Exposed the function "setConstantPerObservableAndLinkEndsWeights" to Python

[rashikajain11]

Added the python exposure of the function "setConstantPerObservableAndLinkEndsWeights". This allows for adding constant weights to observations depending on the observable type and link-ends for the covariance analysis.

Python example:

noise_level = 1e-3
range_type = observation.one_way_range_type
link_ends_dict = { observation.transmitter: observation.body_origin_link_end_id(<transmitter_name>),                        
                   observation.receiver: observation.body_origin_link_end_id(<receiver_name>)
                 }

pod_input = estimation.CovarianceAnalysisInput(<simulated_observations>)
pod_input.set_constant_weight_per_observable_and_link_end(range_type, link_ends_dict, noise_level)

Note: the python syntax for this function is different from other functions used to define the weight matrix. The other functions require a dictionary input, for example:

weights_per_observable = {range_type: noise_level}  # a dictionary of noise for each range type
pod_input.set_constant_weight_per_observable(weights_per_observable)