changed jsr_run in FD to reach exactely tau #144
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## main #144 +/- ##
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+ Coverage 73.46% 75.01% +1.54%
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Files 22 24 +2
Lines 2891 3330 +439
Branches 762 856 +94
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+ Hits 2124 2498 +374
- Misses 552 601 +49
- Partials 215 231 +16
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Thanks @NellyMitnik
Please see some comments below. Feel free to squash the commits together. Also, could you add a test to the new function you added and an overall test for this feature?
t3/utils/flux.py
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| t = 0 | ||
| while t < tau: | ||
| dt = tau/num_steps |
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minor: please add a space before and after operators, e.g. tau / num_steps
Also below for tau*tau_tolerance and other cases
t3/utils/flux.py
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| @@ -295,6 +295,15 @@ def set_batch_p(gas: ct.Solution, | |||
| network.rtol = r_tol | |||
| return network, reactor | |||
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| def closest_bigger_number(array, target): | |||
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please add a docstring and typehints for each new function, e.g., as we have for run_jsr below
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Was this comment addressed? (please add the docstring in the same commit as the function)
t3/utils/flux.py
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| candidates = [(num, i) for i, num in enumerate(array) if num > target] | ||
| # Find the closest bigger number | ||
| if candidates: | ||
| m, i = min(candidates, key=lambda x: x[0]) |
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Can you explain what is being returned as m, i? Looks to me like the min() func only returns one value, I'm probably missing something
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Let's use consistent variable names, for example if we call the content of the tuple pair (num, i), then let's also call them num, i on this line if indeed they represent the same type of data
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Candidates is a list of tuples (num, i), with numbers from the original array which are bigger that the target number. Let's call this process as filtering out smaller numbers.
In the next step, the min function is used over a list of tuples, while the key for the min function is the first variable in tuple (which is the value number itself). The min function returns a value from the list (which is a tuple consists of a value and its index in the original array).
t3/utils/flux.py
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| @@ -304,6 +313,7 @@ def run_jsr(gas: ct.Solution, | |||
| V: Optional[float] = None, | |||
| a_tol: float = 1e-16, | |||
| r_tol: float = 1e-10, | |||
| tau_tolerance: float = 0.1, | |||
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Are we sure that 0.1 isn't too risky? In the PR comment you wrote that it reached from 0.8 to 8 sec. So if next time it will jump from 0.9 to 8 sec it would be enough for the 0.1 tolerance to miss this jump in t. We need the tolerance just for the lower range, let's make it ~0.01 or 0.005?
NellyMitnik
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Thanks @NellyMitnik!
Please squash the commits that are fixups
I added some comments, some of them were probably addressed by the fixups, but please take a look
t3/utils/flux.py
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| t = tau | ||
| t = time_steps_array[t_step_index] | ||
| network.advance(time_steps_array[t_step_index]) | ||
| print(network.time) |
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Should this print statement be removed?
t3/utils/flux.py
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| @@ -351,6 +355,7 @@ def run_jsr(gas: ct.Solution, | |||
| rops[spc.name][rxn.equation] += cantera_reaction_rops[i] * stoichiometry[spc.name][i] | |||
| profile = {'P': gas.P, 'T': gas.T, 'X': {s.name: x for s, x in zip(gas.species(), gas.X)}, 'ROPs': rops} | |||
| if t == tau: | |||
| print("exactly tau") | |||
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same here, I think this print should be removed
t3/utils/flux.py
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| @@ -295,6 +295,15 @@ def set_batch_p(gas: ct.Solution, | |||
| network.rtol = r_tol | |||
| return network, reactor | |||
|
|
|||
| def closest_bigger_number(array, target): | |||
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Was this comment addressed? (please add the docstring in the same commit as the function)
t3/utils/flux.py
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| @@ -304,6 +313,7 @@ def run_jsr(gas: ct.Solution, | |||
| V: Optional[float] = None, | |||
| a_tol: float = 1e-16, | |||
| r_tol: float = 1e-10, | |||
| tau_tolerance: float = 0.1, | |||
| @@ -2,6 +2,13 @@ | |||
| "python.testing.pytestArgs": [ | |||
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please uncommit the changes to this file
| num, i = flux.closest_bigger_number([1, 2, 2.5, 3, 3.5, 4.5, 5], 3.7) | ||
| assert num == 4.5 | ||
| assert i == 5 | ||
| print("\npassed test case 1") |
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please remove the prints in the test
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@NellyMitnik , what's the status of this PR? |
run_jsrinflux.py, was passing tau by a huge factor, and then, it was time-integrating backward to tau.For example, if tau = 2 sec, by stepping, it reached from 0.8 to 8 sec. When reaching 8 sec, it advanced to 2 sec (time-integrating backward).
To fix that, an array of 500 steps in time was defined. A loop iterates over this array and advances by equal time-steps until it reaches exactly tau.