQL query and params
:rtype: (str, dict)
"""
sql = """
select
acquisition.acquisition_id, satellite_tag as satellite, start_datetime, end_datetime,
extract(year from end_datetime) as end_datetime_year, extract(month from end_datetime) as end_datetime_month,
nbar.x_index, nbar.y_index, point(nbar.x_index, nbar.y_index) as xy,
"""
sql += """
ARRAY[
"""
sql += """
['ARG25', nbar.tile_pathname]
"""
if DatasetType.PQ25 in dataset_types:
sql += """
,['PQ25', pqa.tile_pathname]
"""
if DatasetType.FC25 in dataset_types:
sql += """
,['FC25', fc.tile_pathname]
"""
if DatasetType.NDVI in dataset_types:
sql += """
,['NDVI', nbar.tile_pathname]
"""
if DatasetType.EVI in dataset_types:
sql += """
,['EVI', nbar.tile_pathname]
"""
if DatasetType.NBR in dataset_types:
sql += """
,['NBR', nbar.tile_pathname]
"""
if DatasetType.TCI in dataset_types:
sql += """
,['TCI', nbar.tile_pathname]
"""
if DatasetType.DSM in dataset_types:
sql += """
,['DSM', dsm.tile_pathname]
"""
if DatasetType.DEM in dataset_types:
sql += """
,['DEM', dem.tile_pathname]
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
,['DEM_HYDROLOGICALLY_ENFORCED', dem_h.tile_pathname]
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
,['DEM_SMOOTHED', dem_s.tile_pathname]
"""
sql += """
] as datasets
"""
sql += """
from acquisition
join satellite on satellite.satellite_id=acquisition.satellite_id
"""
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_nbar)s
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
"""
if DatasetType.PQ25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_pqa)s
) as pqa on
pqa.acquisition_id=acquisition.acquisition_id
and pqa.x_index=nbar.x_index and pqa.y_index=nbar.y_index
and pqa.tile_type_id=nbar.tile_type_id and pqa.tile_class_id=nbar.tile_class_id
"""
if DatasetType.FC25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_fc)s
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DSM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dsm)s
) as dsm on
dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem)s
) as dem on
dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_h)s
) as dem_h on
dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_s)s
) as dem_s on
dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
"""
sql += """
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and nbar.x_index = ANY(%(x)s) and nbar.y_index = ANY(%(y)s)
and end_datetime::date between %(acq_min)s and %(acq_max)s
"""
if exclude:
for index, exclusion in enumerate(exclude):
esql = ""
if type(exclusion) is SatelliteDateExclusion:
esql += " and (satellite.satellite_tag <> %(exclude_satellite_{0})s".format(index)
if exclusion.acq_min and exclusion.acq_max:
esql += " or end_datetime not between %(exclude_acq_min_{0})s and %(exclude_acq_min_{0})s".format(index)
elif exclusion.acq_min:
esql += " or end_datetime < %(exclude_acq_min_{0})s".format(index)
elif exclusion.acq_max:
esql += " or end_datetime > %(exclude_acq_max_{0})s".format(index)
esql += ")"
sql += esql
if months:
sql += " and extract(month from end_datetime) = ANY(%(month)s)"
sql += """
order by nbar.x_index, nbar.y_index, end_datetime {sort}, satellite asc
""".format(sort=sort.value)
params = {"tile_type": [TILE_TYPE.value],
"tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"x": x, "y": y,
"acq_min": acq_min, "acq_max": acq_max,
"level_nbar": ProcessingLevel.NBAR.value}
if DatasetType.PQ25 in dataset_types:
params["level_pqa"] = ProcessingLevel.PQA.value
if DatasetType.FC25 in dataset_types:
params["level_fc"] = ProcessingLevel.FC.value
if DatasetType.DSM in dataset_types:
params["level_dsm"] = ProcessingLevel.DSM.value
if DatasetType.DEM in dataset_types:
params["level_dem"] = ProcessingLevel.DEM.value
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
params["level_dem_h"] = ProcessingLevel.DEM_H.value
if DatasetType.DEM_SMOOTHED in dataset_types:
params["level_dem_s"] = ProcessingLevel.DEM_S.value
if exclude:
for index, exclusion in enumerate(exclude):
if type(exclusion) is SatelliteDateExclusion:
params["exclude_satellite_{0}".format(index)] = exclusion.satellite.value
if exclusion.acq_min:
params["exclude_acq_min_{0}".format(index)] = exclusion.acq_min
if exclusion.acq_max:
params["exclude_acq_max_{0}".format(index)] = exclusion.acq_max
if months:
params["month"] = [month.value for month in months]
return sql, params
# Tiles that we DON'T have
def list_tiles_missing(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of tiles matching the criteria as a SINGLE-USE generator
.. note::
The dataset types supplied are tested for NOT being present
.. note::
The NBAR dataset is ALWAYS the only dataset returned
.. warning::
Deprecated: use either datacube.api.query.list_tiles_missing_as_list() or datacube.api.query.list_tiles_missing_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
return list_tiles_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort, config)
def list_tiles_missing_as_list(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of tiles matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
.. note::
The dataset types supplied are tested for NOT being present
.. note::
The NBAR dataset is ALWAYS the only dataset returned
.. warning::
Deprecated: use either datacube.api.query.list_tiles_missing_as_list() or datacube.api.query.list_tiles_missing_as_generator()
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
return list(list_tiles_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort, config))
def list_tiles_missing_as_generator(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of tiles matching the criteria as a SINGLE-USE generator
.. note::
The dataset types supplied are tested for NOT being present
.. note::
The NBAR dataset is ALWAYS the only dataset returned
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of tiles
:rtype: list[datacube.api.model.Tile]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_tiles_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort)
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Tile.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_tiles_missing_to_file(x, y, satellites, acq_min, acq_max, dataset_types, filename, sort=SortType.ASC, config=None):
"""
Write the list of tiles matching the criteria to the specified file
.. note::
The dataset types supplied are tested for NOT being present
.. note::
The NBAR dataset is ALWAYS the only dataset returned
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param filename: The output file
:type filename: str
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
"""
conn = cursor = None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_tiles_missing_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort)
sql = to_file_ify_sql(sql)
if filename:
with open(filename, "w") as f:
cursor.copy_expert(cursor.mogrify(sql, params), f)
else:
cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def build_list_tiles_missing_sql_and_params(x, y, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC):
"""
Build the SQL query string and parameters required to return the cells matching the criteria
:param x: X cell range
:type x: list[int]
:param y: Y cell range
:type y: list[int]
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:return: The SQL query and params
:rtype: (str, dict)
"""
sql = """
select
acquisition.acquisition_id, satellite_tag as satellite, start_datetime, end_datetime,
extract(year from end_datetime) as end_datetime_year, extract(month from end_datetime) as end_datetime_month,
NBAR.x_index, NBAR.y_index, point(NBAR.x_index, NBAR.y_index) as xy,
ARRAY[
['ARG25', NBAR.tile_pathname]
] as datasets
from acquisition
join satellite on satellite.satellite_id=acquisition.satellite_id
"""
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_nbar)s
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
"""
if DatasetType.PQ25 in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_pqa)s
) as pqa on
pqa.acquisition_id=acquisition.acquisition_id
and pqa.x_index=nbar.x_index and pqa.y_index=nbar.y_index
and pqa.tile_type_id=nbar.tile_type_id and pqa.tile_class_id=nbar.tile_class_id
"""
if DatasetType.FC25 in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_fc)s
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DSM in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dsm)s
) as dsm on
dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem)s
) as dem on
dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_h)s
) as dem_h on
dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
left outer join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_s)s
) as dem_s on
dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
"""
sql += """
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and nbar.x_index = ANY(%(x)s) and nbar.y_index = ANY(%(y)s)
and end_datetime::date between %(acq_min)s and %(acq_max)s
"""
if DatasetType.PQ25 in dataset_types:
sql += """
and pqa.x_index is null
"""
if DatasetType.FC25 in dataset_types:
sql += """
and fc.x_index is null
"""
if DatasetType.DSM in dataset_types:
sql += """
and dsm.x_index is null
"""
if DatasetType.DEM in dataset_types:
sql += """
and dem.x_index is null
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
and dem_h.x_index is null
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
and dem_s.x_index is null
"""
sql += """
order by nbar.x_index {sort}, nbar.y_index {sort}
""".format(sort=sort.value)
params = {"tile_type": [TILE_TYPE.value], "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"x": x, "y": y,
"acq_min": acq_min, "acq_max": acq_max,
"level_nbar": ProcessingLevel.NBAR.value}
if DatasetType.PQ25 in dataset_types:
params["level_pqa"] = ProcessingLevel.PQA.value
if DatasetType.FC25 in dataset_types:
params["level_fc"] = ProcessingLevel.FC.value
if DatasetType.DSM in dataset_types:
params["level_dsm"] = ProcessingLevel.DSM.value
if DatasetType.DEM in dataset_types:
params["level_dem"] = ProcessingLevel.DEM.value
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
params["level_dem_h"] = ProcessingLevel.DEM_H.value
if DatasetType.DEM_SMOOTHED in dataset_types:
params["level_dem_s"] = ProcessingLevel.DEM_S.value
return sql, params
# DEM/DSM tiles - quickie to get DEM/DSM tiles for WOFS - note they have no acquisition information!!!!
def list_tiles_dtm(x, y, datasets, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
Deprecated: Move to using explicit as_list or as_generator
:type x: list[int]
:type y: list[int]
:type datasets: list[datacube.api.model.DatasetType]
:type database: str
:type user: str
:type password: str
:type host: str
:type port: int
:type sort: SortType
:rtype: list[datacube.api.model.Tile]
"""
return list_tiles_dtm_as_generator(x, y, datasets, sort, config)
def list_tiles_dtm_as_list(x, y, datasets, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:type x: list[int]
:type y: list[int]
:type datasets: list[datacube.api.model.DatasetType]
:type database: str
:type user: str
:type password: str
:type host: str
:type port: int
:type sort: SortType
:rtype: list[datacube.api.model.Tile]
"""
return list(list_tiles(x, y, datasets, sort, config))
def list_tiles_dtm_as_generator(x, y, datasets, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
:type x: list[int]
:type y: list[int]
:type datasets: list[datacube.api.model.DatasetType]
:type database: str
:type user: str
:type password: str
:type host: str
:type port: int
:type sort: SortType
:rtype: list[datacube.api.model.Tile]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql = """
select
null acquisition_id, null satellite, null start_datetime, null end_datetime,
null end_datetime_year, null end_datetime_month,
dsm.x_index, dsm.y_index, point(dsm.x_index, dsm.y_index) as xy,
ARRAY[
['DSM', DSM.tile_pathname],
['DEM', DEM.tile_pathname],
['DEM_HYDROLOGICALLY_ENFORCED', DEM_H.tile_pathname],
['DEM_SMOOTHED', DEM_S.tile_pathname]
] as datasets
from
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 100
) as DSM
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 110
) as DEM on
DEM.x_index=DSM.x_index and DEM.y_index=DSM.y_index
and DEM.tile_type_id=DSM.tile_type_id and DEM.tile_class_id=DSM.tile_class_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 120
) as DEM_S on
DEM_S.x_index=DSM.x_index and DEM_S.y_index=DSM.y_index
and DEM_S.tile_type_id=DSM.tile_type_id and DEM_S.tile_class_id=DSM.tile_class_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 130
) as DEM_H on
DEM_H.x_index=DSM.x_index and DEM_H.y_index=DSM.y_index
and DEM_H.tile_type_id=DSM.tile_type_id and DEM_H.tile_class_id=DSM.tile_class_id
where
DSM.tile_type_id = ANY(%(tile_type)s) and DSM.tile_class_id = ANY(%(tile_class)s) -- mandatory
and DSM.x_index = ANY(%(x)s) and DSM.y_index = ANY(%(y)s)
;
""".format()
params = {"tile_type": [1], "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"x": x, "y": y}
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Tile.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
###
# Other stuff - mostly incomplete...
###
def visit_tiles(x, y, satellites, years, datasets, func=print_tile, sort=SortType.ASC, config=None):
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql = """
select
acquisition.acquisition_id, satellite_tag, start_datetime, end_datetime,
extract(year from end_datetime) as end_datetime_year, extract(month from end_datetime) as end_datetime_month,
nbar.x_index, nbar.y_index, point(nbar.x_index, nbar.y_index) as xy,
ARRAY[
['ARG25', nbar.tile_pathname],
['PQ25', pq.tile_pathname],
['FC25', fc.tile_pathname]
] as datasets
from acquisition
join satellite on satellite.satellite_id=acquisition.satellite_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 2
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 3
) as pq on
pq.acquisition_id=acquisition.acquisition_id
and pq.x_index=nbar.x_index and pq.y_index=nbar.y_index
and pq.tile_type_id=nbar.tile_type_id and pq.tile_class_id=nbar.tile_class_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 4
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_id = ANY(%(satellite)s)
and nbar.x_index = ANY(%(x)s) and nbar.y_index = ANY(%(y)s)
and extract(year from end_datetime) = ANY(%(year)s)
;
"""
params = {"tile_type": [1], "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"x": [x], "y": [y],
"year": years}
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in cursor:
_log.debug(record)
func(Tile.from_db_record(record))
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_tiles_wkt(wkt, satellites, years, datasets, sort=SortType.ASC, config=None):
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql = """
select
acquisition.acquisition_id, satellite_tag as satellite, start_datetime, end_datetime,
extract(year from end_datetime)::integer as end_datetime_year, extract(month from end_datetime)::integer as end_datetime_month,
nbar.x_index, nbar.y_index, point(nbar.x_index, nbar.y_index) as xy,
ARRAY[
['ARG25', nbar.tile_pathname],
['PQ25', pq.tile_pathname],
['FC25', fc.tile_pathname]
] as datasets
from acquisition
join satellite on satellite.satellite_id=acquisition.satellite_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 2
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 3
) as pq on
pq.acquisition_id=acquisition.acquisition_id
and pq.x_index=nbar.x_index and pq.y_index=nbar.y_index
and pq.tile_type_id=nbar.tile_type_id and pq.tile_class_id=nbar.tile_class_id
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = 4
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
join tile_footprint on
tile_footprint.x_index = nbar.x_index
and tile_footprint.y_index = nbar.y_index
and tile_footprint.tile_type_id = nbar.tile_type_id
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and st_intersects(tile_footprint.bbox, st_geomfromtext(%(polygon)s, 4326))
and extract(year from end_datetime) = ANY(%(year)s)
order by end_datetime asc, satellite asc
;
"""
params = {"tile_type": [1], "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"polygon": wkt,
"year": years}
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
tiles = []
for record in cursor:
_log.debug(record)
tiles.append(Tile.from_db_record(record))
return tiles
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_tiles_wkt_to_file(wkt, years, datasets, format, filename, sort=SortType.ASC, config=None):
pass
def visit_tiles_wkt(wkt, years, datasets, sort=SortType.ASC, config=None):
pass
def result_generator(cursor, size=100):
while True:
results = cursor.fetchmany(size)
if not results:
break
for result in results:
yield result
###
# AREA OF INTEREST / POLYGON QUERIES
###
# CELL
def list_cells_vector_file(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
.. warning::
Deprecated: use either datacube.api.query.list_cells_wkb_as_list() or datacube.api.query.list_cells_wkb_as_generator()
:param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
:type vector_file: str
:param vector_layer: Layer (0 based index) within the vector file
:type vector_layer: int
:param vector_feature: Feature (0 based index) within the layer
:type vector_feature: int
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list_cells_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort, config)
def list_cells_vector_file_as_list(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
:type vector_file: str
:param vector_layer: Layer (0 based index) within the vector file
:type vector_layer: int
:param vector_feature: Feature (0 based index) within the layer
:type vector_feature: int
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list(list_cells_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort, config))
def list_cells_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
:type vector_file: str
:param vector_layer: Layer (0 based index) within the vector file
:type vector_layer: int
:param vector_layer: Feature (0 based index) within the layer
:type vector_layer: int
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list_cells_wkb_as_generator(extract_feature_geometry_wkb(vector_file, vector_layer, vector_feature),
satellites, acq_min, acq_max, dataset_types, sort, config)
def list_cells_wkb(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
.. warning::
Deprecated: use either datacube.api.query.list_cells_wkb_as_list() or datacube.api.query.list_cells_wkb_as_generator()
:param wkb: Shape as WKB format
:type wkb: WKB
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list_cells_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort, config)
def list_cells_wkb_as_list(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
:param wkb: Shape as WKB format
:type wkb: WKB
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
return list(list_cells_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort, config))
def list_cells_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
"""
Return a list of cells matching the criteria as a SINGLE-USE generator
:param wkb: Shape as WKB format
:type wkb: WKB
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
:return: List of cells
:rtype: list[datacube.api.model.Cell]
"""
conn, cursor = None, None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort)
_log.debug(cursor.mogrify(sql, params))
cursor.execute(sql, params)
for record in result_generator(cursor):
_log.debug(record)
yield Cell.from_db_record(record)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def list_cells_wkb_to_file(wkb, satellites, acq_min, acq_max, dataset_types, filename, sort=SortType.ASC, config=None):
"""
Write the list of cells matching the criteria to the specified file
:param wkb: Shape as WKB format
:type wkb: WKB
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param filename: The output file
:type filename: str
:param sort: Sort order
:type sort: datacube.api.query.SortType
:param config: Config
:type config: datacube.config.Config
"""
conn = cursor = None
try:
# connect to database
conn, cursor = connect_to_db(config=config)
sql, params = build_list_cells_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort)
sql = to_file_ify_sql(sql)
if filename:
with open(filename, "w") as f:
cursor.copy_expert(cursor.mogrify(sql, params), f)
else:
cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
except Exception as e:
_log.error("Caught exception %s", e)
conn.rollback()
raise
finally:
conn = cursor = None
def build_list_cells_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC):
"""
Build the SQL query string and parameters required to return the cells matching the criteria
:param wkb: Shape as WKB format
:type wkb: WKB
:param satellites: Satellites
:type satellites: list[datacube.api.model.Satellite]
:param acq_min: Acquisition date range
:type acq_min: datetime.datetime
:param acq_max: Acquisition date range
:type acq_max: datetime.datetime
:param dataset_types: Dataset types
:type dataset_types: list[datacube.api.model.DatasetType]
:param sort: Sort order
:type sort: datacube.api.query.SortType
:return: The SQL query and params
:rtype: (str, dict)
"""
sql = """
SELECT DISTINCT nbar.x_index, nbar.y_index
FROM acquisition
JOIN satellite ON satellite.satellite_id=acquisition.satellite_id
"""
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_nbar)s
) as nbar on nbar.acquisition_id=acquisition.acquisition_id
"""
sql += """
join tile_footprint on tile_footprint.x_index=nbar.x_index and tile_footprint.y_index=nbar.y_index
"""
if DatasetType.PQ25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_pqa)s
) as pq on
pq.acquisition_id=acquisition.acquisition_id
and pq.x_index=nbar.x_index and pq.y_index=nbar.y_index
and pq.tile_type_id=nbar.tile_type_id and pq.tile_class_id=nbar.tile_class_id
"""
if DatasetType.FC25 in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_fc)s
) as fc on
fc.acquisition_id=acquisition.acquisition_id
and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DSM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dsm)s
) as dsm on
dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem)s
) as dem on
dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_h)s
) as dem_h on
dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
"""
if DatasetType.DEM_SMOOTHED in dataset_types:
sql += """
join
(
select
dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
from tile
join dataset on dataset.dataset_id=tile.dataset_id
where dataset.level_id = %(level_dem_s)s
) as dem_s on
dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
"""
sql += """
where
nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
and satellite.satellite_tag = ANY(%(satellite)s)
and st_intersects(tile_footprint.bbox, st_setsrid(st_geomfromwkb(%(geom)s), 4326))
and end_datetime::date between %(acq_min)s and %(acq_max)s
"""
sql += """
order by nbar.x_index {sort}, nbar.y_index {sort}
""".format(sort=sort.value)
params = {"tile_type": [TILE_TYPE.value],
"tile_class": [tile_class.value for tile_class in TILE_CLASSES],
"satellite": [satellite.value for satellite in satellites],
"geom": bytearray(wkb),
"acq_min": acq_min, "acq_max": acq_max,
"level_nbar": ProcessingLevel.NBAR.value}
if DatasetType.PQ25 in dataset_types:
params["level_pqa"] = ProcessingLevel.PQA.value
if DatasetType.FC25 in dataset_types:
params["level_fc"] = ProcessingLevel.FC.value
if DatasetType.DSM in dataset_types:
params["level_dsm"] = ProcessingLevel.DSM.value
if DatasetType.DEM in dataset_types:
params["level_dem"] = ProcessingLevel.DEM.value
if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
params["level_dem_h"] = ProcessingLevel.DEM_H.value
if DatasetType.DEM_SMOOTHED in dataset_types:
params["level_dem_s"] = ProcessingLevel.DEM_S.value
return sql, params
# TODO - disabling this for now as I don't think the queries perform very well and nothing is currently using them
# as the AOI filtering is done at the CELL level rather than the TILE level.
# I'll do some work on the queries shortly!
# # TILE
#
# def list_tiles_vector_file(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of tiles matching the criteria as a SINGLE-USE generator
#
# .. warning::
# Deprecated: use either datacube.api.query.list_tiles_wkb_as_list() or datacube.api.query.list_tiles_wkb_as_generator()
#
# :param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
# :type vector_file: str
# :param vector_layer: Layer (0 based index) within the vector file
# :type vector_layer: int
# :param vector_feature: Feature (0 based index) within the layer
# :type vector_feature: int
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
# return list_tiles_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort, config)
#
#
# def list_tiles_vector_file_as_list(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of tiles matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
#
# :param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
# :type vector_file: str
# :param vector_layer: Layer (0 based index) within the vector file
# :type vector_layer: int
# :param vector_feature: Feature (0 based index) within the layer
# :type vector_feature: int
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
# return list(list_tiles_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort, config))
#
#
# def list_tiles_vector_file_as_generator(vector_file, vector_layer, vector_feature, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
#
# :param vector_file: Vector (ESRI Shapefile, KML, ...) file containing the shape
# :type vector_file: str
# :param vector_layer: Layer (0 based index) within the vector file
# :type vector_layer: int
# :param vector_feature: Feature (0 based index) within the layer
# :type vector_feature: int
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
#
# return list_tiles_wkb_as_generator(extract_feature_geometry_wkb(vector_file, vector_layer, vector_feature),
# satellites, acq_min, acq_max, dataset_types, sort, config)
#
#
# def list_tiles_wkb(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of tiles matching the criteria as a SINGLE-USE generator
#
# .. warning::
# Deprecated: use either datacube.api.query.list_tiles_as_list() or datacube.api.query.list_tiles_as_generator()
#
# :param wkb: Shape as WKB format
# :type wkb: WKB
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
# return list_tiles_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort, config)
#
#
# def list_tiles_wkb_as_list(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of cells matching the criteria AS A REUSABLE LIST rather than as a one-use-generator
#
# :param wkb: Shape as WKB format
# :type wkb: WKB
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
# return list(list_tiles_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort))
#
#
# def list_tiles_wkb_as_generator(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC, config=None):
#
# """
# Return a list of tiles matching the criteria as a SINGLE-USE generator
#
# :param wkb: Shape as WKB format
# :type wkb: WKB
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
#
# :return: List of tiles
# :rtype: list[datacube.api.model.Tile]
# """
#
# conn, cursor = None, None
#
# try:
# # connect to database
#
# conn, cursor = connect_to_db(config=config)
#
# sql, params = build_list_tiles_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort)
#
# _log.debug(cursor.mogrify(sql, params))
#
# cursor.execute(sql, params)
#
# for record in result_generator(cursor):
# _log.debug(record)
# yield Tile.from_db_record(record)
#
# except Exception as e:
#
# _log.error("Caught exception %s", e)
# conn.rollback()
# raise
#
# finally:
#
# conn = cursor = None
#
#
# def list_tiles_wkb_to_file(wkb, satellites, acq_min, acq_max, dataset_types, filename, sort=SortType.ASC, config=None):
#
# """
# Write the list of tiles matching the criteria to the specified file
#
# :param wkb: Shape as WKB format
# :type wkb: WKB
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param filename: The output file
# :type filename: str
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
# :param config: Config
# :type config: datacube.config.Config
# """
#
# conn = cursor = None
#
# try:
# # connect to database
#
# conn, cursor = connect_to_db(config=config)
#
# sql, params = build_list_tiles_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort)
#
# sql = to_file_ify_sql(sql)
#
# if filename:
# with open(filename, "w") as f:
# cursor.copy_expert(cursor.mogrify(sql, params), f)
# else:
# cursor.copy_expert(cursor.mogrify(sql, params), sys.stdout)
#
# except Exception as e:
#
# _log.error("Caught exception %s", e)
# conn.rollback()
# raise
#
# finally:
#
# conn = cursor = None
#
#
# def build_list_tiles_wkb_sql_and_params(wkb, satellites, acq_min, acq_max, dataset_types, sort=SortType.ASC):
#
# """
# Build the SQL query string and parameters required to return the tiles matching the criteria
#
# :param wkb: Shape as WKB format
# :type wkb: WKB
# :param satellites: Satellites
# :type satellites: list[datacube.api.model.Satellite]
# :param acq_min: Acquisition date range
# :type acq_min: datetime.datetime
# :param acq_max: Acquisition date range
# :type acq_max: datetime.datetime
# :param dataset_types: Dataset types
# :type dataset_types: list[datacube.api.model.DatasetType]
# :param sort: Sort order
# :type sort: datacube.api.query.SortType
#
# :return: The SQL query and params
# :rtype: (str, dict)
# """
#
# sql = """
# select
# acquisition.acquisition_id, satellite_tag as satellite, start_datetime, end_datetime,
# extract(year from end_datetime) as end_datetime_year, extract(month from end_datetime) as end_datetime_month,
# nbar.x_index, nbar.y_index, point(nbar.x_index, nbar.y_index) as xy,
# """
#
# sql += """
# ARRAY[
# """
#
# sql += """
# ['ARG25', nbar.tile_pathname]
# """
#
# if DatasetType.PQ25 in dataset_types:
# sql += """
# ,['PQ25', pqa.tile_pathname]
# """
#
# if DatasetType.FC25 in dataset_types:
# sql += """
# ,['FC25', fc.tile_pathname]
# """
#
# if DatasetType.NDVI in dataset_types:
# sql += """
# ,['NDVI', nbar.tile_pathname]
# """
#
# if DatasetType.EVI in dataset_types:
# sql += """
# ,['EVI', nbar.tile_pathname]
# """
#
# if DatasetType.NBR in dataset_types:
# sql += """
# ,['NBR', nbar.tile_pathname]
# """
#
# if DatasetType.TCI in dataset_types:
# sql += """
# ,['TCI', nbar.tile_pathname]
# """
#
# if DatasetType.DSM in dataset_types:
# sql += """
# ,['DSM', dsm.tile_pathname]
# """
#
# if DatasetType.DEM in dataset_types:
# sql += """
# ,['DEM', dem.tile_pathname]
# """
#
# if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
# sql += """
# ,['DEM_HYDROLOGICALLY_ENFORCED', dem_h.tile_pathname]
# """
#
# if DatasetType.DEM_SMOOTHED in dataset_types:
# sql += """
# ,['DEM_SMOOTHED', dem_s.tile_pathname]
# """
#
# sql += """
# ] as datasets
# """
#
# sql += """
# from acquisition
# join satellite on satellite.satellite_id=acquisition.satellite_id
# """
#
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_nbar)s
# ) as nbar on nbar.acquisition_id=acquisition.acquisition_id
# """
#
# sql += """
# join tile_footprint on tile_footprint.x_index=nbar.x_index and tile_footprint.y_index=nbar.y_index
# """
#
# if DatasetType.PQ25 in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_pqa)s
# ) as pqa on
# pqa.acquisition_id=acquisition.acquisition_id
# and pqa.x_index=nbar.x_index and pqa.y_index=nbar.y_index
# and pqa.tile_type_id=nbar.tile_type_id and pqa.tile_class_id=nbar.tile_class_id
#
# """
#
# if DatasetType.FC25 in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_fc)s
# ) as fc on
# fc.acquisition_id=acquisition.acquisition_id
# and fc.x_index=nbar.x_index and fc.y_index=nbar.y_index
# and fc.tile_type_id=nbar.tile_type_id and fc.tile_class_id=nbar.tile_class_id
# """
#
# if DatasetType.DSM in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_dsm)s
# ) as dsm on
# dsm.x_index=nbar.x_index and dsm.y_index=nbar.y_index
# and dsm.tile_type_id=nbar.tile_type_id and dsm.tile_class_id=nbar.tile_class_id
# """
#
# if DatasetType.DEM in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_dem)s
# ) as dem on
# dem.x_index=nbar.x_index and dem.y_index=nbar.y_index
# and dem.tile_type_id=nbar.tile_type_id and dem.tile_class_id=nbar.tile_class_id
# """
#
# if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_dem_h)s
# ) as dem_h on
# dem_h.x_index=nbar.x_index and dem_h.y_index=nbar.y_index
# and dem_h.tile_type_id=nbar.tile_type_id and dem_h.tile_class_id=nbar.tile_class_id
# """
#
# if DatasetType.DEM_SMOOTHED in dataset_types:
# sql += """
# join
# (
# select
# dataset.acquisition_id, tile.dataset_id, tile.x_index, tile.y_index, tile.tile_pathname, tile.tile_type_id, tile.tile_class_id
# from tile
# join dataset on dataset.dataset_id=tile.dataset_id
# where dataset.level_id = %(level_dem_s)s
# ) as dem_s on
# dem_s.x_index=nbar.x_index and dem_s.y_index=nbar.y_index
# and dem_s.tile_type_id=nbar.tile_type_id and dem_s.tile_class_id=nbar.tile_class_id
# """
#
# sql += """
# where
# nbar.tile_type_id = ANY(%(tile_type)s) and nbar.tile_class_id = ANY(%(tile_class)s) -- mandatory
# and satellite.satellite_tag = ANY(%(satellite)s)
# and st_intersects(tile_footprint.bbox, st_setsrid(st_geomfromwkb(%(geom)s), 4326))
# and end_datetime::date between %(acq_min)s and %(acq_max)s
# """
#
# sql += """
# order by nbar.x_index, nbar.y_index, end_datetime {sort}, satellite asc
# """.format(sort=sort.value)
#
# params = {"tile_type": [TILE_TYPE.value],
# "tile_class": [tile_class.value for tile_class in TILE_CLASSES],
# "satellite": [satellite.value for satellite in satellites],
# "geom": bytearray(wkb),
# "acq_min": acq_min, "acq_max": acq_max,
# "level_nbar": ProcessingLevel.NBAR.value}
#
# if DatasetType.PQ25 in dataset_types:
# params["level_pqa"] = ProcessingLevel.PQA.value
#
# if DatasetType.FC25 in dataset_types:
# params["level_fc"] = ProcessingLevel.FC.value
#
# if DatasetType.DSM in dataset_types:
# params["level_dsm"] = ProcessingLevel.DSM.value
#
# if DatasetType.DEM in dataset_types:
# params["level_dem"] = ProcessingLevel.DEM.value
#
# if DatasetType.DEM_HYDROLOGICALLY_ENFORCED in dataset_types:
# params["level_dem_h"] = ProcessingLevel.DEM_H.value
#
# if DatasetType.DEM_SMOOTHED in dataset_types:
# params["level_dem_s"] = ProcessingLevel.DEM_S.value
#
# return sql, params
#
#
| 35.861049
| 162
| 0.636967
| 14,909
| 112,783
| 4.608357
| 0.031189
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| 0.021687
| 0.022705
| 0.935799
| 0.933165
| 0.929206
| 0.923369
| 0.9204
| 0.915728
| 0
| 0.003268
| 0.270191
| 112,783
| 3,144
| 163
| 35.872455
| 0.831454
| 0.396922
| 0
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| 0
| 0.048799
| 0.530753
| 0.143814
| 0
| 0
| 0
| 0.000318
| 0
| 1
| 0.029279
| false
| 0.003003
| 0.008258
| 0
| 0.087087
| 0.006757
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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|
0
| 7
|
64aa3d37b85cc243ccdf71e09e84298f1e19d500
| 25,744
|
py
|
Python
|
venv/Lib/site-packages/dateparser/data/date_translation_data/pt.py
|
realwaynesun/cryptotradedata
|
4bd4d3f80438507fbb2b611a9cf451ed1aa2bde1
|
[
"MIT"
] | 2
|
2020-09-28T21:23:59.000Z
|
2021-11-10T15:01:15.000Z
|
venv/Lib/site-packages/dateparser/data/date_translation_data/pt.py
|
realwaynesun/cryptotradedata
|
4bd4d3f80438507fbb2b611a9cf451ed1aa2bde1
|
[
"MIT"
] | 21
|
2021-02-04T01:37:44.000Z
|
2022-03-12T01:00:55.000Z
|
venv/Lib/site-packages/dateparser/data/date_translation_data/pt.py
|
realwaynesun/cryptotradedata
|
4bd4d3f80438507fbb2b611a9cf451ed1aa2bde1
|
[
"MIT"
] | 1
|
2021-03-27T19:41:10.000Z
|
2021-03-27T19:41:10.000Z
|
info = {
"name": "pt",
"date_order": "DMY",
"january": [
"janeiro",
"jan"
],
"february": [
"fevereiro",
"fev"
],
"march": [
"março",
"mar"
],
"april": [
"abril",
"abr"
],
"may": [
"maio",
"mai"
],
"june": [
"junho",
"jun"
],
"july": [
"julho",
"jul"
],
"august": [
"agosto",
"ago"
],
"september": [
"setembro",
"set",
"Septembro"
],
"october": [
"outubro",
"out"
],
"november": [
"novembro",
"nov"
],
"december": [
"dezembro",
"dez"
],
"monday": [
"segunda-feira",
"seg",
"Segunda"
],
"tuesday": [
"terça-feira",
"ter",
"Terça"
],
"wednesday": [
"quarta-feira",
"qua",
"Quarta"
],
"thursday": [
"quinta-feira",
"qui",
"Quinta"
],
"friday": [
"sexta-feira",
"sex",
"Sexta"
],
"saturday": [
"sábado",
"sáb",
"Sab"
],
"sunday": [
"domingo",
"dom"
],
"am": [
"am"
],
"pm": [
"pm"
],
"year": [
"ano",
"anos"
],
"month": [
"mês",
"meses"
],
"week": [
"semana",
"sem",
"semanas"
],
"day": [
"dia",
"dias"
],
"hour": [
"hora",
"h",
"horas"
],
"minute": [
"minuto",
"min",
"m",
"minutos"
],
"second": [
"segundo",
"seg",
"s",
"segundos"
],
"relative-type": {
"1 year ago": [
"ano passado"
],
"0 year ago": [
"este ano"
],
"in 1 year": [
"próximo ano"
],
"1 month ago": [
"mês passado"
],
"0 month ago": [
"este mês"
],
"in 1 month": [
"próximo mês"
],
"1 week ago": [
"semana passada"
],
"0 week ago": [
"esta semana"
],
"in 1 week": [
"próxima semana"
],
"1 day ago": [
"ontem"
],
"0 day ago": [
"hoje"
],
"in 1 day": [
"amanhã"
],
"0 hour ago": [
"esta hora"
],
"0 minute ago": [
"este minuto"
],
"0 second ago": [
"agora"
],
"2 day ago": [
"anteontem"
]
},
"relative-type-regex": {
"in \\1 year": [
"em (\\d+) ano",
"em (\\d+) anos"
],
"\\1 year ago": [
"há (\\d+) ano",
"há (\\d+) anos"
],
"in \\1 month": [
"em (\\d+) mês",
"em (\\d+) meses"
],
"\\1 month ago": [
"há (\\d+) mês",
"há (\\d+) meses"
],
"in \\1 week": [
"em (\\d+) semana",
"em (\\d+) semanas",
"em (\\d+) sem"
],
"\\1 week ago": [
"há (\\d+) semana",
"há (\\d+) semanas",
"há (\\d+) sem"
],
"in \\1 day": [
"em (\\d+) dia",
"em (\\d+) dias"
],
"\\1 day ago": [
"há (\\d+) dia",
"há (\\d+) dias"
],
"in \\1 hour": [
"em (\\d+) hora",
"em (\\d+) horas",
"em (\\d+) h"
],
"\\1 hour ago": [
"há (\\d+) hora",
"há (\\d+) horas",
"há (\\d+) h"
],
"in \\1 minute": [
"em (\\d+) minuto",
"em (\\d+) minutos",
"em (\\d+) min",
"em (\\d+) mins"
],
"\\1 minute ago": [
"há (\\d+) minuto",
"há (\\d+) minutos",
"há (\\d+) min",
"há (\\d+) mins"
],
"in \\1 second": [
"em (\\d+) segundo",
"em (\\d+) segundos",
"em (\\d+) seg",
"em (\\d+) segs"
],
"\\1 second ago": [
"há (\\d+) segundo",
"há (\\d+) segundos",
"há (\\d+) seg"
]
},
"locale_specific": {
"pt-CH": {
"name": "pt-CH",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-GW": {
"name": "pt-GW",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-MZ": {
"name": "pt-MZ",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-CV": {
"name": "pt-CV",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-LU": {
"name": "pt-LU",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-MO": {
"name": "pt-MO",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-GQ": {
"name": "pt-GQ",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-ST": {
"name": "pt-ST",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-PT": {
"name": "pt-PT",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-AO": {
"name": "pt-AO",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
},
"pt-TL": {
"name": "pt-TL",
"monday": [
"segunda"
],
"tuesday": [
"terça"
],
"wednesday": [
"quarta"
],
"thursday": [
"quinta"
],
"friday": [
"sexta"
],
"am": [
"manhã",
"da manhã"
],
"pm": [
"tarde",
"da tarde"
],
"relative-type-regex": {
"in \\1 year": [
"dentro de (\\d+) ano",
"dentro de (\\d+) anos"
],
"in \\1 month": [
"dentro de (\\d+) meses",
"dentro de (\\d+) mês"
],
"in \\1 week": [
"dentro de (\\d+) sem",
"dentro de (\\d+) semana",
"dentro de (\\d+) semanas"
],
"in \\1 day": [
"dentro de (\\d+) dias",
"dentro de (\\d+) dia"
],
"in \\1 hour": [
"dentro de (\\d+) h",
"dentro de (\\d+) hora",
"dentro de (\\d+) horas"
],
"in \\1 minute": [
"dentro de (\\d+) min",
"dentro de (\\d+) minuto",
"dentro de (\\d+) minutos"
],
"in \\1 second": [
"dentro de (\\d+) segundo",
"dentro de (\\d+) segundos",
"dentro de (\\d+) s"
],
"\\1 second ago": [
"há (\\d+) s"
]
}
}
},
"skip": [
"de",
"cerca",
"e",
"às",
" ",
".",
",",
";",
"-",
"/",
"'",
"|",
"@",
"[",
"]",
"，"
],
"pertain": [
"de"
],
"sentence_splitter_group": 1,
"ago": [
"atrás",
"há"
],
"in": [
"em"
],
"simplifications": [
{
"uma": "1"
},
{
"um": "1"
},
{
"alguns segundos": "44 segundos"
}
]
}
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0
| 9
|
b3fd52b152656f94f2cb493a192011c38a0ebbef
| 1,246
|
py
|
Python
|
test.py
|
lvreynoso/Call-Routing-Project
|
8aeafe6c26d92ed93f32a0fc830bb53e339cb83a
|
[
"MIT"
] | null | null | null |
test.py
|
lvreynoso/Call-Routing-Project
|
8aeafe6c26d92ed93f32a0fc830bb53e339cb83a
|
[
"MIT"
] | null | null | null |
test.py
|
lvreynoso/Call-Routing-Project
|
8aeafe6c26d92ed93f32a0fc830bb53e339cb83a
|
[
"MIT"
] | null | null | null |
import scenario1
import unittest
cost = scenario1.findCost
class Scenario1Test(unittest.TestCase):
def test_init(self):
pass
def test_ten(self):
routes = 'data/route-costs-10.txt'
# find longest route
assert cost(routes, '+449275049230') == '0.49'
assert cost(routes, '+449938419843') == None
assert cost(routes, '+8197753314') == '0.75'
def test_hundred(self):
routes = 'data/route-costs-100.txt'
def test_6hundred(self):
routes = 'data/route-costs-600.txt'
def test_35thousand(self):
routes = 'data/route-costs-35000.txt'
def test_106thousand(self):
routes = 'data/route-costs-106000.txt'
class Scenario2Test(unittest.TestCase):
def test_init(self):
pass
def test_ten(self):
routes = 'data/route-costs-10.txt'
def test_hundred(self):
routes = 'data/route-costs-100.txt'
def test_6hundred(self):
routes = 'data/route-costs-600.txt'
def test_35thousand(self):
routes = 'data/route-costs-35000.txt'
def test_106thousand(self):
routes = 'data/route-costs-106000.txt'
if __name__ == '__main__':
unittest.main()
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0
| 10
|
3744a67ac0806df733d0a009949e03e1e837df25
| 4,565
|
py
|
Python
|
waterspout_api/migrations/0014_auto_20210406_1439.py
|
Water-Systems-Management-UCM/Waterspout
|
78965f1e53b09f442e278dff72c290ceac22ed60
|
[
"MIT"
] | 1
|
2020-09-10T20:43:24.000Z
|
2020-09-10T20:43:24.000Z
|
waterspout_api/migrations/0014_auto_20210406_1439.py
|
Water-Systems-Management-UCM/Waterspout
|
78965f1e53b09f442e278dff72c290ceac22ed60
|
[
"MIT"
] | 72
|
2020-05-28T17:20:12.000Z
|
2022-03-28T14:11:40.000Z
|
waterspout_api/migrations/0014_auto_20210406_1439.py
|
Water-Systems-Management-UCM/Waterspout
|
78965f1e53b09f442e278dff72c290ceac22ed60
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2 on 2021-04-06 21:39
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('waterspout_api', '0013_modelarea_main_help_page_content'),
]
operations = [
migrations.AlterField(
model_name='calibratedparameter',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='calibrationset',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='crop',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='cropgroup',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='cropmodification',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='helpdocument',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='infeasibility',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='inputdataitem',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='inputdataset',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='modelarea',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='modelareapreferences',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='modelrun',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='organization',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='region',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='regionextra',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='regiongroup',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='regionmodification',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='result',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resultset',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='userprofile',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
]
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| 111
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| 451
| 4,565
| 5.94235
| 0.135255
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| 0.216418
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| 0.839925
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| 0.839925
| 0.839925
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| 0
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| 0.274699
| 4,565
| 113
| 112
| 40.39823
| 0.803987
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| 1
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| 0.080531
| 0.008186
| 0
| 0
| 0
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| 1
| 0
| false
| 0
| 0.009346
| 0
| 0.037383
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
37b2a8a74db0c97f1c16318f9a8201defdfcd005
| 129
|
py
|
Python
|
bangoo/theming/context_processors.py
|
slapec/bangoo
|
34facf122f15943a4368d5c2f45fe178ff01edaa
|
[
"MIT"
] | null | null | null |
bangoo/theming/context_processors.py
|
slapec/bangoo
|
34facf122f15943a4368d5c2f45fe178ff01edaa
|
[
"MIT"
] | null | null | null |
bangoo/theming/context_processors.py
|
slapec/bangoo
|
34facf122f15943a4368d5c2f45fe178ff01edaa
|
[
"MIT"
] | null | null | null |
from django.conf import settings
def act_theme(request):
return {'ACT_THEME': getattr(request, 'ACT_THEME', settings.THEME)}
| 32.25
| 71
| 0.75969
| 18
| 129
| 5.277778
| 0.611111
| 0.252632
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 129
| 4
| 71
| 32.25
| 0.833333
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| 0.138462
| 0
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| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
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| 0
| null | 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
8077f1dd7b219933df17d90b224491a92c222729
| 174
|
py
|
Python
|
secrets.py
|
bethskw/botcocktails
|
1090d6e85161efb174891911a4cdb4523c97719a
|
[
"MIT"
] | 3
|
2019-02-20T21:47:08.000Z
|
2021-03-19T19:23:17.000Z
|
secrets.py
|
bethskw/botcocktails
|
1090d6e85161efb174891911a4cdb4523c97719a
|
[
"MIT"
] | null | null | null |
secrets.py
|
bethskw/botcocktails
|
1090d6e85161efb174891911a4cdb4523c97719a
|
[
"MIT"
] | 1
|
2018-11-27T22:03:24.000Z
|
2018-11-27T22:03:24.000Z
|
C_KEY = "XXXXX" # replace with your own
C_SECRET = "XXXXX" # replace with your own
A_TOKEN = "xxxxx" # replace with your own
A_TOKEN_SECRET = "XXXXX" # replace with your own
| 43.5
| 48
| 0.724138
| 29
| 174
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| 4
| 48
| 43.5
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| 0
|
0
| 7
|
80c11af0a9e842802c77c72389868b3417a646eb
| 87
|
py
|
Python
|
sam-app/sam-checker/get_contents.py
|
Aishwarya26l/sam-testing
|
bb2d78944ba5f816edc4de7d48a57c00bc4dfc21
|
[
"MIT"
] | null | null | null |
sam-app/sam-checker/get_contents.py
|
Aishwarya26l/sam-testing
|
bb2d78944ba5f816edc4de7d48a57c00bc4dfc21
|
[
"MIT"
] | null | null | null |
sam-app/sam-checker/get_contents.py
|
Aishwarya26l/sam-testing
|
bb2d78944ba5f816edc4de7d48a57c00bc4dfc21
|
[
"MIT"
] | 1
|
2019-10-22T02:48:04.000Z
|
2019-10-22T02:48:04.000Z
|
import os
def getIndexPage():
return os.popen('cat /var/task/index.html').read()
| 14.5
| 54
| 0.678161
| 13
| 87
| 4.538462
| 0.923077
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0.149425
| 87
| 5
| 55
| 17.4
| 0.797297
| 0
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| 0
| 0.275862
| 0
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| 1
| 0.333333
| true
| 0
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| null | 0
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| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
80d072dd7f1dfab164b7c6fe152a6fe9d577cdab
| 111,008
|
py
|
Python
|
src/libraries/reactive_planner_lib.py
|
vvasilo/semnav
|
e1141035fa4ea8ad3d5f077198a141209693625b
|
[
"MIT"
] | 10
|
2020-02-28T22:26:55.000Z
|
2021-12-14T08:34:18.000Z
|
src/libraries/reactive_planner_lib.py
|
KodlabPenn/semnav
|
489cfe203516e359cc488740b99c8e208a757c2d
|
[
"MIT"
] | 1
|
2020-08-24T23:37:51.000Z
|
2021-11-10T14:29:34.000Z
|
src/libraries/reactive_planner_lib.py
|
vvasilo/semnav
|
e1141035fa4ea8ad3d5f077198a141209693625b
|
[
"MIT"
] | 3
|
2020-02-27T20:22:57.000Z
|
2022-01-21T12:58:32.000Z
|
#!/usr/bin/env python
"""
MIT License (modified)
Copyright (c) 2020 The Trustees of the University of Pennsylvania
Authors:
Vasileios Vasilopoulos <vvasilo@seas.upenn.edu>
Omur Arslan <omur@seas.upenn.edu>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this **file** (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import time
import itertools
import shapely as sp
from matplotlib.collections import PatchCollection
from shapely.geometry import Polygon, Point, LineString, LinearRing
from shapely.ops import cascaded_union
from scipy.spatial import ConvexHull
from scipy.signal import butter, lfilter
from operator import itemgetter
# Geometry imports
from polygeom_lib import cvxpolyxhplane, cvxpolyerode, polydist, polyxline, inpolygon, cvxpolyintersect, polyxray
from polygeom_lib import polytriangulation, polycvxdecomp, polyconvexdecomposition
class LIDARClass:
"""
Class that describes a LIDAR object and is updated as new measurements are received
Properties:
1) RangeMeasurements: Range measurements received
2) Range: Range of the sensor
3) Infinity: Range to be considered as infinity
4) MinAngle: Minimum angle of the sensor
5) MaxAngle: Maximum angle of the sensor
6) Resolution: Sensor angular resolution
"""
def __init__(self, RangeMeasurements, Range, Infinity, MinAngle, MaxAngle, Resolution):
self.RangeMeasurements = RangeMeasurements # 1D numpy array
self.Range = Range # float
self.Infinity = Infinity # float
self.MinAngle = MinAngle # float
self.MaxAngle = MaxAngle # float
self.Resolution = Resolution # float
self.NumSample = int(1+round((MaxAngle-MinAngle)/Resolution)) # integer
self.Angle = np.linspace(MinAngle, MaxAngle, self.NumSample) # 1D numpy array
def completeLIDAR2D(LIDAR):
"""
Function that completes missing LIDAR data due to limited field of view
Input:
1) LIDAR: Incomplete LIDAR object
Output:
1) LIDAR: Complete modified LIDAR object
"""
if ((LIDAR.MaxAngle-LIDAR.MinAngle) < 2*np.pi):
tempR = LIDAR.RangeMeasurements
tempAngle = LIDAR.Angle
tempResolution = LIDAR.Resolution
# Updated LIDAR model
LIDAR.MaxAngle = LIDAR.MinAngle + 2*np.pi
LIDAR.NumSample = int(round((2*np.pi/tempResolution)+1))
LIDAR.Resolution = (LIDAR.MaxAngle-LIDAR.MinAngle)/(LIDAR.NumSample-1)
LIDAR.Angle = np.linspace(LIDAR.MinAngle, LIDAR.MaxAngle, LIDAR.NumSample)
# Completed Range Data
R = LIDAR.Infinity*np.ones(LIDAR.NumSample)
Indices = np.floor(((tempAngle-LIDAR.MinAngle+LIDAR.Resolution/2)%(2*np.pi))/LIDAR.Resolution)
Indices = Indices.astype(int)
R[Indices] = tempR
R[R>LIDAR.Range] = LIDAR.Range
LIDAR.RangeMeasurements = R
return LIDAR
def constructLIDAR2D(DataLIDAR, CutoffRange, AllowableRange, Pitch=0.):
"""
Function that constructs a LIDAR object to be used by the reactive planner
Input:
1) DataLIDAR: Received LIDAR data
2) CutoffRange: Cutoff range below which the range measurement is ignored
3) AllowableRange: Maximum allowable LIDAR range
4) Pitch: Robot pitch to be considered for range compensation (default is 0)
Output:
1) LIDAR: Constructed LIDAR object
"""
# LIDAR operations
Range = AllowableRange
Infinity = AllowableRange+10.
MinAngle = float(DataLIDAR.angle_min)
MaxAngle = float(DataLIDAR.angle_max)
Resolution = float(DataLIDAR.angle_increment)
RangeMeasurements = np.array(DataLIDAR.ranges)
# Project on the robot plane
RangeMeasurements = RangeMeasurements*np.cos(Pitch)
# Reject NaN
Inan = np.nonzero(np.isnan(RangeMeasurements))
Inan = Inan[0]
for k in Inan:
RangeMeasurements[k] = Infinity
# Cutoff LIDAR range
Icutoff = np.nonzero(RangeMeasurements <= CutoffRange)
for k in Icutoff:
RangeMeasurements[k] = Infinity
# Construct LIDAR object
LIDAR = LIDARClass(RangeMeasurements, Range, Infinity, MinAngle, MaxAngle, Resolution)
return LIDAR
def obstaclePointsLIDAR2D(RobotState, LIDAR):
"""
Function that returns the coordinates of observed obstacle points from the LIDAR
Input:
1) RobotState: Robot position and orientation
2) LIDAR: Current LIDAR object
Output:
1) PointsAll: Coordinates of the observed obstacle points from the LIDAR - Nx2 numpy.array
"""
# Find robot position and orientation
RobotPosition = RobotState[0:2]
RobotOrientation = RobotState[2]
# Range measurements
R = np.array(LIDAR.RangeMeasurements)
# Find observed LIDAR points coordinates
PointsAll = np.zeros((R.shape[0], 2))
for i in range(PointsAll.shape[0]):
PointsAll[i][0] = RobotPosition[0]+R[i]*np.cos(LIDAR.Angle[i]+RobotOrientation)
PointsAll[i][1] = RobotPosition[1]+R[i]*np.sin(LIDAR.Angle[i]+RobotOrientation)
return PointsAll
def compensateObstacleLIDAR2D(RobotState, Obstacle, LIDAR):
"""
Function that checks if the LIDAR hits a specific obstacle whose polygon is known
Input:
1) RobotState: Robot position and orientation
2) Obstacle: shapely.geometry.Polygon obstacle defining the polygonal obstacle
3) LIDAR: Current LIDAR object
Output:
1) LIDAR: Final LIDAR object
"""
# Find robot position and orientation
RobotPosition = RobotState[0:2]
RobotOrientation = RobotState[2]
# Find the indices that correspond to a LIDAR range less than the maximum range
lidar_indices = np.where(LIDAR.RangeMeasurements < LIDAR.Range)
for k in lidar_indices[0]:
lidar_point_x = RobotPosition[0]+LIDAR.RangeMeasurements[k]*np.cos(LIDAR.Angle[k]+RobotOrientation)
lidar_point_y = RobotPosition[1]+LIDAR.RangeMeasurements[k]*np.sin(LIDAR.Angle[k]+RobotOrientation)
if Obstacle.contains(Point(lidar_point_x, lidar_point_y)):
LIDAR.RangeMeasurements[k] = LIDAR.Range
return LIDAR
def readLIDAR2D(RobotState,Obstacles,Range,MinAngle,MaxAngle,NumSample):
"""
Function that generates a virtual LIDAR object, based on the position of the robot and the surrounding obstacles
Input:
1) RobotState: Robot position and orientation
2) Obstacles: shapely.geometry.Polygon obstacle array defining the polygonal obstacles
3) Range: Range of the LIDAR object to be generated
4) MinAngle: Minimum angle of the LIDAR object to be generated
5) MaxAngle: Maximum angle of the LIDAR object to be generated
6) NumSample: Number of samples used in the process
Output:
1) virtualLIDAR: Complete virtual LIDAR object
"""
# Find robot position and orientation
RobotPosition = RobotState[0:2]
RobotOrientation = RobotState[2]
# Initialize LIDAR object
Resolution = (MaxAngle-MinAngle)/(NumSample-1)
RangeMeasurements = np.zeros(NumSample)
Infinity = Range + 20.0
virtualLIDAR = LIDARClass(RangeMeasurements, Range, Infinity, MinAngle, MaxAngle, Resolution)
# Rotation matrix from the global frame to the local sensor frame
RotMat = np.array([[np.cos(-RobotOrientation),-np.sin(-RobotOrientation)],[np.sin(-RobotOrientation),np.cos(-RobotOrientation)]])
# Determine distance to the workspace boundary and obstacles
virtualLIDAR.RangeMeasurements = virtualLIDAR.Infinity*np.ones(virtualLIDAR.NumSample)
for co in range(len(Obstacles)):
# Obstacle in the local sensor frame
Obs = np.vstack((Obstacles[co].exterior.coords.xy[0],Obstacles[co].exterior.coords.xy[1])).transpose() - RobotPosition
Obs = np.matmul(Obs,RotMat.transpose())
# Compute distance to every obstacle edge
for cv in range(Obs.shape[0]):
cn = ((cv+1)%Obs.shape[0])
vc = Obs[cv][:] # current vertex
vn = Obs[cn][:] # next vertex
# Compute the distance to the origin
dist = np.min([np.linalg.norm(vc),np.linalg.norm(vn)])
w = (np.dot(vn,(vn-vc).transpose()))/(np.linalg.norm(vn-vc)**2)
if (w>=0.0) and (w<=1.0):
vx = w*vc + (1-w)*vn
dist = np.min([dist, np.linalg.norm(vx)])
ac = np.arctan2(vc[1],vc[0]) # Relative angle of the current vertex
an = np.arctan2(vn[1],vn[0]) # Relative angle of the next vertex
flagDist = (dist <= virtualLIDAR.Range)
flagAngle = (np.min([ac,an]) <= virtualLIDAR.MaxAngle) and (np.max([ac,an]) >= virtualLIDAR.MinAngle)
# Compute LIDAR range if the obstacle segment is in the sensing region
if flagDist and flagAngle:
# Closest LIDAR ray index
I = int(round((np.max([np.min([ac,an]),virtualLIDAR.MinAngle])-virtualLIDAR.MinAngle)/virtualLIDAR.Resolution))
I = (I%virtualLIDAR.NumSample)
# Compute the intersection of the LIDAR ray with the sensor footprint
vtemp = np.array([np.cos(virtualLIDAR.Angle[I]), np.sin(virtualLIDAR.Angle[I])])
vRtemp = np.array([-np.sin(virtualLIDAR.Angle[I]), np.cos(virtualLIDAR.Angle[I])])
w = -(np.dot(vn,vRtemp.transpose()))/(np.dot(vc-vn,vRtemp.transpose()))
if (w>=0.0) and (w<=1.0):
xtemp = w*vc + (1-w)*vn
if (np.dot(xtemp,vtemp.transpose()) >= 0):
virtualLIDAR.RangeMeasurements[I] = np.min([virtualLIDAR.RangeMeasurements[I],np.linalg.norm(xtemp)])
# Compute the intersection of adjacent LIDAR rays
J = ((I+1)%virtualLIDAR.NumSample)
flagValid = True
while flagValid and (J is not I):
vtemp = np.array([np.cos(virtualLIDAR.Angle[J]),np.sin(virtualLIDAR.Angle[J])])
vRtemp = np.array([-np.sin(virtualLIDAR.Angle[J]),np.cos(virtualLIDAR.Angle[J])])
w = -(np.dot(vn,vRtemp.transpose()))/(np.dot(vc-vn,vRtemp.transpose()))
if (w>=0.0) and (w<=1.0):
xtemp = w*vc + (1-w)*vn
if (np.dot(xtemp,vtemp.transpose()) >= 0):
virtualLIDAR.RangeMeasurements[J] = np.min([virtualLIDAR.RangeMeasurements[J],np.linalg.norm(xtemp)])
J = ((J+1)%virtualLIDAR.NumSample)
else:
flagValid = False
else:
flagValid = False
J = (I-1)%virtualLIDAR.NumSample
flagValid = True
while flagValid and (J is not I):
vtemp = np.array([np.cos(virtualLIDAR.Angle[J]),np.sin(virtualLIDAR.Angle[J])])
vRtemp = np.array([-np.sin(virtualLIDAR.Angle[J]),np.cos(virtualLIDAR.Angle[J])])
w = -(np.dot(vn,vRtemp.transpose()))/(np.dot(vc-vn,vRtemp.transpose()))
if (w>=0.0) and (w<=1.0):
xtemp = w*vc + (1-w)*vn
if (np.dot(xtemp,vtemp.transpose()) >= 0):
virtualLIDAR.RangeMeasurements[J] = np.min([virtualLIDAR.RangeMeasurements[J],np.linalg.norm(xtemp)])
J = (J-1)%virtualLIDAR.NumSample
else:
flagValid = False
else:
flagValid = False
# Check sensor range
virtualLIDAR.RangeMeasurements[virtualLIDAR.RangeMeasurements > virtualLIDAR.Range] = virtualLIDAR.Range
return virtualLIDAR
def translateLIDAR2D(RobotState, RobotStateTransformed, RobotRadius, LIDAR):
"""
Rebase LIDAR readings from RobotState to RobotStateTransformed
Input:
1) RobotState: Original robot position and orientation
2) RobotStateTransformed: Transformed robot position and orientation
3) RobotRadius: Robot radius
4) LIDAR: Current LIDAR object
Output:
1) newLIDAR: Transformed LIDAR object
"""
# Find robot position and orientation
RobotPosition = RobotState[0:2]
RobotOrientation = RobotState[2]
# Find transformed robot position and orientation
RobotPositionTransformed = RobotStateTransformed[0:2]
RobotOrientationTransformed = RobotStateTransformed[2]
# Account for the robot radius
LIDAR.RangeMeasurements = LIDAR.RangeMeasurements - RobotRadius
# Find obstacle points
obstacle_points = obstaclePointsLIDAR2D(RobotState, LIDAR) - RobotPosition
# Rotation matrix from the global frame to the local sensor frame
RotMat = np.array([[np.cos(-RobotOrientation),-np.sin(-RobotOrientation)],[np.sin(-RobotOrientation),np.cos(-RobotOrientation)]])
# Points in the local sensor frame
obstacle_points = np.matmul(obstacle_points,RotMat.transpose())
# Rotation matrix from the local sensor frame to the global frame in the transformed space
RotMatTransformed = np.array([[np.cos(RobotOrientationTransformed),-np.sin(RobotOrientationTransformed)],[np.sin(RobotOrientationTransformed),np.cos(RobotOrientationTransformed)]])
# Points in the transformed space
obstacle_points_transformed = np.matmul(obstacle_points,RotMatTransformed.transpose())
obstacle_points_transformed = obstacle_points_transformed + RobotPositionTransformed
# Find new ranges
obstacle_points_transformedT = obstacle_points_transformed.transpose()
obstacle_points_transformed_x = obstacle_points_transformedT[0][:]
obstacle_points_transformed_y = obstacle_points_transformedT[1][:]
R = np.sqrt((obstacle_points_transformed_x-RobotPositionTransformed[0])**2+(obstacle_points_transformed_y-RobotPositionTransformed[1])**2)
newLIDAR = LIDARClass(R, LIDAR.Range-np.linalg.norm(RobotPositionTransformed-RobotPosition), LIDAR.Infinity, LIDAR.MinAngle, LIDAR.MaxAngle, LIDAR.Resolution)
return newLIDAR
def localminLIDAR2D(LIDAR):
"""
Function that finds the indices of local minima of the LIDAR range data
Input:
1) LIDAR: Current LIDAR object
Output:
1) Imin: Indices of local minima of the LIDAR range data
"""
R = LIDAR.RangeMeasurements
# Compute the indices of strictly local minima of the LIDAR range data
if ((LIDAR.MaxAngle-LIDAR.MinAngle)<2*np.pi):
# Assume that the region outside the angular range of LIDAR is free
Rp = np.hstack((np.array([LIDAR.Range]), R[0:-1]))
Rn = np.hstack((R[1:], np.array([LIDAR.Range])))
else:
Rp = np.hstack((np.array([R[-2]]), R[0:-1]))
Rn = np.hstack((R[1:], np.array([R[1]])))
# Logical tests
logical_test = np.logical_or(np.logical_and(R<=Rp, R<Rn), np.logical_and(R<Rp, R<=Rn))
Imin = logical_test+0
return Imin
def localworkspaceLIDAR2D(RobotState, RobotRadius, LIDAR):
"""
Function that returns the local workspace
Input:
1) RobotState: Robot position and orientation
2) RobotRadius: Robot radius
3) LIDAR: Current LIDAR object
Output:
1) LW: Local workspace polygon array
"""
X = RobotState
epsilon = 0.000000001
R = LIDAR.RangeMeasurements
if R.min(0)<epsilon:
LW = np.array([[]])
return LW
else:
# Complete missing data due to the LIDAR's angular range limits
LIDAR = completeLIDAR2D(LIDAR)
# Modified range data defining the local workspace
R = 0.5*(LIDAR.RangeMeasurements+RobotRadius)
# Initialize the local workspace with the minimum square that respects
# the LIDAR's sensing range
LW = (0.5*(LIDAR.Range+RobotRadius))*np.array([[-1,-1], [-1,1], [1,1], [1,-1]])
Imin = np.nonzero(localminLIDAR2D(LIDAR))
Imin = Imin[0]
for k in Imin:
if not LW.any():
return LW
else:
# Local minimum parameters
Ak = LIDAR.Angle[k] # Angle
Rk = R[k] # Range
# Separating hyperplane parameters
n = np.array([-np.cos(Ak+X[2]), -np.sin(Ak+X[2])]) # Hyperplane normal
m = -Rk*n # A point on the separating hyperplane
# Update the local workspace by taking its intersection with the associated halfplane
LW = cvxpolyxhplane(LW, m, n)
# Local workspace footprint
LocalFootprint = np.vstack((R*np.cos(LIDAR.Angle+X[2]), R*np.sin(LIDAR.Angle+X[2])))
LocalFootprint = LocalFootprint.transpose()
# Update local workspace
if Polygon(LW).is_valid and Polygon(LocalFootprint).is_valid:
LW = cvxpolyintersect(LW, LocalFootprint)
if LW.any():
LW = LW + np.array([[X[0], X[1]]])
else:
LW = np.array([[]])
return LW
# Make local workspace convex
convhullind = ConvexHull(LW)
LW = LW[convhullind.vertices]
return LW
else:
LW = np.array([[]])
return LW
def localfreespaceLIDAR2D(RobotState, RobotRadius, LIDAR):
"""
Function that returns the local freespace
Input:
1) RobotState: Robot position and orientation
2) RobotRadius: Robot radius
3) LIDAR: Current LIDAR object
Output:
1) LF: Local freespace polygon array
"""
X = RobotState
epsilon = 0.000000001
R = LIDAR.RangeMeasurements
if R.min(0)<epsilon:
LF = np.array([[]])
return LF
else:
# Complete missing data due to the LIDAR's angular range limits
LIDAR = completeLIDAR2D(LIDAR)
# Modified range data defining the local freespace
R = 0.5*(LIDAR.RangeMeasurements-RobotRadius)
# Initialize the local freespace with the minimum square that respects
# the LIDAR's sensing range
LF = (0.5*(LIDAR.Range-RobotRadius))*np.array([[-1,-1], [-1,1], [1,1], [1,-1]])
Imin = np.nonzero(localminLIDAR2D(LIDAR))
Imin = Imin[0]
for k in Imin:
if not LF.any():
return LF
else:
# Local minimum parameters
Ak = LIDAR.Angle[k] # Angle
Rk = R[k] # Range
# Separating hyperplane parameters
n = np.array([-np.cos(Ak+X[2]), -np.sin(Ak+X[2])]) # Hyperplane normal
m = -Rk*n # A point on the separating hyperplane
# Update the local freespace by taking its intersection with the associated halfplane
LF = cvxpolyxhplane(LF, m, n)
LocalFootprint = np.vstack((R*np.cos(LIDAR.Angle+X[2]), R*np.sin(LIDAR.Angle+X[2])))
LocalFootprint = LocalFootprint.transpose()
# Update local freespace
if Polygon(LF).is_valid and Polygon(LocalFootprint).is_valid:
LF = cvxpolyintersect(LF, LocalFootprint)
if LF.any():
LF = LF + np.array([[X[0], X[1]]])
else:
LF = np.array([[]])
return LF
# Make local freespace convex
convhullind = ConvexHull(LF)
LF = LF[convhullind.vertices]
return LF
else:
LF = np.array([[]])
return LF
def localfreespace_linearLIDAR2D(RobotState, LF):
"""
Function that returns the linear local freespace as the intersection of the local freespace with the current heading line
Input:
1) RobotState: Robot position and orientation
2) LF: Local freespace
Output:
1) LFL: Linear freespace
"""
X = RobotState
if not LF.any():
LFL = np.array([[]])
return LFL
else:
RobotPosition = np.array([X[0], X[1]])
RobotDirection = np.array([np.cos(X[2]), np.sin(X[2])])
LFL = polyxray(LF, RobotPosition, RobotDirection)
LFL = np.vstack((RobotPosition, LFL))
return LFL
def localfreespace_angularLIDAR2D(RobotState, LF, Goal):
"""
Function that returns the angular local freespace as the intersection of the local freespace with the line connecting the robot to the goal
Input:
1) RobotState: Robot position and orientation
2) LF: Local freespace
Output:
1) LFA: Angular freespace
"""
X = RobotState
if not LF.any():
LFA = np.array([[]])
return LFA
else:
RobotPosition = np.array([X[0], X[1]])
GoalDirection = np.array([Goal[0]-RobotPosition[0], Goal[1]-RobotPosition[1]])
LFA = polyxray(LF, RobotPosition, GoalDirection)
LFA = np.vstack((RobotPosition, LFA))
return LFA
def localgoalLIDAR2D(LF, Goal):
"""
Function that computes the local goal as the projection of the global goal on the local freespace
Input:
1) LF: Local freespace
2) Goal: Global goal
Output:
1) LGA1: Local goal
"""
# Compute local goal --- the closest point of local free space to the global goal
if not LF.any():
LGA1 = np.array([Goal])
else:
if inpolygon(LF, Goal):
LGA1 = np.array([Goal])
else:
D, LGA1 = polydist(LF, Goal)
return LGA1
def localgoal_linearLIDAR2D(RobotState, LF, Goal):
"""
Function that computes the linear local goal as the projection of the global goal on the linear local freespace
Input:
1) RobotState: Robot position and orientation
2) LF: Local freespace
3) Goal: Global goal
Output:
1) LGL: Local linear goal
"""
# Compute linear local free space
LFL = localfreespace_linearLIDAR2D(RobotState, LF)
# Compute local goal for unicycle
if not LFL.any():
LGL = np.array([[RobotState[0], RobotState[1]]])
else:
D, LGL = polydist(LFL, Goal)
return LGL
def localgoal_angularLIDAR2D(RobotState, LF, Goal):
"""
Function that computes the angular local goal as the projection of the global goal on the angular local freespace
Input:
1) RobotState: Robot position and orientation
2) LF: Local freespace
3) Goal: Global goal
Output:
1) LGA2: Local angular goal
"""
# Compute angular local free space
LFA = localfreespace_angularLIDAR2D(RobotState, LF, Goal)
# Compute local goal for unicycle
if not LFA.any():
LGA2 = np.array([[Goal[0], Goal[1]]])
else:
D, LGA2 = polydist(LFA, Goal)
return LGA2
def diffeoTreeTriangulation(PolygonVertices, DiffeoParams):
"""
Function that calculates the triangulation tree of a polygon and augments it with properties used in semantic navigation
Input:
1) PolygonVertices: Vertex Coordinates of input polygon - Nx2 numpy.array (start and end vertices must be the same)
2) DiffeoParams: Options for the diffeomorphism construction
Output:
1) tree: Modified tree with added properties
a) For the root:
i) 'radius': the radius of the final sphere to be constructed
b) For the children:
i) 'r_center_t': The tangents from vertices 0 and 1 to the center - 2x2 numpy array
2) 'r_center_n': The normals corresponding to 'r_center_t'
c) For the root and the children:
i) 'vertices': the vertices of the triangle - 3x2 numpy array in CCW order
ii) 'vertices_tilde': the vertices of the polygonal collar that encompasses the triangle - Mx2 numpy array in CCW order starting from the center in the parent
iii) 'r_t': the unit tangents for the triangle to be deformed in a 3x2 numpy array in CCW order
(the 1st row is the tangent shared between the parent and the child)
iv) 'r_n': the unit normals for the triangle to be deformed in a 3x2 array corresponding to 'r_t'
v) 'r_tilde_t': the unit tangents for the polygonal collar in a Mx2 numpy array in CCW order
vi) 'r_tilde_n': the unit normals for the polygonal collar in a Mx2 numpy array corresponding to 'r_tilde_t'
vii) 'center': the center in the parent, used for the purging transformation, or the center of the root used for the final transformation
"""
# Unpack diffeomorphism parameters
varepsilon = DiffeoParams['varepsilon']
workspace = DiffeoParams['workspace']
# Check if the polygon intersects the workspace boundary
if Polygon(PolygonVertices).intersects(LineString(workspace)):
# Compute the intersection with the workspace
polygon_to_use = sp.geometry.polygon.orient(Polygon(PolygonVertices).intersection(Polygon(workspace)), 1.0)
PolygonVertices = np.vstack((polygon_to_use.exterior.coords.xy[0], polygon_to_use.exterior.coords.xy[1])).transpose()
# Compute the triangulation tree of the polygon with its dual (adjacency) graph
tree = polytriangulation(PolygonVertices, workspace, True)
# Find the center and the adjacency edge to the boundary
D, C = polydist(workspace,tree[-1]['vertices'])
inds = D.argsort()
tree[-1]['vertices'] = tree[-1]['vertices'][inds]
root_polygon_coords = np.vstack((tree[-1]['vertices'], tree[-1]['vertices'][0]))
if not LinearRing(root_polygon_coords).is_ccw:
tree[-1]['vertices'][[0,1]] = tree[-1]['vertices'][[1,0]]
tree[-1]['adj_edge'] = np.vstack((tree[-1]['vertices'][0], tree[-1]['vertices'][1]))
median_point = 0.5*np.array([[tree[-1]['adj_edge'][1][0]+tree[-1]['adj_edge'][0][0], tree[-1]['adj_edge'][1][1]+tree[-1]['adj_edge'][0][1]]])
median_ray = np.array([[median_point[0][0]-tree[-1]['vertices'][2][0], median_point[0][1]-tree[-1]['vertices'][2][1]]])
median_ray = median_ray/np.linalg.norm(median_ray[0])
tree[-1]['center'] = np.array([[median_point[0][0]+1.0*median_ray[0][0], median_point[0][1]+1.0*median_ray[0][1]]])
# Compute the tangent and normal vectors of the root triangle
tree[-1]['r_t'] = np.array(tree[-1]['vertices'][1]-tree[-1]['vertices'][0])/np.linalg.norm(tree[-1]['vertices'][1]-tree[-1]['vertices'][0])
tree[-1]['r_t'] = np.vstack((tree[-1]['r_t'], np.array(tree[-1]['vertices'][2]-tree[-1]['vertices'][1])/np.linalg.norm(tree[-1]['vertices'][2]-tree[-1]['vertices'][1])))
tree[-1]['r_t'] = np.vstack((tree[-1]['r_t'], np.array(tree[-1]['vertices'][0]-tree[-1]['vertices'][2])/np.linalg.norm(tree[-1]['vertices'][0]-tree[-1]['vertices'][2])))
tree[-1]['r_n'] = np.array([-tree[-1]['r_t'][0][1], tree[-1]['r_t'][0][0]])
tree[-1]['r_n'] = np.vstack((tree[-1]['r_n'], np.array([-tree[-1]['r_t'][1][1],tree[-1]['r_t'][1][0]])))
tree[-1]['r_n'] = np.vstack((tree[-1]['r_n'], np.array([-tree[-1]['r_t'][2][1],tree[-1]['r_t'][2][0]])))
# Find the remaining tangents and normals from vertices 0 and 1 to the center
tree[-1]['r_center_t'] = (tree[-1]['center'][0]-tree[-1]['vertices'][0])/np.linalg.norm(tree[-1]['center'][0]-tree[-1]['vertices'][0])
tree[-1]['r_center_n'] = np.array([-tree[-1]['r_center_t'][1], tree[-1]['r_center_t'][0]])
tree[-1]['r_center_t'] = np.vstack((tree[-1]['r_center_t'], (tree[-1]['vertices'][1]-tree[-1]['center'][0])/np.linalg.norm(tree[-1]['vertices'][1]-tree[-1]['center'][0])))
tree[-1]['r_center_n'] = np.vstack((tree[-1]['r_center_n'], np.array([-tree[-1]['r_center_t'][1][1],tree[-1]['r_center_t'][1][0]])))
# Compute the dilated polygon and truncate it by the rays emanating from the center
original_polygon = np.array([tree[-1]['center'][0], tree[-1]['vertices'][1], tree[-1]['vertices'][2], tree[-1]['vertices'][0], tree[-1]['center'][0]])
