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qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/tracking_url_parameter_validation.yml | name: tracking_url_parameter_validation
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: '%Y%m%d-%H%M%S-newsletter-automation'
expectation_suite_name:
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource_new
data_connector_name: default_runtime_data_connector_name
data_asset_name: newsletter_automation.articles
runtime_parameters:
query: SELECT * from newsletter_automation.articles WHERE title !="" and description !="" and time !="" and newsletter_id = ""
batch_identifiers:
default_identifier_name: validation_stage
expectation_suite_name: tracking_url_parameter_validation
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id:
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/description_validation.yml | name: description_validation
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: 'All Articles Description Validation'
expectation_suite_name:
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: All Articles
runtime_parameters:
query: SELECT * from newsletter_automation.articles WHERE newsletter_id is null and category_id != 1 and description != ''
batch_identifiers:
default_identifier_name: test_identifier
expectation_suite_name: description_title_validations
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id:
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/all_articles_count_validations.yml | name: all_articles_count_validations
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: 'Ready count'
expectation_suite_name:
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {
result_format: {
result_format: SUMMARY,
include_unexpected_rows: True
}
}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Comics
runtime_parameters:
query: select * from newsletter_automation.articles where title !="" and description !="" and newsletter_id is null and category_id = 1
batch_identifiers:
default_identifier_name: ready_comics
expectation_suite_name: all_articles_count_validations
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Past articles
runtime_parameters:
query: select * from newsletter_automation.articles where title !="" and description !="" and newsletter_id is null and category_id = 2
batch_identifiers:
default_identifier_name: ready_past_articles
expectation_suite_name: all_articles_count_validations
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Current week
runtime_parameters:
query: select * from newsletter_automation.articles where title !="" and description !="" and newsletter_id is null and category_id = 3
batch_identifiers:
default_identifier_name: ready_current_week_articles
expectation_suite_name: all_articles_count_validations
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Automation corner
runtime_parameters:
query: select * from newsletter_automation.articles where title !="" and description !="" and newsletter_id is null and category_id = 4
batch_identifiers:
default_identifier_name: ready_automation_corner_articles
expectation_suite_name: all_articles_count_validations
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id: | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/past_articles_count_validations.yml | name: past_articles_count_validations
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: 'Past Article Count'
expectation_suite_name: verify_past_articles_expectation_suite
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Past week
runtime_parameters:
query: select * from newsletter_automation.articles where newsletter_id is null and description != "" and title != "" and category_id=2
batch_identifiers:
default_identifier_name: default_identifier
expectation_suite_name: verify_past_articles_expectation_suite
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id:
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/current_week_article_validation.yml | name: current_week_article_validation
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: 'Ready Current Week Article with Title and Description'
expectation_suite_name:
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Current Week
runtime_parameters:
query: SELECT * from newsletter_automation.articles WHERE newsletter_id is null
batch_identifiers:
default_identifier_name: test_identifier
expectation_suite_name: verify_current_week_article
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id:
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/comic_title_validations.yml | name: comic_title_validations
config_version: 1.0
template_name:
module_name: great_expectations.checkpoint
class_name: Checkpoint
run_name_template: 'Ready Comic with Title'
expectation_suite_name:
batch_request: {}
action_list:
- name: store_validation_result
action:
class_name: StoreValidationResultAction
- name: store_evaluation_params
action:
class_name: StoreEvaluationParametersAction
- name: update_data_docs
action:
class_name: UpdateDataDocsAction
site_names: []
evaluation_parameters: {}
runtime_configuration: {}
validations:
- batch_request:
datasource_name: newsletter_automation_datasource
data_connector_name: default_runtime_data_connector_name
data_asset_name: Comics
runtime_parameters:
query: SELECT * from newsletter_automation.articles WHERE newsletter_id is null
batch_identifiers:
default_identifier_name: comic_title_validator
expectation_suite_name: comic_title_validations
profilers: []
ge_cloud_id:
expectation_suite_ge_cloud_id:
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/checkpoints/README.md | Checkpoints are the primary means of validating data against the defined expectations.
Place the Checkpoints in this directory.
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/verify_current_week_article.json | {
"data_asset_type": null,
"expectation_suite_name": "verify_current_week_article",
"expectations": [
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "description",
"condition_parser": "great_expectations__experimental__",
"min_value": 10,
"mostly": 0.1,
"row_condition": "col(\"category_id\")==3"
},
"meta": {}
},
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "title",
"condition_parser": "great_expectations__experimental__",
"min_value": 2,
"mostly": 0.1,
"row_condition": "col(\"category_id\")==3"
},
"meta": {}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_inferred_data_connector_name",
"datasource_name": "newsletter_automation_datasource",
"limit": 1000
},
"citation_date": "2022-07-22T10:42:58.622567Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.15"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/comic_title_validations.json | {
"data_asset_type": null,
"expectation_suite_name": "comic_title_validations",
"expectations": [
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "title",
"condition_parser": "great_expectations__experimental__",
"min_value": 2,
"mostly": 0.1,
"row_condition": "col(\"category_id\")==1"
},
"meta": {}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_inferred_data_connector_name",
"datasource_name": "newsletter_automation_datasource",
"limit": 1000
},
"citation_date": "2022-07-27T06:06:14.035562Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.15"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/.ge_store_backend_id | store_backend_id = 4236ae2e-0b47-4b23-8b7e-5c38fd283e7a
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/description_title_validations.json | {
"data_asset_type": null,
"expectation_suite_name": "description_title_validations",
"expectations": [
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "description",
"min_value": 90
},
"meta": {}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_inferred_data_connector_name",
"datasource_name": "newsletter_automation_datasource",
"limit": 1000
},
"citation_date": "2022-09-29T10:54:59.637264Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.25"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/verify_past_articles_expectation_suite.json | {
"data_asset_type": null,
"expectation_suite_name": "verify_past_articles_expectation_suite",
"expectations": [
{
"expectation_type": "expect_table_row_count_to_be_between",
"kwargs": {
"min_value": 2
},
"meta": {}
},
{
"expectation_type": "expect_column_values_to_be_null",
"kwargs": {
"column": "newsletter_id",
"condition_parser": "great_expectations__experimental__",
"mostly": 0.2,
"row_condition": "col(\"category_id\")==2"
},
"meta": {}
},
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "description",
"condition_parser": "great_expectations__experimental__",
"min_value": 10,
"mostly": 0.2,
"row_condition": "col(\"category_id\")==2"
},
"meta": {}
},
{
"expectation_type": "expect_column_value_lengths_to_be_between",
"kwargs": {
"column": "title",
"condition_parser": "great_expectations__experimental__",
"min_value": 2,
"mostly": 0.2,
"row_condition": "col(\"category_id\")==2"
},
"meta": {}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_runtime_data_connector_name",
"datasource_name": "newsletter_automation_datasource",
"limit": 1000
},
"citation_date": "2022-09-21T04:23:03.283243Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.22"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/all_articles_count_validations.json | {
"data_asset_type": null,
"expectation_suite_name": "all_articles_count_validations",
"expectations": [
{
"expectation_type": "expect_table_row_count_to_be_between",
"kwargs": {
"max_value": 10,
"min_value": 1
}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_inferred_data_connector_name",
"datasource_name": "newsletter_automation_datasource",
"limit": 1000
},
"citation_date": "2022-07-22T10:04:52.942359Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.14"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/expectations/tracking_url_parameter_validation.json | {
"data_asset_type": null,
"expectation_suite_name": "tracking_url_parameter_validation",
"expectations": [
{
"expectation_type": "expect_column_values_to_be_unique",
"kwargs": {
"column": "url"
},
"meta": {}
}
],
"ge_cloud_id": null,
"meta": {
"citations": [
{
"batch_request": {
"data_asset_name": "newsletter_automation.articles",
"data_connector_name": "default_inferred_data_connector_name",
"datasource_name": "newsletter_automation_datasource_new",
"limit": 1000
},
"citation_date": "2022-09-16T08:29:25.841899Z",
"comment": "Created suite added via CLI"
}
],
"great_expectations_version": "0.15.22"
}
} | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/pytest.ini | [pytest]
markers =
CheckpointWednesday10am : Checkpoints that ought to be run at 10:00 AM, every Wednesday
CheckpointFriday04pm : Checkpoints that ought to be run at 04:00 PM, every Friday
CheckpointWednesday04pm : Checkpoints that ought to be run at 04:00 PM, every Wednesday | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/send_skype_message.py | """
This script is used to send message to provided Skype channel
"""
import os
import sys
import requests
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from conf import netlify_conf
from conf import skype_conf
def post_message_on_skype():
"""
Posts a message on the set Skype channel
"""
try:
skype_message = f"""<b>Newsletter Automation - data validations outcome: </b> {netlify_conf.NETLIFY_URL}"""
headers = {"Content-Type": "application/json"}
payload = {
"msg": skype_message,
"channel": skype_conf.SKYPE_CHANNEL,
"API_KEY": skype_conf.SKYPE_API_KEY,
}
response = requests.post(
url=skype_conf.SKYPE_URL,
json=payload,
headers=headers,
)
if response.status_code == 200:
print(f"Successfully sent the Skype message - {skype_message}")
else:
print("Failed to send Skype message!")
response.raise_for_status()
except requests.exceptions.HTTPError as error:
print (error.response.text)
except Exception as err:
raise Exception(f"Unable to post message to Skype channel, due to {err}")
post_message_on_skype()
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_current_week_article_validations.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/uncommitted/run_newsletter_automation.py`.
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.CheckpointWednesday10am
def test_run_checkpoint():
data_context: DataContext = DataContext(
context_root_dir="tests/data_validation/great_expectations"
)
checkpoint_name : str = "current_week_article_validation"
batch_request : str = None
run_name : str = None
result: CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
print(result)
else:
print("Validation succeeded!")
# print result to assist debug
print(f"The Result is {result}")
assert result["success"], "No articles from current week are ready with a description and title!"
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_past_articles_count_validations.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/utils/test_run_past_articles_count_validations.py`
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.CheckpointWednesday10am
def test_run_past_articles_count_validations():
data_context = DataContext(context_root_dir="tests/data_validation/great_expectations")
checkpoint_name : str ="past_articles_count_validations"
batch_request : str = None
run_name : str = None
result : CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
print(result)
else:
print("Validation succeeded!")
# print result to assist debug
print(f"The Result is {result}")
assert result["success"],"Looks like 2 past articles are not ready with description and title!"
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_tracking_url_parameter_validation.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/uncommitted/run_newsletter_automation.py`.
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.checkpointtracking
def test_tracking_url_param_validation():
data_context = DataContext(context_root_dir="tests/data_validation/great_expectations")
checkpoint_name : str = "tracking_url_parameter_validation"
batch_request : str = None
run_name : str = None
result : CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
print(result)
else:
print("Validation succeeded!")
assert result["success"]
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_all_articles_count_validations.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/utils/test_run_all_articles_count_validations.py`
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.CheckpointWednesday10am
def test_run_all_articles_count_validations():
data_context = DataContext(context_root_dir="tests/data_validation/great_expectations")
checkpoint_name : str ="all_articles_count_validations"
batch_request : str = None
run_name : str = None
result : CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
else:
print("Validation succeeded!")
# print result to assist debug
print(f"The Result is {result}")
assert result["success"], f"We either have no article ready or too many articles ready!" | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_comic_title_validations.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/utils/test_run_comic_title_validations.py`
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.CheckpointWednesday10am
def test_comic_title_validations():
data_context = DataContext(context_root_dir="tests/data_validation/great_expectations")
checkpoint_name : str ="comic_title_validations"
batch_request : str = None
run_name : str = None
result : CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
print(result)
else:
print("Validation succeeded!")
# print result to assist debug
print(f"The Result is {result}")
assert result["success"], "No comic is ready with a title!"
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/utils/test_run_description_validation.py | """
This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending a
notification to team members about validation results, or storing a result in a shared cloud storage.
See also <cyan>https://docs.greatexpectations.io/en/latest/guides/how_to_guides/validation/how_to_create_a_new_checkpoint_using_test_yaml_config.html</cyan> for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command. This script
is provided for those who wish to run Checkpoints in python.
Usage:
- Run this file: `python great_expectations/uncommitted/run_description_title_validation.py`.
- This can be run manually or via a scheduler such, as cron.
- If your pipeline runner supports python snippets, then you can paste this into your pipeline.
"""
import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResult
from great_expectations.data_context import DataContext
import pytest
@pytest.mark.CheckpointFriday04pm
def test_run_checkpoint():
data_context: DataContext = DataContext(
context_root_dir="tests/data_validation/great_expectations"
)
checkpoint_name : str = "description_validation"
batch_request : str = None
run_name : str = None
result: CheckpointResult = data_context.run_checkpoint(checkpoint_name=checkpoint_name,
batch_request=batch_request,
run_name=run_name)
if not result["success"]:
print("Validation failed!")
sys.exit(1)
print("Validation succeeded!")
sys.exit(0)
# print result to assist debug
print(f"The Result is {result}")
assert result["success"], "Lazy one liners in the description of the articles!"
| 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/conf/skype_conf.py | import os
SKYPE_URL = os.environ['SKYPE_URL']
SKYPE_CHANNEL = os.environ['SKYPE_CHANNEL']
SKYPE_API_KEY = os.environ['SKYPE_API_KEY'] | 0 |
qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations | qxf2_public_repos/newsletter_automation/tests/data_validation/great_expectations/conf/netlify_conf.py | import os
NETLIFY_URL = os.environ['NETLIFY_URL'] | 0 |
qxf2_public_repos | qxf2_public_repos/streaming-data-gen/logging_config.py | """
Logging configuration for the FastAPI Data Streaming Application.
"""
import logging
import os
def setup_logging():
"""
Set up logging configuration - configures the root logger with console and file handlers,
sets their formatters, log levels, and adds them to the root logger.
"""
try:
# Define the base directory and log file path
base_dir = os.path.dirname(os.path.abspath(__file__))
log_file_path = os.path.join(base_dir, "app.log")
# Configure the root logger
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
if logger.hasHandlers():
logger.handlers.clear()
# Create console handler with a normal formatter
c_handler = logging.StreamHandler()
console_format = (
"%(asctime)s loglevel=%(levelname)-6s "
"logger=%(name)s %(funcName)s() L%(lineno)-4d "
"%(message)s"
)
c_format = logging.Formatter(console_format)
c_handler.setFormatter(c_format)
c_handler.setLevel(logging.DEBUG)
# Create file handler with a detailed formatter
f_handler = logging.FileHandler(log_file_path)
log_format = (
"%(asctime)s loglevel=%(levelname)-6s "
"logger=%(name)s %(funcName)s() L%(lineno)-4d "
"%(message)s call_trace=%(pathname)s L%(lineno)-4d"
)
f_format = logging.Formatter(log_format)
f_handler.setFormatter(f_format)
f_handler.setLevel(logging.DEBUG)
# Add handlers to the root logger
logger.addHandler(c_handler)
logger.addHandler(f_handler)
return logger
except Exception as error:
print(f"Failed to configure logging: {error}")
raise
| 0 |
qxf2_public_repos | qxf2_public_repos/streaming-data-gen/LICENSE | MIT License
Copyright (c) 2024 sravantit25
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
qxf2_public_repos | qxf2_public_repos/streaming-data-gen/requirements.txt | fastapi
uvicorn[standard]
numpy
scipy
pyjwt
passlib[bcrypt]
python-multipart
sqlalchemy
httpx
pytest
bcrypt
apitally[fastapi] | 0 |
qxf2_public_repos | qxf2_public_repos/streaming-data-gen/README.md | # streaming-data-gen
This project is a data streaming generator built using FastAPI. It produces streaming data of various waveforms and distributions, which can be accessed using HTTP endpoints.
This project has been designed, developed and maintained by [Qxf2 Services](https://qxf2.com/?utm_source=github&utm_medium=click&utm_campaign=Streaming%20datagen). Qxf2 provides flexible and custom QA services to startups and early-stage products.
## Key Features
- **Multiple Data Distributions**: Supports various waveforms such as sine, cosine, square, sawtooth and distributions such as normal, uniform, and exponential.
- **Customizable Parameters**: Users can fine-tune data generation parameters to suit their specific requirements.
- **FastAPI-based**: Utilizes FastAPI to provide efficient and scalable data streaming capabilities.
- **Easy Integration**: Can be seamlessly incorporated into existing applications and workflows via simple HTTP requests to the provided endpoints.
- **Authentication**: Requires bearer token authentication for accessing the endpoints.
## Usage
The app is accessible at http://datagen.pythonanywhere.com. The available endpoints include:
**Regular Data Streams**:
Append the desired endpoint to the base_url.
- `/sine`: Generates a sine wave data stream.
- `/cosine`: Generates a cosine wave data stream.
- `/square`: Generates a square wave data stream.
- `/sawtooth`: Generates a sawtooth wave data stream.
- `/normal`: Generates a data stream with values sampled from a normal distribution.
- `/uniform`: Generates a data stream with values sampled from a uniform distribution.
- `/exponential`: Generates a data stream with values sampled from an exponential distribution.
**Anomalous Data Streams**:
Append the desired endpoint to base_url/anomalies.
- `/random`: Generates data with random anomalies.
- `/random-square`: Generates data with random square wave anomalies.
- `/clustered`: Generates data with clustered anomalies.
- `/periodic-spike`: Generates data with periodic spike anomalies within a 1-hour duration.
- `/count-per-duration`: Generates data with a specified number of anomalies within a 1-hour duration.
For example:
- http://datagen.pythonanywhere.com/sine
- http://datagen.pythonanywhere.com/anomalies/random
To access these endpoints, provide a valid bearer token in the request headers.
Customize the parameters of the requested waveform or distribution by passing query parameters in the URL. If no parameters are provided, the default parameters for each endpoint will be applied.
## Documentation
Documentation for the API endpoints is available at <a href="http://datagen.pythonanywhere.com" target="_blank">http://datagen.pythonanywhere.com/ </a>. It has information on how to use each endpoint and the available parameters.
## Obtaining Bearer Token
To obtain a bearer token for accessing these endpoints, follow these steps:
1. **Register**:
- Navigate to the registration section at http://datagen.pythonanywhere.com/#register-section.
- Enter your desired username and password and submit to register with our application.
2. **Login**:
- Go to the login section at http://datagen.pythonanywhere.com/#login-section.
- Enter your registered username and password to log in.
3. **Use the Token**:
- After successful login, copy the generated bearer token provided.
- Include the token in the Authorization header of your HTTP requests to access the streaming endpoints.
Alternatively, you can use curl commands to register and obtain the token:
```bash
curl -X POST http://datagen.pythonanywhere.com/register -H "Content-Type: application/json" -d '{"username": "youruser", "password": "yourpassword"}'
curl -X POST http://datagen.pythonanywhere.com/token -H "Content-Type: application/x-www-form-urlencoded" -d "username=youruser&password=yourpassword"
curl -X GET http://datagen.pythonanywhere.com/sine -H "Authorization: Bearer YOUR_TOKEN_HERE"
```
## Examples
### 1. Example: Direct Endpoint Access
To consume streaming data directly from the endpoint, you can use a simple Python script like the following:
```python
import requests
import json
def consume_stream(url):
try:
headers = {"Authorization": f"Bearer {token}"}
response = requests.get(url, headers=headers, stream=True)
print(f"Connected to {url}")
if response.status_code == 200:
for line in response.iter_lines():
if line:
decoded_line = line.decode('utf-8')
data = json.loads(decoded_line)
print(data)
else:
print(f"Failed to connect, status code: {response.status_code}")
print(response.text)
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
if __name__ == "__main__":
stream_url = 'http://datagen.pythonanywhere.com/sine' #In this case, the default values for the parameters will be used
consume_stream(stream_url)
```
### 3.2. Example: Access with Parameters
You can also pass parameters to customize the generated data. Here's an example of how to request a sine wave with specific parameters:
```python
import requests
def consume_stream(url, amplitude, frequency, phase, sample_rate, interval):
try:
url = f"{url}?amplitude={amplitude}&frequency={frequency}&phase={phase}&sample_rate={sample_rate}&interval={interval}"
headers = {"Authorization": f"Bearer {token}"}
response = requests.get(url, headers=headers, stream=True)
print(f"Connected to {url}")
if response.status_code == 200:
for line in response.iter_lines():
if line:
decoded_line = line.decode('utf-8')
print(decoded_line)
else:
print(f"Failed to connect, status code: {response.status_code}")
print(response.text)
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
if __name__ == "__main__":
stream_url = 'http://datagen.pythonanywhere.com/sine'
amplitude = 10
frequency = 2
phase = 0
sample_rate = 100
interval = 2
consume_stream(stream_url, amplitude, frequency, phase, sample_rate, interval)
```
## Setup Instructions
To set up the project locally, follow these instructions:
### Installation
1. Clone the repository:
```bash
git clone https://github.com/qxf2/streaming-data-gen.git
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
### Configuration
1. Generate 'SECRET_KEY'
This is essential for cryptograhic signing within the application. To generate the key, run the following command in your terminal:
```bash
openssl rand -hex 32
```
2. Set 'SECRET_KEY' as Environment Variable.
* Create a '.env' file in the project root
* Add the following line to '.env' replaceing 'your_secret_key_value_here' with the generated 'SECRET_KEY'"
```bash
SECRET_KEY=your_secret_key_value_here
```
* Ensure '.emv' is listed in your '.gitignore' file to prevent it from being committed to version control.
