access

Module for computing the intersection of the various accessibility maps for all targets for the following constraints:

• telescope pointing

• telescope allocation

• sky brightness

• moon separation

• internight cadence from past history

• PI custom windows

• simulated weather loss

• enough time to complete the exposure tonight

The Access class is saved as an attribute of the splan object and used again in plotting.

class astroq.access.Access(semester_start_date, semester_length, n_nights_in_semester, today_starting_night, current_day, all_dates_dict, all_dates_array, slot_size, slots_needed_for_exposure_dict, custom_file, allocation_file, past_history, output_directory, run_weather_loss, run_band3, observatory_string, request_frame)

Bases: object

The Access class encapsulates all the parameters needed for accessibility computation and provides an object-oriented interface to the accessibility computation.

compute_allocated()

Compute boolean mask of is_allocated for all targets according to the allocated times.

compute_altaz(tel_min)

Compute boolean mask of is_altaz for targets according to a minimum elevation. May be superceded by a specific compute_altaz method for a specific observatory, see astroq/queue/ modules.

Parameters:

tel_min (float) – the minimum elevation for the telescope

Returns:

boolean mask of is_altaz for targets

Return type:

is_altaz (array)

compute_clear(weather_loss_file=None)

Compute boolean mask of is_clear for all targets according to the clear times.

Parameters:

weather_loss_file – Path to file with weather loss statistics information

compute_custom()

Compute boolean mask of is_custom for all targets according to the custom times.

compute_future()

Compute boolean mask of is_future for all targets according to today’s current_day.

Args: :returns: boolean mask of is_altaz for targets :rtype: is_altaz (array)

compute_inter()

Compute boolean mask of is_inter for all targets according to the internight cadence.

compute_moon()

Compute boolean mask of is_moon for all targets according to the moon’s position.

get_loss_stats(weather_loss_file)

Gather the loss probabilities for each night in the semester from the saved historical weather data.

observability(requests_frame, access=None)

Extract a dictionary of the available indices from the record array returned by produce_ultimate_map

Parameters:

• requests_frame – DataFrame containing request information

• access – Optional record array from produce_ultimate_map (if None, this function will compute it)

Returns:

Dictionary where keys are target names and values are lists of available slots per night

Return type:

df (dict)

produce_ultimate_map(running_backup_stars=False)

Compute boolean mask of is_observable for all targets according to the ultimate map.

simulate_weather_losses(covariance=0.14)

Simulate nights totally lost to weather using historical data

Parameters:

covariance (float) – the added percent chance that tomorrow will be lost if today is lost

Returns:

Trues represent clear nights, Falses represent weathered nights

Return type:

is_clear (array)

astroq.access.build_twilight_allocation_file(semester_planner)

Build an allocation.csv file where every night of the semester is allocated from evening to morning 12-degree twilight times. This is used exclusively by the football plot in the webapp.

Parameters:

semester_planner (SemesterPlanner) – a semester planner object from splan.py

Returns:

Path to the created allocation.csv file

Return type:

twilight_file (str)