code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def managers_one_page_two_components_two_controls(vizro_app, dash_data_table_with_id):
"""Instantiates managers with one page that contains two controls and two components."""
vm.Dashboard(
pages=[
vm.Page(
id="test_page",
title="First page",
c... | Instantiates managers with one page that contains two controls and two components. | managers_one_page_two_components_two_controls | python | mckinsey/vizro | vizro-core/tests/unit/vizro/actions/_action_loop/test_get_action_loop_components.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/actions/_action_loop/test_get_action_loop_components.py | Apache-2.0 |
def managers_one_page_no_actions(vizro_app):
"""Instantiates managers with one "empty" page."""
vm.Dashboard(
pages=[
vm.Page(
id="test_page_no_actions",
title="Second page",
components=[
vm.Card(text=""),
],... | Instantiates managers with one "empty" page. | managers_one_page_no_actions | python | mckinsey/vizro | vizro-core/tests/unit/vizro/actions/_action_loop/test_get_action_loop_components.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/actions/_action_loop/test_get_action_loop_components.py | Apache-2.0 |
def test_model_type_none_root_model_none(self):
"""model_type is None | page is None -> return all elements."""
result = [model.id for model in model_manager._get_models()]
expected = {
"page_1_id",
"page_1_button_id",
"page_1_graph_id",
"page_2_i... | model_type is None | page is None -> return all elements. | test_model_type_none_root_model_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_root_model_none(self):
"""model_type is vm.Button | root_model is None -> return all vm.Button from the dashboard."""
result = [model.id for model in model_manager._get_models(model_type=vm.Button)]
expected = {"page_1_button_id", "page_2_button_id"}
excluded = {"pag... | model_type is vm.Button | root_model is None -> return all vm.Button from the dashboard. | test_model_type_root_model_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_none_root_model_not_none(self, page_1):
"""model_type is None | root_model is page_1 -> return all elements from the page_1."""
result = [model.id for model in model_manager._get_models(root_model=page_1)]
expected = {"page_1_id", "page_1_button_id", "page_1_graph_id"}
... | model_type is None | root_model is page_1 -> return all elements from the page_1. | test_model_type_none_root_model_not_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_not_none_page_not_none(self, page_1):
"""model_type is vm.Button | page is page_1 -> return all vm.Button from the page_1."""
result = [model.id for model in model_manager._get_models(model_type=vm.Button, root_model=page_1)]
expected = {"page_1_button_id"}
excluded ... | model_type is vm.Button | page is page_1 -> return all vm.Button from the page_1. | test_model_type_not_none_page_not_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_no_match_root_model_none(self):
"""model_type matches no type | root_model is None -> return empty list."""
# There is no AgGrid in the dashboard
result = [model.id for model in model_manager._get_models(model_type=vm.AgGrid)]
assert result == [] | model_type matches no type | root_model is None -> return empty list. | test_model_type_no_match_root_model_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_no_match_root_model_not_none(self, page_1):
"""model_type matches no type | root_model is page_1 -> return empty list."""
# There is no AgGrid in the page_1
result = [model.id for model in model_manager._get_models(model_type=vm.AgGrid, root_model=page_1)]
assert res... | model_type matches no type | root_model is page_1 -> return empty list. | test_model_type_no_match_root_model_not_none | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_tuple_of_models(self):
"""model_type is tuple of models -> return all elements of the specified types from the dashboard."""
result = [model.id for model in model_manager._get_models(model_type=(vm.Button, vm.Graph))]
expected = {"page_1_button_id", "page_1_graph_id", "page_... | model_type is tuple of models -> return all elements of the specified types from the dashboard. | test_model_type_tuple_of_models | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_type_figure_models(self):
"""model_type is FIGURE_MODELS | root_model is None -> return all figure elements from the dashboard."""
result = [model.id for model in model_manager._get_models(model_type=FIGURE_MODELS)]
expected = {"page_1_graph_id", "page_2_figure_id"}
exclu... | model_type is FIGURE_MODELS | root_model is None -> return all figure elements from the dashboard. | test_model_type_figure_models | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_subclass_model_type(self, page_1, standard_px_chart):
"""model_type is subclass of vm.Graph -> return all elements of the specified type and its subclasses."""
class CustomGraph(vm.Graph):
pass
page_1.components.append(CustomGraph(id="page_1_custom_graph_id", figure=standa... | model_type is subclass of vm.Graph -> return all elements of the specified type and its subclasses. | test_subclass_model_type | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_nested_models(self, page_1, make_nested_control):
"""Model is nested under another model and known property in different ways -> return the model."""
class ControlGroup(vm.VizroBaseModel):
controls: Any
page_1.controls.append(
ControlGroup(controls=make_nested_... | Model is nested under another model and known property in different ways -> return the model. | test_nested_models | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_under_unknown_field(self, page_1):
"""Model is nested under another model but under an unknown field -> don't return the model."""
class ControlGroup(vm.VizroBaseModel):
unknown_field: Any
page_1.controls.append(ControlGroup(unknown_field=vm.Filter(id="page_1_control... | Model is nested under another model but under an unknown field -> don't return the model. | test_model_under_unknown_field | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_root_model_container(self, container_1):
"""model_type is None | root_model is container_1 -> return all elements from the container_1."""
result = [model.id for model in model_manager._get_models(root_model=container_1)]
expected = {"container_1_id", "container_1_button_id", "containe... | model_type is None | root_model is container_1 -> return all elements from the container_1. | test_root_model_container | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_in_page(self, page_1):
"""Model is in page -> return page."""
result = model_manager._get_model_page(page_1.components[0])
assert result == page_1 | Model is in page -> return page. | test_model_in_page | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_not_in_page(self, page_1):
"""Model is not in page -> return None."""