### Usage
1. Run the FastAPI server locally:
```bash
python app/main.py
```
2. Access the application at 'http://localhost:8000'.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Qxf2 Services - custom testing services for startups
This application is an off-shoot of some technical testing that [Qxf2](https://qxf2.com/?utm_source=github&utm_medium=click&utm_campaign=Streaming%20datagen) performed at a client. **Qxf2 provides flexible QA services for startups and early-stage products**. We frequently support employ advanced testing techniques and develop QA tools to test what matters to our clients. Our vast experience with early-stage products has lead us to design and offer flexible testing services that are unique in the market. Some examples of QA services that you might not know about include - [lightweight testing service for startups](https://qxf2.com/essential-service-offering), our [foundational testing service](https://qxf2.com/foundational-service-offering), fractional QA advisor role, [deep AI/ML testing](https://qxf2.com/aiml-testing-offering) and so much more. If this streaming data generator helped you, consider spreading the word about Qxf2 and give this repo a star. And if you want to hire us for some advanced, technical testing, simply drop an note to Arun ([email protected]). | 0 |
qxf2_public_repos | qxf2_public_repos/streaming-data-gen/endpoints_demo.ipynb | pip install requestspip install matplotlibimport requests
token = "YOUR_TOKEN_HERE"
def consume_stream(url):
"""
Consumes a data stream from the given URL and yields each data point.
Args:
url (str): The URL of the data stream to consume.
Yields:
str: The data point from the stream.
"""
try:
headers = {"Authorization": f"Bearer {token}"}
response = requests.get(url, headers=headers, stream=True)
print(f"Connected to {url}")
if response.status_code == 200:
for line in response.iter_lines():
if line:
decoded_line = line.decode('utf-8')
yield decoded_line
else:
print(f"Failed to connect, status code: {response.status_code}")
print(response.text)
except requests.exceptions.RequestException as error:
print(f"Request failed: {error}")
def collect_data(url, num_points):
"""
Collects a specified number of data points from the stream.
Args:
url (str): The URL of the data stream to consume.
num_points (int): The number of data points to collect.
Returns:
list: A list of collected data points.
"""
data_points = []
stream = consume_stream(url)
for i, data_point in enumerate(stream):
if i < num_points:
data_points.append(data_point)
else:
break
return data_pointsimport matplotlib.pyplot as plt
def plot_data(data_points, num_points, title="Data Stream"):
"""
Plot the collected data points.
Args:
data_points (list): A list of data points to plot.
num_points (int): The number of data points to plot.
title (str): The title of the plot.
"""
data_points = [float(value) for value in data_points]
print(data_points)
plt.figure(figsize=(10, 5))
plt.plot(data_points[:num_points], marker='o', linestyle='-')
plt.title(title)
plt.xlabel("Data Point Index")
plt.ylabel("Value")
plt.grid(True)
plt.show()
# Endpoint URL for generating random anomalies
stream_url = 'http://localhost:8000/anomalies/random'# Consume the data stream from the endpoint and print it
for data_point in consume_stream(stream_url):
print(data_point)# Collect the specified number of data points
data_points = collect_data(stream_url, num_points=20)# Print the collected data points
for point in data_points:
print(point)# Plot the collected data points
plot_data(data_points, num_points=20, title="Random Anomalies Data")def construct_url(base_url, base_value, anomaly_probability, anomaly_range, data_interval):
return f"{base_url}?base_value={base_value}&anomaly_probability={anomaly_probability}&anomaly_range={anomaly_range}&data_interval={data_interval}"
# Using the constructed URL with custom parameters
base_url = 'http://localhost:8000/anomalies/random'
base_value = 5
anomaly_probability = 0.1
anomaly_range = 5.0
data_interval = 3.0
# Construct the custom URL
stream_url = construct_url(base_url, base_value, anomaly_probability, anomaly_range, data_interval)
# Consume the data stream from the custom URL
print(stream_url)# Function to construct the URL with custom parameters
def construct_url(base_url, base_value, num_anomalies, min_anomaly_range, max_anomaly_range, data_interval):
return f"{base_url}?base_value={base_value}&num_anomalies={num_anomalies}&min_anomaly_range={min_anomaly_range}&max_anomaly_range={max_anomaly_range}&data_interval={data_interval}"
# Using the constructed URL with custom parameters
base_url = 'http://localhost:8000/anomalies/count-per-duration'
base_value = 10
num_anomalies = 5
min_anomaly_range = 5.0
max_anomaly_range = 15.0
data_interval = 0.01
# Construct the custom URL
stream_url = construct_url(base_url, base_value, num_anomalies, min_anomaly_range, max_anomaly_range, data_interval)
# Consume the data stream from the custom URL
print(stream_url)# Function to construct the URL with custom parameters
def construct_url(base_url, base_value, spike_interval, min_spike_range, max_spike_range, data_interval):
return f"{base_url}?base_value={base_value}&spike_interval={spike_interval}&min_spike_range={min_spike_range}&max_spike_range={max_spike_range}&data_interval={data_interval}"
# Using the constructed URL with custom parameters
base_url = 'http://localhost:8000/anomalies/periodic-spike'
base_value = 10
spike_interval = 7
min_spike_range = -5.0
max_spike_range = 5.0
data_interval = 0.01
# Construct the custom URL
stream_url = construct_url(base_url, base_value, spike_interval, min_spike_range, max_spike_range, data_interval)
# Consume the data stream from the custom URL
print(stream_url)# Function to construct the URL with custom parameters
def construct_url(base_url, base_value, anomaly_magnitude, minimum_interval, maximum_interval, min_anomaly_length, max_anomaly_length, data_interval):
return f"{base_url}?base_value={base_value}&anomaly_magnitude={anomaly_magnitude}&minimum_interval={minimum_interval}&maximum_interval={maximum_interval}&min_anomaly_length={min_anomaly_length}&max_anomaly_length={max_anomaly_length}&data_interval={data_interval}"
# Using the constructed URL with custom parameters
base_url = 'http://localhost:8000/anomalies/clustered'
base_value = 10
anomaly_magnitude = 10.0
minimum_interval = 5.0
maximum_interval = 15.0
min_anomaly_length = 7.0
max_anomaly_length = 12.0
data_interval = 0.01
# Construct the custom URL
stream_url = construct_url(base_url, base_value, anomaly_magnitude, minimum_interval, maximum_interval, min_anomaly_length, max_anomaly_length, data_interval)
# Consume the data stream from the custom URL
print(stream_url)def construct_url(base_url, base_value, anomaly_magnitude, minimum_interval, maximum_interval, min_anomaly_duration, max_anomaly_duration, data_interval):
return f"{base_url}?base_value={base_value}&anomaly_magnitude={anomaly_magnitude}&minimum_interval={minimum_interval}&maximum_interval={maximum_interval}&min_anomaly_duration={min_anomaly_duration}&max_anomaly_duration={max_anomaly_duration}&data_interval={data_interval}"
# Using the constructed URL with custom parameters
base_url = 'http://localhost:8000/anomalies/random-square'
base_value = 2
anomaly_magnitude = 7
minimum_interval = 2.0
maximum_interval = 10.0
min_anomaly_duration = 7.0
max_anomaly_duration = 12.0
data_interval = 0.01
# Construct the custom URL
stream_url = construct_url(base_url, base_value, anomaly_magnitude, minimum_interval, maximum_interval, min_anomaly_duration, max_anomaly_duration,data_interval)
# Consume the data stream from the custom URL
print(stream_url) | 0 |
qxf2_public_repos/streaming-data-gen | qxf2_public_repos/streaming-data-gen/app/main.py | """
Main module for the Streaming Data Generator application.
"""
import os
import sys
import logging
import uvicorn
from apitally.fastapi import ApitallyMiddleware
from starlette.exceptions import HTTPException as StarletteHTTPException
from fastapi import (
FastAPI,
Request,
status,
)
from fastapi.exceptions import RequestValidationError
from fastapi.responses import (
FileResponse,
JSONResponse,
)
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from fastapi.encoders import jsonable_encoder
# Import from local modules
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(base_dir)
from app.endpoints.endpoints import router as api_router
from app.endpoints.anomaly_endpoints import router as anomaly_api_router
from app.endpoints.auth_endpoint import router as auth_router
from logging_config import setup_logging
from app.db_utils.database import Base, engine
# Configure the root logger using setup_logging function
setup_logging()
# Create a FastAPI instance
app = FastAPI()
origins = ["http://localhost", "http://localhost:8000"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Add Apitally middleware
apitally_client_id = os.environ.get('APITALLY_CLIENT_ID')
if apitally_client_id:
app.add_middleware(ApitallyMiddleware,
client_id=apitally_client_id,
env="prod")
logger = logging.getLogger(__name__)
app.mount("/static", StaticFiles(directory="app/static"), name="static")
@app.get("/")
async def serve_main_page():
"""
Serves the main page of the Data Stream Generator App.
"""
return FileResponse("app/static/index.html")
@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request: Request, exc: RequestValidationError):
"""
Handle exceptions related to request validation errors.
"""
logger.error("Validation error occurred: %s", exc.errors())
return JSONResponse(
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
content=jsonable_encoder({"detail": exc.errors()}),
)
@app.exception_handler(StarletteHTTPException)
async def http_exception_handler(request: Request, exc: StarletteHTTPException):
"""
Handle exceptions related to HTTP errors.
"""
logger.error("HTTP error occurred: %s", {exc.detail})
return JSONResponse(
status_code=exc.status_code,
content=jsonable_encoder({"detail": exc.detail}),
)
@app.exception_handler(Exception)
async def general_exception_handler(request: Request, exc: Exception):
"""
Handle all other excetions that are not explicitly handled by other exception handlers.
"""
logger.error("An exception occurred: %s", exc)
return JSONResponse(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
content=jsonable_encoder({"detail": "Internal server error"}),
)
app.include_router(api_router)
app.include_router(anomaly_api_router, prefix="/anomalies")
app.include_router(auth_router)
Base.metadata.create_all(bind=engine)
logger.info("Starting the Streaming Data Generator")
# Start the FastAPI server
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/endpoints/anomaly_endpoints.py | """
Module for defining FastAPI endpoints that generates data with anomalies.
Endpoints:
/random: Endpoint for generating data with random anomalies
/random-square: Endpoint for generating data with random square wave anomalies
/clustered: Endpoint for generating data with clustered anomalies
/periodic-spike: Endpoint for generating data with periodic spike anomalies
within 1 hour duration
/count-per-duration: Endopint for generating data with specified
number of anomalies within 1 hour duration
"""
import logging
from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse
from app.generators.anomalies import (
periodic_spike,
clustered,
count_duration,
random_anomaly,
random_square,
)
from app.db_utils.crud import verify_token
from app.models.auth_model import TokenData
router = APIRouter()
logger = logging.getLogger(__name__)
@router.get("/random", response_class=StreamingResponse)
async def generate_random_anomaly(
random_anomaly_model: random_anomaly.RandomAnomalyModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate streaming data with random anomalies.
Parameters:
- random_anomaly: An instance of the RandomAnomalyModel class defining the parameters
of the data to be generated. If not provided, default parameters will be used.
The parameters include:
- base_value (float): The constant value for regular data points.
- anomaly_probability(float): The probability of an anomaly occurring
- anomaly_range(float): The range within which the anomaly values can vary
- data_interval(float): The time interval between data points
Returns:
- StreamingResponse: A streaming response containing the generated data with anomalies.
"""
try:
logger.info(
"Generating data with random anomalies for user '%s' with parameters: %s",
token_data.username,
random_anomaly_model,
)
return StreamingResponse(
random_anomaly.generate_random_anomalies(random_anomaly_model),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/random-square", response_class=StreamingResponse)
async def random_square_anomaly(
pos_square: random_square.RandomSquareModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate streaming data with random square wave anomalies.
Parameters:
- pos_square: An instance of the RandomSquareModel class defining the parameters
of the data to be generated. If not provided, default parameters will be used.
The parameters include:
- base_value (float): The constant value for regular data points.
- anomaly_magnitude (float): The magnitude of the anomaly to add to the base value.
- min_anomaly_duration (int): The minimum duration of the anomaly in data points.
- max_anomaly_duration (int): The maximum duration of the anomaly in data points.
- data_interval (float): The time interval between data points in seconds.
Returns:
- StreamingResponse: A streaming response containing the generated data with anomalies.
"""
try:
logger.info(
"Generating data with random square wave anomalies for user '%s' with parameters: %s",
token_data.username,
pos_square,
)
return StreamingResponse(
random_square.generate_random_square(pos_square),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/clustered", response_class=StreamingResponse)
async def generate_clustered_anomaly(
clustered_anomaly: clustered.ClusteredAnomalyModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate streaming data with clustered anomalies.
Parameters:
- clustered_anomaly: An instance of the ClusteredAnomalyModel class defining the parameters
of the data to be generated. If not provided, default parameters will be used.
The parameters include:
- constant_value (float): The base or constant value for regular data points.
- anomaly_magnitude (float): The magnitude of the anomalies.
- anomaly_interval_range (float):
minimum and maximum interval between clusters of anomalies
- anomaly_length_range (float):
The minimum and maximum length of a cluster of anomalies.
- data_interval (float): The time interval between data points.
Returns:
- StreamingResponse: A streaming response containing the generated data with anomalies.
"""
try:
logger.info(
"Generating data with clustered anomalies for user '%s' with parameters: %s",
token_data.username,
clustered_anomaly,
)
return StreamingResponse(
clustered.generate_clustered_anomalies(clustered_anomaly),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/periodic-spike", response_class=StreamingResponse)
async def generate_spike_anomaly(
spike_anomaly: periodic_spike.SpikeAnomalyModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate streaming data with regular spikes occurring at
specified interval within a duration of 1 hour.
Parameters:
- spike_anomaly: An instance of the SpikeAnomalyModel class defining the parameters
of the data to be generated. If not provided, default parameters will be used.
The parameters include:
- base_value (float): The base or constant value around which anomalies will occur.
- spike_interval (int): The interval in seconds at which spikes should occur.
- spike_range (tuple): The range (lower and upper bounds) for the spike values.
- data_interval (float): The time interval between data points.
Returns:
- StreamingResponse: A streaming response containing the generated data with anomalies.
"""
try:
logger.info(
"Generating data with periodic spike anomalies for user '%s' with parameters: %s",
token_data.username,
spike_anomaly,
)
return StreamingResponse(
periodic_spike.generate_periodic_spike_data(spike_anomaly),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/count-per-duration", response_class=StreamingResponse)
async def count_per_duration(
count_based: count_duration.CountBasedAnomalyModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate streaming data with specified number of anomalies within a duration of 1 hour.
Parameters:
- count_based: An instance of the CountBasedAnomalyModel class defining the parameters
of the data to be generated. If not provided, default parameters will be used.
The parameters include:
- base_value (float): The base or constant value around which anomalies will occur.
- num_anomalies (int): The number of anomalies to introduce within the duration.
- anomaly_range (tuple): The range (lower and upper bounds) for the anomaly values.
- data_interval (float): The rate at which data points are generated.
Returns:
- StreamingResponse: A streaming response containing the generated data with anomalies.
"""
try:
logger.info(
"Generating data with count based anomalies for user '%s' with parameters: %s",
token_data.username,
count_based,
)
return StreamingResponse(
count_duration.generate_count_based_anomalies_data(count_based),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/endpoints/auth_endpoint.py | """
Module for defining FastAPI endpoints for user authentication and token generation.
Endpoints:
/register: Endpoint for registering a new user.
/token: Endpoint for logging in a user and generating a token.
"""
from datetime import timedelta
import logging
from typing import Annotated
from fastapi import APIRouter, Depends, HTTPException, status
from fastapi.security import OAuth2PasswordRequestForm
from sqlalchemy.orm import Session
from app.db_utils import crud
from app.db_utils.database import SessionLocal
from app.db_utils import schemas
from app.models.auth_model import Token
from app.db_utils.crud import verify_password, create_access_token
logger = logging.getLogger(__name__)
ACCESS_TOKEN_EXPIRY = 7
router = APIRouter()
def get_db():
"""
Generator function that yields a SQLAlchemy SessionLocal instance.
Yields:
SessionLocal: A database session object that can be used within a context manager.
"""
db = SessionLocal()
try:
yield db
finally:
db.close()
@router.post("/register", response_model=schemas.User)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
"""
Endpoint to creates a new user.
Parameters:
user (schemas.UserCreate): The user data including username and password
db (Session, optional): The database session. Defaults to the result of `get_db()`.
Returns:
schemas.User : The newly created user object from the database.
"""
db_user = crud.get_user_by_username(db, username=user.username)
if db_user:
raise HTTPException(status_code=400, detail="User already registered")
new_user = crud.create_user(db=db, user=user)
return new_user
def authenticate_user(username: str, password: str, db: Session):
"""
Authenticates a user based on the provided username and password.
Parameters:
username (str): The username of the user to authenticate.
password (str): The password of the user to authenticate.
db (Session): The database session to query user information.
Returns:
db_user: The authenticated user if successful, False otherwise.
"""
db_user = crud.get_user_by_username(db, username=username)
if not db_user:
logger.debug("No user found with username: %s", username)
return False
if not verify_password(password, db_user.hashed_password):
logger.debug("Password verification failed for user: %s", username)
return False
logger.debug("User %s authenticated successfully", username)
return db_user
@router.post("/token")
async def login_for_access_token(
form_data: Annotated[OAuth2PasswordRequestForm, Depends()],
db: Session = Depends(get_db),
) -> Token:
"""
Endpoint for logging in a user and generating an access token.
Args:
form_data (OAuth2PasswordRequestForm): The form data containing the username and password.
db (Session, optional): The database session. Defaults to Depends(get_db).
Returns:
Token: The access token with the username as the subject.
"""
user = authenticate_user(form_data.username, form_data.password, db)
if not user:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect username or password",
headers={"WWW-Authenticate": "Bearer"},
)
logger.debug("Login successful for username: %s", form_data.username)
access_token_expires = timedelta(days=ACCESS_TOKEN_EXPIRY)
access_token = create_access_token(
data={"sub": user.username}, expires_delta=access_token_expires
)
return Token(access_token=access_token, token_type="bearer")
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/endpoints/endpoints.py | """
Module for defining FastAPI endpoints.
Endpoints:
/sine: Endpoint for generating a sine wave.
/cosine: Endpoint for generating a cosine wave.
/sawtooth: Endpoint for generating a sawtooth wave.
/square: Endpoint for generating a square wave.
/normal: Endpoint for generating a normal distribution.
/uniform: Endpoint for generating a uniform distribution.
/exponential: Endpoint for generating an exponential distribution.
