Tm/synthetics #22
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Tm/synthetics #22
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+ This adds a new class `ProteomicsExperimentSample` to represent sample being pushed through proteomics setup
1. `ProteomicsExperimentSampleSlice` is considered
the working bulk of data, that is loaded as dataframe for
processing
2. `ProteomicsExperimentDatabaseHandle` is a wrapper class
for sql database management
1. python/proteolizardalgo/feature.py:
+ Class for charge distribution `ChargeProfile`
2. python/proteolizardalgo/hardware_models.py:
+ `LiquidChromatography`
* support for `irt_to_rt` method
* methods returning time interval (start,end)
and center of frames
+ implemented `EMGChromatographyProfileModel`
+ implemented `NormalIonMobilityProfileModel`
3. python/proteolizardalgo/proteome.py
+ method to make columns with `Profile` data types
SQL compatible
TODO:
+ IonMobilityModel must support multiple charge states.
+ realistic parameter sampling
+ realistic `irt_to_rt` method -> must be provided by user
1. model params were null in sql table
+ This was due to np datatypes (not serializable)
+ now stored as python built-ins
2. charge profile is stored in peptides table
1. changed hyphen - to underscore _ in sql columns
2. In experiment.py, added structure for TOF spectra assembly
3. replaced assertion with in averagine_generator concerning
proper masses for averagine model
This adds a prototype end to end synthetics generator that returns a dictionary (frames) of dictionaries (scans) of `MzSpectrum`
orienting synthetics workflow on experiment
1. In chemistry.py new class `ChemicalCompound`
with subclass `BufferGas` for handling of
e.g. ion mobility gas properties.
+ ChemicalCompound gets elemental properties
from new dependency
['mendeleev'](https://github.com/lmmentel/mendeleev)
2. CCS to ion mobility / reduced ion mobility is now handled
within device class `IonMobilitySeparation`
3. Scan to ion mobility and reverse are now relying
on converters defined by user
1. check for empty spectra 2. use __repr__ for file output instead of json
1. instead of repeatedly adding spectra
`.push` just copies data and lets `to_resolution`
at the end do the sorting and adding.
1. tf models use a lot of RAM, and a variety of
approaches to free the RAM did not work.
Running the model inference inside a child process
worked.
1. fixed bug in which sequence tokens were read as
string
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This Pull request implements a platform for synthetic data generation in proteolizard.