# Instantiate standalone model
button = vm.Button(id="standalone_button_id")
result = model_manager._get_model_page(button)
assert result is None | Model is not in page -> return None. | test_model_not_in_page | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_model_is_page(self, page_1):
"""Model is Page -> return that page."""
result = model_manager._get_model_page(page_1)
assert result == page_1 | Model is Page -> return that page. | test_model_is_page | python | mckinsey/vizro | vizro-core/tests/unit/vizro/managers/test_model_manager.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/managers/test_model_manager.py | Apache-2.0 |
def test_add_same_model(self, Parent):
"""Test whether adding same model re-defined avoids pydantic discriminator error."""
class MultipleChild(vm.VizroBaseModel):
type: Literal["derived"] = "derived"
Parent.add_type("child", MultipleChild)
class MultipleChild(vm.VizroBase... | Test whether adding same model re-defined avoids pydantic discriminator error. | test_add_same_model | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/test_base.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/test_base.py | Apache-2.0 |
def test_add_duplicate_type(self, Parent):
"""Test whether adding model of same type avoids pydantic discriminator error."""
class MultipleChild(ChildX):
pass
Parent.add_type("child", MultipleChild) | Test whether adding model of same type avoids pydantic discriminator error. | test_add_duplicate_type | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/test_base.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/test_base.py | Apache-2.0 |
def test_button_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
result = vm.Button(
id="button", text="Click me!", extra={"color": "success", "outline": True, "href": "www.google.com"}
).build()
assert_component_equal(
result,
... | Test that extra arguments correctly override defaults. | test_button_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/test_button.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/test_button.py | Apache-2.0 |
def test_button_build_with_description(self):
"""Test that description argument correctly builds icon and tooltip."""
result = vm.Button(
id="button",
text="Click me",
description=vm.Tooltip(text="Test description", icon="info", id="info"),
).build()
... | Test that description argument correctly builds icon and tooltip. | test_button_build_with_description | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/test_button.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/test_button.py | Apache-2.0 |
def test_card_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
card = vm.Card(id="card_id", text="Hello", extra={"class_name": "bg-primary p-1 mt-2 text-center h2"}).build()
assert_component_equal(
card,
dbc.Card(
id="ca... | Test that extra arguments correctly override defaults. | test_card_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/test_card.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/test_card.py | Apache-2.0 |
def test_container_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
result = vm.Container(
id="container",
title="Title",
components=[vm.Button()],
extra={"fluid": False, "class_name": "bg-container"},
).build()
... | Test that extra arguments correctly override defaults. | test_container_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/test_container.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/test_container.py | Apache-2.0 |
def test_text_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
text = vm.Text(id="text_id", text="Test", extra={"className": "bg-primary p-1 mt-2 text-center h2"})
text = text.build()
expected = dcc.Markdown(
id="text_id",
chil... | Test that extra arguments correctly override defaults. | test_text_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/test_text.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/test_text.py | Apache-2.0 |
def test_checklist_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
checklist = Checklist(
id="checklist_id",
options=["A", "B", "C"],
value=["A"],
title="Title",
extra={
"switch": True,
... | Test that extra arguments correctly override defaults. | test_checklist_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_checklist.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_checklist.py | Apache-2.0 |
def test_checklist_build_with_description(self):
"""Test that description arguments correctly builds icon and tooltip."""
checklist = Checklist(
options=["A", "B", "C"],
value=["A"],
title="Title",
description=Tooltip(text="Test description", icon="info", ... | Test that description arguments correctly builds icon and tooltip. | test_checklist_build_with_description | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_checklist.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_checklist.py | Apache-2.0 |
def test_datepicker_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
date_picker = vm.DatePicker(
id="datepicker_id",
min="2023-01-01",
max="2023-07-01",
value="2023-01-05",
range=False,
title="Ti... | Test that extra arguments correctly override defaults. | test_datepicker_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_date_picker.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_date_picker.py | Apache-2.0 |
def test_datepicker_build_with_description(self):
"""Test that extra arguments correctly override defaults."""
date_picker = vm.DatePicker(
id="datepicker_id",
min="2023-01-01",
max="2023-07-01",
value="2023-01-05",
range=False,
tit... | Test that extra arguments correctly override defaults. | test_datepicker_build_with_description | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_date_picker.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_date_picker.py | Apache-2.0 |
def test_dropdown_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
dropdown = Dropdown(
options=["A", "B", "C"],
title="Title",
id="dropdown_id",
extra={
"clearable": True,
"optionHeight":... | Test that extra arguments correctly override defaults. | test_dropdown_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_dropdown.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_dropdown.py | Apache-2.0 |
def test_radio_items_build_with_extra(self):
"""Test that extra arguments correctly override defaults."""
