predict_pyvip#

class predict_backend.predict_pyvip.predict_pyvip.PredictpyVIP(user, flow_id, step_name, force=False)#

Bases: object

ad(features=None, exclude=None, return_anomalies_df=True, plus_minus='both', stdev=0.5, and_or='and', apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True)#: Alias to anomaly_detection

add_column(data, name=None)#

Add a pandas series to the currently loaded dataset in Virtualitics Explore. Uses column dtype to determine column type in Virtualitics Explore.

Parameters:

data – pandas.core.Series object that contains a column of the user’s data.
name – if not None, sets this as the name of the series when it is added.

Returns:

None

add_dashboard_tile(name, dashboard, tile_type, header_text=None, width=None, height=None, body_text=None, mapping_id=None, plot=None, dataset_name=None, column_name=None, column_bins=8, breakdown_column_name=None, breakdown_index=-1, breakdown_bins=4, breakdown_bin_dist='equal')#

Create a new VipDashboardTile instance in Virtualitics Explore and assign it to a dashboard.

Parameters:

name (str) – str Desired name of the dashboard (may be displayed in Virtualitics Explore).
dashboard (VipDashboard) – vip_dashboard.VipDashboard object on which to add a tile.
tile_type (str) – str Desired tile type (i.e., text, histogram, piechart, snapshot)
header_text – str Text to be displayed in the header bar of the tile in Virtualitics Explore.
body_text – str Text to be displayed in the body of the tile in Virtualitics Explore (for text tile_type only).
width – int or float Desired tile width in pixels (may be clamped by Virtualitics Explore, [370,1110]).
height – int or float Desired tile height in pixels (may be clamped by Virtualitics Explore [290,870]).
mapping_id – int Mapping index used to produce a snapshot tile. (optional - for snapshot tile_type only.)
plot – VipPlot object used to generate a new mapping to use when producing a snapshot tile. (optional - for snapshot tile_type only)
dataset_name – str Name of the dataset in Virtualitics Explore that holds the data needed for the tile (optional - for histogram and piechart tile_type only)
column_name – str Name of the column used to the tile (optional - for histogram and piechart tile_type only)
column_bins – int Number of bins to display for a numerical histogram or piechart (optional - for histogram and piechart tile_type only, default value is 8 for histogram, 4 for piechart)
breakdown_column_name – str Name of breakdown column used to divide histogram bins further into groups (optional - for histogram tile_type only)
breakdown_index – int Preferred breakdown category, by index. Default value of -1 will display an aggregate of all categories in the breakdown. (optional - for histogram tile_type only)
breakdown_bins – int Number of bins to divide the breakdown column. (optional - for histogram tile_type only.) Default value: 4.
breakdown_bin_dist – str Preferred breakdown bin distribution for numerical breakdown columns. equal or range. (optional - for histogram tile_type only)

Returns:

VipDashboardTile

add_histogram_dashboard_tile(name, dashboard, header_text=None, width=None, height=None, dataset_name=None, column_name=None, column_bins=8, breakdown_column_name=None, breakdown_index=-1, breakdown_bins=4, breakdown_bin_dist='equal')#

Helper method for generating a histogram dashboard tile.

Parameters:

name (str) – str Desired name of the dashboard (may be displayed in Virtualitics Explore).
dashboard (VipDashboard) – vip_dashboard.VipDashboard object on which to add a tile.
header_text – str Text to be displayed in the header bar of the tile in Virtualitics Explore.
width – int or float Desired tile width in pixels (may be clamped by Virtualitics Explore, [370,1110]).
height – int or float Desired tile height in pixels (may be clamped by Virtualitics Explore [290,870]).
dataset_name – str Name of the dataset in Virtualitics Explore that holds the data needed for the tile (required for histogram, piechart, and snapshot tile_type only)
column_name – str Name of the column used to the tile (optional - for histogram and piechart tile_type only)
column_bins – int Number of bins to display for a numerical histogram or piechart (optional - for histogram and piechart tile_type only, default value is 8 for histogram, 4 for piechart)
breakdown_column_name – str Name of breakdown column used to divide histogram bins further into groups (optional - for histogram tile_type only)
breakdown_index – int Preferred breakdown category, by index. Default value of -1 will display an aggregate of all categories in the breakdown. (optional - for histogram tile_type only)
breakdown_bins – int Number of bins to divide the breakdown column. (optional - for histogram tile_type only.) Default value: 4.
breakdown_bin_dist – str Preferred breakdown bin distribution for numerical breakdown columns. equal or range. (optional - for histogram tile_type only)

Returns:

VipDashboardTile

add_piechart_dashboard_tile(name, dashboard, header_text=None, width=None, height=None, dataset_name=None, column_name=None, column_bins=8)#

Helper method for generating a piechart dashboard tile.

Parameters:

name (str) – str Desired name of the dashboard (may be displayed in Virtualitics Explore).
dashboard (VipDashboard) – vip_dashboard.VipDashboard object on which to add a tile.
header_text – str Text to be displayed in the header bar of the tile in Virtualitics Explore.
width – int or float Desired tile width in pixels (may be clamped by Virtualitics Explore, [370,1110]).
height – int or float Desired tile height in pixels (may be clamped by Virtualitics Explore [290,870]).
dataset_name – str Name of the dataset in Virtualitics Explore that holds the data needed for the tile (required for histogram, piechart, and snapshot tile_type only)
column_name – str Name of the column used to the tile (optional - for histogram and piechart tile_type only)
column_bins – int Number of bins to display for a numerical histogram or piechart (optional - for histogram and piechart tile_type only, default value is 8 for histogram, 4 for piechart)

Returns:

VipDashboardTile

add_rows(data, replot=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, show_legend=True)#

Append a pandas data frame of rows to the currently loaded dataset in Virtualitics Explore.

Parameters:

data – pandas.core.frame.DataFrame object that contains rows of the user’s data.
replot – bool Whether to replot the current mapping after the rows have been added.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

add_snapshot_dashboard_tile(name, dashboard, header_text=None, width=None, height=None, dataset_name=None, mapping_id=None, plot=None)#

Helper method for generating a snapshot dashboard tile.

Parameters:

name (str) – str Desired name of the dashboard (may be displayed in Virtualitics Explore).
dashboard (VipDashboard) – vip_dashboard.VipDashboard object on which to add a tile.
header_text – str Text to be displayed in the header bar of the tile in Virtualitics Explore.
width – int or float Desired tile width in pixels (may be clamped by Virtualitics Explore, [370,1110]).
height – int or float Desired tile height in pixels (may be clamped by Virtualitics Explore [290,870]).
dataset_name – str Name of the dataset in Virtualitics Explore that holds the data needed for the tile (required for histogram, piechart, and snapshot tile_type only)
mapping_id – int Mapping index used to produce a snapshot tile. (for snapshot tile_type only.)
plot – VipPlot object used to generate a new mapping to use when producing a snapshot tile. (for snapshot tile_type only)

Returns:

VipDashboardTile

add_text_dashboard_tile(name, dashboard, header_text=None, body_text=None, width=None, height=None)#

Helper method for generating a text dashboard tile.