polygon_tilde = sp.geometry.polygon.orient(Polygon(original_polygon).buffer(varepsilon, join_style=1), 1.0)
dilation = np.vstack((polygon_tilde.exterior.coords.xy[0], polygon_tilde.exterior.coords.xy[1])).transpose()
intersect_1 = cvxpolyxhplane(dilation[0:-1], tree[-1]['center'][0], tree[-1]['r_center_n'][0])
intersect_2 = cvxpolyxhplane(intersect_1, tree[-1]['center'][0], tree[-1]['r_center_n'][1])
polygon_tilde_vertices = np.vstack((intersect_2,intersect_2[0]))
# Compute the intersection with the workspace
final_polygon = sp.geometry.polygon.orient(Polygon(polygon_tilde_vertices).intersection(Polygon(workspace).union(Polygon(np.array([tree[-1]['center'][0], tree[-1]['vertices'][1], tree[-1]['vertices'][2], tree[-1]['vertices'][0], tree[-1]['center'][0]])))).simplify(0.01), 1.0)
tree[-1]['vertices_tilde'] = np.vstack((final_polygon.exterior.coords.xy[0][0:-1], final_polygon.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
vertices_to_consider = np.vstack((tree[-1]['vertices_tilde'],tree[-1]['vertices_tilde'][0]))
tree[-1]['r_tilde_t'] = (vertices_to_consider[1]-vertices_to_consider[0])/np.linalg.norm(vertices_to_consider[1]-vertices_to_consider[0])
tree[-1]['r_tilde_n'] = np.array([-tree[-1]['r_tilde_t'][1],tree[-1]['r_tilde_t'][0]])
for j in range(1,vertices_to_consider.shape[0]-1):
tree[-1]['r_tilde_t'] = np.vstack((tree[-1]['r_tilde_t'],(vertices_to_consider[j+1]-vertices_to_consider[j])/np.linalg.norm(vertices_to_consider[j+1]-vertices_to_consider[j])))
tree[-1]['r_tilde_n'] = np.vstack((tree[-1]['r_tilde_n'],np.array([-tree[-1]['r_tilde_t'][j][1],tree[-1]['r_tilde_t'][j][0]])))
# Add a dummy radius
tree[-1]['radius'] = 0.0
else:
# Compute the triangulation tree of the polygon with its dual (adjacency) graph
tree = polytriangulation(PolygonVertices, workspace, False)
# Start with the root and find the center and the radius
root_coords = tree[-1]['vertices'].transpose()
tree[-1]['center'] = np.array([[sum(root_coords[0])/len(root_coords[0]), sum(root_coords[1])/len(root_coords[1])]])
D, closest_point = polydist(tree[-1]['vertices'], tree[-1]['center'])
tree[-1]['radius'] = 0.8*D[0]
# Compute the tangent and normal vectors of the root triangle
tree[-1]['r_t'] = np.array(tree[-1]['vertices'][1]-tree[-1]['vertices'][0])/np.linalg.norm(tree[-1]['vertices'][1]-tree[-1]['vertices'][0])
tree[-1]['r_t'] = np.vstack((tree[-1]['r_t'], np.array(tree[-1]['vertices'][2]-tree[-1]['vertices'][1])/np.linalg.norm(tree[-1]['vertices'][2]-tree[-1]['vertices'][1])))
tree[-1]['r_t'] = np.vstack((tree[-1]['r_t'], np.array(tree[-1]['vertices'][0]-tree[-1]['vertices'][2])/np.linalg.norm(tree[-1]['vertices'][0]-tree[-1]['vertices'][2])))
tree[-1]['r_n'] = np.array([-tree[-1]['r_t'][0][1], tree[-1]['r_t'][0][0]])
tree[-1]['r_n'] = np.vstack((tree[-1]['r_n'], np.array([-tree[-1]['r_t'][1][1],tree[-1]['r_t'][1][0]])))
tree[-1]['r_n'] = np.vstack((tree[-1]['r_n'], np.array([-tree[-1]['r_t'][2][1],tree[-1]['r_t'][2][0]])))
# Find the polygonal collar for the root by dilating the triangle by varepsilon
polygon_tilde = sp.geometry.polygon.orient(Polygon(tree[-1]['vertices']).buffer(varepsilon, join_style=1).intersection(Polygon(workspace)).simplify(0.01), 1.0)
tree[-1]['vertices_tilde'] = np.vstack((polygon_tilde.exterior.coords.xy[0][0:-1], polygon_tilde.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
vertices_to_consider = np.vstack((tree[-1]['vertices_tilde'],tree[-1]['vertices_tilde'][0]))
tree[-1]['r_tilde_t'] = (vertices_to_consider[1]-vertices_to_consider[0])/np.linalg.norm(vertices_to_consider[1]-vertices_to_consider[0])
tree[-1]['r_tilde_n'] = np.array([-tree[-1]['r_tilde_t'][1],tree[-1]['r_tilde_t'][0]])
for j in range(1,vertices_to_consider.shape[0]-1):
tree[-1]['r_tilde_t'] = np.vstack((tree[-1]['r_tilde_t'],(vertices_to_consider[j+1]-vertices_to_consider[j])/np.linalg.norm(vertices_to_consider[j+1]-vertices_to_consider[j])))
tree[-1]['r_tilde_n'] = np.vstack((tree[-1]['r_tilde_n'],np.array([-tree[-1]['r_tilde_t'][j][1],tree[-1]['r_tilde_t'][j][0]])))
# Identify all the children properties
for i in range(0,len(tree)-1):
# Compute the tangent and normal vectors of the child hyperplanes
# r0 is always the shared edge between the parent and the child, r1 and r2 the rest in CCW order
tree[i]['r_t'] = np.array(tree[i]['vertices'][1]-tree[i]['vertices'][0])/np.linalg.norm(tree[i]['vertices'][1]-tree[i]['vertices'][0])
tree[i]['r_t'] = np.vstack((tree[i]['r_t'], np.array(tree[i]['vertices'][2]-tree[i]['vertices'][1])/np.linalg.norm(tree[i]['vertices'][2]-tree[i]['vertices'][1])))
tree[i]['r_t'] = np.vstack((tree[i]['r_t'], np.array(tree[i]['vertices'][0]-tree[i]['vertices'][2])/np.linalg.norm(tree[i]['vertices'][0]-tree[i]['vertices'][2])))
tree[i]['r_n'] = np.array([-tree[i]['r_t'][0][1], tree[i]['r_t'][0][0]])
tree[i]['r_n'] = np.vstack((tree[i]['r_n'], np.array([-tree[i]['r_t'][1][1],tree[i]['r_t'][1][0]])))
tree[i]['r_n'] = np.vstack((tree[i]['r_n'], np.array([-tree[i]['r_t'][2][1],tree[i]['r_t'][2][0]])))
# Find the median from the 3rd point to the shared edge and from that compute the center for the purging transformation
median_point = 0.5*np.array([[tree[i]['adj_edge'][1][0]+tree[i]['adj_edge'][0][0], tree[i]['adj_edge'][1][1]+tree[i]['adj_edge'][0][1]]])
median_ray = np.array([[median_point[0][0]-tree[i]['vertices'][2][0], median_point[0][1]-tree[i]['vertices'][2][1]]])
median_ray = median_ray/np.linalg.norm(median_ray[0])
intersection_point = polyxray(tree[tree[i]['predecessor']]['vertices'], median_point[0], median_ray[0]) # offset median point by a little bit to avoid numerical problems
tree[i]['center'] = np.array([[0.2*median_point[0][0]+0.8*intersection_point[0], 0.2*median_point[0][1]+0.8*intersection_point[1]]])
# Find the remaining tangents and normals from vertices 0 and 1 to the center
tree[i]['r_center_t'] = (tree[i]['center'][0]-tree[i]['vertices'][0])/np.linalg.norm(tree[i]['center'][0]-tree[i]['vertices'][0])
tree[i]['r_center_n'] = np.array([-tree[i]['r_center_t'][1], tree[i]['r_center_t'][0]])
tree[i]['r_center_t'] = np.vstack((tree[i]['r_center_t'], (tree[i]['vertices'][1]-tree[i]['center'][0])/np.linalg.norm(tree[i]['vertices'][1]-tree[i]['center'][0])))
tree[i]['r_center_n'] = np.vstack((tree[i]['r_center_n'], np.array([-tree[i]['r_center_t'][1][1],tree[i]['r_center_t'][1][0]])))
# Compute the dilated polygon and truncate it by the rays emanating from the center
original_polygon = np.array([tree[i]['center'][0], tree[i]['vertices'][1], tree[i]['vertices'][2], tree[i]['vertices'][0], tree[i]['center'][0]])
polygon_tilde = sp.geometry.polygon.orient(Polygon(original_polygon).buffer(varepsilon, join_style=1).simplify(0.01), 1.0)
dilation = np.vstack((polygon_tilde.exterior.coords.xy[0], polygon_tilde.exterior.coords.xy[1])).transpose()
intersect_1 = cvxpolyxhplane(dilation[0:-1], tree[i]['center'][0], tree[i]['r_center_n'][0])
intersect_2 = cvxpolyxhplane(intersect_1, tree[i]['center'][0], tree[i]['r_center_n'][1])
candidate_polygon_vertices = np.vstack((intersect_2,intersect_2[0]))
candidate_polygon = Polygon(candidate_polygon_vertices)
# Check for collisions with all the triangles that will succeed i in the diffeomorphism construction except for its parent
for j in range(i+1,len(tree)):
if (j == tree[i]['predecessor']):
continue
else:
polygon_to_test = Polygon(tree[j]['vertices'])
candidate_polygon = (candidate_polygon.buffer(0)).difference(polygon_to_test.buffer(0))
# If the difference operation created a multipolygon, keep only the polygon that contains the barycenter of the extended triangle
if candidate_polygon.geom_type == 'MultiPolygon':
point_to_consider = Point((tree[i]['vertices'][0][0]+tree[i]['vertices'][1][0]+tree[i]['center'][0][0])/3.0, (tree[i]['vertices'][0][1]+tree[i]['vertices'][1][1]+tree[i]['center'][0][1])/3.0)
for k in range(len(candidate_polygon)):
if candidate_polygon[k].contains(point_to_consider):
candidate_polygon = candidate_polygon[k]
break
# Extract final vertices
candidate_polygon = sp.geometry.polygon.orient(candidate_polygon.simplify(0.01), 1.0)
candidate_polygon_vertices = np.vstack((candidate_polygon.exterior.coords.xy[0], candidate_polygon.exterior.coords.xy[1])).transpose()
# Decompose the polygon into its convex pieces and find the piece that includes the barycenter of the extended triangle
decomposition = polycvxdecomp(candidate_polygon_vertices.tolist())
for j in range(len(decomposition)):
point_to_consider = Point((tree[i]['vertices'][0][0]+tree[i]['vertices'][1][0]+tree[i]['center'][0][0])/3.0, (tree[i]['vertices'][0][1]+tree[i]['vertices'][1][1]+tree[i]['center'][0][1])/3.0)
polygon_to_consider = Polygon(decomposition[j])
if polygon_to_consider.buffer(0.01).contains(point_to_consider):
final_polygon_vertices = np.vstack((polygon_to_consider.exterior.coords.xy[0], polygon_to_consider.exterior.coords.xy[1])).transpose()
break
# Generate the outer polygonal collar
final_polygon = sp.geometry.polygon.orient(Polygon(final_polygon_vertices).intersection(Polygon(workspace)), 1.0)
tree[i]['vertices_tilde'] = np.vstack((final_polygon.exterior.coords.xy[0][0:-1], final_polygon.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
vertices_to_consider = np.vstack((tree[i]['vertices_tilde'],tree[i]['vertices_tilde'][0]))
tree[i]['r_tilde_t'] = (vertices_to_consider[1]-vertices_to_consider[0])/np.linalg.norm(vertices_to_consider[1]-vertices_to_consider[0])
tree[i]['r_tilde_n'] = np.array([-tree[i]['r_tilde_t'][1],tree[i]['r_tilde_t'][0]])
for j in range(1,vertices_to_consider.shape[0]-1):
tree[i]['r_tilde_t'] = np.vstack((tree[i]['r_tilde_t'],(vertices_to_consider[j+1]-vertices_to_consider[j])/np.linalg.norm(vertices_to_consider[j+1]-vertices_to_consider[j])))
tree[i]['r_tilde_n'] = np.vstack((tree[i]['r_tilde_n'],np.array([-tree[i]['r_tilde_t'][j][1],tree[i]['r_tilde_t'][j][0]])))
return tree
def diffeoTreeConvex(PolygonVertices, DiffeoParams):
"""
Function that calculates the convex decomposition of a polygon and augments it with properties used in semantic navigation
Input:
1) PolygonVertices: Vertex Coordinates of input polygon - Nx2 numpy.array (start and end vertices must be the same)
2) DiffeoParams: Options for the diffeomorphism construction
Output:
1) tree: Modified tree with added properties
a) For the root:
i) 'radius': the radius of the final sphere to be constructed
b) For the children:
i) 'r_center_t': The tangents from vertices 0 and 1 to the center - 2x2 numpy array
2) 'r_center_n': The normals corresponding to 'r_center_t'
c) For the root and the children:
i) 'vertices': the vertices of the polygon - Nx2 numpy array in CCW order
ii) 'augmented_vertices': the vertices of the polygon including the center of deformation (the second element in this array) - (N+1)x2 numpy array in CCW order
ii) 'vertices_tilde': the vertices of the polygonal collar that encompasses the polygon - Mx2 numpy array in CCW order starting from the center in the parent
iii) 'r_t': the unit tangents corresponding to augmented_vertices in CCW order
iv) 'r_n': the unit normals for the polygon to be deformed in an array corresponding to 'r_t'
v) 'r_tilde_t': the unit tangents for the polygonal collar in a Mx2 numpy array in CCW order
vi) 'r_tilde_n': the unit normals for the polygonal collar in a Mx2 numpy array corresponding to 'r_tilde_t'
vii) 'center': the center in the parent, used for the purging transformation, or the center of the root used for the final transformation
"""
# Unpack diffeomorphism parameters
varepsilon = DiffeoParams['varepsilon']
workspace = DiffeoParams['workspace']
# Check if the polygon intersects the workspace boundary
if Polygon(PolygonVertices).intersects(LineString(workspace)):
# Compute the intersection with the workspace
polygon_to_use = sp.geometry.polygon.orient(Polygon(PolygonVertices).intersection(Polygon(workspace)), 1.0)
PolygonVertices = np.vstack((polygon_to_use.exterior.coords.xy[0], polygon_to_use.exterior.coords.xy[1])).transpose()
# Compute the convex decomposition tree of the polygon with its dual (adjacency) graph
tree = polyconvexdecomposition(PolygonVertices, workspace, True)
# Find the center and the adjacency edge to the boundary
D, C = polydist(workspace,tree[-1]['vertices'])
inds = D.argsort()
if D[(inds[0]+1)%tree[-1]['vertices'].shape[0]] >= D[(inds[0]-1)%tree[-1]['vertices'].shape[0]]:
tree[-1]['vertices'] = np.roll(tree[-1]['vertices'],-(inds[0]-1)%tree[-1]['vertices'].shape[0],axis=0)
else:
tree[-1]['vertices'] = np.roll(tree[-1]['vertices'],-(inds[0])%tree[-1]['vertices'].shape[0],axis=0)
tree[-1]['adj_edge'] = np.vstack((tree[-1]['vertices'][0], tree[-1]['vertices'][1]))
median_point = 0.5*np.array([[tree[-1]['adj_edge'][1][0]+tree[-1]['adj_edge'][0][0], tree[-1]['adj_edge'][1][1]+tree[-1]['adj_edge'][0][1]]])
median_ray = np.array([[median_point[0][0]-tree[-1]['vertices'][2][0], median_point[0][1]-tree[-1]['vertices'][2][1]]])
median_ray = median_ray/np.linalg.norm(median_ray[0])
tree[-1]['center'] = np.array([[median_point[0][0]+0.3*median_ray[0][0], median_point[0][1]+0.3*median_ray[0][1]]])
# Compute the tangent and normal vectors of the child hyperplanes
# r0 is always the shared edge between the parent and the child, the rest in CCW order
tree[-1]['r_t'] = []
for j in range(0,tree[-1]['vertices'].shape[0]):
tree[-1]['r_t'].append(np.array(tree[-1]['vertices'][(j+1)%tree[-1]['vertices'].shape[0]]-tree[-1]['vertices'][j%tree[-1]['vertices'].shape[0]])/np.linalg.norm(tree[-1]['vertices'][(j+1)%tree[-1]['vertices'].shape[0]]-tree[-1]['vertices'][j%tree[-1]['vertices'].shape[0]]))
tree[-1]['r_t'] = np.array(tree[-1]['r_t'])
tree[-1]['r_n'] = np.zeros((tree[-1]['r_t'].shape[0],2))
for j in range(0,tree[-1]['r_n'].shape[0]):
tree[-1]['r_n'][j][0] = -tree[-1]['r_t'][j][1]
tree[-1]['r_n'][j][1] = tree[-1]['r_t'][j][0]
# Find the remaining tangents and normals from vertices 0 and 1 to the center
tree[-1]['r_center_t'] = (tree[-1]['center'][0]-tree[-1]['vertices'][0])/np.linalg.norm(tree[-1]['center'][0]-tree[-1]['vertices'][0])
tree[-1]['r_center_n'] = np.array([-tree[-1]['r_center_t'][1], tree[-1]['r_center_t'][0]])
tree[-1]['r_center_t'] = np.vstack((tree[-1]['r_center_t'], (tree[-1]['vertices'][1]-tree[-1]['center'][0])/np.linalg.norm(tree[-1]['vertices'][1]-tree[-1]['center'][0])))
tree[-1]['r_center_n'] = np.vstack((tree[-1]['r_center_n'], np.array([-tree[-1]['r_center_t'][1][1],tree[-1]['r_center_t'][1][0]])))
# Compute the dilated polygon and truncate it by the rays emanating from the center
original_polygon = np.vstack((tree[-1]['vertices'][0], tree[-1]['center'], tree[-1]['vertices'][1:]))
polygon_tilde = sp.geometry.polygon.orient(Polygon(original_polygon).buffer(varepsilon, join_style=1), 1.0)
dilation = np.vstack((polygon_tilde.exterior.coords.xy[0], polygon_tilde.exterior.coords.xy[1])).transpose()
intersect_1 = cvxpolyxhplane(dilation[0:-1], tree[-1]['center'][0], tree[-1]['r_center_n'][0])
intersect_2 = cvxpolyxhplane(intersect_1, tree[-1]['center'][0], tree[-1]['r_center_n'][1])
polygon_tilde_vertices = np.vstack((intersect_2,intersect_2[0]))
# Compute the intersection with the workspace
final_polygon = sp.geometry.polygon.orient(((Polygon(polygon_tilde_vertices).intersection(Polygon(workspace))).union(Polygon(np.vstack((tree[-1]['center'][0], tree[-1]['vertices'][1:], tree[-1]['vertices'][0], tree[-1]['center'][0]))))).simplify(0.01), 1.0)
tree[-1]['vertices_tilde'] = np.vstack((final_polygon.exterior.coords.xy[0][0:-1], final_polygon.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
tree[-1]['r_tilde_t'] = []
for j in range(0,tree[-1]['vertices_tilde'].shape[0]):
tree[-1]['r_tilde_t'].append(np.array(tree[-1]['vertices_tilde'][(j+1)%tree[-1]['vertices_tilde'].shape[0]]-tree[-1]['vertices_tilde'][j%tree[-1]['vertices_tilde'].shape[0]])/np.linalg.norm(tree[-1]['vertices_tilde'][(j+1)%tree[-1]['vertices_tilde'].shape[0]]-tree[-1]['vertices_tilde'][j%tree[-1]['vertices_tilde'].shape[0]]))
tree[-1]['r_tilde_t'] = np.array(tree[-1]['r_tilde_t'])
tree[-1]['r_tilde_n'] = np.zeros((tree[-1]['r_tilde_t'].shape[0],2))
for j in range(0,tree[-1]['r_tilde_n'].shape[0]):
tree[-1]['r_tilde_n'][j][0] = -tree[-1]['r_tilde_t'][j][1]
tree[-1]['r_tilde_n'][j][1] = tree[-1]['r_tilde_t'][j][0]
# Finally, compute the augmented inner polygon that includes the center of deformation and update
tree[-1]['augmented_vertices'] = np.vstack((tree[-1]['vertices'][0], tree[-1]['center'], tree[-1]['vertices'][1:]))
tree[-1]['r_t'] = np.vstack((tree[-1]['r_center_t'][0], tree[-1]['r_center_t'][1], tree[-1]['r_t'][1:]))
tree[-1]['r_n'] = np.vstack((tree[-1]['r_center_n'][0], tree[-1]['r_center_n'][1], tree[-1]['r_n'][1:]))
# Add a dummy radius
tree[-1]['radius'] = 0.0
else:
# Compute the convex decomposition tree of the polygon with its dual (adjacency) graph
tree = polyconvexdecomposition(PolygonVertices, workspace, False)
# Start with the root and find the center and the radius
root_coords = tree[-1]['vertices'].transpose()
tree[-1]['center'] = np.array([[sum(root_coords[0])/len(root_coords[0]), sum(root_coords[1])/len(root_coords[1])]])
D, closest_point = polydist(tree[-1]['vertices'], tree[-1]['center'])
tree[-1]['radius'] = 0.8*D[0]
# Compute the tangent and normal vectors of the root polygon
tree[-1]['r_t'] = []
for j in range(0,tree[-1]['vertices'].shape[0]):
tree[-1]['r_t'].append(np.array(tree[-1]['vertices'][(j+1)%tree[-1]['vertices'].shape[0]]-tree[-1]['vertices'][j%tree[-1]['vertices'].shape[0]])/np.linalg.norm(tree[-1]['vertices'][(j+1)%tree[-1]['vertices'].shape[0]]-tree[-1]['vertices'][j%tree[-1]['vertices'].shape[0]]))
tree[-1]['r_t'] = np.array(tree[-1]['r_t'])
tree[-1]['r_n'] = np.zeros((tree[-1]['r_t'].shape[0],2))
for j in range(0,tree[-1]['r_n'].shape[0]):
tree[-1]['r_n'][j][0] = -tree[-1]['r_t'][j][1]
tree[-1]['r_n'][j][1] = tree[-1]['r_t'][j][0]
# Find the polygonal collar for the root by dilating the polygon by varepsilon
polygon_tilde = sp.geometry.polygon.orient(Polygon(tree[-1]['vertices']).buffer(varepsilon, join_style=1).intersection(Polygon(workspace)).simplify(0.01), 1.0)
tree[-1]['vertices_tilde'] = np.vstack((polygon_tilde.exterior.coords.xy[0][0:-1], polygon_tilde.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
tree[-1]['r_tilde_t'] = []
for j in range(0,tree[-1]['vertices_tilde'].shape[0]):
tree[-1]['r_tilde_t'].append(np.array(tree[-1]['vertices_tilde'][(j+1)%tree[-1]['vertices_tilde'].shape[0]]-tree[-1]['vertices_tilde'][j%tree[-1]['vertices_tilde'].shape[0]])/np.linalg.norm(tree[-1]['vertices_tilde'][(j+1)%tree[-1]['vertices_tilde'].shape[0]]-tree[-1]['vertices_tilde'][j%tree[-1]['vertices_tilde'].shape[0]]))
tree[-1]['r_tilde_t'] = np.array(tree[-1]['r_tilde_t'])
tree[-1]['r_tilde_n'] = np.zeros((tree[-1]['r_tilde_t'].shape[0],2))
for j in range(0,tree[-1]['r_tilde_n'].shape[0]):
tree[-1]['r_tilde_n'][j][0] = -tree[-1]['r_tilde_t'][j][1]
tree[-1]['r_tilde_n'][j][1] = tree[-1]['r_tilde_t'][j][0]
# In this case the augmented vertices are the same
tree[-1]['augmented_vertices'] = tree[-1]['vertices']
# Identify all the children properties
for i in range(0,len(tree)-1):
# Compute the tangent and normal vectors of the child hyperplanes
# r0 is always the shared edge between the parent and the child, the rest in CCW order
tree[i]['r_t'] = []
for j in range(0,tree[i]['vertices'].shape[0]):
tree[i]['r_t'].append(np.array(tree[i]['vertices'][(j+1)%tree[i]['vertices'].shape[0]]-tree[i]['vertices'][j%tree[i]['vertices'].shape[0]])/np.linalg.norm(tree[i]['vertices'][(j+1)%tree[i]['vertices'].shape[0]]-tree[i]['vertices'][j%tree[i]['vertices'].shape[0]]))
tree[i]['r_t'] = np.array(tree[i]['r_t'])
tree[i]['r_n'] = np.zeros((tree[i]['r_t'].shape[0],2))
for j in range(0,tree[i]['r_n'].shape[0]):
tree[i]['r_n'][j][0] = -tree[i]['r_t'][j][1]
tree[i]['r_n'][j][1] = tree[i]['r_t'][j][0]
# Find the median from the point furthest away from the adjacency edge and from that compute the center for the purging transformation
# To compute the center, first compute the intersection of the parent polygon with the hyperplanes of the child polygon next to the adjacency edge - this defines the admissible region within which you are allowed to search for a center
dot_product_list = []
for j in range(0,tree[i]['vertices'].shape[0]):
dot_product_list.append(np.dot(tree[i]['vertices'][j]-tree[i]['vertices'][0],tree[i]['r_n'][0]))
inds = np.array(dot_product_list).argsort()
median_point = 0.5*np.array([[tree[i]['adj_edge'][1][0]+tree[i]['adj_edge'][0][0], tree[i]['adj_edge'][1][1]+tree[i]['adj_edge'][0][1]]])
median_ray = np.array([[median_point[0][0]-tree[i]['vertices'][inds[-1]][0], median_point[0][1]-tree[i]['vertices'][inds[-1]][1]]])
median_ray = median_ray/np.linalg.norm(median_ray[0])
intersect_1_cvx = cvxpolyxhplane(tree[tree[i]['predecessor']]['vertices'], tree[i]['adj_edge'][0], tree[i]['r_n'][-1])
intersect_2_cvx = cvxpolyxhplane(intersect_1_cvx, tree[i]['adj_edge'][1], tree[i]['r_n'][1])
intersection_point = polyxray(intersect_2_cvx, median_point[0]+0.05*median_ray[0], median_ray[0]) # offset median point by a little bit to avoid numerical problems
tree[i]['center'] = np.array([[0.5*median_point[0][0]+0.5*intersection_point[0], 0.5*median_point[0][1]+0.5*intersection_point[1]]])
# Find the remaining tangents and normals from vertices 0 and 1 to the center
tree[i]['r_center_t'] = (tree[i]['center'][0]-tree[i]['vertices'][0])/np.linalg.norm(tree[i]['center'][0]-tree[i]['vertices'][0])
tree[i]['r_center_n'] = np.array([-tree[i]['r_center_t'][1], tree[i]['r_center_t'][0]])
tree[i]['r_center_t'] = np.vstack((tree[i]['r_center_t'], (tree[i]['vertices'][1]-tree[i]['center'][0])/np.linalg.norm(tree[i]['vertices'][1]-tree[i]['center'][0])))
tree[i]['r_center_n'] = np.vstack((tree[i]['r_center_n'], np.array([-tree[i]['r_center_t'][1][1],tree[i]['r_center_t'][1][0]])))
# Compute the dilated polygon and truncate it by the rays emanating from the center
# Make sure that the intersection of the dilation with the convex pieces succeeding the current convex piece in the transformation does not generate a multipolygon - otherwise reduce the radius of dilation until we have a single piece
succeeding_polygons = []
for j in range(i+1,len(tree)):
succeeding_polygons.append(Polygon(tree[j]['vertices']))
succeeding_polygons_union = cascaded_union(succeeding_polygons)
original_polygon = np.vstack((tree[i]['vertices'][0], tree[i]['center'], tree[i]['vertices'][1:]))
varepsilon_used = varepsilon
polygon_tilde = sp.geometry.polygon.orient(Polygon(original_polygon).buffer(varepsilon_used, join_style=1).simplify(0.01), 1.0)
while (polygon_tilde.intersection(succeeding_polygons_union)).geom_type == 'MultiPolygon':
varepsilon_used = 0.5*varepsilon_used
polygon_tilde = sp.geometry.polygon.orient(Polygon(original_polygon).buffer(varepsilon_used, join_style=1).simplify(0.01), 1.0)
dilation = np.vstack((polygon_tilde.exterior.coords.xy[0], polygon_tilde.exterior.coords.xy[1])).transpose()
intersect_1 = cvxpolyxhplane(dilation[0:-1], tree[i]['center'][0], tree[i]['r_center_n'][0])
intersect_2 = cvxpolyxhplane(intersect_1, tree[i]['center'][0], tree[i]['r_center_n'][1])
candidate_polygon_vertices = np.vstack((intersect_2,intersect_2[0]))
candidate_polygon = Polygon(candidate_polygon_vertices)
# Check for collisions with all the polygons that will succeed i in the diffeomorphism construction except for its parent
for j in range(i+1,len(tree)):
if (j == tree[i]['predecessor']):
continue
else:
polygon_to_test = Polygon(tree[j]['vertices'])
candidate_polygon = (candidate_polygon.buffer(0)).difference(polygon_to_test.buffer(0))
# If the difference operation created a multipolygon, keep only the polygon that contains the barycenter of the extended triangle
if candidate_polygon.geom_type == 'MultiPolygon':
point_to_consider = Point((tree[i]['vertices'][0][0]+tree[i]['vertices'][1][0]+tree[i]['center'][0][0])/3.0, (tree[i]['vertices'][0][1]+tree[i]['vertices'][1][1]+tree[i]['center'][0][1])/3.0)
for k in range(len(candidate_polygon)):
if candidate_polygon[k].contains(point_to_consider):
candidate_polygon = candidate_polygon[k]
break
# Extract final vertices
candidate_polygon = sp.geometry.polygon.orient(candidate_polygon.simplify(0.01), 1.0)
candidate_polygon_vertices = np.vstack((candidate_polygon.exterior.coords.xy[0], candidate_polygon.exterior.coords.xy[1])).transpose()
# Decompose the polygon into its convex pieces and find the piece that includes the barycenter of the extended triangle
decomposition = polycvxdecomp(candidate_polygon_vertices.tolist())
for j in range(len(decomposition)):
point_to_consider = Point((tree[i]['vertices'][0][0]+tree[i]['vertices'][1][0]+tree[i]['center'][0][0])/3.0, (tree[i]['vertices'][0][1]+tree[i]['vertices'][1][1]+tree[i]['center'][0][1])/3.0)
polygon_to_consider = Polygon(decomposition[j])
if polygon_to_consider.buffer(0.01).contains(point_to_consider):
final_polygon_vertices = np.vstack((polygon_to_consider.exterior.coords.xy[0], polygon_to_consider.exterior.coords.xy[1])).transpose()
break
# Generate the outer polygonal collar
final_polygon = sp.geometry.polygon.orient(Polygon(final_polygon_vertices).intersection(Polygon(workspace)), 1.0)
tree[i]['vertices_tilde'] = np.vstack((final_polygon.exterior.coords.xy[0][0:-1], final_polygon.exterior.coords.xy[1][0:-1])).transpose()
# Find the tangent and normal vectors for the generated polygonal collar
tree[i]['r_tilde_t'] = []
for j in range(0,tree[i]['vertices_tilde'].shape[0]):
tree[i]['r_tilde_t'].append(np.array(tree[i]['vertices_tilde'][(j+1)%tree[i]['vertices_tilde'].shape[0]]-tree[i]['vertices_tilde'][j%tree[i]['vertices_tilde'].shape[0]])/np.linalg.norm(tree[i]['vertices_tilde'][(j+1)%tree[i]['vertices_tilde'].shape[0]]-tree[i]['vertices_tilde'][j%tree[i]['vertices_tilde'].shape[0]]))
tree[i]['r_tilde_t'] = np.array(tree[i]['r_tilde_t'])
tree[i]['r_tilde_n'] = np.zeros((tree[i]['r_tilde_t'].shape[0],2))
for j in range(0,tree[i]['r_tilde_n'].shape[0]):
tree[i]['r_tilde_n'][j][0] = -tree[i]['r_tilde_t'][j][1]
tree[i]['r_tilde_n'][j][1] = tree[i]['r_tilde_t'][j][0]
# Finally, compute the augmented inner polygon that includes the center of deformation and update
tree[i]['augmented_vertices'] = np.vstack((tree[i]['vertices'][0], tree[i]['center'], tree[i]['vertices'][1:]))
tree[i]['r_t'] = np.vstack((tree[i]['r_center_t'][0], tree[i]['r_center_t'][1], tree[i]['r_t'][1:]))
tree[i]['r_n'] = np.vstack((tree[i]['r_center_n'][0], tree[i]['r_center_n'][1], tree[i]['r_n'][1:]))
return tree
def polygonDiffeoTriangulation(Position, DiffeoTree, DiffeoParams):
"""
Function that computes h(x) (i.e., the position of point x in the model layer), Dh(x) and the derivatives of Dh, when purging a specific polygon whose dual graph and diffeomorphism properties are known, using the ear clipping method
Input:
1) Position: Point to consider - 1x2 numpy.array
2) DiffeoTree: Tree that contains the diffeomorphism properties for a particular polygon
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) map_h: Value of the map
2) map_hd: Value of the map differential - 1x2 numpy.array
3) map_hdd: Values of the derivatives of the differential - 8-element numpy array
"""
# Begin purging process with default values
if Position.shape == (2,):
Position = np.array([Position])
map_h = Position
map_hd = np.eye(2)
map_hdd = np.zeros(8)
# Iterate through the polygon triangles
for i in range(len(DiffeoTree)):
map_h_new, map_hd_new, map_hdd_new = triangleDiffeo(map_h, DiffeoTree[i], DiffeoParams)
res1 = map_hd_new[0][0]*map_hdd[0] + map_hd_new[0][1]*map_hdd[4] + map_hd[0][0]*(map_hdd_new[0]*map_hd[0][0] + map_hdd_new[1]*map_hd[1][0]) + map_hd[1][0]*(map_hdd_new[2]*map_hd[0][0] + map_hdd_new[3]*map_hd[1][0])
res2 = map_hd_new[0][0]*map_hdd[1] + map_hd_new[0][1]*map_hdd[5] + map_hd[0][0]*(map_hdd_new[0]*map_hd[0][1] + map_hdd_new[1]*map_hd[1][1]) + map_hd[1][0]*(map_hdd_new[2]*map_hd[0][1] + map_hdd_new[3]*map_hd[1][1])
res3 = map_hd_new[0][0]*map_hdd[2] + map_hd_new[0][1]*map_hdd[6] + map_hd[0][1]*(map_hdd_new[0]*map_hd[0][0] + map_hdd_new[1]*map_hd[1][0]) + map_hd[1][1]*(map_hdd_new[2]*map_hd[0][0] + map_hdd_new[3]*map_hd[1][0])
res4 = map_hd_new[0][0]*map_hdd[3] + map_hd_new[0][1]*map_hdd[7] + map_hd[0][1]*(map_hdd_new[0]*map_hd[0][1] + map_hdd_new[1]*map_hd[1][1]) + map_hd[1][1]*(map_hdd_new[2]*map_hd[0][1] + map_hdd_new[3]*map_hd[1][1])
res5 = map_hd_new[1][0]*map_hdd[0] + map_hd_new[1][1]*map_hdd[4] + map_hd[0][0]*(map_hdd_new[4]*map_hd[0][0] + map_hdd_new[5]*map_hd[1][0]) + map_hd[1][0]*(map_hdd_new[6]*map_hd[0][0] + map_hdd_new[7]*map_hd[1][0])
res6 = map_hd_new[1][0]*map_hdd[1] + map_hd_new[1][1]*map_hdd[5] + map_hd[0][0]*(map_hdd_new[4]*map_hd[0][1] + map_hdd_new[5]*map_hd[1][1]) + map_hd[1][0]*(map_hdd_new[6]*map_hd[0][1] + map_hdd_new[7]*map_hd[1][1])
res7 = map_hd_new[1][0]*map_hdd[2] + map_hd_new[1][1]*map_hdd[6] + map_hd[0][1]*(map_hdd_new[4]*map_hd[0][0] + map_hdd_new[5]*map_hd[1][0]) + map_hd[1][1]*(map_hdd_new[6]*map_hd[0][0] + map_hdd_new[7]*map_hd[1][0])
res8 = map_hd_new[1][0]*map_hdd[3] + map_hd_new[1][1]*map_hdd[7] + map_hd[0][1]*(map_hdd_new[4]*map_hd[0][1] + map_hdd_new[5]*map_hd[1][1]) + map_hd[1][1]*(map_hdd_new[6]*map_hd[0][1] + map_hdd_new[7]*map_hd[1][1])
map_hdd[0] = res1
map_hdd[1] = res2
map_hdd[2] = res3
map_hdd[3] = res4
map_hdd[4] = res5
map_hdd[5] = res6
map_hdd[6] = res7
map_hdd[7] = res8
map_hd = np.matmul(map_hd_new, map_hd)
map_h = map_h_new
return map_h, map_hd, map_hdd
def polygonDiffeoConvex(Position, DiffeoTree, DiffeoParams):
"""
Function that computes h(x) (i.e., the position of point x in the model layer), Dh(x) and the derivatives of Dh, when purging a specific polygon whose dual graph and diffeomorphism properties are known, using the convex decomposition method
Input:
1) Position: Point to consider - 1x2 numpy.array
2) DiffeoTree: Tree that contains the diffeomorphism properties for a particular polygon
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) map_h: Value of the map
2) map_hd: Value of the map differential - 1x2 numpy.array
3) map_hdd: Values of the derivatives of the differential - 8-element numpy array
"""
# Begin purging process with default values
if Position.shape == (2,):
Position = np.array([Position])
map_h = Position
map_hd = np.eye(2)
map_hdd = np.zeros(8)
# Iterate through the polygon triangles
for i in range(len(DiffeoTree)):
map_h_new, map_hd_new, map_hdd_new = polygonDiffeo(map_h, DiffeoTree[i], DiffeoParams)
res1 = map_hd_new[0][0]*map_hdd[0] + map_hd_new[0][1]*map_hdd[4] + map_hd[0][0]*(map_hdd_new[0]*map_hd[0][0] + map_hdd_new[1]*map_hd[1][0]) + map_hd[1][0]*(map_hdd_new[2]*map_hd[0][0] + map_hdd_new[3]*map_hd[1][0])
res2 = map_hd_new[0][0]*map_hdd[1] + map_hd_new[0][1]*map_hdd[5] + map_hd[0][0]*(map_hdd_new[0]*map_hd[0][1] + map_hdd_new[1]*map_hd[1][1]) + map_hd[1][0]*(map_hdd_new[2]*map_hd[0][1] + map_hdd_new[3]*map_hd[1][1])
res3 = map_hd_new[0][0]*map_hdd[2] + map_hd_new[0][1]*map_hdd[6] + map_hd[0][1]*(map_hdd_new[0]*map_hd[0][0] + map_hdd_new[1]*map_hd[1][0]) + map_hd[1][1]*(map_hdd_new[2]*map_hd[0][0] + map_hdd_new[3]*map_hd[1][0])
res4 = map_hd_new[0][0]*map_hdd[3] + map_hd_new[0][1]*map_hdd[7] + map_hd[0][1]*(map_hdd_new[0]*map_hd[0][1] + map_hdd_new[1]*map_hd[1][1]) + map_hd[1][1]*(map_hdd_new[2]*map_hd[0][1] + map_hdd_new[3]*map_hd[1][1])
res5 = map_hd_new[1][0]*map_hdd[0] + map_hd_new[1][1]*map_hdd[4] + map_hd[0][0]*(map_hdd_new[4]*map_hd[0][0] + map_hdd_new[5]*map_hd[1][0]) + map_hd[1][0]*(map_hdd_new[6]*map_hd[0][0] + map_hdd_new[7]*map_hd[1][0])
res6 = map_hd_new[1][0]*map_hdd[1] + map_hd_new[1][1]*map_hdd[5] + map_hd[0][0]*(map_hdd_new[4]*map_hd[0][1] + map_hdd_new[5]*map_hd[1][1]) + map_hd[1][0]*(map_hdd_new[6]*map_hd[0][1] + map_hdd_new[7]*map_hd[1][1])
res7 = map_hd_new[1][0]*map_hdd[2] + map_hd_new[1][1]*map_hdd[6] + map_hd[0][1]*(map_hdd_new[4]*map_hd[0][0] + map_hdd_new[5]*map_hd[1][0]) + map_hd[1][1]*(map_hdd_new[6]*map_hd[0][0] + map_hdd_new[7]*map_hd[1][0])
res8 = map_hd_new[1][0]*map_hdd[3] + map_hd_new[1][1]*map_hdd[7] + map_hd[0][1]*(map_hdd_new[4]*map_hd[0][1] + map_hdd_new[5]*map_hd[1][1]) + map_hd[1][1]*(map_hdd_new[6]*map_hd[0][1] + map_hdd_new[7]*map_hd[1][1])
map_hdd[0] = res1
map_hdd[1] = res2
map_hdd[2] = res3
map_hdd[3] = res4
map_hdd[4] = res5
map_hdd[5] = res6
map_hdd[6] = res7
map_hdd[7] = res8
map_hd = np.matmul(map_hd_new, map_hd)
map_h = map_h_new
return map_h, map_hd, map_hdd
def triangleDiffeo(Position, Triangle, DiffeoParams):
"""
Function that computes h(x) (i.e., the position of point x in the model layer), Dh(x) and the derivatives of Dh, when purging a specific triangle in space
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) map_h: Value of the map
2) map_hd: Value of the map differential - 1x2 numpy.array
3) map_hdd: Values of the derivatives of the differential - 8-element numpy array
"""
# Compute the triangle switch and its gradient
sigma, sigmad, sigmadd = triangleSwitch(Position, Triangle, DiffeoParams)
# Compute the triangle deforming factor
nu, nud, nudd = deformingFactor(Position, Triangle)
# Find the map and its differential
map_h = sigma*(Triangle['center']+nu*(Position-Triangle['center'])) + (1-sigma)*Position
map_hd = (nu-1)*np.dot((Position-Triangle['center']).transpose(),sigmad) + sigma*np.dot((Position-Triangle['center']).transpose(),nud) + (1+sigma*(nu-1))*np.eye(2)
# Find the derivatives of the jacobian
map_hdd_m0_r0_s0 = 2*sigma*nud[0][0]+2*(nu-1)*sigmad[0][0]+2*(Position[0][0]-Triangle['center'][0][0])*sigmad[0][0]*nud[0][0]+(Position[0][0]-Triangle['center'][0][0])*sigma*nudd[0][0]+(Position[0][0]-Triangle['center'][0][0])*(nu-1)*sigmadd[0][0]
map_hdd_m0_r0_s1 = sigma*nud[0][1]+(nu-1)*sigmad[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigmad[0][1]*nud[0][0]+(Position[0][0]-Triangle['center'][0][0])*sigma*nudd[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigmad[0][0]*nud[0][1]+(Position[0][0]-Triangle['center'][0][0])*(nu-1)*sigmadd[0][1]
map_hdd_m0_r1_s0 = sigma*nud[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigmad[0][0]*nud[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigma*nudd[0][1]+(nu-1)*sigmad[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigmad[0][1]*nud[0][0]+(Position[0][0]-Triangle['center'][0][0])*(nu-1)*sigmadd[0][1]
map_hdd_m0_r1_s1 = 2*(Position[0][0]-Triangle['center'][0][0])*sigmad[0][1]*nud[0][1]+(Position[0][0]-Triangle['center'][0][0])*sigma*nudd[1][1]+(Position[0][0]-Triangle['center'][0][0])*(nu-1)*sigmadd[1][1]
map_hdd_m1_r0_s0 = 2*(Position[0][1]-Triangle['center'][0][1])*sigmad[0][0]*nud[0][0]+(Position[0][1]-Triangle['center'][0][1])*sigma*nudd[0][0]+(Position[0][1]-Triangle['center'][0][1])*(nu-1)*sigmadd[0][0]
map_hdd_m1_r0_s1 = sigma*nud[0][0]+(Position[0][1]-Triangle['center'][0][1])*sigmad[0][1]*nud[0][0]+(Position[0][1]-Triangle['center'][0][1])*sigma*nudd[0][1]+(nu-1)*sigmad[0][0]+(Position[0][1]-Triangle['center'][0][1])*sigmad[0][0]*nud[0][1]+(Position[0][1]-Triangle['center'][0][1])*(nu-1)*sigmadd[0][1]
map_hdd_m1_r1_s0 = sigma*nud[0][0]+(nu-1)*sigmad[0][0]+(Position[0][1]-Triangle['center'][0][1])*sigmad[0][0]*nud[0][1]+(Position[0][1]-Triangle['center'][0][1])*sigma*nudd[0][1]+(Position[0][1]-Triangle['center'][0][1])*sigmad[0][1]*nud[0][0]+(Position[0][1]-Triangle['center'][0][1])*(nu-1)*sigmadd[0][1]
map_hdd_m1_r1_s1 = 2*sigma*nud[0][1]+2*(nu-1)*sigmad[0][1]+2*(Position[0][1]-Triangle['center'][0][1])*sigmad[0][1]*nud[0][1]+(Position[0][1]-Triangle['center'][0][1])*sigma*nudd[1][1]+(Position[0][1]-Triangle['center'][0][1])*(nu-1)*sigmadd[1][1]
map_hdd = np.array([map_hdd_m0_r0_s0, map_hdd_m0_r0_s1, map_hdd_m0_r1_s0, map_hdd_m0_r1_s1, map_hdd_m1_r0_s0, map_hdd_m1_r0_s1, map_hdd_m1_r1_s0, map_hdd_m1_r1_s1])
return map_h, map_hd, map_hdd
def polygonDiffeo(Position, PolygonUsed, DiffeoParams):
"""
Function that computes h(x) (i.e., the position of point x in the model layer), Dh(x) and the derivatives of Dh, when purging a specific polygon in space
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) map_h: Value of the map
2) map_hd: Value of the map differential - 1x2 numpy.array
3) map_hdd: Values of the derivatives of the differential - 8-element numpy array
"""
# Compute the polygon switch and its gradient
sigma, sigmad, sigmadd = polygonSwitch(Position, PolygonUsed, DiffeoParams)
# Compute the polygon deforming factor
nu, nud, nudd = deformingFactor(Position, PolygonUsed)
# Find the map and its differential
map_h = sigma*(PolygonUsed['center']+nu*(Position-PolygonUsed['center'])) + (1-sigma)*Position
map_hd = (nu-1)*np.dot((Position-PolygonUsed['center']).transpose(),sigmad) + sigma*np.dot((Position-PolygonUsed['center']).transpose(),nud) + (1+sigma*(nu-1))*np.eye(2)
# Find the derivatives of the jacobian
map_hdd_m0_r0_s0 = 2*sigma*nud[0][0]+2*(nu-1)*sigmad[0][0]+2*(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][0]*nud[0][0]+(Position[0][0]-PolygonUsed['center'][0][0])*sigma*nudd[0][0]+(Position[0][0]-PolygonUsed['center'][0][0])*(nu-1)*sigmadd[0][0]
map_hdd_m0_r0_s1 = sigma*nud[0][1]+(nu-1)*sigmad[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][1]*nud[0][0]+(Position[0][0]-PolygonUsed['center'][0][0])*sigma*nudd[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][0]*nud[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*(nu-1)*sigmadd[0][1]
map_hdd_m0_r1_s0 = sigma*nud[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][0]*nud[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigma*nudd[0][1]+(nu-1)*sigmad[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][1]*nud[0][0]+(Position[0][0]-PolygonUsed['center'][0][0])*(nu-1)*sigmadd[0][1]
map_hdd_m0_r1_s1 = 2*(Position[0][0]-PolygonUsed['center'][0][0])*sigmad[0][1]*nud[0][1]+(Position[0][0]-PolygonUsed['center'][0][0])*sigma*nudd[1][1]+(Position[0][0]-PolygonUsed['center'][0][0])*(nu-1)*sigmadd[1][1]
map_hdd_m1_r0_s0 = 2*(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][0]*nud[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*sigma*nudd[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*(nu-1)*sigmadd[0][0]
map_hdd_m1_r0_s1 = sigma*nud[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][1]*nud[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*sigma*nudd[0][1]+(nu-1)*sigmad[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][0]*nud[0][1]+(Position[0][1]-PolygonUsed['center'][0][1])*(nu-1)*sigmadd[0][1]
map_hdd_m1_r1_s0 = sigma*nud[0][0]+(nu-1)*sigmad[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][0]*nud[0][1]+(Position[0][1]-PolygonUsed['center'][0][1])*sigma*nudd[0][1]+(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][1]*nud[0][0]+(Position[0][1]-PolygonUsed['center'][0][1])*(nu-1)*sigmadd[0][1]
map_hdd_m1_r1_s1 = 2*sigma*nud[0][1]+2*(nu-1)*sigmad[0][1]+2*(Position[0][1]-PolygonUsed['center'][0][1])*sigmad[0][1]*nud[0][1]+(Position[0][1]-PolygonUsed['center'][0][1])*sigma*nudd[1][1]+(Position[0][1]-PolygonUsed['center'][0][1])*(nu-1)*sigmadd[1][1]
map_hdd = np.array([map_hdd_m0_r0_s0, map_hdd_m0_r0_s1, map_hdd_m0_r1_s0, map_hdd_m0_r1_s1, map_hdd_m1_r0_s0, map_hdd_m1_r0_s1, map_hdd_m1_r1_s0, map_hdd_m1_r1_s1])
return map_h, map_hd, map_hdd
def triangleSwitch(Position, Triangle, DiffeoParams):
"""
Function that computes the overall switch value, its gradient and hessian for a point x outside a triangle
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 1x2 numpy.array
"""
# Distinguish whether the triangle to consider is the root or some child
# Find the separate switch values, gradients and hessians
sigma_beta, sigma_betad, sigma_betadd = betaSwitchTriangle(Position, Triangle, DiffeoParams)
sigma_gamma, sigma_gammad, sigma_gammadd = gammaSwitchTriangle(Position, Triangle, DiffeoParams)
# Find the overall switch value, gradient and hessian
if (sigma_beta == 1.0) and (sigma_gamma == 0.0):
sigma = 1.0
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
nom = sigma_gamma*sigma_beta
denom = sigma_gamma*sigma_beta + (1-sigma_beta)
sigma = nom/denom
nomd = sigma_gamma*sigma_betad + sigma_beta*sigma_gammad
denomd = sigma_gamma*sigma_betad + sigma_beta*sigma_gammad - sigma_betad
sigmad = (1/denom)*nomd - (nom/denom**2)*denomd
nomdd = sigma_gamma*sigma_betadd + np.dot(sigma_betad.transpose(), sigma_gammad) + np.dot(sigma_gammad.transpose(), sigma_betad) + sigma_beta*sigma_gammadd
denomdd = sigma_gamma*sigma_betadd + np.dot(sigma_betad.transpose(), sigma_gammad) + np.dot(sigma_gammad.transpose(), sigma_betad) + sigma_beta*sigma_gammadd - sigma_betadd
sigmadd = (1/denom)*nomdd - (1/denom**2)*(np.dot(nomd.transpose(),denomd)+np.dot(denomd.transpose(),nomd)) + 2*(nom/(denom**3))*np.dot(denomd.transpose(),denomd) - (nom/(denom**2))*denomdd
return sigma, sigmad, sigmadd
def polygonSwitch(Position, PolygonUsed, DiffeoParams):
"""
Function that computes the overall switch value, its gradient and hessian for a point x outside a triangle
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 1x2 numpy.array
"""
# Find the separate switch values, gradients and hessians
sigma_beta, sigma_betad, sigma_betadd = betaSwitchPolygon(Position, PolygonUsed, DiffeoParams)
sigma_gamma, sigma_gammad, sigma_gammadd = gammaSwitchPolygon(Position, PolygonUsed, DiffeoParams)
# Find the overall switch value, gradient and hessian
if (sigma_beta == 1.0) and (sigma_gamma == 0.0):
sigma = 1.0
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
nom = sigma_gamma*sigma_beta
denom = sigma_gamma*sigma_beta + (1-sigma_beta)
sigma = nom/denom
nomd = sigma_gamma*sigma_betad + sigma_beta*sigma_gammad
denomd = sigma_gamma*sigma_betad + sigma_beta*sigma_gammad - sigma_betad
sigmad = (1/denom)*nomd - (nom/denom**2)*denomd
nomdd = sigma_gamma*sigma_betadd + np.dot(sigma_betad.transpose(), sigma_gammad) + np.dot(sigma_gammad.transpose(), sigma_betad) + sigma_beta*sigma_gammadd
denomdd = sigma_gamma*sigma_betadd + np.dot(sigma_betad.transpose(), sigma_gammad) + np.dot(sigma_gammad.transpose(), sigma_betad) + sigma_beta*sigma_gammadd - sigma_betadd
sigmadd = (1/denom)*nomdd - (1/denom**2)*(np.dot(nomd.transpose(),denomd)+np.dot(denomd.transpose(),nomd)) + 2*(nom/(denom**3))*np.dot(denomd.transpose(),denomd) - (nom/(denom**2))*denomdd
return sigma, sigmad, sigmadd
def deformingFactor(Position, PolygonUsed):
"""
Function that computes the value, gradient and hessian of the deforming factor for a point x outside a triangle
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
Output:
1) nu: Value of the deforming factor
2) nud: Value of the deforming factor gradient - 1x2 numpy.array
3) nudd: Value of the deforming factor hessian - 2x2 numpy.array
"""
# Distinguish whether the polygon to consider is the root or some child
if (PolygonUsed['depth'] == 0) and (not PolygonUsed['adj_edge'].any()):
nu = PolygonUsed['radius']/np.linalg.norm(Position[0]-PolygonUsed['center'][0])
nud = -(PolygonUsed['radius']/(np.linalg.norm(Position[0]-PolygonUsed['center'][0])**3))*np.array([Position[0]-PolygonUsed['center'][0]])
nudd = ((3*PolygonUsed['radius'])/(np.linalg.norm(Position[0]-PolygonUsed['center'][0])**5))*np.dot(np.array([Position[0]-PolygonUsed['center'][0]]).transpose(),np.array([Position[0]-PolygonUsed['center'][0]])) - (PolygonUsed['radius']/(np.linalg.norm(Position[0]-PolygonUsed['center'][0])**3))*np.eye(2)
else:
# First compute the normal for the adjacency edge
shared_tangent = np.array(PolygonUsed['adj_edge'][1]-PolygonUsed['adj_edge'][0])/np.linalg.norm(PolygonUsed['adj_edge'][1]-PolygonUsed['adj_edge'][0])
shared_normal = np.array([-shared_tangent[1],shared_tangent[0]])
nu = np.dot(PolygonUsed['vertices'][0]-PolygonUsed['center'][0],shared_normal)/np.dot(Position[0]-PolygonUsed['center'][0],shared_normal)
nud = -((np.dot(PolygonUsed['vertices'][0]-PolygonUsed['center'][0],shared_normal))/(np.dot(Position[0]-PolygonUsed['center'][0],shared_normal))**2)*np.array([shared_normal])
nudd = 2*((np.dot(PolygonUsed['vertices'][0]-PolygonUsed['center'][0],shared_normal))/(np.dot(Position[0]-PolygonUsed['center'][0],shared_normal))**3)*np.dot(np.array([shared_normal]).transpose(),np.array([shared_normal]))
return nu, nud, nudd
def betaSwitchTriangle(Position, Triangle, DiffeoParams):
"""
Function that computes the value, gradient and hessian of the beta-switch for a point x outside a triangle
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 2x2 numpy.array
"""
# Unwrap parameters
mu_1 = DiffeoParams['mu_1']
epsilon = DiffeoParams['epsilon']
# Compute the value of beta and its gradient and hessian
beta, betad, betadd = outsideImplicitTriangle(Position, Triangle, DiffeoParams)
# Compute the value of the switch
if beta >= epsilon:
sigma = 0.
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
sigma = np.exp(-mu_1/(epsilon-beta))/np.exp(-mu_1/(epsilon))
sigmad = -mu_1*(sigma/((epsilon-beta)**2))*betad
sigmadd = (mu_1**2*(sigma/((epsilon-beta)**4))-2*mu_1*(sigma/((epsilon-beta)**3)))*np.dot(betad.transpose(),betad) - mu_1*(sigma/((epsilon-beta)**2))*betadd
return sigma, sigmad, sigmadd
def betaSwitchPolygon(Position, PolygonUsed, DiffeoParams):
"""
Function that computes the value, gradient and hessian of the beta-switch for a point x outside a polygon
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 2x2 numpy.array
"""
# Unwrap parameters
mu_1 = DiffeoParams['mu_1']
epsilon = DiffeoParams['epsilon']
# Compute the value of beta and its gradient and hessian
beta, betad, betadd = outsideImplicitPolygon(Position, PolygonUsed, DiffeoParams)
# Compute the value of the switch
if beta >= epsilon:
sigma = 0.
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
sigma = np.exp(-mu_1/(epsilon-beta))/np.exp(-mu_1/(epsilon))
sigmad = -mu_1*(sigma/((epsilon-beta)**2))*betad
sigmadd = (mu_1**2*(sigma/((epsilon-beta)**4))-2*mu_1*(sigma/((epsilon-beta)**3)))*np.dot(betad.transpose(),betad) - mu_1*(sigma/((epsilon-beta)**2))*betadd
return sigma, sigmad, sigmadd
def gammaSwitchTriangle(Position, Triangle, DiffeoParams):
"""
Function that computes the value, gradient and hessian of the gamma-switch for a point x outside a triangle
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 2x2 numpy.array
"""
# Unwrap parameters
mu_2 = DiffeoParams['mu_2']
# Compute the value of gamma and its gradient
gamma, gammad, gammadd = insideImplicitTriangle(Position, Triangle, DiffeoParams)
# Compute the value of the switch
if gamma <= 0.:
sigma = 0.
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
# Compute the value of alpha and its gradient and hessian
nom = gamma
denom = np.linalg.norm(Position[0]-Triangle['center'][0])
alpha = nom/denom
nomd = gammad
denomd = np.array([Position[0]-Triangle['center'][0]])/np.linalg.norm(Position[0]-Triangle['center'][0])
alphad = (1/denom)*nomd - (nom/denom**2)*denomd
nomdd = gammadd
denomdd = (1/np.linalg.norm(Position[0]-Triangle['center'][0]))*np.eye(2) - (1/np.linalg.norm(Position[0]-Triangle['center'][0])**3)*np.dot(np.array([Position[0]-Triangle['center'][0]]).transpose(),np.array([Position[0]-Triangle['center'][0]]))
alphadd = (1/denom)*nomdd - (1/denom**2)*(np.dot(nomd.transpose(),denomd)+np.dot(denomd.transpose(),nomd)) + 2*(nom/(denom**3))*np.dot(denomd.transpose(),denomd) - (nom/(denom**2))*denomdd
sigma = np.exp(-mu_2/alpha)
sigmad = mu_2*(sigma/(alpha**2))*alphad
sigmadd = (mu_2**2*(sigma/(alpha**4))-2*mu_2*(sigma/(alpha**3)))*np.dot(alphad.transpose(),alphad) + mu_2*(sigma/(alpha**2))*alphadd
return sigma, sigmad, sigmadd
def gammaSwitchPolygon(Position, PolygonUsed, DiffeoParams):
"""
Function that computes the value, gradient and hessian of the gamma-switch for a point x outside a polygon
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) sigma: Value of the switch
2) sigmad: Value of the switch gradient - 1x2 numpy.array
3) sigmadd: Value of the switch hessian - 2x2 numpy.array
"""
# Unwrap parameters
mu_2 = DiffeoParams['mu_2']
# Compute the value of gamma and its gradient
gamma, gammad, gammadd = insideImplicitPolygon(Position, PolygonUsed, DiffeoParams)
# Compute the value of the switch
if gamma <= 0.:
sigma = 0.
sigmad = np.array([[0.,0.]])
sigmadd = np.zeros((2,2))
else:
# Compute the value of alpha and its gradient and hessian
nom = gamma
denom = np.linalg.norm(Position[0]-PolygonUsed['center'][0])
alpha = nom/denom
nomd = gammad
denomd = np.array([Position[0]-PolygonUsed['center'][0]])/np.linalg.norm(Position[0]-PolygonUsed['center'][0])
alphad = (1/denom)*nomd - (nom/denom**2)*denomd
nomdd = gammadd
denomdd = (1/np.linalg.norm(Position[0]-PolygonUsed['center'][0]))*np.eye(2) - (1/np.linalg.norm(Position[0]-PolygonUsed['center'][0])**3)*np.dot(np.array([Position[0]-PolygonUsed['center'][0]]).transpose(),np.array([Position[0]-PolygonUsed['center'][0]]))
alphadd = (1/denom)*nomdd - (1/denom**2)*(np.dot(nomd.transpose(),denomd)+np.dot(denomd.transpose(),nomd)) + 2*(nom/(denom**3))*np.dot(denomd.transpose(),denomd) - (nom/(denom**2))*denomdd
sigma = np.exp(-mu_2/alpha)
sigmad = mu_2*(sigma/(alpha**2))*alphad
sigmadd = (mu_2**2*(sigma/(alpha**4))-2*mu_2*(sigma/(alpha**3)))*np.dot(alphad.transpose(),alphad) + mu_2*(sigma/(alpha**2))*alphadd
return sigma, sigmad, sigmadd
def outsideImplicitTriangle(Position, Triangle, DiffeoParams):
"""
Function that computes beta(x) (i.e., the R-function) for a point x outside a triangle, and its gradient and hessian
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) beta: beta(x)
2) betad: Gradient of beta(x)
3) betadd: Hessian of beta(x)
"""
# Unwrap parameters
p = DiffeoParams['p']
# Distinguish between the root triangle and all the other triangles
if (Triangle['depth'] == 0) and (not Triangle['adj_edge'].any()):
# Find the hyperplane values
hyperplane_1 = np.dot(Position[0]-Triangle['vertices'][2],Triangle['r_n'][1])
hyperplane_2 = np.dot(Position[0]-Triangle['vertices'][2],Triangle['r_n'][2])
hyperplane_3 = np.dot(Position[0]-Triangle['vertices'][0],Triangle['r_n'][0])
# Compute the R-function
hyperplane_12 = hyperplane_1 + hyperplane_2 - (hyperplane_1**p+hyperplane_2**p)**(1/p)
hyperplane_123 = hyperplane_12 + hyperplane_3 - (hyperplane_12**p+hyperplane_3**p)**(1/p)
beta = -hyperplane_123
# Compute the gradients
hyperplane_1d = np.array([Triangle['r_n'][1]])
hyperplane_2d = np.array([Triangle['r_n'][2]])
hyperplane_3d = np.array([Triangle['r_n'][0]])
hyperplane_12d = (1-((hyperplane_1**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_1d + (1-((hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_2d
hyperplane_123d = (1-((hyperplane_12**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p))))*hyperplane_12d + (1-((hyperplane_3**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p))))*hyperplane_3d
betad = -hyperplane_123d
# Compute the hessian
hyperplane_1dd = np.zeros((2,2))
hyperplane_2dd = np.zeros((2,2))
hyperplane_3dd = np.zeros((2,2))
hyperplane_12dd = (-(p-1)*((hyperplane_1**(p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p)))+(p-1)*((hyperplane_1**(2*p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_1d.transpose(),hyperplane_1d) + ((p-1)*((hyperplane_1**(p-1)*hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_1d.transpose(),hyperplane_2d) + (1-((hyperplane_1**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_1dd + (-(p-1)*((hyperplane_2**(p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p)))+(p-1)*((hyperplane_2**(2*p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_2d.transpose(),hyperplane_2d) + ((p-1)*((hyperplane_1**(p-1)*hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_2d.transpose(),hyperplane_1d) + (1-((hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_2dd
hyperplane_123dd = (-(p-1)*((hyperplane_12**(p-2))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p)))+(p-1)*((hyperplane_12**(2*p-2))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p+1))))*np.dot(hyperplane_12d.transpose(),hyperplane_12d) + ((p-1)*((hyperplane_12**(p-1)*hyperplane_3**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p+1))))*np.dot(hyperplane_12d.transpose(),hyperplane_3d) + (1-((hyperplane_12**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p))))*hyperplane_12dd + (-(p-1)*((hyperplane_3**(p-2))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p)))+(p-1)*((hyperplane_3**(2*p-2))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p+1))))*np.dot(hyperplane_3d.transpose(),hyperplane_3d) + ((p-1)*((hyperplane_12**(p-1)*hyperplane_3**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p+1))))*np.dot(hyperplane_3d.transpose(),hyperplane_12d) + (1-((hyperplane_3**(p-1))/((hyperplane_12**p+hyperplane_3**p)**((p-1)/p))))*hyperplane_3dd
betadd = -hyperplane_123dd
else:
# Find the hyperplane values
hyperplane_1 = np.dot(Position[0]-Triangle['vertices'][2],Triangle['r_n'][1])
hyperplane_2 = np.dot(Position[0]-Triangle['vertices'][2],Triangle['r_n'][2])
hyperplane_3 = np.dot(Position[0]-Triangle['center'][0],Triangle['r_center_n'][0])
hyperplane_4 = np.dot(Position[0]-Triangle['center'][0],Triangle['r_center_n'][1])
# Compute the R-function
hyperplane_12 = hyperplane_1 + hyperplane_2 - (hyperplane_1**p+hyperplane_2**p)**(1/p)
hyperplane_34 = hyperplane_3 + hyperplane_4 - (hyperplane_3**p+hyperplane_4**p)**(1/p)
hyperplane_1234 = hyperplane_12 + hyperplane_34 - (hyperplane_12**p+hyperplane_34**p)**(1/p)
beta = -hyperplane_1234
# Compute the gradients
hyperplane_1d = np.array([Triangle['r_n'][1]])
hyperplane_2d = np.array([Triangle['r_n'][2]])
hyperplane_3d = np.array([Triangle['r_center_n'][0]])
hyperplane_4d = np.array([Triangle['r_center_n'][1]])
hyperplane_12d = (1-((hyperplane_1**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_1d + (1-((hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_2d
hyperplane_34d = (1-((hyperplane_3**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p))))*hyperplane_3d + (1-((hyperplane_4**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p))))*hyperplane_4d
hyperplane_1234d = (1-((hyperplane_12**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p))))*hyperplane_12d + (1-((hyperplane_34**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p))))*hyperplane_34d
betad = -hyperplane_1234d
# Compute the hessian
hyperplane_1dd = np.zeros((2,2))
hyperplane_2dd = np.zeros((2,2))
hyperplane_3dd = np.zeros((2,2))
hyperplane_4dd = np.zeros((2,2))
hyperplane_12dd = (-(p-1)*((hyperplane_1**(p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p)))+(p-1)*((hyperplane_1**(2*p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_1d.transpose(),hyperplane_1d) + ((p-1)*((hyperplane_1**(p-1)*hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_1d.transpose(),hyperplane_2d) + (1-((hyperplane_1**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_1dd + (-(p-1)*((hyperplane_2**(p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p)))+(p-1)*((hyperplane_2**(2*p-2))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_2d.transpose(),hyperplane_2d) + ((p-1)*((hyperplane_1**(p-1)*hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p+1))))*np.dot(hyperplane_2d.transpose(),hyperplane_1d) + (1-((hyperplane_2**(p-1))/((hyperplane_1**p+hyperplane_2**p)**((p-1)/p))))*hyperplane_2dd
hyperplane_34dd = (-(p-1)*((hyperplane_3**(p-2))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p)))+(p-1)*((hyperplane_3**(2*p-2))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p+1))))*np.dot(hyperplane_3d.transpose(),hyperplane_3d) + ((p-1)*((hyperplane_3**(p-1)*hyperplane_4**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p+1))))*np.dot(hyperplane_3d.transpose(),hyperplane_4d) + (1-((hyperplane_3**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p))))*hyperplane_3dd + (-(p-1)*((hyperplane_4**(p-2))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p)))+(p-1)*((hyperplane_4**(2*p-2))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p+1))))*np.dot(hyperplane_4d.transpose(),hyperplane_4d) + ((p-1)*((hyperplane_3**(p-1)*hyperplane_4**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p+1))))*np.dot(hyperplane_4d.transpose(),hyperplane_3d) + (1-((hyperplane_4**(p-1))/((hyperplane_3**p+hyperplane_4**p)**((p-1)/p))))*hyperplane_4dd
hyperplane_1234dd = (-(p-1)*((hyperplane_12**(p-2))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p)))+(p-1)*((hyperplane_12**(2*p-2))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p+1))))*np.dot(hyperplane_12d.transpose(),hyperplane_12d) + ((p-1)*((hyperplane_12**(p-1)*hyperplane_34**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p+1))))*np.dot(hyperplane_12d.transpose(),hyperplane_34d) + (1-((hyperplane_12**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p))))*hyperplane_12dd + (-(p-1)*((hyperplane_34**(p-2))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p)))+(p-1)*((hyperplane_34**(2*p-2))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p+1))))*np.dot(hyperplane_34d.transpose(),hyperplane_34d) + ((p-1)*((hyperplane_12**(p-1)*hyperplane_34**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p+1))))*np.dot(hyperplane_34d.transpose(),hyperplane_12d) + (1-((hyperplane_34**(p-1))/((hyperplane_12**p+hyperplane_34**p)**((p-1)/p))))*hyperplane_34dd
betadd = -hyperplane_1234dd
return beta, betad, betadd
def outsideImplicitPolygon(Position, PolygonUsed, DiffeoParams):
"""
Function that computes beta(x) (i.e., the R-function) for a point x outside a polygon, and its gradient and hessian
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) beta: beta(x)
2) betad: Gradient of beta(x)
3) betadd: Hessian of beta(x)
"""
# Unwrap parameters
p = DiffeoParams['p']
# Compute hyperplane functions
hyperplane = np.zeros(PolygonUsed['augmented_vertices'].shape[0])
for i in range(0, PolygonUsed['augmented_vertices'].shape[0]):
hyperplane[i] = np.dot(Position[0]-PolygonUsed['augmented_vertices'][i],PolygonUsed['r_n'][i])
# Compute the R-function and its gradient and hessian
betadd = (-(p-1)*((hyperplane[0]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[0]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][0]]).transpose(),np.array([PolygonUsed['r_n'][0]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][0]]).transpose(),np.array([PolygonUsed['r_n'][1]])) + (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2)) + (-(p-1)*((hyperplane[1]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[1]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][1]]).transpose(),np.array([PolygonUsed['r_n'][1]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][1]]).transpose(),np.array([PolygonUsed['r_n'][0]])) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2))
betad = (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([PolygonUsed['r_n'][0]]) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([PolygonUsed['r_n'][1]])
beta = hyperplane[0] + hyperplane[1] - (hyperplane[0]**p+hyperplane[1]**p)**(1/p)
for i in range(2,len(hyperplane)):
betadd = (-(p-1)*((beta**(p-2))/((beta**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((beta**(2*p-2))/((beta**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(betad.transpose(),betad) + ((p-1)*((beta**(p-1)*hyperplane[i]**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(betad.transpose(),np.array([PolygonUsed['r_n'][i]])) + (1-((beta**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p))))*betadd + (-(p-1)*((hyperplane[i]**(p-2))/((beta**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((hyperplane[i]**(2*p-2))/((beta**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][i]]).transpose(),np.array([PolygonUsed['r_n'][i]])) + ((p-1)*((beta**(p-1)*hyperplane[i]**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_n'][i]]).transpose(),betad) + (1-((hyperplane[i]**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p))))*np.zeros((2,2))
betad = (1-((beta**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p))))*betad + (1-((hyperplane[i]**(p-1))/((beta**p+hyperplane[i]**p)**((p-1)/p))))*np.array([PolygonUsed['r_n'][i]])
beta = beta + hyperplane[i] - (beta**p+hyperplane[i]**p)**(1/p)
return -beta, -betad, -betadd
def insideImplicitTriangle(Position, Triangle, DiffeoParams):
"""
Function that computes gamma(x) (i.e., the R-function) for a point x inside an enclosing polygon, and its gradient and hessian
Input:
1) Position: Point to consider - 1x2 numpy.array
2) Triangle: Description of the triangle - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) gamma: gamma(x)
2) gammad: Gradient of gamma(x)
3) gammadd: Hessian of gamma(x)
"""
# Unwrap parameters
p = DiffeoParams['p']
# Compute hyperplane functions
hyperplane = np.zeros(Triangle['vertices_tilde'].shape[0])
for i in range(0, Triangle['vertices_tilde'].shape[0]):
hyperplane[i] = np.dot(Position[0]-Triangle['vertices_tilde'][i],Triangle['r_tilde_n'][i])
# Compute the R-function and its gradient and hessian
gammadd = (-(p-1)*((hyperplane[0]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[0]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][0]]).transpose(),np.array([Triangle['r_tilde_n'][0]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][0]]).transpose(),np.array([Triangle['r_tilde_n'][1]])) + (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2)) + (-(p-1)*((hyperplane[1]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[1]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][1]]).transpose(),np.array([Triangle['r_tilde_n'][1]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][1]]).transpose(),np.array([Triangle['r_tilde_n'][0]])) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2))
gammad = (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([Triangle['r_tilde_n'][0]]) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([Triangle['r_tilde_n'][1]])
gamma = hyperplane[0] + hyperplane[1] - (hyperplane[0]**p+hyperplane[1]**p)**(1/p)
for i in range(2,len(hyperplane)):
gammadd = (-(p-1)*((gamma**(p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((gamma**(2*p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(gammad.transpose(),gammad) + ((p-1)*((gamma**(p-1)*hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(gammad.transpose(),np.array([Triangle['r_tilde_n'][i]])) + (1-((gamma**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*gammadd + (-(p-1)*((hyperplane[i]**(p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((hyperplane[i]**(2*p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][i]]).transpose(),np.array([Triangle['r_tilde_n'][i]])) + ((p-1)*((gamma**(p-1)*hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([Triangle['r_tilde_n'][i]]).transpose(),gammad) + (1-((hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*np.zeros((2,2))
gammad = (1-((gamma**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*gammad + (1-((hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*np.array([Triangle['r_tilde_n'][i]])
gamma = gamma + hyperplane[i] - (gamma**p+hyperplane[i]**p)**(1/p)
return gamma, gammad, gammadd
def insideImplicitPolygon(Position, PolygonUsed, DiffeoParams):
"""
Function that computes gamma(x) (i.e., the R-function) for a point x inside an enclosing polygon, and its gradient and hessian
Input:
1) Position: Point to consider - 1x2 numpy.array
2) PolygonUsed: Description of the polygon - Dictionary
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) gamma: gamma(x)
2) gammad: Gradient of gamma(x)
3) gammadd: Hessian of gamma(x)
"""
# # Unpack the values and compute the hyperplanes
# PolygonVertices = PolygonUsed['vertices_tilde']
# PolygonNormals = PolygonUsed['r_tilde_n']
# # Find length of polygon vertices
# m = PolygonVertices.shape[0]
# # Span to find values and derivatives
# hyperplane_values = []
# hyperplane_values_d = []
# for k in range(0,m):
# hyperplane_values.append(np.dot(Position[0]-PolygonVertices[k], PolygonNormals[k]))
# hyperplane_values_d.append(np.array([PolygonNormals[k]]))
# # Initialize gamma and derivatives
# gamma = 0.0
# gamma_d = np.array([[0.0, 0.0]])
# gamma_dd = np.zeros((2,2))
# for k in range(0,m):
# gamma = gamma + hyperplane_values[k]
# gamma_d = gamma_d + hyperplane_values_d[k]
# # Span through the polygon to find the m by k combinations
# for k in range(2,m+1):
# sorting = list(itertools.combinations(range(0,m),k))
# power_k = (-1)**(k+1)
# # Span through a specific sort tuple
# for sort in sorting:
# alpha_k_before_root = 0
# alpha_k_before_root_d = np.array([[0.0, 0.0]])
# alpha_k_before_root_dd = np.zeros((2,2))
# # Span the indices of the sort to find the values, gradients and hessian inside the root - (a_1^2 + ... + a_n^2)
# for j in sort:
# alpha_k_before_root = alpha_k_before_root + hyperplane_values[j]**2
# alpha_k_before_root_d = alpha_k_before_root_d + 2.0*hyperplane_values[j]*hyperplane_values_d[j]
# alpha_k_before_root_dd = alpha_k_before_root_dd + 2.0*np.dot(hyperplane_values_d[j].transpose(),hyperplane_values_d[j])
# # Precompute some terms
# alpha_k_before_root_sqrt = np.sqrt(alpha_k_before_root)
# # Find the final values, gradients and hessians with the root and add it to the current gamma - sqrt(a_1^2 + ... + a_n^2)
# gamma = gamma + power_k * alpha_k_before_root_sqrt
# gamma_d = gamma_d + power_k * alpha_k_before_root_d/(2.0*alpha_k_before_root_sqrt)
# gamma_dd = gamma_dd + power_k*alpha_k_before_root_dd/(2.0*alpha_k_before_root_sqrt) - power_k*np.dot(alpha_k_before_root_d.transpose(),alpha_k_before_root_d)/(4.0*alpha_k_before_root_sqrt**3)
# return gamma, gamma_d, gamma_dd
# Unwrap parameters
p = DiffeoParams['p']
# Compute hyperplane functions
hyperplane = np.zeros(PolygonUsed['vertices_tilde'].shape[0])
for i in range(0, PolygonUsed['vertices_tilde'].shape[0]):
hyperplane[i] = np.dot(Position[0]-PolygonUsed['vertices_tilde'][i],PolygonUsed['r_tilde_n'][i])
# Compute the R-function and its gradient and hessian
gammadd = (-(p-1)*((hyperplane[0]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[0]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][0]]).transpose(),np.array([PolygonUsed['r_tilde_n'][0]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][0]]).transpose(),np.array([PolygonUsed['r_tilde_n'][1]])) + (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2)) + (-(p-1)*((hyperplane[1]**(p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p)))+(p-1)*((hyperplane[1]**(2*p-2))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][1]]).transpose(),np.array([PolygonUsed['r_tilde_n'][1]])) + ((p-1)*((hyperplane[0]**(p-1)*hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][1]]).transpose(),np.array([PolygonUsed['r_tilde_n'][0]])) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.zeros((2,2))
gammad = (1-((hyperplane[0]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([PolygonUsed['r_tilde_n'][0]]) + (1-((hyperplane[1]**(p-1))/((hyperplane[0]**p+hyperplane[1]**p)**((p-1)/p))))*np.array([PolygonUsed['r_tilde_n'][1]])
gamma = hyperplane[0] + hyperplane[1] - (hyperplane[0]**p+hyperplane[1]**p)**(1/p)
for i in range(2,len(hyperplane)):
gammadd = (-(p-1)*((gamma**(p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((gamma**(2*p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(gammad.transpose(),gammad) + ((p-1)*((gamma**(p-1)*hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(gammad.transpose(),np.array([PolygonUsed['r_tilde_n'][i]])) + (1-((gamma**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*gammadd + (-(p-1)*((hyperplane[i]**(p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p)))+(p-1)*((hyperplane[i]**(2*p-2))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][i]]).transpose(),np.array([PolygonUsed['r_tilde_n'][i]])) + ((p-1)*((gamma**(p-1)*hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p+1))))*np.dot(np.array([PolygonUsed['r_tilde_n'][i]]).transpose(),gammad) + (1-((hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*np.zeros((2,2))
gammad = (1-((gamma**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*gammad + (1-((hyperplane[i]**(p-1))/((gamma**p+hyperplane[i]**p)**((p-1)/p))))*np.array([PolygonUsed['r_tilde_n'][i]])
gamma = gamma + hyperplane[i] - (gamma**p+hyperplane[i]**p)**(1/p)
return gamma, gammad, gammadd
def polygonImplicit(Position, DiffeoTree, DiffeoParams):
"""
Function that computes b(x) (i.e., the implicit function corresponding to a polygon), and its derivative and hessian
Input:
1) Position: Point to consider - 1x2 numpy.array
2) DiffeoTree: Tree that contains the diffeomorphism properties for a particular polygon
3) DiffeoParams: Options for the diffeomorphism construction
Output:
1) b: Value of the implicit function
2) bd: Value of the implicit function gradient - 1x2 numpy.array
3) bdd: Values of the implicit function hessian - 2x2 numpy.array
"""
# Begin purging process with default values
if Position.shape == (2,):
Position = np.array([Position])
# Unwrap parameters
p = DiffeoParams['p']
# Compute the R-function and its gradient and Hessian
b, bd, bdd = outsideImplicitTriangle(Position, DiffeoTree[0], DiffeoParams)
for i in range(1,len(DiffeoTree)):
btemp, btempd, btempdd = outsideImplicitTriangle(Position, DiffeoTree[i], DiffeoParams)
bdd = (-(p-1)*((b**(p-2))/((b**p+btemp**p)**((p-1)/p)))+(p-1)*((b**(2*p-2))/((b**p+btemp**p)**((p-1)/p+1))))*np.dot(bd.transpose(),bd) + ((p-1)*((b**(p-1)*btemp**(p-1))/((b**p+btemp**p)**((p-1)/p+1))))*np.dot(bd.transpose(),btempd) + (1-((b**(p-1))/((b**p+btemp**p)**((p-1)/p))))*bdd + (-(p-1)*((btemp**(p-2))/((b**p+btemp**p)**((p-1)/p)))+(p-1)*((btemp**(2*p-2))/((b**p+btemp**p)**((p-1)/p+1))))*np.dot(btempd.transpose(),btempd) + ((p-1)*((b**(p-1)*btemp**(p-1))/((b**p+btemp**p)**((p-1)/p+1))))*np.dot(btempd.transpose(),bd) + (1-((btemp**(p-1))/((b**p+btemp**p)**((p-1)/p))))*btempdd
bd = (1-((b**(p-1))/((b**p+btemp**p)**((p-1)/p))))*bd + (1-((btemp**(p-1))/((b**p+btemp**p)**((p-1)/p))))*btempd
b = b + btemp - (b**p+btemp**p)**(1/p)
return b, bd, bdd
def butterworthLowPass(cutoff, fs, order=5):
"""
Function that generates the filter coefficients based on a cutoff frequency and a sampling rate
Input:
1) cutoff: Cutoff frequency
2) fs: Sampling frequency
3) order: Order of the filter
Output:
1) b: Numerator coefficient array
2) a: Denominator coefficient array
"""
nyq = 0.5 * fs
normal_cutoff = cutoff/nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a
def butterworthLowPassFilter(data, cutoff, fs, order=5):
"""
Function that filters the desired data based on a cutoff frequency and a sampling frequency
Input:
1) data: Data array
2) cutoff: Cutoff frequency
3) fs: Sampling frequency
4) order: Order of the filter
Output:
1) y: Filtered version of the input data
"""
b, a = butterworthLowPass(cutoff, fs, order=order)
y = lfilter(b, a, data)
return y
| 61.432208
| 1,155
| 0.628982
| 16,738
| 111,008
| 4.052814
| 0.045346
| 0.01026
| 0.006368
| 0.007076
| 0.800239
| 0.774559
| 0.75202
| 0.728035
| 0.709771
| 0.698243
| 0
| 0.042397
| 0.182383
| 111,008
| 1,806
| 1,156
| 61.466224
| 0.705009
| 0.263657
| 0
| 0.568946
| 0
| 0
| 0.06476
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.040556
| false
| 0.003476
| 0.017381
| 0
| 0.110081
| 0.00927
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
80d5c63ff69dba8134d7d97f4b5da991272fad1f
| 8,536
|
py
|
Python
|
test/partial_log/test_partial_log.py
|
splunk/docker-logging-plugin
|
6a5816b4ccba58c33e431b3c763442da65c2a5b3
|
[
"Apache-2.0"
] | 63
|
2017-10-19T16:44:05.000Z
|
2022-02-18T23:22:12.000Z
|
test/partial_log/test_partial_log.py
|
splunk/docker-logging-plugin
|
6a5816b4ccba58c33e431b3c763442da65c2a5b3
|
[
"Apache-2.0"
] | 41
|
2017-12-05T12:30:05.000Z
|
2022-01-11T21:06:58.000Z
|
test/partial_log/test_partial_log.py
|
splunk/docker-logging-plugin
|
6a5816b4ccba58c33e431b3c763442da65c2a5b3
|
[
"Apache-2.0"
] | 27
|
2017-11-21T09:45:25.000Z
|
2022-03-27T09:45:42.000Z
|
"""
Copyright 2018 Splunk, Inc..