radio_items = RadioItems(
id="radio_items",
options=["A", "B", "C"],
title="Title",
extra={
"inline": True,
"id": "ove... | Test that extra arguments correctly override defaults. | test_radio_items_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_radio_items.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_radio_items.py | Apache-2.0 |
def test_range_slider_build_with_extra(self, expected_range_slider_with_extra):
"""Test that extra arguments correctly override defaults."""
range_slider = vm.RangeSlider(
id="range_slider",
min=0.0,
max=10.0,
step=2,
marks={1: "1", 5: "5", 10:... | Test that extra arguments correctly override defaults. | test_range_slider_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_range_slider.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_range_slider.py | Apache-2.0 |
def test_range_slider_build_with_description(self, expected_range_slider_with_description):
"""Test that description arguments correctly builds icon and tooltip."""
range_slider = vm.RangeSlider(
id="range_slider",
min=0.0,
max=10.0,
step=2,
ma... | Test that description arguments correctly builds icon and tooltip. | test_range_slider_build_with_description | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_range_slider.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_range_slider.py | Apache-2.0 |
def test_slider_build_with_extra(self, expected_slider_extra):
"""Test that extra arguments correctly override defaults."""
slider = vm.Slider(
id="slider_id",
min=0,
max=10,
step=1,
value=5,
title="Title",
extra={
... | Test that extra arguments correctly override defaults. | test_slider_build_with_extra | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_components/form/test_slider.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_components/form/test_slider.py | Apache-2.0 |
def managers_one_page_two_graphs(gapminder):
"""Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data."""
vm.Page(
id="test_page",
title="My first dashboard",
components=[
vm.Graph(id="scatter_chart", figure=px.scatter(gapmi... | Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data. | managers_one_page_two_graphs | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_controls/conftest.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_controls/conftest.py | Apache-2.0 |
def managers_one_page_container_controls(gapminder):
"""Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data."""
vm.Page(
id="test_container",
title="My first dashboard",
components=[
vm.Container(
title="",... | Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data. | managers_one_page_container_controls | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_controls/conftest.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_controls/conftest.py | Apache-2.0 |
def managers_one_page_container_controls_invalid(gapminder):
"""Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data."""
vm.Page(
id="test_container",
title="My first dashboard",
components=[
vm.Container(
i... | Instantiates a simple model_manager and data_manager with a page, and two graph models and gapminder data. | managers_one_page_container_controls_invalid | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_controls/conftest.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_controls/conftest.py | Apache-2.0 |
def managers_column_different_type():
"""Instantiates the managers with a page and two graphs sharing the same column but of different data types."""
df_numerical = pd.DataFrame({"shared_column": [1]})
df_temporal = pd.DataFrame({"shared_column": [datetime(2024, 1, 1)]})
df_categorical = pd.DataFrame({"... | Instantiates the managers with a page and two graphs sharing the same column but of different data types. | managers_column_different_type | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_controls/test_filter.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_controls/test_filter.py | Apache-2.0 |
def managers_column_only_exists_in_some():
"""Dataframes with column_numerical and column_categorical, which can be different lengths."""
vm.Page(
id="test_page",
title="Page Title",
components=[
vm.Graph(id="column_numerical_exists_1", figure=px.scatter(pd.DataFrame({"column... | Dataframes with column_numerical and column_categorical, which can be different lengths. | managers_column_only_exists_in_some | python | mckinsey/vizro | vizro-core/tests/unit/vizro/models/_controls/test_filter.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tests/unit/vizro/models/_controls/test_filter.py | Apache-2.0 |
def fetch_extracted_url(source_url: str, pattern: str, headers: dict[str, str]) -> bytes:
"""Look at the file at source_url, search for pattern and then download `url_to_download`."""
response = requests.get(source_url, timeout=TIMEOUT, headers=headers)
response.raise_for_status()
match = re.search(pat... | Look at the file at source_url, search for pattern and then download `url_to_download`. | fetch_extracted_url | python | mckinsey/vizro | vizro-core/tools/download_static_files.py | https://github.com/mckinsey/vizro/blob/master/vizro-core/tools/download_static_files.py | Apache-2.0 |
def get_sample_data_info(data_name: Literal["iris", "tips", "stocks", "gapminder"]) -> DFMetaData:
"""If user provides no data, use this tool to get sample data information.
Use the following data for the below purposes:
- iris: mostly numerical with one categorical column, good for scatter, histogram,... | If user provides no data, use this tool to get sample data information.
Use the following data for the below purposes:
- iris: mostly numerical with one categorical column, good for scatter, histogram, boxplot, etc.
- tips: contains mix of numerical and categorical columns, good for bar, pie, etc.
... | get_sample_data_info | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def validate_model_config(
dashboard_config: dict[str, Any],
data_infos: list[DFMetaData], # Should be Optional[..]=None, but Cursor complains..
auto_open: bool = True,
) -> ValidationResults:
"""Validate Vizro model configuration. Run ALWAYS when you have a complete dashboard configuration.
If su... | Validate Vizro model configuration. Run ALWAYS when you have a complete dashboard configuration.
If successful, the tool will return the python code and, if it is a remote file, the py.cafe link to the chart.
