[v2.3] Pass RNG to evolution steps

[sgossage]

This PR is branched from sg_v2.3_fix_ram_usage (PR#818), so contains those changes as well.

Changes in this PR occur in:

1. posydon/binary_evol/simulationproperties.py
2. posydon/binary_evol/SN/step_SN.py
3. posydon/binary_evol/DT/step_detached.py
4. posydon/binary_evol/MESA/step_mesa.py
5. posydon/popsyn/binarypopulation.py
6. posydon/utils/common_functions.py
7. posydon/unit_tests/utils/test_common_functions.py

In order, the following has been changed:

1. New RNG keyword arguments for from_ini, load_steps and load_a_step so that a RNG object may be passed to an evolution step.
2. The StepSN class now looks for/builds an RNG object and uses it to generate randomized SN kicks.
3. The detached_step class now looks for/builds an RNG object and uses it to calculate Bondi-Hoyle accretion.
4. The MesaGridStep class now looks for/builds an RNG object and uses it to calculate Bondi-Hoyle accretion.
5. When creating and evolving a BinaryPopulation, the entropy and resultant RNG object set in your .ini may be passed on to evolution steps. Currently, only step_SN, detached_step, and MesaGridStep pick this up.
6. The bondi_hoyle() function can be passed an RNG object and it will be used to calculate the Bondi-Hoyle accretion rate.
7. The unit test for bondi_hoyle() has been updated accordingly.

Now step_SN can have fixed kick outcomes if entropy is set to a specific value in the .ini. Likewise for MesaGridStep and detached_step regarding Bondi-Hoyle accretion. This should allow us to run reproducible binary populations, fixed by the entropy setting.

[astroJeff]

Can we check if this addresses issue #764?

[sgossage]

Can we check if this addresses issue #764?

It does @astroJeff , running two separate populations with equal settings produces identical history data frames (ignoring step_times which measures how long each step takes and can differ) with entropy set to a fixed integer. E.g.,

from pandas.testing import assert_frame_equal

# my .ini has kicks enabled, etc, default settings, except entropy = 0
pop1 = BinaryPopulation.from_ini(my_ini_filename, verbose=True)
pop2 = BinaryPopulation.from_ini(my_ini_filename, verbose=True)

pop1.evolve(breakdown_to_df=False, optimize_ram=False, tqdm=True)
pop2.evolve(breakdown_to_df=False, optimize_ram=False, tqdm=True)

hist_df1 = pop1.to_df()
hist_df2 = pop2.to_df()

compare1 = hist_df1.drop(columns=['step_times'])
compare2 = hist_df2.drop(columns=['step_times'])

# this shows no differences exist between the data frames
assert_frame_equal(compare1, compare2)