"""
import logging
from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse
from app.generators import sine, cosine, square, sawtooth, normal, uniform, exponential
from app.db_utils.crud import verify_token
from app.models.auth_model import TokenData
router = APIRouter()
logger = logging.getLogger(__name__)
@router.get("/sine", response_class=StreamingResponse)
async def sine_wave(
sine_model: sine.SineModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming sine wave.
Parameters:
- sine_model: An instance of the SineModel class defining the parameters
of the sine wave to be generated. If not provided, default parameters will be used.
The parameters include:
- amplitude (float): The distance from height of a peak to center line.
- frequency (float): The number of cycles per second of the sine wave (in Hertz).
- phase (int): The offset of the sine wave (in degrees).
- sample_rate (int): The frequency of generated data points in samples per second.
- interval (float): The time interval between data points (in seconds).
Returns:
- StreamingResponse: A streaming response containing the generated sine wave data.
"""
try:
logger.info(
"Generating sine wave for user '%s' with parameters: %s",
token_data.username,
sine_model,
)
return StreamingResponse(
sine.generate_sine_data(sine_model), media_type="text/event-stream"
)
except Exception as error:
logger.error("An error occurred while generating sine wave: %s", error)
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/cosine", response_class=StreamingResponse)
async def cosine_wave(
cosine_model: cosine.CosineModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming cosine wave.
Parameters:
- cosine_model: An instance of the CosineModel class defining the parameters
of the cosine wave to be generated. If not provided, default parameters will be used.
The parameters include:
- amplitude (float): The distance from height of a peak to center line.
- frequency (float): The number of cycles per second of the cosine wave (in Hertz).
- phase (int): The offset of the cosine wave (in degrees).
- sample_rate (int): The frequency of generated data points in samples per second.
- interval (float): The time interval between data points in seconds.
Returns:
- StreamingResponse: A streaming response containing the generated cosine wave data.
"""
try:
logger.info(
"Generating cosine wave for user '%s' with parameters: %s",
token_data.username,
cosine_model,
)
return StreamingResponse(
cosine.generate_cosine_data(cosine_model), media_type="text/event-stream"
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/sawtooth", response_class=StreamingResponse)
async def sawtooth_wave(
sawtooth_model: sawtooth.SawtoothModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming sawtooth wave.
Parameters:
- sawtooth_model : An instance of the SawtoothModel class defining the parameters
of the sawtooth wave to be generated. If not provided, default parameters will be used.
The parameters include:
- amplitude (float): The distance from height of a peak to center line.
- frequency (float): The number of cycles per second of the sawtooth wave (in Hertz).
- sample_rate (int): The frequency of generated data points in samples per second.
- interval (float): The time interval between data points (in seconds).
Returns:
- StreamingResponse: A streaming response containing the generated sawtooth wave data.
"""
try:
logger.info(
"Generating sawtooth wave for user '%s' with parameters: %s",
token_data.username,
sawtooth_model,
)
return StreamingResponse(
sawtooth.generate_sawtooth_data(sawtooth_model),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/square", response_class=StreamingResponse)
async def square_wave(
square_model: square.SquareModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming square wave.
Parameters:
- square_model: An instance of the SquareModel class defining the parameters
of the square wave to be generated. If not provided, default parameters will be used.
The parameters include:
- frequency (float): The number of cycles per second of the square wave (in Hertz).
- sample_rate (int): The frequency of generated data points in samples per second.
- interval (float): The time interval between data points (in seconds).
Returns:
- StreamingResponse: A streaming response containing the generated square wave data.
"""
try:
logger.info(
"Generating square wave for user '%s' with parameters: %s",
token_data.username,
square_model,
)
return StreamingResponse(
square.generate_square_data(square_model), media_type="text/event-stream"
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/normal", response_class=StreamingResponse)
async def normal_wave(
normal_model: normal.NormalModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming normal distribution.
Parameters:
- normal_model: An instance of the NormalModel class defining the parameters
of the normal distribution to be generated. If not provided, default parameters will be used.
The parameters include:
- mean (float): The mean of the normal distribution.
- std_dev (float): The standard deviation of the normal distribution.
- interval (float): The time interval between data points in seconds.
Returns:
- StreamingResponse: A streaming response containing the generated normal distribution data.
"""
try:
logger.info(
"Generating normal distribution for user '%s' with parameters: %s",
token_data.username,
normal_model,
)
return StreamingResponse(
normal.generate_normal_data(normal_model), media_type="text/event-stream"
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/uniform", response_class=StreamingResponse)
async def uniform_wave(
uniform_model: uniform.UniformModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming uniform distribution.
Parameters:
- uniform_model: An instance of the UniformModel class defining the parameters
of the uniform distribution to be generated. If not provided, default parameters will be used.
The parameters include:
- min_val (float): The minimum value of the uniform distribution.
- max_val (float): The maximum value of the uniform distribution.
- interval (float): The time interval between data points in seconds.
Returns:
- StreamingResponse: A streaming response containing the generated uniform distribution data.
"""
try:
logger.info(
"Generating uniform distribution for user '%s' with parameters: %s",
token_data.username,
uniform_model,
)
return StreamingResponse(
uniform.generate_uniform_data(uniform_model), media_type="text/event-stream"
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
@router.get("/exponential", response_class=StreamingResponse)
async def exponential_wave(
exponential_model: exponential.ExponentialModel = Depends(),
token_data: TokenData = Depends(verify_token),
):
"""
Generate a streaming exponential distribution.
Parameters:
- exponential_model: An instance of the ExponentialModel class defining the parameters
of the exponential distribution to be generated.
If not provided, default parameters will be used.
The parameters include:
- scale (float): The inverse of the rate parameter controlling
the rate at which events occur.
- interval (float): The time interval between data points in seconds.
Returns:
- StreamingResponse: A streaming response containing the generated
exponential distribution data.
"""
try:
logger.info(
"Generating exponential distribution for user '%s' with parameters: %s",
token_data.username,
exponential_model,
)
return StreamingResponse(
exponential.generate_exponential_data(exponential_model),
media_type="text/event-stream",
)
except Exception as error:
raise HTTPException(status_code=500, detail=str(error)) from error
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/tests/test_endpoints.py | """
This script contains tests for validating the streaming data endpoints
The endpoints require bearer token authentication.
"""
import pytest
import requests
import json
token = ""
@pytest.mark.parametrize(
"endpoint",
[
"http://localhost:8000/sine",
"http://localhost:8000/cosine",
"http://localhost:8000/square",
"http://localhost:8000/sawtooth",
"http://localhost:8000/normal",
"http://localhost:8000/uniform",
"http://localhost:8000/exponential",
"http://localhost:8000/anomalies/random",
"http://localhost:8000/anomalies/random-square",
"http://localhost:8000/anomalies/clustered",
"http://localhost:8000/anomalies/periodic-spike",
"http://localhost:8000/anomalies/count-per-duration"
],
)
def test_streaming_endpoint(endpoint):
"""
Test the streaming endpoint by making requests to different endpoints and handling the streaming data.
"""
headers = {"Authorization": f"Bearer {token}"}
print(f"Testing endpoint: {endpoint}")
try:
response = requests.get(endpoint, headers=headers, stream=True)
print(f"Response Status Code: {response.status_code}")
assert (
response.status_code == 200
), f"Failed to connect, status code: {response.status_code}"
data = []
for line in response.iter_lines():
if line:
decoded_line = line.decode("utf-8")
data.append(json.loads(decoded_line))
print(f"Received data: {decoded_line}")
if len(data) >= 10:
break
assert len(data) > 0, "No data received from the stream"
except requests.exceptions.RequestException as e:
pytest.fail(f"Request failed: {e}")
if __name__ == "__main__":
pytest.main()
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/models/distribution_models.py | """
This script defines Pydantic models representing various distributions.
"""
from pydantic import BaseModel, Field
class NormalModel(BaseModel):
"""
A model representing a normal distribution.
"""
mean: float = Field(
default=0, description="The mean of the normal distribution."
)
std_dev: float = Field(
default=1, description="The standard deviation of the normal distribution."
)
interval: float = Field(
default=1.0, description="The time interval between data points in seconds."
)
class UniformModel(BaseModel):
"""
A model representing a uniform distribution.
"""
min_val: float = Field(
default=0, description="The minimum value of the uniform distribution."
)
max_val: float = Field(
default=1, description="The maximum value of the uniform distribution."
)
interval: float = Field(
default=1.0, description="The time interval between data points in seconds."
)
class ExponentialModel(BaseModel):
"""
A model representing an exponential distribution.
"""
scale: float = Field(
default=1.0,
description="The inverse of the rate parameter controlling the rate at which events occur.",
)
interval: float = Field(
default=1.0, description="The time interval between data points in seconds."
)
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/models/auth_model.py | from pydantic import BaseModel
from typing import Union
class Token(BaseModel):
"""
Pydantic model representing a token.
Attributes:
access_token (str): The access token.
token_type (str): The type of the token.
"""
access_token: str
token_type: str
class TokenData(BaseModel):
"""
Pydantic model representing token data.
Attributes:
username (Union[str, None]): The username associated with the token,
or None if the token is not associated with any user.
"""
username: Union[str, None] = None
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/models/waveform_models.py | """
This script defines Pydantic models representing various waveforms.
"""
from pydantic import BaseModel, Field
class SineModel(BaseModel):
"""
A model representing a sine wave.
"""
amplitude: float = Field(
default=4, description="The distance from height of a peak to center line."
)
frequency: float = Field(
default=2,
description="The number of cycles per second of the sine wave (in Hertz).",
)
phase: int = Field(
default=0, description="The offset of the sine wave (in degrees)."
)
sample_rate: int = Field(
default=100,
description="The frequency of generated data points in samples per second.",
)
interval: float = Field(
default=1.0, description="The time interval between data points (in seconds)."
)
class CosineModel(BaseModel):
"""
A model representing a cosine wave.
"""
amplitude: float = Field(
default=4, description="The distance from height of a peak to center line."
)
frequency: float = Field(
default=2,
description="The number of cycles per second of the cosine wave (in Hertz).",
)
phase: int = Field(
default=0, description="The offset of the cosine wave (in degrees)."
)
sample_rate: int = Field(
default=100,
description="The frequency of generated data points in samples per second.",
)
interval: float = Field(
default=1.0, description="The time interval between data points (in seconds)."
)
class SquareModel(BaseModel):
"""
A model representing a square wave.
"""
frequency: float = Field(
default=2.0,
description="The number of cycles per second of the square wave (in Hertz).",
)
sample_rate: int = Field(
default=100,
description="The frequency of generated data points in samples per second.",
)
interval: float = Field(
default=1.0, description="The time interval between data points (in seconds)."
)
class SawtoothModel(BaseModel):
"""
A model representing a sawtooth wave.
"""
amplitude: float = Field(
default=1.0,
title="Amplitude",
description="The distance from height of a peak to center line.",
)
frequency: float = Field(
default=2.0,
title="Frequency",
description="The number of cycles per second of the sawtooth wave (in Hertz).",
)
sample_rate: int = Field(
default=100,
title="Sample Rate",
description="The frequency of generated data points in samples per second.",
)
interval: float = Field(
default=1.0,
title="Interval",
description="The time interval between data points (in seconds).",
)
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/models/anomaly_models.py | """
This script defines Pydantic models representing various anomaly models.
"""
from pydantic import BaseModel, Field
class RandomAnomalyModel(BaseModel):
"""
A model representing the parameters for generating random anomalies.
"""
base_value: float = Field(
default=0.0,
title="Base Value",
description="The constant value for regular data points.",
)
anomaly_probability: float = Field(
default=0.05,
title="Anomaly Probability",
description="The probability of an anomaly occurring at each data point.",
)
anomaly_range: float = Field(
default=10.0,
title="Anomaly Range",
description="The range within which the anomaly values can vary.",
)
data_interval: float = Field(
default=1.0,
title="Data Interval",
description="The time interval between data points.",
)
class RandomSquareModel(BaseModel):
"""
A model representing the parameters for generating random positive square wave anomalies.
"""
base_value: float = Field(
default=0.0,
title="Constant Value",
description="The constant value for regular data points.",
)
anomaly_magnitude: float = Field(
default=10.0,
title="Anomaly Magnitude",
description="The magnitude of the anomaly to add to the base value",
)
minimum_interval: float = Field(
default=50.0,
title="Minimum Interval",
description="The minimum interval between wave of anomalies",
)
maximum_interval: float = Field(
default=150.0,
title="Maximum Interval",
description="The maximum interval between wave of anomalies",
)
min_anomaly_duration: float = Field(
default=5.0,
title="Minimum Anomaly Duration",
description="The minimum length of the wave",
)
max_anomaly_duration: float = Field(
default=10.0,
title="Maximum Anomaly Duration",
description="The maximum length of the wave",
)
data_interval: float = Field(
default=1.0,
title="Interval",
description="The rate at which data points are generated.",
)
class ClusteredAnomalyModel(BaseModel):
"""
A model representing the parameters for generating clustered anomalies.
"""
constant_value: float = Field(
default=0.0,
title="Constant Value",
description="The constant value for regular data points.",
)
anomaly_magnitude: float = Field(
default=10.0,
title="Anomaly Magnitude",
description="The magnitude of the anomaly to add to the base value",
)
minimum_interval: float = Field(
default=50.0,
title="Minimum Interval",
description="The minimum interval between cluster of anomalies",
)
maximum_interval: float = Field(
default=150.0,
title="Maximum Interval",
description="The maximum interval between cluster of anomalies",
)
min_anomaly_length: float = Field(
default=5.0,
title="Minimum Anomaly Length",
description="The minimum length of the anomalies in the cluster",
)
max_anomaly_length: float = Field(
default=10.0,
title="Maximum Anomaly Length",
description="The maximum length of the anomalies in the cluster",
)
data_interval: float = Field(
default=1.0,
title="Interval",
description="The rate at which data points are generated.",
)
class SpikeAnomalyModel(BaseModel):
"""
A model representing the parameters for generating spiked anomalies at regular intervals.
"""
base_value: float = Field(
default=0.0,
title="Base Value",
description="The constant value for regular data points.",
)
spike_interval: int = Field(
default=1200,
title="Spike Interval",
description="The interval in seconds at which anomaly spike should occur.",
)
min_spike_range: float = Field(
default=5.0,
title="Minimum Spike Range",
description="The minimum range for the spike values.",
)
max_spike_range: float = Field(
default=10.0,
title="Maximum Spike Range",
description="The maximum range for the spike values.",
)
data_interval: float = Field(
default=1.0,
title="Interval",
description="The rate at which data points are generated.",
)
class CountBasedAnomalyModel(BaseModel):
"""
A model representing the parameters for generating specified number of anomalies within 1 hour.