Parameters:

name (str) – str Desired name of the dashboard (may be displayed in Virtualitics Explore).
dashboard (VipDashboard) – vip_dashboard.VipDashboard object on which to add a tile.
header_text – str Text to be displayed in the header bar of the tile in Virtualitics Explore.
body_text – str Text to be displayed in the body of the tile in Virtualitics Explore (for text tile_type only).
width – int or float Desired tile width in pixels (may be clamped by Virtualitics Explore, [370,1110]).
height – int or float Desired tile height in pixels (may be clamped by Virtualitics Explore [290,870]).

Returns:

VipDashboardTile

anomaly_detection(features=None, exclude=None, return_anomalies_df=True, plus_minus='both', stdev=0.5, and_or='and', apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Runs anomaly detection in Virtualitics Explore

Parameters:

features – List of column names that user wants to analyze for outliers
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
plus_minus – Include outliers that are above, below, or above and below the desired standard deviation mark. Defaults to both. Can be “both”, “plus”, or “minus”
stdev – User defined standard deviation on which to classify outliers.
and_or – “and” identifies data points that are outliers in all input features. “or” identifies data points that are outliers in any of the input features.
apply – [Deprecated] bool for whether to apply the result to the halo dimension.
return_anomalies_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for anomaly detection. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

apply_suggested_mapping(result_index, mapping_index, method='smart_mapping', export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Applies a suggested mapping from a given smart mapping or pca result.

Parameters:

result_index – int; The desired Smart Mapping or PCA Result index (use -1 for currently selected result).
mapping_index – int; The desired Suggested Mapping index (warning will be returned if no mapping exists for the provided index).
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
export – Specify whether to export a capture of the plot. defaults to “ortho”. If the plot type is “MAPS2D”, the export setting will be set to “front” regardless of requested parameter, unless the user passes None/False.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

attach_object_annotation(id, objectAnchorX=None, objectAnchorY=None, objectAnchorZ=None)#

Given an object annotation by id, attach to to its object at the specified anchor position.

Returns:: None

change_annotation_type(id, new_type)#

Convert an annotation from one type to another. Only certain transformations are allowed (i.e. Point->Mapping, Point->Dataset, Mapping->Dataset, Dataset->Mapping)

Parameters:

id (str) – str ID of the annotation to be changed.
new_type (AnnotationType) – AnnotationType New desired type for the annotation. (i.e. mapping, dataset)

Returns:

VipAnnotation

clear_custom_dashboard(guid=None)#

Clears all VipDashboardTiles from a VipDashboard instance in Virtualitics Explore.

Parameters:: guid – str The GUID of the dashboard to clear.
Returns:: None

clustering(num_clusters=None, features=None, exclude=None, keep_missing_value_columns=True, apply=True, return_clusters_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Runs K-means clustering in Virtualitics Explore

Parameters:

num_clusters – int between 1 and 16, specifying the number of clusters to compute. Default is None and enables ‘auto’-mode where the number of clusters to compute is algorithmically determined based on stability.
features – List of column names that user wants to analyze
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
return_clusters_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the clustering result to the color dimension. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for clustering. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the clustering. If return_data is false, this returns None.

clustering_coefficient(apply=True, use_color_normalization=True, return_clustering_coefficient_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Runs the clustering coefficient algorithm on the visible network that is currently loaded in Virtualitics Explore.

Parameters:

return_clustering_coefficient_df – bool determining whether to return a pandas.DataFrame containing the clustering coefficient results to the caller. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – bool determining whether to apply the clustering coefficient result to the color dimension. Default is True. When True, color_type is automatically changed to gradient.
use_color_normalization – bool determining whether to use softmax color normalization when the clustering coefficient result has been applied to color. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the clustering coefficient. If return_clustering_coefficient_df is False, this returns None.

collapse_all_annotations()#

Triggers the global toggle to collapse all expanded Annotations in Virtualitics Explore.

Returns:: None

convert_column(column, column_type)#

Converts column to the specified type.

Parameters:

column – expects column name (str) or a pandas.Series
column_type (str) – {“Continuous”, “Categorical”}

Returns:

None

convex_hull(show_points=True, x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates Convex Hull plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

show_points – Setting for how to view the convex hull. Valid options are {True, False, “show”, “hide”}
x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “bin” or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

create_annotation(a_type, name=None, comment=None, userID=None, isCollapsed=False, windowColor=None, textColor=None, pipPosition=None, screenPositionX=None, screenPositionY=None, screenOffsetX=None, screenOffsetY=None, width=None, height=None, datasetName=None, mappingID=None, rowIndex=None, objectName=None, objectID=None, isAttached=False, objectAnchorX=None, objectAnchorY=None, objectAnchorZ=None)#

Base method to create an annotation of any type.

Parameters:

a_type (AnnotationType) – AnnotationType specifies the type of annotation.
name – str The name of the annotation (will be displayed in the header bar of the Annotation UI.)
comment – str The main body text of the annotation.
userID – str The user-defined id (will be displayed in the pip/badge for the annotation).
datasetName – str The name of the dataset the annotation belongs to (if a DATASET, MAPPING, or POINT annotation).
mappingID – int The index of the mapping the annotation belongs to (if a MAPPING ANNOTATION).
objectName – str The name of the object the annotation belongs to (if an OBJECT annotation). This can be used in place of objectID.
objectID – str The id of the object the annotation belongs to (if an OBJECT annotation). This can be used in place of objectName.
rowIndex – int The rowIndex of the data point the annotation belongs to (if a POINT annotation).
windowColor – str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED)
textColor – str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)
pipPosition – AnnotationPipPosition or str The position that the pip/badge should take with respect to the annotation (LEFT or RIGHT)
screenPositionX – float The screen-space X position of the annotation, normalized from 0 (left) to 1 (right). This setting is only used for DATASET, MAPPING, and Detached OBJECT annotations.
screenPositionY – float The screen-space Y position of the annotation, normalized from 0 (bottom) to 1 (top). This setting is only used for DATASET, MAPPING, and Detached OBJECT annotations.
screenOffsetX – float The screen-space X offset of the annotation, normalized from -1 (full screen width to the left) to 1 (full screen width to the right). This setting is only used for POINT annotations and Attached OBJECT annotations.
screenOffsetY – float The screen-space Y offset of the annotation, normalized from -1 (full screen height below) to 1 (full screen height above). This setting is only used for POINT annotations and Attached OBJECT annotations.
objectAnchorX – float The object-space X position of the object annotation attach point, or anchor.
objectAnchorY – float The object-space Y position of the object annotation attach point, or anchor.
objectAnchorZ – float The object-space Z position of the object annotation attach point, or anchor.
width – int The width, as a ratio of screen width, of the annotation window.
height – int The height, as a ratio of screen height, of the annotation window.
isAttached – bool The attached/detached state of an annotation (if an OBJECT annotation).
isCollapsed – bool The collapsed/expanded state of an annotation.

