wallaroo.assay

class Assay(wallaroo.object.Object):

An Assay represents a record in the database. An assay contains some high level attributes such as name, status, active, etc. as well as the sub objects Baseline, Window and Summarizer which specify how the Baseline is derived, how the Windows should be created and how the analysis should be conducted.

Assay( client: Union[wallaroo.client.Client, NoneType], data: Dict[str, Any])

Base constructor.

Each object requires:

  • a GraphQL client - in order to fill its missing members dynamically
  • an initial data blob - typically from unserialized JSON, contains at
  • least the data for required members (typically the object's primary key) and optionally other data members.
def id(self) -> int:
def active(*args, **kwargs):
def status(*args, **kwargs):
def name(*args, **kwargs):
def warning_threshold(*args, **kwargs):
def alert_threshold(*args, **kwargs):
def pipeline_id(*args, **kwargs):
def pipeline_name(*args, **kwargs):
def workspace_id(*args, **kwargs):
def workspace_name(*args, **kwargs):
@staticmethod
def get_assay_info( client: wallaroo.client.Client, assay_id: int, workspace_id: Union[int, NoneType] = None, workspace_name: Union[str, NoneType] = None) -> pandas.core.frame.DataFrame:

Get the assay information for the given assay_id

Parameters
  • client: Client object
  • assay_id: int Assay Identifier
  • workspace_id: Optiona[int] Workspace Identifier
  • workspace_name: Optional[str] Workspace Name
Returns

pd.DataFrame

def turn_on(self):

Sets the Assay to active causing it to run and backfill any missing analysis.

def turn_off(self):

Disables the Assay. No further analysis will be conducted until the assay is enabled.

def set_alert_threshold(self, threshold: float):

Sets the alert threshold at the specified level. The status in the AssayAnalysis will show if this level is exceeded however currently alerting/notifications are not implemented.

def set_warning_threshold(self, threshold: float):

Sets the warning threshold at the specified level. The status in the AssayAnalysis will show if this level is exceeded however currently alerting/notifications are not implemented.

def meta_df(assay_result: Dict, index_name) -> pandas.core.frame.DataFrame:

Creates a dataframe for the meta data in the baseline or window excluding the edge information.

Parameters
  • assay_result: The dict of the raw asset result
def edge_df(window_or_baseline: Dict) -> pandas.core.frame.DataFrame:

Creates a dataframe specifically for the edge information in the baseline or window.

Parameters
  • window_or_baseline: The dict from the assay result of either the window or baseline
class AssayAnalysis:

The AssayAnalysis class helps handle the assay analysis logs from the Plateau logs. These logs are a json document with meta information on the assay and analysis as well as summary information on the baseline and window and information on the comparison between them.

AssayAnalysis( raw: Dict[str, Any], client: Union[wallaroo.client.Client, NoneType] = None, assay_name: Union[str, NoneType] = None)
def compare_basic_stats(self) -> pandas.core.frame.DataFrame:

Creates a simple dataframe making it easy to compare a baseline and window.

def baseline_stats(self) -> pandas.core.frame.DataFrame:

Creates a simple dataframe with the basic stats data for a baseline.

def compare_bins(self) -> pandas.core.frame.DataFrame:

Creates a simple dataframe to compare the bin/edge information of baseline and window.

def baseline_bins(self) -> pandas.core.frame.DataFrame:

Creates a simple dataframe to with the edge/bin data for a baseline.

def chart(self, show_scores=True):

Quickly create a chart showing the bins, values and scores of an assay analysis. show_scores will also label each bin with its final weighted (if specified) score.

class AssayAnalysisList:

Helper class primarily to easily create a dataframe from a list of AssayAnalysis objects.

AssayAnalysisList(raw: List[wallaroo.assay.AssayAnalysis])
def to_dataframe(self) -> pandas.core.frame.DataFrame:

Creates and returns a summary dataframe from the assay results.

def to_full_dataframe(self) -> pandas.core.frame.DataFrame:

Creates and returns a dataframe with all values including inputs and outputs from the assay results.

def chart_df( self, df: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], title: str, nth_x_tick=None):

Creates a basic chart of the scores from dataframe created from assay analysis list

def chart_scores(self, title: Union[str, NoneType] = None, nth_x_tick=4):

Creates a basic chart of the scores from an AssayAnalysisList

def chart_iopaths( self, labels: Union[List[str], NoneType] = None, selected_labels: Union[List[str], NoneType] = None, nth_x_tick=None):

Creates a basic charts of the scores for each unique iopath of an AssayAnalysisList

class Assays(typing.List[wallaroo.assay.Assay]):

Wraps a list of assays for display in an HTML display-aware environment like Jupyter.

Inherited Members
builtins.list
list
clear
copy
append
insert
extend
pop
remove
index
count
reverse
sort