Coverage for python/lsst/sims/seeingModel/seeingData.py : 27%
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"""Read the seeing data from disk and return appropriate FWHM_500 value at a given time.
Parameters ---------- time_handler : :class:`.TimeHandler` The instance of the simulation time handler. seeing_db : str or None, opt The name of the seeing database. If None (default), this will use the Seeing.db file in the 'data' directory of this package. """ self.seeing_db = seeing_db if self.seeing_db is None: self.seeing_db = os.path.join(getPackageDir('sims_seeingModel'), 'data', 'seeing.db') model_time_start = datetime(time_handler.initial_dt.year, 1, 1) self.offset = time_handler.time_since_given_datetime(model_time_start, reverse=True) self.seeing_dates = None self.seeing_values = None
"""Get the FWHM_500 value for the specified time.
Parameters ---------- delta_time : int The time (seconds) from the start of the simulation.
Returns ------- float The FWHM_500(") closest to the specified time. """ delta_time += self.offset # Find the date to look for in the time range of the data. # Note that data dates should not necessarily start at zero. date = delta_time % self.time_range + self.min_time idx = np.searchsorted(self.seeing_dates, date) # searchsorted ensures that left < date < right # but we need to know if date is closer to left or to right left = self.seeing_dates[idx - 1] right = self.seeing_dates[idx] if date - left < right - date: idx -= 1 return self.seeing_values[idx]
"""Read the seeing information from disk.
The default behavior is to use the module stored database. However, an alternate database file can be provided. The alternate database file needs to have a table called *Seeing* with the following columns:
seeingId int : A unique index for each seeing entry. s_date int : The time (in seconds) from the start of the simulation, for the seeing observation. seeing float : The FWHM of the atmospheric PSF (in arcseconds) at zenith. """ with sqlite3.connect(self.seeing_db) as conn: cur = conn.cursor() query = "select s_date, seeing from Seeing order by s_date;" cur.execute(query) results = np.array(cur.fetchall()) self.seeing_dates = np.hsplit(results, 2)[0].flatten() self.seeing_values = np.hsplit(results, 2)[1].flatten() cur.close() # Make sure seeing dates are ordered appropriately (monotonically increasing). ordidx = self.seeing_dates.argsort() self.seeing_dates = self.seeing_dates[ordidx] self.seeing_values = self.seeing_values[ordidx] self.min_time = self.seeing_dates[0] self.max_time = self.seeing_dates[-1] self.time_range = self.max_time - self.min_time |