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* added external plugin * upgrade the build.sh jupyterlab 2 support * change version to 1.0.1
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@@ -44,6 +44,10 @@ To install JupyterLab plugin, install the following dependence libraries: | |
| ```bash | ||
| conda install nodejs ipywidgets | ||
| ``` | ||
| Build the ipywidgets Jupyterlab plugin | ||
| ```bash | ||
| jupyter labextension install @jupyter-widgets/[email protected] | ||
| ``` | ||
| Then install the gquantlab lib: | ||
| ```bash | ||
| pip install gquantlab==0.1.1 | ||
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| ## Simple External Plugin Example | ||
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| This is a simple example to show how to write an external gQuant plugin. gQuant take advantage of the `entry point` inside the `setup.py` file to register the plugin. gQuant can discover all the plugins that has the entry point group name `gquant.plugin`. Check the `setup.py` file to see details. | ||
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| ### Create an new Python enviroment | ||
| ```bash | ||
| conda create -n test python=3.8 | ||
| ``` | ||
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| ### Install the gQuant lib | ||
| To install the gQuant graph computation library, first install the dependence libraries: | ||
| ```bash | ||
| pip install dask[dataframe] distributed networkx | ||
| conda install python-graphviz ruamel.yaml numpy pandas | ||
| ``` | ||
| Then install gquant lib: | ||
| ```bash | ||
| pip install gquant | ||
| ``` | ||
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| ### Install the gQuantlab plugin | ||
| To install JupyterLab plugin, install the following dependence libraries: | ||
| ```bash | ||
| conda install nodejs ipywidgets | ||
| ``` | ||
| Build the ipywidgets Jupyterlab plugin | ||
| ```bash | ||
| jupyter labextension install @jupyter-widgets/[email protected] | ||
| ``` | ||
| Then install the gquantlab lib: | ||
| ```bash | ||
| pip install gquantlab | ||
| ``` | ||
| If you launch the JupyterLab, it will prompt to build the new plugin. You can | ||
| explicitly build it by: | ||
| ```bash | ||
| jupyter lab build | ||
| ``` | ||
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| ### Install the external example plugin | ||
| To install the external plugin, in the plugin diretory, run following command | ||
| ```bash | ||
| pip install . | ||
| ``` | ||
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| ### Launch the Jupyter lab | ||
| After launching the JupyterLab by, | ||
| ```bash | ||
| jupyter-lab --allow-root --ip=0.0.0.0 --no-browser --NotebookApp.token='' | ||
| ``` | ||
| You can see the `DistanceNode` and `PointNode` under the name `custom_node` in the menu. |
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| from .distanceNode import DistanceNode | ||
| from .pointNode import PointNode | ||
| import pandas as pd | ||
| import numpy as np | ||
| from .client import validation, display # noqa: F40 | ||
| from gquant.dataframe_flow._node_flow import register_validator | ||
| from gquant.dataframe_flow._node_flow import register_copy_function | ||
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| def _validate_df(df_to_val, ref_cols, obj): | ||
| '''Validate a pandas DataFrame. | ||
| :param df_to_val: A dataframe typically of type pd.DataFrame | ||
| :param ref_cols: Dictionary of column names and their expected types. | ||
| :returns: True or False based on matching all columns in the df_to_val | ||
| and columns spec in ref_cols. | ||
| :raises: Exception - Raised when invalid dataframe length or unexpected | ||
| number of columns. TODO: Create a ValidationError subclass. | ||
| ''' | ||
| if (isinstance(df_to_val, pd.DataFrame) and len(df_to_val) == 0): | ||
| err_msg = 'Node "{}" produced empty output'.format(obj.uid) | ||
| raise Exception(err_msg) | ||
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| if not isinstance(df_to_val, pd.DataFrame): | ||
| return True | ||
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| i_cols = df_to_val.columns | ||
