Ca H&K Algorithm

class modules.ca_hk.src.alg.CaHKAlg(data, fibers, output_exts=None, output_wl_exts=None, config=None, logger=None)

Ca H&K spectrum extraction.

This module defines class ‘HKExtractionAlg’ and methods to extract spectrum from H&K science data.

Parameters:

• data (numpy.ndarray) – Ca H&K 2D image data.

• fibers (list) – List containing the interested fibers to be extracted.

• output_exts (list) – List with the name of the extension to contain extraction data

• output_wl_exts (list) – List with the name of the extension to contain wavelength data

• config (configparser.ConfigParser) – config context.

• logger (logging.Logger) – Instance of logging.Logger from external application.

instrument

Imaging instrument.

Type:

str

hk_data

Numpy array storing 2d image data.

Type:

numpy.ndarray

fibers

List storing fibers to be processed.

Type:

list

data_range

Index range of all pixels.

Type:

list

trace_location

Trace location per order per fiber.

Type:

dict

order_buffer

Buffer to contain flux computation.

Type:

numpy.ndarray

Raises:

• AttributeError – The Raises section is a list of all exceptions that are relevant to the interface.

• TypeError – If there is type error for data or config.

• Exception – If the size of data is less than 20 pixels by 20 pixels.

extract_spectrum(fiber_name, order_set=None, show_time=False, print_debug=None)

Spectrum extraction from 2D flux to 1D by doing summation on extracted data.

Parameters:

• fiber_name (str, optional) – Name of the fiber to be processed.

• order_set (numpy.ndarray, optional) – Set of selected orders to extract. Defaults to None for all orders.

• show_time (bool, optional) – Show running time of the steps. Defaults to False.

• print_debug (str, optional) – Print debug information to stdout if it is provided as empty string, a file with path print_debug if it is non empty string, or no print if it is None. Defaults to None.

Returns:

spectral extraction result from 2D spectrum data, like:

{
    'spectral_extraction_result':  Padas.DataFrame
}

Return type:

dict

get_config_value(param: str, default)

Get defined value from the config file.

Search the value of the specified property from config section. The default value is returned if no found.

Parameters:

• param (str) – Name of the parameter to be searched.

• default (str/int/float) – Default value for the searched parameter.

Returns:

Value for the searched parameter.

Return type:

int/float/str

get_data_range()

Get image size range

Returns:

image range in order of y1, y2, x1 and x2.

Return type:

numpy.ndarray

get_fibers()

Get imaging fibers

Returns:

list with fibers

Return type:

list

get_instrument()

Get imaging instrument.

Returns:

Instrument name.

Return type:

str

get_order_buffer()

Get a pre-allocated buffer with all zeros to contain sum of spectrum extraction

Returns:

1 x <spectrum width> array with all zeros

Return type:

numpy.ndarray

get_spectral_data()

Get spectral information including data and dimension.

Returns:

Information of spectral data,

• (numpy.ndarray): 2D spectral data.

• nx (int): Width of the data.

• ny (int): Height of the data.

Return type:

tuple

get_trace_location(fiber=None)

Get the trace location on specified fibers

Parameters:

fiber (str) – Fiber name. Defaults to None for all fibers

Returns:

fiber location on one fiber or all fibers, like:

{
    <fiber name 1>: {<order_1>: {'x1': , 'x2', 'y1': 'y2' },
                     <order_2>: {'x1': , 'x2', 'y1': 'y2' }, ...},
    <fiber name n>: {<order_1>: {'x1': , 'x2', 'y1': 'y2' }, ...}
}

Return type:

dict

img_scaling()

Scale the hk data based on the defined gain that converts the image from the count to electron charge

img_subtraction(dark_img, bias_img)

Hk image processing by subtracting the dark image and the bias image if existing.

Parameters:

• dark_img (numpy.array) – dark image

• bias_img (numpy.array) – biase image

Returns:

False in case the image size doesn’t match, or True.

Return type:

bool

load_trace_location(trace_path)

Load the file containing trace definition and record the trace information per order and per fiber

Parameters:

trace_path – the path to a file with order trace information. The file is assumed in csv format containing the header and the space as the delimiter for each row.

Returns:

each item in dict object has the trace value for each fiber, like:

{
    <fiber name>: <fiber_trace>
        # where <fiber trace> is a dict containing location for each order,
        {<order_number_1>: {'x1': , 'y1':, 'x2': , 'y2': },
         <order_number_2>: {'x1': , 'y1':, 'x2': , 'y2': }, .....,
         <order_number_n>: {'x1': , 'y1':, 'x2': , 'y2': }}
}

Return type:

dict

load_wavelength_table(wave_table_file: str, fiber: str)

load the csv file with wavelength solution table. The table size is also checked.

Parameters:

• wave_table_file (str) – path to the csv table file with hk wavelength solution data

• fiber (str) – the associated fiber name

Returns:

Array containing the wavelength solution with the size <total_order>*<image width>

Return type:

numpy.array

summation_extraction(trace_location, selected_orders=None)

Extract spectrum for selected orders and perform summation on the extracted data

Parameters:

• trace_location (dict) – trace location of selected orders

• selected_orders (list) – selected orders to be processed. Defaults to None for all orders.

Returns:

2D data containing the summation on selected orders.

Return type:

numpy.ndarray

summation_extraction_one_order(order_loc)

Spectrum extraction and summation on extracted pixels per order location

Parameters:

order_loc (dict) – Order location in terms of x1, x2, y1, y2 per order.

Returns:

extracted and summed result with dimension 1xn where n is the image width, like:

{
        'extraction': numpy.ndarray   # summation of extraction
}

Return type:

dict

static write_data_to_dataframe(out_data, fiber_name, extraction_dim)

Write Ca H&K extraction result to an instance of Pandas DataFrame.

Parameters:

• out_data (numpy.ndarray) – H&K spectrum extraction result. Each row of the array corresponds to the reduced 1D data of one order.

• fiber_name (str) – Fiber name.

• extraction_dim (dict) – Dimension in orders for the specified fiber.

Returns:

Instance of DataFrame containing the extraction result plus the following attributes:

• FIBER: fiber name.

• Ordern: Dimension data for order n.

Return type:

Pandas.DataFrame