"""
base_value: float = Field(
default=0.0,
title="Base Value",
description="The constant value for regular data points.",
)
num_anomalies: int = Field(
default=1,
title="Anomaly Count",
description="The number of anomalies to introduce within the duration.",
)
min_anomaly_range: float = Field(
default=-10.0,
title="Minimum Anomaly Range",
description="The minimum range for the anomaly values.",
)
max_anomaly_range: float = Field(
default=10.0,
title="Maximum Anomaly Range",
description="The maximum range for the anomaly values.",
)
data_interval: float = Field(
default=1.0,
title="Interval",
description="The rate at which data points are generated.",
)
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/static/index.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Streaming Data Generator</title>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@picocss/pico@1/css/pico.min.css">
<link rel="stylesheet" href="/static/styles.css">
</head>
<body>
<nav class="container-fluid">
<ul>
<li><strong>Streaming Data Generator</strong></li>
</ul>
<ul>
<li><a href="/">Home</a></li>
<li><a href="https://github.com/qxf2/streaming-data-gen" target="_blank" rel="noopener noreferrer">GitHub</a></li>
</ul>
</nav>
<section id="intro" style="text-align: center; margin-bottom: 2em;">
<hgroup>
<h2>Welcome to FastAPI Data Streaming Application</h2>
<h3>Real-time data streaming through HTTP endpoints</h3>
</hgroup>
</section>
<main class="container">
<section id="endpoints" style="text-align: center;">
<hgroup>
<h2>Endpoints</h2>
<h3>Explore the various endpoints provided by our application. </h3>
</hgroup>
<div class="tabs">
<div class="tab-buttons">
<button class="button-fixed-width" onclick="openTab(event, 'regular-streams')">Regular Data Streams</button>
<button class="button-fixed-width" onclick="openTab(event, 'anomalous-streams')">Anomalous Data Streams</button>
</div>
<div id="regular-streams" class="tab-content">
<br>
<h3>Regular Data Streams</h3>
<div class="buttons-container">
<button class="button-fixed-width" onclick="openModal('sineModal')">/sine</button>
<button class="button-fixed-width" onclick="openModal('cosineModal')">/cosine</button>
<button class="button-fixed-width" onclick="openModal('squareModal')">/square</button>
<button class="button-fixed-width" onclick="openModal('sawtoothModal')">/sawtooth</button>
<button class="button-fixed-width" onclick="openModal('normalModal')">/normal</button>
<button class="button-fixed-width" onclick="openModal('uniformModal')">/uniform</button>
<button class="button-fixed-width" onclick="openModal('exponentialModal')">/exponential</button>
</div>
</div>
<div id="anomalous-streams" class="tab-content">
<br>
<h3>Anomalous Data Streams</h3>
<div class="buttons-container">
<button class="button-fixed-width" onclick="openModal('randomAnomalyModal')">/anomalies/random</button>
<button class="button-fixed-width" onclick="openModal('randomSquareModal')">/anomalies/random-square</button>
<button class="button-fixed-width" onclick="openModal('clusteredModal')">/anomalies/clustered</button>
<button class="button-fixed-width" onclick="openModal('periodicSpikeModal')">/anomalies/periodic-spike</button>
<button class="button-fixed-width" onclick="openModal('countPerDurationModal')">/anomalies/count-per-duration</button>
</div>
</div>
</section>
<section id="register-section" style="display: none; text-align: center;">
<hgroup>
<h2>Sign Up</h2>
<h3>Register to start accessing the endpoints</h3>
</hgroup>
<form id="register-form" style="max-width: 400px; margin: auto;">
<label for="username">Username:</label>
<input type="text" id="username" name="username" required>
<label for="password">Password:</label>
<input type="password" id="password" name="password" required>
<button type="submit">Register</button>
</form>
<p id="register-message"></p>
<p>Already have an account? <a href="#login-section" onclick="showSection('login-section')">Login and generate Token</a></p>
</section>
<section id="login-section" style="display: none; text-align: center;">
<hgroup>
<h2>Login</h2>
<h3>Login to generate your access token</h3>
</hgroup>
<form id="login-form" style="max-width: 400px; margin: auto;">
<label for="login-username">Username:</label>
<input type="text" id="login-username" name="username" required>
<label for="login-password">Password:</label>
<input type="password" id="login-password" name="password" required>
<button type="submit">Generate Token</button>
</form>
<p id="login-message"></p>
<p>New user? <a href="#register-section" onclick="showSection('register-section')">Sign Up</a></p>
</section>
</div>
</main>
<div id="tokenModal" class="modal" style="display: none;">
<div class="modal-content">
<span class="close" onclick="closeModal('tokenModal')">×</span>
<h4>Generated Token</h4>
<p id="modal-token-message"></p>
<p id="modal-token-expiry"></p>
<button id="copyTokenButton">Copy Token</button>
</div>
</div>
<div id="notification">Token copied to clipboard!</div>
<div id="sineModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('sineModal')">×</span>
<h4>Sine Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The sine wave approach generates a continuous stream of data points that follow a sinusoidal pattern. This method is useful for simulating smooth, periodic oscillations in data, commonly observed in various natural and man-made systems.</p>
<p><strong>How It Works:</strong>
<br>Generates data points that form a sine wave, characterized by its amplitude, frequency, and phase. The amplitude determines the height of the wave, the frequency defines the number of cycles per second, and the phase sets the wave's horizontal shift. Data points are produced at a specified sample rate, with each point separated by a fixed interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>amplitude</strong> (float): <em>Default: 4.0</em>
<br>The distance from the height of a peak to the center line, representing the wave's strength.</li>
<li><strong>frequency</strong> (float): <em>Default: 2.0</em>
<br>The number of cycles per second of the sine wave, measured in Hertz.</li>
<li><strong>phase</strong> (int): <em>Default: 0</em>
<br>The offset of the sine wave, in degrees, determining where the wave starts.</li>
<li><strong>sample_rate</strong> (int): <em>Default: 100</em>
<br>The frequency at which data points are generated, measured in samples per second.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="cosineModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('cosineModal')">×</span>
<h4>Cosine Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The cosine wave approach generates a continuous stream of data points that follow a cosine pattern. This method is useful for simulating smooth, periodic oscillations in data, similar to sine waves but shifted in phase.</p>
<p><strong>How It Works:</strong>
<br>Generates data points that form a cosine wave, characterized by its amplitude, frequency, and phase. The amplitude determines the height of the wave, the frequency defines the number of cycles per second, and the phase sets the wave's horizontal shift. Data points are produced at a specified sample rate, with each point separated by a fixed interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>amplitude</strong> (float): <em>Default: 4.0</em>
<br>The distance from the height of a peak to the center line, representing the wave's strength.</li>
<li><strong>frequency</strong> (float): <em>Default: 2.0</em>
<br>The number of cycles per second of the cosine wave, measured in Hertz.</li>
<li><strong>phase</strong> (int): <em>Default: 0</em>
<br>The offset of the cosine wave, in degrees, determining where the wave starts.</li>
<li><strong>sample_rate</strong> (int): <em>Default: 100</em>
<br>The frequency at which data points are generated, measured in samples per second.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="squareModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('squareModal')">×</span>
<h4>Square Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The square wave approach generates a continuous stream of data points that alternate between two levels, creating a rectangular waveform.</p>
<p><strong>How It Works:</strong>
<br>Generates data points that form a square wave, characterized by its frequency. The frequency determines the number of cycles per second, producing sharp transitions between high and low levels. Data points are produced at a specified sample rate, with each point separated by a fixed interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>frequency</strong> (float): <em>Default: 2.0</em>
<br>The number of cycles per second of the square wave, measured in Hertz.</li>
<li><strong>sample_rate</strong> (int): <em>Default: 100</em>
<br>The frequency at which data points are generated, measured in samples per second.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="sawtoothModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('sawtoothModal')">×</span>
<h4>Sawtooth Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The sawtooth wave approach generates a continuous stream of data points that linearly rise and then sharply drop, creating a wave that resembles the teeth of a saw. This method is useful for simulating linear changes followed by abrupt resets.</p>
<p><strong>How It Works:</strong>
<br>Generates data points that form a sawtooth wave, characterized by a linear rise followed by a sudden drop. The frequency determines the number of cycles per second. Data points are produced at a specified sample rate, with each point separated by a fixed interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>amplitude</strong> (float): <em>Default: 1.0</em>
<br>The distance from the height of a peak to the center line, representing the wave's peak value.</li>
<li><strong>frequency</strong> (float): <em>Default: 2.0</em>
<br>The number of cycles per second of the sawtooth wave, measured in Hertz.</li>
<li><strong>sample_rate</strong> (int): <em>Default: 100</em>
<br>The frequency at which data points are generated, measured in samples per second.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="normalModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('normalModal')">×</span>
<h4>Normal Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The normal distribution model generates a continuous stream of data points that follow a normal (Gaussian) distribution. This is useful for simulating naturally occurring phenomena and testing systems that expect normally distributed inputs.</p>
<p><strong>How It Works:</strong>
<br>Generates data points according to a normal distribution, characterized by a specific mean and standard deviation. Data points are produced at regular intervals, determined by the specified time interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>mean</strong> (float): <em>Default: 0</em>
<br>The mean of the normal distribution, representing the central value around which data points are distributed.</li>
<li><strong>std_dev</strong> (float): <em>Default: 1</em>
<br>The standard deviation of the normal distribution, indicating the spread or variability of the data points around the mean.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="uniformModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('uniformModal')">×</span>
<h4>Uniform Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The uniform distribution model generates a continuous stream of data points that are uniformly distributed between a specified minimum and maximum value. This model is useful for simulating scenarios where every value within a range has an equal chance of occurring.</p>
<p><strong>How It Works:</strong>
<br>Generates data points according to a uniform distribution, where each value between the specified minimum and maximum is equally likely to occur. Data points are produced at regular intervals, determined by the specified time interval.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>min_val</strong> (float): <em>Default: 0</em>
<br>The minimum value of the uniform distribution, representing the lower bound of the generated data points.</li>
<li><strong>max_val</strong> (float): <em>Default: 1</em>
<br>The maximum value of the uniform distribution, representing the upper bound of the generated data points.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="exponentialModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('exponentialModal')">×</span>
<h4>Exponential Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The exponential distribution model generates a continuous stream of data points where the time between events follows an exponential distribution. This model is useful for simulating scenarios where events occur continuously and independently at a constant average rate.</p>
<p><strong>How It Works:</strong>
<br>Generates data points according to an exponential distribution, determined by the specified scale parameter. Data points are produced at regular intervals, as defined by the time interval parameter.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>scale</strong> (float): <em>Default: 1.0</em>
<br>The inverse of the rate parameter, controlling the rate at which events occur in the exponential distribution.</li>
<li><strong>interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point, controlling the generation rate.</li>
</ul>
</div>
</div>
<div id="randomAnomalyModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('randomAnomalyModal')">×</span>
<h4>Random Anomalies Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The Random Anomalies approach is designed to generate a stream of data where each data point has a random chance of being an anomaly.</p>
<p><strong>How It Works:</strong>
<br>Data points are generated continuously at specified intervals. Each data point typically holds a constant base value. However, at each step, there is a predefined probability that the data point will be an anomaly. When an anomaly occurs, its value is randomly determined within a specified range around the base value.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>base_value</strong> (float): <em>Default: 0.0</em>
<br>This is the default value for data points when no anomaly is present. It represents the normal state of the system.</li>
<li><strong>anomaly_probability</strong> (float): <em>Default: 0.1</em>
<br>This parameter defines the likelihood of an anomaly occurring at any given data point. For example, a 0.1 probability means there is a 10% chance that each data point will be an anomaly.</li>
<li><strong>anomaly_range</strong> (float): <em>Default: (10.0)</em>
<br>This range determines how much the anomaly can deviate from the base value. Anomalies are randomly selected within this range, ensuring variability in the values.</li>
<li><strong>data_interval</strong> (float): <em>Default: 1.0</em>
<br>This is the time interval between consecutive data points. It controls the frequency of data generation, with a smaller interval producing data more rapidly.</li>
</ul>
</div>
</div>
<div id="countPerDurationModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('countPerDurationModal')">×</span>
<h4>Count Per Duration Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The count-per-duration approach introduces a specified number of anomalies within a fixed duration of 1 hour. This can be useful for simulating scenarios where anomalies are expected to appear within regular intervals.</p>
<p><strong>How It Works:</strong>
<br>Data points are continuously generated, and within a specified duration of one hour, a predetermined number of anomalies are introduced. This method also allows for control over the range of anomaly values, providing flexibility in the magnitude of deviations from the regular data points.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>base_value</strong> (float): <em>Default: 0.0</em>
<br>The base value for regular data points, representing the normal state of the data.</li>
<li><strong>num_anomalies</strong> (int): <em>Default: 1</em>
<br>The total number of anomalies to be introduced within the duration. </li>
<li><strong>min_anomaly_range</strong> (float): <em>Default: -10.0</em>
<br>The lower bound for how much an anomaly can deviate from the base value.</li>
<li><strong>max_anomaly_range</strong> (float): <em>Default: 10.0</em>
<br>The upper bound for how much an anomaly can deviate from the base value.</li>
<li><strong>data_interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point. It controls the frequency of data generation.</li>
</ul>
</div>
</div>
<div id="periodicSpikeModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('periodicSpikeModal')">×</span>
<h4>Periodic Spike Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The periodic-spike approach introduces spikes at regular intervals within a fixed duration of 1 hour. It is designed to simulate scenarios where sudden changes or spikes occur at predictable, consistent intervals.</p>
<p><strong>How It Works:</strong>
<br>Generates a continuous stream of data points, with spikes introduced at specified intervals within each hour-long cycle. For instance, introducing a spike every 20th minute within the hour. The spike values are randomly chosen within a specified range.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>base_value</strong> (float): <em>Default: 0.0</em>
<br>The constant value for regular data points, representing the normal state of the data.</li>
<li><strong>spike_interval</strong> (int): <em>Default: 1200</em>
<br>The interval at which spikes occur, specified in seconds. This determines how frequently the spikes will appear within the duration.</li>
<li><strong>min_spike_range</strong> (float): <em>Default: 5.0</em>
<br>The smallest possible deviation from the base value for a spike</li>
<li><strong>max_spike_range</strong> (float): <em>Default: 10.0</em>
<br>The largest possible deviation from the base value for a spike.</li>
<li><strong>data_interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point. It controls the frequency of data generation.</li>
</ul>
</div>
</div>
<div id="randomSquareModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('randomSquareModal')">×</span>
<h4>Random Square Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>The random-square approach generates positive or negative square wave anomalies at random intervals throughout the data stream. The anomalies occur unpredictably, both in terms of their start times and durations, creating irregular, spontaneous square waves.</p>
<p><strong>How It Works:</strong>
<br>Generates continous stream of data points, typically representing a base value. And at random intervals, a square wave anomaly is introduced by adding a specified magnitude to the base value for a random duration. The intervals between these anomalies and their durations are randomly determined, ensuring that the anomalies occur unpredictably.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>base_value</strong> (float): <em>Default: 0.0</em>
<br>The constant value for regular data points, representing the normal state of the data.</li>
<li><strong>anomaly_magnitude</strong> (float): <em>Default: 10.0</em>
<br>The amount by which the anomaly value deviates from the base value, introducing a significant change to the data point.</li>
<li><strong>minimum_interval</strong> (float): <em>Default: 50.0</em>
<br>The shortest possible time between consecutive waves of anomalies.</li>
<li><strong>maximum_interval</strong> (float): <em>Default: 150.0</em>
<br>The longest possible time between consecutive waves of anomalies.</li>
<li><strong>min_anomaly_duration</strong> (float): <em>Default: 5.0</em>
<br>The shortest duration that an anomaly wave can last.</li>
<li><strong>max_anomaly_duration</strong> (float): <em>Default: 10.0</em>
<br>The longest duration that an anomaly wave can last.</li>
<li><strong>data_interval</strong> (float): <em>Default: 1.0</em>
<br>The rate at which data points are generated. It controls the frequency of data generation.</li>
</ul>
</div>
</div>
<div id="clusteredModal" class="modal">
<div class="modal-content">
<span class="close" onclick="closeModal('clusteredModal')">×</span>
<h4>Clustered Endpoint Details</h4>
<p><strong>Overview:</strong>
<br>This approach introduces clusters of anomalies at random intervals throughout the data stream, where multiple anomalies occur in close succession. Clustered anomalies appear in groups, creating bursts of anomalous activity.</p>
<p><strong>How It Works:</strong>
<br>Generates a continuous stream of data, typically representing a base value. Anomalies occur in clusters at random intervals, with the timing and duration of each cluster randomized within specified ranges. Each anomaly within a cluster deviates from the base value by a magnitude chosen randomly within a defined range, creating varied and realistic anomaly patterns.</p>
<p><strong>Key Parameters:</strong></p>
<ul>
<li><strong>base_value</strong> (float): <em>Default: 0.0</em>
<br>The base value for regular data points, representing the normal state of the data.</li>
<li><strong>anomaly_magnitude</strong> (float): <em>Default: 10.0</em>
<br>The range within which each anomaly value can vary around the base value.</li>
<li><strong>minimum_interval</strong> (float): <em>Default: 50.0</em>
<br>The shortest possible interval between clusters of anomalies.</li>
<li><strong>maximum_interval</strong> (float): <em>Default: 150.0</em>
<br>The longest possible interval between clusters of anomalies.</li>
<li><strong>min_anomaly_length</strong> (float): <em>Default: 5.0</em>
<br>The minimum length of the anomalies in the cluster.</li>
<li><strong>max_anomaly_length</strong> (float): <em>Default: 10.0</em>
<br>The maximum length of the anomalies in the cluster.</li>
<li><strong>data_interval</strong> (float): <em>Default: 1.0</em>
<br>The time interval between each data point. It controls the frequency of data generation.</li>
</ul>
</div>
</div>
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</html>
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/static/styles.css | .graph {
width: 100%;
height: 400px;
margin: 20px 0;
}
.tabs {
display: flex;
flex-direction: column;
}
.tab-buttons {
display: flex;
justify-content: space-around;
}
.tab-buttons button {
padding: 10px;
margin: 5px;
cursor: pointer;
}
.tab-content {
display: none;
}
.tab-content.active {
display: block;
}
.modal {
display: none;
position: fixed;
z-index: 1;
left: 0;
top: 0;
width: 100%;
height: 100%;
overflow: auto;
background-color: rgb(0,0,0);
background-color: rgba(0,0,0,0.4);
padding-top: 60px;
}
.modal-content {
background-color: #fefefe;
margin: 5% auto;
padding: 20px;
border: 1px solid #888;
width: 80%;
}
.close {
color: #aaa;
float: right;
font-size: 28px;
font-weight: bold;
}
.close:hover,
.close:focus {
color: black;
text-decoration: none;
cursor: pointer;
}
.button-fixed-width {
width: 400px;
display: inline-block;
margin: 10px auto;
text-align: center;
}
.buttons-container {
text-align: center;
}
#copyTokenButton {
font-size: 12px;
padding: 5px 10px;
background-color: #007bff;
color: white;
border: none;
border-radius: 3px;
cursor: pointer;
width: 100px;
transition: background-color 0.3s ease;
}
#copyTokenButton:hover {
background-color: #0056b3;
}
#notification {
position: fixed;
bottom: 10px;
left: 50%;
transform: translateX(-50%);
background-color: #007bff;
color: white;
padding: 10px 20px;
border-radius: 5px;
opacity: 0;
visibility: hidden;
transition: opacity 0.5s ease, visibility 0.5s ease;
}
#notification.show {
opacity: 1;
visibility: visible;
}
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/static/token.js | document.addEventListener('DOMContentLoaded', function () {
const form = document.getElementById('register-form');
form.addEventListener('submit', async function (event) {
event.preventDefault();
const formData = new FormData(event.target);
const username = document.getElementById('username').value;
const password = document.getElementById('password').value;
console.log('Register form submitted');
console.log('Username:', username);
console.log('Password:', password);
try {
const response = await fetch('/register', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ username, password }),
});
console.log('Response status:', response.status);
console.log('Response OK:', response.ok);
const message = document.getElementById('register-message');
if (response.ok) {
const data = await response.json();
message.textContent = "Registration successful! Login with the registered credentials to generate a token.";
console.log('Registration successful:', data);
form.reset();
setTimeout(function () {
message.textContent = '';
}, 5000);
setTimeout(function () {
showSection('login-section');
}, 2000);
} else {
const contentType = response.headers.get('content-type');
if (contentType && contentType.indexOf('application/json') !== -1) {
const errorData = await response.json();
message.textContent = `Registration failed: ${errorData.detail}`;
console.log('Registration failed:', errorData.detail);
} else {
const errorText = await response.text();
message.textContent = `Registration failed: ${errorText}`;
console.log('Registration failed:', errorText);
}
}
} catch (error) {
document.getElementById('register-message').textContent = `Error: ${error}`;
console.log('Error:', error);
}
});
});
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/static/script.js | function openTab(event, tabName) {
const tabContents = document.getElementsByClassName('tab-content');
for (let i = 0; i < tabContents.length; i++) {
tabContents[i].classList.remove('active');
}
document.getElementById(tabName).classList.add('active');
}
function openModal(modalId) {
document.getElementById(modalId).style.display = "block";
}
function closeModal(modalId) {
document.getElementById(modalId).style.display = "none";
}
function showSection(sectionId) {
document.getElementById('register-section').style.display = 'none';
document.getElementById('login-section').style.display = 'none';
document.getElementById(sectionId).style.display = 'block';
}
document.addEventListener('DOMContentLoaded', function() {
showSection('register-section'); // Show register section by default
}); | 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/static/login.js | document.addEventListener('DOMContentLoaded', function () {
const loginForm = document.getElementById('login-form');
const loginMessage = document.getElementById('login-message');
const modalTokenMessage = document.getElementById('modal-token-message');
const modalTokenExpiry = document.getElementById('modal-token-expiry');
const copyTokenButton = document.getElementById('copyTokenButton');
loginForm.addEventListener('submit', async function (event) {
event.preventDefault();
const username = document.getElementById('login-username').value;
const password = document.getElementById('login-password').value;
try {
const response = await fetch('/token', {
method: 'POST',
headers: {
'Content-Type': 'application/x-www-form-urlencoded',
},
body: new URLSearchParams({ username, password }),
});
console.log('Response status:', response.status);
console.log('Response OK:', response.ok);
if (response.ok) {
const data = await response.json();
modalTokenMessage.textContent = `${data.access_token}`;
modalTokenExpiry.textContent = `Token expires in 7 days.`;
console.log('Token generated:', data.access_token);
openModal('tokenModal');
loginForm.reset();
loginMessage.textContent = '';
copyTokenButton.addEventListener('click', function () {
copyToClipboard(data.access_token);
});
} else {
const contentType = response.headers.get('content-type');
if (contentType && contentType.indexOf('application/json') !== -1) {
const errorData = await response.json();
loginMessage.textContent = `Login failed: ${errorData.detail}`;
console.log('Login failed:', errorData.detail);
} else {
const errorText = await response.text();
loginMessage.textContent = `Login failed: ${errorText}`;
console.log('Login failed:', errorText);
}
}
} catch (error) {
loginMessage.textContent = `Error: ${error}`;
console.log('Error:', error);
}
});
});
function copyToClipboard(text) {
const tempInput = document.createElement('input');
tempInput.style.position = 'absolute';
tempInput.style.left = '-9999px';
tempInput.value = text;
document.body.appendChild(tempInput);
tempInput.select();
document.execCommand('copy');
document.body.removeChild(tempInput);
notification.classList.add('show');
setTimeout(function () {
notification.classList.remove('show');
}, 2000);
} | 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/db_utils/models.py | """
SQLAlchemy User model definition.
This script defines the User model for the database, including the columns and table name.
"""
from sqlalchemy import Column, Integer, String
from app.db_utils.database import Base
class User(Base):
"""
User model for the SQLAlchemy ORM.
Attributes:
id (int): The primary key for the user.
username (str): The unique username of the user.
hashed_password (str): The hashed password of the user.
"""
__tablename__ = "users"
id = Column(Integer, primary_key=True)
username = Column(String, unique=True, index=True)
hashed_password = Column(String)
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/db_utils/database.py | """
This script defines the database connection and session for
the Streaming Data Generator application.
It uses SQLAlchemy to create and manage the database connection and session.
"""
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
SQLALCHEMY_DATABASE_URL = "sqlite:///./sql_app.db"
engine = create_engine(
SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False}
)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/db_utils/schemas.py | """
Pydantic models for User operations.
This script defines the Pydantic models used for user-related operations.
"""
from pydantic import BaseModel
class UserBase(BaseModel):
"""
Base model for user information.
Attributes:
username (str): The username of the user.
"""
username: str
class UserCreate(UserBase):
"""
Model for creating a new user.
Attributes:
username (str): The username of the user.
password (str): The password of the user.
"""
password: str
class User(UserBase):
"""
Complete user model.
Attributes:
username (str): The username of the user.
"""
class Config:
"""
Pydantic configuration for ORM mode.
"""
from_attributes = True
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/db_utils/crud.py | """
This module provides utility functions for user authentication and token management.
