libgunshotmatch.project¶
Represents a collection of repeat analyses.
Classes:
|
A project represents the aligned peaks from multiple datafiles. |
Functions:
|
Consolidate the compound identification from the experiments into a single dataset. |
-
class
Project(name, alignment, datafile_data, consolidated_peaks=None)[source]¶ A project represents the aligned peaks from multiple datafiles.
- Parameters
name (
str) – The name of the project.alignment (
Alignment) – Peak alignment for the repeats in this project.datafile_data (
Dict[str,Repeat]) – Mapping of repeat names toRepeatobjects.consolidated_peaks (
Optional[List[ConsolidatedPeak]]) – List of peaks afterconsolidate()is performed.Noneinitially. DefaultNone.
Attributes:
Peak alignment for the repeats in this project.
List of peaks after
consolidate()is performed.Mapping of repeat names to
Repeatobjects.The name of the project.
Methods:
consolidate(engine[, peak_filter])Consolidate the compound identification from the experiments into a single dataset.
export(output_dir)Export as a
gsmpfile.from_dict(d)Construct a
Projectfrom a dictionary.from_file(filename)Parse a
gsmpfile.to_dict()Returns a dictionary representation of this
Project.-
consolidate(engine, peak_filter=None)[source]¶ Consolidate the compound identification from the experiments into a single dataset.
- Parameters
engine (
Engine)peak_filter (
Optional[ConsolidatedPeakFilter]) – Filter for the consolidated peaks. DefaultNone.
- Return type
- Returns
pandas.DataFramegiving the results of pairwise mass spectral comparisons between the repeats for each aligned peak.
-
consolidated_peaks¶ Type:
Optional[List[ConsolidatedPeak]]List of peaks after
consolidate()is performed.Noneinitially.
-
consolidate(project, engine)[source]¶ Consolidate the compound identification from the experiments into a single dataset.
- Parameters
- Return type
- Returns
List of consolidated peaks and
pandas.DataFramegiving the results of pairwise mass spectral comparisons between the repeats for each aligned peak.
New in version 0.10.0.