The PyCafe link will be automatically opened in your default browser if auto_open is True.
Args:
... | validate_model_config | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def get_model_json_schema(model_name: str) -> dict[str, Any]:
"""Get the JSON schema for the specified Vizro model.
Args:
model_name: Name of the Vizro model to get schema for (e.g., 'Card', 'Dashboard', 'Page')
Returns:
JSON schema of the requested Vizro model
"""
# Dictionary map... | Get the JSON schema for the specified Vizro model.
Args:
model_name: Name of the Vizro model to get schema for (e.g., 'Card', 'Dashboard', 'Page')
Returns:
JSON schema of the requested Vizro model
| get_model_json_schema | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def get_vizro_chart_or_dashboard_plan(user_plan: Literal["chart", "dashboard"]) -> str:
"""Get instructions for creating a Vizro chart or dashboard. Call FIRST when asked to create Vizro things."""
if user_plan == "chart":
return """
IMPORTANT:
- KEEP IT SIMPLE: rather than iterating yourself, ask t... | Get instructions for creating a Vizro chart or dashboard. Call FIRST when asked to create Vizro things. | get_vizro_chart_or_dashboard_plan | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def load_and_analyze_data(path_or_url: str) -> DataAnalysisResults:
"""Load data from various file formats into a pandas DataFrame and analyze its structure.
Supported formats:
- CSV (.csv)
- JSON (.json)
- HTML (.html, .htm)
- Excel (.xls, .xlsx)
- OpenDocument Spreadsheet (.ods)
- Par... | Load data from various file formats into a pandas DataFrame and analyze its structure.
Supported formats:
- CSV (.csv)
- JSON (.json)
- HTML (.html, .htm)
- Excel (.xls, .xlsx)
- OpenDocument Spreadsheet (.ods)
- Parquet (.parquet)
Args:
path_or_url: Local file path or URL to a... | load_and_analyze_data | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def create_starter_dashboard():
"""Prompt template for getting started with Vizro."""
content = f"""
Create a super simple Vizro dashboard with one page and one chart and one filter:
- No need to call any tools except for validate_model_config
- Call this tool with the precise config as shown below
- The PyCafe... | Prompt template for getting started with Vizro. | create_starter_dashboard | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def create_eda_dashboard(
file_path_or_url: str,
) -> str:
"""Prompt template for creating an EDA dashboard based on one dataset."""
content = f"""
Create an EDA dashboard based on the following dataset:{file_path_or_url}. Proceed as follows:
1. Analyze the data using the load_and_analyze_data tool first, p... | Prompt template for creating an EDA dashboard based on one dataset. | create_eda_dashboard | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def validate_chart_code(
chart_config: ChartPlan,
data_info: DFMetaData,
auto_open: bool = True,
) -> ValidationResults:
"""Validate the chart code created by the user and optionally open the PyCafe link in a browser.
Args:
chart_config: A ChartPlan object with the chart configuration
... | Validate the chart code created by the user and optionally open the PyCafe link in a browser.
Args:
chart_config: A ChartPlan object with the chart configuration
data_info: Metadata for the dataset to be used in the chart
auto_open: Whether to automatically open the PyCafe link in a browser... | validate_chart_code | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def create_vizro_chart(
chart_type: str,
file_path_or_url: Optional[str] = None,
) -> str:
"""Prompt template for creating a Vizro chart."""
content = f"""
- Create a chart using the following chart type: {chart_type}.
- You MUST name the function containing the fig `custom_chart`
- Make sure to anal... | Prompt template for creating a Vizro chart. | create_vizro_chart | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/server.py | Apache-2.0 |
def main():
"""Run the Vizro MCP server - makes charts and dashboards available to AI assistants."""
# Configure logging to show warnings by default
logging.basicConfig(level=logging.WARNING, stream=sys.stderr)
# Run the MCP server
mcp.run() | Run the Vizro MCP server - makes charts and dashboards available to AI assistants. | main | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/__init__.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/__init__.py | Apache-2.0 |
def _strip_markdown(code_string: str) -> str:
"""Remove any code block wrappers (markdown or triple quotes)."""
wrappers = [("```python\n", "```"), ("```py\n", "```"), ("```\n", "```"), ('"""', '"""'), ("'''", "'''")]
for start, end in wrappers:
if code_string.startswith(start) and code_string.ends... | Remove any code block wrappers (markdown or triple quotes). | _strip_markdown | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_schemas/schemas.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_schemas/schemas.py | Apache-2.0 |
def get_dashboard_template(self, data_info: DFMetaData) -> str:
"""Create a simple dashboard template for displaying the chart.
Args:
data_info: The metadata of the dataset to use.
Returns:
Complete Python code for a Vizro dashboard displaying the chart.
"""
... | Create a simple dashboard template for displaying the chart.
Args:
data_info: The metadata of the dataset to use.
Returns:
Complete Python code for a Vizro dashboard displaying the chart.
| get_dashboard_template | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_schemas/schemas.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_schemas/schemas.py | Apache-2.0 |
def get_overview_vizro_models() -> dict[str, list[dict[str, str]]]:
"""Get all available models in the vizro.models namespace.