| if len(i_cols) != len(ref_cols): | ||
| print("expect %d columns, only see %d columns" | ||
| % (len(ref_cols), len(i_cols))) | ||
| print("ref:", ref_cols) | ||
| print("columns", i_cols) | ||
| raise Exception("not valid for node %s" % (obj.uid)) | ||
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| for col in ref_cols.keys(): | ||
| if col not in i_cols: | ||
| print("error for node %s, column %s is not in the required " | ||
| "output df" % (obj.uid, col)) | ||
| return False | ||
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| if ref_cols[col] is None: | ||
| continue | ||
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| err_msg = "for node {} type {}, column {} type {} "\ | ||
| "does not match expected type {}".format( | ||
| obj.uid, type(obj), col, df_to_val[col].dtype, | ||
| ref_cols[col]) | ||
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| if ref_cols[col] == 'category': | ||
| # comparing pandas.core.dtypes.dtypes.CategoricalDtype to | ||
| # numpy.dtype causes TypeError. Instead, let's compare | ||
| # after converting all types to their string representation | ||
| # d_type_tuple = (pd.core.dtypes.dtypes.CategoricalDtype(),) | ||
| d_type_tuple = (str(pd.CategoricalDtype()),) | ||
| elif ref_cols[col] == 'date': | ||
| # Cudf read_csv doesn't understand 'datetime64[ms]' even | ||
| # though it reads the data in as 'datetime64[ms]', but | ||
| # expects 'date' as dtype specified passed to read_csv. | ||
| d_type_tuple = ('datetime64[ms]', 'date', 'datetime64[ns]') | ||
| else: | ||
| d_type_tuple = (str(np.dtype(ref_cols[col])),) | ||
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| if (str(df_to_val[col].dtype) not in d_type_tuple): | ||
| print("ERROR: {}".format(err_msg)) | ||
| # Maybe raise an exception here and have the caller | ||
| # try/except the validation routine. | ||
| return False | ||
| return True | ||
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| def copy_df(df_obj): | ||
| return df_obj.copy(deep=False) | ||
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| register_validator(pd.DataFrame, _validate_df) | ||
| register_copy_function(pd.DataFrame, copy_df) |
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| display_fun = """ | ||
| const columnKeys = Object.keys(metaObj); | ||
| let header = ''; | ||
| if (columnKeys.length > 0) { | ||
| header += '<table>'; | ||
| header += '<tr>'; | ||
| header += '<th>Column Name</th>'; | ||
| for (let i = 0; i < columnKeys.length; i++) { | ||
| header += `<th>${columnKeys[i]}</th>`; | ||
| } | ||
| header += '</tr>'; | ||
| header += '<tr>'; | ||
| header += '<th>Type</th>'; | ||
| for (let i = 0; i < columnKeys.length; i++) { | ||
| header += `<td>${metaObj[columnKeys[i]]}</td>`; | ||
| } | ||
| header += '</tr>'; | ||
| header += '</table>'; | ||
| } | ||
| return header; | ||
| """ | ||
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| validation = {} | ||
| display = {} | ||
| display['pandas.core.frame.DataFrame'] = display_fun |
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| import pandas as pd | ||
| import numpy as np | ||
| from gquant.dataframe_flow import Node, MetaData | ||
| from gquant.dataframe_flow import NodePorts, PortsSpecSchema | ||
| from gquant.dataframe_flow import ConfSchema | ||
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| class DistanceNode(Node): | ||
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| def ports_setup(self): | ||
| port_type = PortsSpecSchema.port_type | ||
| input_ports = { | ||
| 'points_df_in': { | ||
| port_type: [pd.DataFrame] | ||
| } | ||
| } | ||
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| output_ports = { | ||
| 'distance_df': { | ||
| port_type: [pd.DataFrame] | ||
| }, | ||
| 'distance_abs_df': { | ||
| PortsSpecSchema.port_type: [pd.DataFrame] | ||
| } | ||
| } | ||
| input_connections = self.get_connected_inports() | ||
| if 'points_df_in' in input_connections: | ||
| types = input_connections['points_df_in'] | ||
| # connected, use the types passed in from parent | ||
| return NodePorts(inports={'points_df_in': {port_type: types}}, | ||
| outports={'distance_df': {port_type: types}, | ||
