From 8d04803eb341c63d25b347b011e6a0b709c9438f Mon Sep 17 00:00:00 2001
From: Deepak Mallubhotla <dmallubhotla+github@gmail.com>
Date: Sun, 19 May 2024 02:29:59 -0500
Subject: [PATCH] fmt: formatting, nicer log, removing comment

---
 .../cost_function_filter.py                   | 11 +++++++---
 .../subset_simulation_impl.py                 | 21 ++-----------------
 .../test_cost_function_filter.py              | 14 ++++++++-----
 3 files changed, 19 insertions(+), 27 deletions(-)

diff --git a/deepdog/direct_monte_carlo/cost_function_filter.py b/deepdog/direct_monte_carlo/cost_function_filter.py
index dec937e..c9e4d0f 100644
--- a/deepdog/direct_monte_carlo/cost_function_filter.py
+++ b/deepdog/direct_monte_carlo/cost_function_filter.py
@@ -1,9 +1,14 @@
 from deepdog.direct_monte_carlo.direct_mc import DirectMonteCarloFilter
-from typing import Sequence, Callable
+from typing import Callable
 import numpy
 
+
 class CostFunctionTargetFilter(DirectMonteCarloFilter):
-	def __init__(self, cost_function: Callable[[numpy.ndarray], numpy.ndarray], target_cost: float):
+	def __init__(
+		self,
+		cost_function: Callable[[numpy.ndarray], numpy.ndarray],
+		target_cost: float,
+	):
 		"""
 		Filters dipoles by cost, only leaving dipoles with cost below target_cost
 		"""
@@ -12,7 +17,7 @@ class CostFunctionTargetFilter(DirectMonteCarloFilter):
 
 	def filter_samples(self, samples: numpy.ndarray) -> numpy.ndarray:
 		current_sample = samples
-		
+
 		costs = self.cost_function(current_sample)
 
 		current_sample = current_sample[costs < self.target_cost]
diff --git a/deepdog/subset_simulation/subset_simulation_impl.py b/deepdog/subset_simulation/subset_simulation_impl.py
index 11d6d22..9344645 100644
--- a/deepdog/subset_simulation/subset_simulation_impl.py
+++ b/deepdog/subset_simulation/subset_simulation_impl.py
@@ -94,9 +94,8 @@ class SubsetSimulation:
 		_logger.info(f"\tn_c: {self.n_c}")
 		_logger.info(f"\tn_s: {self.n_s}")
 		_logger.info(f"\tm: {self.m_max}")
-		_logger.info(f"\tseeds:")
-		_logger.info(f"\t\t{mcmc_seed=}")
-		_logger.info(f"\t\t{level_0_seed=}")
+		_logger.info(f"\t{mcmc_seed=}")
+		_logger.info(f"\t{level_0_seed=}")
 		_logger.info("let's do level 0...")
 
 		self.target_cost = target_cost
@@ -272,22 +271,6 @@ class SubsetSimulation:
 					samples_generated += self.n_s
 					samples_rejected += rejected_count
 
-				# for seed_index, (c, s) in enumerate(next_seeds):
-				# 	# chain = mcmc(s, threshold_cost, n_s, model, dot_inputs_array, actual_measurement_array, mcmc_rng, curr_cost=c, stdevs=stdevs)
-				# 	# until new version gotta do
-				# 	_logger.debug(
-				# 		f"\t{seed_index}: getting another chain from the next seed"
-				# 	)
-				# 	rejected_count, chain = self.model.get_repeat_counting_mcmc_chain(
-				# 		s,
-				# 		self.cost_function_to_use,
-				# 		self.n_s,
-				# 		threshold_cost,
-				# 		stdevs,
-				# 		initial_cost=c,
-				# 		rng_arg=mcmc_rng,
-				# 	)
-
 				_logger.debug("finished mcmc")
 				_logger.debug(f"{samples_rejected=} out of {samples_generated=}")
 				if samples_rejected * 2 > samples_generated:
diff --git a/tests/direct_monte_carlo/test_cost_function_filter.py b/tests/direct_monte_carlo/test_cost_function_filter.py
index 82e3864..2e6482c 100644
--- a/tests/direct_monte_carlo/test_cost_function_filter.py
+++ b/tests/direct_monte_carlo/test_cost_function_filter.py
@@ -1,6 +1,7 @@
 import deepdog.direct_monte_carlo.cost_function_filter
 import numpy
 
+
 def test_px_cost_function_filter_example():
 
 	dipoles_1 = [
@@ -18,21 +19,24 @@ def test_px_cost_function_filter_example():
 	def cost_function(dipoleses: numpy.ndarray) -> numpy.ndarray:
 		return dipoleses[:, :, 0].max(axis=-1)
 
-	
 	expected_costs = numpy.array([2, 30])
 
 	numpy.testing.assert_array_equal(cost_function(dipoleses), expected_costs)
 
-	filter = deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(cost_function, 5)
+	filter = deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(
+		cost_function, 5
+	)
 
 	actual_filtered = filter.filter_samples(dipoleses)
 	expected_filtered = numpy.array([dipoles_1])
 	assert actual_filtered.size != 0
 	numpy.testing.assert_array_equal(actual_filtered, expected_filtered)
 
-
-
-	filter_stricter = deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(cost_function, 0.5)
+	filter_stricter = (
+		deepdog.direct_monte_carlo.cost_function_filter.CostFunctionTargetFilter(
+			cost_function, 0.5
+		)
+	)
 
 	actual_filtered_stricter = filter_stricter.filter_samples(dipoleses)
 	assert actual_filtered_stricter.size == 0