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import pytest
import time
import uuid
import os
import logging
from ..common import request_start_logging, \
check_events_from_splunk, request_stop_logging, \
start_log_producer_from_input, start_log_producer_from_file, kill_logging_plugin
@pytest.mark.parametrize("test_input, expected", [
([("start", True), ("in the middle", True), ("end", False)], 1)
])
def test_partial_log_1(setup, test_input, expected):
'''
Test that the logging plugin can handle partial logs correctly.
Expected behavior is that the plugin keeps appending the message
hen partial flag is True and stop and flush when it reaches False
'''
logging.getLogger().info("testing test_partial_log input={0} \
expected={1} event(s)".format(test_input, expected))
u_id = str(uuid.uuid4())
file_path = setup["fifo_path"]
start_log_producer_from_input(file_path, test_input, u_id)
request_start_logging(file_path,
setup["splunk_hec_url"],
setup["splunk_hec_token"])
# wait for 15 seconds to allow messages to be sent
time.sleep(15)
request_stop_logging(file_path)
# check that events get to splunk
events = check_events_from_splunk(id=u_id,
start_time="-15m@m",
url=setup["splunkd_url"],
user=setup["splunk_user"],
password=setup["splunk_password"])
logging.getLogger().info("Splunk received %s events in \
the last minute with u_id=%s",
len(events), u_id)
assert len(events) == expected
kill_logging_plugin
@pytest.mark.parametrize("test_input, expected", [
([("start2", False), ("new start", True), ("end2", False)], 2)
])
def test_partial_log_2(setup, test_input, expected):
'''
Test that the logging plugin can handle partial logs correctly.
Expected behavior is that the plugin keeps appending the message
hen partial flag is True and stop and flush when it reaches False
'''
logging.getLogger().info("testing test_partial_log input={0} \
expected={1} event(s)".format(test_input, expected))
u_id = str(uuid.uuid4())
file_path = setup["fifo_path"]
start_log_producer_from_input(file_path, test_input, u_id)
request_start_logging(file_path,
setup["splunk_hec_url"],
setup["splunk_hec_token"])
# wait for 15 seconds to allow messages to be sent
time.sleep(15)
request_stop_logging(file_path)
# check that events get to splunk
events = check_events_from_splunk(id=u_id,
start_time="-15m@m",
url=setup["splunkd_url"],
user=setup["splunk_user"],
password=setup["splunk_password"])
logging.getLogger().info("Splunk received %s events in \
the last minute with u_id=%s",
len(events), u_id)
assert len(events) == expected
kill_logging_plugin
@pytest.mark.parametrize("test_input, expected", [
([("start", True), ("mid", True), ("end", False)], 2)
])
def test_partial_log_flush_timeout_1(setup, test_input, expected):
'''
Test that the logging plugin can flush the buffer for partial
log. If the next partial message didn't arrive in expected
time (default 5 sec), it should flush the buffer anyway. There
is an architectural restriction that the buffer flush can only
occur on receipt of a new message beyond the timeout of the buffer.
'''
logging.getLogger().info("testing test_partial_log_flush_timeout input={0} \
expected={1} event(s)".format(test_input, expected))
u_id = str(uuid.uuid4())
file_path = setup["fifo_path"]
start_log_producer_from_input(file_path, test_input, u_id, 10)
request_start_logging(file_path,
setup["splunk_hec_url"],
setup["splunk_hec_token"])
# wait for 70 seconds to allow messages to be sent
time.sleep(70)
request_stop_logging(file_path)
# check that events get to splunk
events = check_events_from_splunk(id=u_id,
start_time="-15m@m",
url=setup["splunkd_url"],
user=setup["splunk_user"],
password=setup["splunk_password"])
logging.getLogger().info("Splunk received %s events in the last minute " +
"with u_id=%s",
len(events), u_id)
assert len(events) == expected
kill_logging_plugin
@pytest.mark.parametrize("test_input, expected", [
([("start2", True), ("new start", False), ("end2", True), ("start3", False), ("new start", True), ("end3", False)], 3)
])
def test_partial_log_flush_timeout_2(setup, test_input, expected):
'''
Test that the logging plugin can flush the buffer for partial
log. If the next partial message didn't arrive in expected
time (default 5 sec), it should flush the buffer anyway. There
is an architectural restriction that the buffer flush can only
occur on receipt of a new message beyond the timeout of the buffer.
'''
logging.getLogger().info("testing test_partial_log_flush_timeout input={0} \
expected={1} event(s)".format(test_input, expected))
u_id = str(uuid.uuid4())
file_path = setup["fifo_path"]
start_log_producer_from_input(file_path, test_input, u_id, 10)
request_start_logging(file_path,
setup["splunk_hec_url"],
setup["splunk_hec_token"])
# wait for 70 seconds to allow messages to be sent
time.sleep(70)
request_stop_logging(file_path)
# check that events get to splunk
events = check_events_from_splunk(id=u_id,
start_time="-15m@m",
url=setup["splunkd_url"],
user=setup["splunk_user"],
password=setup["splunk_password"])
logging.getLogger().info("Splunk received %s events in the last minute " +
"with u_id=%s",
len(events), u_id)
assert len(events) == expected
kill_logging_plugin
def test_partial_log_flush_size_limit(setup):
'''
Test that the logging plugin can flush the buffer when it reaches
the buffer size limit (1mb)
'''
logging.getLogger().info("testing test_partial_log_flush_size_limit")
u_id = str(uuid.uuid4())
file_path = setup["fifo_path"]
__location__ = os.path.realpath(os.path.join(os.getcwd(),
os.path.dirname(__file__)))
test_file_path = os.path.join(__location__, "test_file.txt")
start_log_producer_from_file(file_path, u_id, test_file_path)
request_start_logging(file_path,
setup["splunk_hec_url"],
setup["splunk_hec_token"])
# wait for 15 seconds to allow messages to be sent
time.sleep(15)
request_stop_logging(file_path)
# check that events get to splunk
events = check_events_from_splunk(id=u_id,
start_time="-15m@m",
url=setup["splunkd_url"],
user=setup["splunk_user"],
password=setup["splunk_password"])
logging.getLogger().info("Splunk received %s events in the last minute "
"with u_id=%s",
len(events), u_id)
assert len(events) == 2
kill_logging_plugin
| 39.88785
| 121
| 0.606256
| 1,080
| 8,536
| 4.566667
| 0.165741
| 0.015207
| 0.041363
| 0.028386
| 0.822993
| 0.807583
| 0.782441
| 0.782441
| 0.773114
| 0.764801
| 0
| 0.012222
| 0.300258
| 8,536
| 214
| 122
| 39.88785
| 0.813494
| 0.243088
| 0
| 0.811024
| 0
| 0
| 0.127255
| 0.005223
| 0
| 0
| 0
| 0
| 0.03937
| 1
| 0.03937
| false
| 0.03937
| 0.047244
| 0
| 0.086614
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
03f0e951e703bc07082b6a1a6a828adfe88ff594
| 797
|
py
|
Python
|
CanChickensFly.py
|
griledchicken/VAMPY-2017-CS
|
1bc71734751850b580b481eac51c5c235d0ca9e2
|
[
"MIT"
] | null | null | null |
CanChickensFly.py
|
griledchicken/VAMPY-2017-CS
|
1bc71734751850b580b481eac51c5c235d0ca9e2
|
[
"MIT"
] | null | null | null |
CanChickensFly.py
|
griledchicken/VAMPY-2017-CS
|
1bc71734751850b580b481eac51c5c235d0ca9e2
|
[
"MIT"
] | null | null | null |
answer = input("Can Chickens Fly? YES/NO: ")
if answer == "YES":
answer = input("Can they fly well? YES/NO: ")
if answer == "YES":
answer = input("Have you ever seen a Chicken? YES/NO: ")
if answer == "YES":
print("You're lying to me")
else:
print("Go take a look at one")
else:
answer = input("Have you ever seen a Chicken? YES/NO: ")
if answer == "YES":
print("I figured")
else:
print("You're smart")
else:
answer = input("Can they jump? YES/NO: ")
if answer == "YES":
answer = input("Have you ever seen a Chicken? YES/NO: ")
if answer == "YES":
print("I figured")
else:
print("You're smart")
else:
answer = input("Have you ever seen a Chicken? YES/NO: ")
if answer == "YES":
print("You're lying to me")
else:
print("Go take a look at one")
| 26.566667
| 58
| 0.607277
| 130
| 797
| 3.723077
| 0.238462
| 0.159091
| 0.10124
| 0.188017
| 0.896694
| 0.896694
| 0.896694
| 0.840909
| 0.840909
| 0.840909
| 0
| 0
| 0.224592
| 797
| 29
| 59
| 27.482759
| 0.783172
| 0
| 0
| 0.896552
| 0
| 0
| 0.462986
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.275862
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
208d55bf2af77c0581700d580f1805b2e51b5213
| 30,297
|
py
|
Python
|
model/dualposenet.py
|
wx-b/DualPoseNet
|
393f786a28f857eee36d913f335912da72b28371
|
[
"MIT"
] | 14
|
2021-08-19T09:12:23.000Z
|
2022-03-22T06:58:58.000Z
|
model/dualposenet.py
|
wx-b/DualPoseNet
|
393f786a28f857eee36d913f335912da72b28371
|
[
"MIT"
] | 4
|
2021-09-16T05:48:53.000Z
|
2022-02-28T16:23:40.000Z
|
model/dualposenet.py
|
wx-b/DualPoseNet
|
393f786a28f857eee36d913f335912da72b28371
|
[
"MIT"
] | 7
|
2021-10-11T05:38:39.000Z
|
2022-01-04T03:22:57.000Z
|
# Copyright (c) Gorilla Lab, SCUT.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Dual Pose Network with Refined Learning of Pose Consistency.
Author: Jiehong Lin
"""
import os
import tensorflow as tf
import numpy as np
import math
import cmath
import glob
import _pickle as cPickle
from tqdm import tqdm
import cv2
from transforms3d.euler import quat2mat
from layers import point_transformation
from modules import encoder, explicit_decoder, implicit_decoder
from dataloder import Fetcher
from pc_utils import load_depth, backproject, pc2sphericalmap
class DualPoseNet(object):
def __init__(self, opts, sess):
self.sess = sess
self.opts = opts
if self.opts.dataset == 'REAL275':
self.intrinsics = np.array(
[[591.0125, 0, 322.525], [0, 590.16775, 244.11084], [0, 0, 1]])
elif self.opts.dataset == 'CAMERA25':
self.intrinsics = np.array(
[[577.5, 0, 319.5], [0, 577.5, 239.5], [0, 0, 1]])
def allocate_placeholders(self):
self.is_training = tf.placeholder_with_default(
True, shape=[], name='is_training')
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.input_dis = tf.placeholder(tf.float32, shape=[
self.opts.batch_size, self.opts.input_res, self.opts.input_res, 1])
self.input_rgb = tf.placeholder(tf.float32, shape=[
self.opts.batch_size, self.opts.input_res, self.opts.input_res, 3])
self.observed_pc = tf.placeholder(
tf.float32, shape=[self.opts.batch_size, 1024, 3])
self.gt_rotation = tf.placeholder(
tf.float32, shape=[self.opts.batch_size, 4])
self.gt_translation = tf.placeholder(
tf.float32, shape=[self.opts.batch_size, 3])
self.gt_scale = tf.placeholder(
tf.float32, shape=[self.opts.batch_size, 3])
def build_model(self):
# model
self.encoder = encoder(self.opts, self.is_training, name='encoder')
self.explicit_decoder = explicit_decoder(
self.opts, self.is_training, name='explicit_decoder')
self.implicit_decoder = implicit_decoder(
self.opts, self.is_training, name="implicit_decoder")
# graphs
self.pose_feat = self.encoder(self.input_dis, self.input_rgb)
self.pred_translation, self.pred_rotation, self.pred_scale = self.explicit_decoder(
self.pose_feat)
self.pred_canonical_points = self.implicit_decoder(
self.observed_pc, self.pose_feat)
self.gt_canonical_points = point_transformation(
self.observed_pc, self.gt_rotation, self.gt_translation, self.gt_scale)
# loss
self.translation_loss = tf.losses.huber_loss(
self.pred_translation, self.gt_translation)
self.rotation_loss = tf.losses.huber_loss(
self.pred_rotation, self.gt_rotation)
self.scale_loss = tf.losses.huber_loss(self.pred_scale, self.gt_scale)
self.implicit_loss = tf.losses.huber_loss(
self.pred_canonical_points, self.gt_canonical_points)
self.loss = self.rotation_loss + self.translation_loss + \
self.scale_loss + self.opts.implicit_loss_weight*self.implicit_loss
def setup_optimizer(self):
self.learning_rate = tf.train.exponential_decay(self.opts.learning_rate, self.global_step,
self.opts.lr_decay_steps, self.opts.lr_decay_rate, staircase=True)
self.learning_rate = tf.maximum(self.learning_rate, 0.000001)
all_update_ops = [op for op in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if op.name.startswith(
"encoder") or op.name.startswith("explicit_decoder") or op.name.startswith("implicit_decoder")]
all_tvars = [var for var in tf.trainable_variables() if var.name.startswith(
"encoder") or var.name.startswith("explicit_decoder") or var.name.startswith("implicit_decoder")]
with tf.control_dependencies(all_update_ops):
self.all_optimizers = tf.train.AdamOptimizer(self.learning_rate).minimize(
self.loss, var_list=all_tvars, colocate_gradients_with_ops=True, global_step=self.global_step)
def train(self):
print('\n*********** Training of DualPoseNet ***********')
# model & graph
print('building model ...')
self.allocate_placeholders()
self.build_model()
self.setup_optimizer()
print('model built !')
# dataset
print('loading data ...')
fetchworker = Fetcher(self.opts)
fetchworker.start()
print('data loaded !')
print('starting training ...')
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(max_to_keep=None)
step = self.sess.run(self.global_step)
for epoch in range(1, self.opts.training_epoch+1):
sum_loss = 0.0
sum_r_loss = 0.0
sum_t_loss = 0.0
sum_s_loss = 0.0
sum_i_loss = 0.0
count = 0
for batch_idx in range(fetchworker.num_batches):
batch_input_dis, batch_input_rgb, batch_observed_pc, batch_rotation, batch_translation, batch_scale = fetchworker.fetch()
curr_bs = batch_input_dis.shape[0]
feed_dict = {self.input_dis: batch_input_dis,
self.input_rgb: batch_input_rgb,
self.observed_pc: batch_observed_pc,
self.gt_rotation: batch_rotation,
self.gt_translation: batch_translation,
self.gt_scale: batch_scale,
self.is_training: True}
_, loss, r_loss, t_loss, s_loss, i_loss = self.sess.run(
[self.all_optimizers, self.loss, self.rotation_loss, self.translation_loss, self.scale_loss, self.implicit_loss], feed_dict=feed_dict)
sum_loss += (loss*curr_bs)
sum_r_loss += (r_loss*curr_bs)
sum_t_loss += (t_loss*curr_bs)
sum_s_loss += (s_loss*curr_bs)
sum_i_loss += (i_loss*curr_bs)
count += curr_bs
print('[{}][{}/{}] loss: {:.5f}({:.5f}) r_loss: {:.5f}({:.5f}) t_loss: {:.5f}({:.5f}) s_loss: {:.5f}({:.5f}) i_loss: {:.5f}({:.5f})'.format(
epoch, batch_idx, fetchworker.num_batches, sum_loss /
float(count), loss, sum_r_loss /
float(count), r_loss, sum_t_loss/float(count),
t_loss, sum_s_loss/float(count), s_loss, sum_i_loss/float(count), i_loss))
step += 1
if epoch % 10 == 0:
self.saver.save(self.sess, os.path.join(
self.opts.log_dir, 'model'), epoch)
fetchworker.shutdown()
print('training finished !')
def test(self):
print('\n*********** Testing on {} ***********'.format(self.opts.dataset))
# inputs
self.input_dis = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 1])
self.input_rgb = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 3])
self.observed_pc = tf.placeholder(tf.float32, shape=[1, 1024, 3])
self.is_training = tf.placeholder_with_default(
False, shape=[], name='is_training')
# modules
self.encoder = encoder(self.opts, self.is_training, name='encoder')
self.explicit_decoder = explicit_decoder(
self.opts, self.is_training, name='explicit_decoder')
self.implicit_decoder = implicit_decoder(
self.opts, self.is_training, name="implicit_decoder")
# graphs
self.pose_feat = self.encoder(self.input_dis, self.input_rgb)
self.pred_translation, self.pred_rotation, self.pred_scale = self.explicit_decoder(
self.pose_feat)
self.pred_canonical_points = self.implicit_decoder(
self.observed_pc, self.pose_feat)
# checkpoints
print("loading from checkpoint ...")
saver = tf.train.Saver()
checkpoint_path = os.path.join(
self.opts.log_dir, 'model-'+str(self.opts.test_epoch))
saver.restore(self.sess, checkpoint_path)
# test
result_pkl_list = glob.glob(
'data/segmentation_results/{}/results_*.pkl'.format(self.opts.dataset))
result_pkl_list = sorted(result_pkl_list)
n_image = len(result_pkl_list)
print('no. of test images: {}\n'.format(n_image))
for i, path in tqdm(enumerate(result_pkl_list)):
with open(path, 'rb') as f:
data = cPickle.load(f)
image_path = data['image_path']
num_instance = len(data['pred_class_ids'])
image_path = os.path.join(
'/data/linjiehong/posed3Ddet/NOCS_CVPR2019-master/', image_path)
image = cv2.imread(image_path + '_color.png')[:, :, :3]
image = image[:, :, ::-1]
depth = load_depth(image_path)
pred_mask = data['pred_masks']
pred_RTs = np.zeros((num_instance, 4, 4))
pred_scales = np.zeros((num_instance, 3))
pred_RTs[:, 3, 3] = 1
if num_instance != 0:
for j in range(num_instance):
inst_mask = 255 * pred_mask[:, :, j].astype('uint8')
pts_ori, idx = backproject(
depth, self.intrinsics, inst_mask)
pts_ori = pts_ori/1000.0
rgb_ori = image[idx[0], idx[1], :]
rgb_ori = (
rgb_ori - np.array([123.7, 116.8, 103.9])[np.newaxis, :])/255.0
FLAG = pts_ori.shape[0] > 32
pts = pts_ori.copy()
rgb = rgb_ori.copy()
for k in range(3):
if FLAG:
centroid = np.mean(pts, axis=0)
pts = pts - centroid[np.newaxis, :]
input_dis, input_rgb = pc2sphericalmap(
pts, rgb, resolution=self.opts.input_res)
if pts.shape[0] > 1024:
input_pts = pts[np.random.choice(
pts.shape[0], 1024, replace=False), :]
else:
input_pts = pts[np.random.choice(
pts.shape[0], 1024), :]
feed_dict = {self.input_dis: input_dis, self.input_rgb: input_rgb,
self.observed_pc: input_pts[np.newaxis, :, :], self.is_training: False}
pred_rotation, pred_translation, pred_size = self.sess.run(
[self.pred_rotation, self.pred_translation, self.pred_scale], feed_dict=feed_dict)
pred_rotation = quat2mat(pred_rotation[0])
pred_translation = pred_translation[0] + centroid
pred_scale = np.linalg.norm(pred_size[0])
pred_size = pred_size[0]/pred_scale
pred_canonical_pts = (
(pts_ori - pred_translation[np.newaxis, :])/pred_scale) @ np.transpose(pred_rotation)
dis = np.linalg.norm(pred_canonical_pts, axis=1)
FLAG = np.sum(dis < 0.6) > 32
pts = pts_ori[dis < 0.6].copy()
rgb = rgb_ori[dis < 0.6].copy()
else:
break
if pts_ori.shape[0] > 32:
pred_RTs[j, :3, :3] = np.diag(
np.ones((3))*pred_scale) @ pred_rotation.transpose()
pred_RTs[j, :3, 3] = pred_translation
pred_scales[j] = pred_size
z_180_RT = np.zeros((4, 4), dtype=np.float32)
z_180_RT[:3, :3] = np.diag([-1, -1, 1])
z_180_RT[3, 3] = 1
pred_RTs[j, :, :] = z_180_RT @ pred_RTs[j, :, :]
else:
pred_RTs[j] = np.eye(4)
pred_scales[j] = np.ones((3))
data.pop('pred_masks')
data['pred_RTs'] = pred_RTs
data['pred_scales'] = pred_scales
with open(os.path.join(self.opts.test_log_dir, path.split('/')[-1]), 'wb') as f:
cPickle.dump(data, f)
def test_refine_encoder(self):
print(
'\n*********** Testing & Refining on {} ***********'.format(self.opts.dataset))
# inputs
self.input_dis = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 1])
self.input_rgb = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 3])
self.observed_pc = tf.placeholder(tf.float32, shape=[1, 1024, 3])
self.is_training = tf.placeholder_with_default(
False, shape=[], name='is_training')
self.global_step = tf.Variable(0, trainable=False, name='global_step')
# modules
self.encoder = encoder(self.opts, self.is_training, name='encoder')
self.explicit_decoder = explicit_decoder(
self.opts, self.is_training, name='explicit_decoder')
self.implicit_decoder = implicit_decoder(
self.opts, self.is_training, name="implicit_decoder")
# graphs
self.pose_feat = self.encoder(self.input_dis, self.input_rgb)
self.pred_translation, self.pred_rotation, self.pred_scale = self.explicit_decoder(
self.pose_feat)
self.ex_canonical_points = point_transformation(
self.observed_pc, self.pred_rotation, self.pred_translation, self.pred_scale)
self.im_canonical_points = self.implicit_decoder(
self.observed_pc, self.pose_feat)
# self-adaptive loss
self.loss = tf.losses.huber_loss(
self.ex_canonical_points, self.im_canonical_points)
# optimizer
self.learning_rate = tf.train.exponential_decay(
self.opts.refine_learning_rate, self.global_step, 100000000, 0, staircase=True)
all_update_ops = [op for op in tf.get_collection(
tf.GraphKeys.UPDATE_OPS) if op.name.startswith("encoder")]
all_tvars = [var for var in tf.trainable_variables()
if var.name.startswith("encoder")]
with tf.control_dependencies(all_update_ops):
self.all_optimizers = tf.train.AdamOptimizer(self.learning_rate).minimize(
self.loss, var_list=all_tvars, colocate_gradients_with_ops=True, global_step=self.global_step)
# checkpoints
print("loading from checkpoint ...")
self.sess.run(tf.global_variables_initializer())
checkpoint_path = os.path.join(
self.opts.log_dir, 'model-'+str(self.opts.test_epoch))
var_to_restore = [
val for val in tf.global_variables() if ('/Adam' not in val.name) and ('encoder' in val.name or 'explicit_decoder' in val.name or 'implicit_decoder' in val.name)]
saver = tf.train.Saver(var_to_restore)
saver.restore(self.sess, checkpoint_path)
step = self.sess.run(self.global_step)
# test
result_pkl_list = glob.glob(
'data/segmentation_results/{}/results_*.pkl'.format(self.opts.dataset))
result_pkl_list = sorted(result_pkl_list)
n_image = len(result_pkl_list)
print('no. of test images: {}\n'.format(n_image))
for i, path in tqdm(enumerate(result_pkl_list)):
with open(path, 'rb') as f:
data = cPickle.load(f)
image_path = data['image_path']
num_instance = len(data['pred_class_ids'])
image_path = os.path.join(
'/data/linjiehong/posed3Ddet/NOCS_CVPR2019-master/', image_path)
image = cv2.imread(image_path + '_color.png')[:, :, :3]
image = image[:, :, ::-1]
depth = load_depth(image_path)
pred_mask = data['pred_masks']
pred_RTs = np.zeros((num_instance, 4, 4))
pred_scales = np.zeros((num_instance, 3))
pred_RTs[:, 3, 3] = 1
if num_instance != 0:
for j in range(num_instance):
saver.restore(self.sess, checkpoint_path) # re-load model
inst_mask = 255 * pred_mask[:, :, j].astype('uint8')
pts_ori, idx = backproject(
depth, self.intrinsics, inst_mask)
pts_ori = pts_ori/1000.0
rgb_ori = image[idx[0], idx[1], :]
rgb_ori = (
rgb_ori - np.array([123.7, 116.8, 103.9])[np.newaxis, :])/255.0
# test
dis = np.zeros((pts_ori.shape[0]))
for k in range(3):
pts = pts_ori[dis < 0.6].copy()
rgb = rgb_ori[dis < 0.6].copy()
if pts.shape[0] > 32:
centroid = np.mean(pts, axis=0)
pts = pts - centroid[np.newaxis, :]
input_dis, input_rgb = pc2sphericalmap(
pts, rgb, resolution=self.opts.input_res)
if pts.shape[0] > 1024:
input_pts = pts[np.random.choice(
pts.shape[0], 1024, replace=False), :]
else:
input_pts = pts[np.random.choice(
pts.shape[0], 1024), :]
feed_dict = {self.input_dis: input_dis, self.input_rgb: input_rgb,
self.observed_pc: input_pts[np.newaxis, :, :], self.is_training: False}
iter_rotation, iter_translation, iter_size, iter_loss = self.sess.run(
[self.pred_rotation, self.pred_translation, self.pred_scale, self.loss], feed_dict=feed_dict)
pred_rotation = quat2mat(iter_rotation[0])
pred_translation = iter_translation[0] + centroid
pred_scale = np.linalg.norm(iter_size[0])
pred_size = iter_size[0]/pred_scale
pred_loss = iter_loss
pred_canonical_pts = (
(pts_ori - pred_translation[np.newaxis, :])/pred_scale) @ np.transpose(pred_rotation)
dis = np.linalg.norm(pred_canonical_pts, axis=1)
else:
break
# refinement
if pts.shape[0] > 32:
for k in range(self.opts.refine_iteration):
_, iter_rotation, iter_translation, iter_size, iter_loss = self.sess.run(
[self.all_optimizers, self.pred_rotation, self.pred_translation, self.pred_scale, self.loss], feed_dict=feed_dict)
if iter_loss < pred_loss:
pred_rotation = quat2mat(iter_rotation[0])
pred_translation = iter_translation[0] + centroid
pred_scale = np.linalg.norm(iter_size[0])
pred_size = iter_size[0]/pred_scale
pred_loss = iter_loss
if pred_loss <= self.opts.refine_threshold:
break
if pts_ori.shape[0] > 32:
pred_RTs[j, :3, :3] = np.diag(
np.ones((3))*pred_scale) @ pred_rotation.transpose()
pred_RTs[j, :3, 3] = pred_translation
pred_scales[j] = pred_size
z_180_RT = np.zeros((4, 4), dtype=np.float32)
z_180_RT[:3, :3] = np.diag([-1, -1, 1])
z_180_RT[3, 3] = 1
pred_RTs[j, :, :] = z_180_RT @ pred_RTs[j, :, :]
else:
pred_RTs[j] = np.eye(4)
pred_scales[j] = np.ones((3))
data.pop('pred_masks')
data['pred_RTs'] = pred_RTs
data['pred_scales'] = pred_scales
with open(os.path.join(self.opts.test_log_dir, path.split('/')[-1]), 'wb') as f:
cPickle.dump(data, f)
def test_refine_feature(self):
print(
'\n*********** Testing & Refining on {} ***********'.format(self.opts.dataset))
# inputs
self.input_dis = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 1])
self.input_rgb = tf.placeholder(
tf.float32, shape=[1, self.opts.input_res, self.opts.input_res, 3])
self.observed_pc = tf.placeholder(tf.float32, shape=[1, 1024, 3])
self.new_pose_feat = tf.Variable(tf.zeros([1,1024]), trainable=True, name='new_pose_feat')
self.is_training = tf.placeholder_with_default(
False, shape=[], name='is_training')
self.global_step = tf.Variable(0, trainable=False, name='global_step')
# modules
self.encoder = encoder(self.opts, self.is_training, name='encoder')
self.explicit_decoder = explicit_decoder(
self.opts, self.is_training, name='explicit_decoder')
self.implicit_decoder = implicit_decoder(
self.opts, self.is_training, name="implicit_decoder")
# graphs
self.pose_feat = self.encoder(self.input_dis, self.input_rgb)
self.pred_translation0, self.pred_rotation0, self.pred_scale0 = self.explicit_decoder(
self.pose_feat)
self.ex_canonical_points0 = point_transformation(
self.observed_pc, self.pred_rotation0, self.pred_translation0, self.pred_scale0)
self.im_canonical_points0 = self.implicit_decoder(
self.observed_pc, self.pose_feat)
# refine_graphs
self.pred_translation, self.pred_rotation, self.pred_scale = self.explicit_decoder(
self.new_pose_feat)
self.ex_canonical_points = point_transformation(
self.observed_pc, self.pred_rotation, self.pred_translation, self.pred_scale)
self.im_canonical_points = self.implicit_decoder(
self.observed_pc, self.new_pose_feat)
# self.adaptive loss
self.loss = tf.losses.huber_loss(
self.ex_canonical_points, self.im_canonical_points)
# optimizer
self.learning_rate = tf.train.exponential_decay(
self.opts.refine_learning_rate, self.global_step, 100000000, 0, staircase=True)
self.all_optimizers = tf.train.AdamOptimizer(self.opts.refine_f_learning_rate).minimize(
self.loss, var_list=[self.new_pose_feat], colocate_gradients_with_ops=True, global_step=self.global_step)
# checkpoints
print("loading from checkpoint ...")
self.sess.run(tf.global_variables_initializer())
checkpoint_path = os.path.join(
self.opts.log_dir, 'model-'+str(self.opts.test_epoch))
var_to_restore = [
val for val in tf.global_variables() if ('/Adam' not in val.name) and ('encoder' in val.name or 'explicit_decoder' in val.name or 'implicit_decoder' in val.name)]
saver = tf.train.Saver(var_to_restore)
saver.restore(self.sess, checkpoint_path)
step = self.sess.run(self.global_step)
# test
result_pkl_list = glob.glob(
'data/segmentation_results/{}/results_*.pkl'.format(self.opts.dataset))
result_pkl_list = sorted(result_pkl_list)
n_image = len(result_pkl_list)
print('no. of test images: {}\n'.format(n_image))
for i, path in tqdm(enumerate(result_pkl_list)):
with open(path, 'rb') as f:
data = cPickle.load(f)
image_path = data['image_path']
num_instance = len(data['pred_class_ids'])
image_path = os.path.join(
'/data/linjiehong/posed3Ddet/NOCS_CVPR2019-master/', image_path)
image = cv2.imread(image_path + '_color.png')[:, :, :3]
image = image[:, :, ::-1]
depth = load_depth(image_path)
pred_mask = data['pred_masks']
pred_RTs = np.zeros((num_instance, 4, 4))
pred_scales = np.zeros((num_instance, 3))
pred_RTs[:, 3, 3] = 1
if num_instance != 0:
for j in range(num_instance):
inst_mask = 255 * pred_mask[:, :, j].astype('uint8')
pts_ori, idx = backproject(
depth, self.intrinsics, inst_mask)
pts_ori = pts_ori/1000.0
rgb_ori = image[idx[0], idx[1], :]
rgb_ori = (
rgb_ori - np.array([123.7, 116.8, 103.9])[np.newaxis, :])/255.0
# test
dis = np.zeros((pts_ori.shape[0]))
for k in range(3):
pts = pts_ori[dis < 0.6].copy()
rgb = rgb_ori[dis < 0.6].copy()
if pts.shape[0] > 32:
centroid = np.mean(pts, axis=0)
pts = pts - centroid[np.newaxis, :]
input_dis, input_rgb = pc2sphericalmap(
pts, rgb, resolution=self.opts.input_res)
if pts.shape[0] > 1024:
input_pts = pts[np.random.choice(
pts.shape[0], 1024, replace=False), :]
else:
input_pts = pts[np.random.choice(
pts.shape[0], 1024), :]
feed_dict = {self.input_dis: input_dis, self.input_rgb: input_rgb,
self.observed_pc: input_pts[np.newaxis, :, :], self.is_training: False}
iter_rotation, iter_translation, iter_size = self.sess.run(
[self.pred_rotation0, self.pred_translation0, self.pred_scale0], feed_dict=feed_dict)
pred_rotation = quat2mat(iter_rotation[0])
pred_translation = iter_translation[0] + centroid
pred_scale = np.linalg.norm(iter_size[0])
pred_size = iter_size[0]/pred_scale
pred_canonical_pts = (
(pts_ori - pred_translation[np.newaxis, :])/pred_scale) @ np.transpose(pred_rotation)
dis = np.linalg.norm(pred_canonical_pts, axis=1)
else:
break
# refinement
if pts.shape[0] > 32:
pose_feat = self.sess.run([self.pose_feat], feed_dict=feed_dict)
update = tf.assign(self.new_pose_feat, pose_feat[0])
self.sess.run(update)
pred_loss = np.inf
for k in range(self.opts.refine_f_iteration):
_, iter_rotation, iter_translation, iter_size, iter_loss = self.sess.run(
[self.all_optimizers, self.pred_rotation, self.pred_translation, self.pred_scale, self.loss], feed_dict=feed_dict)
if iter_loss < pred_loss:
pred_rotation = quat2mat(iter_rotation[0])
pred_translation = iter_translation[0] + centroid
pred_scale = np.linalg.norm(iter_size[0])
pred_size = iter_size[0]/pred_scale
pred_loss = iter_loss
if pred_loss <= self.opts.refine_f_threshold:
break
if pts_ori.shape[0] > 32:
pred_RTs[j, :3, :3] = np.diag(
np.ones((3))*pred_scale) @ pred_rotation.transpose()
pred_RTs[j, :3, 3] = pred_translation
pred_scales[j] = pred_size
z_180_RT = np.zeros((4, 4), dtype=np.float32)
z_180_RT[:3, :3] = np.diag([-1, -1, 1])
z_180_RT[3, 3] = 1
pred_RTs[j, :, :] = z_180_RT @ pred_RTs[j, :, :]
else:
pred_RTs[j] = np.eye(4)
pred_scales[j] = np.ones((3))
data.pop('pred_masks')
data['pred_RTs'] = pred_RTs
data['pred_scales'] = pred_scales
with open(os.path.join(self.opts.test_log_dir, path.split('/')[-1]), 'wb') as f:
cPickle.dump(data, f)
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0
| 7
|
20e69aa1204b71354dd606f9dd415d38b56a677a
| 15,298
|
py
|
Python
|
tests/integration/test_occ.py
|
aavcc/taiga-openshift
|
7c33284573ceed38f755b8159ad83f3f68d2f7cb
|
[
"MIT"
] | null | null | null |
tests/integration/test_occ.py
|
aavcc/taiga-openshift
|
7c33284573ceed38f755b8159ad83f3f68d2f7cb
|
[
"MIT"
] | 12
|
2019-11-25T14:08:32.000Z
|
2021-06-24T10:35:51.000Z
|
tests/integration/test_occ.py
|
threefoldtech/Threefold-Circles
|
cbc433796b25cf7af9a295af65d665a4a279e2d6
|
[
"Apache-2.0"
] | 1
|
2018-06-07T10:58:15.000Z
|
2018-06-07T10:58:15.000Z
|
# -*- coding: utf-8 -*-
# Copyright (C) 2014-2017 Andrey Antukh <niwi@niwi.nz>
# Copyright (C) 2014-2017 Jesús Espino <jespinog@gmail.com>
# Copyright (C) 2014-2017 David Barragán <bameda@dbarragan.com>
# Copyright (C) 2014-2017 Alejandro Alonso <alejandro.alonso@kaleidos.net>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import pytest
from unittest.mock import patch
from django.core.urlresolvers import reverse
from taiga.base.utils import json
from .. import factories as f
pytestmark = pytest.mark.django_db
def test_valid_us_creation(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
url = reverse("userstories-list")
data = {
'project': project.id,
'subject': 'test',
}
response = client.post(url, json.dumps(data), content_type="application/json")
assert response.status_code == 201
def test_invalid_concurrent_save_for_issue(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.issues.api.IssueViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("issues-list")
data = {"subject": "test",
"project": project.id,
"status": f.IssueStatusFactory.create(project=project).id,
"severity": f.SeverityFactory.create(project=project).id,
"type": f.IssueTypeFactory.create(project=project).id,
"priority": f.PriorityFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.content
issue_id = response.data["id"]
url = reverse("issues-detail", args=(issue_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 400
def test_valid_concurrent_save_for_issue_different_versions(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.issues.api.IssueViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("issues-list")
data = {"subject": "test",
"project": project.id,
"status": f.IssueStatusFactory.create(project=project).id,
"severity": f.SeverityFactory.create(project=project).id,
"type": f.IssueTypeFactory.create(project=project).id,
"priority": f.PriorityFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.content
issue_id = response.data["id"]
url = reverse("issues-detail", args=(issue_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 2, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_valid_concurrent_save_for_issue_different_fields(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.issues.api.IssueViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("issues-list")
data = {"subject": "test",
"project": project.id,
"status": f.IssueStatusFactory.create(project=project).id,
"severity": f.SeverityFactory.create(project=project).id,
"type": f.IssueTypeFactory.create(project=project).id,
"priority": f.PriorityFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.content
issue_id = response.data["id"]
url = reverse("issues-detail", args=(issue_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "description": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_invalid_concurrent_save_for_wiki_page(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.wiki.api.WikiViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("wiki-list")
data = {"project": project.id, "slug": "test"}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.content
wiki_id = response.data["id"]
url = reverse("wiki-detail", args=(wiki_id,))
data = {"version": 1, "content": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "content": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 400
def test_valid_concurrent_save_for_wiki_page_different_versions(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.wiki.api.WikiViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("wiki-list")
data = {"project": project.id, "slug": "test"}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201, response.content
wiki_id = response.data["id"]
url = reverse("wiki-detail", args=(wiki_id,))
data = {"version": 1, "content": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 2, "content": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_invalid_concurrent_save_for_us(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
f.UserStoryFactory.create(version=10, project=project)
client.login(user)
mock_path = "taiga.projects.userstories.api.UserStoryViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("userstories-list")
data = {"subject": "test",
"project": project.id,
"status": f.UserStoryStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
userstory_id = response.data["id"]
url = reverse("userstories-detail", args=(userstory_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 400
def test_valid_concurrent_save_for_us_different_versions(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.userstories.api.UserStoryViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("userstories-list")
data = {"subject": "test",
"project": project.id,
"status": f.UserStoryStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
userstory_id = response.data["id"]
url = reverse("userstories-detail", args=(userstory_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 2, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_valid_concurrent_save_for_us_different_fields(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.userstories.api.UserStoryViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("userstories-list")
data = {"subject": "test",
"project": project.id,
"status": f.UserStoryStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
userstory_id = response.data["id"]
url = reverse("userstories-detail", args=(userstory_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "description": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_invalid_concurrent_save_for_task(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.tasks.api.TaskViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("tasks-list")
data = {"subject": "test",
"project": project.id,
"status": f.TaskStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
task_id = response.data["id"]
url = reverse("tasks-detail", args=(task_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 400
def test_valid_concurrent_save_for_task_different_versions(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.tasks.api.TaskViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("tasks-list")
data = {"subject": "test",
"project": project.id,
"status": f.TaskStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
task_id = response.data["id"]
url = reverse("tasks-detail", args=(task_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 2, "subject": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_valid_concurrent_save_for_task_different_fields(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.tasks.api.TaskViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("tasks-list")
data = {"subject": "test",
"project": project.id,
"status": f.TaskStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
task_id = response.data["id"]
url = reverse("tasks-detail", args=(task_id,))
data = {"version": 1, "subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
data = {"version": 1, "description": "test 2"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 200
def test_invalid_save_without_version_parameter(client):
user = f.UserFactory.create()
project = f.ProjectFactory.create(owner=user)
f.MembershipFactory.create(project=project, user=user, is_admin=True)
client.login(user)
mock_path = "taiga.projects.tasks.api.TaskViewSet.pre_conditions_on_save"
with patch(mock_path):
url = reverse("tasks-list")
data = {"subject": "test",
"project": project.id,
"status": f.TaskStatusFactory.create(project=project).id}
response = client.json.post(url, json.dumps(data))
assert response.status_code == 201
task_id = response.data["id"]
url = reverse("tasks-detail", args=(task_id,))
data = {"subject": "test 1"}
response = client.patch(url, json.dumps(data), content_type="application/json")
assert response.status_code == 400
| 42.376731
| 88
| 0.664727
| 1,873
| 15,298
| 5.302723
| 0.097704
| 0.064841
| 0.043496
| 0.057994
| 0.914217
| 0.903141
| 0.896798
| 0.889952
| 0.887737
| 0.887737
| 0
| 0.015511
| 0.203491
| 15,298
| 360
| 89
| 42.494444
| 0.79959
| 0.0587
| 0
| 0.897436
| 0
| 0
| 0.15881
| 0.051801
| 0
| 0
| 0
| 0
| 0.131868
| 1
| 0.047619
| false
| 0
| 0.018315
| 0
| 0.065934
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
20eced0df909101c4f435345f41c4866a1674f9a
| 138
|
py
|
Python
|
test/resources/inspectors/python/case27_using_requests.py
|
nihalshetty-boop/hyperstyle
|
9a6d53cd1ca220d97d296c0087056b5885b26281
|
[
"Apache-2.0"
] | 18
|
2020-10-05T16:48:11.000Z
|
2022-03-22T04:15:38.000Z
|
test/resources/inspectors/python/case27_using_requests.py
|
nihalshetty-boop/hyperstyle
|
9a6d53cd1ca220d97d296c0087056b5885b26281
|
[
"Apache-2.0"
] | 60
|
2020-10-05T17:01:05.000Z
|
2022-01-27T12:46:14.000Z
|
test/resources/inspectors/python/case27_using_requests.py
|
nihalshetty-boop/hyperstyle
|
9a6d53cd1ca220d97d296c0087056b5885b26281
|
[
"Apache-2.0"
] | 6
|
2021-02-09T09:31:19.000Z
|
2021-08-13T07:45:51.000Z
|
import requests
def do_search(bookstore_url, params):
return requests.get(url=bookstore_url,
params=params)
| 19.714286
| 42
| 0.652174
| 16
| 138
| 5.4375
| 0.625
| 0.275862
| 0.413793
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.275362
| 138
| 6
| 43
| 23
| 0.87
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
4555aaa5efb8e9633d6c3233732fefd9345316e0
| 41
|
py
|
Python
|
Python/Tests/TestData/FormattingTests/linereduction.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/FormattingTests/linereduction.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/FormattingTests/linereduction.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
(a +
b +
c +
d+
e+
f
)
| 4.555556
| 8
| 0.146341
| 6
| 41
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.682927
| 41
| 8
| 9
| 5.125
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
afff826c89cf84dbe2b809d9f7fa9ddb7ae851a3
| 86
|
py
|
Python
|
chainerltr/evaluation/__init__.py
|
rjagerman/chainerltr
|
1fdb6a0a304a465d27149011951a01a5e3de4bbc
|
[
"MIT"
] | 1
|
2019-04-10T03:18:23.000Z
|
2019-04-10T03:18:23.000Z
|
chainerltr/evaluation/__init__.py
|
rjagerman/chainerltr
|
1fdb6a0a304a465d27149011951a01a5e3de4bbc
|
[
"MIT"
] | null | null | null |
chainerltr/evaluation/__init__.py
|
rjagerman/chainerltr
|
1fdb6a0a304a465d27149011951a01a5e3de4bbc
|
[
"MIT"
] | null | null | null |
from chainerltr.evaluation.ndcg import ndcg
from chainerltr.evaluation.dcg import dcg
| 28.666667
| 43
| 0.860465
| 12
| 86
| 6.166667
| 0.5
| 0.378378
| 0.648649
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 86
| 2
| 44
| 43
| 0.948718
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
b370d17d5c8b1ddccff4041e2effd7503de95a5e
| 8,292
|
py
|
Python
|
home/templatetags/table_tags.py
|
davidjrichardson/toucans
|
7446b78ec2a09ff90eb83d4a78638c909deb06e1
|
[
"MIT"
] | 1
|
2020-04-20T05:37:09.000Z
|
2020-04-20T05:37:09.000Z
|
home/templatetags/table_tags.py
|
davidjrichardson/toucans
|
7446b78ec2a09ff90eb83d4a78638c909deb06e1
|
[
"MIT"
] | 23
|
2019-03-13T10:54:36.000Z
|
2022-03-11T23:33:59.000Z
|
home/templatetags/table_tags.py
|
davidjrichardson/toucans
|
7446b78ec2a09ff90eb83d4a78638c909deb06e1
|
[
"MIT"
] | null | null | null |
from functools import reduce
from django import template
register = template.Library()
def rank_leg(scores):
points = {}
ranked = sorted(scores, key=lambda x: x[1], reverse=True)
for i in range(0, len(ranked)):
if ranked[i][1] != (0, 0, 0):
points[ranked[i][0]] = (len(ranked) - i, ranked[i][1][0], ranked[i][1][1], ranked[i][1][2])
else:
# If the team has no hits/points/golds, they get 0 points
points[ranked[i][0]] = (0, ranked[i][1][0], ranked[i][1][1], ranked[i][1][2])
return list(points.items())
def align_results(reference, results):
# results is the tuple list from a dict (.items())
results_dict = dict(results)
reordered = []
# Reference is already sorted, so simply re-insert in order of the reference
for team, _ in reference:
reordered.append((team, results_dict[team]))
return reordered
@register.filter()
def contract(name):
return {
'loughborough': 'L\'boro',
'de montfort': 'DMU'
}.get(name.lower(), name)
@register.filter()
def dashify(val):
if int(val) >= 0:
return val
else:
return '‒'
def generate_3leg_table(standings):
# Check if all of the standings provided are empty
standings_are_empty = reduce(lambda x, y: x and y, map(lambda x: x.is_empty, standings))
if standings_are_empty:
standings_sorted = sorted(map(lambda x: (x.team_name, x.results), standings))
# The per-leg column of if there are results: True means 0-points will be displayed, otherwise a dash (-)
# will be shown instead
standings_has_results = [False, False, False, False, False]
standings_aggregate = [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0)]
else:
# Rank each leg individually
leg_1 = rank_leg(list(map(lambda x: (x.team_name, x.leg_1), standings)))
leg_2 = rank_leg(list(map(lambda x: (x.team_name, x.leg_2), standings)))
leg_3 = rank_leg(list(map(lambda x: (x.team_name, x.leg_3), standings)))
champs = rank_leg(list(map(lambda x: (x.team_name, x.champs), standings)))
pts_combined = leg_1 + leg_2 + leg_3 + champs
pts_dict = {}
for team, rank in pts_combined:
if pts_dict.get(team):
pts_dict.get(team).append(rank)
else:
pts_dict[team] = [rank]
# Collapse the list of results per-team into a 4-tuple: (points, agg. score, agg. hits, agg. golds)
# And sort by that
standings_sorted = sorted(pts_dict.items(),
key=lambda x: reduce(
lambda z, y: (z[0] + y[0], z[1] + y[1], z[2] + y[2], z[3] + y[3]),
x[1]), reverse=True)
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_1)))
# The per-leg column of if there are results: True means 0-points will be displayed, otherwise a dash (-)
# will be shown instead
standings_has_results = [
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_1))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_2))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_3))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), champs))),
]
standings_aggregate = list(
map(lambda x: reduce(lambda z, y: (z[0] + y[0], z[1] + y[1], z[2] + y[2], z[3] + y[3]), x[1]),
standings_sorted))
return standings_sorted, standings_aggregate, standings_has_results
@register.inclusion_tag('home/tags/3leg_results_table.html', takes_context=True)
def overall_3leg_standings(context, standings):
standings_sorted, standings_aggregate, standings_has_results = generate_3leg_table(standings)
return {
'request': context,
'standings': standings_sorted,
'standings_agg': standings_aggregate,
'results_mask': standings_has_results,
# Results empty is simply if there are no results through the entire table
'results_empty': reduce(lambda x, y: x or y, standings_has_results)
}
def generate_4leg_table(standings):
# Check if all of the standings provided are empty
standings_are_empty = reduce(lambda x, y: x and y, map(lambda x: x.is_empty, standings))
if standings_are_empty:
standings_sorted = sorted(map(lambda x: (x.team_name, x.results), standings))
# The per-leg column of if there are results: True means 0-points will be displayed, otherwise a dash (-)
# will be shown instead
standings_has_results = [False, False, False, False, False]
standings_aggregate = [(0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0), (0, 0, 0, 0)]
else:
# Rank each leg individually
leg_1 = rank_leg(list(map(lambda x: (x.team_name, x.leg_1), standings)))
leg_2 = rank_leg(list(map(lambda x: (x.team_name, x.leg_2), standings)))
leg_3 = rank_leg(list(map(lambda x: (x.team_name, x.leg_3), standings)))
leg_4 = rank_leg(list(map(lambda x: (x.team_name, x.leg_4), standings)))
champs = rank_leg(list(map(lambda x: (x.team_name, x.champs), standings)))
pts_combined = leg_1 + leg_2 + leg_3 + leg_4 + champs
pts_dict = {}
for team, rank in pts_combined:
if pts_dict.get(team):
pts_dict.get(team).append(rank)
else:
pts_dict[team] = [rank]
# Collapse the list of results per-team into a 4-tuple: (points, agg. score, agg. hits, agg. golds)
# And sort by that
standings_sorted = sorted(pts_dict.items(),
key=lambda x: reduce(
lambda z, y: (z[0] + y[0], z[1] + y[1], z[2] + y[2], z[3] + y[3]),
x[1]), reverse=True)
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_1)))
# The per-leg column of if there are results: True means 0-points will be displayed, otherwise a dash (-)
# will be shown instead
standings_has_results = [
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_1))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_2))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_3))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), leg_4))),
reduce(lambda x, y: x or y, list(map(lambda x: x[1] != (0, 0, 0, 0), champs))),
]
standings_aggregate = list(
map(lambda x: reduce(lambda z, y: (z[0] + y[0], z[1] + y[1], z[2] + y[2], z[3] + y[3]), x[1]),
standings_sorted))
return standings_sorted, standings_aggregate, standings_has_results
@register.inclusion_tag('home/tags/aggregate_results_table.html', takes_context=True)
def aggregated_standings(context, results):
standings = results[0]
num_legs = results[1]
if num_legs == 3:
standings_sorted, standings_aggregate, standings_has_results = generate_3leg_table(standings)
else:
standings_sorted, standings_aggregate, standings_has_results = generate_4leg_table(standings)
return {
'request': context['request'],
'standings': standings_sorted,
'standings_agg': standings_aggregate,
# Results empty is simply if there are no results through the entire table
'results_empty': not reduce(lambda x, y: x or y, standings_has_results)
}
@register.inclusion_tag('home/tags/4leg_results_table.html', takes_context=True)
def overall_4leg_standings(context, standings):
standings_sorted, standings_aggregate, standings_has_results = generate_4leg_table(standings)
return {
'request': context,
'standings': standings_sorted,
'standings_agg': standings_aggregate,
'results_mask': standings_has_results,
# Results empty is simply if there are no results through the entire table
'results_empty': reduce(lambda x, y: x or y, standings_has_results)
}
| 43.873016
| 113
| 0.599735
| 1,243
| 8,292
| 3.860016
| 0.103781
| 0.029179
| 0.034389
| 0.034181
| 0.834931
| 0.834931
| 0.834931
| 0.827637
| 0.793039
| 0.793039
| 0
| 0.032693
| 0.265919
| 8,292
| 188
| 114
| 44.106383
| 0.75538
| 0.154848
| 0
| 0.628788
| 0
| 0.212121
| 0.041518
| 0.014889
| 0
| 0
| 0
| 0
| 0
| 1
| 0.068182
| false
| 0
| 0.015152
| 0.007576
| 0.159091
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2fb06c21eb891caf2ba79588b761d865ccf7d6e3
| 21,869
|
py
|
Python
|
sdk/python/pulumi_newrelic/service_level.py
|
pulumi/pulumi-newrelic
|
cd9a882f3524883ed155f87ff26c4c17cd048c9a
|
[
"ECL-2.0",
"Apache-2.0"
] | 6
|
2019-09-17T20:41:26.000Z
|
2022-01-13T23:54:14.000Z
|
sdk/python/pulumi_newrelic/service_level.py
|
pulumi/pulumi-newrelic
|
cd9a882f3524883ed155f87ff26c4c17cd048c9a
|
[
"ECL-2.0",
"Apache-2.0"
] | 136
|
2019-04-29T21:34:57.000Z
|
2022-03-30T17:07:03.000Z
|
sdk/python/pulumi_newrelic/service_level.py
|
pulumi/pulumi-newrelic
|
cd9a882f3524883ed155f87ff26c4c17cd048c9a
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2019-10-05T10:33:59.000Z
|
2021-06-15T16:37:49.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
from . import outputs
from ._inputs import *
__all__ = ['ServiceLevelArgs', 'ServiceLevel']
@pulumi.input_type
class ServiceLevelArgs:
def __init__(__self__, *,
events: pulumi.Input['ServiceLevelEventsArgs'],
guid: pulumi.Input[str],
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
objectives: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]] = None):
"""
The set of arguments for constructing a ServiceLevel resource.
:param pulumi.Input['ServiceLevelEventsArgs'] events: The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
:param pulumi.Input[str] guid: The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
:param pulumi.Input[str] description: The description of the SLI.
:param pulumi.Input[str] name: A short name for the SLI that will help anyone understand what it is about.
:param pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]] objectives: An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
"""
pulumi.set(__self__, "events", events)
pulumi.set(__self__, "guid", guid)
if description is not None:
pulumi.set(__self__, "description", description)
if name is not None:
pulumi.set(__self__, "name", name)
if objectives is not None:
pulumi.set(__self__, "objectives", objectives)
@property
@pulumi.getter
def events(self) -> pulumi.Input['ServiceLevelEventsArgs']:
"""
The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
"""
return pulumi.get(self, "events")
@events.setter
def events(self, value: pulumi.Input['ServiceLevelEventsArgs']):
pulumi.set(self, "events", value)
@property
@pulumi.getter
def guid(self) -> pulumi.Input[str]:
"""
The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
"""
return pulumi.get(self, "guid")
@guid.setter
def guid(self, value: pulumi.Input[str]):
pulumi.set(self, "guid", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
The description of the SLI.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A short name for the SLI that will help anyone understand what it is about.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def objectives(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]]:
"""
An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
"""
return pulumi.get(self, "objectives")
@objectives.setter
def objectives(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]]):
pulumi.set(self, "objectives", value)
@pulumi.input_type
class _ServiceLevelState:
def __init__(__self__, *,
description: Optional[pulumi.Input[str]] = None,
events: Optional[pulumi.Input['ServiceLevelEventsArgs']] = None,
guid: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
objectives: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]] = None,
sli_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering ServiceLevel resources.
:param pulumi.Input[str] description: The description of the SLI.
:param pulumi.Input['ServiceLevelEventsArgs'] events: The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
:param pulumi.Input[str] guid: The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
:param pulumi.Input[str] name: A short name for the SLI that will help anyone understand what it is about.
:param pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]] objectives: An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
:param pulumi.Input[str] sli_id: The unique entity identifier of the Service Level Indicator.
"""
if description is not None:
pulumi.set(__self__, "description", description)
if events is not None:
pulumi.set(__self__, "events", events)
if guid is not None:
pulumi.set(__self__, "guid", guid)
if name is not None:
pulumi.set(__self__, "name", name)
if objectives is not None:
pulumi.set(__self__, "objectives", objectives)
if sli_id is not None:
pulumi.set(__self__, "sli_id", sli_id)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
The description of the SLI.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def events(self) -> Optional[pulumi.Input['ServiceLevelEventsArgs']]:
"""
The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
"""
return pulumi.get(self, "events")
@events.setter
def events(self, value: Optional[pulumi.Input['ServiceLevelEventsArgs']]):
pulumi.set(self, "events", value)
@property
@pulumi.getter
def guid(self) -> Optional[pulumi.Input[str]]:
"""
The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
"""
return pulumi.get(self, "guid")
@guid.setter
def guid(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "guid", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A short name for the SLI that will help anyone understand what it is about.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def objectives(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]]:
"""
An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
"""
return pulumi.get(self, "objectives")
@objectives.setter
def objectives(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ServiceLevelObjectiveArgs']]]]):
pulumi.set(self, "objectives", value)
@property
@pulumi.getter(name="sliId")
def sli_id(self) -> Optional[pulumi.Input[str]]:
"""
The unique entity identifier of the Service Level Indicator.
"""
return pulumi.get(self, "sli_id")
@sli_id.setter
def sli_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sli_id", value)
class ServiceLevel(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
events: Optional[pulumi.Input[pulumi.InputType['ServiceLevelEventsArgs']]] = None,
guid: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
objectives: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceLevelObjectiveArgs']]]]] = None,
__props__=None):
"""
> **New Relic Service Level Management is in preview**
Please contact your account team or fill in this [form](https://forms.gle/v1y3PDJ2P6sSfviC7) if you'd like to enroll your account.
Use this resource to create, update, and delete New Relic Service Level Indicators and Objectives.
A New Relic User API key is required to provision this resource. Set the `api_key`
attribute in the `provider` block or the `NEW_RELIC_API_KEY` environment
variable with your User API key.
Important:
- Only roles that provide [permissions](https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-user-management/new-relic-one-user-model-understand-user-structure/) to create events to metric rules can create SLI/SLOs.
- Only [Full users](https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-user-management/new-relic-one-user-model-understand-user-structure/#user-type) can view SLI/SLOs.
## Example Usage
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.ServiceLevel("foo",
description="SLI that measures the availability of the service.",
events=newrelic.ServiceLevelEventsArgs(
account_id=12345678,
bad_events=newrelic.ServiceLevelEventsBadEventsArgs(
from_="TransactionError",
where="appName = 'Example application' AND error.expected is false",
),
valid_events=newrelic.ServiceLevelEventsValidEventsArgs(
from_="Transaction",
where="appName = 'Example application'",
),
),
guid="MXxBUE18QVBQTElDQVRJT058MQ",
objectives=[newrelic.ServiceLevelObjectiveArgs(
description="A realistic objective.",
name="Realistic",
target=99,
time_window=newrelic.ServiceLevelObjectiveTimeWindowArgs(
rolling=newrelic.ServiceLevelObjectiveTimeWindowRollingArgs(
count=7,
unit="DAY",
),
),
)])
```
## Import
New Relic Service Levels can be imported using a concatenated string of the format
`<account_id>:<sli_id>:<guid>`, where the `guid` is the entity the SLI relates to. Examplebash
```sh
$ pulumi import newrelic:index/serviceLevel:ServiceLevel foo 12345678:4321:MXxBUE18QVBQTElDQVRJT058MQ
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the SLI.
:param pulumi.Input[pulumi.InputType['ServiceLevelEventsArgs']] events: The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
:param pulumi.Input[str] guid: The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
:param pulumi.Input[str] name: A short name for the SLI that will help anyone understand what it is about.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceLevelObjectiveArgs']]]] objectives: An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ServiceLevelArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
> **New Relic Service Level Management is in preview**
Please contact your account team or fill in this [form](https://forms.gle/v1y3PDJ2P6sSfviC7) if you'd like to enroll your account.
Use this resource to create, update, and delete New Relic Service Level Indicators and Objectives.
A New Relic User API key is required to provision this resource. Set the `api_key`
attribute in the `provider` block or the `NEW_RELIC_API_KEY` environment
variable with your User API key.
Important:
- Only roles that provide [permissions](https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-user-management/new-relic-one-user-model-understand-user-structure/) to create events to metric rules can create SLI/SLOs.
- Only [Full users](https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-user-management/new-relic-one-user-model-understand-user-structure/#user-type) can view SLI/SLOs.
## Example Usage
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.ServiceLevel("foo",
description="SLI that measures the availability of the service.",
events=newrelic.ServiceLevelEventsArgs(
account_id=12345678,
bad_events=newrelic.ServiceLevelEventsBadEventsArgs(
from_="TransactionError",
where="appName = 'Example application' AND error.expected is false",
),
valid_events=newrelic.ServiceLevelEventsValidEventsArgs(
from_="Transaction",
where="appName = 'Example application'",
),
),
guid="MXxBUE18QVBQTElDQVRJT058MQ",
objectives=[newrelic.ServiceLevelObjectiveArgs(
description="A realistic objective.",
name="Realistic",
target=99,
time_window=newrelic.ServiceLevelObjectiveTimeWindowArgs(
rolling=newrelic.ServiceLevelObjectiveTimeWindowRollingArgs(
count=7,
unit="DAY",
),
),
)])
```
## Import
New Relic Service Levels can be imported using a concatenated string of the format
`<account_id>:<sli_id>:<guid>`, where the `guid` is the entity the SLI relates to. Examplebash
```sh
$ pulumi import newrelic:index/serviceLevel:ServiceLevel foo 12345678:4321:MXxBUE18QVBQTElDQVRJT058MQ
```
:param str resource_name: The name of the resource.
:param ServiceLevelArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ServiceLevelArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
events: Optional[pulumi.Input[pulumi.InputType['ServiceLevelEventsArgs']]] = None,
guid: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
objectives: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceLevelObjectiveArgs']]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ServiceLevelArgs.__new__(ServiceLevelArgs)
__props__.__dict__["description"] = description
if events is None and not opts.urn:
raise TypeError("Missing required property 'events'")
__props__.__dict__["events"] = events
if guid is None and not opts.urn:
raise TypeError("Missing required property 'guid'")
__props__.__dict__["guid"] = guid
__props__.__dict__["name"] = name
__props__.__dict__["objectives"] = objectives
__props__.__dict__["sli_id"] = None
super(ServiceLevel, __self__).__init__(
'newrelic:index/serviceLevel:ServiceLevel',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
events: Optional[pulumi.Input[pulumi.InputType['ServiceLevelEventsArgs']]] = None,
guid: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
objectives: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceLevelObjectiveArgs']]]]] = None,
sli_id: Optional[pulumi.Input[str]] = None) -> 'ServiceLevel':
"""
Get an existing ServiceLevel resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the SLI.
:param pulumi.Input[pulumi.InputType['ServiceLevelEventsArgs']] events: The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
:param pulumi.Input[str] guid: The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
:param pulumi.Input[str] name: A short name for the SLI that will help anyone understand what it is about.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceLevelObjectiveArgs']]]] objectives: An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
:param pulumi.Input[str] sli_id: The unique entity identifier of the Service Level Indicator.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ServiceLevelState.__new__(_ServiceLevelState)
__props__.__dict__["description"] = description
__props__.__dict__["events"] = events
__props__.__dict__["guid"] = guid
__props__.__dict__["name"] = name
__props__.__dict__["objectives"] = objectives
__props__.__dict__["sli_id"] = sli_id
return ServiceLevel(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
"""
The description of the SLI.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter
def events(self) -> pulumi.Output['outputs.ServiceLevelEvents']:
"""
The events that define the NRDB data for the SLI/SLO calculations.
See Events below for details.
"""
return pulumi.get(self, "events")
@property
@pulumi.getter
def guid(self) -> pulumi.Output[str]:
"""
The GUID of the entity (e.g, APM Service, Browser application, Workload, etc.) that you want to relate this SLI to.
"""
return pulumi.get(self, "guid")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
A short name for the SLI that will help anyone understand what it is about.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def objectives(self) -> pulumi.Output[Optional[Sequence['outputs.ServiceLevelObjective']]]:
"""
An objective for the SLI. Multiple objective blocks can be defined for an SLI.
See Nested objective blocks below for details.
"""
return pulumi.get(self, "objectives")
@property
@pulumi.getter(name="sliId")
def sli_id(self) -> pulumi.Output[str]:
"""
The unique entity identifier of the Service Level Indicator.
"""
return pulumi.get(self, "sli_id")
| 44.630612
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0
| 7
|
2fbeda5ce9c154a9ab00881c49315865e5d2e8ad
| 197,887
|
py
|
Python
|
boto3_type_annotations_with_docs/boto3_type_annotations/lex_models/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 119
|
2018-12-01T18:20:57.000Z
|
2022-02-02T10:31:29.000Z
|
boto3_type_annotations_with_docs/boto3_type_annotations/lex_models/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 15
|
2018-11-16T00:16:44.000Z
|
2021-11-13T03:44:18.000Z
|
boto3_type_annotations_with_docs/boto3_type_annotations/lex_models/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 11
|
2019-05-06T05:26:51.000Z
|
2021-09-28T15:27:59.000Z
|
from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from botocore.paginate import Paginator
from botocore.waiter import Waiter
from typing import Union
from typing import List
class Client(BaseClient):
def can_paginate(self, operation_name: str = None):
"""
Check if an operation can be paginated.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:return: ``True`` if the operation can be paginated,
``False`` otherwise.
"""
pass
def create_bot_version(self, name: str, checksum: str = None) -> Dict:
"""
Creates a new version of the bot based on the ``$LATEST`` version. If the ``$LATEST`` version of this resource hasn't changed since you created the last version, Amazon Lex doesn't create a new version. It returns the last created version.
.. note::
You can update only the ``$LATEST`` version of the bot. You can't update the numbered versions that you create with the ``CreateBotVersion`` operation.
When you create the first version of a bot, Amazon Lex sets the version to 1. Subsequent versions increment by 1. For more information, see versioning-intro .
This operation requires permission for the ``lex:CreateBotVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/CreateBotVersion>`_
**Request Syntax**
::
response = client.create_bot_version(
name='string',
checksum='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'intents': [
{
'intentName': 'string',
'intentVersion': 'string'
},
],
'clarificationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'abortStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'status': 'BUILDING'|'READY'|'READY_BASIC_TESTING'|'FAILED'|'NOT_BUILT',
'failureReason': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'idleSessionTTLInSeconds': 123,
'voiceId': 'string',
'checksum': 'string',
'version': 'string',
'locale': 'en-US'|'en-GB'|'de-DE',
'childDirected': True|False
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the bot.
- **description** *(string) --*
A description of the bot.
- **intents** *(list) --*
An array of ``Intent`` objects. For more information, see PutBot .
- *(dict) --*
Identifies the specific version of an intent.
- **intentName** *(string) --*
The name of the intent.
- **intentVersion** *(string) --*
The version of the intent.
- **clarificationPrompt** *(dict) --*
The message that Amazon Lex uses when it doesn't understand the user's request. For more information, see PutBot .
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **abortStatement** *(dict) --*
The message that Amazon Lex uses to abort a conversation. For more information, see PutBot .
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **status** *(string) --*
When you send a request to create or update a bot, Amazon Lex sets the ``status`` response element to ``BUILDING`` . After Amazon Lex builds the bot, it sets ``status`` to ``READY`` . If Amazon Lex can't build the bot, it sets ``status`` to ``FAILED`` . Amazon Lex returns the reason for the failure in the ``failureReason`` response element.
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex provides the reason that it failed to build the bot.
- **lastUpdatedDate** *(datetime) --*
The date when the ``$LATEST`` version of this bot was updated.
- **createdDate** *(datetime) --*
The date when the bot version was created.
- **idleSessionTTLInSeconds** *(integer) --*
The maximum time in seconds that Amazon Lex retains the data gathered in a conversation. For more information, see PutBot .
- **voiceId** *(string) --*
The Amazon Polly voice ID that Amazon Lex uses for voice interactions with the user.
- **checksum** *(string) --*
Checksum identifying the version of the bot that was created.
- **version** *(string) --*
The version of the bot.
- **locale** *(string) --*
Specifies the target locale for the bot.
- **childDirected** *(boolean) --*
For each Amazon Lex bot created with the Amazon Lex Model Building Service, you must specify whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to the Children's Online Privacy Protection Act (COPPA) by specifying ``true`` or ``false`` in the ``childDirected`` field. By specifying ``true`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. By specifying ``false`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is not** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. You may not specify a default value for the ``childDirected`` field that does not accurately reflect whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA.
If your use of Amazon Lex relates to a website, program, or other application that is directed in whole or in part, to children under age 13, you must obtain any required verifiable parental consent under COPPA. For information regarding the use of Amazon Lex in connection with websites, programs, or other applications that are directed or targeted, in whole or in part, to children under age 13, see the `Amazon Lex FAQ. <https://aws.amazon.com/lex/faqs#data-security>`__
:type name: string
:param name: **[REQUIRED]**
The name of the bot that you want to create a new version of. The name is case sensitive.
:type checksum: string
:param checksum:
Identifies a specific revision of the ``$LATEST`` version of the bot. If you specify a checksum and the ``$LATEST`` version of the bot has a different checksum, a ``PreconditionFailedException`` exception is returned and Amazon Lex doesn\'t publish a new version. If you don\'t specify a checksum, Amazon Lex publishes the ``$LATEST`` version.
:rtype: dict
:returns:
"""
pass
def create_intent_version(self, name: str, checksum: str = None) -> Dict:
"""
Creates a new version of an intent based on the ``$LATEST`` version of the intent. If the ``$LATEST`` version of this intent hasn't changed since you last updated it, Amazon Lex doesn't create a new version. It returns the last version you created.
.. note::
You can update only the ``$LATEST`` version of the intent. You can't update the numbered versions that you create with the ``CreateIntentVersion`` operation.
When you create a version of an intent, Amazon Lex sets the version to 1. Subsequent versions increment by 1. For more information, see versioning-intro .
This operation requires permissions to perform the ``lex:CreateIntentVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/CreateIntentVersion>`_
**Request Syntax**
::
response = client.create_intent_version(
name='string',
checksum='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'slots': [
{
'name': 'string',
'description': 'string',
'slotConstraint': 'Required'|'Optional',
'slotType': 'string',
'slotTypeVersion': 'string',
'valueElicitationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'priority': 123,
'sampleUtterances': [
'string',
],
'responseCard': 'string'
},
],
'sampleUtterances': [
'string',
],
'confirmationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'followUpPrompt': {
'prompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
}
},
'conclusionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'dialogCodeHook': {
'uri': 'string',
'messageVersion': 'string'
},
'fulfillmentActivity': {
'type': 'ReturnIntent'|'CodeHook',
'codeHook': {
'uri': 'string',
'messageVersion': 'string'
}
},
'parentIntentSignature': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the intent.
- **description** *(string) --*
A description of the intent.
- **slots** *(list) --*
An array of slot types that defines the information required to fulfill the intent.
- *(dict) --*
Identifies the version of a specific slot.
- **name** *(string) --*
The name of the slot.
- **description** *(string) --*
A description of the slot.
- **slotConstraint** *(string) --*
Specifies whether the slot is required or optional.
- **slotType** *(string) --*
The type of the slot, either a custom slot type that you defined or one of the built-in slot types.
- **slotTypeVersion** *(string) --*
The version of the slot type.
- **valueElicitationPrompt** *(dict) --*
The prompt that Amazon Lex uses to elicit the slot value from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **priority** *(integer) --*
Directs Lex the order in which to elicit this slot value from the user. For example, if the intent has two slots with priorities 1 and 2, AWS Lex first elicits a value for the slot with priority 1.
If multiple slots share the same priority, the order in which Lex elicits values is arbitrary.
- **sampleUtterances** *(list) --*
If you know a specific pattern with which users might respond to an Amazon Lex request for a slot value, you can provide those utterances to improve accuracy. This is optional. In most cases, Amazon Lex is capable of understanding user utterances.
- *(string) --*
- **responseCard** *(string) --*
A set of possible responses for the slot type used by text-based clients. A user chooses an option from the response card, instead of using text to reply.
- **sampleUtterances** *(list) --*
An array of sample utterances configured for the intent.
- *(string) --*
- **confirmationPrompt** *(dict) --*
If defined, the prompt that Amazon Lex uses to confirm the user's intent before fulfilling it.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in ``confirmationPrompt`` , Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **followUpPrompt** *(dict) --*
If defined, Amazon Lex uses this prompt to solicit additional user activity after the intent is fulfilled.
- **prompt** *(dict) --*
Prompts for information from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in the ``prompt`` field, Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **conclusionStatement** *(dict) --*
After the Lambda function specified in the ``fulfillmentActivity`` field fulfills the intent, Amazon Lex conveys this statement to the user.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **dialogCodeHook** *(dict) --*
If defined, Amazon Lex invokes this Lambda function for each user input.
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **fulfillmentActivity** *(dict) --*
Describes how the intent is fulfilled.
- **type** *(string) --*
How the intent should be fulfilled, either by running a Lambda function or by returning the slot data to the client application.
- **codeHook** *(dict) --*
A description of the Lambda function that is run to fulfill the intent.
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **parentIntentSignature** *(string) --*
A unique identifier for a built-in intent.
- **lastUpdatedDate** *(datetime) --*
The date that the intent was updated.
- **createdDate** *(datetime) --*
The date that the intent was created.
- **version** *(string) --*
The version number assigned to the new version of the intent.
- **checksum** *(string) --*
Checksum of the intent version created.
:type name: string
:param name: **[REQUIRED]**
The name of the intent that you want to create a new version of. The name is case sensitive.
:type checksum: string
:param checksum:
Checksum of the ``$LATEST`` version of the intent that should be used to create the new version. If you specify a checksum and the ``$LATEST`` version of the intent has a different checksum, Amazon Lex returns a ``PreconditionFailedException`` exception and doesn\'t publish a new version. If you don\'t specify a checksum, Amazon Lex publishes the ``$LATEST`` version.
:rtype: dict
:returns:
"""
pass
def create_slot_type_version(self, name: str, checksum: str = None) -> Dict:
"""
Creates a new version of a slot type based on the ``$LATEST`` version of the specified slot type. If the ``$LATEST`` version of this resource has not changed since the last version that you created, Amazon Lex doesn't create a new version. It returns the last version that you created.
.. note::
You can update only the ``$LATEST`` version of a slot type. You can't update the numbered versions that you create with the ``CreateSlotTypeVersion`` operation.
When you create a version of a slot type, Amazon Lex sets the version to 1. Subsequent versions increment by 1. For more information, see versioning-intro .
This operation requires permissions for the ``lex:CreateSlotTypeVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/CreateSlotTypeVersion>`_
**Request Syntax**
::
response = client.create_slot_type_version(
name='string',
checksum='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'enumerationValues': [
{
'value': 'string',
'synonyms': [
'string',
]
},
],
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string',
'valueSelectionStrategy': 'ORIGINAL_VALUE'|'TOP_RESOLUTION'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the slot type.
- **description** *(string) --*
A description of the slot type.
- **enumerationValues** *(list) --*
A list of ``EnumerationValue`` objects that defines the values that the slot type can take.
- *(dict) --*
Each slot type can have a set of values. Each enumeration value represents a value the slot type can take.
For example, a pizza ordering bot could have a slot type that specifies the type of crust that the pizza should have. The slot type could include the values
* thick
* thin
* stuffed
- **value** *(string) --*
The value of the slot type.
- **synonyms** *(list) --*
Additional values related to the slot type value.
- *(string) --*
- **lastUpdatedDate** *(datetime) --*
The date that the slot type was updated. When you create a resource, the creation date and last update date are the same.
- **createdDate** *(datetime) --*
The date that the slot type was created.
- **version** *(string) --*
The version assigned to the new slot type version.
- **checksum** *(string) --*
Checksum of the ``$LATEST`` version of the slot type.
- **valueSelectionStrategy** *(string) --*
The strategy that Amazon Lex uses to determine the value of the slot. For more information, see PutSlotType .
:type name: string
:param name: **[REQUIRED]**
The name of the slot type that you want to create a new version for. The name is case sensitive.
:type checksum: string
:param checksum:
Checksum for the ``$LATEST`` version of the slot type that you want to publish. If you specify a checksum and the ``$LATEST`` version of the slot type has a different checksum, Amazon Lex returns a ``PreconditionFailedException`` exception and doesn\'t publish the new version. If you don\'t specify a checksum, Amazon Lex publishes the ``$LATEST`` version.
:rtype: dict
:returns:
"""
pass
def delete_bot(self, name: str):
"""
Deletes all versions of the bot, including the ``$LATEST`` version. To delete a specific version of the bot, use the DeleteBotVersion operation.
If a bot has an alias, you can't delete it. Instead, the ``DeleteBot`` operation returns a ``ResourceInUseException`` exception that includes a reference to the alias that refers to the bot. To remove the reference to the bot, delete the alias. If you get the same exception again, delete the referring alias until the ``DeleteBot`` operation is successful.
This operation requires permissions for the ``lex:DeleteBot`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteBot>`_
**Request Syntax**
::
response = client.delete_bot(
name='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the bot. The name is case sensitive.
:returns: None
"""
pass
def delete_bot_alias(self, name: str, botName: str):
"""
Deletes an alias for the specified bot.
You can't delete an alias that is used in the association between a bot and a messaging channel. If an alias is used in a channel association, the ``DeleteBot`` operation returns a ``ResourceInUseException`` exception that includes a reference to the channel association that refers to the bot. You can remove the reference to the alias by deleting the channel association. If you get the same exception again, delete the referring association until the ``DeleteBotAlias`` operation is successful.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteBotAlias>`_
**Request Syntax**
::
response = client.delete_bot_alias(
name='string',
botName='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the alias to delete. The name is case sensitive.
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot that the alias points to.
:returns: None
"""
pass
def delete_bot_channel_association(self, name: str, botName: str, botAlias: str):
"""
Deletes the association between an Amazon Lex bot and a messaging platform.
This operation requires permission for the ``lex:DeleteBotChannelAssociation`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteBotChannelAssociation>`_
**Request Syntax**
::
response = client.delete_bot_channel_association(
name='string',
botName='string',
botAlias='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the association. The name is case sensitive.
:type botName: string
:param botName: **[REQUIRED]**
The name of the Amazon Lex bot.
:type botAlias: string
:param botAlias: **[REQUIRED]**
An alias that points to the specific version of the Amazon Lex bot to which this association is being made.
:returns: None
"""
pass
def delete_bot_version(self, name: str, version: str):
"""
Deletes a specific version of a bot. To delete all versions of a bot, use the DeleteBot operation.
This operation requires permissions for the ``lex:DeleteBotVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteBotVersion>`_
**Request Syntax**
::
response = client.delete_bot_version(
name='string',
version='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the bot.
:type version: string
:param version: **[REQUIRED]**
The version of the bot to delete. You cannot delete the ``$LATEST`` version of the bot. To delete the ``$LATEST`` version, use the DeleteBot operation.
:returns: None
"""
pass
def delete_intent(self, name: str):
"""
Deletes all versions of the intent, including the ``$LATEST`` version. To delete a specific version of the intent, use the DeleteIntentVersion operation.
You can delete a version of an intent only if it is not referenced. To delete an intent that is referred to in one or more bots (see how-it-works ), you must remove those references first.
.. note::
If you get the ``ResourceInUseException`` exception, it provides an example reference that shows where the intent is referenced. To remove the reference to the intent, either update the bot or delete it. If you get the same exception when you attempt to delete the intent again, repeat until the intent has no references and the call to ``DeleteIntent`` is successful.
This operation requires permission for the ``lex:DeleteIntent`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteIntent>`_
**Request Syntax**
::
response = client.delete_intent(
name='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the intent. The name is case sensitive.
:returns: None
"""
pass
def delete_intent_version(self, name: str, version: str):
"""
Deletes a specific version of an intent. To delete all versions of a intent, use the DeleteIntent operation.
This operation requires permissions for the ``lex:DeleteIntentVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteIntentVersion>`_
**Request Syntax**
::
response = client.delete_intent_version(
name='string',
version='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the intent.