Returns:
Dictionary with categories of models and their descriptions
"""
result: dict[str, list[dict[str, str]]] = {}
for category, models_list in MODEL_GROUPS.item... | Get all available models in the vizro.models namespace.
Returns:
Dictionary with categories of models and their descriptions
| get_overview_vizro_models | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_schemas/schemas.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_schemas/schemas.py | Apache-2.0 |
def convert_github_url_to_raw(path_or_url: str) -> str:
"""Convert a GitHub URL to a raw URL if it's a GitHub URL, otherwise return the original path or URL."""
github_pattern = r"https?://(?:www\.)?github\.com/([^/]+)/([^/]+)/(?:blob|raw)/([^/]+)/(.+)"
github_match = re.match(github_pattern, path_or_url)
... | Convert a GitHub URL to a raw URL if it's a GitHub URL, otherwise return the original path or URL. | convert_github_url_to_raw | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_utils/utils.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_utils/utils.py | Apache-2.0 |
def load_dataframe_by_format(
path_or_url: Union[str, Path], mime_type: Optional[str] = None
) -> tuple[pd.DataFrame, Literal["pd.read_csv", "pd.read_json", "pd.read_html", "pd.read_excel", "pd.read_parquet"]]:
"""Load a dataframe based on file format determined by MIME type or file extension."""
file_path_... | Load a dataframe based on file format determined by MIME type or file extension. | load_dataframe_by_format | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_utils/utils.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_utils/utils.py | Apache-2.0 |
def path_or_url_check(string: str) -> str:
"""Check if a string is a link or a file path."""
if string.startswith(("http://", "https://", "www.")):
return "remote"
if Path(string).is_file():
return "local"
return "invalid" | Check if a string is a link or a file path. | path_or_url_check | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_utils/utils.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_utils/utils.py | Apache-2.0 |
def create_pycafe_url(python_code: str) -> str:
"""Create a PyCafe URL for a given Python code."""
# Create JSON object for py.cafe
json_object = {
"code": python_code,
"requirements": "vizro==0.1.38",
"files": [],
}
# Convert to compressed base64 URL
json_text = json.du... | Create a PyCafe URL for a given Python code. | create_pycafe_url | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_utils/utils.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_utils/utils.py | Apache-2.0 |
def get_python_code_and_preview_link(
model_object: vm.VizroBaseModel, data_infos: list[DFMetaData]
) -> VizroCodeAndPreviewLink:
"""Get the Python code and preview link for a Vizro model object."""
# Get the Python code
python_code = model_object._to_python()
# Add imports after the first empty li... | Get the Python code and preview link for a Vizro model object. | get_python_code_and_preview_link | python | mckinsey/vizro | vizro-mcp/src/vizro_mcp/_utils/utils.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/src/vizro_mcp/_utils/utils.py | Apache-2.0 |
def dashboard_config_validation_result() -> ValidationResults:
"""Fixture for a dashboard configuration validation result."""
return ValidationResults(
valid=True,
message="Configuration is valid for Dashboard!",
python_code="""############ Imports ##############
import vizro.models as v... | Fixture for a dashboard configuration validation result. | dashboard_config_validation_result | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def graph_dashboard_config() -> dict[str, Any]:
"""Fixture for a dashboard configuration with a scatter graph."""
return {
"title": "Graph Dashboard",
"pages": [
{
"id": "graph_page",
"title": "Scatter Graph Page",
"components": [
... | Fixture for a dashboard configuration with a scatter graph. | graph_dashboard_config | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def graph_dashboard_validation_result() -> ValidationResults:
"""Fixture for a dashboard configuration with graph validation result."""
return ValidationResults(
valid=True,
message="Configuration is valid for Dashboard!",
python_code="""############ Imports ##############
import vizro.p... | Fixture for a dashboard configuration with graph validation result. | graph_dashboard_validation_result | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def invalid_chart_plan() -> dict[str, Any]:
"""Fixture for an invalid chart plan."""
return {
"chart_type": "scatter",
"imports": ["import pandas as pd", "import plotly.express as px"],
"chart_code": """def scatter_chart(data_frame):
return px.scatter(data_frame, x="sepal_length"... | Fixture for an invalid chart plan. | invalid_chart_plan | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def chart_plan_validation_result() -> ValidationResults:
"""Fixture for a chart plan validation result."""
return ValidationResults(
valid=True,
message="Chart only dashboard created successfully!",
python_code="""@capture('graph')
def custom_chart(data_frame):
return px.scatter(... | Fixture for a chart plan validation result. | chart_plan_validation_result | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_successful_validation(
self, valid_dashboard_config: dict[str, Any], dashboard_config_validation_result: ValidationResults
) -> None:
"""Test successful validation of a dashboard configuration."""
result = validate_model_config(dashboard_config=valid_dashboard_config, data_infos=[],... | Test successful validation of a dashboard configuration. | test_successful_validation | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_graph_dashboard_validation(
self,
graph_dashboard_config: dict[str, Any],
graph_dashboard_validation_result: ValidationResults,
iris_metadata: DFMetaData,
) -> None:
"""Test validation of a dashboard with a scatter graph component."""
result = validate_model_... | Test validation of a dashboard with a scatter graph component. | test_graph_dashboard_validation | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_validation_error(self, valid_dashboard_config: dict[str, Any], iris_metadata: DFMetaData) -> None:
"""Test validation error for an invalid dashboard configuration."""
