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: wallaroo.client.Client, 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.
client
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: Optional[int] = None, workspace_name: Optional[str] = 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: wallaroo.client.Client, assay_name: Optional[str] = None)
id
assay_id
assay_name
pipeline_id
pipeline_name
workspace_id
workspace_name
raw
iopath
score
status
alert_threshold
warning_threshold
window_summary: Dict[str, Any]
is_baseline_run
def baseline_stats(self) -> pandas.core.frame.DataFrame:

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

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: bool = True) -> None:

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

Parameters
  • show_scores: Whether to show the scores for each bin.
def compare_basic_stats(self) -> pandas.core.frame.DataFrame:

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

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

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

class AssayAnalysisList:

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

AssayAnalysisList(raw: List[AssayAnalysis])
raw
def chart_iopaths( self, labels: Optional[List[str]] = None, selected_labels: Optional[List[str]] = None, nth_x_tick: Optional[int] = None) -> None:

Creates a basic charts of the scores for each unique iopath of a list of assay results.

Parameters
  • labels: Custom labels for each unique iopath. If provided, these labels will be used in chart titles instead of raw iopath values.
  • selected_labels: Labels to filter which iopaths to chart. If provided, only iopaths with labels in this list will be charted.
  • nth_x_tick: Controls the density of x ticks. Every nth x tick will be used for the chart.
def chart_scores( self, title: Optional[str] = None, nth_x_tick: Optional[int] = 4, start: Optional[datetime.datetime] = None, end: Optional[datetime.datetime] = None) -> None:

Creates a chart of the scores from dataframe representation of a list of assay results.

Parameters
  • title: The title of the chart.
  • nth_x_tick: Controls the density of x ticks. Every nth x tick will be used for the chart.
  • start: The start time of the chart. Both start and end have to be provided to be used.
  • end: The end time of the chart. Both start and end have to be provided to be used.
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.

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

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