"""
import os
from dotenv import load_dotenv
from datetime import datetime, timedelta, timezone
import logging
from typing import Union
import jwt
from fastapi import APIRouter, Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContext
from sqlalchemy.orm import Session
from app.db_utils import models, schemas
from app.models.auth_model import TokenData
logger = logging.getLogger(__name__)
router = APIRouter()
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
load_dotenv()
# To generate SECRET_KEY run: openssl rand -hex 32
SECRET_KEY = os.getenv("SECRET_KEY")
ALGORITHM = "HS256"
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
def verify_password(plain_password, hashed_password):
"""
Verify if a plain password matches a hashed password.
Args:
plain_password (str): The plain password to be verified.
hashed_password (str): The hashed password to be compared against.
Returns:
bool: True if the plain password matches the hashed password, False otherwise.
"""
return pwd_context.verify(plain_password, hashed_password)
def get_password_hash(password):
"""
Generates a hash of the provided password using the password hashing context.
Args:
password (str): The password to be hashed.
Returns:
str: The hashed password.
"""
return pwd_context.hash(password)
def get_user(db: Session, user_id: int):
"""
Retrieves a user from the database based on the provided user_id.
Args:
db (Session): The database session.
user_id (int): The unique identifier of the user to retrieve.
Returns:
User: The user corresponding to the provided user_id, or None if not found.
"""
return db.query(models.User).filter(models.User.id == user_id).first()
def get_user_by_username(db: Session, username: str):
"""
Retrieves a user from the database based on the provided username.
Args:
db (Session): The database session.
username (str): The username of the user to retrieve.
Returns:
User: The user corresponding to the provided username, or None if not found.
"""
user = db.query(models.User).filter(models.User.username == username).first()
return user
def get_users(db: Session, skip: int = 0, limit: int = 100):
"""
Retrieves a list of users from the database.
Args:
db (Session): The database session.
skip (int, optional): The number of users to skip. Defaults to 0.
limit (int, optional): The maximum number of users to retrieve. Defaults to 100.
Returns:
list: A list of User objects representing the retrieved users.
"""
return db.query(models.User).offset(skip).limit(limit).all()
def create_user(db: Session, user: schemas.UserCreate):
"""
Creates a new user in the database.
Args:
db (Session): The database session.
user (UserCreate): The user data to create.
Returns:
User: The newly created user in the database.
"""
hashed_password = get_password_hash(user.password)
db_user = models.User(username=user.username, hashed_password=hashed_password)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def create_access_token(data: dict, expires_delta: Union[timedelta, None] = None):
"""
Creates an access token with the given data and optional expiration time.
Args:
data (dict): The data to be encoded into the token.
expires_delta (Union[timedelta, None], optional): The expiration time of the token.
If None, the token will expire after 7 days. Defaults to None.
Returns:
str: The encoded JWT access token.
"""
to_encode = data.copy()
if expires_delta:
expire = datetime.now(timezone.utc) + expires_delta
else:
expire = datetime.now(timezone.utc) + timedelta(days=7)
to_encode.update({"exp": expire})
encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
return encoded_jwt
def verify_token(token: str = Depends(oauth2_scheme)) -> TokenData:
"""
Verify the provided JWT token and return the username contained in the payload.
Args:
token (str, optional): The JWT token to be verified.
Defaults to the result of the `oauth2_scheme` dependency.
Returns:
TokenData: An object containing the username extracted from the token payload.
"""
try:
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
username: str = payload.get("sub")
if username is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Could not validate credentials",
headers={"WWW-Authenticate": "Bearer"},
)
token_data = TokenData(username=username)
except InvalidTokenError as exc:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Incorrect token",
headers={"WWW-Authenticate": "Bearer"},
) from exc
return token_data
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/cosine.py | """
Module for generating a cosine wave data stream based on the given CosineModel parameters.
"""
import asyncio
import logging
import numpy as np
from app.models.waveform_models import CosineModel
logger = logging.getLogger(__name__)
async def generate_cosine_data(cosine_model: CosineModel):
"""
Generates a cosine wave data stream based on the given Cosine model parameters.
Args:
cosine_model (CosineModel): The model containing the parameters
for generating the cosine wave.
Yields:
str: A string representation of a data point in the cosine wave.
"""
time_index = 0
while True:
try:
data_point = cosine_model.amplitude * np.cos(
2 * np.pi * cosine_model.frequency * (time_index / cosine_model.sample_rate)
)
yield f"{data_point:.3f}\n"
await asyncio.sleep(cosine_model.interval)
time_index += 1
except ValueError as value_error:
logger.error("Value error occurred while generating cosine wave: %s", value_error)
continue
except asyncio.CancelledError:
logger.info("Cosine wave generation was cancelled.")
break
except Exception as error:
logger.exception("An unexpected error occurred while generating cosine wave: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/normal.py | """
Module for generating a normal distribution data stream based on the given NormalModel parameters.
"""
import asyncio
import logging
from scipy.stats import norm
from app.models.distribution_models import NormalModel
logger = logging.getLogger(__name__)
async def generate_normal_data(normal_model: NormalModel):
"""
Generates a normal distribution data stream based on the given Normal model parameters.
Args:
normal_model (NormalModel): The model containing the parameters for
generating the normal distribution.
Yields:
str: A string representation of a data point in the normal distribution.
"""
while True:
try:
data_point = norm.rvs(loc=normal_model.mean, scale=normal_model.std_dev)
yield f"{data_point:.3f}\n"
await asyncio.sleep(normal_model.interval)
except ValueError as value_error:
logger.error(
"Value error occurred while generating normal distribution: %s", value_error)
continue
except asyncio.CancelledError:
logger.info("Normal distribution data generation was cancelled.")
break
except Exception as error:
logger.exception(
"An unexpected error occurred while generating normal distribution data: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/sine.py | """
Module for generating a sine wave data stream based on the given SineModel parameters.
"""
import asyncio
import logging
import numpy as np
from app.models.waveform_models import SineModel
logger = logging.getLogger(__name__)
async def generate_sine_data(sine_model: SineModel):
"""
Generates a sine wave data stream based on the given Sine model parameters.
Args:
sine_model (SineModel): The model containing the parameters for generating the sine wave.
Yields:
str: A string representation of a data point in the sine wave.
"""
time_index = 0
while True:
try:
data_point = sine_model.amplitude * np.sin(
2 * np.pi * sine_model.frequency * (time_index / sine_model.sample_rate)
+ sine_model.phase
)
yield f"{data_point:.3f}\n"
await asyncio.sleep(sine_model.interval)
time_index += 1
except ValueError as value_error:
logger.error(
"Value error occurred while generating sine wave: %s", value_error
)
continue
except asyncio.CancelledError:
logger.info("Sine wave generation was cancelled.")
break
except Exception as error:
logger.exception("An unexpected error occurred while generating sine wave: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/exponential.py | """
Module for generating an exponential distribution data stream based on the
given ExponentialModel parameters.
"""
import asyncio
import logging
from scipy.stats import expon
from app.models.distribution_models import ExponentialModel
logger = logging.getLogger(__name__)
async def generate_exponential_data(exponential_model: ExponentialModel):
"""
Generates exponential data stream based on the given Exponential model parameters.
Parameters:
exponential_model (ExponentialModel): The model containing the parameters for
generating the exponential data.
Yields:
str: A string representation of a data point in the exponential distribution.
"""
while True:
try:
data_point = expon.rvs(scale=exponential_model.scale)
yield f"{data_point:.3f}\n"
await asyncio.sleep(exponential_model.interval)
except ValueError as value_error:
logger.error(
"Value error occurred while generating exponential distribution: %s",
value_error,
)
continue
except asyncio.CancelledError:
logger.info("Exponential distribution data generation was cancelled.")
break
except Exception as error:
logger.exception(
"An unexpected error occurred while generating exponential distribution data: %s",
error,
)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/uniform.py | """
Module for generating an uniform distribution data stream based on
the given UniformModel parameters.
"""
import asyncio
import logging
from scipy.stats import uniform
from app.models.distribution_models import UniformModel
logger = logging.getLogger(__name__)
async def generate_uniform_data(uniform_model: UniformModel):
"""
Generates a uniform distribution data based on the provided Uniform model parameters.
Args:
uniform_model (UniformModel): The model containing the parameters for
generating the uniform data.
Yields:
str: A string representation of a data point in the uniform distribution.
"""
while True:
try:
data_point = uniform.rvs(
loc=uniform_model.min_val,
scale=uniform_model.max_val - uniform_model.min_val,
)
yield f"{data_point:.3f}\n"
await asyncio.sleep(uniform_model.interval)
except ValueError as value_error:
logger.error(
"Value error occurred while generating uniform distribution: %s",
value_error,
)
continue
except asyncio.CancelledError:
logger.info("Uniform distribution data generation was cancelled.")
break
except Exception as error:
logger.exception(
"An unexpected error occurred while generating uniform distribution data: %s",
error,
)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/square.py | """
Module for generating a square wave data stream based on the given SquareModel parameters.
"""
import asyncio
import logging
import numpy as np
from scipy.signal import square
from app.models.waveform_models import SquareModel
logger = logging.getLogger(__name__)
async def generate_square_data(square_model: SquareModel):
"""
Generates square wave data based on the given Square model parameters.
Args:
square_model (SquareModel): The model containing the parameters for
generating the square wave.
Yields:
str: A string representation of a data point in the square wave.
"""
time_index = 0
while True:
try:
data_point = square(
2 * np.pi *
square_model.frequency *
(time_index / square_model.sample_rate)
)
yield f"{data_point:.3f}\n"
await asyncio.sleep(square_model.interval)
time_index += 1
except ValueError as value_error:
logger.error(
"Value error occurred while generating square wave: %s", value_error
)
continue
except asyncio.CancelledError:
logger.info("Square wave generation was cancelled.")
break
except Exception as error:
logger.exception("An unexpected error occurred while generating square wave: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app | qxf2_public_repos/streaming-data-gen/app/generators/sawtooth.py | """
Module for generating a sawtooth wave data stream based on the given SawtoothModel parameters.
"""
import asyncio
import logging
import numpy as np
from app.models.waveform_models import SawtoothModel
logger = logging.getLogger(__name__)
async def generate_sawtooth_data(sawtooth_model: SawtoothModel):
"""
Generates a sawtooth wave data stream based on the given Sawtooth model parameters.
Args:
sawtooth_model (SawtoothModel): The model containing the parameters for
generating the Sawtooth distribution.
Yields:
str: A string representation of a data point in the Sawtooth distribution.
"""
time_index = 0
while True:
try:
cycles = (
2 * np.pi *
sawtooth_model.frequency *
(time_index / sawtooth_model.sample_rate)
)
data_point = sawtooth_model.amplitude * (cycles - np.floor(cycles))
yield f"{data_point:.3f}\n"
await asyncio.sleep(sawtooth_model.interval)
time_index += 1
except ValueError as value_error:
logger.error(
"Value error occurred while generating sawtooth wave: %s", value_error
)
continue
except asyncio.CancelledError:
logger.info("Sawtooth wave generation was cancelled.")
break
except Exception as error:
logger.exception("An unexpected error occurred while generating sawtooth wave: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app/generators | qxf2_public_repos/streaming-data-gen/app/generators/anomalies/random_anomaly.py | """
Module for generating a continuous stream of data points with random anomalies
based on specified parameters.
"""
import random
import asyncio
import logging
from app.models.anomaly_models import RandomAnomalyModel
logger = logging.getLogger(__name__)
async def generate_random_anomalies(random_anomaly: RandomAnomalyModel):
"""
Generates data points with random anomalies.
Args:
random_anomaly (RandomAnomalyModel): The model containing the parameters
for generating the random anomalies.
Yields:
str: A string representation of a data point.
"""
try:
while True:
if random.random() < random_anomaly.anomaly_probability:
anomaly = random.uniform(
-random_anomaly.anomaly_range, random_anomaly.anomaly_range
)
anomaly_value = random_anomaly.base_value + anomaly
else:
anomaly_value = random_anomaly.base_value
yield f"{anomaly_value:.3f}\n"
await asyncio.sleep(random_anomaly.data_interval)
except asyncio.CancelledError:
logger.info("Data generation was cancelled.")
except Exception as error:
logger.exception("Error occurred while generating data: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app/generators | qxf2_public_repos/streaming-data-gen/app/generators/anomalies/count_duration.py | """
Module for generating a specified number of anomalies within a fixed duration (1 hour).
"""
import random
import asyncio
import logging
import itertools
from app.models.anomaly_models import CountBasedAnomalyModel
logger = logging.getLogger(__name__)
async def generate_count_based_anomalies_data(
count_based_anomaly: CountBasedAnomalyModel,
):
"""
Generates data points with specified number of anomalies within a duration of 1 hour.
Args:
count_based_anomaly (CountBasedAnomalyModel): The model containing the parameters
used for generating the data.
Yields:
str: A string representation of a data point.
"""
try:
total_time_seconds = 30
anomaly_start_times = sorted(
random.sample(range(total_time_seconds), count_based_anomaly.num_anomalies)
)
for x in itertools.count():
current_time = x % total_time_seconds
if current_time in anomaly_start_times:
anomaly_value = random.uniform(
count_based_anomaly.min_anomaly_range,
count_based_anomaly.max_anomaly_range,
)
yield f"{anomaly_value:.3f}\n"
else:
yield f"{count_based_anomaly.base_value:.3f}\n"
await asyncio.sleep(count_based_anomaly.data_interval)
except asyncio.CancelledError:
logger.info("Data generation was cancelled.")
except Exception as error:
logger.exception("Error occurred while generating data: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app/generators | qxf2_public_repos/streaming-data-gen/app/generators/anomalies/periodic_spike.py | """
Module for generateing data points with regular spikes occurring at
specified interval within a duration of 1 hour.
"""
import random
import asyncio
import logging
import itertools
from app.models.anomaly_models import SpikeAnomalyModel
logger = logging.getLogger(__name__)
async def generate_periodic_spike_data(spike_anomaly: SpikeAnomalyModel):
"""
Generates data points with spikes at regular intervals.
Args:
spike_anomaly (SpikeAnomalyModel): The model containing the parameters
used for generating the data.
Yields:
str: A string representation of a data point.
"""
try:
total_time_seconds = 30
for each_val in itertools.count():
current_time = (each_val + 1) % total_time_seconds
if current_time % spike_anomaly.spike_interval == 0 and current_time != 0:
spike_value = random.uniform(spike_anomaly.min_spike_range, spike_anomaly.max_spike_range)
yield f"{spike_value:.3f}\n"
else:
yield f"{spike_anomaly.base_value:.3f}\n"
await asyncio.sleep(spike_anomaly.data_interval)
except asyncio.CancelledError:
logger.info("Data generation was cancelled.")
except Exception as error:
logger.exception("Error occurred while generating data: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app/generators | qxf2_public_repos/streaming-data-gen/app/generators/anomalies/clustered.py | """
Module for generating clustered anomalies occuring at random intervals.
"""
import random
import asyncio
import logging
from app.models.anomaly_models import ClusteredAnomalyModel
logger = logging.getLogger(__name__)
async def generate_clustered_anomalies(clustered_model: ClusteredAnomalyModel):
"""
Generates data points with clustered anomalies.
Args:
clustered_model (ClusteredAnomalyModel): The model containing the parameters
used for generating the data.
Yields:
str: A string representation of a data point.
"""
anomaly_countdown = random.randint(clustered_model.minimum_interval, clustered_model.maximum_interval)
anomaly_length = 0
try:
while True:
if anomaly_countdown <= 0:
anomaly_countdown = random.randint(
clustered_model.minimum_interval, clustered_model.maximum_interval
)
anomaly_length = random.randint(
clustered_model.min_anomaly_length,
clustered_model.max_anomaly_length,
)
if anomaly_length > 0:
anomaly_value = clustered_model.constant_value + random.uniform(
-clustered_model.anomaly_magnitude,
clustered_model.anomaly_magnitude,
)
yield f"{anomaly_value:.3f}\n"
anomaly_length -= 1
else:
yield f"{clustered_model.constant_value:.3f}\n"
anomaly_countdown -= 1
await asyncio.sleep(clustered_model.data_interval)
except asyncio.CancelledError:
logger.info("Data generation was cancelled.")
except Exception as error:
logger.exception("Error occurred while generating data: %s", error)
raise
| 0 |
qxf2_public_repos/streaming-data-gen/app/generators | qxf2_public_repos/streaming-data-gen/app/generators/anomalies/random_square.py | """
Module for generating random positive square wave anomalies with
varying durations at irregular intervals.
"""
import random
import asyncio
import logging
from app.models.anomaly_models import RandomSquareModel
logger = logging.getLogger(__name__)
async def generate_random_square(square_model: RandomSquareModel):
"""
Generates data points with random square wave anomalies.
Yields:
str: A string representation of a data point.
"""
anomaly_countdown = random.randint(
square_model.minimum_interval, square_model.maximum_interval
)
anomaly_end = 0
try:
while True:
if anomaly_countdown <= 0:
anomaly_countdown = random.randint(
square_model.minimum_interval, square_model.maximum_interval
)
anomaly_duration = random.randint(
square_model.min_anomaly_duration, square_model.max_anomaly_duration
)
anomaly_end = anomaly_duration
if anomaly_end > 0:
anomaly_value = square_model.base_value + square_model.anomaly_magnitude
yield f"{anomaly_value:.3f}\n"
anomaly_end -= 1
else:
yield f"{square_model.base_value:.3f}\n"
anomaly_countdown -= 1
await asyncio.sleep(square_model.data_interval)
except asyncio.CancelledError:
logger.info("Data generation was cancelled.")
except Exception as error:
logger.exception("Error occurred while generating data: %s", error)
raise
| 0 |
qxf2_public_repos | qxf2_public_repos/acc-model-app/LICENSE | MIT License
Copyright (c) 2024 sravantit25
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| 0 |
qxf2_public_repos | qxf2_public_repos/acc-model-app/README.md | # ACC Model App
## Overview
The ACC Model App is a web application for managing Attributes, Components, and Capabilities (ACC) for software projects. It provides a user-friendly interface for evaluating various capabilities against defined attributes and viewing aggregated ratings across users.
## Background
This project is built around the concept of ACC (Attributes, Components, Capabilities) modeling, originally introduced by Google. ACC modeling offers a streamlined approach to defining, testing, and assessing the essential elements of your project. This model replaces conventional test plans by providing a more dynamic, comprehensive, and easily maintainable framework. It helps you visualize the intersections between Attributes and Components, making it easier to identify gaps, prioritize testing efforts, and ensure that your project meets its intended quality standards.
ACC modeling is utilized to break down a software project into three key parts:
**Attributes**: The qualities or characteristics (adjectives) that describe the desired properties of your product, such as "Accuracy," "Responsiveness," or "User-Friendly."
**Components**: The major sections or building blocks (nouns) of your product, such as "User Management", "Shopping Cart" etc.
**Capabilities**: The specific features or functionalities (verbs) that each Component has such as "Add User", "Edit User" etc.
## What Does This Application Do?
This application allows you to efficiently construct, manage, and assess an ACC model for your project. It enables you to:
- **Define Attributes:** Specify the adjectives that describe the core qualities of your product.
- **List Components:** Break down your project into its major sections or modules.
- **Map Capabilities:** Identify features of each Component
- **Rate Capabiltiies:** Evaluate and rate the effectivenss of each Capability.
- **View Ratings:** See a consolidated view of ratings for each Capability to better understand overall performance.
## Technologies Used
- **Frontend:** React, Material-UI
- **Backend:** FastAPI
- **Database:** Postgres
- **Authentication:** JWT (JSON Web Tokens)
## Prerequisites
- **Node.js** (version 14.x or later)
- **Python** (version 3.8 or later)
- **FastAPI** and other Python dependencies
- **React** and other JavaScript dependencies
- **PostgreSQL**
## Setup Instructions (for Ubuntu)
To set up the project locally, follow these instructions:
1. Clone the repository:
```bash
git clone https://github.com/qxf2/acc-model-app.git
cd acc-model-app
2. Generate 'SECRET_KEY'.
This is essential for cryptograhic signing within the application. To generate the key, run the following command in your terminal:
```bash
openssl rand -hex 32
```
3. Set 'SECRET_KEY' as Environment Variable.
* Create a '.env' file in the project root
* Add the following line to '.env' replacing 'your_secret_key_value_here' with the generated 'SECRET_KEY'"
```bash
SECRET_KEY=your_secret_key_value_here
```
* Ensure '.env' is listed in your '.gitignore' file to prevent it from being committed to version control.
