Source code for transcriptic.analysis.kinetics

    import pandas as pd
    import plotly as py
    import plotly.graph_objs as go
except ImportError:
    raise ImportError(
        "Please run `pip install transcriptic[analysis] if you "
        "would like to use the Transcriptic analysis module."

class _Kinetics(object):
    A Kinetics object generalizes the parsing of a time series of datasets
    datasets: List[dataset]
        List of Datasets

    def __init__(self, datasets):
        self.datasets = datasets
        self.readings = pd.concat([ for ds in datasets])
        self.readings.index = pd.to_datetime(
            [ds.attributes["warp"]["completed_at"] for ds in datasets]
        self.readings = self.readings.transpose()

[docs]class Spectrophotometry(_Kinetics): """ A Spectrophotomery object is used to analyze a kinetic series of PlateRead datasets Attributes ---------- properties: DataFrame DataFrame of aliquot properties for each well, useful for groupby operations during plots readings: DataFrame DataFrame of readings for each well at different time points operation: str Operation used for generating these growth curves (e.g. Absorbance) """ def __init__(self, datasets): """ Parameters ---------- datasets: List[dataset] List of Datasets objects. Currently restricted to those generated by 'absorbance', 'fluorescence' and 'luminescence' operations """ operation_set = set([ds.operation for ds in datasets]) if len(operation_set) > 1: raise RuntimeError("Input Datasets must all be of the same type.") self.operation = operation_set.pop() if self.operation not in ["absorbance", "fluorescence", "luminescence"]: raise RuntimeError( f"{self.operation} has to be of type absorbance, " f"fluorescence or luminescence" ) super(Spectrophotometry, self).__init__(datasets) # Assume that well names are consistent across all runs ref_dataset = datasets[0] ref_container = ref_dataset.container # Check if well_map is defined if len(ref_container.well_map) != 0: = pd.DataFrame.from_dict( ref_container.well_map, orient="index" ) else: = pd.DataFrame.from_dict( { ref_container.container_type.robotize(x): x for x in if x not in ["GAIN"] }, orient="index", ) = ["name"] 1, "column", ( % ref_container.container_type.col_count), ) 1, "row", ( // ref_container.container_type.col_count) ) = lambda x: "ABCDEFGHIJKLMNOPQRSTUVWXYZ"[x] ) = [ ref_container.container_type.humanize(int(x)) for x in list( ]
[docs] def plot( self, wells="*", groupby=None, title=None, xlabel=None, ylabel=None, max_legend_len=20, ): """ This generates a plot of the kinetics curve. Note that this function is meant for use under a Jupyter notebook environment Example Usage: .. code-block:: python from transcriptic.analysis.kinetics import Spectrophotometry growth_curve = Spectrophotometry( growth_curve.plot(wells=["A1", "A2", "B1", "B2"]) growth_curve.plot(wells=["A1", "A2", "B1", "B2"], groupby="row", title="Row Groups") growth_curve.plot(wells=["A1", "A2", "B1", "B2"], groupby="name", ylabel="Absorbance Units") growth_curve.plot(groupby="name", max_legend_len=40) Parameters ---------- wells: Optional[list or str] If not specified, this plots all the wells associated with the Datasets given. Otherwise, specifiy a list of well indices (["A1", "B1"]) or a specific well ("A1") groupby: Optional[str] When specified, this groups the wells with the same property value together. On the plot, each group will be represented by a single curve with the mean values and error bars of 1 std. dev. away from the mean title: Optional[str] Plot title. Default: "Kinectics Curve (`run-id`)" xlabel: Optional[str] Plot x-axis label. Default: "Time" ylabel: Optional[str] Plot y-axis label. Default: "`Operation` (`Wavelength`)" max_legend_len Maximum number of characters for the legend labels before truncating. Default: 20 Returns ------- IPlot Plotly iplot object. Will be rendered nicely in Jupyter notebook instance """ # TODO: Shift init_notebook_mode() to start of notebook instance py.offline.init_notebook_mode() if isinstance(wells, str): if wells != "*": wells = [wells] else: well_readings = self.readings wells = list( if isinstance(wells, list): well_readings = self.readings.loc[wells] if not groupby: traces = [ go.Scatter( x=self.readings.columns, y=well_readings.loc[well],["name"].loc[well], ) for well in wells ] else: if groupby not in raise ValueError( f"'{groupby}' not found in the properties table. " f"Please specify a column which exists" ) grouped = index_list = [grouped.get_group(group).index for group in grouped.groups] reading_map = [] for indx in index_list: common_set = set(well_readings.index).intersection(set(indx)) if len(common_set) != 0: reading_map.append(well_readings.loc[common_set]) if len(reading_map) != 0: traces = [ go.Scatter( x=self.readings.columns, y=reading.mean(), name=self._truncate_name([groupby].loc[reading.iloc[0].name], max_legend_len, ), error_y=dict(type="data", array=reading.std(), visible=True), ) for reading in reading_map ] else: raise ValueError( f"No common groups found for specified groupby: {groupby}" ) # Assume all data is generated from the same run-id for now if not title: title = f"Kinetics Curve ({self.datasets[0].attributes['instruction']['run']['id']})" if not xlabel: xlabel = "Time" if not ylabel: if self.operation == "absorbance": ylabel = f"RAU ({self.datasets[0].attributes['instruction']['operation']['wavelength']})" elif self.operation == "fluorescence": ylabel = ( f"RFU ({self.datasets[0].attributes['instruction']['operation']['excitation']}/" f"{self.datasets[0].attributes['instruction']['operation']['emission']})" ) elif self.operation == "luminescence": ylabel = "Luminescence" layout = go.Layout( title=title, xaxis=dict( title=xlabel, titlefont=dict( family="Courier New, monospace", size=18, color="#7f7f7f" ), ), yaxis=dict( title=ylabel, titlefont=dict( family="Courier New, monospace", size=18, color="#7f7f7f" ), ), legend=dict(x=100, y=1), ) fig = go.Figure(data=traces, layout=layout) return py.offline.iplot(fig)
@staticmethod def _truncate_name(string, max_len=20): """Truncates string to max_len number of characters, adds ellipses instead if its too long""" if len(string) > max_len: return string[: (max_len - 3)] + "..." else: return string