# Create an invalid config by removing a required field
invalid_config = valid_dashboard_config.copy()
invalid_c... | Test validation error for an invalid dashboard configuration. | test_validation_error | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_successful_validation(
self,
valid_chart_plan: dict[str, Any],
iris_metadata: DFMetaData,
chart_plan_validation_result: ValidationResults,
) -> None:
"""Test successful validation of chart code."""
result = validate_chart_code(chart_config=valid_chart_plan, d... | Test successful validation of chart code. | test_successful_validation | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_validation_error(
self,
invalid_chart_plan: dict[str, Any],
iris_metadata: DFMetaData,
) -> None:
"""Test validation error for an invalid chart plan."""
result = validate_chart_code(chart_config=invalid_chart_plan, data_info=iris_metadata, auto_open=False)
a... | Test validation error for an invalid chart plan. | test_validation_error | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_model_json_schema(self, model_name: str, model_class: type) -> None:
"""Test getting JSON schema for various models."""
schema = get_model_json_schema(model_name=model_name)
# Get the schema directly from the model class
expected_schema = model_class.model_json_schema()
... | Test getting JSON schema for various models. | test_model_json_schema | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def test_nonexistent_model(self) -> None:
"""Test getting schema for a nonexistent model."""
schema = get_model_json_schema("NonExistentModel")
assert isinstance(schema, dict)
assert "error" in schema
assert "not found" in schema["error"] | Test getting schema for a nonexistent model. | test_nonexistent_model | python | mckinsey/vizro | vizro-mcp/tests/unit/vizro_mcp/test_server.py | https://github.com/mckinsey/vizro/blob/master/vizro-mcp/tests/unit/vizro_mcp/test_server.py | Apache-2.0 |
def setup(self) -> None:
"""Load the model into memory to make running multiple predictions efficient"""
# Download the model weights
if not os.path.exists(MODEL_CACHE):
download_weights(MODEL_URL, MODEL_CACHE)
# Soft links for the auxiliary models
os.system("mkdir -... | Load the model into memory to make running multiple predictions efficient | setup | python | bytedance/LatentSync | predict.py | https://github.com/bytedance/LatentSync/blob/master/predict.py | Apache-2.0 |
def predict(
self,
video: Path = Input(description="Input video", default=None),
audio: Path = Input(description="Input audio to ", default=None),
guidance_scale: float = Input(description="Guidance scale", ge=1, le=3, default=2.0),
inference_steps: int = Input(description="Infer... | Run a single prediction on the model | predict | python | bytedance/LatentSync | predict.py | https://github.com/bytedance/LatentSync/blob/master/predict.py | Apache-2.0 |
def resnet50_backbone(lda_out_channels, in_chn, pretrained=False, **kwargs):
"""Constructs a ResNet-50 model_hyper.
Args:
pretrained (bool): If True, returns a model_hyper pre-trained on ImageNet
"""
model = ResNetBackbone(lda_out_channels, in_chn, Bottleneck, [3, 4, 6, 3], **kwargs)
if pre... | Constructs a ResNet-50 model_hyper.
Args:
pretrained (bool): If True, returns a model_hyper pre-trained on ImageNet
| resnet50_backbone | python | bytedance/LatentSync | eval/hyper_iqa.py | https://github.com/bytedance/LatentSync/blob/master/eval/hyper_iqa.py | Apache-2.0 |
def nms_(dets, thresh):
"""
Courtesy of Ross Girshick
[https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py]
"""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1) * (y2 - y1)
order = scores.argsort... |
Courtesy of Ross Girshick
[https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py]
| nms_ | python | bytedance/LatentSync | eval/detectors/s3fd/box_utils.py | https://github.com/bytedance/LatentSync/blob/master/eval/detectors/s3fd/box_utils.py | Apache-2.0 |
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form... | Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
... | decode | python | bytedance/LatentSync | eval/detectors/s3fd/box_utils.py | https://github.com/bytedance/LatentSync/blob/master/eval/detectors/s3fd/box_utils.py | Apache-2.0 |
def nms(boxes, scores, overlap=0.5, top_k=200):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the ... | Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The o... | nms | python | bytedance/LatentSync | eval/detectors/s3fd/box_utils.py | https://github.com/bytedance/LatentSync/blob/master/eval/detectors/s3fd/box_utils.py | Apache-2.0 |
def set_attention_slice(self, slice_size):
r"""
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.... |
Enable sliced attention computation.
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
in several steps. This is useful to save some memory in exchange for a small speed decrease.
Args:
slice_size (`str` or `int` ... | set_attention_slice | python | bytedance/LatentSync | latentsync/models/unet.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/models/unet.py | Apache-2.0 |
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
encoder_hidden_states: torch.Tensor = None,
class_labels: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
# support controlnet
down_block... |
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
... | forward | python | bytedance/LatentSync | latentsync/models/unet.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/models/unet.py | Apache-2.0 |
def get_random_clip_from_video(self, idx: int) -> tuple:
'''
Sample a random clip starting index from the video.
Args:
idx: Index of the video.
'''
# Note that some videos may not contain enough frames, we skip those videos here.
while self._clips.clips[idx].... |
Sample a random clip starting index from the video.