### PostgreSQL Setup
1. Install PostgreSQL(if you don't have it already):
```bash
sudo apt update
sudo apt install postgresql postgresql-contrib
```
Verify the installation:
```bash
psql --version
```
2. Start the PostgreSQL service:
```bash
sudo service postgresql start
sudo service postgresql status
3. Setup a new PostgreSQL user and database:
Log in as the default postgres user:
```bash
sudo -u postgres psql
```
Inside the psql prompt, run the following commands to create a new user and database:
```bash
CREATE USER your_username WITH PASSWORD 'your_password';
CREATE DATABASE your_db_name;
GRANT ALL PRIVILEGES ON DATABASE your_db_name TO your_username;
\q
4. Verify the connection.
```bash
psql -U your_username -d your_db_name -h 127.0.0.1 -W
```
When prompted, enter the password you set for the user. If the connection is successful, you will see a PostgreSQL prompt (e.g., your_db_name=#), indicating that you can interact with the database and run queries.
5. Update the .env file
Once you have created the PostgreSQL user and database, you'll need to specify the connection details in the .env file. The DATABASE_URL is a string that defines how your application connects to the PostgreSQL database, including the username, password, host, and database name.
Add the following line to your .env file, replacing the placeholders with your actual values:
```bash
DATABASE_URL='postgresql://your_username:your_password@localhost/your_db_name'
### Backend (FastAPI)
1. Navigate to the backend directory:
```bash
cd backend
2. Create a virtual environment and activate it:
```bash
python3.11 -m venv venv
source venv/bin/activate
3. Install the required Python packages:
```bash
pip install -r requirements.txt
4. Set up database migrations with Alembic:
**Note**: The migration files for the initial setup have already been generated and are included in the repository. You only need to apply the migration.
To apply existing migrations, run (from the backend folder):
```bash
alembic upgrade head
5. Run the backend server:
```bash
cd backend
uvicorn app.main:app --reload
The API will be available at `http://127.0.0.1:8000`.
For a full list of available endpoints, refer to the FastAPI auto-generated docs at `http://127.0.0.1:8000/docs`.
### Frontend (React)
1. Navigate to the frontend directory:
```bash
cd frontend
2. Install the required Node.js packages:
```bash
npm install
3. Create a `.env` file with the following:
```bash
REACT_APP_API_BASE_URL=http://localhost:8000
4. Run the React development server:
```bash
npm start
The application will be available at `http://localhost:3000`.
## Usage
1. Access the application by navigating to `http://localhost:3000` in your web browser.
2. Register as a new user by using the Registration page.
3. Login with your credentials. (Note that there is no 'Forgot Password' option at the moment.)
4. If starting new, create ACC Model to begin with. Then divide your project into Components and then define the Capabilities. All these can be done on the respective pages (ACC Models, Components, Capabilities, Attributes) which are available under the dropdown menu (present) on the Navigation bar.
5. To provide ratings for your capabilities, use the Ratings page.
## App Setup Instructions (using Docker)
To set up the project locally, follow these instructions:
## Prerequisites
- **Docker**
- **Docker Compose**
## Steps to Run the App Using Docker
1. Clone the repository:
```bash
git clone https://github.com/qxf2/acc-model-app.git
cd acc-model-app
2. Generate 'SECRET_KEY'.
This is essential for cryptograhic signing within the application. To generate the key, run the following command in your terminal:
```bash
openssl rand -hex 32
```
3. Set 'SECRET_KEY' as Environment Variable.
* Create a '.env' file in the project root
* Add the following lines to '.env'
```bash
SECRET_KEY=your_secret_key_value_here
POSTGRES_USER=your_username
POSTGRES_PASSWORD=your_password
POSTGRES_DB=your_databasename
DATABASE_URL="postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@database:5432/${POSTGRES_DB}"
```
4. Navigate to the frontend folder and create another .env file with the following
```bash
REACT_APP_API_BASE_URL=http://localhost:8000
```
* Ensure '.env' is listed in your '.gitignore' file to prevent it from being committed to version control.
5. Navigate to the root directory and run the following command to start the app and all associated services.
```bash
docker-compose up --build
```
Once the services are up and running, you can access the application through the following URLs:
Frontend: http://localhost:3000
Backend: http://localhost:8000/docs
### License
This project is licensed under the MIT Licesnse. See the `LICENSE` for more details.
| 0 |
qxf2_public_repos | qxf2_public_repos/acc-model-app/docker-compose.yml | version: "3.8"
services:
backend:
build:
context: ./backend
ports:
- "8000:8000"
env_file:
- .env
depends_on:
database:
condition: service_healthy
command: >
sh -c "sleep 10 && alembic upgrade head && uvicorn app.main:app --reload --host 0.0.0.0 --port 8000"
networks:
- acc_network
frontend:
build:
context: ./frontend
ports:
- "3000:3000"
env_file:
- .env
networks:
- acc_network
database:
image: postgres:14
environment:
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
POSTGRES_DB: ${POSTGRES_DB}
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
interval: 5s
timeout: 5s
retries: 5
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
networks:
- acc_network
volumes:
postgres_data:
networks:
acc_network:
| 0 |
qxf2_public_repos/acc-model-app | qxf2_public_repos/acc-model-app/frontend/Dockerfile | # Use Node.js as the base image
FROM node:18
# Set working directory
WORKDIR /app
# Copy frontend code
COPY . /app
# Install dependencies
RUN npm install
# Build the React app for production
RUN npm run build
# Expose the React port
EXPOSE 3000
# Serve the production build
CMD ["npx", "serve", "-s", "build", "-l", "3000"]
| 0 |
qxf2_public_repos/acc-model-app | qxf2_public_repos/acc-model-app/frontend/README.md | # Getting Started with Create React App
This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
## Available Scripts
In the project directory, you can run:
### `npm start`
Runs the app in the development mode.\
Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
The page will reload when you make changes.\
You may also see any lint errors in the console.
### `npm test`
Launches the test runner in the interactive watch mode.\
See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
### `npm run build`
Builds the app for production to the `build` folder.\
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.\
Your app is ready to be deployed!
See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
### `npm run eject`
**Note: this is a one-way operation. Once you `eject`, you can't go back!**
If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
## Learn More
You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
To learn React, check out the [React documentation](https://reactjs.org/).
### Code Splitting
This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
### Analyzing the Bundle Size
This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
### Making a Progressive Web App
This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
### Advanced Configuration
This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
### Deployment
This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
### `npm run build` fails to minify
This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
| 0 |
qxf2_public_repos/acc-model-app | qxf2_public_repos/acc-model-app/frontend/package-lock.json | {
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| 0 |
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"chartjs-plugin-datalabels": "^2.2.0",
"react": "^18.3.1",
"react-chartjs-2": "^5.2.0",
"react-dom": "^18.3.1",
"react-router-dom": "^6.26.0",
"react-scripts": "5.0.1",
"web-vitals": "^2.1.4"
},
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject"
},
"eslintConfig": {
"extends": [
"react-app",
"react-app/jest"
]
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"devDependencies": {
"@babel/plugin-proposal-private-property-in-object": "^7.21.11"
}
}
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/public/index.html | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="theme-color" content="#000000" />
<meta
name="description"
content="Web site created using create-react-app"
/>
<link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
<!--
manifest.json provides metadata used when your web app is installed on a
user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
-->
<link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
<!--
Notice the use of %PUBLIC_URL% in the tags above.
It will be replaced with the URL of the `public` folder during the build.
Only files inside the `public` folder can be referenced from the HTML.
Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
work correctly both with client-side routing and a non-root public URL.
Learn how to configure a non-root public URL by running `npm run build`.
-->
<title>ACC Model App</title>
</head>
<body>
<noscript>You need to enable JavaScript to run this app.</noscript>
<div id="root"></div>
<!--
This HTML file is a template.
If you open it directly in the browser, you will see an empty page.
You can add webfonts, meta tags, or analytics to this file.
The build step will place the bundled scripts into the <body> tag.
To begin the development, run `npm start` or `yarn start`.
To create a production bundle, use `npm run build` or `yarn build`.
-->
</body>
</html>
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/public/manifest.json | {
"short_name": "React App",
"name": "Create React App Sample",
"icons": [
{
"src": "favicon.ico",
"sizes": "64x64 32x32 24x24 16x16",
"type": "image/x-icon"
},
{
"src": "logo192.png",
"type": "image/png",
"sizes": "192x192"
},
{
"src": "logo512.png",
"type": "image/png",
"sizes": "512x512"
}
],
"start_url": ".",
"display": "standalone",
"theme_color": "#000000",
"background_color": "#ffffff"
}
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/public/robots.txt | # https://www.robotstxt.org/robotstxt.html
User-agent: *
Disallow:
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/reportWebVitals.js | const reportWebVitals = onPerfEntry => {
if (onPerfEntry && onPerfEntry instanceof Function) {
import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {
getCLS(onPerfEntry);
getFID(onPerfEntry);
getFCP(onPerfEntry);
getLCP(onPerfEntry);
getTTFB(onPerfEntry);
});
}
};
export default reportWebVitals;
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/App.css | .App {
text-align: left;
width: 100%;
}
.App-logo {
height: 40vmin;
pointer-events: none;
}
@media (prefers-reduced-motion: no-preference) {
.App-logo {
animation: App-logo-spin infinite 20s linear;
}
}
.App-header {
align-items: flex-start;
justify-content: flex-start;
padding: 2rem;
}
.App-link {
color: #61dafb;
}
@keyframes App-logo-spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
.fixed-column-capability {
font-size: 1.125rem;
}
.custom-container {
margin-top: 2rem;
padding: 0.25rem;
width: 100%;
}
.PageContainer {
display: flex;
flex-direction: column;
align-items: flex-start;
justify-content: flex-start;
width: 100%;
padding: 1rem 2rem;
}
.toolpad-layout {
padding: 0;
width: 100%;
max-width: none;
}
.table-container {
width: 100%;
overflow-x: auto;
}
.custom-divider {
background-color: white;
}
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/index.js | import React from 'react';
import ReactDOM from 'react-dom/client';
import './index.css';
import App from './App';
import { ThemeProvider } from '@mui/material/styles';
import reportWebVitals from './reportWebVitals';
import theme from './theme';
const root = ReactDOM.createRoot(document.getElementById('root'));
root.render(
<React.StrictMode>
<ThemeProvider theme={theme}>
<App />
</ThemeProvider>
</React.StrictMode>
);
// If you want to start measuring performance in your app, pass a function
// to log results (for example: reportWebVitals(console.log))
// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals
reportWebVitals();
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/theme.js | import { createTheme } from "@mui/material/styles";
const theme = createTheme({
palette: {
primary: {
main: "#4B8077", // Sage green
light: "#8BB3A1", // Lighter sage green
dark: "#2E5D53", // Darker sage green
},
secondary: {
main: "#FF6F61", // Coral
light: "#FF8A75", // Lighter coral
dark: "#D75948", // Darker coral
},
background: {
default: "#F2F8F6", // Light sage green for main background
paper: "#E4EFED", // Slightly darker sage for card background
},
text: {
primary: "#2E403B", // Dark green-gray for primary text
secondary: "#5F7269", // Medium gray-green for secondary text
},
table: {
header: {
background: "#C7D9D3", // Dark sage green for table header
color: "#C7D9D3",
},
row: {
background: "#E5EFEA", // Dark sage green for table row
},
},
action: {
active: "#3A756B", // Dark sage green for active icons or links
},
tableHeader: {
background: "#E0F2EF", // Dark sage green for table header
color: "#283593",
},
custom: {
itemBackground: "#E0F2EF",
},
},
typography: {
fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif',
button: {
textTransform: "none",
},
},
components: {
MuiTableCell: {
styleOverrides: {
head: {
backgroundColor: "#E0F2EF",
color: "#283593",
fontWeight: "bold",
padding: "0.5rem",
},
},
},
MuiCard: {
styleOverrides: {
root: {
padding: "16px",
borderRadius: "8px",
boxShadow: "0px 4px 12px rgba(0, 0, 0, 0.1)",
backgroundColor: "#EDF7F2",
},
},
},
MuiListItemText: {
styleOverrides: {
primary: {
fontWeight: "bold",
color: "#CBE7E0",
},
},
},
MuiListItemIcon: {
styleOverrides: {
root: {
color: "#CBE7E0",
},
},
},
MuiDivider: {
styleOverrides: {
root: {
backgroundColor: "#CBE7E0",
},
},
},
},
});
export default theme;
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/index.css | body {
margin: 0;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
sans-serif;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
code {
font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New',
monospace;
}
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/DrawerComponent.js | import React from "react";
import {
Drawer,
List,
ListItem,
ListItemIcon,
ListItemText,
Divider,
Collapse,
} from "@mui/material";
import { Link } from "react-router-dom";
import {
Dashboard as DashboardIcon,
Compare as CompareIcon,
RateReview,
ExpandLess,
ExpandMore,
People,
MiscellaneousServices,
Settings,
} from "@mui/icons-material";
const DrawerComponent = ({ isAuthenticated, drawerOpen, toggleDrawer, toggleModels, openModels }) => {
return (
<Drawer
variant="permanent"
open={drawerOpen}
onClose={toggleDrawer}
sx={{
width: drawerOpen ? 240 : 60,
flexShrink: 0,
"& .MuiDrawer-paper": {
width: drawerOpen ? 240 : 60,
boxSizing: "border-box",
backgroundColor: "#2E5D53",
color: "white",
boxShadow: "2px 0px 5px rgba(0, 0, 0, 0.1)",
marginTop: "64px",
height: `calc(100% - 64px)`,
},
}}
>
<Divider />
{isAuthenticated && (
<List>
<ListItem component={Link} to="/dashboard">
<ListItemIcon>
<DashboardIcon />
</ListItemIcon>
<ListItemText primary="Dashboard" />
</ListItem>
<Divider />
<ListItem component={Link} to="/historical-comparison">
<ListItemIcon>
<CompareIcon />
</ListItemIcon>
<ListItemText primary="Ratings Trends" />
</ListItem>
<Divider />
<ListItem component={Link} to="/ratings">
<ListItemIcon>
<RateReview />
</ListItemIcon>
<ListItemText primary="Submit Ratings" />
</ListItem>
<Divider />
<ListItem onClick={toggleModels}>
<ListItemIcon>
<MiscellaneousServices />
</ListItemIcon>
<ListItemText primary="Manage" />
{openModels ? <ExpandLess /> : <ExpandMore />}
</ListItem>
<Collapse in={openModels} timeout="auto" unmountOnExit>
<List component="div" disablePadding>
<ListItem component={Link} to="/acc-models" sx={{ pl: 4 }}>
<ListItemIcon>
<Settings />
</ListItemIcon>
<ListItemText primary="ACC Models" />
</ListItem>
<ListItem component={Link} to="/attributes" sx={{ pl: 4 }}>
<ListItemIcon>
<Settings />
</ListItemIcon>
<ListItemText primary="Attributes" />
</ListItem>
<ListItem component={Link} to="/components" sx={{ pl: 4 }}>
<ListItemIcon>
<Settings />
</ListItemIcon>
<ListItemText primary="Components" />
</ListItem>
<ListItem component={Link} to="/capabilities" sx={{ pl: 4 }}>
<ListItemIcon>
<Settings />
</ListItemIcon>
<ListItemText primary="Capabilities" />
</ListItem>
</List>
</Collapse>
<Divider />
<ListItem component={Link} to="/users">
<ListItemIcon>
<People />
</ListItemIcon>
<ListItemText primary="Users" />
</ListItem>
<Divider />
</List>
)}
</Drawer>
);
};
export default DrawerComponent;
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/AppBarComponent.js | import React from "react";
import {
AppBar,
Toolbar,
IconButton,
Typography,
Box,
Button } from "@mui/material";
import MenuIcon from "@mui/icons-material/Menu";
import { Link } from "react-router-dom";
const AppBarComponent = ({ isAuthenticated, user, toggleDrawer, handleLogout }) => {
return (
<AppBar position="fixed" sx={{ backgroundColor: "#2E5D53", zIndex: (theme) => theme.zIndex.drawer + 1 }}>
<Toolbar>
<Box
component={Link}
to="/"
sx={{
flexGrow: 1,
textDecoration: "none",
color: "white",
display: "flex",
alignItems: "center",
py: 2,
backgroundColor: "#2E5D53",
}}
>
<IconButton color="inherit" edge="start" onClick={toggleDrawer}>
<MenuIcon />
</IconButton>
<Typography variant="h6" sx={{ fontWeight: "bold", color: "white" }}>
ACC Model App
</Typography>
</Box>
<Box sx={{ display: "flex", alignItems: "center", gap: 2 }}>
{isAuthenticated ? (
<>
<Typography variant="body1" component="span" sx={{ fontWeight: "bold", color: "white" }}>
Welcome, {user?.username || "Loading..."}
</Typography>
<Button
color="inherit"
variant="contained"
sx={{
fontWeight: "bold",
backgroundColor: "#3CAEA3",
"&:hover": { backgroundColor: "#2E8E83" },
}}
onClick={handleLogout}
>
Logout
</Button>
</>
) : (
<>
<Button
color="inherit"
component={Link}
to="/login"
sx={{
fontWeight: "bold",
backgroundColor: "#3CAEA3",
"&:hover": { backgroundColor: "#2E8E83" },
}}
>
Login
</Button>
<Button
color="inherit"
component={Link}
to="/registration"
sx={{
fontWeight: "bold",
backgroundColor: "#3CAEA3",
"&:hover": { backgroundColor: "#2E8E83" },
}}
>
Register
</Button>
</>
)}
</Box>
</Toolbar>
</AppBar>
);
};
export default AppBarComponent;
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/App.test.js | import { render, screen } from '@testing-library/react';
import App from './App';
test('renders learn react link', () => {
render(<App />);
const linkElement = screen.getByText(/learn react/i);
expect(linkElement).toBeInTheDocument();
});
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/setupTests.js | // jest-dom adds custom jest matchers for asserting on DOM nodes.