| 'distance_abs_df': {port_type: types}, | ||
| }) | ||
| else: | ||
| return NodePorts(inports=input_ports, outports=output_ports) | ||
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| def conf_schema(self): | ||
| return ConfSchema() | ||
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| def init(self): | ||
| self.delayed_process = True | ||
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| def meta_setup(self): | ||
| req_cols = { | ||
| 'x': 'float64', | ||
| 'y': 'float64' | ||
| } | ||
| required = { | ||
| 'points_df_in': req_cols, | ||
| } | ||
| input_meta = self.get_input_meta() | ||
| output_cols = ({ | ||
| 'distance_df': { | ||
| 'distance_cudf': 'float64', | ||
| 'x': 'float64', | ||
| 'y': 'float64' | ||
| }, | ||
| 'distance_abs_df': { | ||
| 'distance_abs_cudf': 'float64', | ||
| 'x': 'float64', | ||
| 'y': 'float64' | ||
| } | ||
| }) | ||
| if 'points_df_in' in input_meta: | ||
| col_from_inport = input_meta['points_df_in'] | ||
| # additional ports | ||
| output_cols['distance_df'].update(col_from_inport) | ||
| output_cols['distance_abs_df'].update(col_from_inport) | ||
| return MetaData(inports=required, outports=output_cols) | ||
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| def process(self, inputs): | ||
| df = inputs['points_df_in'] | ||
| output = {} | ||
| if self.outport_connected('distance_df'): | ||
| copy_df = df.copy() | ||
| copy_df['distance_cudf'] = np.sqrt((df['x'] ** 2 + df['y'] ** 2)) | ||
| output.update({'distance_df': copy_df}) | ||
| if self.outport_connected('distance_abs_df'): | ||
| copy_df = df.copy() | ||
| copy_df['distance_abs_cudf'] = np.abs(df['x']) + np.abs(df['y']) | ||
| output.update({'distance_abs_df': copy_df}) | ||
| return output |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,58 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| from gquant.dataframe_flow import Node, MetaData | ||
| from gquant.dataframe_flow import NodePorts, PortsSpecSchema | ||
| from gquant.dataframe_flow import ConfSchema | ||
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| class PointNode(Node): | ||
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| def ports_setup(self): | ||
| input_ports = {} | ||
| output_ports = { | ||
| 'points_df_out': { | ||
| PortsSpecSchema.port_type: pd.DataFrame | ||
| } | ||
| } | ||
| return NodePorts(inports=input_ports, outports=output_ports) | ||
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| def conf_schema(self): | ||
| json = { | ||
| "title": "PointNode configure", | ||
| "type": "object", | ||
| "properties": { | ||
| "npts": { | ||
| "type": "number", | ||
| "description": "number of data points", | ||
| "minimum": 10 | ||
| } | ||
| }, | ||
| "required": ["npts"], | ||
| } | ||
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| ui = { | ||
| "npts": {"ui:widget": "updown"} | ||
| } | ||
| return ConfSchema(json=json, ui=ui) | ||
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| def init(self): | ||
| pass | ||
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| def meta_setup(self): | ||
| columns_out = { | ||
| 'points_df_out': { | ||
| 'x': 'float64', | ||
| 'y': 'float64' | ||
| }, | ||
| } | ||
| return MetaData(inports={}, outports=columns_out) | ||
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| def process(self, inputs): | ||
| npts = self.conf['npts'] | ||
| df = pd.DataFrame() | ||
| df['x'] = np.random.rand(npts) | ||
| df['y'] = np.random.rand(npts) | ||
| output = {} | ||
| if self.outport_connected('points_df_out'): | ||
| output.update({'points_df_out': df}) | ||
| return output |
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| Original file line number | Diff line number | Diff line change |
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| from setuptools import setup, find_packages | ||
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| setup( | ||
| name='example_plugin', | ||
| packages=find_packages(include=['example']), | ||
| entry_points={ | ||
| 'gquant.plugin': [ | ||
| 'custom_nodes = example', | ||
| ], | ||
| } | ||
| ) |
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