:type version: string
:param version: **[REQUIRED]**
The version of the intent to delete. You cannot delete the ``$LATEST`` version of the intent. To delete the ``$LATEST`` version, use the DeleteIntent operation.
:returns: None
"""
pass
def delete_slot_type(self, name: str):
"""
Deletes all versions of the slot type, including the ``$LATEST`` version. To delete a specific version of the slot type, use the DeleteSlotTypeVersion operation.
You can delete a version of a slot type only if it is not referenced. To delete a slot type that is referred to in one or more intents, you must remove those references first.
.. note::
If you get the ``ResourceInUseException`` exception, the exception provides an example reference that shows the intent where the slot type is referenced. To remove the reference to the slot type, either update the intent or delete it. If you get the same exception when you attempt to delete the slot type again, repeat until the slot type has no references and the ``DeleteSlotType`` call is successful.
This operation requires permission for the ``lex:DeleteSlotType`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteSlotType>`_
**Request Syntax**
::
response = client.delete_slot_type(
name='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the slot type. The name is case sensitive.
:returns: None
"""
pass
def delete_slot_type_version(self, name: str, version: str):
"""
Deletes a specific version of a slot type. To delete all versions of a slot type, use the DeleteSlotType operation.
This operation requires permissions for the ``lex:DeleteSlotTypeVersion`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteSlotTypeVersion>`_
**Request Syntax**
::
response = client.delete_slot_type_version(
name='string',
version='string'
)
:type name: string
:param name: **[REQUIRED]**
The name of the slot type.
:type version: string
:param version: **[REQUIRED]**
The version of the slot type to delete. You cannot delete the ``$LATEST`` version of the slot type. To delete the ``$LATEST`` version, use the DeleteSlotType operation.
:returns: None
"""
pass
def delete_utterances(self, botName: str, userId: str):
"""
Deletes stored utterances.
Amazon Lex stores the utterances that users send to your bot. Utterances are stored for 15 days for use with the GetUtterancesView operation, and then stored indefinitely for use in improving the ability of your bot to respond to user input.
Use the ``DeleteStoredUtterances`` operation to manually delete stored utterances for a specific user.
This operation requires permissions for the ``lex:DeleteUtterances`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/DeleteUtterances>`_
**Request Syntax**
::
response = client.delete_utterances(
botName='string',
userId='string'
)
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot that stored the utterances.
:type userId: string
:param userId: **[REQUIRED]**
The unique identifier for the user that made the utterances. This is the user ID that was sent in the `PostContent <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostContent.html>`__ or `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ operation request that contained the utterance.
:returns: None
"""
pass
def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None):
"""
Generate a presigned url given a client, its method, and arguments
:type ClientMethod: string
:param ClientMethod: The client method to presign for
:type Params: dict
:param Params: The parameters normally passed to
``ClientMethod``.
:type ExpiresIn: int
:param ExpiresIn: The number of seconds the presigned url is valid
for. By default it expires in an hour (3600 seconds)
:type HttpMethod: string
:param HttpMethod: The http method to use on the generated url. By
default, the http method is whatever is used in the method\'s model.
:returns: The presigned url
"""
pass
def get_bot(self, name: str, versionOrAlias: str) -> Dict:
"""
Returns metadata information for a specific bot. You must provide the bot name and the bot version or alias.
This operation requires permissions for the ``lex:GetBot`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBot>`_
**Request Syntax**
::
response = client.get_bot(
name='string',
versionOrAlias='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'intents': [
{
'intentName': 'string',
'intentVersion': 'string'
},
],
'clarificationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'abortStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'status': 'BUILDING'|'READY'|'READY_BASIC_TESTING'|'FAILED'|'NOT_BUILT',
'failureReason': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'idleSessionTTLInSeconds': 123,
'voiceId': 'string',
'checksum': 'string',
'version': 'string',
'locale': 'en-US'|'en-GB'|'de-DE',
'childDirected': True|False
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the bot.
- **description** *(string) --*
A description of the bot.
- **intents** *(list) --*
An array of ``intent`` objects. For more information, see PutBot .
- *(dict) --*
Identifies the specific version of an intent.
- **intentName** *(string) --*
The name of the intent.
- **intentVersion** *(string) --*
The version of the intent.
- **clarificationPrompt** *(dict) --*
The message Amazon Lex uses when it doesn't understand the user's request. For more information, see PutBot .
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **abortStatement** *(dict) --*
The message that Amazon Lex returns when the user elects to end the conversation without completing it. For more information, see PutBot .
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **status** *(string) --*
The status of the bot. If the bot is ready to run, the status is ``READY`` . If there was a problem with building the bot, the status is ``FAILED`` and the ``failureReason`` explains why the bot did not build. If the bot was saved but not built, the status is ``NOT BUILT`` .
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex explains why it failed to build the bot.
- **lastUpdatedDate** *(datetime) --*
The date that the bot was updated. When you create a resource, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot was created.
- **idleSessionTTLInSeconds** *(integer) --*
The maximum time in seconds that Amazon Lex retains the data gathered in a conversation. For more information, see PutBot .
- **voiceId** *(string) --*
The Amazon Polly voice ID that Amazon Lex uses for voice interaction with the user. For more information, see PutBot .
- **checksum** *(string) --*
Checksum of the bot used to identify a specific revision of the bot's ``$LATEST`` version.
- **version** *(string) --*
The version of the bot. For a new bot, the version is always ``$LATEST`` .
- **locale** *(string) --*
The target locale for the bot.
- **childDirected** *(boolean) --*
For each Amazon Lex bot created with the Amazon Lex Model Building Service, you must specify whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to the Children's Online Privacy Protection Act (COPPA) by specifying ``true`` or ``false`` in the ``childDirected`` field. By specifying ``true`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. By specifying ``false`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is not** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. You may not specify a default value for the ``childDirected`` field that does not accurately reflect whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA.
If your use of Amazon Lex relates to a website, program, or other application that is directed in whole or in part, to children under age 13, you must obtain any required verifiable parental consent under COPPA. For information regarding the use of Amazon Lex in connection with websites, programs, or other applications that are directed or targeted, in whole or in part, to children under age 13, see the `Amazon Lex FAQ. <https://aws.amazon.com/lex/faqs#data-security>`__
:type name: string
:param name: **[REQUIRED]**
The name of the bot. The name is case sensitive.
:type versionOrAlias: string
:param versionOrAlias: **[REQUIRED]**
The version or alias of the bot.
:rtype: dict
:returns:
"""
pass
def get_bot_alias(self, name: str, botName: str) -> Dict:
"""
Returns information about an Amazon Lex bot alias. For more information about aliases, see versioning-aliases .
This operation requires permissions for the ``lex:GetBotAlias`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBotAlias>`_
**Request Syntax**
::
response = client.get_bot_alias(
name='string',
botName='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'botVersion': 'string',
'botName': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'checksum': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the bot alias.
- **description** *(string) --*
A description of the bot alias.
- **botVersion** *(string) --*
The version of the bot that the alias points to.
- **botName** *(string) --*
The name of the bot that the alias points to.
- **lastUpdatedDate** *(datetime) --*
The date that the bot alias was updated. When you create a resource, the creation date and the last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot alias was created.
- **checksum** *(string) --*
Checksum of the bot alias.
:type name: string
:param name: **[REQUIRED]**
The name of the bot alias. The name is case sensitive.
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot.
:rtype: dict
:returns:
"""
pass
def get_bot_aliases(self, botName: str, nextToken: str = None, maxResults: int = None, nameContains: str = None) -> Dict:
"""
Returns a list of aliases for a specified Amazon Lex bot.
This operation requires permissions for the ``lex:GetBotAliases`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBotAliases>`_
**Request Syntax**
::
response = client.get_bot_aliases(
botName='string',
nextToken='string',
maxResults=123,
nameContains='string'
)
**Response Syntax**
::
{
'BotAliases': [
{
'name': 'string',
'description': 'string',
'botVersion': 'string',
'botName': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'checksum': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **BotAliases** *(list) --*
An array of ``BotAliasMetadata`` objects, each describing a bot alias.
- *(dict) --*
Provides information about a bot alias.
- **name** *(string) --*
The name of the bot alias.
- **description** *(string) --*
A description of the bot alias.
- **botVersion** *(string) --*
The version of the Amazon Lex bot to which the alias points.
- **botName** *(string) --*
The name of the bot to which the alias points.
- **lastUpdatedDate** *(datetime) --*
The date that the bot alias was updated. When you create a resource, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot alias was created.
- **checksum** *(string) --*
Checksum of the bot alias.
- **nextToken** *(string) --*
A pagination token for fetching next page of aliases. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of aliases, specify the pagination token in the next request.
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot.
:type nextToken: string
:param nextToken:
A pagination token for fetching the next page of aliases. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of aliases, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of aliases to return in the response. The default is 50. .
:type nameContains: string
:param nameContains:
Substring to match in bot alias names. An alias will be returned if any part of its name matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\"
:rtype: dict
:returns:
"""
pass
def get_bot_channel_association(self, name: str, botName: str, botAlias: str) -> Dict:
"""
Returns information about the association between an Amazon Lex bot and a messaging platform.
This operation requires permissions for the ``lex:GetBotChannelAssociation`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBotChannelAssociation>`_
**Request Syntax**
::
response = client.get_bot_channel_association(
name='string',
botName='string',
botAlias='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'botAlias': 'string',
'botName': 'string',
'createdDate': datetime(2015, 1, 1),
'type': 'Facebook'|'Slack'|'Twilio-Sms'|'Kik',
'botConfiguration': {
'string': 'string'
},
'status': 'IN_PROGRESS'|'CREATED'|'FAILED',
'failureReason': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the association between the bot and the channel.
- **description** *(string) --*
A description of the association between the bot and the channel.
- **botAlias** *(string) --*
An alias pointing to the specific version of the Amazon Lex bot to which this association is being made.
- **botName** *(string) --*
The name of the Amazon Lex bot.
- **createdDate** *(datetime) --*
The date that the association between the bot and the channel was created.
- **type** *(string) --*
The type of the messaging platform.
- **botConfiguration** *(dict) --*
Provides information that the messaging platform needs to communicate with the Amazon Lex bot.
- *(string) --*
- *(string) --*
- **status** *(string) --*
The status of the bot channel.
* ``CREATED`` - The channel has been created and is ready for use.
* ``IN_PROGRESS`` - Channel creation is in progress.
* ``FAILED`` - There was an error creating the channel. For information about the reason for the failure, see the ``failureReason`` field.
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex provides the reason that it failed to create the association.
:type name: string
:param name: **[REQUIRED]**
The name of the association between the bot and the channel. The name is case sensitive.
:type botName: string
:param botName: **[REQUIRED]**
The name of the Amazon Lex bot.
:type botAlias: string
:param botAlias: **[REQUIRED]**
An alias pointing to the specific version of the Amazon Lex bot to which this association is being made.
:rtype: dict
:returns:
"""
pass
def get_bot_channel_associations(self, botName: str, botAlias: str, nextToken: str = None, maxResults: int = None, nameContains: str = None) -> Dict:
"""
Returns a list of all of the channels associated with the specified bot.
The ``GetBotChannelAssociations`` operation requires permissions for the ``lex:GetBotChannelAssociations`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBotChannelAssociations>`_
**Request Syntax**
::
response = client.get_bot_channel_associations(
botName='string',
botAlias='string',
nextToken='string',
maxResults=123,
nameContains='string'
)
**Response Syntax**
::
{
'botChannelAssociations': [
{
'name': 'string',
'description': 'string',
'botAlias': 'string',
'botName': 'string',
'createdDate': datetime(2015, 1, 1),
'type': 'Facebook'|'Slack'|'Twilio-Sms'|'Kik',
'botConfiguration': {
'string': 'string'
},
'status': 'IN_PROGRESS'|'CREATED'|'FAILED',
'failureReason': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **botChannelAssociations** *(list) --*
An array of objects, one for each association, that provides information about the Amazon Lex bot and its association with the channel.
- *(dict) --*
Represents an association between an Amazon Lex bot and an external messaging platform.
- **name** *(string) --*
The name of the association between the bot and the channel.
- **description** *(string) --*
A text description of the association you are creating.
- **botAlias** *(string) --*
An alias pointing to the specific version of the Amazon Lex bot to which this association is being made.
- **botName** *(string) --*
The name of the Amazon Lex bot to which this association is being made.
.. note::
Currently, Amazon Lex supports associations with Facebook and Slack, and Twilio.
- **createdDate** *(datetime) --*
The date that the association between the Amazon Lex bot and the channel was created.
- **type** *(string) --*
Specifies the type of association by indicating the type of channel being established between the Amazon Lex bot and the external messaging platform.
- **botConfiguration** *(dict) --*
Provides information necessary to communicate with the messaging platform.
- *(string) --*
- *(string) --*
- **status** *(string) --*
The status of the bot channel.
* ``CREATED`` - The channel has been created and is ready for use.
* ``IN_PROGRESS`` - Channel creation is in progress.
* ``FAILED`` - There was an error creating the channel. For information about the reason for the failure, see the ``failureReason`` field.
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex provides the reason that it failed to create the association.
- **nextToken** *(string) --*
A pagination token that fetches the next page of associations. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of associations, specify the pagination token in the next request.
:type botName: string
:param botName: **[REQUIRED]**
The name of the Amazon Lex bot in the association.
:type botAlias: string
:param botAlias: **[REQUIRED]**
An alias pointing to the specific version of the Amazon Lex bot to which this association is being made.
:type nextToken: string
:param nextToken:
A pagination token for fetching the next page of associations. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of associations, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of associations to return in the response. The default is 50.
:type nameContains: string
:param nameContains:
Substring to match in channel association names. An association will be returned if any part of its name matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\" To return all bot channel associations, use a hyphen (\"-\") as the ``nameContains`` parameter.
:rtype: dict
:returns:
"""
pass
def get_bot_versions(self, name: str, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Gets information about all of the versions of a bot.
The ``GetBotVersions`` operation returns a ``BotMetadata`` object for each version of a bot. For example, if a bot has three numbered versions, the ``GetBotVersions`` operation returns four ``BotMetadata`` objects in the response, one for each numbered version and one for the ``$LATEST`` version.
The ``GetBotVersions`` operation always returns at least one version, the ``$LATEST`` version.
This operation requires permissions for the ``lex:GetBotVersions`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBotVersions>`_
**Request Syntax**
::
response = client.get_bot_versions(
name='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'bots': [
{
'name': 'string',
'description': 'string',
'status': 'BUILDING'|'READY'|'READY_BASIC_TESTING'|'FAILED'|'NOT_BUILT',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **bots** *(list) --*
An array of ``BotMetadata`` objects, one for each numbered version of the bot plus one for the ``$LATEST`` version.
- *(dict) --*
Provides information about a bot. .
- **name** *(string) --*
The name of the bot.
- **description** *(string) --*
A description of the bot.
- **status** *(string) --*
The status of the bot.
- **lastUpdatedDate** *(datetime) --*
The date that the bot was updated. When you create a bot, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot was created.
- **version** *(string) --*
The version of the bot. For a new bot, the version is always ``$LATEST`` .
- **nextToken** *(string) --*
A pagination token for fetching the next page of bot versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type name: string
:param name: **[REQUIRED]**
The name of the bot for which versions should be returned.
:type nextToken: string
:param nextToken:
A pagination token for fetching the next page of bot versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of bot versions to return in the response. The default is 10.
:rtype: dict
:returns:
"""
pass
def get_bots(self, nextToken: str = None, maxResults: int = None, nameContains: str = None) -> Dict:
"""
Returns bot information as follows:
* If you provide the ``nameContains`` field, the response includes information for the ``$LATEST`` version of all bots whose name contains the specified string.
* If you don't specify the ``nameContains`` field, the operation returns information about the ``$LATEST`` version of all of your bots.
This operation requires permission for the ``lex:GetBots`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBots>`_
**Request Syntax**
::
response = client.get_bots(
nextToken='string',
maxResults=123,
nameContains='string'
)
**Response Syntax**
::
{
'bots': [
{
'name': 'string',
'description': 'string',
'status': 'BUILDING'|'READY'|'READY_BASIC_TESTING'|'FAILED'|'NOT_BUILT',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **bots** *(list) --*
An array of ``botMetadata`` objects, with one entry for each bot.
- *(dict) --*
Provides information about a bot. .
- **name** *(string) --*
The name of the bot.
- **description** *(string) --*
A description of the bot.
- **status** *(string) --*
The status of the bot.
- **lastUpdatedDate** *(datetime) --*
The date that the bot was updated. When you create a bot, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot was created.
- **version** *(string) --*
The version of the bot. For a new bot, the version is always ``$LATEST`` .
- **nextToken** *(string) --*
If the response is truncated, it includes a pagination token that you can specify in your next request to fetch the next page of bots.
:type nextToken: string
:param nextToken:
A pagination token that fetches the next page of bots. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of bots, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of bots to return in the response that the request will return. The default is 10.
:type nameContains: string
:param nameContains:
Substring to match in bot names. A bot will be returned if any part of its name matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\"
:rtype: dict
:returns:
"""
pass
def get_builtin_intent(self, signature: str) -> Dict:
"""
Returns information about a built-in intent.
This operation requires permission for the ``lex:GetBuiltinIntent`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBuiltinIntent>`_
**Request Syntax**
::
response = client.get_builtin_intent(
signature='string'
)
**Response Syntax**
::
{
'signature': 'string',
'supportedLocales': [
'en-US'|'en-GB'|'de-DE',
],
'slots': [
{
'name': 'string'
},
]
}
**Response Structure**
- *(dict) --*
- **signature** *(string) --*
The unique identifier for a built-in intent.
- **supportedLocales** *(list) --*
A list of locales that the intent supports.
- *(string) --*
- **slots** *(list) --*
An array of ``BuiltinIntentSlot`` objects, one entry for each slot type in the intent.
- *(dict) --*
Provides information about a slot used in a built-in intent.
- **name** *(string) --*
A list of the slots defined for the intent.
:type signature: string
:param signature: **[REQUIRED]**
The unique identifier for a built-in intent. To find the signature for an intent, see `Standard Built-in Intents <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/standard-intents>`__ in the *Alexa Skills Kit* .
:rtype: dict
:returns:
"""
pass
def get_builtin_intents(self, locale: str = None, signatureContains: str = None, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Gets a list of built-in intents that meet the specified criteria.
This operation requires permission for the ``lex:GetBuiltinIntents`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBuiltinIntents>`_
**Request Syntax**
::
response = client.get_builtin_intents(
locale='en-US'|'en-GB'|'de-DE',
signatureContains='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'intents': [
{
'signature': 'string',
'supportedLocales': [
'en-US'|'en-GB'|'de-DE',
]
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **intents** *(list) --*
An array of ``builtinIntentMetadata`` objects, one for each intent in the response.
- *(dict) --*
Provides metadata for a built-in intent.
- **signature** *(string) --*
A unique identifier for the built-in intent. To find the signature for an intent, see `Standard Built-in Intents <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/standard-intents>`__ in the *Alexa Skills Kit* .
- **supportedLocales** *(list) --*
A list of identifiers for the locales that the intent supports.
- *(string) --*
- **nextToken** *(string) --*
A pagination token that fetches the next page of intents. If the response to this API call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of intents, specify the pagination token in the next request.
:type locale: string
:param locale:
A list of locales that the intent supports.
:type signatureContains: string
:param signatureContains:
Substring to match in built-in intent signatures. An intent will be returned if any part of its signature matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\" To find the signature for an intent, see `Standard Built-in Intents <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/standard-intents>`__ in the *Alexa Skills Kit* .
:type nextToken: string
:param nextToken:
A pagination token that fetches the next page of intents. If this API call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of intents, use the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of intents to return in the response. The default is 10.
:rtype: dict
:returns:
"""
pass
def get_builtin_slot_types(self, locale: str = None, signatureContains: str = None, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Gets a list of built-in slot types that meet the specified criteria.
For a list of built-in slot types, see `Slot Type Reference <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/slot-type-reference>`__ in the *Alexa Skills Kit* .
This operation requires permission for the ``lex:GetBuiltInSlotTypes`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetBuiltinSlotTypes>`_
**Request Syntax**
::
response = client.get_builtin_slot_types(
locale='en-US'|'en-GB'|'de-DE',
signatureContains='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'slotTypes': [
{
'signature': 'string',
'supportedLocales': [
'en-US'|'en-GB'|'de-DE',
]
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **slotTypes** *(list) --*
An array of ``BuiltInSlotTypeMetadata`` objects, one entry for each slot type returned.
- *(dict) --*
Provides information about a built in slot type.
- **signature** *(string) --*
A unique identifier for the built-in slot type. To find the signature for a slot type, see `Slot Type Reference <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/slot-type-reference>`__ in the *Alexa Skills Kit* .
- **supportedLocales** *(list) --*
A list of target locales for the slot.
- *(string) --*
- **nextToken** *(string) --*
If the response is truncated, the response includes a pagination token that you can use in your next request to fetch the next page of slot types.
:type locale: string
:param locale:
A list of locales that the slot type supports.
:type signatureContains: string
:param signatureContains:
Substring to match in built-in slot type signatures. A slot type will be returned if any part of its signature matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\"
:type nextToken: string
:param nextToken:
A pagination token that fetches the next page of slot types. If the response to this API call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of slot types, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of slot types to return in the response. The default is 10.
:rtype: dict
:returns:
"""
pass
def get_export(self, name: str, version: str, resourceType: str, exportType: str) -> Dict:
"""
Exports the contents of a Amazon Lex resource in a specified format.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetExport>`_
**Request Syntax**
::
response = client.get_export(
name='string',
version='string',
resourceType='BOT'|'INTENT'|'SLOT_TYPE',
exportType='ALEXA_SKILLS_KIT'|'LEX'
)
**Response Syntax**
::
{
'name': 'string',
'version': 'string',
'resourceType': 'BOT'|'INTENT'|'SLOT_TYPE',
'exportType': 'ALEXA_SKILLS_KIT'|'LEX',
'exportStatus': 'IN_PROGRESS'|'READY'|'FAILED',
'failureReason': 'string',
'url': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the bot being exported.
- **version** *(string) --*
The version of the bot being exported.
- **resourceType** *(string) --*
The type of the exported resource.
- **exportType** *(string) --*
The format of the exported data.
- **exportStatus** *(string) --*
The status of the export.
* ``IN_PROGRESS`` - The export is in progress.
* ``READY`` - The export is complete.
* ``FAILED`` - The export could not be completed.
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex provides the reason that it failed to export the resource.
- **url** *(string) --*
An S3 pre-signed URL that provides the location of the exported resource. The exported resource is a ZIP archive that contains the exported resource in JSON format. The structure of the archive may change. Your code should not rely on the archive structure.
:type name: string
:param name: **[REQUIRED]**
The name of the bot to export.
:type version: string
:param version: **[REQUIRED]**
The version of the bot to export.
:type resourceType: string
:param resourceType: **[REQUIRED]**
The type of resource to export.
:type exportType: string
:param exportType: **[REQUIRED]**
The format of the exported data.
:rtype: dict
:returns:
"""
pass
def get_import(self, importId: str) -> Dict:
"""
Gets information about an import job started with the ``StartImport`` operation.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetImport>`_
**Request Syntax**
::
response = client.get_import(
importId='string'
)
**Response Syntax**
::
{
'name': 'string',
'resourceType': 'BOT'|'INTENT'|'SLOT_TYPE',
'mergeStrategy': 'OVERWRITE_LATEST'|'FAIL_ON_CONFLICT',
'importId': 'string',
'importStatus': 'IN_PROGRESS'|'COMPLETE'|'FAILED',
'failureReason': [
'string',
],
'createdDate': datetime(2015, 1, 1)
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name given to the import job.
- **resourceType** *(string) --*
The type of resource imported.
- **mergeStrategy** *(string) --*
The action taken when there was a conflict between an existing resource and a resource in the import file.
- **importId** *(string) --*
The identifier for the specific import job.
- **importStatus** *(string) --*
The status of the import job. If the status is ``FAILED`` , you can get the reason for the failure from the ``failureReason`` field.
- **failureReason** *(list) --*
A string that describes why an import job failed to complete.
- *(string) --*
- **createdDate** *(datetime) --*
A timestamp for the date and time that the import job was created.
:type importId: string
:param importId: **[REQUIRED]**
The identifier of the import job information to return.
:rtype: dict
:returns:
"""
pass
def get_intent(self, name: str, version: str) -> Dict:
"""
Returns information about an intent. In addition to the intent name, you must specify the intent version.
This operation requires permissions to perform the ``lex:GetIntent`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetIntent>`_
**Request Syntax**
::
response = client.get_intent(
name='string',
version='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'slots': [
{
'name': 'string',
'description': 'string',
'slotConstraint': 'Required'|'Optional',
'slotType': 'string',
'slotTypeVersion': 'string',
'valueElicitationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'priority': 123,
'sampleUtterances': [
'string',
],
'responseCard': 'string'
},
],
'sampleUtterances': [
'string',
],
'confirmationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'followUpPrompt': {
'prompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
}
},
'conclusionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'dialogCodeHook': {
'uri': 'string',
'messageVersion': 'string'
},
'fulfillmentActivity': {
'type': 'ReturnIntent'|'CodeHook',
'codeHook': {
'uri': 'string',
'messageVersion': 'string'
}
},
'parentIntentSignature': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the intent.
- **description** *(string) --*
A description of the intent.
- **slots** *(list) --*
An array of intent slots configured for the intent.
- *(dict) --*
Identifies the version of a specific slot.
- **name** *(string) --*
The name of the slot.
- **description** *(string) --*
A description of the slot.
- **slotConstraint** *(string) --*
Specifies whether the slot is required or optional.
- **slotType** *(string) --*
The type of the slot, either a custom slot type that you defined or one of the built-in slot types.
- **slotTypeVersion** *(string) --*
The version of the slot type.
- **valueElicitationPrompt** *(dict) --*
The prompt that Amazon Lex uses to elicit the slot value from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **priority** *(integer) --*
Directs Lex the order in which to elicit this slot value from the user. For example, if the intent has two slots with priorities 1 and 2, AWS Lex first elicits a value for the slot with priority 1.
If multiple slots share the same priority, the order in which Lex elicits values is arbitrary.
- **sampleUtterances** *(list) --*
If you know a specific pattern with which users might respond to an Amazon Lex request for a slot value, you can provide those utterances to improve accuracy. This is optional. In most cases, Amazon Lex is capable of understanding user utterances.
- *(string) --*
- **responseCard** *(string) --*
A set of possible responses for the slot type used by text-based clients. A user chooses an option from the response card, instead of using text to reply.
- **sampleUtterances** *(list) --*
An array of sample utterances configured for the intent.
- *(string) --*
- **confirmationPrompt** *(dict) --*
If defined in the bot, Amazon Lex uses prompt to confirm the intent before fulfilling the user's request. For more information, see PutIntent .
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in ``confirmationPrompt`` , Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **followUpPrompt** *(dict) --*
If defined in the bot, Amazon Lex uses this prompt to solicit additional user activity after the intent is fulfilled. For more information, see PutIntent .
- **prompt** *(dict) --*
Prompts for information from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in the ``prompt`` field, Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **conclusionStatement** *(dict) --*
After the Lambda function specified in the ``fulfillmentActivity`` element fulfills the intent, Amazon Lex conveys this statement to the user.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **dialogCodeHook** *(dict) --*
If defined in the bot, Amazon Amazon Lex invokes this Lambda function for each user input. For more information, see PutIntent .
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **fulfillmentActivity** *(dict) --*
Describes how the intent is fulfilled. For more information, see PutIntent .
- **type** *(string) --*
How the intent should be fulfilled, either by running a Lambda function or by returning the slot data to the client application.
- **codeHook** *(dict) --*
A description of the Lambda function that is run to fulfill the intent.
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **parentIntentSignature** *(string) --*
A unique identifier for a built-in intent.
- **lastUpdatedDate** *(datetime) --*
The date that the intent was updated. When you create a resource, the creation date and the last updated date are the same.
- **createdDate** *(datetime) --*
The date that the intent was created.
- **version** *(string) --*
The version of the intent.
- **checksum** *(string) --*
Checksum of the intent.
:type name: string
:param name: **[REQUIRED]**
The name of the intent. The name is case sensitive.
:type version: string
:param version: **[REQUIRED]**
The version of the intent.
:rtype: dict
:returns:
"""
pass
def get_intent_versions(self, name: str, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Gets information about all of the versions of an intent.
The ``GetIntentVersions`` operation returns an ``IntentMetadata`` object for each version of an intent. For example, if an intent has three numbered versions, the ``GetIntentVersions`` operation returns four ``IntentMetadata`` objects in the response, one for each numbered version and one for the ``$LATEST`` version.
The ``GetIntentVersions`` operation always returns at least one version, the ``$LATEST`` version.
This operation requires permissions for the ``lex:GetIntentVersions`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetIntentVersions>`_
**Request Syntax**
::
response = client.get_intent_versions(
name='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'intents': [
{
'name': 'string',
'description': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **intents** *(list) --*
An array of ``IntentMetadata`` objects, one for each numbered version of the intent plus one for the ``$LATEST`` version.
- *(dict) --*
Provides information about an intent.
- **name** *(string) --*
The name of the intent.
- **description** *(string) --*
A description of the intent.
- **lastUpdatedDate** *(datetime) --*
The date that the intent was updated. When you create an intent, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the intent was created.
- **version** *(string) --*
The version of the intent.
- **nextToken** *(string) --*
A pagination token for fetching the next page of intent versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type name: string
:param name: **[REQUIRED]**
The name of the intent for which versions should be returned.
:type nextToken: string
:param nextToken:
A pagination token for fetching the next page of intent versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of intent versions to return in the response. The default is 10.
:rtype: dict
:returns:
"""
pass
def get_intents(self, nextToken: str = None, maxResults: int = None, nameContains: str = None) -> Dict:
"""
Returns intent information as follows:
* If you specify the ``nameContains`` field, returns the ``$LATEST`` version of all intents that contain the specified string.
* If you don't specify the ``nameContains`` field, returns information about the ``$LATEST`` version of all intents.
The operation requires permission for the ``lex:GetIntents`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetIntents>`_
**Request Syntax**
::
response = client.get_intents(
nextToken='string',
maxResults=123,
nameContains='string'
)
**Response Syntax**
::
{
'intents': [
{
'name': 'string',
'description': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **intents** *(list) --*
An array of ``Intent`` objects. For more information, see PutBot .
- *(dict) --*
Provides information about an intent.
- **name** *(string) --*
The name of the intent.
- **description** *(string) --*
A description of the intent.
- **lastUpdatedDate** *(datetime) --*
The date that the intent was updated. When you create an intent, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the intent was created.
- **version** *(string) --*
The version of the intent.
- **nextToken** *(string) --*
If the response is truncated, the response includes a pagination token that you can specify in your next request to fetch the next page of intents.
:type nextToken: string
:param nextToken:
A pagination token that fetches the next page of intents. If the response to this API call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of intents, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of intents to return in the response. The default is 10.
:type nameContains: string
:param nameContains:
Substring to match in intent names. An intent will be returned if any part of its name matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\"
:rtype: dict
:returns:
"""
pass
def get_paginator(self, operation_name: str = None) -> Paginator:
"""
Create a paginator for an operation.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:raise OperationNotPageableError: Raised if the operation is not
pageable. You can use the ``client.can_paginate`` method to
check if an operation is pageable.
:rtype: L{botocore.paginate.Paginator}
:return: A paginator object.
"""
pass
def get_slot_type(self, name: str, version: str) -> Dict:
"""
Returns information about a specific version of a slot type. In addition to specifying the slot type name, you must specify the slot type version.
This operation requires permissions for the ``lex:GetSlotType`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetSlotType>`_
**Request Syntax**
::
response = client.get_slot_type(
name='string',
version='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'enumerationValues': [
{
'value': 'string',
'synonyms': [
'string',
]
},
],
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string',
'valueSelectionStrategy': 'ORIGINAL_VALUE'|'TOP_RESOLUTION'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the slot type.
- **description** *(string) --*
A description of the slot type.
- **enumerationValues** *(list) --*
A list of ``EnumerationValue`` objects that defines the values that the slot type can take.
- *(dict) --*
Each slot type can have a set of values. Each enumeration value represents a value the slot type can take.
For example, a pizza ordering bot could have a slot type that specifies the type of crust that the pizza should have. The slot type could include the values
* thick
* thin
* stuffed
- **value** *(string) --*
The value of the slot type.
- **synonyms** *(list) --*
Additional values related to the slot type value.
- *(string) --*
- **lastUpdatedDate** *(datetime) --*
The date that the slot type was updated. When you create a resource, the creation date and last update date are the same.
- **createdDate** *(datetime) --*
The date that the slot type was created.
- **version** *(string) --*
The version of the slot type.
- **checksum** *(string) --*
Checksum of the ``$LATEST`` version of the slot type.
- **valueSelectionStrategy** *(string) --*
The strategy that Amazon Lex uses to determine the value of the slot. For more information, see PutSlotType .
:type name: string
:param name: **[REQUIRED]**
The name of the slot type. The name is case sensitive.
:type version: string
:param version: **[REQUIRED]**
The version of the slot type.
:rtype: dict
:returns:
"""
pass
def get_slot_type_versions(self, name: str, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Gets information about all versions of a slot type.
The ``GetSlotTypeVersions`` operation returns a ``SlotTypeMetadata`` object for each version of a slot type. For example, if a slot type has three numbered versions, the ``GetSlotTypeVersions`` operation returns four ``SlotTypeMetadata`` objects in the response, one for each numbered version and one for the ``$LATEST`` version.
The ``GetSlotTypeVersions`` operation always returns at least one version, the ``$LATEST`` version.
This operation requires permissions for the ``lex:GetSlotTypeVersions`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetSlotTypeVersions>`_
**Request Syntax**
::
response = client.get_slot_type_versions(
name='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'slotTypes': [
{
'name': 'string',
'description': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **slotTypes** *(list) --*
An array of ``SlotTypeMetadata`` objects, one for each numbered version of the slot type plus one for the ``$LATEST`` version.
- *(dict) --*
Provides information about a slot type..
- **name** *(string) --*
The name of the slot type.
- **description** *(string) --*
A description of the slot type.
- **lastUpdatedDate** *(datetime) --*
The date that the slot type was updated. When you create a resource, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the slot type was created.
- **version** *(string) --*
The version of the slot type.
- **nextToken** *(string) --*
A pagination token for fetching the next page of slot type versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type name: string
:param name: **[REQUIRED]**
The name of the slot type for which versions should be returned.
:type nextToken: string
:param nextToken:
A pagination token for fetching the next page of slot type versions. If the response to this call is truncated, Amazon Lex returns a pagination token in the response. To fetch the next page of versions, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of slot type versions to return in the response. The default is 10.
:rtype: dict
:returns:
"""
pass
def get_slot_types(self, nextToken: str = None, maxResults: int = None, nameContains: str = None) -> Dict:
"""
Returns slot type information as follows:
* If you specify the ``nameContains`` field, returns the ``$LATEST`` version of all slot types that contain the specified string.
* If you don't specify the ``nameContains`` field, returns information about the ``$LATEST`` version of all slot types.
The operation requires permission for the ``lex:GetSlotTypes`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetSlotTypes>`_
**Request Syntax**
::
response = client.get_slot_types(
nextToken='string',
maxResults=123,
nameContains='string'
)
**Response Syntax**
::
{
'slotTypes': [
{
'name': 'string',
'description': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **slotTypes** *(list) --*
An array of objects, one for each slot type, that provides information such as the name of the slot type, the version, and a description.
- *(dict) --*
Provides information about a slot type..
- **name** *(string) --*
The name of the slot type.
- **description** *(string) --*
A description of the slot type.
- **lastUpdatedDate** *(datetime) --*
The date that the slot type was updated. When you create a resource, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the slot type was created.
- **version** *(string) --*
The version of the slot type.
- **nextToken** *(string) --*
If the response is truncated, it includes a pagination token that you can specify in your next request to fetch the next page of slot types.
:type nextToken: string
:param nextToken:
A pagination token that fetches the next page of slot types. If the response to this API call is truncated, Amazon Lex returns a pagination token in the response. To fetch next page of slot types, specify the pagination token in the next request.
:type maxResults: integer
:param maxResults:
The maximum number of slot types to return in the response. The default is 10.
:type nameContains: string
:param nameContains:
Substring to match in slot type names. A slot type will be returned if any part of its name matches the substring. For example, \"xyz\" matches both \"xyzabc\" and \"abcxyz.\"
:rtype: dict
:returns:
"""
pass
def get_utterances_view(self, botName: str, botVersions: List, statusType: str) -> Dict:
"""
Use the ``GetUtterancesView`` operation to get information about the utterances that your users have made to your bot. You can use this list to tune the utterances that your bot responds to.
For example, say that you have created a bot to order flowers. After your users have used your bot for a while, use the ``GetUtterancesView`` operation to see the requests that they have made and whether they have been successful. You might find that the utterance "I want flowers" is not being recognized. You could add this utterance to the ``OrderFlowers`` intent so that your bot recognizes that utterance.
After you publish a new version of a bot, you can get information about the old version and the new so that you can compare the performance across the two versions.
.. note::
Utterance statistics are generated once a day. Data is available for the last 15 days. You can request information for up to 5 versions in each request. The response contains information about a maximum of 100 utterances for each version.
This operation requires permissions for the ``lex:GetUtterancesView`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/GetUtterancesView>`_
**Request Syntax**
::
response = client.get_utterances_view(
botName='string',
botVersions=[
'string',
],
statusType='Detected'|'Missed'
)
**Response Syntax**
::
{
'botName': 'string',
'utterances': [
{
'botVersion': 'string',
'utterances': [
{
'utteranceString': 'string',
'count': 123,
'distinctUsers': 123,
'firstUtteredDate': datetime(2015, 1, 1),
'lastUtteredDate': datetime(2015, 1, 1)
},
]
},
]
}
**Response Structure**
- *(dict) --*
- **botName** *(string) --*
The name of the bot for which utterance information was returned.
- **utterances** *(list) --*
An array of UtteranceList objects, each containing a list of UtteranceData objects describing the utterances that were processed by your bot. The response contains a maximum of 100 ``UtteranceData`` objects for each version.
- *(dict) --*
Provides a list of utterances that have been made to a specific version of your bot. The list contains a maximum of 100 utterances.
- **botVersion** *(string) --*
The version of the bot that processed the list.
- **utterances** *(list) --*
One or more UtteranceData objects that contain information about the utterances that have been made to a bot. The maximum number of object is 100.
- *(dict) --*
Provides information about a single utterance that was made to your bot.
- **utteranceString** *(string) --*
The text that was entered by the user or the text representation of an audio clip.
- **count** *(integer) --*
The number of times that the utterance was processed.
- **distinctUsers** *(integer) --*
The total number of individuals that used the utterance.
- **firstUtteredDate** *(datetime) --*
The date that the utterance was first recorded.
- **lastUtteredDate** *(datetime) --*
The date that the utterance was last recorded.
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot for which utterance information should be returned.
:type botVersions: list
:param botVersions: **[REQUIRED]**
An array of bot versions for which utterance information should be returned. The limit is 5 versions per request.
- *(string) --*
:type statusType: string
:param statusType: **[REQUIRED]**
To return utterances that were recognized and handled, use``Detected`` . To return utterances that were not recognized, use ``Missed`` .
:rtype: dict
:returns:
"""
pass
def get_waiter(self, waiter_name: str = None) -> Waiter:
"""
Returns an object that can wait for some condition.
:type waiter_name: str
:param waiter_name: The name of the waiter to get. See the waiters
section of the service docs for a list of available waiters.
:returns: The specified waiter object.
:rtype: botocore.waiter.Waiter
"""
pass
def put_bot(self, name: str, locale: str, childDirected: bool, description: str = None, intents: List = None, clarificationPrompt: Dict = None, abortStatement: Dict = None, idleSessionTTLInSeconds: int = None, voiceId: str = None, checksum: str = None, processBehavior: str = None, createVersion: bool = None) -> Dict:
"""
Creates an Amazon Lex conversational bot or replaces an existing bot. When you create or update a bot you are only required to specify a name, a locale, and whether the bot is directed toward children under age 13. You can use this to add intents later, or to remove intents from an existing bot. When you create a bot with the minimum information, the bot is created or updated but Amazon Lex returns the response ``FAILED`` . You can build the bot after you add one or more intents. For more information about Amazon Lex bots, see how-it-works .
If you specify the name of an existing bot, the fields in the request replace the existing values in the ``$LATEST`` version of the bot. Amazon Lex removes any fields that you don't provide values for in the request, except for the ``idleTTLInSeconds`` and ``privacySettings`` fields, which are set to their default values. If you don't specify values for required fields, Amazon Lex throws an exception.
This operation requires permissions for the ``lex:PutBot`` action. For more information, see auth-and-access-control .
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/PutBot>`_
**Request Syntax**
::
response = client.put_bot(
name='string',
description='string',
intents=[
{
'intentName': 'string',
'intentVersion': 'string'
},
],
clarificationPrompt={
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
abortStatement={
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
idleSessionTTLInSeconds=123,
voiceId='string',
checksum='string',
processBehavior='SAVE'|'BUILD',
locale='en-US'|'en-GB'|'de-DE',
childDirected=True|False,
createVersion=True|False
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'intents': [
{
'intentName': 'string',
'intentVersion': 'string'
},
],
'clarificationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'abortStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'status': 'BUILDING'|'READY'|'READY_BASIC_TESTING'|'FAILED'|'NOT_BUILT',
'failureReason': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'idleSessionTTLInSeconds': 123,
'voiceId': 'string',
'checksum': 'string',
'version': 'string',
'locale': 'en-US'|'en-GB'|'de-DE',
'childDirected': True|False,
'createVersion': True|False
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the bot.
- **description** *(string) --*
A description of the bot.
- **intents** *(list) --*
An array of ``Intent`` objects. For more information, see PutBot .
- *(dict) --*
Identifies the specific version of an intent.
- **intentName** *(string) --*
The name of the intent.
- **intentVersion** *(string) --*
The version of the intent.
- **clarificationPrompt** *(dict) --*
The prompts that Amazon Lex uses when it doesn't understand the user's intent. For more information, see PutBot .
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **abortStatement** *(dict) --*
The message that Amazon Lex uses to abort a conversation. For more information, see PutBot .
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **status** *(string) --*
When you send a request to create a bot with ``processBehavior`` set to ``BUILD`` , Amazon Lex sets the ``status`` response element to ``BUILDING`` . After Amazon Lex builds the bot, it sets ``status`` to ``READY`` . If Amazon Lex can't build the bot, Amazon Lex sets ``status`` to ``FAILED`` . Amazon Lex returns the reason for the failure in the ``failureReason`` response element.
When you set ``processBehavior`` to ``SAVE`` , Amazon Lex sets the status code to ``NOT BUILT`` .
- **failureReason** *(string) --*
If ``status`` is ``FAILED`` , Amazon Lex provides the reason that it failed to build the bot.
- **lastUpdatedDate** *(datetime) --*
The date that the bot was updated. When you create a resource, the creation date and last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot was created.
- **idleSessionTTLInSeconds** *(integer) --*
The maximum length of time that Amazon Lex retains the data gathered in a conversation. For more information, see PutBot .
- **voiceId** *(string) --*
The Amazon Polly voice ID that Amazon Lex uses for voice interaction with the user. For more information, see PutBot .
- **checksum** *(string) --*
Checksum of the bot that you created.
- **version** *(string) --*
The version of the bot. For a new bot, the version is always ``$LATEST`` .
- **locale** *(string) --*
The target locale for the bot.
- **childDirected** *(boolean) --*
For each Amazon Lex bot created with the Amazon Lex Model Building Service, you must specify whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to the Children's Online Privacy Protection Act (COPPA) by specifying ``true`` or ``false`` in the ``childDirected`` field. By specifying ``true`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. By specifying ``false`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is not** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. You may not specify a default value for the ``childDirected`` field that does not accurately reflect whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA.
If your use of Amazon Lex relates to a website, program, or other application that is directed in whole or in part, to children under age 13, you must obtain any required verifiable parental consent under COPPA. For information regarding the use of Amazon Lex in connection with websites, programs, or other applications that are directed or targeted, in whole or in part, to children under age 13, see the `Amazon Lex FAQ. <https://aws.amazon.com/lex/faqs#data-security>`__
- **createVersion** *(boolean) --*
:type name: string
:param name: **[REQUIRED]**
The name of the bot. The name is *not* case sensitive.
:type description: string
:param description:
A description of the bot.
:type intents: list
:param intents:
An array of ``Intent`` objects. Each intent represents a command that a user can express. For example, a pizza ordering bot might support an OrderPizza intent. For more information, see how-it-works .
- *(dict) --*
Identifies the specific version of an intent.
- **intentName** *(string) --* **[REQUIRED]**
The name of the intent.
- **intentVersion** *(string) --* **[REQUIRED]**
The version of the intent.
:type clarificationPrompt: dict
:param clarificationPrompt:
When Amazon Lex doesn\'t understand the user\'s intent, it uses this message to get clarification. To specify how many times Amazon Lex should repeate the clarification prompt, use the ``maxAttempts`` field. If Amazon Lex still doesn\'t understand, it sends the message in the ``abortStatement`` field.
When you create a clarification prompt, make sure that it suggests the correct response from the user. for example, for a bot that orders pizza and drinks, you might create this clarification prompt: \"What would you like to do? You can say \'Order a pizza\' or \'Order a drink.\'\"
- **messages** *(list) --* **[REQUIRED]**
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --* **[REQUIRED]**
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
:type abortStatement: dict
:param abortStatement:
When Amazon Lex can\'t understand the user\'s input in context, it tries to elicit the information a few times. After that, Amazon Lex sends the message defined in ``abortStatement`` to the user, and then aborts the conversation. To set the number of retries, use the ``valueElicitationPrompt`` field for the slot type.
For example, in a pizza ordering bot, Amazon Lex might ask a user \"What type of crust would you like?\" If the user\'s response is not one of the expected responses (for example, \"thin crust, \"deep dish,\" etc.), Amazon Lex tries to elicit a correct response a few more times.
For example, in a pizza ordering application, ``OrderPizza`` might be one of the intents. This intent might require the ``CrustType`` slot. You specify the ``valueElicitationPrompt`` field when you create the ``CrustType`` slot.
- **messages** *(list) --* **[REQUIRED]**
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
:type idleSessionTTLInSeconds: integer
:param idleSessionTTLInSeconds:
The maximum time in seconds that Amazon Lex retains the data gathered in a conversation.
A user interaction session remains active for the amount of time specified. If no conversation occurs during this time, the session expires and Amazon Lex deletes any data provided before the timeout.
For example, suppose that a user chooses the OrderPizza intent, but gets sidetracked halfway through placing an order. If the user doesn\'t complete the order within the specified time, Amazon Lex discards the slot information that it gathered, and the user must start over.
If you don\'t include the ``idleSessionTTLInSeconds`` element in a ``PutBot`` operation request, Amazon Lex uses the default value. This is also true if the request replaces an existing bot.
The default is 300 seconds (5 minutes).
:type voiceId: string
:param voiceId:
The Amazon Polly voice ID that you want Amazon Lex to use for voice interactions with the user. The locale configured for the voice must match the locale of the bot. For more information, see `Available Voices <http://docs.aws.amazon.com/polly/latest/dg/voicelist.html>`__ in the *Amazon Polly Developer Guide* .
:type checksum: string
:param checksum:
Identifies a specific revision of the ``$LATEST`` version.
When you create a new bot, leave the ``checksum`` field blank. If you specify a checksum you get a ``BadRequestException`` exception.
When you want to update a bot, set the ``checksum`` field to the checksum of the most recent revision of the ``$LATEST`` version. If you don\'t specify the ``checksum`` field, or if the checksum does not match the ``$LATEST`` version, you get a ``PreconditionFailedException`` exception.
:type processBehavior: string
:param processBehavior:
If you set the ``processBehavior`` element to ``BUILD`` , Amazon Lex builds the bot so that it can be run. If you set the element to ``SAVE`` Amazon Lex saves the bot, but doesn\'t build it.
If you don\'t specify this value, the default value is ``BUILD`` .
:type locale: string
:param locale: **[REQUIRED]**
Specifies the target locale for the bot. Any intent used in the bot must be compatible with the locale of the bot.
The default is ``en-US`` .
:type childDirected: boolean
:param childDirected: **[REQUIRED]**
For each Amazon Lex bot created with the Amazon Lex Model Building Service, you must specify whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to the Children\'s Online Privacy Protection Act (COPPA) by specifying ``true`` or ``false`` in the ``childDirected`` field. By specifying ``true`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. By specifying ``false`` in the ``childDirected`` field, you confirm that your use of Amazon Lex **is not** related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA. You may not specify a default value for the ``childDirected`` field that does not accurately reflect whether your use of Amazon Lex is related to a website, program, or other application that is directed or targeted, in whole or in part, to children under age 13 and subject to COPPA.
If your use of Amazon Lex relates to a website, program, or other application that is directed in whole or in part, to children under age 13, you must obtain any required verifiable parental consent under COPPA. For information regarding the use of Amazon Lex in connection with websites, programs, or other applications that are directed or targeted, in whole or in part, to children under age 13, see the `Amazon Lex FAQ. <https://aws.amazon.com/lex/faqs#data-security>`__
:type createVersion: boolean
:param createVersion:
:rtype: dict
:returns:
"""
pass
def put_bot_alias(self, name: str, botVersion: str, botName: str, description: str = None, checksum: str = None) -> Dict:
"""
Creates an alias for the specified version of the bot or replaces an alias for the specified bot. To change the version of the bot that the alias points to, replace the alias. For more information about aliases, see versioning-aliases .
This operation requires permissions for the ``lex:PutBotAlias`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/PutBotAlias>`_
**Request Syntax**
::
response = client.put_bot_alias(
name='string',
description='string',
botVersion='string',
botName='string',
checksum='string'
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'botVersion': 'string',
'botName': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'checksum': 'string'
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the alias.
- **description** *(string) --*
A description of the alias.
- **botVersion** *(string) --*
The version of the bot that the alias points to.
- **botName** *(string) --*
The name of the bot that the alias points to.
- **lastUpdatedDate** *(datetime) --*
The date that the bot alias was updated. When you create a resource, the creation date and the last updated date are the same.
- **createdDate** *(datetime) --*
The date that the bot alias was created.
- **checksum** *(string) --*
The checksum for the current version of the alias.
:type name: string
:param name: **[REQUIRED]**
The name of the alias. The name is *not* case sensitive.
:type description: string
:param description:
A description of the alias.
:type botVersion: string
:param botVersion: **[REQUIRED]**
The version of the bot.
:type botName: string
:param botName: **[REQUIRED]**
The name of the bot.
:type checksum: string
:param checksum:
Identifies a specific revision of the ``$LATEST`` version.
When you create a new bot alias, leave the ``checksum`` field blank. If you specify a checksum you get a ``BadRequestException`` exception.
When you want to update a bot alias, set the ``checksum`` field to the checksum of the most recent revision of the ``$LATEST`` version. If you don\'t specify the ``checksum`` field, or if the checksum does not match the ``$LATEST`` version, you get a ``PreconditionFailedException`` exception.
:rtype: dict
:returns:
"""
pass
def put_intent(self, name: str, description: str = None, slots: List = None, sampleUtterances: List = None, confirmationPrompt: Dict = None, rejectionStatement: Dict = None, followUpPrompt: Dict = None, conclusionStatement: Dict = None, dialogCodeHook: Dict = None, fulfillmentActivity: Dict = None, parentIntentSignature: str = None, checksum: str = None, createVersion: bool = None) -> Dict:
"""
Creates an intent or replaces an existing intent.
To define the interaction between the user and your bot, you use one or more intents. For a pizza ordering bot, for example, you would create an ``OrderPizza`` intent.
To create an intent or replace an existing intent, you must provide the following:
* Intent name. For example, ``OrderPizza`` .
* Sample utterances. For example, "Can I order a pizza, please." and "I want to order a pizza."
* Information to be gathered. You specify slot types for the information that your bot will request from the user. You can specify standard slot types, such as a date or a time, or custom slot types such as the size and crust of a pizza.
* How the intent will be fulfilled. You can provide a Lambda function or configure the intent to return the intent information to the client application. If you use a Lambda function, when all of the intent information is available, Amazon Lex invokes your Lambda function. If you configure your intent to return the intent information to the client application.
You can specify other optional information in the request, such as:
* A confirmation prompt to ask the user to confirm an intent. For example, "Shall I order your pizza?"
* A conclusion statement to send to the user after the intent has been fulfilled. For example, "I placed your pizza order."
* A follow-up prompt that asks the user for additional activity. For example, asking "Do you want to order a drink with your pizza?"
If you specify an existing intent name to update the intent, Amazon Lex replaces the values in the ``$LATEST`` version of the intent with the values in the request. Amazon Lex removes fields that you don't provide in the request. If you don't specify the required fields, Amazon Lex throws an exception. When you update the ``$LATEST`` version of an intent, the ``status`` field of any bot that uses the ``$LATEST`` version of the intent is set to ``NOT_BUILT`` .
For more information, see how-it-works .
This operation requires permissions for the ``lex:PutIntent`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/PutIntent>`_
**Request Syntax**
::
response = client.put_intent(
name='string',
description='string',
slots=[
{
'name': 'string',
'description': 'string',
'slotConstraint': 'Required'|'Optional',
'slotType': 'string',
'slotTypeVersion': 'string',
'valueElicitationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'priority': 123,
'sampleUtterances': [
'string',
],
'responseCard': 'string'
},
],
sampleUtterances=[
'string',
],
confirmationPrompt={
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
rejectionStatement={
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
followUpPrompt={
'prompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
}
},
conclusionStatement={
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
dialogCodeHook={
'uri': 'string',
'messageVersion': 'string'
},
fulfillmentActivity={
'type': 'ReturnIntent'|'CodeHook',
'codeHook': {
'uri': 'string',
'messageVersion': 'string'
}
},
parentIntentSignature='string',
checksum='string',
createVersion=True|False
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'slots': [
{
'name': 'string',
'description': 'string',
'slotConstraint': 'Required'|'Optional',
'slotType': 'string',
'slotTypeVersion': 'string',
'valueElicitationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'priority': 123,
'sampleUtterances': [
'string',
],
'responseCard': 'string'
},
],
'sampleUtterances': [
'string',
],
'confirmationPrompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'followUpPrompt': {
'prompt': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'maxAttempts': 123,
'responseCard': 'string'
},
'rejectionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
}
},
'conclusionStatement': {
'messages': [
{
'contentType': 'PlainText'|'SSML'|'CustomPayload',
'content': 'string',
'groupNumber': 123
},
],
'responseCard': 'string'
},
'dialogCodeHook': {
'uri': 'string',
'messageVersion': 'string'
},
'fulfillmentActivity': {
'type': 'ReturnIntent'|'CodeHook',
'codeHook': {
'uri': 'string',
'messageVersion': 'string'
}
},
'parentIntentSignature': 'string',
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string',
'createVersion': True|False
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the intent.
- **description** *(string) --*
A description of the intent.
- **slots** *(list) --*
An array of intent slots that are configured for the intent.
- *(dict) --*
Identifies the version of a specific slot.
- **name** *(string) --*
The name of the slot.
- **description** *(string) --*
A description of the slot.
- **slotConstraint** *(string) --*
Specifies whether the slot is required or optional.
- **slotType** *(string) --*
The type of the slot, either a custom slot type that you defined or one of the built-in slot types.
- **slotTypeVersion** *(string) --*
The version of the slot type.
- **valueElicitationPrompt** *(dict) --*
The prompt that Amazon Lex uses to elicit the slot value from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **priority** *(integer) --*
Directs Lex the order in which to elicit this slot value from the user. For example, if the intent has two slots with priorities 1 and 2, AWS Lex first elicits a value for the slot with priority 1.
If multiple slots share the same priority, the order in which Lex elicits values is arbitrary.
- **sampleUtterances** *(list) --*
If you know a specific pattern with which users might respond to an Amazon Lex request for a slot value, you can provide those utterances to improve accuracy. This is optional. In most cases, Amazon Lex is capable of understanding user utterances.
- *(string) --*
- **responseCard** *(string) --*
A set of possible responses for the slot type used by text-based clients. A user chooses an option from the response card, instead of using text to reply.
- **sampleUtterances** *(list) --*
An array of sample utterances that are configured for the intent.
- *(string) --*
- **confirmationPrompt** *(dict) --*
If defined in the intent, Amazon Lex prompts the user to confirm the intent before fulfilling it.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in ``confirmationPrompt`` Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **followUpPrompt** *(dict) --*
If defined in the intent, Amazon Lex uses this prompt to solicit additional user activity after the intent is fulfilled.
- **prompt** *(dict) --*
Prompts for information from the user.
- **messages** *(list) --*
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --*
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --*
If the user answers "no" to the question defined in the ``prompt`` field, Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **conclusionStatement** *(dict) --*
After the Lambda function specified in the``fulfillmentActivity`` intent fulfills the intent, Amazon Lex conveys this statement to the user.
- **messages** *(list) --*
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --*
The content type of the message string.
- **content** *(string) --*
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
- **dialogCodeHook** *(dict) --*
If defined in the intent, Amazon Lex invokes this Lambda function for each user input.
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **fulfillmentActivity** *(dict) --*
If defined in the intent, Amazon Lex invokes this Lambda function to fulfill the intent after the user provides all of the information required by the intent.
- **type** *(string) --*
How the intent should be fulfilled, either by running a Lambda function or by returning the slot data to the client application.
- **codeHook** *(dict) --*
A description of the Lambda function that is run to fulfill the intent.
- **uri** *(string) --*
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --*
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
- **parentIntentSignature** *(string) --*
A unique identifier for the built-in intent that this intent is based on.
- **lastUpdatedDate** *(datetime) --*
The date that the intent was updated. When you create a resource, the creation date and last update dates are the same.
- **createdDate** *(datetime) --*
The date that the intent was created.
- **version** *(string) --*
The version of the intent. For a new intent, the version is always ``$LATEST`` .
- **checksum** *(string) --*
Checksum of the ``$LATEST`` version of the intent created or updated.
- **createVersion** *(boolean) --*
:type name: string
:param name: **[REQUIRED]**
The name of the intent. The name is *not* case sensitive.
The name can\'t match a built-in intent name, or a built-in intent name with \"AMAZON.\" removed. For example, because there is a built-in intent called ``AMAZON.HelpIntent`` , you can\'t create a custom intent called ``HelpIntent`` .
For a list of built-in intents, see `Standard Built-in Intents <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/standard-intents>`__ in the *Alexa Skills Kit* .
:type description: string
:param description:
A description of the intent.
:type slots: list
:param slots:
An array of intent slots. At runtime, Amazon Lex elicits required slot values from the user using prompts defined in the slots. For more information, see how-it-works .
- *(dict) --*
Identifies the version of a specific slot.
- **name** *(string) --* **[REQUIRED]**
The name of the slot.
- **description** *(string) --*
A description of the slot.
- **slotConstraint** *(string) --* **[REQUIRED]**
Specifies whether the slot is required or optional.
- **slotType** *(string) --*
The type of the slot, either a custom slot type that you defined or one of the built-in slot types.
- **slotTypeVersion** *(string) --*
The version of the slot type.
- **valueElicitationPrompt** *(dict) --*
The prompt that Amazon Lex uses to elicit the slot value from the user.
- **messages** *(list) --* **[REQUIRED]**
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --* **[REQUIRED]**
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **priority** *(integer) --*
Directs Lex the order in which to elicit this slot value from the user. For example, if the intent has two slots with priorities 1 and 2, AWS Lex first elicits a value for the slot with priority 1.
If multiple slots share the same priority, the order in which Lex elicits values is arbitrary.
- **sampleUtterances** *(list) --*
If you know a specific pattern with which users might respond to an Amazon Lex request for a slot value, you can provide those utterances to improve accuracy. This is optional. In most cases, Amazon Lex is capable of understanding user utterances.
- *(string) --*
- **responseCard** *(string) --*
A set of possible responses for the slot type used by text-based clients. A user chooses an option from the response card, instead of using text to reply.
:type sampleUtterances: list
:param sampleUtterances:
An array of utterances (strings) that a user might say to signal the intent. For example, \"I want {PizzaSize} pizza\", \"Order {Quantity} {PizzaSize} pizzas\".
In each utterance, a slot name is enclosed in curly braces.
- *(string) --*
:type confirmationPrompt: dict
:param confirmationPrompt:
Prompts the user to confirm the intent. This question should have a yes or no answer.
Amazon Lex uses this prompt to ensure that the user acknowledges that the intent is ready for fulfillment. For example, with the ``OrderPizza`` intent, you might want to confirm that the order is correct before placing it. For other intents, such as intents that simply respond to user questions, you might not need to ask the user for confirmation before providing the information.
.. note::
You you must provide both the ``rejectionStatement`` and the ``confirmationPrompt`` , or neither.
- **messages** *(list) --* **[REQUIRED]**
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --* **[REQUIRED]**
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
:type rejectionStatement: dict
:param rejectionStatement:
When the user answers \"no\" to the question defined in ``confirmationPrompt`` , Amazon Lex responds with this statement to acknowledge that the intent was canceled.
.. note::
You must provide both the ``rejectionStatement`` and the ``confirmationPrompt`` , or neither.
- **messages** *(list) --* **[REQUIRED]**
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
:type followUpPrompt: dict
:param followUpPrompt:
Amazon Lex uses this prompt to solicit additional activity after fulfilling an intent. For example, after the ``OrderPizza`` intent is fulfilled, you might prompt the user to order a drink.
The action that Amazon Lex takes depends on the user\'s response, as follows:
* If the user says \"Yes\" it responds with the clarification prompt that is configured for the bot.
* if the user says \"Yes\" and continues with an utterance that triggers an intent it starts a conversation for the intent.
* If the user says \"No\" it responds with the rejection statement configured for the the follow-up prompt.
* If it doesn\'t recognize the utterance it repeats the follow-up prompt again.
The ``followUpPrompt`` field and the ``conclusionStatement`` field are mutually exclusive. You can specify only one.
- **prompt** *(dict) --* **[REQUIRED]**
Prompts for information from the user.
- **messages** *(list) --* **[REQUIRED]**
An array of objects, each of which provides a message string and its type. You can specify the message string in plain text or in Speech Synthesis Markup Language (SSML).
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **maxAttempts** *(integer) --* **[REQUIRED]**
The number of times to prompt the user for information.
- **responseCard** *(string) --*
A response card. Amazon Lex uses this prompt at runtime, in the ``PostText`` API response. It substitutes session attributes and slot values for placeholders in the response card. For more information, see ex-resp-card .
- **rejectionStatement** *(dict) --* **[REQUIRED]**
If the user answers \"no\" to the question defined in the ``prompt`` field, Amazon Lex responds with this statement to acknowledge that the intent was canceled.
- **messages** *(list) --* **[REQUIRED]**
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
:type conclusionStatement: dict
:param conclusionStatement:
The statement that you want Amazon Lex to convey to the user after the intent is successfully fulfilled by the Lambda function.
This element is relevant only if you provide a Lambda function in the ``fulfillmentActivity`` . If you return the intent to the client application, you can\'t specify this element.
.. note::
The ``followUpPrompt`` and ``conclusionStatement`` are mutually exclusive. You can specify only one.
- **messages** *(list) --* **[REQUIRED]**
A collection of message objects.
- *(dict) --*
The message object that provides the message text and its type.
- **contentType** *(string) --* **[REQUIRED]**
The content type of the message string.
- **content** *(string) --* **[REQUIRED]**
The text of the message.
- **groupNumber** *(integer) --*
Identifies the message group that the message belongs to. When a group is assigned to a message, Amazon Lex returns one message from each group in the response.
- **responseCard** *(string) --*
At runtime, if the client is using the `PostText <http://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html>`__ API, Amazon Lex includes the response card in the response. It substitutes all of the session attributes and slot values for placeholders in the response card.
:type dialogCodeHook: dict
:param dialogCodeHook:
Specifies a Lambda function to invoke for each user input. You can invoke this Lambda function to personalize user interaction.
For example, suppose your bot determines that the user is John. Your Lambda function might retrieve John\'s information from a backend database and prepopulate some of the values. For example, if you find that John is gluten intolerant, you might set the corresponding intent slot, ``GlutenIntolerant`` , to true. You might find John\'s phone number and set the corresponding session attribute.
- **uri** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --* **[REQUIRED]**
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
:type fulfillmentActivity: dict
:param fulfillmentActivity:
Required. Describes how the intent is fulfilled. For example, after a user provides all of the information for a pizza order, ``fulfillmentActivity`` defines how the bot places an order with a local pizza store.
You might configure Amazon Lex to return all of the intent information to the client application, or direct it to invoke a Lambda function that can process the intent (for example, place an order with a pizzeria).
- **type** *(string) --* **[REQUIRED]**
How the intent should be fulfilled, either by running a Lambda function or by returning the slot data to the client application.
- **codeHook** *(dict) --*
A description of the Lambda function that is run to fulfill the intent.
- **uri** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Lambda function.
- **messageVersion** *(string) --* **[REQUIRED]**
The version of the request-response that you want Amazon Lex to use to invoke your Lambda function. For more information, see using-lambda .
:type parentIntentSignature: string
:param parentIntentSignature:
A unique identifier for the built-in intent to base this intent on. To find the signature for an intent, see `Standard Built-in Intents <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/standard-intents>`__ in the *Alexa Skills Kit* .
:type checksum: string
:param checksum:
Identifies a specific revision of the ``$LATEST`` version.
When you create a new intent, leave the ``checksum`` field blank. If you specify a checksum you get a ``BadRequestException`` exception.
When you want to update a intent, set the ``checksum`` field to the checksum of the most recent revision of the ``$LATEST`` version. If you don\'t specify the ``checksum`` field, or if the checksum does not match the ``$LATEST`` version, you get a ``PreconditionFailedException`` exception.
:type createVersion: boolean
:param createVersion:
:rtype: dict
:returns:
"""
pass
def put_slot_type(self, name: str, description: str = None, enumerationValues: List = None, checksum: str = None, valueSelectionStrategy: str = None, createVersion: bool = None) -> Dict:
"""
Creates a custom slot type or replaces an existing custom slot type.
To create a custom slot type, specify a name for the slot type and a set of enumeration values, which are the values that a slot of this type can assume. For more information, see how-it-works .
If you specify the name of an existing slot type, the fields in the request replace the existing values in the ``$LATEST`` version of the slot type. Amazon Lex removes the fields that you don't provide in the request. If you don't specify required fields, Amazon Lex throws an exception. When you update the ``$LATEST`` version of a slot type, if a bot uses the ``$LATEST`` version of an intent that contains the slot type, the bot's ``status`` field is set to ``NOT_BUILT`` .
This operation requires permissions for the ``lex:PutSlotType`` action.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/PutSlotType>`_
**Request Syntax**
::
response = client.put_slot_type(
name='string',
description='string',
enumerationValues=[
{
'value': 'string',
'synonyms': [
'string',
]
},
],
checksum='string',
valueSelectionStrategy='ORIGINAL_VALUE'|'TOP_RESOLUTION',
createVersion=True|False
)
**Response Syntax**
::
{
'name': 'string',
'description': 'string',
'enumerationValues': [
{
'value': 'string',
'synonyms': [
'string',
]
},
],
'lastUpdatedDate': datetime(2015, 1, 1),
'createdDate': datetime(2015, 1, 1),
'version': 'string',
'checksum': 'string',
'valueSelectionStrategy': 'ORIGINAL_VALUE'|'TOP_RESOLUTION',
'createVersion': True|False
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name of the slot type.
- **description** *(string) --*
A description of the slot type.
- **enumerationValues** *(list) --*
A list of ``EnumerationValue`` objects that defines the values that the slot type can take.
- *(dict) --*
Each slot type can have a set of values. Each enumeration value represents a value the slot type can take.
For example, a pizza ordering bot could have a slot type that specifies the type of crust that the pizza should have. The slot type could include the values
* thick
* thin
* stuffed
- **value** *(string) --*
The value of the slot type.
- **synonyms** *(list) --*
Additional values related to the slot type value.
- *(string) --*
- **lastUpdatedDate** *(datetime) --*
The date that the slot type was updated. When you create a slot type, the creation date and last update date are the same.
- **createdDate** *(datetime) --*
The date that the slot type was created.
- **version** *(string) --*
The version of the slot type. For a new slot type, the version is always ``$LATEST`` .
- **checksum** *(string) --*
Checksum of the ``$LATEST`` version of the slot type.
- **valueSelectionStrategy** *(string) --*
The slot resolution strategy that Amazon Lex uses to determine the value of the slot. For more information, see PutSlotType .
- **createVersion** *(boolean) --*
:type name: string
:param name: **[REQUIRED]**
The name of the slot type. The name is *not* case sensitive.
The name can\'t match a built-in slot type name, or a built-in slot type name with \"AMAZON.\" removed. For example, because there is a built-in slot type called ``AMAZON.DATE`` , you can\'t create a custom slot type called ``DATE`` .
For a list of built-in slot types, see `Slot Type Reference <https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/built-in-intent-ref/slot-type-reference>`__ in the *Alexa Skills Kit* .
:type description: string
:param description:
A description of the slot type.
:type enumerationValues: list
:param enumerationValues:
A list of ``EnumerationValue`` objects that defines the values that the slot type can take. Each value can have a list of ``synonyms`` , which are additional values that help train the machine learning model about the values that it resolves for a slot.
When Amazon Lex resolves a slot value, it generates a resolution list that contains up to five possible values for the slot. If you are using a Lambda function, this resolution list is passed to the function. If you are not using a Lambda function you can choose to return the value that the user entered or the first value in the resolution list as the slot value. The ``valueSelectionStrategy`` field indicates the option to use.
- *(dict) --*
Each slot type can have a set of values. Each enumeration value represents a value the slot type can take.
For example, a pizza ordering bot could have a slot type that specifies the type of crust that the pizza should have. The slot type could include the values
* thick
* thin
* stuffed
- **value** *(string) --* **[REQUIRED]**
The value of the slot type.
- **synonyms** *(list) --*
Additional values related to the slot type value.
- *(string) --*
:type checksum: string
:param checksum:
Identifies a specific revision of the ``$LATEST`` version.
When you create a new slot type, leave the ``checksum`` field blank. If you specify a checksum you get a ``BadRequestException`` exception.
When you want to update a slot type, set the ``checksum`` field to the checksum of the most recent revision of the ``$LATEST`` version. If you don\'t specify the ``checksum`` field, or if the checksum does not match the ``$LATEST`` version, you get a ``PreconditionFailedException`` exception.
:type valueSelectionStrategy: string
:param valueSelectionStrategy:
Determines the slot resolution strategy that Amazon Lex uses to return slot type values. The field can be set to one of the following values:
* ``ORIGINAL_VALUE`` - Returns the value entered by the user, if the user value is similar to the slot value.
* ``TOP_RESOLUTION`` - If there is a resolution list for the slot, return the first value in the resolution list as the slot type value. If there is no resolution list, null is returned.
If you don\'t specify the ``valueSelectionStrategy`` , the default is ``ORIGINAL_VALUE`` .
:type createVersion: boolean
:param createVersion:
:rtype: dict
:returns:
"""
pass
def start_import(self, payload: bytes, resourceType: str, mergeStrategy: str) -> Dict:
"""
Starts a job to import a resource to Amazon Lex.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/lex-models-2017-04-19/StartImport>`_
**Request Syntax**
::
response = client.start_import(
payload=b'bytes',
resourceType='BOT'|'INTENT'|'SLOT_TYPE',
mergeStrategy='OVERWRITE_LATEST'|'FAIL_ON_CONFLICT'
)
**Response Syntax**
::
{
'name': 'string',
'resourceType': 'BOT'|'INTENT'|'SLOT_TYPE',
'mergeStrategy': 'OVERWRITE_LATEST'|'FAIL_ON_CONFLICT',
'importId': 'string',
'importStatus': 'IN_PROGRESS'|'COMPLETE'|'FAILED',
'createdDate': datetime(2015, 1, 1)
}
**Response Structure**
- *(dict) --*
- **name** *(string) --*
The name given to the import job.
- **resourceType** *(string) --*
The type of resource to import.
- **mergeStrategy** *(string) --*
The action to take when there is a merge conflict.
- **importId** *(string) --*
The identifier for the specific import job.
- **importStatus** *(string) --*
The status of the import job. If the status is ``FAILED`` , you can get the reason for the failure using the ``GetImport`` operation.
- **createdDate** *(datetime) --*
A timestamp for the date and time that the import job was requested.
:type payload: bytes
:param payload: **[REQUIRED]**
A zip archive in binary format. The archive should contain one file, a JSON file containing the resource to import. The resource should match the type specified in the ``resourceType`` field.
:type resourceType: string
:param resourceType: **[REQUIRED]**
Specifies the type of resource to export. Each resource also exports any resources that it depends on.
* A bot exports dependent intents.
* An intent exports dependent slot types.
:type mergeStrategy: string
:param mergeStrategy: **[REQUIRED]**
Specifies the action that the ``StartImport`` operation should take when there is an existing resource with the same name.
* FAIL_ON_CONFLICT - The import operation is stopped on the first conflict between a resource in the import file and an existing resource. The name of the resource causing the conflict is in the ``failureReason`` field of the response to the ``GetImport`` operation. OVERWRITE_LATEST - The import operation proceeds even if there is a conflict with an existing resource. The $LASTEST version of the existing resource is overwritten with the data from the import file.
