Source code for lsst.sims.maf.web.mafTracking

from builtins import object
import os
from collections import OrderedDict
import numpy as np
import lsst.sims.maf.db as db
from .mafRunResults import MafRunResults

__all__ = ['MafTracking']

[docs]class MafTracking(object): """ Class to read MAF's tracking SQLite database (tracking a set of MAF runs) and handle the output for web display. """ def __init__(self, database=None): """ Instantiate the (multi-run) layout visualization class. Parameters ---------- database :str Path to the sqlite tracking database file. If not set, looks for 'trackingDb_sqlite.db' file in current directory. """ if database is None: database = os.path.join(os.getcwd(), 'trackingDb_sqlite.db') # Read in the results database. tdb = db.Database(database=database, longstrings=True) cols = ['mafRunId', 'opsimRun', 'opsimGroup', 'mafComment', 'opsimComment', 'dbFile', 'mafDir', 'opsimVersion', 'opsimDate', 'mafVersion', 'mafDate'] self.runs = tdb.query_columns('runs', colnames=cols) self.runs = self.sortRuns(self.runs) self.runsPage = {}
[docs] def runInfo(self, run): """ Provide the tracking database information relevant for a given run in a format that the jinja2 templates can use. Parameters ---------- run : numpy.NDarray One line from self.runs Returns ------- OrderedDict Ordered dict version of the numpy structured array. """ runInfo = OrderedDict() runInfo['OpsimRun'] = run['opsimRun'] runInfo['OpsimGroup'] = run['opsimGroup'] runInfo['MafComment'] = run['mafComment'] runInfo['OpsimComment'] = run['opsimComment'] runInfo['SQLite File'] = [os.path.relpath(run['dbFile']), os.path.split(run['dbFile'])[1]] runInfo['ResultsDb'] = os.path.relpath(os.path.join(run['mafDir'], 'resultsDb_sqlite.db')) runInfo['MafDir'] = run['mafDir'] runInfo['OpsimVersion'] = run['opsimVersion'] runInfo['OpsimDate'] = run['opsimDate'] runInfo['MafVersion'] = run['mafVersion'] runInfo['MafDate'] = run['mafDate'] return runInfo
[docs] def sortRuns(self, runs, order=['opsimRun', 'mafComment', 'mafRunId']): """ Sort the numpy array of run data. Parameters ---------- runs : numpy.NDarray The runs from self.runs to sort. order : list The fields to use to sort the runs array. Returns ------- numpy.NDarray A sorted numpy array. """ return np.sort(runs, order=order)
[docs] def getRun(self, mafRunId): """ Set up a mafRunResults object to read and handle the data from an individual run. Caches the mafRunResults object, meaning the metric information from a particular run is only read once from disk. Parameters ---------- mafRunId : int mafRunId value in the tracking database corresponding to a particular MAF run. Returns ------- MafRunResults A MafRunResults object containing the information about a particular run. Stored internally in self.runsPage dict, but also passed back to the tornado server. """ if not isinstance(mafRunId, int): if isinstance(mafRunId, dict): mafRunId = int(mafRunId['runId'][0][0]) if isinstance(mafRunId, list): mafRunId = int(mafRunId[0]) if mafRunId in self.runsPage: return self.runsPage[mafRunId] match = (self.runs['mafRunId'] == mafRunId) mafDir = self.runs[match]['mafDir'][0] runName = self.runs[match]['opsimRun'][0] if runName == 'NULL': runName = None self.runsPage[mafRunId] = MafRunResults(mafDir, runName) return self.runsPage[mafRunId]