// allows you to do things like:
// expect(element).toHaveTextContent(/react/i)
// learn more: https://github.com/testing-library/jest-dom
import '@testing-library/jest-dom';
| 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/logo.svg | <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 841.9 595.3"><g fill="#61DAFB"><path d="M666.3 296.5c0-32.5-40.7-63.3-103.1-82.4 14.4-63.6 8-114.2-20.2-130.4-6.5-3.8-14.1-5.6-22.4-5.6v22.3c4.6 0 8.3.9 11.4 2.6 13.6 7.8 19.5 37.5 14.9 75.7-1.1 9.4-2.9 19.3-5.1 29.4-19.6-4.8-41-8.5-63.5-10.9-13.5-18.5-27.5-35.3-41.6-50 32.6-30.3 63.2-46.9 84-46.9V78c-27.5 0-63.5 19.6-99.9 53.6-36.4-33.8-72.4-53.2-99.9-53.2v22.3c20.7 0 51.4 16.5 84 46.6-14 14.7-28 31.4-41.3 49.9-22.6 2.4-44 6.1-63.6 11-2.3-10-4-19.7-5.2-29-4.7-38.2 1.1-67.9 14.6-75.8 3-1.8 6.9-2.6 11.5-2.6V78.5c-8.4 0-16 1.8-22.6 5.6-28.1 16.2-34.4 66.7-19.9 130.1-62.2 19.2-102.7 49.9-102.7 82.3 0 32.5 40.7 63.3 103.1 82.4-14.4 63.6-8 114.2 20.2 130.4 6.5 3.8 14.1 5.6 22.5 5.6 27.5 0 63.5-19.6 99.9-53.6 36.4 33.8 72.4 53.2 99.9 53.2 8.4 0 16-1.8 22.6-5.6 28.1-16.2 34.4-66.7 19.9-130.1 62-19.1 102.5-49.9 102.5-82.3zm-130.2-66.7c-3.7 12.9-8.3 26.2-13.5 39.5-4.1-8-8.4-16-13.1-24-4.6-8-9.5-15.8-14.4-23.4 14.2 2.1 27.9 4.7 41 7.9zm-45.8 106.5c-7.8 13.5-15.8 26.3-24.1 38.2-14.9 1.3-30 2-45.2 2-15.1 0-30.2-.7-45-1.9-8.3-11.9-16.4-24.6-24.2-38-7.6-13.1-14.5-26.4-20.8-39.8 6.2-13.4 13.2-26.8 20.7-39.9 7.8-13.5 15.8-26.3 24.1-38.2 14.9-1.3 30-2 45.2-2 15.1 0 30.2.7 45 1.9 8.3 11.9 16.4 24.6 24.2 38 7.6 13.1 14.5 26.4 20.8 39.8-6.3 13.4-13.2 26.8-20.7 39.9zm32.3-13c5.4 13.4 10 26.8 13.8 39.8-13.1 3.2-26.9 5.9-41.2 8 4.9-7.7 9.8-15.6 14.4-23.7 4.6-8 8.9-16.1 13-24.1zM421.2 430c-9.3-9.6-18.6-20.3-27.8-32 9 .4 18.2.7 27.5.7 9.4 0 18.7-.2 27.8-.7-9 11.7-18.3 22.4-27.5 32zm-74.4-58.9c-14.2-2.1-27.9-4.7-41-7.9 3.7-12.9 8.3-26.2 13.5-39.5 4.1 8 8.4 16 13.1 24 4.7 8 9.5 15.8 14.4 23.4zM420.7 163c9.3 9.6 18.6 20.3 27.8 32-9-.4-18.2-.7-27.5-.7-9.4 0-18.7.2-27.8.7 9-11.7 18.3-22.4 27.5-32zm-74 58.9c-4.9 7.7-9.8 15.6-14.4 23.7-4.6 8-8.9 16-13 24-5.4-13.4-10-26.8-13.8-39.8 13.1-3.1 26.9-5.8 41.2-7.9zm-90.5 125.2c-35.4-15.1-58.3-34.9-58.3-50.6 0-15.7 22.9-35.6 58.3-50.6 8.6-3.7 18-7 27.7-10.1 5.7 19.6 13.2 40 22.5 60.9-9.2 20.8-16.6 41.1-22.2 60.6-9.9-3.1-19.3-6.5-28-10.2zM310 490c-13.6-7.8-19.5-37.5-14.9-75.7 1.1-9.4 2.9-19.3 5.1-29.4 19.6 4.8 41 8.5 63.5 10.9 13.5 18.5 27.5 35.3 41.6 50-32.6 30.3-63.2 46.9-84 46.9-4.5-.1-8.3-1-11.3-2.7zm237.2-76.2c4.7 38.2-1.1 67.9-14.6 75.8-3 1.8-6.9 2.6-11.5 2.6-20.7 0-51.4-16.5-84-46.6 14-14.7 28-31.4 41.3-49.9 22.6-2.4 44-6.1 63.6-11 2.3 10.1 4.1 19.8 5.2 29.1zm38.5-66.7c-8.6 3.7-18 7-27.7 10.1-5.7-19.6-13.2-40-22.5-60.9 9.2-20.8 16.6-41.1 22.2-60.6 9.9 3.1 19.3 6.5 28.1 10.2 35.4 15.1 58.3 34.9 58.3 50.6-.1 15.7-23 35.6-58.4 50.6zM320.8 78.4z"/><circle cx="420.9" cy="296.5" r="45.7"/><path d="M520.5 78.1z"/></g></svg> | 0 |
qxf2_public_repos/acc-model-app/frontend | qxf2_public_repos/acc-model-app/frontend/src/App.js | import React, { useState, useEffect } from "react";
import {
BrowserRouter as Router,
Route,
Routes,
Navigate,
} from "react-router-dom";
import {
CssBaseline,
Toolbar,
Box,
} from "@mui/material";
import AccModels from "./pages/AccModels";
import Attributes from "./pages/Attributes";
import Components from "./pages/Components";
import Capabilities from "./pages/Capabilities";
import Ratings from "./pages/Ratings";
import Dashboard from "./pages/AggregateRatings";
import Users from "./pages/Users";
import RegistrationForm from "./pages/RegistrationForm";
import Login from "./pages/Login";
import HistoricalComparison from "./pages/HistoricalComparison";
import { apiRequest } from "./services/apiService";
import Home from "./pages/Home";
import "./App.css";
import AppBarComponent from "./AppBarComponent";
import DrawerComponent from "./DrawerComponent";
function App() {
const [isAuthenticated, setIsAuthenticated] = useState(false);
const [drawerOpen, setDrawerOpen] = useState(true);
const [isLoading, setIsLoading] = useState(true);
const [openModels, setOpenModels] = useState(true);
const [user, setUser] = useState(null); // Store user info
const toggleModels = () => {
setOpenModels(!openModels);
};
useEffect(() => {
const handleAuthentication = async () => {
const token = localStorage.getItem("authToken");
const tokenExpiry = localStorage.getItem("tokenExpiry");
const currentTime = Date.now();
if (token && tokenExpiry) {
if (currentTime < parseInt(tokenExpiry, 10)) {
setIsAuthenticated(true);
} else {
const refreshToken = localStorage.getItem("refreshToken");
if (refreshToken) {
try {
const response = await apiRequest("/refresh-token", {
method: "POST",
data: { refresh_token: refreshToken },
});
const newAccessToken = response.newAccessToken;
localStorage.setItem("authToken", newAccessToken);
setIsAuthenticated(true);
} catch (error) {
localStorage.removeItem("authToken");
localStorage.removeItem("refreshToken");
setIsAuthenticated(false);
}
} else {
localStorage.removeItem("authToken");
localStorage.removeItem("refreshToken");
setIsAuthenticated(false);
}
}
} else {
setIsAuthenticated(false);
}
setIsLoading(false);
};
handleAuthentication();
}, []);
// Fetch user details if authenticated
useEffect(() => {
if (isAuthenticated) {
const fetchUserDetails = async () => {
try {
const response = await apiRequest("/users/users/me/");
setUser(response); // Set the user data
} catch (error) {
console.error("Error fetching user details:", error);
}
};
fetchUserDetails();
} else {
setUser(null); // Reset user if not authenticated
}
}, [isAuthenticated]);
if (isLoading) {
return <div>Loading...</div>;
}
const handleLoginSuccess = () => {
setIsAuthenticated(true);
};
const handleLogout = () => {
localStorage.removeItem("authToken");
localStorage.removeItem("refreshToken");
setIsAuthenticated(false);
setUser(null);
};
const toggleDrawer = () => {
setDrawerOpen(!drawerOpen);
};
return (
<Router>
<CssBaseline />
<Box sx={{ display: "flex", flexDirection: "column", height: "100vh" }}>
<AppBarComponent
isAuthenticated={isAuthenticated}
user={user}
toggleDrawer={toggleDrawer}
handleLogout={handleLogout}
/>
<Toolbar />
<Box sx={{ display: "flex", flex: 1 }}>
<DrawerComponent
isAuthenticated={isAuthenticated}
drawerOpen={drawerOpen}
toggleDrawer={toggleDrawer}
toggleModels={toggleModels}
openModels={openModels}
/>
{/* Main Content Area */}
<Box component="main" sx={{ flexGrow: 1, p: 3 }}>
<Routes>
<Route path="/" element={<Home isAuthenticated={isAuthenticated}/>} />
<Route path="/token" element={<Login onLoginSuccess={handleLoginSuccess} />} />
<Route path="/registration" element={<RegistrationForm />} />
{isAuthenticated ? (
<>
<Route path="/dashboard" element={<Dashboard />} />
<Route path="/historical-comparison" element={<HistoricalComparison />} />
<Route path="/acc-models" element={<AccModels />} />
<Route path="/attributes" element={<Attributes />} />
<Route path="/components" element={<Components />} />
<Route path="/capabilities" element={<Capabilities />} />
<Route path="/users" element={<Users />} />
<Route path="/ratings" element={<Ratings />} />
</>
) : (
<Route path="*" element={<Navigate to="/token" />} />
)}
</Routes>
</Box>
</Box>
</Box>
</Router>
);
}
export default App;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/capabilities/CapabilityForm.js | import React from "react";
import { Box, Button, Modal, TextField, Typography } from "@mui/material";
/**
* A React component for creating or editing a capability through a form.
*
* @param {bool} isOpen - Whether the form should be shown or not.
* @param {function} onClose - A function to be called when the user clicks
* the "Cancel" button.
* @param {Object} capability - The capability to be edited, or an empty object
* if a new capability is being created.
* @param {function} onChange - A function to be called when the user changes
* the value of one of the form fields.
* @param {function} onSave - A function to be called when the user clicks
* the "Save" button.
* @param {string} errorMessage - An error message to be shown to the user,
* or an empty string if no error message should be shown.
*/
const CapabilityForm = ({ isOpen, onClose, capability, onChange, onSave, errorMessage }) => {
return (
<Modal open={isOpen} onClose={onClose}>
<Box
p={3}
bgcolor="white"
style={{ margin: "auto", marginTop: "10%", maxWidth: "400px" }}
>
<Typography variant="h6" component="h2" gutterBottom>
{capability.id ? "Edit Capability" : "Create New Capability"}
</Typography>
<TextField
fullWidth
label="Name"
name="name"
value={capability.name}
onChange={onChange}
margin="normal"
/>
<TextField
fullWidth
label="Description"
name="description"
value={capability.description}
onChange={onChange}
margin="normal"
/>
{errorMessage && (
<Typography color="error" style={{ marginTop: '10px' }}>
{errorMessage}
</Typography>
)}
<Box mt={2}>
<Button
variant="contained"
color="primary"
onClick={onSave}
style={{ marginRight: "8px" }}
>
Save
</Button>
<Button variant="outlined" color="secondary" onClick={onClose}>
Cancel
</Button>
</Box>
</Box>
</Modal>
);
};
export default CapabilityForm;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/capabilities/AccModelSelector.js | import React from "react";
import { FormControl, InputLabel, Select, MenuItem } from "@mui/material";
/**
* A React component for selecting an ACC model from a list.
*
* @param {Object[]} accModels - An array of ACC model objects to display.
* @param {string} selectedAccModel - The id of the currently selected ACC model.
* @param {function} onSelectAccModel - A function to be called when the user selects an ACC model.
* It should take the id of the selected ACC model as an argument.
*/
const AccModelSelector = ({
accModels,
selectedAccModel,
onSelectAccModel,
}) => {
return (
<FormControl sx={{ width: "400px" }} margin="normal" variant="outlined">
<InputLabel id="acc-model-select-label">Select ACC Model</InputLabel>
<Select
labelId="acc-model-select-label"
value={selectedAccModel || ""}
onChange={(e) => onSelectAccModel(e.target.value)}
label="Select ACC Model"
>
{accModels.length === 0 ? (
<MenuItem disabled value="">
No ACC Models Available
</MenuItem>
) : (
accModels.map((model) => (
<MenuItem key={model.id} value={model.id}>
{model.name}
</MenuItem>
))
)}
</Select>
</FormControl>
);
};
export default AccModelSelector;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/capabilities/CapabilityList.js | import React from "react";
import {
Box,
Button,
Card,
CardContent,
Typography,
Stack,
} from "@mui/material";
/**
* The component renders a list of components, each with its associated
* capabilities. The component also renders a "Create New Capability" button
* for each component.
*/
const CapabilityList = ({
components,
capabilities,
onEdit,
onDelete,
onCreate,
}) => {
return (
<Stack spacing={4} sx={{ maxWidth: "80%", ml: 0 }}>
{components.map((component) => (
<Box key={component.id}>
<Box display="flex" justifyContent="space-between" alignItems="center" mb={2}>
<Typography variant="h6" style={{ color: "#1B3A34", fontSize: "1.5rem" }} gutterBottom>
{component.name}
</Typography>
<Button
variant="contained"
color="primary"
onClick={() => onCreate(component.id)}
>
Create New Capability
</Button>
</Box>
<Stack spacing={2}>
{capabilities[component.id] &&
capabilities[component.id].map((capability) => (
<Card key={capability.id} sx={(theme) => theme.customCard}>
<CardContent>
<Box
display="flex"
justifyContent="space-between"
alignItems="center"
>
<Box>
<Typography variant="h6">
{capability.name}
</Typography>
<Typography color="textSecondary">
{capability.description}
</Typography>
</Box>
<Box>
<Button
variant="outlined"
color="primary"
size="small"
onClick={() => onEdit(component.id, capability)}
sx={{ marginRight: 1 }} // Use MUI's spacing system
>
Edit
</Button>
<Button
variant="outlined"
color="secondary"
size="small"
onClick={() =>
onDelete(capability.id, component.id)
}
>
Delete
</Button>
</Box>
</Box>
</CardContent>
</Card>
))}
</Stack>
</Box>
))}
</Stack>
);
};
export default CapabilityList;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/capabilities/ConfirmDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogContentText,
DialogTitle,
Button,
} from "@mui/material";
/**
* A dialog that asks the user to confirm that they want to delete a capability.
*
* @param {{isOpen: boolean, onClose: function, onConfirm: function}} props
* @prop {boolean} isOpen - Whether the dialog is open or not.
* @prop {function} onClose - Called when the user clicks the cancel button.
* @prop {function} onConfirm - Called when the user clicks the confirm button.
* @returns {React.ReactElement} The ConfirmDialog component.
*/
const ConfirmDialog = ({ isOpen, onClose, onConfirm }) => {
return (
<Dialog open={isOpen} onClose={onClose}>
<DialogTitle>Confirm Delete</DialogTitle>
<DialogContent>
<DialogContentText>
Are you sure you want to delete this capability? This action cannot be
undone.
</DialogContentText>
</DialogContent>
<DialogActions>
<Button onClick={onClose} color="primary">
Cancel
</Button>
<Button onClick={onConfirm} color="secondary">
Delete
</Button>
</DialogActions>
</Dialog>
);
};
export default ConfirmDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/ratings/RatingsTable.js | import React from "react";
import {
Box,
Typography,
IconButton,
TableCell,
TableRow,
Select,
MenuItem,
} from "@mui/material";
import { ExpandMore, ExpandLess, Edit } from "@mui/icons-material";
/**
* A table for displaying and editing capability ratings.
*
* @param {object} components A list of component objects with their IDs and names.
* @param {object} attributes A list of attribute objects with their IDs and names.
* @param {object} expandedComponents A dictionary mapping component IDs to booleans
* of whether the component is currently expanded.
* @param {function} handleToggleExpand A function to call when a component row is
* clicked. It takes the ID of the component row.
* @param {object} capabilities A list of capability objects with their IDs, names,
* and component IDs.
* @param {object} ratings A dictionary mapping capability IDs and attribute IDs to
* rating values.
* @param {function} handleRatingChange A function to call when a rating value is
* changed. It takes the capability ID, attribute ID, and new rating value.
* @param {function} handleOpenModal A function to call when the edit icon is
* clicked. It takes the capability ID and attribute ID.
* @param {array} ratingOptions A list of rating options.
*
* @returns {ReactElement} A table element.
*/
const RatingsTable = ({
components,
attributes,
expandedComponents,
handleToggleExpand,
capabilities,
ratings,
handleRatingChange,
handleOpenModal,
ratingOptions,
}) => (
<>
{components.map((component) => (
<React.Fragment key={component.id}>
<TableRow>
<TableCell>
<Box display="flex" alignItems="center">
<IconButton onClick={() => handleToggleExpand(component.id)}>
{expandedComponents[component.id] ? (
<ExpandLess />
) : (
<ExpandMore />
)}
</IconButton>
<Typography
variant="h6"
component="h2"
style={{ fontSize: "1.25rem" }}
>
{component.name}
</Typography>
</Box>
</TableCell>
{attributes.map((attribute) => (
<TableCell key={`${component.id}-${attribute.id}`}>
{/* Empty component row cells */}
</TableCell>
))}
</TableRow>
{expandedComponents[component.id] &&
capabilities
.filter((cap) => cap.componentId === component.id)
.flatMap((cap) => cap.capabilities)
.map((capability) => (
<TableRow key={capability.id}>
<TableCell style={{ paddingLeft: "2rem" }}>
{capability.name}
</TableCell>
{attributes.map((attribute) => (
<TableCell
key={`${capability.id}-${attribute.id}`}
style={{ position: "relative" }}
>
<Select
value={
ratings[`${capability.id}-${attribute.id}`]?.rating ||
""
}
onChange={(e) =>
handleRatingChange(
capability.id,
attribute.id,
e.target.value
)
}
displayEmpty
fullWidth
>
<MenuItem value="">
<em>None</em>
</MenuItem>
{ratingOptions.map((option) => (
<MenuItem key={option} value={option}>
{option}
</MenuItem>
))}
</Select>
<IconButton
style={{ position: "absolute", top: 0, right: 0 }}
onClick={() =>
handleOpenModal(capability.id, attribute.id)
}
>
<Edit />
</IconButton>
</TableCell>
))}
</TableRow>
))}
</React.Fragment>
))}
</>
);
export default RatingsTable;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/ratings/CommentDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogTitle,
Button,
TextareaAutosize,
} from "@mui/material";
/**
* A dialog for adding a comment to a rating.
* @param {bool} open - Whether the dialog is open or not.
* @param {string} currentComment - The current comment being edited.
* @param {function} handleCommentChange - Called when the user changes the comment.
* @param {function} handleSubmitComment - Called when the user clicks the save button.
* @param {function} handleCloseModal - Called when the user clicks the cancel button.
* @returns {React.ReactElement} The CommentDialog component.
*/
const CommentDialog = ({
open,
currentComment,
handleCommentChange,
handleSubmitComment,
handleCloseModal,
}) => (
<Dialog open={open} onClose={handleCloseModal}>
<DialogTitle>Add Comment</DialogTitle>
<DialogContent>
<TextareaAutosize
minRows={4}
placeholder="Enter your comments here"
value={currentComment}
onChange={handleCommentChange}
style={{ width: "100%" }}
/>
</DialogContent>
<DialogActions>
<Button onClick={handleCloseModal} color="primary">
Cancel
</Button>
<Button onClick={handleSubmitComment} color="primary">
Save
</Button>
</DialogActions>
</Dialog>
);
export default CommentDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/attributes/AttributeList.js | import React from "react";
import {
Card,
CardContent,
Typography,
Button,
Box,
Stack,
} from "@mui/material";
/**
* A React component for displaying a list of attributes.
*
* @param {Object[]} attributes - An array of attribute objects to display.
* @param {Function} handleOpenModal - A function to be called when the user
* clicks the "Edit" button on an attribute. It should take the attribute
* being edited as an argument.
* @param {Function} handleOpenDialog - A function to be called when the user
* clicks the "Delete" button on an attribute. It should take the id of the
* attribute being deleted as an argument.
*/
const AttributeList = ({ attributes, handleOpenModal, handleOpenDialog }) => {
return (
<Stack spacing={2} sx={{ maxWidth: "80%", ml: 0 }}> {/* Left-aligned */}
{attributes.map((attribute) => (
<Card
key={attribute.id}
sx={(theme) => theme.customCard} // Custom card style from theme
>
<CardContent>
<Box display="flex" justifyContent="space-between" alignItems="center">
<Box>
<Typography variant="h6">{attribute.name}</Typography>
<Typography color="textSecondary">{attribute.description}</Typography>
</Box>
<Box>
<Button
variant="outlined"
color="primary"
size="small"
onClick={() => handleOpenModal(attribute)}
sx={{ marginRight: 1 }} // Use MUI's spacing system
>
Edit
</Button>
<Button
variant="outlined"
color="secondary"
size="small"
onClick={() => handleOpenDialog(attribute.id)}
>
Delete
</Button>
</Box>
</Box>
</CardContent>
</Card>
))}
</Stack>
);
};
export default AttributeList;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/attributes/AttributeForm.js | import React from "react";
import { Modal, Box, Typography, TextField, Button } from "@mui/material";
/**
* A React component for creating or editing an attribute through a form.