Returns:

VipAnnotation

create_annotation_from_instance(annotation)#: Creates a Virtualitics Explore annotation from an instance of a VipAnnotation. :type annotation: VipAnnotation :param annotation: VipAnnotation Annotation instance holding all relevant information for the annotation. :return: VipAnnotation

create_custom_dashboard(name=None)#

Create a new VipDashboard instance in Virtualitics Explore.

Parameters:: name – str user-specified name for the VipDashboard instance.
Returns:: VipDashboard

create_dataset_annotation(name=None, comment=None, userID=None, isCollapsed=False, windowColor=None, textColor=None, pipPosition=None, screenPositionX=None, screenPositionY=None, width=None, height=None, datasetName=None)#

Helper method to create a dataset annotation.

Parameters:

name – str The name of the annotation (will be displayed in the header bar of the Annotation UI.)
comment – str The main body text of the annotation.
userID – str The user-defined id (will be displayed in the pip/badge for the annotation).
datasetName – str The name of the dataset to which the annotation belongs.
windowColor – str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED)
textColor – str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)
pipPosition – AnnotationPipPosition or str The position that the pip/badge should take with respect to the annotation (LEFT or RIGHT)
screenPositionX – float The screen-space X position of the annotation, normalized from 0 (left) to 1 (right).
screenPositionY – float The screen-space Y position of the annotation, normalized from 0 (bottom) to 1 (top).
width – int The width, as a ratio of screen width, of the annotation window.
height – int The height, as a ratio of screen height, of the annotation window.
isCollapsed – bool The collapsed/expanded state of an annotation.

Returns:

VipAnnotation

create_mapping_annotation(name=None, comment=None, userID=None, isCollapsed=False, windowColor=None, textColor=None, pipPosition=None, screenPositionX=None, screenPositionY=None, width=None, height=None, datasetName=None, mappingID=None)#

Helper method to create a mapping annotation.

Parameters:

name – str The name of the annotation (will be displayed in the header bar of the Annotation UI.)
comment – str The main body text of the annotation.
userID – str The user-defined id (will be displayed in the pip/badge for the annotation).
datasetName – str The name of the dataset to which the annotation belongs.
mappingID – int The index of the mapping to which the annotation belongs.
windowColor – str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED)
textColor – str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)
pipPosition – AnnotationPipPosition or str The position that the pip/badge should take with respect to the annotation (LEFT or RIGHT)
screenPositionX – float The screen-space X position of the annotation, normalized from 0 (left) to 1 (right).
screenPositionY – float The screen-space Y position of the annotation, normalized from 0 (bottom) to 1 (top).
width – int The width, as a ratio of screen width, of the annotation window.
height – int The height, as a ratio of screen height, of the annotation window.
isCollapsed – bool The collapsed/expanded state of an annotation.

Returns:

VipAnnotation

create_object_annotation(name=None, comment=None, userID=None, isCollapsed=False, windowColor=None, textColor=None, pipPosition=None, screenPositionX=None, screenPositionY=None, screenOffsetX=None, screenOffsetY=None, width=None, height=None, objectID=None, isAttached=False, objectAnchorX=None, objectAnchorY=None, objectAnchorZ=None)#

Helper method to create an object annotation.

Parameters:

name – str The name of the annotation (will be displayed in the header bar of the Annotation UI.)
comment – str The main body text of the annotation.
userID – str The user-defined id (will be displayed in the pip/badge for the annotation).
objectID – str The id of the object the annotation belongs to (if an OBJECT annotation).
windowColor – str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED)
textColor – str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)
pipPosition – AnnotationPipPosition or str The position that the pip/badge should take with respect to the annotation (LEFT or RIGHT)
screenPositionX – float The screen-space X position of the annotation, normalized from 0 (left) to 1 (right). This setting is only used if isAttached is False.
screenPositionY – float The screen-space Y position of the annotation, normalized from 0 (bottom) to 1 (top). This setting is only used if isAttached is False.
screenOffsetX – float The screen-space X offset of the annotation from its attached point, normalized from -1 (full screen width to the left) to 1 (full screen width to the right). This setting is only used if isAttached is True.
screenOffsetY – float The screen-space Y offset of the annotation from its attached point, normalized from -1 (full screen height below) to 1 (full screen height above). This setting is only used if isAttached is True.
width – int The width, as a ratio of screen width, of the annotation window.
height – int The height, as a ratio of screen height, of the annotation window.
isAttached – bool The attached/detached state of an annotation.
isCollapsed – bool The collapsed/expanded state of an annotation.
objectAnchorX – float The object-space X position of the object annotation attach point, or anchor.
objectAnchorY – float The object-space Y position of the object annotation attach point, or anchor.
objectAnchorZ – float The object-space Z position of the object annotation attach point, or anchor.

Returns:

VipAnnotation

create_point_annotation(name=None, comment=None, userID=None, isCollapsed=False, windowColor=None, textColor=None, pipPosition=None, screenOffsetX=None, screenOffsetY=None, width=None, height=None, datasetName=None, rowIndex=None)#

Helper method to create a point annotation. Only one point annotation per row index is allowed. Any attempts to add an annotation to a row index that already contains one will result in an error being returned.

Parameters:

name – str The name of the annotation (will be displayed in the header bar of the Annotation UI.)
comment – str The main body text of the annotation.
userID – str The user-defined id (will be displayed in the pip/badge for the annotation).
datasetName – str The name of the dataset to which the annotation belongs.
rowIndex – int The rowIndex of the data point to which the annotation belongs.
windowColor – str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED)
textColor – str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)
pipPosition – AnnotationPipPosition or str The position that the pip/badge should take with respect to the annotation (LEFT or RIGHT)
screenOffsetX – float The screen-space X offset of the annotation from its attached point, normalized from -1 (full screen width to the left) to 1 (full screen width to the right).
screenOffsetY – float The screen-space Y offset of the annotation from its attached point, normalized from -1 (full screen height below) to 1 (full screen height above).
width – int The width, as a ratio of screen width, of the annotation window.
height – int The height, as a ratio of screen height, of the annotation window.
isCollapsed – bool The collapsed/expanded state of an annotation.

Returns:

VipAnnotation

delete_all_annotations()#

Deletes all Annotations (globally) in Virtualitics Explore.

Returns:: None

delete_all_objects()#: Deletes all objects loaded into Virtualitics Explore.

delete_all_objs()#: Shortcut for delete_all_objects method.

delete_annotation(id)#

Delete an annotation based on a given id.

Parameters:: id (str) – str ID for an annotation (provided) by Virtualitics Explore.
Returns:: None

delete_dataset(name=None)#

Deletes a dataset or network from Virtualitics Explore. This is particularly useful when you have a lot of data loaded into Virtualitics Explore and there is a performance slow down. If ‘dataset_name’ is passed, Virtualitics Explore will delete the dataset or network with the corresponding name. If ‘dataset_name’ is left as None, the currently loaded dataset or network will be deleted from Virtualitics Explore if there is a dataset loaded.

Parameters:: name – str specifying the name of the dataset or network to delete from Virtualitics Explore. Defaults to None
Returns:: None

delete_obj(id=None)#: Shortcut for delete_object method.

delete_object(id=None)#: Delete an object in Virtualitics Explore, given an id. If no id is passed, Virtualitics Explore will delete the current object.

destroy_custom_dashboard(guid=None)#

Destroys a VipDashboard instance in Virtualitics Explore.