:rtype: dict
:returns:
"""
pass
| 59.74849
| 1,198
| 0.557945
| 21,454
| 197,887
| 5.130046
| 0.041158
| 0.01581
| 0.012993
| 0.007741
| 0.827013
| 0.805406
| 0.778712
| 0.755061
| 0.740187
| 0.728757
| 0
| 0.006678
| 0.349932
| 197,887
| 3,311
| 1,199
| 59.766536
| 0.848888
| 0.844836
| 0
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.454545
| false
| 0.454545
| 0.102273
| 0
| 0.568182
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 11
|
2fc9f7217040262d94ce5133df432d66dd60db6a
| 48
|
py
|
Python
|
src/ui/menus.py
|
KathTheDragon/spindizzy-client
|
7ad7c5b330e707ab40fa572c2c25049959544d11
|
[
"MIT"
] | null | null | null |
src/ui/menus.py
|
KathTheDragon/spindizzy-client
|
7ad7c5b330e707ab40fa572c2c25049959544d11
|
[
"MIT"
] | null | null | null |
src/ui/menus.py
|
KathTheDragon/spindizzy-client
|
7ad7c5b330e707ab40fa572c2c25049959544d11
|
[
"MIT"
] | null | null | null |
import tkinter as tk
def menubar(ui):
pass
| 9.6
| 20
| 0.6875
| 8
| 48
| 4.125
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 48
| 4
| 21
| 12
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 1
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
ff43b73960dca68d62dec6fa9b19a0ca5ed2c18c
| 43
|
py
|
Python
|
shufflenet/__init__.py
|
TropComplique/ShuffleNet-tensorflow
|
a3e531d23364af0d38a336241a07eb8b37993b74
|
[
"MIT"
] | 45
|
2017-08-27T17:31:03.000Z
|
2021-03-01T11:01:40.000Z
|
shufflenet/__init__.py
|
kingzhengguang/ImageNet-ShuffleNet-tensorflow
|
a3e531d23364af0d38a336241a07eb8b37993b74
|
[
"MIT"
] | 9
|
2017-08-24T09:53:26.000Z
|
2019-08-22T08:24:50.000Z
|
shufflenet/__init__.py
|
kingzhengguang/ImageNet-ShuffleNet-tensorflow
|
a3e531d23364af0d38a336241a07eb8b37993b74
|
[
"MIT"
] | 20
|
2017-08-28T16:33:03.000Z
|
2019-10-31T13:01:55.000Z
|
from .get_shufflenet import get_shufflenet
| 21.5
| 42
| 0.883721
| 6
| 43
| 6
| 0.666667
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 43
| 1
| 43
| 43
| 0.923077
| 0
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| 0
| true
| 0
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| null | 1
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| 0
| 0
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| 1
| 0
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| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ff6894db50194aa75df64958c810c45cece30015
| 108
|
py
|
Python
|
app/database/base.py
|
Sephyr3s/Store-backend
|
c5e9cc91069e52b0ab11002094a88b1776b669bd
|
[
"Apache-2.0"
] | null | null | null |
app/database/base.py
|
Sephyr3s/Store-backend
|
c5e9cc91069e52b0ab11002094a88b1776b669bd
|
[
"Apache-2.0"
] | null | null | null |
app/database/base.py
|
Sephyr3s/Store-backend
|
c5e9cc91069e52b0ab11002094a88b1776b669bd
|
[
"Apache-2.0"
] | null | null | null |
from app.database.base_class import Base
from app.models.place import Place
from app.models.user import User
| 36
| 40
| 0.842593
| 19
| 108
| 4.736842
| 0.473684
| 0.233333
| 0.288889
| 0
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| 108
| 3
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| 36
| 0.927835
| 0
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| true
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| 0
|
0
| 7
|
ffa7ea79eec8298d7bcdbef4fe9e8d9ea1a8557e
| 109
|
py
|
Python
|
messenger/health_check/__init__.py
|
EducationalTestingService/halef-messenger
|
59eccdbf021a5a1e8290b4f61fdc2b1d74947993
|
[
"Apache-2.0"
] | null | null | null |
messenger/health_check/__init__.py
|
EducationalTestingService/halef-messenger
|
59eccdbf021a5a1e8290b4f61fdc2b1d74947993
|
[
"Apache-2.0"
] | 1
|
2017-06-05T22:57:47.000Z
|
2017-06-05T22:58:45.000Z
|
messenger/health_check/__init__.py
|
EducationalTestingService/halef-messenger
|
59eccdbf021a5a1e8290b4f61fdc2b1d74947993
|
[
"Apache-2.0"
] | null | null | null |
from flask import Blueprint
health_check = Blueprint('health_check', __name__)
from . import health # noqa
| 21.8
| 50
| 0.779817
| 14
| 109
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0
| 7
|
4460e4feed0f72a43de6ee2899df2357b585c9b2
| 4,134
|
py
|
Python
|
tests/render-out/Drawing13.py
|
debragail/reportlab-mirror
|
1e5814e1313ed50d5abb65487b207711cb4f7595
|
[
"BSD-3-Clause"
] | 1
|
2020-05-21T23:34:55.000Z
|
2020-05-21T23:34:55.000Z
|
tests/render-out/Drawing13.py
|
debragail/reportlab-mirror
|
1e5814e1313ed50d5abb65487b207711cb4f7595
|
[
"BSD-3-Clause"
] | null | null | null |
tests/render-out/Drawing13.py
|
debragail/reportlab-mirror
|
1e5814e1313ed50d5abb65487b207711cb4f7595
|
[
"BSD-3-Clause"
] | null | null | null |
#Autogenerated by ReportLab guiedit do not edit
from reportlab.graphics.shapes import _DrawingEditorMixin, Drawing, Group, Rect, String
from reportlab.lib.colors import Color, CMYKColor, PCMYKColor
class ExplodedDrawing_Drawing(_DrawingEditorMixin,Drawing):
def __init__(self,width=756.284,height=240.20000000000002,*args,**kw):
Drawing.__init__(self,width,height,*args,**kw)
self.transform = (1,0,0,1,0,0)
self.add(Rect(8,208.4,569.068,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,215.8,'Times-Roman: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='Times-Roman',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,179,631.832,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,186.4,'Vera: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='Vera',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,149.6,598.216,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,157,'Times-BoldItalic: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='Times-BoldItalic',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,120.2,740.284,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,127.6,'Courier: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='Courier',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,90.8,592.504,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,98.2,'Helvetica: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='Helvetica',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,61.4,716.459,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,68.8,'VeraBd: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='VeraBd',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,32,640.0762,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,39.4,'VeraIt: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='VeraIt',fontSize=12,fillColor=Color(0,0,0,1)))
self.add(Rect(8,2.6,712.334,24.4,rx=0,ry=0,fillColor=Color(.827451,.827451,.827451,1),fillOpacity=None,strokeColor=Color(1,0,0,1),strokeWidth=.5,strokeLineCap=0,strokeLineJoin=0,strokeMiterLimit=0,strokeDashArray=None,strokeOpacity=None))
self.add(String(10,10,'VeraBI: I should be totally horizontal and enclosed in a box and end in alphabetagamma ¢©®£ʥЖփאقকαβγ',textAnchor='start',fontName='VeraBI',fontSize=12,fillColor=Color(0,0,0,1)))
if __name__=="__main__": #NORUNTESTS
ExplodedDrawing_Drawing().save(formats=['pdf'],outDir='.',fnRoot=None)
| 142.551724
| 242
| 0.779149
| 668
| 4,134
| 4.83982
| 0.185629
| 0.016084
| 0.015775
| 0.011135
| 0.808537
| 0.808537
| 0.808537
| 0.808537
| 0.799876
| 0.799876
| 0
| 0.116463
| 0.050798
| 4,134
| 28
| 243
| 147.642857
| 0.699286
| 0.013546
| 0
| 0
| 1
| 0
| 0.229146
| 0
| 0
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| 0
| 0
| 0
| 1
| 0.041667
| false
| 0
| 0.083333
| 0
| 0.166667
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
9230b5dfc3dc5973a4a8bb85e64adb2810164e90
| 154
|
py
|
Python
|
data/test_xunit.py
|
dustprotocol/go2xunit
|
15f9176b63ac73928fea9106350a53d7556ddf27
|
[
"MIT"
] | 163
|
2015-03-18T09:42:39.000Z
|
2022-02-09T01:22:29.000Z
|
data/test_xunit.py
|
dustprotocol/go2xunit
|
15f9176b63ac73928fea9106350a53d7556ddf27
|
[
"MIT"
] | 50
|
2015-05-28T08:27:51.000Z
|
2019-09-26T08:07:31.000Z
|
data/test_xunit.py
|
dustprotocol/go2xunit
|
15f9176b63ac73928fea9106350a53d7556ddf27
|
[
"MIT"
] | 50
|
2015-01-10T18:44:32.000Z
|
2021-11-18T03:52:03.000Z
|
def test_add():
assert 1 + 2 == 3, "bad add"
def test_sub():
assert 2 - 1 == 1, "bad sub"
def test_sub_fail():
assert 2 - 2 == 1, "bad sub"
| 17.111111
| 32
| 0.538961
| 28
| 154
| 2.821429
| 0.357143
| 0.265823
| 0.253165
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.081818
| 0.285714
| 154
| 8
| 33
| 19.25
| 0.636364
| 0
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| 0
| 0
| 0.136364
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.5
| true
| 0
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| 0.5
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| null | 1
| 1
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| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
92aeeb3e9d61abc362adc14aaad14c366f08ca7b
| 47,930
|
py
|
Python
|
src/vbr/tableclasses/redcap/autogenerated/qst_mcc1_v03.py
|
a2cps/python-vbr
|
9d5d4480386d0530450d59157e0da6937320f928
|
[
"BSD-3-Clause"
] | 1
|
2021-05-26T19:08:29.000Z
|
2021-05-26T19:08:29.000Z
|
src/vbr/tableclasses/redcap/autogenerated/qst_mcc1_v03.py
|
a2cps/python-vbr
|
9d5d4480386d0530450d59157e0da6937320f928
|
[
"BSD-3-Clause"
] | 7
|
2021-05-04T13:12:39.000Z
|
2022-03-09T21:04:33.000Z
|
src/vbr/tableclasses/redcap/autogenerated/qst_mcc1_v03.py
|
a2cps/python-vbr
|
9d5d4480386d0530450d59157e0da6937320f928
|
[
"BSD-3-Clause"
] | 2
|
2021-04-20T14:46:52.000Z
|
2021-06-07T20:28:28.000Z
|
"""Autogenerated 2021-11-16T11:37:36.551949 by redcap_classfiles.py
"""
from ....pgrest import *
from ...constants import Constants
from ..rcconstants import REDCapConstants
from ..rcaptable import RcapTable
__all__ = ["RcapQstMcc1V03"]
class RcapQstMcc1V03(RcapTable):
"""Qst Mcc1 V03"""
__redcap_form_name = "qst_mcc1_v03"
qst_mcc1_v03_id = Constants.SERIAL_PRIMARY_KEY_COLUMN
qst_mcc1_v03_complete = Column(Integer, ForeignKey("status.status_id"))
# Location of greater knee pain(index site)
# Field Type: radio
# Choices: 1, medial | 2, lateral | 3, equal
pptpainlocation = Column(Integer, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
pptremote1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
pptremote1val1 = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
pptremote2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
pptremote2val1 = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
pptremote3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
pptremote3val1 = Column(String, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
pptindex1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
pptindex1val1 = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
pptindex2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
pptindex2val1 = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
pptindex3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
pptindex3val1 = Column(String, nullable=True, comments=None)
# Test completed?
# Field Type: radio
# Choices: 1, yes, both sites | 2, only remote (shoulder) | 3, only index (chest) | 0, no, neither site
pptcompleteyn = Column(Integer, nullable=True, comments=None)
# Additional notes for baseline PPTs
# Field Type: notes
# Choices: N/A
pptnotes = Column(FreeText, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1initialpainrem = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1initialpainrem_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1finalpainrem = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1finalpainrem_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep2initialpainrem = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep2initialpainrem_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep2finalpainrem = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep2finalpainrem_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainremsclr3 = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainremscl1r3 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainremsclr3 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainremscl1r3 = Column(Numeric, nullable=True, comments=None)
# Remote After-sensations: 15 sec
# Field Type: text
# Choices: N/A
tsfinalpainremafts15 = Column(String, nullable=True, comments=None)
# (single entry) 30 sec
# Field Type: text
# Choices: N/A
tsfinalpainremafts30 = Column(String, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1initialpainindex = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1initialpainindex_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1finalpainindex = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsrep1finalpainindex_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainindexsclr2 = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainindexscl1r2 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainindexsclr2 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainindexscl1r2 = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainindexsclr3 = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsinitialpainindexscl1r3 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainindexsclr3 = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainindexscl1r3 = Column(Numeric, nullable=True, comments=None)
# Index After-sensations: 15 sec
# Field Type: text
# Choices: N/A
tsfinalpainindafts15 = Column(String, nullable=True, comments=None)
# (single entry) 30 sec
# Field Type: text
# Choices: N/A
tsfinalpainindafts30 = Column(String, nullable=True, comments=None)
# Test completed?
# Field Type: radio
# Choices: 1, yes, at least 1 repetition for both sites | 2, only remote (shoulder) | 3, only index (chest) | 0, no, neither site
tscompleted = Column(Integer, nullable=True, comments=None)
# Additional notes for Temporal Summation
# Field Type: notes
# Choices: N/A
tsnotes = Column(FreeText, nullable=True, comments=None)
# Confirm water temp 10 deg C (+/-1 deg)
# Field Type: radio
# Choices: 1, Yes | 0, No
cpmcoldwatertempc = Column(Boolean, nullable=True, comments=None)
# Cold Water Pain Rating at 30 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain30sscl = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 30 sec: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain30sscl1 = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 60 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain60sscl = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 60 sec: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain60sscl1 = Column(Numeric, nullable=True, comments=None)
# Water bath duration?
# Field Type: radio
# Choices: 1, Standard range (60sec +/- 5 sec) | 2, Outside of range: < 55sec or > 65 sec
cpmbathrangeyn = Column(Integer, nullable=True, comments=None)
# If outside of range, enter time (sec)
# Field Type: text
# Choices: N/A
cpmoutrangetime = Column(String, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
cpmppt1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
cpmppt1val1 = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
cpmppt2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
cpmppt2val1 = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
cpmppt3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
cpmppt3val1 = Column(String, nullable=True, comments=None)
# Test Completed
# Field Type: yesno
# Choices: N/A
cpmcompleteyn = Column(Boolean, nullable=True, comments=None)
# Additional notes for Conditioned Pain Modulation
# Field Type: notes
# Choices: N/A
cpmnotes = Column(FreeText, nullable=True, comments=None)
# Cuff pressure contraindicated
# Field Type: radio
# Choices: 1, Yes | 0, No
fmricuffcontrayn = Column(Boolean, nullable=True, comments=None)
# Calf pressure, contralateral calf, Pressure 1
# Field Type: text
# Choices: N/A
fmricuffcalfpressure = Column(String, nullable=True, comments=None)
# Calf pressure, contralateral calf, Pressure 1: (double entry)
# Field Type: text
# Choices: N/A
fmricuffcalfpressure2 = Column(String, nullable=True, comments=None)
# New chest pain since surgery?
# Field Type: radio
# Choices: 1, Yes | 0, No
pmachgchestpainyn = Column(Boolean, nullable=True, comments=None)
# General region of pain
# Field Type: radio
# Choices: 1, Anterior chest | 2, Axillary/lateral | 3, Posterior chest
pmapainregion = Column(Integer, nullable=True, comments=None)
# Highest pain location
# Field Type: radio
# Choices: 1, T1 | 2, T2 | 3, T3 | 4, T4 | 5, T5 | 6, T6 | 7, T7 | 8, T8 | 9, T9 | 10, T10 | 11, T11 | 12, T12 | 13, Above or below thoracic levels
pmahighestloc = Column(Integer, nullable=True, comments=None)
# Lowest pain location
# Field Type: radio
# Choices: 1, T1 | 2, T2 | 3, T3 | 4, T4 | 5, T5 | 6, T6 | 7, T7 | 8, T8 | 9, T9 | 10, T10 | 11, T11 | 12, T12 | 13, Above or below thoracic levels
pmalowestloc = Column(Integer, nullable=True, comments=None)
# Most painful region
# Field Type: radio
# Choices: 1, At the scar | 2, Immediately around the scar (<= 4 cm) | 3, Distant from the scar (> 4 cm)
pmamostpain = Column(Integer, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
pptremoter1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
pptremoter1val_d = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
pptremoter2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
pptremoter2val_d = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
pptremoter3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
pptremoter3val_d = Column(String, nullable=True, comments=None)
# Rep 1
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr1pain = Column(Numeric, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr1pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 2
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr2pain = Column(Numeric, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr2pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 3
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr3pain = Column(Numeric, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr3pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 4
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr4pain = Column(Numeric, nullable=True, comments=None)
# Rep 4: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr4pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 5
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr5pain = Column(Numeric, nullable=True, comments=None)
# Rep 5: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmacontr5pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 1
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr1pain = Column(Numeric, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr1pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 2
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr2pain = Column(Numeric, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr2pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 3
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr3pain = Column(Numeric, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr3pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 4
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr4pain = Column(Numeric, nullable=True, comments=None)
# Rep 4: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr4pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 5
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr5pain = Column(Numeric, nullable=True, comments=None)
# Rep 5: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaindxr5pain_d = Column(Numeric, nullable=True, comments=None)
# Sensation comparison standardized site
# Field Type: dropdown
# Choices: 1, Equal on both sides | 2, Stronger on the surgery side | 3, Stronger on contralateral side
dmasenscompare = Column(Integer, nullable=True, comments=None)
# Rep 1
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr1pain = Column(Numeric, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr1pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 2
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr2pain = Column(Numeric, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr2pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 3
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr3pain = Column(Numeric, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr3pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 4
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr4pain = Column(Numeric, nullable=True, comments=None)
# Rep 4: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr4pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 5
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr5pain = Column(Numeric, nullable=True, comments=None)
# Rep 5: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptcontr5pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 1
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr1pain = Column(Numeric, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr1pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 2
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr2pain = Column(Numeric, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr2pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 3
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr3pain = Column(Numeric, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr3pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 4
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr4pain = Column(Numeric, nullable=True, comments=None)
# Rep 4: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr4pain_d = Column(Numeric, nullable=True, comments=None)
# Rep 5
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr5pain = Column(Numeric, nullable=True, comments=None)
# Rep 5: (double entry)
# Field Type: dropdown
# Choices: 0.0, 0.0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10.0, 10.0
dmaptindxr5pain_d = Column(Numeric, nullable=True, comments=None)
# Patient-Specific site assessed?
# Field Type: radio
# Choices: 1, 1. No, no new pain site or < 4 cm from standardized site | 2, 2. Yes, tested pt-specific site
dmaptspecsite = Column(Integer, nullable=True, comments=None)
# Sensation comparison patient-specific site
# Field Type: radio
# Choices: 1, 1. Equal on both sides | 2, 2. Stronger on surgery side | 3, 3. Stronger on contralateral side
dmaptsenscompare = Column(Integer, nullable=True, comments=None)
# DMA Test(s) completed
# Field Type: radio
# Choices: 1, Yes, all 4 sites | 2, Yes, but only some sites | 0, None
dmatestcompyn = Column(Integer, nullable=True, comments=None)
# Choose all completed sites
# Field Type: checkbox
# Choices: 1, 1. Standardized control site (contralateral) | 2, 2. Standardized index site (surgical) | 3, 3. Patient specific control site (contralateral) | 4, 4. Patient specific index site (surgical)
dmatestcompwhich = Column(Integer, nullable=True, comments=None)
# Additional notes for DMA
# Field Type: notes
# Choices: N/A
dmanotes = Column(FreeText, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
pptindxr1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
pptindxr1val_d = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
pptindxr2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
pptindxr2val_d = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
pptindxr3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
pptindxr3val_d = Column(String, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter1initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter1initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter1final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter1final_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter2initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter2initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter2final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter2final_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter3initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter3initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter3final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoter3final_d = Column(Numeric, nullable=True, comments=None)
# 7. 15 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsremoteafter15 = Column(Numeric, nullable=True, comments=None)
# 8. 30 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsfinalpainremafts31 = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr1initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr1initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr1final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr1final_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr2initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr2initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr2final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr2final_d = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr3initial = Column(Numeric, nullable=True, comments=None)
# Initial Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr3initial_d = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr3final = Column(Numeric, nullable=True, comments=None)
# Final Pain Rating: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxr3final_d = Column(Numeric, nullable=True, comments=None)
# 15. 15 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxafter15 = Column(Numeric, nullable=True, comments=None)
# 16. 30 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
tsindxafter30 = Column(Numeric, nullable=True, comments=None)
# Confirm water temp 10 deg C (+/-1 deg)
# Field Type: radio
# Choices: 1, Yes | 0, No
cpmcoldwatertemp = Column(Boolean, nullable=True, comments=None)
# Cold Water Pain Rating at 30 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain30 = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 30 sec: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain30_d = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 60 sec
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain60 = Column(Numeric, nullable=True, comments=None)
# Cold Water Pain Rating at 60 sec: (double entry)
# Field Type: dropdown
# Choices: 0, 0 | 0.5, 0.5 | 1.0, 1.0 | 1.5, 1.5 | 2.0, 2.0 | 2.5, 2.5 | 3.0, 3.0 | 3.5, 3.5 | 4.0, 4.0 | 4.5, 4.5 | 5.0, 5.0 | 5.5, 5.5 | 6.0, 6.0 | 6.5, 6.5 | 7.0, 7.0 | 7.5, 7.5 | 8.0, 8.0 | 8.5, 8.5 | 9.0, 9.0 | 9.5, 9.5 | 10, 10
cpmcoldwaterpain60_d = Column(Numeric, nullable=True, comments=None)
# Rep 1
# Field Type: text
# Choices: N/A
cpmpptremr1val = Column(String, nullable=True, comments=None)
# Rep 1: (double entry)
# Field Type: text
# Choices: N/A
cpmpptremr1val_d = Column(String, nullable=True, comments=None)
# Rep 2
# Field Type: text
# Choices: N/A
cpmpptremr2val = Column(String, nullable=True, comments=None)
# Rep 2: (double entry)
# Field Type: text
# Choices: N/A
cpmpptremr2val_d = Column(String, nullable=True, comments=None)
# Rep 3
# Field Type: text
# Choices: N/A
cpmpptremr3val = Column(String, nullable=True, comments=None)
# Rep 3: (double entry)
# Field Type: text
# Choices: N/A
cpmpptremr3val_d = Column(String, nullable=True, comments=None)
# Cuff pressure contraindicated
# Field Type: radio
# Choices: 1, Yes | 0, No
cuffpfmricontraindyn = Column(Boolean, nullable=True, comments=None)
# Which leg is non-dominant?
# Field Type: radio
# Choices: 1, Right | 2, Left
cuffpfmrinondomleg = Column(Integer, nullable=True, comments=None)
# Calf of non-dominant leg pressure
# Field Type: text
# Choices: N/A
cuffpfmripressure = Column(String, nullable=True, comments=None)
# Calf of non-dominant leg pressure 1: (double entry)
# Field Type: text
# Choices: N/A
cuffpfmripressure_d = Column(String, nullable=True, comments=None)
| 68.964029
| 245
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| 47,930
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| 0.902647
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0
| 10
|
92b26fb10b69150eb5abb2d1e46488489f0a691c
| 55
|
py
|
Python
|
play_motion/play_motion/src/play_motion/__init__.py
|
hect1995/Robotics_intro
|
1b687585c20db5f1114d8ca6811a70313d325dd6
|
[
"BSD-3-Clause"
] | 7
|
2018-10-24T14:52:20.000Z
|
2021-01-12T14:59:00.000Z
|
play_motion/play_motion/src/play_motion/__init__.py
|
hect1995/Robotics_intro
|
1b687585c20db5f1114d8ca6811a70313d325dd6
|
[
"BSD-3-Clause"
] | null | null | null |
play_motion/play_motion/src/play_motion/__init__.py
|
hect1995/Robotics_intro
|
1b687585c20db5f1114d8ca6811a70313d325dd6
|
[
"BSD-3-Clause"
] | 17
|
2019-09-29T10:22:41.000Z
|
2021-04-08T12:38:37.000Z
|
from .move_joint import move_joint, print_err, print_ok
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2bac10073b3ae6eec7697f798e5c4e87de6ae3a5
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py
|
Python
|
src/containerapp/azext_containerapp/tests/latest/test_containerapp_commands.py
|
saisankargochhayat/azure-cli-extensions
|
f89443a766961f984977a2cf1b682973fcb61edd
|
[
"MIT"
] | null | null | null |
src/containerapp/azext_containerapp/tests/latest/test_containerapp_commands.py
|
saisankargochhayat/azure-cli-extensions
|
f89443a766961f984977a2cf1b682973fcb61edd
|
[
"MIT"
] | null | null | null |
src/containerapp/azext_containerapp/tests/latest/test_containerapp_commands.py
|
saisankargochhayat/azure-cli-extensions
|
f89443a766961f984977a2cf1b682973fcb61edd
|
[
"MIT"
] | null | null | null |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import os
import time
import unittest
from azure.cli.testsdk.scenario_tests import AllowLargeResponse, live_only
from azure.cli.testsdk import (ScenarioTest, ResourceGroupPreparer, JMESPathCheck)
from msrestazure.tools import parse_resource_id
TEST_DIR = os.path.abspath(os.path.join(os.path.abspath(__file__), '..'))
class ContainerappIdentityTests(ScenarioTest):
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="eastus2")
@live_only() # encounters 'CannotOverwriteExistingCassetteException' only when run from recording (passes when run live)
def test_containerapp_identity_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
user_identity_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {}'.format(resource_group, ca_name, env_name))
self.cmd('containerapp identity assign --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('identity create -g {} -n {}'.format(resource_group, user_identity_name))
self.cmd('containerapp identity assign --user-assigned {} -g {} -n {}'.format(user_identity_name, resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned, UserAssigned'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned, UserAssigned'),
])
self.cmd('containerapp identity remove --user-assigned {} -g {} -n {}'.format(user_identity_name, resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity remove --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="canadacentral")
def test_containerapp_identity_system(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {} --system-assigned'.format(resource_group, ca_name, env_name))
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity remove --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
self.cmd('containerapp identity assign --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity remove --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
@AllowLargeResponse(8192)
@live_only() # encounters 'CannotOverwriteExistingCassetteException' only when run from recording (passes when run live)
@ResourceGroupPreparer(location="westeurope")
def test_containerapp_identity_user(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
user_identity_name1 = self.create_random_name(prefix='containerapp-user1', length=24)
user_identity_name2 = self.create_random_name(prefix='containerapp-user2', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {}'.format(resource_group, ca_name, env_name))
self.cmd('identity create -g {} -n {}'.format(resource_group, user_identity_name1))
self.cmd('identity create -g {} -n {}'.format(resource_group, user_identity_name2))
self.cmd('containerapp identity assign --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity assign --user-assigned {} {} -g {} -n {}'.format(user_identity_name1, user_identity_name2, resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned, UserAssigned'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned, UserAssigned'),
])
self.cmd('containerapp identity remove --user-assigned {} -g {} -n {}'.format(user_identity_name1, resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned, UserAssigned'),
])
self.cmd('containerapp identity remove --user-assigned {} -g {} -n {}'.format(user_identity_name2, resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'SystemAssigned'),
])
self.cmd('containerapp identity remove --system-assigned -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
self.cmd('containerapp identity show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('type', 'None'),
])
class ContainerappIngressTests(ScenarioTest):
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="eastus2")
def test_containerapp_ingress_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {} --ingress external --target-port 80'.format(resource_group, ca_name, env_name))
self.cmd('containerapp ingress show -g {} -n {}'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('external', True),
JMESPathCheck('targetPort', 80),
])
self.cmd('containerapp ingress disable -g {} -n {}'.format(resource_group, ca_name, env_name))
containerapp_def = self.cmd('containerapp show -g {} -n {}'.format(resource_group, ca_name)).get_output_in_json()
self.assertEqual("fqdn" in containerapp_def["properties"]["configuration"], False)
self.cmd('containerapp ingress enable -g {} -n {} --type internal --target-port 81 --allow-insecure --transport http2'.format(resource_group, ca_name, env_name))
self.cmd('containerapp ingress show -g {} -n {}'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('external', False),
JMESPathCheck('targetPort', 81),
JMESPathCheck('allowInsecure', True),
JMESPathCheck('transport', "Http2"),
])
self.cmd('containerapp ingress show -g {} -n {}'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('external', False),
JMESPathCheck('targetPort', 81),
JMESPathCheck('allowInsecure', True),
JMESPathCheck('transport', "Http2"),
])
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="eastus2")
def test_containerapp_ingress_traffic_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {} --ingress external --target-port 80 --revisions-mode multiple'.format(resource_group, ca_name, env_name))
self.cmd('containerapp ingress show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('external', True),
JMESPathCheck('targetPort', 80),
])
self.cmd('containerapp ingress traffic set -g {} -n {} --revision-weight latest=100'.format(resource_group, ca_name), checks=[
JMESPathCheck('[0].latestRevision', True),
JMESPathCheck('[0].weight', 100),
])
self.cmd('containerapp update -g {} -n {} --cpu 1.0 --memory 2Gi'.format(resource_group, ca_name))
revisions_list = self.cmd('containerapp revision list -g {} -n {}'.format(resource_group, ca_name)).get_output_in_json()
self.cmd('containerapp ingress traffic set -g {} -n {} --revision-weight latest=50 {}=50'.format(resource_group, ca_name, revisions_list[0]["name"]), checks=[
JMESPathCheck('[0].latestRevision', True),
JMESPathCheck('[0].weight', 50),
JMESPathCheck('[1].revisionName', revisions_list[0]["name"]),
JMESPathCheck('[1].weight', 50),
])
self.cmd('containerapp ingress traffic show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('[0].latestRevision', True),
JMESPathCheck('[0].weight', 50),
JMESPathCheck('[1].revisionName', revisions_list[0]["name"]),
JMESPathCheck('[1].weight', 50),
])
revisions_list = self.cmd('containerapp revision list -g {} -n {}'.format(resource_group, ca_name)).get_output_in_json()
for revision in revisions_list:
self.assertEqual(revision["properties"]["trafficWeight"], 50)
@AllowLargeResponse(8192)
@live_only() # encounters 'CannotOverwriteExistingCassetteException' only when run from recording (passes when run live)
@ResourceGroupPreparer(location="westeurope")
def test_containerapp_custom_domains_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
app = self.cmd('containerapp create -g {} -n {} --environment {} --ingress external --target-port 80'.format(resource_group, ca_name, env_name)).get_output_in_json()
self.cmd('containerapp hostname list -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('length(@)', 0),
])
# list hostnames with a wrong location
self.cmd('containerapp hostname list -g {} -n {} -l "{}"'.format(resource_group, ca_name, "eastus2"), checks={
JMESPathCheck('length(@)', 0),
}, expect_failure=True)
# create an App service domain and update its txt records
contacts = os.path.join(TEST_DIR, 'domain-contact.json')
zone_name = "{}.com".format(ca_name)
subdomain_1 = "devtest"
subdomain_2 = "clitest"
txt_name_1 = "asuid.{}".format(subdomain_1)
txt_name_2 = "asuid.{}".format(subdomain_2)
hostname_1 = "{}.{}".format(subdomain_1, zone_name)
hostname_2 = "{}.{}".format(subdomain_2, zone_name)
verification_id = app["properties"]["customDomainVerificationId"]
self.cmd("appservice domain create -g {} --hostname {} --contact-info=@'{}' --accept-terms".format(resource_group, zone_name, contacts)).get_output_in_json()
self.cmd('network dns record-set txt add-record -g {} -z {} -n {} -v {}'.format(resource_group, zone_name, txt_name_1, verification_id)).get_output_in_json()
self.cmd('network dns record-set txt add-record -g {} -z {} -n {} -v {}'.format(resource_group, zone_name, txt_name_2, verification_id)).get_output_in_json()
# upload cert, add hostname & binding
pfx_file = os.path.join(TEST_DIR, 'cert.pfx')
pfx_password = 'test12'
cert_id = self.cmd('containerapp ssl upload -n {} -g {} --environment {} --hostname {} --certificate-file "{}" --password {}'.format(ca_name, resource_group, env_name, hostname_1, pfx_file, pfx_password), checks=[
JMESPathCheck('[0].name', hostname_1),
]).get_output_in_json()[0]["certificateId"]
self.cmd('containerapp hostname list -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].name', hostname_1),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
])
# get cert thumbprint
cert_thumbprint = self.cmd('containerapp env certificate list -n {} -g {} -c {}'.format(env_name, resource_group, cert_id), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].id', cert_id),
]).get_output_in_json()[0]["properties"]["thumbprint"]
# add binding by cert thumbprint
self.cmd('containerapp hostname bind -g {} -n {} --hostname {} --thumbprint {}'.format(resource_group, ca_name, hostname_2, cert_thumbprint), expect_failure=True)
self.cmd('containerapp hostname bind -g {} -n {} --hostname {} --thumbprint {} -e {}'.format(resource_group, ca_name, hostname_2, cert_thumbprint, env_name), checks=[
JMESPathCheck('length(@)', 2),
])
self.cmd('containerapp hostname list -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('length(@)', 2),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
JMESPathCheck('[1].bindingType', "SniEnabled"),
JMESPathCheck('[1].certificateId', cert_id),
])
# delete hostname with a wrong location
self.cmd('containerapp hostname delete -g {} -n {} --hostname {} -l "{}" --yes'.format(resource_group, ca_name, hostname_1, "eastus2"), expect_failure=True)
self.cmd('containerapp hostname delete -g {} -n {} --hostname {} -l "{}" --yes'.format(resource_group, ca_name, hostname_1, app["location"]), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].name', hostname_2),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
]).get_output_in_json()
self.cmd('containerapp hostname list -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].name', hostname_2),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
])
self.cmd('containerapp hostname delete -g {} -n {} --hostname {} --yes'.format(resource_group, ca_name, hostname_2), checks=[
JMESPathCheck('length(@)', 0),
]).get_output_in_json()
# add binding by cert id
self.cmd('containerapp hostname bind -g {} -n {} --hostname {} --certificate {}'.format(resource_group, ca_name, hostname_2, cert_id), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
JMESPathCheck('[0].name', hostname_2),
]).get_output_in_json()
self.cmd('containerapp hostname delete -g {} -n {} --hostname {} --yes'.format(resource_group, ca_name, hostname_2), checks=[
JMESPathCheck('length(@)', 0),
]).get_output_in_json()
# add binding by cert name, with and without environment
cert_name = parse_resource_id(cert_id)["resource_name"]
self.cmd('containerapp hostname bind -g {} -n {} --hostname {} --certificate {}'.format(resource_group, ca_name, hostname_1, cert_name), expect_failure=True)
self.cmd('containerapp hostname bind -g {} -n {} --hostname {} --certificate {} -e {}'.format(resource_group, ca_name, hostname_1, cert_name, env_name), checks=[
JMESPathCheck('length(@)', 1),
JMESPathCheck('[0].bindingType', "SniEnabled"),
JMESPathCheck('[0].certificateId', cert_id),
JMESPathCheck('[0].name', hostname_1),
]).get_output_in_json()
self.cmd('containerapp hostname delete -g {} -n {} --hostname {} --yes'.format(resource_group, ca_name, hostname_1), checks=[
JMESPathCheck('length(@)', 0),
]).get_output_in_json()
class ContainerappDaprTests(ScenarioTest):
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="eastus2")
def test_containerapp_dapr_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {}'.format(resource_group, ca_name, env_name))
self.cmd('containerapp dapr enable -g {} -n {} --dapr-app-id containerapp1 --dapr-app-port 80 --dapr-app-protocol http'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('appId', "containerapp1"),
JMESPathCheck('appPort', 80),
JMESPathCheck('appProtocol', "http"),
JMESPathCheck('enabled', True),
])
self.cmd('containerapp show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('properties.configuration.dapr.appId', "containerapp1"),
JMESPathCheck('properties.configuration.dapr.appPort', 80),
JMESPathCheck('properties.configuration.dapr.appProtocol', "http"),
JMESPathCheck('properties.configuration.dapr.enabled', True),
])
self.cmd('containerapp dapr disable -g {} -n {}'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('appId', "containerapp1"),
JMESPathCheck('appPort', 80),
JMESPathCheck('appProtocol', "http"),
JMESPathCheck('enabled', False),
])
self.cmd('containerapp show -g {} -n {}'.format(resource_group, ca_name), checks=[
JMESPathCheck('properties.configuration.dapr.appId', "containerapp1"),
JMESPathCheck('properties.configuration.dapr.appPort', 80),
JMESPathCheck('properties.configuration.dapr.appProtocol', "http"),
JMESPathCheck('properties.configuration.dapr.enabled', False),
])
class ContainerappEnvStorageTests(ScenarioTest):
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="eastus")
def test_containerapp_env_storage(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
storage_name = self.create_random_name(prefix='storage', length=24)
shares_name = self.create_random_name(prefix='share', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('storage account create -g {} -n {} --kind StorageV2 --sku Standard_ZRS --enable-large-file-share'.format(resource_group, storage_name))
self.cmd('storage share-rm create -g {} -n {} --storage-account {} --access-tier "TransactionOptimized" --quota 1024'.format(resource_group, shares_name, storage_name))
storage_keys = self.cmd('az storage account keys list -g {} -n {}'.format(resource_group, storage_name)).get_output_in_json()[0]
self.cmd('containerapp env storage set -g {} -n {} --storage-name {} --azure-file-account-name {} --azure-file-account-key {} --access-mode ReadOnly --azure-file-share-name {}'.format(resource_group, env_name, storage_name, storage_name, storage_keys["value"], shares_name), checks=[
JMESPathCheck('name', storage_name),
])
self.cmd('containerapp env storage show -g {} -n {} --storage-name {}'.format(resource_group, env_name, storage_name), checks=[
JMESPathCheck('name', storage_name),
])
self.cmd('containerapp env storage list -g {} -n {}'.format(resource_group, env_name), checks=[
JMESPathCheck('[0].name', storage_name),
])
self.cmd('containerapp env storage remove -g {} -n {} --storage-name {} --yes'.format(resource_group, env_name, storage_name))
self.cmd('containerapp env storage list -g {} -n {}'.format(resource_group, env_name), checks=[
JMESPathCheck('length(@)', 0),
])
class ContainerappRevisionTests(ScenarioTest):
@AllowLargeResponse(8192)
@ResourceGroupPreparer(location="northeurope")
def test_containerapp_revision_label_e2e(self, resource_group):
env_name = self.create_random_name(prefix='containerapp-env', length=24)
ca_name = self.create_random_name(prefix='containerapp', length=24)
logs_workspace_name = self.create_random_name(prefix='containerapp-env', length=24)
logs_workspace_id = self.cmd('monitor log-analytics workspace create -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["customerId"]
logs_workspace_key = self.cmd('monitor log-analytics workspace get-shared-keys -g {} -n {}'.format(resource_group, logs_workspace_name)).get_output_in_json()["primarySharedKey"]
self.cmd('containerapp env create -g {} -n {} --logs-workspace-id {} --logs-workspace-key {}'.format(resource_group, env_name, logs_workspace_id, logs_workspace_key))
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
while containerapp_env["properties"]["provisioningState"].lower() == "waiting":
time.sleep(5)
containerapp_env = self.cmd('containerapp env show -g {} -n {}'.format(resource_group, env_name)).get_output_in_json()
self.cmd('containerapp create -g {} -n {} --environment {} --ingress external --target-port 80'.format(resource_group, ca_name, env_name))
self.cmd('containerapp ingress show -g {} -n {}'.format(resource_group, ca_name, env_name), checks=[
JMESPathCheck('external', True),
JMESPathCheck('targetPort', 80),
])
self.cmd('containerapp create -g {} -n {} --environment {} --ingress external --target-port 80 --image nginx'.format(resource_group, ca_name, env_name))
self.cmd('containerapp revision set-mode -g {} -n {} --mode multiple'.format(resource_group, ca_name, env_name))
revision_names = self.cmd(f"containerapp revision list -g {resource_group} -n {ca_name} --all --query '[].name'").get_output_in_json()
self.assertEqual(len(revision_names), 2)
labels = []
for revision in revision_names:
label = self.create_random_name(prefix='label', length=12)
labels.append(label)
self.cmd(f"containerapp revision label add -g {resource_group} -n {ca_name} --revision {revision} --label {label}")
traffic_weight = self.cmd(f"containerapp ingress traffic show -g {resource_group} -n {ca_name} --query '[].name'").get_output_in_json()
for traffic in traffic_weight:
if "label" in traffic:
self.assertEqual(traffic["label"] in labels, True)
self.cmd(f"containerapp ingress traffic set -g {resource_group} -n {ca_name} --revision-weight latest=50 --label-weight {labels[0]}=25 {labels[1]}=25")
traffic_weight = self.cmd(f"containerapp ingress traffic show -g {resource_group} -n {ca_name} --query '[].name'").get_output_in_json()
for traffic in traffic_weight:
if "label" in traffic:
self.assertEqual(traffic["weight"], 25)
else:
self.assertEqual(traffic["weight"], 50)
traffic_weight = self.cmd(f"containerapp revision label swap -g {resource_group} -n {ca_name} --source {labels[0]} --target {labels[1]}").get_output_in_json()
for revision in revision_names:
traffic = [w for w in traffic_weight if "revisionName" in w and w["revisionName"] == revision][0]
self.assertEqual(traffic["label"], labels[(revision_names.index(revision) + 1) % 2])
self.cmd(f"containerapp revision label remove -g {resource_group} -n {ca_name} --label {labels[0]}", checks=[
JMESPathCheck('length(@)', 3),
])
self.cmd(f"containerapp revision label remove -g {resource_group} -n {ca_name} --label {labels[1]}", checks=[
JMESPathCheck('length(@)', 3),
])
traffic_weight = self.cmd(f"containerapp ingress traffic show -g {resource_group} -n {ca_name}").get_output_in_json()
self.assertEqual(len([w for w in traffic_weight if "label" in w]), 0)
| 58.809609
| 291
| 0.663399
| 3,856
| 33,051
| 5.465768
| 0.067427
| 0.086971
| 0.104574
| 0.055418
| 0.865819
| 0.836307
| 0.814196
| 0.801907
| 0.784304
| 0.756453
| 0
| 0.011297
| 0.183141
| 33,051
| 561
| 292
| 58.914439
| 0.769353
| 0.028743
| 0
| 0.711111
| 0
| 0.059259
| 0.330653
| 0.013869
| 0
| 0
| 0
| 0
| 0.019753
| 1
| 0.022222
| false
| 0.004938
| 0.014815
| 0
| 0.049383
| 0.009877
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9208f7346cadc419a62a686dd5dba387c6a93c38
| 8,531
|
py
|
Python
|
examples/seq2seq/penman_denoising.py
|
ahoho/transformers
|
366c305e5d4d122b47dd7a0a5e5e481e18e0a6ff
|
[
"Apache-2.0"
] | null | null | null |
examples/seq2seq/penman_denoising.py
|
ahoho/transformers
|
366c305e5d4d122b47dd7a0a5e5e481e18e0a6ff
|
[
"Apache-2.0"
] | null | null | null |
examples/seq2seq/penman_denoising.py
|
ahoho/transformers
|
366c305e5d4d122b47dd7a0a5e5e481e18e0a6ff
|
[
"Apache-2.0"
] | null | null | null |
AMR_SOURCES = [
"(w / want-01 :ARG0 (b / boy) :ARG1 (g / go-01 :ARG0 b))"
]
AMR_TARGETS = [
"The boy wants to go."
]
AMR_NOISED = {
"convert-to-triples": {
"src": "order Graph: ( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "<t> want :ARG0 boy <t> want :ARG1 go <t> go :ARG0 boy"
},
"generate-from-triples": {
"src": "order Graph: <t> want :ARG0 boy <t> want :ARG1 go <t> go :ARG0 boy",
"tgt": "The boy wants to go."
},
"mask-all": {
"src": "denoise Graph: ( want :ARG0 <extra_id_0> boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"mask-all-drop": {
"src": "denoise Graph: ( want :ARG0 boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": " ( "
},
"mask-all-mass": {
"src": "denoise Graph: ( want :ARG0 <extra_id_0> boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"mask-components": {
"src": "denoise Graph: ( want :ARG0 <extra_id_0> boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"mask-components-corrupt": {
"src": "denoise Graph: ( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"mask-components-drop": {
"src": "denoise Graph: ( want :ARG0 boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": " ( "
},
"mask-components-mass": {
"src": "denoise Graph: ( want :ARG0 <extra_id_0> boy ) :ARG1 ( go :ARG0 boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"mask-nodes": {
"src": "denoise Graph: ( <extra_id_0> :ARG0 ( boy ) :ARG1 ( go :ARG0 <extra_id_1> ) )",
"tgt": "<extra_id_0> want <extra_id_1> boy <extra_id_2>"
},
"mask-nodes-drop": {
"src": "denoise Graph: ( :ARG0 ( boy ) :ARG1 ( go :ARG0 ) )",
"tgt": " want boy "
},
"mask-nodes-mass": {
"src": "denoise Graph: ( <extra_id_0> :ARG0 ( boy ) :ARG1 ( go :ARG0 <extra_id_0> ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"mask-surface": {
"src": "denoise Graph: The boy wants <extra_id_0> go.",
"tgt": "<extra_id_0> to <extra_id_1>"
},
"parse-from-triples": {
"src": "order Graph: <t> want :ARG0 boy <t> want :ARG1 go <t> go :ARG0 boy",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"randomize": {
"src": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "The boy wants to go."
},
"randomize_convert-to-triples": {
"src": "order Graph: ( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "<t> want :ARG1 go <t> want :ARG0 boy <t> go :ARG0 boy"
},
"randomize_generate-from-triples": {
"src": "order Graph: <t> want :ARG1 go <t> want :ARG0 boy <t> go :ARG0 boy",
"tgt": "The boy wants to go."
},
"randomize_mask-all": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"randomize_mask-all-drop": {
"src": "denoise Graph: ( go :ARG1-of want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": " ( "
},
"randomize_mask-all-mass": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )"
},
"randomize_mask-all-mass-unshuffle": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"randomize_mask-components": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"randomize_mask-components-corrupt": {
"src": "denoise Graph: ( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )"
},
"randomize_mask-components-corrupt-unshuffle": {
"src": "denoise Graph: ( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"randomize_mask-components-drop": {
"src": "denoise Graph: ( go :ARG1-of want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": " ( "
},
"randomize_mask-components-mass": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )"
},
"randomize_mask-components-mass-unshuffle": {
"src": "denoise Graph: ( go :ARG1-of <extra_id_0> want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"randomize_mask-nodes": {
"src": "denoise Graph: ( <extra_id_0> :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 <extra_id_1> )",
"tgt": "<extra_id_0> go <extra_id_1> boy <extra_id_2>"
},
"randomize_mask-nodes-drop": {
"src": "denoise Graph: ( :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 )",
"tgt": " go boy "
},
"randomize_mask-nodes-mass": {
"src": "denoise Graph: ( <extra_id_0> :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 <extra_id_0> )",
"tgt": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )"
},
"randomize_mask-nodes-mass-unshuffle": {
"src": "denoise Graph: ( <extra_id_0> :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 <extra_id_0> )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"randomize_parse-from-triples": {
"src": "order Graph: <t> want :ARG1 go <t> want :ARG0 boy <t> go :ARG0 boy",
"tgt": "( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )"
},
"randomize_reorder": {
"src": "order Graph: ( go :ARG1-of ( want :ARG0 ( boy ) ) :ARG0 boy )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"reconfigure": {
"src": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "The boy wants to go."
},
"reconfigure_convert-to-triples": {
"src": "order Graph: ( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "<t> want :ARG1 go <t> go :ARG0 boy <t> want :ARG0 boy"
},
"reconfigure_generate-from-triples": {
"src": "order Graph: <t> want :ARG1 go <t> go :ARG0 boy <t> want :ARG0 boy",
"tgt": "The boy wants to go."
},
"reconfigure_mask-all": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"reconfigure_mask-all-drop": {
"src": "denoise Graph: ( want :ARG1 go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": " ( "
},
"reconfigure_mask-all-mass": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )"
},
"reconfigure_mask-all-mass-unshuffle": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"reconfigure_mask-components": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "<extra_id_0> ( <extra_id_1>"
},
"reconfigure_mask-components-corrupt": {
"src": "denoise Graph: ( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )"
},
"reconfigure_mask-components-corrupt-unshuffle": {
"src": "denoise Graph: ( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"reconfigure_mask-components-drop": {
"src": "denoise Graph: ( want :ARG1 go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": " ( "
},
"reconfigure_mask-components-mass": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )"
},
"reconfigure_mask-components-mass-unshuffle": {
"src": "denoise Graph: ( want :ARG1 <extra_id_0> go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"reconfigure_mask-nodes": {
"src": "denoise Graph: ( <extra_id_0> :ARG1 ( go :ARG0 boy ) :ARG0 ( <extra_id_1> ) )",
"tgt": "<extra_id_0> want <extra_id_1> boy <extra_id_2>"
},
"reconfigure_mask-nodes-drop": {
"src": "denoise Graph: ( :ARG1 ( go :ARG0 boy ) :ARG0 ( ) )",
"tgt": " want boy "
},
"reconfigure_mask-nodes-mass": {
"src": "denoise Graph: ( <extra_id_0> :ARG1 ( go :ARG0 boy ) :ARG0 ( <extra_id_0> ) )",
"tgt": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )"
},
"reconfigure_mask-nodes-mass-unshuffle": {
"src": "denoise Graph: ( <extra_id_0> :ARG1 ( go :ARG0 boy ) :ARG0 ( <extra_id_0> ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
},
"reconfigure_parse-from-triples": {
"src": "order Graph: <t> want :ARG1 go <t> go :ARG0 boy <t> want :ARG0 boy",
"tgt": "( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )"
},
"reconfigure_reorder": {
"src": "order Graph: ( want :ARG1 ( go :ARG0 boy ) :ARG0 ( boy ) )",
"tgt": "( want :ARG0 ( boy ) :ARG1 ( go :ARG0 boy ) )"
}
}
| 39.133028
| 94
| 0.563006
| 1,217
| 8,531
| 3.822514
| 0.036976
| 0.215176
| 0.104471
| 0.108985
| 0.955718
| 0.938951
| 0.915735
| 0.866939
| 0.86092
| 0.829536
| 0
| 0.044783
| 0.214746
| 8,531
| 218
| 95
| 39.133028
| 0.649649
| 0
| 0
| 0.398148
| 0
| 0.175926
| 0.80872
| 0.117206
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a6f4cd68d6d364156f5fdb3da318997921fa8760
| 314
|
py
|
Python
|
randomsubgroups/__init__.py
|
rebelosa/random-subgroups
|
5fa795d0251e2d5dfac678729d091ddf5c34f82e
|
[
"MIT"
] | 4
|
2020-10-15T20:30:15.000Z
|
2021-02-23T16:38:13.000Z
|
randomsubgroups/__init__.py
|
rebelosa/random-subgroups
|
5fa795d0251e2d5dfac678729d091ddf5c34f82e
|
[
"MIT"
] | 2
|
2021-02-14T07:03:58.000Z
|
2021-02-21T17:16:57.000Z
|
randomsubgroups/__init__.py
|
rebelosa/random-subgroups
|
5fa795d0251e2d5dfac678729d091ddf5c34f82e
|
[
"MIT"
] | null | null | null |
from ._randomsubgroups import SubgroupPredictorBase
from ._randomsubgroups import RandomSubgroupClassifier
from ._randomsubgroups import RandomSubgroupRegressor
from ._version import __version__
__all__ = ['SubgroupPredictorBase', 'RandomSubgroupClassifier', 'RandomSubgroupRegressor',
'__version__']
| 34.888889
| 90
| 0.83121
| 21
| 314
| 11.666667
| 0.380952
| 0.232653
| 0.306122
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111465
| 314
| 8
| 91
| 39.25
| 0.878136
| 0
| 0
| 0
| 0
| 0
| 0.251592
| 0.216561
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
47029060be442baf1023a0a0e23b74ce3ae356e8
| 8,240
|
py
|
Python
|
aoi_envs/__init__.py
|
landonbutler/Learning-Connectivity
|
2ce4c148878e2dab98c53a7cc2bd8d1362c2bf87
|
[
"MIT"
] | 2
|
2021-04-13T19:50:40.000Z
|
2021-05-04T09:27:37.000Z
|
aoi_envs/__init__.py
|
landonbutler/Learning-Connectivity
|
2ce4c148878e2dab98c53a7cc2bd8d1362c2bf87
|
[
"MIT"
] | null | null | null |
aoi_envs/__init__.py
|
landonbutler/Learning-Connectivity
|
2ce4c148878e2dab98c53a7cc2bd8d1362c2bf87
|
[
"MIT"
] | 2
|
2021-04-13T19:51:05.000Z
|
2021-05-12T16:07:41.000Z
|
from aoi_envs.MultiAgent import MultiAgentEnv
from aoi_envs.Mobile import MobileEnv
from gym.envs.registration import register
MAX_EPISODE_STEPS = 10000
register(
id='StationaryEnv-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
)
###################################################################
register(
id='PowerLevel10Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'fractional_power_levels': [1.0, 0.0]},
)
register(
id='PowerLevel075Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'fractional_power_levels': [0.75, 0.0]},
)
register(
id='PowerLevel05Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'fractional_power_levels': [0.5, 0.0]},
)
register(
id='PowerLevel025Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'fractional_power_levels': [0.25, 0.0]},
)
register(
id='PowerLevel02Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'fractional_power_levels': [0.2, 0.0]},
)
###################################################################
register(
id='Stationary10Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 10},
)
register(
id='Stationary20Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 20},
)
register(
id='Stationary30Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 30},
)
register(
id='Stationary40Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 40},
)
register(
id='Stationary50Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 50},
)
register(
id='Stationary60Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 60},
)
register(
id='Stationary80Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 80},
)
register(
id='Stationary100Env-v0',
entry_point='aoi_envs:MultiAgentEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'num_agents': 100},
)
###################################################################
register(
id='MobileEnv-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
)
register(
id='MobileEnv00-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.0},
)
register(
id='MobileEnv005-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.05},
)
register(
id='MobileEnv01-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.1},
)
register(
id='MobileEnv015-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15},
)
register(
id='MobileEnv025-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.25},
)
register(
id='MobileEnv05-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.5},
)
# register(
# id='MobileEnv075-v0',
# entry_point='aoi_envs:MobileEnv',
# max_episode_steps=MAX_EPISODE_STEPS,
# kwargs={'agent_velocity': 0.75},
# )
#
# register(
# id='MobileEnv10-v0',
# entry_point='aoi_envs:MobileEnv',
# max_episode_steps=MAX_EPISODE_STEPS,
# kwargs={'agent_velocity': 1.0},
# )
###################################################################
register(
id='MobileEnv10N10-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 10},
)
register(
id='MobileEnv10N20-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 20},
)
register(
id='MobileEnv10N40-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 40},
)
register(
id='MobileEnv10N60-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 60},
)
register(
id='MobileEnv10N80-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 80},
)
register(
id='MobileEnv10N100-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'num_agents': 100},
)
###################################################################
register(
id='FlockingEnv-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 1.0, 'flocking': True, 'aoi_reward': False},
)
register(
id='FlockingAOIEnv-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 1.0, 'flocking': True},
)
###################################################################
register(
id='Flocking015Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking025Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.25, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking0325Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.325, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking05Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.5, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking0625Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.625, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking075Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.75, 'flocking': True, 'aoi_reward': False},
)
register(
id='Flocking10Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 1.0, 'flocking': True, 'aoi_reward': False},
)
register(
id='FlockingAOI015Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.15, 'flocking': True},
)
register(
id='FlockingAOI025Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.25, 'flocking': True},
)
register(
id='FlockingAOI0325Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.325, 'flocking': True},
)
register(
id='FlockingAOI05Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.5, 'flocking': True},
)
register(
id='FlockingAOI0625Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.625, 'flocking': True},
)
register(
id='FlockingAOI075Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 0.75, 'flocking': True},
)
register(
id='FlockingAOI10Env-v0',
entry_point='aoi_envs:MobileEnv',
max_episode_steps=MAX_EPISODE_STEPS,
kwargs={'agent_velocity': 1.0, 'flocking': True},
)
| 24.744745
| 76
| 0.672209
| 993
| 8,240
| 5.239678
| 0.093656
| 0.174899
| 0.262349
| 0.129733
| 0.80319
| 0.77513
| 0.77513
| 0.747453
| 0.743609
| 0.743609
| 0
| 0.042746
| 0.142597
| 8,240
| 332
| 77
| 24.819277
| 0.693701
| 0.037015
| 0
| 0.511538
| 0
| 0
| 0.323224
| 0.061852
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.011538
| 0
| 0.011538
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
471541a5cf52f118d9542ca2f78a5e604679f92d
| 194
|
py
|
Python
|
manager/integration/tests/cloudprovider.py
|
kaxing/longhorn-tests
|
e8e95f1446155f20cbbb9b47d8e139de09d567f0
|
[
"Apache-2.0"
] | 10
|
2021-01-25T00:52:46.000Z
|
2022-02-20T01:49:56.000Z
|
manager/integration/tests/cloudprovider.py
|
kaxing/longhorn-tests
|
e8e95f1446155f20cbbb9b47d8e139de09d567f0
|
[
"Apache-2.0"
] | 273
|
2019-06-12T17:43:49.000Z
|
2022-03-29T09:06:02.000Z
|
manager/integration/tests/cloudprovider.py
|
kaxing/longhorn-tests
|
e8e95f1446155f20cbbb9b47d8e139de09d567f0
|
[
"Apache-2.0"
] | 24
|
2019-06-12T04:03:00.000Z
|
2022-03-21T08:08:47.000Z
|
class cloudprovider:
def instance_id(self):
pass
def instance_stop(self):
pass
def instance_start(self):
pass
def instance_status(self):
pass
| 13.857143
| 30
| 0.592784
| 22
| 194
| 5.045455
| 0.454545
| 0.396396
| 0.297297
| 0.513514
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.340206
| 194
| 13
| 31
| 14.923077
| 0.867188
| 0
| 0
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.444444
| false
| 0.444444
| 0
| 0
| 0.555556
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
472541c1fb45432416447fbd232a3ef995de7235
| 35
|
py
|
Python
|
cstool/input_data/__init__.py
|
Nebula-simulator/cstool
|
4d8a6434fa0d05287876f656842257fbd5d196bd
|
[
"BSD-3-Clause"
] | null | null | null |
cstool/input_data/__init__.py
|
Nebula-simulator/cstool
|
4d8a6434fa0d05287876f656842257fbd5d196bd
|
[
"BSD-3-Clause"
] | 2
|
2021-08-05T12:36:13.000Z
|
2022-02-05T15:51:39.000Z
|
cstool/input_data/__init__.py
|
Nebula-simulator/cstool
|
4d8a6434fa0d05287876f656842257fbd5d196bd
|
[
"BSD-3-Clause"
] | 1
|
2021-08-06T09:20:43.000Z
|
2021-08-06T09:20:43.000Z
|
from .param_file import param_file
| 17.5
| 34
| 0.857143
| 6
| 35
| 4.666667
| 0.666667
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.903226
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
753028e8ebdf1300490ed13c919d16e0ca4beb04
| 7,068
|
py
|
Python
|
tests/test_dataset.py
|
ttecles/aidl-lyrics-recognition
|
33a7cf449a0a5e45d0575493b8b92b64787c8b8e
|
[
"CC0-1.0"
] | 8
|
2021-06-07T09:16:56.000Z
|
2022-03-30T13:30:31.000Z
|
tests/test_dataset.py
|
ttecles/aidl-lyrics-recognition
|
33a7cf449a0a5e45d0575493b8b92b64787c8b8e
|
[
"CC0-1.0"
] | 1
|
2021-06-27T13:56:29.000Z
|
2021-06-30T20:52:08.000Z
|
tests/test_dataset.py
|
ttecles/aidl-lyrics-recognition
|
33a7cf449a0a5e45d0575493b8b92b64787c8b8e
|
[
"CC0-1.0"
] | 2
|
2021-12-05T06:58:27.000Z
|
2022-02-09T15:18:02.000Z
|
import pathlib
from unittest import TestCase
from unittest.mock import patch
from DALI import Annotations
from lyre.data import DaliDataset, Chunk
from lyre.utils import sample2time
class TestDaliDataset(TestCase):
def test_creation_of_dataset(self):
self.maxDiff = None
with patch("lyre.data.dali_code") as mock_dali:
# length of our test audio: 1147
# sample of our test audio: 22050
ann = Annotations()
ann.info["id"] = "test"
ann.info["metadata"]["language"] = "english"
ann.info["scores"]["NCC"] = .9
ann.annotations["annot"]["notes"] = [
{"time": (sample2time(200, 44100), sample2time(500, 44100)), "text": "I"},
{"time": (sample2time(600, 44100), sample2time(700, 44100)), "text": "am"},
{"time": (sample2time(900, 44100), sample2time(1000, 44100)), "text": "an"},
{"time": (sample2time(1100, 44100), sample2time(1700, 44100)), "text": "a"},
{"time": (sample2time(1800, 44100), sample2time(2000, 44100)), "text": "mazing"}]
ann.annotations["annot"]["paragraphs"] = [{"text": "I am an amazing"}]
mock_dali.get_the_DALI_dataset.return_value = {"test": ann}
chunk_length = 800
dt = DaliDataset(dali_data=pathlib.Path("."), dali_audio_path=pathlib.Path("./audio"), length=chunk_length)
self.assertListEqual(dt.chunk_map, [
Chunk(song_id='test',
init_sample=0 * chunk_length, end_sample=1 * chunk_length - 1,
audio_start=0, audio_end=1 * chunk_length - 1,
lyrics='I AM'),
Chunk(song_id='test',
init_sample=1 * chunk_length, end_sample=2 * chunk_length - 1,
audio_start=1 * chunk_length, audio_end=1099,
lyrics='AN'),
Chunk(song_id='test',
init_sample=2 * chunk_length, end_sample=3 * chunk_length - 1,
audio_start=1701, audio_end=1147 * 2 - 1,
lyrics='MAZING')
])
self.assertEqual(tuple(dt[0][0].size()), (2, chunk_length))
self.assertEqual(tuple(dt[1][0].size()), (2, chunk_length))
self.assertEqual(tuple(dt[2][0].size()), (2, chunk_length))
def test_filters(self):
self.maxDiff = None
with patch("lyre.data.dali_code") as mock_dali:
# length of our test audio: 1147
# sample of our test audio: 22050
ann1 = Annotations()
ann1.info["id"] = "test1"
ann1.info["metadata"]["language"] = "english"
ann1.info["scores"]["NCC"] = .9
ann1.annotations["annot"]["notes"] = []
ann1.annotations["annot"]["paragraphs"] = []
ann2 = Annotations()
ann2.info["id"] = "test2"
ann2.info["metadata"]["language"] = "catala"
ann2.info["scores"]["NCC"] = .9
ann3 = Annotations()
ann3.info["id"] = "test3"
ann3.info["metadata"]["language"] = "english"
ann3.info["scores"]["NCC"] = .8
mock_dali.get_the_DALI_dataset.return_value = {"test1": ann1, "test2": ann2, "test3": ann3}
chunk_length = 800
dt = DaliDataset(dali_data=pathlib.Path("."), dali_audio_path=pathlib.Path("./audio"),
length=chunk_length, ncc=(.85, .95))
self.assertListEqual(dt.dali_data_subset_ident, ["test1"])
def test_notes_bigger_than_chunk(self):
self.maxDiff = None
with patch("lyre.data.dali_code") as mock_dali:
# length of our test audio: 1147 - 2294
# sample of our test audio: 22050 - 44100
ann = Annotations()
ann.info["id"] = "test"
ann.info["metadata"]["language"] = "english"
ann.annotations["annot"]["notes"] = [
{"time": (sample2time(200, 44100), sample2time(2000, 44100)), "text": "I"}]
ann.annotations["annot"]["paragraphs"] = [{"text": "I"}]
mock_dali.get_the_DALI_dataset.return_value = {"test": ann}
chunk_length = 800
dt = DaliDataset(dali_data=pathlib.Path("."), dali_audio_path=pathlib.Path("./audio"), length=chunk_length)
self.assertListEqual(dt.chunk_map, [
Chunk(song_id='test',
init_sample=0 * chunk_length, end_sample=1 * chunk_length - 1,
audio_start=0, audio_end=199,
lyrics=''),
Chunk(song_id='test',
init_sample=2 * chunk_length, end_sample=3 * chunk_length - 1,
audio_start=2001, audio_end=1147 * 2 - 1,
lyrics='')
])
def test_last_chunk_without_notes(self):
self.maxDiff = None
with patch("lyre.data.dali_code") as mock_dali:
# length of our test audio: 1147
# sample of our test audio: 22050
ann = Annotations()
ann.info["id"] = "test"
ann.info["metadata"]["language"] = "english"
ann.info["scores"]["NCC"] = .9
ann.annotations["annot"]["notes"] = [
{"time": (sample2time(200, 44100), sample2time(500, 44100)), "text": "I"},
{"time": (sample2time(600, 44100), sample2time(700, 44100)), "text": "am"},
{"time": (sample2time(900, 44100), sample2time(1000, 44100)), "text": "an"},
{"time": (sample2time(1100, 44100), sample2time(1700, 44100)), "text": "a"},
{"time": (sample2time(1800, 44100), sample2time(2000, 44100)), "text": "mazing"}]
ann.annotations["annot"]["paragraphs"] = [{"text": "I am an amazing"}]
mock_dali.get_the_DALI_dataset.return_value = {"test": ann}
chunk_length = 800
dt = DaliDataset(dali_data=pathlib.Path("."), dali_audio_path=pathlib.Path("./audio"), length=chunk_length)
self.assertListEqual(dt.chunk_map, [
Chunk(song_id='test',
init_sample=0 * chunk_length, end_sample=1 * chunk_length - 1,
audio_start=0, audio_end=1 * chunk_length - 1,
lyrics='I AM'),
Chunk(song_id='test',
init_sample=1 * chunk_length, end_sample=2 * chunk_length - 1,
audio_start=1 * chunk_length, audio_end=1099,
lyrics='AN'),
Chunk(song_id='test',
init_sample=2 * chunk_length, end_sample=3 * chunk_length - 1,
audio_start=1701, audio_end=1147 * 2 - 1,
lyrics='MAZING')
])
self.assertEqual(tuple(dt[0][0].size()), (2, chunk_length))
self.assertEqual(tuple(dt[1][0].size()), (2, chunk_length))
self.assertEqual(tuple(dt[2][0].size()), (2, chunk_length))
| 47.436242
| 119
| 0.535088
| 787
| 7,068
| 4.637865
| 0.13723
| 0.102466
| 0.036164
| 0.030685
| 0.816712
| 0.813151
| 0.791507
| 0.791507
| 0.781644
| 0.766575
| 0
| 0.081143
| 0.316497
| 7,068
| 148
| 120
| 47.756757
| 0.674395
| 0.037917
| 0
| 0.705882
| 0
| 0
| 0.099102
| 0
| 0
| 0
| 0
| 0
| 0.084034
| 1
| 0.033613
| false
| 0
| 0.05042
| 0
| 0.092437
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f337866812b474c09e2202fff6887c80f6dcd01a
| 31,982
|
py
|
Python
|
sdk/python/pulumi_databricks/mws_customer_managed_keys.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_databricks/mws_customer_managed_keys.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_databricks/mws_customer_managed_keys.py
|
pulumi/pulumi-databricks
|
43580d4adbd04b72558f368ff0eef3d03432ebc1
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
from . import outputs
from ._inputs import *
__all__ = ['MwsCustomerManagedKeysArgs', 'MwsCustomerManagedKeys']
@pulumi.input_type
class MwsCustomerManagedKeysArgs:
def __init__(__self__, *,
account_id: pulumi.Input[str],
aws_key_info: pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs'],
use_cases: pulumi.Input[Sequence[pulumi.Input[str]]],
creation_time: Optional[pulumi.Input[int]] = None,
customer_managed_key_id: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a MwsCustomerManagedKeys resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
:param pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs'] aws_key_info: This field is a block and is documented below.
:param pulumi.Input[Sequence[pulumi.Input[str]]] use_cases: *(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
:param pulumi.Input[int] creation_time: (Integer) Time in epoch milliseconds when the customer key was created.
:param pulumi.Input[str] customer_managed_key_id: (String) ID of the encryption key configuration object.
"""
pulumi.set(__self__, "account_id", account_id)
pulumi.set(__self__, "aws_key_info", aws_key_info)
pulumi.set(__self__, "use_cases", use_cases)
if creation_time is not None:
pulumi.set(__self__, "creation_time", creation_time)
if customer_managed_key_id is not None:
pulumi.set(__self__, "customer_managed_key_id", customer_managed_key_id)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> pulumi.Input[str]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
"""
return pulumi.get(self, "account_id")
@account_id.setter
def account_id(self, value: pulumi.Input[str]):
pulumi.set(self, "account_id", value)
@property
@pulumi.getter(name="awsKeyInfo")
def aws_key_info(self) -> pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs']:
"""
This field is a block and is documented below.
"""
return pulumi.get(self, "aws_key_info")
@aws_key_info.setter
def aws_key_info(self, value: pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs']):
pulumi.set(self, "aws_key_info", value)
@property
@pulumi.getter(name="useCases")
def use_cases(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
*(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
return pulumi.get(self, "use_cases")
@use_cases.setter
def use_cases(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "use_cases", value)
@property
@pulumi.getter(name="creationTime")
def creation_time(self) -> Optional[pulumi.Input[int]]:
"""
(Integer) Time in epoch milliseconds when the customer key was created.
"""
return pulumi.get(self, "creation_time")
@creation_time.setter
def creation_time(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "creation_time", value)
@property
@pulumi.getter(name="customerManagedKeyId")
def customer_managed_key_id(self) -> Optional[pulumi.Input[str]]:
"""
(String) ID of the encryption key configuration object.
"""
return pulumi.get(self, "customer_managed_key_id")
@customer_managed_key_id.setter
def customer_managed_key_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_id", value)
@pulumi.input_type
class _MwsCustomerManagedKeysState:
def __init__(__self__, *,
account_id: Optional[pulumi.Input[str]] = None,
aws_key_info: Optional[pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs']] = None,
creation_time: Optional[pulumi.Input[int]] = None,
customer_managed_key_id: Optional[pulumi.Input[str]] = None,
use_cases: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
Input properties used for looking up and filtering MwsCustomerManagedKeys resources.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
:param pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs'] aws_key_info: This field is a block and is documented below.
:param pulumi.Input[int] creation_time: (Integer) Time in epoch milliseconds when the customer key was created.
:param pulumi.Input[str] customer_managed_key_id: (String) ID of the encryption key configuration object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] use_cases: *(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
if account_id is not None:
pulumi.set(__self__, "account_id", account_id)
if aws_key_info is not None:
pulumi.set(__self__, "aws_key_info", aws_key_info)
if creation_time is not None:
pulumi.set(__self__, "creation_time", creation_time)
if customer_managed_key_id is not None:
pulumi.set(__self__, "customer_managed_key_id", customer_managed_key_id)
if use_cases is not None:
pulumi.set(__self__, "use_cases", use_cases)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> Optional[pulumi.Input[str]]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
"""
return pulumi.get(self, "account_id")
@account_id.setter
def account_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "account_id", value)
@property
@pulumi.getter(name="awsKeyInfo")
def aws_key_info(self) -> Optional[pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs']]:
"""
This field is a block and is documented below.
"""
return pulumi.get(self, "aws_key_info")
@aws_key_info.setter
def aws_key_info(self, value: Optional[pulumi.Input['MwsCustomerManagedKeysAwsKeyInfoArgs']]):
pulumi.set(self, "aws_key_info", value)
@property
@pulumi.getter(name="creationTime")
def creation_time(self) -> Optional[pulumi.Input[int]]:
"""
(Integer) Time in epoch milliseconds when the customer key was created.
"""
return pulumi.get(self, "creation_time")
@creation_time.setter
def creation_time(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "creation_time", value)
@property
@pulumi.getter(name="customerManagedKeyId")
def customer_managed_key_id(self) -> Optional[pulumi.Input[str]]:
"""
(String) ID of the encryption key configuration object.
"""
return pulumi.get(self, "customer_managed_key_id")
@customer_managed_key_id.setter
def customer_managed_key_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_id", value)
@property
@pulumi.getter(name="useCases")
def use_cases(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
*(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
return pulumi.get(self, "use_cases")
@use_cases.setter
def use_cases(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "use_cases", value)
class MwsCustomerManagedKeys(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account_id: Optional[pulumi.Input[str]] = None,
aws_key_info: Optional[pulumi.Input[pulumi.InputType['MwsCustomerManagedKeysAwsKeyInfoArgs']]] = None,
creation_time: Optional[pulumi.Input[int]] = None,
customer_managed_key_id: Optional[pulumi.Input[str]] = None,
use_cases: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
"""
## Example Usage
> **Note** If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.
### Customer-managed key for managed services
You must configure this during workspace creation
```python
import pulumi
import pulumi_aws as aws
import pulumi_databricks as databricks
config = pulumi.Config()
databricks_account_id = config.require_object("databricksAccountId")
databricks_managed_services_cmk = aws.iam.get_policy_document(version="2012-10-17",
statements=[
aws.iam.GetPolicyDocumentStatementArgs(
sid="Enable IAM User Permissions",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["*"],
)],
actions=["kms:*"],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for control plane managed services",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:Encrypt",
"kms:Decrypt",
],
resources=["*"],
),
])
managed_services_customer_managed_key = aws.kms.Key("managedServicesCustomerManagedKey", policy=databricks_managed_services_cmk.json)
managed_services_customer_managed_key_alias = aws.kms.Alias("managedServicesCustomerManagedKeyAlias", target_key_id=managed_services_customer_managed_key.key_id)
managed_services = databricks.MwsCustomerManagedKeys("managedServices",
account_id=databricks_account_id,
aws_key_info=databricks.MwsCustomerManagedKeysAwsKeyInfoArgs(
key_arn=managed_services_customer_managed_key.arn,
key_alias=managed_services_customer_managed_key_alias.name,
),
use_cases=["MANAGED_SERVICES"])
```
### Customer-managed key for workspace storage
```python
import pulumi
import pulumi_aws as aws
import pulumi_databricks as databricks
config = pulumi.Config()
databricks_account_id = config.require_object("databricksAccountId")
databricks_cross_account_role = config.require_object("databricksCrossAccountRole")
databricks_storage_cmk = aws.iam.get_policy_document(version="2012-10-17",
statements=[
aws.iam.GetPolicyDocumentStatementArgs(
sid="Enable IAM User Permissions",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["*"],
)],
actions=["kms:*"],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for DBFS",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:Encrypt",
"kms:Decrypt",
"kms:ReEncrypt*",
"kms:GenerateDataKey*",
"kms:DescribeKey",
],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for DBFS (Grants)",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:CreateGrant",
"kms:ListGrants",
"kms:RevokeGrant",
],
resources=["*"],
conditions=[aws.iam.GetPolicyDocumentStatementConditionArgs(
test="Bool",
variable="kms:GrantIsForAWSResource",
values=["true"],
)],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for EBS",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=[databricks_cross_account_role],
)],
actions=[
"kms:Decrypt",
"kms:GenerateDataKey*",
"kms:CreateGrant",
"kms:DescribeKey",
],
resources=["*"],
conditions=[aws.iam.GetPolicyDocumentStatementConditionArgs(
test="ForAnyValue:StringLike",
variable="kms:ViaService",
values=["ec2.*.amazonaws.com"],
)],
),
])
storage_customer_managed_key = aws.kms.Key("storageCustomerManagedKey", policy=databricks_storage_cmk.json)
storage_customer_managed_key_alias = aws.kms.Alias("storageCustomerManagedKeyAlias", target_key_id=storage_customer_managed_key.key_id)
storage = databricks.MwsCustomerManagedKeys("storage",
account_id=databricks_account_id,
aws_key_info=databricks.MwsCustomerManagedKeysAwsKeyInfoArgs(
key_arn=storage_customer_managed_key.arn,
key_alias=storage_customer_managed_key_alias.name,
),
use_cases=["STORAGE"])
```
## Related Resources
The following resources are used in the same context:
* Provisioning Databricks on AWS guide.
* MwsCredentials to configure the cross-account role for creation of new workspaces within AWS.
* MwsLogDelivery to configure delivery of [billable usage logs](https://docs.databricks.com/administration-guide/account-settings/billable-usage-delivery.html) and [audit logs](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html).
* MwsNetworks to [configure VPC](https://docs.databricks.com/administration-guide/cloud-configurations/aws/customer-managed-vpc.html) & subnets for new workspaces within AWS.
* MwsStorageConfigurations to configure root bucket new workspaces within AWS.
* MwsWorkspaces to set up [workspaces in E2 architecture on AWS](https://docs.databricks.com/getting-started/overview.html#e2-architecture-1).
## Import
-> **Note** Importing this resource is not currently supported.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
:param pulumi.Input[pulumi.InputType['MwsCustomerManagedKeysAwsKeyInfoArgs']] aws_key_info: This field is a block and is documented below.
:param pulumi.Input[int] creation_time: (Integer) Time in epoch milliseconds when the customer key was created.
:param pulumi.Input[str] customer_managed_key_id: (String) ID of the encryption key configuration object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] use_cases: *(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: MwsCustomerManagedKeysArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
## Example Usage
> **Note** If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.
### Customer-managed key for managed services
You must configure this during workspace creation
```python
import pulumi
import pulumi_aws as aws
import pulumi_databricks as databricks
config = pulumi.Config()
databricks_account_id = config.require_object("databricksAccountId")
databricks_managed_services_cmk = aws.iam.get_policy_document(version="2012-10-17",
statements=[
aws.iam.GetPolicyDocumentStatementArgs(
sid="Enable IAM User Permissions",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["*"],
)],
actions=["kms:*"],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for control plane managed services",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:Encrypt",
"kms:Decrypt",
],
resources=["*"],
),
])
managed_services_customer_managed_key = aws.kms.Key("managedServicesCustomerManagedKey", policy=databricks_managed_services_cmk.json)
managed_services_customer_managed_key_alias = aws.kms.Alias("managedServicesCustomerManagedKeyAlias", target_key_id=managed_services_customer_managed_key.key_id)
managed_services = databricks.MwsCustomerManagedKeys("managedServices",
account_id=databricks_account_id,
aws_key_info=databricks.MwsCustomerManagedKeysAwsKeyInfoArgs(
key_arn=managed_services_customer_managed_key.arn,
key_alias=managed_services_customer_managed_key_alias.name,
),
use_cases=["MANAGED_SERVICES"])
```
### Customer-managed key for workspace storage
```python
import pulumi
import pulumi_aws as aws
import pulumi_databricks as databricks
config = pulumi.Config()
databricks_account_id = config.require_object("databricksAccountId")
databricks_cross_account_role = config.require_object("databricksCrossAccountRole")
databricks_storage_cmk = aws.iam.get_policy_document(version="2012-10-17",
statements=[
aws.iam.GetPolicyDocumentStatementArgs(
sid="Enable IAM User Permissions",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["*"],
)],
actions=["kms:*"],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for DBFS",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:Encrypt",
"kms:Decrypt",
"kms:ReEncrypt*",
"kms:GenerateDataKey*",
"kms:DescribeKey",
],
resources=["*"],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for DBFS (Grants)",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=["arn:aws:iam::414351767826:root"],
)],
actions=[
"kms:CreateGrant",
"kms:ListGrants",
"kms:RevokeGrant",
],
resources=["*"],
conditions=[aws.iam.GetPolicyDocumentStatementConditionArgs(
test="Bool",
variable="kms:GrantIsForAWSResource",
values=["true"],
)],
),
aws.iam.GetPolicyDocumentStatementArgs(
sid="Allow Databricks to use KMS key for EBS",
effect="Allow",
principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
type="AWS",
identifiers=[databricks_cross_account_role],
)],
actions=[
"kms:Decrypt",
"kms:GenerateDataKey*",
"kms:CreateGrant",
"kms:DescribeKey",
],
resources=["*"],
conditions=[aws.iam.GetPolicyDocumentStatementConditionArgs(
test="ForAnyValue:StringLike",
variable="kms:ViaService",
values=["ec2.*.amazonaws.com"],
)],
),
])
storage_customer_managed_key = aws.kms.Key("storageCustomerManagedKey", policy=databricks_storage_cmk.json)
storage_customer_managed_key_alias = aws.kms.Alias("storageCustomerManagedKeyAlias", target_key_id=storage_customer_managed_key.key_id)
storage = databricks.MwsCustomerManagedKeys("storage",
account_id=databricks_account_id,
aws_key_info=databricks.MwsCustomerManagedKeysAwsKeyInfoArgs(
key_arn=storage_customer_managed_key.arn,
key_alias=storage_customer_managed_key_alias.name,
),
use_cases=["STORAGE"])
```
## Related Resources
The following resources are used in the same context:
* Provisioning Databricks on AWS guide.
* MwsCredentials to configure the cross-account role for creation of new workspaces within AWS.
* MwsLogDelivery to configure delivery of [billable usage logs](https://docs.databricks.com/administration-guide/account-settings/billable-usage-delivery.html) and [audit logs](https://docs.databricks.com/administration-guide/account-settings/audit-logs.html).
* MwsNetworks to [configure VPC](https://docs.databricks.com/administration-guide/cloud-configurations/aws/customer-managed-vpc.html) & subnets for new workspaces within AWS.
* MwsStorageConfigurations to configure root bucket new workspaces within AWS.
* MwsWorkspaces to set up [workspaces in E2 architecture on AWS](https://docs.databricks.com/getting-started/overview.html#e2-architecture-1).
## Import
-> **Note** Importing this resource is not currently supported.
:param str resource_name: The name of the resource.
:param MwsCustomerManagedKeysArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(MwsCustomerManagedKeysArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
account_id: Optional[pulumi.Input[str]] = None,
aws_key_info: Optional[pulumi.Input[pulumi.InputType['MwsCustomerManagedKeysAwsKeyInfoArgs']]] = None,
creation_time: Optional[pulumi.Input[int]] = None,
customer_managed_key_id: Optional[pulumi.Input[str]] = None,
use_cases: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = MwsCustomerManagedKeysArgs.__new__(MwsCustomerManagedKeysArgs)
if account_id is None and not opts.urn:
raise TypeError("Missing required property 'account_id'")
__props__.__dict__["account_id"] = account_id
if aws_key_info is None and not opts.urn:
raise TypeError("Missing required property 'aws_key_info'")
__props__.__dict__["aws_key_info"] = aws_key_info
__props__.__dict__["creation_time"] = creation_time
__props__.__dict__["customer_managed_key_id"] = customer_managed_key_id
if use_cases is None and not opts.urn:
raise TypeError("Missing required property 'use_cases'")
__props__.__dict__["use_cases"] = use_cases
super(MwsCustomerManagedKeys, __self__).__init__(
'databricks:index/mwsCustomerManagedKeys:MwsCustomerManagedKeys',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
account_id: Optional[pulumi.Input[str]] = None,
aws_key_info: Optional[pulumi.Input[pulumi.InputType['MwsCustomerManagedKeysAwsKeyInfoArgs']]] = None,
creation_time: Optional[pulumi.Input[int]] = None,
customer_managed_key_id: Optional[pulumi.Input[str]] = None,
use_cases: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None) -> 'MwsCustomerManagedKeys':
"""
Get an existing MwsCustomerManagedKeys resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] account_id: Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
:param pulumi.Input[pulumi.InputType['MwsCustomerManagedKeysAwsKeyInfoArgs']] aws_key_info: This field is a block and is documented below.
:param pulumi.Input[int] creation_time: (Integer) Time in epoch milliseconds when the customer key was created.
:param pulumi.Input[str] customer_managed_key_id: (String) ID of the encryption key configuration object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] use_cases: *(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _MwsCustomerManagedKeysState.__new__(_MwsCustomerManagedKeysState)
__props__.__dict__["account_id"] = account_id
__props__.__dict__["aws_key_info"] = aws_key_info
__props__.__dict__["creation_time"] = creation_time
__props__.__dict__["customer_managed_key_id"] = customer_managed_key_id
__props__.__dict__["use_cases"] = use_cases
return MwsCustomerManagedKeys(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="accountId")
def account_id(self) -> pulumi.Output[str]:
"""
Account Id that could be found in the bottom left corner of [Accounts Console](https://accounts.cloud.databricks.com/)
"""
return pulumi.get(self, "account_id")
@property
@pulumi.getter(name="awsKeyInfo")
def aws_key_info(self) -> pulumi.Output['outputs.MwsCustomerManagedKeysAwsKeyInfo']:
"""
This field is a block and is documented below.
"""
return pulumi.get(self, "aws_key_info")
@property
@pulumi.getter(name="creationTime")
def creation_time(self) -> pulumi.Output[int]:
"""
(Integer) Time in epoch milliseconds when the customer key was created.
"""
return pulumi.get(self, "creation_time")
@property
@pulumi.getter(name="customerManagedKeyId")
def customer_managed_key_id(self) -> pulumi.Output[str]:
"""
(String) ID of the encryption key configuration object.