*
* @param {bool} isOpen - Whether the form should be shown or not.
* @param {Object} attribute - The attribute to be edited, or an empty object
* if a new attribute is being created.
* @param {function} handleChange - A function to be called when the user
* changes the value of one of the form fields.
* @param {function} handleSave - A function to be called when the user clicks
* the "Save" button.
* @param {function} handleClose - A function to be called when the user clicks
* the "Cancel" button.
* @param {string} errorMessage - An error message to be shown to the user,
* or an empty string if no error message should be shown.
*/
const AttributeForm = ({
isOpen,
attribute,
handleChange,
handleSave,
handleClose,
errorMessage,
}) => {
return (
<Modal open={isOpen} onClose={handleClose}>
<Box
p={3}
bgcolor="white"
style={{ margin: "auto", marginTop: "10%", maxWidth: "400px" }}
>
<Typography variant="h6" component="h2" gutterBottom>
{attribute.id ? "Edit Attribute" : "Create New Attribute"}
</Typography>
<TextField
fullWidth
label="Name"
name="name"
value={attribute.name}
onChange={handleChange}
margin="normal"
/>
<TextField
fullWidth
label="Description"
name="description"
value={attribute.description}
onChange={handleChange}
margin="normal"
/>
{errorMessage && (
<Typography color="error" style={{ marginTop: '10px' }}>
{errorMessage}
</Typography>
)}
<Box mt={2}>
<Button
variant="contained"
color="primary"
onClick={handleSave}
style={{ marginRight: "8px" }}
>
Save
</Button>
<Button variant="outlined" color="secondary" onClick={handleClose}>
Cancel
</Button>
</Box>
</Box>
</Modal>
);
};
export default AttributeForm;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/attributes/ConfirmDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogContentText,
DialogTitle,
Button,
} from "@mui/material";
/**
* A dialog that asks the user to confirm that they want to delete an attribute.
*
* @param {bool} isOpen - Whether the dialog is open or not.
* @param {function} handleClose - Called when the user clicks the cancel button.
* @param {function} handleConfirm - Called when the user clicks the confirm button.
* @returns {React.ReactElement} The ConfirmDialog component.
*/
const ConfirmDialog = ({ isOpen, handleClose, handleConfirm }) => {
return (
<Dialog open={isOpen} onClose={handleClose}>
<DialogTitle>Confirm Delete</DialogTitle>
<DialogContent>
<DialogContentText>
Are you sure you want to delete this attribute?
</DialogContentText>
</DialogContent>
<DialogActions>
<Button onClick={handleClose} color="primary">
Cancel
</Button>
<Button onClick={handleConfirm} color="secondary">
Delete
</Button>
</DialogActions>
</Dialog>
);
};
export default ConfirmDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/components/ComponentForm.js | import React from "react";
import { Box, Button, Modal, TextField, Typography } from "@mui/material";
/**
* A React component for creating or editing a component through a form.
*
* @param {bool} isOpen - Whether the form should be shown or not.
* @param {Object} component - The component to be edited, or an empty object
* if a new component is being created.
* @param {function} handleChange - A function to be called when the user
* changes the value of one of the form fields.
* @param {function} handleSave - A function to be called when the user clicks
* the "Save" button.
* @param {function} handleClose - A function to be called when the user clicks
* the "Cancel" button.
* @param {string} errorMessage - An error message to be shown to the user,
* or an empty string if no error message should be shown.
*/
const ComponentForm = ({
isOpen,
component,
handleChange,
handleSave,
handleClose,
errorMessage,
}) => {
return (
<Modal open={isOpen} onClose={handleClose}>
<Box
p={3}
bgcolor="white"
style={{ margin: "auto", marginTop: "10%", maxWidth: "400px" }}
>
<Typography variant="h6" component="h2" gutterBottom>
{component.id ? "Edit Component" : "Create New Component"}
</Typography>
<TextField
fullWidth
label="Name"
name="name"
value={component.name}
onChange={handleChange}
margin="normal"
/>
<TextField
fullWidth
label="Description"
name="description"
value={component.description}
onChange={handleChange}
margin="normal"
/>
{errorMessage && (
<Typography color="error" style={{ marginTop: '10px' }}>
{errorMessage}
</Typography>
)}
<Box mt={2}>
<Button
variant="contained"
color="primary"
onClick={handleSave}
style={{ marginRight: "8px" }}
>
Save
</Button>
<Button variant="outlined" color="secondary" onClick={handleClose}>
Cancel
</Button>
</Box>
</Box>
</Modal>
);
};
export default ComponentForm;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/components/AccModelSelector.js | import React from "react";
import { FormControl, InputLabel, MenuItem, Select } from "@mui/material";
/**
* A React component for selecting an ACC model from a list.
*
* @param {Object[]} accModels - An array of ACC model objects to display.
* @param {string} selectedAccModel - The id of the currently selected ACC model.
* @param {function} handleSelect - A function to be called when the user selects an ACC model.
* It should take the id of the selected ACC model as an argument.
*/
const AccModelSelector = ({ accModels, selectedAccModel, handleSelect }) => {
return (
<FormControl sx={{ width: "400px" }} margin="normal" variant="outlined">
<InputLabel id="acc-model-select-label">Select ACC Model</InputLabel>
<Select
labelId="acc-model-select-label"
value={selectedAccModel || ""}
onChange={(e) => handleSelect(e.target.value)}
label="Select ACC Model"
>
{accModels.length === 0 ? (
<MenuItem disabled value="">
No ACC Models Available
</MenuItem>
) : (
accModels.map((model) => (
<MenuItem key={model.id} value={model.id}>
{model.name}
</MenuItem>
))
)}
</Select>
</FormControl>
);
};
export default AccModelSelector;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/components/ConfirmDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogContentText,
DialogTitle,
Button,
} from "@mui/material";
/**
* A dialog that asks the user to confirm that they want to delete a component.
*
* @param {bool} isOpen - Whether the dialog is open or not.
* @param {function} handleClose - Called when the user clicks the cancel button.
* @param {function} handleConfirm - Called when the user clicks the confirm button.
* @returns {React.ReactElement} The ConfirmDialog component.
*/
const ConfirmDialog = ({ isOpen, handleClose, handleConfirm }) => {
return (
<Dialog open={isOpen} onClose={handleClose}>
<DialogTitle>Confirm Delete</DialogTitle>
<DialogContent>
<DialogContentText>
Are you sure you want to delete this component?
</DialogContentText>
</DialogContent>
<DialogActions>
<Button onClick={handleClose} color="primary">
Cancel
</Button>
<Button onClick={handleConfirm} color="secondary">
Delete
</Button>
</DialogActions>
</Dialog>
);
};
export default ConfirmDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/components/ComponentList.js | import React from "react";
import {
Card,
CardContent,
Typography,
Box,
Button,
Stack,
} from "@mui/material";
/**
* A React component for displaying a list of components.
*
* @param {Object[]} components - An array of component objects to display.
* @param {Function} handleOpenModal - A function to be called when the user
* clicks the "Edit" button on a component. It should take the component
* being edited as an argument.
* @param {Function} handleOpenDialog - A function to be called when the user
* clicks the "Delete" button on a component. It should take the id of the
* component being deleted as an argument.
*/
const ComponentList = ({ components, handleOpenModal, handleOpenDialog }) => {
return (
<Stack spacing={2} sx={{ maxWidth: "80%", ml: 0 }}>
{components.map((component) => (
<Card key={component.id} sx={(theme) => theme.customCard}>
<CardContent>
<Box display="flex" justifyContent="space-between" alignItems="center">
<Box>
<Typography variant="h6">{component.name}</Typography>
<Typography color="textSecondary">
{component.description}
</Typography>
</Box>
<Box>
<Button
variant="outlined"
color="primary"
size="small"
onClick={() => handleOpenModal(component)}
sx={{ marginRight: 1 }} // Use MUI's spacing system
>
Edit
</Button>
<Button
variant="outlined"
color="secondary"
size="small"
onClick={() => handleOpenDialog(component.id)}
>
Delete
</Button>
</Box>
</Box>
</CardContent>
</Card>
))}
</Stack>
);
};
export default ComponentList;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/users/UserList.js | import React from "react";
import {
Card,
CardContent,
Typography,
Box,
Button,
Stack,
} from "@mui/material";
/**
* A React component for displaying a list of users.
*
* @param {Object[]} users - An array of user objects to display.
* @param {Function} handleOpenModal - A function to be called when the user
* clicks the "Edit" button on a user. It should take the user being edited as
* an argument.
* @param {Function} handleOpenDialog - A function to be called when the user
* clicks the "Delete" button on a user. It should take the id of the user being
* deleted as an argument.
*/
const UserList = ({ users, handleOpenModal, handleOpenDialog }) => {
return (
<Stack spacing={2} sx={{ maxWidth: "80%", ml: 0 }}>
{users.map((user) => (
<Card key={user.id} sx={(theme) => theme.customCard}>
<CardContent>
<Box display="flex" justifyContent="space-between" alignItems="center">
<Box>
<Typography variant="h6">{user.username}</Typography>
<Typography color="textSecondary">{user.email}</Typography>
</Box>
<Box>
<Button
variant="outlined"
color="primary"
size="small"
onClick={() => handleOpenModal(user)}
sx={{ marginRight: 1 }} // Use MUI's spacing system
>
Edit
</Button>
<Button
variant="outlined"
color="secondary"
size="small"
onClick={() => handleOpenDialog(user.id)}
>
Delete
</Button>
</Box>
</Box>
</CardContent>
</Card>
))}
</Stack>
);
};
export default UserList;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/users/UserForm.js | import React, { useState } from "react";
import { Box, Button, Modal, TextField, Typography } from "@mui/material";
/**
* A React component for creating or editing a user through a form.
*
* @param {bool} isOpen - Whether the form should be shown or not.
* @param {Object} user - The user to be edited, or an empty object
* if a new user is being created.
* @param {function} handleChange - A function to be called when the user
* changes the value of one of the form fields.
* @param {function} handleSave - A function to be called when the user clicks
* the "Save" button.
* @param {function} handleClose - A function to be called when the user clicks
* the "Cancel" button.
* @param {string} errorMessage - An error message to be shown to the user,
* or an empty string if no error message should be shown.
*/
const UserForm = ({ isOpen, user, handleChange, handleSave, handleClose, errorMessage }) => {
return (
<Modal open={isOpen} onClose={handleClose}>
<Box
p={3}
bgcolor="white"
style={{ margin: "auto", marginTop: "10%", maxWidth: "400px" }}
>
<Typography variant="h6" component="h2" gutterBottom>
{user.id ? "Edit User" : "Create New User"}
</Typography>
<TextField
fullWidth
label="Username"
name="username"
value={user.username}
onChange={handleChange}
margin="normal"
placeholder="Username should be min 3 characters"
/>
<TextField
fullWidth
label="Email"
name="email"
value={user.email}
onChange={handleChange}
margin="normal"
placeholder="Email should be min 3 characters"
/>
<TextField
fullWidth
label="Password"
name="password"
type="password"
value={user.password}
onChange={handleChange}
margin="normal"
placeholder="Password must be atleast 6 characters long"
/>
{errorMessage && (
<Typography color="error" style={{ marginTop: '10px' }}>
{errorMessage}
</Typography>
)}
<Box mt={2}>
<Button
variant="contained"
color="primary"
onClick={handleSave}
style={{ marginRight: "8px" }}
>
Save
</Button>
<Button variant="outlined" color="secondary" onClick={handleClose}>
Cancel
</Button>
</Box>
</Box>
</Modal>
);
};
export default UserForm;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/users/ConfirmDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogContentText,
DialogTitle,
Button,
} from "@mui/material";
/**
* A dialog that asks the user to confirm that they want to delete a user.
*
* @param {bool} isOpen - Whether the dialog is open or not.
* @param {function} handleClose - Called when the user clicks the cancel button.
* @param {function} handleConfirm - Called when the user clicks the confirm button.
* @returns {React.ReactElement} The ConfirmDialog component.
*/
const ConfirmDialog = ({ isOpen, handleClose, handleConfirm }) => {
return (
<Dialog open={isOpen} onClose={handleClose}>
<DialogTitle>Confirm Delete</DialogTitle>
<DialogContent>
<DialogContentText>
Are you sure you want to delete this user?
</DialogContentText>
</DialogContent>
<DialogActions>
<Button onClick={handleClose} color="primary">
Cancel
</Button>
<Button onClick={handleConfirm} color="secondary">
Delete
</Button>
</DialogActions>
</Dialog>
);
};
export default ConfirmDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/accModels/AccModelList.js | import React from "react";
import {
Card,
CardContent,
Stack,
Typography,
Box,
Button,
} from "@mui/material";
/**
* A React component for displaying a list of ACC models.
*
* @param {Object[]} models - An array of ACC model objects to display.
* @param {Function} handleOpenModal - A function to be called when the user
* clicks the "Edit" button on an ACC model. It should take the ACC model
* being edited as an argument.
* @param {Function} handleOpenDialog - A function to be called when the user
* clicks the "Delete" button on an ACC model. It should take the id of the
* ACC model being deleted as an argument.
*/
const AccModelList = ({ models, handleOpenModal, handleOpenDialog }) => {
return (
<Stack spacing={2} sx={{ maxWidth: "80%", ml: 0 }}> {/* Left-aligned */}
{models.map((model) => (
<Card
key={model.id}
sx={(theme) => theme.customCard} // Custom card style from theme
>
<CardContent>
<Box display="flex" justifyContent="space-between" alignItems="center">
<Box>
<Typography variant="h6">{model.name}</Typography>
<Typography color="textSecondary">{model.description}</Typography>
</Box>
<Box>
<Button
variant="outlined"
color="primary"
size="small"
onClick={() => handleOpenModal(model)}
sx={{ marginRight: 1 }}
>
Edit
</Button>
<Button
variant="outlined"
color="secondary"
size="small"
onClick={() => handleOpenDialog(model.id)}
>
Delete
</Button>
</Box>
</Box>
</CardContent>
</Card>
))}
</Stack>
);
};
export default AccModelList;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/accModels/ConfirmDialog.js | import React from "react";
import {
Dialog,
DialogActions,
DialogContent,
DialogContentText,
DialogTitle,
Button,
} from "@mui/material";
/**
* A dialog that asks the user to confirm that they want to delete an ACC model.
*
* @param {boolean} isOpen - Whether the dialog is open or not.
* @param {function} handleClose - Called when the user clicks the cancel button.
* @param {function} handleConfirm - Called when the user clicks the confirm button.
* @returns {React.ReactElement} The ConfirmDialog component.
*/
const ConfirmDialog = ({ isOpen, handleClose, handleConfirm }) => {
return (
<Dialog open={isOpen} onClose={handleClose}>
<DialogTitle>Confirm Delete</DialogTitle>
<DialogContent>
<DialogContentText>
Are you sure you want to delete this ACC model?
</DialogContentText>
</DialogContent>
<DialogActions>
<Button onClick={handleClose} color="primary">
Cancel
</Button>
<Button onClick={handleConfirm} color="secondary">
Delete
</Button>
</DialogActions>
</Dialog>
);
};
export default ConfirmDialog;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src/components | qxf2_public_repos/acc-model-app/frontend/src/components/accModels/AccModelForm.js | import React from 'react';
import { Box, Button, Modal, TextField, Typography } from '@mui/material';
/**
* A React component for creating or editing an ACC model through a form.
*
* @param {bool} isOpen - Whether the form should be shown or not.
* @param {Object} model - The ACC model to be edited, or an empty object
* if a new ACC model is being created.
* @param {function} handleChange - A function to be called when the user
* changes the value of one of the form fields.
* @param {function} handleSave - A function to be called when the user clicks
* the "Save" button.
* @param {function} handleClose - A function to be called when the user clicks
* the "Cancel" button.
* @param {string} errorMessage - An error message to be shown to the user,
* or an empty string if no error message should be shown.
*/
const AccModelForm = ({ isOpen, model, handleChange, handleSave, handleClose, errorMessage }) => {
return (
<Modal open={isOpen} onClose={handleClose}>
<Box p={3} bgcolor="white" style={{ margin: 'auto', marginTop: '10%', maxWidth: '400px' }}>
<Typography variant="h6" component="h2" gutterBottom>
{model.id ? 'Edit ACC Model' : 'Create New ACC Model'}
</Typography>
<TextField
fullWidth
label="Name"
name="name"
value={model.name}
onChange={handleChange}
margin="normal"
/>
<TextField
fullWidth
label="Description"
name="description"
value={model.description}
onChange={handleChange}
margin="normal"
/>
{errorMessage && (
<Typography color="error" style={{ marginTop: '10px' }}>
{errorMessage}
</Typography>
)}
<Box mt={2}>
<Button
variant="contained"
color="primary"
onClick={handleSave}
style={{ marginRight: '8px' }}
>
Save
</Button>
<Button variant="outlined" color="secondary" onClick={handleClose}>
Cancel
</Button>
</Box>
</Box>
</Modal>
);
};
export default AccModelForm;
| 0 |
qxf2_public_repos/acc-model-app/frontend/src | qxf2_public_repos/acc-model-app/frontend/src/pages/RegistrationForm.js | import React, { useState } from "react";
import { TextField, Button, Container, Typography, Box } from "@mui/material";
import { registerUser } from "../services/registerService";
/**
* RegistrationForm is a component that renders a registration form for
* users. It accepts a username, email, designation, password and
* confirmation password and submits the data to the backend for
* registration. It also displays any error messages that occur during
* registration.
*/
const RegistrationForm = () => {
const [username, setUsername] = useState("");
const [email, setEmail] = useState("");
const [designation, setDesignation] = useState("");
const [password, setPassword] = useState("");
const [confirmPassword, setConfirmPassword] = useState("");
const [error, setError] = useState("");
const [success, setSuccess] = useState("");
const handleSubmit = async (e) => {
e.preventDefault();
console.log("Form submission initiated.");
if (password !== confirmPassword) {
setError("Passwords do not match!");
return;
}
try {
const userData = { username, email, designation, password };
const response = await registerUser(userData);
if (response.status === 201 || response.status === 200) {
setSuccess("Registration successful! Please log in.");
setError("");
setUsername("");
setEmail("");
setDesignation("");
setPassword("");
setConfirmPassword("");
setTimeout(() => {
window.location.href = "/login";
}, 2000);
} else {
setError("Failed to register. Please try again.");
console.log("Registration failed with status:", response.status);
}
} catch (err) {
setError(err.message || "An error occurred during registration.");
console.error("An error occurred:", err);
}
};
return (
<Container maxWidth="sm">
<Box mt={5}>
<Typography variant="h4" gutterBottom>
Register
</Typography>
{error && <Typography color="error">{error}</Typography>}
{success && <Typography color="primary">{success}</Typography>}
<form onSubmit={handleSubmit}>
<TextField
label="Username"
fullWidth
margin="normal"
value={username}
onChange={(e) => setUsername(e.target.value)}
placeholder="Username should be min 3 characters"
required
/>
<TextField
label="Email"
type="email"
fullWidth
margin="normal"
value={email}
onChange={(e) => setEmail(e.target.value)}
placeholder="Email should be min 3 characters"
required
/>
<TextField
label="Designation"
fullWidth
margin="normal"
value={designation}
onChange={(e) => setDesignation(e.target.value)}
required
/>
<TextField
label="Password"
type="password"
fullWidth
margin="normal"
value={password}
onChange={(e) => setPassword(e.target.value)}
placeholder="Password must be atleast 6 characters long"
required
/>
<TextField
label="Confirm Password"
type="password"
fullWidth
margin="normal"
value={confirmPassword}
onChange={(e) => setConfirmPassword(e.target.value)}
required
/>
<Box mt={2}>
<Button type="submit" variant="contained" color="primary">
Register
</Button>
</Box>
</form>
</Box>
</Container>
);
};
export default RegistrationForm;
| 0 |
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