Parameters:: guid – str The GUID of the dashboard to destroy.
Returns:: None

detach_object_annotation(id)#

Given an object annotation by id, detach it from its object anchor position.

Returns:: None

ellipsoid(confidence=95.0, show_points=True, x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates Ellipsoid plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

confidence – float confidence probability that must be in {99.5, 99.0, 97.5, 95.0, 90.0, 80.0, 75.0, 70.0, 50.0, 30.0, 25.0, 20.0, 10.0, 5.0, 2.5, 1.0, 0.5}
show_points – Setting for how to view the confidence ellipsoids. Valid options are {True, False, “show”, “hide”}
x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “bin” or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

expand_all_annotations()#

Triggers the global toggle to expand all collapsed Annotations in VIP.

Returns:: None

explainable_ai(function, targetColumn, associativeColumns)#

Configures and runs the Explainable AI tool in Virtualitics Explore.

Parameters:

function – The type of explainability function to run. Can be “IdentificationTree”, “RelativeEdgeDensity”, “KolmogorovSmirnov”.
targetColumn – pandas.Series containing values which will be treated as the target categories for explainability.
associativeColumns – [pandas.Series] containing list of columns that will be used to as input alongside the target column.

Returns:

None

filter(feature_name, min=None, max=None, include=None, exclude=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Parameters:

feature_name – Name of feature to add filter to
min – If feature is continuous, set the lower bound of the filter to “min”
max – If feature is continuous, set the upper bound of the filter to “max”
include – If feature is categorical, set these categories to be visible
exclude – If feature is categorical, set these categories to be invisible
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for filtering. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

get_all_annotations(a_type=None)#

Get all annotations of a specific type in the current Virtualitics Explore project.

Parameters:: a_type – vip_annotation.AnnotationType The desired type of annotations to retrieve. If set to None, all annotations of all types will be retrieved.
Returns:: [AnnotationType]

get_all_dataset_annotations(dataset_name=None)#

Get all dataset annotations. (optional: limit to a particular dataset, by name)

Parameters:: dataset_name – str (optional) Dataset name of the desired data from which to retrieve annotations.
Returns:: [AnnotationType]

get_all_mapping_annotations(dataset_name=None)#

Get all mapping annotations. (optional: limit to a particular dataset, by name)

Parameters:: dataset_name – str (optional) Dataset name of the desired data from which to retrieve annotations.
Returns:: [AnnotationType]

get_all_object_annotations(object_id=None)#

Get all object annotations. (optional: limit to a particular object, by id)

Parameters:: object_id – str (optional) Object ID of the desired object from which to retrieve annotations.
Returns:: [AnnotationType]

get_all_point_annotations(dataset_name=None)#

Get all point annotations. (optional: limit to a particular dataset, by name)

Parameters:: dataset_name – str (optional) Dataset name of the desired data from which to retrieve annotations.
Returns:: [AnnotationType]

get_column(feature_name)#

Gets the column named <feature_name> from the currently loaded dataset

Parameters:: feature_name – Name of column to get
Returns:: pandas.core.Series

get_column_stats(feature_name)#

Gets the basic information and stats for a column named <feature_name> from the currently loaded dataset.

Parameters:: feature_name – Name of the column to get stats for.
Returns:: dict Dictionary of column information & stats.

get_dataset(name=None, visible_points_only=False)#

Gets the entire loaded dataset from the software in its current state

Parameters:

name – If specified, get the dataset named <name>. Else, gets the currently loaded dataset.
visible_points_only – bool Whether to return visible points only or not. Default value is False.

Returns:

pandas.DataFrame

get_legend(color=True, shape=True, groupby=True, background='light')#

Returns the legend information from the active dataset in Virtualitics Explore.

Parameters:

color – bool Whether to return Color legend information.
shape – bool Whether to return Shape legend information.
groupby – bool Whether to return Group By legend information.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.

Returns:

None

get_network(as_edgelist=False)#

This function fetches the network data for the currently loaded dataset. The data can be returned as an edgelist (pandas.DataFrame) or as a networkx.Graph object. When the data is returned as a networkx.Graph object, it will also encode the additional columns of data that were recorded for each node in the network. By default, the function returns the data as a networkx.Graph object.

Parameters:: as_edgelist – bool determining whether to return the data as a pandas.DataFrame.
Returns:: networkx.Graph object by default. If the as_edgelist is set to True, then this method returns a pandas.DataFrame containing the weighted edgelist.

get_orientation()#

Returns the Plot or OBJ orientation and position in Virtualitics Explore.

Returns:: None

get_plot(save_to_local_history=False)#: Fetch the VipPlot information for the current plot. :type save_to_local_history: :param save_to_local_history: bool Optionally, save the VipPlot local history. :return: VipPlot

get_screen(export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#: Exports a snapshot of the visible mapping in Virtualitics Explore and fetches a Plot object. If save_to_local_history is set to True, the VipPlot instance will be appended to the local_history :type position: :param position: { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters. :type orientation: :param orientation: { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees. :type show_legend: :param show_legend: bool; whether to show the legend in the plot capture. Default value is True. :return: None

get_visible_points()#

Returns indices of points visible in Virtualitics Explore in a pandas DataFrame.

Returns:: pandas.DataFrame with one column containing an indicator of whether each point is currently visible in Virtualitics Explore.

get_workflow()#

Returns the workflow for the active dataset in Virtualitics Explore.

Returns:: JSON

graph_distance(apply=True, use_color_normalization=True, return_centralities_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Runs the graph distance algorithms on the visible network that is currently loaded in Virtualitics Explore. The graph distance algorithms include betweenness centrality, closeness centrality, and eccentricity.

Parameters:

return_centralities_df – bool determining whether to return a pandas.DataFrame containing the centralities to the caller. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – bool determining whether to apply the betweeenness centrality result to the color dimension. Default is True. When True, color_type is automatically changed to gradient.
use_color_normalization – bool determining whether to use softmax color normalization when the betweenness centrality result has been applied to color. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the graph distance algorithms. If return_centralities_df is False, this returns None.

hide_annotations()#

Triggers the global toggle to hide all Annotations in Virtualitics Explore.

Returns:: None

hist(x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=False, volume_by=None, x_bins=None, y_bins=None, z_bins=None, name=None, position=None, orientation=None, show_legend=True)#

Generates Histogram in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
groupby – Group By dimension. Works with categorical columns.
arrow – Arrow dimension. Works with continuous and categorical features. The arrow dimension is not visible for this plot type.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “bin” or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
volume_by – setting for metric used for height of histogram bins; {“count”, “avg”, “sum”, “uniform”}
x_bins – int between 1 and 1000 that sets the number of bins to use in the ‘x’ dimension
y_bins – int between 1 and 1000 that sets the number of bins to use in the ‘y’ dimension
z_bins – int between 1 and 1000 that sets the number of bins to use in the ‘z’ dimension
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

history(index=None, name=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Allows users to re-plot mappings in Virtualitics Explore’s history entries for the current dataset. The user must specify a desired index (negative indexing is allowed) or pass the name of the desired plot. If there are multiple history entries with the requested name, the last entry with the requested name will be plotted. Users have the ability to rename a plot through the software. The user should not specify an index and a name in the same function call.