Args:
idx: Index of the video.
| get_random_clip_from_video | python | bytedance/LatentSync | latentsync/trepa/utils/data_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/data_utils.py | Apache-2.0 |
def load_video_frames(self, dataroot: str) -> list:
'''
Loads all the video frames under the dataroot and returns a list of all the video frames.
Args:
dataroot: The root directory containing the video frames.
Returns:
A list of all the video frames.
''... |
Loads all the video frames under the dataroot and returns a list of all the video frames.
Args:
dataroot: The root directory containing the video frames.
Returns:
A list of all the video frames.
| load_video_frames | python | bytedance/LatentSync | latentsync/trepa/utils/data_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/data_utils.py | Apache-2.0 |
def getTensor(self, index: int) -> torch.Tensor:
'''
Returns a tensor of the video frames at the given index.
Args:
index: The index of the video frames to return.
Returns:
A BCTHW tensor in the range `[0, 1]` of the video frames at the given index.
'''... |
Returns a tensor of the video frames at the given index.
Args:
index: The index of the video frames to return.
Returns:
A BCTHW tensor in the range `[0, 1]` of the video frames at the given index.
| getTensor | python | bytedance/LatentSync | latentsync/trepa/utils/data_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/data_utils.py | Apache-2.0 |
def set_num_features(self, num_features: int):
'''
Set the number of features diminsions.
Args:
num_features: Number of features diminsions.
'''
if self.num_features is not None:
assert num_features == self.num_features
else:
self.num_... |
Set the number of features diminsions.
Args:
num_features: Number of features diminsions.
| set_num_features | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def append(self, x: np.ndarray):
'''
Add the newly computed features to the list. Update the mean and covariance.
Args:
x: New features to record.
'''
x = np.asarray(x, dtype=np.float32)
assert x.ndim == 2
if (self.max_items is not None) and (self.num... |
Add the newly computed features to the list. Update the mean and covariance.
Args:
x: New features to record.
| append | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def append_torch(self, x: torch.Tensor, rank: int, num_gpus: int):
'''
Add the newly computed PyTorch features to the list. Update the mean and covariance.
Args:
x: New features to record.
rank: Rank of the current GPU.
num_gpus: Total number of GPUs.
... |
Add the newly computed PyTorch features to the list. Update the mean and covariance.
Args:
x: New features to record.
rank: Rank of the current GPU.
num_gpus: Total number of GPUs.
| append_torch | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def get_all(self) -> np.ndarray:
'''
Get all the stored features as NumPy Array.
Returns:
Concatenation of the stored features.
'''
assert self.capture_all
return np.concatenate(self.all_features, axis=0) |
Get all the stored features as NumPy Array.
Returns:
Concatenation of the stored features.
| get_all | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def get_mean_cov(self) -> Tuple[np.ndarray, np.ndarray]:
'''
Get the mean and covariance of the stored features.
Returns:
Mean and covariance of the stored features.
'''
assert self.capture_mean_cov
mean = self.raw_mean / self.num_items
cov = self.raw... |
Get the mean and covariance of the stored features.
Returns:
Mean and covariance of the stored features.
| get_mean_cov | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def load(pkl_file: str) -> 'FeatureStats':
'''
Load the features and statistics from a pickle file.
Args:
pkl_file: Path to the pickle file.
'''
with open(pkl_file, 'rb') as f:
s = pickle.load(f)
obj = FeatureStats(capture_all=s['capture_all'], ma... |
Load the features and statistics from a pickle file.
Args:
pkl_file: Path to the pickle file.
| load | python | bytedance/LatentSync | latentsync/trepa/utils/metric_utils.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/trepa/utils/metric_utils.py | Apache-2.0 |
def num_frames(length, fsize, fshift):
"""Compute number of time frames of spectrogram"""
pad = fsize - fshift
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M | Compute number of time frames of spectrogram | num_frames | python | bytedance/LatentSync | latentsync/utils/audio.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/utils/audio.py | Apache-2.0 |
def __getitem__(self, idx):
"""Get audio samples and video frame at `idx`.
Parameters
----------
idx : int or slice
The frame index, can be negative which means it will index backwards,
or slice of frame indices.
Returns
-------
(ndarray/... | Get audio samples and video frame at `idx`.
Parameters
----------
idx : int or slice
The frame index, can be negative which means it will index backwards,
or slice of frame indices.
Returns
-------
(ndarray/list of ndarray, ndarray)
F... | __getitem__ | python | bytedance/LatentSync | latentsync/utils/av_reader.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/utils/av_reader.py | Apache-2.0 |
def get_batch(self, indices):
"""Get entire batch of audio samples and video frames.
Parameters
----------
indices : list of integers
A list of frame indices. If negative indices detected, the indices will be indexed from backward
Returns
-------
(lis... | Get entire batch of audio samples and video frames.