"""
return pulumi.get(self, "customer_managed_key_id")
@property
@pulumi.getter(name="useCases")
def use_cases(self) -> pulumi.Output[Sequence[str]]:
"""
*(since v0.3.4)* List of use cases for which this key will be used. *If you've used the resource before, please add `use_cases = ["MANAGED_SERVICES"]` to keep the previous behaviour.* Possible values are:
"""
return pulumi.get(self, "use_cases")
| 49.738725
| 272
| 0.616253
| 3,267
| 31,982
| 5.811142
| 0.089073
| 0.047511
| 0.052146
| 0.032657
| 0.895128
| 0.884277
| 0.87685
| 0.865894
| 0.863208
| 0.850988
| 0
| 0.005882
| 0.287724
| 31,982
| 642
| 273
| 49.816199
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| 1
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| 0.142752
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| false
| 0.004831
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| null | 0
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0
| 7
|
f36495e33d596f7361caa6c8ba8d2e5cf0761c52
| 25,617
|
py
|
Python
|
devops/ops/api/assets_api.py
|
jinjin123/devops2.0
|
8241aff0c6677cef55c9e13b7d182fb75a814bbe
|
[
"MIT"
] | null | null | null |
devops/ops/api/assets_api.py
|
jinjin123/devops2.0
|
8241aff0c6677cef55c9e13b7d182fb75a814bbe
|
[
"MIT"
] | null | null | null |
devops/ops/api/assets_api.py
|
jinjin123/devops2.0
|
8241aff0c6677cef55c9e13b7d182fb75a814bbe
|
[
"MIT"
] | null | null | null |
# !/usr/bin/env python
# _*_ coding:utf-8 _*_
from rest_framework import viewsets, permissions
from ops.serializers import *
from ops.models import *
from rest_framework import status
from django.http import Http404
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.decorators import api_view
from ops.views.tasks.tasks import recordAssets
from django.contrib.auth.decorators import permission_required
@api_view(['GET', 'POST'])
def idc_list(request, format=None):
if request.method == 'GET':
snippets = Idc_Assets.objects.all()
serializer = IdcSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = IdcSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加机房：{name}".format(name=request.data.get("name")),
type="idc", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def idc_detail(request, id, format=None):
try:
snippet = Idc_Assets.objects.get(id=id)
except Idc_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = IdcSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = IdcSerializer(snippet, data=request.data)
old_name = snippet.name
print old_name,snippet,serializer
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="更新资产：{name}".format(name=snippet.name), type="idc",id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE' and request.user.has_perm('ops.can_delete_assets'):
if not request.user.has_perm('ops.can_delete_service_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user),
content="删除idc：{name}".format(name=snippet.name), type="idc",id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def business_list(request, format=None):
if request.method == 'GET':
snippets = Business_Assets.objects.all()
serializer = BusinessSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = BusinessSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加业务分组名称：{business_name}".format(business_name=request.data.get("business_name")),
type="business", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def business_detail(request, id, format=None):
try:
snippet = Business_Assets.objects.get(id=id)
except Business_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = BusinessSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = BusinessSerializer(snippet, data=request.data)
old_name = snippet.business_name
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改业务分组为：{old_name} -> {business_name}".format(old_name=old_name,
business_name=request.data.get(
"business_name")),
type="business", id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE' and request.user.has_perm('ops.can_delete_assets'):
if not request.user.has_perm('ops.can_delete_service_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user),
content="删除业务类型：{business_name}".format(business_name=snippet.business_name), type="business",
id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def service_list(request, format=None):
"""
List all order, or create a server assets order.
"""
if request.method == 'GET':
snippets = Service_Assets.objects.all()
serializer = ServiceSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = ServiceSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加业务类型名称：{service_name}".format(service_name=request.data.get("service_name")),
type="service", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def service_detail(request, id, format=None):
"""
Retrieve, update or delete a server assets instance.
"""
try:
snippet = Service_Assets.objects.get(id=id)
except Service_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = ServiceSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = ServiceSerializer(snippet, data=request.data)
old_name = snippet.service_name
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改业务类型为：{old_name} -> {service_name}".format(old_name=old_name,
service_name=request.data.get(
"service_name")),
type="service", id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE' and request.user.has_perm('ops.can_delete_assets'):
if not request.user.has_perm('ops.can_delete_service_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user),
content="删除业务类型：{service_name}".format(service_name=snippet.service_name), type="service",
id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def group_list(request, format=None):
"""
List all order, or create a server assets order.
"""
if request.method == 'GET':
snippets = RoleList.objects.all()
serializer = GroupSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
if not request.user.has_perm('ops.change_group'):
return Response(status=status.HTTP_403_FORBIDDEN)
serializer = GroupSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加用户组：{group_name}".format(group_name=request.data.get("name")), type="group",
id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
@permission_required('ops.change_group', raise_exception=True)
def group_detail(request, id, format=None):
"""
Retrieve, update or delete a server assets instance.
"""
try:
snippet = RoleList.objects.get(id=id)
except RoleList.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = GroupSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = GroupSerializer(snippet, data=request.data)
old_name = snippet.name
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改用户组名称：{old_name} -> {group_name}".format(old_name=old_name,
group_name=request.data.get("name")),
type="group", id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.delete_group'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user), content="删除用户组：{group_name}".format(group_name=snippet.group_name),
type="group", id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
@permission_required('ops.can_add_zone_assets', raise_exception=True)
def zone_list(request, format=None):
"""
List all order, or create a server assets order.
"""
if request.method == 'GET':
snippets = Zone_Assets.objects.all()
serializer = ZoneSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = ZoneSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加出口线路：{zone_name}".format(zone_name=request.data.get("zone_name")),
type="zone", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
@permission_required('ops.can_change_zone_assets', raise_exception=True)
def zone_detail(request, id, format=None):
"""
Retrieve, update or delete a server assets instance.
"""
try:
snippet = Zone_Assets.objects.get(id=id)
except Zone_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = ZoneSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
old_name = snippet.zone_name
serializer = ZoneSerializer(snippet, data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改出口线路类型：{old_name} -> {zone_name}".format(old_name=old_name,
zone_name=request.data.get(
"zone_name")), type="zone",
id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.can_delete_zone_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user), content="删除出口线路：{zone_name}".format(zone_name=snippet.zone_name),
type="zone", id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
@permission_required('ops.can_add_line_assets', raise_exception=True)
def line_list(request, format=None):
"""
List all order, or create a server assets order.
"""
if request.method == 'GET':
snippets = Line_Assets.objects.all()
serializer = LineSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = LineSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加出口线路：{line_name}".format(line_name=request.data.get("line_name")),
type="line", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
@permission_required('OpsManage.can_change_line_assets', raise_exception=True)
def line_detail(request, id, format=None):
"""
Retrieve, update or delete a server assets instance.
"""
try:
snippet = Line_Assets.objects.get(id=id)
except Line_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = LineSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = LineSerializer(snippet, data=request.data)
old_name = snippet.line_name
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改出口线路类型：{old_name} -> {line_name}".format(old_name=old_name,
line_name=request.data.get(
"line_name")), type="line",
id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.can_delete_line_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user), content="删除出口线路：{line_name}".format(line_name=snippet.line_name),
type="line", id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def raid_list(request, format=None):
"""
List all order, or create a server assets order.
"""
if request.method == 'GET':
snippets = Raid_Assets.objects.all()
serializer = RaidSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = RaidSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="添加Raid类型：{raid_name}".format(raid_name=request.data.get("raid_name")),
type="raid", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def raid_detail(request, id, format=None):
"""
Retrieve, update or delete a server assets instance.
"""
try:
snippet = Raid_Assets.objects.get(id=id)
except Raid_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = RaidSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
old_name = snippet.raid_name
serializer = RaidSerializer(snippet, data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user),
content="修改Raid类型：{old_name} -> {raid_name}".format(old_name=old_name,
raid_name=request.data.get(
"raid_name")), type="raid",
id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.can_delete_raid_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user), content="删除Raid类型：{raid_name}".format(raid_name=snippet.raid_name),
type="raid", id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def asset_list(request, format=None):
if request.method == 'GET':
snippets = Assets.objects.all()
serializer = AssetsSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = AssetsSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="添加资产：{name}".format(name=request.data.get("name")),
type="assets", id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def asset_detail(request, id, format=None):
try:
snippet = Assets.objects.get(id=id)
except Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = AssetsSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = AssetsSerializer(snippet, data=request.data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="更新资产：{name}".format(name=snippet.name), type="assets",id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.delete_asset_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
recordAssets.delay(user=str(request.user), content="删除资产：{name}".format(name=snippet.name), type="assets",
id=id)
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def asset_server_list(request, format=None):
if request.method == 'GET':
snippets = HostInfo.objects.all()
serializer = ServerSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
if (request.data.get('data')):
data = request.data.get('data')
else:
data = request.data
# keyword = ['ip','user','login_type','pwd','key']
"""
添加资产不检查server login , 使用crontab定时抽取 user不为空的资产信息 sshCheck 在更新状态 到数据库里
"""
print(data)
serializer = ServerSerializer(data=data)
# print serializer
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="添加服务器资产：{ip}".format(ip=data.get("ip")), type="server",
id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def asset_server_detail(request, id, format=None):
try:
snippet = HostInfo.objects.get(id=id)
except HostInfo.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = ServerSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
'''如果更新字段包含assets则先更新总资产表'''
print(request.data.get('data'))
if (request.data.get('data')):
data = request.data.get('data')
else:
data = request.data
if (data.get('assets')):
assets_data = data.pop('assets')
try:
assets_snippet = Assets.objects.get(id=snippet.assets.id)
assets = AssetsSerializer(assets_snippet, data=assets_data)
except Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if assets.is_valid():
assets.save()
recordAssets.delay(user=str(request.user), content="修改服务器资产：{ip}".format(ip=snippet.ip), type="server",
id=id)
print(data)
serializer = ServerSerializer(snippet,data=data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.can_delete_server_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
try:
assets_snippet = Assets.objects.get(id=snippet.assets.id)
assets_snippet.delete()
recordAssets.delay(user=str(request.user), content="删除服务器资产：{ip}".format(ip=snippet.ip), type="server",
id=id)
except Assets.DoesNotExist:
pass
return Response(status=status.HTTP_204_NO_CONTENT)
@api_view(['GET', 'POST'])
def asset_net_list(request, format=None):
"""
List all order, or create a new net assets.
"""
if request.method == 'GET':
snippets = Network_Assets.objects.all()
serializer = NetworkSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
if (request.data.get('data')):
data = request.data.get('data')
else:
data = request.data
serializer = NetworkSerializer(data=data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="添加网络设备资产：{ip}".format(ip=data.get("ip")), type="net",
id=serializer.data.get('id'))
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def asset_net_detail(request, id, format=None):
"""
Retrieve, update or delete a net assets instance.
"""
try:
snippet = Network_Assets.objects.get(id=id)
except Network_Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = NetworkSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
'''如果更新字段包含assets则先更新总资产表'''
if (request.data.get('data')):
data = request.data.get('data')
else:
data = request.data
if (data.get('assets')):
assets_data = data.pop('assets')
try:
assets_snippet = Assets.objects.get(id=snippet.assets.id)
assets = AssetsSerializer(assets_snippet, data=assets_data)
except Assets.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if assets.is_valid():
assets.save()
serializer = NetworkSerializer(snippet, data=data)
if serializer.is_valid():
serializer.save()
recordAssets.delay(user=str(request.user), content="更新网络设备资产：{ip}".format(ip=snippet.ip), type="net", id=id)
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
if not request.user.has_perm('ops.delete_net_assets'):
return Response(status=status.HTTP_403_FORBIDDEN)
snippet.delete()
try:
assets_snippet = Assets.objects.get(id=snippet.assets.id)
assets_snippet.delete()
recordAssets.delay(user=str(request.user), content="删除网络设备资产：{ip}".format(ip=snippet.ip), type="net", id=id)
except Assets.DoesNotExist:
pass
return Response(status=status.HTTP_204_NO_CONTENT)
| 43.640545
| 124
| 0.615099
| 2,814
| 25,617
| 5.445984
| 0.057569
| 0.084959
| 0.065775
| 0.073083
| 0.861338
| 0.840261
| 0.817684
| 0.786949
| 0.765024
| 0.737031
| 0
| 0.010303
| 0.268728
| 25,617
| 586
| 125
| 43.715017
| 0.807772
| 0.004177
| 0
| 0.637131
| 0
| 0
| 0.072248
| 0.020107
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.004219
| 0.021097
| null | null | 0.008439
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f3a3102984d588e2abc373267f16845fca67864a
| 17,427
|
py
|
Python
|
import_models.py
|
CHREC/nn_benching-1
|
3b2e21e244edc20a226930104f9eb82f097296b0
|
[
"MIT"
] | 3
|
2020-01-07T17:51:10.000Z
|
2021-03-27T02:03:53.000Z
|
import_models.py
|
CHREC/nn_benching-1
|
3b2e21e244edc20a226930104f9eb82f097296b0
|
[
"MIT"
] | null | null | null |
import_models.py
|
CHREC/nn_benching-1
|
3b2e21e244edc20a226930104f9eb82f097296b0
|
[
"MIT"
] | 1
|
2021-07-13T16:30:04.000Z
|
2021-07-13T16:30:04.000Z
|
import torchvision.models as models
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
import torch
#from models_dir.p_s_s.models import duc_hdc, fcn8s, fcn16s, fcn32s, gcn, psp_net, seg_net, u_net
from efficientnet_pytorch import EfficientNet
def import_models(download):
"""
Commented out models do not have pre-trained variants available.
This function will load and cache all of these models if they are not already cached.
On CRC we can just run this initially as its own python script to download and cache the models.
Afterwards, this function will be implemented into some other testing code that will call it in
a similar fashion to what is shown below.
"""
print("Loading or checking if models are cached...\n")
print("This may take a while. Progress will be shown if models are being downloaded.\n ", flush=True)
############################################################################################
# Image Classification
alexnet = models.alexnet(pretrained=download, progress=True)
squeezenet1_0 = models.squeezenet1_0(pretrained=download, progress=True)
squeezenet1_1 = models.squeezenet1_1(pretrained=download, progress=True)
vgg16 = models.vgg16_bn(pretrained=download, progress=True)
vgg19 = models.vgg19_bn(pretrained=download, progress=True)
resnet18 = models.resnet18(pretrained=download, progress=True)
resnet34 = models.resnet34(pretrained=download, progress=True)
resnet50 = models.resnet50(pretrained=download, progress=True)
resnet101 = models.resnet101(pretrained=download, progress=True)
resnet152 = models.resnet152(pretrained=download, progress=True)
densenet121 = models.densenet121(pretrained=download, progress=True, memory_efficient=False)
densenet161 = models.densenet161(pretrained=download, progress=True, memory_efficient=False)
densenet201 = models.densenet201(pretrained=download, progress=True, memory_efficient=False)
densenet121_efficient = models.densenet121(pretrained=download, progress=True, memory_efficient=True)
densenet161_efficient = models.densenet161(pretrained=download, progress=True, memory_efficient=True)
densenet201_efficient = models.densenet201(pretrained=download, progress=True, memory_efficient=True)
googlenet = models.googlenet(pretrained=download, progress=True)
shufflenet_v2_1 = models.shufflenet_v2_x1_0(pretrained=download, progress=True)
shufflenet_v2_0_5 = models.shufflenet_v2_x0_5(pretrained=download, progress=True)
#shufflenet_v2_1_5 = models.shufflenet_v2_x1_5(pretrained=download, progress=True)
#shufflenet_v2_2 = models.shufflenet_v2_x2_0(pretrained=download, progress=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=download, progress=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=download, progress=True)
resnext101_32x8d = models.resnext101_32x8d(pretrained=download, progress=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=download, progress=True)
wide_resnet101_2 = models.wide_resnet101_2(pretrained=download, progress=True)
mnasnet1_0 = models.mnasnet1_0(pretrained=download, progress=True)
mnasnet0_5 = models.mnasnet0_5(pretrained=download, progress=True)
#mnasnet0_75 = models.mnasnet0_75(pretrained=download, progress=True)
#mnasnet1_3 = models.mnasnet1_3(pretrained=download, progress=True)
print("Checking efficientnet", flush=True)
efficientnet_b0 = EfficientNet.from_pretrained('efficientnet-b0')
efficientnet_b1 = EfficientNet.from_pretrained('efficientnet-b1')
efficientnet_b2 = EfficientNet.from_pretrained('efficientnet-b2')
efficientnet_b3 = EfficientNet.from_pretrained('efficientnet-b3')
efficientnet_b4 = EfficientNet.from_pretrained('efficientnet-b4')
efficientnet_b5 = EfficientNet.from_pretrained('efficientnet-b5')
efficientnet_b6 = EfficientNet.from_pretrained('efficientnet-b6')
efficientnet_b7 = EfficientNet.from_pretrained('efficientnet-b7')
###########################################################################################
# Video Classification
# resnet_3d = models.video.r3d_18(pretrained=download, progress=True)
# resnet_mixed_conv = models.video.mc3_18(pretrained=download, progress=True)
# resnet_2_1D = models.video.r2plus1d_18(pretrained=download, progress=True)
###########################################################################################
# Object Detection
fasterrcnn_resnet50 = models.detection.fasterrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
maskcnn_resnet50 = models.detection.maskrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
keypointrcnn_resnet50 = models.detection.keypointrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True)
###########################################################################################
# Semantic Segmentation
fcn_resnet50 = models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
fcn_resnet101 = models.segmentation.fcn_resnet101(pretrained=download, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet50 = models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet101 = models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)
###########################################################################################
# Generative Adversarial Networks
###########################################################################################
checking_input = True
while (checking_input):
model_type = int(input("Choose the type of model you want:\n1 (Image Classification)\n2 (Video Classification)\n3 (Object Detection)\n4 (Semantic Segmentation)\n5 (GAN)\nInput: "))
print(model_type)
# Convolutional Neual Networks
if model_type == 1:
models_dict = {
"alexnet" : alexnet,
"squeezenet1_0" : squeezenet1_0,
"squeezenet1_1": squeezenet1_1,
"vgg16" : vgg16,
"vgg19" : vgg19,
"resnet18" : resnet18,
"resnet34" : resnet34,
"resnet50" : resnet50,
"resnet101" : resnet101,
"resnet152" : resnet152,
"densenet121" : densenet121,
"densenet161" : densenet161,
"densenet201" : densenet201,
"densenet121_efficient": densenet121_efficient,
"densenet161_efficient": densenet161_efficient,
"densenet201_efficient": densenet201_efficient,
"googlenet" : googlenet,
"shufflenet_v2_1" : shufflenet_v2_1,
"shufflenet_v2_0_5": shufflenet_v2_0_5,
#"shufflenet_v2_1_5": shufflenet_v2_1_5,
#"shufflenet_v2_2": shufflenet_v2_2,
"mobilenet_v2" : mobilenet_v2,
"resnext50_32x4d" : resnext50_32x4d,
"resnext101_32x4d": resnext101_32x8d,
"wide_resnet50_2" : wide_resnet50_2,
"wide_resnet101_2" : wide_resnet101_2,
"mnasnet1_0" : mnasnet1_0,
#"mnasnet1_3": mnasnet1_3,
"mnasnet0_5": mnasnet0_5,
#"mnasnet0_75": mnasnet0_75,
"efficientnet_b0" : efficientnet_b0,
"efficientnet_b1" : efficientnet_b1,
"efficientnet_b2" : efficientnet_b2,
"efficientnet_b3" : efficientnet_b3,
"efficientnet_b4" : efficientnet_b4,
"efficientnet_b5" : efficientnet_b5,
"efficientnet_b6" : efficientnet_b6,
"efficientnet_b7" : efficientnet_b7
}
checking_input = False
return models_dict
# Video Classification
elif model_type == 2:
checking_input = False
models_dict = {
"resnet_3d" : resnet_3d,
"resnet_mixed_conv" : resnet_mixed_conv,
"resnet_2_1D" : resnet_2_1D
}
return models_dict
# Object Detection
elif model_type == 3:
checking_input = False
models_dict = {
"fasterrcnn_resnet50" : fasterrcnn_resnet50,
"maskcnn_resnet50" : maskcnn_resnet50,
"keypointrcnn_resnet50" : keypointrcnn_resnet50
}
return models_dict
# Semantic Segmentation
elif model_type == 4:
checking_input = False
models_dict = {
"fcn_resnet50" : fcn_resnet50,
"fcn_resnet101" : fcn_resnet101,
"deeplabv3_resnet50" : deeplabv3_resnet50,
"deeplabv3_resnet101" : deeplabv3_resnet101
}
return models_dict
# Generative Adversarial Networks
elif model_type == 5:
checking_input = False
models_dict = {
}
return models_dict
else:
print("You did not choose a valid input...")
def import_all(download):
"""
Commented out models do not have pre-trained variants available.
This function will load and cache all of these models if they are not already cached.
On CRC we can just run this initially as its own python script to download and cache the models.
Afterwards, this function will be implemented into some other testing code that will call it in
a similar fashion to what is shown below.
"""
print("Loading or checking if models are cached...\n")
print("This may take a while. Progress will be shown if models are being downloaded.\n ")
############################################################################################
# Image Classification
alexnet = models.alexnet(pretrained=download, progress=True)
squeezenet1_0 = models.squeezenet1_0(pretrained=download, progress=True)
squeezenet1_1 = models.squeezenet1_1(pretrained=download, progress=True)
vgg16 = models.vgg16_bn(pretrained=download, progress=True)
vgg19 = models.vgg19_bn(pretrained=download, progress=True)
resnet18 = models.resnet18(pretrained=download, progress=True)
resnet34 = models.resnet34(pretrained=download, progress=True)
resnet50 = models.resnet50(pretrained=download, progress=True)
resnet101 = models.resnet101(pretrained=download, progress=True)
resnet152 = models.resnet152(pretrained=download, progress=True)
densenet121 = models.densenet121(pretrained=download, progress=True, memory_efficient=False)
densenet161 = models.densenet161(pretrained=download, progress=True, memory_efficient=False)
densenet201 = models.densenet201(pretrained=download, progress=True, memory_efficient=False)
densenet121_efficient = models.densenet121(pretrained=download, progress=True, memory_efficient=True)
densenet161_efficient = models.densenet161(pretrained=download, progress=True, memory_efficient=True)
densenet201_efficient = models.densenet201(pretrained=download, progress=True, memory_efficient=True)
googlenet = models.googlenet(pretrained=download, progress=True)
shufflenet_v2_1 = models.shufflenet_v2_x1_0(pretrained=download, progress=True)
shufflenet_v2_0_5 = models.shufflenet_v2_x0_5(pretrained=download, progress=True)
#shufflenet_v2_1_5 = models.shufflenet_v2_x1_5(pretrained=download, progress=True)
#shufflenet_v2_2 = models.shufflenet_v2_x2_0(pretrained=download, progress=True)
mobilenet_v2 = models.mobilenet_v2(pretrained=download, progress=True)
resnext50_32x4d = models.resnext50_32x4d(pretrained=download, progress=True)
resnext101_32x8d = models.resnext101_32x8d(pretrained=download, progress=True)
wide_resnet50_2 = models.wide_resnet50_2(pretrained=download, progress=True)
wide_resnet101_2 = models.wide_resnet101_2(pretrained=download, progress=True)
mnasnet1_0 = models.mnasnet1_0(pretrained=download, progress=True)
mnasnet0_5 = models.mnasnet0_5(pretrained=download, progress=True)
#mnasnet0_75 = models.mnasnet0_75(pretrained=download, progress=True)
#mnasnet1_3 = models.mnasnet1_3(pretrained=download, progress=True)
efficientnet_b0 = EfficientNet.from_pretrained('efficientnet-b0')
efficientnet_b1 = EfficientNet.from_pretrained('efficientnet-b1')
efficientnet_b2 = EfficientNet.from_pretrained('efficientnet-b2')
efficientnet_b3 = EfficientNet.from_pretrained('efficientnet-b3')
efficientnet_b4 = EfficientNet.from_pretrained('efficientnet-b4')
efficientnet_b5 = EfficientNet.from_pretrained('efficientnet-b5')
efficientnet_b6 = EfficientNet.from_pretrained('efficientnet-b6')
efficientnet_b7 = EfficientNet.from_pretrained('efficientnet-b7')
#
#
# ###########################################################################################
# # Video Classification
# # resnet_3d = models.video.r3d_18(pretrained=download, progress=True)
# # resnet_mixed_conv = models.video.mc3_18(pretrained=download, progress=True)
# # resnet_2_1D = models.video.r2plus1d_18(pretrained=download, progress=True)
#
# ###########################################################################################
# # Object Detection
#
fasterrcnn_resnet50 = models.detection.fasterrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
maskcnn_resnet50 = models.detection.maskrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=91, pretrained_backbone=True)
keypointrcnn_resnet50 = models.detection.keypointrcnn_resnet50_fpn(pretrained=download, progress=True, num_classes=2, num_keypoints=17, pretrained_backbone=True)
#
# ###########################################################################################
# # Semantic Segmentation
#
fcn_resnet50 = models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
fcn_resnet101 = models.segmentation.fcn_resnet101(pretrained=download, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet50 = models.segmentation.deeplabv3_resnet50(pretrained=False, progress=True, num_classes=21, aux_loss=None)
deeplabv3_resnet101 = models.segmentation.deeplabv3_resnet101(pretrained=False, progress=True, num_classes=21, aux_loss=None)
###########################################################################################
# Generative Adversarial Networks
###########################################################################################
models_dict = {
"alexnet" : alexnet,
"fcn_resnet50" : fcn_resnet50,
"fcn_resnet101" : fcn_resnet101,
"deeplabv3_resnet50" : deeplabv3_resnet50,
"deeplabv3_resnet101" : deeplabv3_resnet101,
"squeezenet1_0" : squeezenet1_0,
"squeezenet1_1": squeezenet1_1,
"vgg16" : vgg16,
"vgg19" : vgg19,
"resnet18" : resnet18,
"resnet34" : resnet34,
"resnet50" : resnet50,
"resnet101" : resnet101,
"resnet152" : resnet152,
"densenet121" : densenet121,
"densenet161" : densenet161,
"densenet201" : densenet201,
"densenet121_efficient": densenet121_efficient,
"densenet161_efficient": densenet161_efficient,
"densenet201_efficient": densenet201_efficient,
"googlenet" : googlenet,
"shufflenet_v2_1" : shufflenet_v2_1,
"shufflenet_v2_0_5": shufflenet_v2_0_5,
#"shufflenet_v2_1_5": shufflenet_v2_1_5,
#"shufflenet_v2_2": shufflenet_v2_2,
"mobilenet_v2" : mobilenet_v2,
"resnext50_32x4d" : resnext50_32x4d,
"resnext101_32x4d": resnext101_32x8d,
"wide_resnet50_2" : wide_resnet50_2,
"wide_resnet101_2" : wide_resnet101_2,
"mnasnet1_0" : mnasnet1_0,
#"mnasnet1_3": mnasnet1_3,
"mnasnet0_5": mnasnet0_5,
#"mnasnet0_75": mnasnet0_75,
"efficientnet_b0" : efficientnet_b0,
"efficientnet_b1" : efficientnet_b1,
"efficientnet_b2" : efficientnet_b2,
"efficientnet_b3" : efficientnet_b3,
"efficientnet_b4" : efficientnet_b4,
"efficientnet_b5" : efficientnet_b5,
"efficientnet_b6" : efficientnet_b6,
"efficientnet_b7" : efficientnet_b7,
}
# "maskcnn_resnet50" : maskcnn_resnet50,
# "keypointrcnn_resnet50" : keypointrcnn_resnet50,
# "fasterrcnn_resnet50" : fasterrcnn_resnet50,
# "resnet_3d" : resnet_3d,
# "resnet_mixed_conv" : resnet_mixed_conv,
# "resnet_2_1D" : resnet_2_1D,
return models_dict
| 48.008264
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| false
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| 0.033175
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| null | 0
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0
| 8
|
caca9e7e4c7e9fcaa5eda9ce8eed667687e6876a
| 34,263
|
py
|
Python
|
tests/scheduling/test_condition.py
|
bdsinger/PsyNeuLink
|
71d8a0bb1691ff85061d4ad3de866d9930a69a73
|
[
"Apache-2.0"
] | null | null | null |
tests/scheduling/test_condition.py
|
bdsinger/PsyNeuLink
|
71d8a0bb1691ff85061d4ad3de866d9930a69a73
|
[
"Apache-2.0"
] | null | null | null |
tests/scheduling/test_condition.py
|
bdsinger/PsyNeuLink
|
71d8a0bb1691ff85061d4ad3de866d9930a69a73
|
[
"Apache-2.0"
] | null | null | null |
import logging
import pytest
from psyneulink.core.components.functions.transferfunctions import Linear
from psyneulink.core.components.mechanisms.processing.transfermechanism import TransferMechanism
from psyneulink.core.components.projections.pathway.mappingprojection import MappingProjection
from psyneulink.core.compositions.composition import Composition
from psyneulink.core.scheduling.condition import AfterCall, AfterNCalls, AfterNCallsCombined, AfterNPasses, AfterNTrials, AfterPass, AfterTrial, All, AllHaveRun, Always, Any, AtPass, AtTimeStep, AtTrial, BeforeNCalls, BeforePass, BeforeTimeStep, BeforeTrial, Condition, ConditionError, ConditionSet, EveryNCalls, EveryNPasses, NWhen, Not, WhenFinished, WhenFinishedAll, WhenFinishedAny, WhileNot
from psyneulink.core.scheduling.scheduler import Scheduler
from psyneulink.core.scheduling.time import TimeScale
logger = logging.getLogger(__name__)
class TestCondition:
def test_invalid_input_WhenFinished(self):
with pytest.raises(ConditionError):
WhenFinished(None).is_satisfied()
def test_invalid_input_WhenFinishedAny_1(self):
with pytest.raises(ConditionError):
WhenFinished(None).is_satisfied()
def test_invalid_input_WhenFinishedAny_2(self):
with pytest.raises(ConditionError):
WhenFinished({None}).is_satisfied()
def test_invalid_input_WhenFinishedAll_1(self):
with pytest.raises(ConditionError):
WhenFinished(None).is_satisfied()
def test_invalid_input_WhenFinishedAll_2(self):
with pytest.raises(ConditionError):
WhenFinished({None}).is_satisfied()
def test_additional_args(self):
class OneSatisfied(Condition):
def __init__(self, a):
def func(a, b):
return a or b
super().__init__(func, a)
cond = OneSatisfied(True)
assert cond.is_satisfied(True)
assert cond.is_satisfied(False)
cond = OneSatisfied(False)
assert cond.is_satisfied(True)
assert not cond.is_satisfied(False)
def test_additional_kwargs(self):
class OneSatisfied(Condition):
def __init__(self, a, c=True):
def func(a, b, c=True):
return a or b or c
super().__init__(func, a, c=True)
cond = OneSatisfied(True)
assert cond.is_satisfied(True)
assert cond.is_satisfied(False, c=True)
assert cond.is_satisfied(False, c=False)
cond = OneSatisfied(True, c=False)
assert cond.is_satisfied(True)
assert cond.is_satisfied(False, c=True)
assert cond.is_satisfied(False, c=False)
cond = OneSatisfied(False)
assert cond.is_satisfied(True)
assert cond.is_satisfied(False, c=True)
assert not cond.is_satisfied(False, c=False)
assert not cond.is_satisfied(False, c=False, extra_arg=True)
class TestGeneric:
def test_WhileNot_AtPass(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, WhileNot(lambda sched: sched.clock.get_total_times_relative(TimeScale.PASS, TimeScale.TRIAL) == 0, sched))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhileNot_AtPass_in_middle(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, WhileNot(lambda sched: sched.clock.get_total_times_relative(TimeScale.PASS, TimeScale.TRIAL) == 2, sched))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, set(), A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
class TestRelative:
def test_Any_end_before_one_finished(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
for m in [A]:
comp.add_node(m)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = Any(AfterNCalls(A, 10), AtPass(5))
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A for _ in range(5)]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_All_end_after_one_finished(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
for m in [A]:
comp.add_node(m)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = Any(AfterNCalls(A, 5), AtPass(10))
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A for _ in range(5)]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_Not_AtPass(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, Not(AtPass(0)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_Not_AtPass_in_middle(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, Not(AtPass(2)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, set(), A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
@pytest.mark.parametrize(
'n,expected_output', [
(0, ['A', 'A', 'A', 'A', 'A', 'A']),
(1, ['A', 'A', 'A', 'B', 'A', 'A', 'A']),
(2, ['A', 'A', 'A', 'B', 'A', 'B', 'A', 'A']),
]
)
def test_NWhen_AfterNCalls(self, n, expected_output):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, NWhen(AfterNCalls(A, 3), n))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(A, 6)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A if x == 'A' else B for x in expected_output]
assert output == pytest.helpers.setify_expected_output(expected_output)
class TestTime:
def test_BeforeTimeStep(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforeTimeStep(2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_BeforeTimeStep_2(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(name='B')
comp.add_node(A)
comp.add_node(B)
comp.add_projection(MappingProjection(), A, B)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforeTimeStep(2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, B, B, B, B, B]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtTimeStep(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtTimeStep(0))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, set(), set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_BeforePass(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforePass(2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtPass(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(0))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, set(), set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtPass_underconstrained(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(0))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 2)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, B, C, B, C]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtPass_in_middle(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), set(), A, set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtPass_at_end(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(5))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), set(), set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtPass_after_end(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(6))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), set(), set(), set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterPass(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AfterPass(0))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterNPasses(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AfterNPasses(1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_BeforeTrial(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforeTrial(4))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(5)
termination_conds[TimeScale.TRIAL] = AtPass(1)
comp.run(
inputs={A: range(6)},
scheduler_processing=sched,
termination_processing=termination_conds
)
output = sched.execution_list[comp.default_execution_id]
expected_output = [A, A, A, A, set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AtTrial(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
termination_conds = {}
termination_conds[TimeScale.RUN] = AtTrial(4)
termination_conds[TimeScale.TRIAL] = AtPass(1)
comp.run(
inputs={A: range(6)},
scheduler_processing=sched,
termination_processing=termination_conds
)
output = sched.execution_list[comp.default_execution_id]
expected_output = [A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterTrial(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterTrial(4)
termination_conds[TimeScale.TRIAL] = AtPass(1)
comp.run(
inputs={A: range(6)},
scheduler_processing=sched,
termination_processing=termination_conds
)
output = sched.execution_list[comp.default_execution_id]
expected_output = [A, A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterNTrials(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, AfterNPasses(1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [set(), A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
class TestComponentBased:
def test_BeforeNCalls(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
comp.add_node(A)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforeNCalls(A, 3))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, A, set(), set()]
assert output == pytest.helpers.setify_expected_output(expected_output)
# NOTE:
# The behavior is not desired (i.e. depending on the order mechanisms are checked, B running AtCall(A, x))
# may run on both the xth and x+1st call of A; if A and B are not parent-child
# A fix could invalidate key assumptions and affect many other conditions
# Since this condition is unlikely to be used, it's best to leave it for now
# def test_AtCall(self):
# comp = Composition()
# A = TransferMechanism(function = Linear(slope=5.0, intercept = 2.0), name = 'A')
# B = TransferMechanism(function = Linear(intercept = 4.0), name = 'B')
# C = TransferMechanism(function = Linear(intercept = 1.5), name = 'C')
# for m in [A,B]:
# comp.add_node(m)
# sched = Scheduler(composition=comp)
# sched.add_condition(A, Always())
# sched.add_condition(B, AtCall(A, 3))
# termination_conds = {}
# termination_conds[TimeScale.RUN] = AfterNTrials(1)
# termination_conds[TimeScale.TRIAL] = AtPass(5)
# output = list(sched.run(termination_conds=termination_conds))
# expected_output = [A, A, set([A, B]), A, A]
# assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterCall(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_node(m)
sched = Scheduler(composition=comp)
sched.add_condition(B, AfterCall(A, 3))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, A, set([A, B]), set([A, B])]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterNCalls(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_node(m)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, AfterNCalls(A, 3))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, set([A, B]), set([A, B]), set([A, B])]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_composite_condition_multi(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, All(
Any(
AfterPass(6),
AfterNCalls(B, 2)
),
Any(
AfterPass(2),
AfterNCalls(B, 3)
)
)
)
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 3)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
A, A, B, A, A, B, C, A, C, A, B, C
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAny_1(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
A._is_finished = True
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B._is_finished = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNPasses(1))
sched.add_condition(C, WhenFinishedAny(A, B))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 1)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A, B]), C
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAny_2(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
A._is_finished = False
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B._is_finished = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNPasses(1))
sched.add_condition(C, WhenFinishedAny(A, B))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(A, 5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A, B]), C, set([A, B]), C, set([A, B]), C, set([A, B]), C, set([A, B])
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAny_noargs(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, Always())
sched.add_condition(C, Always())
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = WhenFinishedAny()
output = []
i = 0
for step in sched.run(termination_conds=termination_conds):
if i == 3:
A._is_finished = True
B._is_finished = True
if i == 4:
C._is_finished = True
output.append(step)
i += 1
expected_output = [
set([A, B]), C, set([A, B]), C,
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAll_1(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
A._is_finished = True
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B._is_finished = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNPasses(1))
sched.add_condition(C, WhenFinishedAll(A, B))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 1)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A, B]), C
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAll_2(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
A._is_finished = False
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B._is_finished = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNPasses(1))
sched.add_condition(C, WhenFinishedAll(A, B))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(A, 5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A, B]), set([A, B]), set([A, B]), set([A, B]), set([A, B]),
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAll_noargs(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, Always())
sched.add_condition(C, Always())
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = WhenFinishedAll()
output = []
i = 0
for step in sched.run(termination_conds=termination_conds):
if i == 3:
A._is_finished = True
B._is_finished = True
if i == 4:
C._is_finished = True
output.append(step)
i += 1
expected_output = [
set([A, B]), C, set([A, B]), C, set([A, B]),
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AfterNCallsCombined(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(B, 2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCallsCombined(B, C, n=4)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
A, A, B, A, A, B, C, A, A, B
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AllHaveRun(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(B, 2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AllHaveRun(A, B, C)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
A, A, B, A, A, B, C
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_AllHaveRun_2(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0), name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(B, 2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AllHaveRun()
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
A, A, B, A, A, B, C
]
assert output == pytest.helpers.setify_expected_output(expected_output)
| 41.330519
| 395
| 0.613636
| 3,871
| 34,263
| 5.269181
| 0.049083
| 0.138844
| 0.088248
| 0.108251
| 0.903956
| 0.903074
| 0.903074
| 0.899201
| 0.891945
| 0.891945
| 0
| 0.013774
| 0.27108
| 34,263
| 828
| 396
| 41.380435
| 0.802923
| 0.032134
| 0
| 0.776235
| 0
| 0
| 0.003018
| 0
| 0
| 0
| 0
| 0
| 0.075617
| 1
| 0.070988
| false
| 0.100309
| 0.013889
| 0.003086
| 0.098765
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
1b6d09dfc4f1b4dc68d58889456369a59b9e4ee0
| 92
|
py
|
Python
|
parameters_8000.py
|
rchen33/Web2py_memo
|
71387aaef5b2220e4442514944a39266e44a8871
|
[
"BSD-3-Clause"
] | null | null | null |
parameters_8000.py
|
rchen33/Web2py_memo
|
71387aaef5b2220e4442514944a39266e44a8871
|
[
"BSD-3-Clause"
] | null | null | null |
parameters_8000.py
|
rchen33/Web2py_memo
|
71387aaef5b2220e4442514944a39266e44a8871
|
[
"BSD-3-Clause"
] | null | null | null |
password="pbkdf2(1000,20,sha512)$b38a05727410f589$82dc4c62c2132fdc53b2e69c6a362f5369ad31dd"
| 46
| 91
| 0.891304
| 7
| 92
| 11.714286
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.516484
| 0.01087
| 92
| 1
| 92
| 92
| 0.384615
| 0
| 0
| 0
| 0
| 0
| 0.869565
| 0.869565
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
948936c51930c143342f75833616fd120dea2868
| 68,602
|
py
|
Python
|
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_pinned/cmp_libquantum/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_pinned/cmp_libquantum/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_pinned/cmp_libquantum/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202689,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.0,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.144202,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.249705,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.143213,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.53712,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.142538,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 5.13891,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00522743,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0378011,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.03866,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0378011,
'Execution Unit/Register Files/Runtime Dynamic': 0.0438875,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0913431,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.257839,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 1.44691,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00126299,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00126299,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00111063,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000435721,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000555355,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00419197,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.011732,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0371649,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.36401,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.168797,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.126229,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.69901,
'Instruction Fetch Unit/Runtime Dynamic': 0.348114,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0612548,
'L2/Runtime Dynamic': 0.0190464,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 1.87664,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.337467,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0206901,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.02069,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 1.97474,
'Load Store Unit/Runtime Dynamic': 0.460193,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0510181,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.102036,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0181065,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0190262,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.146985,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0276724,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.336786,
'Memory Management Unit/Runtime Dynamic': 0.0466986,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 16.7724,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.00737368,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0758891,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.0832628,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 2.40423,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202689,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.0,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0554129,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.0893789,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0451155,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.189907,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.063376,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 3.94672,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00232427,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0168073,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0171894,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0168073,
'Execution Unit/Register Files/Runtime Dynamic': 0.0195136,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0354083,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.0996096,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 0.917096,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00056203,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00056203,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000494145,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000193817,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000246927,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00186513,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00522369,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0165246,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.05111,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0748018,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0561249,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 3.32063,
'Instruction Fetch Unit/Runtime Dynamic': 0.15454,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0269275,
'L2/Runtime Dynamic': 0.0082039,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 1.51841,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.148152,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.00910026,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.00910027,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 1.56138,
'Load Store Unit/Runtime Dynamic': 0.202132,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0224397,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.0448794,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.00796392,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.00836825,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.0653539,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0122629,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.235143,
'Memory Management Unit/Runtime Dynamic': 0.0206312,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
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'Peak Dynamic': 12.6803,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00250008,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0285324,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0310325,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 1.33364,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202689,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.0,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0556844,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.0898168,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0453365,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.190838,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.0636879,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 3.94733,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00233565,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.01689,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0172736,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.01689,
'Execution Unit/Register Files/Runtime Dynamic': 0.0196092,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0355825,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.100084,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 0.918597,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000564757,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000564757,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000496549,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000194763,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000248136,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.0018742,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00524884,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0166055,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.05625,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0751467,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0563999,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 3.32603,
'Instruction Fetch Unit/Runtime Dynamic': 0.155275,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0270445,
'L2/Runtime Dynamic': 0.00823457,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 1.51961,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.148779,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.00913928,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0091392,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 1.56277,
'Load Store Unit/Runtime Dynamic': 0.202989,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0225359,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.0450714,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.00799806,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.00840408,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.235522,
'Memory Management Unit/Runtime Dynamic': 0.0207236,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 12.6882,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
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'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
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'Renaming Unit/Runtime Dynamic': 0.0311846,
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'Runtime Dynamic': 1.337,
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{'Area': 32.0201,
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0
| 7
|
b6e01231f6ddaed17eabf22f2cf909de2da3fd64
| 8,289
|
py
|
Python
|
tests/test_backend.py
|
pbaehr/django-cas-ng
|
470fc9ab4d6110cf39cc434c8317097546aa4ced
|
[
"MIT"
] | null | null | null |
tests/test_backend.py
|
pbaehr/django-cas-ng
|
470fc9ab4d6110cf39cc434c8317097546aa4ced
|
[
"MIT"
] | 1
|
2017-07-17T12:31:28.000Z
|
2017-07-31T08:59:35.000Z
|
tests/test_backend.py
|
WernerRaath/django-cas-ng
|
2999648af76e888ea0122c8f727451396cf9f724
|
[
"MIT"
] | 1
|
2019-03-06T09:44:08.000Z
|
2019-03-06T09:44:08.000Z
|
from __future__ import absolute_import
import sys
import pytest
from django.test import RequestFactory
from django_cas_ng import backends
@pytest.mark.django_db
def test_backend_authentication_creating_a_user(monkeypatch, django_user_model):
"""
Test the case where CAS authentication is creating a new user.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'ticket': ticket, 'service': service}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
# sanity check
assert not django_user_model.objects.filter(
username='test@example.com',
).exists()
backend = backends.CASBackend()
user = backend.authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is not None
assert user.username == 'test@example.com'
assert django_user_model.objects.filter(
username='test@example.com',
).exists()
def test_backend_authentication_do_not_create_user(monkeypatch, django_user_model, settings):
"""
Test the case where CAS authentication is creating a new user.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'ticket': ticket, 'service': service}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
# sanity check
assert not django_user_model.objects.filter(
username='test@example.com',
).exists()
settings.CAS_CREATE_USER = False
backend = backends.CASBackend()
user = backend.authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is None
assert not django_user_model.objects.filter(
username='test@example.com',
).exists()
@pytest.mark.django_db
def test_backend_for_existing_user(monkeypatch, django_user_model):
"""
Test the case where CAS authenticates an existing user.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'ticket': ticket, 'service': service}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
existing_user = django_user_model.objects.create_user('test@example.com', '')
backend = backends.CASBackend()
user = backend.authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is not None
assert user.username == 'test@example.com'
assert user == existing_user
@pytest.mark.django_db
def test_backend_for_existing_user(monkeypatch, django_user_model):
"""
Test the case where CAS authenticates an existing user, but request argument is None.
"""
def mock_verify(ticket, service):
return 'test@example.com', {'ticket': ticket, 'service': service}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
existing_user = django_user_model.objects.create_user('test@example.com', '')
backend = backends.CASBackend()
user = backend.authenticate(
None, ticket='fake-ticket', service='fake-service',
)
assert user is not None
assert user.username == 'test@example.com'
assert user == existing_user
@pytest.mark.django_db
def test_backend_for_failed_auth(monkeypatch, django_user_model):
"""
Test CAS authentication failure.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return None, {}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
assert not django_user_model.objects.filter(
username='test@example.com',
).exists()
backend = backends.CASBackend()
user = backend.authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is None
assert not django_user_model.objects.filter(
username='test@example.com',
).exists()
@pytest.mark.django_db
def test_backend_user_can_authenticate(monkeypatch, django_user_model):
"""
Test CAS authentication failure.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'ticket': ticket, 'service': service}, None
# we mock out the verify method so that we can bypass the external http
# calls needed for real authentication since we are testing the logic
# around authentication.
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
user = backends.CASBackend().authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is not None
class AllowNoneBackend(backends.CASBackend):
def user_can_authenticate(self, user):
return False
user = AllowNoneBackend().authenticate(
request, ticket='fake-ticket', service='fake-service',
)
assert user is None
@pytest.mark.django_db
def test_backend_does_not_apply_attributes_by_default(monkeypatch):
"""
Test to make sure attributes returned from the provider are not assigned to
the User model by default.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'is_staff': 'True', 'is_superuser': 'False'}, None
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
backend = backends.CASBackend()
user = backend.authenticate(request, ticket='fake-ticket',
service='fake-service')
assert user is not None
assert not user.is_staff
@pytest.mark.django_db
def test_backend_applies_attributes_when_set(monkeypatch, settings):
"""
If CAS_APPLY_ATTRIBUTES_TO_USER is set, make sure the attributes returned
with the ticket are added to the User model.
"""
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'is_staff': 'True', 'is_superuser': 'False'}, None
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
settings.CAS_APPLY_ATTRIBUTES_TO_USER = True
backend = backends.CASBackend()
user = backend.authenticate(request, ticket='fake-ticket',
service='fake-service')
assert user is not None
assert user.is_staff
@pytest.mark.django_db
def test_boolean_attributes_applied_as_booleans(monkeypatch, settings):
factory = RequestFactory()
request = factory.get('/login/')
request.session = {}
def mock_verify(ticket, service):
return 'test@example.com', {'is_staff': 'True', 'is_superuser': 'False'}, None
monkeypatch.setattr('cas.CASClientV2.verify_ticket', mock_verify)
settings.CAS_APPLY_ATTRIBUTES_TO_USER = True
backend = backends.CASBackend()
user = backend.authenticate(request, ticket='fake-ticket',
service='fake-service')
assert user is not None
assert user.is_superuser is False
assert user.is_staff is True
| 31.51711
| 93
| 0.694414
| 1,008
| 8,289
| 5.565476
| 0.109127
| 0.055615
| 0.047415
| 0.040998
| 0.870232
| 0.860606
| 0.860606
| 0.84795
| 0.84795
| 0.836185
| 0
| 0.001365
| 0.204488
| 8,289
| 262
| 94
| 31.637405
| 0.849409
| 0.186754
| 0
| 0.79085
| 0
| 0
| 0.152131
| 0.039587
| 0
| 0
| 0
| 0
| 0.163399
| 1
| 0.124183
| false
| 0
| 0.03268
| 0.065359
| 0.228758
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
8e04477a8705dc2344d3c75791a0441dfe9889b8
| 6,269
|
py
|
Python
|
loldib/getratings/models/NA/na_jinx/na_jinx_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_jinx/na_jinx_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_jinx/na_jinx_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Jinx_Mid_Aatrox(Ratings):
pass
class NA_Jinx_Mid_Ahri(Ratings):
pass
class NA_Jinx_Mid_Akali(Ratings):
pass
class NA_Jinx_Mid_Alistar(Ratings):
pass
class NA_Jinx_Mid_Amumu(Ratings):
pass
class NA_Jinx_Mid_Anivia(Ratings):
pass
class NA_Jinx_Mid_Annie(Ratings):
pass
class NA_Jinx_Mid_Ashe(Ratings):
pass
class NA_Jinx_Mid_AurelionSol(Ratings):
pass
class NA_Jinx_Mid_Azir(Ratings):
pass
class NA_Jinx_Mid_Bard(Ratings):
pass
class NA_Jinx_Mid_Blitzcrank(Ratings):
pass
class NA_Jinx_Mid_Brand(Ratings):
pass
class NA_Jinx_Mid_Braum(Ratings):
pass
class NA_Jinx_Mid_Caitlyn(Ratings):
pass
class NA_Jinx_Mid_Camille(Ratings):
pass
class NA_Jinx_Mid_Cassiopeia(Ratings):
pass
class NA_Jinx_Mid_Chogath(Ratings):
pass
class NA_Jinx_Mid_Corki(Ratings):
pass
class NA_Jinx_Mid_Darius(Ratings):
pass
class NA_Jinx_Mid_Diana(Ratings):
pass
class NA_Jinx_Mid_Draven(Ratings):
pass
class NA_Jinx_Mid_DrMundo(Ratings):
pass
class NA_Jinx_Mid_Ekko(Ratings):
pass
class NA_Jinx_Mid_Elise(Ratings):
pass
class NA_Jinx_Mid_Evelynn(Ratings):
pass
class NA_Jinx_Mid_Ezreal(Ratings):
pass
class NA_Jinx_Mid_Fiddlesticks(Ratings):
pass
class NA_Jinx_Mid_Fiora(Ratings):
pass
class NA_Jinx_Mid_Fizz(Ratings):
pass
class NA_Jinx_Mid_Galio(Ratings):
pass
class NA_Jinx_Mid_Gangplank(Ratings):
pass
class NA_Jinx_Mid_Garen(Ratings):
pass
class NA_Jinx_Mid_Gnar(Ratings):
pass
class NA_Jinx_Mid_Gragas(Ratings):
pass
class NA_Jinx_Mid_Graves(Ratings):
pass
class NA_Jinx_Mid_Hecarim(Ratings):
pass
class NA_Jinx_Mid_Heimerdinger(Ratings):
pass
class NA_Jinx_Mid_Illaoi(Ratings):
pass
class NA_Jinx_Mid_Irelia(Ratings):
pass
class NA_Jinx_Mid_Ivern(Ratings):
pass
class NA_Jinx_Mid_Janna(Ratings):
pass
class NA_Jinx_Mid_JarvanIV(Ratings):
pass
class NA_Jinx_Mid_Jax(Ratings):
pass
class NA_Jinx_Mid_Jayce(Ratings):
pass
class NA_Jinx_Mid_Jhin(Ratings):
pass
class NA_Jinx_Mid_Jinx(Ratings):
pass
class NA_Jinx_Mid_Kalista(Ratings):
pass
class NA_Jinx_Mid_Karma(Ratings):
pass
class NA_Jinx_Mid_Karthus(Ratings):
pass
class NA_Jinx_Mid_Kassadin(Ratings):
pass
class NA_Jinx_Mid_Katarina(Ratings):
pass
class NA_Jinx_Mid_Kayle(Ratings):
pass
class NA_Jinx_Mid_Kayn(Ratings):
pass
class NA_Jinx_Mid_Kennen(Ratings):
pass
class NA_Jinx_Mid_Khazix(Ratings):
pass
class NA_Jinx_Mid_Kindred(Ratings):
pass
class NA_Jinx_Mid_Kled(Ratings):
pass
class NA_Jinx_Mid_KogMaw(Ratings):
pass
class NA_Jinx_Mid_Leblanc(Ratings):
pass
class NA_Jinx_Mid_LeeSin(Ratings):
pass
class NA_Jinx_Mid_Leona(Ratings):
pass
class NA_Jinx_Mid_Lissandra(Ratings):
pass
class NA_Jinx_Mid_Lucian(Ratings):
pass
class NA_Jinx_Mid_Lulu(Ratings):
pass
class NA_Jinx_Mid_Lux(Ratings):
pass
class NA_Jinx_Mid_Malphite(Ratings):
pass
class NA_Jinx_Mid_Malzahar(Ratings):
pass
class NA_Jinx_Mid_Maokai(Ratings):
pass
class NA_Jinx_Mid_MasterYi(Ratings):
pass
class NA_Jinx_Mid_MissFortune(Ratings):
pass
class NA_Jinx_Mid_MonkeyKing(Ratings):
pass
class NA_Jinx_Mid_Mordekaiser(Ratings):
pass
class NA_Jinx_Mid_Morgana(Ratings):
pass
class NA_Jinx_Mid_Nami(Ratings):
pass
class NA_Jinx_Mid_Nasus(Ratings):
pass
class NA_Jinx_Mid_Nautilus(Ratings):
pass
class NA_Jinx_Mid_Nidalee(Ratings):
pass
class NA_Jinx_Mid_Nocturne(Ratings):
pass
class NA_Jinx_Mid_Nunu(Ratings):
pass
class NA_Jinx_Mid_Olaf(Ratings):
pass
class NA_Jinx_Mid_Orianna(Ratings):
pass
class NA_Jinx_Mid_Ornn(Ratings):
pass
class NA_Jinx_Mid_Pantheon(Ratings):
pass
class NA_Jinx_Mid_Poppy(Ratings):
pass
class NA_Jinx_Mid_Quinn(Ratings):
pass
class NA_Jinx_Mid_Rakan(Ratings):
pass
class NA_Jinx_Mid_Rammus(Ratings):
pass
class NA_Jinx_Mid_RekSai(Ratings):
pass
class NA_Jinx_Mid_Renekton(Ratings):
pass
class NA_Jinx_Mid_Rengar(Ratings):
pass
class NA_Jinx_Mid_Riven(Ratings):
pass
class NA_Jinx_Mid_Rumble(Ratings):
pass
class NA_Jinx_Mid_Ryze(Ratings):
pass
class NA_Jinx_Mid_Sejuani(Ratings):
pass
class NA_Jinx_Mid_Shaco(Ratings):
pass
class NA_Jinx_Mid_Shen(Ratings):
pass
class NA_Jinx_Mid_Shyvana(Ratings):
pass
class NA_Jinx_Mid_Singed(Ratings):
pass
class NA_Jinx_Mid_Sion(Ratings):
pass
class NA_Jinx_Mid_Sivir(Ratings):
pass
class NA_Jinx_Mid_Skarner(Ratings):
pass
class NA_Jinx_Mid_Sona(Ratings):
pass
class NA_Jinx_Mid_Soraka(Ratings):
pass
class NA_Jinx_Mid_Swain(Ratings):
pass
class NA_Jinx_Mid_Syndra(Ratings):
pass
class NA_Jinx_Mid_TahmKench(Ratings):
pass
class NA_Jinx_Mid_Taliyah(Ratings):
pass
class NA_Jinx_Mid_Talon(Ratings):
pass
class NA_Jinx_Mid_Taric(Ratings):
pass
class NA_Jinx_Mid_Teemo(Ratings):
pass
class NA_Jinx_Mid_Thresh(Ratings):
pass
class NA_Jinx_Mid_Tristana(Ratings):
pass
class NA_Jinx_Mid_Trundle(Ratings):
pass
class NA_Jinx_Mid_Tryndamere(Ratings):
pass
class NA_Jinx_Mid_TwistedFate(Ratings):
pass
class NA_Jinx_Mid_Twitch(Ratings):
pass
class NA_Jinx_Mid_Udyr(Ratings):
pass
class NA_Jinx_Mid_Urgot(Ratings):
pass
class NA_Jinx_Mid_Varus(Ratings):
pass
class NA_Jinx_Mid_Vayne(Ratings):
pass
class NA_Jinx_Mid_Veigar(Ratings):
pass
class NA_Jinx_Mid_Velkoz(Ratings):
pass
class NA_Jinx_Mid_Vi(Ratings):
pass
class NA_Jinx_Mid_Viktor(Ratings):
pass
class NA_Jinx_Mid_Vladimir(Ratings):
pass
class NA_Jinx_Mid_Volibear(Ratings):
pass
class NA_Jinx_Mid_Warwick(Ratings):
pass
class NA_Jinx_Mid_Xayah(Ratings):
pass
class NA_Jinx_Mid_Xerath(Ratings):
pass
class NA_Jinx_Mid_XinZhao(Ratings):
pass
class NA_Jinx_Mid_Yasuo(Ratings):
pass
class NA_Jinx_Mid_Yorick(Ratings):
pass
class NA_Jinx_Mid_Zac(Ratings):
pass
class NA_Jinx_Mid_Zed(Ratings):
pass
class NA_Jinx_Mid_Ziggs(Ratings):
pass
class NA_Jinx_Mid_Zilean(Ratings):
pass
class NA_Jinx_Mid_Zyra(Ratings):
pass
| 15.033573
| 46
| 0.75642
| 972
| 6,269
| 4.452675
| 0.151235
| 0.223198
| 0.350739
| 0.446396
| 0.791359
| 0.791359
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177221
| 6,269
| 416
| 47
| 15.069712
| 0.839085
| 0
| 0
| 0.498195
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.498195
| 0.00361
| 0
| 0.501805
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
8e22442365d785a783e5e25a9bd0e144d5615dbf
| 586
|
py
|
Python
|
pset_challenging_ext/exercises/p54.py
|
mottaquikarim/pydev-psets
|
9749e0d216ee0a5c586d0d3013ef481cc21dee27
|
[
"MIT"
] | 5
|
2019-04-08T20:05:37.000Z
|
2019-12-04T20:48:45.000Z
|
pset_challenging_ext/exercises/p54.py
|
mottaquikarim/pydev-psets
|
9749e0d216ee0a5c586d0d3013ef481cc21dee27
|
[
"MIT"
] | 8
|
2019-04-15T15:16:05.000Z
|
2022-02-12T10:33:32.000Z
|
pset_challenging_ext/exercises/p54.py
|
mottaquikarim/pydev-psets
|
9749e0d216ee0a5c586d0d3013ef481cc21dee27
|
[
"MIT"
] | 2
|
2019-04-10T00:14:42.000Z
|
2020-02-26T20:35:21.000Z
|
"""
Define a class named Shape and its subclass Square. The Square class has an init function which takes a length as argument. Both classes have a area function which can print the area of the shape where Shape's area is 0 by default.
"""
"""Define a class named Shape and its subclass Square. The Square class has an init function which takes a length as argument. Both classes have a area function which can print the area of the shape where Shape's area is 0 by default.
Hints:
To override a method in super class, we can define a method with the same name in the super class.
"""
| 65.111111
| 234
| 0.771331
| 109
| 586
| 4.146789
| 0.385321
| 0.115044
| 0.053097
| 0.075221
| 0.818584
| 0.818584
| 0.818584
| 0.818584
| 0.818584
| 0.818584
| 0
| 0.004211
| 0.18942
| 586
| 9
| 235
| 65.111111
| 0.947368
| 0.394198
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
8e4a79c3b1a36fed4482fd94b962204fce143c66
| 46,378
|
py
|
Python
|
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_ipv4_autorp_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_ipv4_autorp_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_ipv4_autorp_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
""" Cisco_IOS_XR_ipv4_autorp_oper
This module contains a collection of YANG definitions
for Cisco IOS\-XR ipv4\-autorp package operational data.
This module contains definitions
for the following management objects\:
auto\-rp\: AutoRP operational data
Copyright (c) 2013\-2016 by Cisco Systems, Inc.
All rights reserved.
"""
import re
import collections
from enum import Enum
from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict
from ydk.errors import YPYError, YPYModelError
class AutorpProtocolModeEnum(Enum):
"""
AutorpProtocolModeEnum
Autorp protocol mode
.. data:: sparse = 0
sparse
.. data:: bidirectional = 1
bidirectional
"""
sparse = 0
bidirectional = 1
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutorpProtocolModeEnum']
class AutoRp(object):
"""
AutoRP operational data
.. attribute:: active
Active Process
**type**\: :py:class:`Active <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active>`
.. attribute:: standby
Standby Process
**type**\: :py:class:`Standby <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.active = AutoRp.Active()
self.active.parent = self
self.standby = AutoRp.Standby()
self.standby.parent = self
class Standby(object):
"""
Standby Process
.. attribute:: candidate_rps
AutoRP Candidate RP Table
**type**\: :py:class:`CandidateRps <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.CandidateRps>`
.. attribute:: mapping_agent
AutoRP Mapping Agent Table
**type**\: :py:class:`MappingAgent <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.MappingAgent>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.candidate_rps = AutoRp.Standby.CandidateRps()
self.candidate_rps.parent = self
self.mapping_agent = AutoRp.Standby.MappingAgent()
self.mapping_agent.parent = self
class CandidateRps(object):
"""
AutoRP Candidate RP Table
.. attribute:: candidate_rp
AutoRP Candidate RP Entry
**type**\: list of :py:class:`CandidateRp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.CandidateRps.CandidateRp>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.candidate_rp = YList()
self.candidate_rp.parent = self
self.candidate_rp.name = 'candidate_rp'
class CandidateRp(object):
"""
AutoRP Candidate RP Entry
.. attribute:: access_list_name
ACL Name
**type**\: str
.. attribute:: announce_period
Announce Period
**type**\: int
**range:** \-2147483648..2147483647
.. attribute:: candidate_rp_address
Candidate RP IP Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: interface_name
Interface Name
**type**\: str
**pattern:** (([a\-zA\-Z0\-9\_]\*\\d+/){3,4}\\d+)\|(([a\-zA\-Z0\-9\_]\*\\d+/){3,4}\\d+\\.\\d+)\|(([a\-zA\-Z0\-9\_]\*\\d+/){2}([a\-zA\-Z0\-9\_]\*\\d+))\|(([a\-zA\-Z0\-9\_]\*\\d+/){2}([a\-zA\-Z0\-9\_]+))\|([a\-zA\-Z0\-9\_\-]\*\\d+)\|([a\-zA\-Z0\-9\_\-]\*\\d+\\.\\d+)\|(mpls)\|(dwdm)
.. attribute:: protocol_mode
Protocol Mode
**type**\: :py:class:`AutoRpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_datatypes.AutoRpProtocolModeEnum>`
.. attribute:: protocol_mode_xr
Protocol Mode
**type**\: :py:class:`AutorpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutorpProtocolModeEnum>`
.. attribute:: ttl
TTL
**type**\: int
**range:** \-2147483648..2147483647
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.access_list_name = None
self.announce_period = None
self.candidate_rp_address = None
self.interface_name = None
self.protocol_mode = None
self.protocol_mode_xr = None
self.ttl = None
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rps/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rp'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.access_list_name is not None:
return True
if self.announce_period is not None:
return True
if self.candidate_rp_address is not None:
return True
if self.interface_name is not None:
return True
if self.protocol_mode is not None:
return True
if self.protocol_mode_xr is not None:
return True
if self.ttl is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.CandidateRps.CandidateRp']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rps'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.candidate_rp is not None:
for child_ref in self.candidate_rp:
if child_ref._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.CandidateRps']['meta_info']
class MappingAgent(object):
"""
AutoRP Mapping Agent Table
.. attribute:: rp_addresses
AutoRP Mapping Agent Table Entries
**type**\: :py:class:`RpAddresses <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.MappingAgent.RpAddresses>`
.. attribute:: summary
AutoRP Mapping Agent Summary Information
**type**\: :py:class:`Summary <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.MappingAgent.Summary>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_addresses = AutoRp.Standby.MappingAgent.RpAddresses()
self.rp_addresses.parent = self
self.summary = AutoRp.Standby.MappingAgent.Summary()
self.summary.parent = self
class RpAddresses(object):
"""
AutoRP Mapping Agent Table Entries
.. attribute:: rp_address
AutoRP Mapping Agent Entry
**type**\: list of :py:class:`RpAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.MappingAgent.RpAddresses.RpAddress>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_address = YList()
self.rp_address.parent = self
self.rp_address.name = 'rp_address'
class RpAddress(object):
"""
AutoRP Mapping Agent Entry
.. attribute:: rp_address <key>
RP Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: expiry_time
Time for expiration in seconds
**type**\: int
**range:** 0..18446744073709551615
**units**\: second
.. attribute:: pim_version
PIM version of the CRP
**type**\: int
**range:** 0..255
.. attribute:: range
Array of ranges
**type**\: list of :py:class:`Range <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Standby.MappingAgent.RpAddresses.RpAddress.Range>`
.. attribute:: rp_address_xr
Candidate\-RP address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_address = None
self.expiry_time = None
self.pim_version = None
self.range = YList()
self.range.parent = self
self.range.name = 'range'
self.rp_address_xr = None
class Range(object):
"""
Array of ranges
.. attribute:: check_point_object_id
Checkpoint object id
**type**\: int
**range:** 0..4294967295
.. attribute:: create_type
Source of the entry
**type**\: int
**range:** 0..255
.. attribute:: is_advertised
Is this entry advertised ?
**type**\: bool
.. attribute:: prefix
Prefix of the range
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: prefix_length
Prefix length of the range
**type**\: int
**range:** 0..255
.. attribute:: protocol_mode
Protocol Mode
**type**\: :py:class:`AutorpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutorpProtocolModeEnum>`
.. attribute:: uptime
Uptime in seconds
**type**\: int
**range:** 0..18446744073709551615
**units**\: second
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.check_point_object_id = None
self.create_type = None
self.is_advertised = None
self.prefix = None
self.prefix_length = None
self.protocol_mode = None
self.uptime = None
@property
def _common_path(self):
if self.parent is None:
raise YPYModelError('parent is not set . Cannot derive path.')
return self.parent._common_path +'/Cisco-IOS-XR-ipv4-autorp-oper:range'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.check_point_object_id is not None:
return True
if self.create_type is not None:
return True
if self.is_advertised is not None:
return True
if self.prefix is not None:
return True
if self.prefix_length is not None:
return True
if self.protocol_mode is not None:
return True
if self.uptime is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.MappingAgent.RpAddresses.RpAddress.Range']['meta_info']
@property
def _common_path(self):
if self.rp_address is None:
raise YPYModelError('Key property rp_address is None')
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:rp-addresses/Cisco-IOS-XR-ipv4-autorp-oper:rp-address[Cisco-IOS-XR-ipv4-autorp-oper:rp-address = ' + str(self.rp_address) + ']'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_address is not None:
return True
if self.expiry_time is not None:
return True
if self.pim_version is not None:
return True
if self.range is not None:
for child_ref in self.range:
if child_ref._has_data():
return True
if self.rp_address_xr is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.MappingAgent.RpAddresses.RpAddress']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:rp-addresses'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_address is not None:
for child_ref in self.rp_address:
if child_ref._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.MappingAgent.RpAddresses']['meta_info']
class Summary(object):
"""
AutoRP Mapping Agent Summary Information
.. attribute:: cache_count
Number of group to RP mapping entries in the cache
**type**\: int
**range:** 0..4294967295
.. attribute:: cache_limit
Maximum group to RP mapping entries allowed
**type**\: int
**range:** 0..4294967295
.. attribute:: is_maximum_disabled
Is maximum enforcement disabled ?
**type**\: bool
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.cache_count = None
self.cache_limit = None
self.is_maximum_disabled = None
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:summary'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.cache_count is not None:
return True
if self.cache_limit is not None:
return True
if self.is_maximum_disabled is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.MappingAgent.Summary']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_addresses is not None and self.rp_addresses._has_data():
return True
if self.summary is not None and self.summary._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby.MappingAgent']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:standby'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.candidate_rps is not None and self.candidate_rps._has_data():
return True
if self.mapping_agent is not None and self.mapping_agent._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Standby']['meta_info']
class Active(object):
"""
Active Process
.. attribute:: candidate_rps
AutoRP Candidate RP Table
**type**\: :py:class:`CandidateRps <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.CandidateRps>`
.. attribute:: mapping_agent
AutoRP Mapping Agent Table
**type**\: :py:class:`MappingAgent <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.MappingAgent>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.candidate_rps = AutoRp.Active.CandidateRps()
self.candidate_rps.parent = self
self.mapping_agent = AutoRp.Active.MappingAgent()
self.mapping_agent.parent = self
class CandidateRps(object):
"""
AutoRP Candidate RP Table
.. attribute:: candidate_rp
AutoRP Candidate RP Entry
**type**\: list of :py:class:`CandidateRp <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.CandidateRps.CandidateRp>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.candidate_rp = YList()
self.candidate_rp.parent = self
self.candidate_rp.name = 'candidate_rp'
class CandidateRp(object):
"""
AutoRP Candidate RP Entry
.. attribute:: access_list_name
ACL Name
**type**\: str
.. attribute:: announce_period
Announce Period
**type**\: int
**range:** \-2147483648..2147483647
.. attribute:: candidate_rp_address
Candidate RP IP Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: interface_name
Interface Name
**type**\: str
**pattern:** (([a\-zA\-Z0\-9\_]\*\\d+/){3,4}\\d+)\|(([a\-zA\-Z0\-9\_]\*\\d+/){3,4}\\d+\\.\\d+)\|(([a\-zA\-Z0\-9\_]\*\\d+/){2}([a\-zA\-Z0\-9\_]\*\\d+))\|(([a\-zA\-Z0\-9\_]\*\\d+/){2}([a\-zA\-Z0\-9\_]+))\|([a\-zA\-Z0\-9\_\-]\*\\d+)\|([a\-zA\-Z0\-9\_\-]\*\\d+\\.\\d+)\|(mpls)\|(dwdm)
.. attribute:: protocol_mode
Protocol Mode
**type**\: :py:class:`AutoRpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_datatypes.AutoRpProtocolModeEnum>`
.. attribute:: protocol_mode_xr
Protocol Mode
**type**\: :py:class:`AutorpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutorpProtocolModeEnum>`
.. attribute:: ttl
TTL
**type**\: int
**range:** \-2147483648..2147483647
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.access_list_name = None
self.announce_period = None
self.candidate_rp_address = None
self.interface_name = None
self.protocol_mode = None
self.protocol_mode_xr = None
self.ttl = None
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rps/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rp'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.access_list_name is not None:
return True
if self.announce_period is not None:
return True
if self.candidate_rp_address is not None:
return True
if self.interface_name is not None:
return True
if self.protocol_mode is not None:
return True
if self.protocol_mode_xr is not None:
return True
if self.ttl is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.CandidateRps.CandidateRp']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:candidate-rps'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.candidate_rp is not None:
for child_ref in self.candidate_rp:
if child_ref._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.CandidateRps']['meta_info']
class MappingAgent(object):
"""
AutoRP Mapping Agent Table
.. attribute:: rp_addresses
AutoRP Mapping Agent Table Entries
**type**\: :py:class:`RpAddresses <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.MappingAgent.RpAddresses>`
.. attribute:: summary
AutoRP Mapping Agent Summary Information
**type**\: :py:class:`Summary <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.MappingAgent.Summary>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_addresses = AutoRp.Active.MappingAgent.RpAddresses()
self.rp_addresses.parent = self
self.summary = AutoRp.Active.MappingAgent.Summary()
self.summary.parent = self
class RpAddresses(object):
"""
AutoRP Mapping Agent Table Entries
.. attribute:: rp_address
AutoRP Mapping Agent Entry
**type**\: list of :py:class:`RpAddress <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.MappingAgent.RpAddresses.RpAddress>`
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_address = YList()
self.rp_address.parent = self
self.rp_address.name = 'rp_address'
class RpAddress(object):
"""
AutoRP Mapping Agent Entry
.. attribute:: rp_address <key>
RP Address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: expiry_time
Time for expiration in seconds
**type**\: int
**range:** 0..18446744073709551615
**units**\: second
.. attribute:: pim_version
PIM version of the CRP
**type**\: int
**range:** 0..255
.. attribute:: range
Array of ranges
**type**\: list of :py:class:`Range <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutoRp.Active.MappingAgent.RpAddresses.RpAddress.Range>`
.. attribute:: rp_address_xr
Candidate\-RP address
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.rp_address = None
self.expiry_time = None
self.pim_version = None
self.range = YList()
self.range.parent = self
self.range.name = 'range'
self.rp_address_xr = None
class Range(object):
"""
Array of ranges
.. attribute:: check_point_object_id
Checkpoint object id
**type**\: int
**range:** 0..4294967295
.. attribute:: create_type
Source of the entry
**type**\: int
**range:** 0..255
.. attribute:: is_advertised
Is this entry advertised ?
**type**\: bool
.. attribute:: prefix
Prefix of the range
**type**\: str
**pattern:** (([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])\\.){3}([0\-9]\|[1\-9][0\-9]\|1[0\-9][0\-9]\|2[0\-4][0\-9]\|25[0\-5])(%[\\p{N}\\p{L}]+)?
.. attribute:: prefix_length
Prefix length of the range
**type**\: int
**range:** 0..255
.. attribute:: protocol_mode
Protocol Mode
**type**\: :py:class:`AutorpProtocolModeEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_ipv4_autorp_oper.AutorpProtocolModeEnum>`
.. attribute:: uptime
Uptime in seconds
**type**\: int
**range:** 0..18446744073709551615
**units**\: second
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.check_point_object_id = None
self.create_type = None
self.is_advertised = None
self.prefix = None
self.prefix_length = None
self.protocol_mode = None
self.uptime = None
@property
def _common_path(self):
if self.parent is None:
raise YPYModelError('parent is not set . Cannot derive path.')
return self.parent._common_path +'/Cisco-IOS-XR-ipv4-autorp-oper:range'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.check_point_object_id is not None:
return True
if self.create_type is not None:
return True
if self.is_advertised is not None:
return True
if self.prefix is not None:
return True
if self.prefix_length is not None:
return True
if self.protocol_mode is not None:
return True
if self.uptime is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.MappingAgent.RpAddresses.RpAddress.Range']['meta_info']
@property
def _common_path(self):
if self.rp_address is None:
raise YPYModelError('Key property rp_address is None')
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:rp-addresses/Cisco-IOS-XR-ipv4-autorp-oper:rp-address[Cisco-IOS-XR-ipv4-autorp-oper:rp-address = ' + str(self.rp_address) + ']'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_address is not None:
return True
if self.expiry_time is not None:
return True
if self.pim_version is not None:
return True
if self.range is not None:
for child_ref in self.range:
if child_ref._has_data():
return True
if self.rp_address_xr is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.MappingAgent.RpAddresses.RpAddress']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:rp-addresses'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_address is not None:
for child_ref in self.rp_address:
if child_ref._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.MappingAgent.RpAddresses']['meta_info']
class Summary(object):
"""
AutoRP Mapping Agent Summary Information
.. attribute:: cache_count
Number of group to RP mapping entries in the cache
**type**\: int
**range:** 0..4294967295
.. attribute:: cache_limit
Maximum group to RP mapping entries allowed
**type**\: int
**range:** 0..4294967295
.. attribute:: is_maximum_disabled
Is maximum enforcement disabled ?
**type**\: bool
"""
_prefix = 'ipv4-autorp-oper'
_revision = '2015-11-09'
def __init__(self):
self.parent = None
self.cache_count = None
self.cache_limit = None
self.is_maximum_disabled = None
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent/Cisco-IOS-XR-ipv4-autorp-oper:summary'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.cache_count is not None:
return True
if self.cache_limit is not None:
return True
if self.is_maximum_disabled is not None:
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.MappingAgent.Summary']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active/Cisco-IOS-XR-ipv4-autorp-oper:mapping-agent'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.rp_addresses is not None and self.rp_addresses._has_data():
return True
if self.summary is not None and self.summary._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active.MappingAgent']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp/Cisco-IOS-XR-ipv4-autorp-oper:active'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.candidate_rps is not None and self.candidate_rps._has_data():
return True
if self.mapping_agent is not None and self.mapping_agent._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp.Active']['meta_info']
@property
def _common_path(self):
return '/Cisco-IOS-XR-ipv4-autorp-oper:auto-rp'
def is_config(self):
''' Returns True if this instance represents config data else returns False '''
return False
def _has_data(self):
if not self.is_config():
return False
if self.active is not None and self.active._has_data():
return True
if self.standby is not None and self.standby._has_data():
return True
return False
@staticmethod
def _meta_info():
from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_ipv4_autorp_oper as meta
return meta._meta_table['AutoRp']['meta_info']
| 36.691456
| 310
| 0.449351
| 4,488
| 46,378
| 4.444742
| 0.040998
| 0.054943
| 0.068679
| 0.068077
| 0.956036
| 0.953429
| 0.951073
| 0.951073
| 0.951073
| 0.951073
| 0
| 0.035263
| 0.455798
| 46,378
| 1,263
| 311
| 36.720507
| 0.755101
| 0.262538
| 0
| 0.891182
| 0
| 0.026266
| 0.130958
| 0.100596
| 0
| 0
| 0
| 0
| 0
| 1
| 0.161351
| false
| 0
| 0.043152
| 0.02439
| 0.515947
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
ed9319b4c643894e862dc180400d3e4c6e873027
| 68
|
py
|
Python
|
src/models/__init__.py
|
renyi-ai/drfrankenstein
|
b9064cdea67698f70af07849bc5decaafccac9f3
|
[
"MIT"
] | 4
|
2021-12-08T14:27:19.000Z
|
2022-01-05T20:19:03.000Z
|
src/models/__init__.py
|
renyi-ai/drfrankenstein
|
b9064cdea67698f70af07849bc5decaafccac9f3
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
renyi-ai/drfrankenstein
|
b9064cdea67698f70af07849bc5decaafccac9f3
|
[
"MIT"
] | null | null | null |
from src.models.classifiers import *
from src.models.frank import *
| 22.666667
| 36
| 0.794118
| 10
| 68
| 5.4
| 0.6
| 0.259259
| 0.481481
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 68
| 2
| 37
| 34
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
b62ff9e5eec8b6d69d6eaf98f80cfd17b261bf8f
| 253,617
|
py
|
Python
|
src/MSI/optimization/matrix_loader.py
|
carlylagrotta/MSI
|
e958beb5df2a2d1018bbb2f96382b5c99b08c3ef
|
[
"MIT"
] | 1
|
2021-06-25T15:46:06.000Z
|
2021-06-25T15:46:06.000Z
|
src/MSI/optimization/matrix_loader.py
|
TheBurkeLab/MSI
|
e958beb5df2a2d1018bbb2f96382b5c99b08c3ef
|
[
"MIT"
] | null | null | null |
src/MSI/optimization/matrix_loader.py
|
TheBurkeLab/MSI
|
e958beb5df2a2d1018bbb2f96382b5c99b08c3ef
|
[
"MIT"
] | 2
|
2019-12-18T23:45:25.000Z
|
2021-06-10T20:37:20.000Z
|
import numpy as np
import pandas as pd
from ..master_equation import master_equation as meq
#import MSI.master_equation.master_equation as meq
import copy
import re
import cantera as ct
class OptMatrix(object):
def __init__(self):
self.S_matrix = None
self.s_matrix = None
self.Y_matrix = None
self.y_matrix = None
self.z_matrix = None
self.delta_X = None
self.X = None
self.sigma = None
# #loads one experiment into self.matrix. Decides padding based on previous matrix or handle based on total exp num?
def build_Z(self, exp_dict_list:list,
parsed_yaml_file_list:list,
loop_counter:int = 0,
reaction_uncertainty=None,
master_equation_uncertainty_df=None,
master_equation_reaction_list=[],
master_equation_flag = False):
'''
Builds the Z vector.