Parameters:

index – int index to be used on the list of previously created plots through Virtualitics Explore. Default value is None. For the past 1…N plots, access via index=[-1 (latest), -N] or index=[0, N - 1 (latest)].
name – str plot name checked against the list of previously created plots through Virtualitics Explore. Default value is None
export – Specify whether to export a capture of the plot. Defaults to “ortho”.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

insights(insight_type='default')#

Runs insights in Virtualitics Explore. Returns a pandas data frame containing the requested insights. :type insight_type: :param insight_type: The type of insights to retrieve. “default”, “standard”, or “network”. Default will automatically choose based on the current dataset type of plot type.

Returns:: pandas.DataFrame

line(x=None, y=None, z=None, show_points=True, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, color_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, viewby=None, color_inverted=None, name=None, trend_lines=None, line_plot_point_mode=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates line plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
show_points – Setting for how to view the points. Valid options are {True, False, “show”, “hide”}
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “bin” or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
color_normalization – Normalization setting for Color. This can only be set if the feature mapped to this dimension is numerical and continuous, the color_type is set to “gradient” and the view-by mode is set to “groupby”. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
viewby – str Specify the line plot series grouping dimension. Options are {“color”, “groupby”}. The default option is “color”
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
trend_lines – str specifying whether to build trend lines for the plot, and how they should be broken down. Options: None, Color, GroupBy, All. Note: Trend lines are only available for scatter plot and line plot types.
line_plot_point_mode – str specifies whether to show or hide points and lines in the line plot visualization.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

link_annotation(id=None, linkedObjectID=None, linkedObjectName=None, linkedDatasetName=None, linkedMappingID=None, linkLatestMapping=False)#

Link an annotation, by id, to a specificed object or dataset/mapping.

Parameters:

id – str ID of the annotation to link.
linkedObjectName – str Name of the object to link to (if linking to an object).
linkedObjectID – str ID of the object to link to (if object name not provided).
linkedDatasetName – str Name of the dataset to link to (if linking to a mapping).
linkedMappingID – int ID of the mapping to link to (if linking to a mapping).
linkLatestMapping – bool Whether to ignore the linkedMappingID and simply link to the latest mapping for the specified dataset.

Returns:

None

load_data(data, dataset_name=None)#

Loads pandas.DataFrame into Virtualitics Explore. Uses column dtype to determine column type in Virtualitics Explore.

Parameters:

data – pandas.DataFrame object that contains the users data.
dataset_name – optionally pass in a name for this dataset to show in Virtualitics Explore

Returns:

None

load_network(network, network_name=None, edge_weight_format='similarity', export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, edge_transparency=None, show_legend=True)#

Loads a network dataset into Virtualitics Explore. Datasets can be loaded as networkx.Graph objects, Virtualitics Explore’s JSON format as a string to a file or Python dictionary, or pandas.DataFrame (for edgelists) objects. The Virtualitics Explore API does not support Adjacency Matrix format. Virtualitics Explore automatically computes structure (community detection and Force Directed Layout - ForceAtlas3D) upon load of the network dataset.

Parameters:

network – Can be a networkx.Graph object, pandas.DataFrame containing an edgelist, str of path to JSON file, or dict representing the JSON as a dictionary.
network_name – str containing the desired name of the network dataset.
edge_weight_format – str containing edge weight format for this data (given that the data is weighted). “Similarity” should be used when larger edge weights indicate a closer/tighter relationship between the adjacent nodes. “Distance” should be used when larger edge weight represent a looser/weaker relationship between the adjacent nodes.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

load_object(path=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, imgPath=None, position=None, orientation=None)#

Load an OBJ file into Virtualitics Explore.

Parameters:

path – str Desired path of the OBJ file (must be local to machine running Virtualitics Explore instance).
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.

load_project(path, send_project_xml=False)#

Loads Virtualitics Explore project file (.vip) into software from a path local to the machine running Virtualitics Explore. Note that any project currently open will be discarded. To save the project first, please use VIP.save_project().

Parameters:

path – str
send_project_xml – bool

Returns:

None

load_workflow(path=None, workflow_json=None, workflow_name=None)#

Loads Virtualitics Explore workflow file into software from a path local to the machine running Virtualitics Explore. Note that any workflow currently open will be discarded. To save the workflow first, please use VIP.save_workflow().

Parameters:

path – str desired path of the workflow file (must be local to machine running Virtualitics Explore instance).
workflow_json – object that contains the workflow data.
workflow_name – optionally pass in a name for this workflow to show in Virtualitics Explore.

Returns:

None

property local_history#

maps2d(x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, z_scale=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, z_normalization=None, color_normalization=None, x_range_min=None, x_range_max=None, y_range_min=None, y_range_max=None, z_range_min=None, z_range_max=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='front', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, map_provider='ArcGIS', map_style='default', heatmap_enabled=False, heatmap_intensity=None, heatmap_radius=None, heatmap_radius_unit=None, heatmap_feature=False, return_data=False, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, edge_transparency=None, show_legend=True)#

Generates 2D Map plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
groupby – Group By dimension. Works with categorical columns.
arrow – Arrow dimension. Works with continuous and categorical features. The arrow dimension is not visible for this plot type.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
map_provider – {“ArcGIS”, “OpenStreetMap”}
map_style – depends on the map_provider. See documentation for options.
heatmap_enabled – bool setting for whether to use heatmap of the mapped data.
heatmap_intensity – float to determine the intensity of the heatmap. heatmap_enabled must be True for this parameter to be used.
heatmap_radius – float determining the radius of sensitivity for heatmap functionality. heatmap_enabled must be True for this parameter to be used.
heatmap_radius_unit – determines the units of the heatmap_radius. Must be a str and one of {“Kilometers”, “Miles”, “NauticalMiles”}. heatmap_enabled must be True for this parameter to be used.
heatmap_feature – bool to determine whether to compute a heatmap feature (computes density of points).
return_data – bool to determine whether to send back the computed heatmap feature.
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None or pd.DataFrame if return_data is True for heatmap_feature

maps3d(x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, groupby=None, arrow=None, z_scale=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, z_normalization=None, color_normalization=None, x_range_min=None, x_range_max=None, y_range_min=None, y_range_max=None, z_range_min=None, z_range_max=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, lat_long_lines=True, country_lines=None, country_labels=None, globe_style='natural', heatmap_enabled=False, heatmap_intensity=None, heatmap_radius=None, heatmap_radius_unit=None, heatmap_feature=False, return_data=False, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates 3D Map plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features. The arrow dimension is not visible for this plot type.
groupby – Group By dimension. Works with categorical columns.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
lat_long_lines – bool visibility setting for Latitude/Longitude lines.
country_lines – bool visibility setting for country border lines.
country_labels – bool visibility setting for country labels.
globe_style – {“natural”, “dark”, “black ocean”, “blue ocean”, “gray ocean”, “water color”, “topographic”, “moon”, “night”}
heatmap_enabled – bool setting for whether to use heatmap of the mapped data.
heatmap_intensity – float to determine the intensity of the heatmap. heatmap_enabled must be True for this parameter to be used.
heatmap_radius – float determining the radius of sensitivity for heatmap functionality. heatmap_enabled must be True for this parameter to be used.
heatmap_radius_unit – determines the units of the heatmap_radius. Must be a str and one of {“Kilometers”, “Miles”, “NauticalMiles”}. heatmap_enabled must be True for this parameter to be used.
heatmap_feature – bool to determine whether to compute a heatmap feature (computes density of points).
return_data – bool to determine whether to send back the computed heatmap feature.
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None or pd.DataFrame if return_data is True for heatmap_feature

network_extractor(node_column, associative_columns, pivot_type='mean', export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, bypass_warning=False, extraction_type='Categorical', standard_scale=True, position=None, orientation=None, show_legend=True)#

