wkerzendorf

class QSeries(pd.Series):
    _metadata = ["unit", "descriptor"]
    @property
    def _constructor(self):
        return QSeries

    @property
    def _constructor_expanddim(self):
        return QDataFrame

wkerzendorf

wkerzendorf

#### implementation 1 ####
class GenericModel(object):
    def __init__(self, *args):
        for property in args:
          self.cells_property = {some dictionary containing each cell property}
          # example
          self.cells_property = {'velocity':Velocity(asdasd)}
          self.__dict__[property.name] = property
          

wkerzendorf

#!/bin/bash

#SBATCH --job-name=dalek-jobarray
#SBATCH --nodes=1
#SBATCH --cpus-per-task=20
#SBATCH --mem=40GB
#SBATCH --time=02:00:00
#SBATCH --array=0-300

### ACTIVATING CONDA ####

wkerzendorf

wkerzendorf

from collections import OrderedDict

from astropy import units as u
import numpy as np
import pandas as pd

def read_cmfgen(fname):
    nu = []
    flux = []
    current_data = nu

wkerzendorf

from collections import OrderedDict

from astropy import units as u
import numpy as np
import pandas as pd

def read_cmfgen(fname):
    nu = []
    flux = []
    current_data = nu

wkerzendorf

import luigi
from glob import glob
import os
raw_zips = '/1002/*.gz'


class ExtractGZ(luigi.Task):
    document_id = luigi.Parameter()

    def output(self):

wkerzendorf

wkerzendorf

from astropy import wcs
from scipy import optimize
# this doesn't include the precise function names - just look them up on the astropy wcs website

def fit_func(crval1, crval2, x, y, ra, dec):
  w = wcs.WCS(naxis=2)
  w.wcs.crval = [crval1, crval2]
  ra_wcs, dec_wcs = w.pix_to_world(x, y)
  return (ra_wcs - ra)**2 + (dec_wcs - dec)**2