Arguments:
exp_dic_list -- the dictonary that is built after a simulation
that contains things like sensitivity coefficients
parsed_yaml_file_list -- a list of dictonaries that contain the
information stored in the yaml files.
Keyword Arguments:
loop_counter -- keeps track of the iteration number for the optimization (default 0)
reaction_uncertainty -- a csv file that contains all the reactions
in the cti file being used for optimization and their corresponding
A,n and Ea uncertainty values (default None)
master_equation_uncertainty_df -- a pandas dataframe that contains
the reactions being treated with theory paramters along with the
associated uncertainty values of those paramters (default None)
master_equation_reaction_list -- a list of the reactions being treated
with theory paramters (default [])
master_equation_flag -- a boolean that indicates if reactions being
represented by theory parameters are being used in the optimization (default False)
'''
Z = []
Z_data_Frame = []
sigma = []
def jsr_temp_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)
else:
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['Temperature'].values))
temp_uncertainties = list(temp_uncertainties)
return temp_uncertainties
def flow_reactor_time_shift_uncertainties(parsed_yaml_file_list,experiment_dict):
if len(parsed_yaml_file_list['timeShiftOriginal']) ==1:
time_shift_uncertainties = [experiment_dict['uncertainty']['time_shift_uncertainty']]
elif len(parsed_yaml_file_list['timeShiftOriginal']) >1:
time_shift_uncertainties = [experiment_dict['uncertainty']['time_shift_uncertainty']]*len(parsed_yaml_file_list['timeShiftOriginal'])
return time_shift_uncertainties
def flow_reactor_temp_uncertainties(experiment_dict):
if 'Temperature_Uncertainty' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['experimental_data'][0]['Temperature_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)
else:
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['Temperature'].values))
temp_uncertainties = list(temp_uncertainties)
return temp_uncertainties
def flame_speed_temp_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'Temperature' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)
elif 'Temperature' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['Temperature'].values))
temp_uncertainties = list(temp_uncertainties)
elif 'Pressure' in list(experiment_dict['experimental_data'][0].columns) or 'Phi' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties)
return temp_uncertainties
def flame_speed_press_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'Pressure' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
press_uncertainties = list(press_uncertainties)
elif 'Pressure' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['Pressure'].values))
press_uncertainties = list(temp_uncertainties)
elif 'Temperature' in list(experiment_dict['experimental_data'][0].columns) or 'Phi' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(temp_uncertainties)
return press_uncertainties
def igdelay_temp_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'pressure' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures) and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1:
temp_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)*len(experiment_dict['simulation'].temperatures)
elif 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'temperature' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)
elif 'temperature' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['temperature'].values))
temp_uncertainties = list(temp_uncertainties)
#stub this is where we are editing
elif 'pressure' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures) and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1 :
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['pressure'].values))
temp_uncertainties = list(temp_uncertainties)* len(experiment_dict['simulation'].temperatures)
elif 'pressure' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) != len(experiment_dict['simulation'].pressures):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties)
elif len(experiment_dict['conditions_to_run'])>1 and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1 and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties) * len(experiment_dict['simulation'].temperatures)
elif len(experiment_dict['conditions_to_run'])>1:
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties)
return temp_uncertainties
def igdelay_press_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'temperature' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures) and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1:
press_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
press_uncertainties = list(press_uncertainties)*len(experiment_dict['simulation'].temperatures)
elif 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'pressure' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
press_uncertainties = list(press_uncertainties)
elif 'pressure' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['pressure'].values))
press_uncertainties = list(press_uncertainties)
elif 'temperature' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures) and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1:
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(press_uncertainties) * len(experiment_dict['simulation'].temperatures)
#stub this is where editing is happening
elif 'temperature' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].temperatures) != len(experiment_dict['simulation'].pressures):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(press_uncertainties)
elif len(experiment_dict['conditions_to_run'])>1 and len(experiment_dict['simulation'].temperatures)>1 and len(experiment_dict['simulation'].pressures)>1 and len(experiment_dict['simulation'].temperatures) == len(experiment_dict['simulation'].pressures):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(press_uncertainties)* len(experiment_dict['simulation'].temperatures)
elif len(experiment_dict['conditions_to_run'])>1:
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(press_uncertainties)
return press_uncertainties
def rcm_temp_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'temperature' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
temp_uncertainties = list(temp_uncertainties)
elif 'temperature' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].fullParsedYamlFile['temperatures'])==len(experiment_dict['simulation'].fullParsedYamlFile['pressures']):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['temperature'].values))
temp_uncertainties = list(temp_uncertainties)
elif 'pressure' in list(experiment_dict['experimental_data'][0].columns):
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties)
elif len(experiment_dict['conditions_to_run'])>1:
temp_uncertainties=experiment_dict['uncertainty']['temperature_relative_uncertainty']
temp_uncertainties = list(temp_uncertainties)
return temp_uncertainties
def rcm_press_uncertainties(experiment_dict):
if 'Relative_Uncertainty' in list(experiment_dict['experimental_data'][0].columns) and 'pressure' in list(experiment_dict['experimental_data'][0].columns):
press_uncertainties=experiment_dict['experimental_data'][0]['Relative_Uncertainty'].values
press_uncertainties = list(press_uncertainties)
elif 'pressure' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].fullParsedYamlFile['temperatures'])==len(experiment_dict['simulation'].fullParsedYamlFile['pressures']):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['pressure'].values))
press_uncertainties = list(press_uncertainties)
elif 'temperature' in list(experiment_dict['experimental_data'][0].columns) and len(experiment_dict['simulation'].fullParsedYamlFile['temperatures'])==len(experiment_dict['simulation'].fullParsedYamlFile['pressures']):
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']*np.ones(np.shape(experiment_dict['experimental_data'][0]['temperature'].values))
press_uncertainties = list(press_uncertainties)
elif len(experiment_dict['conditions_to_run'])>1:
press_uncertainties=experiment_dict['uncertainty']['pressure_relative_uncertainty']
press_uncertainties = list(press_uncertainties)
return press_uncertainties
#need to append to sigma
def uncertainty_calc(relative_uncertainty,absolute_uncertainty,data,experimental_data):
absolute_uncertainty=float(absolute_uncertainty)
length_of_data = data.shape[0]
if 'Relative_Uncertainty' in list(experimental_data.columns):
x_dependent_uncertainty = experimental_data['Relative_Uncertainty'].values
relative_uncertainty_array = copy.deepcopy(x_dependent_uncertainty)
relative_uncertainty_array = relative_uncertainty_array.reshape((relative_uncertainty_array.shape[0],1))
elif 'Relative_Uncertainty' not in list(experimental_data.columns):
relative_uncertainty_array = np.full((length_of_data,1),relative_uncertainty)
relative_uncertainty_array = relative_uncertainty_array.reshape((relative_uncertainty_array.shape[0],1))
if 'Absolute_Uncertainty' in list(experimental_data.columns):
x_dependent_a_uncertainty = experimental_data['Absolute_Uncertainty'].values
absolute_uncertainty_array = copy.deepcopy(x_dependent_a_uncertainty)
#Fix this to deal with 0 data.
absolute_uncertainty_array = np.divide(absolute_uncertainty_array,data)
absolute_uncertainty_array = absolute_uncertainty_array.reshape((absolute_uncertainty_array.shape[0],1))
elif 'Absolute_Uncertainty' not in list(experimental_data.columns):
absolute_uncertainty_array = np.divide(absolute_uncertainty,data)
absolute_uncertainty_array = absolute_uncertainty_array.reshape((absolute_uncertainty_array.shape[0],1))
total_uncertainty = np.sqrt(np.square(relative_uncertainty_array) + np.square(absolute_uncertainty_array))
un_weighted_uncertainty = copy.deepcopy(total_uncertainty)
if 'W' not in list(experimental_data.columns):
weighting_factor = (1/length_of_data**.5)
total_uncertainty = np.divide(total_uncertainty,weighting_factor)
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0],1))
elif 'W' in list(experimental_data.columns):
weighting_factor = experimental_data['W'].values
weighting_factor = weighting_factor.reshape((weighting_factor.shape[0],1))
total_uncertainty = np.divide(total_uncertainty,weighting_factor)
#total_uncertainty = total_uncertainty/weighting_factor
return total_uncertainty,un_weighted_uncertainty
#tab, start working here tomorrow with how we want to read in csv file
for i,exp_dic in enumerate(exp_dict_list):
counter = 0
#print(exp_dic)
for j,observable in enumerate(exp_dic['mole_fraction_observables']+
exp_dic['concentration_observables']+
exp_dic['flame_speed_observables']+
exp_dic['ignition_delay_observables']):
if observable == None:
pass
else:
if observable in exp_dic['mole_fraction_observables']:
## add ppm statment here ? check if it exists? and add concentration statment below just for parcing
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['mole_fraction_relative_uncertainty'][counter],
exp_dic['uncertainty']['mole_fraction_absolute_uncertainty'][counter],
exp_dic['experimental_data'][counter][observable].values,exp_dic['experimental_data'][counter])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0],1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
elif observable in exp_dic['concentration_observables'] and '_ppm' in exp_dic['experimental_data'][counter].columns[1]:
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['concentration_relative_uncertainty'][counter],
exp_dic['uncertainty']['concentration_absolute_uncertainty'][counter],
exp_dic['experimental_data'][counter][observable+'_ppm'].values,exp_dic['experimental_data'][counter])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0], 1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
elif observable in exp_dic['concentration_observables'] and '_mol/cm^3' in exp_dic['experimental_data'][counter].columns[1]:
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['concentration_relative_uncertainty'][counter],
exp_dic['uncertainty']['concentration_absolute_uncertainty'][counter],
exp_dic['experimental_data'][counter][observable+'_mol/cm^3'].values,exp_dic['experimental_data'][counter])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0],1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
elif observable in exp_dic['flame_speed_observables'] and '_cm/s' in exp_dic['experimental_data'][counter].columns[1]:
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['flame_speed_relative_uncertainty'][counter],
exp_dic['uncertainty']['flame_speed_absolute_uncertainty'][counter],
exp_dic['experimental_data'][counter][observable+'_cm/s'].values,exp_dic['experimental_data'][counter])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0],1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
elif observable in exp_dic['ignition_delay_observables'] and '_s'in exp_dic['experimental_data'][counter].columns[1]:
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['ignition_delay_relative_uncertainty'][counter],
exp_dic['uncertainty']['ignition_delay_absolute_uncertainty'][counter],
exp_dic['experimental_data'][counter][observable+'_s'].values,exp_dic['experimental_data'][counter])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0],1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
else:
raise Exception('We Do Not Have This Unit Installed, Please Use Mole Fraction, ppm, mol/cm^3 or cm/s')
Z.append(total_uncertainty)
sigma.append(un_weighted_uncertainty)
tempList = [observable+'_'+'experiment'+str(i)]*np.shape(total_uncertainty)[0]
Z_data_Frame.extend(tempList)
#print(Z_data_Frame)
counter+=1
if 'absorbance_observables' in list(exp_dic.keys()):
wavelengths = parsed_yaml_file_list[i]['absorbanceCsvWavelengths']
for k,wl in enumerate(wavelengths):
total_uncertainty,un_weighted_uncertainty = uncertainty_calc(exp_dic['uncertainty']['absorbance_relative_uncertainty'][k],
exp_dic['uncertainty']['absorbance_absolute_uncertainty'][k],
exp_dic['absorbance_experimental_data'][k]['Absorbance_'+str(wl)].values,exp_dic['absorbance_experimental_data'][k])
total_uncertainty = total_uncertainty.reshape((total_uncertainty.shape[0], 1))
un_weighted_uncertainty = un_weighted_uncertainty.reshape((un_weighted_uncertainty.shape[0], 1))
tempList = [str(wl)+'_'+'experiment'+'_'+str(i)]*np.shape(total_uncertainty)[0]
Z_data_Frame.extend(tempList)
Z.append(total_uncertainty)
sigma.append(un_weighted_uncertainty)
Z = np.vstack((Z))
sigma = np.vstack((sigma))
#Here we are adding A,n,and Ea uncertainty
#we go do not through an additional step to make sure that the A,N and Ea
#values are paired with the correct reactions as in the old code,
#because we wrote a function to make the excel sheet which will arrange things in the correct order
#We also need to decide if we want to put this in as ln values or not in the spreadsheet
active_parameters = []
reaction_uncertainty = pd.read_csv(reaction_uncertainty)
#Flatten master equation reaction list
flatten = lambda *n: (e for a in n
for e in (flatten(*a) if isinstance(a, (tuple, list)) else (a,)))
flattened_master_equation_reaction_list = list(flatten(master_equation_reaction_list))
if master_equation_flag:
for reaction in flattened_master_equation_reaction_list:
index = reaction_uncertainty.loc[reaction_uncertainty['Reaction'] == reaction].index[0]
reaction_uncertainty = reaction_uncertainty.drop([index])
#tab fix this correctly, this unit needs to be fixed when we make a decision what the spreadsheet looks like
uncertainty_As = reaction_uncertainty['Uncertainty A (unit)'].values
uncertainty_As = uncertainty_As.reshape((uncertainty_As.shape[0],
1))
#uncertainty_As = np.log(uncertainty_As)
Z = np.vstack((Z,uncertainty_As))
sigma = np.vstack((sigma,uncertainty_As))
for variable in range(uncertainty_As.shape[0]):
Z_data_Frame.append('A'+'_'+str(variable))
active_parameters.append('A'+'_'+str(variable))
uncertainty_ns = reaction_uncertainty['Uncertainty N (unit)'].values
uncertainty_ns = uncertainty_ns.reshape((uncertainty_ns.shape[0],
1))
Z = np.vstack((Z,uncertainty_ns))
sigma = np.vstack((sigma,uncertainty_ns))
for variable in range(uncertainty_ns.shape[0]):
Z_data_Frame.append('n'+'_'+str(variable))
active_parameters.append('n'+'_'+str(variable))
uncertainty_Eas = reaction_uncertainty['Uncertainty Ea (unit)'].values
uncertainty_Eas = uncertainty_Eas.reshape((uncertainty_Eas.shape[0],
1))
Z = np.vstack((Z,uncertainty_Eas))
sigma = np.vstack((sigma,uncertainty_Eas))
for variable in range(uncertainty_Eas.shape[0]):
Z_data_Frame.append('Ea'+'_'+str(variable))
active_parameters.append('Ea'+'_'+str(variable))
if master_equation_flag == True:
master_equation_uncertainty = []
for i,reaction in enumerate(master_equation_reaction_list):
if type(reaction)==str:
master_equation_uncertainty.append(list(master_equation_uncertainty_df[reaction].dropna().values))
elif type(reaction)==tuple:
column_headers = master_equation_uncertainty_df.columns.to_list()
for sub_reaction in reaction:
if sub_reaction in column_headers:
master_equation_uncertainty.append(list(master_equation_uncertainty_df[sub_reaction].dropna().values))
# if master_equation_flag ==True:
# master_equation_uncertainty = []
# for col in master_equation_uncertainty_df:
# master_equation_uncertainty.append(list(master_equation_uncertainty_df[col].dropna().values))
if master_equation_flag == True:
for i,reaction in enumerate(master_equation_reaction_list):
if type(reaction)==str:
for j,paramter in enumerate(master_equation_uncertainty_df[reaction].dropna()):
Z_data_Frame.append(str(reaction)+'_'+'P'+'_'+str(j))
active_parameters.append(master_equation_reaction_list[i]+'_P_'+str(j))
elif type(reaction)==tuple:
column_headers = master_equation_uncertainty_df.columns.to_list()
for sub_reaction in reaction:
if sub_reaction in column_headers:
for j,paramter in enumerate(master_equation_uncertainty_df[sub_reaction].dropna()):
Z_data_Frame.append(str(reaction)+'_'+'P'+'_'+str(j))
active_parameters.append(str(master_equation_reaction_list[i])+'_P_'+str(j))
# for i,reaction in enumerate(master_equation_uncertainty):
# for j,uncer in enumerate(reaction):
# Z_data_Frame.append('R'+'_'+str(i)+'_'+'P'+str(j))
# #This might not look right in the data frame but we can try
# #stub
# active_parameters.append(master_equation_reaction_list[i]+'_P_'+str(j))
##check this
master_equation_uncertainty = [item for sublist in master_equation_uncertainty for item in sublist]
master_equation_uncertainty = np.array(master_equation_uncertainty)
master_equation_uncertainty = master_equation_uncertainty.reshape((master_equation_uncertainty.shape[0],
1))
Z = np.vstack((Z,master_equation_uncertainty))
sigma = np.vstack((sigma,master_equation_uncertainty))
#This is going to have to be simulation specific
if exp_dict_list[0]['simulation'].physicalSens ==1:
for i, exp_dic in enumerate(exp_dict_list):
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']):
#for i,exp_dic in enumerate(exp_dict_list):
experiment_physical_uncertainty = []
#Temperature Uncertainty
experiment_physical_uncertainty.append(exp_dic['uncertainty']['temperature_relative_uncertainty'])
Z_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
active_parameters.append('T'+'_'+'experiment'+'_'+str(i))
#Pressure Uncertainty
experiment_physical_uncertainty.append(exp_dic['uncertainty']['pressure_relative_uncertainty'])
Z_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+'_'+'experiment'+'_'+str(i))
#Species Uncertainty
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
dilluant = ['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']
for specie in species_to_loop:
if specie in dilluant:
continue
experiment_physical_uncertainty.append(species_uncertainties[specie])
Z_data_Frame.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
active_parameters.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty.append(exp_dic['uncertainty']['time_shift_absolute_uncertainty'])
Z_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
active_parameters.append('Time_shift'+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],
1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
elif re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']):
#ASK MARK WHAT TO ADD HERE
#for i,exp_dic in enumerate(exp_dict_list):
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=jsr_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
#experiment_physical_uncertainty.append(exp_dic['uncertainty']['temperature_relative_uncertainty'])
Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
experiment_physical_uncertainty.append(exp_dic['uncertainty']['pressure_relative_uncertainty'])
Z_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+'_'+'experiment'+'_'+str(i))
#Species Uncertainty
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
dilluant = ['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']
for specie in species_to_loop:
if specie in dilluant:
continue
experiment_physical_uncertainty.append(species_uncertainties[specie])
Z_data_Frame.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
active_parameters.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty.append(exp_dic['uncertainty']['restime_relative_uncertainty'])
Z_data_Frame.append('R_experiment_'+str(i))
active_parameters.append('R_experiment_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
#print(Z_data_Frame)
elif re.match('[Ff]lame[- ][Ss]peed',exp_dict_list[i]['simulation_type']) and re.match('[Oo][Nn][Ee]|[1][ -][dD][ -][Ff]lame',exp_dict_list[i][['experiment_type']]):
#for i,exp_dic in enumerate(exp_dict_list):
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=flame_speed_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
press_uncertainties = flame_speed_press_uncertainties(exp_dic)
Z_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))*len(press_uncertainties)
active_parameters.append('P'+'_'+'experiment'+'_'+str(i))*len(press_uncertainties)
#Species Uncertainty
conditions = exp_dic['conditions']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
if 'Diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluant = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluant']
for nmbr_of_species_sets in range(max_species):
for specie in species_to_loop:
if specie in dilluant:
continue
experiment_physical_uncertainty.append(species_uncertainties[specie])
Z_data_Frame.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
active_parameters.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],
1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
#ASK MARK WHAT TO ADD HERE
#for i,exp_dic in enumerate(exp_dict_list):
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=flow_reactor_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
#experiment_physical_uncertainty.append(exp_dic['uncertainty']['temperature_relative_uncertainty'])
Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
experiment_physical_uncertainty.append(exp_dic['uncertainty']['pressure_relative_uncertainty'])
Z_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+'_'+'experiment'+'_'+str(i))
#Species Uncertainty
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
dilluant = ['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']
for specie in species_to_loop:
if specie in dilluant:
continue
experiment_physical_uncertainty.append(species_uncertainties[specie])
Z_data_Frame.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
active_parameters.append('X'+'_'+str(specie)+'_'+'experiment'+'_'+str(i))
time_shift_uncertainties = flow_reactor_time_shift_uncertainties(parsed_yaml_file_list[i],exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+time_shift_uncertainties
Z_data_Frame=Z_data_Frame+['Time_Shift'+'_'+'experiment'+'_'+str(i)]*len(time_shift_uncertainties)
active_parameters=active_parameters+['Time_Shift'+'_'+'experiment'+'_'+str(i)]*len(time_shift_uncertainties)
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
elif re.match('[Ss]hock[- ][Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
#for i,exp_dic in enumerate(exp_dict_list):
if len(exp_dic['simulation'].temperatures) == len(exp_dic['simulation'].pressures) and len(exp_dic['simulation'].temperatures) >1 and len(exp_dic['simulation'].pressures) >1:
# print('inside z matrix')
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=igdelay_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
for index in range(len(temp_uncertainties)):
Z_data_Frame.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
press_uncertainties = igdelay_press_uncertainties(exp_dic)
for index in range(len(press_uncertainties)):
Z_data_Frame.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
#active_parameters=active_parameters+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
experiment_physical_uncertainty=experiment_physical_uncertainty+press_uncertainties
#print(len(press_uncertainties))
#Species Uncertainty
conditions = exp_dic['conditions_dict_list']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty.append(exp_dic['uncertainty']['time_shift_absolute_uncertainty'])
Z_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
active_parameters.append('Time_shift'+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],
1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
else:
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=igdelay_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
for index in range(len(temp_uncertainties)):
Z_data_Frame.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
press_uncertainties = igdelay_press_uncertainties(exp_dic)
for index in range(len(press_uncertainties)):
Z_data_Frame.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
#active_parameters=active_parameters+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
experiment_physical_uncertainty=experiment_physical_uncertainty+press_uncertainties
#print(len(press_uncertainties))
#Species Uncertainty
conditions = exp_dic['conditions_dict_list']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty.append(exp_dic['uncertainty']['time_shift_absolute_uncertainty'])
Z_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
active_parameters.append('Time_shift'+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],
1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
elif re.match('[Rr][Cc][Mm]',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
#for i,exp_dic in enumerate(exp_dict_list):
experiment_physical_uncertainty = []
#Temperature Uncertainty
temp_uncertainties=rcm_temp_uncertainties(exp_dic)
experiment_physical_uncertainty=experiment_physical_uncertainty+temp_uncertainties
for index in range(len(temp_uncertainties)):
Z_data_Frame.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('T'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#active_parameters=active_parameters+['T'+'_'+'experiment'+'_'+str(i)]*len(temp_uncertainties)
#Pressure Uncertainty
press_uncertainties = rcm_press_uncertainties(exp_dic)
for index in range(len(press_uncertainties)):
Z_data_Frame.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
active_parameters.append('P'+str(index+1)+'_'+'experiment'+'_'+str(i))
#Z_data_Frame=Z_data_Frame+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
#active_parameters=active_parameters+['P'+'_'+'experiment'+'_'+str(i)]*len(press_uncertainties)
experiment_physical_uncertainty=experiment_physical_uncertainty+press_uncertainties
#print(len(press_uncertainties))
#Species Uncertainty
conditions = exp_dic['conditions_dict_list']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Z_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
experiment_physical_uncertainty.append(species_uncertainties[specie])
active_parameters.append('X'+str(x+1)+'_'+species+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty.append(exp_dic['uncertainty']['time_shift_absolute_uncertainty'])
Z_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
active_parameters.append('Time_shift'+'_'+'experiment'+'_'+str(i))
experiment_physical_uncertainty = np.array(experiment_physical_uncertainty)
experiment_physical_uncertainty = experiment_physical_uncertainty.reshape((experiment_physical_uncertainty.shape[0],
1))
Z = np.vstack((Z,experiment_physical_uncertainty))
sigma = np.vstack((sigma,experiment_physical_uncertainty))
#print(exp_dict_list[i]['simulation_type'],exp_dict_list[i]['experiment_type'])
#building dictonary to keep track of independtend coupled coefficients
count = 0
coef_dict = {}
uncertainties_of_coefficents = []
for i,exp_dic in enumerate(exp_dict_list):
if 'perturbed_coef' not in exp_dic.keys():
continue
dictonary_of_coef_and_uncertainty = exp_dic['uncertainty']['coupled_coef_and_uncertainty']
for x in dictonary_of_coef_and_uncertainty:
if x not in coef_dict.keys():
coef_dict[x] = dictonary_of_coef_and_uncertainty[x]
for x in coef_dict:
for y in coef_dict[x]:
if y[0]!=0: #this might cause a problem in the future
count+=1
uncertainties_of_coefficents.append(y)
Z_data_Frame.append('Sigma'+'_'+str(count))
active_parameters.append('Sigma'+'_'+str(count))
uncertainties_of_coefficents = np.array(uncertainties_of_coefficents)
if uncertainties_of_coefficents.any() == True:
uncertainties_of_coefficents = uncertainties_of_coefficents.reshape((uncertainties_of_coefficents.shape[0],
1))
Z = np.vstack((Z,uncertainties_of_coefficents))
sigma = np.vstack((sigma,uncertainties_of_coefficents))
#return(Z,Z_data_Frame)
#print('THIS IS Z',Z_data_Frame)
Z_data_Frame = pd.DataFrame({'value': Z_data_Frame,'Uncertainty': Z.reshape((Z.shape[0],))})
self.z_matrix = Z
self.sigma = sigma
#print(Z.shape)
return Z,Z_data_Frame,sigma,active_parameters
def load_Y(self, exp_dict_list:list,parsed_yaml_file_list:list,
loop_counter:int = 0,
X:dict={},
master_equation_reactions = [],
master_equation_uncertainty_df = None,
master_equation_flag = False):
def natural_log_difference(experiment,model):
natural_log_diff = np.log(np.array(experiment)) - np.log(np.array(model))
return natural_log_diff
Y = []
Y_data_Frame = []
for i,exp_dic in enumerate(exp_dict_list):
counter = 0
for j,observable in enumerate((exp_dic['mole_fraction_observables']+
exp_dic['concentration_observables'] +
exp_dic['flame_speed_observables']+
exp_dic['ignition_delay_observables'])):
if observable == None:
pass
else:
#if you need to add something with concentration add it here
if 'ppm' in exp_dic['experimental_data'][counter].columns.tolist()[1]:
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_ppm'].values,
(exp_dic['simulation'].timeHistoryInterpToExperiment[observable].dropna().values)*1e6)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0],1))
if re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_ppm'].values,
(exp_dic['simulation'].timeHistories[0][observable].dropna().values)*1e6)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0],1))
if re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_ppm'].values,
(exp_dic['simulation'].timeHistories[0][observable].dropna().values)*1e6)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0],1))
elif 'mol/cm^3' in exp_dic['experimental_data'][counter].columns.tolist()[1]:
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']):
concentration = np.true_divide(1,exp_dic['simulation'].pressureAndTemperatureToExperiment[counter]['temperature'].to_numpy())*exp_dic['simulation'].pressureAndTemperatureToExperiment[counter]['pressure'].to_numpy()
concentration *= (1/(8.314e6))*exp_dic['simulation'].timeHistoryInterpToExperiment[observable].dropna().to_numpy()
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_mol/cm^3'].to_numpy(),concentration)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
if re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
concentration = np.true_divide(1,exp_dic['simulation'].timeHistories[0]['temperature'].to_numpy())*exp_dic['simulation'].timeHistories[0]['pressure'].to_numpy()
concentration *= (1/(8.314e6))*exp_dic['simulation'].timeHistories[0][observable].dropna().to_numpy()
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_mol/cm^3'].to_numpy(),concentration)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
if re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']):
concentration = np.true_divide(1.0,exp_dic['simulation'].pressure*ct.one_atm)*np.array(exp_dic['simulation'].temperatures)
concentration *= (1/(8.314e6))*exp_dic['simulation'].timeHistories[0][observable].dropna().to_numpy()
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_mol/cm^3'].to_numpy(),concentration)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
elif 'cm/s' in exp_dic['experimental_data'][counter].columns.tolist()[1]:
if re.match('[Ff]lame [Ss]peed',exp_dict_list[i]['simulation_type']) and re.match('[Oo][Nn][Ee]|[1][ -][dD][ -][Ff]lame',exp_dict_list[i]['experiment_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_cm/s'].to_numpy(),
exp_dic['simulation'].timeHistories[0][observable])
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
elif 's' in exp_dic['experimental_data'][counter].columns.tolist()[1]:
if re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
#check these units would be in seconds of ms?
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable+'_s'].to_numpy(),
exp_dic['simulation'].timeHistories[0]['delay'])
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
else:
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable].values,
exp_dic['simulation'].timeHistoryInterpToExperiment[observable].dropna().values)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
if re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable].values,
exp_dic['simulation'].timeHistories[0][observable].values)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
if re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
natural_log_diff = natural_log_difference(exp_dic['experimental_data'][counter][observable].values,
exp_dic['simulation'].timeHistories[0][observable].values)
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0], 1))
tempList = [observable+'_'+'experiment'+str(i)]*np.shape(natural_log_diff)[0]
Y_data_Frame.extend(tempList)
Y.append(natural_log_diff)
counter+=1
if 'absorbance_observables' in list(exp_dic.keys()):
wavelengths = parsed_yaml_file_list[i]['absorbanceCsvWavelengths']
for k,wl in enumerate(wavelengths):
natural_log_diff = natural_log_difference(exp_dic['absorbance_experimental_data'][k]['Absorbance_'+str(wl)].values,exp_dic['absorbance_model_data'][wl])
natural_log_diff = natural_log_diff.reshape((natural_log_diff.shape[0],
1))
tempList = [str(wl)+'_'+'experiment'+'_'+str(i)]*np.shape(natural_log_diff)[0]
Y_data_Frame.extend(tempList)
Y.append(natural_log_diff)
Y = np.vstack((Y))
#YdataFrame = pd.DataFrame({'value': YdataFrame,'ln_difference': Y})
reactions_in_cti_file = exp_dict_list[0]['simulation'].processor.solution.reaction_equations()
#assembling the target values portion of the Y matrix
#getting the size of the cti file from the first simulation because
#they all use the same cti file and it shouldn't matter
# add in a conditional statment for if there is master equation data
#which is getting included in the simulation
#Flatten master equation reaction list
flatten = lambda *n: (e for a in n
for e in (flatten(*a) if isinstance(a, (tuple, list)) else (a,)))
flattened_master_equation_reaction_list = list(flatten(master_equation_reactions))
if master_equation_flag ==True:
A_n_Ea_length = int((len(reactions_in_cti_file) - len(flattened_master_equation_reaction_list))*3)
number_of_molecular_parameters_list = []
for col in master_equation_uncertainty_df:
number_of_molecular_parameters_list.append(len(master_equation_uncertainty_df[col].dropna().values))
number_of_molecular_parameters = sum(number_of_molecular_parameters_list)
#print('we do not have master equation installed yet')
#subtract out the necessary target values and add the other ones in
else:
A_n_Ea_length = len(reactions_in_cti_file)*3
#addint the zeros to the Y array
#adding the strings to the dictonary
## making a,n and Ea zero list
A_n_Ea_zeros = np.zeros((A_n_Ea_length,1))
if master_equation_flag ==True:
molecular_paramter_zeros = np.zeros((number_of_molecular_parameters,1))
for variable in range(A_n_Ea_length//3):
Y_data_Frame.append('A'+'_'+str(variable))
for variable in range(A_n_Ea_length//3):
Y_data_Frame.append('n'+'_'+str(variable))
for variable in range(A_n_Ea_length//3):
Y_data_Frame.append('Ea'+'_'+str(variable))
#make this the order of master equation list
if master_equation_flag == True:
for i,reaction in enumerate(master_equation_reactions):
if type(reaction)==str:
for j,paramter in enumerate(master_equation_uncertainty_df[reaction].dropna()):
Y_data_Frame.append(str(reaction)+'_P'+'_'+str(j))
elif type(reaction)==tuple:
column_headers = master_equation_uncertainty_df.columns.to_list()
for sub_reaction in reaction:
if sub_reaction in column_headers:
for j,paramter in enumerate(master_equation_uncertainty_df[sub_reaction].dropna()):
Y_data_Frame.append(str(reaction)+'_P'+'_'+str(j))
# if master_equation_flag == True:
# for i,value in enumerate(number_of_molecular_parameters_list):
# for j,parameter in enumerate(range(value)):
# Y_data_Frame.append('R'+'_'+str(i)+'P'+'_'+str(j))
if loop_counter == 0:
Y = np.vstack((Y,A_n_Ea_zeros))
if master_equation_flag ==True:
Y = np.vstack((Y,molecular_paramter_zeros))
else:
#print('we do not have loop counter installed yet')
#need to check what we would need to do here
#should be tottal X ?
#clean this part of the code up here
temp_array = np.array(X['As_ns_Eas'])*-1
temp_array = temp_array.reshape((temp_array.shape[0],
1))
Y = np.vstack((Y, temp_array))
#clean this part of the code up here
#tab
if master_equation_flag == True:
temp_array = np.array(X['molecular_parameters'])*-1
temp_array = temp_array.reshape((temp_array.shape[0],
1))
Y = np.vstack((Y,temp_array))
#Assembling the phsycial portion of the Y matrix
if exp_dict_list[0]['simulation'].physicalSens ==1:
#print(exp_dict_list)
for i,exp_dic in enumerate(exp_dict_list):
if loop_counter ==0:
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[ -][Pp]rofile',exp_dict_list[i]['experiment_type']):
dic_of_conditions = exp_dic['simulation'].conditions
#subtract out the dilluant
species_in_simulation = len(set(dic_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
#add two for Temperature and Pressure
len_of_phsycial_observables_in_simulation = species_in_simulation + 2 + 1
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
#stacking the zeros onto the Y array
Y = np.vstack((Y,temp_zeros))
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
elif re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[ -][Pp]rofile',exp_dict_list[i]['experiment_type']):
dict_of_conditions = exp_dic['simulation'].conditions
species_in_simulation = len(set(dict_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressure_in_simulation = 1
restime_in_simulation = 1
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressure_in_simulation+restime_in_simulation
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
Y = np.vstack((Y,temp_zeros))
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('R_experiment_'+str(i))
elif re.match('[Ff]lame [Ss]peed',exp_dict_list[i]['simulation_type']) and re.match('[Oo][Nn][Ee]|[1][ -][dD][ -][Ff]lame',exp_dict_list[i]['experimentType']):
conditions = exp_dic['conditions_dict_list']
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
pressures_in_simulation = len(exp_dic['simulation'].pressures)
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
if 'Diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluant = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluant']
# species_in_simulation = list(exp_dic['conditions_to_run'][0].keys())
species_in_simulation = len(set(species_in_simulation).difference(diluant)) * max_species
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressure_in_simulation = len(exp_dic['simulation'].pressures)
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressure_in_simulation
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
Y = np.vstack((Y,temp_zeros))
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
elif re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
conditions = exp_dic['conditions_dict_list']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
#species_in_simulation = len(set(dict_of_conditions.keys()).difference(diluant)) * max_species
species = copy.deepcopy(species_to_loop)
species_in_simulation = int(len(singular_species)+((len(set(exp_dic['simulation'].fullParsedYamlFile['speciesNames']).difference(diluent))-len(singular_species))*len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressures_in_simulation = len(exp_dic['simulation'].pressures)
time_shift_length = 1
#print(species_in_simulation,temperatures_in_simulation,pressures_in_simulation)
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressures_in_simulation + time_shift_length
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
Y = np.vstack((Y,temp_zeros))
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+str(value+1)+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+str(value+1)+'_'+'experiment'+'_'+str(i))
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
elif re.match('[Rr][Cc][Mm]',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
conditions = exp_dic['conditions_dict_list']
species_uncertainties = exp_dic['uncertainty']['species_relative_uncertainty']['dictonary_of_values']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
#species_in_simulation = len(set(dict_of_conditions.keys()).difference(diluant)) * max_species
species = copy.deepcopy(species_to_loop)
species_in_simulation = int(len(singular_species)+((len(set(exp_dic['simulation'].fullParsedYamlFile['speciesNames']).difference(diluent))-len(singular_species))*len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressures_in_simulation = len(exp_dic['simulation'].pressures)
time_shift_length = 1
#print(species_in_simulation,temperatures_in_simulation,pressures_in_simulation)
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressures_in_simulation + time_shift_length
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
Y = np.vstack((Y,temp_zeros))
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+str(value+1)+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+str(value+1)+'_'+'experiment'+'_'+str(i))
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
dict_of_conditions = exp_dic['simulation'].conditions
species_in_simulation = len(set(dict_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
time_shift_in_simulation = len(parsed_yaml_file_list[i]['timeShiftOriginal'])
pressure_in_simulation = 1
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressure_in_simulation+time_shift_in_simulation
temp_zeros = np.zeros((len_of_phsycial_observables_in_simulation,1))
Y = np.vstack((Y,temp_zeros))
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
for variable in range(time_shift_in_simulation):
Y_data_Frame.append('Time_shift'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
else:
if re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']):
dic_of_conditions = exp_dic['simulation'].conditions
#subtract out the dilluant
species_in_simulation = len(set(dic_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
elif re.match('[Jj][Ss][Rr]',exp_dict_list[i]['simulation_type']):
dict_of_conditions = exp_dic['simulation'].conditions
species_in_simulation = len(set(dict_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressure_in_simulation = 1
restime_in_simulation = 1
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressure_in_simulation+restime_in_simulation
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('R_experiment_'+str(i))
elif re.match('[Ff]lame [Ss]peed',exp_dict_list[i]['simulation_type']) and re.match('[Oo][Nn][Ee]|[1][ -][dD][ -][Ff]lame',exp_dict_list[i]['experimentType']):
species_to_loop = exp_dic['uncertainty']['species_relative_uncertainty']['species']
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
if 'Diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluant' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluant = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluant']
species_in_simulation = len(set(dict_of_conditions.keys()).difference(diluant)) * max_species
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressure_in_simulation = len(exp_dic['simulation'].pressures)
len_of_phsycial_observables_in_simulation = species_in_simulation+temperatures_in_simulation+pressure_in_simulation
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
dict_of_conditions = exp_dic['simulation'].conditions
species_in_simulation = len(set(dict_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
time_shift_in_simulation = len(parsed_yaml_file_list[i]['timeShiftOriginal'])
pressure_in_simulation = 1
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+'_'+'experiment'+'_'+str(i))
Y_data_Frame.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
Y_data_Frame.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
for variable in range(time_shift_in_simulation):
Y_data_Frame.append('Time_shift'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
elif re.match('[Ss]hock [Tt]ube',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
conditions = exp_dic['conditions_dict_list']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressures_in_simulation = len(exp_dic['simulation'].pressures)
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluant=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluant = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+str(value+1)+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+str(value+1)+'_'+'experiment'+'_'+str(i))
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
elif re.match('[Rr][Cc][Mm]',exp_dict_list[i]['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dict_list[i]['experiment_type']):
conditions = exp_dic['conditions_dict_list']
species_to_loop = list(exp_dic['conditions_dict_list'].keys())
temperatures_in_simulation = len(exp_dic['simulation'].temperatures)
pressures_in_simulation = len(exp_dic['simulation'].pressures)
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
max_species = max(list_with_most_species_in_them)
diluant=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluant = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
for value in range(temperatures_in_simulation):
Y_data_Frame.append('T'+str(value+1)+'_'+'experiment'+'_'+str(i))
for value in range(pressures_in_simulation):
Y_data_Frame.append('P'+str(value+1)+'_'+'experiment'+'_'+str(i))
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
Y_data_Frame.append('X'+str(x+1)+'_'+species+'_experiment_'+str(i))
Y_data_Frame.append('Time_shift'+'_'+'experiment'+'_'+str(i))
if i==len(exp_dict_list)-1:
temp_array = np.array(X['physical_observables'])*-1
temp_array = temp_array.reshape((temp_array.shape[0],
1))
Y = np.vstack((Y,temp_array))
#Assembling the portion of the Y matrix for the absorbance coefficient sensitiviteis
pert_coef = {} #build a dict matching pert_coef to their experiment and wavelength.
#length of the dict gives padding information
for exp in exp_dict_list:
if 'perturbed_coef' not in exp.keys():
continue
perturbed_for_exp = exp['perturbed_coef']
for x in perturbed_for_exp:
if x[0][2] not in pert_coef.keys():
pert_coef[x[0][2]] = [x[1]]
else:
pert_coef[x[0][2]].append(x[1])
num_ind_pert_coef = len(pert_coef)
temp_zeros = np.zeros((num_ind_pert_coef,1))
if loop_counter == 0:
Y = np.vstack((Y,temp_zeros))
else:
if 'absorbance_coefficent_observables' in X.keys():
#temp_array = np.array(X['absorbance_coefficent_observables'])
temp_array = X['absorbance_coefficent_observables']
temp_array = [a for a in temp_array if a != 'null']
#temp_array = temp_array[temp_array!=0]
#temp_array = temp_array[temp_array!=0]
temp_array = np.array(temp_array)
temp_array = np.array(temp_array)*-1
temp_array = temp_array.reshape((temp_array.shape[0],
1))
Y = np.vstack((Y,temp_array))
for x in range(num_ind_pert_coef):
Y_data_Frame.append('Sigma'+'_'+str(x))
Y_data_Frame = pd.DataFrame({'value': Y_data_Frame,'ln_difference': Y.reshape((Y.shape[0],))})
self.Y_matrix = Y
#print(Y.shape,'Y matrix without k targets')
return Y, Y_data_Frame
def load_S(self, exp_dict_list:list,parsed_yaml_list:list,
dk=.01,
master_equation_reactions = [],
mapped_master_equation_sensitivites=np.array(()),
master_equation_uncertainty_df = None,
master_equation_flag = False):
#preprocessing for padding
num_exp = len(exp_dict_list)
pert_coef = {} #build a dict matching pert_coef to their experiment and wavelength.
#length of the dict gives padding information
list_to_keep_order_of_coef = []
for exp in exp_dict_list:
if 'perturbed_coef' not in exp.keys():
continue
perturbed_for_exp = exp['perturbed_coef']
for x in perturbed_for_exp:
if x[0][2] not in pert_coef.keys():
pert_coef[x[0][2]] = [x[1]]
else:
pert_coef[x[0][2]].append(x[1])
if x[0][2] not in list_to_keep_order_of_coef:
list_to_keep_order_of_coef.append(x[0][2])
num_ind_pert_coef = len(pert_coef)
#print(pert_coef.keys())
#print(num_ind_pert_coef," sigmas")
#establish # of independent pert before hand, to proper pad the observables, put in list, make a dict of cc,
# values will be a list of tabs data?
# use the list to get the padding size
k_sens_for_whole_simulation = []
p_sens_for_whole_simulation = []
abs_coef_sens_for_whole_simulation = []
temps = []
for i,exp in enumerate(exp_dict_list):
ttl_kinetic_observables_for_exp = []
obs_counter =0
for j,observable in enumerate(exp['mole_fraction_observables'] + exp['concentration_observables']+ exp['ignition_delay_observables']):
if observable == None:
continue
#return exp['ksens']['A']
#print(np.shape(exp['ksens']['A'][obs_counter]))
#print(np.shape(exp['ksens']['N'][obs_counter]))
#print(np.shape(exp['ksens']['Ea'][obs_counter]))
single_obs_matrix = np.hstack((exp['ksens']['A'][obs_counter],
exp['ksens']['N'][obs_counter],
exp['ksens']['Ea'][obs_counter]))
#print(single_obs_matrix)
ttl_kinetic_observables_for_exp.append(single_obs_matrix)
obs_counter +=1
if 'perturbed_coef' in exp.keys():
wavelengths = parsed_yaml_list[i]['absorbanceCsvWavelengths']
for k,wl in enumerate(wavelengths):
single_obs_matrix = np.hstack((exp['absorbance_ksens'][wl][0],
exp['absorbance_ksens'][wl][1],
exp['absorbance_ksens'][wl][2]))
ttl_kinetic_observables_for_exp.append(single_obs_matrix)
ttl_kinetic_observables_for_exp = np.vstack((ttl_kinetic_observables_for_exp))
k_sens_for_whole_simulation.append(ttl_kinetic_observables_for_exp)
#print(np.shape(k_sens_for_whole_simulation))
####vstack ttl_kinetic_observables_for_exp and append somwehre else
if exp['simulation'].physicalSens ==1:
ttl_phsycal_obs_for_exp = []
for j,observable in enumerate(exp['mole_fraction_observables'] + exp['concentration_observables'] + exp['ignition_delay_observables']):
obs_counter = 0
if observable == None:
continue
if re.match('[Ss]hock [Tt]ube',exp['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp['experiment_type']):
temperature_sensitivity = exp['temperature'][observable].dropna().values
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
time_shift_sensitivity = exp['time_shift'][observable].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
pressure_sensitivity = exp['pressure'][observable].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df[observable].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0]
,1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
elif re.match('[Jj][Ss][Rr]',exp['simulation_type']):
temperature_sensitivity=np.array(exp['temperature'][observable])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = exp['pressure'][observable].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
restime_sensitivity=exp['restime_sens'][observable].dropna().values
restime_sensitivity = restime_sensitivity.reshape((restime_sensitivity.shape[0],1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df[observable].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
restime_sensitivity=exp['restime_sens'][observable].dropna().values
restime_sensitivity = restime_sensitivity.reshape((restime_sensitivity.shape[0],1))
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
temperature_sensitivity=np.array(exp['temperature'][observable])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = exp['pressure'][observable].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df[observable].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
if len(parsed_yaml_list[i]['timeShiftOriginal'])>1:
time_shift_sensitivity = np.array(exp['time_shift'][observable])*np.identity(len(exp['simulation'].temperatures))
else:
time_shift_sensitivity = np.array(exp['time_shift'][observable])
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif re.match('[Ii]gnition[- ][Dd]elay',exp['experiment_type']) and re.match('[Ss]hock[- ][Tt]ube',exp['simulation_type']):
#CHECK HOW MANY SPECIES THERE ARE.
conditions = exp['conditions_dict_list']
species_to_loop = list(exp['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
if len(exp['simulation'].temperatures)>1 and len(exp['simulation'].pressures)==1:
temperature_sensitivity=np.array(exp['temperature']['delay'])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = exp['pressure']['delay'].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
#print("INSIDE HERE")
elif len(exp['simulation'].pressures)>1 and len(exp['simulation'].temperatures)==1:
pressure_sensitivity = np.array(exp['pressure']['delay'])*np.identity(len(exp['simulation'].pressures))
temperature_sensitivity = exp['temperature']['delay'].dropna().values
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif len(exp['simulation'].pressures)==1 and len(exp['simulation'].temperatures)==1 and len(list_with_most_species_in_them)>1:
pressure_sensitivity = exp['pressure']['delay'].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
temperature_sensitivity = exp['temperature']['delay'].dropna().values
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
species_sensitivty=[]
conditions = exp['conditions_dict_list']
species_to_loop = list(exp['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
diluent=[]
if 'Diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
single_species_sensitivty = exp['species'][x]['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
#print(single_species_sensitivty)
species_sensitivty.append(single_species_sensitivty)
elif species not in singular_species and species not in diluent:
single_species_sensitivty = np.array(exp['species'][x]['delay'])*np.identity(len(exp['species'][x]['delay']))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty=np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif len(exp['simulation'].pressures)>1 and len(exp['simulation'].temperatures)>1 and len(list_with_most_species_in_them)>1 and len(exp['simulation'].pressures)==len(exp['simulation'].temperatures):
temperature_sensitivity=np.array(exp['temperature']['delay'])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = np.array(exp['pressure']['delay'])*np.identity(len(exp['simulation'].pressures))
species_sensitivty=[]
conditions = exp['conditions_dict_list']
species_to_loop = list(exp['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
diluent=[]
if 'Diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
single_species_sensitivty = exp['species'][x]['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
#print(single_species_sensitivty)
species_sensitivty.append(single_species_sensitivty)
elif species not in singular_species and species not in diluent:
single_species_sensitivty = np.array(exp['species'][x]['delay'])*np.identity(len(exp['species'][x]['delay']))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty=np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif len(exp['simulation'].pressures)>1 and len(exp['simulation'].temperatures)>1 and len(exp['simulation'].pressures) == len(exp['simulation'].temperatures):
temperature_sensitivity=np.array(exp['temperature']['delay'])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = np.array(exp['pressure']['delay'])*np.identity(len(exp['simulation'].pressures))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif re.match('[Ii]gnition[- ][Dd]elay',exp['experiment_type']) and re.match('[Rr][Cc][Mm]',exp['simulation_type']):
if len(exp['simulation'].temperatures)>1 and len(exp['simulation'].pressures)>1:
temperature_sensitivity=np.array(exp['temperature']['delay'])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = np.array(exp['pressure']['delay'])*np.identity(len(exp['simulation'].pressures))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif len(exp['simulation'].temperatures)>1:
temperature_sensitivity=np.array(exp['temperature']['delay'])*np.identity(len(exp['simulation'].temperatures))
pressure_sensitivity = exp['pressure']['delay'].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
#print("INSIDE HERE")
elif len(exp['simulation'].pressures)>1:
pressure_sensitivity = np.array(exp['pressure']['delay'])*np.identity(len(exp['simulation'].pressures))
temperature_sensitivity = exp['temperature']['delay'].dropna().values
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif len(exp['simulation'].pressures)==1 and len(exp['simulation'].temperatures)==1:
pressure_sensitivity = exp['pressure']['delay'].dropna().values
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
temperature_sensitivity = exp['temperature']['delay'].dropna().values
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
species_sensitivty=[]
conditions = exp['conditions_dict_list']
species_to_loop = list(exp['conditions_dict_list'].keys())
list_with_most_species_in_them = []
for specie in species_to_loop:
list_with_most_species_in_them.append(len(conditions[specie]))
diluent=[]
if 'Diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
for x,species in enumerate(exp['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
single_species_sensitivty = exp['species'][x]['delay'].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0],1))
#print(single_species_sensitivty)
species_sensitivty.append(single_species_sensitivty)
elif species not in singular_species and species not in diluent:
single_species_sensitivty = np.array(exp['species'][x]['delay'])*np.identity(len(exp['species'][x]['delay']))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty=np.hstack((species_sensitivty))
time_shift_sensitivity = exp['time_shift']['delay'].dropna().values
time_shift_sensitivity = time_shift_sensitivity.reshape((time_shift_sensitivity.shape[0], 1))
elif re.match('[Ff]lame[- ][Ss]peed',exp['simulation_type']) and re.match('[Oo][Nn][Ee]|[1][ -][dD][ -][Ff]lame',exp['experiment_type']):
len_of_temperature_list = len(exp['simulation'].temperatures)
if len_of_temperature_list > 1:
temperature_sensitivity=np.array(exp['temperature'][observable])*np.identity(len(exp['simulation'].temperatures))
else:
temperature_sensitivity = np.array(exp['temperature'][observable])
temperature_sensitivity = temperature_sensitivity.reshape((temperature_sensitivity.shape[0], 1))
len_of_pressure_list = len(exp['simulation'].pressures)
if len_of_pressure_list >1:
pressure_sensitivity=np.array(exp['pressure'][observable])*np.identity(len(exp['simulation'].pressures))
else:
pressure_sensitivity=np.array(exp['pressure'][observable])
pressure_sensitivity = pressure_sensitivity.reshape((pressure_sensitivity.shape[0], 1))
#FIX THIS
#print('FIXXXX')
species_sensitivty = []
for df in exp['species']:
single_species_sensitivty = df[observable].dropna().values
single_species_sensitivty = single_species_sensitivty.reshape((single_species_sensitivty.shape[0]
,1))
species_sensitivty.append(single_species_sensitivty)
species_sensitivty = np.hstack((species_sensitivty))
if re.match('[Jj][Ss][Rr]',exp['simulation_type']):
single_obs_physical = np.hstack((temperature_sensitivity,pressure_sensitivity,species_sensitivty,restime_sensitivity))
elif re.match('[Ss]hock [Tt]ube',exp['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp['experiment_type']):
single_obs_physical = np.hstack((temperature_sensitivity,pressure_sensitivity,species_sensitivty,time_shift_sensitivity))
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
single_obs_physical = np.hstack((temperature_sensitivity,pressure_sensitivity,species_sensitivty,time_shift_sensitivity))
elif re.match('[Ii]gnition[- ][Dd]elay',exp['experiment_type']) and re.match('[Ss]hock[- ][Tt]ube',exp['simulation_type']):
single_obs_physical = np.hstack((temperature_sensitivity,pressure_sensitivity,species_sensitivty,time_shift_sensitivity))
#print("INSIDE HERE")
elif re.match('[Ii]gnition[- ][Dd]elay',exp['experiment_type']) and re.match('[Rr][Cc][Mm]',exp['simulation_type']):
single_obs_physical = np.hstack((temperature_sensitivity,pressure_sensitivity,species_sensitivty,time_shift_sensitivity))
ttl_phsycal_obs_for_exp.append(single_obs_physical)
obs_counter +=1
if 'perturbed_coef' in exp.keys():
wavelengths = parsed_yaml_list[i]['absorbanceCsvWavelengths']
for k,wl in enumerate(wavelengths):
physical_sens = []
for p_sens in exp['absorbance_psens']:
array = p_sens[wl]
array = array.reshape((array.shape[0],1))
physical_sens.append(array)
for time_sens in exp['absorbance_time_shift']:
array2 = p_sens[wl]
array2 = array2.reshape((array2.shape[0],1))
physical_sens.append(array2)
physical_sens = np.hstack((physical_sens))
ttl_phsycal_obs_for_exp.append(physical_sens)
ttl_phsycal_obs_for_exp = np.vstack((ttl_phsycal_obs_for_exp))
p_sens_for_whole_simulation.append(ttl_phsycal_obs_for_exp)
#######################################################################################################################################################
if 'perturbed_coef' in exp.keys():
ttl_absorbance_obs_for_exp = []
wavelengths = parsed_yaml_list[i]['absorbanceCsvWavelengths']
for k,wl in enumerate(wavelengths):
perturbed_coefficeints = []
index_list = []
for xx in range(len(parsed_yaml_list[i]['coupledCoefficients'])):
for yy in range(len(parsed_yaml_list[i]['coupledCoefficients'][xx])):
ff = parsed_yaml_list[i]['functionalForm'][xx][yy]
#temp = list(parsed_yaml_list[i]['coupledCoefficients'][xx][yy])
for zz in range(len(parsed_yaml_list[i]['coupledCoefficients'][xx][yy])):
temp = list(parsed_yaml_list[i]['coupledCoefficients'][xx][yy])
coefficent = parsed_yaml_list[i]['coupledCoefficients'][xx][yy][zz]
if coefficent!=0:
perturbed_coefficent=coefficent+coefficent*dk
if zz==1 and ff =='F':
#change back tab
perturbed_coefficent = coefficent + .01*coefficent
temp[zz] = perturbed_coefficent
key = tuple(temp)
indx = list_to_keep_order_of_coef.index(key)
index_list.append(indx)
exp_index_sigma = temps.count(key)
temps.append(key)
array = pert_coef[key][exp_index_sigma][wl]
array = array.reshape((array.shape[0],1))
perturbed_coefficeints.append(array)
missing_sigmas = []
for indp_sigma in range(len(list_to_keep_order_of_coef)):
if indp_sigma not in index_list:
missing_sigmas.append(indp_sigma)
perturbed_coefficents_padded_with_zeros = []
count_sigma=0
for indp_sigma in range(len(list_to_keep_order_of_coef)):
if indp_sigma in missing_sigmas:
zero_array = np.zeros((perturbed_coefficeints[0].shape[0],1))
perturbed_coefficents_padded_with_zeros.append(zero_array)
else:
perturbed_coefficents_padded_with_zeros.append(perturbed_coefficeints[count_sigma])
count_sigma +=1
perturbed_coefficents_padded_with_zeros = np.hstack((perturbed_coefficents_padded_with_zeros))
ttl_absorbance_obs_for_exp.append(perturbed_coefficents_padded_with_zeros)
ttl_absorbance_obs_for_exp = np.vstack((ttl_absorbance_obs_for_exp))
abs_coef_sens_for_whole_simulation.append(ttl_absorbance_obs_for_exp)
#vstack ttl_absorbance_obs_for_exp and append somehwere else
else:
abs_coef_sens_for_whole_simulation.append(0)
######################################################################################################################################################
flatten = lambda *n: (e for a in n
for e in (flatten(*a) if isinstance(a, (tuple, list)) else (a,)))
flattened_master_equation_reaction_list = list(flatten(master_equation_reactions))
#assembling the S matrix from the individual experiments
#master_equation = False
if master_equation_flag == True:
S_ksens = np.vstack((k_sens_for_whole_simulation))
A_k = np.hsplit(S_ksens,3)[0]
N_k = np.hsplit(S_ksens,3)[1]
Ea_k = np.hsplit(S_ksens,3)[2]
number_of_master_equation_reactions = len(flattened_master_equation_reaction_list)
A_k = A_k[:,:-number_of_master_equation_reactions]
N_k = N_k[:,:-number_of_master_equation_reactions]
Ea_k = Ea_k[:,:-number_of_master_equation_reactions]
S_ksens = np.hstack((A_k,N_k,Ea_k))
#print(np.shape(S_ksens),'this is the shape of the S matrix before MP')
S_ksens = np.hstack((S_ksens,mapped_master_equation_sensitivites))
else:
S_ksens = np.vstack((k_sens_for_whole_simulation))
def sum_of_zeros(idx,array,column_list):
rows_behind = array.shape[0]
rows_infront = array.shape[0]
columns_behind = sum(column_list[:idx])
columns_infront = sum(column_list[idx+1:])
behind_tuple = (rows_behind,columns_behind)
infront_tuple = (rows_infront,columns_infront)
return (behind_tuple,infront_tuple)
if exp_dict_list[0]['simulation'].physicalSens ==1:
number_of_columns_in_psens_arrays = []
number_of_rows_in_psens_arrays=[]
for i,array in enumerate(p_sens_for_whole_simulation):
number_of_rows_in_psens_arrays.append(array.shape[0])
number_of_columns_in_psens_arrays.append(array.shape[1])
p_sens_whole_simulation_with_padding = []
for i,array in enumerate(p_sens_for_whole_simulation):
zero_array_behind = np.zeros(sum_of_zeros(i,array,number_of_columns_in_psens_arrays)[0])
if zero_array_behind.shape[1] != 0:
array = np.hstack((zero_array_behind,array))
zero_array_infront = np.zeros(sum_of_zeros(i,array,number_of_columns_in_psens_arrays)[1])
if zero_array_infront.shape[1] != 0:
array = np.hstack((array,zero_array_infront))
p_sens_whole_simulation_with_padding.append(array)
S_psens = np.vstack((p_sens_whole_simulation_with_padding))
##############################################################################################
absorb_coef_whole_simulation_with_padding = []
for i,exp in enumerate(exp_dict_list):
single_experiment_absorption = []
if exp['mole_fraction_observables'][0] != None or exp['concentration_observables'][0] != None or exp['ignition_delay_observables'][0] != None:
if 'perturbed_coef' not in exp.keys():
zero_array_for_observables_padding = np.zeros((number_of_rows_in_psens_arrays[i],
num_ind_pert_coef))
single_experiment_absorption.append(zero_array_for_observables_padding)
if 'perturbed_coef' in exp.keys():
zero_padded_aborption_coef_array = abs_coef_sens_for_whole_simulation[i]
combined = abs_coef_sens_for_whole_simulation[i]
if exp['mole_fraction_observables'][0] != None or exp['concentration_observables'][0] != None or exp['ignition_delay_observables'][0] != None:
zero_array_for_observables_padding = np.zeros((number_of_rows_in_psens_arrays[i]-zero_padded_aborption_coef_array.shape[0],
num_ind_pert_coef))
combined = np.vstack((zero_array_for_observables_padding,zero_padded_aborption_coef_array))
single_experiment_absorption.append(combined)
single_experiment_absorption = np.vstack((single_experiment_absorption))
absorb_coef_whole_simulation_with_padding.append(single_experiment_absorption)
absorb_coef_whole_simulation_with_padding = np.vstack((absorb_coef_whole_simulation_with_padding))
S_abs_coef = absorb_coef_whole_simulation_with_padding
#return((S_ksens,S_psens,S_abs_coef))
#print(np.shape(S_ksens),np.shape(S_psens),np.shape(S_abs_coef))
S_matrix = np.hstack((S_ksens,S_psens,S_abs_coef))
shape = np.shape(S_matrix)[1]
#append identy matrix
identity_matrix = np.identity(shape)
# identity_matrix[1,0]=.1
# identity_matrix[0,1]=.1
# identity_matrix[0,20]=.1
# identity_matrix[20,0]=.1
# identity_matrix[39,0]=.1
# identity_matrix[0,39]=.1
####making edits to this just for masten test
S_matrix = np.vstack((S_matrix,identity_matrix))
self.S_matrix = S_matrix
S_matrix_wo_k_targets = copy.deepcopy(self.S_matrix)
self.S_matrix_wo_k_targets = S_matrix_wo_k_targets
#print(S_matrix_wo_k_targets.shape,'S matrix without k targets')
S_matrix_df = pd.DataFrame(S_matrix)
return S_matrix
def grouping_physical_model_parameters(self,exp:list):
final_groups=[]
for i in exp['simulation'].fullParsedYamlFile['overallDict'].keys():
if not re.match('[dD]iluent',i['type']):
final_groups.append(i)
def breakup_X(self, X,
exp_dict_list:list,
exp_uncertainty_dict_list_original:list,
loop_counter:int = 0,
master_equation_uncertainty_df=None,
master_equation_reactions = [],
master_equation_flag = False):
X_to_subtract_from_Y = {}
reactions_in_cti_file = exp_dict_list[0]['simulation'].processor.solution.reaction_equations()
number_of_reactions = len(reactions_in_cti_file)
####Grab off updates directly for the CTI file
####need to add master equation reactions
##################################################################
if loop_counter !=0:
X_new = X
else:
X_new = X
##################################################################
#print('USING BURKE X VALUES')
#X = pd.read_csv('MSI/data/test_data/burke_X_values.csv')
#X= X['Burke_Value'].values
#X = X.reshape(X.shape[0],1)
################################################################
##################################################################
#print('RUNNING TEST')
#X_new = np.zeros(np.shape(X_new))
#X_new[79] = .01
# print(X_new)
# X_new[847] = -0.007258986471821074
# X_new[848] = -0.07160891432785314
# X_new[849] = -0.038747789992729584
# X_new[850] = -0.09184808671928052
# X_new[851] = -0.13343314153597205
# X_new[852] = 0.0046931837946472
# X_new[853] = -0.007191276020250346
#X= X['Burke_Value'].values
#X = X.reshape(X.shape[0],1)
#zeros = np.zeros((X_new.shape))
#X_new = zeros
# X_new[873,0] = .01
# print("X_NEW")
################################################################
flatten = lambda *n: (e for a in n
for e in (flatten(*a) if isinstance(a, (tuple, list)) else (a,)))
flattened_master_equation_reaction_list = list(flatten(master_equation_reactions))
X_new = list(X_new.flatten())
if exp_dict_list[0]['simulation'].kineticSens ==1:
value1 = 3*(number_of_reactions - len(flattened_master_equation_reaction_list))
AsNsEas = X_new[:value1]
X_to_subtract_from_Y['As_ns_Eas'] = AsNsEas
#### pickup here
dividedBy = int(len(AsNsEas) / 3)
def list_slice(S,step):
return [S[i::step] for i in range(step)]
resortedList = list_slice(AsNsEas,dividedBy)
innerDict = ['A','n','Ea']
l = [dict(zip(innerDict,resortedList[x])) for x in range(len(resortedList))]
Keys= []
for xx in range(int(value1/3)):
Keys.append('r'+str(xx))
deltaXAsNsEas = dict(zip(Keys,l))
innerDictNew = ['A_update','n_update','Ea_update']
ll = [dict(zip(innerDictNew,resortedList[x])) for x in range(len(resortedList))]
kinetic_paramter_dict = dict(zip(reactions_in_cti_file,ll))
#molecularParams = np.array([.1,.2,.3,.4,.2,.3,.4]).flatten().tolist()
# might need to fix this based on how lei is passing me information, check in notebook
if master_equation_flag == True:
# number_of_molecular_parameters_list = []
# for col in master_equation_uncertainty_df:
# number_of_molecular_parameters_list.append(len(master_equation_uncertainty_df[col].dropna().values))
number_of_molecular_parameters_list = []
for i,reaction in enumerate(master_equation_reactions):
if type(reaction)==str:
number_of_molecular_parameters_list.append(len(list(master_equation_uncertainty_df[reaction].dropna().values)))
elif type(reaction)==tuple:
column_headers = master_equation_uncertainty_df.columns.to_list()
for sub_reaction in reaction:
if sub_reaction in column_headers:
number_of_molecular_parameters_list.append(len(list(master_equation_uncertainty_df[sub_reaction].dropna().values)))
sum_of_moleular_paramters = sum(number_of_molecular_parameters_list)
value2 = sum_of_moleular_paramters
deltaXmolecularParams = X_new[value1:(value1+value2)]
X_to_subtract_from_Y['molecular_parameters'] = deltaXmolecularParams
molecular_paramters_by_reaction = []
reaction_numbers = []
start_mp = 0
for r,number in enumerate(number_of_molecular_parameters_list):
stop_mp = start_mp + number
molecular_paramters_by_reaction.append(deltaXmolecularParams[start_mp:stop_mp])
start_mp = stop_mp
reaction_numbers.append('R_'+str(r))
delta_x_molecular_params_by_reaction_dict = dict(zip(master_equation_reactions,molecular_paramters_by_reaction))
list_of_mp = []
for i,reaction in enumerate(molecular_paramters_by_reaction):
temp=[]
for j,value in enumerate(reaction):
temp.append('Paramter_'+str(j)+'_Update')
list_of_mp.append(temp)
inner_dict_temp = [dict(zip(list_of_mp[x],molecular_paramters_by_reaction[x])) for x in range(len(molecular_paramters_by_reaction))]
inner_dict_temp_2 = dict(zip(master_equation_reactions,inner_dict_temp))
kinetic_paramter_dict.update(inner_dict_temp_2)
#its possible this kinetic paramters dict might break
else:
value2 = 0
physical_observables = []
previous_value = 0
physical_observables_for_Y = []
if exp_dict_list[0]['simulation'].physicalSens ==1:
for i,exp_dic in enumerate(exp_dict_list):
if re.match('[Ss]hock [Tt]ube',exp_dic['simulation_type']) and re.match('[Ss]pecies[- ][Pp]rofile',exp_dic['experiment_type']):
dic_of_conditions = exp_dic['simulation'].conditions
#subtract out the dilluant
species_in_simulation = len(set(dic_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
#add two for Temperature and Pressure
len_of_phsycial_observables_in_simulation = species_in_simulation + 2 +1
new_value = previous_value + len_of_phsycial_observables_in_simulation
single_experiment_physical_observables = X_new[(value1+value2+previous_value):(value1+value2+new_value)]
physical_observables_for_Y.append(single_experiment_physical_observables)
temp_keys = []
#stacking the zeros onto the Y array
temp_keys.append('T'+'_'+'experiment'+'_'+str(i))
temp_keys.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
temp_keys.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
temp_keys.append('Time_shift'+'_'+'experiment'+'_'+str(i))
temp_dict = dict(zip(temp_keys,single_experiment_physical_observables))
physical_observables.append(temp_dict)
##come back to this and do a test on paper
previous_value = new_value
elif re.match('[Ss]hock [Tt]ube',exp_dic['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dic['experiment_type']):
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
species_in_simulation = int(len(singular_species)+((len(exp_dic['simulation'].fullParsedYamlFile['speciesNames'])-len(singular_species))*len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])))
len_of_phsycial_observables_in_simulation = species_in_simulation + len(exp_dic['simulation'].pressures)+len(exp_dic['simulation'].temperatures)+1
new_value = previous_value + len_of_phsycial_observables_in_simulation
single_experiment_physical_observables = X_new[(value1+value2+previous_value):(value1+value2+new_value)]
physical_observables_for_Y.append(single_experiment_physical_observables)
temp_keys = []
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].temperatures)):
temp_keys.append('T'+str(j+1)+'_'+'experiment'+'_'+str(i))
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].pressures)):
temp_keys.append('P'+str(j+1)+'_'+'experiment'+'_'+str(i))
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
temp_keys.append('X'+str(x+1)+'_cond'+str(0)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
temp_keys.append('X'+str(x+1)+'_cond'+str(j)+'_'+species+'_experiment_'+str(i))
temp_keys.append('Time_shift'+'_'+'experiment'+'_'+str(i))
temp_dict = dict(zip(temp_keys,single_experiment_physical_observables))
physical_observables.append(temp_dict)
##come back to this and do a test on paper
previous_value = new_value
#print(temp_dict)
elif re.match('[Rc][Cc][Mm]',exp_dic['simulation_type']) and re.match('[Ii]gnition[- ][Dd]elay',exp_dic['experiment_type']):
diluent=[]
if 'Diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys() or 'diluent' in exp_dic['uncertainty']['species_relative_uncertainty']['type_dict'].keys():
diluent = exp_dic['uncertainty']['species_relative_uncertainty']['type_dict']['diluent']
singular_species=[]
for species in list(exp_dic['simulation'].fullParsedYamlFile['conditions'].keys()):
if len(exp_dic['simulation'].fullParsedYamlFile['conditions'][species])==1 and species not in diluent:
singular_species.append(species)
species_in_simulation = int(len(singular_species)+((len(exp_dic['simulation'].fullParsedYamlFile['speciesNames'])-len(singular_species))*len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])))
len_of_phsycial_observables_in_simulation = species_in_simulation + len(exp_dic['simulation'].pressures)+len(exp_dic['simulation'].temperatures)+1
new_value = previous_value + len_of_phsycial_observables_in_simulation
single_experiment_physical_observables = X_new[(value1+value2+previous_value):(value1+value2+new_value)]
physical_observables_for_Y.append(single_experiment_physical_observables)
temp_keys = []
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].temperatures)):
temp_keys.append('T'+str(j+1)+'_'+'experiment'+'_'+str(i))
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].pressures)):
temp_keys.append('P'+str(j+1)+'_'+'experiment'+'_'+str(i))
for x,species in enumerate(exp_dic['simulation'].fullParsedYamlFile['speciesNames']):
if species in singular_species and species not in diluent:
temp_keys.append('X'+str(x+1)+'_cond'+str(0)+'_'+species+'_experiment_'+str(i))
elif species not in singular_species and species not in diluent:
for j in range(len(exp_dic['simulation'].fullParsedYamlFile['conditions_to_run'])):
temp_keys.append('X'+str(x+1)+'_cond'+str(j)+'_'+species+'_experiment_'+str(i))
temp_keys.append('Time_shift'+'_'+'experiment'+'_'+str(i))
temp_dict = dict(zip(temp_keys,single_experiment_physical_observables))
physical_observables.append(temp_dict)
##come back to this and do a test on paper
previous_value = new_value
elif re.match('[Jj][Ss][Rr]',exp_dic['simulation_type']):
dic_of_conditions = exp_dic['simulation'].conditions
#subtract out the dilluant
species_in_simulation = len(set(dic_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
#add two for Temperature and Pressure
len_of_phsycial_observables_in_simulation = species_in_simulation + 1+len(exp_dic['simulation'].temperatures)+1
#print(len_of_phsycial_observables_in_simulation)
new_value = previous_value + len_of_phsycial_observables_in_simulation
single_experiment_physical_observables = X_new[(value1+value2+previous_value):(value1+value2+new_value)]
#print(len(single_experiment_physical_observables))
physical_observables_for_Y.append(single_experiment_physical_observables)
temp_keys = []
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].temperatures)):
temp_keys.append('T'+str(j+1)+'_'+'experiment'+'_'+str(i))
temp_keys.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
temp_keys.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
temp_keys.append('R'+'_'+'experiment'+'_'+str(i))
temp_dict = dict(zip(temp_keys,single_experiment_physical_observables))
physical_observables.append(temp_dict)
##come back to this and do a test on paper
previous_value = new_value
elif re.match('[Ss]pecies[- ][Pp]rofile',exp_dict_list[i]['experiment_type']) and re.match('[Ff]low[ -][Rr]eactor',exp_dict_list[i]['simulation_type']):
dic_of_conditions = exp_dic['simulation'].conditions
#subtract out the dilluant
species_in_simulation = len(set(dic_of_conditions.keys()).difference(['Ar','AR','ar','HE','He','he','Kr','KR','kr','Xe','XE','xe','NE','Ne','ne']))
#add two for Temperature and Pressure
time_shift_length = len(exp_dic['simulation'].fullParsedYamlFile['timeShiftOriginal'])
len_of_phsycial_observables_in_simulation = species_in_simulation + 1+len(exp_dic['simulation'].temperatures)+time_shift_length
#print(len_of_phsycial_observables_in_simulation)
new_value = previous_value + len_of_phsycial_observables_in_simulation
single_experiment_physical_observables = X_new[(value1+value2+previous_value):(value1+value2+new_value)]
#print(len(single_experiment_physical_observables))
physical_observables_for_Y.append(single_experiment_physical_observables)
temp_keys = []
#stacking the zeros onto the Y array
for j in range(len(exp_dic['simulation'].temperatures)):
temp_keys.append('T'+str(j+1)+'_'+'experiment'+'_'+str(i))
temp_keys.append('P'+'_'+'experiment'+'_'+str(i))
for variable in range(species_in_simulation):
temp_keys.append('X'+'_'+str(variable)+'_'+'experiment'+'_'+str(i))
for j in range(time_shift_length):
temp_keys.append('Time_Shift'+str(j+1)+'_'+'experiment'+'_'+str(i))
temp_dict = dict(zip(temp_keys,single_experiment_physical_observables))
physical_observables.append(temp_dict)
##come back to this and do a test on paper
previous_value = new_value
physical_observables_for_Y = [item for sublist in physical_observables_for_Y for item in sublist]
X_to_subtract_from_Y['physical_observables'] = physical_observables_for_Y
test_abs = []
absorbance_coefficients_for_Y = []
coef_dict = {}
coef_dict_list = []
absorbance_coef_update_dict = {}
for i,exp_dic in enumerate(exp_uncertainty_dict_list_original):
if 'coupled_coef_and_uncertainty' not in exp_dic.keys():
continue
dictonary_of_coef_and_uncertainty = exp_dic['coupled_coef_and_uncertainty']
#tab start working here tomorrow, need to pass in the original version of this dict
#dictonary_of_coef_and_uncertainty = {(140000, 0.0): ([0.7], [0.0]), (1270000, 0.0): ([0.7], [0.0])}
for x in dictonary_of_coef_and_uncertainty:
if x not in coef_dict.keys():
coef_dict[x] = dictonary_of_coef_and_uncertainty[x]
if x not in coef_dict_list:
coef_dict_list.append(x)
start_abs = 0
stop_abs = 1
for i,cof in enumerate(coef_dict_list):
temp=[]
temp2=[]
# counter=1
for value in cof:
if value==0:
temp.append([0])
temp2.append(['null'])
else:
temp.append(X_new[(value1+value2+new_value+start_abs):(value1+value2+new_value+stop_abs)])
temp2.append(X_new[(value1+value2+new_value+start_abs):(value1+value2+new_value+stop_abs)])
start_abs = stop_abs
stop_abs +=1
temp = [item for sublist in temp for item in sublist]
temp2 = [item for sublist in temp2 for item in sublist]
absorbance_coef_update_dict[cof] = temp
absorbance_coefficients_for_Y.append(temp2)
test_abs.append(temp2)