Network extractor is a beta functionality (please submit feedback to “support@virtualitics.com”). With this method, you can extract network structures from non-network data. You must specify a column containing entities you would like to use as nodes as the ‘node_column.’ Furthermore, you must specify a list containing at least one column of values that will be used to find associations between the selected nodes. There can be multiple rows of data about any given node. This tool is especially useful for analyzing categorical columns of data.

Parameters:

node_column – pandas.Series containing values which will be treated as nodes in a network.
associative_columns – [pandas.Series] containing list of columns that will be used to find associations between the nodes.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
bypass_warning – bool; whether to bypass warning from Network Extractor tool that warns the user that the variety and size of the data will require large computational resources and memory. Use with care.
extraction_type – str; whether the extraction should be based on Categorical or Numerical associative features.
standard_scale – bool; whether to scale numerical values with respect to column mean and standard-deviation.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

network_structure(apply=True, return_structure_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Runs the network structure algorithms on the visible network that is currently loaded in Virtualitics Explore. Network structure algorithms include community detection and ForceAtlas3D. The results will also included degree and weighted degree results.

Parameters:

return_structure_df – bool determining whether to return a pandas.DataFrame containing the structure results to the caller. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – bool determining whether to map the network structure. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the network structure. If return_structure_df is False, this returns None.

normalize(norm_type='Softmax', export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None)#

Normalizes the axis for spatial dimensions in Virtualitics Explore if applicable.

Parameters:

norm_type – The type of normalization to apply to the data. Can be “softmax”, “log10”, or “ihst”
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.

Returns:

None

pagerank(damping_factor=0.85, apply=True, use_color_normalization=True, return_pagerank_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Runs PageRank algorithm on the visible network that is currently loaded in Virtualitics Explore.

Parameters:

damping_factor – float between 0 and 1 representing the damping factor for the PageRank algorithm. Defaults to 0.85 which is widely considered a good value. Users are recommended not to change this unless they are familiar with the PageRank algorithm.
return_pagerank_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – bool determining whether to apply the pagerank result to the color dimension. Default is True. When True, color_type is automatically changed to gradient.
use_color_normalization – bool determining whether to use softmax color normalization when the pagerank result has been applied to color. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the pagerank. If return_pagerank_df is false, this returns None.

pca(num_components, features=None, exclude=None, apply=True, return_components_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Runs Principal Component Analysis (PCA) in Virtualitics Explore

Parameters:

num_components – int for the number of principle components to compute from the input data. The number of components must be within [1, 10] and cannot be greater than the number of features to run on.
features – List of column names that user wants to analyze
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
return_components_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the first 3 computed components to the spatial dimensions. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for pca. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

if return_data is True, this returns a pandas.DataFrame containing the user specified number of principal components. Otherwise, this returns None.

pca_ad(features=None, exclude=None, return_anomalies_df=True, threshold=1, apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Alias to pca_anomaly_detection

Parameters:

features – List of column names that user wants to analyze for outliers
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
threshold – Percent threshold on which to classify outliers. Takes values from 0 to 100 exclusive. Defaults to a threshold of 1.
return_anomalies_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the anomaly detection result to the halo dimension. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for pca based anomaly detection. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

pca_anomaly_detection(features=None, exclude=None, return_anomalies_df=True, threshold=1, apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

PCA based Anomaly Detection.

Parameters:

features – List of column names that user wants to analyze for outliers
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
threshold – Percent threshold on which to classify outliers. Takes values from 0 to 100 exclusive. Defaults to a threshold of 1.
return_anomalies_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the anomaly detection result to the halo dimension. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for pca based anomaly detection. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

plot(plot_type='scatter', x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, color_normalization=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, trend_lines=None, scatter_plot_point_mode=None, line_plot_point_mode=None, viewby=None, edge_transparency=None, show_legend=True)#

Requests Virtualitics Explore to make the specified plot. Expects column name or pandas.Series dimension parameters. Plot type is expected to be string.

Parameters:

plot_type – {“scatter”, “hist”, “line”, “maps3d”, “maps2d”, “ellipsoid”, “surface”, “convex_hull”}; default is “scatter”
x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
x_range_min – Minimum visible value for the X dimension.
x_range_max – Maximum visible value for the X dimension.
x_limit_min – Minimum value displayed for the X dimension on the axis/grid box.
x_limit_max – Maximum value displayed for the X dimension on the axis/grid box.
x_limit_link – Whether limit is locked to range.
y_range_min – Minimum visible value for the Y dimension.
y_range_max – Maximum visible value for the Y dimension.
y_limit_min – Minimum value displayed for the Y dimension on the axis/grid box.
y_limit_max – Maximum value displayed for the Y dimension on the axis/grid box.
y_limit_link – Whether limit is locked to range.
z_range_min – Minimum visible value for the Z dimension.
z_range_max – Maximum visible value for the Z dimension.
z_limit_min – Minimum value displayed for the Z dimension on the axis/grid box.
z_limit_max – Maximum value displayed for the Z dimension on the axis/grid box.
z_limit_link – Whether limit is locked to range.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
trend_lines – str specifying whether to build trend lines for the plot, and how they should be broken down. Options: None, Color, GroupBy, All. Note: Trend lines are only available for scatter plot and line plot types.
scatter_plot_point_mode – str specifies whether to show or hide points in a scatter plot visualization. (Only valid for plot_type = ‘scatter_plot’)
line_plot_point_mode – str specifies whether to show or hide points and lines in a line plot visualization. (Only valid for plot_type = ‘line_plot’)
viewby – str specifies which viewby mode (“color” or “groupby”) to use in a line plot visualization. (Only valid for plot_type = ‘line_plot’)
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

pull_new_columns()#

Gets new columns that were added to the currently loaded dataset since the last invocation of this method. This does not include columns from the initial loading of a dataset (call get_dataset() to access these) or columns created from via ML routines, such as clustering and PCA, that have not been added to the feature list.