Parameters
----------
indices : list of integers
A list of frame indices. If negative indices detected, the indices will be indexed from backward
Returns
-------
(list of ndarray, ndarray)
First ... | get_batch | python | bytedance/LatentSync | latentsync/utils/av_reader.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/utils/av_reader.py | Apache-2.0 |
def _validate_indices(self, indices):
"""Validate int64 integers and convert negative integers to positive by backward search"""
assert self.__video_reader is not None and self.__audio_reader is not None
indices = np.array(indices, dtype=np.int64)
# process negative indices
indic... | Validate int64 integers and convert negative integers to positive by backward search | _validate_indices | python | bytedance/LatentSync | latentsync/utils/av_reader.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/utils/av_reader.py | Apache-2.0 |
def cuda_to_int(cuda_str: str) -> int:
"""
Convert the string with format "cuda:X" to integer X.
"""
if cuda_str == "cuda":
return 0
device = torch.device(cuda_str)
if device.type != "cuda":
raise ValueError(f"Device type must be 'cuda', got: {device.type}")
return device.ind... |
Convert the string with format "cuda:X" to integer X.
| cuda_to_int | python | bytedance/LatentSync | latentsync/utils/face_detector.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/utils/face_detector.py | Apache-2.0 |
def get_sliced_feature(self, feature_array, vid_idx, fps=25):
"""
Get sliced features based on a given index
:param feature_array:
:param start_idx: the start index of the feature
:param audio_feat_length:
:return:
"""
length = len(feature_array)
s... |
Get sliced features based on a given index
:param feature_array:
:param start_idx: the start index of the feature
:param audio_feat_length:
:return:
| get_sliced_feature | python | bytedance/LatentSync | latentsync/whisper/audio2feature.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/audio2feature.py | Apache-2.0 |
def get_sliced_feature_sparse(self, feature_array, vid_idx, fps=25):
"""
Get sliced features based on a given index
:param feature_array:
:param start_idx: the start index of the feature
:param audio_feat_length:
:return:
"""
length = len(feature_array)
... |
Get sliced features based on a given index
:param feature_array:
:param start_idx: the start index of the feature
:param audio_feat_length:
:return:
| get_sliced_feature_sparse | python | bytedance/LatentSync | latentsync/whisper/audio2feature.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/audio2feature.py | Apache-2.0 |
def load_audio(file: str, sr: int = SAMPLE_RATE):
"""
Open an audio file and read as mono waveform, resampling as necessary
Parameters
----------
file: str
The audio file to open
sr: int
The sample rate to resample the audio if necessary
Returns
-------
A NumPy arr... |
Open an audio file and read as mono waveform, resampling as necessary
Parameters
----------
file: str
The audio file to open
sr: int
The sample rate to resample the audio if necessary
Returns
-------
A NumPy array containing the audio waveform, in float32 dtype.
| load_audio | python | bytedance/LatentSync | latentsync/whisper/whisper/audio.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/audio.py | Apache-2.0 |
def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
"""
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
"""
if torch.is_tensor(array):
if array.shape[axis] > length:
array = array.index_select(dim=axis, index=torch.arange(length))
if arr... |
Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
| pad_or_trim | python | bytedance/LatentSync | latentsync/whisper/whisper/audio.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/audio.py | Apache-2.0 |
def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
"""
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
Allows decoupling librosa dependency; saved using:
np.savez_compressed(
"mel_filters.npz",
mel_80=librosa.filters.mel(sr=16000, n_fft... |
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
Allows decoupling librosa dependency; saved using:
np.savez_compressed(
"mel_filters.npz",
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
)
| mel_filters | python | bytedance/LatentSync | latentsync/whisper/whisper/audio.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/audio.py | Apache-2.0 |
def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
"""
Compute the log-Mel spectrogram of
Parameters
----------
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
The path to audio or either a NumPy array or Tensor containing the audio waveform... |
Compute the log-Mel spectrogram of
Parameters
----------
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
n_mels: int
The number of Mel-frequency filters, only 80 is supported
... | log_mel_spectrogram | python | bytedance/LatentSync | latentsync/whisper/whisper/audio.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/audio.py | Apache-2.0 |
def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
"""
Detect the spoken language in the audio, and return them as list of strings, along with the ids
of the most probable language tokens and the probability distribution over all language tokens.
... |
Detect the spoken language in the audio, and return them as list of strings, along with the ids
of the most probable language tokens and the probability distribution over all language tokens.
This is performed outside the main decode loop in order to not interfere with kv-caching.
Returns
-------
... | detect_language | python | bytedance/LatentSync | latentsync/whisper/whisper/decoding.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/decoding.py | Apache-2.0 |
def finalize(
self, tokens: Tensor, sum_logprobs: Tensor
) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
"""Finalize search and return the final candidate sequences
Parameters
----------
tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
... | Finalize search and return the final candidate sequences
Parameters
----------
tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
all tokens in the context so far, including the prefix and sot_sequence
sum_logprobs : Tensor, shape = (n_audio, n_group)
... | finalize | python | bytedance/LatentSync | latentsync/whisper/whisper/decoding.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/decoding.py | Apache-2.0 |
def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
"""
Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
Parameters
----------
model: Whisper
the Whisper model instance
m... |
Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
Parameters
----------
model: Whisper
the Whisper model instance
mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
A tensor containing the Mel spectrogram(s)
options: DecodingOptions
... | decode | python | bytedance/LatentSync | latentsync/whisper/whisper/decoding.py | https://github.com/bytedance/LatentSync/blob/master/latentsync/whisper/whisper/decoding.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.