ccd_process

ccdproc.ccd_process(ccd, oscan=None, trim=None, error=False, master_bias=None, dark_frame=None, master_flat=None, bad_pixel_mask=None, gain=None, readnoise=None, oscan_median=True, oscan_model=None, min_value=None, dark_exposure=None, data_exposure=None, exposure_key=None, exposure_unit=None, dark_scale=False, add_keyword=True)[source]

Perform basic processing on ccd data.

The following steps can be included: * overscan correction * trimming of the image * create deviation frame * gain correction * add a mask to the data * subtraction of master bias * subtraction of a dark frame * correction of flat field

The task returns a processed ccdproc.CCDData object.

Parameters:

ccd: `~ccdproc.CCDData`

Frame to be reduced

oscan: None, str, or, `~ccdproc.ccddata.CCDData`

For no overscan correction, set to None. Otherwise proivde a region of ccd from which the overscan is extracted, using the FITS conventions for index order and index start, or a slice from ccd that contains the overscan.

trim: None or str

For no trim correction, set to None. Otherwise proivde a region of ccd from which the image should be trimmed, using the FITS conventions for index order and index start.

error: boolean

If True, create an uncertainty array for ccd

master_bias: None or `~ccdproc.CCDData`

A master bias frame to be subtracted from ccd.

dark_frame: None or `~ccdproc.CCDData`

A dark frame to be subtracted from the ccd.

master_flat: None or `~ccdproc.CCDData`

A master flat frame to be divided into ccd.

bad_pixel_mask: None or `~numpy.ndarray`

A bad pixel mask for the data. The bad pixel mask should be in given such that bad pixels havea value of 1 and good pixels a value of 0.

gain: None or `~astropy.Quantity`

Gain value to multiple the image by to convert to electrons

readnoise: None or `~astropy.Quantity`

Read noise for the observations. The read noise should be in electrons.

oscan_median : bool, optional

If true, takes the median of each line. Otherwise, uses the mean

oscan_model : Model, optional

Model to fit to the data. If None, returns the values calculated by the median or the mean.

min_value : None or float

Minimum value for flat field. The value can either be None and no minimum value is applied to the flat or specified by a float which will replace all values in the flat by the min_value.

dark_exposure : Quantity

Exposure time of the dark image; if specified, must also provided data_exposure.

data_exposure : Quantity

Exposure time of the science image; if specified, must also provided dark_exposure.

exposure_key : str or Keyword

Name of key in image metadata that contains exposure time.

exposure_unit : Unit

Unit of the exposure time if the value in the meta data does not include a unit.

dark_scale: boolean

If True, scale the dark frame by the exposure times

Returns:

occd: CCDData

Reduded ccd

Examples

  1. To overscan, trim, and gain correct a data set:
>>> import numpy as np
>>> from astropy import units as u
>>> from ccdproc import CCDData
>>> from ccdproc import ccd_process
>>> ccd = CCDData(np.ones([100, 100]), unit=u.adu)
>>> nccd = ccd_process(ccd, oscan='[1:10,1:100]', trim='[10:100, 1:100]',                           error=False, gain=2.0*u.electron/u.adu)