Returns:: pandas.DataFrame

refresh_plot(export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, position=None, orientation=None, show_legend=True)#: Requests Virtualitics Explore to refresh the current plot. This is the equivalent of pressing the Plot button again. It may be helpful to use this to refresh the visualizations after manipulating or adding data to the active dataset. :type position: :param position: { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters. :type orientation: :param orientation: { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees. :type show_legend: :param show_legend: bool; whether to show the legend in the plot capture. Default value is True.

remove_all_filters(export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Parameters:

export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

remove_dashboard_tiles(dashboard, guids=None)#

Remove a list of dashboards by GUID from a specified VipDashboard.

Parameters:

dashboard (VipDashboard) – VipDashboard Dashboard from which to remove tiles.
guids – str[] Array of tile guids to remove from the dashboard.

Returns:

None

remove_filter(feature_name, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, position=None, orientation=None, show_legend=True)#

Parameters:

feature_name – Name of feature to remove any filter on if it exists
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

reset_annotation_color(id)#

Resets the annotation color back to default.

Returns:: None

response_callback(body, message)#

Callback method used to handle messages recieved on the response queue. Since this method cannot directly return to the line where processing a message from the queue is called, it puts the result into a local queue to be immediately read.

Parameters:

body (bytes) – actual content that was written to the message queue.
message (Message) – message object containing body and metadata. Must have ack called on it to clear it from the queue.

save_project(filename, overwrite=False)#

Saves Virtualitics Explore project to the specified filepath.

Parameters:

filename (str) – absolute path to the desired save location.
overwrite – bool that controls whether to write over a file that may exist at the specified path.

Returns:

None

save_workflow(filename, overwrite=False)#

Saves Virtualitics Explore workflow to the specified filepath.

Parameters:

filename (str) – absolute path to the desired save location.
overwrite – bool that controls whether to write over a file that may exist at the specified path.

Returns:

None

scatter(x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, color_normalization=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, color_inverted=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, name=None, trend_lines=None, scatter_plot_point_mode=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates scatter plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
color_inverted – bool controlling the order of colors for all color types.
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
trend_lines – str specifies whether to build trend lines for the plot, and how they should be broken down. Options: None, Color, GroupBy, All. Note: Trend lines are only available for scatter plot and line plot types.
scatter_plot_point_mode – str specifies whether to show or hide points in a scatter plot.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

search(search_term=None, features=None, exclude=None, exact_match=False, return_search_df=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Runs Search tool in Virtualitics Explore.

Parameters:

search_term – str string to search for in the current dataset.
features – List of column names that user wants to include in the search area
exclude – List of column names to exclude in the search area; this overrides any features listed in the features parameter.
return_search_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for Search. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

pandas.DataFrame containing the results of the search. If return_data is false, this returns None.

set_annotation_color(id, windowColor=None, textColor=None)#

Set the window and/or text color of an annotation. :type id: str :param id: str Annotation id provided by Virtualitics Explore. :type windowColor: :param windowColor: str The color of the header in the annotation window, represented as an HTML color string (i.e. FF0000 for RED) :type textColor: :param textColor: str The color of the header text in the annotation, represented as an HTML color string (i.e. FF0000 for RED)

Returns:: None

set_annotation_dimensions(id, width, height)#

Set the dimensions for an annotation.

Parameters:

id (str) – str id of the annotation to position.
width (float) – float width as a ratio of screen size.
height (float) – float height as a ratio of screen size.

Returns:

None

set_annotation_position(id, posX, posY)#

Set the screen position of an annotation.

Parameters:

id (str) – str id of the annotation to position.
posX (float) – float Screen X position, normalized 0.0-1.0 (left to right). Values outside of this range will results in off-screen annotations.
posY (float) – float Screen Y position, normalized 0.0-1.0 (bottom to top). Values outside of this range will results in off-screen annotations.

Returns:

None

set_camera_angle(angle)#

Sets the camera angle in Virtualitics Explore (does not effect export camera angle).

Parameters:: angle – str controlling camera angle in Virtualitics Explore. {“Default”, “Top”, “Front”, “Side”}
Returns:: None

set_current_dashboard(guid=None)#

Sets the current VipDashboard in the dashboard window in Virtualitics Explore, based on the specified GUID.

Parameters:: guid – str The GUID of the dashboard to activate.
Returns:: None

set_gridbox_tickmarks_view(gridbox=None, tickmarks=None)#

Sets the visibility of the gridbox and tickmarks. Expects one or both of gridbox and tickmarks to be not None.

Parameters:

gridbox – bool controlling visibility of gridbox. True sets to “show”, False sets to “hide”
tickmarks – bool controlling visibility of tickmarks. True sets to “show”, False sets to “hide’

Returns:

None

set_obj_mode(mode)#: Shortcut method for set_object_mode.

set_object_mode(mode)#

Sets the current mode (object or plot) if possible.

Parameters:: mode (str) – str Desired mode, object or plot.

shape_options(render_mode)#

Updates optimization mode of software by setting the shape options render mode.

Parameters:: render_mode – str to set the shape options (formerly point render) mode. Can be { “Default”, “2D”, “Points”, “Point Cloud”, or “PointCloud”}. The “Default” case yields 2D billboard rendering of the data points.
Returns:: None

show(plot, display=True, save_to_local_history=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, position=None, orientation=None, show_legend=True)#

Recreates a plot in Virtualitics Explore from a VipPlot instance.

Parameters:

plot (VipPlot) – VipPlot instance that contains all important details to send to Virtualitics Explore
display – bool flag for whether to show this plot to the user
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
export – Specify whether to export a capture of the plot. defaults to “ortho”. If the plot type is “MAPS2D”, the export setting will be set to “front” regardless of requested parameter, unless the user passes None/False.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

show_annotations()#

Triggers the global toggle to show all Annotations in Virtualitics Explore.

Returns:: None

shutdown()#: Method called when the client is done with Predict PyVIP. Purges any remaining messages from the queues and closes the connections they depend on.

smart_mapping(target, features=None, exclude=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, return_results_df=False, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Runs smart mapping in Virtualitics Explore.

Parameters:

target – Target column that the user wants to find insights about; this feature will be dropped automatically from Smart Mapping input regardless of what is listed in the features and exclude parameters.
features – List of column names that user wants to analyze in comparison to target
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
return_results_df – bool for whether to return the feature ranking and correlation groups pd.DataFrame. The default is False; in which case the head of the feature ranking pd.DataFrame is displayed.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for smart mapping. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

if ‘return_results_df’ is True, this returns the feature importance and correlation groups of the input features as a pd.DataFrame.

statistics(feature=None)#

Runs statistics in Virtualitics Explore

Returns:: None

stats(feature=None)#: Alias for statistics

surface(show_points=False, x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, color_normalization=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, position=None, orientation=None, show_legend=True)#

Generates Surface plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

show_points – Setting for how to view the surface. Valid options are {True, False, “show”, “hide”}
x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

switch_dataset(name)#

Switches Dataset context in Virtualitics Explore.

Parameters:: name (str) – str for the name of the dataset or network to bring into context.
Returns:: None

switch_obj(id)#: Shortcut for switch_object method.

switch_object(id)#

Switches the current Object context in Virtualitics Explore.