# return everything in a dictonary??
absorbance_coefficients_for_Y = [item for sublist in absorbance_coefficients_for_Y for item in sublist]
X_to_subtract_from_Y['absorbance_coefficent_observables'] = absorbance_coefficients_for_Y
#
if master_equation_flag == False:
return deltaXAsNsEas,physical_observables,absorbance_coef_update_dict,X_to_subtract_from_Y,kinetic_paramter_dict
else:
return deltaXAsNsEas,physical_observables,absorbance_coef_update_dict,X_to_subtract_from_Y,delta_x_molecular_params_by_reaction_dict,kinetic_paramter_dict
def matrix_manipulation(self,runCounter,S_matrix,Y_matrix,z_matrix,XLastItteration = np.array(()),active_parameters=[]):
#RUnning test to link up to paramters
##################################################
#s_temp = np.zeros((1,S_matrix.shape[1]))
#s_temp[0,886]=1
#s_temp[0,888]=-1
#y_temp = np.zeros((1,1))
#y_temp[0,0]=0
#z_temp=np.zeros((1,1))
#z_temp[0,0]=.00001
#S_matrix=np.vstack((S_matrix,s_temp))
#Y_matrix = np.vstack((Y_matrix,y_temp))
#z_matrix = np.vstack((z_matrix,z_temp))
##################################################
# print("ONLY CONSIDERING RATE CONSTANT TARGETS")
# for value in np.arange(0,401):
# z_matrix[value,0] =1000000
##################################################
one_over_z = np.true_divide(1,z_matrix)
#print(Y_matrix)
y_matrix = Y_matrix * one_over_z
s_matrix = S_matrix * (one_over_z.flatten()[:,np.newaxis])
self.y_matrix = y_matrix
sTimesZ = S_matrix * (z_matrix.flatten())[:,np.newaxis]
#calculate covariance matrix
shape = np.shape(self.S_matrix_wo_k_targets)
s_wo_k_targets = s_matrix[:shape[0],:shape[1]]
identity_matrix = s_wo_k_targets[shape[0]-len(active_parameters):,:]
#try:
if runCounter==0:
c = np.dot(np.transpose(identity_matrix),identity_matrix)
c = np.linalg.inv(c)
prior_diag = np.diag(c)
prior_sigmas = np.sqrt(prior_diag)
covariance_prior_df = pd.DataFrame(c)
if active_parameters:
covariance_prior_df.columns = active_parameters
covariance_prior_df.reindex(labels = active_parameters)
prior_diag_df = pd.DataFrame({'parameter': active_parameters,'value': prior_diag.reshape((prior_diag.shape[0],))})
sorted_prior_diag = prior_diag_df.sort_values(by=['value'])
prior_sigmas_df = pd.DataFrame({'parameter': active_parameters,'value': prior_sigmas.reshape((prior_sigmas.shape[0],))})
else:
c = np.dot(np.transpose(s_matrix),s_matrix)
c = np.linalg.inv(c)
covariance_posterior_df = pd.DataFrame(c)
if active_parameters:
covariance_posterior_df.columns = active_parameters
covariance_posterior_df.reindex(labels = active_parameters)
posterior_diag = np.diag(c)
posterior_sigmas = np.sqrt(posterior_diag)
posterior_sigmas_df = pd.DataFrame({'parameter': active_parameters,'value': posterior_sigmas.reshape((posterior_sigmas.shape[0],))})
posterior_diag_df = pd.DataFrame({'parameter': active_parameters,'value': posterior_diag.reshape((posterior_diag.shape[0],))})
sorted_posterior_diag = posterior_diag_df.sort_values(by=['value'])
# except:
# #stub
# print('WE ARE IN THE EXCEPT STATMENT')
# if runCounter==0:
# c = -1
# c = -1
# prior_diag = -1
# prior_sigmas = -1
# covariance_prior_df = -1
# prior_diag_df = -1
# sorted_prior_diag = -1
# prior_sigmas_df = -1
# else:
# c = -1
# c =-1
# covariance_posterior_df = -1
# posterior_diag = -1
# posterior_sigmas = -1
# posterior_sigmas_df = -1
# posterior_diag_df = -1
# sorted_posterior_diag = -1
self.covariance = c
self.s_matrix = s_matrix
psudoInverse = np.linalg.pinv(s_matrix)
delta_X = np.dot(psudoInverse,y_matrix)
self.delta_X = delta_X
if runCounter == 0:
XlastItteration = np.zeros(np.shape(delta_X))
else:
XlastItteration = XLastItteration
X = XlastItteration + delta_X
#STUB THIS IS FOR A TESTING ITTERATION
#####################################################################
#X = np.zeros(np.shape(delta_X))
# X[564] = .01
#####################################################################
self.X = X
#STUB THIS
try:
X_data_frame = pd.DataFrame({'value': active_parameters,'Parameter': X.reshape((X.shape[0],))})
except:
X_data_frame = -1
if runCounter==0:
return X,c,s_matrix,y_matrix,delta_X,z_matrix,X_data_frame,prior_diag,prior_diag_df,sorted_prior_diag,covariance_prior_df,prior_sigmas_df
else:
return X,c,s_matrix,y_matrix,delta_X,z_matrix,X_data_frame,posterior_diag,posterior_diag_df,sorted_posterior_diag,covariance_posterior_df,posterior_sigmas_df
class Adding_Target_Values(meq.Master_Equation):
def __init__(self,S_matrix,Y_matrix,z_matrix,sigma,Y_data_Frame,z_data_Frame):
self.S_matrix = S_matrix
self.Y_matrix = Y_matrix
self.z_matrix = z_matrix
self.sigma = sigma
self.Y_data_Frame = Y_data_Frame
self.z_data_Frame = z_data_Frame
meq.Master_Equation.__init__(self)
def target_values_Y(self,target_value_csv,
exp_dict_list:list,
Y_data_Frame,
master_equation_reactions):
import cantera as ct
Y_df_list = []
Y_values = []
#make sure we put the reactions into the file in the units cantera uses
target_value_csv = pd.read_csv(target_value_csv)
target_reactions = target_value_csv['Reaction']
target_temp = target_value_csv['temperature']
target_press = target_value_csv['pressure']
target_k = target_value_csv['k']
bath_gas = target_value_csv['M']
reactions_in_cti_file = exp_dict_list[0]['simulation'].processor.solution.reaction_equations()
gas = ct.Solution(exp_dict_list[0]['simulation'].processor.cti_path)
diff_in_ks_for_Y = []
def check_if_M_in_reactants(list_to_append_to,
gas,
reactants_in_target_reactions,
reverse_reactants_in_target_reaction):
if reverse_reactants_in_target_reaction !=None:
for reaction_number_in_cti_file in range(gas.n_reactions):
if (gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' + M' or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction + ' + M') :
list_to_append_to.append(reactions_in_cti_file[reaction_number_in_cti_file])
elif reverse_reactants_in_target_reaction==None:
for reaction_number_in_cti_file in range(gas.n_reactions):
if (gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' + M'):
list_to_append_to.append(reactions_in_cti_file[reaction_number_in_cti_file])
return list_to_append_to
for i,reaction in enumerate(target_reactions):
#ask about the mixture composition
#if reaction not in flattened_linked_channel_reactions:
if '*' not in reaction and reaction != 'More Complex Combination Rule' and '(+)' not in reaction:
index_in_cti_file = gas.reaction_equations().index(reaction)
units_reaction_types=['ElementaryReaction',
'PlogReaction',
'ChebyshevReaction',
'ThreeBodyReaction',
'FalloffReaction']
coeff_sum = sum(gas.reaction(index_in_cti_file).reactants.values())
if target_press[i] == 0:
pressure = 1e-9
else:
pressure = target_press[i]
if bath_gas[i] !=0:
gas.TPX = target_temp[i],pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = target_temp[i],pressure*101325,{'Ar':.99}
reaction_number_in_cti = reactions_in_cti_file.index(reaction)
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k = k
elif coeff_sum==2:
k=k*1000
elif coeff_sum==3:
k=k*1000000
#check and make sure we are subtracting in the correct order
difference = np.log(target_k[i]) - np.log(k)
diff_in_ks_for_Y.append(difference)
Y_df_list.append(reaction)
Y_values.append(difference)
#elif reaction in flattened_linked_channel_reactions:
elif '*' in reaction and reaction != 'More Complex Combination Rule' and '/' not in reaction:
reactions_in_cti_file_with_these_reactants = []
#might be a more comprehensive way to do this
reactants_in_target_reactions = reaction.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction=None
if len(reactants_in_target_reactions.split('+'))>1:
reverse_reactants_in_target_reaction = reactants_in_target_reactions.split('+')
temp = reverse_reactants_in_target_reaction[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction:
reactions_in_cti_file_with_these_reactants.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_cti_file_with_these_reactants = check_if_M_in_reactants(reactions_in_cti_file_with_these_reactants,
gas,
reactants_in_target_reactions,
reactants_in_target_reactions)
if target_press[i] == 0:
pressure = 1e-9
else:
pressure = target_press[i]
if bath_gas[i] !=0:
gas.TPX = target_temp[i],pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = target_temp[i],pressure*101325,{'Ar':.99}
tottal_k = []
for secondary_reaction in reactions_in_cti_file_with_these_reactants:
reaction_number_in_cti = reactions_in_cti_file.index(secondary_reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k= k*1000000
tottal_k.append(k)
#check and make sure we are subtracting in the correct order
k=sum(tottal_k)
difference = np.log(target_k[i]) - np.log(k)
diff_in_ks_for_Y.append(difference)
#I guess i could append the tuple
Y_df_list.append(reaction)
Y_values.append(difference)
elif '/' in reaction:
reactants_in_numerator = reaction.split('/')[0].rstrip()
reactants_in_numerator = reactants_in_numerator.lstrip()
reactants_in_denominator = reaction.split('/')[1].rstrip()
reactants_in_denominator = reactants_in_denominator.lstrip()
reactions_in_cti_file_with_these_reactants_numerator = []
reactions_in_cti_file_with_these_reactants_denominator = []
#take back here
if '*' in reactants_in_numerator:
reactants_in_target_reactions_numerator = reactants_in_numerator.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction_in_numerator=None
if len(reactants_in_target_reactions_numerator.split('+'))>1:
reverse_reactants_in_target_reaction_in_numerator = reactants_in_target_reactions_numerator.split('+')
temp = reverse_reactants_in_target_reaction_in_numerator[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction_in_numerator[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction_in_numerator = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions_numerator or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction_in_numerator:
reactions_in_cti_file_with_these_reactants_numerator.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_cti_file_with_these_reactants_numerator = check_if_M_in_reactants(reactions_in_cti_file_with_these_reactants_numerator,
gas,
reactants_in_target_reactions_numerator,
reverse_reactants_in_target_reaction_in_numerator)
else:
#need to figure out how to split addition of reactions
if '(+)' not in reactants_in_numerator:
reactions_in_cti_file_with_these_reactants_numerator.append(reactants_in_numerator)
else:
list_of_reactions_in_numerator = reactants_in_numerator.split('(+)')
list_of_reactions_in_numerator_cleaned=[]
for reaction in list_of_reactions_in_numerator:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_in_numerator_cleaned.append(reaction)
reactions_in_cti_file_with_these_reactants_numerator = list_of_reactions_in_numerator_cleaned
if '*' in reactants_in_denominator:
reactants_in_target_reactions_denominator = reactants_in_denominator.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction_in_denominator=None
if len(reactants_in_target_reactions_denominator.split('+'))>1:
reverse_reactants_in_target_reaction_in_denominator = reactants_in_target_reactions_denominator.split('+')
temp = reverse_reactants_in_target_reaction_in_denominator[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction_in_denominator[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction_in_denominator = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions_denominator or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction_in_denominator:
reactions_in_cti_file_with_these_reactants_denominator.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_cti_file_with_these_reactants_denominator = check_if_M_in_reactants(reactions_in_cti_file_with_these_reactants_denominator,
gas,
reactants_in_target_reactions_denominator,
reverse_reactants_in_target_reaction_in_denominator)
else:
#need to figure out how to split addition of reactions
if '(+)' not in reactants_in_denominator:
reactions_in_cti_file_with_these_reactants_denominator.append(reactants_in_denominator)
else:
list_of_reactions_in_denominator = reactants_in_denominator.split('(+)')
list_of_reactions_in_denominator_cleaned=[]
for reaction in list_of_reactions_in_denominator:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_in_denominator_cleaned.append(reaction)
reactions_in_cti_file_with_these_reactants_denominator = list_of_reactions_in_denominator_cleaned
if target_press[i] == 0:
pressure = 1e-9
else:
pressure = target_press[i]
if bath_gas[i] !=0:
gas.TPX = target_temp[i],pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = target_temp[i],pressure*101325,{'Ar':.99}
tottal_k_numerator = []
for secondary_reaction in reactions_in_cti_file_with_these_reactants_numerator:
reaction_number_in_cti = reactions_in_cti_file.index(secondary_reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k = k*1000000
tottal_k_numerator.append(k)
#check and make sure we are subtracting in the correct order
k_numerator=sum(tottal_k_numerator)
tottal_k_denominator = []
for secondary_reaction in reactions_in_cti_file_with_these_reactants_denominator:
reaction_number_in_cti = reactions_in_cti_file.index(secondary_reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k = k*1000000
tottal_k_denominator.append(k)
k_denominator=sum(tottal_k_denominator)
k = k_numerator/k_denominator
difference = np.log(target_k[i]) - np.log(k)
#print(k_numerator,k_denominator)
##print(target_k[i],k)
diff_in_ks_for_Y.append(difference)
#I guess i could append the tuple
Y_df_list.append(reaction)
Y_values.append(difference)
elif '(+)' in reaction and '/' not in reaction and '*' not in reaction:
list_of_reactions = reaction.split('(+)')
list_of_reactions_cleaned=[]
for reaction in list_of_reactions:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_cleaned.append(reaction)
reactions_in_cti_file_with_these_reactants = list_of_reactions_cleaned
if target_press[i] == 0:
pressure = 1e-9
else:
pressure = target_press[i]
if bath_gas[i] !=0:
gas.TPX = target_temp[i],pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = target_temp[i],pressure*101325,{'Ar':.99}
tottal_k = []
for secondary_reaction in reactions_in_cti_file_with_these_reactants:
reaction_number_in_cti = reactions_in_cti_file.index(secondary_reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k= k*1000000
tottal_k.append(k)
#check and make sure we are subtracting in the correct order
k=sum(tottal_k)
difference = np.log(target_k[i]) - np.log(k)
diff_in_ks_for_Y.append(difference)
#I guess i could append the tuple
Y_df_list.append(reaction)
Y_values.append(difference)
elif reaction == 'More Complex Combination Rule':
print('do someting else ')
k_targets_for_y = np.array(diff_in_ks_for_Y)
k_targets_for_y = k_targets_for_y.reshape((k_targets_for_y.shape[0],1))
Y_values = np.array(Y_values)
Y_df_temp = pd.DataFrame({'value': Y_df_list,'ln_difference': Y_values.reshape((Y_values.shape[0],))})
Y_data_Frame = Y_data_Frame.append(Y_df_temp, ignore_index=True)
#print(k_targets_for_y.shape,'k targets for y')
return k_targets_for_y,Y_data_Frame
def target_values_for_Z(self,target_value_csv,z_data_Frame):
z_over_w = []
sigma = []
target_value_csv = pd.read_csv(target_value_csv)
target_ln_uncertainty = target_value_csv['ln_unc_k']
target_W = target_value_csv['W']
target_reactions = target_value_csv['Reaction']
z_df_list=[]
z_values = []
for i,value in enumerate(target_ln_uncertainty):
temp = np.divide(value,target_W[i])
sigma.append(value)
z_over_w.append(temp)
z_values.append(temp)
z_df_list.append(target_reactions[i])
k_targets_for_z = np.array(z_over_w)
sigma = np.array(sigma)
sigma = sigma.reshape((sigma.shape[0],1))
z_values = np.array(z_values)
k_targets_for_z = k_targets_for_z.reshape((k_targets_for_z.shape[0],1))
Z_data_Frame_temp = pd.DataFrame({'value': z_df_list,'Uncertainty': z_values.reshape((z_values.shape[0],))})
z_data_Frame = z_data_Frame.append(Z_data_Frame_temp, ignore_index=True)
return k_targets_for_z,sigma,z_data_Frame
def target_values_for_S(self,target_value_csv,
exp_dict_list,
S_matrix,
master_equation_reaction_list = [],
master_equation_sensitivites = {}):
target_value_csv = pd.read_csv(target_value_csv)
target_reactions = target_value_csv['Reaction']
target_temp = target_value_csv['temperature']
target_press = target_value_csv['pressure']
target_k = target_value_csv['k']
bath_gas = target_value_csv['M']
reactions_in_cti_file = exp_dict_list[0]['simulation'].processor.solution.reaction_equations()
number_of_reactions_in_cti = len(reactions_in_cti_file)
gas = ct.Solution(exp_dict_list[0]['simulation'].processor.cti_path)
As = []
Ns = []
Eas = []
flatten = lambda *n: (e for a in n
for e in (flatten(*a) if isinstance(a, (tuple, list)) else (a,)))
flattened_master_equation_reaction_list = list(flatten(master_equation_reaction_list))
coupled_reaction_list = []
list_of_reaction_tuples = []
for reaction in master_equation_reaction_list:
if type(reaction)==tuple:
list_of_reaction_tuples.append(reaction)
for secondary_reaction in reaction:
coupled_reaction_list.append(secondary_reaction)
def reactants_in_master_equation_reactions(flattened_master_equation_reaction_list):
reactants = []
for me_reaction in flattened_master_equation_reaction_list:
reactants_in_master_equation_reaction = me_reaction.split('<=>')[0].rstrip()
reactants.append(reactants_in_master_equation_reaction)
if len(reactants_in_master_equation_reaction.split('+')) >1:
reverse_reactants_in_target_reaction = reactants_in_master_equation_reaction.split('+')
temp = reverse_reactants_in_target_reaction[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction = temp
reactants.append(reverse_reactants_in_target_reaction)
return reactants
master_equation_reactants_and_reverse_reactants = reactants_in_master_equation_reactions(flattened_master_equation_reaction_list)
#print(master_equation_reactants_and_reverse_reactants)
def calculate_weighting_factor_summation(rate_constant_list,gas,temperature,Press,bath_gas):
if Press == 0:
pressure = 1e-9
else:
pressure = Press
if bath_gas !=0:
gas.TPX = temperature,pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = temperature,pressure*101325,{'Ar':.99}
tottal_k = []
original_rc_dict = {}
for reaction in rate_constant_list:
reaction_number_in_cti = reactions_in_cti_file.index(reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k = k*1000000
original_rc_dict[reaction] = k
tottal_k.append(k)
#check and make sure we are subtracting in the correct order
k_summation=sum(tottal_k)
weighting_factor_dict = {}
for reaction in rate_constant_list:
weighting_factor_dict[reaction] = original_rc_dict[reaction] / k_summation
return weighting_factor_dict
def calculate_weighting_factor_summation_with_denominator(numerator_rate_constant_list,denominator_rate_constant_list,gas,temperature,Press,bath_gas):
if Press == 0:
pressure = 1e-9
else:
pressure = Press
if bath_gas !=0:
gas.TPX = temperature,pressure*101325,{'H2O':.013,'O2':.0099,'H':.0000007,'Ar':.9770993}
else:
gas.TPX = temperature,pressure*101325,{'Ar':.99}
tottal_k_numerator = []
original_rc_dict = {}
for reaction in numerator_rate_constant_list:
reaction_number_in_cti = reactions_in_cti_file.index(reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k = k*1000000
original_rc_dict[reaction] = k
tottal_k_numerator.append(k)
#check and make sure we are subtracting in the correct order
k_summation_numerator=sum(tottal_k_numerator)
weighting_factor_dict_numerator = {}
for reaction in numerator_rate_constant_list:
weighting_factor_dict_numerator[reaction] = original_rc_dict[reaction] / k_summation_numerator
tottal_k_denominator = []
original_rc_dict = {}
for reaction in denominator_rate_constant_list:
reaction_number_in_cti = reactions_in_cti_file.index(reaction)
coeff_sum = sum(gas.reaction(reaction_number_in_cti).reactants.values())
k = gas.forward_rate_constants[reaction_number_in_cti]
if coeff_sum==1:
k=k
elif coeff_sum==2:
k = k*1000
elif coeff_sum==3:
k = k*1000000
original_rc_dict[reaction] = k
tottal_k_denominator.append(k)
#check and make sure we are subtracting in the correct order
k_summation_denominator=sum(tottal_k_denominator)
weighting_factor_dict_denominator = {}
for reaction in denominator_rate_constant_list:
weighting_factor_dict_denominator[reaction] = -(original_rc_dict[reaction] / k_summation_denominator)
reactions_in_common = weighting_factor_dict_numerator.keys() & weighting_factor_dict_denominator.keys()
weighting_factor_dict = {}
for reaction in reactions_in_common:
weighting_factor_dict[reaction] = weighting_factor_dict_numerator[reaction] + weighting_factor_dict_denominator[reaction]
for reaction in weighting_factor_dict_numerator.keys():
if reaction in reactions_in_common:
pass
else:
weighting_factor_dict[reaction] = weighting_factor_dict_numerator[reaction]
for reaction in weighting_factor_dict_denominator.keys():
if reaction in reactions_in_common:
pass
else:
weighting_factor_dict[reaction] = weighting_factor_dict_denominator[reaction]
return weighting_factor_dict
def add_tuple_lists(nested_list,master_euqation_reactions_list):
if any(isinstance(x, tuple) for x in master_euqation_reactions_list) == False:
return nested_list
else:
all_tuple_summations = []
indexes_that_need_to_be_removed = []
indexes_to_replace_with = []
counter = 0
for i,reaction in enumerate(master_euqation_reactions_list):
if type(reaction) == str:
counter+=1
elif type(reaction) == tuple:
tuple_sublist=[]
indexes_to_replace_with.append(counter)
for j,secondary_reaction in enumerate(reaction):
tuple_sublist.append(np.array(nested_list[counter]))
if j!= 0:
indexes_that_need_to_be_removed.append(counter)
counter+=1
sum_of_tupe_sublist = list(sum(tuple_sublist))
all_tuple_summations.append(sum_of_tupe_sublist)
new_nested_list = copy.deepcopy(nested_list)
for i,replacment in enumerate(indexes_to_replace_with):
new_nested_list[replacment] = all_tuple_summations[i]
new_nested_list = [x for i,x in enumerate(new_nested_list) if i not in indexes_that_need_to_be_removed]
return new_nested_list
def create_empty_nested_reaction_list():
nested_reaction_list = [[] for x in range(len(flattened_master_equation_reaction_list))]
for reaction in flattened_master_equation_reaction_list:
for i,MP in enumerate(master_equation_sensitivites[reaction]):
nested_reaction_list[flattened_master_equation_reaction_list.index(reaction)].append(0)
return nested_reaction_list
def create_tuple_list(array_of_sensitivities):
tuple_list = []
for ix,iy in np.ndindex(array_of_sensitivities.shape):
tuple_list.append((ix,iy))
return tuple_list
def check_if_M_in_reactants(list_to_append_to,
gas,
reactants_in_target_reactions,
reverse_reactants_in_target_reaction):
if reverse_reactants_in_target_reaction !=None:
for reaction_number_in_cti_file in range(gas.n_reactions):
if (gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' + M' or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction + ' + M') :
list_to_append_to.append(reactions_in_cti_file[reaction_number_in_cti_file])
elif reverse_reactants_in_target_reaction==None:
for reaction_number_in_cti_file in range(gas.n_reactions):
if (gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or
gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' (+M)' or
gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions + ' + M'):
list_to_append_to.append(reactions_in_cti_file[reaction_number_in_cti_file])
return list_to_append_to
def check_if_reaction_is_theory_or_not(reaction):
is_reaction_in_master_equation_list = False
is_reacton_in_normal_reaction_list = False
if '/' in reaction:
#check numerator and denominator
reactants_in_numerator = reaction.split('/')[0].rstrip()
reactants_in_numerator = reactants_in_numerator.lstrip()
reactants_in_denominator = reaction.split('/')[1].rstrip()
reactants_in_denominator = reactants_in_denominator.lstrip()
if '*' in reactants_in_numerator and '(+)' not in reactants_in_numerator:
reactions_in_numerator_with_these_reactants = []
#might be a more comprehensive way to do this
reactants_in_target_reactions = reaction.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction=None
if len(reactants_in_target_reactions.split('+'))>1:
reverse_reactants_in_target_reaction = reactants_in_target_reactions.split('+')
temp = reverse_reactants_in_target_reaction[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction:
reactions_in_numerator_with_these_reactants.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_numerator_with_these_reactants = check_if_M_in_reactants(reactions_in_numerator_with_these_reactants,
gas,
reactants_in_target_reactions,
reverse_reactants_in_target_reaction)
elif '(+)' in reactants_in_numerator and '*' not in reactants_in_numerator:
list_of_reactions_in_numerator = reactants_in_numerator.split('(+)')
list_of_reactions_in_numerator_cleaned=[]
for reaction in list_of_reactions_in_numerator:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_in_numerator_cleaned.append(reaction)
reactions_in_numerator_with_these_reactants = list_of_reactions_in_numerator_cleaned
elif '(+)' in reactants_in_numerator and '*' in reactants_in_numerator:
print('need to make rule')
else:
reactions_in_numerator_with_these_reactants = []
reactions_in_numerator_with_these_reactants.append(reactants_in_numerator)
#check reactants in numerator
if '*' in reactants_in_denominator and '(+)' not in reactants_in_denominator:
reactions_in_denominator_with_these_reactants = []
#might be a more comprehensive way to do this
reactants_in_target_reactions = reaction.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction=None
if len(reactants_in_target_reactions.split('+'))>1:
reverse_reactants_in_target_reaction = reactants_in_target_reactions.split('+')
temp = reverse_reactants_in_target_reaction[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction:
reactions_in_denominator_with_these_reactants.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_denominator_with_these_reactants = check_if_M_in_reactants(reactions_in_denominator_with_these_reactants,
gas,
reactants_in_target_reactions,
reverse_reactants_in_target_reaction)
elif '(+)' in reactants_in_denominator and '*' not in reactants_in_denominator:
list_of_reactions_in_denominator = reactants_in_numerator.split('(+)')
list_of_reactions_in_denominator_cleaned=[]
for reaction in list_of_reactions_in_denominator:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_in_denominator_cleaned.append(reaction)
reactions_in_denominator_with_these_reactants = list_of_reactions_in_numerator_cleaned
elif '(+)' in reactants_in_denominator and '*' in reactants_in_denominator:
print('need to make rule')
else:
reactions_in_denominator_with_these_reactants=[]
reactions_in_denominator_with_these_reactants.append(reactants_in_denominator)
reactions_in_numerator_and_denominator = reactions_in_numerator_with_these_reactants+reactions_in_denominator_with_these_reactants
for reaction_check in reactions_in_numerator_and_denominator:
if reaction_check in flattened_master_equation_reaction_list:
is_reaction_in_master_equation_list = True
else:
is_reacton_in_normal_reaction_list = True
if is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==False:
return 'master_equations_only', (reactions_in_numerator_with_these_reactants,reactions_in_denominator_with_these_reactants)
elif is_reaction_in_master_equation_list == False and is_reacton_in_normal_reaction_list==True:
return 'not_master_equations_only', (reactions_in_numerator_with_these_reactants,reactions_in_denominator_with_these_reactants)
elif is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==True:
return 'mixed', (reactions_in_numerator_with_these_reactants,reactions_in_denominator_with_these_reactants)
elif '(+)' in reaction and '/' not in reaction and '*' not in reaction:
list_of_reactions = reaction.split('(+)')
list_of_reactions_cleaned=[]
for reaction in list_of_reactions:
reaction = reaction.rstrip()
reaction = reaction.lstrip()
list_of_reactions_cleaned.append(reaction)
reactions_in_cti_file_with_these_reactants = list_of_reactions_cleaned
for reaction_check in reactions_in_cti_file_with_these_reactants:
if reaction_check in flattened_master_equation_reaction_list:
is_reaction_in_master_equation_list = True
else:
is_reacton_in_normal_reaction_list = True
if is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==False:
return 'master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == False and is_reacton_in_normal_reaction_list==True:
return 'not_master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==True:
return 'mixed', (reactions_in_cti_file_with_these_reactants,)
elif '*' in reaction and '/' not in reaction and '(+)' not in reaction:
reactions_in_cti_file_with_these_reactants = []
#might be a more comprehensive way to do this
reactants_in_target_reactions = reaction.split('<=>')[0].rstrip()
reverse_reactants_in_target_reaction=None
if len(reactants_in_target_reactions.split('+'))>1:
reverse_reactants_in_target_reaction = reactants_in_target_reactions.split('+')
temp = reverse_reactants_in_target_reaction[1] + ' '+ '+' +' '+ reverse_reactants_in_target_reaction[0]
temp = temp.lstrip()
temp = temp.rstrip()
reverse_reactants_in_target_reaction = temp
for reaction_number_in_cti_file in range(gas.n_reactions):
if gas.reactants(reaction_number_in_cti_file) == reactants_in_target_reactions or gas.reactants(reaction_number_in_cti_file) == reverse_reactants_in_target_reaction:
reactions_in_cti_file_with_these_reactants.append(reactions_in_cti_file[reaction_number_in_cti_file])
reactions_in_cti_file_with_these_reactants = check_if_M_in_reactants(reactions_in_cti_file_with_these_reactants,
gas,
reactants_in_target_reactions,
reverse_reactants_in_target_reaction)
for reaction_check in reactions_in_cti_file_with_these_reactants:
if reaction_check in flattened_master_equation_reaction_list:
is_reaction_in_master_equation_list = True
else:
is_reacton_in_normal_reaction_list = True
if is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==False:
return 'master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == False and is_reacton_in_normal_reaction_list==True:
return 'not_master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==True:
return 'mixed', (reactions_in_cti_file_with_these_reactants,)
else:
#normal reaction
reactions_in_cti_file_with_these_reactants=[]
for reaction_check in [reaction]:
if reaction_check in flattened_master_equation_reaction_list:
is_reaction_in_master_equation_list = True
else:
is_reacton_in_normal_reaction_list = True
reactions_in_cti_file_with_these_reactants.append(reaction)
if is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==False:
return 'master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == False and is_reacton_in_normal_reaction_list==True:
return 'not_master_equations_only', (reactions_in_cti_file_with_these_reactants,)
elif is_reaction_in_master_equation_list == True and is_reacton_in_normal_reaction_list==True:
return 'mixed', (reactions_in_cti_file_with_these_reactants,)
MP_stack = []
target_values_to_stack = []
for i,reaction in enumerate(target_reactions):
type_of_reaction, reaction_tuple = check_if_reaction_is_theory_or_not(reaction)
if type_of_reaction== 'master_equations_only':
if len(reaction_tuple)==1:
if len(reaction_tuple[0])==1:
nested_reaction_list = create_empty_nested_reaction_list()
for j, MP_array in enumerate(master_equation_sensitivites[reaction]):
tuple_list = create_tuple_list(MP_array)
temp = []
counter = 0
for sensitivity in np.nditer(MP_array,order='C'):
k = tuple_list[counter][0]
l= tuple_list[counter][1]
counter +=1
#need to add reduced p and t, and check these units were using to map
#these might not work
t_alpha= meq.Master_Equation.chebyshev_specific_poly(self,k,meq.Master_Equation.calc_reduced_T(self,target_temp[i]))
if target_press[i] ==0:
target_press_new = 1e-9
else:
target_press_new=target_press[i]
p_alpha = meq.Master_Equation.chebyshev_specific_poly(self,l,meq.Master_Equation.calc_reduced_P(self,target_press_new*101325))
#these might nowt work
single_alpha_map = t_alpha*p_alpha*sensitivity
temp.append(single_alpha_map)
temp =sum(temp)
#should there be an = temp here
#nested_reaction_list[master_equation_reaction_list.index(reaction)][j]=temp
nested_reaction_list[flattened_master_equation_reaction_list.index(reaction)][j]=temp
temp2 = nested_reaction_list
temp2_summed = add_tuple_lists(temp2,master_equation_reaction_list)
flat_list = [item for sublist in temp2_summed for item in sublist]
#print(flat_list)
MP_stack.append(nested_reaction_list)
flat_list = np.array(flat_list)
flat_list = flat_list.reshape((1,flat_list.shape[0]))
target_values_to_stack.append(flat_list)
elif len(reaction_tuple[0])>1:
reactions_in_cti_file_with_these_reactants = reaction_tuple[0]
weighting_factor_dictonary = calculate_weighting_factor_summation(reactions_in_cti_file_with_these_reactants,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
nested_reaction_list = create_empty_nested_reaction_list()
for secondary_reaction in reactions_in_cti_file_with_these_reactants:
for j, MP_array in enumerate(master_equation_sensitivites[secondary_reaction]):
tuple_list = create_tuple_list(MP_array)
temp = []
counter = 0
for sensitivity in np.nditer(MP_array,order='C'):
k = tuple_list[counter][0]
l= tuple_list[counter][1]
counter +=1
#need to add reduced p and t, and check these units were using to map
#these might not work
t_alpha= meq.Master_Equation.chebyshev_specific_poly(self,k,meq.Master_Equation.calc_reduced_T(self,target_temp[i]))
if target_press[i] ==0:
target_press_new = 1e-9
else:
target_press_new=target_press[i]
p_alpha = meq.Master_Equation.chebyshev_specific_poly(self,l,meq.Master_Equation.calc_reduced_P(self,target_press_new*101325))
#these might nowt work
single_alpha_map = t_alpha*p_alpha*sensitivity
temp.append(single_alpha_map)
temp =sum(temp)
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)][j]=temp
sub_array_to_apply_weighting_factor_to = list(np.array(nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)])*weighting_factor_dictonary[secondary_reaction])
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)] = sub_array_to_apply_weighting_factor_to
temp2 = nested_reaction_list
#print('THIS IS TEMP:',temp2)
temp2_summed = add_tuple_lists(temp2,master_equation_reaction_list)
#print('THIS IS TEMP SUMMED:',temp2_summed)
flat_list = [item for sublist in temp2_summed for item in sublist]
#print(flat_list)
MP_stack.append(nested_reaction_list)
flat_list = np.array(flat_list)
flat_list = flat_list.reshape((1,flat_list.shape[0]))
target_values_to_stack.append(flat_list)
elif len(reaction_tuple)==2:
reactions_in_cti_file_with_these_reactants_numerator = reaction_tuple[0]
reactions_in_cti_file_with_these_reactants_denominator= reaction_tuple[1]
weighting_factor_dictonary = calculate_weighting_factor_summation_with_denominator(reactions_in_cti_file_with_these_reactants_numerator,
reactions_in_cti_file_with_these_reactants_denominator,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
#now need to add to S matrix
for secondary_reaction in (reactions_in_cti_file_with_these_reactants_numerator+reactions_in_cti_file_with_these_reactants_denominator):
for j, MP_array in enumerate(master_equation_sensitivites[secondary_reaction]):
tuple_list = create_tuple_list(MP_array)
temp = []
counter = 0
for sensitivity in np.nditer(MP_array,order='C'):
k = tuple_list[counter][0]
l= tuple_list[counter][1]
counter +=1
#need to add reduced p and t, and check these units were using to map
#these might not work
t_alpha= meq.Master_Equation.chebyshev_specific_poly(self,k,meq.Master_Equation.calc_reduced_T(self,target_temp[i]))
if target_press[i] ==0:
target_press_new = 1e-9
else:
target_press_new=target_press[i]
p_alpha = meq.Master_Equation.chebyshev_specific_poly(self,l,meq.Master_Equation.calc_reduced_P(self,target_press_new*101325))
#these might nowt work
single_alpha_map = t_alpha*p_alpha*sensitivity
temp.append(single_alpha_map)
temp =sum(temp)
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)][j]=temp
sub_array_to_apply_weighting_factor_to = list(np.array(nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)])*weighting_factor_dictonary[secondary_reaction])
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)] = sub_array_to_apply_weighting_factor_to
temp2 = nested_reaction_list
#print('THIS IS TEMP:',temp2)
temp2_summed = add_tuple_lists(temp2,master_equation_reaction_list)
#print('THIS IS TEMP SUMMED:',temp2_summed)
flat_list = [item for sublist in temp2_summed for item in sublist]
#print(flat_list)
MP_stack.append(nested_reaction_list)
flat_list = np.array(flat_list)
flat_list = flat_list.reshape((1,flat_list.shape[0]))
target_values_to_stack.append(flat_list)
elif type_of_reaction== 'not_master_equations_only':
if len(reaction_tuple)==1:
if len(reaction_tuple[0])==1:
A_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
N_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
Ea_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
#decide if this mapping is correct
A_temp[0,reactions_in_cti_file.index(reaction)] = 1
N_temp [0,reactions_in_cti_file.index(reaction)] = np.log(target_temp[i])
Ea_temp[0,reactions_in_cti_file.index(reaction)] = (-1/target_temp[i])
As.append(A_temp)
Ns.append(N_temp)
Eas.append(Ea_temp)
A_temp = A_temp.reshape((1,A_temp.shape[1]))
N_temp = N_temp.reshape((1,N_temp.shape[1]))
Ea_temp = Ea_temp.reshape((1,Ea_temp.shape[1]))
target_values_to_stack.append(np.hstack((A_temp,N_temp,Ea_temp)))
elif len(reaction_tuple[0])>1:
reactions_in_cti_file_with_these_reactants = reaction_tuple[0]
weighting_factor_dictonary = calculate_weighting_factor_summation(reactions_in_cti_file_with_these_reactants,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
A_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
N_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
Ea_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
for secondary_reaction in reactions_in_cti_file_with_these_reactants:
#need to multiply by the weighting factor for the reaction
A_temp[0,reactions_in_cti_file.index(secondary_reaction)] = 1 * weighting_factor_dictonary[secondary_reaction]
N_temp [0,reactions_in_cti_file.index(secondary_reaction)] = np.log(target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
Ea_temp[0,reactions_in_cti_file.index(secondary_reaction)] = (-1/target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
As.append(A_temp)
Ns.append(N_temp)
Eas.append(Ea_temp)
A_temp = A_temp.reshape((1,A_temp.shape[1]))
N_temp = N_temp.reshape((1,N_temp.shape[1]))
Ea_temp = Ea_temp.reshape((1,Ea_temp.shape[1]))
target_values_to_stack.append(np.hstack((A_temp,N_temp,Ea_temp)))
elif len(reaction_tuple)==2:
reactions_in_cti_file_with_these_reactants_numerator = reaction_tuple[0]
reactions_in_cti_file_with_these_reactants_denominator= reaction_tuple[1]
weighting_factor_dictonary = calculate_weighting_factor_summation_with_denominator(reactions_in_cti_file_with_these_reactants_numerator,
reactions_in_cti_file_with_these_reactants_denominator,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
A_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
N_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
Ea_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
for secondary_reaction in (reactions_in_cti_file_with_these_reactants_numerator+reactions_in_cti_file_with_these_reactants_denominator):
if reaction not in flattened_master_equation_reaction_list:
A_temp[0,reactions_in_cti_file.index(secondary_reaction)] = 1 * weighting_factor_dictonary[secondary_reaction]
N_temp [0,reactions_in_cti_file.index(secondary_reaction)] = np.log(target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
Ea_temp[0,reactions_in_cti_file.index(secondary_reaction)] = (-1/target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
As.append(A_temp)
Ns.append(N_temp)
Eas.append(Ea_temp)
A_temp = A_temp.reshape((1,A_temp.shape[1]))
N_temp = N_temp.reshape((1,N_temp.shape[1]))
Ea_temp = Ea_temp.reshape((1,Ea_temp.shape[1]))
target_values_to_stack.append(np.hstack((A_temp,N_temp,Ea_temp)))
elif type_of_reaction== 'mixed':
#need to figure out what is going in here
if len(reaction_tuple) == 1:
reactions_in_cti_file_with_these_reactants = reaction_tuple[0]
weighting_factor_dictonary = calculate_weighting_factor_summation(reactions_in_cti_file_with_these_reactants,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
#fill in respective lists and figure out what to do with them?
A_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
N_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
Ea_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
nested_reaction_list = create_empty_nested_reaction_list()
for secondary_reaction in reactions_in_cti_file_with_these_reactants:
if secondary_reaction not in flattened_master_equation_reaction_list:
A_temp[0,reactions_in_cti_file.index(secondary_reaction)] = 1 * weighting_factor_dictonary[secondary_reaction]
N_temp [0,reactions_in_cti_file.index(secondary_reaction)] = np.log(target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
Ea_temp[0,reactions_in_cti_file.index(secondary_reaction)] = (-1/target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
elif secondary_reaction in flattened_master_equation_reaction_list:
for j, MP_array in enumerate(master_equation_sensitivites[secondary_reaction]):
tuple_list = create_tuple_list(MP_array)
temp = []
counter = 0
for sensitivity in np.nditer(MP_array,order='C'):
k = tuple_list[counter][0]
l= tuple_list[counter][1]
counter +=1
#need to add reduced p and t, and check these units were using to map
#these might not work
t_alpha= meq.Master_Equation.chebyshev_specific_poly(self,k,meq.Master_Equation.calc_reduced_T(self,target_temp[i]))
if target_press[i] ==0:
target_press_new = 1e-9
else:
target_press_new=target_press[i]
p_alpha = meq.Master_Equation.chebyshev_specific_poly(self,l,meq.Master_Equation.calc_reduced_P(self,target_press_new*101325))
#these might nowt work
single_alpha_map = t_alpha*p_alpha*sensitivity
temp.append(single_alpha_map)
temp =sum(temp)
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)][j]=temp
sub_array_to_apply_weighting_factor_to = list(np.array(nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)])*weighting_factor_dictonary[secondary_reaction])
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)] = sub_array_to_apply_weighting_factor_to
temp2 = nested_reaction_list
temp2_summed = add_tuple_lists(temp2,master_equation_reaction_list)
flat_list = [item for sublist in temp2_summed for item in sublist]
MP_stack.append(nested_reaction_list)
flat_list = np.array(flat_list)
flat_list = flat_list.reshape((1,flat_list.shape[0]))
master_equation_stacked = flat_list
As.append(A_temp)
Ns.append(N_temp)
Eas.append(Ea_temp)
A_temp = A_temp.reshape((1,A_temp.shape[1]))
N_temp = N_temp.reshape((1,N_temp.shape[1]))
Ea_temp = Ea_temp.reshape((1,Ea_temp.shape[1]))
A_n_Ea_stacked = (np.hstack((A_temp,N_temp,Ea_temp)))
combined_master_and_A_n_Ea= np.hstack((A_n_Ea_stacked,master_equation_stacked))
target_values_to_stack.append(combined_master_and_A_n_Ea)
elif len(reaction_tuple) == 2:
reactions_in_cti_file_with_these_reactants_numerator = reaction_tuple[0]
reactions_in_cti_file_with_these_reactants_denominator = reaction_tuple[1]
weighting_factor_dictonary = calculate_weighting_factor_summation_with_denominator(reactions_in_cti_file_with_these_reactants_numerator,
reactions_in_cti_file_with_these_reactants_denominator,
gas,
target_temp[i],
target_press[i],
bath_gas[i])
#fill in respective lists and figure out what to do with them?
A_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
N_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
Ea_temp = np.zeros((1,number_of_reactions_in_cti-len(flattened_master_equation_reaction_list)))
nested_reaction_list = create_empty_nested_reaction_list()
for secondary_reaction in (reactions_in_cti_file_with_these_reactants_numerator+reactions_in_cti_file_with_these_reactants_denominator):
if secondary_reaction not in flattened_master_equation_reaction_list:
A_temp[0,reactions_in_cti_file.index(secondary_reaction)] = 1 * weighting_factor_dictonary[secondary_reaction]
N_temp [0,reactions_in_cti_file.index(secondary_reaction)] = np.log(target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
Ea_temp[0,reactions_in_cti_file.index(secondary_reaction)] = (-1/target_temp[i]) * weighting_factor_dictonary[secondary_reaction]
elif secondary_reaction in flattened_master_equation_reaction_list:
for j, MP_array in enumerate(master_equation_sensitivites[secondary_reaction]):
tuple_list = create_tuple_list(MP_array)
temp = []
counter = 0
for sensitivity in np.nditer(MP_array,order='C'):
k = tuple_list[counter][0]
l= tuple_list[counter][1]
counter +=1
#need to add reduced p and t, and check these units were using to map
#these might not work
t_alpha= meq.Master_Equation.chebyshev_specific_poly(self,k,meq.Master_Equation.calc_reduced_T(self,target_temp[i]))
if target_press[i] ==0:
target_press_new = 1e-9
else:
target_press_new=target_press[i]
p_alpha = meq.Master_Equation.chebyshev_specific_poly(self,l,meq.Master_Equation.calc_reduced_P(self,target_press_new*101325))
#these might nowt work
single_alpha_map = t_alpha*p_alpha*sensitivity
temp.append(single_alpha_map)
temp =sum(temp)
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)][j]=temp
sub_array_to_apply_weighting_factor_to = list(np.array(nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)])*weighting_factor_dictonary[secondary_reaction])
nested_reaction_list[flattened_master_equation_reaction_list.index(secondary_reaction)] = sub_array_to_apply_weighting_factor_to
temp2 = nested_reaction_list
temp2_summed = add_tuple_lists(temp2,master_equation_reaction_list)
flat_list = [item for sublist in temp2_summed for item in sublist]
MP_stack.append(nested_reaction_list)
flat_list = np.array(flat_list)
flat_list = flat_list.reshape((1,flat_list.shape[0]))
master_equation_stacked = flat_list
As.append(A_temp)
Ns.append(N_temp)
Eas.append(Ea_temp)
A_temp = A_temp.reshape((1,A_temp.shape[1]))
N_temp = N_temp.reshape((1,N_temp.shape[1]))
Ea_temp = Ea_temp.reshape((1,Ea_temp.shape[1]))
A_n_Ea_stacked = (np.hstack((A_temp,N_temp,Ea_temp)))
combined_master_and_A_n_Ea= np.hstack((A_n_Ea_stacked,master_equation_stacked))
target_values_to_stack.append(combined_master_and_A_n_Ea)
S_matrix = S_matrix
shape_s = S_matrix.shape
S_target_values = []
#print(target_values_to_stack,target_values_to_stack[0].shape)
#this whole part needs to be edited
for i,row in enumerate(target_values_to_stack):
type_of_reaction, reaction_tuple = check_if_reaction_is_theory_or_not(target_reactions[i])
if type_of_reaction=='master_equations_only':
#zero_to_append_infront = np.zeros((1,((number_of_reactions_in_cti-len(master_equation_reaction_list))*3)))
zero_to_append_infront = np.zeros((1,((number_of_reactions_in_cti-len(flattened_master_equation_reaction_list))*3)))
zero_to_append_behind = np.zeros((1, shape_s[1] - ((number_of_reactions_in_cti-len(flattened_master_equation_reaction_list))*3) - np.shape(row)[1] ))
temp_array = np.hstack((zero_to_append_infront,row,zero_to_append_behind))
S_target_values.append(temp_array)
elif type_of_reaction=='not_master_equations_only':
zero_to_append_behind = np.zeros((1,shape_s[1]-np.shape(row)[1]))
temp_array = np.hstack((row,zero_to_append_behind))
S_target_values.append(temp_array)
elif type_of_reaction=='mixed':
zero_to_append_behind = np.zeros((1,shape_s[1]-np.shape(row)[1]))
temp_array = np.hstack((row,zero_to_append_behind))
S_target_values.append(temp_array)
S_target_values = np.vstack((S_target_values))
return S_target_values
def preprocessing_rate_constant_target_csv(self,target_value_csv,
master_equation_reactions):
#split up the master equations into multiple data frames
master_equation_df_list = []
master_equation_df_sorted_list = []
df_summation_list = []
for reaction in master_equation_reactions:
if type(reaction) == tuple:
master_equation_df_list.append([])
master_equation_df_sorted_list.append([])
df_ttl = pd.read_csv(target_value_csv)
counter = 0
for reaction in master_equation_reactions:
if type(reaction) == tuple:
for secondary_reaction in reaction:
temp = df_ttl.loc[df_ttl['Reaction'] == secondary_reaction]
if not temp.empty:
master_equation_df_list[counter].append(temp)
counter +=1
for i,lst in enumerate(master_equation_df_list):
for j,df in enumerate(lst):
df = df.sort_values(["temperature", "pressure"], ascending = (True, True))
master_equation_df_sorted_list[i].append(df)
for i,lst in enumerate(master_equation_df_sorted_list):
df_summation = pd.DataFrame()
df_summation['Reaction'] = lst[0]['Reaction']
df_summation['temperature'] = lst[0]['temperature']
df_summation['pressure'] = lst[0]['pressure']
df_summation['M'] = lst[0]['M']
df_summation['ln_unc_k'] = lst[0]['ln_unc_k']
df_summation['W'] = lst[0]['W']
k_summation_list=[]
for j,df in enumerate(lst):
k_summation_list.append(df['k'].to_numpy())
df_summation['k'] = sum(k_summation_list)
df_summation_list.append(df_summation)
reactions_to_remove = []
for reaction in master_equation_reactions:
if type(reaction) == tuple:
for secondary_reaction in reaction:
reactions_to_remove.append(secondary_reaction)
df_cleaned = df_ttl[~df_ttl['Reaction'].isin(reactions_to_remove)]
df_concat_list = [df_cleaned]+ df_summation_list
df_new_tottal = pd.concat(df_concat_list)
new_file_name = target_value_csv[:-4]
new_file_path = new_file_name+'_combined_channels.csv'
df_new_tottal.to_csv(new_file_path,
index=False)
return df_new_tottal,new_file_path
def appending_target_values(self,target_values_for_z,
target_values_for_Y,
target_values_for_S,
sigma_target_values,
S_matrix,
Y_matrix,
z_matrix,
sigma):
z_matrix = np.vstack((z_matrix ,target_values_for_z))
Y_matrix = np.vstack((Y_matrix,target_values_for_Y))
S_matrix = np.vstack((S_matrix,target_values_for_S))
sigma = np.vstack((sigma,sigma_target_values))
self.S_matrix = S_matrix
self.Y_matrix = Y_matrix
self.z_matrix = z_matrix
self.sigma = sigma
return S_matrix,Y_matrix,z_matrix,sigma
| 62.590573
| 375
| 0.541718
| 24,945
| 253,617
| 5.133333
| 0.028864
| 0.013682
| 0.011386
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| 0.838596
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| 0
| 0.009446
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| 253,617
| 4,051
| 376
| 62.606023
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| 0
| 0
| 0
| 0
| 1
| 0.013213
| false
| 0.00151
| 0.002643
| 0.000378
| 0.033975
| 0.001133
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
|
0
| 7
|
b649d55b955af3b2e169f07a8038551b9e42e9ff
| 17,165
|
py
|
Python
|
placer/placer_lib_test.py
|
beomyeol/baechi
|
796dd45f6db6eb11bb2fba2acfd56f51da024df8
|
[
"NCSA"
] | 4
|
2020-09-27T01:32:23.000Z
|
2022-03-06T22:49:19.000Z
|
placer/placer_lib_test.py
|
beomyeol/baechi
|
796dd45f6db6eb11bb2fba2acfd56f51da024df8
|
[
"NCSA"
] | null | null | null |
placer/placer_lib_test.py
|
beomyeol/baechi
|
796dd45f6db6eb11bb2fba2acfd56f51da024df8
|
[
"NCSA"
] | 1
|
2021-11-15T06:36:29.000Z
|
2021-11-15T06:36:29.000Z
|
# Copyright 2020 University of Illinois Board of Trustees. All Rights Reserved.
# Author: Beomyeol Jeon, DPRG (https://dprg.cs.uiuc.edu)
# This file is part of Baechi, which is released under specific terms. See file License.txt file for full license details.
# ==============================================================================
"""Unit tests for placer_lib module."""
# pylint: disable=missing-function-docstring
import unittest
import networkx as nx
from placer import placer_lib
class FusedOpPlacerTest(unittest.TestCase):
"""FusedOpPlacer test."""
def test_generate_fused_op_graph1(self):
op_graph = nx.DiGraph()
# op0 -> op1 -> op2
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 2,
'persistent_memory': 0, 'colocation_group': 'group0',
'output_memory': [2]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 4,
'persistent_memory': 0, 'colocation_group': 'group0',
'output_memory': []}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_edge(
0, 1, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
1, 2, id=1, weight=2,
tensor=[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
# all ops are fused
self.assertEqual(fused_op_graph.number_of_nodes(), 1)
fused_op = fused_op_graph.nodes[0]
self.assertEqual(fused_op['weight'], 8)
self.assertEqual(fused_op['temporary_memory'], 4)
self.assertEqual(fused_op['persistent_memory'], 5)
self.assertEqual(len(fused_op['output_memory']), 0)
fused_op_names = [op['name'] for op in fused_op['fused_ops']]
self.assertIn('op1', fused_op_names)
self.assertIn('op2', fused_op_names)
def test_generate_fused_op_graph2(self):
op_graph = nx.DiGraph()
# op0 -> op1 -> op2
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 2,
'persistent_memory': 0, 'colocation_group': 'group0',
'output_memory': [2]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 4,
'persistent_memory': 0, 'colocation_group': 'group1',
'output_memory': []}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_edge(
0, 1, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
1, 2, id=1, weight=2,
tensor=[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
# op0 and op1 are fused
self.assertEqual(fused_op_graph.number_of_nodes(), 2)
fused_op0 = fused_op_graph.nodes[0]
self.assertEqual(fused_op0['weight'], 5)
self.assertEqual(fused_op0['temporary_memory'], 2)
self.assertEqual(fused_op0['persistent_memory'], 5)
self.assertListEqual(fused_op0['output_memory'], [2])
fused_op1 = fused_op_graph.nodes[1]
self.assertEqual(fused_op1['weight'], 3)
self.assertEqual(fused_op1['temporary_memory'], 4)
self.assertEqual(fused_op1['persistent_memory'], 0)
self.assertListEqual(fused_op1['output_memory'], [])
self.assertEqual(fused_op_graph.number_of_edges(), 1)
edge = fused_op_graph[0][1]
self.assertEqual(edge['id'], 0)
self.assertEqual(edge['weight'], 2)
self.assertListEqual(edge['tensor'],
[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
def test_generate_fused_op_graph3(self):
op_graph = nx.DiGraph()
# op0 -> op2
# op1 /
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 0,
'persistent_memory': 3, 'colocation_group': 'group1',
'output_memory': [2]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 4,
'persistent_memory': 2, 'colocation_group': 'group0',
'output_memory': []}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_edge(
0, 2, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
1, 2, id=1, weight=2,
tensor=[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
# op0 and op2 are fused.
self.assertEqual(fused_op_graph.number_of_nodes(), 2)
fused_op0 = fused_op_graph.nodes[0]
self.assertEqual(fused_op0['weight'], 5)
self.assertEqual(fused_op0['temporary_memory'], 4)
self.assertEqual(fused_op0['persistent_memory'], 7)
self.assertListEqual(fused_op0['output_memory'], [])
fused_op1 = fused_op_graph.nodes[1]
self.assertEqual(fused_op1['weight'], 3)
self.assertEqual(fused_op1['temporary_memory'], 0)
self.assertEqual(fused_op1['persistent_memory'], 3)
self.assertListEqual(fused_op1['output_memory'], [2])
self.assertEqual(fused_op_graph.number_of_edges(), 1)
edge = fused_op_graph[1][0]
self.assertEqual(edge['id'], 0)
self.assertEqual(edge['weight'], 2)
self.assertListEqual(edge['tensor'],
[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
def test_generate_fused_op_graph4(self):
op_graph = nx.DiGraph()
# op0 -> op2
# op1 /
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 0,
'persistent_memory': 3, 'colocation_group': 'group0',
'output_memory': [2]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 4,
'persistent_memory': 2, 'colocation_group': 'group0',
'output_memory': []}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_edge(
0, 2, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
1, 2, id=1, weight=2,
tensor=[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
# all ops are fused
self.assertEqual(fused_op_graph.number_of_nodes(), 1)
fused_op = fused_op_graph.nodes[0]
self.assertEqual(fused_op['weight'], 8)
self.assertEqual(fused_op['temporary_memory'], 4)
self.assertEqual(fused_op['persistent_memory'], 10)
self.assertListEqual(fused_op['output_memory'], [])
def test_generate_fused_op_graph5(self):
op_graph = nx.DiGraph()
# op0 -> op2 -> op3 -> op4
# op1 /
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 0,
'persistent_memory': 3, 'colocation_group': 'group0',
'output_memory': [2]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 4,
'persistent_memory': 0, 'colocation_group': 'group1',
'output_memory': [3]}
op3 = {'id': 3, 'name': 'op4', 'weight': 1, 'temporary_memory': 7,
'persistent_memory': 3, 'colocation_group': 'group1',
'output_memory': [4]}
op4 = {'id': 4, 'name': 'op4', 'weight': 5, 'temporary_memory': 2,
'persistent_memory': 0, 'colocation_group': 'group0',
'output_memory': [0]}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_node(3, **op3)
op_graph.add_node(4, **op4)
op_graph.add_edge(
0, 2, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
1, 2, id=1, weight=2,
tensor=[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
op_graph.add_edge(
2, 3, id=2, weight=3,
tensor=[{'name': 'op2:0', 'weight': 3, 'num_bytes': 3}])
op_graph.add_edge(
3, 4, id=3, weight=4,
tensor=[{'name': 'op3:0', 'weight': 4, 'num_bytes': 4}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
# op2 and op3 are fused.
self.assertEqual(fused_op_graph.number_of_nodes(), 4)
self.assertEqual(fused_op_graph.nodes[0], {**op0, 'old_id': 0})
self.assertEqual(fused_op_graph.nodes[1], {**op1, 'old_id': 1})
fused_op = fused_op_graph.nodes[2]
self.assertEqual(fused_op['weight'], 4)
self.assertEqual(fused_op['temporary_memory'], 7)
self.assertEqual(fused_op['persistent_memory'], 3)
self.assertListEqual(fused_op['output_memory'], [4])
expected_dict = {**op4, 'old_id': 4}
expected_dict['id'] = 3
self.assertEqual(fused_op_graph.nodes[3], expected_dict)
self.assertEqual(fused_op_graph.number_of_edges(), 3)
edge_ids = set()
edge_ids.add(fused_op_graph[0][2]['id'])
self.assertEqual(fused_op_graph[0][2]['weight'], 1)
self.assertListEqual(
fused_op_graph[0][2]['tensor'],
[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
edge_ids.add(fused_op_graph[1][2]['id'])
self.assertEqual(fused_op_graph[1][2]['weight'], 2)
self.assertListEqual(
fused_op_graph[1][2]['tensor'],
[{'name': 'op1:0', 'weight': 2, 'num_bytes': 2}])
edge_ids.add(fused_op_graph[2][3]['id'])
self.assertEqual(fused_op_graph[2][3]['weight'], 4)
self.assertListEqual(
fused_op_graph[2][3]['tensor'],
[{'name': 'op3:0', 'weight': 4, 'num_bytes': 4}])
self.assertSetEqual(edge_ids, set(list(range(3))))
def test_generate_fused_op_graph6(self):
op_graph = nx.DiGraph()
# -> op2 -> op3
# / /
# op0 -> op1 ->/
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1, 2]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 5,
'persistent_memory': 3, 'colocation_group': 'group0',
'output_memory': [3]}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 2,
'persistent_memory': 2, 'colocation_group': 'group1',
'output_memory': [4]}
op3 = {'id': 3, 'name': 'op4', 'weight': 1, 'temporary_memory': 7,
'persistent_memory': 0, 'colocation_group': 'group1',
'output_memory': []}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_node(3, **op3)
op_graph.add_edge(
0, 1, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
0, 2, id=1, weight=2,
tensor=[{'name': 'op0:1', 'weight': 2, 'num_bytes': 2}])
op_graph.add_edge(
1, 3, id=2, weight=3,
tensor=[{'name': 'op1:0', 'weight': 3, 'num_bytes': 3}])
op_graph.add_edge(
2, 3, id=3, weight=4,
tensor=[{'name': 'op2:0', 'weight': 4, 'num_bytes': 4}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
self.assertEqual(fused_op_graph.number_of_nodes(), 2)
fused_op0 = fused_op_graph.nodes[0] # op0, op1
self.assertEqual(fused_op0['weight'], 5)
self.assertEqual(fused_op0['temporary_memory'], 5)
self.assertEqual(fused_op0['persistent_memory'], 8)
self.assertEqual(sum(fused_op0['output_memory']), 5)
fused_op1 = fused_op_graph.nodes[1] # op2, op3
self.assertEqual(fused_op1['weight'], 4)
self.assertEqual(fused_op1['temporary_memory'], 7)
self.assertEqual(fused_op1['persistent_memory'], 2)
self.assertListEqual(fused_op1['output_memory'], [])
self.assertEqual(fused_op_graph.number_of_edges(), 1)
fused_edge = fused_op_graph[0][1]
self.assertEqual(fused_edge['id'], 0)
self.assertEqual(fused_edge['weight'], 5)
self.assertListEqual(
fused_edge['tensor'],
[{'name': 'op0:1', 'weight': 2, 'num_bytes': 2},
{'name': 'op1:0', 'weight': 3, 'num_bytes': 3}])
def test_generate_fused_op_graph7(self):
op_graph = nx.DiGraph()
# -> op2 -> op3
# / /
# op0 -> op1 <-/
op0 = {'id': 0, 'name': 'op0', 'weight': 2, 'temporary_memory': 0,
'persistent_memory': 5, 'colocation_group': 'group0',
'output_memory': [1, 2]}
op1 = {'id': 1, 'name': 'op1', 'weight': 3, 'temporary_memory': 5,
'persistent_memory': 3, 'colocation_group': 'group0',
'output_memory': []}
op2 = {'id': 2, 'name': 'op2', 'weight': 3, 'temporary_memory': 2,
'persistent_memory': 2, 'colocation_group': 'group1',
'output_memory': [4]}
op3 = {'id': 3, 'name': 'op4', 'weight': 1, 'temporary_memory': 7,
'persistent_memory': 0, 'colocation_group': 'group1',
'output_memory': [3]}
op_graph.add_node(0, **op0)
op_graph.add_node(1, **op1)
op_graph.add_node(2, **op2)
op_graph.add_node(3, **op3)
op_graph.add_edge(
0, 1, id=0, weight=1,
tensor=[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
op_graph.add_edge(
0, 2, id=1, weight=2,
tensor=[{'name': 'op0:1', 'weight': 2, 'num_bytes': 2}])
op_graph.add_edge(
3, 1, id=2, weight=3,
tensor=[{'name': 'op3:0', 'weight': 3, 'num_bytes': 3}])
op_graph.add_edge(
2, 3, id=3, weight=4,
tensor=[{'name': 'op2:0', 'weight': 4, 'num_bytes': 4}])
# pylint: disable=protected-access
fused_op_graph = placer_lib.FusedOpPlacer._generate_fused_op_graph(
op_graph, False)
self.assertEqual(fused_op_graph.number_of_nodes(), 3)
self.assertEqual(fused_op_graph.nodes[0], {**op0, 'old_id': 0})
self.assertEqual(fused_op_graph.nodes[1], {**op1, 'old_id': 1})
fused_op = fused_op_graph.nodes[2] # op2, op3
self.assertEqual(fused_op['weight'], 4)
self.assertEqual(fused_op['temporary_memory'], 7)
self.assertEqual(fused_op['persistent_memory'], 2)
self.assertEqual(sum(fused_op['output_memory']), 3)
self.assertEqual(fused_op_graph.number_of_edges(), 3)
self.assertEqual(fused_op_graph[0][1]['weight'], 1)
self.assertListEqual(
fused_op_graph[0][1]['tensor'],
[{'name': 'op0:0', 'weight': 1, 'num_bytes': 1}])
self.assertEqual(fused_op_graph[0][2]['weight'], 2)
self.assertListEqual(
fused_op_graph[0][2]['tensor'],
[{'name': 'op0:1', 'weight': 2, 'num_bytes': 2}])
self.assertEqual(fused_op_graph[2][1]['weight'], 3)
self.assertListEqual(
fused_op_graph[2][1]['tensor'],
[{'name': 'op3:0', 'weight': 3, 'num_bytes': 3}])
self.assertSetEqual(
{edge[-1] for edge in fused_op_graph.edges(data='id')},
set(range(3)))
if __name__ == "__main__":
unittest.main()
| 45.651596
| 122
| 0.55887
| 2,159
| 17,165
| 4.192219
| 0.058824
| 0.092034
| 0.079549
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| 0.878908
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| 0.777704
| 0.74942
| 0.723898
| 0
| 0.051886
| 0.267929
| 17,165
| 375
| 123
| 45.773333
| 0.668391
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| 0.260317
| 1
| 0.022222
| false
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| null | 0
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0
| 7
|
b69ef294d653b88033a59117fedccaa5a2a0264b
| 73
|
py
|
Python
|
ai/algorithms/reinforcement_learning/monte_carlo/__init__.py
|
rbak/ai-implementations
|
5b773c23a5582b05b8aef55ea70e800cf4ffa376
|
[
"MIT"
] | null | null | null |
ai/algorithms/reinforcement_learning/monte_carlo/__init__.py
|
rbak/ai-implementations
|
5b773c23a5582b05b8aef55ea70e800cf4ffa376
|
[
"MIT"
] | null | null | null |
ai/algorithms/reinforcement_learning/monte_carlo/__init__.py
|
rbak/ai-implementations
|
5b773c23a5582b05b8aef55ea70e800cf4ffa376
|
[
"MIT"
] | null | null | null |
from .monte_carlo_prediction import *
from .monte_carlo_control import *
| 24.333333
| 37
| 0.835616
| 10
| 73
| 5.7
| 0.6
| 0.315789
| 0.491228
| 0
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| 0.109589
| 73
| 2
| 38
| 36.5
| 0.876923
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| true
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| null | 1
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| 1
| 0
|
0
| 7
|
b6a970b2171f56ad789b97a6380209b5c0b4639a
| 34,014
|
py
|
Python
|
dit_helpdesk/countries/migrations/0004_auto_20201021_1041.py
|
uktrade/dit-helpdesk
|
f5127daa46e920d33ec3f0b982136b65bba0353a
|
[
"MIT"
] | 3
|
2019-10-24T10:39:38.000Z
|
2021-07-13T11:46:18.000Z
|
dit_helpdesk/countries/migrations/0004_auto_20201021_1041.py
|
uktrade/dit-helpdesk
|
f5127daa46e920d33ec3f0b982136b65bba0353a
|
[
"MIT"
] | 20
|
2020-01-22T11:16:24.000Z
|
2022-02-02T10:38:54.000Z
|
dit_helpdesk/countries/migrations/0004_auto_20201021_1041.py
|
uktrade/dit-helpdesk
|
f5127daa46e920d33ec3f0b982136b65bba0353a
|
[
"MIT"
] | null | null | null |
# Generated by Django 2.2.13 on 2020-10-21 09:41
from django.db import migrations
import logging
trade_data = [
{
"Country code": "CO",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-andean-countries-trade-agreement",
"Mendel agreement label": "ANDEAN-COUNTRIES",
"TWUK content template label": "ANDEAN-COUNTRIES",
},
{
"Country code": "EC",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-andean-countries-trade-agreement",
"Mendel agreement label": "ANDEAN-COUNTRIES",
"TWUK content template label": "ANDEAN-COUNTRIES",
},
{
"Country code": "PE",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-andean-countries-trade-agreement",
"Mendel agreement label": "ANDEAN-COUNTRIES",
"TWUK content template label": "ANDEAN-COUNTRIES",
},
{
"Country code": "AG",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "BB",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "BZ",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "DM",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "DO",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "GD",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "GY",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "JM",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "KN",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "LC",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "VC",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "SR",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "BS",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "TT",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/cariforum-uk-economic-partnership-agreement",
"Mendel agreement label": "CARIFORUM",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "CR",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "SV",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "GT",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "HN",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "NI",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "PA",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-central-america-association-agreement",
"Mendel agreement label": "CENTRAL-AMERICA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "CL",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-chile-association-agreement",
"Mendel agreement label": "CHILE",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "MG",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/esa-uk-economic-partnership-agreement-epa--2",
"Mendel agreement label": "ESA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "MU",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/esa-uk-economic-partnership-agreement-epa--2",
"Mendel agreement label": "ESA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "SC",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/esa-uk-economic-partnership-agreement-epa--2",
"Mendel agreement label": "ESA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "ZW",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/esa-uk-economic-partnership-agreement-epa--2",
"Mendel agreement label": "ESA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "CI",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-AGR-SIGNED-NO-LINK",
"TWUK content template label": "EU-AGR-SIGNED-NO-LINK",
},
{
"Country code": "UA",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-AGR-SIGNED-NO-LINK",
"TWUK content template label": "EU-AGR-SIGNED-NO-LINK",
},
{
"Country code": "AT",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "BE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "BG",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "HR",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "CY",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "CZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "DK",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "EE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "FI",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "FR",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "DE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "GR",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "HU",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "IE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "IT",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "LV",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "LT",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "LU",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "MT",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "NL",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "PL",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "PT",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "RO",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "SK",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "SI",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "ES",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "SE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-MEMBER",
"TWUK content template label": "EU-MEMBER",
},
{
"Country code": "DZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "AD",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "BA",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "SZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "MK",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "SM",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "XS",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-NON-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-NON-WTO",
},
{
"Country code": "AL",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "CM",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "CA",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "EG",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "GH",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "KE",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "MX",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "MD",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "ME",
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"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
},
{
"Country code": "SG",
"GOVUK FTA URL": "",
"Mendel agreement label": "EU-NOAGR-FOR-EXIT-WTO",
"TWUK content template label": "EU-NOAGR-FOR-EXIT-WTO",
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{
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"Mendel agreement label": "FAROE-ISLANDS",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "GE",
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"Mendel agreement label": "GEORGIA",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
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{
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"Mendel agreement label": "ICELAND-NORWAY",
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{
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"Mendel agreement label": "ISRAEL",
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{
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"Mendel agreement label": "JAPAN",
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{
"Country code": "JO",
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"Mendel agreement label": "JORDAN",
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{
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"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-kosovopartnership-trade-and-cooperationagreement",
"Mendel agreement label": "KOSOVO",
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},
{
"Country code": "LB",
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"Mendel agreement label": "LEBANON",
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},
{
"Country code": "LI",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-switzerland-liechtenstein-trade-agreement",
"Mendel agreement label": "LIECHTENSTEIN",
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},
{
"Country code": "MA",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-morocco-association-agreement",
"Mendel agreement label": "MOROCCO",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
},
{
"Country code": "FJ",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-pacific-economic-partnership-agreement",
"Mendel agreement label": "PACIFIC-STATES",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
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{
"Country code": "PG",
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"Mendel agreement label": "PACIFIC-STATES",
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{
"Country code": "PS",
"GOVUK FTA URL": "https://www.gov.uk/government/collections/uk-palestinian-authority-political-trade-and-partnership-agreement",
"Mendel agreement label": "PALESTINIAN-AUTHORITY",
"TWUK content template label": "EU-AGR-SIGNED-LINK",
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{
"Country code": "BW",
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"Mendel agreement label": "SACUM",
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{
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{
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{
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"GOVUK FTA URL": "",
"Mendel agreement label": "TURKEY",
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"Mendel agreement label": "VN",
"TWUK content template label": "VN",
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{
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"Mendel agreement label": "WTO",
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"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "BR",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "BN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "BF",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "BI",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "KH",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CV",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CF",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "TD",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CG",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CD",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "CU",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "DJ",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "GA",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "GN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "GW",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "HT",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "IN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "KZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "KW",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "KG",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "LA",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "LR",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MW",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MY",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MV",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "ML",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MR",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "MM",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "NP",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "NZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "NE",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "NG",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "OM",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "PK",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "PY",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "PH",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "QA",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "RU",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "RW",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "WS",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "SA",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "SN",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "SL",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "SB",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "LK",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "TJ",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "TZ",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "TH",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "GM",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "TG",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "UG",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "AE",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "US",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "UY",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "VU",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "VE",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "YE",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "ZM",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "HK",
"GOVUK FTA URL": "",
"Mendel agreement label": "WTO",
"TWUK content template label": "WTO",
},
{
"Country code": "EU",
"GOVUK FTA URL": "",
"Mendel agreement label": "",
"TWUK content template label": "EU-MEMBER",
},
]
def populate_trade_scenarios(apps, schema_editor):
Country = apps.get_model("countries", "Country")
for item in trade_data:
country_code = item["Country code"]
trade_scenario = item["TWUK content template label"]
content_url = item["GOVUK FTA URL"]
try:
country = Country.objects.get(country_code=country_code)
except Country.DoesNotExist:
logging.error("Could not find country with country_code=%s", country_code)
else:
country.scenario = trade_scenario
country.content_url = content_url
country.save()
def unpopulate_trade_scenarios(apps, schema_editor):
Country = apps.get_model("countries", "Country")
Country.objects.all().update(scenario="WTO")
class Migration(migrations.Migration):
dependencies = [("countries", "0003_auto_20201021_1041")]
operations = [
migrations.RunPython(populate_trade_scenarios, unpopulate_trade_scenarios)
]
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0
| 8
|
fcef799a7e5f6146dab0e6c5b4b7d8fd2a2c8798
| 10,058
|
py
|
Python
|
steps/optimize.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | null | null | null |
steps/optimize.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | null | null | null |
steps/optimize.py
|
bartubisgin/z-quantum-core
|
b61aef12cc86f0a8234229b9b26b21cde950d6f1
|
[
"Apache-2.0"
] | 1
|
2022-03-19T02:23:53.000Z
|
2022-03-19T02:23:53.000Z
|
import json
import numpy as np
from openfermion import SymbolicOperator
from typing import Union, Dict, Optional, List
from zquantum.core.circuit import (
Circuit,
load_circuit,
load_circuit_template_params,
save_circuit_template_params,
load_parameter_grid,
)
from zquantum.core.cost_function import (
get_ground_state_cost_function,
AnsatzBasedCostFunction,
)
from zquantum.core.estimation import (
estimate_expectation_values_by_averaging,
)
from zquantum.core.serialization import save_optimization_results
from zquantum.core.utils import create_object, load_list
from zquantum.core.typing import Specs
from zquantum.core.openfermion import load_qubit_operator
def optimize_parametrized_circuit_for_ground_state_of_operator(
optimizer_specs: Specs,
target_operator: Union[SymbolicOperator, str],
parametrized_circuit: Union[Circuit, str],
backend_specs: Specs,
estimation_method_specs: Optional[Specs] = None,
estimation_preprocessors_specs: Optional[List[Specs]] = None,
initial_parameters: Union[str, np.ndarray, List[float]] = None,
fixed_parameters: Optional[Union[np.ndarray, str]] = None,
parameter_precision: Optional[float] = None,
parameter_precision_seed: Optional[int] = None,
**kwargs
):
"""Optimize the parameters of a parametrized quantum circuit to prepare the ground state of a target operator.
Args:
optimizer_specs: The specs of the optimizer to use to refine the parameter values
target_operator: The operator of which to prepare the ground state
parametrized_circuit: The parametrized quantum circuit that prepares trial states
backend_specs: The specs of the quantum backend (or simulator) to use to run the circuits
estimation_method_specs: A reference to a callable to use to estimate the expectation value of the operator.
The default is the estimate_expectation_values_by_averaging function.
estimation_preprocessors_specs: A list of Specs that describe callable functions that adhere to the
EstimationPreprocessor protocol.
initial_parameters: The initial parameter values to begin optimization
fixed_parameters: values for the circuit parameters that should be fixed.
parameter_precision: the standard deviation of the Gaussian noise to add to each parameter, if any.
parameter_precision_seed: seed for randomly generating parameter deviation if using parameter_precision
kwaargs:
The following key word arguments are handled explicitly when appropriate:
parameter_grid: A parameter grid artifact that defines a 2D grid for parameter values
"""
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
parameter_grid = kwargs.pop("parameter_grid", None)
# Load parameter grid
if parameter_grid is not None:
parameter_grid = load_parameter_grid(parameter_grid)
optimizer_specs["grid"] = parameter_grid
optimizer = create_object(optimizer_specs)
if isinstance(target_operator, str):
target_operator = load_qubit_operator(target_operator)
if isinstance(parametrized_circuit, str):
parametrized_circuit = load_circuit(parametrized_circuit)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = json.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = []
if estimation_preprocessors_specs is not None:
for estimation_preprocessor_specs in estimation_preprocessors_specs:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = json.loads(
estimation_preprocessor_specs
)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs)
)
if initial_parameters is not None:
if isinstance(initial_parameters, str):
initial_parameters = load_circuit_template_params(initial_parameters)
if fixed_parameters is not None:
if isinstance(fixed_parameters, str):
fixed_parameters = load_circuit_template_params(fixed_parameters)
cost_function = get_ground_state_cost_function(
target_operator,
parametrized_circuit,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
fixed_parameters=fixed_parameters,
parameter_precision=parameter_precision,
parameter_precision_seed=parameter_precision_seed,
)
optimization_results = optimizer.minimize(cost_function, initial_parameters)
save_optimization_results(optimization_results, "optimization-results.json")
save_circuit_template_params(
optimization_results.opt_params, "optimized-parameters.json"
)
def optimize_ansatz_based_cost_function(
optimizer_specs: Specs,
target_operator: Union[SymbolicOperator, str],
ansatz_specs: Specs,
backend_specs: Specs,
estimation_method_specs: Optional[Specs] = None,
estimation_preprocessors_specs: Optional[List[Specs]] = None,
initial_parameters: Union[str, np.ndarray, List[float]] = None,
fixed_parameters: Optional[Union[np.ndarray, str]] = None,
parameter_precision: Optional[float] = None,
parameter_precision_seed: Optional[int] = None,
**kwargs
):
"""Optimize the parameters of an ansatz circuit to prepare the ground state of a target operator.
Args:
optimizer_specs: The specs of the optimizer to use to refine the parameter values
target_operator: The operator of which to prepare the ground state
ansatz_specs: The specs describing an Ansatz which will prepare the quantum circuit
backend_specs: The specs of the quantum backend (or simulator) to use to run the circuits
estimation_method_specs: A reference to a callable to use to estimate the expectation value of the operator.
The default is the estimate_expectation_values_by_averaging function.
estimation_preprocessors_specs: A list of Specs that describe callable functions that adhere to the
EstimationPreprocessor protocol.
initial_parameters: The initial parameter values to begin optimization
fixed_parameters: values for the circuit parameters that should be fixed.
parameter_precision: the standard deviation of the Gaussian noise to add to each parameter, if any.
parameter_precision_seed: seed for randomly generating parameter deviation if using parameter_precision
kwaargs:
The following key word arguments are handled explicitly when appropriate:
parameter_grid: A parameter grid artifact that defines a 2D grid for parameter values
thetas: A list of thetas used to initialize the WarmStartQAOAAnsatz
"""
if isinstance(optimizer_specs, str):
optimizer_specs = json.loads(optimizer_specs)
parameter_grid = kwargs.pop("parameter_grid", None)
# Load parameter grid
if parameter_grid is not None:
parameter_grid = load_parameter_grid(parameter_grid)
optimizer_specs["grid"] = parameter_grid
optimizer = create_object(optimizer_specs)
if isinstance(target_operator, str):
target_operator = load_qubit_operator(target_operator)
if isinstance(ansatz_specs, str):
ansatz_specs = json.loads(ansatz_specs)
if "WarmStartQAOAAnsatz" in ansatz_specs["function_name"]:
ansatz_specs["thetas"] = np.array(load_list(kwargs.pop("thetas")))
ansatz_specs["cost_hamiltonian"] = target_operator
elif "QAOA" in ansatz_specs["function_name"]:
ansatz_specs["cost_hamiltonian"] = target_operator
ansatz = create_object(ansatz_specs)
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
backend = create_object(backend_specs)
if estimation_method_specs is not None:
if isinstance(estimation_method_specs, str):
estimation_method_specs = json.loads(estimation_method_specs)
estimation_method = create_object(estimation_method_specs)
else:
estimation_method = estimate_expectation_values_by_averaging
estimation_preprocessors = []
if estimation_preprocessors_specs is not None:
for estimation_preprocessor_specs in estimation_preprocessors_specs:
if isinstance(estimation_preprocessor_specs, str):
estimation_preprocessor_specs = json.loads(
estimation_preprocessor_specs
)
estimation_preprocessors.append(
create_object(estimation_preprocessor_specs)
)
if initial_parameters is not None:
if isinstance(initial_parameters, str):
initial_parameters = load_circuit_template_params(initial_parameters)
if fixed_parameters is not None:
if isinstance(fixed_parameters, str):
fixed_parameters = load_circuit_template_params(fixed_parameters)
cost_function = AnsatzBasedCostFunction(
target_operator,
ansatz,
backend,
estimation_method=estimation_method,
estimation_preprocessors=estimation_preprocessors,
fixed_parameters=fixed_parameters,
parameter_precision=parameter_precision,
parameter_precision_seed=parameter_precision_seed,
)
optimization_results = optimizer.minimize(cost_function, initial_parameters)
save_optimization_results(optimization_results, "optimization-results.json")
save_circuit_template_params(
optimization_results.opt_params, "optimized-parameters.json"
)
| 43.921397
| 116
| 0.741897
| 1,156
| 10,058
| 6.179066
| 0.123702
| 0.049279
| 0.041159
| 0.00924
| 0.840403
| 0.828083
| 0.818424
| 0.808344
| 0.792384
| 0.792384
| 0
| 0.00025
| 0.205906
| 10,058
| 228
| 117
| 44.114035
| 0.894078
| 0.278485
| 0
| 0.705128
| 0
| 0
| 0.03224
| 0.014079
| 0
| 0
| 0
| 0
| 0
| 1
| 0.012821
| false
| 0
| 0.070513
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 1
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| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
1e2e52b3d0a6b948e6c8f945ff393631222f7c91
| 123
|
py
|
Python
|
ievv_opensource/demo/demoapp/translation.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | null | null | null |
ievv_opensource/demo/demoapp/translation.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | 37
|
2015-10-26T09:14:12.000Z
|
2022-02-10T10:35:33.000Z
|
ievv_opensource/demo/demoapp/translation.py
|
appressoas/ievv_opensource
|
63e87827952ddc8f6f86145b79478ef21d6a0990
|
[
"BSD-3-Clause"
] | 1
|
2015-11-06T07:56:34.000Z
|
2015-11-06T07:56:34.000Z
|
from django.utils.translation import gettext_lazy as _
def get_translated_string():
return _('Testing translations')
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| 54
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| 123
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| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
1e909bbac52b8d09611a8c081d2717c3f3ceb7b5
| 73
|
py
|
Python
|
sim_common/client_utils.py
|
mfatihaktas/edge-load-balance
|
b866ca47ba37a605eeba05658b1d302f6855a23f
|
[
"MIT"
] | null | null | null |
sim_common/client_utils.py
|
mfatihaktas/edge-load-balance
|
b866ca47ba37a605eeba05658b1d302f6855a23f
|
[
"MIT"
] | null | null | null |
sim_common/client_utils.py
|
mfatihaktas/edge-load-balance
|
b866ca47ba37a605eeba05658b1d302f6855a23f
|
[
"MIT"
] | null | null | null |
def min_wait_time(cl_l):
return min(cl.min_wait_time() for cl in cl_l)
| 18.25
| 46
| 0.753425
| 17
| 73
| 2.882353
| 0.529412
| 0.285714
| 0.44898
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136986
| 73
| 3
| 47
| 24.333333
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
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