Parameters:: id (str) – str id of the object (as provided by Virtualitics Explore).
Returns:: None

threshold_ad(features=None, exclude=None, return_anomalies_df=True, threshold=1, apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Alias to pca_anomaly_detection

Parameters:

features – List of column names that user wants to analyze for outliers
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
threshold – Percent threshold on which to classify outliers. Takes values from 0 to 100 exclusive. Defaults to a threshold of 1.
return_anomalies_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the anomaly detection result to the halo dimension. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for pca based anomaly detection. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

threshold_anomaly_detection(features=None, exclude=None, return_anomalies_df=True, threshold=1, apply=True, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, keep_missing_value_columns=True, position=None, orientation=None, show_legend=True)#

Alias to pca_anomaly_detection

Parameters:

features – List of column names that user wants to analyze for outliers
exclude – List of column names to exclude in the analysis; this overrides any features listed in the features parameter.
threshold – Percent threshold on which to classify outliers. Takes values from 0 to 100 exclusive. Defaults to a threshold of 1.
return_anomalies_df – Whether to return the output of the process to the notebook. Defaults to True.
export – Specify whether to export a capture of the plot. Can be None/False or “ortho”, “front”, “side” or “right”, “top”, or “perspective”
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
apply – [Deprecated] bool determining whether to apply the anomaly detection result to the halo dimension. Default is True.
keep_missing_value_columns – bool for whether to keep features with more than 50% missing values as part of the input for pca based anomaly detection. Default is True.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

toggle_obj_mode()#: Shortcut method for toggle_object_mode.

toggle_object_mode()#: Toggles the current mode (object or plot) if possible.

violin(x=None, y=None, z=None, color=None, size=None, shape=None, transparency=None, halo=None, halo_highlight=None, pulsation=None, pulsation_highlight=None, playback=None, playback_highlight=None, arrow=None, groupby=None, x_scale=None, y_scale=None, z_scale=None, x_range_min=None, x_range_max=None, x_limit_min=None, x_limit_max=None, x_limit_link=None, y_range_min=None, y_range_max=None, y_limit_min=None, y_limit_max=None, y_limit_link=None, z_range_min=None, z_range_max=None, z_limit_min=None, z_limit_max=None, z_limit_link=None, size_scale=None, transparency_scale=None, halo_scale=None, arrow_scale=None, color_type=None, color_palette_id=None, color_normalization=None, x_normalization=None, y_normalization=None, z_normalization=None, size_normalization=None, transparency_normalization=None, arrow_normalization=None, export='ortho', background='light', imsize=(2048, 2048), autocrop=True, path=None, save_to_local_history=True, color_bins=None, color_bin_dist=None, color_inverted=None, name=None, position=None, orientation=None, edge_transparency=None, show_legend=True)#

Generates violin plot in Virtualitics Explore. Expects column name or pandas data series dimension parameters.

Parameters:

x – X dimension
y – Y dimension
z – Z dimension
color – Color dimension. Automatically uses quartile/categorical coloring.
size – Size dimension. Works best with continuous features
shape – Shape dimension. Works best with categorical features
transparency – Transparency dimension. Works best with continuous features.
halo – Halo dimension. Works with binary features
halo_highlight – Optionally select a single value of the feature mapped to the Halo dimension. All points with this value will show a halo.
pulsation – Pulsation dimension. Works best with categorical features
pulsation_highlight – Optionally select a single value of the feature mapped to the Pulsation dimension. All points with this value will pulsate.
playback – Playback dimension. Requires user interaction to be activated; otherwise shows all.
playback_highlight – Optionally select a single value of the feature mapped to the Playback dimension. All points with this value will be shown and all other points will be hidden.
arrow – Arrow dimension. Works with continuous and categorical features.
groupby – Group By dimension. Works with categorical columns.
x_scale – Scaling factor for X dimension. Value must be between .5 and 10.
y_scale – Scaling factor for Y dimension. Value must be between .5 and 10.
z_scale – Scaling factor for Z dimension. Value must be between .5 and 10.
size_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
transparency_scale – Scaling factor for Transparency dimension. Value must be between .5 and 10.
halo_scale – Scaling factor for Halo dimension. Value must be between .5 and 10.
arrow_scale – Scaling factor for Size dimension. Value must be between .5 and 10.
color_type – User can select “gradient”, “bin”, or “palette” or None (which uses Virtualitics Explore defaults). For categorical data, the only option is color “palette”. For numeric data, “bin” is the default but “gradient” can also be used.
color_palette_id – User can select the color palette based on the available palettes for the specified color_type.
color_inverted – bool controlling the order of colors for all color types.
color_normalization – Normalization setting for color. This can only be set if the color type is set to “Gradient”. The options are “Log10”, “Softmax”, “IHST”
x_normalization – Normalization setting for X. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
y_normalization – Normalization setting for Y.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
z_normalization – Normalization setting for Z. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
size_normalization – Normalization setting for Size. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
transparency_normalization – Normalization setting for Transparency.This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
arrow_normalization – Normalization setting for Arrow. This can only be set if the feature mapped to this dimension is numerical and continuous. The options are “Log10”, “Softmax”, “IHST”
export – Specify whether to export a capture of the plot. Defaults to “ortho”. Options are {“ortho”, “front”, “right”, “side” (same as “right”), “top”, “perspective”, None, False}.
background – Specify whether to export a plot capture with a light or dark theme, or the current color scheme configured in Virtualitics Explore. Options are {“light”, “dark”, “current”}. Light is used if the value is not specified.
imsize – size of the returned dimension; [w, h]. Only used if export is not None. Defaults to (2048, 2048)
autocrop – Specify whether to automatically crop the plot capture to remove whitespace
save_to_local_history – bool; whether to save VipPlot object to this.local_history list. Default value is True.
path – Filepath to save snapshot; filepath should end with a jpg/jpeg/png/bmp extension
color_bins – sets the number of color bins to use. The max number of bins is 16. You must have at least as many unique values (in the column mapped to color) as the number of bins you set.
color_bin_dist – str with options: {“equal”, “range”}
name – str specifying the name of the plot. Default to None. A name will be automatically generated in Virtualitics Explore.
position – { pos_x: float, pos_y: float, pos_z: float } horizontal, vertical and depth values for plot position in meters.
orientation – { rot_x: float, rot_y: float, rot_z: float } pitch, yaw, roll values for plot orientation in degrees.
edge_transparency – Determines how transparent the edges will be. Fully transparent is 0 and fully opaque is 1.
show_legend – bool; whether to show the legend in the plot capture. Default value is True.

Returns:

None

class predict_backend.predict_pyvip.predict_pyvip.PyVIPMetaClass(classname, bases, classDict)#

Bases: type

Metaclass used to add wrapped versions of all PyVIP methods to PredictPyVIP. Iterates over all attributes of the VIP class and adds wrapped versions of the functions to the parent class.

predict_backend.predict_pyvip.predict_pyvip.serialization_wrapper(f)#: Wrapper to convert method name and arguments into serialized format, write the serialization to a message queue, wait for a response, and return the serialized response. This wrapper is applied to all pyVIP methods by the PyVIP MetaClass.